A clothing retail member operation decision method and device based on an RFIMD model

Abstract

The application relates to the technical field of data processing, and discloses a clothing retail member operation decision method and device based on an RFIMD model. The method comprises the following steps: extracting user transaction details, commodity life cycle parameters and recommended conversion topological relationship data in an evaluation period, performing benchmark calibration operation, and constructing a dynamic RFIMD feature vector; matching a graphic-text component in a mapping matrix and generating a communication tracking voucher, and encapsulating a communication data packet; performing interface available and concurrent quota decrement release before the data packet is issued, and freezing single product inventory in a distributed cache layer when a set condition is met; recording an external message arrival timestamp and an internal gateway trigger timestamp after the data packet is issued; when the time delay exceeds a blocking timeout threshold and no trigger timestamp is recorded, the inventory freezing is released, the concurrent quota is restored, and a degraded communication data packet is assembled. The application effectively avoids transaction blocking of an underlying relational database and invalid occupation of physical resources in a high concurrency scenario.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a method and apparatus for decision-making in apparel retail membership operations based on the RFIMD model. Background Technology

[0002] In the digital operation system of apparel retail, companies typically accumulate massive amounts of user transaction logs and interaction behavior data. To improve marketing efficiency and customer retention rates, technical personnel have developed various data processing systems to analyze the underlying business logs, thereby establishing a member value assessment system and automatically generating corresponding business distribution strategies. Automating data mining and operational command issuance through computer systems has become a crucial technological means for customer relationship management in the retail industry.

[0003] Existing data processing systems mostly employ the traditional RFM model to analyze business data. The system uses conventional aggregate queries to extract a user's most recent purchase time, purchase frequency, and cumulative amount as evaluation indicators, thereby categorizing users into different static levels. When executing specific operational tasks, the backend server assembles image and text data packages containing specific product links based on these static category tags and pushes them concurrently to external communication platform terminals. Subsequently, only when a user clicks on the terminal and ultimately initiates the actual payment instruction will the system directly access the underlying relational database to perform the corresponding inventory deduction operation in the main table.

[0004] However, in actual operation, the aforementioned systems typically perform indiscriminate absolute value accumulation of historical data when extracting feature indicators, failing to absorb the decaying impact of time span on user recommendation activity. Furthermore, they directly use fixed pricing for comparison, ignoring the objective attribute of clothing price reductions over its lifecycle. This makes the feature quantification results highly prone to deviating from the user's true value. Simultaneously, when facing large-scale concurrent pushes, the system directly relies on relational databases for post-processing inventory reads and writes. Instantaneous concurrent requests can penetrate directly to the underlying data, frequently causing severe row-level lock contention and transaction queuing blockages. Moreover, the existing communication link loop relies excessively on message delivery receipts from third-party platforms, making it difficult to accurately ascertain the actual parsing and access actions of the terminal. If a task is not substantially triggered by the user, resulting in a link interruption, the system struggles to promptly identify this abnormal state. Therefore, this invention provides a method and apparatus for apparel retail member operation decision-making based on the RFIMD model to address the shortcomings of existing technologies. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and apparatus for apparel retail member operation decision-making based on the RFIMD model. This solves the problems of existing data processing systems having a single evaluation dimension when executing operational decisions, lacking dynamic quantification of individual recommendation networks and price sensitivity, which leads to biased data analysis results. Furthermore, when handling concurrent communication requests, existing systems lack concurrency control and state synchronization mechanisms for underlying physical resources, and directly reading and writing to the underlying relational database can easily cause transaction blocking or overselling of inventory.

[0006] To achieve the above objectives, the present invention provides the following technical solution: The first aspect of this invention provides a decision-making method for apparel retail membership operations based on the RFIMD model, comprising: Extract user transaction details, product lifecycle parameters, and recommendation conversion topology data within the evaluation period; Based on the extracted data, time-depreciation calculations and benchmark calibration calculations based on the product lifecycle are performed to construct a dynamic RFIMD feature vector; Extract the two-dimensional addressing coordinates of the dynamic RFIMD feature vector, match the data components in the two-dimensional feature mapping matrix, obtain the target product identifier corresponding to the business task and generate a communication tracking certificate, and encapsulate the matched data components, the target product identifier and the communication tracking certificate into an initial communication data packet; Before the initial communication data packet is sent, the available concurrent quota of the interface is reduced and released. The target item identifier in the initial communication data packet is extracted. When the dynamic RFIMD feature vector threshold condition and the low inventory condition based on the preset low inventory level are met, the target item inventory corresponding to the target item identifier is frozen in the distributed cache layer. The initial communication data packet is sent to the external communication platform terminal, and the message delivery timestamp and the trigger timestamp of the internal application gateway receiving the communication tracking credential parsing request are recorded. If the delay between the current system time and the message delivery timestamp exceeds the blocking timeout threshold and no trigger timestamp is recorded, the inventory freeze is lifted if a temporary freeze record exists, and the available concurrent quota of the interface reduced due to the initial communication data packet is restored, and the degraded communication data packet is assembled.

[0007] Through the above design, this invention utilizes a feature vector construction mechanism to quantify multi-source business data. Combining system resource verification and distributed caching technologies, a pre-deduction channel independent of the underlying relational database is established at the application layer to resolve the database row-level lock blocking problem caused by concurrent requests. Simultaneously, a state feedback closed loop is constructed using communication tracing credentials and a dual-node timing monitoring mechanism. Upon identifying communication task blocking anomalies, system resource rollback and parameter scheduling are triggered, establishing a collaborative mechanism between feature calculation and resource control.

[0008] Furthermore, the step of performing time-depreciation calculations and benchmark calibration calculations based on the extracted data to construct a dynamic RFIMD feature vector includes: Based on the aforementioned recommendation conversion topology data, a time decay factor is introduced and combined with the first transaction feature weight generated by the receiving node and the conversion time difference, and the social recommendation conversion feature value is calculated through time decay calculation. Based on the user transaction details and the product lifecycle parameters, when the number of valid orders is greater than zero within a set time period, the actual payment amount of a single item is compared with the dynamic benchmark price corresponding to the single item to calculate the price sensitivity feature value. The independently calculated social recommendation conversion feature values, price-sensitive feature values, and basic consumption feature values ​​are processed to unify their dimensions and then encapsulated and combined to generate a dynamic RFIMD feature vector.

[0009] The above processing steps reduce the computational weight of historical data through exponential decay operations and use dynamic benchmark prices over the life cycle for comparison, thereby reducing the impact of commodity price reductions on feature quantification and improving the accuracy of feature vector calculation.

[0010] Furthermore, the step of extracting the two-dimensional addressing coordinates of the dynamic RFIMD feature vector, matching data components in the two-dimensional feature mapping matrix, obtaining the target item identifier corresponding to the business task and generating a communication tracking credential, and encapsulating the matched data component, the target item identifier, and the communication tracking credential into an initial communication data packet includes: Social recommendation conversion feature values ​​and price-sensitive feature values ​​are extracted from the dynamic RFIMD feature vector and discretized binning is performed. The discrete integer index values ​​obtained from the conversion are combined to construct two-dimensional addressing coordinates. The pre-configured two-dimensional feature mapping matrix is ​​input to locate the hit resource identifier, so as to extract the data component matching the resource identifier. The data component includes a graphic data component, which includes a visual material component and a text business component. Extract the target user identifier, target product identifier, and task generation timestamp corresponding to the business task, and call the one-way hash algorithm to perform string concatenation operation to generate communication tracking credentials; The communication tracking credentials are appended as network request parameters to the basic gateway request address to generate a mount request address. The matched and extracted image and text data components are then encapsulated with the mount request address into a standard data exchange format structure to generate an initial communication data packet.

[0011] The above processing steps discretize continuous feature variables to adapt to matrix addressing logic, ensuring accurate extraction of data components. At the same time, a one-way hash algorithm is used to generate unique communication tracking credentials, providing a data foundation for subsequent cross-node tracking of system status.

[0012] Furthermore, the step of performing a decremental release operation on the available concurrent capacity of the interface before the initial communication data packet is sent, extracting the target item identifier from the initial communication data packet, and freezing the target item inventory corresponding to the target item identifier in the distributed cache layer when the dynamic RFIMD feature vector threshold condition and the low inventory condition based on the preset low inventory level are met, includes: Instantiate a concurrent controller built on the token bucket mechanism in memory. If the available concurrent quota of the interface in the concurrent controller is found to be greater than zero, perform an atomic operation to decrement the available concurrent quota of the interface and mark the initial communication data packet as a resource ready state. Extract the total transaction amount feature value and the price sensitivity feature value from the dynamic RFIMD feature vector. When the total transaction amount feature value is greater than or equal to a preset total transaction amount threshold and the price sensitivity feature value is greater than or equal to a preset price sensitivity feature threshold, it is determined that the set high value feature condition is met. When the set high-value characteristic conditions are met, query the actual available inventory. When the actual available inventory is lower than the preset low inventory level and greater than or equal to the preset pre-deduction inventory amount, generate a temporary freeze record containing the target item identifier and the target user identifier key value in the distributed cache layer to freeze the inventory.

[0013] The above processing steps control the call frequency of external interfaces based on the token bucket mechanism, generate temporary freeze records in the distributed cache, establish a binding relationship between communication tasks and underlying physical resources, and avoid overselling of inventory due to concurrent requests.

[0014] Furthermore, the step of sending the initial communication data packet to the external communication platform terminal and recording the message delivery timestamp and the trigger timestamp of the internal application gateway receiving the communication tracking credential parsing request includes: The application programming interface is called to send the initial communication data packet to the external communication platform terminal, and a record with the communication tracking credential as the globally unique key value is created in the status mapping table and the timestamp is initialized to an empty value. The asynchronous listening interface receives asynchronous callback confirmation messages from external platforms, extracts the system time as the message delivery timestamp, parses the communication tracking credentials from the transparent field of the asynchronous callback confirmation message, performs mapping table matching, and writes them into the status mapping table. Continuously monitor the access logs of the internal application gateway. When a parsing request with the communication tracing credentials is captured, extract the system time as the trigger timestamp and update the corresponding field in the status mapping table to complete the closed-loop recording.

[0015] The above processing steps utilize a state mapping table to uniformly manage the timestamps of callback messages and access logs, and establish data association through communication tracking credentials to avoid the problem of misjudgment of state caused by relying on a single external receipt.

[0016] Furthermore, when the delay between the current system time and the message delivery timestamp exceeds the blocking timeout threshold and no trigger timestamp is recorded, the inventory freeze is lifted if a temporary freeze record exists, and the available concurrent interface capacity reduced due to the initial communication data packet is restored, and a degraded communication data packet is assembled, including: Poll the status mapping table. If the message delivery timestamp is not null and the delay between the current system time and the message delivery timestamp is greater than the set blocking timeout threshold and the trigger timestamp is still the initial null value, determine that the communication delivery task has been blocked. When the blocking task has an inventory freeze flag, the distributed cache layer is triggered to perform an unlock operation, which releases the frozen inventory amount to the available inventory pool by reversing the accumulation, and sends a release signal to the concurrency controller to execute the increment operation of the available concurrency amount of the interface. Extract the social recommendation conversion feature value corresponding to the blocking task and apply a predefined downgrade coefficient for downgrade calibration. Combine the downgrade calibration feature with the original price-sensitive feature value and input it into a two-dimensional feature mapping matrix to extract the image and text components without inventory dependence. Then, concatenate them to generate downgrade communication data packets and write them to an asynchronous queue.

[0017] The above processing steps determine the interruption status of the communication task through timing data. When an interruption occurs, the cache layer is unlocked and the concurrency controller is reset. The original characteristic parameters are downgraded and calibrated to generate a degraded communication task, thus avoiding long-term occupation of system resources by unresponsive tasks.

[0018] Furthermore, the comparison of the actual payment amount for a single item with the corresponding dynamic benchmark price includes: Extract the lifecycle stage tags associated with the target product at the time the transaction occurs; When the lifecycle stage label indicates that the target product is in the full-price sales period, the dynamic benchmark price is determined as the system's original price for the target product. When the product lifecycle stage label indicates that the target product is in the clearance sale period, the dynamic benchmark price is determined to be the original system price multiplied by the set system clearance discount rate parameter.

[0019] The above processing steps dynamically match the benchmark price based on the product's life cycle stage, thereby improving the reliability of the price sensitivity feature calculation.

[0020] Furthermore, the extraction of user transaction details, product lifecycle parameters, and recommendation conversion topology data within the evaluation period includes: Read the underlying system configuration parameters and extract the set start and end timestamps to define the time window boundaries for system evaluation; Send data extraction commands to external enterprise resource planning and customer relationship management systems with which connections have been established to extract the target data set; Perform a time filter operation to extract business logs whose timestamps fall between the start and end timestamps and load them into the memory cache.

[0021] The above processing steps extract business logs by defining time window boundaries, unify and align external system data, and provide basic data input for the construction of feature vectors.

[0022] Furthermore, after loading the business logs into the memory cache, the process also includes: Filter out abnormal records in the business log collection where key fields are empty; Duplicate log entries caused by network retransmission mechanisms are removed by comparing globally unique identifiers or hash values ​​of data rows. Heterogeneous log files from different external systems are parsed into a unified structured data format, and unstructured text logs are mapped into a standardized relational field matrix to output a standardized set of business data.

[0023] The above processing steps eliminate redundant data and format differences in the original logs through anomaly filtering, deduplication, and structured parsing operations, ensuring the stability of subsequent data processing.

[0024] A second aspect of the present invention provides a decision-making device for apparel retail membership operations based on the RFIMD model, comprising: The data acquisition and preprocessing module is configured to extract user transaction details, product lifecycle parameters, and recommendation conversion topology data within the evaluation period. The feature vector calculation module is configured to perform time-depreciation calculation and benchmark calibration calculation based on the product life cycle based on the extracted data to construct dynamic RFIMD feature vectors; The data packet assembly module is configured to extract the two-dimensional addressing coordinates of the dynamic RFIMD feature vector, match data components in the two-dimensional feature mapping matrix, obtain the target single item identifier corresponding to the business task and generate a communication tracking certificate, and encapsulate the matched data components, the target single item identifier and the communication tracking certificate into an initial communication data packet. The system resource control module is configured to perform a decremental release operation of the available concurrent quota of the interface before the initial communication data packet is sent, extract the target item identifier in the initial communication data packet, and freeze the target item inventory corresponding to the target item identifier in the distributed cache layer when the dynamic RFIMD feature vector threshold condition and the low inventory condition based on the preset low inventory level are met. The communication and event monitoring module is configured to send the initial communication data packet to the external communication platform terminal, record the message delivery timestamp and the trigger timestamp of the internal application gateway receiving the communication tracking credential parsing request; The blocking assessment and system rollback module is configured to, when the delay between the current system time and the message delivery timestamp exceeds the blocking timeout threshold and no trigger timestamp is recorded, release the inventory freeze if a temporary freeze record exists, restore the interface available concurrency quota that was reduced due to the initial communication data packet, and assemble the degraded communication data packet.

[0025] This invention provides a method and apparatus for apparel retail membership operation decision-making based on the RFIMD model. It has the following beneficial effects: 1. This invention introduces a time decay factor to calculate social recommendation conversion feature values ​​when constructing dynamic RFIMD feature vectors, and combines the dynamic benchmark price of the product lifecycle to calculate price-sensitive feature values. These technical features reduce the impact of historical data and seasonal price reductions on feature quantification, improve the accuracy of feature evaluation, and provide accurate data input for subsequent data packet assembly and resource scheduling.

[0026] 2. Before the communication data packet is sent, this invention uses a concurrency controller to decrement the available concurrency limit of the interface. When both high-value and low-inventory conditions are met, a temporary freeze record is generated in the distributed cache layer to freeze the target item's inventory. These technical features establish an inventory pre-deduction mechanism at the application layer that is independent of the underlying relational database, avoiding transaction blocking issues caused by concurrent requests directly reading and writing to the database, and preventing overselling of system inventory.

[0027] 3. This invention utilizes communication tracking credentials to associate message delivery timestamps with internal gateway trigger timestamps. When the delay exceeds a set blocking timeout threshold and no trigger timestamp is recorded, it automatically triggers inventory unlocking in the distributed cache layer and quota restoration in the concurrency controller. These technical features enable the system to promptly reclaim physical resources occupied by blocked tasks based on the actual response status of the communication link, and to reschedule communication data packets without inventory dependencies through parameter downgrading, thereby improving the overall resource utilization of the system. Attached Figure Description

[0028] Figure 1 This is a diagram of the device architecture of the present invention; Figure 2 This is a flowchart of the method steps of the present invention; Figure 3 This is a flowchart of the data acquisition and preprocessing process of the present invention; Figure 4 This is a flowchart of the feature vector calculation process of the present invention; Figure 5This is a flowchart of the data packet assembly process of the present invention; Figure 6 This is a flowchart illustrating the system resource control of the present invention. Figure 7 This is a flowchart illustrating the communication and event monitoring process of the present invention. Figure 8 This is a flowchart of the blocking assessment and system rollback process of the present invention; Figure 9 This is a line graph comparing the time series of resource occupancy status of the system of this invention. Detailed Implementation

[0029] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] See attached document Figure 1 This invention provides a decision-making device for apparel retail membership operations based on the RFIMD model, which may include: The data acquisition and preprocessing module is configured to extract user transaction details, product lifecycle parameters, and recommendation conversion topology data from the enterprise resource planning system, customer relationship management system, and gateway logs.

[0031] The feature vector calculation module is connected to the data acquisition and preprocessing module. The feature vector calculation module is configured to receive the extracted data, perform time-related decay calculations and benchmark calibration calculations based on the product lifecycle, and output a feature vector containing five dimensions.

[0032] The data packet assembly module is connected to the feature vector calculation module. The data packet assembly module is configured to extract data components based on the two-dimensional addressing coordinates in the feature vector, call a one-way hash algorithm to generate communication tracking credentials, and encapsulate the extracted data components and communication tracking credentials into a structured communication data packet.

[0033] The system resource control module is connected to the data packet assembly module. The system resource control module is configured to execute interface available concurrency limit verification logic and perform logical freeze operation on the entity inventory before initiating a communication request.

[0034] The communication and event monitoring module is connected to the system resource control module. The communication and event monitoring module is configured to interface with an external instant messaging platform's application programming interface (API) for data distribution and deploys a distributed time-series state machine to monitor external callback events and internal gateway-triggered events.

[0035] The blocking assessment and system rollback module is connected to the communication and event monitoring module, the system resource control module, and the packet assembly module, respectively. The blocking assessment and system rollback module is configured to perform timeout determination based on the time difference. When a communication blockage is determined to have occurred, the blocking assessment and system rollback module sends a resource rollback command to the system resource control module and a parameter downgrade calibration command to the packet assembly module.

[0036] See attached document Figure 2 This invention provides a decision-making method for apparel retail membership operations based on the RFIMD model, comprising the following steps: S10, extract user transaction details, product lifecycle parameters, and recommendation conversion topology data within the set evaluation period; S20 receives the extracted data, performs time-depreciation calculation and benchmark calibration calculation based on the product life cycle, and constructs a dynamic RFIMD feature vector containing five dimensions. S30: Extract the two-dimensional addressing coordinates from the feature vector as addressing parameters, match the corresponding data components in the two-dimensional feature mapping matrix, call the one-way hash algorithm to generate communication tracking credentials, and encapsulate the matched data components and communication tracking credentials into an initial communication data packet; wherein, the data components include graphic data components, and the graphic data components include visual material components and text business components; S40 performs system resource verification before the communication data packet is sent, performs a decremental release operation on the available concurrent quota of the interface, and executes a logical pre-deduction instruction in the distributed cache layer to freeze the inventory of the corresponding quota of the target single item when the feature vector threshold condition and low inventory condition are met. S50 calls the application programming interface of the external platform to send the communication data packet with resource verification to the external communication platform terminal, and records the delivery time of the message confirming the asynchronous callback from the external platform and the trigger time of the internal application gateway receiving the communication tracking credential parsing request. S60, calculate the delay between the current system time and the message delivery timestamp. If the delay exceeds the set blocking timeout threshold and no trigger timestamp is recorded, trigger the system resource control module to unfreeze the inventory when there is a temporary freeze record, restore the interface available concurrency quota that was reduced due to the initial communication data packet, and feed back the degradation calibration feature to the data packet assembly module to reassemble the degradation communication data packet.

[0037] The technical solution of the present invention will be described in detail below with reference to specific implementation steps.

[0038] See attached document Figure 3 Specifically, step S10 extracts user transaction details, product lifecycle parameters, and recommendation conversion topology data within the set evaluation period. Its detailed implementation may include the following sub-steps: S110, the data acquisition and preprocessing module reads the underlying system configuration parameters and extracts the set start and end timestamps to define the time window boundaries for system evaluation.

[0039] In one specific embodiment, the time window boundary can be configured as a rolling time window that extrapolates forward based on the current system clock, such as the last thirty calendar days or a complete financial cycle, so that the system can achieve continuous dynamic data tracking.

[0040] The data acquisition and preprocessing module establishes system-level data communication connections with external enterprise resource planning systems and customer relationship management systems through database connection pool components or standard application programming interfaces.

[0041] S120, based on the set time window boundaries, the data acquisition and preprocessing module sends a data extraction command to the external system with which it has established a connection. The data acquisition and preprocessing module extracts the target data set from the underlying database logs of the external system. The target data set includes user transaction details, product lifecycle parameters, and recommendation conversion topology data. Specifically, the extracted user transaction details record the user identifier, order serial number, target product identifier, actual payment amount, and order settlement timestamp.

[0042] The product lifecycle parameters record the lifecycle stage tags corresponding to the target product identifier. These lifecycle stage tags are configured to indicate whether the product is in a full-price sales period, a regular discount period, or a clearance sale period. Recommended conversion topology data is obtained by parsing promotional link click records with specific user suffix parameters or by binding relationship tables. This data records the sharing end node identifier, the receiving end node identifier, and the conversion timestamp of the receiving end generating a valid purchase.

[0043] During the data extraction process, the data acquisition and preprocessing module performs a time filter operation, extracting business logs that conform to the evaluation period based on conditional expressions: ; In the formula, The starting timestamp indicating the boundary of the time window. The end timestamp indicating the boundary of the time window. This represents the timestamp of the business events recorded in each business log. The data acquisition and preprocessing module loads business logs that meet the above timing conditions into the system's memory cache.

[0044] S130, the data acquisition and preprocessing module performs data cleaning operations on the user transaction details, product lifecycle parameters and recommendation conversion topology data temporarily stored in the memory cache.

[0045] The data acquisition and preprocessing module identifies and filters abnormal records in the log set where key fields are empty, and removes duplicate log entries caused by the network retransmission mechanism by comparing globally unique identifiers or hash values ​​of data rows.

[0046] After data cleaning, the data acquisition and preprocessing module performs format unification processing. This module parses heterogeneous log files from different external systems into a unified structured data format, maps unstructured text logs into a standardized relational field matrix, outputs a standardized set of business data, and transmits this data as input for subsequent feature calculations to the next processing level.

[0047] See attached document Figure 4 Specifically, step S20 receives the extracted data, performs time-depreciation calculation and benchmark calibration calculation based on the product lifecycle, and constructs a dynamic RFIMD feature vector containing five dimensions. Its detailed implementation may include the following sub-steps: S210, the feature vector calculation module reads the user transaction details that have been standardized by the data acquisition and preprocessing module, and performs routine aggregation calculations of basic consumption features.

[0048] RFIMD feature vectors are used to characterize the dynamic membership features of target users from dimensions such as basic consumption behavior, social recommendation conversion behavior, and price response behavior. In RFIMD, R represents recent consumption characteristics, F represents consumption frequency characteristics, M represents total transaction amount characteristics, I represents social recommendation conversion characteristics, and D represents price sensitivity characteristics.

[0049] Accordingly, the dynamic RFIMD feature vector of the target user It can be represented as: ; In the formula, This represents a recent consumption characteristic value, used to characterize the time interval between a target user's most recent valid transaction; This represents the frequency of purchases, used to characterize the frequency with which a target user places a valid order within a set time period. This represents the total transaction amount characteristic value, used to characterize the total effective transaction amount generated by the target user within a set time period; This represents the social recommendation conversion feature value, used to characterize the ability of a target user to facilitate a valid transaction at the receiving node through recommendation relationships; This represents the price-sensitive characteristic value, used to characterize the ratio of the target user's actual payment amount to the dynamic benchmark price of the product.

[0050] Among them, recent consumption characteristics Consumption frequency characteristic value and the characteristic value of total transaction amount Together, they constitute the basic consumption characteristic values, and the social recommendation conversion characteristic values. and price-sensitive eigenvalues These constitute extended feature values ​​used for content addressing and operational decisions.

[0051] The feature vector calculation module calculates the time difference between the timestamp of the target user's most recent valid transaction record and the timestamp calculated by the current system to generate a recent consumption feature value; combined with the set time window boundaries, it counts the total number of valid orders generated by the target user within the set time period to generate a consumption frequency feature value; and it sums the actual payment amounts of all valid orders within the time period to generate a total transaction amount feature value.

[0052] S220, the feature vector calculation module performs time-lapse calculation on social recommendation conversion features based on the extracted recommendation conversion topology data.

[0053] The feature vector calculation module introduces a time decay factor into the computational model and calculates the social recommendation conversion feature value according to the following formula: ; In the formula, This represents the feature value of social recommendation conversion; This represents the total number of new receiving nodes that successfully registered and generated purchase conversions through the target user's sharing node identifier within the time window boundary. Indicates the first The first transaction feature weight generated by each receiving node; It is a natural constant; Indicates the first The time difference between the conversion timestamp corresponding to each recommended conversion event and the current system calculation timestamp; This represents the system's preset time decay constant, the value of which controls the rate at which the weights of historical behaviors decrease over time. Typically, It is a real number greater than 0. For example, if the half-life of the recommendation behavior is set to be... Heaven, then Values .

[0054] The weight of the first transaction feature is determined based on the settlement amount of the first transaction at the receiving node, according to a pre-configured tiered mapping rule.

[0055] In one exemplary implementation, when the first settlement amount of the receiving node is less than 100 yuan, the first transaction feature weight is adjusted. Set to 50; when the first order settlement amount is greater than or equal to 100 yuan and less than or equal to 300 yuan, the first order transaction characteristic weight will be adjusted. Set to 100; when the first order settlement amount is greater than 300 yuan, the first order transaction feature weight will be adjusted. Set it to 150.

[0056] The above-mentioned amount ranges and corresponding weight values ​​are only used to illustrate one implementation of the tiered mapping rule. In other implementations, the amount ranges, the number of ranges, and the corresponding weight values ​​can be adjusted according to product category, average order value distribution, membership operation cycle, or historical conversion data, and the tiered mapping rule can be stored as a system configuration parameter in the rule configuration table.

[0057] S230, the feature vector calculation module performs a benchmark calibration operation based on the product lifecycle for price-sensitive features, based on the user's transaction details and product lifecycle parameters.

[0058] The system identifies the number of valid orders within the currently set time period. .like If the value is zero, the system assigns a preset neutral price-sensitive default value to the user to avoid abnormal division by zero operations; if If the value is greater than zero, the feature vector calculation module calculates the price-sensitive feature value according to the following formula: ; In the formula, Indicates price-sensitive characteristic value; This represents the total number of individual items included in all valid orders generated by the target user within the time window boundary; Indicates the first The actual payment amount for each individual item; Indicates the first The dynamic benchmark price corresponding to each individual item.

[0059] The feature vector calculation module extracts the lifecycle stage tag associated with the product at the time of the transaction and assigns a corresponding dynamic benchmark price accordingly. Specifically, when the lifecycle stage tag indicates that the product is in a regular-price sales period, the dynamic benchmark price is the product's original system price; when the lifecycle stage tag indicates that the product is in a regular discount period, the dynamic benchmark price is determined as the product's original system price multiplied by a set regular discount rate parameter; when the lifecycle stage tag indicates that the product is in a clearance sale period, the dynamic benchmark price is the original price multiplied by a set system clearance discount rate parameter.

[0060] S240, the feature vector calculation module performs dynamic RFIMD feature vector fusion and construction processing, reading the basic consumption feature values, social recommendation conversion feature values, and price-sensitive feature values ​​into a unified memory array. The basic consumption feature values ​​include recent consumption feature values, consumption frequency feature values, and total transaction amount feature values.

[0061] The feature vector calculation module calls the standard deviation standardization algorithm or the minimum-maximum normalization algorithm to unify the dimensions of recent consumption feature values, consumption frequency feature values, total transaction amount feature values, social recommendation conversion feature values, and price-sensitive feature values.

[0062] The feature vector calculation module encapsulates and combines the five dimension values ​​that have undergone dimensional unification according to a fixed sequence rule to generate a dynamic RFIMD feature vector that represents the target user, and outputs and stores the feature vector for use in the next system-level operation.

[0063] See attached document Figure 5 Specifically, step S30 extracts the two-dimensional addressing coordinates from the feature vector as addressing parameters, matches the corresponding data components in the two-dimensional feature mapping matrix, calls the one-way hash algorithm to generate a communication tracking credential, and encapsulates the matched data components and the communication tracking credential into an initial communication data packet. Its detailed implementation may include the following sub-steps: S310, the data packet assembly module receives the dynamic RFIMD feature vector output by the feature vector calculation module, and extracts social recommendation conversion feature value and price-sensitive feature value from the dynamic RFIMD feature vector.

[0064] The data packet assembly module performs discretization binning on the two extracted feature values ​​based on a preset threshold range, converting continuous values ​​into discrete integer index values, and combining these two discrete values ​​to construct two-dimensional addressing coordinates.

[0065] The system's memory or cache is pre-configured with a two-dimensional feature mapping matrix. The two-dimensional feature mapping matrix uses the discrete integer index value corresponding to the social recommendation conversion feature value as the first dimension index and the discrete integer index value corresponding to the price sensitive feature value as the second dimension index. Each matrix unit stores one or more content resource identifiers.

[0066] In one exemplary implementation, the system converts social recommendation features into values ​​respectively. and price-sensitive eigenvalues It is divided into three feature intervals: low, medium, and high. For social recommendation conversion feature values... The low, medium, and high feature intervals are converted into discrete integer index values ​​0, 1, and 2, respectively; for price-sensitive feature values... The three feature intervals—low, medium, and high—are also converted into discrete integer index values ​​of 0, 1, and 2, respectively. This constructs a 3×3 two-dimensional feature mapping matrix.

[0067] Specifically, the system can pre-set a first social recommendation conversion threshold. Second social recommendation conversion threshold First price sensitivity threshold and the second price sensitivity threshold .when Less than When, its discrete integer index value is 0; when Greater than or equal to and less than When, its discrete integer index value is 1; when Greater than or equal to At that time, its discrete integer index value is 2. Price-sensitive eigenvalue According to the same method and Convert to the corresponding discrete integer index value.

[0068] The data packet assembly module combines two discrete integer index values ​​to construct a two-dimensional addressing coordinate system and uses this coordinate system to access the corresponding matrix unit. Each matrix unit can store a single content resource identifier or multiple content resource identifiers arranged by priority. The content resource identifier points to the corresponding data component in the content management system. In one implementation, the matrix unit of the two-dimensional feature mapping matrix includes at least a first-dimensional index value, a second-dimensional index value, a content resource identifier, a data component identifier, a target item identifier, an inventory dependency attribute marker, and a resource priority field. The inventory dependency attribute marker indicates whether the data component hit by the matrix unit needs to be bound to the target item's inventory, perform inventory verification, or trigger an inventory freeze operation.

[0069] For example, when the social recommendation conversion feature is in the high range and the price sensitivity feature is in the high range, the corresponding two-dimensional addressing coordinates can be (2, 2). This matrix unit can store the resource identifiers of graphic and text data components for users with high recommendation activity and high price sensitivity. When the social recommendation conversion feature is in the low range and the price sensitivity feature is in the middle range, the corresponding two-dimensional addressing coordinates can be (0, 1). This matrix unit can store the resource identifiers of general member maintenance graphic and text data components.

[0070] The 3×3 matrix described above is merely an example. In other implementations, other numbers of binning intervals and matrix dimensions can be set based on business data distribution, quantile intervals, clustering results, or preset operational rules.

[0071] The data packet assembly module inputs the constructed two-dimensional addressing coordinates into the two-dimensional feature mapping matrix for location and obtains the matched resource identifier. Based on the resource identifier, the data packet assembly module initiates a retrieval request to the content management system's database and extracts the data components that match the resource identifier.

[0072] In one implementation, the matrix unit corresponding to the resource identifier is further configured with a target item identifier, a target item selection rule, or a business task configuration index. When the hit data component is bound to a specific item, the data packet assembly module determines the item identifier bound to that data component as the target item identifier; when the hit data component is a general content component, the data packet assembly module determines the target item identifier according to the business task configuration index or operational strategy rule.

[0073] In this embodiment, the data component includes a graphic data component, which further includes a visual material component and a text business component. The visual material component includes at least one of product images, event images, page cover images, or icon materials; the text business component includes at least one of product titles, recommendation text, discount notices, price descriptions, inventory information, or jump button text.

[0074] S320, after acquiring the text and image data required for the business, the data packet assembly module performs a dynamic communication tracking credential generation operation by extracting the target user identifier corresponding to the current business task, the target item identifier assigned by the system, and the task generation timestamp of the system that currently generated the task. The data packet assembly module calls the cryptographic component to generate the communication tracking credential according to the following formula: ; In the formula, This indicates the generated communication tracking credentials; This indicates the target user identifier corresponding to the current business link; This represents the current system timestamp when the system performed the generation operation; This indicates the target product identifier for the current operational decision-making task allocation; This indicates a string concatenation operation performed on the variables before and after it. This indicates a one-way hash algorithm, such as the SHA-256 algorithm.

[0075] S330, the data packet assembly module extracts the system's basic gateway request address and combines it with the communication tracking credentials generated by the aforementioned operations. As a network request parameter, it is appended to the end of the underlying gateway request address in the format of a standard Uniform Resource Locator (URL) to generate a mount request address with a unique verification attribute.

[0076] The data packet assembly module encapsulates the data components extracted in S310, the target item identifier extracted in S320, and the mount request address with communication tracking credentials into a standard data exchange format structure according to the application programming interface specification requirements of the external communication platform. In one embodiment, the standard data exchange format structure is a JavaScript object simplified format structure.

[0077] The standard data exchange format structure includes at least a data component field, a target item identifier field, and a mount request address field; the mount request address carries communication tracking credentials.

[0078] After the data packet assembly module completes the format encapsulation, it generates an initial communication data packet for the target user and writes the initial communication data packet into the system cache so that the system resource control module can extract the target item identifier and perform subsequent resource constraint verification.

[0079] See attached document Figure 6 Specifically, step S40 performs system resource verification before the communication data packet is sent, performs a decremental release operation on the available concurrent quota of the interface, and, under the conditions of satisfying the feature vector threshold and low inventory, executes a logical pre-deduction instruction in the distributed cache layer to freeze the inventory corresponding to the target single item. Its detailed implementation may include the following sub-steps: S410, the system resource control module performs an interface availability and concurrency check before sending communication data packets.

[0080] The system resource control module instantiates a concurrent controller built on the token bucket mechanism in memory and adds available concurrent quotas to the concurrent controller at a set fixed rate until the set capacity limit is reached.

[0081] When the initial communication data packet output by the data packet assembly module is received, the system resource control module queries the current available concurrency quota of the interface in the concurrency controller. .

[0082] exist Under these conditions, the system resource control module performs a decreasing release operation, that is, it performs an atomic operation to release the resource. The value is decremented by one, and the initial communication data packet is marked as resource-ready and allowed to proceed to the next system level.

[0083] exist Under these conditions, the system resource control module transfers the initial communication data packet to the message queue for asynchronous waiting until a new available concurrency quota is generated in the concurrency controller due to fixed-rate replenishment.

[0084] After completing the verification of the available concurrent quota of the interface, the system resource control module performs pre-deduction of distributed cache logic based on characteristic thresholds for specific business states.

[0085] The system resource control module reads the dynamic RFIMD feature vector corresponding to the target user and extracts the total transaction amount feature value from it. Price-sensitive eigenvalues In this embodiment, the dynamic RFIMD feature vector threshold condition refers to a judgment condition formed based on at least one dimension feature value in the dynamic RFIMD feature vector. Specifically, this threshold condition may include a feature value based on the total transaction amount. and price-sensitive eigenvalues The identified high-value characteristics can also be combined with recent consumption characteristics, consumption frequency characteristics, or social recommendation conversion characteristics to form a set of judgment conditions based on business strategies.

[0086] The system resource control module compares the extracted feature values ​​with a preset judgment threshold for verification. When the system determines that the condition is met... and At that time, the system resource control module determines that the target user meets the set high-value characteristic conditions. In the formula, This indicates the system's preset threshold for the total transaction amount. This represents the system's preset price-sensitive feature threshold.

[0087] S430: For users who meet the high-value characteristic conditions, the system resource control module further extracts the target item identifier associated with the initial communication data packet.

[0088] The system resource control module queries the current actual available inventory of the target item in the global database. Simultaneously, the system reads the preset pre-deduction inventory quantity limit configured for the target item. When the actual available inventory obtained from the query is lower than the set low inventory level, and the actual available inventory is greater than or equal to the preset pre-deducted inventory quantity limit. At this time, the system resource control module sends a logical pre-deduction instruction to the system's distributed cache layer. If the actual available inventory is less than... If so, the system skips the pre-deduction step.

[0089] The system resource control module allocates a quota independently in the distributed cache layer through logical pre-deduction instructions. The inventory quantity is determined and its status is frozen. The frozen inventory quantity is then associated with the target user identifier and the target product identifier.

[0090] To avoid duplicate inventory freezes caused by multiple concurrent tasks reading the same inventory value, the system sets up the actual available inventory reading, low inventory condition judgment, preset pre-deduction inventory quantity limit verification, available inventory value deduction, and temporary freeze record writing as an indivisible atomic process.

[0091] In one implementation, atomic processing can be achieved using Redis Lua scripts, allowing the distributed cache server to sequentially complete inventory reads, condition checks, inventory deductions, and freeze record writes within a single script execution. In other implementations, the same atomic processing effect can be achieved using distributed locks, Compare and Swap (CAS) operations, or cache transaction mechanisms.

[0092] When the actual available inventory is lower than the preset low inventory level but greater than or equal to the preset pre-deduction inventory amount. At that time, the atomic processing procedure will deduct the available inventory. A temporary freeze record is generated in the distributed cache. This temporary freeze record can use a combination of the target item identifier and the target user identifier as the key, and a preset pre-deduction amount from the inventory quantity. The record includes the freeze timestamp, communication tracking credentials, and freeze status.

[0093] Temporary freeze records can also be set with a preset expiration period. When the expiration period expires and the system has not recorded a corresponding valid trigger event or transaction event, the system will add the frozen inventory amount back to the available inventory pool through an atomic release operation, and delete or update the temporary freeze record. This ensures data consistency between inventory freeze and inventory release operations under concurrent conditions.

[0094] See attached document Figure 7 Specifically, in step S50 above, calling the application programming interface of the external platform will send the communication data packet with resource verification to the external communication platform terminal, and record the message delivery timestamp of receiving the asynchronous callback confirmation from the external platform and the trigger timestamp of the internal application gateway receiving the communication tracking credential parsing request. Its detailed implementation may include the following sub-steps: S510, the communication and event monitoring module obtains the initial communication data packet authorized and released by the system resource control module. The communication and event monitoring module reads the external communication platform terminal address specified in the system configuration file, calls the corresponding application programming interface to establish a network connection, and sends the initial communication data packet to the external terminal based on the Hypertext Transfer Protocol. The external terminal may include an external communication platform terminal, a user mobile terminal, an instant messaging platform message receiver, or other network terminals capable of receiving the initial communication data packet.

[0095] Simultaneously, the communication and event monitoring module creates a record corresponding to the issued task in the system's state mapping table. This record uses the communication tracing credentials generated by the packet assembly module. As a globally unique key-value pair, and also includes the message delivery timestamp. With trigger timestamp Initialize to null.

[0096] In the S520, the communication and event monitoring module receives asynchronous callback confirmation messages from external platforms via an asynchronous monitoring interface. When it parses a status code indicating that the message has been successfully pushed to the terminal, the communication and event monitoring module extracts the current system time as the message delivery timestamp. And match the corresponding communication tracing credentials based on the parameters in the callback message. .

[0097] Specifically, when issuing the initial communication data packet in step S510, the system will use the communication tracking credentials. The communication tracking credential is written into a custom pass-through field of the application programming interface (API), so that when the corresponding asynchronous callback confirmation message is received, the communication tracking credential can be directly parsed and extracted from the pass-through field of the message. Perform mapping table matching. The communication and event listening module will then... Write the corresponding state mapping table The field.

[0098] S530, the communication and event monitoring module continuously monitors the access logs of the internal application gateway. When a target user triggers a request address with a communication tracking credential on an external communication platform terminal, the internal application gateway receives the request with that communication tracking credential. The parsing request.

[0099] The communication and event listening module captures the parsing request and extracts the current system time as the trigger timestamp. and the Update the corresponding field in the status mapping table to complete the closed-loop record of this communication task.

[0100] See attached document Figure 8 Specifically, step S60 calculates the delay between the current system time and the message delivery timestamp. If it is determined that the delay exceeds the set blocking timeout threshold and no trigger timestamp is recorded, the system resource control module is triggered to unfreeze the inventory if a temporary freeze record exists, and restore the available concurrent interface quota that was reduced due to the initial communication data packet. The module then feeds back the degradation calibration feature to the data packet assembly module to reassemble the degraded communication data packet. The implementation method includes the following sub-steps: S610, the blocking assessment and system rollback module continuously polls the timestamp data recorded in the state mapping table based on a set system clock cycle. The blocking assessment and system rollback module first performs a filtering operation on the state mapping table, excluding message delivery timestamps. Task records with empty values.

[0101] For successfully delivered tasks, the blocking assessment and system rollback module extracts the message delivery timestamp of any task from the status mapping table. With trigger timestamp The blocking assessment and system rollback module calculates the current system time. With the message delivery timestamp The difference is used to obtain the latency. The system has a preset blocking timeout threshold. The blocking assessment and system rollback module executes the following logical expression to determine the outcome: ; In the formula, This indicates that the trigger timestamp is in an initial null value state that has not been written. When the above conditions are met, that is, when the latency of the task exceeds the blocking timeout threshold and the trigger timestamp is still an initial null value, the blocking assessment and system rollback module determines that the current communication delivery task has experienced a blocking anomaly.

[0102] S620: After determining that a blocking anomaly has occurred, the blocking assessment and system rollback module sends a resource rollback command to the system resource control module.

[0103] The blocking assessment and system rollback module queries whether the blocking task has an inventory freeze flag in the system. If it does, the blocking assessment and system rollback module triggers the distributed cache layer to perform an unlocking operation based on the target item flag and pre-deduction amount associated with the current blocking task.

[0104] After receiving the instruction, the caching system will increment the target item inventory amount frozen in the previous step in reverse and release it to the available inventory pool. If there is no inventory freeze flag, this inventory release step will be skipped.

[0105] Subsequently, the blocking assessment and system rollback module sends a release signal of the available concurrency quota to the concurrency controller. After receiving the signal, the concurrency controller executes an increment operation of the available concurrency quota of its internal interface to restore the call quota of the external communication interface.

[0106] S630, the blocking assessment and system rollback module reports the communication blocking event to the packet assembly module. This is achieved by extracting the social recommendation conversion feature values ​​of the user corresponding to the blocking task. and apply a predefined downsampling coefficient. Perform calibration calculations: ; In the formula, This represents the social recommendation conversion feature value after downgrade calibration; This represents a real number greater than zero and less than one. The data packet assembly module receives the downgraded and calibrated social recommendation transformation feature values. Compared with the original price-sensitive characteristic value And according to the same discretization binning rules as in step S310, respectively... and The two discrete integer index values ​​are converted into corresponding discrete integer index values. The packet assembly module combines the two discrete integer index values ​​to construct a downgraded two-dimensional addressing coordinate, and then inputs the downgraded two-dimensional addressing coordinate into the two-dimensional feature mapping matrix for re-addressing.

[0107] The content resource identifier in the two-dimensional feature mapping matrix can be associated with inventory dependency attribute markers. Inventory dependency attribute markers indicate whether the corresponding data component needs to freeze, pre-deduct, or verify the target item's inventory when it is issued or triggered. When generating degraded communication data packets, the data packet assembly module only extracts data components whose inventory dependency attribute markers are marked as having no inventory dependency.

[0108] In this embodiment, an inventory-free graphic component refers to a data component that meets at least one of the following conditions: it is not bound to a low-inventory target item identifier; it does not trigger inventory pre-deduction or inventory freeze operations in the distributed caching layer; it does not contain a direct purchase entry for limited-quantity, limited-time, or scarce inventory items; or it does not require verification of the available inventory of the target item when the target user triggers the corresponding request address.

[0109] Inventory-free graphic and text components can include brand content, membership benefits descriptions, fashion information, recommendations for non-limited-time items, general promotional information, or other graphic and text content that does not consume inventory resources of the target item.

[0110] The data packet assembly module removes data components marked as having inventory dependencies from the original initial communication data packet, and concatenates the re-addressed non-inventory-dependent graphic components with the mount request address to generate a downgraded communication data packet.

[0111] Degraded communication data packets do not trigger inventory freeze operations for the original target item. The data packet assembly module writes the degraded communication data packets into a low-priority asynchronous queue. Based on the overall concurrent load, the system re-executes resource scheduling for the degraded communication data packets when the system load meets preset scheduling conditions. Preset scheduling conditions may include the interface's available concurrent capacity recovering to above a preset limit, the system's current communication load falling below a preset load threshold, or reaching a timed polling scheduling cycle in the asynchronous queue.

[0112] The following is a specific application example, using a periodic marketing task in an apparel retail system as an example.

[0113] The system is set to evaluate over the past 30 calendar days, with a time decay constant. Based on a 7-day half-life setting, its value is The target item is a limited-edition coat with low inventory levels; the system has set a threshold for the total transaction amount. The price-sensitive feature threshold is 1000 yuan. The blocking timeout threshold is 0.6. It lasts for 15 minutes.

[0114] For target user A, the data acquisition and preprocessing module extracts their historical transaction and topological correlation data. In the feature vector calculation module, the system aggregates and calculates the feature value of user A's total transaction amount. The amount is 1500 yuan. The system detected that user A recommended 2 new users within the period, and the weight corresponding to the first order settlement amount is... and The values ​​are 100 and 150, corresponding to the conversion time difference. For 2 days, The timeframe is 10 days. The social recommendation conversion feature value is calculated based on the time decay formula. : ; The system detected that user A purchased two items, with actual payment amounts of 500 yuan and 200 yuan respectively. The corresponding system dynamic benchmark prices for these two items are 600 yuan and 500 yuan respectively. The price-sensitive characteristic value is calculated based on the lifecycle benchmark calibration formula. : ; After normalizing the above feature values, the system determines the feature value of user A. The condition of exceeding the set threshold is met. The data packet assembly module generates an initial communication data packet containing the limited edition coat components and communication tracking credentials.

[0115] Before the distribution, the system resource control module reduces the available quota of the concurrent controller and sends a logical pre-deduction instruction to the distributed cache layer to establish a temporary freeze relationship between the inventory of one limited edition coat and user A.

[0116] The communication and event listening module receives a callback from the external platform at system time 10:00:00 and records the message delivery timestamp. The trigger timestamp is 10:00:00. Initialize to null.

[0117] When the system time reaches 10:16:00, the blocking assessment and system rollback module performs polling to calculate the latency. The timeout period is 16 minutes. Because 16 minutes exceeds the set 15-minute timeout threshold, and... The value is still empty, and the system determines that a block has occurred. The system then triggers the caching layer to add one unit of the limited edition coat's inventory back to the available inventory pool, restores the interface's available concurrency limit, and processes user A's characteristic value. Apply a downgrade factor of 0.2 to calculate the degradation characteristics. : ; The system reorganizes the degraded communication data packets that do not depend on inventory and transfers them to an asynchronous queue, thereby avoiding the long-term invalid occupation of scarce physical inventory.

[0118] Experiments were conducted to verify the above application scenarios, and the results were compared with existing conventional push methods based on static rules.

[0119] See attached document Figure 9 The experimental environment simulated concurrent communication task delivery for a target user scale of 100,000, with the observation window set at 60 minutes after the task delivery started. The core metrics observed included the target inventory deadlock rate and the interface rate limiting error rate. The target inventory deadlock rate represents the percentage of invalid inventory that was locked but ultimately did not result in any actual transaction, relative to the total available inventory. The interface rate limiting error rate represents the percentage of requests rejected by the external application programming interface due to instantaneous high concurrency.

[0120] Based on the appendix Figure 9 The trends shown validate the conclusions. Under conventional push methods, due to the lack of underlying inventory logic pre-deduction and dynamic recovery mechanisms, the system blindly locks a large amount of physical inventory in the initial stages of delivery. This causes the target inventory deadlock rate to rapidly climb to its peak within fifteen minutes of task initiation. Furthermore, with the concurrency backlog from external platforms, the interface rate limiting error rate also fluctuates at a high level, severely impacting the overall system throughput stability. After applying the operational decision-making method based on the RFIMD model provided in this invention, the system resource occupancy status exhibits stable characteristics. The system accurately filters and implements distributed logical deduction through feature thresholds, and relies on the latency judgment mechanism of the state mapping table to promptly trigger inventory release and concurrent quota recovery at the blocking threshold. This ensures that the target inventory deadlock rate remains at an extremely low level throughout the entire 60-minute observation period, while the interface rate limiting error rate remains at a low level. Experimental results show that this method, while ensuring the independence of the high-value user conversion link, eliminates the defects of underlying database lock contention and external communication link congestion in large-scale concurrent scenarios, achieving a virtuous cycle between business conversion and underlying system resource scheduling.

[0121] While specific embodiments of the present invention have been described above, those skilled in the art should understand that these specific embodiments are merely illustrative. Those skilled in the art can omit, substitute, and modify the details of the above methods and systems in various ways without departing from the principles and essence of the present invention. For example, combining the above method steps to perform substantially the same function and achieve substantially the same result using substantially the same method falls within the scope of the present invention.

Claims

1. A decision-making method for apparel retail membership operations based on the RFIMD model, characterized in that, Includes the following steps: Extract user transaction details, product lifecycle parameters, and recommendation conversion topology data within the evaluation period; Based on the extracted data, time-depreciation calculations and benchmark calibration calculations based on the product lifecycle are performed to construct a dynamic RFIMD feature vector; Extract the two-dimensional addressing coordinates of the dynamic RFIMD feature vector, match the data components in the two-dimensional feature mapping matrix, obtain the target product identifier corresponding to the business task and generate a communication tracking certificate, and encapsulate the matched data components, the target product identifier and the communication tracking certificate into an initial communication data packet; Before the initial communication data packet is sent, the available concurrent quota of the interface is reduced and released. The target item identifier in the initial communication data packet is extracted. When the dynamic RFIMD feature vector threshold condition and the low inventory condition based on the preset low inventory level are met, the target item inventory corresponding to the target item identifier is frozen in the distributed cache layer. The initial communication data packet is sent to the external communication platform terminal, and the message delivery timestamp and the trigger timestamp of the internal application gateway receiving the communication tracking credential parsing request are recorded. If the delay between the current system time and the message delivery timestamp exceeds the blocking timeout threshold and no trigger timestamp is recorded, the inventory freeze is lifted if a temporary freeze record exists, and the available concurrent quota of the interface reduced due to the initial communication data packet is restored. The degraded communication data packet is then assembled and written to the asynchronous queue.

2. The apparel retail membership operation decision-making method based on the RFIMD model according to claim 1, characterized in that, The process of performing time-depreciation calculations and benchmark calibration calculations based on the extracted data to construct a dynamic RFIMD feature vector includes: Based on the aforementioned recommendation conversion topology data, a time decay factor is introduced and combined with the first transaction feature weight generated by the receiving node and the conversion time difference, and the social recommendation conversion feature value is calculated through time decay calculation. Based on the user transaction details and the product lifecycle parameters, when the number of valid orders is greater than zero within a set time period, the actual payment amount of a single item is compared with the dynamic benchmark price corresponding to the single item to calculate the price sensitivity feature value. The independently calculated social recommendation conversion feature values, price-sensitive feature values, and basic consumption feature values ​​are processed to unify their dimensions and then encapsulated and combined to generate a dynamic RFIMD feature vector.

3. The apparel retail membership operation decision-making method based on the RFIMD model according to claim 1, characterized in that, The process involves extracting the two-dimensional addressing coordinates of the dynamic RFIMD feature vector, matching data components in the two-dimensional feature mapping matrix, obtaining the target item identifier corresponding to the business task, generating a communication tracking credential, and encapsulating the matched data component, the target item identifier, and the communication tracking credential into an initial communication data packet, including: Social recommendation conversion feature values ​​and price-sensitive feature values ​​are extracted from the dynamic RFIMD feature vector and discretized binning is performed. The discrete integer index values ​​obtained from the conversion are combined to construct two-dimensional addressing coordinates. The pre-configured two-dimensional feature mapping matrix is ​​input to locate the hit resource identifier, so as to extract the data component matching the resource identifier. The data component includes a graphic data component, which includes a visual material component and a text business component. Extract the target user identifier, target product identifier, and task generation timestamp corresponding to the business task, and call the one-way hash algorithm to perform string concatenation operation to generate communication tracking credentials; The communication tracking credentials are appended as network request parameters to the basic gateway request address to generate a mount request address. The matched and extracted image and text data components are then encapsulated with the mount request address into a standard data exchange format structure to generate an initial communication data packet.

4. The apparel retail membership operation decision-making method based on the RFIMD model according to claim 1, characterized in that, The step of performing a decremental release operation on the available concurrent capacity of the interface before the initial communication data packet is sent, extracting the target item identifier from the initial communication data packet, and freezing the target item inventory corresponding to the target item identifier in the distributed cache layer when the dynamic RFIMD feature vector threshold condition and the low inventory condition based on the preset low inventory level are met, includes: Instantiate a concurrent controller built on the token bucket mechanism in memory. If the available concurrent quota of the interface in the concurrent controller is found to be greater than zero, perform an atomic operation to decrement the available concurrent quota of the interface and mark the initial communication data packet as a resource ready state. Extract the total transaction amount feature value and the price sensitivity feature value from the dynamic RFIMD feature vector. When the total transaction amount feature value is greater than or equal to a preset total transaction amount threshold and the price sensitivity feature value is greater than or equal to a preset price sensitivity feature threshold, it is determined that the set high value feature condition is met. When the set high-value characteristic conditions are met, query the actual available inventory. When the actual available inventory is lower than the preset low inventory level and greater than or equal to the preset pre-deduction inventory amount, generate a temporary freeze record containing the target item identifier and the target user identifier key value in the distributed cache layer to freeze the inventory.

5. The apparel retail membership operation decision-making method based on the RFIMD model according to claim 1, characterized in that, The step of sending the initial communication data packet to the external communication platform terminal and recording the message delivery timestamp and the trigger timestamp of the internal application gateway receiving the communication tracking credential parsing request includes: The application programming interface is called to send the initial communication data packet to the external communication platform terminal, and a record with the communication tracking credential as the globally unique key value is created in the status mapping table and the timestamp is initialized to an empty value. The system receives asynchronous callback confirmation messages from external platforms via an asynchronous listening interface, extracts the system time as the message delivery timestamp, parses the communication tracking credentials from the transparent field of the asynchronous callback confirmation message to match the corresponding record in the status mapping table, and writes the message delivery timestamp into the corresponding field of the corresponding record. Continuously monitor the access logs of the internal application gateway. When a parsing request with the communication tracing credentials is captured, extract the system time as the trigger timestamp and update the corresponding field in the status mapping table to complete the closed-loop recording.

6. The apparel retail membership operation decision-making method based on the RFIMD model according to claim 1, characterized in that, When the delay between the current system time and the message delivery timestamp exceeds the blocking timeout threshold and no trigger timestamp is recorded, the inventory freeze is lifted if a temporary freeze record exists, and the available concurrent interface capacity reduced due to the initial communication data packet is restored. The degraded communication data packet is then assembled, including: Poll the status mapping table. If the message delivery timestamp is not null and the delay between the current system time and the message delivery timestamp is greater than the set blocking timeout threshold and the trigger timestamp is still the initial null value, determine that the communication delivery task has been blocked. When the blocking task has an inventory freeze flag, the distributed cache layer is triggered to perform an unlock operation, which releases the frozen inventory amount to the available inventory pool by reversing the accumulation, and sends a release signal to the concurrency controller to execute the increment operation of the available concurrency amount of the interface. Extract the social recommendation conversion feature value corresponding to the blocking task and apply a predefined downgrade coefficient for downgrade calibration. Combine the downgrade calibration feature with the original price-sensitive feature value and input it into a two-dimensional feature mapping matrix to extract the image and text components without inventory dependence. Then, concatenate them to generate downgrade communication data packets and write them to an asynchronous queue.

7. The apparel retail membership operation decision-making method based on the RFIMD model according to claim 2, characterized in that, The comparison of the actual payment amount for a single item with the corresponding dynamic benchmark price includes: Extract the lifecycle stage tags associated with the target product at the time the transaction occurs; When the lifecycle stage label indicates that the target product is in the full-price sales period, the dynamic benchmark price is determined as the system's original price for the target product. When the lifecycle stage label indicates that the target product is in the regular discount period, the dynamic benchmark price is determined to be the system's original price for the target product multiplied by the set regular discount rate parameter; When the lifecycle stage label indicates that the target item is in the clearance sale period, the dynamic benchmark price is determined as the system's original price for the target item multiplied by the set system clearance discount rate parameter.

8. The apparel retail membership operation decision-making method based on the RFIMD model according to claim 1, characterized in that, The extracted user transaction details, product lifecycle parameters, and recommendation conversion topology data within the evaluation period include: Read the underlying system configuration parameters and extract the set start and end timestamps to define the time window boundaries for system evaluation; Send data extraction commands to external enterprise resource planning and customer relationship management systems with which connections have been established to extract the target data set; Perform a time filter operation to extract business logs whose timestamps fall between the start and end timestamps and load them into the memory cache.

9. The apparel retail membership operation decision-making method based on the RFIMD model according to claim 8, characterized in that, After the business logs whose occurrence timestamps are between the start and end timestamps are loaded into the memory cache, the process further includes: Filter out abnormal records in the business log collection where key fields are empty; Duplicate log entries caused by network retransmission mechanisms are removed by comparing globally unique identifiers or hash values ​​of data rows. Heterogeneous log files from different external systems are parsed into a unified structured data format, and unstructured text logs are mapped into a standardized relational field matrix to output a standardized set of business data.

10. A decision-making device for apparel retail membership operations based on the RFIMD model, used to execute the decision-making method for apparel retail membership operations based on the RFIMD model as described in any one of claims 1-9, characterized in that, include: The data acquisition and preprocessing module is configured to extract user transaction details, product lifecycle parameters, and recommendation conversion topology data within the evaluation period. The feature vector calculation module is configured to perform time-depreciation calculation and benchmark calibration calculation based on the product life cycle based on the extracted data to construct dynamic RFIMD feature vectors; The data packet assembly module is configured to extract the two-dimensional addressing coordinates of the dynamic RFIMD feature vector, match data components in the two-dimensional feature mapping matrix, obtain the target single item identifier corresponding to the business task and generate a communication tracking certificate, and encapsulate the matched data components, the target single item identifier and the communication tracking certificate into an initial communication data packet. The system resource control module is configured to perform a decremental release operation of the available concurrent quota of the interface before the initial communication data packet is sent, extract the target item identifier in the initial communication data packet, and freeze the target item inventory corresponding to the target item identifier in the distributed cache layer when the dynamic RFIMD feature vector threshold condition and the low inventory condition based on the preset low inventory level are met. The communication and event monitoring module is configured to send the initial communication data packet to the external communication platform terminal, record the message delivery timestamp and the trigger timestamp of the internal application gateway receiving the communication tracking credential parsing request; The blocking assessment and system rollback module is configured to, when the delay between the current system time and the message delivery timestamp exceeds the blocking timeout threshold and no trigger timestamp is recorded, release the inventory freeze if a temporary freeze record exists, restore the interface available concurrency quota that was reduced due to the initial communication data packet, assemble the degraded communication data packet and write it to the asynchronous queue.

Images