A payment scenario-oriented SaaS template intelligent generation and adaptation method

Abstract

The application relates to the technical field of electronic commerce, and discloses a SaaS template intelligent generation and adaptation method for a payment scene. The method first acquires a target payment channel operation sequence and a cross-currency exchange rate, calculates a comprehensive channel health degree and an exchange rate fluctuation rate to construct a multi-dimensional state feature vector; secondly, a symmetric semi-positive definite component interference matrix updated offline is extracted, the feature vector is mapped into a benchmark prediction probability, and multi-dimensional feature cross coupling is combined to calculate an expected comprehensive utility value of a candidate template with a non-negative interference penalty term; subsequently, a dynamic strategy exploration temperature clamped by a threshold is calculated by fusing features, and an adaptation template is generated by sampling in combination with the utility value; and finally, actual conversion results are collected, the order amount proportion with a maximum threshold cut-off is taken as a value weighting factor, and a value weighting approximate gradient is used to perform online adaptive closed-loop updating on a conversion matrix. The application effectively quantifies component bottom layer conflicts, and realizes adaptive closed-loop optimization of a payment interface in a high dynamic environment.

Description

Technical Field

[0001] This invention belongs to the field of e-commerce technology, and in particular relates to a method for intelligent generation and adaptation of SaaS templates for payment scenarios. Background Technology

[0002] With the booming development of global digital commerce, payment checkouts under SaaS architecture have become a core infrastructure connecting merchants and consumers. Currently, most payment systems rely on static templates or fixed component display mechanisms based on simple preset rules when presenting their user interfaces, such as sorting channels solely based on geographic information. Such methods fail to fully consider the complex and dynamic characteristics of the real online payment environment. The technical operational status of the underlying payment channels, including network latency fluctuations and instantaneous success rate changes, as well as macroeconomic financial indicators in cross-regional transactions, such as unpredictable fluctuations in real-time exchange rates, are all constantly evolving. Traditional static template rendering mechanisms lack the ability to perceive these multi-dimensional environmental factors in real time, and cannot achieve integrated analysis of technical and financial indicators, resulting in a serious disconnect between the front-end user interface display and the back-end business operation status. When faced with complex scenarios such as payment channel congestion, system technology degradation, or drastic exchange rate fluctuations, existing solutions cannot generate adaptive interface defense strategies or provide effective user guidance mechanisms, thus significantly increasing the risk of transaction interruption. Furthermore, traditional systems fail to establish a closed-loop learning mechanism based on actual transaction feedback. They cannot dynamically adjust template configurations to match environmental changes, nor can they achieve global optimization of system conversion efficiency between merchant revenue goals and business risk control. This technological limitation keeps payment conversion efficiency at a suboptimal level for a long time, making it difficult to meet the core requirements of modern e-commerce for high reliability and adaptability. Summary of the Invention

[0003] The purpose of this invention is to provide a method for intelligent generation and adaptation of SaaS templates for payment scenarios, aiming to solve the above-mentioned problems.

[0004] This invention is implemented as follows: a method for intelligent generation and adaptation of SaaS templates for payment scenarios, comprising the following steps: Step S10: Real-time acquisition of the operating sequence and cross-currency exchange rate quotes of the target payment channel, calculation of the comprehensive channel health and exchange rate volatility, and construction of a multi-dimensional state feature vector of the payment environment; Step S20: Acquiring real-time order amounts and the merchant's historical average order amount, extracting the offline updated symmetric positive semi-definite component interference matrix; mapping the multi-dimensional state feature vector to a benchmark prediction probability, and cross-coupling it with the logarithmic proportional smoothing term of the order amount, the exchange rate volatility, and the component interference matrix to calculate the expected value of each candidate template configuration vector with a non-negative interference penalty term. Comprehensive utility value; Step S30: Integrate the comprehensive channel health, exchange rate volatility and order amount ratio characteristics to calculate the dynamic strategy exploration temperature, which is constrained by both the comprehensive channel health and the minimum threshold; Based on the dynamic strategy exploration temperature and the expected comprehensive utility value, calculate the selection probability of each candidate template configuration vector and sample to generate the final adapted SaaS template; Step S40: Collect the actual conversion results after the SaaS template interacts with the user; Using the order amount ratio with the maximum threshold truncation as the value weighting factor, calculate the residual between the actual conversion result and the benchmark predicted probability, and use the value weighted approximate gradient to perform online adaptive closed-loop update of the state-action interaction transformation matrix.

[0005] A further technical solution is that the calculation process of the multi-dimensional state feature vector of the payment environment constructed in step S10 includes: extracting the comprehensive channel health of all target candidate payment channels in the system, and performing dimensional concatenation and vectorization representation with the exchange rate volatility of the current payment environment to construct the multi-dimensional state feature vector of the payment environment.

[0006] A further technical solution is that the calculation process of the comprehensive channel health includes: obtaining the real-time success rate and real-time latency of the corresponding payment channel; performing dimensionless processing on the real-time latency based on a preset tolerance limit to construct a nonlinear decay penalty factor; and positively coupling the real-time success rate and the nonlinear decay penalty factor to obtain the comprehensive channel health of the payment channel.

[0007] A further technical solution involves calculating the exchange rate volatility by: extracting a cross-currency exchange rate sampling sequence within a historical time window, and calculating the dimensionless relative dispersion of the real-time quote relative to the historical average quote, which is then used as the exchange rate volatility.

[0008] A further technical solution, wherein the calculation process of the expected comprehensive utility value in step S20 includes: mapping the multidimensional state feature vector to a baseline prediction probability through a state-action interaction transformation matrix; extracting the dimensionless logarithmic proportion feature constructed from the real-time order amount and the merchant's historical average order amount; performing multidimensional orthogonal decoupling and nonlinear mapping on the dimensionless logarithmic proportion feature, exchange rate volatility, and symmetric positive semi-definite component interference matrix to construct a non-negative interference penalty term with environmental risk and component conflict characteristics; subtracting the non-negative interference penalty term from the baseline prediction probability to obtain the expected comprehensive utility value of each candidate template configuration vector.

[0009] A further technical solution involves the following offline statistical steps for extracting the offline-updated symmetric positive semi-definite component interference matrix: obtaining original co-occurrence data; extracting the total number of times each candidate SaaS component was simultaneously rendered and loaded within a historical preset time window, as well as the number of co-occurrence failures leading to payment loss; constructing the original interference matrix between components using the ratio of the number of co-occurrence failures to the total number of co-occurrences as elements; adding the original interference matrix to its transpose and dividing by two to generate a symmetric interference matrix; performing eigenvalue decomposition on the symmetric interference matrix, representing it as the product of an eigenvector matrix and a diagonal matrix containing eigenvalues; forcibly truncating all eigenvalues ​​less than zero in the diagonal matrix to zero, obtaining a non-negative eigendiagonal matrix; and reconstructing the updated symmetric positive semi-definite component interference matrix based on the eigenvector matrix and the non-negative eigendiagonal matrix.

[0010] A further technical solution, wherein the calculation process of the dynamic strategy exploration temperature and the selection probability in step S30 includes: setting a system temperature boundary that includes a minimum temperature threshold and an initial benchmark temperature; constructing a risk adjustment coefficient based on the dimensionless proportional characteristics of order amount and exchange rate volatility; making the dynamic strategy exploration temperature decrease non-linearly and monotonically with the increase of the risk adjustment coefficient, and constrained by the lower limit of the minimum temperature threshold; and performing exponential smoothing normalization on the expected comprehensive utility value of each candidate template configuration vector based on the dynamic strategy exploration temperature to generate the selection probability distribution of each candidate template configuration vector.

[0011] A further technical solution, wherein the calculation process of online adaptive closed-loop update using value-weighted approximate gradient in step S40 includes: extracting the dimensionless proportional feature of the order amount and applying a maximum threshold truncation to it to generate a value-weighted factor; calculating the target residual term of the actual conversion result and the benchmark predicted probability; fusing the outer product matrix of the value-weighted factor, the target residual term, and the actually generated template configuration vector with the multidimensional state feature vector to calculate the value-weighted approximate gradient; and adaptively iterating and replacing the parameters of the state-action interaction transition matrix along the direction of the value-weighted approximate gradient according to a preset step size.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0013] 1. This invention abandons traditional black-box recommendation rules based on experience, decoupling the complex payment environment into channel health, exchange rate volatility, and order amount ratio. It innovatively introduces a symmetric positive semi-definite component interference matrix reconstructed through feature decomposition and negative feature truncation. This mathematically guarantees the non-negativity of the quadratic interference penalty term, precisely quantifying implicit conflicts between interface components as error-prevention penalties, thus ensuring the rigor and high reliability of the template configuration scheme in terms of business logic.

[0014] 2. This invention overcomes the technical bottleneck of static template rendering and constructs an online learning closed loop based on real conversion feedback. By extracting the residual between the actual conversion results and the predicted probability, and innovatively integrating the order amount ratio as a value weighting factor, the interactive conversion matrix is ​​updated using an approximate gradient-oriented approach. This mechanism achieves deep fusion of multi-source interactive data and continuous self-correction of model accuracy, enabling the system to dynamically optimize according to environmental changes, significantly improving the conversion rate of high-value orders in extremely volatile environments. Attached Figure Description

[0015] Figure 1 The present invention provides a general flowchart of a SaaS template intelligent generation and adaptation method for payment scenarios;

[0016] Figure 2 The following is a flowchart of the offline generation sub-process for the interference matrix of the symmetric positive semidefinite component provided by the present invention;

[0017] Figure 3 The logical architecture diagram of the SaaS template intelligent generation and adaptation system provided by this invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0019] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0020] Payment scenarios refer to the business environment involving the flow of funds, encompassing users, merchants, payment channels, and related financial services. SaaS templates refer to a set of configurable user interfaces or functional modules provided under a Software as a Service (SaaS) model, used to build application interfaces such as payment checkouts.

[0021] like Figure 1 , Figure 2 and Figure 3As shown, an embodiment of the present invention provides a method for intelligent generation and adaptation of SaaS templates for payment scenarios, comprising the following steps:

[0022] Step S10: The system acquires the real-time operational sequence and cross-currency exchange rate quotes of the target payment channel, calculates the overall channel health and exchange rate volatility, and constructs a multi-dimensional state feature vector for the payment environment. To achieve this, the system periodically sends requests to the application programming interfaces (APIs) of each payment channel to obtain operational data such as current transaction success rate and average response time. Simultaneously, the system can subscribe to or query real-time cross-currency exchange rate quotes from multiple financial data providers. When calculating the overall channel health, a simple weighted average method can be used, for example, multiplying the real-time success rate by a weight and subtracting a penalty proportional to the average delay time from a baseline value. Exchange rate volatility can be calculated based on the sum of squared differences between the simple moving average of historical exchange rate data and the current exchange rate. Finally, these calculated channel health values ​​and exchange rate volatility values ​​are arranged in a preset order to form a multi-dimensional state feature vector, which concisely summarizes the key information of the current payment environment. The target payment channel refers to the specific payment method or service provider that the user can choose during the payment process, such as bank card payment or a third-party payment platform. The operational sequence refers to the series of operational steps and state changes that a payment channel undergoes during transaction processing. Cross-currency exchange rate quotes refer to real-time exchange rate information between different currencies, which is crucial for payment scenarios involving international transactions. Comprehensive channel health refers to an indicator measuring the overall operational status and reliability of a payment channel, typically considering factors such as success rate and latency. Exchange rate volatility refers to the degree of fluctuation in cross-currency exchange rate quotes over a certain period, reflecting the stability of the exchange rate market. The multi-dimensional state feature vector of the payment environment is a mathematical vector used to quantify and represent multiple key characteristics of the current payment environment, such as the health of each payment channel and exchange rate volatility.

[0023] Step S20: Obtain the real-time order amount and the merchant's historical average order amount, and extract the offline-updated symmetric positive semi-definite component interference matrix; map the multidimensional state feature vector to the baseline prediction probability, and combine the logarithmic proportional smoothing term of the order amount, exchange rate volatility, and component interference matrix for cross-coupling to calculate the expected comprehensive utility value of each candidate template configuration vector with a non-negative interference penalty term; specifically, the real-time order amount can be directly obtained from the user's current transaction request, while the merchant's historical average order amount can be queried from a pre-statistical database. The symmetric positive semi-definite component interference matrix can be set manually based on experience; for example, experts can preset a fixed negative interference value for certain UI component combinations based on past experience. The multidimensional state feature vector can be input into a pre-trained linear model, and the baseline prediction probability can be generated through simple linear transformations and activation functions (such as the sigmoid function). The logarithmic proportional smoothing term of the order amount can be simply obtained by calculating the ratio of the current order amount to the historical average order amount and then taking its logarithm. When calculating the expected overall utility value, the baseline predicted probability can be used as a foundation. Then, through addition or multiplication operations, a logarithmic proportional smoothing term for the order amount and an adjustment factor for exchange rate volatility are introduced. Simultaneously, based on the UI components included in the candidate template configuration, the corresponding interference value is found from the preset component interference matrix and subtracted from the utility value as a non-negative interference penalty term to reflect the negative impact between components. Real-time order amount refers to the transaction amount currently being paid by the user. Merchant historical average order amount refers to the average amount of all transaction orders of a specific merchant over a past period. The symmetric positive semi-definite component interference matrix is ​​a mathematical matrix used to quantify the potential negative interaction effects between different UI components in the SaaS template. This matrix is ​​symmetric and positive semi-definite, ensuring that the interference penalty term is non-negative. The baseline predicted probability refers to the system's preliminary estimate of the likelihood of a successful transaction or conversion under a given payment environment. The logarithmic proportional smoothing term for the order amount is a value obtained by smoothing the ratio of the real-time order amount to the merchant's historical average order amount, used to reflect the relative size of the order amount. The non-negative interference penalty term refers to the penalty value applied based on the component interference matrix when calculating template utility. This value is always non-negative to reduce the utility of templates with negative interaction components. The expected overall utility value refers to the predicted evaluation value of the overall benefits or performance that the system may bring to a specific SaaS template configuration in the current payment environment.

[0024] Step S30: Integrate the comprehensive channel health, exchange rate volatility, and order amount ratio characteristics to calculate the dynamic strategy exploration temperature, which is constrained by both the comprehensive channel health and the minimum threshold. Based on the dynamic strategy exploration temperature and the expected comprehensive utility value, calculate the selection probability of each candidate template configuration vector and sample to generate the final adapted SaaS template. To calculate the dynamic strategy exploration temperature, the average value of the comprehensive channel health, exchange rate volatility, and order amount ratio characteristics can be simply linearly weighted and summed. Then, based on the result of this weighted sum, an exploration temperature value is determined through a preset lookup table or piecewise function. For example, when high exchange rate volatility or a high order amount ratio leads to enhanced environmental risk characteristics, based on the exponential decay constraint of the formula, the system will calculate a lower exploration temperature, forcing the system to converge to a known safe strategy to avoid high-risk situations. At the same time, to prevent the exploration temperature from being too low and causing the system to completely lose its potential global optimization ability, a fixed minimum temperature threshold is set to ensure that the exploration temperature is never lower than this threshold. When calculating the selection probability of each candidate template configuration vector, the expected comprehensive utility value of each template can be divided by the dynamic strategy exploration temperature. The result is then input into a standard Softmax function to obtain the selection probability of each template. Finally, based on these selection probabilities, the system selects a final SaaS template configuration through random sampling. The dynamic strategy exploration temperature is a variable parameter used to adjust the balance between exploring new configurations and utilizing known optimal configurations when selecting a SaaS template. The safety threshold is a minimum limit set for a parameter to ensure it does not fall below a preset level. The candidate template configuration vector is a mathematical vector representing various possible combinations of UI component layouts, payment channel ordering, etc., within a SaaS template. The selection probability is the likelihood that a specific template configuration will be selected by the system among all candidate template configurations.

[0025] Step S40: Collect the actual conversion results after the SaaS template interacts with the user; using the order amount ratio with a maximum threshold truncation as the value weighting factor, calculate the residual between the actual conversion result and the baseline predicted probability, and use the value-weighted approximate gradient to perform online adaptive closed-loop updates on the state-action interaction transition matrix; specifically, the actual conversion result can be obtained by listening to the callback notification of the payment channel or the interaction event of the user interface. For example, a successful payment is recorded as 1, and a failed payment or user abandonment is recorded as 0. The value weighting factor can simply be based on the ratio of the real-time order amount to the merchant's historical average order amount, and a fixed maximum value is set to prevent extremely high order amounts from having too much impact on the update process. The residual is calculated by directly subtracting the baseline predicted probability evaluated in step S20 for the actual generated template configuration from the actual conversion result. When performing online adaptive closed-loop updates, the system can use a basic gradient descent algorithm. This algorithm adjusts the parameters in the state-action interaction transition matrix based on the calculated residual, multiplied by the value weighting factor and a fixed learning rate parameter. This adjustment is performed in real time, enabling the system to continuously optimize its internal model based on the latest user feedback. Actual conversion result refers to the actual transaction outcome after a user interacts with the SaaS template, such as successful or failed payment. Value-weighted factor refers to a weighting coefficient used to adjust the intensity of learning updates based on the value of the transaction. Maximum threshold truncation refers to limiting a value to a preset maximum value to prevent it from exceeding a reasonable range. Residual refers to the difference between the actual conversion result and the system's baseline predicted probability. Value-weighted approximate gradient refers to a model update method that adjusts the system's internal parameters by combining transaction value weights and approximate gradient information. State-action interaction transition matrix refers to the knowledge representation used within the system to store and learn the results of different template configuration actions under different payment environment states. Online adaptive closed-loop update refers to the process by which the system continuously and automatically adjusts and optimizes its internal model and strategies based on real-time feedback data.

[0026] The following example will provide a more detailed explanation of the above technical solution:

[0027] For example, suppose an online merchant operates in location A, with its main customer base distributed globally, involving transactions in multiple currencies. This merchant uses the method proposed in this application to intelligently generate and adapt SaaS templates for its payment checkout.

[0028] At a certain time t, the system first executes step S10. The system acquires real-time operational sequence data for all target payment channels integrated by the merchant (e.g., payment channel 1, payment channel 2, and payment channel 3), including their real-time transaction success rates and average latency. Simultaneously, the system also obtains real-time exchange rate quotes for all relevant cross-currency currencies from financial data sources. Based on this data, the system calculates the overall channel health of each payment channel. For example, payment channel 1 has a high success rate and low latency, and its overall channel health is assessed as good; while payment channel 2 may experience increased latency due to network congestion, and its health is assessed as average. Next, the system calculates the cross-currency exchange rate volatility under the current market environment; for example, exchange rate volatility may be high due to international financial events. Finally, these calculated overall channel health values ​​and exchange rate volatility are integrated to construct a multi-dimensional state feature vector of the payment environment. This vector accurately reflects the complex dynamics of the current payment environment.

[0029] The system then proceeds to step S20. The system obtains the real-time order amount of user A's current order, for example, $1000. Simultaneously, the system queries the merchant's historical average order amount, for example, $500. The system also extracts a pre-updated offline symmetric positive semi-definite component interference matrix. This matrix records the potential negative impacts of different UI components (e.g., the "Credit Card Payment" button and the "Installment Payment" option) appearing simultaneously in historical data. Next, the system inputs the multi-dimensional state feature vector of the payment environment constructed in step S10 into an internal model, mapping it to a baseline prediction probability. This probability represents the likelihood of a successful transaction in the current environment, without considering specific template configurations. Then, the system cross-couples the logarithmic proportional smoothing term of the order amount (based on the ratio of $1000 to $500), the exchange rate volatility, and the component interference matrix. Specifically, for each candidate SaaS template configuration vector (e.g., template configuration A contains "Credit Card Payment" and "PayPal" options, and template configuration B contains "Alipay" and "WeChat Pay" options), the system calculates its expected comprehensive utility value with a non-negative interference penalty term. If a candidate template configuration contains UI components that exhibit high negative interference in the interference matrix, its expected overall utility value will be reduced accordingly. In this way, the system can comprehensively evaluate the potential performance of each template configuration in the current environment and avoid component combinations that may lead to user churn.

[0030] Next, the system executes step S30. The system integrates the comprehensive channel health calculated in step S10 (e.g., the average health of all channels), exchange rate volatility, and the order amount ratio characteristic used in step S20. Based on these integrated characteristics, the system calculates a dynamic strategy exploration temperature. For example, if the current exchange rate volatility is high and the order amount ratio is amplified, the system will calculate an extremely low exploration temperature (approaching a lower bound constraint) based on a formula. This means the system will reduce its trial-and-error exposure, strongly favoring the safest template configuration with the most stable historical performance and the smallest anti-interference penalty, in order to ensure a robust closed loop for large payments in a volatile financial environment. Conversely, if the environment is extremely stable and involves low-risk, small orders, the exploration temperature will relatively rise. The system will allow the allocation of a certain random probability to try suboptimal candidate templates, seeking interface configurations with better conversion potential under safe conditions. This exploration temperature is also subject to a double constraint of a safety threshold, ensuring that exploration behavior will not completely stop or become excessive even under extremely stable or unstable conditions. Based on this dynamic strategy, exploring the temperature and the expected comprehensive utility value of each candidate template configuration vector calculated in step S20, the system calculates the selection probability of each candidate template configuration vector. Finally, the system samples based on these selection probabilities to generate the final SaaS template adapted to user A. For example, if template configuration A has the highest selection probability, then that template is selected and presented to user A.

[0031] Finally, the system proceeds to step S40. User A interacts with the generated SaaS template and completes the payment. The system collects the actual conversion result of User A; for example, if User A successfully completes the payment, the actual conversion result is 1. The system uses the order amount ratio with a maximum threshold truncation (e.g., the ratio of $1000 to $500, but truncated to a preset maximum value) as a value weighting factor. Then, the system calculates the residual between the actual conversion result (1) and the baseline predicted probability evaluated for the actual generated template configuration in step S20. For example, if the baseline predicted probability is 0.8, the residual is 1-0.8=0.2. The system uses this value weighting factor and the residual to perform online adaptive closed-loop updates to the internal state-action interaction transformation matrix through a value-weighted approximate gradient method. This means that the system adjusts its perception of the utility of different template configurations under different environmental states in real time based on User A's actual transaction feedback. If the actual conversion result is better than expected, the utility weight of the corresponding template configuration in a similar environment will increase; otherwise, it will decrease. Through this continuous online learning, the system can continuously optimize its template generation strategy to better adapt to the ever-changing payment environment, thereby improving the overall payment success rate and user experience.

[0032] Based on the above examples, the method proposed in this application demonstrates a significant technical contribution in addressing the limitations of existing SaaS template rendering mechanisms in payment scenarios.

[0033] This application further proposes a calculation process for the multi-dimensional state feature vector of the payment environment constructed in step S10, including: extracting the comprehensive channel health of all target candidate payment channels in the system, and performing dimensional concatenation and vectorization representation with the exchange rate volatility of the current payment environment to construct a multi-dimensional state feature vector of the payment environment; wherein, the calculation process of the comprehensive channel health includes: obtaining the real-time success rate and real-time latency of the corresponding payment channel, performing dimensionless processing on the real-time latency based on a preset tolerance upper limit, and constructing a nonlinear decay penalty factor; positively coupling the real-time success rate and the nonlinear decay penalty factor to obtain the comprehensive channel health of the payment channel; wherein, the calculation process of the exchange rate volatility includes: extracting the cross-currency exchange rate sampling sequence within the historical time window, calculating the dimensionless relative dispersion of the real-time quote relative to the historical average quote, as the exchange rate volatility; the calculation formula for the constructed multi-dimensional state feature vector of the payment environment is as follows:

[0034]

[0035] in, This represents the multidimensional state feature vector of the payment environment constructed at time t; , to These represent the dimensionless comprehensive channel health of payment channels 1, 2 to N at time t; The expression represents the cross-currency exchange rate volatility at time t; T represents the matrix transpose operation; this scheme constructs a multi-dimensional state feature vector of the payment environment. This enables a refined quantitative representation of the complex dynamic factors in payment scenarios. (Multi-dimensional state feature vector of the payment environment) This method is used to comprehensively and quantitatively describe the complex state of the current payment scenario, integrating multiple heterogeneous payment environment indicators into a unified mathematical representation to provide input for subsequent intelligent decision-making. It can be implemented by standardizing and normalizing data from different sources and arranging them into a vector form according to a preset order; or by using dimensionality reduction techniques such as principal component analysis to extract the most representative features from the original high-dimensional data to form the vector.

[0036] The formula for calculating the channel health of the i-th payment channel is:

[0037]

[0038] in, This represents the overall channel health of the i-th payment channel at time t; This represents the real-time statistical success rate of the i-th payment channel at time t; This represents the real-time average latency of the i-th payment channel at time t. This indicates the preset maximum tolerable delay time limit; dimensionless integrated channel health. The aim is to evaluate the overall operational status of a single payment channel at a specific moment and eliminate the dimensional differences in performance indicators across different channels. It comprehensively considers the success rate and latency of payment channels, transforming them into a unified and comparable value through a mathematical model. This can be achieved by acquiring raw data through a real-time monitoring system and then applying predefined functions for calculation; or by using a machine learning model combined with historical data to predict and evaluate channel health. Real-time success rate statistics are also provided. This reflects the success rate of transactions processed by a particular payment channel within a specific time window and is a key indicator of the channel's reliability. It can be obtained in real-time through API interfaces provided by payment gateways or third-party payment service providers; or through real-time statistics and aggregation of internal system transaction logs. Real-time average latency. This represents the average time required for a payment channel to complete a transaction within a specific time window, and is an important indicator of payment channel efficiency. It can be obtained through real-time calculation using internal system transaction timestamp records, or through real-time monitoring of payment channel response times using network probing tools. A preset maximum tolerable latency limit is also included. This defines the maximum tolerance level for payment transaction delays for the system or users. Any delay exceeding this limit will be considered a serious impact on user experience or business efficiency. This limit can be set based on business needs, user experience standards, or industry norms; or it can be determined through historical data analysis to find a reasonable threshold that covers the vast majority of normal transaction scenarios.

[0039] The formula for calculating cross-currency exchange rate volatility is:

[0040]

[0041] in, This represents the cross-currency exchange rate volatility at time t; This represents the total number of historical exchange rate sampling points used to calculate volatility; Indicates the step size index for the summation operation; Indicates in Real-time cross-currency exchange rate quotes; In the past Average cross-currency exchange rate quotes within each sampling point; This represents the absolute value of the average exchange rate quote across currencies. Represents a pre-defined, extremely small positive real number. Cross-currency exchange rate volatility. This quantifies the volatility of exchange rate fluctuations between different currencies over a specific period; high volatility typically implies higher trading risk. Calculation methods include obtaining real-time exchange rate data from a financial data provider's API and then applying statistical methods to calculate volatility; or using historical exchange rate data and employing methods such as moving averages and exponential smoothing for prediction and estimation. The total number of historical exchange rate sampling points is also considered. This specifies the amount of historical exchange rate data to be considered when calculating exchange rate volatility. The value of K directly affects the smoothness and responsiveness of the volatility. The K value can be configured based on the business's sensitivity to exchange rate changes; for example, it can be set to the number of sampling points from the past hour, day, or week. Alternatively, a K value that effectively captures market fluctuations and avoids overfitting can be selected through backtesting. Actual cross-currency exchange rate quotes. This represents the actual exchange rate value at a specific point in the past, and it is the fundamental data for calculating exchange rate volatility. It can be obtained through data synchronization with banks, foreign exchange trading platforms, or financial data service providers via interfaces; or by scraping publicly available financial market data sources. Cross-currency average exchange rate quotes. This represents the average exchange rate over the past K sampling points. It serves as a benchmark to measure the deviation of a single exchange rate quote from its historical average. The calculation method can be an arithmetic average of the past K actual exchange rate quotes, or a weighted average, giving higher weight to recent exchange rates. A preset minimum positive real number is used. It is a very small positive number used to avoid misinterpreting the average exchange rate quote when calculating exchange rate volatility. A division-by-zero error occurs when the value is close to or equal to zero, ensuring the numerical stability of the calculation. This value can be a very small floating-point number, such as 1e-9 or 1e-12; or it can be set according to the precision requirements of floating-point operations.

[0042] The solution proposed in this application constructs a multi-dimensional state feature vector of the payment environment. This enables a refined quantitative representation of the complex and dynamic factors in payment scenarios. The vector organically integrates heterogeneous payment channel operation data with macroeconomic exchange rate fluctuation data. Specifically, the system first acquires the operational sequence of each target payment channel in real time, including real-time statistics on success rates. and real-time average latency To eliminate the dimensional differences in performance metrics across different channels and to comprehensively evaluate their usability, the system employs an exponentially decaying weighted average to calculate the real-time success rate. Compared with real-time average latency and the preset maximum tolerable latency limit The attenuation factor is multiplied to calculate the dimensionless overall channel health. This approach allows for the comparison and analysis of performance metrics across payment channels on a unified scale. Simultaneously, the system obtains cross-currency exchange rate quotes in real time and, based on past data... A number of historical exchange rate sampling points were used to calculate actual cross-currency exchange rate quotes. relative to cross-currency average exchange rate quote The standardization bias is determined, and a pre-defined minimum positive real number is introduced. To ensure computational stability, thus obtaining cross-currency exchange rate volatility. Ultimately, the dimensionless comprehensive health status of all payment channels will be assessed. , to Cross-currency exchange rate volatility Integrate into a unified payment environment multidimensional state feature vector This vector, serving as the core input to the intelligent generation and adaptation method of SaaS templates, provides comprehensive, accurate, and quantitatively represented payment environment information for subsequent calculations of expected comprehensive utility values, dynamic strategy exploration, and online adaptive updates. In this way, the system overcomes the insufficient perception problems caused by data heterogeneity and inconsistent dimensions in traditional methods, enabling the generation and adaptation of SaaS templates to make intelligent decisions based on objective and real-time environmental conditions, thereby significantly improving the adaptability of payment scenarios and transaction conversion efficiency.

[0043] As a specific implementation method, in constructing a multi-dimensional state feature vector of the payment environment In this scenario, the system can deploy a real-time data acquisition module that continuously monitors API interfaces or message queues from various payment channels (e.g., Alipay, WeChat Pay, Visa, MasterCard, etc.). For each payment channel, such as Alipay, the data acquisition module can retrieve its transaction success rate for the most recent minute every 5 seconds as a real-time statistical success rate. The average transaction processing time in the most recent minute is used as the real-time average latency. The preset maximum tolerable latency limit. It can be set to 5 seconds. Then, the system uses a formula. The system calculates the dimensionless overall channel health of Alipay. Simultaneously, it can integrate a financial data service interface to retrieve real-time USD / CNY exchange rate quotes every 10 seconds. This is used to calculate cross-currency exchange rate volatility. The system can maintain a list containing the past 100 sampling points (i.e. The queue of historical exchange rate data. When a new exchange rate quote... Upon arrival, the system adds it to the queue and removes the oldest quote. Then, the system calculates the average exchange rate across these 100 sample points. and apply the formula To calculate the current exchange rate volatility, where It can be set to 1e-6. Ultimately, the dimensionless comprehensive channel health score is calculated for all payment channels (e.g., Alipay, WeChat Pay). and the calculated cross-currency exchange rate volatility Combined into a vector, for example As the multidimensional state feature vector of the payment environment at the current moment This vector is then passed to subsequent decision-making modules for intelligent generation and adaptation of SaaS templates.

[0044] Through the above technical solutions, this application enables a refined quantitative representation of complex and dynamic factors in the payment environment. By exponentially decaying weighting of the real-time success rate and latency of each payment channel, a dimensionless comprehensive channel health is calculated, effectively solving the problem of large differences in performance indicators among different channels and the difficulty in direct comparison. This allows the system to accurately assess the real-time availability of each channel. Simultaneously, by introducing a formula for calculating exchange rate volatility based on the mean normalized historical sampling points, macroeconomic financial indicators are transformed into numerical features that match the payment environment status, ensuring that the impact of exchange rate fluctuations on payment decisions can be effectively captured by the model. Integrating the processed channel health and exchange rate volatility into a unified state feature vector provides high-quality input data for subsequent intelligent template generation, enabling the system to achieve deep perception and dynamic adaptation of the payment environment status based on objective quantitative indicators, rather than simple rules. This significantly improves the adaptability of SaaS templates in complex and volatile scenarios such as channel congestion, technology degradation, or drastic exchange rate fluctuations, thereby effectively reducing transaction churn and optimizing the overall conversion efficiency for merchants.

[0045] This application further proposes a calculation process for the expected comprehensive utility value in step S20, including: mapping the multidimensional state feature vector to a baseline prediction probability through a state-action interaction transformation matrix; extracting the dimensionless logarithmic proportion feature constructed from the real-time order amount and the merchant's historical average order amount; performing multidimensional orthogonal decoupling and nonlinear mapping on the dimensionless logarithmic proportion feature, exchange rate volatility, and symmetric positive semi-definite component interference matrix to construct a non-negative interference penalty term with environmental risk and component conflict characteristics; subtracting the non-negative interference penalty term from the baseline prediction probability to obtain the expected comprehensive utility value of each candidate template configuration vector; the calculation formula for the expected comprehensive utility value is as follows:

[0046]

[0047] in, Indicates the state The following uses candidate template configuration vectors The expected overall utility value; Represents a multidimensional state feature vector; This represents the configuration vector for the j-th candidate template. Representing vectors The transpose of the matrix; This represents the state-action interaction transition matrix of the system at time t; This represents the preset dimensional normalization balance coefficient; This indicates the current user's real-time order amount; This represents the merchant's historical average order amount; This represents the preset minimum positive real number order lower limit; Indicates exchange rate volatility; Represents the symmetric positive semidefinite component interference matrix updated offline; This indicates the non-negative interference penalty for global UI components under the candidate template configuration.

[0048] Expected total utility value It measures the status in a specific payment environment. Below, a candidate template is used to configure the vector. A quantitative indicator of the overall value or benefits it can bring. This value comprehensively considers the potential success rate of template configuration and possible negative impacts, aiming to provide a basis for subsequent template selection decisions. Multidimensional state feature vector It provides a comprehensive quantitative description of the current payment environment, including the operational status of payment channels and macroeconomic financial indicators, used to capture dynamic changes in the payment environment. Candidate template configuration vector. It is a mathematical representation of the available, predefined, or dynamically generated combinations and arrangements of UI components in a SaaS template. Each vector represents a specific template configuration scheme. For example, it can be a binary column vector with the same dimensions as the total number of candidate components, where each element corresponds to a UI component, with a value of 1 indicating that the component is included and a value of 0 indicating that it is not included; or it can be a vector containing the component ID and its position on the interface. These vectors can be pre-stored in a database or dynamically generated according to business rules.

[0049] State-Action Interaction Transition Matrix It is a weight matrix used to capture the payment environment state. With candidate template configuration vector The complex nonlinear relationship between them. This matrix, learned from historical data, reflects the degree of influence of different template configurations on payment conversion results under different environmental conditions. Its implementation can be a parameter matrix dynamically updated through online learning or reinforcement learning algorithms (initialized at the initial moment by a zero matrix or offline historical data pre-training), or a pre-trained neural network layer weight matrix. Dimensionally normalized balance coefficients. It is a preset hyperparameter used to adjust the relative weights between the two main components (i.e., the baseline prediction probability term and the non-negative interference penalty term) in the formula for calculating the expected comprehensive utility value. By adjusting... The value of this coefficient can balance the system's pursuit of payment success rate with the degree of avoidance of potential risks. This coefficient can be set based on historical data analysis, expert experience, or through hyperparameter tuning algorithms.

[0050] Real-time order amount This refers to the transaction amount currently being processed by a user for payment. This amount is a key factor influencing user payment behavior and system risk assessment, and can be obtained in real-time through the payment gateway or front-end page. Merchant's historical average order amount. This refers to the average amount of all transaction orders for a specific merchant over a past period. This value reflects the typical transaction size of the merchant and can be compared with real-time order amounts. This data is compared to assess the relative importance or anomaly of the current transaction. It can be periodically compiled and updated from the merchant's transaction history database. Minimal positive real number order lower limit. It is a preset, near-zero positive number used to avoid using the merchant's historical average order amount when calculating the logarithm of the order amount ratio. A value of zero or extremely small will result in a zero denominator or unstable calculations. Exchange rate volatility. It is an indicator that measures the degree of volatility in cross-currency exchange rates over a period of time. High volatility usually means higher trading risk, and this value can be calculated using statistical methods based on historical exchange rate data.

[0051] Offline updated symmetric positive semidefinite component interference matrix This matrix is ​​used to quantify the potential interference effects between different UI components in a SaaS template, particularly negative interferences that could lead to payment failures or user churn. The elements of this matrix reflect the probability or degree of negative impact when components co-occur. Besides being constructed using eigenvalue decomposition and truncation of negative eigenvalues, this matrix can also be obtained through other offline statistical methods, such as analyzing the failure rate of component combinations based on historical A / B testing data, or evaluating component compatibility using an expert system. Global UI component non-negative interference penalty term. Based on candidate template configuration vector Interference matrix of components A non-negative value is calculated to quantify the potential negative interference effects between UI components under a specific template configuration. The larger the penalty term, the higher the risk of negative interference between components in that template configuration. This is achieved by mapping the effects of the component interference matrix to the specific template configuration through matrix multiplication and vector inner product operations, thus obtaining a scalar penalty value.

[0052] The solution proposed in this application constructs the expected comprehensive utility value. This enables a refined and quantitative evaluation of the quality of payment template configurations, thus solving the problems of insufficient comprehensive measurement of template performance relying solely on a single probability mapping and inadequate consideration of UI component interference effects. The utility value consists of two parts: the first part is based on multi-dimensional state feature vectors. and candidate template configuration vector Through the state-action interaction transition matrix The baseline prediction probability obtained through mapping. This part uses logistic regression to transform the matching degree between the current payment environment and a specific template configuration into a success probability, reflecting the basic conversion potential of the template configuration in a given environment.

[0053] Building upon this, this application introduces a key penalty term, which consists of a logarithmic proportional smoothing term of the order amount and exchange rate volatility. and the offline updated symmetric positive semidefinite component interference matrix Together they constitute. Specifically, the logarithmic proportional smoothing term of the order amount. This enables the system to determine the current order amount. Compared with the merchant's historical average order amount The relative size of this value dynamically adjusts the sensitivity of the penalty. When the order amount is significantly higher than average, this value increases, thus imposing a heavier penalty on the risk of the template configuration and ensuring the robustness of high-value transactions. Exchange rate volatility. As a multiplier factor, it further enhances the responsiveness of the penalty term to external uncertainties. During periods of sharp exchange rate fluctuations, even minor component interventions can be amplified, prompting the system to choose a more robust template configuration to mitigate financial risks.

[0054] Most importantly, by introducing an offline-updated symmetric positive semi-definite component interference matrix... And calculate the non-negative interference penalty term for global UI components under the candidate template configuration. This application effectively identifies and quantifies potential negative interactions between UI components in a specific template configuration. The matrix, through offline statistics and optimization, captures component co-occurrence patterns leading to payment loss in historical data, thus directly reflecting the inherent risk of component configuration in the expected comprehensive utility value. This penalty term is subtracted from the baseline predicted probability, and the balance coefficient is normalized using dimensionality. Adjustments are made to ensure that the final expected overall utility value comprehensively balances the conversion potential and potential negative risks of the template. Through the above mechanism, the solution proposed in this application can not only dynamically evaluate the conversion success rate of the template based on the real-time payment environment, but also deeply consider the negative interference effects that different UI component combinations may bring. Especially in sensitive scenarios such as high-value orders and high exchange rate fluctuations, the system can be guided to select more robust and lower-risk SaaS templates through dynamic adjustment of penalty items.

[0055] As a specific implementation method, in calculating the expected comprehensive utility value At this time, the system first obtains the multidimensional state feature vector of the current time t. This vector may include the real-time success rate, latency, and current cross-currency exchange rate volatility of multiple payment channels. Simultaneously, the system will select a candidate template configuration vector to be evaluated from the candidate template library. For example, this vector It can be a binary vector of length 10, where each position represents a specific UI component (such as "coupon selector", "payment method recommendation", "risk warning pop-up", etc.), with a value of 1 indicating that the component is activated and a value of 0 indicating that it is not activated.

[0056] The system will utilize the current state-action interaction transition matrix. ,Will and Perform interactive calculations, for example through Calculate an intermediate value and then substitute it into the logistic regression function. This yields a preliminary baseline probability prediction, representing the likelihood of the user completing the payment under the current environment and template configuration. Simultaneously, the system retrieves the user's real-time order amount. And query the merchant's historical average order amount Assuming It is 1000 yuan. The minimum order value is 200 yuan, which is the preset minimum positive real number. The value is 1 yuan. Therefore, the logarithmic smoothing term of the order amount is... Next, the system will obtain the current exchange rate volatility. For example, 0.05. Offline updated symmetric positive semi-definite component interference matrix. It can be a 3x3 matrix whose elements reflect the historical level of interference between the three core UI components (e.g., the "payment method selection list," the "user agreement checkbox," and the "payment button"). If the candidate template configuration vector... With these three components activated, then through calculation A specific non-negative interference penalty value can be obtained, for example, 0.2. Finally, these calculation results are substituted into the formula for the expected comprehensive utility value. Assume a pre-defined dimensional normalization equilibrium coefficient. The expected overall utility value is 0.5, and the baseline predicted probability is 0.8. It will be 0.8 minus ,Right now In this way, the system can calculate a comprehensive utility score for each candidate template configuration, which not only considers its conversion potential but also quantifies its potential risks and negative impacts.

[0057] Through the aforementioned technical solution, this application effectively addresses the shortcomings of traditional methods that rely solely on a single probability mapping to comprehensively measure the overall performance of different payment template configurations in complex and dynamic environments, and fail to adequately consider the potential mutual interference effects of different UI components in payment scenarios. This application achieves a precise quantitative assessment of SaaS template configuration quality by constructing an expected comprehensive utility value that includes a baseline predicted probability and multi-dimensional penalty terms. Specifically, the introduction of a logarithmic proportional smoothing term for order amounts allows the system to dynamically adjust penalty weights based on transaction size sensitivity, resulting in more stringent control over configuration risk in high-value transaction scenarios. Simultaneously, exchange rate volatility serves as a multiplier factor, making the system more sensitive to the interference effects of template configurations during periods of financial instability, thereby effectively mitigating additional user churn caused by improper interface configuration. More importantly, through an offline-updated symmetric semi-definite component interference matrix, this application can identify and suppress component combinations exhibiting high failure rates in historical data, thus effectively balancing the potential negative impacts of payment conversion rates and component configurations during template decision-making. This comprehensive evaluation mechanism enables the system to take a holistic view, ensuring payment success rates while maximizing the intelligent adaptation and utility of payment templates through refined constraints on component configurations, significantly improving user experience and transaction success rates in payment scenarios.

[0058] This application further proposes extracting the interference matrix of symmetric positive semidefinite components updated offline. The preliminary offline statistical steps include: acquiring raw co-occurrence data, extracting the total number of times each candidate SaaS component was simultaneously rendered and loaded within a historical preset time window, and the number of co-occurrence failures that led to payment loss; and constructing the original interference matrix between components using the ratio of the number of co-occurrence failures to the total number of co-occurrences as elements. Add the original interference matrix to its transpose and divide by two to generate a symmetric interference matrix. For the symmetric interference matrix Eigenvalue decomposition is performed, representing it as the product of an eigenvector matrix and a diagonal matrix containing the eigenvalues. All eigenvalues ​​less than zero in the diagonal matrix are forcibly truncated to zero, yielding a non-negative eigendiagonal matrix. Based on the eigenvector matrix and the non-negative eigendiagonal matrix, an updated symmetric positive semi-definite component interference matrix is ​​obtained. .

[0059] The process involves acquiring raw co-occurrence data, extracting the total number of times each candidate SaaS component was simultaneously rendered and loaded within a historical preset time window, and the number of co-occurrence failures leading to payment loss. The aim is to collect empirical evidence regarding the actual interactions of SaaS components and their negative consequences. This can be understood as analyzing the payment system's operational logs or transaction records within a specific historical time period to identify which SaaS components (e.g., different payment method buttons, coupon pop-ups, risk warning messages, etc.) appeared simultaneously on the user interface, and counting the total number of these co-occurrences. Simultaneously, it's also necessary to record the number of times users ultimately failed to complete the payment (i.e., payment loss occurred) in these co-occurrence scenarios. For example, the system can parse front-end logs, record the list of components rendered each time the page loads, and combine this with the back-end transaction status to determine whether the payment for that session was successful. Alternatively, the system can use database queries to aggregate component display events and transaction result events within a specific time window to obtain the required total number of co-occurrences and the number of co-occurrence failures.

[0060] Using the ratio of the number of co-occurrence failures to the total number of co-occurrences as elements, the original interference matrix between components is constructed. This is to transform the aforementioned raw statistical data into a quantified interferometric metric. Original interferometric matrix It is a square matrix, whose elements This ratio represents the probability or intensity of payment churn when components i and j appear simultaneously. This ratio intuitively reflects the strength of negative interactions between components; a higher ratio indicates a stronger interference effect between components. In practice, the impact of each component's interaction on payment churn can be directly calculated. To handle data sparsity or avoid division by zero errors, smoothing techniques can be used, such as Laplace smoothing. ,in and It is a pre-defined, extremely small positive number.

[0061] Add the original interference matrix to its transpose and divide by two to generate a symmetric interference matrix. The purpose of this step is to eliminate any potential asymmetry in the original interference matrix and ensure the mathematical consistency of the interference relationships between components. In actual data acquisition, due to various random factors or data biases, the interference intensity of component A on component B may not be exactly the same as the interference intensity of component B on component A. By taking the average of the original matrix and its transpose, a symmetric matrix can be obtained, i.e. This makes the interference relationship mathematically more stable and easier to handle, laying the foundation for subsequent eigenvalue decomposition.

[0062] For the symmetric interference matrix Eigenvalue decomposition is performed, representing it as the product of an eigenvector matrix and a diagonal matrix containing the eigenvalues. All eigenvalues ​​less than zero in the diagonal matrix are forcibly truncated to zero, yielding a non-negative eigendiagonal matrix. Based on the eigenvector matrix and the non-negative eigendiagonal matrix, an updated symmetric positive semi-definite component interference matrix is ​​obtained. This series of operations is crucial to ensuring the good mathematical properties of the interference matrix. Eigenvalue decomposition reveals the matrix's intrinsic structure and main interference modes. However, if negative values ​​are present in the decomposed eigenvalues, the matrix is ​​not positive semi-definite, which may lead to negative interference penalty terms in subsequent utility calculations, thus incorrectly treating interference as a "reward." By forcing all eigenvalues ​​less than zero to zero and reconstructing the matrix using the modified non-negative eigendiagonal matrix and the original eigenvector matrix, the final matrix can be ensured to have good mathematical properties. It is a symmetric positive semi-definite matrix. This means that for any non-zero vector... Quadratic The value will always be non-negative, thus ensuring the rationality of the interference penalty term.

[0063] This application's solution employs a rigorous matrix processing procedure to transform raw, potentially noisy, component co-occurrence data into a symmetric positive semi-definite matrix with sound mathematical properties, thus providing a reliable basis for interference assessment in subsequent template configuration optimization. First, by acquiring the raw co-occurrence data and calculating the co-occurrence failure rate, the direct impact of different component combinations on payment churn can be quantified, laying the data foundation for constructing the interference matrix. Second, by averaging the raw interference matrix with its transpose, a symmetric interference matrix is ​​constructed. This step eliminates the asymmetry of interactions between components, ensuring the objectivity of the interference relationship; that is, the interference of component A on component B and the interference of component B on component A are mathematically consistent. Finally, by performing eigenvalue decomposition on the symmetric interference matrix and forcibly truncating and setting the negative eigenvalues ​​to zero, a symmetric positive semi-definite matrix is ​​reconstructed. This core step not only eliminates abnormal fluctuations and negative noise in the data, but also ensures that the interference matrix satisfies positive semi-definiteness in subsequent utility calculations. This guarantees the stability and convergence of the expected comprehensive utility value calculation process, enabling the system to more accurately identify and avoid component combinations that may lead to payment loss, and improving the scientific nature of template adaptation.

[0064] The following example illustrates this. Suppose we have three candidate SaaS components: component A (e.g., a coupon pop-up), component B (e.g., a payment method selection list), and component C (e.g., a risk warning message). First, the system will analyze historical data to calculate the total number of times each pair of these components co-occurred, as well as the number of failures that led to payment aborts. For example, components A and B co-occurred 1000 times, with 200 of those resulting in payment aborts; components B and A co-occurred 950 times, with 210 of those resulting in payment aborts. Based on this data, an initial interference matrix can be constructed. .For example, ,and Next, in order to eliminate this asymmetry, the system will use the original interference matrix... Its transpose Add them together and divide by two to obtain the symmetric interference matrix. .For example, Finally, regarding this symmetric interference matrix... Eigenvalue decomposition is performed. If a negative eigenvalue, such as -0.05, appears in the decomposition result, the system will forcibly truncate it to zero. Then, the final symmetric positive semi-definite component interference matrix is ​​reconstructed using the modified non-negative eigendiagonal matrix and eigenvector matrix. .this The matrix will then be used to calculate the expected overall utility value as a non-negative interference penalty, ensuring that negative interactions between components are penalized reasonably and consistently when templates are selected.

[0065] Through the above technical solution, this application can effectively solve the problem that the original co-occurrence data contains noise and lacks mathematical stability in traditional methods. Specifically, by systematically processing the original co-occurrence data, including constructing the original interference matrix, generating a symmetric interference matrix, and further performing eigenvalue decomposition, truncation and zeroing of negative eigenvalues, and reconstruction, a symmetric positive semi-definite component interference matrix with good mathematical properties is finally obtained. This matrix ensures that the interference penalty term between components is always non-negative when calculating the expected overall utility value, thus avoiding erroneous evaluations due to data noise or mathematical instability. Furthermore, this symmetric positive semi-definite component interference matrix... Applied to the calculation of expected overall utility value, this "global UI component non-negative interference penalty term" can accurately quantify the potential negative impacts of different SaaS component combinations. Due to the mathematical stability of this penalty term, the system can more accurately identify and avoid conflicting component combinations that could lead to payment loss when optimizing template configurations, thereby significantly improving the accuracy of SaaS template adaptation and payment conversion rates. This solution provides a stable and reliable component interference evaluation mechanism, enabling intelligently generated and adapted SaaS templates to better adapt to complex payment scenarios and reduce the risk of transaction loss.

[0066] This application further proposes the following steps for calculating the dynamic strategy exploration temperature and selection probability in step S30: setting a system temperature boundary that includes a minimum temperature threshold and an initial benchmark temperature; constructing a risk adjustment coefficient based on the dimensionless proportional characteristics of order amount and exchange rate volatility; ensuring that the dynamic strategy exploration temperature decreases non-linearly and monotonically with the increase of the risk adjustment coefficient, and is constrained by the lower limit of the minimum temperature threshold; and performing exponential smoothing normalization on the expected comprehensive utility value of each candidate template configuration vector based on the dynamic strategy exploration temperature to generate the selection probability distribution of each candidate template configuration vector; the calculation formula for the dynamic strategy exploration temperature is as follows:

[0067]

[0068] in, This indicates the temperature explored by the generated dynamic strategy; This represents the preset minimum positive real number minimum temperature threshold. Indicates the initial baseline exploration temperature of the system; Indicates exchange rate volatility; This indicates the current user's real-time order amount; This represents the merchant's historical average order amount; This represents the minimum positive real number of orders; N represents the total number of target candidate payment channels; i represents the channel traversal index; This represents the overall channel health of the i-th payment channel at time t; dynamic strategy explores temperature. This is a key parameter used to balance exploration and utilization when the system selects SaaS templates. Its core function is to dynamically adjust the randomness of strategy selection based on the complexity and uncertainty of the real-time payment environment. In scenarios with high uncertainty, such as high-risk or large-value transactions, the temperature value will adaptively decrease, forcing the system to restrain blind trial and error and instead favor the template with the highest current evaluation utility and the most robust performance (i.e., entering a conservative utilization mode). Conversely, when the environment is relatively stable and the transaction amount is small with extremely low trial and error costs, the temperature value will rise, prompting the system to conduct moderate random exploration and try more different potential optimal template configurations (i.e., entering an exploration and optimization mode). Besides the calculation using exchange rate volatility, order amount ratio, and overall channel health as described in this application, the dynamic strategy exploration temperature can also be evaluated in other ways, such as by combining factors like the volatility of historical payment success rates, the dispersion of user behavior patterns, or the current business risk level of the system. Minimum Temperature Threshold It is a preset, minimal positive real number whose function is to ensure the exploration temperature of the dynamic strategy. It is consistently maintained above a non-zero minimum value. This ensures that even in extremely stable environments with very low exploration requirements, the system maintains a certain degree of randomness, avoiding complete entrapment in local optima and thus preserving the ability to continuously explore potentially better strategies. This threshold can be a fixed small positive number, or it can be adaptively adjusted based on long-term system data or business needs. For example, it can be dynamically set based on the average historical payment churn rate or the performance during the observation period after a new template is launched. Initial baseline exploration temperature of the system. This is a basic exploration parameter set by the system upon startup or reset, providing an initial reference point for calculating the exploration temperature of dynamic strategies. This parameter reflects the system's exploration tendency under normal or default conditions. Its setting can be based on the experience and knowledge of domain experts, for example, according to industry average payment conversion rates or risk preferences; or it can be calibrated through offline simulations and backtesting historical data to find the optimal initial value that balances exploration and utilization in typical scenarios. Exchange rate volatility. Exchange rate volatility is an indicator that measures the degree of fluctuation in cross-currency exchange rates over a specific period, reflecting financial uncertainty in the payment environment. In payment scenarios, high exchange rate volatility may mean increased uncertainty in transaction costs or returns, thus requiring the system to adopt more cautious or exploratory strategies. Besides the method of calculating standard deviation using historical exchange rate sampling points as described in this application, exchange rate volatility can also be calculated using other financial models. For example, an Exponentially Weighted Moving Average (EWMA) model can be used to focus more on recent volatility, or a GARCH model can be used to capture the clustering effect of volatility. (Order amount ratio) Used to measure the current user's real-time order amount. Compared to the merchant's historical average order amount The relative size of the order amount reflects its importance or potential risk. When the order amount is significantly higher than the average, the system may need to be more cautious in selecting templates to ensure success; when the order amount is lower, the system may be more inclined to explore new channels or strategies. Besides simple ratio calculations, logarithmic ratio transformations, quantile rankings, or weighted averages combining the absolute and relative values ​​of the order amount can also be used to characterize the importance of the order amount. Overall Channel Health Comprehensive channel health is a comprehensive indicator that assesses the operational status of a single payment channel at a specific moment, reflecting its reliability and efficiency. High health typically means the channel is stable, fast, and has a high success rate. Besides the method used in this application—calculating success rate and average latency in real-time—comprehensive channel health can also be assessed by integrating data from more dimensions. For example, it can incorporate information such as channel error rate, system load, maintenance status, or historical failure frequency, and employ fuzzy logic or expert systems for comprehensive scoring.

[0069] Furthermore, the formula for calculating the selection probability of each candidate template configuration vector based on the dynamic strategy exploration temperature is as follows:

[0070]

[0071] in, Indicates the environmental state Select candidate template configuration vector The probability value; Indicates the use of candidate template configuration vectors The expected overall utility value M represents the total number of candidate template configuration vectors; m represents the traversal index of the candidate template in the summation term; This indicates the use of template configuration vectors. The expected overall utility value. The probability of the candidate template configuration vector being selected. The system is given the current payment environment state. Below, select a specific candidate template configuration vector. The probability of finding a single optimal solution. This probabilistic selection mechanism allows the system to avoid relying entirely on a single optimal solution, instead exploring suboptimal solutions with a certain probability, thus maintaining flexibility in uncertain environments. Besides the Boltzmann distribution-based (Softmax function) form used in this application, other methods can also be employed. - Greedy strategy (epsilon-greedy strategy), which is to use a small probability... Randomly select templates, to The template with the highest current utility is selected based on probability; alternatively, the Upper Confidence Bound (UCB) algorithm is used to select the template by balancing the average utility of the template with exploration uncertainty. The expected total utility value is... It measures the status in a specific payment environment. Below, a candidate template is used to configure the vector. A quantitative indicator of the overall benefits or value that can be generated. It forms the basis for the system's decision-making and reflects the potential performance of the template in the current environment. In addition to the calculation method in this application that combines the benchmark prediction probability, order amount ratio smoothing term, exchange rate volatility, and component interference penalty term, the expected comprehensive utility value can also be defined and calculated in other ways. For example, it can incorporate factors such as user satisfaction rating, merchant's long-term revenue expectations, risk exposure, or compliance costs, and use a multi-objective optimization method for comprehensive evaluation.

[0072] The solution in this application explores temperature by introducing a dynamic strategy. This mechanism enables adaptive probabilistic control of the payment template selection process, effectively solving the problem of rigid strategy selection in complex and ever-changing payment environments. The scheme obtains multi-dimensional state feature vectors of the payment environment. And calculate the expected comprehensive utility value of each candidate template configuration vector. Building upon this foundation, the final template selection process has been further optimized. Specifically, the system first integrates comprehensive channel health data... Exchange rate volatility Characteristics of the ratio of order amount Calculate the minimum temperature threshold. and system initial baseline exploration temperature Dynamic strategy of dual-clamping to explore temperature This temperature value is not fixed but automatically adjusts based on real-time environmental fluctuations. For example, when exchange rates fluctuate sharply or order amounts deviate significantly from the average, the temperature value changes accordingly, guiding the system to find a balance between conservative and aggressive strategies. Subsequently, the system explores the temperature based on this dynamic strategy. The expected combined utility value of each candidate template configuration vector calculated previously. The selection probability of each candidate template configuration vector is calculated using the Boltzmann distribution (Softmax function). This probability distribution increases the likelihood of selecting templates with higher utility values ​​while preserving room for exploration of other templates. Ultimately, the system samples based on these selection probabilities to generate the final adapted SaaS template. In this way, the proposed solution smoothly handles utility differences between different templates and, when facing uncertainty, dynamically adjusts the concentration of the probability distribution by regulating temperature parameters. This ensures payment conversion effectiveness while enhancing the system's adaptability to environmental changes. This ability to dynamically adjust the balance between exploration and utilization allows the system to more intelligently and robustly address the complexity and uncertainty of payment scenarios, building upon a comprehensive understanding of the payment environment and accurate assessment of template utility. It avoids decision-making errors caused by sudden environmental changes, thereby effectively improving the robustness and conversion rate of template adaptation.

[0073] The following is a concrete example to illustrate this. In calculating the temperature of a dynamic strategy... When calculating the probability of each candidate template being selected, it is assumed that at a certain moment, the system monitors the cross-currency exchange rate volatility. The significant increase to 1.0 indicates substantial uncertainty in the current financial environment. Meanwhile, the current user's real-time order amount... The amount was 2,500 yuan, far exceeding the merchant's historical average order amount. (For example, 500 yuan, with a preset minimum lower limit) If the transaction value is 1 yuan, and the order amount ratio is 5%, this transaction has high value for the merchant. However, at this point, the overall channel health of some payment channels... There has been a slight decline; for example, the current health scores of the two target payment channels are 0.9 and 0.7 respectively, with their average values... The value is 0.8, but it is still within an acceptable range. In this case, the system will calculate a dynamic strategy to explore the temperature based on this real-time data. Set the initial baseline exploration temperature for the system. The minimum guaranteed temperature threshold is 1.0. The value is 0.01. Substituting the above parameters into the formula, we get: Due to high exchange rate volatility and large order amounts, the system determines that the current trading environment is high-risk, high-value, and sensitive, and calculates the exploration temperature. (0.018) Significantly reduced (towards the minimum temperature threshold) (To avoid the system randomly testing interfaces in high-risk environments, which could lead to significant transaction losses. For example, when calculating the probability of selection, the system, due to the extremely low exploration temperature, will drastically amplify the expected comprehensive utility value among various candidate templates.) The slight differences in these parameters result in an overwhelming advantage in the probability of the most robust template being selected (i.e., exhibiting a greedy selection characteristic). Specifically, suppose there exists template A (a historically extremely robust and safe template with an expected overall utility of Q=0.80) and template B (an exploratory template with lower rates but unknown volatility risk, with an expected overall utility of Q=0.70). At extremely low exploration temperatures, substituting this into the Softmax probability distribution formula, the exponential weights of template A are: The exponential weights of template B are After normalization, the probability of template A being selected is as high as approximately... Template B is only about The system will almost always lock onto template A, eliminating any exposure to trial and error caused by conflicts in interface components. Conversely, if the environment is extremely stable, such as when the exchange rate volatility is measured... Furthermore, the real-time order amount is equal to the average (proportional characteristic is 1), the channel average health is 0.95, and the dynamic exploration temperature is calculated by substituting these values ​​into the formula. Now, faced with templates A and B, with utility values ​​of 0.80 and 0.70 respectively, due to the temperature rise, the probability of template A being selected will smooth out to approximately [value missing]. Template B is approximately At this point, the cost of trial and error is extremely low, and the probability distribution of each candidate template tends to be smooth, thus allowing the system to allocate a certain probability to try suboptimal templates and discover configuration schemes with greater long-term return potential under low-risk conditions.

[0074] Through the above technical solution, this application effectively addresses the problem that in highly dynamic and uncertain payment environments, relying solely on static utility evaluation is insufficient to balance the development of optimal strategies with the exploration of potentially high-yield strategies. This leads to the system's inability to flexibly adjust the randomness of strategies when exchange rate fluctuations are severe or order amounts are abnormal, thus limiting the robustness of template adaptation and conversion rates. This solution introduces a dynamic strategy exploration temperature, enabling the system to adaptively adjust the randomness of strategy selection based on real-time payment environment fluctuations (such as exchange rate volatility, order amount ratios, and overall channel health). This ensures that in high-risk environments such as volatile exchange rates or large, high-net-worth orders, the system can force convergence and significantly reduce the exploration probability, prioritizing the use of the oldest, most robust strategies with the highest known utility to secure transactions and prevent decision-making errors caused by component conflicts. Conversely, in extremely stable environments with small transaction amounts and very low trial-and-error costs, the system can appropriately increase the exploration temperature to flexibly discover long-tail strategies with higher conversion potential. Combined with a minimum temperature threshold, the system maintains basic exploration capabilities even in extreme environments, preventing it from getting trapped in local optima. This ability to dynamically adjust the balance between exploration and utilization significantly enhances the robustness of SaaS template adaptation and its adaptability to environmental changes. As a result, it can more effectively optimize payment conversion rates and reduce the risk of transaction loss in complex and ever-changing payment scenarios.

[0075] This application further proposes a calculation process for online adaptive closed-loop update using value-weighted approximate gradient in step S40, including: extracting the dimensionless proportional feature of the order amount and applying a maximum threshold truncation to generate a value-weighting factor; calculating the target residual term of the actual conversion result and the benchmark predicted probability; fusing the outer product matrix of the value-weighting factor, the target residual term, and the actually generated template configuration vector with the multidimensional state feature vector to calculate the value-weighted approximate gradient; and adaptively iterating and replacing the parameters of the state-action interaction transition matrix along the direction of the value-weighted approximate gradient according to a preset step size. The calculation formula for online adaptive closed-loop update using value-weighted approximate gradient is as follows:

[0076]

[0077] in, This represents the state-action interaction transition matrix of the system in the next updated moment; This represents the state-action interaction transition matrix of the system at time t; The learning rate parameter represents the model update; This indicates the current user's real-time order amount; This represents the merchant's historical average order amount; This represents the lower bound of orders with extremely small positive real numbers; This represents the preset maximum weight cutoff threshold; This represents the actual conversion result; a value of 1 indicates successful payment, while a value of 0 indicates failure or churn. This represents the baseline prediction probability evaluated by the system for the actual generated template configuration vector, and its calculation formula is: ; This represents the actual generated template configuration vector; Represents the transpose of a multidimensional state feature vector; This represents the outer product of the actual generated template configuration vector and the multidimensional state feature vector.

[0078] The calculation formula for this online adaptive closed-loop update defines how the system adjusts its internal decision-making model based on actual payment results, enabling continuous learning and optimization. This is a reinforcement learning or adaptive control mechanism designed to continuously improve the SaaS template generation strategy through a feedback loop. The formula can be implemented in a dedicated model training service on the payment system backend, which receives payment result data in real time and performs model parameter updates periodically or event-driven; alternatively, it can be implemented in a distributed computing framework, leveraging its stream processing capabilities to incrementally update real-time data, ensuring the timeliness of model parameters. The state-action interaction transition matrix is ​​included. This is a core parameter of the model, encoding the expected utility derived from selecting different SaaS template configurations under specific payment environment conditions. Updating this matrix signifies that the system is learning and adjusting its evaluation of the value of different state-action combinations. This matrix can be stored in an in-memory database for rapid read, write, and update, or stored in a distributed file system and managed through a machine learning framework. The learning rate parameter is used for model updates. The learning rate controls the impact of new information on the old model parameters with each update. A larger learning rate may lead to rapid model adaptation to new data but is prone to oscillations, while a smaller learning rate results in smoother model updates but slower convergence. The learning rate can be a preset fixed constant, determined through experimental tuning; or a dynamic learning rate strategy can be used, such as gradually decaying as training progresses, or adaptively adjusting based on model performance metrics. The order amount ratio with a maximum threshold truncation is used as a value-weighted factor to measure the value of the current order and is applied as a weight to the model update. Compared with the merchant's historical average order amount By comparing, high-value orders can be identified. Maximum threshold truncation. This prevents extremely high-value orders from having an excessively large and unstable impact on model updates. This factor can be calculated by the data processing module after the payment transaction is completed and passed to the model update service along with the actual conversion result; alternatively, it can be calculated in the real-time data stream through a preprocessing step and appended to each transaction record as one of the input features for model updates. The residual between the actual conversion result and the baseline predicted probability... The actual result (Success is 1, failure is 0) and the success probability predicted by the model. The difference between the two. This residual is a signal of model learning, indicating the accuracy of the model's predictions. A positive residual indicates that the model underestimates the success rate, while a negative residual indicates that the model overestimates the success rate. After each payment transaction, the system records the actual conversion result. And calculate based on the current model parameters Then calculate the difference between the two; alternatively, in an event-driven architecture, the residual can be calculated immediately when the payment result event is triggered and used as part of the update signal. The outer product of the actually generated template configuration vector and the multidimensional state feature vector. Generate a matrix whose dimensions are the same as the state-action interaction transition matrix. Same. It indicates the current state. Below is the actual template configuration selected. right The direction of influence of each parameter. This term indicates the direction of gradient update, indicating... Which parameters need adjustment and by how much? During model updates, the actual selected template configuration vector will be... and the current multidimensional state feature vector Perform matrix multiplication to obtain the outer product; this can also be done efficiently in a dedicated matrix operation library to support large-scale matrix updates.

[0079] This application's online adaptive closed-loop update mechanism, by introducing a value-weighted approximate gradient update formula, enables the system to transition from passive response to active learning, ensuring that the payment template generation strategy can evolve in real time with changes in the business environment. In the payment scenario, the system first calculates the comprehensive channel health and exchange rate volatility based on the real-time acquired target payment channel's operational sequence and cross-currency exchange rate quotes, thereby constructing a multi-dimensional state feature vector of the payment environment. Next, the system obtains the real-time order amount and the merchant's historical average order amount, and combines this with the offline-updated symmetric positive semi-definite component interference matrix. Multidimensional state feature vector The mapping is performed to the baseline prediction probability, and cross-coupling is applied to calculate the configuration vector for each candidate template. Expected overall utility value with non-negative interference penalty term Subsequently, the system integrates the characteristics of comprehensive channel health, exchange rate volatility, and order amount ratio to calculate the dynamic strategy exploration temperature, which is subject to the dual constraints of comprehensive channel health and minimum guarantee threshold. Based on this dynamic strategy, temperature is explored. Compared with expected total utility value The system calculates the selection probability of each candidate template configuration vector and samples them to generate the final adapted SaaS template. After the aforementioned SaaS template is generated and put into practical use, the online adaptive closed-loop update mechanism of this application begins to function. The system will collect the actual conversion results after the SaaS template interacts with the user. At the same time, the system will configure vectors based on the actual generated template. and current state feature vector and the current system state-action interaction transition matrix Calculate the baseline prediction success probability of this template. By comparing the actual conversion results Compared with the baseline prediction probability The system can obtain a residual term. The residual term directly reflects the accuracy of the current model's prediction of the transaction outcome. To fine-tune the model, this application introduces the order amount ratio with a maximum threshold truncation as a value-weighted factor. This factor can be based on the current order amount. Compared to the merchant's historical average order amount The model assigns different learning weights to transactions of varying values. Higher-value orders have a greater impact on model updates, guiding the model to prioritize optimizing the conversion performance of high-value orders. Simultaneously, a preset maximum weight truncation threshold is used. This effectively avoids excessive disturbances to model parameters caused by extreme abnormal orders, ensuring the stability of model updates. Finally, the system utilizes the learning rate parameter updated in the model update. Value weighting factor, residual term and the actual generated template configuration vector With multidimensional state feature vectors outer product By using an online adaptive closed-loop update formula, the state-action interaction transition matrix is ​​adjusted. Update the matrix to obtain the matrix at the next time step. This outer product term It can accurately pinpoint the feature dimensions that cause deviations in conversion results, enabling targeted optimization of specific parameters in the state-action interaction transition matrix. Through this closed-loop feedback mechanism, the system transforms real-time interaction data in payment scenarios into continuous iterative power for model parameters. The actual result of each transaction is used to correct the model's evaluation of future decisions, allowing the system to continuously learn and adapt to the dynamically changing payment environment. This continuously optimizes the generation and adaptation strategies of SaaS templates, achieving global optimization of payment conversion utility. This mechanism, combined with the previous approach of generating SaaS templates by exploring temperature and expected comprehensive utility values ​​through dynamic strategies, forms a complete intelligent decision-making and learning loop. This allows the system to not only make optimal decisions based on the current environment but also continuously improve its decision-making capabilities through actual feedback.

[0080] The following is a concrete example to illustrate this. Suppose in an e-commerce payment scenario, a user is making a transaction, and the order amount is... The amount is 1000 yuan. This is the merchant's historical average order amount. The minimum order value is 500 yuan, which is the preset minimum positive real number. The maximum weight cutoff threshold is set to 1 yuan. The value is 5. The system first generates a multi-dimensional state feature vector based on the current payment environment. And based on the current system state-action interaction transition matrix By combining the candidate template configuration vectors, calculating the expected comprehensive utility value and the probability of selection, a suitable SaaS template is ultimately generated. Assume the system predicts the probability of successful payment for this template. The actual conversion rate is 0.85. After the user interacts with the SaaS template and the payment is successful, the actual conversion result is... The value is 1. At this point, the system will perform an online adaptive closed-loop update. First, calculate the value weighting factor: the order amount ratio is... The value weighting factor with maximum threshold truncation is: Next, the residual between the actual conversion result and the baseline predicted probability is calculated: the residual is... Assume the learning rate parameter updated by the model... The value is 0.01. The system will calculate the actual generated template configuration vector. With multidimensional state feature vectors outer product For example, if It is a 2D vector , It is a 3D vector Then the outer product will be a The matrix. Finally, the system will update the matrix according to the formula. State-action interaction transition matrix Update it. Specifically, Through this update, the system adjusts the state-action interaction transition matrix based on actual payment success results. Fine-tuning was performed. Because the order amount was higher than average, this update received a relatively higher weight, allowing the model to learn more quickly the effectiveness of the current SaaS template configuration in high-value order scenarios. This continuous online update mechanism enables the system to continuously learn from actual transactions and optimize its decision-making capabilities.

[0081] Through the above technical solution, this application addresses the problem of the system lacking closed-loop adaptive optimization based on actual transaction feedback. This solution introduces an online adaptive closed-loop update mechanism, enabling the system to transition from passive response to active learning, ensuring that the payment template generation strategy can evolve in real time with changes in the business environment. Specifically, using a value-weighted approximate gradient update formula, the system can use the residual between the actual conversion result and the baseline predicted probability to correct the state-action interaction transition matrix. This residual-based update method allows the model to identify performance deviations of the current template configuration under specific environmental conditions, thereby making targeted adjustments in subsequent decisions and improving the accuracy of template adaptation. Furthermore, by using the order amount ratio as a value-weighting factor and setting a maximum weight truncation threshold, the impact of different transaction sizes on model updates can be effectively balanced. The system can assign higher weights to high-value transactions, guiding the model to prioritize optimizing the conversion performance of high-value orders, while the truncation mechanism prevents extreme abnormal orders from causing excessive disturbances to model parameters, ensuring the efficiency and stability of model learning. By combining the outer product of the generated template configuration vector and the multidimensional state feature vector, the system can accurately locate the feature dimensions that cause deviations in the conversion results. This enables targeted optimization of specific parameters in the state-action interaction transition matrix, ensuring the model's learning efficiency and accuracy in complex multidimensional feature spaces. This closed-loop feedback mechanism transforms real-time interaction data in payment scenarios into continuous iteration power for model parameters, solving the problem that traditional static rules cannot adapt to dynamic environments. This allows the system to continuously optimize payment conversion utility and achieve global optimization. Combined with the previous solution that uses dynamic strategies to explore temperature and expected comprehensive utility values ​​to generate SaaS templates, this solution further enhances the system's adaptability and long-term performance, enabling it to better cope with complex and ever-changing payment environments, reduce transaction churn rates, and improve user experience and merchant revenue.

[0082] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent generation and adaptation of SaaS templates for payment scenarios, characterized in that, Includes the following steps: Step S10: Obtain the operating sequence and cross-currency exchange rate quotes of the target payment channel in real time, calculate the overall channel health and exchange rate volatility, and construct a multi-dimensional state feature vector of the payment environment; Step S20: Obtain the real-time order amount and the merchant's historical average order amount, and extract the offline updated symmetric positive semi-definite component interference matrix; the symmetric positive semi-definite component interference matrix is ​​used to quantify the possible negative interaction effects between different UI components in the SaaS template; The multidimensional state feature vector is mapped to the baseline prediction probability, and the logarithmic proportional smoothing term of the order amount, the exchange rate volatility and the component interference matrix are cross-coupled to calculate the expected comprehensive utility value of each candidate template configuration vector with a non-negative interference penalty term. Step S30: Integrate the characteristics of comprehensive channel health, exchange rate volatility and order amount ratio to calculate the dynamic strategy exploration temperature, which is constrained by both comprehensive channel health and minimum threshold. The dynamic strategy exploration temperature is a variable parameter used to adjust the balance between exploring new configurations and utilizing known optimal configurations when the system selects SaaS templates. Based on a dynamic strategy, explore the temperature and expected comprehensive utility value, calculate the selection probability of each candidate template configuration vector, and sample to generate the final adapted SaaS template; Step S40: Collect the actual conversion results after the SaaS template interacts with the user; Using the order amount ratio with maximum threshold truncation as the value weighting factor, the residual between the actual conversion result and the baseline predicted probability is calculated, and the value-weighted approximate gradient is used to perform online adaptive closed-loop update of the state-action interaction transition matrix.

2. The method for intelligent generation and adaptation of SaaS templates for payment scenarios according to claim 1, characterized in that, The calculation process of the multi-dimensional state feature vector of the payment environment constructed in step S10 includes: Extract the comprehensive channel health of all target candidate payment channels in the system, and concatenate and vectorize it with the exchange rate volatility of the current payment environment to construct a multi-dimensional state feature vector of the payment environment.

3. The method for intelligent generation and adaptation of SaaS templates for payment scenarios according to claim 2, characterized in that, The calculation process for the overall channel health status includes: The real-time success rate and real-time latency of the corresponding payment channel are obtained. The real-time latency is then processed into a dimensionless form based on a preset tolerance limit, and a nonlinear decay penalty factor is constructed. By positively coupling the real-time success rate with the nonlinear decay penalty factor, the overall channel health of the payment channel is obtained.

4. The method for intelligent generation and adaptation of SaaS templates for payment scenarios according to claim 2, characterized in that, The calculation process for the exchange rate volatility includes: Extract cross-currency exchange rate sampling sequences within historical time windows, and calculate the dimensionless relative dispersion of real-time quotes relative to historical average quotes as exchange rate volatility.

5. The method for intelligent generation and adaptation of SaaS templates for payment scenarios according to claim 1, characterized in that, The calculation process for the expected comprehensive utility value in step S20 includes: The multidimensional state feature vector is mapped to the baseline prediction probability through the state-action interaction transformation matrix; Extract the dimensionless logarithmic ratio feature constructed from the real-time order amount and the merchant's historical average order amount; By performing multidimensional orthogonal decoupling and nonlinear mapping on the dimensionless logarithmic proportion feature, exchange rate volatility and symmetric positive semidefinite component interference matrix, a non-negative interference penalty term with environmental risk and component conflict characteristics is constructed. The expected comprehensive utility value of each candidate template configuration vector is obtained by subtracting the non-negative interference penalty term from the baseline prediction probability.

6. The method for intelligent generation and adaptation of SaaS templates for payment scenarios according to claim 5, characterized in that, The preliminary offline statistical steps for extracting the offline-updated symmetric positive semidefinite component interference matrix include: Obtain raw co-occurrence data, extract the total number of times each candidate SaaS component was simultaneously rendered and loaded within a historical preset time window, and the number of co-occurrence failures that led to payment loss; The original interference matrix between components is constructed using the ratio of the number of co-occurrence failures to the total number of co-occurrences as elements; Add the original interference matrix to its transpose and divide by two to generate a symmetric interference matrix; The symmetric interference matrix is ​​decomposed into eigenvalues ​​and expressed as the product of an eigenvector matrix and a diagonal matrix containing eigenvalues. All eigenvalues ​​less than zero in the diagonal matrix are forcibly truncated and set to zero to obtain a non-negative eigendiagonal matrix. Based on the eigenvector matrix and the non-negative eigendiagonal matrix, the updated symmetric positive semi-definite component interference matrix is ​​obtained.

7. The method for intelligent generation and adaptation of SaaS templates for payment scenarios according to claim 6, characterized in that, The calculation process of the dynamic strategy exploration temperature and the probability of selection in step S30 includes: Set system temperature boundaries that include a minimum protection temperature threshold and an initial reference temperature; A risk adjustment coefficient is constructed based on the dimensionless proportional characteristics of order amount and exchange rate volatility; the dynamic strategy exploration temperature decreases nonlinearly and monotonically with the increase of the risk adjustment coefficient, and is constrained by the lower limit of the guaranteed temperature threshold. The expected comprehensive utility value of each candidate template configuration vector is exponentially smoothed and normalized based on the dynamic strategy exploration temperature to generate the selection probability distribution of each candidate template configuration vector.

8. The method for intelligent generation and adaptation of SaaS templates for payment scenarios according to claim 7, characterized in that, The calculation process for online adaptive closed-loop update using value-weighted approximate gradient in step S40 includes: Extract the dimensionless proportional feature of the order amount and apply a maximum threshold truncation to it to generate a value weighting factor; Calculate the target residual term between the actual transformation result and the baseline predicted probability; The outer product matrix of the value weighting factor, the target residual term, and the actually generated template configuration vector with the multidimensional state feature vector is fused to calculate the value weighted approximate gradient. Along the direction of the value-weighted approximate gradient, the parameters of the state-action interaction transition matrix are adaptively iterated and replaced according to a preset step size.

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