A multi-payment channel adaptive switching method and device based on environmental perception

Abstract

The application provides an environment perception-based multi-payment channel adaptive switching method and device, and relates to the online payment technical field.The method comprises the following steps: monitoring environment parameters in real time through an environment perception module, and mapping the monitored environment parameters into adjustment factors of switching control parameters; establishing a threshold deviation quantization model according to the environment parameters, calculating a threshold deviation expected value in real time, and superimposing the threshold deviation expected value on an original switching threshold before a switching decision to form a dynamic correction threshold; and performing adaptive switching of the multi-payment channel based on the dynamic correction threshold.The application can effectively improve the success rate and stability of the multi-payment channel switching.

Description

Technical Field

[0001] This invention relates to the field of online payment technology, and in particular to a method and apparatus for adaptive switching of multiple payment channels based on environmental awareness. Background Technology

[0002] With the rapid development of mobile payment, the coexistence of multiple payment channels has become the mainstream. Users can choose between different network operators, payment interfaces, or channels to ensure a smooth and reliable payment process. Among these, the mobile payment channel is directly related to the success of daily transactions, and its stability and real-time performance are of crucial importance.

[0003] Existing methods often struggle to handle the complex changes in real-world payment channel switching environments. Network latency and bandwidth fluctuate frequently due to signal strength, base station load, or user location movement. Device performance can also be affected by simultaneous operation of multiple applications or changes in battery power. Sudden shifts in user payment behavior, such as from frequent small transactions to large single payments, can also have unexpected impacts. These changes make switching decisions prone to inaccuracy. Pre-defined fixed conditions may trigger prematurely, causing unnecessary interruptions, or trigger too late, missing the optimal timing, ultimately making channel switching timing unstable. These factors interact, further exacerbating the problem. When network latency suddenly increases, relying solely on latency for switching decisions may overlook the combined effect of simultaneous bandwidth reduction. When device performance temporarily decreases, slower payment responses can easily be mistaken for channel issues. Furthermore, when users switch from frequent small payments to large payments, their tolerance for latency decreases significantly, making it difficult for existing judgment criteria to simultaneously meet the requirements of different scenarios. Especially in actual payment processes, such as when a user moves quickly in the subway causing the network to frequently switch base stations, latency and bandwidth fluctuate drastically. At the same time, the phone's performance degrades due to background applications consuming resources. If a large transfer is made at this time, and the system still uses a fixed threshold to judge the channel quality, it is easy to misjudge at the critical moment, resulting in untimely or excessively frequent switching, causing the payment process to lag or fail.

[0004] How to accurately grasp the timing of payment channel switching under the real-time changes and mutual influence of multiple environmental factors such as network latency, bandwidth, device performance and user payment behavior, so as to ensure that the switching decision is neither too early nor too late, has become a key issue to improve payment stability and user experience in multi-payment channel scenarios. Summary of the Invention

[0005] This invention provides an adaptive switching method and apparatus for multiple payment channels based on environmental awareness, aiming to improve the success rate and stability of switching multiple payment channels.

[0006] In a first aspect, the present invention provides an adaptive switching method for multiple payment channels based on environment awareness, mainly comprising: The environmental sensing module monitors environmental parameters in real time and maps the monitored environmental parameters to adjustment factors for switching control parameters. A threshold deviation quantification model is established based on environmental parameters, the expected value of threshold deviation is calculated in real time, and the expected value of threshold deviation is superimposed on the original switching threshold before the switching decision to form a dynamically corrected threshold. Adaptive switching of multiple payment channels is performed based on dynamically adjusted thresholds.

[0007] Furthermore, the environmental sensing module monitors environmental parameters in real time, including: The environmental sensing module monitors network latency, bandwidth fluctuations, and device performance indicators to obtain environmental parameters. The adjustment factor is determined based on environmental parameters. The adjustment factor is used to adjust the switching trigger threshold, waiting time window, and number of retries.

[0008] Furthermore, the threshold deviation quantization model calculates the expected value of the threshold deviation in real time, including: The threshold deviation quantification model collects deviation samples between the threshold setting value and the actual trigger value in historical switching data, and constructs a deviation distribution function. By using network latency fluctuations, device performance jitter, and sudden changes in user payment behavior as deviation influencing factors into the deviation distribution function, the expected value of the threshold deviation is determined.

[0009] Furthermore, determining the adjustment factor based on environmental parameters includes: Establish a parameter sensitivity matrix and calculate the sensitivity coefficients of environmental parameters to the handover trigger threshold, waiting time window, and number of retries based on historical handover data; When the environment awareness module detects that the network environment changes from stable to fluctuating, it relaxes the switching threshold tolerance and increases the waiting time window based on the sensitivity coefficient.

[0010] Furthermore, the adaptive switching of multiple payment channels based on dynamically adjusted thresholds includes: Set different sets of control parameters for small-amount, high-frequency payment scenarios and large-amount, low-frequency payment scenarios; Select the corresponding set of control parameters based on the current payment scenario, determine the switching timing based on the dynamically adjusted threshold, and execute the channel switching.

[0011] Furthermore, the step of superimposing the expected value of the threshold deviation onto the original switching threshold to form a dynamically corrected threshold includes: If the expected value of the threshold deviation is positive, the original threshold is switched in advance to form a dynamically corrected threshold. If the expected value of the threshold deviation is negative, the original switching threshold is delayed to form a dynamically corrected threshold. At the channel switching critical point, the multi-payment channel switching is triggered based on the dynamically adjusted threshold.

[0012] Furthermore, the establishment of the parameter sensitivity matrix includes: Calculate the optimal combination of control parameters based on historical switching data under different environmental conditions; When changes in environmental parameters are detected in real time, the switching trigger threshold, waiting time window, and number of retries are updated based on the parameter sensitivity matrix.

[0013] Furthermore, the step of selecting the corresponding set of control parameters based on the current payment scenario includes: Detect the current payment amount and payment frequency characteristics to determine whether it belongs to a small-amount, high-frequency payment scenario or a large-amount, low-frequency payment scenario; Select the set of control parameters corresponding to the identified payment scenario and combine it with the dynamically adjusted threshold to perform adaptive switching.

[0014] Secondly, the present invention provides an environment-aware multi-payment channel adaptive switching device, comprising: The monitoring and mapping module is used to monitor environmental parameters in real time through the environmental sensing module and map the monitored environmental parameters into adjustment factors for switching control parameters. The threshold correction module is used to establish a threshold deviation quantification model based on environmental parameters, calculate the expected value of threshold deviation in real time, and add the expected value of threshold deviation to the original switching threshold before the switching decision to form a dynamic correction threshold. The adaptive switching module is used to perform adaptive switching of multiple payment channels based on dynamically adjusted thresholds.

[0015] Furthermore, the monitoring mapping module includes a monitoring unit and an adjustment unit; The monitoring unit is used to monitor network latency, bandwidth fluctuations, and device performance indicators to obtain environmental parameters; The adjustment unit is used to determine the adjustment factor based on environmental parameters. The adjustment factor is used to adjust the switching trigger threshold, the waiting time window, and the number of retries.

[0016] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: This invention discloses an environment-aware adaptive switching method for multiple payment channels. Addressing the issues of inaccurate switching timing, low success rate, and poor user experience caused by network latency fluctuations, unstable bandwidth, device performance jitter, and sudden changes in user payment behavior in multi-payment channel scenarios, this invention uses a real-time environment-aware module to monitor network latency, bandwidth fluctuations, device performance, and user payment behavior. These parameters are mapped to adjustment parameters for the switching trigger threshold, waiting time window, and number of retries via a parameter sensitivity matrix. Simultaneously, a threshold deviation quantification model is constructed, predicting the expected threshold deviation in real time based on historical deviation distribution and superimposing it onto the original threshold to form a dynamically corrected threshold. Differentiated control parameter sets are selected for different payment scenarios, such as small-amount, high-frequency and large-amount, low-frequency, thereby automatically loosening or tightening the threshold, adjusting buffer time, and retrying strategies when the environment changes, accurately executing adaptive switching at the switching critical point. This invention significantly improves the success rate and stability of multi-payment channel switching, optimizes the payment experience, and reduces the risk of payment failure caused by environmental fluctuations. Attached Figure Description

[0017] Figure 1 The flowchart illustrates an adaptive switching method for multiple payment channels based on environmental awareness, as provided in an embodiment of the present invention.

[0018] Figure 2 Applications provided in the embodiments of the present invention Figure 1 A schematic diagram of the module structure in the document.

[0019] Figure 3 This is a functional block diagram of a multi-payment channel adaptive switching device based on environmental perception, provided in an embodiment of the present invention. Detailed Implementation

[0020] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] like Figures 1-2 As shown in the figure, the adaptive switching method for multiple payment channels based on environment awareness provided by this embodiment of the invention may specifically include the following steps: S1: The environmental sensing module monitors environmental parameters in real time and maps the monitored environmental parameters to adjustment factors for switching control parameters.

[0022] Specifically, the environmental awareness module collects network latency, bandwidth fluctuations, device performance metrics, and user payment behavior data in real time. The collected raw data is standardized to form a unified initial parameter set for subsequent mapping processing. This initial parameter set is mapped to adjustment factors for handover control parameters. For key indicators such as network latency and bandwidth fluctuations, and in conjunction with pre-established mapping rules, corresponding adjustment factor sets are generated to reflect the impact of environmental changes on handover decisions.

[0023] For example, in practical applications of multi-payment channel switching, the environmental perception module, as a real-time data acquisition component, integrates sensors and network monitoring tools to capture network latency (such as data packet transmission time), bandwidth fluctuations (such as transmission rate changes), device performance indicators (such as CPU utilization), and user payment behavior data (such as transaction frequency). After these data are collected, they are standardized, that is, data in different units are converted into a dimensionless numerical range for unified comparison, forming an initial parameter set. The beneficial effect of doing so is to eliminate data heterogeneity and ensure the accuracy of subsequent mapping processing.

[0024] In one possible implementation, if the network latency is too high, it will cause payment channel switching delays. By standardizing the data, the latency value can be mapped to the range of 0 to 1, which makes it easier to integrate with other indicators and improves the system's response speed to environmental changes.

[0025] Specifically, user payment behavior data (such as frequent small transactions) will be standardized into behavior intensity indicators, which helps to reflect user preferences and support personalized adjustments to switching decisions. The beneficial effect of doing so is to reduce the switching failure rate and improve payment efficiency.

[0026] In one possible implementation, mapping the initial parameter set to adjustment factors involves pre-established mapping rules. These rules are correspondences trained using historical data. For example, for network latency metrics, the rule is defined as generating a higher adjustment factor if the standardized latency value exceeds a preset threshold, thereby amplifying handover sensitivity. By generating a set of adjustment factors to reflect environmental changes, the beneficial effect of this approach is to make handover decisions more adaptable to dynamic environments and avoid channel congestion caused by bandwidth fluctuations.

[0027] Specifically, regarding the mapping of device performance indicators, if the CPU utilization rate is high after standardization, the rules will generate a factor to suppress switching in order to prevent device overload and help maintain system stability.

[0028] In one possible implementation, combining the mapping of key indicators can also generate factors from the perspective of user payment behavior, such as high-frequency payment behavior corresponding to rapid switching factors. This complements network indicators and is beneficial to the robustness of overall decision-making.

[0029] S2: Establish a threshold deviation quantification model based on environmental parameters, calculate the expected value of threshold deviation in real time, and add the expected value of threshold deviation to the original switching threshold before the switching decision to form a dynamically corrected threshold.

[0030] Real-time data is acquired from current environmental parameters. Key variables, such as network latency and bandwidth changes, are extracted to address the impact of environmental fluctuations. These variables are mapped to initial deviations using pre-established quantification relationships, forming preliminary expected deviation values. Dynamic calibration is then performed on these preliminary expected deviation values. Historical environmental change records are used to weight the deviations, generating corrected expected deviation values ​​that reflect the true fluctuation trends under the current environmental conditions. These corrected expected deviation values ​​are then superimposed on a preset original handover threshold to generate dynamically corrected handover threshold data, adapting to the impact of environmental changes. Before executing a handover decision, the timing of the handover is predicted based on the dynamically corrected handover threshold data to determine the final handover trigger point, ensuring that this trigger point matches the environmental conditions and achieving the goal of threshold deviation quantification.

[0031] For example, in practical applications, the process of acquiring real-time data from current environmental parameters can be understood as collecting key indicators such as network latency and bandwidth changes in real time within a network communication scenario. Assuming a mobile network environment, network latency may fluctuate due to user location changes. In this case, sensors or monitoring tools are used to acquire this changing data and convert it into quantifiable initial deviations. The benefit of this approach is that it can promptly capture subtle characteristics of environmental changes, laying the foundation for subsequent processing and ensuring that the deviations remain consistent with the actual environmental conditions.

[0032] For example, when dynamically calibrating initial deviations, historical environmental change records can be used to optimize the data. Assuming that network latency has shown a regular increase during peak hours over a period of time, weighted analysis of these historical records can adjust the current deviation to better reflect the actual fluctuation trend. This improves the accuracy of the expected deviation value, avoids misjudgments due to data anomalies at a single moment, and provides a reliable basis for subsequent threshold adjustments.

[0033] When generating a dynamically corrected threshold by superimposing the corrected deviation amount onto the original switching threshold, we can imagine a payment channel switching scenario where the original threshold is set to a fixed value, but due to network fluctuations, the actual switching timing may deviate from expectations. By superimposing the corrected deviation amount, the new threshold can dynamically adapt to the current environment, ensuring more accurate switching decisions. The advantage of this approach is that it reduces the risk of switching failures caused by sudden environmental changes.

[0034] In the pre-decision process before the handover decision is executed, the final handover trigger point is determined based on dynamically adjusted threshold data, allowing for early triggering of handover actions in scenarios with sudden increases in network load. For example, in densely populated user areas where network bandwidth suddenly drops, the adjusted threshold can identify handover needs earlier, thus ensuring service continuity. The benefit of this approach is that it optimizes the selection of handover timing, making it highly compatible with environmental conditions and improving overall operational efficiency.

[0035] Based on the set of adjustment factors, the control parameters for switching multiple payment channels are dynamically adjusted, and the switching threshold is updated in real time when environmental parameters change, ensuring that the switching decision matches the current environmental state.

[0036] For example, dynamically adjusting control parameters based on a set of adjustment factors updates the switching threshold in real time as environmental parameters change. For instance, when bandwidth fluctuations increase, a new threshold is calculated using the factor set to trigger switching in advance. This compensates for prediction errors and reduces latency. Specifically, the update process involves gradually refining the threshold formula by applying factors to ensure the decision matches the current state, thus improving the success rate of channel switching. This adjustment can also integrate load balancing in multi-channel scenarios, preventing single-channel overload.

[0037] S3: Perform adaptive switching of multiple payment channels based on dynamically adjusted thresholds.

[0038] Real-time fluctuation information is obtained from the environmental data of the payment channel. The critical point for channel switching is determined based on this fluctuation information, and the initial threshold range corresponding to the critical point is recorded. Based on the changing trend of the environmental data, the upper and lower limits of the threshold range are dynamically adjusted to form a corrected threshold interval. Within the corrected threshold interval, it is determined whether the environmental data of the current payment channel meets the switching conditions. If the switching conditions are met, a channel switching command is triggered. According to the channel switching command, switching operations between multiple payment channels are executed, ensuring that the switching process adaptively adjusts based on the corrected threshold interval.

[0039] In one implementation, real-time fluctuation information is obtained from environmental data of the payment channel. For example, in a mobile payment scenario, environmental data may include network latency and signal strength, and this fluctuation information reflects the channel stability. When the fluctuation information indicates a sudden increase in network latency, the critical point for channel switching is determined based on the fluctuation information. This allows for timely identification of potential problems, avoids payment interruptions, and records the initial threshold range corresponding to the critical point. This range is set based on historical data and provides a benchmark for subsequent adjustments, thereby improving the accuracy of switching decisions.

[0040] Specifically, for the initial threshold range, combined with the changing trend of environmental data, for example, if the changing trend shows that the signal strength is continuously decreasing, the upper and lower limits of the threshold range are dynamically adjusted to form a corrected threshold range. This adjustment can adapt to real-time environmental changes, reduce the losses caused by switching delays, and ensure that the threshold range is more in line with actual needs, which is beneficial to improving the system's response speed and reliability.

[0041] In one implementation, within the modified threshold range, it is determined whether the environmental data of the current payment channel meets the switching conditions. For example, if the environmental data exceeds the upper limit of the range, and the switching conditions are met, a channel switching instruction is triggered. This judgment process can prevent unnecessary switching, optimize resource utilization, and achieve a seamless transition through instruction triggering, effectively reducing the risk of interruption in user experience.

[0042] Specifically, according to the channel switching instruction, a switching operation between multiple payment channels is performed, such as switching from a high-latency channel to a low-latency backup channel. This ensures that the switching process is adaptively adjusted based on the corrected threshold range. This execution can dynamically compensate for environmental errors, improve overall payment efficiency, and support smooth conversion between multiple channels through an adaptive mechanism, thereby achieving adaptive switching based on dynamically corrected thresholds.

[0043] In one embodiment, the above process starts with acquiring fluctuation information, gradually proceeds to threshold adjustment and judgment, and then executes the switch, forming a complete chain. For example, during peak e-commerce payment periods, fluctuation information causes the initial threshold range to be adjusted to a wider range, which then determines the trigger instruction and finally completes the switch. This not only reduces the failure rate but also enhances the robustness of the system.

[0044] Specifically, in the cross-border payment environment, the changing trends of environmental data, such as exchange rate fluctuations, combined with network factors, dynamically form a correction threshold range. After judgment, the switch is executed, which can support the switch decision from multiple aspects such as stability, speed and cost. These aspects are interconnected, ensuring that the switch does not fail due to a single factor deviation, thereby fully achieving the adaptive goal.

[0045] In another embodiment, on the IoT payment device, the acquired fluctuation information (such as the battery power influence signal) is used to adjust the threshold and determine the conditions to trigger and execute the switch. This is consistent with the aforementioned scenario support, emphasizing the role of real-time compensation, resulting in lower latency and higher success rate, which together enhance the execution effect of dynamically correcting the threshold.

[0046] Based on the adjusted control parameters and combined with user payment behavior data, the adaptive switching process of multiple payment channels is monitored in real time and the parameters are fine-tuned to ensure the smoothness and accuracy of the switching process, thus realizing the adaptive switching method of multiple payment channels based on environment awareness.

[0047] In one possible implementation, the adjusted control parameters are monitored and fine-tuned in real time in conjunction with user payment behavior data. For example, monitoring behavioral changes such as transaction failure rate during the switching process and fine-tuning parameters to optimize smoothness. The benefit of doing so is to ensure the accuracy of adaptive switching and ultimately complete the implementation of the method.

[0048] Specifically, fine-tuning involves iterative feedback loops that refine thresholds based on behavioral data, which helps maintain efficient payment channel switching in complex environments.

[0049] In one possible implementation, by establishing a parameter sensitivity matrix, the optimal combination of control parameters is automatically calculated based on historical switching data, and the switching trigger threshold, waiting time window, and number of retries are adaptively adjusted when a change in environmental state is detected.

[0050] Handover behavior data under different environmental conditions is obtained from historical handover records to construct a parameter sensitivity matrix. This matrix reflects the influence of various control parameters on the handover outcome, and the initial combination of handover trigger threshold, waiting time window, and retry count is determined using this matrix. When an environmental state changes from stable to fluctuating, the key indicators in the parameter sensitivity matrix are updated in real time, dynamically adjusting the tolerance range of the handover trigger threshold and extending the waiting time window to adapt to environmental changes.

[0051] Based on real-time sensing data of environmental changes, the handover trigger threshold is dynamically adjusted. Historical behavior data recorded in the parameter sensitivity matrix is ​​used to pre-calculate the possible error range, and the number of retries is adjusted near the critical point to reduce the probability of handover failure. After each handover operation, the actual handover result is compared with the expected value in the parameter sensitivity matrix, and the parameter weights in the matrix are updated to ensure that subsequent adaptive adjustments can more accurately determine the combination of handover trigger threshold, waiting time window, and number of retries.

[0052] In one possible implementation, switching behavior data under different environmental conditions is obtained from historical switching records to construct a parameter sensitivity matrix. This matrix is ​​essentially a two-dimensional array that reflects the influence of each control parameter on the switching result. For example, the rows of the matrix represent various environmental states such as stable or fluctuating, while the columns represent control parameters such as switching trigger thresholds or waiting time windows. The matrix is ​​filled by calculating the sensitivity score of parameter changes to the switching success rate in each cell. This approach can lead to a more accurate determination of the initial combination values ​​because it avoids blind settings based on actual historical data, thereby improving the switching efficiency of the system in the initial stage.

[0053] It should be noted that this construction process involves statistical analysis of historical records, such as collecting events that switch from stable to fluctuating states in the past week, and statistically analyzing the changes in success rates before and after threshold adjustments. This quantifies sensitivity, making the matrix a reliable reference base and helping to reduce the blindness of subsequent adjustments.

[0054] In one possible implementation, when an environmental state is detected to change from stable to fluctuating, the key indicators in the parameter sensitivity matrix are updated in real time. These key indicators refer to the sensitivity scores in the matrix that match the current environment. The scores are updated by comparing the current network latency with the historical average. For example, if the latency suddenly increases, the sensitivity value of the fluctuating state row is increased accordingly. This dynamic adjustment of the tolerance range of the switching trigger threshold and the extension of the waiting time window can bring about the effect of adapting to environmental changes because it prevents unnecessary switching from being triggered frequently during the fluctuating period, thereby reducing system load and improving stability.

[0055] It should be noted that this real-time update is similar to a feedback loop. For example, in a mobile network, when the signal strength drops, the matrix update will relax the threshold from the original strict standard to a more lenient range, thus giving the system more time to buffer the impact of fluctuations, which is beneficial to maintaining connection continuity rather than causing an immediate switch and interruption.

[0056] In one possible implementation, the handover trigger threshold is dynamically adjusted based on real-time sensing data of environmental changes. The possible error range is pre-calculated using historical behavior data recorded in the parameter sensitivity matrix. For example, the current deviation is estimated by the average of historical errors in the matrix, and the number of retries is adjusted near the critical point. This can reduce the probability of handover failure because it provides a compensation mechanism at the handover edge, avoiding misjudgments caused by sudden fluctuations.

[0057] It should be noted that this correction process can be supported from multiple aspects. For example, in the scenario of switching from WiFi to cellular network, if a signal attenuation trend is detected, the system will calculate the error based on the matrix historical data. If the delay deviation is within 5%, the threshold will be fine-tuned, and the number of retries will be increased from 2 to 3. These multi-directional adjustments support each other to ensure more reliable switching and are beneficial to the overall network performance optimization.

[0058] In one possible implementation, after each switching action, the actual switching result is compared with the expected value in the parameter sensitivity matrix to update the parameter weights in the matrix. For example, if the actual success rate is higher than expected, the weight coefficient of the corresponding parameter is increased. This ensures that subsequent adaptive adjustments more accurately determine the combination of switching trigger threshold, waiting time window and number of retries, which can bring about continuous optimization because it forms a closed-loop learning mechanism. The iteration from history to real time improves the accuracy of the matrix.

[0059] It should be noted that this comparison and update is examined from multiple perspectives, such as success rate and latency. For example, in a high-load environment, if the latency decreases after switching, the weight update will strengthen the parameters of the waiting time window, thereby making more effective adjustments during the next fluctuation. This is beneficial for long-term adaptation to various environmental changes and maintaining the optimal combination of control parameters.

[0060] Payment scenarios are categorized based on payment amount and frequency into small-amount, high-frequency payment scenarios and large-amount, low-frequency payment scenarios. Corresponding control parameter sets for small-amount, high-frequency and large-amount, low-frequency transactions are pre-established in storage. Upon each payment request, the current payment amount and frequency characteristics are retrieved from the request, and the payment scenario type is determined based on these characteristics. If it is determined to be a small-amount, high-frequency payment scenario, the pre-established control parameter set is loaded as the parameter for the current switching control; if it is determined to be a large-amount, low-frequency payment scenario, the pre-established control parameter set is loaded as the parameter for the current switching control. During multi-payment channel switching, the loaded control parameter sets are used to monitor the channel status in real time and compare thresholds, thereby achieving adaptive control for the current payment scenario.

[0061] In one possible implementation, classifying payment scenarios based on payment amount and frequency involves pre-defining thresholds. For example, payments below a certain level are considered small-amount, while payments with a frequency exceeding a certain number are considered high-amount. This division into small-amount, high-frequency payment scenarios and large-amount, low-frequency payment scenarios facilitates optimized resource allocation for different scenarios. Corresponding control parameter sets for small-amount, high-frequency and large-amount, low-frequency transactions are pre-established in storage. These parameter sets include elements such as switching thresholds and compensation factors. This improves the system's response efficiency to diverse payment demands and avoids switching delays caused by uniform parameters. For instance, in small-amount, high-frequency payment scenarios, if a user frequently purchases virtual goods with small amounts each time, the system ensures that a more sensitive set of control parameters is used to quickly switch channels, thereby reducing waiting time and increasing transaction success rates. This classification facilitates refined management.

[0062] In one possible implementation, the process of obtaining the current payment amount and frequency characteristics from each payment request upon arrival specifically involves parsing the request data packet to extract the amount value and calculating the frequency based on recent transaction records. Then, based on these payment amount and frequency characteristics, the system determines the payment scenario type to which the current payment request belongs. For example, if the amount is small and the frequency is high, it is classified as a small-amount, high-frequency scenario. This acquisition and judgment is beneficial for real-time adaptation to dynamic environments and ensures the accuracy of switching decisions. For instance, in large-amount, low-frequency payment scenarios such as bulk corporate purchases, the system determines the type after obtaining the high amount and low frequency from the request, thereby selecting a robust parameter set that prioritizes security over speed. This method reduces risk and improves overall system stability.

[0063] In one possible implementation, if the scenario is determined to be a small-amount, high-frequency payment scenario, the process of loading a pre-established set of small-amount, high-frequency control parameters as the parameters for the current switching control includes reading parameters from storage and applying them to the control logic. If the scenario is determined to be a large-amount, low-frequency payment scenario, the process of loading a pre-established set of large-amount, low-frequency control parameters as the parameters for the current switching control is as follows: for example, loading a lower switching threshold in a high-frequency scenario to allow for frequent adjustments. This loading is beneficial for differentiated control and optimizing performance under different scenarios.

[0064] In practical applications, for small-amount, high-frequency transactions such as mobile payments, loading the corresponding parameter set can accelerate channel switching and reduce congestion, while for large-amount, low-frequency transactions such as cross-border transfers, loading a robust parameter set can enhance reliability. This differentiation is beneficial for balancing speed and security.

[0065] In one possible implementation, during the switching of multiple payment channels, the process of real-time monitoring and threshold comparison of channel status using a loaded set of control parameters is specifically achieved by continuously collecting channel load and latency data and comparing it with thresholds in the parameter set. If the threshold is exceeded, a switch is triggered, thereby completing adaptive control for the current payment scenario. For example, in a small-amount, high-frequency scenario, when the load increases, a quick comparison is made and a switch to a backup channel is initiated. This monitoring and comparison helps reduce the failure rate and improve the user experience.

[0066] In high-value, low-frequency scenarios, threshold comparison focuses more on stability thresholds to ensure that switching only occurs when necessary, thereby avoiding unnecessary interruptions. This adaptive control is beneficial to the robustness and efficiency of the overall system.

[0067] The environmental parameters monitored in real time by the environmental perception module include network latency, bandwidth fluctuations, device CPU utilization, and user payment behavior deviations. The adjustment factor converts the environmental parameters into adjustment amounts for the switching trigger threshold, waiting time window, and number of retries based on the parameter sensitivity matrix. When the environment changes from stable to fluctuating, the system automatically relaxes the tolerance of the switching threshold and increases the buffer time.

[0068] The environmental awareness module collects environmental parameters in real time, such as network latency, bandwidth fluctuations, and device CPU utilization. It converts the collected raw data into initial adjustment factors using a pre-established parameter mapping table, which are then used to adjust the handover control parameters. Based on these initial adjustment factors and a pre-established parameter sensitivity matrix, the module further converts them into specific adjustment values ​​for the handover trigger threshold, waiting time window, and retry count, ensuring that the adjustment values ​​match the current environmental state. When the environmental state changes from stable to fluctuating, the tolerance range of the handover trigger threshold is automatically widened based on the adjustment values, while the waiting time window is extended as a buffer mechanism to adapt to environmental changes. If environmental fluctuations persist, the adjusted handover trigger threshold and waiting time window are used to monitor and update handover behavior in real time, ensuring the adaptability of handover control parameters in fluctuating environments and maintaining the stability of handover behavior.

[0069] In one embodiment, the environmental perception module collects network latency data in real time through sensors and network interfaces. For example, when the network latency rises from a low value, it indicates that there may be congestion on the transmission path. This collection process involves periodically sampling the raw signal and filtering noise to obtain accurate environmental parameters, thereby providing a reliable basis for subsequent conversion. This can improve the system's response accuracy to dynamic environments and help reduce misjudgments.

[0070] Specifically, the pre-established parameter mapping table is a correspondence structure in which each environmental parameter, such as bandwidth fluctuation, corresponds to a predefined conversion rule. For example, when the bandwidth fluctuation is large, it is mapped to a higher initial adjustment factor. This table is formed by training with historical data. The conversion process involves inputting the original data into the table, finding matching items, and outputting factor values. This ensures that the generation of adjustment factors matches the actual environment and is beneficial for optimizing the adaptability of switching control.

[0071] In one embodiment, the parameter sensitivity matrix is ​​a multidimensional array used to quantify the degree of influence of each environmental parameter on the switching parameter. For example, the rows in the matrix represent parameters such as device CPU utilization, and the columns represent switching thresholds, etc. The conversion process involves matrix multiplication to multiply the initial adjustment factor by the sensitivity value to obtain the specific adjustment amount. This can accurately match the current state and is beneficial for the system to maintain stability in complex environments.

[0072] Specifically, when the environment changes from stable to fluctuating, the tolerance range of the switching trigger threshold is automatically adjusted according to the adjustment amount. For example, the originally strict threshold is relaxed to accommodate more fluctuations, while the waiting time window is extended as a buffer, such as from a short window to a longer period of time to observe the trend. This can avoid the waste of resources caused by frequent switching and help improve the overall switching efficiency.

[0073] In one embodiment, if fluctuations persist, the switching behavior is monitored by adjusting the threshold and window, for example, by comparing the current parameter with the threshold in real time. If the threshold is exceeded, an update is triggered. This process ensures dynamic adaptation of the parameters, which is beneficial for maintaining behavioral stability under persistent fluctuations.

[0074] Specifically, from multiple perspectives, for example, in high-load scenarios, when the collected CPU utilization is high, it is converted into a large adjustment factor through a mapping table, and then converted into a threshold relaxation through a matrix. This is mutually supportive with the processing of network latency, such as relaxing the threshold when latency increases, forming a consistent adaptation mechanism, which is beneficial to robustness in comprehensive environments.

[0075] In one embodiment, another example of this approach is the integration of user behavior deviations, such as inputting abnormal payment behavior as an additional parameter into a mapping table. After conversion, this is combined with bandwidth fluctuations to adjust the window. This multi-parameter support ensures comprehensive monitoring and helps prevent potential risks.

[0076] Specifically, in the chain from data collection to adjustment, each step of these implementation methods, such as matrix transformation, depends on the output of the previous step. For example, the initial factor is directly input into the matrix to generate the adjustment amount, forming a progressive logic that is beneficial for achieving efficient environmental adaptation.

[0077] In one embodiment, this can be extended to payment scenarios, such as relaxing thresholds during fluctuations to avoid transaction interruptions, which can improve user experience and benefit business continuity.

[0078] The threshold deviation quantification model constructs a deviation distribution function by collecting deviation samples between the threshold setting value and the actual trigger value in historical handover data. It inputs network latency fluctuations, device performance jitter, and sudden changes in user payment behavior as deviation influencing factors into the model, calculates the expected value of the threshold deviation in real time, and adds it to the original threshold before the handover decision to form a dynamically corrected threshold.

[0079] The threshold setting and actual trigger value are collected from historical switching records during multiple payment channel switching events. A deviation sample set is calculated between these two values ​​to form a deviation sample sequence. For this deviation sample sequence, a deviation distribution function incorporating the corresponding network latency fluctuations, device performance jitter, and sudden changes in user payment behavior are constructed. Before each switching decision, the current real-time network latency fluctuations, device performance jitter, and sudden changes in user payment behavior are obtained and input into the constructed deviation distribution function to calculate the expected threshold deviation value under the current environment. The calculated expected threshold deviation value is directly superimposed on the original threshold to generate a dynamically corrected threshold for subsequent switching trigger judgments.

[0080] For example, when collecting threshold settings and actual trigger values ​​from historical switching records, one can first filter out all payment channel switching event records within the past month, and calculate the difference between the set value and the actual trigger value for each event, thus obtaining a set of deviation samples. This collection method helps to capture deviation patterns in real-world scenarios because historical records reflect changes in the actual operating environment, ensuring that the functions subsequently built are more aligned with business needs.

[0081] In one possible implementation, after forming a sequence of biased samples, these bias values ​​are sorted and correlated with network latency data at corresponding times, such as when the latency jumps from 50 milliseconds to 200 milliseconds, thus providing basic data for the distribution function. This correlation can improve the model's sensitivity to environmental fluctuations, making predictions more accurate.

[0082] It should be noted that the process of constructing the deviation distribution function involves taking the deviation sample sequence as input and using a Gaussian distribution fitting method to describe the probability density of the deviation. Among them, network latency fluctuations affect the deviation mean as an independent variable, device performance jitter affects the variance, and sudden changes in user payment behavior introduce a nonlinear adjustment term.

[0083] Specifically, the mean of the Gaussian distribution is calculated by averaging historical deviations, while the variance is derived from the sample divergence. This construction quantifies the weights of influencing factors, ensuring that the function outputs reliable expected deviation values ​​under different conditions. This approach leads to more accurate predictions of the error range, which helps avoid payment interruptions caused by switching too early or too late.

[0084] In one possible implementation, when obtaining the current real-time status, instantaneous network latency values, device CPU utilization jitter metrics, and sudden changes in user payment frequency can be extracted from system logs. These are then input into a distribution function to calculate the expected value. For example, when latency fluctuations increase, the expected value shifts positively accordingly to trigger a handover earlier. This real-time calculation is beneficial for dynamically adapting to environmental changes and improving the robustness of handover decisions.

[0085] For example, the process of adding the expected threshold deviation to the original threshold to generate a dynamically corrected threshold can be done directly in the decision-making module. For instance, if the original threshold is 100 milliseconds and the expected deviation is 20 milliseconds, it is corrected to 120 milliseconds to determine whether the switching condition has been met. This addition can compensate for the time difference caused by environmental fluctuations, ensuring that the switching is performed within the optimal window, which is beneficial to improving the stability of the payment channel and the user experience.

[0086] In one possible implementation, collecting deviation samples from multiple payment scenarios, such as peak and off-peak periods, can mutually support the function's generalization ability. For example, when the deviation is larger during peak periods, the function's adjustment factor has a higher weight, thus providing a more robust expected value in real-time calculations and supporting threshold correction under different business loads. These multi-faceted examples demonstrate that the collection, construction, calculation, and overlay stages are interconnected, forming a complete deviation quantification process, which is beneficial for accurately predicting the error range of switching timing.

[0087] The different payment scenarios include small-amount, high-frequency payment scenarios and large-amount, low-frequency payment scenarios. The system pre-configures a differentiated set of control parameters for each payment scenario, and selects the corresponding parameter set to apply to the adjustment of switching control parameters and threshold correction after detecting the current payment amount and frequency characteristics.

[0088] For different payment scenarios, control parameter sets for small-amount, high-frequency payment scenarios and large-amount, low-frequency payment scenarios are pre-established. These control parameter sets include switching thresholds and adjustment factors, corresponding to the characteristic ranges of payment amount and frequency, respectively. Parameter set data matching the payment scenario is retrieved from a pre-set storage unit. When the amount and frequency characteristics of the current payment scenario are detected, a control parameter set matching the current characteristics is selected from the aforementioned parameter set data. The switching threshold and adjustment factor in the selected parameter set are initially loaded for subsequent switching control adjustments. For the loaded switching threshold and adjustment factor, environmental change data is dynamically sensed at the critical point of payment channel switching. Fluctuation characteristics are extracted from the environmental change data, and the switching threshold is adjusted in real time based on the adjustment factor to adapt to the fluctuations of the current payment scenario. After the switching threshold correction is completed, the corrected threshold is applied to the payment channel switching control, ensuring that the adjustment of switching control parameters and threshold correction conform to the actual characteristics of the current payment scenario, addressing the differentiated needs of small-amount, high-frequency payment scenarios and large-amount, low-frequency payment scenarios.

[0089] For example, when a set of control parameters is pre-established in a payment system, for small-amount, high-frequency payment scenarios such as daily small-amount QR code payments, the switching threshold can be set at a lower level to quickly respond to frequent transactions, while the adjustment factor is used to fine-tune frequency fluctuations. This improves the agility of channel switching and avoids transaction failures due to latency in high-frequency scenarios. In one possible implementation, for large-amount, low-frequency payment scenarios such as large corporate transfers, the switching threshold is set higher to ensure security, and the adjustment factor corresponds to a range of amounts to enhance stability. This differentiated configuration can bring a more reliable payment experience because it matches actual business needs. After obtaining the parameter set data from the pre-set storage unit, it can directly support accurate matching in subsequent detection stages.

[0090] Specifically, when the current payment amount and frequency characteristics are detected, matching items are selected from the previously acquired parameter set data. For example, in a small-amount, high-frequency scenario, if the amount is below a certain range and the frequency is above the average, the corresponding switching threshold and adjustment factor are loaded for initial adjustment. This selection process optimizes the accuracy of switching control because it avoids the inefficiency of general parameters based on real-time characteristics. In one possible implementation, for large-amount, low-frequency scenarios, if a high amount and low frequency are detected, a parameter set emphasizing security is loaded first. This reduces risk and provides a solid foundation for subsequent dynamic correction, ensuring the consistency of the entire switching adjustment process.

[0091] For example, for the loaded switching thresholds and adjustment factors, environmental change data such as network load fluctuations are dynamically sensed at the channel switching critical point. After extracting the fluctuation characteristics, the thresholds are adjusted in real time in conjunction with the adjustment factors. For example, if the environmental change shows a sudden peak, the adjustment factor is amplified to compensate for the delay. This correction can adapt to the actual fluctuations in the payment scenario, resulting in more stable switching performance. In one possible implementation, fluctuation characteristics for small-amount, high-frequency scenarios can be extracted and quickly corrected to maintain efficiency, while large-amount, low-frequency scenarios focus on conservative correction. This not only improves the adaptive capability but also ensures close connection with the aforementioned loaded parameters, forming a reliable control chain.

[0092] Specifically, after threshold correction is completed, it is applied to payment channel switching control. For small-amount, high-frequency scenarios, the adjustment ensures that it meets the requirements of rapid response, while for large-amount, low-frequency scenarios, the safety orientation of threshold correction is emphasized. This application can bring about the beneficial effect of refined adaptive control because it directly responds to the actual characteristics of different scenarios, avoids switching failures, and forms a complete closed loop with the perception and correction of the previous steps.

[0093] The construction of the parameter sensitivity matrix includes calculating the sensitivity coefficients of each environmental parameter to the switching trigger threshold, waiting time window, and number of retries based on historical switching success rate and failure rate data under different environmental conditions. When the system detects changes in environmental parameters in real time, it calculates the adjustment amount based on the sensitivity coefficients and updates the control parameter combination.

[0094] Historical handover records are categorized based on different environmental states. Handover data under the same environmental state is grouped and summarized, and the number of successful and failed handovers for each group is counted to calculate the success rate and failure rate for the corresponding environmental state. For each environmental state, the values ​​of individual parameters—the handover trigger threshold, waiting time window, and retry count—are changed, and the changes in the success rate and failure rate are recorded. The sensitivity coefficient of each environmental parameter to each control parameter is calculated, forming a parameter sensitivity matrix. When real-time monitoring of changes in current environmental parameters, the corresponding sensitivity coefficients are obtained from the parameter sensitivity matrix, and the adjustment amount of each control parameter is calculated based on the magnitude of the environmental parameter change. The calculated adjustment amount is directly added to the original control parameter combination to update the handover trigger threshold, waiting time window, and retry count, forming a real-time adaptive control parameter combination.

[0095] For example, in the field of network communication, the classification of historical handover records can be achieved by categorizing environmental states into three types: high load, low load, and medium load. For instance, in a high load state, network congestion is severe. In this case, all relevant handover data can be aggregated, and the number of successful handovers (e.g., 80%) and the number of failed handovers (e.g., 20%) can be counted to calculate the success rate and failure rate. This classification helps to identify the stability of handover under specific conditions, thereby providing a data basis for subsequent parameter adjustments. Doing so can improve the accuracy of the system's response to environmental fluctuations.

[0096] In one possible implementation, the value of the switching trigger threshold parameter is gradually changed from the default -10 to -5 in a high-load environment. The change in success rate from 75% to 85% and the corresponding decrease in failure rate after each change are recorded. Then, the sensitivity coefficient of environmental parameters such as signal strength to this threshold is calculated. The coefficient value is obtained by dividing the change by the parameter adjustment range. This method ensures that the sensitivity coefficient reflects the influence of the parameter on the switching result, which is beneficial for constructing an accurate parameter sensitivity matrix to optimize the overall control.

[0097] It should be noted that the parameter sensitivity matrix is ​​formed by organizing the sensitivity coefficients of all environmental states into a matrix structure, where the rows represent different environmental parameters such as delay and bandwidth, the columns represent control parameters such as waiting time window and number of retries, and each element is the corresponding coefficient. This matrix facilitates quick querying and calculation of adjustment amounts, thereby enabling adaptive updates of parameters when the environment changes, and improving the reliability and timeliness of switching.

[0098] In one possible implementation, when monitoring changes in current environmental parameters in real time, coefficients matching the current state are extracted from the matrix. For example, when the delay increases by 10%, the adjustment amount is calculated as the negative two of the threshold based on the coefficients. This can compensate for potential fluctuations in advance and reduce the technical effect of switching failures.

[0099] For example, for the parameter of the waiting time window, its value is changed from five seconds to ten seconds in a low-load environment, and the failure rate is observed to decrease from 15% to 5%. The sensitivity coefficient is then calculated and incorporated into the matrix. This is connected with the aforementioned threshold adjustment to ensure the consistency of the adjustment amount calculation of all control parameters, which is beneficial to forming a unified update mechanism.

[0100] In one possible implementation, the obtained adjustment is added to the original combination to update the number of retries from three to four, forming an appropriate parameter combination. This update process is closely related to real-time monitoring, which can reduce latency issues and improve the overall system performance during actual switching.

[0101] It should be noted that all parameters are processed similarly under moderate load conditions, the changes are recorded and the coefficients are calculated, thus the matrix is ​​complete, supporting dynamic correction, thereby maintaining a high success rate in volatile environments and benefiting long-term stable operation.

[0102] For example, from another perspective, if environmental parameters such as bandwidth suddenly drop, the system obtains coefficients from the matrix to calculate adjustment amounts and update thresholds and windows. This multi-parameter linkage, supported by historical data aggregation, jointly ensures the optimization effect of the switching action.

[0103] The formation of the dynamic correction threshold includes triggering the switching threshold in advance when the expected value of the predicted threshold deviation is positive, and delaying the triggering of the switching threshold when the expected value of the predicted threshold deviation is negative. The system performs the switching action according to the dynamic correction threshold at the critical point of multi-payment channel switching.

[0104] The expected threshold deviation under the current environmental conditions is calculated in real time. This expected threshold deviation is then added to the original threshold to obtain the dynamically corrected threshold. The sign of the expected threshold deviation corresponding to the dynamically corrected threshold is determined. If the expected threshold deviation is positive, the dynamically corrected threshold is set lower than the original threshold to trigger the switch earlier; if the expected threshold deviation is negative, the dynamically corrected threshold is set higher than the original threshold to delay the switch. At the multi-payment channel switching critical point, the relationship between the actual measured value and the dynamically corrected threshold is continuously monitored. Once the actual measured value reaches the dynamically corrected threshold, the switch is immediately executed. An independent expected threshold deviation is maintained for each payment channel, forming a corresponding dynamically corrected threshold. At the same critical moment, the switch is determined and executed separately based on the dynamic corrected threshold of each channel.

[0105] In one possible implementation, the process of calculating the expected threshold deviation in real time under the current environmental conditions involves obtaining deviation indicators from network load and latency data. For example, in a payment channel, when network congestion leads to increased latency, the calculated expected threshold deviation may be positive. This helps to add this value to the original threshold to obtain a dynamically corrected threshold, making the switching decision more sensitive to environmental changes. The benefit of doing so is to reduce switching latency and ensure a smooth payment process.

[0106] For example, after adding the expected threshold deviation value to the original threshold to obtain the dynamically corrected threshold, the process of determining the sign of the corresponding expected threshold deviation value can start from the sign. For example, if the expected threshold deviation value is positive, the dynamically corrected threshold is lower than the original threshold to trigger the switch in advance. This can avoid channel congestion in advance in high-load scenarios, and the beneficial effect is to improve the system response speed and reduce the failure rate.

[0107] In one possible implementation, if the expected threshold deviation is negative, the dynamically adjusted threshold is set higher than the original threshold to delay the triggering of the switch. For example, when the network is stable but fluctuates briefly, this delay can prevent unnecessary frequent switches, which has the beneficial effect of optimizing resource utilization and maintaining channel stability.

[0108] For example, the process of continuously monitoring the relationship between the actual measured value and the dynamic correction threshold at the critical point of multi-payment channel switching, and immediately executing the switching action once the actual measured value reaches the dynamic correction threshold, can be achieved through real-time comparison. For example, when the measured value, such as signal strength, approaches the correction threshold, the switching is triggered. This supports precise timing control from a monitoring perspective, and the beneficial effect is to reduce switching failures caused by environmental fluctuations.

[0109] In one possible implementation, the process of maintaining an independent threshold deviation expectation value for each payment channel and forming a corresponding dynamically corrected threshold involves channel isolation calculation. For example, a positive deviation expectation value is calculated and the threshold is corrected for channel A, while a negative deviation expectation value is calculated and adjusted accordingly for channel B. This ensures personalized processing and improves the efficiency of multi-channel parallel processing.

[0110] For example, at the same critical moment, the switching action can be judged and executed separately according to the dynamic correction threshold of each channel. This can start with synchronous judgment, such as switching channel A in advance and channel B in a delayed manner. The support from these multiple directions forms the argument for overall switching optimization. The beneficial effect is to enhance the robustness of the system and the reliability of payment. Through these aspects of analysis, it can be seen that the judgment of the sign of the deviation expectation value and the formation of the correction threshold support each other, and the monitoring and execution are progressively advanced to ensure optimal switching in the face of environmental fluctuations.

[0111] like Figure 3As shown in the figure, an embodiment of the present invention provides a multi-payment channel adaptive switching device based on environment awareness, comprising: The monitoring and mapping module 100 is used to monitor environmental parameters in real time through the environmental sensing module and map the monitored environmental parameters into adjustment factors for switching control parameters. The threshold correction module 200 is used to establish a threshold deviation quantification model based on environmental parameters, calculate the expected value of threshold deviation in real time, and add the expected value of threshold deviation to the original switching threshold before the switching decision to form a dynamic correction threshold. The adaptive switching module 300 is used to perform adaptive switching of multiple payment channels based on dynamically adjusted thresholds.

[0112] Furthermore, the monitoring mapping module 100 includes a monitoring unit 101 and an adjustment unit 102; The monitoring unit 101 is used to monitor network latency, bandwidth fluctuations, and device performance indicators to obtain environmental parameters. The adjustment unit 102 is used to determine the adjustment factor based on environmental parameters. The adjustment factor is used to adjust the switching trigger threshold, the waiting time window, and the number of retries.

[0113] It should be noted that the module units provided in the embodiments of the present invention have the same implementation principle and technical effects as those in the aforementioned method embodiments. For the sake of brevity, the specific working process of the modules described above can be referred to the corresponding process in the aforementioned method embodiments, and will not be repeated here.

[0114] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

Claims

1. A multi-payment channel adaptive switching method based on environment awareness, characterized in that, include: The environmental sensing module monitors environmental parameters in real time and maps the monitored environmental parameters to adjustment factors for switching control parameters. A threshold deviation quantification model is established based on environmental parameters, the expected value of threshold deviation is calculated in real time, and the expected value of threshold deviation is superimposed on the original switching threshold before the switching decision to form a dynamically corrected threshold. Adaptive switching of multiple payment channels is performed based on dynamically adjusted thresholds.

2. The method as described in claim 1, characterized in that, The environmental sensing module monitors environmental parameters in real time, including: The environmental sensing module monitors network latency, bandwidth fluctuations, and device performance indicators to obtain environmental parameters. The adjustment factor is determined based on environmental parameters. The adjustment factor is used to adjust the switching trigger threshold, waiting time window, and number of retries.

3. The method as described in claim 1, characterized in that, The threshold deviation quantization model calculates the expected value of the threshold deviation in real time, including: The threshold deviation quantification model collects deviation samples between the threshold setting value and the actual trigger value in historical switching data, and constructs a deviation distribution function. By using network latency fluctuations, device performance jitter, and sudden changes in user payment behavior as deviation influencing factors into the deviation distribution function, the expected value of the threshold deviation is determined.

4. The method as described in claim 2, characterized in that, The process of determining the adjustment factor based on environmental parameters includes: Establish a parameter sensitivity matrix and calculate the sensitivity coefficients of environmental parameters to the handover trigger threshold, waiting time window, and number of retries based on historical handover data; When the environment awareness module detects that the network environment changes from stable to fluctuating, it relaxes the switching threshold tolerance and increases the waiting time window based on the sensitivity coefficient.

5. The method as described in claim 1, characterized in that, The adaptive switching of multiple payment channels based on dynamically adjusted thresholds includes: Set different sets of control parameters for small-amount, high-frequency payment scenarios and large-amount, low-frequency payment scenarios; Select the corresponding set of control parameters based on the current payment scenario, determine the switching timing based on the dynamically adjusted threshold, and execute the channel switching.

6. The method as described in claim 3, characterized in that, The step of superimposing the expected value of the threshold deviation onto the original switching threshold to form a dynamically corrected threshold includes: If the expected value of the threshold deviation is positive, the original threshold is switched in advance to form a dynamically corrected threshold. If the expected value of the threshold deviation is negative, the original switching threshold is delayed to form a dynamically corrected threshold. At the critical point of channel switching, multiple payment channels are switched based on a dynamically adjusted threshold.

7. The method as described in claim 4, characterized in that, The establishment of the parameter sensitivity matrix includes: Calculate the optimal combination of control parameters based on historical switching data under different environmental conditions; When changes in environmental parameters are detected in real time, the switching trigger threshold, waiting time window, and number of retries are updated based on the parameter sensitivity matrix.

8. The method as described in claim 5, characterized in that, The step of selecting the corresponding set of control parameters based on the current payment scenario includes: Detect the current payment amount and payment frequency characteristics to determine whether it belongs to a small-amount, high-frequency payment scenario or a large-amount, low-frequency payment scenario; Select the set of control parameters corresponding to the identified payment scenario and combine it with the dynamically adjusted threshold to perform adaptive switching.

9. A multi-payment channel adaptive switching device based on environmental perception, characterized in that, include: The monitoring and mapping module is used to monitor environmental parameters in real time through the environmental sensing module and map the monitored environmental parameters into adjustment factors for switching control parameters. The threshold correction module is used to establish a threshold deviation quantification model based on environmental parameters, calculate the expected value of threshold deviation in real time, and add the expected value of threshold deviation to the original switching threshold before the switching decision to form a dynamic correction threshold. The adaptive switching module is used to perform adaptive switching of multiple payment channels based on dynamically adjusted thresholds.

10. The apparatus according to claim 9, characterized in that, The monitoring mapping module includes a monitoring unit and an adjustment unit; The monitoring unit is used to monitor network latency, bandwidth fluctuations, and device performance indicators to obtain environmental parameters; The adjustment unit is used to determine the adjustment factor based on environmental parameters. The adjustment factor is used to adjust the switching trigger threshold, the waiting time window, and the number of retries.

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