Methods and devices for flow control processing of credit information access

Abstract

This application relates to the field of traffic scheduling technology and discloses a method and apparatus for traffic control processing of credit reporting inquiries. One method for traffic control processing of credit reporting inquiries includes: obtaining a calling mode by adapting the mode of the credit reporting inquiry period during the traffic control scheduling process; calculating the real-time traffic deviation for the real-time time window of the calling period when the calling mode is a traffic clearing mode; combining the real-time traffic deviation with steady-state deviation data for adaptive traffic control scheduling to obtain adaptive traffic control parameters; finally, sending the adaptive traffic control parameters to the calling task; and executing the calling task to realize the credit reporting inquiries from the credit reporting platform by the resource organization. This achieves dynamic adaptive traffic control during the credit reporting inquiries process, which helps the resource organization clear the credit reporting inquiries traffic.

Description

Technical Field

[0001] This application relates to the field of traffic scheduling technology, and in particular to a method and apparatus for traffic control processing of credit information invocation. Background Technology

[0002] With the continuous development and promotion of Internet technology, credit reporting services based on Internet technology have emerged. For example, resource institutions often need to call the credit inquiry interface of the credit reporting platform to conduct credit inquiries during the process of resource processing, and then carry out corresponding resource processing based on the credit inquiries. However, as more and more resource institutions call the credit reporting platform for credit inquiries, and the volume of credit inquiries continues to grow, it has brought certain pressure and challenges to the credit reporting platform. Summary of the Invention

[0003] This specification provides one or more embodiments of a flow control method for credit reporting invocation, comprising: performing mode adaptation for the invocation period of the credit reporting invocation to obtain the invocation mode of the invocation period; if the invocation mode is a traffic clearing mode, calculating the real-time traffic deviation for the real-time time window of the invocation period to obtain the real-time traffic deviation; performing adaptive flow control scheduling based on the real-time traffic deviation and steady-state deviation data to obtain adaptive flow control parameters for the target time window; and issuing the adaptive flow control parameters to the invocation task to perform credit reporting invocation to the credit reporting platform by executing the invocation task.

[0004] This specification provides one or more embodiments of a flow control processing device for credit reporting inquiries, comprising: an inquiry mode adaptation module configured to perform mode adaptation for the inquiry period of the credit reporting inquiry to obtain the inquiry mode of the inquiry period; a flow deviation calculation module configured to calculate the real-time flow deviation for a real-time time window of the inquiry period when the inquiry mode is a flow zeroing mode; a flow control scheduling module configured to perform adaptive flow control scheduling based on the real-time flow deviation and steady-state deviation data to obtain adaptive flow control parameters for a target time window; and a flow control execution module configured to send the adaptive flow control parameters to the inquiry task to perform a credit reporting inquiry to the credit reporting platform by executing the inquiry task.

[0005] This specification provides one or more embodiments of a flow control processing device for credit reporting inquiries, including: a processor; and a memory configured to store computer-executable instructions, which, when executed, cause the processor to: perform mode adaptation for a credit reporting inquiry period to obtain an inquiry mode for the inquiry period; if the inquiry mode is a traffic zeroing mode, calculate a real-time traffic deviation for a real-time time window of the inquiry period to obtain a real-time traffic deviation; perform adaptive flow control scheduling based on the real-time traffic deviation and steady-state deviation data to obtain adaptive flow control parameters for a target time window; and send the adaptive flow control parameters to an inquiry task to perform a credit reporting inquiry to the credit reporting platform by executing the inquiry task.

[0006] This specification provides one or more embodiments of a computer-readable storage medium for storing computer-executable instructions. When executed, these computer-executable instructions implement the following process: Mode adaptation is performed for a credit reporting inquiry time period to obtain the inquiry mode for that time period. If the inquiry mode is a traffic clearing mode, a traffic deviation is calculated for the real-time time window of the inquiry time period to obtain a real-time traffic deviation. Adaptive flow control scheduling is performed based on the real-time traffic deviation and steady-state deviation data to obtain adaptive flow control parameters for the target time window. The adaptive flow control parameters are then sent to the inquiry task to execute the inquiry task and initiate a credit reporting inquiry to the credit reporting platform.

[0007] This specification provides one or more embodiments of a flow control processing method for credit reporting invocation. During the flow control scheduling process for credit reporting invocation, mode adaptation is performed for the invocation period to obtain the invocation mode for that period. When the invocation mode is a traffic zeroing mode, traffic deviation is calculated for the real-time time window of the invocation period to obtain the real-time traffic deviation. Adaptive flow control scheduling is then performed based on the real-time traffic deviation and steady-state deviation data to obtain adaptive flow control parameters for the target time window. Finally, the adaptive flow control parameters are sent to the invocation task to execute the task according to the adaptive flow control parameters. This allows for credit reporting invocation to the credit reporting platform through the execution of the invocation task, thereby combining instantaneous and steady-state traffic deviations to achieve more comprehensive and accurate flow control scheduling for the time window. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in one or more embodiments or prior art of this specification, the drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Figure 1A schematic diagram illustrating the implementation environment of a flow control processing method for credit reporting invocation provided in one or more embodiments of this specification; Figure 2 A flowchart illustrating a flow control method for credit reporting invocation provided in one or more embodiments of this specification; Figure 3 A timing diagram of a flow control method for credit inquiry applied to a resource agency credit inquiry scenario, provided for one or more embodiments of this specification; Figure 4 A schematic diagram of an embodiment of a flow control processing device for credit reporting in accordance with one or more embodiments of this specification; Figure 5 This is a schematic diagram of the structure of a flow control processing device for credit reporting, provided for one or more embodiments of this specification. Detailed Implementation

[0009] To enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of this application.

[0010] The credit reporting invocation flow control method provided in one or more embodiments of this specification is applicable to the implementation environment of a credit reporting invocation flow control system. (Refer to...) Figure 1 The implementation environment includes at least: Time period segmentation engine 101, flow control calculation module 102, scheduling execution module 103, credit scoring platform 104; Among them, the time period segmentation engine 101 is used to segment the time period for credit investigation calls by resource institutions. It can divide the time period for credit investigation calls by resource institutions into one or more call periods, and can also perform mode adaptation for the call periods of credit investigation calls to obtain the call mode of the call period. The flow control calculation module 102 is used to calculate the flow control parameters for resource institutions to make credit calls to the credit platform 104 under each call mode. Specifically, it can calculate the flow control parameters for each time window under the call mode of traffic clearing mode to obtain the adaptive flow control parameters for each time window under the traffic clearing mode. The scheduling execution module 103 is used to receive the flow control parameters issued by the flow control calculation module 102, and to make a credit query call to the credit platform 104 by executing the call task in the call task pool according to the flow control parameters. Credit reporting platform 104 is used to respond to credit reporting requests from resource institutions, perform corresponding credit reporting processing, and return the credit reporting processing results to the resource institution that made the credit reporting request.

[0011] In this implementation environment, during the call flow control process for credit reporting inquiries, the call mode of each call period can be obtained by the time period segmentation engine 101 through mode adaptation of the call period. Based on this, when the call mode is the traffic clearing mode, the adaptive flow control parameters of the time window of the traffic clearing mode can be calculated by the flow control calculation module 102. Specifically, the real-time flow deviation is calculated for the real-time time window of the call period to obtain the real-time flow deviation, and the adaptive flow control parameters of the target time window are obtained by adaptive flow control scheduling based on the real-time flow deviation and steady-state deviation data. The adaptive flow control parameters are then sent to the scheduling execution module 103. The scheduling execution module 103 executes the call task to make a credit reporting inquiry to the credit reporting platform 104 according to the adaptive flow control parameters issued for the call task, thereby realizing dynamic adaptive flow control during the credit reporting inquiry to the credit reporting platform 104.

[0012] Reference Figure 2 The flow control processing method for credit information invocation provided in this embodiment specifically includes steps S202 to S208.

[0013] Step S202: Perform pattern adaptation for the call period of the credit reporting call to obtain the call pattern of the call period.

[0014] The call period mentioned in this embodiment refers to dividing the time when resource institutions make credit information calls to the credit information platform into several time periods, each time period is called a call period. Specifically, the call period can be divided according to the call load distribution of the credit information platform. For example, according to the call load distribution of the credit information platform, the call of the resource institution's institutional system to the credit information platform every day is divided into 4 call periods. Call period 1 (00:00 to 08:00) is the time when the call load of the credit information platform is at a low point, call period 2 (08:00 to 20:00) is the time when the call load of the credit information platform is relatively high, call period 3 (20:00 to 22:00) is the time when the call load of the credit information platform is at a peak, and call period 4 (22:00 to 23:59) is the time when the call load of the credit information platform is relatively low. In addition, the call time period can be divided based on the success rate of credit investigation calls. Specifically, the call time period can be divided based on the success distribution of credit investigation calls made by the resource institution's institutional system, or it can be divided based on the call load distribution of the credit investigation platform and the success distribution of credit investigation calls made by the resource institution. Optionally, the call time period can be determined based on the call load distribution of the credit investigation platform, or based on the success distribution and call load distribution of credit investigation calls made by the resource institution.

[0015] The aforementioned invocation mode refers to the internal flow control mode of a resource organization making credit invocations to a credit reporting platform. The flow control processing method for credit invocations provided in this embodiment can be applied to the organizational system or services of a resource organization. In this case, the invocation mode can be the flow control mode during the credit invocation process when the organizational system or services of a resource organization make credit inquiries or access to a credit reporting platform.

[0016] Optionally, the invocation mode includes fixed flow control mode, hybrid flow control mode and / or dynamic flow control mode.

[0017] Among them, the fixed flow control mode refers to the mode in which the institutional system or service of the resource institution makes credit calls to the credit reporting platform according to a fixed call traffic indicator. The call traffic indicator can be the call traffic frequency. For example, during the call period 1 (00:00 to 08:00) when the call load of the credit reporting platform is at its lowest, the fixed flow control mode can be used to make credit inquiries or accesses to the credit reporting platform, making x1 credit inquiries or accesses per second.

[0018] Hybrid flow control mode refers to a hybrid flow control mode that combines credit reporting calls to the credit reporting platform based on fixed call traffic indicators with online real-time calls. For example, during call periods 2 (08:00 to 20:00) and 3 (20:00 to 22:00) when the credit reporting platform has a high call load, hybrid flow control mode can be used to query or access the credit reporting platform. Dynamic flow control mode refers to a mode in which credit reporting calls are made to the credit reporting platform based on real-time dynamically calculated call traffic indicators. Specifically, dynamic flow control mode can be a call mode that aims to clear the call traffic quota allocated by the credit reporting platform to resource institutions. It calculates the call traffic indicators in real time with the goal of clearing the call traffic quota, and then makes credit reporting calls to the credit reporting platform according to the calculated dynamic call traffic indicators. In this case, dynamic flow control mode can be called traffic clearing mode. For example, during call period 4 (22:00 to 23:59), dynamic flow control mode is used to calculate the call traffic indicators dynamically, and then make credit reporting calls to the credit reporting platform according to the calculated dynamic call traffic indicators.

[0019] In practice, when resource agencies make credit inquiries to the credit reporting platform, they adapt the inquiries to the inquiries during the inquiries to obtain the inquiries during the inquiries. Specifically, if the obtained call mode is a fixed flow control mode, the credit reporting platform can be called according to the flow control parameters corresponding to the call time period; If the obtained call mode is a hybrid flow control mode, a credit call can be made to the credit reporting platform for online call tasks, and a credit call can be made to the credit reporting platform according to the offline flow control parameters corresponding to the call time period; If the call mode is traffic clearing mode, then the following operation is performed to calculate the real-time traffic deviation for the real-time time window of the call period.

[0020] In one optional implementation of this embodiment, after the step of performing mode adaptation for the call period of the credit reporting call and obtaining the call mode of the call period is executed, the method further includes: if the call mode is a fixed flow control mode, executing the offline call task in the offline task pool to make a credit reporting call to the credit reporting platform according to the offline traffic parameters corresponding to the call period; and / or, if the call mode is a hybrid flow control mode, making a credit reporting call to the credit reporting platform for the online call task and making a credit reporting call to the credit reporting platform according to the offline traffic parameters corresponding to the call period.

[0021] In practical applications, to improve the real-time performance of flow control under the traffic clearing mode, the call period corresponding to the traffic clearing mode can be divided into fine-grained time windows. Dynamic flow control calculations can be performed according to the time windows. In this way, the dynamic flow control calculations can ensure that the call traffic quota allocated by the credit reporting platform to the resource agency is cleared as much as possible, or that the call traffic quota purchased by the resource agency from the credit reporting platform is cleared.

[0022] Specifically, in one optional implementation of this embodiment, after obtaining the calling mode of the calling time period for the credit reporting call by performing mode adaptation, if the calling mode is the traffic clearing mode, flow control trigger detection of the time window is performed for the traffic clearing mode. If the flow control trigger condition for triggering the time window is detected, the following operation of calculating the real-time flow deviation for the real-time time window of the calling time period is performed to obtain the real-time flow deviation.

[0023] It should be noted that the process of obtaining the call mode for the call period based on the call mode adaptation for the credit reporting call can also be performed as a pre-executed process. Based on the execution of this process, the process of detecting the flow control trigger for the time window of the traffic clearing mode is executed. If the flow control trigger condition for the trigger time window is detected, the following operation of calculating the real-time flow deviation for the real-time time window of the call period is executed. That is, the process of obtaining the call mode for the call period based on the call mode adaptation for the credit reporting call, and the condition that the call mode is the traffic clearing mode, can be replaced by: the process of detecting the flow control trigger for the time window of the traffic clearing mode. If the flow control trigger condition for the trigger time window is detected, the traffic clearing mode can be obtained by pattern adaptation for the call period of the credit reporting call.

[0024] Step S204: If the calling mode is the traffic clearing mode, calculate the real-time traffic deviation for the real-time time window of the calling period.

[0025] Based on the above-mentioned mode adaptation for the call period of credit reporting calls to obtain the call mode, if the call mode is the traffic clearing mode, the traffic deviation is calculated for the real-time time window of the call period to obtain the real-time traffic deviation. This is used to determine the instantaneous traffic deviation of the current real-time time window. The real-time time window refers to the currently completed time window. The real-time traffic deviation refers to the deviation between the actual traffic used in the current time window and the traffic that should be used according to the preset schedule. Specifically, the difference between the traffic that should be used according to the preset schedule and the actual traffic used in the current time window can be used as the real-time traffic deviation.

[0026] In the specific execution process, during the calculation of traffic deviation for the real-time time window of the call period, the real-time target traffic to be used can be calculated based on the window flow control parameters and window time information of the real-time time window, and the real-time traffic deviation can be calculated further based on the real-time target traffic and the actual traffic used in the real-time time window.

[0027] In one optional implementation of this embodiment, the real-time traffic deviation is obtained by calculating the traffic deviation for the real-time time window of the calling period, including: The time information of the real-time time window is indexed and transformed to obtain the time window index, and the window flow control parameters of the real-time time window are read. The real-time target flow is calculated based on the time window index and window flow control parameters, and the real-time flow deviation is calculated based on the real-time target flow and the corresponding actual flow.

[0028] Optionally, the window flow control parameters can be calculated based on the remaining traffic quota and the number of windows in the calling period, or based on the remaining traffic quota corresponding to the real-time time window and the number of windows in the remaining time window.

[0029] For example, in the process of dynamically calculating the call traffic index using dynamic flow control mode for call period 4 (22:00 to 23:59), call period 4 is divided into several time windows with a duration of 10 minutes. For any time window in call period 4, the integer index of the time window can be calculated based on the time information and the duration of 10 minutes. The integer index is then corrected by adding 1 to obtain the corrected integer index of the time window. The corrected integer index is then multiplied by the preset traffic of a single time window to obtain the real-time target traffic of the time window. The difference between the real-time target traffic and the actual traffic of the time window is calculated as the real-time traffic deviation of the time window.

[0030] Step S206: Based on the real-time flow deviation and steady-state deviation data, perform adaptive flow control scheduling to obtain the adaptive flow control parameters for the target time window.

[0031] In practice, after obtaining the instantaneous real-time flow deviation of the real-time time window through flow deviation calculation in the flow zeroing mode, adaptive flow control scheduling is performed by combining the instantaneous real-time flow deviation and steady-state deviation data. In this way, more comprehensive and accurate flow control scheduling is performed on the next time window (i.e. the target time window) of the current real-time time window by combining the instantaneous flow deviation and steady-state flow deviation.

[0032] In the specific implementation process, during adaptive flow control scheduling by combining real-time flow deviation and steady-state deviation data, adaptive flow control scheduling can be carried out from multiple flow control scheduling dimensions. Specifically, in the deviation response dimension, deviation response flow can be obtained by calculating the deviation response based on real-time flow deviation and deviation response coefficient; in the steady-state deviation compensation dimension, steady-state deviation parameters can be obtained by calculating the steady-state deviation compensation based on real-time flow deviation and steady-state deviation data; or, in the flow control amplitude dimension, flow control amplitude parameters can be obtained by predicting the flow control amplitude based on real-time flow deviation and steady-state deviation data. On this basis, at least two of the above three flow control scheduling dimensions can be fused, specifically, at least two of the deviation response flow, steady-state deviation parameters, and flow control amplitude parameters can be fused to obtain adaptive flow control parameters.

[0033] The steady-state deviation data refers to the relevant data used to eliminate the cumulative deviation that still exists when traffic scheduling is performed according to the deviation response flow. Specifically, the steady-state deviation data includes real-time traffic deviation, deviation saturation threshold, deviation response coefficient, steady-state deviation coefficient and / or flow control amplitude coefficient. The steady-state deviation data may also include credit call records.

[0034] Adaptive flow control parameters refer to the flow control parameters that can be adaptively and dynamically adjusted during the dynamic scheduling of call traffic from resource institutions to the credit reporting platform in the flow clearing mode.

[0035] In one optional implementation of this embodiment, adaptive flow control scheduling is performed based on real-time flow deviation and steady-state deviation data to obtain adaptive flow control parameters for the target time window, including: The deviation response flow is obtained by calculating the deviation response based on the real-time flow deviation and the deviation response coefficient. Based on real-time flow deviation and steady-state deviation data, steady-state deviation parameters are obtained by performing steady-state deviation compensation calculations, and flow control amplitude parameters are obtained by performing flow control amplitude prediction. Adaptive flow control parameters are obtained by fusing flow control parameters based on deviation response flow, steady-state deviation parameters, and / or flow control amplitude parameters.

[0036] In the specific execution process, on the one hand, deviation response is calculated based on real-time flow deviation and deviation response coefficient in the deviation response dimension to obtain the deviation response flow, which is the dynamic flow control parameter of the deviation response dimension. On the other hand, steady-state deviation compensation is calculated based on real-time flow deviation and steady-state deviation data in the steady-state deviation compensation dimension to obtain the steady-state deviation parameter, which is the dynamic flow control parameter of the steady-state deviation compensation dimension. On the third hand, flow control amplitude is predicted based on real-time flow deviation and steady-state deviation data in the flow control amplitude dimension to obtain the flow control amplitude parameter, which is the dynamic flow control parameter of the flow control amplitude dimension. Based on this, the calculation results of these three dimensions are fused to obtain adaptive flow control parameters. The calculation process and fusion process of these three dimensions are explained in detail below.

[0037] (1) Deviation response dimension Based on the real-time flow deviation obtained by the above flow deviation calculation, in the process of adaptive flow control scheduling in the deviation response dimension, the deviation response flow is obtained by deviation response calculation based on the real-time flow deviation and the deviation response coefficient. Specifically, in the process of deviation response calculation, the product of the real-time flow deviation and the deviation response coefficient can be used as the deviation response flow, which is also used as the scheduling parameter for adaptive flow control scheduling in the deviation response dimension.

[0038] Among them, deviation response traffic refers to the traffic control parameters of the deviation response dimension, that is, the dynamic traffic parameters of the resource institution making credit inquiries to the credit reporting platform as determined by the deviation response dimension, such as dynamic traffic frequency; deviation response coefficient refers to the flow control scheduling coefficient of the deviation response dimension, specifically the deviation amplification coefficient used to characterize deviation amplification. The specific value of the deviation response coefficient can be preset, or it can be determined based on the historical call parameters of the call mode; the larger the real-time traffic deviation, the stronger the adaptive flow control scheduling in the deviation response dimension. In this case, the flow control scheduling of the deviation response dimension is positively correlated with the real-time traffic deviation, that is: the deviation response coefficient can be set to be positively correlated with the real-time traffic deviation.

[0039] (2) Steady-state deviation compensation dimension In the process of adaptive flow control scheduling in the steady-state deviation compensation dimension, specifically in the steady-state deviation compensation calculation based on real-time flow deviation and steady-state deviation data, the total flow deviation can be obtained by cumulative calculation based on the cumulative flow deviation in the real-time flow deviation and steady-state deviation data, and the steady-state deviation parameter can be obtained by compensation calculation based on the total flow deviation and steady-state deviation coefficient, so as to compensate for the historical cumulative flow deviation in the steady-state deviation compensation dimension.

[0040] The cumulative traffic deviation refers to the total traffic deviation accumulated over all time windows in history (excluding the current real-time time window). The total traffic deviation can be obtained by summing the cumulative traffic deviation with the real-time traffic deviation of the current real-time time window.

[0041] The cumulative flow deviation can be obtained by summing the total flow deviation of each historical time window (excluding the current real-time time window), or by summing the real-time flow deviation of the current real-time time window with the cumulative flow deviation obtained from the previous adaptive flow control scheduling calculation, or by calculating the preset flow based on the preset window flow control parameters and the number of windows for the historical time windows, and then calculating the difference between the preset flow and the total actual flow of each historical time window as the cumulative flow deviation.

[0042] In practical implementation, to avoid excessive expansion of the steady-state deviation parameters obtained from steady-state deviation compensation during adaptive flow control scheduling, deviation compensation detection can be performed during the steady-state deviation compensation calculation. If the deviation compensation detection passes, steady-state deviation compensation calculation can be performed. Specifically, during the deviation compensation detection process, deviation compensation detection can be performed based on the cumulative flow deviation in the real-time flow deviation and steady-state deviation data. Furthermore, if the detection passes, the steady-state deviation parameters can be obtained by compensation calculation based on the real-time flow deviation, cumulative flow deviation, and steady-state deviation coefficient.

[0043] In one optional implementation of this embodiment, steady-state deviation parameters are obtained by performing steady-state deviation compensation calculations based on real-time flow deviation and steady-state deviation data, including: Deviation compensation detection is performed based on the cumulative flow deviation in real-time flow deviation and steady-state deviation data; If the detection passes, the total flow deviation is calculated by accumulating the real-time flow deviation and the cumulative flow deviation, and the steady-state deviation parameter is obtained by compensation calculation based on the total flow deviation and the steady-state deviation coefficient.

[0044] In this process, assuming the deviation compensation detection passes, the sum of the real-time and cumulative flow deviations can be calculated as the total flow deviation during the cumulative calculation based on the real-time flow deviation and the cumulative flow deviation. Furthermore, during the compensation calculation based on the total flow deviation and the steady-state deviation coefficient, the steady-state deviation coefficient can be used as a deviation scheduling weight to weight the total flow deviation to obtain the steady-state deviation flow as the steady-state deviation parameter. Alternatively, the product of the steady-state deviation coefficient and the total flow deviation can be used as the steady-state deviation parameter. The steady-state deviation coefficient refers to the flow control scheduling coefficient in the steady-state deviation compensation dimension; specifically, it is the compensation coefficient used to compensate for or eliminate historical cumulative deviations. The specific value of the steady-state deviation coefficient can be preset or determined based on historical call parameters of the call mode.

[0045] In the specific implementation process, during the deviation compensation detection, on the one hand, deviation compensation detection can be performed on real-time traffic deviation to avoid excessive accumulation of traffic deviation due to excessive short-term traffic deviation in the current real-time time window. For example, the traffic changes are large after the initialization of the resource organization's system. Deviation compensation detection through real-time traffic deviation can eliminate this special case and avoid the impact of similar special cases on deviation compensation calculation. On the other hand, deviation detection can also be performed on cumulative traffic deviation to avoid excessive expansion of steady-state deviation parameters due to excessive historical cumulative total traffic deviation. Specifically, in one optional implementation of this embodiment, deviation compensation detection is performed based on the cumulative flow deviation in the real-time flow deviation and steady-state deviation data, including: detecting whether the real-time flow deviation is less than the real-time deviation threshold; if so, detecting whether the cumulative flow deviation is less than the deviation saturation threshold; if so, determining that the detection has passed; otherwise, no processing is required.

[0046] In addition, during the deviation compensation detection process, besides the aforementioned implementation methods of detecting whether the real-time flow deviation is less than the real-time deviation threshold, and detecting whether the cumulative flow deviation is less than the deviation saturation threshold when it is less than the real-time deviation threshold, deviation compensation detection based on the cumulative flow deviation in the real-time flow deviation and steady-state deviation data can also be replaced with: deviation compensation detection based on the cumulative flow deviation in the real-time flow deviation and / or steady-state deviation data. Correspondingly, the process of detecting whether the real-time flow deviation is less than the real-time deviation threshold, and detecting whether the cumulative flow deviation is less than the deviation saturation threshold when it is less than the real-time deviation threshold, can be replaced with: detecting whether the real-time flow deviation is less than the real-time deviation threshold; if so, the detection is deemed successful. Alternatively, it can be replaced with: detecting whether the cumulative flow deviation is less than the deviation saturation threshold; if so, the detection is deemed successful. Furthermore, it can be replaced with: detecting whether the real-time flow deviation is less than the real-time deviation threshold and whether the cumulative flow deviation is less than the deviation saturation threshold; if so, the detection is deemed successful.

[0047] (3) Flow control amplitude dimension In the process of adaptive flow control scheduling based on the flow control amplitude dimension, specifically in the process of predicting the flow control amplitude based on real-time flow deviation and steady-state deviation data, the flow control amplitude parameter can be obtained by calculating the flow control amplitude based on the real-time flow deviation of the real-time time window and the real-time flow deviation of the previous time window. Alternatively, the flow control amplitude parameter can be obtained by calculating the flow control amplitude coefficient, the real-time flow deviation of the real-time time window and the real-time flow deviation of the previous time window. In this way, adaptive flow control scheduling is performed through the flow control amplitude dimension to analyze the change magnitude of flow deviation, and then the trend of the deviation change of the scheduled flow can be predicted based on the change magnitude of flow deviation. This allows for flow control scheduling based on the trend of the change magnitude of the flow deviation, thereby improving the stability of flow control scheduling and improving the stability and success rate of resource institutions' credit information invocation to the credit information platform.

[0048] In one optional implementation of this embodiment, flow control amplitude parameters are obtained by predicting the flow control amplitude based on real-time flow deviation and steady-state deviation data, including: Read the real-time flow deviation of the previous time window in the steady-state deviation data as the historical flow deviation; The flow control amplitude parameters are obtained by calculating the flow control amplitude based on real-time flow deviation and historical flow deviation.

[0049] In the specific execution process, during the flow control amplitude calculation based on real-time and historical flow deviations, the deviation change amplitude can be obtained by calculating the flow control amplitude based on these two deviations. Then, the flow control amplitude parameter in the flow control amplitude dimension can be obtained by calculating the flow control amplitude based on the deviation change amplitude and the flow control amplitude coefficient. Specifically, the difference between the real-time and historical flow deviations can be used as the deviation change amplitude; the flow control amplitude coefficient can be used as a weight to weight the deviation change amplitude to obtain the flow control amplitude flow as the flow control amplitude parameter; or, the product of the flow control amplitude coefficient and the deviation change amplitude can be used as the flow control amplitude parameter. The flow control amplitude coefficient refers to the flow control scheduling coefficient in the flow control amplitude dimension, specifically a coefficient used for predicting or adjusting deviation change trends. The specific value of the flow control amplitude coefficient can be preset or determined based on historical call parameters of the call mode.

[0050] In specific implementation, after obtaining the dynamic flow control parameters in the three dimensions of deviation response, steady-state deviation compensation, and flow control amplitude, i.e., after obtaining the deviation response flow, steady-state deviation parameter, and flow control amplitude parameter, adaptive flow control parameters are obtained by fusing these parameters. Specifically, during the flow control parameter fusion process, dynamic flow can be obtained as an adaptive flow control parameter by performing deviation fusion calculation based on the deviation response flow, steady-state deviation parameter, and flow control amplitude parameter. In addition, during the flow control parameter fusion process, dynamic flow can also be obtained by performing deviation fusion calculation based on the deviation response flow, steady-state deviation parameter, and flow control amplitude parameter, and flow correction can be applied to the dynamic flow. The adaptive flow obtained by the flow correction is then used as the adaptive flow control parameter.

[0051] In one optional implementation of this embodiment, adaptive flow control parameters are obtained by fusing flow control parameters based on deviation response flow, steady-state deviation parameters, and flow control amplitude parameters, including: Dynamic flow rate is obtained by deviation fusion calculation based on deviation response flow rate, steady-state deviation parameters and flow control amplitude parameters; The dynamic traffic is positively corrected to obtain corrected dynamic traffic, and the corrected dynamic traffic is further corrected offline based on the offline flow control threshold to obtain adaptive traffic as adaptive flow control parameters.

[0052] In the specific execution process, during the deviation fusion calculation based on the deviation response flow, steady-state deviation parameter, and flow control amplitude parameter, the sum of the deviation response flow, steady-state deviation parameter, and flow control amplitude parameter can be calculated as the dynamic flow. That is, the deviation response flow is adaptively adjusted using the steady-state deviation parameter and flow control amplitude parameter to obtain the dynamic flow. Furthermore, the dynamic flow is positively corrected to obtain the corrected dynamic flow. Then, based on the offline flow control threshold, the corrected dynamic flow is offline flow corrected to obtain the adaptive flow as the adaptive flow control parameter. Specifically, in the offline flow control threshold correction process, the smaller of the offline flow control threshold and the corrected dynamic flow can be selected as the adaptive flow through an algorithm to achieve adaptive adjustment of the dynamic flow.

[0053] In practical applications, to improve the accuracy of adaptive flow control scheduling, flow control coefficients can be determined by combining historical credit call records. Dynamic flow control coefficients include deviation response coefficients, steady-state deviation coefficients, and / or flow control amplitude coefficients. This allows for the acquisition of more accurate deviation response coefficients, steady-state deviation coefficients, and / or flow control amplitude coefficients, thereby improving the accuracy of adaptive flow control scheduling based on deviation response coefficients, steady-state deviation coefficients, and / or flow control amplitude coefficients. Specifically, in one optional implementation of this embodiment, after calculating the real-time traffic deviation for the real-time time window of the call period to obtain the real-time traffic deviation, the credit reporting call records of the historical time windows mapped to the target time window in the historical call period are determined, and call parameters are calculated based on the credit reporting call records to obtain historical call parameters; flow control coefficients are then determined based on the historical call parameters to obtain dynamic flow control coefficients. Optionally, the dynamic flow control coefficients include at least one of the following: deviation response coefficient, steady-state deviation coefficient, and flow control amplitude coefficient. The historical call parameters refer to parameters used to characterize the call situation or call status of resource institutions making credit reporting calls to the credit reporting platform within the time window mapped to the target time window, such as the call success rate. In this case, the aforementioned deviation response coefficient, steady-state deviation coefficient, and / or flow control amplitude coefficient are determined based on the historical call parameters obtained from the call parameter calculation based on the credit reporting call records mapped to the target time window.

[0054] Step S208: The adaptive flow control parameters are sent to the calling task to make a credit reporting call to the credit reporting platform by executing the calling task.

[0055] After obtaining the adaptive flow control parameters for the traffic clearing mode, the adaptive flow control parameters are sent to the calling task to make a credit inquiry to the credit reporting platform by executing the calling task. Specifically, the adaptive flow control parameters can be sent to the scheduling execution module, which will then process the calling tasks in the calling task pool according to the adaptive flow control parameters to make a credit inquiry to the credit reporting platform. Similarly, when the call mode is fixed flow control mode or hybrid flow control mode, the call traffic indicators of fixed flow control mode or hybrid flow control mode are sent to the call task so that the call task can be executed to make a credit call to the credit reporting platform. The call traffic indicators of fixed flow control mode or hybrid flow control mode can also be sent to the scheduling execution module, which will then execute the call tasks in the call task pool according to the call traffic indicators to make a credit call to the credit reporting platform.

[0056] Optionally, the call task pool includes: an offline task pool for offline call tasks that perform offline credit reporting calls, and / or an online task pool for online call tasks that perform real-time online calls. Call tasks include offline call tasks and / or online call tasks. Specifically, the configuration of the online task pool and / or offline task pool in the call task pool can be determined according to the call mode. For example, in a fixed flow control mode, the call task pool includes an offline task pool; or in a hybrid flow control mode, the call task pool includes both an offline task pool and an online task pool.

[0057] To ensure that online call tasks can be processed quickly and in real time, online call tasks can be processed first. If there is still remaining traffic in the call traffic indicators or adaptive flow control parameters, offline call tasks can be processed. Optionally, the execution priority of online call tasks is higher than that of offline call tasks.

[0058] Furthermore, to ensure the availability of offline and / or real-time online credit reporting calls from resource agencies' systems to the credit reporting platform, a distributed architecture can be used to deploy distributed call nodes to execute call tasks in the call task pool. Optionally, the offline and / or online call tasks in the call task pool are executed by the distributed call nodes.

[0059] In summary, the flow control processing method for credit reporting invocation provided in this embodiment, during the flow control scheduling process of resource institutions making credit reporting invocations to the credit reporting platform, performs mode adaptation for the invocation period to obtain the invocation mode of the invocation period. When the invocation mode is a traffic zeroing mode, it calculates the real-time traffic deviation for the real-time time window of the invocation period to obtain the real-time traffic deviation, and performs adaptive flow control scheduling based on the real-time traffic deviation and steady-state deviation data to obtain the adaptive flow control parameters for the target time window. Finally, the adaptive flow control parameters are sent to the invocation task to execute the invocation task according to the adaptive flow control parameters. In this way, the credit reporting invocation is made to the credit reporting platform through the execution of the invocation task. This combines the instantaneous traffic deviation and the steady-state traffic deviation to perform more comprehensive and accurate flow control scheduling for the time window. At the same time, it also helps to improve the utilization rate of the invocation traffic quota allocated by the credit reporting platform to the resource institutions, or improve the utilization rate of the invocation traffic quota purchased by the resource institutions from the credit reporting platform.

[0060] The following example uses the flow control processing method for credit information inquiries provided in this embodiment in the scenario of credit information inquiries by resource institutions as an example, combined with Figure 3 The flow control processing method for credit reporting calls provided in this embodiment will be further explained below. See [link to relevant documentation]. Figure 3 The flow control processing method for credit information inquiries applied to resource agency credit information inquiries includes the following steps.

[0061] Step S302: Perform mode adaptation for the call time period when the resource system makes credit information calls to the credit information platform to obtain the call mode for the call time period.

[0062] Step S304: If the calling mode is traffic clearing mode, calculate the real-time traffic deviation for the real-time time window of the calling period.

[0063] Step S306: Calculate the deviation response flow frequency based on the real-time flow deviation and the deviation response coefficient.

[0064] Step S308: Based on real-time flow deviation and steady-state deviation data, perform steady-state deviation compensation calculation to obtain the steady-state deviation flow frequency.

[0065] Step S310: Flow control amplitude and flow frequency are obtained by predicting the flow control amplitude based on real-time flow deviation and steady-state deviation data.

[0066] Step S312: Obtain the adaptive flow frequency by fusing the flow frequency based on the deviation response flow frequency, the steady-state deviation flow frequency, and the flow control amplitude flow frequency.

[0067] Step S314: The adaptive traffic frequency is sent to the offline task pool, and the offline call tasks in the offline task pool are executed according to the adaptive flow control parameters to make credit call to the credit reporting platform.

[0068] It should be noted that any one or more steps in steps S302 to S314 can be combined with any one or more steps in steps S202 to S208 to form a new implementation method according to the needs of implementation and deployment. In addition, any one or more technical features in steps S302 to S314 can be selected and combined with any one or more technical features provided in steps S202 to S208 to form a new implementation method according to the actual deployment needs. Alternatively, any one or more technical features in steps S302 to S314 can be replaced with any one or more technical features provided in steps S202 to S208 to form a new implementation method according to the actual deployment needs. These will not be elaborated on here.

[0069] The following is an embodiment of a flow control processing device for credit information invocation provided in this specification: In the above embodiments, a flow control processing method for credit information invocation is provided, and correspondingly, a flow control processing device for credit information invocation is also provided, which will be described below with reference to the accompanying drawings.

[0070] Reference Figure 4 This illustration shows a schematic diagram of an embodiment of a flow control processing device for credit reporting in this embodiment.

[0071] Since the apparatus embodiments correspond to the method embodiments, the descriptions are relatively simple. For relevant parts, please refer to the corresponding descriptions of the method embodiments provided above. The apparatus embodiments described below are merely illustrative.

[0072] This embodiment provides a flow control processing device for credit information invocation, the device comprising: The call mode adaptation module 402 is configured to perform mode adaptation for the call period of the credit investigation call, and obtain the call mode of the call period; The traffic deviation calculation module 404 is configured to perform traffic deviation calculation for the real-time time window of the calling period to obtain the real-time traffic deviation when the calling mode is the traffic clearing mode. The flow control scheduling module 406 is configured to perform adaptive flow control scheduling based on the real-time flow deviation and steady-state deviation data to obtain adaptive flow control parameters for the target time window; The flow control execution module 408 is configured to send the adaptive flow control parameters to the calling task so as to make a credit reporting call to the credit reporting platform by executing the calling task.

[0073] For ease of description, the above devices are described by dividing them into various modules or units based on their functions. Of course, when implementing one or more embodiments of this specification, the functions of each module or unit can be implemented in one or more software and / or hardware components, or a module that performs the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0074] The following is an example of a flow control processing device for credit information invocation provided in this specification: Corresponding to the flow control processing method for credit information invocation described above, based on the same technical concept, one or more embodiments of this specification also provide a flow control processing device for credit information invocation, which is used to execute the flow control processing method for credit information invocation provided above. Figure 5 This is a schematic diagram of the structure of a flow control processing device for credit reporting, provided for one or more embodiments of this specification.

[0075] This embodiment provides a flow control processing device for credit information invocation, comprising: like Figure 5As shown, device 500 mainly consists of a communication interface 502, a user interface 504, a processor 506, and a data storage 508. These components are interconnected and communicate with each other via a system bus, network, or other connection mechanism 510. The communication interface 502 enables device 500 to communicate with other devices, access networks, and transmission networks via analog or digital modulation. For example, the communication interface 502 may include a chipset and antenna for wireless communication with a radio access network or access point. Furthermore, the communication interface 502 can be a wired interface such as Ethernet, Token Ring, or a USB port, or a wireless interface such as Wi-Fi, Bluetooth, Global Positioning System (GPS), or a wide-area wireless interface (e.g., WiMAX or LTE). Of course, the communication interface 502 can also support other forms of physical layer interfaces and standard or proprietary communication protocols. The communication interface 502 may also include multiple physical communication interfaces, such as Wi-Fi, Bluetooth, and wide-area wireless interfaces. The user interface 504 includes receiving user input and providing output to the user. Therefore, user interface 504 may include input components such as a keypad, keyboard, touch panel, computer mouse, trackball, joystick, microphone, still camera, and video camera, and output components such as a display screen (which may be combined with the touch panel), CRT, LCD, LED, display using DLP technology, printer, and other similar devices known or developed in the future. User interface 504 may also generate auditory output via speakers, speaker jacks, audio output ports, audio output devices, headphones, and other similar devices known or developed in the future. In some embodiments, user interface 504 may include software, circuitry, or other forms of logic capable of transmitting and receiving data to and from external user input / output devices. Additionally or alternatively, device 500 may support remote access from other devices via communication interface 502 or another physical interface (not shown). User interface 504 may be configured to receive user input, the position and movement of which may be indicated by indicators or cursors described herein. User interface 504 may also be configured as a display device for rendering or displaying text fragments.

[0076] Processor 506 may include one or more general-purpose processors and / or special-purpose processors. Data storage 508 may include one or more volatile and / or non-volatile storage components and may be integrated wholly or partially with processor 506. Data storage 508 may include removable and non-removable components.

[0077] Processor 506 is capable of executing program instructions 518 (e.g., compiled or uncompiled program logic and / or machine code) stored in data storage 508 to perform the various functions described herein. Data storage 508 may contain a non-transitory computer-readable medium on which program instructions are stored, which, when executed by device 500, enable device 500 to perform any methods, processes, or functions disclosed in this specification and / or the accompanying drawings. Execution of program instructions 518 by processor 506 may result in processor 506 using data 512. For example, program instructions 518 may include an operating system 522 (e.g., an operating system kernel, device drivers, and / or other modules) installed on device 500 and one or more application programs 520 (e.g., a browser, social application, or game application). Similarly, data 512 may include operating system data 516 and application data 514. Operating system data 516 is primarily accessible to operating system 522, while application data 514 is primarily accessible to one or more application programs 520. Application data 514 may reside in a file system visible or hidden from the user of device 500. Application 520 can communicate with operating system 522 through one or more application programming interfaces (APIs). These APIs facilitate application 520 in reading and / or writing application data 514, transmitting or receiving information via communication interface 502, and receiving or displaying information on user interface 504. In some terms, application 520 may be simply referred to as "app". Furthermore, application 520 can be downloaded to device 500 through one or more online app stores or app markets. However, applications can also be installed on device 500 in other ways, such as through a web browser or a physical interface on device 500 (e.g., a USB port).

[0078] In one specific embodiment, the flow control processing device for credit reporting includes a memory and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer-executable instructions for the flow control processing device for credit reporting, and is configured to be executed by one or more processors. The one or more programs include computer-executable instructions for performing the following: Pattern adaptation is performed for the call period of the credit reporting call to obtain the call pattern of the call period; If the calling mode is the traffic clearing mode, the real-time traffic deviation is calculated for the real-time time window of the calling period; Adaptive flow control scheduling is performed based on the real-time flow deviation and steady-state deviation data to obtain adaptive flow control parameters for the target time window; The adaptive flow control parameters are sent to the calling task so that the calling task can be executed to make a credit reporting call to the credit reporting platform.

[0079] This specification provides an embodiment of a computer-readable storage medium as follows: Corresponding to the flow control processing method for credit information invocation described above, based on the same technical concept, one or more embodiments of this specification also provide a computer-readable storage medium.

[0080] The computer-readable storage medium provided in this embodiment is used to store computer-executable instructions, which, when executed, implement the following process: Pattern adaptation is performed for the call period of the credit reporting call to obtain the call pattern of the call period; If the calling mode is the traffic clearing mode, the real-time traffic deviation is calculated for the real-time time window of the calling period; Adaptive flow control scheduling is performed based on the real-time flow deviation and steady-state deviation data to obtain adaptive flow control parameters for the target time window; The adaptive flow control parameters are sent to the calling task so that the calling task can be executed to make a credit reporting call to the credit reporting platform.

[0081] It should be noted that the embodiments of a computer-readable storage medium described in this specification and the embodiments of a flow control processing method for credit reporting in this specification are based on the same inventive concept. Therefore, the specific implementation of this embodiment can be referred to the implementation of the corresponding method described above, and the repeated parts will not be described again.

[0082] This specification provides an example of a computer program product as follows: Corresponding to the flow control processing method for credit information invocation described above, based on the same technical concept, one or more embodiments of this specification also provide a computer program product.

[0083] A computer program product includes a computer program / instructions that, when executed by a processor, perform the following steps: Pattern adaptation is performed for the call period of the credit reporting call to obtain the call pattern of the call period; If the calling mode is the traffic clearing mode, the real-time traffic deviation is calculated for the real-time time window of the calling period; Adaptive flow control scheduling is performed based on the real-time flow deviation and steady-state deviation data to obtain adaptive flow control parameters for the target time window; The adaptive flow control parameters are sent to the calling task so that the calling task can be executed to make a credit reporting call to the credit reporting platform.

[0084] It should be noted that the embodiments of a computer program product described in this specification and the embodiments of a flow control processing method for credit information inquiries described in this specification are based on the same inventive concept. Therefore, the specific implementation of this embodiment can be referred to the implementation of the corresponding method described above, and the repeated parts will not be described again.

[0085] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments. For example, the device embodiment, equipment embodiment and computer-readable storage medium embodiment are all similar to the method embodiment, so the description is relatively simple. When reading the relevant content of the device embodiment, equipment embodiment and computer-readable storage medium embodiment, please refer to the description of the method embodiment.

[0086] While one or more embodiments of this specification provide method steps as described in the embodiments or flowcharts, it is understood that the order of steps listed in the embodiments or flowcharts is merely one possible execution order among many steps, and does not represent the only execution order. Therefore, when the claims involve method steps, any changes or adjustments to the order of such steps, or the parallelism between steps, are also within the scope of protection of the claims. This specification uses specific terms to describe embodiments of this specification. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "an embodiment," "one embodiment," or "an alternative embodiment" mentioned twice or more in different locations in this specification do not necessarily refer to the same embodiment. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0087] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0088] In the 1930s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many improvements to the methodology today can be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that an improvement to the methodology cannot be implemented using hardware physical modules. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program and "integrate" a digital system onto a PLD themselves, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. Similar to the software compiler used in program development, the original code before compilation must also be written in a specific programming language, called a Hardware Description Language (HDL). There are many HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also understand that by simply performing some logic programming on the method flow using one of these hardware description languages ​​and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.

[0089] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0090] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0091] For ease of description, the above apparatus is described by dividing it into various functional units. Of course, when implementing the embodiments of this specification, the functions of each unit can be implemented in one or more software and / or hardware.

[0092] Those skilled in the art will understand that one or more embodiments of this specification can be provided as a method, system, or computer program product. Therefore, one or more embodiments of this specification may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this specification may take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0093] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0094] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0095] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0096] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0097] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0098] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer-readable storage media include, but are not limited to, phase-change memory (PRAM), static random-access memory (SRAM), dynamic random-access memory (DRAM), other types of random-access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0099] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising at least one…" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0100] One or more embodiments of this specification can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a particular task or implement a particular abstract data type. One or more embodiments of this specification can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0101] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A credit inquiry flow control processing method, characterized in that, The method includes: Pattern adaptation is performed for the call period of the credit reporting call to obtain the call pattern of the call period; If the calling mode is the traffic clearing mode, the real-time traffic deviation is calculated for the real-time time window of the calling period; Adaptive flow control scheduling is performed based on the real-time flow deviation and steady-state deviation data to obtain adaptive flow control parameters for the target time window; The adaptive flow control parameters are sent to the calling task so that the calling task can be executed to make a credit reporting call to the credit reporting platform.

2. The credit inquiry flow control processing method of claim 1, wherein, The calculation of real-time traffic deviation for the real-time time window of the call period to obtain the real-time traffic deviation includes: The time information of the real-time time window is indexed and transformed to obtain the time window index, and the window flow control parameters of the real-time time window are read. The real-time target flow is calculated based on the time window index and the window flow control parameters, and the real-time flow deviation is calculated based on the real-time target flow and the corresponding actual flow.

3. The credit inquiry invoking flow control processing method of claim 1, wherein, The adaptive flow control scheduling based on the real-time flow deviation and steady-state deviation data, to obtain the adaptive flow control parameters for the target time window, includes: The deviation response flow is obtained by calculating the deviation response based on the real-time flow deviation and the deviation response coefficient. Based on the real-time flow deviation and the steady-state deviation data, steady-state deviation compensation calculation is performed to obtain steady-state deviation parameters, and flow control amplitude prediction is performed to obtain flow control amplitude parameters. The adaptive flow control parameters are obtained by fusing the flow control parameters based on the deviation response flow rate, the steady-state deviation parameters, and the flow control amplitude parameters.

4. The credit inquiry flow control processing method of claim 3, wherein, The step of calculating steady-state deviation parameters based on the real-time flow deviation and the steady-state deviation data includes: Deviation compensation detection is performed based on the real-time flow deviation and the cumulative flow deviation in the steady-state deviation data; If the detection passes, the total flow deviation is calculated by accumulating the real-time flow deviation and the cumulative flow deviation, and the steady-state deviation parameter is obtained by compensation calculation based on the total flow deviation and the steady-state deviation coefficient.

5. The credit inquiry invoking flow control processing method of claim 4, wherein, The deviation compensation detection based on the cumulative flow deviation in the real-time flow deviation and the steady-state deviation data includes: Check whether the real-time flow deviation is less than the real-time deviation threshold. If so, check whether the cumulative flow deviation is less than the deviation saturation threshold. If so, determine that the detection has passed.

6. The flow control processing method for credit information access according to claim 3, characterized in that, The process of predicting the flow control amplitude based on the real-time flow deviation and the steady-state deviation data to obtain the flow control amplitude parameters includes: The real-time flow deviation of the previous time window in the steady-state deviation data is read as the historical flow deviation. The flow control amplitude parameter is obtained by calculating the flow control amplitude based on the real-time flow deviation and the historical flow deviation.

7. The flow control processing method for credit information access according to claim 3, characterized in that, The process of obtaining the adaptive flow control parameters by fusing the flow control parameters based on the deviation response flow rate, the steady-state deviation parameter, and the flow control amplitude parameter includes: Dynamic flow rate is obtained by deviation fusion calculation based on the deviation response flow rate, the steady-state deviation parameter, and the flow control amplitude parameter; The dynamic traffic is positively corrected to obtain corrected dynamic traffic, and the corrected dynamic traffic is further corrected offline based on the offline flow control threshold to obtain adaptive traffic as the adaptive flow control parameter.

8. The flow control processing method for credit information access according to claim 3, characterized in that, Also includes: Determine the credit reporting call records of the historical time window mapped to the target time window in the historical call period, and calculate the call parameters based on the credit reporting call records to obtain the historical call parameters; Dynamic flow control coefficients are obtained by making flow control coefficient decisions based on the historical call parameters; the dynamic flow control coefficients include at least one of the following: deviation response coefficient, steady-state deviation coefficient, and flow control amplitude coefficient.

9. The flow control processing method for credit information access according to claim 1, characterized in that, After the step of performing pattern adaptation for the credit reporting call period and obtaining the call pattern for the call period is executed, the method further includes: If the calling mode is a fixed flow control mode, the offline calling task in the offline task pool is executed to make a credit calling to the credit platform according to the offline traffic parameters corresponding to the calling time period; If the calling mode is a hybrid flow control mode, a credit reporting call is made to the credit reporting platform for online calling tasks, and a credit reporting call is also made to the credit reporting platform according to the offline traffic parameters corresponding to the calling time period.

10. The flow control processing method for credit information access according to claim 1, characterized in that, In the aforementioned invocation tasks, the execution priority of online invocation tasks is higher than that of offline invocation tasks; The call period is determined based on the call load distribution of the credit reporting platform, or it is determined based on the call success distribution of credit reporting calls made by resource agencies and the call load distribution.

11. The flow control processing method for credit information access according to claim 1, characterized in that, After the step of performing pattern adaptation for the credit reporting call period and obtaining the call pattern for the call period is executed, the method further includes: If the calling mode is the traffic clearing mode, a flow control trigger detection is performed for the traffic clearing mode within a time window. If a flow control trigger condition for triggering a time window is detected, the operation of calculating the real-time traffic deviation for the real-time time window of the calling period is executed to obtain the real-time traffic deviation.

12. A flow control processing device for credit information retrieval, characterized in that, The device includes: The call mode adaptation module is configured to perform mode adaptation for the call period of the credit reporting call, and obtain the call mode of the call period; The traffic deviation calculation module is configured to calculate the real-time traffic deviation for the real-time time window of the calling period when the calling mode is the traffic clearing mode. The flow control scheduling module is configured to perform adaptive flow control scheduling based on the real-time flow deviation and steady-state deviation data to obtain adaptive flow control parameters for the target time window; The flow control execution module is configured to send the adaptive flow control parameters to the calling task, so as to make a credit reporting call to the credit reporting platform by executing the calling task.

13. A flow control processing device for credit information invocation, characterized in that, include: processor; And, a memory configured to store computer-executable instructions, which, when executed, cause the processor to: Pattern adaptation is performed for the call period of the credit reporting call to obtain the call pattern of the call period; If the calling mode is the traffic clearing mode, the real-time traffic deviation is calculated for the real-time time window of the calling period; Adaptive flow control scheduling is performed based on the real-time flow deviation and steady-state deviation data to obtain adaptive flow control parameters for the target time window; The adaptive flow control parameters are sent to the calling task so that the calling task can be executed to make a credit reporting call to the credit reporting platform.

14. A computer-readable storage medium for storing computer-executable instructions, characterized in that, When the computer-executable instructions are executed, they implement the steps of the method of claim 1.

Images