CN116611898B - Online payment optimization system and method based on e-commerce platform - Google Patents
Online payment optimization system and method based on e-commerce platform Download PDFInfo
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Abstract
The invention belongs to the technical field of online payment, and particularly relates to an online payment optimization system and method based on an e-commerce platform. According to the invention, the payment delay parameter can be monitored in real time, an alarm signal is sent out under the condition that the payment delay parameter exceeds the standard delay threshold, and when the payment delay parameter does not exceed the standard delay threshold, the payment delay parameter of the predicted payment information is summarized into the data set to be evaluated, so that the payment delay parameter of the predicted payment information is predicted, an optimization step can be executed before the payment delay parameter exceeds the standard delay threshold, the running fluency of an online payment system is ensured, and in the optimization process, all the historical payment information is not uploaded to a cloud for storage, but part of the historical payment information is reserved in the payment system, so that a user can conveniently read the historical payment information in time, and the experience of the user is further improved.
Description
Technical Field
The invention belongs to the technical field of online payment, and particularly relates to an online payment optimization system and method based on an e-commerce platform.
Background
With the continuous development of network information technology, the way of people obtaining information from the network is also simpler and simpler, compared with the way of obtaining information offline, the way of obtaining information online has timeliness, and meanwhile, the channel of information transfer is reduced, for example, the traditional shopping way needs purchasers to arrive at the position of stores to select commodities, but online shopping can know product information only by browsing pictures and videos, and meanwhile, online payment and offline delivery are supported, so that the shopping way of people is more convenient and faster, and the optimization of an online payment system is essential for ensuring the experience of online shopping of users.
In the prior art, as more commodities of the same type are available on line and stronger in substitutability, the experience of a purchaser can be reduced due to overlarge payment delay parameters, so that the purchaser is most likely to select products of the same type which are available in other platforms, the customer loss can be caused certainly, the historical payment information remained in a payment system is reduced uniformly due to the fact that the payment delay parameters are reduced uniformly, the experience of the user can be reduced certainly, and on the basis of the scheme, the online payment optimization method capable of guaranteeing that the payment delay parameters meet the user demands and part of historical payment information is reserved in the payment system is provided.
Disclosure of Invention
The invention aims to provide an online payment optimizing system and method based on an e-commerce platform, which can ensure that payment delay parameters meet user requirements, and keep part of historical payment information in a payment system, so that a user can conveniently and timely review.
The technical scheme adopted by the invention is as follows:
an online payment optimization method based on an e-commerce platform comprises the following steps:
acquiring online payment information of an e-commerce platform, wherein the online payment information comprises historical payment information and current payment information;
acquiring the settlement time length of the current payment information and calibrating the settlement time length as a payment delay parameter;
acquiring a standard delay threshold value and comparing the standard delay threshold value with the payment delay parameter;
if the payment delay parameter is smaller than a standard delay threshold, the historical payment information redundancy quantity is normal and an online payment environment is normal;
if the payment delay parameter is greater than or equal to a standard delay threshold, the historical payment information redundancy is excessive, the online payment environment is abnormal, the payment delay parameter is calibrated as an abnormal parameter, and an alarm signal is synchronously sent out;
calculating the difference between the abnormal parameter and the standard delay threshold, and calibrating the difference as a payment deviation parameter;
inputting the payment deviation parameters into an evaluation model to obtain deviation grades of abnormal parameters;
inputting the historical payment information into a classification model to obtain a plurality of historical payment information subsets;
and acquiring retrieval amounts of the historical payment information in all the historical payment information subsets, arranging the historical payment information subsets according to the retrieval amounts in order from small to large, and screening out the historical payment information in the historical payment information subsets one by one according to arrangement results.
In a preferred scheme, when the payment delay parameter is smaller than a standard delay threshold, calibrating the payment delay parameter as a parameter to be evaluated, summarizing the parameter to be evaluated into a data set to be evaluated, inputting the data set to be evaluated into a prediction model, and calibrating a prediction result as a payment delay parameter of predicted payment information;
and comparing the predicted payment information with the standard delay threshold, and sending out an early warning signal after the predicted payment information is higher than the standard delay threshold.
In a preferred embodiment, the step of inputting the data set to be evaluated into a prediction model and calibrating the prediction result to be a payment delay parameter of the predicted payment information includes:
invoking parameters to be evaluated from the data set to be evaluated;
inputting the parameter to be evaluated into a trend analysis model to obtain a variation trend value of the parameter to be evaluated;
and calling a prediction function from the prediction model, inputting the change trend value of the parameter to be evaluated and the payment delay parameter of the current payment information into the prediction function, and calibrating the input result as the payment delay parameter of the predicted payment information.
In a preferred embodiment, the step of inputting the parameter to be evaluated into a trend analysis model to obtain a trend value of the parameter to be evaluated includes:
acquiring parameters to be evaluated adjacent to the occurrence node from the data set to be evaluated;
calling a trend analysis function from the trend analysis model;
and inputting the parameter to be evaluated into a trend analysis function, and calibrating an output result of the parameter to be evaluated into a change trend value of the parameter to be evaluated.
In a preferred embodiment, the step of acquiring the parameters to be evaluated adjacent to the occurrence node from the data set to be evaluated includes:
taking the generation node of the current payment information as an ending node for reverse sampling;
acquiring a sampling interval, performing reverse sampling to obtain payment delay parameters of a plurality of pieces of historical payment information, and determining the number of the reverse sampling as a parameter to be compared;
acquiring a sampling threshold value and comparing the sampling threshold value with the parameter to be compared;
if the parameter to be compared is smaller than a sampling threshold, the parameter to be evaluated is insufficient in sampling quantity, the condition input to a trend analysis model is not met, and reverse sampling is carried out again by taking the generation node of the payment information of the next node as an ending node;
and if the parameter to be compared is greater than or equal to a sampling threshold value, the parameter to be evaluated can be input into a trend analysis model.
In a preferred embodiment, after the payment delay parameter of the predicted payment information is determined, the payment delay parameter is input into a verification model, and the verification step includes:
acquiring an actual generation node corresponding to the predicted payment information node, and calibrating the actual generation node as a comparison node;
acquiring an actual delay parameter of the payment information under the comparison node, and carrying out combined operation with the payment delay parameter of the predicted payment information to obtain a predicted deviation value;
acquiring a check interval from the check model, and judging whether the predicted deviation value is in the check interval or not;
if yes, indicating that the change trend value of the parameter to be evaluated meets the prediction standard;
if not, the change trend value of the parameter to be evaluated does not meet the prediction condition, and the payment delay parameters of the historical payment information are screened out one by one according to the occurrence node.
In a preferred embodiment, the step of inputting the payment deviation parameter into an evaluation model to obtain a deviation level of the abnormality parameter includes:
acquiring the payment deviation parameter;
invoking evaluation intervals from the evaluation model, wherein a plurality of evaluation intervals are arranged, and each evaluation interval corresponds to one evaluation grade;
and determining an evaluation interval corresponding to the payment deviation parameter, synchronously outputting a corresponding evaluation grade, determining the evaluation grade as the deviation grade of the abnormal parameter, and determining the intensity of the alarm signal according to the deviation grade of the abnormal parameter.
In a preferred embodiment, the step of inputting the historical payment information into a classification model to obtain a plurality of subsets of historical payment information includes:
acquiring commodity types of an e-commerce platform;
constructing a plurality of parallel classification subsets according to commodity types of the e-commerce platform;
and acquiring commodity types corresponding to the historical payment information, and summarizing the commodity types into corresponding classification subsets to obtain the historical payment information subsets.
The invention also provides an online payment optimization system based on the e-commerce platform, which is applied to the online payment optimization method based on the e-commerce platform, and comprises the following steps:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring online payment information of an e-commerce platform, and the online payment information comprises historical payment information and current payment information;
the second acquisition module is used for acquiring the settlement time length of the current payment information and calibrating the settlement time length as a payment delay parameter;
the comparison module is used for acquiring a standard delay threshold value and comparing the standard delay threshold value with the payment delay parameter;
if the payment delay parameter is smaller than a standard delay threshold, the historical payment information redundancy quantity is normal and an online payment environment is normal;
if the payment delay parameter is greater than or equal to a standard delay threshold, the historical payment information redundancy is excessive, the online payment environment is abnormal, the payment delay parameter is calibrated as an abnormal parameter, and an alarm signal is synchronously sent out;
the measuring and calculating module is used for measuring and calculating the difference value between the abnormal parameter and the standard delay threshold value and calibrating the difference value as a payment deviation parameter;
the evaluation module is used for inputting the payment deviation parameters into an evaluation model to obtain the deviation grade of the abnormal parameters;
the classification module is used for inputting the historical payment information into a classification model to obtain a plurality of historical payment information subsets;
the optimizing module is used for acquiring retrieval amounts of the historical payment information in all the historical payment information subsets, arranging the historical payment information subsets according to the retrieval amounts in sequence from small to large, and screening out the historical payment information in the historical payment information subsets one by one according to arrangement results.
And an online payment optimization terminal based on an e-commerce platform, comprising:
at least one processor;
and a memory communicatively coupled to the at least one processor;
wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the online payment optimization method based on the e-commerce platform described above.
The invention has the technical effects that:
according to the invention, the payment delay parameter can be monitored in real time, an alarm signal is sent out under the condition that the payment delay parameter exceeds the standard delay threshold, and when the payment delay parameter does not exceed the standard delay threshold, the payment delay parameter of the predicted payment information is summarized into the data set to be evaluated, so that the payment delay parameter of the predicted payment information is predicted, an optimization step can be executed before the payment delay parameter exceeds the standard delay threshold, the running fluency of an online payment system is ensured, and in the optimization process, all the historical payment information is not uploaded to a cloud for storage, but part of the historical payment information is reserved in the payment system, so that a user can conveniently read the historical payment information in time, and the experience of the user is further improved.
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FIG. 1 is a flow chart of a method provided by the present invention;
fig. 2 is a block diagram of a system provided by the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one preferred embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Referring to fig. 1 and 2, the invention provides an online payment optimization method based on an e-commerce platform, which comprises the following steps:
s1, acquiring online payment information of an e-commerce platform, wherein the online payment information comprises historical payment information and current payment information;
s2, acquiring settlement time length of current payment information, and calibrating the settlement time length as a payment delay parameter;
s3, acquiring a standard delay threshold value and comparing the standard delay threshold value with a payment delay parameter;
if the payment delay parameter is smaller than the standard delay threshold, the historical payment information redundancy quantity is indicated to be normal and the online payment environment is indicated to be normal;
if the payment delay parameter is greater than or equal to the standard delay threshold, the historical payment information redundancy is excessive, the online payment environment is abnormal, the payment delay parameter is calibrated as an abnormal parameter, and an alarm signal is synchronously sent out;
s4, measuring and calculating the difference between the abnormal parameter and the standard delay threshold value, and calibrating the difference as a payment deviation parameter;
s5, inputting the payment deviation parameters into the evaluation model to obtain the deviation grade of the abnormal parameters;
s6, inputting the historical payment information into a classification model to obtain a plurality of historical payment information subsets;
s7, acquiring retrieval amounts of the historical payment information in all the historical payment information subsets, arranging the historical payment information subsets according to the retrieval amounts in order from small to large, and screening out the historical payment information in the historical payment information subsets one by one according to arrangement results.
As described in the above steps S1-S7, with the continuous development of network information technology, the manner of people obtaining information from the network is also simpler and simpler, compared with the offline information obtaining, the online information obtaining has timeliness, and the information transmission channel is reduced, for example, the traditional shopping manner requires that the purchaser arrives at the position of the store to select goods, but online shopping can know product information only by browsing pictures and videos, and simultaneously supports online payment and offline delivery, so that the shopping manner of people is more convenient, in order to ensure the experience of online shopping of users, optimization of online payment system is necessary, based on this, the embodiment firstly obtains online payment information of an e-commerce platform and classifies the online payment information, and can be divided into historical payment information and current payment information, then determines payment delay parameters according to the settlement time length of the payment information, because the online commodity is more, the substitutability is stronger, the payment delay parameters are too large, the experience of the purchaser can be reduced, thus the purchaser can possibly result in selecting other alternative products, this can be easily caused, the customer can send out a standard for meeting the corresponding delay, the requirements of the user can be met, the user can send out a corresponding delay threshold value, and can be calibrated by the corresponding delay parameters, and the user can send out a real-time standard for the real-time requirement, meanwhile, the strength of the alarm signal is determined according to the deviation level of the abnormal parameter, after the abnormal parameter is determined, the historical payment information needing to be screened is determined according to the retrieval amount of the historical payment information in the historical payment information subsets, so that the data redundancy amount in the electronic commerce platform is reduced, the payment delay parameter is correspondingly reduced, the screened historical payment information can be uploaded to the cloud for storage, the follow-up retrieval of a user is ensured, but the retrieval speed of the user from the cloud is smaller than that of the user directly from the electronic commerce platform, the historical payment information with larger retrieval amount is reserved in the electronic commerce platform, in order to avoid the singleness of the historical payment information in the electronic commerce platform, each historical payment information subset is provided with a screening lower limit, the screening of the historical payment information in the corresponding historical payment information subset reaches the screening lower limit, the historical payment information in the next historical payment information subset is continuously screened, the data redundancy amount in the electronic commerce platform can be reduced, and the user's delay requirement can be met.
In a preferred embodiment, when the payment delay parameter is smaller than the standard delay threshold, calibrating the payment delay parameter as a parameter to be evaluated, summarizing the parameter to be evaluated into a data set to be evaluated, inputting the data set to be evaluated into a prediction model, and calibrating a prediction result as a payment delay parameter for predicting payment information;
and comparing the predicted payment information with a standard delay threshold, and sending out an early warning signal after the predicted payment information is higher than the standard delay threshold.
In this embodiment, when the payment delay parameter is smaller than the standard delay threshold, the payment delay parameter is calibrated as the parameter to be evaluated, after the parameter to be evaluated is determined, the parameter to be evaluated is input into the prediction model, so that the payment delay parameter of the predicted payment information under the prediction node can be obtained, and further, before the parameter exceeds the standard delay parameter, a corresponding early warning signal can be sent out to warn, so that the advanced optimization of the payment delay parameter is realized.
In a preferred embodiment, the step of inputting the data set to be evaluated into the prediction model and calibrating the prediction result to the payment delay parameter of the predicted payment information includes:
stp1, calling parameters to be evaluated from a data set to be evaluated;
stp2, inputting the parameter to be evaluated into a trend analysis model to obtain a change trend value of the parameter to be evaluated;
stp3, calling a prediction function from the prediction model, inputting the change trend value of the parameter to be evaluated and the payment delay parameter of the current payment information into the prediction function, and calibrating the input result as the payment delay parameter of the predicted payment information.
As described in the above steps Stp1-Stp3, when calculating the payment delay parameter of the predicted payment information, the parameter to be evaluated needs to be obtained from the data set to be evaluated, and then the trend analysis model is used to calculate the variation trend value of the parameter to be evaluated, and then the variation trend value and the payment delay parameter of the current payment information are input into the prediction function, wherein the prediction function is:wherein->Payment delay parameter indicative of a predicted payment information, +.>Payment delay parameter representing current payment information, +.>Represents the trend value of the parameter to be evaluated, +.>The time interval between the current payment information and the predicted payment information is represented, and based on the formula, the payment delay parameter of the required predicted payment information can be measured, so that corresponding data support is provided for optimizing the data redundancy in the electronic commerce platform in advance.
In a preferred embodiment, the step of inputting the parameter to be evaluated into the trend analysis model to obtain the trend value of the parameter to be evaluated includes:
stp201, acquiring parameters to be evaluated adjacent to the occurrence node from the data set to be evaluated;
stp202, calling a trend analysis function from the trend analysis model;
stp203, input the parameter to be evaluated into the trend analysis function, and calibrate the output result as the variation trend value of the parameter to be evaluated.
As described in the above steps Stp201-Stp203, when calculating the trend value of the parameter to be evaluated, the parameter to be evaluated adjacent to the occurrence node needs to be obtained first, so as to ensure the continuity of the data participating in the operation and increase the accuracy of the operation result, wherein the trend analysis function is as follows:wherein->Representing the number of parameters to be evaluated,and->Parameters to be evaluated representing the adjacency of occurrence nodes，/>And the total time period between the parameters to be evaluated is represented, based on the total time period, the change trend value of the parameters to be evaluated can be obtained, data support is provided for calculating the payment delay parameters of the predicted payment information, and the accuracy of the predicted result is ensured.
In a preferred embodiment, the step of obtaining the parameters to be evaluated adjacent to the occurrence node from the data set to be evaluated includes:
step 1, taking a current payment information generation node as an ending node for reverse sampling;
step 2, acquiring sampling intervals, performing reverse sampling to obtain payment delay parameters of a plurality of pieces of historical payment information, and determining the number of the reverse sampling as a parameter to be compared;
step 3, acquiring a sampling threshold value and comparing the sampling threshold value with parameters to be compared;
if the parameter to be compared is smaller than the sampling threshold, the parameter to be evaluated is insufficient in sampling quantity, the condition input to the trend analysis model is not met, and the node where the payment information of the next node occurs is taken as an ending node for carrying out reverse sampling again;
if the parameter to be compared is greater than or equal to the sampling threshold value, the parameter to be evaluated can be input into the trend analysis model.
As described in the above steps 1 to 3, before the parameters to be evaluated are input into the trend analysis model, sufficient data volume needs to be ensured, the present embodiment performs evaluation by presetting a sampling threshold, the sampling threshold needs to be set according to practical situations, and is not limited in detail herein, in order to ensure the accuracy of the measurement result of the selected parameters to be evaluated, the present embodiment performs reverse sampling with the generating node of the current payment information as the ending node, in addition, when the number of the payment delay parameters of the current payment information under the generating node of the current payment information is smaller than the standard delay threshold, the data volume required by the trend analysis model is indicated when the number of the payment delay parameters of the history payment information is larger than or equal to the sampling threshold, at this time, the payment delay parameters of the history payment information can be determined as the parameters to be evaluated, and then the parameters to be evaluated are input into the trend analysis model, so as to obtain the variation trend value of the parameters to be evaluated.
In a preferred embodiment, after determining the payment delay parameter of the predicted payment information, the payment delay parameter is input into a verification model, and the verification step includes:
stp4, obtaining an actual generation node corresponding to the predicted payment information node, and calibrating the actual generation node as a comparison node;
stp5, obtaining actual delay parameters of the payment information under the comparison node, and carrying out combined operation with the payment delay parameters of the predicted payment information to obtain a predicted deviation value;
stp6, acquiring a check interval from the check model, and judging whether the predicted deviation value is in the check interval or not;
if yes, indicating that the change trend value of the parameter to be evaluated meets the prediction standard;
if not, the change trend value of the parameter to be evaluated does not meet the prediction condition, and the payment delay parameters of the historical payment information are screened one by one according to the occurrence node.
As described in the above steps Stp4-Stp6, along with the increase of the parameters to be evaluated input into the trend analysis model, the parameters to be evaluated far away from the payment delay parameter of the current payment information gradually affect the accuracy of the measurement result, and further the accuracy of the payment delay parameter of the predicted payment information is also reduced, so as to avoid this phenomenon, in this embodiment, the obtained payment delay parameter of the predicted payment information and the actually occurring actual delay parameter are subjected to a difference operation, so as to obtain a predicted deviation value, and then the predicted deviation value is compared with a preset verification interval, so as to determine whether the variation trend value of the parameters to be evaluated meets the prediction standard, where the verification interval is preferably ±2%, and the specific range needs to be set according to the actual situation, so that excessive redundancy is not required.
In a preferred embodiment, the step of inputting the payment deviation parameter into the evaluation model to obtain the deviation level of the anomaly parameter includes:
s501, acquiring a payment deviation parameter;
s502, calling evaluation intervals from an evaluation model, wherein a plurality of evaluation intervals are arranged, and each evaluation interval corresponds to one evaluation grade;
s503, determining an evaluation interval corresponding to the payment deviation parameter, synchronously outputting a corresponding evaluation grade, determining the evaluation grade as the deviation grade of the abnormal parameter, and determining the intensity of the alarm signal according to the deviation grade of the abnormal parameter.
As described in the above steps S501-S503, in order to determine the intensity of the alarm signal, in daily operation, even if the payment delay parameter exceeds the standard delay threshold, the payment deviation parameter will not be too large, and accordingly, the corresponding alarm intensity will not be too high, and for the period of platform promotion, how large the transaction amount is, the situation that the payment delay parameter exceeds the standard delay threshold will continue to happen, at this time, the payment deviation parameter may gradually increase, at this time, the corresponding evaluation interval will not coincide with the evaluation interval in daily operation, the deviation level will also increase, at this time, the corresponding alarm signal with higher intensity will be found, and when the optimization operation is executed subsequently, the sieving lower limit of the historical payment information subset can be properly reduced, specifically, the intervention setting can be performed by the staff, and this is not limited explicitly.
In a preferred embodiment, the step of inputting the historical payment information into the classification model to obtain a plurality of subsets of the historical payment information comprises:
s601, acquiring the commodity type of an electronic commerce platform;
s602, constructing a plurality of parallel classification subsets according to commodity types of the e-commerce platform;
and S603, acquiring commodity types corresponding to the historical payment information, and summarizing the commodity types into corresponding classification subsets to obtain the historical payment information subsets.
As described in the above steps S601-S603, when determining the subset of the historical payment information, the subset needs to be classified according to the commodity type of the e-commerce platform, and the classification standard is set by the staff, so that when the redundant data generated by the historical payment information is subsequently screened out, the historical payment information with smaller retrieval amount can be screened out preferentially and uploaded to the cloud for storage, and the historical payment information with larger retrieval amount can be ensured to be retained in the payment system, so that the user can conveniently retrieve and browse.
The invention also provides an online payment optimization system based on the e-commerce platform, which is applied to the online payment optimization method based on the e-commerce platform, and comprises the following steps:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring online payment information of an e-commerce platform, and the online payment information comprises historical payment information and current payment information;
the second acquisition module is used for acquiring the settlement time length of the current payment information and calibrating the settlement time length as a payment delay parameter;
the comparison module is used for acquiring a standard delay threshold value and comparing the standard delay threshold value with the payment delay parameter;
if the payment delay parameter is smaller than the standard delay threshold, the historical payment information redundancy quantity is indicated to be normal and the online payment environment is indicated to be normal;
if the payment delay parameter is greater than or equal to the standard delay threshold, the historical payment information redundancy is excessive, the online payment environment is abnormal, the payment delay parameter is calibrated as an abnormal parameter, and an alarm signal is synchronously sent out;
the measuring and calculating module is used for measuring and calculating the difference value between the abnormal parameter and the standard delay threshold value and calibrating the difference value as a payment deviation parameter;
the evaluation module is used for inputting the payment deviation parameters into the evaluation model to obtain the deviation grade of the abnormal parameters;
the classification module is used for inputting the historical payment information into the classification model to obtain a plurality of historical payment information subsets;
the optimizing module is used for acquiring retrieval amounts of the historical payment information in all the historical payment information subsets, arranging the historical payment information subsets according to the retrieval amounts in order from small to large, and screening out the historical payment information in the historical payment information subsets one by one according to arrangement results.
In the above, when the payment system operates, the first acquisition module acquires online payment information of the e-commerce platform, the online payment information includes historical payment information and current payment information, then the second acquisition module acquires settlement time of the current payment information and marks the settlement time as a payment delay parameter, the comparison module compares the payment delay parameter with a standard delay threshold value to judge whether redundancy of the historical payment information is normal, when the payment delay parameter exceeds the standard delay threshold value, an alarm signal is sent, the intensity of the alarm signal is required to be analyzed through combination of the measurement and calculation module and the evaluation module, so that execution of the optimization module is determined, when the optimization module executes, the historical payment information subsets are arranged according to the order from small to large according to the retrieval amount, and then the historical payment information in the historical payment information subsets is screened one by one according to the arrangement result, so that the historical payment information with large retrieval amount can be stored in the payment system preferentially, and corresponding users can be convenient to conduct timely retrieval.
And an online payment optimization terminal based on an e-commerce platform, comprising:
at least one processor;
and a memory communicatively coupled to the at least one processor;
the memory stores a computer program executable by the at least one processor, so that the at least one processor can execute the online payment optimization method based on the e-commerce platform.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, apparatus, article or method that comprises the element.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention. Structures, devices and methods of operation not specifically described and illustrated herein, unless otherwise indicated and limited, are implemented according to conventional means in the art.
Claims (7)
1. An online payment optimization method based on an e-commerce platform is characterized by comprising the following steps of: comprising the following steps:
acquiring online payment information of an e-commerce platform, wherein the online payment information comprises historical payment information and current payment information;
acquiring the settlement time length of the current payment information and calibrating the settlement time length as a payment delay parameter;
acquiring a standard delay threshold value and comparing the standard delay threshold value with the payment delay parameter;
if the payment delay parameter is smaller than a standard delay threshold, the historical payment information redundancy quantity is normal and an online payment environment is normal;
if the payment delay parameter is greater than or equal to a standard delay threshold, the historical payment information redundancy is excessive, the online payment environment is abnormal, the payment delay parameter is calibrated as an abnormal parameter, and an alarm signal is synchronously sent out;
when the payment delay parameter is smaller than the standard delay threshold, calibrating the payment delay parameter as a parameter to be evaluated, summarizing the parameter to be evaluated into a data set to be evaluated, inputting the data set to be evaluated into a prediction model, and calibrating a prediction result as a payment delay parameter of the predicted payment information;
comparing the predicted payment information with the standard delay threshold, and sending out an early warning signal after the predicted payment information is higher than the standard delay threshold;
the step of inputting the data set to be evaluated into a prediction model and calibrating a prediction result into a payment delay parameter of predicted payment information comprises the following steps:
invoking parameters to be evaluated from the data set to be evaluated;
inputting the parameter to be evaluated into a trend analysis model to obtain a variation trend value of the parameter to be evaluated;
calling a prediction function from the prediction model, inputting the change trend value of the parameter to be evaluated and the payment delay parameter of the current payment information into the prediction function, and calibrating the input result as the payment delay parameter of the predicted payment information;
calculating the difference between the abnormal parameter and the standard delay threshold, and calibrating the difference as a payment deviation parameter;
inputting the payment deviation parameters into an evaluation model to obtain deviation grades of abnormal parameters;
the step of inputting the payment deviation parameter into an evaluation model to obtain a deviation grade of an abnormal parameter comprises the following steps:
acquiring the payment deviation parameter;
invoking evaluation intervals from the evaluation model, wherein a plurality of evaluation intervals are arranged, and each evaluation interval corresponds to one evaluation grade;
determining an evaluation interval corresponding to the payment deviation parameter, synchronously outputting a corresponding evaluation grade, determining the evaluation grade as the deviation grade of the abnormal parameter, and determining the intensity of the alarm signal according to the deviation grade of the abnormal parameter;
inputting the historical payment information into a classification model to obtain a plurality of historical payment information subsets;
and acquiring retrieval amounts of the historical payment information in all the historical payment information subsets, arranging the historical payment information subsets according to the retrieval amounts in order from small to large, and screening out the historical payment information in the historical payment information subsets one by one according to arrangement results.
2. The online payment optimization method based on the e-commerce platform according to claim 1, wherein the online payment optimization method comprises the following steps: the step of inputting the parameter to be evaluated into a trend analysis model to obtain a variation trend value of the parameter to be evaluated comprises the following steps:
acquiring parameters to be evaluated adjacent to the occurrence node from the data set to be evaluated;
calling a trend analysis function from the trend analysis model;
and inputting the parameter to be evaluated into a trend analysis function, and calibrating an output result of the parameter to be evaluated into a change trend value of the parameter to be evaluated.
3. An online payment optimization method based on an e-commerce platform according to claim 2, wherein: the step of acquiring the parameters to be evaluated adjacent to the occurrence node from the data set to be evaluated comprises the following steps:
taking the generation node of the current payment information as an ending node for reverse sampling;
acquiring a sampling interval, performing reverse sampling to obtain payment delay parameters of a plurality of pieces of historical payment information, and determining the number of the reverse sampling as a parameter to be compared;
acquiring a sampling threshold value and comparing the sampling threshold value with the parameter to be compared;
if the parameter to be compared is smaller than a sampling threshold, the parameter to be evaluated is insufficient in sampling quantity, the condition input to a trend analysis model is not met, and reverse sampling is carried out again by taking the generation node of the payment information of the next node as an ending node;
and if the parameter to be compared is greater than or equal to a sampling threshold value, the parameter to be evaluated can be input into a trend analysis model.
4. An online payment optimization method based on an e-commerce platform according to claim 2, wherein: after the payment delay parameter of the predicted payment information is determined, the payment delay parameter is input into a verification model, and the verification step comprises the following steps:
acquiring an actual generation node corresponding to the predicted payment information node, and calibrating the actual generation node as a comparison node;
acquiring an actual delay parameter of the payment information under the comparison node, and carrying out combined operation with the payment delay parameter of the predicted payment information to obtain a predicted deviation value;
acquiring a check interval from the check model, and judging whether the predicted deviation value is in the check interval or not;
if yes, indicating that the change trend value of the parameter to be evaluated meets the prediction standard;
if not, the change trend value of the parameter to be evaluated does not meet the prediction condition, and the payment delay parameters of the historical payment information are screened out one by one according to the occurrence node.
5. The online payment optimization method based on the e-commerce platform according to claim 1, wherein the online payment optimization method comprises the following steps: the step of inputting the historical payment information into a classification model to obtain a plurality of historical payment information subsets comprises the following steps:
acquiring commodity types of an e-commerce platform;
constructing a plurality of parallel classification subsets according to commodity types of the e-commerce platform;
and acquiring commodity types corresponding to the historical payment information, and summarizing the commodity types into corresponding classification subsets to obtain the historical payment information subsets.
6. An online payment optimization system based on an e-commerce platform, which is applied to the online payment optimization method based on the e-commerce platform as set forth in any one of claims 1 to 5, and is characterized in that: comprising the following steps:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring online payment information of an e-commerce platform, and the online payment information comprises historical payment information and current payment information;
the second acquisition module is used for acquiring the settlement time length of the current payment information and calibrating the settlement time length as a payment delay parameter;
the comparison module is used for acquiring a standard delay threshold value and comparing the standard delay threshold value with the payment delay parameter;
if the payment delay parameter is smaller than a standard delay threshold, the historical payment information redundancy quantity is normal and an online payment environment is normal;
if the payment delay parameter is greater than or equal to a standard delay threshold, the historical payment information redundancy is excessive, the online payment environment is abnormal, the payment delay parameter is calibrated as an abnormal parameter, and an alarm signal is synchronously sent out;
the measuring and calculating module is used for measuring and calculating the difference value between the abnormal parameter and the standard delay threshold value and calibrating the difference value as a payment deviation parameter;
the evaluation module is used for inputting the payment deviation parameters into an evaluation model to obtain the deviation grade of the abnormal parameters;
the classification module is used for inputting the historical payment information into a classification model to obtain a plurality of historical payment information subsets;
the optimizing module is used for acquiring retrieval amounts of the historical payment information in all the historical payment information subsets, arranging the historical payment information subsets according to the retrieval amounts in sequence from small to large, and screening out the historical payment information in the historical payment information subsets one by one according to arrangement results.
7. An online payment optimization terminal based on an e-commerce platform is characterized in that: comprising the following steps:
at least one processor;
and a memory communicatively coupled to the at least one processor;
wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the e-commerce platform based online payment optimization method of any one of claims 1 to 5.
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| CN117035697B (en) * | 2023-10-09 | 2023-12-15 | 天津云起技术有限公司 | ITSM (integrated traffic simulation) platform optimization method and system based on historical dynamic analysis |
| CN117764587A (en) * | 2023-12-08 | 2024-03-26 | 江苏财经职业技术学院 | Use safety assessment method of financial payment equipment |
| CN117857467B (en) * | 2024-03-08 | 2024-06-21 | 杭州青橄榄网络技术有限公司 | Data encryption method for user payment system |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106097590A (en) * | 2016-06-17 | 2016-11-09 | 浙江右边数字科技有限公司 | Electric bicycle rental system and control method |
| CN106251145A (en) * | 2015-12-30 | 2016-12-21 | 李平 | Electronic fare payment system, electronic payment devices and electric paying method |
| CN107103460A (en) * | 2017-03-27 | 2017-08-29 | 杭州呯嘭智能技术有限公司 | The quick settlement method of cross-border payment based on credit big data |
| CN108876349A (en) * | 2018-08-16 | 2018-11-23 | 平安科技（深圳）有限公司 | Electronic device, payment channel determine method and storage medium |
| CN110210846A (en) * | 2018-02-28 | 2019-09-06 | 维萨国际服务协会 | Message delay estimating system and method |
| WO2019192261A1 (en) * | 2018-04-02 | 2019-10-10 | 阿里巴巴集团控股有限公司 | Payment mode recommendation method and device and equipment |
| CN111105244A (en) * | 2018-10-25 | 2020-05-05 | 阿里巴巴集团控股有限公司 | Service scheme determination method and device based on refund |
| CN112651632A (en) * | 2020-12-25 | 2021-04-13 | 车主邦（北京）科技有限公司 | Information analysis processing method and device based on marketing data |
| WO2023027627A1 (en) * | 2021-08-24 | 2023-03-02 | Grabtaxi Holdings Pte. Ltd. | Method, device and computer readable medium for rendering organic and paid content |
| CN116308306A (en) * | 2023-05-25 | 2023-06-23 | 南京中汇电气科技有限公司 | New energy station intelligent management system and method based on 5G |
| CN116362756A (en) * | 2023-05-30 | 2023-06-30 | 万联易达物流科技有限公司 | Automatic payment wind control method and system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20230073140A1 (en) * | 2021-09-03 | 2023-03-09 | Worldpay, Llc | Systems and methods for data aggregation for transaction settlement |
-
2023
- 2023-07-20 CN CN202310890337.3A patent/CN116611898B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106251145A (en) * | 2015-12-30 | 2016-12-21 | 李平 | Electronic fare payment system, electronic payment devices and electric paying method |
| CN106097590A (en) * | 2016-06-17 | 2016-11-09 | 浙江右边数字科技有限公司 | Electric bicycle rental system and control method |
| CN107103460A (en) * | 2017-03-27 | 2017-08-29 | 杭州呯嘭智能技术有限公司 | The quick settlement method of cross-border payment based on credit big data |
| CN110210846A (en) * | 2018-02-28 | 2019-09-06 | 维萨国际服务协会 | Message delay estimating system and method |
| WO2019192261A1 (en) * | 2018-04-02 | 2019-10-10 | 阿里巴巴集团控股有限公司 | Payment mode recommendation method and device and equipment |
| CN108876349A (en) * | 2018-08-16 | 2018-11-23 | 平安科技（深圳）有限公司 | Electronic device, payment channel determine method and storage medium |
| CN111105244A (en) * | 2018-10-25 | 2020-05-05 | 阿里巴巴集团控股有限公司 | Service scheme determination method and device based on refund |
| CN112651632A (en) * | 2020-12-25 | 2021-04-13 | 车主邦（北京）科技有限公司 | Information analysis processing method and device based on marketing data |
| WO2023027627A1 (en) * | 2021-08-24 | 2023-03-02 | Grabtaxi Holdings Pte. Ltd. | Method, device and computer readable medium for rendering organic and paid content |
| CN116308306A (en) * | 2023-05-25 | 2023-06-23 | 南京中汇电气科技有限公司 | New energy station intelligent management system and method based on 5G |
| CN116362756A (en) * | 2023-05-30 | 2023-06-30 | 万联易达物流科技有限公司 | Automatic payment wind control method and system |
Non-Patent Citations (2)
| Title |
|---|
| 呼叫中心电子支付系统研究与设计;钱敬;;电脑知识与技术(第13期);第3040-3042页 * |
| 考虑消费者效用的电商延迟支付决策研究;马中华等;数学的实践与认识(第24期);第35-44页 * |
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