Data management method for intelligent payment terminal

Abstract

The application discloses a kind of data management methods of intelligent payment terminal, and the application relates to data management technical field, and the data management method of intelligent payment terminal is disclosed in the application, based on the time dimension analysis of payment data, the operation strategy of equipment in high frequency and low frequency period is optimized, the efficient allocation and use of equipment resources are realized;Through efficient data classification and screening mechanism, ensure that key data is uploaded in priority and is processed in real time, reduce the risk of device load and network congestion, while improving the accuracy and reliability of data transmission;In addition, the application adopts the strategy of time-sharing synchronous upload and hierarchical upload, greatly improves the flexibility and efficiency of data management, not only guarantees the timeliness of important data, but also optimizes the performance of overall self-service cash register equipment;Overall method realizes the overall improvement of payment terminal operation efficiency, provides reliable guarantee for data processing and decision support of intelligent payment scene.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data management, in particular to a data management method of an intelligent payment terminal. BACKGROUND

[0002] With the rapid development of e-commerce and digital payment technology, intelligent payment terminals have been widely used in various business scenarios, including retail, catering, transportation and other industries. As an important part of the payment system, intelligent payment terminals not only support multiple payment methods, but also undertake important tasks such as payment data collection, storage, processing and transmission. These terminals are usually equipped with high-performance processors, operating systems and network connection functions, and have strong data processing capabilities.

[0003] In the intelligent development of the retail industry, self-service cash register terminal devices have been widely used in various supermarkets and convenience stores. Such devices can provide efficient and convenient shopping settlement services, effectively alleviating the pressure of manual cash register.

[0004] However, the existing self-service cash register terminal devices still have many deficiencies in data management. For example, during high-frequency use period, the terminal device may cause system performance degradation or network congestion due to frequent data transmission; during low-frequency use period, a large amount of redundant data may be uploaded to the service cloud platform, wasting storage and transmission resources.

[0005] In addition, the existing devices lack effective screening mechanism for the importance of payment data, and fail to reasonably distinguish and prioritize key data, which may affect the timely response of the system to important business data. SUMMARY

[0006] In view of the deficiencies of the prior art, the present application provides a data management method of an intelligent payment terminal, which solves the problems in the background art.

[0007] To achieve the above purpose, the present application is implemented by the following technical scheme: a data management method of an intelligent payment terminal, comprising:

[0008] Step 1: Collect payment data from self-service cash register terminal devices, analyze the payment data according to the generation time, and obtain the high-frequency use period and low-frequency use period of the self-service cash register terminal devices in a day;

[0009] Step 2: When the self-service cash register terminal device is in the low-frequency use period, the payment data generated in the low-frequency use period is uploaded to the service cloud platform in synchronization; when the self-service cash register terminal device is in the high-frequency use period, the payment data generated in the high-frequency use period is first stored in the self-service cash register terminal device;

[0010] Step three: mark the payment data stored in the self-service cash register terminal device in the high-frequency use period as submission data, and when the submission data is uploaded in the low-frequency use period, the submission data is analyzed and screened as first-level important submission data, second-level important submission data and ordinary submission data;

[0011] Step four: according to the screening result of the submission data, first, upload the first-level important submission data to the cloud platform, then upload the second-level important submission data to the service cloud platform after the first-level important submission data is uploaded, and finally upload the ordinary submission data to the service cloud platform.

[0012] As a further scheme of the application: in step one, the payment data refers to the order information of the user purchasing goods at the self-service cash register terminal device.

[0013] As a further scheme of the application: in step one, the specific way of collecting payment data in the self-service cash register terminal device and analyzing the generation time of the payment data to obtain the high-frequency use period and the low-frequency use period of the self-service cash register terminal device in a day is:

[0014] AS1: select a self-service cash register terminal device that needs to be analyzed as a target device;

[0015] AS2: divide a day into n standard time periods, specifically, the division standard time period starts from zero time, wherein n is a preset value;

[0016] AS3: then, an arbitrary standard time period is selected, the number of payment data generated by the target device in each of the s days is obtained, and all the payment data quantities are marked and marked as Bi, wherein i=1, 2, …, s, and s is a preset value;

[0017] The payment data quantity generated by the target device in each of the s days in the standard time period specifically refers to the time period of s days from the current time;

[0018] AS4: then calculate the average value of the s payment data quantities Bi and mark it as Bp; the fluctuation value F corresponding to the s payment data quantities Bi is obtained by the following formula:

[0019]

[0020] AS5: analyze and judge the fluctuation value F to obtain the reference payment data quantity;

[0021] AS6: classify the standard time period as a high-frequency use period or a low-frequency use period according to the size of the reference payment data quantity:

[0022] If the reference payment data quantity is greater than Q3, the standard time period is divided into a high-frequency use time period; wherein Q3 is a preset value.

[0023] If the reference payment data quantity is less than or equal to Q3, the standard time period is divided into a low-frequency use time period.

[0024] AS7: Then, the next standard time period is acquired, and steps AS3-AS6 are repeated to classify all the standard time periods into high-frequency use time periods or low-frequency use time periods.

[0025] As a further scheme of the application, the specific manner of analyzing and judging the fluctuation value F to obtain the reference payment data quantity is that:

[0026] AS51: If F < Q1, the average value Bp of the s payment data quantities Bi is taken as the reference payment data quantity; wherein Q1 is a preset value.

[0027] AS52: If F ≥ Q1, the Bi values are sorted in descending order of |Bi-Bp|, and the corresponding Bi values are sequentially acquired, and each acquired Bi value is deleted, and the fluctuation value F of the remaining Bi values is recalculated, and the judgment is re-performed. If F ≥ Q1, the next Bi value is selected according to the sorting order, the fluctuation value F is recalculated, and the process is repeated until F < Q1.

[0028] AS53: The number of deleted Bi values is acquired, and the number is marked as the deletion quantity r, and the proportion value z is calculated.

[0029] When the proportion value z exceeds Q2, the maximum value, the minimum value, and the median value of the number of deleted Bi values are determined, and the average value of the maximum value, the minimum value, and the median value is taken as the reference payment data quantity; wherein Q2 is a preset value, and Q2 is less than 1.

[0030] When the proportion value does not exceed Q2, the average value of g1 Bi values is calculated as the reference payment data quantity, wherein g1 = s-r.

[0031] As a further scheme of the application, in step two, the payment data generated by the self-service cash register terminal device is synchronized with the service cloud platform, and when the payment data is generated on the self-service cash register terminal device, the payment data is synchronized and uploaded to the service cloud platform for storage.

[0032] As a further scheme of the application, in step three, the specific manner of analyzing and screening the submission data into first-level important submission data, second-level important submission data, and ordinary submission data is that:

[0033] BS1: extract the transaction amount, transaction time interval, commodity category number and user level index in each payment data in the submission data, and mark them as A j , ΔT j , C j , U j , and 1≤j≤m, where m is the total number of payment data in the submission data;

[0034] BS2: make the following judgments on the extracted A j , ΔT j , C j , U j :

[0035] Judge whether A j , ΔT j , C j , U j satisfy all of the following rules:

[0036] A j ≥A thresh , where A thresh is the amount threshold;

[0037] ΔT j ≤ΔT thresh , where ΔT thresh is the transaction time interval threshold;

[0038] C∈[C min , C max ], where C min represents the minimum value of the commodity category number, and C max represents the maximum value of the commodity category number;

[0039] U≥U thresh , where U thresh represents the user level index threshold;

[0040] The above A thresh , ΔT thresh , C min , C max and U thresh are all preset values;

[0041] The payment data that satisfy all of the above conditions are classified as first-level important submission data, otherwise they are all classified as ordinary submission data.

[0042] As a further scheme of the present application, the specific way of analyzing and screening the submission data into first-level important submission data, second-level important submission data and ordinary submission data when uploading the submission data further comprises:

[0043] BS3: Obtain the ordinary submission data determined in step BS2, and extract the transaction amount A in each payment data in the ordinary submission data again j , the transaction time interval ΔT j , the commodity category index C j , and the user level index U j ; determine the judgment coefficient D by the following formula j :

[0044]

[0045] In the formula, A ideal , ΔT ideal , C ideal , and U ideal are the typical high amount value, the typical active transaction interval, the typical important commodity category number, and the typical high-level user index, respectively.

[0046] BS4: Compare the judgment coefficient D of each payment data j with the judgment threshold D:

[0047] Divide the delivery data of D j ≤D into secondary important submission data;

[0048] Divide the delivery data of D j >D into ordinary important submission data again.

[0049] As a further scheme of the present application, it further comprises:

[0050] Step five, after uploading the first important submission data, the secondary important submission data and the ordinary submission data, delete the uploaded first important submission data, the secondary important submission data and the ordinary submission data stored in the self-service cash register terminal device, and free up enough storage space for storing newly generated payment data in subsequent high-frequency use periods.

[0051] The present application provides a data management method of an intelligent payment terminal. Compared with the prior art, the following beneficial effects are possessed:

[0052] The data management method of the intelligent payment terminal optimizes the operation strategy of the device in high-frequency and low-frequency periods based on the time dimension analysis of payment data, realizes efficient allocation and use of device resources. This method ensures that critical data is uploaded and processed in real time through an efficient data classification and screening mechanism, reduces the risk of device load and network congestion, and improves the accuracy and reliability of data transmission.

[0053] In addition, the application adopts a strategy of combining time-sliced synchronous uploading and hierarchical uploading, greatly improves the flexibility and efficiency of data management, not only guarantees the timeliness of important data, but also optimizes the performance and user experience of the overall self-service cash register terminal device. The overall method comprehensively improves the running efficiency of the payment terminal, and provides reliable guarantee for data processing and decision support of the intelligent payment scene. BRIEF DESCRIPTION OF DRAWINGS

[0054] The application will be further described below in combination with the drawings.

[0055] Figure 1 The flowchart of the steps of the data management method of the intelligent payment terminal. DETAILED DESCRIPTION

[0056] The technical solutions in the embodiments of the application will be clearly and completely described below in combination with the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the application.

[0057] Embodiment one

[0058] Please refer to Figure 1 The application provides a data management method of an intelligent payment terminal, comprising;

[0059] Step one: by collecting payment data in the self-service cash register terminal device, analyzing according to the generation time of the payment data, obtaining the high-frequency use period and the low-frequency use period of the self-service cash register terminal device in a day;

[0060] The payment data refers to the order information of the user purchasing goods in the self-service cash register terminal device, and the order information includes order generation time, order amount, commodity category number, user level index and other key information;

[0061] The specific way of collecting payment data in the self-service cash register terminal device, analyzing according to the generation time of the payment data, and obtaining the high-frequency use period and the low-frequency use period of the self-service cash register terminal device in a day is:

[0062] AS1: selecting a self-service cash register terminal device to be analyzed as a target device;

[0063] AS2: dividing a day into n standard time periods, specifically, the division standard time period starts from zero time, wherein n is a preset value, specifically set by professional staff, if n is 24, each standard time period is one hour, in this embodiment, n is 24;

[0064] AS3: Then an arbitrary standard time period is selected, and the number of payment data generated by the target device in each of the s days near the standard time period is obtained; and all the numbers of payment data are marked as Bi, where i = 1, 2,..., s, where s is a preset value, which is specifically set by a professional staff, and in this embodiment, s is 60;

[0065] The number of payment data generated by the target device in each of the s days near the standard time period is specifically referred to as the time period of s days from the present time;

[0066] AS4: Then the average value of the s numbers of payment data Bi is calculated and marked as Bp; and the fluctuation value F corresponding to the s numbers of payment data Bi is obtained by the following formula:

[0067]

[0068] AS5: The fluctuation value F is analyzed and judged to obtain the reference payment data quantity;

[0069] The specific way of analyzing and judging the fluctuation value F to obtain the reference payment data quantity is that:

[0070] AS51: If F < Q1, the average value Bp of the s numbers of payment data Bi is taken as the reference payment data quantity; where Q1 is a preset value, which is specifically set by a professional staff;

[0071] AS52: If F ≥ Q1, the Bi values are sorted in descending order of |Bi-Bp|, and the corresponding Bi values are obtained in turn, and each time a Bi value is obtained, it is deleted, and then the fluctuation value F of the remaining Bi is recalculated, and it is re-determined whether F ≥ Q1; if F ≥ Q1, the next Bi value is selected according to the sorting order, and the fluctuation value F is recalculated until F < Q1;

[0072] AS53: The number of deleted Bi values is obtained, which is marked as the deletion amount r, and the proportion value z is calculated:

[0073] When the proportion value z exceeds Q2, the maximum value, the minimum value and the median value of the number of deleted Bi values are determined, and the average value of the maximum value, the minimum value and the median value is calculated as the reference payment data quantity; where Q2 is a preset value, and Q2 is less than 1, and in this embodiment, Q2 takes the value of 0.5;

[0074] When the proportion value does not exceed Q2, the average value of g1 Bi is calculated as the reference payment data quantity, where g1 = s-r;

[0075] AS6: classifying the standard time period as a high-frequency use period or a low-frequency use period according to the size of the reference payment data quantity:

[0076] If the reference payment data quantity is greater than Q3, the standard time period is divided into a high-frequency use period; wherein Q3 is a preset value, which is determined by the staff according to the running state of the target device;

[0077] If the reference payment data quantity is less than or equal to Q3, the standard time period is divided into a low-frequency use period;

[0078] AS7: then the next standard time period is obtained, and steps AS3-AS6 are repeated to classify all the standard time periods as high-frequency use periods or low-frequency use periods;

[0079] By collecting and analyzing the payment data of the self-service cash register terminal device, and dividing the time period into high-frequency and low-frequency use periods, the use rules of the device in a day can be clearly presented, which is convenient for determining the load management and resource allocation of the device. At the same time, through in-depth analysis and classification processing of the fluctuation value, it ensures that the judgment reference of the payment data quantity is more scientific and accurate. In addition, this process not only optimizes the running efficiency of the device in different time periods, but also provides a reliable basis for subsequent data uploading and management strategy, which helps to improve the stability and accuracy of data analysis;

[0080] Step two: when the self-service cash register terminal device is in a low-frequency use period, the payment data generated in the low-frequency use period is uploaded to the service cloud platform in synchronization; when the self-service cash register terminal device is in a high-frequency use period, the payment data generated in the high-frequency use period is first stored on the self-service cash register terminal device, and then uploaded to the service cloud platform in the subsequent low-frequency use period;

[0081] The synchronization uploading to the service cloud platform is to synchronize the payment data generated by the self-service cash register terminal device with the service cloud platform, and when the payment data is generated on the self-service cash register terminal device, the payment data is uploaded to the service cloud platform for storage in synchronization;

[0082] It should be noted that the payment data stored in the self-service cash register terminal device can be directly accessed by the background management personnel through the service cloud platform through the network;

[0083] According to the division of high-frequency and low-frequency time periods, the uploading strategy of payment data is dynamically adjusted, the data is temporarily stored in the terminal device in the high-frequency time period, and the data is synchronously uploaded in the low-frequency time period, which not only relieves the operation pressure of the network and the device in the high-frequency time period, but also guarantees the integrity and real-time availability of the data; this uploading mode not only improves the response speed of the self-service cash register terminal device, but also maximizes the use of network resources in the low-frequency time period, reduces the risk of network congestion; at the same time, the background management personnel remotely access the stored data through the cloud platform, which greatly improves the flexibility and convenience of data management, and provides an important guarantee for subsequent data analysis and auditing.

[0084] Step three: the payment data stored in the self-service cash register terminal device in the high-frequency use period is marked as submission data, and when the submission data is uploaded, the submission data is analyzed and screened into first-level important submission data, second-level important submission data and ordinary submission data.

[0085] Step four: according to the screening result of the submission data, first, the first-level important submission data is uploaded to the cloud platform, then the second-level important submission data is uploaded to the service cloud platform after the first-level important submission data is uploaded, and finally the ordinary submission data is uploaded to the service cloud platform.

[0086] According to the priority order of first-level, second-level and ordinary data, the payment data is uploaded step by step, which not only ensures that the key data (such as first-level important submission data) can be timely uploaded and processed by the service cloud platform, but also greatly reduces the risk of network congestion caused by too large data upload amount; this phased uploading method fully utilizes the performance resources of the device and the network, ensuring the stability and efficiency of data uploading; in addition, the step-by-step uploading strategy also makes data storage and processing more clear and clear, facilitating subsequent data management and business optimization, and providing more valuable support for the service cloud platform.

[0087] Step five: after uploading the first-level important submission data, the second-level important submission data and the ordinary submission data, the first-level important submission data, the second-level important submission data and the ordinary submission data stored in the self-service cash register terminal device are deleted, so as to free up enough storage space for storing newly generated payment data in the subsequent high-frequency use period.

[0088] Embodiment two

[0089] In the specific implementation process, based on embodiment one and different from embodiment one, the step three process of the embodiment is further described.

[0090] The specific way of analyzing and screening the submission data into first-level important submission data, second-level important submission data and ordinary submission data when uploading the submission data is:

[0091] BS1: extract the transaction amount, transaction time interval, commodity category number and user level index in each payment data in the submission data, and mark them as A j , ΔT j , C j , U j , and 1≤j≤m, where m is the total number of payment data in the submission data;

[0092] BS2: make the following judgments on the extracted A j , ΔT j , C j , U j :

[0093] Judge whether A j , ΔT j , C j , U j satisfy all the following rules:

[0094] A j ≥A thresh , where A thresh is the amount threshold;

[0095] ΔT j ≤ΔT thresh , where ΔT thresh is the transaction time interval threshold, and when the transaction time interval is less than the threshold ΔT thresh , it indicates active transaction;

[0096] C∈[C min , C max ], where C min represents the minimum value of the commodity category number, and C max represents the maximum value of the commodity category number;

[0097] U≥U thresh , where U thresh represents the user level index threshold;

[0098] The above A thresh , ΔT thresh , C min , C max and U thresh are preset values, which are set by professional staff;

[0099] Payment data that satisfy all the above conditions are classified as first-level important submission data, otherwise they are all classified as ordinary submission data;

[0100] BS3: Obtain the common submission data determined in step BS2, and extract the transaction amount A in each payment data in the common submission data again j , the transaction time interval ΔT j , the commodity category index C j , and the user level index U j ; determine the judgment coefficient D by the following formula j :

[0101]

[0102] In the formula, A ideal , ΔT ideal , C ideal , and U ideal are the typical high amount value, the typical active transaction interval, the typical important commodity category number, and the typical high-level user index, respectively.

[0103] It should be noted that the typical high amount value, the typical active transaction interval, the typical important commodity category number, and the typical high-level user index are all preset values, which are determined by professional staff. For example, for the typical high amount value, the average value of the high amount part of the historical payment data can be taken as the typical high amount value A ideal ; for the typical active transaction interval, the average value of the transaction time interval of the high-frequency use period can be taken as a representative time interval as the typical active transaction interval ΔT ideal ; for the typical important commodity category number, according to the business strategy or the commodity classification system, the number of important commodities is selected as the typical important commodity category number C ideal ; for the typical high-level user index, according to the distribution of member levels, the index representing the low-level user is selected.

[0104] BS4: Compare the judgment coefficient D j of each payment data with the judgment threshold D:

[0105] The delivery data with D j ≤D is divided into secondary important submission data;

[0106] The delivery data with D j >D is divided again into common important submission data;

[0107] Through multi-dimension information such as transaction amount, transaction time interval, commodity category number and user level index, the high-frequency period submission data is analyzed and classified in detail, and is divided into first important, second important and ordinary submission data, which is helpful to meet different levels of business requirements; through the hierarchical management mode, the self-service cash register terminal device can preferentially process the first important submission data, and improves the business response speed; in the data screening process, the typical value analysis and threshold judgment of the historical data are combined, the scientificity and practicality of the classification are improved, and a solid foundation is provided for the subsequent uploading strategy.

[0108] Embodiment three

[0109] In the specific implementation process of the present embodiment, all the implementation processes of the above two groups of embodiments are included.

[0110] Part of the data in the above formula is dimensionless for numerical calculation, and the contents not described in detail in the specification all belong to the prior art known to those skilled in the art.

[0111] The above embodiments are only used to illustrate the technical method of the present application but not limit the present application, although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical method of the present application.

Claims

1. A data management method of a smart payment terminal, characterized by, The application relates to a method for uploading payment data of a self-service checkout terminal device to a service cloud platform. Step one: payment data in the self-service checkout terminal device is collected, and the payment data is analyzed according to the generation time to obtain a high-frequency use period and a low-frequency use period of the self-service checkout terminal device in a day; Step two: when the self-service checkout terminal device is in the low-frequency use period, the payment data generated in the low-frequency use period is synchronously uploaded to the service cloud platform; when the self-service checkout terminal device is in the high-frequency use period, the payment data generated in the high-frequency use period is stored in the self-service checkout terminal device; Step three: the payment data stored in the self-service checkout terminal device in the high-frequency use period is marked as submission data; when the self-service checkout terminal device is in the low-frequency use period, the submission data is analyzed and screened into first-level important submission data, second-level important submission data and ordinary submission data; Step four: according to the screening result of the submission data, the first-level important submission data is first uploaded to the cloud platform, the second-level important submission data is uploaded to the service cloud platform after the first-level important submission data is uploaded, and the ordinary submission data is finally uploaded to the service cloud platform; The specific way of analyzing and screening the submission data into the first-level important submission data, the second-level important submission data and the ordinary submission data when the submission data is uploaded is that all the payment data meeting the above conditions are divided into the first-level important submission data, and otherwise, all the payment data are uniformly divided into the ordinary submission data; BS1: extract the transaction amount, transaction time interval, commodity category number, and user level index in each payment data in the submission data, and label them as , and where m is the total number of payment data in the submission data; BS2: to the extracted the following determination is made: determining whether all of the following rules are satisfied: wherein, is an amount threshold; wherein, is a transaction time interval threshold; wherein, represents the minimum value of the commodity category number, represents the maximum value of the commodity category number; wherein, denotes the user rank index threshold value; Above , , , and are preset values; The specific way of analyzing and screening the submission data into the first-level important submission data, the second-level important submission data and the ordinary submission data when the submission data is uploaded further includes that after the first-level important submission data, the second-level important submission data and the ordinary submission data are uploaded, the first-level important submission data, the second-level important submission data and the ordinary submission data stored in the self-service checkout terminal device and having been uploaded are deleted to release storage space for storing newly generated payment data in the subsequent high-frequency use period. In step one, the payment data refers to order information of goods purchased by a user in the self-service checkout terminal device. BS3: Acquire the common submission data determined in step BS2, and extract the transaction amount in each payment data in the common submission data again , a transaction time interval , a commodity category index , and a user level index ; determine the judgment coefficient by the following formula : ； wherein, , , and are a typical high monetary value, a typical active transaction interval, a typical important commodity class number, and a typical high rank user index, respectively. BS4: compares the judgment coefficient of each payment data with the judgment threshold value performs comparison:​ The delivery data of is divided into two levels of important submission data; The delivery data of are again divided into ordinary important submission data; In step one, the specific way of collecting the payment data in the self-service checkout terminal device and analyzing the payment data according to the generation time to obtain the high-frequency use period and the low-frequency use period of the self-service checkout terminal device in a day is as follows:

2. The data management method of an intelligent payment terminal according to claim 1, wherein, AS1: a self-service checkout terminal device to be analyzed is selected as a target device; 3. The data management method of an intelligent payment terminal according to claim 2, wherein, AS6: the standard time period is classified as the high-frequency use period or the low-frequency use period according to the size of the reference payment data quantity; AS7: the next standard time period is obtained, and steps AS3-AS6 are repeated to classify all the standard time periods as the high-frequency use period or the low-frequency use period. AS2: dividing a day into standard time periods, specifically, dividing the standard time periods from the zero time, wherein, is a preset value; AS3: Then randomly select a standard time period, get the target device in the standard time period The number of payment data generated each day; and all payment data quantity is marked, marked as Wherein, Wherein Is a preset value; The acquiring the standard time period target device payment data quantity in each day of the day specifically refers to the time period from the present time to the past The payment data quantity in each day of the day specifically refers to the time period from the present time to the past day. AS4: Then calculate the average value of the number of payment data , and mark it as ; the fluctuation value of the number of payment data corresponding to is obtained by the following formula : ； AS5: to the fluctuation value The analysis and judgment are performed to obtain the reference payment data quantity; In step two, the synchronous uploading to the service cloud platform is synchronizing the payment data generated by the self-service checkout terminal device with the service cloud platform; when the payment data is generated in the self-service checkout terminal device, the payment data is synchronously uploaded to the service cloud platform for storage. If the number of reference payment data is greater than , the standard time period is divided into a high-frequency use time period; wherein, is a preset value. If the reference payment data quantity is less than or equal to If the standard time period is divided into a low-frequency use time period, ​ 4. The data management method of an intelligent payment terminal according to claim 3, wherein, The pair fluctuation value The specific manner of analyzing and determining the reference payment data quantity is that AS51: If At that time, the calculation will be Number of payment data average As a benchmark for payment data quantity; among which... This is the default value; AS52: If At that time, according to Sort in descending order and retrieve the corresponding values ​​in sequence. The value will be obtained each time. When the value is reached, delete it, and then recalculate the remainder. fluctuation value Re-evaluate; if still... When, select the next one according to the sorting order. Value, recalculate fluctuation value ,until ; AS53: Obtain the number of values that are deleted and mark it as the deletion amount , calculate the proportion value : ; When the percentage Exceed When, confirm that it has been deleted The maximum, minimum, and median values ​​are counted, and the average of these three values ​​is calculated as the baseline payment data quantity; among them, It is the preset value, and Less than 1; When the share value does not exceed , the average value of is calculated as the reference payment data amount, wherein .​ 5. The data management method of an intelligent payment terminal according to claim 1, wherein, ​

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