Data management method of intelligent payment terminal

Abstract

The invention discloses a data management method of an intelligent payment terminal, and relates to the technical field of data management, through the data management method of the intelligent payment terminal, operation strategies of equipment in high-frequency and low-frequency periods are optimized based on time dimension analysis of payment data, and efficient allocation and use of equipment resources are realized; through an efficient data classification and screening mechanism, it is ensured that key data are uploaded preferentially and processed in real time, the equipment load and network congestion risks are reduced, and meanwhile the accuracy and reliability of data transmission are improved; besides, a strategy of combining time-phased synchronous uploading and hierarchical uploading is adopted, so that the flexibility and efficiency of data management are greatly improved, the timeliness of important data is guaranteed, and the performance of the whole self-service cashier terminal equipment is optimized; according to the overall method, the operation efficiency of the payment terminal is comprehensively improved, and reliable guarantee is provided for data processing and decision support of an intelligent payment scene.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a data management method for an intelligent payment terminal. Background Art

[0002] With the rapid development of e-commerce and digital payment technology, smart 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, smart payment terminals not only support multiple payment methods, but also undertake important tasks such as collection, storage, processing and transmission of payment data. These terminals are usually equipped with high-performance processors, operating systems and network connection functions, and have strong data processing capabilities.

[0003] With the intelligent development of the retail industry, self-service cashier terminals have been widely used in various supermarkets and convenience stores. Such equipment can provide efficient and convenient shopping settlement services, effectively alleviating the pressure of manual cashiering.

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

[0005] In addition, existing equipment lacks an effective screening mechanism for the importance of payment data and fails to reasonably distinguish and prioritize key data, which may affect the system's timely response to important business data. Summary of the invention

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

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a data management method for a smart payment terminal, comprising:

[0008] Step 1: By collecting payment data from the self-service cashier terminal device, analyzing the payment data according to the generation time, the high-frequency use period and low-frequency use period of the self-service cashier terminal device in a day are obtained;

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

[0010] Step 3: Mark the payment data stored in the self-service cashier terminal device during the high-frequency use period as submitted data. When the submitted data is uploaded during the low-frequency use period, the submitted data is analyzed and screened into primary important submitted data, secondary important submitted data and common submitted data;

[0011] Step 4: According to the screening results of the submitted data, first upload the first-level important submitted data to the cloud platform. After the first-level important submitted data is uploaded, upload the second-level important submitted data to the service cloud platform, and finally upload the ordinary submitted data to the service cloud platform.

[0012] As a further solution of the present invention: in the step 1, the payment data refers to the order information of the user purchasing goods at the self-service checkout terminal device.

[0013] As a further solution of the present invention: In the step 1, the payment data in the self-service cashier terminal device is collected and analyzed according to the generation time of the payment data to obtain the high-frequency use period and low-frequency use period of the self-service cashier terminal device in a day. The specific method is:

[0014] AS1: Select a self-service cashier terminal device to be analyzed as the target device;

[0015] AS2: Divide a day into n standard time periods. Specifically, the standard time periods start from midnight, where n is a preset value.

[0016] AS3: Then, a standard time period is randomly selected to obtain the amount of payment data generated by the target device in the standard time period for each of the last s days; and all the payment data amounts are marked as Bi, where i=1, 2, ..., s, where s is a preset value;

[0017] The obtaining of the amount of payment data generated by the target device in the standard time period on each day in the past s days specifically refers to the time period of s days in the past, starting from the current time;

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

[0019]

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

[0021] AS6: Classify the standard time period into a high-frequency usage period or a low-frequency usage period based on the amount of benchmark payment data:

[0022] If the number of reference payment data is greater than Q3, then the standard time period is divided into a high-frequency usage period; where Q3 is a preset value;

[0023] If the number of reference payment data is less than or equal to Q3, then the standard time period is divided into a low-frequency usage period;

[0024] AS7: Then obtain the next standard time period, and repeat steps AS3 - AS6 to classify all standard time periods as high-frequency usage periods or low-frequency usage periods.

[0025] As a further solution of the present invention: The specific method for analyzing and judging the fluctuation value F to obtain the number of reference payment data is as follows;

[0026] AS51: If F < Q1, take the average value Bp of the s payment data quantities Bi calculated as the number of reference payment data; where Q1 is a preset value;

[0027] AS52: If F ≥ Q1, sort in descending order according to |Bi - Bp|, and at the same time sequentially obtain the corresponding Bi values, and when each Bi value is obtained, delete it. After deletion, recalculate the fluctuation value F of the remaining Bi, and re-judge. If still F ≥ Q1, select the next Bi value in the sorting order and recalculate the fluctuation value F until F < Q1;

[0028] AS53: Obtain the number of deleted Bi values, mark it as the deletion amount r, and calculate the occupancy ratio z:

[0029] When the occupancy ratio z exceeds Q2, determine the maximum value, minimum value and median value among the number of deleted Bi values, and calculate the average value of the maximum value, minimum value and median value as the number of reference payment data; where Q2 is a preset value and Q2 < 1;

[0030] When the occupancy ratio does not exceed Q2, calculate the average value of g1 Bi as the number of reference payment data, where g1 = s - r.

[0031] As a further solution of the present invention: In the second step, the synchronous upload to the service cloud platform is to synchronize the payment data generated by the self-checkout terminal device with the service cloud platform. When payment data is generated on the self-checkout terminal device, the payment data is synchronously uploaded to the service cloud platform for storage.

[0032] As a further solution of the present invention: In the third step, when uploading the submitted data, the specific method for analyzing and screening the submitted data into first-level important submitted data, second-level important submitted data and ordinary submitted data is as follows:

[0033] BS1: Extract the transaction amount, transaction time interval, commodity category number and user level index from each payment data in the submitted 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 submitted;

[0034] BS2: Extracted A j , ΔT j , C j , U j Make the following judgment:

[0035] Judgment A j , ΔT j , C j , U j Do all of the following rules apply?

[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 Indicates the minimum value of the product category code, C max Indicates the maximum value of the product category number;

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

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

[0041] Payment data that meets all the above conditions and rules will be classified as first-level important submission data, otherwise they will be uniformly classified as ordinary submission data.

[0042] As a further solution of the present invention: when uploading the submitted data, the specific method of analyzing and screening the submitted data into primary important submitted data, secondary important submitted data and common submitted data also includes:

[0043] BS3: Get 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 , transaction time interval ΔT j , Commodity Category Index C j and 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 They are typical high amount value, typical active transaction interval, typical important commodity category number and typical high-level user index;

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

[0047] D j Delivery data ≤D is classified as secondary important submission data;

[0048] D j >D's delivery data is further divided into common important submission data.

[0049] As a further solution of the present invention: also include:

[0050] Step 5: After completing uploading the first-level important submission data, the second-level important submission data and the ordinary submission data, delete the uploaded first-level important submission data, the second-level important submission data and the ordinary submission data stored in the self-service checkout terminal device to free up enough storage space for storing the newly generated payment data in subsequent high-frequency usage periods.

[0051] The present invention provides a data management method for an intelligent payment terminal. Compared with the prior art, the present invention has the following beneficial effects:

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

[0053] In addition, the present invention adopts a strategy combining synchronous uploading in different time periods with uploading in different levels, which greatly improves the flexibility and efficiency of data management, not only ensuring the timeliness of important data, but also optimizing the performance and user experience of the overall self-service cashier terminal equipment. The overall method achieves a comprehensive improvement in the operating efficiency of the payment terminal, and provides a reliable guarantee for data processing and decision support in smart payment scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The present invention will be further described below in conjunction with the accompanying drawings.

[0055] Figure 1 The present invention is a flowchart of the steps of a data management method for a smart payment terminal. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] Embodiment 1

[0058] See also Figure 1 ,The present invention provides a data management method for an intelligent payment terminal, comprising:

[0059] Step 1: By collecting payment data from the self-service cashier terminal device, analyzing the payment data according to the generation time, the high-frequency use period and low-frequency use period of the self-service cashier terminal device in a day are obtained;

[0060] The payment data refers to the order information of the user purchasing goods at the self-service cashier terminal device, and the order information includes key information such as order generation time, order amount, product category number, user level index, etc.;

[0061] The specific method of collecting payment data from the self-service cashier terminal device and analyzing it according to the generation time of the payment data to obtain the high-frequency use period and low-frequency use period of the self-service cashier terminal device in a day is:

[0062] AS1: Select a self-service cashier terminal device to be analyzed as the target device;

[0063] AS2: Divide a day into n standard time periods. Specifically, the standard time periods are divided starting from midnight, where n is a preset value, which is specifically set by professional staff. If n is 24 hours, each standard time period is one hour. In this embodiment, n is 24;

[0064] AS3: Next, arbitrarily select a standard time period, and obtain the number of payment data generated by the target device each day in the past s days within this standard time period; and mark all the numbers of payment data as Bi, where i = 1, 2, …, s, and s is a preset value, specifically set by professional staff. In this embodiment, s is 60;

[0065] The obtaining the number of payment data generated by the target device each day in the past s days within this standard time period specifically refers to the time period of the past s days starting from the current time;

[0066] AS4: Next, calculate the average value of the s numbers of payment data Bi, and mark it as Bp; obtain the fluctuation value F corresponding to the s numbers of payment data Bi through the following formula:

[0067]

[0068] AS5: Analyze and judge the fluctuation value F to obtain the benchmark number of payment data;

[0069] The specific method of analyzing and judging the fluctuation value F to obtain the benchmark number of payment data is as follows;

[0070] AS51: If F < Q1, take the average value Bp of the s numbers of payment data Bi calculated as the benchmark number of payment data; where Q1 is a preset value, specifically set by professional staff;

[0071] AS52: If F ≥ Q1, sort in descending order according to |Bi - Bp|, and simultaneously obtain the corresponding Bi values in sequence. When each Bi value is obtained, delete it. After deletion, recalculate the fluctuation value F of the remaining Bi, and re - judge. If still F ≥ Q1, select the next Bi value according to the sorting order and recalculate the fluctuation value F until F < Q1;

[0072] AS53: Obtain the number of deleted Bi values, mark it as the deletion amount r, and calculate the occupancy ratio z:

[0073] When the occupancy ratio z exceeds Q2, determine the maximum value, minimum value, and median value among the number of deleted Bi values, and calculate the average value of the maximum value, minimum value, and median value as the benchmark number of payment data; where Q2 is a preset value, and Q2 < 1. In this embodiment, Q2 takes the value of 0.5;

[0074] When the occupancy ratio does not exceed Q2, calculate the average value of g1 Bi as the benchmark number of payment data, where g1 = s - r;

[0075] AS6: Classify the standard time period into a high-frequency usage period or a low-frequency usage period based on the amount of benchmark payment data:

[0076] If the number of benchmark payment data is greater than Q3, the standard time period is divided into a high-frequency usage period; Q3 is a preset value, which is determined by the staff according to the operating status of the target device;

[0077] If the number of benchmark payment data is less than or equal to Q3, the standard time period is divided into a low-frequency usage period;

[0078] AS7: Next, obtain the next standard time period, repeat steps AS3-AS6, and classify all standard time periods into high-frequency usage time periods or low-frequency usage time periods;

[0079] By collecting and analyzing the payment data of self-service cashier terminal devices and dividing them into high-frequency and low-frequency usage periods according to time periods, the usage patterns of the devices in a day can be clearly presented, which is convenient for determining the load management and resource allocation of the devices. At the same time, through in-depth analysis and classification of fluctuation values, the judgment basis for the amount of payment data is ensured to be more scientific and accurate. In addition, this process not only optimizes the operating efficiency of the equipment in different time periods, but also provides a reliable basis for subsequent data upload and management strategies, which helps to improve stability and the accuracy of data analysis.

[0080] Step 2: When the self-service cashier terminal device is in a low-frequency use period, the payment data generated during the low-frequency use period is synchronously uploaded to the service cloud platform; when the self-service cashier terminal device is in a high-frequency use period, the payment data generated during the high-frequency use period is first stored on the self-service cashier terminal device, and the payment data is uploaded to the service cloud platform when it is in a low-frequency use period later;

[0081] The synchronous uploading to the service cloud platform is to synchronize the payment data generated by the self-service cashier terminal device with the service cloud platform. When the payment data is generated on the self-service cashier terminal device, the payment data is synchronously uploaded to the service cloud platform for storage;

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

[0083] According to the division of high-frequency and low-frequency time periods, the payment data upload strategy is dynamically adjusted. The data is temporarily stored in the terminal device during the high-frequency period and then uploaded synchronously during the low-frequency period. This not only alleviates the operating pressure of the network and equipment during the high-frequency period, but also ensures the integrity and real-time availability of the data. This upload method not only improves the response speed of the self-service cashier terminal equipment, but also makes the most of the network resources during the low-frequency period and reduces the risk of network congestion. At the same time, the back-end management personnel remotely access the stored data through the cloud platform, which greatly improves the flexibility and convenience of data management and provides important guarantees for subsequent data analysis and auditing.

[0084] Step 3: Mark the payment data stored in the self-service cashier terminal device during the high-frequency use period as submitted data. When the submitted data is uploaded during the low-frequency use period, the submitted data is analyzed and screened into primary important submitted data, secondary important submitted data and common submitted data;

[0085] Step 4: According to the screening results of the submitted data, first upload the first-level important submitted data to the cloud platform. After the first-level important submitted data is uploaded, upload the second-level important submitted data to the service cloud platform. Finally, upload the common submitted data to the service cloud platform.

[0086] Payment data is uploaded step by step in the order of priority of primary, secondary and common data, which not only ensures that key data (such as primary important submission data) can be uploaded in time and processed by the service cloud platform in priority, but also greatly reduces the risk of network congestion caused by excessive data upload. This staged upload method fully utilizes the performance resources of equipment and network to ensure the stability and efficiency of data upload. In addition, the strategy of step-by-step upload also makes data storage and processing more organized, facilitates subsequent data management and business optimization, and provides more valuable support for the service cloud platform.

[0087] Step 5: After completing uploading the first-level important submission data, the second-level important submission data and the ordinary submission data, delete the uploaded first-level important submission data, the second-level important submission data and the ordinary submission data stored in the self-service checkout terminal device to free up enough storage space for storing the newly generated payment data in subsequent high-frequency usage periods.

[0088] Embodiment 2

[0089] In the specific implementation process of this embodiment, based on the first embodiment, and different from the first embodiment, this embodiment further explains the process of step three:

[0090] When uploading the submitted data, the specific method of analyzing and screening the submitted data into primary important submitted data, secondary important submitted data and common submitted data is as follows:

[0091] BS1: Extract the transaction amount, transaction time interval, commodity category number and user level index from each payment data in the submitted 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 submitted;

[0092] BS2: Extracted A j , ΔT j , C j , U j Make the following judgment:

[0093] Judgment A j , ΔT j , C j , U j Do all of the following rules apply?

[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. When the transaction time interval is less than the threshold ΔT thresh , indicating active trading;

[0096] C∈[C min ,C max ], where C min Indicates the minimum value of the product category code, C max Indicates the maximum value of the product category number;

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

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

[0099] Payment data that meets all the above conditions will be classified as first-level important submission data, otherwise they will be uniformly classified as ordinary submission data;

[0100] BS3: Get 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 , transaction time interval ΔT j , Commodity Category Index C j and 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 They are typical high amount value, typical active transaction interval, typical important commodity category number and typical high-level user index;

[0103] It should be noted that the typical high amount value, typical active transaction interval, typical important commodity category number and 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 of the high amount part can be taken as the typical high amount value A by analyzing the historical payment data. ideal For the typical active transaction interval, we can calculate the average transaction time interval during the high-frequency usage period and take a representative time interval as the typical active transaction interval ΔT ideal ; For typical important commodity category numbers, select the numbers of important commodities as typical important commodity category numbers C according to business strategies or commodity classification systems ideal ; For the typical high-level user index, select the index representing the lower-level users according to the distribution of membership levels;

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

[0105] D j Delivery data ≤D is classified as secondary important submission data;

[0106] D j >D's delivery data is further divided into common important submission data;

[0107] Through multi-dimensional information such as transaction amount, transaction time interval, commodity category number and user level index, the submitted data in high-frequency time periods are analyzed and classified in detail, and divided into primary important, secondary important and ordinary submitted data, which helps to meet the business needs of different levels in a targeted manner; through this hierarchical management mode, self-service checkout terminal equipment can give priority to primary important submitted data, while improving business response speed; the data screening process also combines typical value analysis and threshold judgment of historical data, which improves the scientificity and practicality of classification and provides a solid foundation for subsequent upload strategies.

[0108] Embodiment 3

[0109] The specific implementation process of this embodiment includes the entire implementation process of the above two groups of embodiments.

[0110] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification 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 invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A data management method for a smart payment terminal, characterized in that: include: Step 1: By collecting payment data from the self-service cashier terminal device, analyzing the payment data according to the generation time, the high-frequency use period and low-frequency use period of the self-service cashier terminal device in a day are obtained; Step 2: When the self-service cashier terminal device is in a low-frequency use period, the payment data generated during the low-frequency use period is synchronously uploaded to the service cloud platform; when the self-service cashier terminal device is in a high-frequency use period, the payment data generated during the high-frequency use period is first stored on the self-service cashier terminal device; Step 3: Mark the payment data stored in the self-service cashier terminal device during the high-frequency use period as submitted data. When the submitted data is uploaded during the low-frequency use period, the submitted data is analyzed and screened into primary important submitted data, secondary important submitted data and common submitted data; Step 4: According to the screening results of the submitted data, first upload the first-level important submitted data to the cloud platform. After the first-level important submitted data is uploaded, upload the second-level important submitted data to the service cloud platform, and finally upload the ordinary submitted data to the service cloud platform.

2. The data management method of a smart payment terminal according to claim 1, characterized in that: In the step 1, the payment data refers to the order information of the user purchasing goods at the self-service checkout terminal device.

3. The data management method of a smart payment terminal according to claim 2, characterized in that: In the step 1, the specific method of collecting payment data from the self-service cashier terminal device and analyzing it according to the generation time of the payment data to obtain the high-frequency use period and low-frequency use period of the self-service cashier terminal device in a day is: AS1: Select a self-service cashier terminal device to be analyzed as the target device; AS2: Divide a day into n standard time periods. Specifically, the standard time periods start from midnight, where n is a preset value. AS3: Then, a standard time period is randomly selected to obtain the amount of payment data generated by the target device in the standard time period for each of the last s days; and all the payment data amounts are marked as Bi, where i=1, 2, ..., s, where s is a preset value; The obtaining of the amount of payment data generated by the target device in the standard time period on each day in the past s days specifically refers to the time period of s days in the past, starting from the current time; AS4: Then calculate the average value of the s payment data quantities Bi and mark it as Bp; obtain the fluctuation value F corresponding to the s payment data quantities Bi by the following formula: AS5: Analyze and judge the fluctuation value F to obtain the benchmark payment data quantity; AS6: Classify the standard time period into a high-frequency usage period or a low-frequency usage period based on the amount of benchmark payment data: If the number of benchmark payment data is greater than Q3, the standard time period is divided into a high-frequency usage period; Q3 is a preset value; If the number of benchmark payment data is less than or equal to Q3, the standard time period is divided into a low-frequency usage period; AS7: Next, the next standard time period is obtained, and steps AS3-AS6 are repeated to classify all standard time periods into high-frequency usage time periods or low-frequency usage time periods.

4. The data management method of a smart payment terminal according to claim 3, characterized in that: The specific method of analyzing and judging the fluctuation value F to obtain the number of benchmark payment data is as follows: AS51: If F < Q1, the average value Bp of the s calculated payment data quantities Bi is used as the reference payment data quantity; where Q1 is a preset value. AS52: If F ≥ Q1, sort in descending order according to |Bi - Bp|, and simultaneously obtain the corresponding Bi values in sequence. When each Bi value is obtained, delete it. After deletion, recalculate the fluctuation value F of the remaining Bi values, and re-determine. If still F ≥ Q1, select the next Bi value according to the sorting order and recalculate the fluctuation value F until F < Q1. AS53: Get the number of deleted Bi values, mark them as the deletion amount r, and calculate the proportion z: When the occupancy ratio z exceeds Q2, determine the maximum value, minimum value, and median value among the number of deleted Bi values, and calculate the average value of the maximum value, minimum value, and median value as the reference payment data quantity; where Q2 is a preset value and Q2 < 1. When the occupancy ratio does not exceed Q2, calculate the average value of g1 Bi values as the reference payment data quantity, where g1 = s - r.

5. The data management method of a smart payment terminal according to claim 1, characterized in that: In step two, the synchronous upload to the service cloud platform is to synchronize the payment data generated by the self-checkout terminal device with the service cloud platform. When payment data is generated on the self-checkout terminal device, the payment data is synchronously uploaded to the service cloud platform for storage.

6. The data management method of a smart payment terminal according to claim 1, characterized in that: In step three, when uploading the submitted data, the specific method of analyzing and screening the submitted data into first-level important submitted data, second-level important submitted data, and ordinary submitted data is as follows: BS1: Extract the transaction amount, transaction time interval, commodity category number and user level index from each payment data in the submitted 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 submitted; BS2: Extracted A j , ΔT j , C j , U j Make the following judgment: Judgment A j , ΔT j , C j , U j Do all of the following rules apply? A j ≥A thresh , where A thresh is the amount threshold; ΔT j ≤ΔT thresh , where ΔT thresh is the transaction time interval threshold; C∈[C min ,C max ], where C min Indicates the minimum value of the product category code, C max Indicates the maximum value of the product category number; U≥U thresh , where U thresh Indicates the user level index threshold; A above thresh , ΔT thresh , C min , C max and U thresh All are preset values; The payment data that meets all the above conditions of the rules is classified as first-level important submitted data, otherwise it is uniformly classified as ordinary submitted data.

7. A data management method for a smart payment terminal according to claim 6, characterized in that: When uploading the submitted data, the specific method of analyzing and screening the submitted data into first-level important submitted data, second-level important submitted data, and ordinary submitted data also includes: BS3: Get 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 , transaction time interval ΔT j , Commodity Category Index C j and user level index U j ; Determine the judgment coefficient D by the following formula j : In the formula, A ideal , ΔT ideal , C ideal and U ideal They are typical high amount value, typical active transaction interval, typical important commodity category number and typical high-level user index; BS4: The judgment coefficient D of each payment data j Compare with the judgment threshold D: D j Delivery data ≤D is classified as secondary important submission data; D j >D's delivery data is further divided into common important submission data.

8. The data management method of a smart payment terminal according to claim 1, characterized in that: It also includes: Step five, after uploading the first-level important submitted data, second-level important submitted data, and ordinary submitted data, delete the first-level important submitted data, second-level important submitted data, and ordinary submitted data that have been uploaded and stored in the self-checkout terminal device to free up enough storage space for storing newly generated payment data during subsequent high-frequency usage periods.

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