A payment data analysis processing method and system for a smart park

Abstract

This invention provides a payment data analysis and processing method and system for smart parks, belonging to the field of data analysis technology. Specifically, it includes: determining an active observation management strategy for active observation terminals based on data from actively observed terminals and combining the analysis results of payment data from different actively observed terminals during the payment matching period; performing active observation processing on different actively observed terminals based on the active observation management strategy to obtain observation processing results; identifying abnormal data in different time periods based on the observation processing results of the active observation terminals; and determining an analysis and processing method for payment data from terminals excluding the actively observed terminals based on the abnormal data from different actively observed terminals in different time periods. This improves the efficiency and reliability of handling payment anomalies.

Description

Technical Field

[0001] This invention belongs to the field of data analysis technology, and in particular relates to a method and system for analyzing and processing payment data in smart parks. Background Technology

[0002] The smart park is equipped with various public facilities, such as charging stations, parking payment systems, and canteens. Specifically, the invention patent application CN201911107198.2, "A Smart Park Payment Management System," upgrades the payment methods for park merchants such as canteens, restaurants, convenience stores, cafes, kiosks, and fruit shops by using electronic payments. Furthermore, as a prerequisite for merchants to enter the park, all shopping and consumption within the park must be paid for through an electronic wallet. This helps improve the image of the smart park and the management level of merchants, providing a safe and convenient payment platform for park merchants and individuals. However, the above technical solution has the following drawbacks: The large number of payment terminals within a smart park inevitably leads to potential order omissions during peak business hours. Therefore, determining proactive monitoring strategies based on terminal distribution data during peak payment periods within the park, and generating corresponding payment data analysis and processing strategies for different terminals, is a pressing technical challenge to improve the efficiency and timeliness of terminal anomaly identification and handling.

[0003] Therefore, there is an urgent need for a method and system for analyzing and processing payment data in smart parks. Summary of the Invention

[0004] To achieve the objectives of this invention, the following technical solution is adopted: Specifically, this application provides a method for payment data analysis and processing in smart parks, which includes: S1 uses the payment data of the terminals in the smart park to determine the payment matching time period of the terminal, and determines the active observation method of the terminal based on the correlation between the payment matching time period of the terminal and the payment matching time periods of other terminals. S2 determines the active observation management strategy for the active observation terminal based on the active observation terminal data and the parsing results of the payment data of different active observation terminals during the payment matching period of the active observation terminal. S3 performs active observation processing on different active observation terminals based on the active observation management strategy, obtains observation processing results, determines abnormal data in different time periods based on the observation processing results of the active observation terminals, and determines the analysis and processing method for payment data of terminals excluding active observation terminals based on the abnormal data in different time periods from different active observation terminals.

[0005] The beneficial effects of this invention are as follows: Based on the correlation between the payment matching time periods of a terminal and those of other terminals, a proactive observation method for terminals is determined. Terminal payment data not only reflects transaction activity but also indirectly reflects its operational health. When a terminal has a small number of payment matching time periods, its payment behavior lacks regularity, and its operational status cannot be judged based on payment reliability; therefore, it should be included in proactive observation. When the number of matching time periods is large, it is necessary to further analyze the degree of overlap between its matching time periods and those of other terminals—a high degree of overlap means that multiple terminals may be simultaneously experiencing payment peaks, posing a risk of payment omissions when the system processes concurrent orders. Therefore, proactive observation is needed to ensure timely detection of anomalies. Through multi-level quantitative judgment, precise allocation of terminal observation resources can be achieved.

[0006] Based on data from actively monitored terminals and the analysis results of payment data from different terminals during their payment matching periods, active monitoring management strategies are determined. For terminals identified as "active monitoring terminals," differentiated active monitoring management strategies are further developed, specifying how long a terminal should remain without payment data before triggering active monitoring (sending virtual transaction requests to check operational status). The proportion of actively monitored terminals (active monitoring percentage) reflects the overall risk level of the park. By analyzing the overlap ratio between these terminals and other actively monitored terminals during their payment matching periods, the reliability of monitoring in the event of payment omissions during the entire payment matching period is assessed. Through the analysis of monitoring reliability and risk level, the allocation of active monitoring resources is optimized, improving the reliability of active monitoring processing.

[0007] Furthermore, the payment data of the smart park's terminals includes the number of payments received by the terminals in different time periods.

[0008] Furthermore, the method for determining the payment matching period of the terminal is as follows: S11 determines the number of times the terminal made payments in different time periods based on the terminal's payment data; S12 uses the number of payments received to determine the busy payment days for the terminal during the time period; S13 determines whether the time period is a payment matching time period for the terminal based on the busy payment date data.

[0009] Furthermore, the active observation terminal data includes the number of active observation terminals.

[0010] Furthermore, during the payment matching period of the active observation terminal, the parsing results of the payment data of different active observation terminals are determined based on the composition data of the active observation terminals among the overlapping payment terminals in the payment matching period.

[0011] Furthermore, the method for determining the active observation management strategy of the active observation terminal is as follows: S31. Based on the data from the active observation terminal, determine the proportion of the active observation terminal among the payment terminals in the smart park, and use the proportion of the active observation terminal among the payment terminals in the smart park as the active observation proportion. S32 uses the parsing results of the payment data of different active observation terminals during the payment matching period of the active observation terminal to determine the composition ratio of active observation terminals among the overlapping payment terminals during the payment matching period of the active observation terminal, and uses the composition ratio of active observation terminals among the overlapping payment terminals during the payment matching period as the overlapping composition ratio. S33 determines the active observation management strategy for the active observation terminal based on the active observation ratio and the overlap ratio in different active observation periods.

[0012] Secondly, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the aforementioned payment data analysis and processing method for a smart park when running the computer program.

[0013] Other features and advantages will be set forth in the following description, and the objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0014] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0015] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0016] Figure 1 This is a flowchart of a payment data analysis and processing method for smart parks; Figure 2 This is a flowchart illustrating the method for determining the payment matching period for the terminal. Figure 3 This is a flowchart illustrating the method for determining the active observation method of the terminal; Figure 4This is a flowchart illustrating the method for determining the analysis and processing of payment data from terminals other than those actively observed. Detailed Implementation

[0017] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0018] Example 1 like Figure 1 As shown, this application provides a payment data analysis and processing method for smart parks, specifically including: S1 uses the payment data of the terminals in the smart park to determine the payment matching time period of the terminal, and determines the active observation method of the terminal based on the correlation between the payment matching time period of the terminal and the payment matching time periods of other terminals. Furthermore, the payment data of the smart park's terminals includes the number of payments received by the terminals in different time periods.

[0019] Specifically, such as Figure 2 As shown, the method for determining the payment matching time period of the terminal is as follows: For various terminals within the smart park (such as vending machines, cafeteria card readers, parking payment machines, etc.), the system accurately identifies their high-frequency payment periods, known as "payment matching periods." The core logic lies in analyzing the number of payments made by the terminals on different historical dates and time periods to identify dates with significantly higher payment volumes than other time periods during the same period—these are designated as "busy payment dates." Then, the frequency of busy dates within each time period is statistically analyzed, calculating a "busy coefficient," which is compared to a preset threshold. If the busy coefficient exceeds the threshold, that time period is determined to be a payment matching period for the terminal. This method helps park operators understand the peak business periods of terminals, providing data support for resource scheduling, equipment maintenance, and promotional activity planning.

[0020] S11 determines the number of times the terminal made payments in different time periods based on the terminal's payment data; Payment data from smart park terminals: This refers to transaction information recorded by various smart terminals (such as vending machines, food and beverage payment terminals, access control payment terminals, etc.) during daily operation within the park. This data includes at least the time and amount of the transaction, with a focus on the number of transactions in this methodology.

[0021] Time period: refers to the number of time periods that a 24-hour day is divided into according to certain time intervals. For example, it can be divided into 24 time periods by hour (0:00-1:00, 1:00-2:00, ...), or it can be divided into custom time periods such as morning peak, noon peak, and evening peak according to business needs.

[0022] Number of transactions: This refers to the total number of transactions successfully completed by the terminal within a specific time period.

[0023] This step forms the data foundation for the entire method. Only by aggregating the raw transaction logs according to time and terminal can we obtain structured "time period-frequency" data, providing input for subsequent statistical analysis.

[0024] This step transforms discrete transaction records into quantitative indicators that reflect the time distribution characteristics of terminal business. For example, it can be visually observed that a vending machine's transaction volume is significantly higher between 12:00 PM and 1:00 PM than at other times, initially revealing its business peak.

[0025] Within the "Smart Industrial Park," the operator selected vending machine T001 as the subject of analysis. Transaction records from the past 30 days were extracted, and the number of transactions per day was calculated by hourly time slots (24 time slots in total). For example, on day 1, T001 processed 15 transactions between 12:00 and 13:00, and 20 transactions between 18:00 and 19:00; on day 2, it processed 12 transactions between 12:00 and 13:00, and so on. Statistics were compiled for all dates and time slots.

[0026] S12 uses the number of payments received to determine the busy payment days for the terminal during the time period; Average number of payments collected during other time periods on the date: For a specific date, sum the number of payments collected during all time periods of that day, then divide by the number of time periods (e.g., 24) to obtain the average number of payments collected on that date. This reflects the average business level across all time periods on that date.

[0027] Busy Payment Days: For a specific time period (e.g., 12:00-13:00), if the number of payments received on a particular day exceeds the average number of payments received across all time periods on that day, then that day is called a "busy payment day" for that time period. This means that on that day, the business volume for that time period is significantly higher than the average for the day, making it a relatively busy day.

[0028] Knowing only the absolute number of transactions for a particular period is insufficient, as total customer traffic can vary across different days. For example, total transaction volume may differ between weekends and weekdays. By comparing to the average for that date, the impact of overall date fluctuations can be eliminated, allowing focus on the relative performance of that period within the day. Only those periods that perform exceptionally well on a given day are considered "busy days" for that period, better reflecting its inherent characteristics.

[0029] Busy payment dates are a relative indicator that identifies which dates are the "highlights" of a specific time period. By counting the number of these dates, the frequency with which a time period has historically stood out can be quantified, providing a basis for subsequently judging the typical busyness level of that time period.

[0030] For vending machine T001, on day 5 of the statistical period (assuming it's a weekday), the average number of transactions across all time slots is 8. The number of transactions during the 12:00-13:00 time slot on that day is 15, which is greater than 8. Therefore, day 5 is marked as a busy day for the 12:00-13:00 time slot. On the same day, the number of transactions during the 18:00-19:00 time slot is 6, less than 8, and therefore not marked. This process is repeated for all dates and time slots to obtain all busy days for each time slot.

[0031] S13 determines whether the time period is a payment matching time period for the terminal based on the busy payment date data.

[0032] Specifically, the busy payment date is defined as a date on which the number of payments received is greater than the average number of payments received in other time periods.

[0033] Specifically, based on the busy payment date data, determining whether the time period is a payment matching time period for the terminal includes: The busyness coefficient of the time period is determined based on the proportion of busy days for receiving payments within that time period. If the busy coefficient of the time period is greater than a preset busy coefficient threshold, then the time period is determined to be the payment matching time period of the terminal; otherwise, the time period is determined not to be the payment matching time period of the terminal.

[0034] Percentage of Busy Payment Days: For a specific period, the percentage of busy payment days is calculated by dividing the number of busy payment days by the total number of days in the statistical period (e.g., 30 days). This percentage reflects how frequently that period has historically been particularly busy.

[0035] Busyness coefficient: This refers to the percentage of the above-mentioned quantities, expressed as a decimal or percentage, and is the core indicator for determining whether a time period is a "matching time period".

[0036] Preset busy coefficient threshold: A pre-set critical value. When the busy coefficient exceeds this threshold, the busy characteristics of that period are considered to be universal and stable, and can be identified as the payment matching period for the terminal, that is, this period is a high-frequency typical period for terminal payment.

[0037] If a period of time occasionally shows exceptional performance on a few days, it may simply be due to accidental factors (such as temporary events). Only when it is significantly higher than the daily average on a sufficient number of days can it be considered a typical peak business period for that terminal. By setting a threshold, accidental fluctuations can be filtered out, identifying statistically significant matching periods.

[0038] This step completes the extraction of business patterns from historical data. The finalized "payment matching time periods" can serve as the basis for operational decisions, such as increasing inventory, arranging promotions, or strengthening equipment inspections during these periods to match high customer traffic demand.

[0039] For vending machine T001, the time slot from 12:00 to 13:00 was marked as busy payment days after 30 days of statistics. Therefore, its busy coefficient = 21 / 30 = 0.7. A preset busy coefficient threshold of 0.6 is set. Since 0.7 > 0.6, the time slot of 12:00 to 13:00 is determined as the matching payment time slot for T001. For the time slot of 18:00 to 19:00, only 10 days were marked, and the busy coefficient = 10 / 30 ≈ 0.33, which is less than 0.6, therefore it is not considered a matching time slot.

[0040] Specifically, such as Figure 3 As shown, the method for determining the active observation method of the terminal is as follows: For various terminals with payment functions within smart parks (such as vending machines, cafeteria card readers, and charging stations), a scientific assessment is needed to determine which terminals require "active monitoring," meaning their operational status needs more frequent and proactive monitoring. The core logic is that terminal payment data not only reflects transaction activity but also indirectly reflects their operational health. When a terminal has a small number of matching payment periods, its payment behavior lacks regularity, and its operational status cannot be judged based on payment reliability; therefore, it should be included in active monitoring. When there are many matching periods, further analysis is needed on the overlap between its matching periods and those of other terminals. High overlap means multiple terminals may be simultaneously experiencing peak payment periods, posing a risk of payment omissions when processing concurrent orders; therefore, active monitoring is necessary to ensure timely detection of anomalies. Low overlap indicates low concurrency risk, and the regularity of its own payment data can be relied upon, eliminating the need for active monitoring. Through multi-level quantitative judgment, precise allocation of terminal monitoring resources can be achieved.

[0041] S21 determines the number of payment matching periods for the terminal based on the payment matching period data of the terminal; In the above steps, the number of payment matching time periods of the terminal is obtained, and it is determined whether the number of payment matching time periods of the terminal is less than the preset threshold for the number of matching time periods. If so, it means that the number of payment matching time periods of the terminal is small, and its operating status cannot be determined by whether its payment is reliable. Therefore, the active observation method of the terminal is determined to be an active observation terminal. If not, proceed to step S22.

[0042] Payment matching period: refers to the typical busy period determined by the aforementioned methods (S11-S13) when the number of payments received by the terminal is significantly higher than the average level of the day.

[0043] Preset matching time period threshold: A pre-set integer threshold used to determine whether the terminal has enough regular busy time periods to support the inference of the operating status based on payment data.

[0044] Active monitoring terminals: These are terminals that need to be included in the active monitoring scope. The system will collect data and check their status more frequently to ensure that faults or anomalies are detected in a timely manner.

[0045] If a terminal has very few matching payment periods (e.g., only one period per day), it indicates that its business peaks are too concentrated or highly random, and the volatility of its payment data is inherently high. It's difficult to judge its operational status simply by whether payments are reliable (e.g., no payments were received within the expected period). Therefore, these types of terminals must be actively monitored directly. This step acts as the first filter, quickly identifying terminals that clearly lack regularity, saving resources for subsequent analysis.

[0046] This step reflects the principle of proactive risk management, prioritizing terminals with "poor data regularity" for active monitoring to avoid delays in fault detection due to reliance on unreliable payment data.

[0047] Within the "Smart Industrial Park," the operator analyzed three terminals: T001 vending machine, T002 cafeteria card reader, and T003 charging station. The preset threshold for the number of matching time periods was set to 3. Based on preliminary analysis, T001 had 2 matching time periods (midday and evening peak hours), T002 had 4, and T003 had 1. Since T001's matching time period count (2 < 3), T001 was directly identified as an active monitoring terminal. Similarly, T003's matching time period count (1 < 3) was also identified as an active monitoring terminal. T002's matching time period count (4 ≥ 3) proceeded to step S22.

[0048] S22 uses the correlation between the payment matching time period of the terminal and the payment matching time period of other terminals to determine other terminals that belong to the payment matching time period of the terminal, and regards the other terminals that belong to the payment matching time period of the terminal as overlapping payment terminals in the payment matching time period. Other terminals: refers to all other payment terminals within the park besides the current analysis terminal.

[0049] Correlation level: This refers to the number of other terminals that also use the same payment matching period for each payment matching period of the current terminal.

[0050] Overlapping payment terminals: Other terminals that also consider a specific payment matching period to be a payment matching period.

[0051] When a terminal has a sufficient number of matching periods, its operational patterns may correlate with those of other terminals. Multiple terminals operating at peak payment times simultaneously mean the system needs to handle a large number of concurrent transactions, increasing the risk of missed payments or system delays. Identifying overlapping payment terminals can quantify this concurrency risk.

[0052] By identifying overlapping payment terminals, the terminals can be placed in an interconnected network of concurrent risks. The higher the degree of overlap, the greater the system load pressure during that period, and the higher the possibility of terminal anomalies.

[0053] Specific examples: For the T002 cafeteria card reader, its designated payment periods are: 11:00-13:00 (lunch peak), 17:00-19:00 (evening peak), 7:00-8:00 (morning peak), and 12:00-13:00 (secondary lunch peak). We need to identify which other terminals also use these designated periods for each time slot. 11:00-13:00: T001 (vending machine) and T004 (coffee machine) are also matched during this period.

[0054] 17:00-19:00: T001 is also matched during this period.

[0055] 7:00-8:00: No other terminals are matched.

[0056] 12:00-13:00: Matching of T001 and T004.

[0057] Therefore, the number of overlapping payment terminals in each time period are: 2, 1, 0, and 2 respectively.

[0058] The above steps include the following: S221 uses overlapping payment terminals in the payment matching time period of the terminal to determine the number of overlapping payment terminals in different payment matching time periods, and determines whether the number of overlapping payment terminals in different payment matching time periods is less than a preset overlapping terminal number threshold. If so, it is determined that the terminal has a high observation reliability due to the large number of payment matching time periods, and the degree of overlap with the payment matching time periods of other terminals is low. Therefore, it is determined that the active observation method of the terminal does not belong to the active observation terminal. If not, proceed to step S222. Preset threshold for the number of overlapping terminals: A pre-set integer threshold used to determine whether the number of other terminals overlapping with this terminal within a certain period of time has reached a level of concurrency risk that warrants attention.

[0059] "All less than": This means that in all payment matching periods, the number of overlapping terminals in each period is less than this threshold, which means that the peak period of this terminal is basically "exclusive" and there is no significant concurrency risk with other terminals.

[0060] If a terminal has a large number of matching time periods (passing S21) and very few overlapping terminals in each time period (less than the threshold), it indicates that the terminal has a unique busy pattern and does not have significant overlap with other terminals' concurrent peaks. In this case, the terminal will not face the systemic risk of payment omissions due to multiple terminals being busy at the same time. Moreover, its own regularity is strong, and its payment data can be relied upon to determine its operational status, so there is no need for active observation.

[0061] This step identifies "independent and low-risk" terminals, grants them trust, and avoids wasting resources.

[0062] Suppose another terminal T005 (the gym access gate in the park) has 5 matching time periods, but the number of overlapping terminals in each time period is 0, 0, 1, 0, and 0 respectively. The preset threshold for the number of overlapping terminals is set to 2. The number of overlaps in all time periods is less than 2, so T005 is determined not to be an actively observed terminal.

[0063] S222 The payment matching period when the number of overlapping payment terminals is not less than the preset threshold for the number of overlapping terminals is taken as the overlapping risk period. It is determined whether the number of overlapping risk periods of the terminal is greater than the preset threshold for the number of risk periods. If yes, it is determined that the active observation method of the terminal belongs to the active observation terminal. If no, proceed to step S23. Overlapping risk periods: These are periods when the number of overlapping terminals reaches or exceeds a preset threshold. This means that during these periods, the terminal is simultaneously in peak payment periods with many other terminals, posing a significant risk of concurrent payments.

[0064] Preset risk period number threshold: Used to determine if there are too many overlapping risk periods, such that the terminal faces high concurrency risk in multiple periods of the day.

[0065] If a terminal overlaps with a large number of other terminals across multiple time periods, it indicates that it faces high concurrency risks across multiple time windows. In this case, even if no problems occur during a particular time period, the cumulative risk across multiple time periods means that the terminal requires closer monitoring to detect and address payment omissions as soon as possible.

[0066] This step identifies terminals with "high concurrency risk" and includes them in proactive monitoring to prevent transaction anomalies caused by excessive system load.

[0067] For T002, the number of overlapping terminals in each time period is: 2, 1, 0, 2. The preset threshold for the number of overlapping terminals is set to 2. Therefore, the overlapping risk periods are those with ≥2 terminals: 11:00-13:00 (2 terminals) and 12:00-13:00 (2 terminals), a total of 2. The preset risk period threshold is set to 1. Since 2 > 1, T002 is determined to be an actively monitored terminal.

[0068] S23 uses the number of payment matching time periods of the terminal and the overlapping payment terminal data in different payment matching time periods to determine the active observation method of the terminal.

[0069] In the above steps, the observation risk value of the terminal is determined based on the number of payment matching time periods of the terminal and the proportion of payment matching time periods where the number of overlapping payment terminals is not less than a preset threshold for the number of overlapping terminals. Based on the observation risk value of the terminal, the active observation method of the terminal is determined.

[0070] It should be noted that the fewer the number of payment matching time periods of the terminal, the greater the proportion of payment matching time periods where the number of overlapping payment terminals is not less than the preset threshold for the number of overlapping terminals, and the greater the observed risk value of the terminal.

[0071] Specifically, when the observation risk value of the terminal is greater than the preset observation risk threshold, the active observation method of the terminal is determined to be an active observation terminal.

[0072] The percentage of payment matching time periods with overlapping payment terminals not less than a preset threshold for overlapping terminals: This is the proportion of the number of payment matching time periods with overlapping risk to the total number of payment matching time periods for that terminal, denoted as R_risk. This proportion reflects the percentage of time periods where the terminal faces concurrency risk among all busy time periods.

[0073] Observational Risk Value: A comprehensive indicator reflecting the degree to which a terminal needs active observation due to its weak regularity (few matching periods) or high concurrency risk (large proportion of risky periods). It can be defined as: Observational Risk Value = (1 / Number of payment matching periods) × (Proportion of overlapping risky periods), or a more complex function, but the original text clearly states that "the fewer the number and the larger the proportion, the greater the risk value."

[0074] Preset observation risk threshold: The critical value used to ultimately determine whether it belongs to an active observation terminal.

[0075] When a terminal remains undecided after the initial screening steps, two dimensions need to be considered: the strength of its own regularity (number of matching time periods) and the degree of concurrent risk (percentage of risky time periods). A small number of matching time periods indicates unreliable information, while a high percentage of risky time periods indicates high concurrent risk. The combination of these two factors results in a high observed risk value, necessitating active monitoring. Conversely, if the number of matching time periods is large (indicating reliability) and the percentage of risky time periods is low (indicating low concurrent risk), the observed risk value is low, and active monitoring is unnecessary.

[0076] Assume terminal T006 has 3 payment matching periods, with 1 period having overlapping risk (approximately 0.33%). Terminal T007 has 4 matching periods, with 3 periods having overlapping risk (approximately 0.75%). Define the observed risk value as (1 / number of matching periods) × (percentage of overlapping risk periods). Calculation: T006: Observation risk value = (1 / 3) × 0.33 ≈ 0.11, T007: Observation risk value = (1 / 4) ×0.75 ≈ 0.1875.

[0077] Let the preset observation risk threshold be 0.15. Then T006 (0.11 ≤ 0.15) will not be actively observed, and T007 (0.1875 > 0.15) will be actively observed.

[0078] S2 determines the active observation management strategy for the active observation terminal based on the active observation terminal data and the parsing results of the payment data of different active observation terminals during the payment matching period of the active observation terminal. Furthermore, the active observation terminal data includes the number of active observation terminals.

[0079] Furthermore, during the payment matching period of the active observation terminal, the parsing results of the payment data of different active observation terminals are determined based on the composition data of the active observation terminals among the overlapping payment terminals in the payment matching period.

[0080] Specifically, the method for determining the active observation management strategy of the active observation terminal is as follows: For payment terminals identified as "actively monitored terminals," a differentiated active monitoring management strategy is further developed. This strategy determines how long a terminal should remain without payment data before triggering active monitoring (sending virtual transaction requests to check operational status). The core logic is that the percentage of actively monitored terminals (active monitoring percentage) reflects the overall risk level of the park. A low percentage allows for a uniform preset strategy, while a high percentage requires more refined management. For each actively monitored terminal, the reliability of monitoring in case of payment omissions is assessed by analyzing the overlap ratio with other actively monitored terminals during the payment matching period. If the overlap ratio of a terminal is low across all periods (i.e., few other actively monitored terminals), its omission risk is high, requiring a more conservative preset strategy (shorter preset duration). If a high percentage exists in certain periods, further comprehensive evaluation is needed to determine whether a more lenient target strategy (longer target duration) can be adopted. Through this dynamic adjustment, the optimal allocation of active monitoring resources is achieved.

[0081] S31. Based on the data from the active observation terminal, determine the proportion of the active observation terminal among the payment terminals in the smart park, and use the proportion of the active observation terminal among the payment terminals in the smart park as the active observation proportion. In the above steps, the active observation ratio is obtained, and it is determined whether the active observation ratio is less than the preset observation ratio threshold. If it is, it is determined that the active observation management strategy of the active observation terminal belongs to the preset active observation strategy. If not, proceed to step S32.

[0082] Active observation terminal data: This includes the number of active observation terminals and other relevant information.

[0083] Payment terminals in a smart park: This refers to the total number of all terminals within the park that have payment functions, including active monitoring terminals and non-active monitoring terminals.

[0084] Active monitoring percentage: The percentage of actively monitored terminals out of all payment terminals in the park reflects the proportion of terminals that require active monitoring in the overall system.

[0085] Preset observation percentage threshold: A pre-defined percentage threshold. When the percentage of active observations is below this threshold, it indicates that there are fewer terminals requiring active monitoring, the system load is lighter, and a relatively conservative observation strategy can be adopted uniformly. When the percentage is above the threshold, it indicates that there are more terminals requiring active monitoring, and a more differentiated strategy is needed to optimize resources.

[0086] Preset Active Observation Strategy: A baseline active observation strategy specifically involves determining whether the active observation terminal is functioning correctly if no payment data is available within a recent preset timeframe. This active observation process involves controlling the terminal to issue virtual transaction request processing data and determining whether the virtual transaction request can be processed promptly.

[0087] This step involves a macro-level assessment. If the proportion of terminals requiring active monitoring across the entire park is low, it indicates that most terminals operate with strong regularity and low risk, allowing for a unified and conservative monitoring strategy to simplify management. Conversely, a high proportion suggests a large number of high-priority terminals within the park, necessitating a more refined strategy to avoid resource waste.

[0088] The proportion of proactive monitoring reflects the overall risk situation of the park and is the first dimension of strategy stratification, ensuring that simplified management is adopted when the risk is low and refined control is initiated when the risk is high.

[0089] The "Smart Industrial Park" has a total of 100 payment terminals. After the previous steps (S21-S23), 20 of them were identified as active observation terminals. Active observation percentage = 20 / 100 = 20%. The preset observation percentage threshold is set to 15%. 20% > 15%, therefore it does not meet the requirement of being less than the threshold, proceed to step S32.

[0090] S32 uses the parsing results of the payment data of different active observation terminals during the payment matching period of the active observation terminal to determine the composition ratio of active observation terminals among the overlapping payment terminals during the payment matching period of the active observation terminal, and uses the composition ratio of active observation terminals among the overlapping payment terminals during the payment matching period as the overlapping composition ratio. Payment matching period of active monitoring terminal: For a specific active monitoring terminal, its own payment matching period (i.e. its peak payment period).

[0091] Overlapping payment terminals: refers to other terminals (including actively monitored terminals and non-actively monitored terminals) that also use the same time period as the payment matching period during the same time period.

[0092] The proportion of actively monitored terminals among overlapping payment terminals (overlapping composition ratio): Within a certain payment matching period, the proportion of actively monitored terminals out of the total number of overlapping payment terminals in that period. This ratio reflects the concentration of other actively monitored terminals during that period.

[0093] For active monitoring terminals, if a large number of other active monitoring terminals are also operating at peak times during their payment matching period, then when a payment omission occurs on that terminal, these other active monitoring terminals may also exhibit similar anomalies, or their operational status can be used to assist in the judgment. A high proportion means that more "eyes" are observing simultaneously, making the risk of omission easier to detect; a low proportion means that the terminal is relatively isolated during this period, and once an omission occurs, it is difficult for data from other terminals to reflect it in a timely manner.

[0094] The overlap ratio is a key indicator for measuring the "observable reliability" of a terminal. A high ratio indicates that there is collective protection, while a low ratio indicates isolation and high risk.

[0095] Taking the active monitoring terminal T002 (cafeteria card reader) as an example, it has four payment matching time periods: 11:00-13:00, 17:00-19:00, 7:00-8:00, and 12:00-13:00. For each time period, the total number of overlapping payment terminals and the number of those belonging to the active monitoring terminal are counted, and the overlap ratio is calculated: 11:00-13:00: There are three overlapping payment terminals: T001, T004, and T007 (assuming that T001, T004, and T007 are all active monitoring terminals). Among them, all three are active monitoring terminals, and the composition ratio is 3 / 3 = 100%.

[0096] 17:00-19:00: There are two overlapping payment terminals, T001 and T005 (T001 is active, T005 is not active), and one active monitoring terminal, with a composition ratio of 50%.

[0097] 7:00-8:00: No overlap, proportion = 0%.

[0098] 12:00-13:00: There are three overlapping payment terminals: T001, T004, and T006 (T001 and T004 are active, T006 is not active). There are two active monitoring terminals, with a composition ratio of approximately 66.7%.

[0099] The above steps include the following: S321 Obtain the number of payment matching time periods of the active observation terminal, and determine whether the number of payment matching time periods of the active observation terminal is less than the preset matching time period number threshold. If yes, determine that the active observation management strategy of the active observation terminal belongs to the preset active observation strategy. If no, proceed to step S32. Preset threshold for the number of matching time periods: Same as the threshold in S21, used here to determine whether the regularity of the actively observed terminal is strong enough. If the number of matching time periods is too small, it means that the terminal has few peak periods, its payment behavior has weak regularity, and the reliability of the observation itself is not high. Therefore, a more conservative preset strategy should be adopted.

[0100] This step is a reconfirmation of the regularity of the actively observed terminals. Even if a terminal is included in the active observation, if the number of its matching time periods is small, it will not have a clear expectation of receiving payments in most time periods, making it difficult to judge anomalies by "no payment". Therefore, more frequent active observation is required (i.e., a preset strategy with a shorter preset duration).

[0101] Specific examples: For T002, the number of matching time periods is 4. A preset threshold for the number of matching time periods is set to 3. Since 4 ≥ 3, the condition is not met, and the process proceeds to step S322.

[0102] S322 determines whether there are payment matching periods where the overlap ratio of the active observation terminal is greater than a preset threshold based on the overlap ratio of the active observation terminal in different payment matching periods. If yes, proceed to step S33. If no, it is determined that the observation reliability of the overlapping payment terminals in the payment matching period of the active observation terminal is poor. Therefore, in order to ensure the reliability of the active observation processing in the payment matching period of the active observation terminal, it is determined that the active observation management strategy of the active observation terminal belongs to the preset active observation strategy.

[0103] Preset Consolidation Ratio Threshold: A pre-defined critical ratio value used to determine whether the concentration of active observation terminals within a certain time period is high enough. If the overlap ratio of a certain time period is higher than this threshold, it indicates that there are a large number of active observation terminals clustered together during that time period, which can form an observation cluster and improve the ability to detect payment omissions through mutual verification.

[0104] Poor observation reliability: If the overlap ratio of any time period exceeds the threshold, it indicates that there are few other actively observed terminals for this terminal across all matching time periods, resulting in scattered observation resources and a relatively isolated terminal. In this case, if a payment omission occurs, there are not enough other terminals to assist in detecting the anomaly, thus the risk of omission is high, requiring a more conservative preset strategy.

[0105] If an active monitoring terminal does not have enough other active monitoring terminals present during any given payment matching period, it cannot rely on the monitoring results of other terminals to detect anomalies in a timely manner when a payment omission occurs during that period (for example, other terminals may be operating normally but cannot reflect the problem with this terminal). Therefore, to ensure that the risk of omissions can be effectively controlled, such isolated terminals should employ more frequent preset strategies.

[0106] This step identifies those "isolated high-risk" active observation terminals and adopts a conservative strategy directly; while terminals "with group protection" have the opportunity to proceed to the next step, where a more lenient strategy can be considered.

[0107] For T002, the overlap ratios of its various time periods are: 100%, 50%, 0%, and 66.7%. A preset threshold for the overlap ratio is set to 60%. There are time periods (100% and 66.7%) that are greater than 60%, so the condition (there are time periods greater than the threshold) is met, and the process proceeds to step S33.

[0108] If another terminal T008 has an overlap ratio of less than 60% for all matching time periods, it is directly determined to be a preset active observation strategy.

[0109] S33 determines the active observation management strategy for the active observation terminal based on the active observation ratio and the overlap ratio in different active observation periods.

[0110] In the above steps, the payment matching time period with an overlap ratio greater than a preset ratio threshold is taken as the active observation reliable time period. The active observation reliability value is determined by using the average of the active observation ratio and the proportion of the active observation reliable time period in the payment matching time period of the active observation terminal. It is then determined whether the active observation reliability value is greater than the preset reliability threshold. If it is, the active observation management strategy of the active observation terminal is determined to belong to the target observation strategy. If not, the active observation management strategy of the active observation terminal is determined to belong to the preset active observation strategy.

[0111] It should be noted that the preset active observation strategy determines whether the active observation terminal is operating normally if there is no payment data within the most recent preset time period.

[0112] Reliable observation periods: These are the periods during which the overlap ratio is greater than a preset threshold for payment matching. In other words, during these periods, there are enough other active observation terminals that can refer to each other to form an observation cluster.

[0113] Percentage of Reliable Time Periods Observed Actively: The percentage of reliable time periods observed actively out of the total number of payment matching time periods for this terminal.

[0114] Active observation reliability value: A comprehensive indicator that combines the overall proportion of active observations in the park with the proportion of reliable observation periods for this terminal itself. It can be defined as: Active observation reliability value = (proportion of active observations) × (proportion of reliable observation periods). The larger this value, the more reliable the observation period for this terminal is, given the high level of attention paid to the park as a whole, allowing for a more lenient target observation strategy.

[0115] Preset reliability threshold: The critical value used to ultimately determine whether to adopt the target strategy.

[0116] Targeted Observation Strategy: A more lenient proactive observation strategy than the preset strategy. Specifically, proactive observation is initiated when no payment data is received within the nearest target duration, where the target duration is longer than the preset duration. This allows for longer periods of no payments before triggering observation, reducing unnecessary proactive observations.

[0117] When the overall proportion of active observations in the park is high (i.e., high attention level), if a certain terminal gathers other active observation terminals for multiple time periods, then the individual status can be inferred from the group behavior during these time periods. Therefore, the observation interval of that terminal can be appropriately relaxed (the target duration can be longer) to save resources. Conversely, if the proportion is low or the proportion of reliable time periods is low, then a shorter preset duration should still be used.

[0118] This step enables differentiated treatment of active observation terminals, allocating resources to terminals that truly require frequent observations, while reducing the observation frequency for terminals with group support.

[0119] For T002, the active observation percentage is 20%, which is 0.2. There are four payment matching periods, of which two are reliable active observation periods (composition percentage > 60%) (11:00-13:00 and 12:00-13:00). Therefore, the reliable period percentage is 2 / 4 = 0.5. The reliable value for active observation is 0.2 × 0.5 = 0.1. The preset reliability threshold is set to 0.15. 0.1 < 0.15, therefore it does not meet the threshold requirement, and the active observation management strategy for T002 still falls under the preset active observation strategy (observation is triggered if there is no payment within 5 minutes).

[0120] Assuming another terminal T007 has a reliable observation period ratio of 0.8 and an active observation percentage of 0.2, then the reliability value = 0.16 > 0.15. Therefore, T007 can adopt a target observation strategy (trigger observation if there is no payment within 10 minutes).

[0121] It is understood that the active observation processing refers to the control terminal issuing virtual transaction request processing data to determine whether the virtual transaction request can be processed in a timely manner.

[0122] Specifically, the target observation strategy is to determine whether the active observation terminal needs to perform active observation processing when there is no payment data within the most recent target duration, thereby determining whether it is operating normally, wherein the target duration is longer than a preset duration.

[0123] S3 performs active observation processing on different active observation terminals based on the active observation management strategy, obtains observation processing results, identifies abnormal data in different concurrent transaction volume ranges based on the observation processing results of the active observation terminals, and determines the analysis and processing method for payment data of terminals excluding active observation terminals based on the abnormal data in different active observation terminals.

[0124] It should be noted that the abnormal data processing includes missing payment order data from the active monitoring terminal, that is, missing payment orders due to high data processing pressure.

[0125] Accurate Identification of High-Risk Isolated Terminals: The core innovation of this method lies in identifying "low overlap ratio" as a high-risk signal. Terminals lacking other actively monitored terminals during peak hours are difficult to detect in a timely manner when payment omissions occur. Therefore, more frequent active monitoring is needed to ensure that anomalies are quickly captured.

[0126] To achieve differentiated allocation of observation resources: The observation interval is dynamically adjusted based on the observation cluster in which the terminal is located. For terminals with group support (high overlap ratio), the observation interval is appropriately relaxed to save resources; for isolated terminals, a short interval is maintained to ensure reliability.

[0127] Improve overall observation efficiency: By allocating resources to high-risk terminals and reducing unnecessary observations of low-risk terminals, the overall observation effect is optimized with limited computing and operational resources.

[0128] Providing support for proactive operation and maintenance in smart parks: The differentiated formulation of proactive observation and management strategies is the key to the transformation of parks from "passive response to faults" to "proactive prevention of anomalies". It helps to build a more intelligent and efficient operation and maintenance system and minimize economic losses and user experience degradation caused by payment omissions.

[0129] Furthermore, such as Figure 4 As shown, the method for determining the analysis and processing method of payment data from terminals excluding actively observed terminals is as follows: For terminals within the park not included in active monitoring (i.e., ordinary terminals), differentiated payment data analysis and processing strategies are developed to determine whether these terminals have also experienced payment order omissions. The core logic is that the omission status of actively monitored terminals reflects the overall payment processing pressure of the system. When omissions are severe (e.g., frequent occurrences, wide impact), ordinary terminals also face a high risk of omissions; therefore, lenient triggering conditions should be adopted (e.g., analysis is triggered upon the omission of any actively monitored terminal) to promptly identify potential problems. When omissions are not severe, further analysis of the reliability of omission terminals, the distribution of risk periods, and the peak characteristics of ordinary terminals themselves is needed to determine whether to adopt a lenient or strict strategy (e.g., triggering only when multiple actively monitored terminals are missed). Through this dynamic adjustment, precise monitoring of ordinary terminals is achieved, providing a basis for potentially upgrading them to actively monitored terminals in the future.

[0130] S41 Based on the abnormal processing data of the active observation terminal in different time periods, determine that there are active observation terminals with missing payment orders on different dates in the time period, and identify the active observation terminals with missing payment orders on the dates as missing terminals. In the above steps, the time period with missing terminals is taken as the missing time period. It is determined whether the average number of missing time periods in different dates is greater than the preset threshold for the number of missing time periods. If so, the analysis and processing method of the payment data of the terminals excluding the actively observed terminals is to perform the analysis and processing of the payment data of the terminals excluding the actively observed terminals as long as any actively observed terminal has a missing order, to determine whether there is a missing situation. If not, proceed to step S42.

[0131] Handling abnormal data: This includes missing payment order data from actively monitored terminals, meaning that some payment orders were missed due to high data processing pressure.

[0132] Missing Terminal: An active monitoring terminal that indicates a payment order was missed on a specific date and within a specific time period.

[0133] Missing time period: A time period (divided by hour or custom time period) during which at least one terminal is missing on a given day.

[0134] Average number of missed periods across different dates: This is the average number of missed periods each day during the observation period (e.g., the past 30 days). This indicator reflects the prevalence of omissions over time.

[0135] Preset threshold for the number of missed time periods: Used to determine whether omissions occur frequently throughout the day. If the average number of missed time periods exceeds the threshold, it indicates that omissions occur in multiple time periods each day, resulting in high system load and requiring more sensitive monitoring of ordinary terminals.

[0136] If payment omissions occur at multiple times each day, it indicates that the system as a whole is under high pressure. At this time, any omission by actively monitored terminals may indicate that ordinary terminals also have problems. Therefore, the most lenient triggering conditions need to be adopted (any omission triggers analysis) to ensure that no possible omissions are missed.

[0137] Over the past 30 days, the "Smart Industrial Park" has recorded the number of missed time periods each day from its active monitoring terminals. Statistics show an average of 4 missed time periods per day. A preset threshold of 3 missed time periods is set. Since 4 > 3, a lenient strategy is triggered: whenever any active monitoring terminal experiences a missed period, payment data analysis is performed on all ordinary terminals to check for further omissions. If the average number of missed time periods is ≤ 3, then proceed to step S42.

[0138] S42 uses the processing anomaly data of the missed terminals to determine the number of missed payment orders of the missed terminals, and uses the number of missed orders to determine the reliable terminals among the missed terminals. It should be noted that the reliable terminal is a terminal whose number of omissions in history is less than a preset omission threshold.

[0139] Number of omissions: refers to the total number of times a payment order was missed at a certain active monitoring terminal during the statistical period.

[0140] Preset omission count threshold: A critical value used to distinguish between "reliable terminals" and "unreliable terminals". Terminals with fewer than the threshold are considered reliable, and their occasional omissions may be due to accidental factors; conversely, terminals with more than the threshold are inherently unstable, and their omissions may be due to their own malfunctions.

[0141] Reliable terminals: Active observation terminals with fewer historical omissions are relatively stable in operation, and omissions are more likely to be systemic problems.

[0142] When omissions are not widespread across multiple time periods each day, it's necessary to distinguish the source of the omissions. Omissions from reliable terminals are more alarming because they are typically stable, and their occurrence may indicate high overall system stress; while omissions from unreliable terminals may be due to their own specific issues and do not represent a general risk.

[0143] During the 30-day statistical period, the actively monitored terminals T002 (cafeteria card reader) had 3 missed occurrences, T007 (convenience store) had 1 missed occurrence, and T008 (park bookstore) had 3 missed occurrences. Assuming T002 missed 2 times (less than 2 is considered reliable), we set the threshold to 2; then, any occurrence less than 2 is considered reliable. If T002 missed 1 time, T007 missed 2 times (equivalent to 3 missed occurrences is not considered reliable), and T008 missed 3 times, then the reliable terminal would be T002 (1 missed occurrence).

[0144] S43 determines the analysis and processing method for payment data of terminals excluding actively observed terminals based on the missing terminal data in different time periods and on different dates, as well as the reliable terminal data in the missing terminals.

[0145] Furthermore, based on the missing terminal data from different time periods and on different dates, as well as the reliable terminal data among the missing terminals, the analysis and processing methods for payment data from terminals excluding actively observed terminals are determined, specifically including: Using the missing terminal data for different dates within the specified time period, determine the average number of missing terminals for different dates within the specified time period. Determine whether the proportion of the average number of missing terminals for different dates within different time periods in all actively observed terminals is less than a preset threshold for the proportion of actively observed terminals. If so, the analysis and processing method for the payment data of terminals excluding actively observed terminals is as follows: If any actively observed terminal with a target number or more has a missing order within the most recent preset time period, the analysis and processing of the payment data of terminals excluding actively observed terminals is performed to determine whether there is a missing situation. If not, proceed to the next step. Average percentage of missed terminals: For each time period, calculate the average number of missed active observation terminals per day during that period, then divide by the total number of active observation terminals to obtain the percentage. If this percentage is below the threshold for all time periods, it indicates that the missed terminals are only sporadic and do not constitute a widespread risk. Therefore, more conclusive evidence (multiple missed terminals) is needed to trigger analysis.

[0146] When omissions are not widespread across different time periods, the omission of a single active observation terminal may be merely accidental and not worth the effort of checking all ordinary terminals. Therefore, a strict strategy is adopted, triggering only when multiple terminals simultaneously omit data, to avoid invalid analysis.

[0147] Assume there are 20 active observation terminals. Calculations show the average percentage of missed terminals in each time period is: 1% for time period A, 1.5% for time period B, and 0.5% for time period C, all less than 3%. Therefore, a strict strategy is adopted: only when at least two active observation terminals are missed within the most recent preset time period will the analysis of ordinary terminals be triggered.

[0148] The period in which the average number of missing terminals on different dates accounts for a proportion of all actively observed terminals that is not less than a preset active observation terminal proportion threshold is taken as the period of missing risk. It is determined whether the number of the missing risk period is greater than the preset risk period number threshold. If so, the method for analyzing and processing the payment data of terminals excluding the actively observed terminals is as follows: whenever any actively observed terminal has a missing order, the payment data of terminals excluding the actively observed terminals is analyzed and processed to determine whether there is a missing situation. If not, proceed to the next step. Omission risk periods: Those periods in which the average number of missed terminals reaches or exceeds the threshold, meaning that omissions are relatively concentrated during these periods.

[0149] Number of high-risk periods: How many such periods are there? If the number is high, it means that there are multiple periods of the day when omissions are frequent, the system is under great pressure, and more lenient monitoring is needed.

[0150] If there are multiple high-risk periods, it means that the omission problem is widely distributed over time. Even if the proportion of a single period is not extremely high, the superposition of multiple periods indicates that the overall pressure on the system is high, so a lenient strategy is needed.

[0151] Assuming that the average percentage of missed terminals in three time periods is 3.5%, 4%, and 3.2% respectively, all exceeding 3%, then the number of risk periods is 3, which is greater than the threshold of 2. Therefore, a lenient strategy is adopted: analysis is triggered if any actively observed terminal is missed.

[0152] The proportion of reliable terminals among the missing terminals is used to determine the proportion of reliable terminals. It is then determined whether the proportion of reliable terminals is less than a preset reliable terminal proportion threshold. If so, the analysis and processing method for the payment data of terminals excluding the actively observed terminals is as follows: if any actively observed terminal with a target number or more has a missing order within the most recent preset time period, the analysis and processing of the payment data of terminals excluding the actively observed terminals is performed to determine whether there is a missing situation. If not, proceed to the next step. Reliable terminal percentage: Among the active observation terminals that were missed, the proportion of reliable terminals out of the total number of missed terminals.

[0153] A high percentage of reliable terminals means that most omissions occur from stable terminals, which usually points to systemic problems (such as high overall processing pressure), thus requiring less stringent monitoring. A low percentage of reliable terminals means that most omissions occur from terminals that are inherently unstable, and the problem may be individual, thus requiring stricter conditions (proceed to the next step for further assessment).

[0154] Specific examples: If a total of 10 terminals are missed in a certain period, of which 6 are reliable terminals (60% > 50%), then a lenient strategy is adopted. If only 4 terminals are reliable (40% ≤ 50%), then proceed to S434.

[0155] The number of payment matching time periods for terminals excluding the actively monitored terminals is obtained. It is then determined whether the number of payment matching time periods for terminals excluding the actively monitored terminals is greater than a preset value for the number of matching time periods. If not, the analysis and processing method for terminals excluding the actively monitored terminals is as follows: if any actively monitored terminal with more than the target number of missed orders occurs within the most recent preset time period, the payment data of terminals excluding the actively monitored terminals is analyzed and processed to determine whether there is a missed order. If so, the analysis and processing method for payment data of terminals excluding the actively monitored terminals is as follows: if any actively monitored terminal has a missed order, the payment data of terminals excluding the actively monitored terminals is analyzed and processed to determine whether there is a missed order. This process determines the actual missed order situation and lays the foundation for quickly converting the terminals into actively monitored terminals. Preset value for the number of matching time periods: Used to determine the richness of peak business periods for a regular terminal. A higher number means that the terminal has high revenue collection in multiple time periods and is more susceptible to system pressure.

[0156] If a typical terminal experiences multiple peak payment periods, it is more susceptible to missed payments due to system pressure during these periods, thus requiring more sensitive monitoring (a lenient strategy). Conversely, if there are fewer peak periods, its impact from system pressure is less pronounced, allowing for a stricter strategy.

[0157] Ordinary terminal R001 has 4 payment matching time periods, which is greater than the preset value of 3. Therefore, a lenient strategy is adopted: analysis is triggered if any active observation terminal misses a period. Ordinary terminal R002 has only 1 matching time period, which is less than or equal to 3. A strict strategy is adopted: analysis is triggered only if more than 2 active observation terminals miss a period.

[0158] Furthermore, if the number of missed observations of the terminal does not meet the requirements, it will be updated to an active observation terminal.

[0159] Finally, when the number of omissions for a regular terminal no longer meets the requirements, it is updated to an active observation terminal. That is, if a regular terminal is found to frequently miss errors during the analysis process, for example, if the average number of omissions per day is more than 0.1, it can be included in the active observation list.

[0160] Furthermore, when the terminal misses a significant number of observations during the active monitoring process, such as more than 0.3 times, active maintenance is performed on the terminal and the payment system to improve the reliability of the payment processing.

[0161] Example 2 Secondly, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the aforementioned payment data analysis and processing method for a smart park when running the computer program.

[0162] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0163] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0164] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A method for analyzing and processing payment data in a smart park, characterized in that, Specifically, it includes: By utilizing the payment data of terminals in the smart park, the payment matching time period of the terminal is determined. Based on the correlation between the payment matching time period of the terminal and the payment matching time periods of other terminals, the active observation method of the terminal is determined. Based on the data from the active observation terminal, and combined with the analysis results of the payment data from different active observation terminals during the payment matching period of the active observation terminal, the active observation management strategy for the active observation terminal is determined. Based on the active observation management strategy, active observation processing is performed on different active observation terminals to obtain observation processing results. Based on the observation processing results of the active observation terminals, abnormal data in different time periods is identified. Based on the abnormal data in different time periods from different active observation terminals, a method for analyzing and processing payment data of terminals excluding active observation terminals is determined.

2. The payment data analysis and processing method for smart parks as described in claim 1, characterized in that, The payment data of the terminals in the smart park includes the number of payments received by the terminals in different time periods.

3. The payment data analysis and processing method for smart parks as described in claim 1, characterized in that, The method for determining the payment matching period of the terminal is as follows: Based on the payment data of the terminal, determine the number of payments received by the terminal in different time periods; Using the number of payments received, the terminal is determined to have busy payment days within the specified time period; Based on the busy payment date data, determine whether the time period is a payment matching time period for the terminal.

4. The payment data analysis and processing method for smart parks as described in claim 3, characterized in that, The busy collection date is defined as a date on which the number of collections is greater than the average number of collections in other time periods.

5. The payment data analysis and processing method for smart parks as described in claim 3, characterized in that, Based on the busy payment date data, determining whether the time period is a payment matching time period for the terminal specifically includes: The busyness coefficient of the time period is determined based on the proportion of busy days for receiving payments within that time period. If the busy coefficient of the time period is greater than a preset busy coefficient threshold, then the time period is determined to be the payment matching time period of the terminal; otherwise, the time period is determined not to be the payment matching time period of the terminal.

6. The payment data analysis and processing method for smart parks as described in claim 1, characterized in that, The method for determining the active observation method of the terminal is as follows: Based on the payment matching time period data of the terminal, determine the number of payment matching time periods of the terminal; By utilizing the correlation between the payment matching time period of the terminal and the payment matching time periods of other terminals, other terminals belonging to the payment matching time period of the terminal are identified, and these other terminals belonging to the payment matching time period of the terminal are designated as overlapping payment terminals in the payment matching time period. The active observation method for the terminal is determined by using the number of payment matching time periods of the terminal and the overlapping payment terminal data in different payment matching time periods.

7. The payment data analysis and processing method for smart parks as described in claim 6, characterized in that, The number of payment matching time periods of the terminal is obtained. If the number of payment matching time periods of the terminal is less than a preset threshold for the number of matching time periods, the active observation method of the terminal is determined to be an active observation terminal.

8. The payment data analysis and processing method for smart parks as described in claim 1, characterized in that, The active observation terminal data includes the number of active observation terminals.

9. The payment data analysis and processing method for smart parks as described in claim 1, characterized in that, During the payment matching period of the active observation terminal, the parsing results of the payment data of different active observation terminals are determined based on the composition data of the active observation terminals among the overlapping payment terminals in the payment matching period.

10. A computer system, comprising: A memory and processor connected by communication, and a computer program stored in the memory and capable of running on the processor, characterized in that, when the processor runs the computer program, it executes a payment data analysis and processing method for a smart park as described in any one of claims 1-9.

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