A cash register data monitoring and management system based on data analysis

Abstract

The present invention relates to the technical field of cash register data monitoring, and specifically discloses a cash register data monitoring and management system based on data analysis, which specifically includes: a cash register data module: obtaining a cash register transaction time value, making a judgment, and obtaining an abnormal transaction. The cash register transaction record time value is obtained, and based on the merchant's normal business hours, it is judged whether the transaction record time value is within the normal business hours. If not, the transaction is marked as an abnormal transaction, so that the abnormal transaction in the cash register record can be obtained in a timely manner. Based on the obtained abnormal transactions, the abnormal time representation value and the abnormal amount representation value of the abnormal transaction are calculated. The abnormal time representation value and the abnormal amount representation value are substituted into a formula to calculate a risk judgment value, and the abnormal transaction risk level of each analysis period during non-business hours is judged, and a low-risk signal or a high-risk signal is generated. Therefore, based on the abnormal transaction risk level, a timely response can be made to reduce the potential dangers caused by abnormal transactions.

Description

Technical Field

[0001] The present invention relates to the technical field of cashier data monitoring, and in particular to a cashier data monitoring and management system based on data analysis. Background Art

[0002] In the modern business environment, cash register data is the core record of the transaction process. Its accuracy and security are directly related to the business efficiency of merchants and the protection of consumers' rights and interests.

[0003] However, with the continuous increase in transaction volume, many potential risks are hidden in the cash register data, such as abnormal transactions and fraudulent behaviors, which pose a serious threat to the financial security and business stability of merchants. Most existing cash register data monitoring systems can only identify simple abnormal transactions, such as excessive transaction amounts, abnormal transaction times, etc., but lack analysis of more complex abnormal transaction patterns, such as frequent small transactions, abnormal transaction time intervals, etc., and some monitoring and management systems lack the ability to deeply analyze and process abnormal accounts, and cannot effectively identify potential fraudulent behaviors. Summary of the Invention

[0004] The purpose of the present invention is to provide a cash register data monitoring and management system based on data analysis to solve the technical problems in the above background.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] The present invention provides a cashier data monitoring and management system based on data analysis, which specifically includes:

[0007] Cashier data module: obtains the cashier transaction time value, makes a judgment, obtains abnormal transactions, and processes them to obtain abnormal data of abnormal transactions; wherein, the abnormal data includes the abnormal time representation value and the abnormal amount representation value of the abnormal transaction; risk judgment value calculation module: calculates the risk judgment value based on the abnormal data of abnormal transactions in each analysis period; abnormal risk degree judgment module: compares the risk judgment value with the risk judgment threshold, and generates a low-risk signal if the risk judgment value is less than or equal to the risk judgment threshold; abnormal judgment data acquisition module: obtains the target analysis account and the abnormal judgment data of the target analysis account based on the generated low-risk signal; wherein, the abnormal judgment data includes the proportion of the number of abnormal transactions and the total value of the abnormal transaction amount deviation ratio; target analysis account abnormal judgment module: calculates the abnormal evaluation value based on the abnormal judgment data of each target analysis account, compares the abnormal evaluation value with the abnormal evaluation threshold, and generates an account abnormality signal if the abnormal evaluation value is greater than the abnormal evaluation threshold; abnormal account risk judgment module: obtains the abnormal account based on the generated account abnormality signal, obtains the historical transaction record data of the abnormal account, and judges the risk level of the abnormal account.

[0008] As a further solution of the present invention: the process of obtaining the abnormal transaction is:

[0009] Get the merchant's normal business hours, where the merchant's daily normal business hours are from the daily business start time to the daily business end time;

[0010] Obtain the daily cash register transaction record time value and compare the cash register transaction record time value with the normal business hours;

[0011] If the cash register transaction record time value is not within the normal business hours, it means that the cash register transaction record is not within the normal business hours, and a transaction time abnormality signal is generated;

[0012] Obtain the transaction record time value corresponding to the generated transaction time abnormality signal, mark it as the transaction abnormality time value, and mark the transaction as an abnormal transaction.

[0013] As a further solution of the present invention: the process of obtaining the risk judgment value is:

[0014] Obtain the abnormal time characterization value and abnormal amount characterization value for each analysis period;

[0015] Substitute into the formula , the risk judgment value FX is calculated, where JE represents the abnormal amount representation value, SJ represents the abnormal time representation value, and a1 and a2 are preset proportional coefficients.

[0016] As a further solution of the present invention: the process of obtaining the abnormal time characterization value is as follows:

[0017] During the analysis period, the difference between two adjacent abnormal transaction time values ​​is calculated to obtain the time interval value, the time interval value is compared with the analysis period to obtain the time interval value ratio, and all time interval value ratios are summed and averaged to obtain the mean time interval value ratio;

[0018] During the analysis period, the number of abnormal transactions is obtained, and the reciprocal of the number of abnormal transactions is taken to obtain the abnormal transaction frequency value;

[0019] The abnormal time representation value is obtained by multiplying the mean of the time interval value ratio with the abnormal transaction frequency value.

[0020] As a further solution of the present invention: the process of obtaining the abnormal amount characterization value is as follows:

[0021] During the analysis period, the value of each abnormal transaction amount is obtained, and the ratio of the abnormal transaction amount value to the abnormal transaction amount threshold is calculated to obtain the abnormal transaction amount ratio. All abnormal transaction amount ratios are summed up to obtain the total value of the abnormal transaction amount ratios, which is marked as the abnormal amount representation value.

[0022] As a further solution of the present invention: the process of obtaining the target analysis account is as follows:

[0023] Based on the generated low-risk signal, obtaining an analysis period corresponding to the generation of the low-risk signal and marking it as an abnormal period;

[0024] Obtain all abnormal transactions within the abnormal period, mark them as transactions to be analyzed, and obtain the number of transactions to be analyzed;

[0025] Obtain the source account of funds corresponding to the transaction to be analyzed, mark it as an abnormal account, extract different abnormal accounts, and mark them as target analysis accounts.

[0026] As a further solution of the present invention: the process of obtaining the abnormal evaluation value is:

[0027] Obtain the percentage of abnormal transactions and the total abnormal transaction amount deviation ratio for each target analysis account;

[0028] Substitute into the formula , calculate the abnormal assessment value PG, where GS represents the percentage of abnormal transactions, PC represents the total value of the abnormal transaction amount deviation ratio, and s1 and s2 are preset proportional coefficients.

[0029] As a further solution of the present invention: the process of obtaining the abnormal transaction number ratio is as follows:

[0030] Obtain abnormal transactions corresponding to the target analysis account, mark them as target analysis transactions, obtain the number of target analysis transactions, and compare the number of target analysis transactions with the number of transactions to be analyzed to obtain the abnormal transaction ratio.

[0031] As a further solution of the present invention: the process of obtaining the total value of the abnormal transaction amount deviation ratio is as follows:

[0032] Obtain several transaction amount values ​​in the historical data, sum and average all the transaction amount values ​​to obtain the average historical transaction amount;

[0033] Obtain the abnormal transaction amount value of each target analysis transaction, calculate the difference between the abnormal transaction amount value and the average historical transaction amount, take the absolute value of the difference to obtain the abnormal transaction amount deviation value, perform ratio processing on the abnormal transaction amount deviation value and the average historical transaction amount to obtain the abnormal transaction amount deviation ratio, and sum up all abnormal transaction amount deviation ratios to obtain the total abnormal transaction amount deviation ratio value.

[0034] As a further solution of the present invention: the risk level of the abnormal account is determined as follows:

[0035] Obtaining the abnormal account corresponding to the generation of the account abnormality signal, and obtaining historical transaction record data of the abnormal account, wherein the historical transaction record data includes historical transaction amount values;

[0036] Sum and average the historical transaction amounts to obtain the historical transaction amount mean, calculate the difference between the historical transaction amount and the historical transaction amount mean, take the absolute value of the difference to obtain the historical transaction amount deviation, and compare the historical transaction amount deviation with the historical transaction amount mean to obtain the historical transaction amount deviation ratio;

[0037] Compare all historical transaction amount deviation ratios with the historical transaction amount deviation ratio threshold respectively;

[0038] If the historical transaction amount deviation ratio is greater than the historical transaction amount deviation ratio threshold, a large deviation signal is generated;

[0039] Obtain the number of generated large deviation signals, and then compare the number of generated large deviation signals with the total number of historical transactions to obtain the percentage of large deviation signals.

[0040] Compare the proportion of large deviations with the threshold of the proportion of large deviations;

[0041] If the proportion of large deviations is less than or equal to the threshold, a low-risk signal is generated for the account;

[0042] If the proportion of large deviations is greater than the threshold of the proportion of large deviations, a high-risk signal for the account is generated.

[0043] Beneficial effects of the present invention:

[0044] (1) The present invention obtains the time value of the cash register transaction record and determines whether the time value of the transaction record is within the normal business time period based on the merchant's normal business time period. If not, the transaction is marked as an abnormal transaction, thereby timely obtaining abnormal transactions in the cash register record. Based on the obtained abnormal transactions, the abnormal time representation value and the abnormal amount representation value of the abnormal transaction are calculated. The abnormal time representation value and the abnormal amount representation value are substituted into the formula to calculate the risk judgment value, and the abnormal transaction risk level of each analysis period during non-business hours is judged to generate a low risk signal or a high risk signal. Therefore, timely response can be made based on the risk level of the abnormal transaction to reduce the potential danger caused by the abnormal transaction;

[0045] (2) The present invention obtains the target analysis account and the abnormal judgment data of the target analysis account, namely, the percentage of the number of abnormal transactions and the total value of the deviation ratio of the amount of abnormal transactions, when generating a low-risk signal, and calculates the abnormal assessment value based on the percentage of the number of abnormal transactions and the total value of the deviation ratio of the amount of abnormal transactions. By comparing the abnormal assessment value with the abnormal assessment threshold, it is evaluated whether the target analysis account is an abnormal account, so that when the risk is low, the source account of abnormal transaction funds can be further analyzed, and then the abnormal account can be discovered and handled in time, thereby reducing the losses caused by fraud and illegal activities, improving transaction security and accuracy, and increasing the ability to identify potential unsafe transactions of accounts. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The present invention will be further described below with reference to the accompanying drawings.

[0047] Figure 1 This is a flow chart of a cash register data monitoring and management system based on data analysis of the present invention;

[0048] Figure 2 The present invention is a flowchart of obtaining risk judgment values ​​in a cashier data monitoring and management system based on data analysis. DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making any creative efforts shall fall within the scope of protection of the present invention.

[0050] Example 1:

[0051] See also Figure 1 and Figure 2 As shown, cashier transaction records occurring during non-normal business hours often have a certain impact on financial security. Therefore, it is important to perform abnormal monitoring and abnormal analysis on abnormal transactions corresponding to the time values ​​of cashier transaction records. The cashier data monitoring and management system based on data analysis described in the embodiment of the present invention specifically includes:

[0052] Cashier data module: obtains the cashier transaction time value, makes judgments, obtains abnormal transactions, processes them, and obtains abnormal data of abnormal transactions;

[0053] In some embodiments, the process of obtaining abnormal transactions is as follows:

[0054] Get the merchant's normal business hours, where the merchant's daily normal business hours are from the daily business start time to the daily business end time;

[0055] Obtain the daily cash register transaction record time value and compare the cash register transaction record time value with the normal business hours;

[0056] If the cash register transaction record time value is within the normal business hours, it means that the cash register transaction record is within the normal business hours, and a normal transaction time signal is generated;

[0057] If the cash register transaction record time value is not within the normal business hours, it means that the cash register transaction record is not within the normal business hours, and a transaction time abnormality signal is generated;

[0058] Obtain the transaction record time value corresponding to the generated transaction time abnormality signal, mark it as the transaction abnormality time value, and mark the transaction as an abnormal transaction;

[0059] The process of obtaining abnormal data of abnormal transactions is as follows:

[0060] During non-business hours, preset analysis periods and obtain abnormal data of abnormal transactions within each analysis period;

[0061] The abnormal data includes abnormal time representation value and abnormal amount representation value of abnormal transaction;

[0062] It should be noted that non-business hours are the time periods outside the normal business hours of each day. For example, if the normal business hours of a merchant are 8:00-22:00, then the non-business hours are from 22:00 on the current day to 8:00 the next day;

[0063] For example, the process of obtaining the abnormal time characterization value is as follows:

[0064] During the analysis period, the difference between two adjacent abnormal transaction time values ​​is calculated to obtain the time interval value, the time interval value is compared with the analysis period to obtain the time interval value ratio, and all time interval value ratios are summed and averaged to obtain the mean time interval value ratio;

[0065] During the analysis period, the number of abnormal transactions is obtained, and the reciprocal of the number of abnormal transactions is taken to obtain the abnormal transaction frequency value;

[0066] Multiply the mean of the time interval ratio by the abnormal transaction frequency to obtain the abnormal time representation value;

[0067] It should be explained that the meaning of the abnormal time characterization value is as follows: the abnormal time characterization value is composed of the mean of the time interval value ratio and the abnormal transaction frequency value. That is, the smaller the mean of the time interval value ratio, the smaller the interval between abnormal transactions during the analysis period, the smaller the abnormal time characterization value, and the higher the transaction risk. That is, the smaller the abnormal transaction frequency value, the more abnormal transactions occurred during the analysis period, the smaller the abnormal time characterization value, and the higher the transaction risk.

[0068] Exemplarily, the process of obtaining the abnormal amount representation value is as follows:

[0069] During the analysis period, obtain the value of each abnormal transaction amount, calculate the ratio of the abnormal transaction amount value to the abnormal transaction amount threshold, and sum all abnormal transaction amount ratios to obtain the total abnormal transaction amount ratio value, which is marked as the abnormal amount representation value;

[0070] The abnormal transaction amount threshold is used to determine whether the abnormal transaction amount is a large or small transaction, and is set by those skilled in the art based on experience.

[0071] It should be explained that the meaning of the abnormal amount representation value is: the abnormal amount representation value is the ratio of the abnormal transaction amount to the total value. The larger the abnormal transaction amount to the total value, the larger the total abnormal transaction amount is and the higher the transaction risk level.

[0072] It should be noted that the above-mentioned abnormal time representation value and abnormal amount representation value are calculated under the condition that abnormal transactions occur during the analysis period;

[0073] Risk judgment value calculation module: calculates the risk judgment value based on the abnormal data of abnormal transactions in each analysis period;

[0074] In some embodiments, abnormal transaction data of each analysis period is obtained, where the abnormal transaction data includes abnormal time characterization values ​​and abnormal amount characterization values;

[0075] Substitute into the formula , calculate the risk judgment value FX, where JE represents the abnormal amount representation value, SJ represents the abnormal time representation value, and a1 and a2 are preset proportional coefficients;

[0076] Abnormal risk level judgment module: compares the risk judgment value with the risk judgment threshold and generates a low-risk signal or a high-risk signal;

[0077] In some embodiments, a risk judgment value is obtained and compared with a risk judgment threshold value, wherein the risk judgment threshold value is set by a person skilled in the art based on a summary of multiple historical experimental data;

[0078] If the risk judgment value is less than or equal to the risk judgment threshold, it means that the risk level of abnormal transactions during the analysis period is low, and a low-risk signal is generated;

[0079] If the risk judgment value is greater than the risk judgment threshold, it means that the risk level of abnormal transactions during the analysis period is high, and a high-risk signal is generated;

[0080] Based on the high-risk signals generated above, the system immediately stops trading;

[0081] The technical solution of the embodiment of the present invention is mainly as follows: by obtaining the time value of the cash register transaction record, based on the merchant's normal business time period, it is judged whether the transaction record time value is within the normal business time period; if not, the transaction is marked as an abnormal transaction, so that the abnormal transactions in the cash register record can be obtained in time, based on the obtained abnormal transactions, the abnormal time representation value and the abnormal amount representation value of the abnormal transaction are calculated, and the abnormal time representation value and the abnormal amount representation value are substituted into the formula to calculate the risk judgment value, and the abnormal transaction risk level of each analysis period during non-business hours is judged, and a low-risk signal or a high-risk signal is generated, so that timely response can be made based on the risk level of the abnormal transaction to reduce the potential dangers caused by abnormal transactions.

[0082] Example 2:

[0083] Based on Example 1, the cashier data monitoring and management system based on data analysis described in this embodiment of the present invention further includes:

[0084] Abnormal judgment data acquisition module: Based on the generated low-risk signals, it obtains the abnormal judgment data of the target analysis account and the target analysis account;

[0085] In some embodiments, based on the generated low-risk signal, obtaining an analysis period corresponding to the generation of the low-risk signal and marking it as an abnormal period;

[0086] Obtain all abnormal transactions within the abnormal period, mark them as transactions to be analyzed, and obtain the number of transactions to be analyzed;

[0087] Obtain the source account of funds corresponding to the transaction to be analyzed, mark it as an abnormal account, extract different abnormal accounts, and mark them as target analysis accounts;

[0088] It should be noted that different abnormal accounts refer to: different fund source accounts that appeared during the abnormal period;

[0089] Obtain abnormality judgment data for each target analysis account, where the abnormality judgment data includes the percentage of abnormal transactions and the total value of the abnormal transaction amount deviation ratio;

[0090] For example, the process of obtaining the percentage of abnormal transactions is as follows:

[0091] Obtain abnormal transactions corresponding to the target analysis account, mark them as target analysis transactions, obtain the number of target analysis transactions, and compare the number of target analysis transactions with the number of transactions to be analyzed to obtain the abnormal transaction ratio.

[0092] For example, the process of obtaining the total value of the abnormal transaction amount deviation ratio is as follows:

[0093] Obtain several transaction amount values ​​in the historical data, sum and average all the transaction amount values ​​to obtain the average historical transaction amount;

[0094] Obtain the abnormal transaction amount value of each target transaction for analysis, calculate the difference between the abnormal transaction amount value and the average historical transaction amount, take the absolute value of the difference to obtain the abnormal transaction amount deviation value, perform ratio processing on the abnormal transaction amount deviation value and the average historical transaction amount to obtain the abnormal transaction amount deviation ratio, and sum all abnormal transaction amount deviation ratios to obtain the total abnormal transaction amount deviation ratio value;

[0095] Target analysis account anomaly judgment module: Calculates anomaly evaluation values ​​based on the anomaly judgment data of each target analysis account to assess whether the target analysis account is an abnormal account;

[0096] In some embodiments, abnormality judgment data of each target analysis account is obtained, and the abnormality judgment data includes a percentage of abnormal transactions and a total value of the abnormal transaction amount deviation ratio;

[0097] Substitute into the formula , calculate the abnormal assessment value PG, where GS represents the percentage of abnormal transactions, PC represents the total deviation ratio of abnormal transaction amounts, and s1 and s2 are preset proportional coefficients;

[0098] It should be explained that the meaning of the abnormality assessment value is as follows: the abnormality assessment value is calculated by the percentage of abnormal transactions and the total value of the abnormal transaction amount deviation ratio. That is, the larger the percentage of abnormal transactions, the larger the abnormality assessment value, and the more abnormal transactions the target analysis account conducted during the analysis period. The more frequent the abnormal transactions, the more abnormal the target analysis account. That is, the larger the total value of the abnormal transaction amount deviation ratio, the larger the abnormality assessment value, and the greater the deviation between the abnormal transaction amount of the target analysis account during the analysis period and the average historical transaction amount. The existence of abnormal transaction amount is a sign of the target analysis account being more abnormal.

[0099] Compare the abnormality assessment value with the abnormality assessment threshold, wherein the abnormality assessment threshold is set by a person skilled in the art based on a summary of multiple historical experimental data;

[0100] If the abnormality assessment value is less than or equal to the abnormality assessment threshold, it means that the target analysis account is a normal account and the corresponding transaction is a reasonable transaction, generating a normal account signal;

[0101] If the abnormality assessment value is greater than the abnormality assessment threshold, it means that the target analysis account is an abnormal account, the corresponding transaction is an unreasonable transaction, and an account abnormality signal is generated;

[0102] Abnormal account risk judgment module: Based on the generated account abnormality signals, obtain abnormal accounts, obtain historical transaction record data of abnormal accounts, and judge the risk level of abnormal accounts;

[0103] In some embodiments, obtaining the abnormal account corresponding to the generation of the account abnormality signal, and obtaining historical transaction record data of the abnormal account, wherein the historical transaction record data includes historical transaction amount values;

[0104] Sum and average the historical transaction amounts to obtain the historical transaction amount mean, calculate the difference between the historical transaction amount and the historical transaction amount mean, take the absolute value of the difference to obtain the historical transaction amount deviation, and compare the historical transaction amount deviation with the historical transaction amount mean to obtain the historical transaction amount deviation ratio;

[0105] Compare all historical transaction amount deviation ratios with the historical transaction amount deviation ratio threshold, where the historical transaction amount deviation ratio threshold is set by those skilled in the art based on a summary of multiple historical experimental data;

[0106] If the historical transaction amount deviation ratio is less than or equal to the historical transaction amount deviation ratio threshold, a small deviation signal is generated;

[0107] If the historical transaction amount deviation ratio is greater than the historical transaction amount deviation ratio threshold, a large deviation signal is generated;

[0108] Get the number of generated large deviation signals, and then compare the number of generated large deviation signals with the total number of historical transactions to obtain the percentage of large deviation signals.

[0109] Compare the percentage of large deviations with the threshold of the percentage of large deviations, where the threshold of the percentage of large deviations is set by a person skilled in the art based on a summary of multiple historical experimental data;

[0110] If the percentage of large deviations is less than or equal to the threshold, it means that although the abnormal account has some large deviation transactions, it is still within the normal range overall, and a low-risk signal is generated for the account.

[0111] Based on the low-risk account signal generated, the system records and continues to monitor the account;

[0112] If the percentage of large deviations exceeds the threshold, it indicates that the abnormal account has a large number of transactions that deviate significantly from the historical transaction amount average, generating a high-risk signal for the account.

[0113] Based on the generated high-risk account signals, take appropriate measures immediately, including but not limited to: freezing the account, notifying the merchant or relevant agencies for further investigation.

[0114] The technical solution of the embodiment of the present invention is mainly as follows: when generating a low-risk signal, by obtaining the target analysis account and the abnormal judgment data of the target analysis account, that is, the proportion of the number of abnormal transactions and the total value of the abnormal transaction amount deviation ratio, and calculating the abnormal assessment value based on the proportion of the number of abnormal transactions and the total value of the abnormal transaction amount deviation ratio, by comparing the abnormal assessment value with the abnormal assessment threshold, it is evaluated whether the target analysis account is an abnormal account, so that when the risk is low, the source account of the abnormal transaction funds can be further analyzed, and then whether the abnormal account is a high-risk account can be discovered and judged in time, thereby reducing losses caused by fraud and illegal activities, improving transaction security and accuracy, and increasing the ability to identify potentially unsafe transactions of accounts.

[0115] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A cash register data monitoring and management system based on data analysis, characterized in that: Specifically include: Cashier data module: obtains the cashier transaction time value, makes judgments, obtains abnormal transactions, processes them, and obtains abnormal data of abnormal transactions; The abnormal data includes abnormal time representation value and abnormal amount representation value of abnormal transaction; Risk judgment value calculation module: calculates the risk judgment value based on the abnormal data of abnormal transactions in each analysis period; Abnormal risk level judgment module: compares the risk judgment value with the risk judgment threshold. If the risk judgment value is less than or equal to the risk judgment threshold, a low risk signal is generated. Abnormal judgment data acquisition module: Based on the generated low-risk signals, it obtains the abnormal judgment data of the target analysis account and the target analysis account; Among them, the abnormal judgment data includes the percentage of abnormal transactions and the total value of the abnormal transaction amount deviation ratio; Target analysis account anomaly judgment module: Calculates an anomaly assessment value based on the anomaly judgment data of each target analysis account, compares the anomaly assessment value with the anomaly assessment threshold, and generates an account anomaly signal if the anomaly assessment value is greater than the anomaly assessment threshold; Abnormal account risk judgment module: Based on the generated account abnormality signals, obtain abnormal accounts, obtain historical transaction record data of abnormal accounts, and judge the risk level of abnormal accounts; The process of obtaining the target analysis account is as follows: Based on the generated low-risk signal, obtaining an analysis period corresponding to the generation of the low-risk signal and marking it as an abnormal period; Obtain all abnormal transactions within the abnormal period, mark them as transactions to be analyzed, and obtain the number of transactions to be analyzed; Obtain the source account of funds corresponding to the transaction to be analyzed, mark it as an abnormal account, extract different abnormal accounts, and mark them as target analysis accounts; The process of obtaining the abnormal evaluation value is as follows: Obtain the percentage of abnormal transactions and the total abnormal transaction amount deviation ratio for each target analysis account; Substitute into the formula , calculate the abnormal assessment value PG, where GS represents the percentage of abnormal transactions, PC represents the total deviation ratio of abnormal transaction amounts, and s1 and s2 are preset proportional coefficients; Compare the proportion of large deviations with the threshold of the proportion of large deviations; If the proportion of large deviations is less than or equal to the threshold, a low-risk signal is generated for the account; If the proportion of large deviations is greater than the threshold of the proportion of large deviations, a high-risk signal for the account is generated.

2. A cash register data monitoring and management system based on data analysis according to claim 1, characterized in that: The acquisition process of the abnormal transaction is as follows: Get the merchant's normal business hours, where the merchant's daily normal business hours are from the daily business start time to the daily business end time; Obtain the daily cash register transaction record time value and compare the cash register transaction record time value with the normal business hours; If the cash register transaction record time value is not within the normal business hours, it means that the cash register transaction record is not within the normal business hours, and a transaction time abnormality signal is generated; Obtain the transaction record time value corresponding to the generated transaction time abnormality signal, mark it as the transaction abnormality time value, and mark the transaction as an abnormal transaction.

3. The cashier data monitoring and management system based on data analysis according to claim 1, characterized in that: The process of obtaining the risk judgment value is as follows: Obtain the abnormal time characterization value and abnormal amount characterization value for each analysis period; Substitute into the formula , the risk judgment value FX is calculated, where JE represents the abnormal amount representation value, SJ represents the abnormal time representation value, and a1 and a2 are preset proportional coefficients.

4. A cash register data monitoring and management system based on data analysis according to claim 1, characterized in that: The process of obtaining the abnormal time characterization value is as follows: During the analysis period, the difference between two adjacent abnormal transaction time values ​​is calculated to obtain the time interval value, the time interval value is compared with the analysis period to obtain the time interval value ratio, and all time interval value ratios are summed and averaged to obtain the mean time interval value ratio; During the analysis period, obtain the number of abnormal transactions, take the inverse of the number of abnormal transactions, and obtain the abnormal transaction frequency value; The abnormal time representation value is obtained by multiplying the mean of the time interval value ratio with the abnormal transaction frequency value.

5. The cash register data monitoring and management system based on data analysis according to claim 1, characterized in that: The process of obtaining the abnormal amount characterization value is as follows: During the analysis period, the value of each abnormal transaction amount is obtained, and the ratio of the abnormal transaction amount value to the abnormal transaction amount threshold is calculated to obtain the abnormal transaction amount ratio. All abnormal transaction amount ratios are summed up to obtain the total value of the abnormal transaction amount ratios, which is marked as the abnormal amount representation value.

6. The cash register data monitoring and management system based on data analysis according to claim 1, characterized in that: The process of obtaining the percentage of abnormal transactions is as follows: Obtain abnormal transactions corresponding to the target analysis account, mark them as target analysis transactions, obtain the number of target analysis transactions, and compare the number of target analysis transactions with the number of transactions to be analyzed to obtain the abnormal transaction ratio.

7. The cash register data monitoring and management system based on data analysis according to claim 1, characterized in that: The process of obtaining the total value of the abnormal transaction amount deviation ratio is as follows: Obtain several transaction amount values ​​in the historical data, sum and average all the transaction amount values ​​to obtain the average historical transaction amount; Obtain the abnormal transaction amount value of each target analysis transaction, calculate the difference between the abnormal transaction amount value and the average historical transaction amount, take the absolute value of the difference to obtain the abnormal transaction amount deviation value, perform ratio processing on the abnormal transaction amount deviation value and the average historical transaction amount to obtain the abnormal transaction amount deviation ratio, and sum up all abnormal transaction amount deviation ratios to obtain the total abnormal transaction amount deviation ratio value.

8. The cash register data monitoring and management system based on data analysis according to claim 1, characterized in that: The process of obtaining the proportion of large deviations is as follows: Obtaining the abnormal account corresponding to the generation of the account abnormality signal, and obtaining historical transaction record data of the abnormal account, wherein the historical transaction record data includes historical transaction amount values; Sum and average the historical transaction amounts to obtain the historical transaction amount mean, calculate the difference between the historical transaction amount and the historical transaction amount mean, take the absolute value of the difference to obtain the historical transaction amount deviation, and compare the historical transaction amount deviation with the historical transaction amount mean to obtain the historical transaction amount deviation ratio; Compare all historical transaction amount deviation ratios with the historical transaction amount deviation ratio threshold respectively; If the historical transaction amount deviation ratio is greater than the historical transaction amount deviation ratio threshold, a large deviation signal is generated; Obtain the number of generated large deviation signals, and then perform a ratio processing on the number of generated large deviation signals and the total number of historical transactions to obtain the proportion of large deviation signals.

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