A third-party-based real-time transaction monitoring method and system

Abstract

This application relates to the field of transaction monitoring technology, and in particular to a third-party-based real-time transaction monitoring method and system. The method includes: the third-party system acquiring a transaction account; if a risky account exists within the transaction account, acquiring the risk value of the risky account; verifying the risk of the risky account based on the risk value; if the risk verification passes, acquiring current transaction information and historical transaction records of the payment account; acquiring historical transaction characteristics based on the historical transaction records; determining whether the current transaction information matches the historical transaction characteristics; if the current transaction information does not match the historical transaction characteristics, generating an alarm message; if the current transaction information matches the historical transaction characteristics, the third-party system allows the transaction account to continue the current transaction. This application helps to strengthen the effective monitoring of irrational consumption by individuals with no or limited capacity for self-determination.

Description

Technical Field

[0001] This application relates to the field of transaction monitoring technology, and in particular to a real-time transaction monitoring method and system based on a third party. Background Technology

[0002] With economic development, people's consumption is increasing, especially in the current internet age, where online shopping and online payment have made transactions more convenient.

[0003] When making online transactions, the most critical issue is security. Therefore, it is necessary to monitor bank account transactions in real time to promptly detect and restrict illegal transactions between bank accounts and other accounts, thereby preventing security issues such as the theft of users' bank accounts and bank account information.

[0004] However, there are frequent instances of individuals with limited or no capacity for independent action engaging in irrational consumption, resulting in economic losses for individuals and families. For example, minors may use their parents' bank accounts to make large purchases of virtual currency or engage in other irrational spending without their parents' knowledge. However, current transaction monitoring methods primarily focus on the security and legality of transactions, restricting transactions only when security issues exist. The aforementioned irrational consumption, however, falls under the category of legal transactions and is therefore not prohibited. Thus, existing transaction monitoring methods are ineffective in effectively monitoring irrational consumption by individuals with limited or no capacity for independent action. Summary of the Invention

[0005] To help strengthen the effective monitoring of irrational consumption by persons without autonomous capacity or with limited capacity, this application provides a real-time transaction monitoring method and system based on a third party. The implementation of this invention is a legal use.

[0006] Firstly, this application provides a third-party-based real-time transaction monitoring method, which adopts the following technical solution:

[0007] A third-party-based real-time transaction monitoring method includes:

[0008] Third-party systems obtain trading accounts;

[0009] If there is a risky account among the trading accounts, then obtain the risk value of the risky account;

[0010] The risk account is verified based on the risk value;

[0011] If the risk verification is successful, the current transaction information and the payment account's historical transaction records will be obtained.

[0012] Based on the historical transaction records, obtain the characteristics of the historical transactions;

[0013] Determine whether the current transaction information matches the historical transaction characteristics;

[0014] If the current transaction information does not match the historical transaction characteristics, an alarm message is generated;

[0015] If the current transaction information matches the historical transaction characteristics, the third-party system allows the transaction account to continue the current transaction.

[0016] By adopting the above technical solution, if a trading account is identified as a risky account, it indicates that the current transaction carries a certain risk. The risk value of the risky account is then obtained, and risk verification is performed based on this value. If the verification passes, it indicates that the risk value corresponding to the risky account is low and has a minimal impact on the current transaction. To further determine whether the transaction can proceed normally, the current transaction information and the trading account's historical transaction records are obtained. Based on these historical transaction records, historical transaction characteristics are acquired, and it is determined whether the transaction information matches these characteristics. If they do not match, it indicates a significant difference between the current transaction information and historical transaction characteristics, suggesting a substantial risk in the current transaction. Therefore, an alarm message is directly generated to alert the first type of user. If they match, it indicates that there is no difference or a small difference between the current transaction information and historical transaction characteristics, suggesting that the current transaction carries no risk or a small risk. Therefore, the third-party system will not block the transaction and will allow the trading account to continue trading.

[0017] First, the transaction account undergoes risk verification. Once the verification is successful, it is then determined whether the current transaction information matches the characteristics of historical transactions. By setting multiple conditions and having a third party monitor the current transaction in real time, an alarm is promptly triggered when the transaction account is at risk or when the current transaction information differs significantly from the characteristics of historical transactions. This not only helps reduce transaction risks but also helps strengthen the effective monitoring of irrational consumption by individuals without or with limited capacity for civil conduct.

[0018] Optionally, obtaining historical transaction features based on the historical transaction records includes:

[0019] Based on the historical transaction records, obtain the highest historical transaction amount and the most frequently used historical transaction amount of the payment account;

[0020] Based on the highest historical transaction amount and the most frequently used historical transaction amount, obtain the characteristics of historical transaction amount;

[0021] Obtain the historical transaction time and historical transaction location for different transactions;

[0022] Based on the historical transaction time and the historical transaction location, obtain historical transaction time features and historical transaction location features;

[0023] Historical transaction features are obtained based on the historical transaction amount features, the historical transaction time features, and the historical transaction location features.

[0024] Optionally, determining whether the current transaction information matches the historical transaction characteristics includes:

[0025] Based on the aforementioned historical transaction records, identify the relevant transaction records;

[0026] Based on the identified transaction records, obtain the identified transaction information;

[0027] Based on the identified transaction information, the characteristics of the identified transactions are obtained;

[0028] Based on the current transaction information, obtain the current transaction time and current transaction location;

[0029] Based on the current transaction time, the current transaction location, and the identified transaction characteristics, a first matching degree is obtained;

[0030] If the first matching degree exceeds a preset degree threshold, it is determined that the current transaction information does not match the historical transaction characteristics.

[0031] Optionally, after obtaining the first matching degree based on the current transaction time, the current transaction location, and the identified transaction characteristics, the method further includes:

[0032] If the first matching degree does not exceed the preset degree threshold, then based on the user information, the first behavior information and the second behavior information are obtained;

[0033] Based on the first behavioral information, obtain the first daily behavior time and the corresponding first daily behavior location;

[0034] Based on the second behavioral information, obtain the time of the second daily behavior and the corresponding location of the second daily behavior;

[0035] Based on the current transaction time, the current transaction location, the first daily behavior time, and the first daily behavior location, a second matching degree is obtained;

[0036] Based on the current transaction time, the current transaction location, the second daily behavior time, and the second daily behavior location, a third matching degree is obtained;

[0037] If the second matching degree exceeds the third matching degree, and the second matching degree exceeds the preset degree threshold, then the current transaction information is determined to match the historical transaction characteristics.

[0038] Optionally, after obtaining the third matching degree based on the current transaction time, the current transaction location, the second daily behavior time, and the second daily behavior location, the method further includes:

[0039] If the second matching degree is equal to the third matching degree, and the second matching degree exceeds the preset degree threshold, then the current first network speed and house information of the target terminal device are obtained;

[0040] If the network corresponding to the target terminal device is a wireless network, then the target distance and obstruction information between different rooms and the target router are obtained based on the house information;

[0041] Based on the obstruction information, the number of obstruction layers and the corresponding obstruction properties are obtained;

[0042] Based on the target distance, the number of obstruction layers, and the properties of the obstruction, the network speed attenuation coefficient is obtained;

[0043] Obtain the current second network speed of the target router, and obtain the target network speed based on the current second network speed and the attenuation coefficient;

[0044] Based on the target network speed and the current first network speed, obtain the target location;

[0045] Based on the target location, determine whether the current transaction information matches the historical transaction characteristics;

[0046] The attenuation coefficient satisfies the following calculation formula:

[0047]

[0048] d is the network speed attenuation coefficient, d is the target distance between the target room and the target router, d0 is the reference distance, n is the number of obstruction layers, and k is the network speed attenuation coefficient. n is a coefficient related to the number of barrier layers, and p is a quantification value of the barrier properties.

[0049] Optionally, determining whether the current transaction information matches the historical transaction characteristics based on the target location includes:

[0050] If the target location is the first region, then the current transaction information is determined to match the historical transaction characteristics;

[0051] If the target location is the second region, then it is determined that the current transaction information does not match the historical transaction characteristics;

[0052] If the target location is the third region, then the video image corresponding to the third region is obtained;

[0053] Based on the video images, the trader can be identified;

[0054] If the trader is the first user, then the current transaction information is determined to match the historical transaction characteristics;

[0055] If the trader is a second user, then it is determined that the current transaction information does not match the historical transaction characteristics.

[0056] Optionally, obtaining the trader based on the video image includes:

[0057] If the video image is obtained, the trader is identified based on the video image;

[0058] If the video image is not obtained, then obtain the transaction attributes;

[0059] If the transaction attribute is an account transfer, then determine whether the account source of the receiving account has been detected;

[0060] If the source of the receiving account is detected, the trader is obtained based on the source of the account.

[0061] If the transaction attribute is online shopping consumption, then obtain the user information of the receiving user;

[0062] Obtain the first consumption record of the first user and the second consumption record of the second user;

[0063] Based on the first consumption record and the second consumption record, generate a first consumption feature and a second consumption feature;

[0064] Based on the first consumption characteristic, the second consumption characteristic, and the user information, the trader is obtained.

[0065] Optionally, obtaining the second matching degree based on the current transaction time, the current transaction location, the first daily behavior time, and the first daily behavior location includes:

[0066] Based on the current transaction time and the first daily behavior time, obtain the target location;

[0067] Based on the current transaction location and the target location, obtain the distance difference;

[0068] Obtain the initial matching degree and unit measurement distance;

[0069] Get the matching increment per unit distance;

[0070] Based on the user information, obtain the interest characteristics of the first user and the account characteristics of the receiving user;

[0071] If the account characteristics and the interest characteristics match, a fourth matching degree is obtained, and a matching compensation coefficient is obtained based on the fourth matching degree.

[0072] Based on the initial matching degree, the unit measurement distance, the unit distance matching increment, and the matching compensation coefficient, a second matching degree is obtained, which satisfies the following calculation formula:

[0073]

[0074] Where F represents the second matching degree, and F0 represents the initial matching degree. This is the distance difference. C is the unit distance measurement distance, and C is the unit distance matching increment. For matching compensation coefficients.

[0075] Optionally, obtaining the unit distance matching increment includes:

[0076] If the current transaction time is the specified behavior time, then obtain the first matching increment and use the first matching increment as the unit distance matching increment;

[0077] If the current transaction time is not the specified behavior time, then the target distance is obtained based on the current transaction time, the specified time, and the target user information;

[0078] Based on the target distance and the distance difference, obtain the target distance difference;

[0079] If the target distance difference is greater than the first distance threshold, then the second matching increment is obtained and used as the unit time matching increment;

[0080] If the target distance difference is less than or equal to the first distance threshold, then the third matching increment is obtained and used as the unit time matching increment;

[0081] Among them, the first matching increment is greater than the second matching increment, which is greater than the third matching increment.

[0082] Secondly, this application also discloses a real-time transaction monitoring system based on a third party, which adopts the following technical solution:

[0083] A third-party-based real-time transaction monitoring system includes:

[0084] The first acquisition module is used to acquire trading accounts based on third-party systems;

[0085] The second acquisition module is used to acquire the risk value of the risk account if there is a risk account in the trading account.

[0086] The risk verification module is used to verify the risk of the risk account based on the risk value;

[0087] If the risk verification is passed, the third acquisition module is used to acquire the current transaction information and the historical transaction records of the transaction account.

[0088] The fourth acquisition module is used to acquire historical transaction characteristics based on the historical transaction records;

[0089] The judgment module is used to determine whether the transaction information matches the historical transaction characteristics;

[0090] An alarm module is used to generate alarm information if the current transaction information does not match the historical transaction characteristics.

[0091] If the current transaction information matches the historical transaction characteristics, the transaction module is used to allow the transaction account to continue the current transaction based on the third-party system.

[0092] By adopting the above technical solution, if a trading account is identified as a risky account, it indicates that the current transaction carries a certain risk. The risk value of the risky account is then obtained, and risk verification is performed based on this value. If the verification passes, it indicates that the risk value corresponding to the risky account is low and has a minimal impact on the current transaction. To further determine whether the transaction can proceed normally, the current transaction information and the trading account's historical transaction records are obtained. Based on these historical transaction records, historical transaction characteristics are acquired, and it is determined whether the transaction information matches these characteristics. If they do not match, it indicates a significant difference between the current transaction information and historical transaction characteristics, suggesting a substantial risk in the current transaction. Therefore, an alarm message is directly generated to alert the first type of user. If they match, it indicates that there is no difference or a small difference between the current transaction information and historical transaction characteristics, suggesting that the current transaction carries no risk or a small risk. Therefore, the third-party system will not block the transaction and will allow the trading account to continue trading.

[0093] First, the transaction account undergoes risk verification. Once the verification is successful, it is then determined whether the current transaction information matches the characteristics of historical transactions. By setting multiple conditions and having a third party monitor the current transaction in real time, an alarm is promptly triggered when the transaction account is at risk or when the current transaction information differs significantly from the characteristics of historical transactions. This not only helps reduce transaction risks but also helps strengthen the effective monitoring of irrational consumption by individuals without or with limited capacity for civil conduct.

[0094] In summary, this application includes the following beneficial technical effects:

[0095] First, the transaction account undergoes risk verification. Once the verification is successful, it is then determined whether the current transaction information matches the characteristics of historical transactions. By setting multiple conditions and having a third party monitor the current transaction in real time, an alarm is promptly triggered when the transaction account is at risk or when the current transaction information differs significantly from the characteristics of historical transactions. This not only helps reduce transaction risks but also helps strengthen the effective monitoring of irrational consumption by individuals without or with limited capacity for civil conduct. Attached Figure Description

[0096] Figure 1 This is a main flowchart of a third-party-based real-time transaction monitoring method according to an embodiment of this application;

[0097] Figure 2 This is a flowchart of steps S201 to S205;

[0098] Figure 3 This is a flowchart of steps S301 to S306;

[0099] Figure 4 This is a flowchart of steps S401 to S406;

[0100] Figure 5 This is a flowchart of steps S501 to S507;

[0101] Figure 6 This is a flowchart of steps S601 to S606;

[0102] Figure 7 This is a flowchart of steps S701 to S708;

[0103] Figure 8 This is a flowchart of steps S801 to S807;

[0104] Figure 9 This is a flowchart of steps S901 to S905;

[0105] Figure 10 This is a block diagram of a third-party-based real-time transaction monitoring system according to an embodiment of this application.

[0106] Explanation of reference numerals in the attached figures:

[0107] 1. First Acquisition Module; 2. Second Acquisition Module; 3. Risk Verification Module; 4. Third Acquisition Module; 5. Fourth Acquisition Module; 6. Judgment Module; 7. Alarm Module; 8. Transaction Module. Detailed Implementation

[0108] Firstly, this application discloses a real-time transaction monitoring method based on a third party.

[0109] Reference Figure 1 A real-time transaction monitoring method based on a third party includes steps S101 to S108:

[0110] Step S101: The third-party system obtains the trading account.

[0111] Specifically, the transaction account refers to the accounts of the two parties involved in the transaction. In this embodiment, after the third-party system identifies the accounts of the two parties involved in the transaction, it will analyze and assess the risks based on the relevant information of the transaction account to determine whether the transaction account is a risky account. For example, if a transaction account is reported or complained about by multiple people, or if there are disputes between the transaction account and multiple accounts, etc.

[0112] Step S102: If there is a risky account in the trading account, obtain the risk value of the risky account.

[0113] Specifically, a risky account refers to an account that has been marked as high-risk by a third-party system or third-party regulatory agency due to abnormal fund transactions, violation of platform rules, or security risks. The risk value is the risk value corresponding to the risky account obtained by analyzing the risk reasons of the risky account. In this embodiment, a large number of risk reasons and corresponding values ​​are stored in advance. The risk value is the sum of the values ​​corresponding to all risk reasons of the risky account.

[0114] Step S103: Perform risk verification on risky accounts based on risk values.

[0115] Specifically, in this embodiment, the risk value can be compared with a preset risk threshold. If the risk value is greater than or equal to the preset risk threshold, it is determined that the risk value of the account is too high and there is a significant security risk. The account cannot pass the risk verification and therefore it is not recommended to transact with it. The third-party system can remind the user who transacted with the risk account according to the corresponding method and prohibit the transaction.

[0116] Step S104: If the risk verification is successful, obtain the current transaction information and the historical transaction records of the payment account.

[0117] Specifically, in this embodiment, the current transaction information refers to the relevant information of the current transaction, such as the transaction time, transaction location, specific transaction content, and transaction amount; the historical transaction record refers to all transaction records of the payment account within a certain period of time in the past (e.g., within one year, or from the time the transaction account was registered to the current time) and the transaction information corresponding to each transaction. It is worth noting that this is the historical transaction record of all transaction accounts.

[0118] Step S105: Obtain historical transaction characteristics based on historical transaction records.

[0119] Specifically, historical transaction characteristics refer to the relevant characteristics of transactions conducted by the payment account at historical moments, including the range of transaction time, transaction location, transaction content, and transaction amount. In this embodiment, all transaction information in the historical transaction records is analyzed, and the corresponding information is statistically analyzed to form a corresponding range (or region). For example, if it is known from the historical transaction records that all transaction times are concentrated between 8 pm and 9 pm, then the time characteristics included in the historical transaction characteristics can be determined to be between 8 pm and 9 pm. It is worth noting that the historical transaction characteristics are the characteristics corresponding to the historical transactions carried out by the true holder of the payment account or the designated guardian. In this embodiment, the true holder of the payment account or the designated guardian is identified as a person with autonomous capacity, referred to as the first type of person, while a person without autonomous capacity or with limited capacity is referred to as the second type of person.

[0120] Step S106: Determine whether the current transaction information matches the characteristics of historical transactions.

[0121] Specifically, in this embodiment, the current transaction information is compared with historical transaction features to determine whether the current transaction information falls within the range formed by the historical transaction features. It is worth noting that the current transaction information should correspond to the historical transaction features. For example, whether the transaction time corresponding to the current transaction information is within the time range included by the historical transaction features, and whether the transaction location is within the transaction location range included by the historical transaction features. Only when all the information in the current transaction information is within the range corresponding to the historical transaction features is it determined that the current transaction information matches the historical transaction features.

[0122] Step S107: If the current transaction information does not match the characteristics of historical transactions, an alarm message is generated.

[0123] Specifically, in this embodiment, if the current transaction information does not match the characteristics of historical transactions, it indicates that there is a significant difference between the current transaction information and the characteristics of historical transactions, which means that there is a significant risk in this transaction. Therefore, an alarm message is directly generated to remind the first type of person.

[0124] Step S108: If the current transaction information matches the characteristics of historical transactions, the third-party system allows the transaction account to continue the current transaction.

[0125] Specifically, in this embodiment, if the current transaction information matches the characteristics of historical transactions, it means that there is no difference or the difference is small between the current transaction information and the characteristics of historical transactions. In other words, there is no risk or the risk is small in this transaction. Therefore, the third-party system will not block this transaction and will allow the transaction account to continue the current transaction.

[0126] The third-party-based real-time transaction monitoring method provided in this embodiment indicates that if a transaction account is identified as a risky account, the current transaction carries a certain risk. The method further obtains the risk value of the risky account and verifies its risk based on this value. If the verification passes, it indicates that the risk value corresponding to the risky account is low and has a minimal impact on the current transaction. To further determine whether the transaction can proceed normally, the method obtains the current transaction information and the historical transaction records of the transaction account. Based on these historical transaction records, it obtains historical transaction characteristics and determines whether the transaction information matches these characteristics. If they do not match, it indicates a significant difference between the current transaction information and historical transaction characteristics, suggesting a high risk in the current transaction. Therefore, an alarm is directly generated to alert the first type of user. If they match, it indicates that there is no difference or a small difference between the current transaction information and historical transaction characteristics, suggesting no or minimal risk in the current transaction. Therefore, the third-party system will not block the transaction and will allow the account to continue trading.

[0127] First, the transaction account undergoes risk verification. Once the verification is successful, it is then determined whether the current transaction information matches the characteristics of historical transactions. By setting multiple conditions and having a third party monitor the current transaction in real time, an alarm is promptly triggered when the transaction account is at risk or when the current transaction information differs significantly from the characteristics of historical transactions. This not only helps reduce transaction risks but also helps strengthen the effective monitoring of irrational consumption by individuals without or with limited capacity for civil conduct.

[0128] Reference Figure 2 In one embodiment of this example, step S105, based on historical transaction records, obtains historical transaction characteristics, including steps S201 to S205:

[0129] Step S201: Based on historical transaction records, obtain the highest historical transaction amount and the most frequently used historical transaction amount of the payment account.

[0130] Specifically, the highest historical transaction amount is the highest transaction amount ever paid by the payment account, and the most frequently used historical transaction amount is the most frequently used transaction amount corresponding to the payment account. In this embodiment, the most frequently used historical transaction amount can be a range of amounts.

[0131] Step S202: Obtain historical transaction volume characteristics based on the highest historical transaction volume and the most frequently used historical transaction volume.

[0132] Specifically, in this embodiment, the historical transaction volume characteristic refers to the amount characteristic corresponding to historical transactions, including the highest transaction volume and frequently used transaction volume corresponding to historical transactions.

[0133] Step S203: Obtain the historical transaction time and historical transaction location for different transactions.

[0134] Specifically, historical transaction time refers to the transaction time corresponding to a historical transaction. In this embodiment, historical transaction time can be specific to a certain day of a certain month, a certain day of the week, or a specific time of day. Historical transaction location refers to the transaction location corresponding to a historical transaction. In this embodiment, historical transaction location can be accurate to a specific floor and a specific room.

[0135] Step S204: Based on historical transaction time and historical transaction location, obtain historical transaction time features and historical transaction location features.

[0136] Specifically, in this embodiment, the historical transaction time feature is a feature composed of the transaction time corresponding to the historical transaction; the historical transaction location feature is a feature composed of the transaction location corresponding to the historical transaction.

[0137] Step S205: Obtain historical transaction features based on historical transaction amount features, historical transaction time features, and historical transaction location features.

[0138] Specifically, in this embodiment, historical transaction features are a combination of all other features of historical transactions, including historical transaction amount features, historical transaction time features, and historical transaction location features.

[0139] Reference Figure 3 In one embodiment of this example, step S106, determining whether the current transaction information matches the characteristics of historical transactions, includes steps S301 to S306:

[0140] Step S301: Based on historical transaction records, obtain the identified transaction records.

[0141] Specifically, in this embodiment, a transaction record is identified as a transaction record that was made by a second type of person.

[0142] Step S302: Obtain the identified transaction information based on the identified transaction records.

[0143] Specifically, identifying transaction information means identifying the transaction information corresponding to the exchange.

[0144] Step S303: Based on the identified transaction information, obtain the identified transaction characteristics.

[0145] Specifically, in this embodiment, identifying transaction characteristics means identifying the relevant characteristics corresponding to the transaction.

[0146] Step S304: Based on the current transaction information, obtain the current transaction time and current transaction location.

[0147] Specifically, in this embodiment, the current trading time is the trading time corresponding to the current exchange, and the current trading position is the trading position corresponding to the current exchange.

[0148] Step S305: Based on the current transaction time, current transaction location, and identified transaction characteristics, obtain the first matching degree.

[0149] Specifically, in this embodiment, the first matching degree is related to the time matching degree and the location matching degree. The time matching degree is the degree of matching between the current transaction time and the time feature in the identified transaction features, and the location matching degree is the degree of matching between the current transaction location and the location feature in the identified transaction features. The first matching degree is the sum of the time matching degree multiplied by its corresponding weight value and the location matching degree multiplied by its corresponding weight value.

[0150] Step S306: If the first matching degree exceeds the preset degree threshold, it is determined that the current transaction information does not match the historical transaction characteristics.

[0151] Specifically, if the first degree of matching exceeds the preset degree threshold, it means that the current transaction is very likely to be a recognized transaction, that is, a transaction made by the second type of person. Therefore, it is determined that the current transaction information does not match the characteristics of historical transactions. The first degree threshold is a preset judgment standard used to determine whether the first degree of matching is too high.

[0152] Reference Figure 4 In one embodiment of this example, after obtaining the first matching degree based on the current transaction time, current transaction location, and identified transaction characteristics in step S305, steps S401 to S406 are further included:

[0153] Step S401: If the first matching degree does not exceed the preset degree threshold, then based on the user information, obtain the first behavior information and the second behavior information.

[0154] Specifically, user information refers to the relevant information of the user corresponding to the transaction account. In this embodiment, the user information is pre-bound to the transaction account and stored in the transaction account in advance; the first behavior information refers to the behavior information of the first type of person, including the person's daily behavior time and daily behavior location, etc. The first daily behavior time refers to the content of the person's daily behavior and the corresponding time. For example, Zhang San gets up at 8:00 every morning from Monday to Friday, washes up and eats breakfast at home (bathroom, kitchen, dining room) between 8:00 and 8:30, goes to work between 8:30 and 9:00, and works at the company from 9:00 to 17:00. The individual's activities include: returning home from get off work between 7:00 and 17:30; preparing dinner at home (kitchen, dining room) between 17:30 and 19:00; relaxing at home (living room) between 19:00 and 22:00; washing up at home (bathroom) between 22:00 and 22:30; and resting at home (bedroom) between 22:30 and 8:00. The second behavioral information refers to the behavioral information of the second type of person corresponding to the payment account, including the person's daily behavioral time and location. The second daily behavioral time refers to the content of the person's daily behavior and the corresponding time. It is worth noting that each payment account has pre-entered relevant information about the family members corresponding to the actual account holder.

[0155] Step S402: Based on the first behavior information, obtain the first daily behavior time and the corresponding first daily behavior location.

[0156] Specifically, in this embodiment, the first daily behavior time is the time set of the historical daily behaviors of the first type of person; the first daily behavior location is the location set of the historical daily behaviors of the first type of person, which can be represented by latitude and longitude coordinates or by two-dimensional coordinates.

[0157] Step S403: Based on the second behavior information, obtain the time of the second daily behavior and the corresponding location of the second daily behavior.

[0158] Specifically, in this embodiment, the second daily behavior time is the set of times when the second type of person's historical daily behavior occurred; the second daily behavior location is the set of locations where the second type of person's historical daily behavior occurred.

[0159] Step S404: Based on the current transaction time, current transaction location, first daily behavior time and first daily behavior location, obtain the second matching degree.

[0160] Specifically, in this embodiment, the second matching degree is the sum of the time matching degree between the current transaction time and the first daily behavior time multiplied by its corresponding weight value, and the location matching degree between the current transaction location and the first daily behavior location multiplied by its corresponding weight value.

[0161] Step S405: Based on the current transaction time, current transaction location, second daily behavior time, and second daily behavior location, obtain the third matching degree.

[0162] Specifically, in this embodiment, the third matching degree is the sum of the time matching degree between the current transaction time and the second daily behavior time multiplied by their corresponding weight value, plus the location matching degree between the current transaction location and the second daily behavior location multiplied by their corresponding weight value.

[0163] Step S406: If the second matching degree exceeds the third matching degree, and the second matching degree exceeds the preset degree threshold, then it is determined that the current transaction information matches the historical transaction characteristics.

[0164] Specifically, if the second matching degree exceeds the third matching degree, it indicates that the current transaction information is more consistent with the shopping habits or characteristics of the first type of person. Furthermore, if the second matching degree exceeds the preset degree threshold, it further indicates that the current transaction is very likely to be carried out by the first type of person. Therefore, it is determined that the current transaction information matches the characteristics of historical transactions.

[0165] Reference Figure 5 In one embodiment of this example, after obtaining the third matching degree based on the current transaction time, current transaction location, second daily behavior time, and second daily behavior location in step S405, steps S501 to S507 are further included:

[0166] Step S501: If the second matching degree is equal to the third matching degree, and the second matching degree exceeds the preset degree threshold, then obtain the current first network speed and house information of the target terminal device.

[0167] Specifically, in this embodiment, if the second matching degree is equal to the third matching degree, it indicates that the current transaction time and the current transaction location have a high matching degree with both the first type of person and the second type of person. Therefore, it can be inferred that the current transaction time is the free time of both the first type of person and the second type of person, and the current transaction location is the permanent residence of both the first type of person and the second type of person. Since it is impossible to determine who is conducting the current transaction based on the magnitude of the second and third matching degrees, it is necessary to further obtain the current first network speed and housing information of the target terminal device. The target terminal device is the terminal device conducting the current transaction, such as a mobile phone or computer. The first network speed is the current network speed of the target terminal device. The housing information is the housing information of the permanent residence, including the housing structure, housing size, and housing attributes.

[0168] Step S502: If the network corresponding to the target terminal device is a wireless network, then obtain the target distance and obstruction information between different rooms and the target router based on the house information.

[0169] Specifically, in this embodiment, the target distance is the straight-line distance between different rooms and the target router, and the obstruction information is the relevant information of the obstructions between different room spaces and the target router, such as the number of obstruction layers and the nature of the obstructions. An obstruction is an object that can block signal transmission, such as a wall or a screen.

[0170] Step S503: Based on the obstruction information, obtain the number of obstruction layers and the corresponding obstruction properties.

[0171] Step S504: Obtain the network speed attenuation coefficient based on the target distance, the number of obstruction layers, and the nature of the obstruction.

[0172] Specifically, in this embodiment, the attenuation coefficient satisfies the following calculation formula:

[0173]

[0174] Here, d represents the network speed attenuation coefficient, d is the target distance between the target room and the target router (the greater the distance, the greater the impact on signal attenuation), d0 is the reference distance (the distance value used as a comparison benchmark), and the ratio of d to d0 reflects the effect of distance on attenuation. n represents the number of obstructions; the more layers, the more significant the signal attenuation may be. k represents the signal strength attenuation coefficient. n The coefficient is related to the number of obstruction layers. Different obstructions attenuate signals to varying degrees. For example, metal obstructions have a stronger attenuation effect on wireless network signals, while obstructions made of materials such as wood have a relatively weaker attenuation effect. In this embodiment, different types of obstructions can be assigned corresponding k values ​​based on experiments or experience. n The value, k, can range from 0 to 1. n The smaller the value, the stronger the attenuation effect of the obstruction on network speed. p is a quantitative value of the obstruction's properties, which is a quantitative value obtained after comprehensively considering the characteristics of the obstruction's material, structure, etc. It is used to further correct the impact of the obstruction on signal attenuation. Generally speaking, metal materials have strong absorption and reflection of wireless signals, and the p value may be between 0.2 and 0.4, while materials such as wood and plastic, which have less impact on the signal, have p values ​​between 0.6 and 0.8.

[0175] Step S505: Obtain the current second network speed of the target router, and obtain the target network speed based on the current second network speed and the attenuation coefficient.

[0176] Specifically, the current second network speed is the real-time network speed of the target router, and the target network speed is the real-time network speed in different rooms after being attenuated by the attenuation coefficient. In this embodiment, the target network speed = the current second network speed × the attenuation coefficient.

[0177] Step S506: Obtain the target location based on the target network speed and the current first network speed.

[0178] Specifically, in this embodiment, the target location is the specific location of the target terminal device. By comparing the target network speed in different rooms with the current first network speed, the target network speed that is closest to the current first network speed is selected, and the room corresponding to the target network speed is taken as the target location.

[0179] Step S507: Based on the target location, determine whether the current transaction information matches the characteristics of historical transactions.

[0180] Specifically, the current transaction information is determined to match the characteristics of historical transactions based on the relationship between the target location and the first and second types of people. For example, if the target location is the master bedroom (the bedroom where the first type of person lives), the current transaction information is determined to match the characteristics of historical transactions. If the target location is the secondary bedroom (the bedroom where the second type of person lives), the current transaction information is determined not to match the characteristics of historical transactions.

[0181] Reference Figure 6 In one embodiment of this example, step S507, based on the target location, determines whether the current transaction information matches the characteristics of historical transactions, including steps S601 to S606:

[0182] Step S601: If the target location is the first region, then determine that the current transaction information matches the characteristics of historical transactions.

[0183] Specifically, in this embodiment, the first area is the room where the first type of person belongs, such as the master bedroom.

[0184] Step S602: If the target location is the second region, then it is determined that the current transaction information does not match the characteristics of historical transactions.

[0185] Specifically, in this embodiment, the second area is the room where the second type of person belongs, such as the secondary bedroom.

[0186] Step S603: If the target location is the third region, then acquire the video image corresponding to the third region.

[0187] Specifically, the third area refers to the public area other than the first and second areas, such as the kitchen, dining room, and living room. In this embodiment, a camera device is provided that can capture video images of the public area.

[0188] Step S604: Obtain the trader based on the video image.

[0189] Specifically, the trader in the current transaction can be identified through video images. In this embodiment, the trader in the current transaction can be identified by combining the current transaction time and video images. For example, the video images can be used to see who is operating the target terminal device at the target location during the current transaction time.

[0190] Step S605: If the trader is the first user, then determine that the current transaction information matches the characteristics of the historical transactions.

[0191] Step S606: If the trader is a second user, then it is determined that the current transaction information does not match the characteristics of historical transactions.

[0192] Reference Figure 7 In one embodiment of this example, step S604, based on video images, to acquire the trader includes steps S701 to S708:

[0193] Step S701: If a video image is obtained, then the trader is obtained based on the video image.

[0194] Step S702: If no video image is obtained, obtain the transaction attributes.

[0195] Specifically, in this embodiment, the transaction attributes are the transaction attributes of the current transaction, including account transfers and transaction deductions.

[0196] Step S703: If the transaction attribute is account transfer, determine whether the account source of the receiving account has been detected.

[0197] Specifically, in this embodiment, the account source is the source of the receiving account, such as a message sent via SMS or other apps.

[0198] Step S704: If the source of the receiving account is detected, the trader is obtained based on the source of the account.

[0199] Specifically, in this embodiment, if the source of the receiving account is detected, the relationship between the owner of the source account and the first type of person and the second type of person can be understood based on the source of the account. For example, if the source of the receiving account is a message sent by an APP, the transaction person who obtained the source of the account can be determined based on the relationship between the message sender and the first type of person and the second type of person. For example, whether the message sender is a friend of the first type of person or a friend of the second type of person.

[0200] Step S705: If the transaction attribute is online shopping consumption, then obtain the user information of the receiving user.

[0201] Specifically, based on the user information, one can know the types of goods sold by the receiving account, the product attributes, and the price range.

[0202] Step S706: Obtain the first consumption record of the first user and the second consumption record of the second user.

[0203] Specifically, in this embodiment, the first user is the first type of person, the second user is the second type of person, the first consumption record is the transaction record of online shopping completed by the first type of person, and the second consumption record is the transaction record of online shopping completed by the second type of person.

[0204] Step S707: Generate first consumption features and second consumption features based on the first consumption record and the second consumption record.

[0205] Specifically, in this embodiment, the first consumption feature is the consumption feature generated based on the first consumption record, including the type and attributes of the goods purchased by the first type of person and the range of transaction amount, etc.; the second consumption feature is the consumption feature generated based on the second consumption record, including the type and attributes of the goods purchased by the second type of person and the range of transaction amount, etc.

[0206] Step S708: Based on the first consumption characteristics, the second consumption characteristics, and user information, obtain the trader.

[0207] Specifically, in this embodiment, the first consumption feature and the second consumption feature are matched with the user information respectively. If the first consumption feature matches the user information more closely, the trader is classified as a first type of person; otherwise, the trader is classified as a second type of person.

[0208] Reference Figure 8 In one embodiment of this example, step S404, based on the current transaction time, current transaction location, first daily behavior time, and first daily behavior location, obtains the second matching degree, including steps S801 to S807:

[0209] Step S801: Obtain the target location based on the current transaction time and the first daily behavior time.

[0210] Specifically, the target location is the location that the person in the first category should be at the current transaction time.

[0211] Step S802: Obtain the distance difference between the current transaction location and the target location.

[0212] Specifically, in this embodiment, the distance difference is the distance between the current transaction location and the first daily behavior location. In this embodiment, since the first daily behavior location is a set of locations, the distance difference is also a set.

[0213] Step S803: Obtain the initial matching degree and unit measurement distance.

[0214] Specifically, the initial matching degree is a pre-set basic matching value used to measure the default matching benchmark between the current transaction and the user's daily behavior. It does not consider specific time differences, distance differences or other compensation factors, and is the starting point for the entire matching degree calculation. In this embodiment, the initial matching degree can be set to 100%. The unit measurement distance represents the basic distance unit used to measure the distance difference. It represents the degree of influence of the distance difference on the second matching degree within each unit distance. The unit measurement distance can be set to 100 meters or other distances.

[0215] Step S804: Obtain the unit distance matching increment.

[0216] Specifically, in this embodiment, the unit distance matching increment refers to the change in the second matching degree when the distance difference increases by one unit of measurement distance.

[0217] Step S805: Based on user information, obtain the interest characteristics of the first user and the account characteristics of the receiving user.

[0218] Specifically, in this embodiment, interest characteristics refer to the interests and preferences of the first type of person, and account characteristics refer to the characteristics corresponding to the payment account, including the attributes of the goods sold.

[0219] Step S806: If the account characteristics and interest characteristics match, obtain the fourth matching degree, and obtain the matching compensation coefficient based on the fourth matching degree.

[0220] Specifically, in this embodiment, the fourth matching degree is the matching degree between interest characteristics and account characteristics. The matching compensation coefficient is an adjustment factor calculated based on the fourth matching degree, which is used to correct the final result of the transaction matching degree. When the account characteristics and interest characteristics are highly matched, the transaction matching degree can be positively compensated (the matching degree is improved); otherwise, negative compensation is performed (the matching degree is reduced).

[0221] Step S807: Obtain the second matching degree based on the initial matching degree, unit measurement distance, unit distance matching increment, and matching compensation coefficient.

[0222] Specifically, in this embodiment, the second matching degree satisfies the following calculation formula:

[0223]

[0224] Where F represents the second matching degree, and F0 represents the initial matching degree. This is the distance difference. C is the unit distance measurement distance, and C is the unit distance matching increment. For matching compensation coefficients.

[0225] Reference Figure 9In one embodiment of this example, step S804, obtaining the unit distance matching increment, includes steps S901 to S905:

[0226] Step S901: If the current transaction time is the specified behavior time, then obtain the first matching increment and use the first matching increment as the unit distance matching increment.

[0227] Specifically, the specified behavior time is the time range corresponding to the specified behavior. In this embodiment, the specified behavior time can be working time; the first matching increment is preset according to the actual situation.

[0228] Step S902: If the current transaction time is not the specified action time, then obtain the target distance based on the current transaction time, the specified time, and the target user information.

[0229] Specifically, the time difference between the current transaction time and the specified behavior time is first calculated. Since the specified behavior time is a time interval, in this embodiment, the start and end nodes of the specified behavior time can be obtained first, and then the absolute values ​​of the differences between the two and the current transaction time can be calculated respectively. The smaller result is selected as the time difference. The target user information is the relevant information of the first type of person, including the specific mode of transportation and average speed of the first type of person when going to or leaving the specified behavior location (workplace). Based on the average speed and the time difference, the target distance can be calculated. Target distance = average speed × time difference.

[0230] Step S903: Obtain the target distance difference based on the target distance and the distance difference.

[0231] Specifically, in this embodiment, the target distance difference = |target distance - distance difference|.

[0232] Step S904: If the target distance difference is greater than the first distance threshold, then obtain the second matching increment and use the second matching increment as the matching increment per unit time.

[0233] Specifically, in this embodiment, the first distance threshold is a pre-set criterion for judging whether the target distance difference is too large.

[0234] Step S905: If the target distance difference is less than or equal to the first distance threshold, then obtain the third matching increment and use the third matching increment as the matching increment per unit time.

[0235] Specifically, in this embodiment, the first matching increment is greater than the second matching increment, which is greater than the third matching increment.

[0236] Secondly, this application also discloses a real-time transaction monitoring system based on a third party.

[0237] Reference Figure 10 A third-party-based real-time transaction monitoring system includes:

[0238] The first acquisition module is used to acquire trading accounts based on third-party systems;

[0239] The second acquisition module is used to acquire the risk value of a risky account if there is a risky account in the trading account.

[0240] The risk verification module is used to verify the risk of risky accounts based on risk values;

[0241] The third acquisition module, if the risk verification is passed, is used to acquire the current transaction information and the historical transaction records of the transaction account;

[0242] The fourth acquisition module is used to acquire historical transaction characteristics based on historical transaction records;

[0243] The judgment module is used to determine whether the transaction information matches the characteristics of historical transactions;

[0244] The alarm module generates alarm information if the current transaction information does not match the characteristics of historical transactions.

[0245] The trading module allows a trading account to continue the current transaction if the current transaction information matches the characteristics of historical transactions, based on a third-party system.

[0246] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A third-party-based real-time transaction monitoring method, characterized in that, include: Third-party systems obtain trading accounts; If there is a risky account among the trading accounts, then obtain the risk value of the risky account; The risk account is verified based on the risk value; If the risk verification is successful, the current transaction information and the payment account's historical transaction records will be obtained. Based on the historical transaction records, obtain the characteristics of the historical transactions; Determine whether the current transaction information matches the historical transaction characteristics; If the current transaction information does not match the historical transaction characteristics, an alarm message is generated; If the current transaction information matches the historical transaction characteristics, the third-party system allows the transaction account to continue the current transaction; The step of obtaining historical transaction characteristics based on the historical transaction records includes: Based on the historical transaction records, obtain the highest historical transaction amount and the most frequently used historical transaction amount of the payment account, where the most frequently used historical transaction amount is a specific range of amounts. Based on the highest historical transaction amount and the most frequently used historical transaction amount, obtain the characteristics of historical transaction amount; Obtain historical transaction times and locations for different transactions. Historical transaction times are accurate to the day of the week and the specific time of the day. Historical transaction locations are accurate to the specific floor and room. Based on the historical transaction time and the historical transaction location, obtain historical transaction time features and historical transaction location features; Based on the historical transaction amount characteristics, the historical transaction time characteristics, and the historical transaction location characteristics, historical transaction characteristics are obtained; The step of determining whether the current transaction information matches the historical transaction characteristics includes: Based on the aforementioned historical transaction records, identify the relevant transaction records; Based on the identified transaction records, obtain the identified transaction information; Based on the identified transaction information, the characteristics of the identified transactions are obtained; Based on the current transaction information, obtain the current transaction time and current transaction location; Based on the current transaction time, the current transaction location, and the identified transaction features, a first matching degree is obtained. The first matching degree = the matching degree between the current transaction time and the time feature in the identified transaction features × time weight + the matching degree between the current transaction location and the location feature in the identified transaction features × location weight. If the first matching degree exceeds a preset degree threshold, it is determined that the current transaction information does not match the historical transaction characteristics; If the first matching degree does not exceed the preset degree threshold, then based on the user information, the first behavior information and the second behavior information are obtained; Each payment account is pre-linked with the information of the account holder and family members, including identity information and daily behavior patterns. The account holder or designated guardian is a person with independent capacity for action, referred to as the first category of person, while persons without independent capacity for action or with limited capacity for action are referred to as the second category of person. Based on the first behavioral information, obtain the first daily behavior time and the corresponding first daily behavior location; Based on the second behavioral information, obtain the time of the second daily behavior and the corresponding location of the second daily behavior; Based on the current transaction time, the current transaction location, the first daily behavior time, and the first daily behavior location, a second matching degree is obtained; Based on the current transaction time, the current transaction location, the second daily behavior time, and the second daily behavior location, a third matching degree is obtained; If the second matching degree exceeds the third matching degree, and the second matching degree exceeds the preset degree threshold, then it is determined that the current transaction information matches the historical transaction characteristics; If the second matching degree is equal to the third matching degree, and the second matching degree exceeds the preset degree threshold, then the current first network speed and house information of the target terminal device are obtained. The house information includes house structure, room size and house attributes. If the network corresponding to the target terminal device is a wireless network, then the target distance and obstruction information between different rooms and the target router are obtained based on the house information; Based on the obstruction information, the number of obstruction layers and the corresponding obstruction properties are obtained; Based on the target distance, the number of obstruction layers, and the properties of the obstruction, the network speed attenuation coefficient is obtained; The attenuation coefficient satisfies the following calculation formula: d is the network speed attenuation coefficient, d is the target distance between the target room and the target router, d0 is the reference distance, n is the number of obstruction layers, kn is the coefficient related to the number of obstruction layers, and p is the quantification value of the obstruction properties. Obtain the current second network speed of the target router, and obtain the target network speed based on the current second network speed and the attenuation coefficient; Based on the target network speed and the current first network speed, obtain the target location; Based on the target location, determine whether the current transaction information matches the historical transaction characteristics.

2. The real-time transaction monitoring method based on a third party according to claim 1, characterized in that, The step of determining whether the current transaction information matches the historical transaction characteristics based on the target location includes: If the target location is the first region, then the current transaction information is determined to match the historical transaction characteristics; If the target location is the second region, then it is determined that the current transaction information does not match the historical transaction characteristics; If the target location is the third region, then the video image corresponding to the third region is obtained; Based on the video images, the trader was identified; If the trader is the first user, then the current transaction information is determined to match the historical transaction characteristics; If the trader is a second user, then it is determined that the current transaction information does not match the historical transaction characteristics.

3. The real-time transaction monitoring method based on a third party according to claim 2, characterized in that, The process of acquiring the trader based on the video image includes: If the video image is obtained, the trader is identified based on the video image; If the video image is not obtained, then obtain the transaction attributes; If the transaction attribute is an account transfer, then determine whether the account source of the receiving account has been detected; If the source of the receiving account is detected, the trader is obtained based on the source of the account. If the transaction attribute is online shopping consumption, then obtain the user information of the receiving user; Obtain the first consumption record of the first user and the second consumption record of the second user; Based on the first consumption record and the second consumption record, generate a first consumption feature and a second consumption feature; Based on the first consumption characteristic, the second consumption characteristic, and the user information, the trader is obtained.

4. The real-time transaction monitoring method based on a third party according to claim 1, characterized in that, The process of obtaining the second matching degree based on the current transaction time, the current transaction location, the first daily behavior time, and the first daily behavior location includes: Based on the current transaction time and the first daily behavior time, obtain the target location; Based on the current transaction location and the target location, obtain the distance difference; Obtain the initial matching degree and unit measurement distance; Get the matching increment per unit distance; Based on the user information, obtain the interest characteristics of the first user and the account characteristics of the receiving user; If the account characteristics and the interest characteristics match, a fourth matching degree is obtained, and a matching compensation coefficient is obtained based on the fourth matching degree. Based on the initial matching degree, the unit measurement distance, the unit distance matching increment, and the matching compensation coefficient, a second matching degree is obtained, which satisfies the following calculation formula: Where F represents the second matching degree, and F0 represents the initial matching degree. This is the distance difference. C is the unit distance measurement distance, and C is the unit distance matching increment. For matching compensation coefficients.

5. The real-time transaction monitoring method based on a third party according to claim 4, characterized in that, The process of obtaining the unit distance matching increment includes: If the current transaction time is the specified behavior time, then obtain the first matching increment and use the first matching increment as the unit distance matching increment; If the current transaction time is not the specified behavior time, then the target distance is obtained based on the current transaction time, the specified behavior time, and the target user information; Based on the target distance and the distance difference, obtain the target distance difference; If the target distance difference is greater than the first distance threshold, then the second matching increment is obtained and used as the unit distance matching increment; If the target distance difference is less than or equal to the first distance threshold, then the third matching increment is obtained and used as the unit distance matching increment; Among them, the first matching increment is greater than the second matching increment, which is greater than the third matching increment.

6. A third-party-based real-time transaction monitoring system for executing the method according to any one of claims 1 to 5, characterized in that, include: The first acquisition module is used to acquire trading accounts based on third-party systems; The second acquisition module is used to acquire the risk value of the risk account if there is a risk account in the trading account. The risk verification module is used to verify the risk of the risk account based on the risk value; If the risk verification is passed, the third acquisition module is used to acquire the current transaction information and the historical transaction records of the transaction account. The fourth acquisition module is used to acquire historical transaction characteristics based on the historical transaction records; The judgment module is used to determine whether the transaction information matches the historical transaction characteristics; An alarm module is used to generate alarm information if the current transaction information does not match the historical transaction characteristics. If the current transaction information matches the historical transaction characteristics, the transaction module is used to allow the transaction account to continue the current transaction based on the third-party system.

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