Data processing method and device, computer device, and storage medium

Abstract

Embodiments of the present application disclose a data processing method and device, computer equipment and a storage medium. The data processing method comprises: obtaining a settled transaction data set, the settled transaction data set comprising N settled transaction data, each settled transaction data comprising transaction information and resource data types, the settled transaction data set corresponding to K resource data types, N and K being positive integers; performing profit and loss statistical processing on the transaction information in the settled transaction data set to obtain the profit and loss of each resource data type; when the profit and loss of M resource data types among the K resource data types is less than a profit and loss threshold, obtaining the profit and loss reasons of the M resource data types, and generating profit and loss alarm information according to the profit and loss reasons of the M resource data types, M being a positive integer; and outputting the profit and loss alarm information. The present application avoids false alarms and fine monitoring of transaction data.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a data processing method, apparatus, computer equipment, and storage medium. Background Technology

[0002] When participating entities support transactions of resource data with multiple data types, they need to monitor the overall profit and loss of all transactions. Currently, the profit and loss of all transactions is calculated manually, and once an anomaly is detected, an alarm is manually generated to notify the relevant personnel to handle the anomaly.

[0003] Inadequate automation in transaction monitoring leads to false alarms, and monitoring only the overall profit and loss of participating entities results in coarse-grained monitoring. Therefore, avoiding false alarms and refining the monitoring of transaction data are current research hotspots. Summary of the Invention

[0004] This application provides a data processing method, apparatus, computer equipment, and storage medium that can avoid false alarms and provide refined monitoring of transaction data.

[0005] One embodiment of this application provides a data processing method, including:

[0006] Obtain a set of settled transaction data, which includes N settled transaction data, each of which includes transaction information and resource data type. The set of settled transaction data corresponds to K resource data types, where N and K are both positive integers.

[0007] Profit and loss statistics are performed on the transaction information in the settled transaction data set to obtain the profit and loss for each type of resource data;

[0008] When the profit or loss of M resource data types out of K resource data types is less than the profit or loss threshold, obtain the reasons for the profit or loss of the M resource data types, and generate profit or loss alarm information based on the reasons for the profit or loss of the M resource data types, where M is a positive integer;

[0009] Output the aforementioned profit and loss alarm information.

[0010] One embodiment of this application provides a data processing apparatus, including:

[0011] The acquisition module is used to acquire a set of settled transaction data, which includes N settled transaction data, each of which includes transaction information and resource data type. The set of settled transaction data corresponds to K resource data types, where N and K are both positive integers.

[0012] The statistics module is used to perform profit and loss statistics processing on the transaction information in the settled transaction data set to obtain the profit and loss of each resource data type;

[0013] The generation module is used to obtain the reasons for the loss or gain of M resource data types when the loss or gain of M resource data types out of K resource data types is less than the loss or gain threshold, and generate loss or gain alarm information based on the reasons for the loss or gain of M resource data types, where M is a positive integer;

[0014] The output module is used to output the loss and gain alarm information.

[0015] One aspect of this application provides a computer device, including a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, the processor performs the methods described in the above embodiments.

[0016] One embodiment of this application provides a computer storage medium storing a computer program, which includes program instructions. When the program instructions are executed by a processor, they perform the methods described in the above embodiments.

[0017] One aspect of this application provides a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. When the computer instructions are executed by the processor of a computer device, the methods described in the above embodiments are performed.

[0018] This application automatically processes transaction information from the settled transaction data set using terminal equipment to obtain the profit and loss for each resource data type. Compared to monitoring only the total profit and loss of all resource data types, this application targets the profit and loss for each resource data type, thus providing finer-grained monitoring of transaction data. Furthermore, this application automatically calculates the profit and loss using terminal equipment and generates alarm information, eliminating human intervention and achieving a high degree of automation, which can avoid false alarms. Moreover, the alarm information in this application also includes the reasons for the small profit and loss of resource data types, enriching the content of the alarm information. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a profit and loss diagram provided in an embodiment of this application;

[0021] Figure 2 This is a schematic diagram of an overall data processing business provided in an embodiment of this application;

[0022] Figure 3 This application provides a schematic diagram of a data processing flow.

[0023] Figure 4 This is a schematic diagram of a profit and loss indicator system provided in an embodiment of this application;

[0024] Figure 5 This is a schematic diagram of a display page provided in an embodiment of this application;

[0025] Figure 6 This application provides a schematic diagram of a data processing flow.

[0026] Figure 7 This is a schematic diagram of a 3-SIGMA algorithm provided in an embodiment of this application;

[0027] Figure 8 This is a schematic diagram of a display page provided in an embodiment of this application;

[0028] Figure 9 This is a schematic diagram of a display page provided in an embodiment of this application;

[0029] Figure 10 This is a schematic diagram of a business processing page provided in an embodiment of this application;

[0030] Figure 11 This is a schematic diagram of a front-end framework provided in an embodiment of this application;

[0031] Figure 12 This is a schematic diagram of an intermediate layer framework provided in an embodiment of this application;

[0032] Figure 13 This is a schematic diagram of a system security framework provided in an embodiment of this application;

[0033] Figure 14 This is a system architecture diagram of a blockchain provided in an embodiment of this application;

[0034] Figure 15 This is a schematic diagram of a data processing flow provided in an embodiment of this application;

[0035] Figure 16 This is a schematic diagram of the structure of a data processing device provided in an embodiment of this application;

[0036] Figure 17 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0037] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0038] This application can be applied to a risk perception and assessment system for cross-border acquiring business. The risk perception module in this system is mainly used to calculate the profit and loss of each type of resource data, the overall profit and loss, and to identify abnormal transaction objects (in this application, transaction objects refer to overseas merchants who have made consumption transactions), enabling relevant business personnel to perceive risk situations in a timely manner. The assessment module in this system is mainly used to identify the causes of losses, issue alarm information, and display corresponding handling pages based on the alarm information, so that business personnel can perform business operations on the handling page, thereby improving the efficiency of business analysis and anomaly handling.

[0039] The following section first explains the definition of cross-border acquiring and the definition of profit and loss for resource data types during cross-border acquiring:

[0040] Cross-border acquiring refers to the process where, after a domestic individual makes a purchase at an overseas merchant, a participating entity collects the RMB payment from the domestic individual and then settles the payment with the overseas merchant through a partner bank.

[0041] Because of the time lag between user spending and merchant settlement, the exchange rate at the time of spending and the exchange rate at the settlement of the same resource data type may fluctuate significantly. This can lead to participating entities needing to purchase more resource data (compared to the transaction time) to settle with merchants for the resource data type they support, resulting in losses. For example, a mainland consumer spends HKD 100 million at merchant A in Hong Kong on day T. At the time of spending, the exchange rate of HKD to RMB is 0.8840. Assuming that due to an external event, the exchange rate of HKD to RMB is 0.9540 at the settlement on day T+1, this results in a loss of HKD 7 million for the participating entity (HKD 100 million × (0.8840 - 0.9540) = -7 million).

[0042] like Figure 1 As shown, Figure 1 This is a profit and loss diagram provided in an embodiment of this application, from... Figure 1 It can be seen that a transaction occurred at 02:00 and was settled at 04:00. The ratio at the time of execution (the ratio at 02:00) was lower than the ratio at the time of settlement (the ratio at 04:00). Therefore, this transaction would result in a loss for the participating entities.

[0043] In summary, the profit and loss of resource data types in cross-border acquiring is defined by the following formula (1):

[0044]

[0045] Among them, Q i t represents the total amount of resource data of the i-th type of resource data. txn t represents the exchange rate of the i-th resource data type during the transaction. settlement This represents the exchange rate for the i-th resource data type at the time of settlement.

[0046] Generally, participating entities can support cross-border acquiring transactions for various resource data types, such as RMB to HKD, RMB to USD, and RMB to JPY. Current technologies often only focus on the overall profit and loss of participating entities in the broader market, resulting in coarse-grained monitoring of profit and loss. Furthermore, when abnormal profit and loss occur, there are no corresponding procedures for broadcasting, attribution of anomalies, and risk management, lacking an integrated handling platform.

[0047] This application proposes a solution based on the aforementioned background technology. It not only determines the overall profit and loss of participating entities but also granularly determines the profit and loss of each resource data type (e.g., determining the profit and loss of Hong Kong dollars, US dollars, and Japanese yen). This granularity allows for more precise monitoring of cross-border acquiring business data. Furthermore, this application forms a closed-loop business process by integrating the discovery of abnormal profits and losses, the investigation of their causes, the generation of alerts, and the display of handling pages, thereby improving the efficiency of anomaly handling. Additionally, this application promptly identifies abnormal merchants by using real-time transaction volume (which can include both settled and unsettled transactions) and predicted transaction volume. Once an abnormal merchant is identified, users can be reminded to exercise caution in their transactions to protect their asset security.

[0048] Please see Figure 2 , Figure 2 This is a schematic diagram of an overall data processing operation provided in an embodiment of this application. The application involves two parts: First, it calculates the profit and loss of various resource data types (commonly known as various currencies) on day T, the overall market profit and loss, and the profit and loss for each time period based on the settled transaction data of day T. It then determines whether to generate an alarm based on the profit and loss thresholds and the calculated profit and loss of each resource data type, the overall market profit and loss, and the profit and loss for each time period. Second, it predicts the predicted trading volume of a trading object on day T using offline transaction data from day T-1. It then uses the trading volume in the real-time transaction data of day T and the predicted trading volume to determine whether the trading object is an abnormal trading object. If it is an abnormal trading object, an alarm should also be issued for that abnormal trading object.

[0049] After generating an alert, this application can further analyze the anomaly from multiple dimensions to determine its cause; and display transaction trends, ratio trends (i.e., exchange rate trends), and profit and loss trends in chart form through a visualization platform. Once the cause of the anomaly is identified, an alert is sent to the corresponding business personnel (i.e., the corresponding...). Figure 2 (In the alarm broadcast), after receiving the alarm, business personnel manually verify and process it. The processing log can then be recorded.

[0050] The data processing scheme provided in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0051] Please see Figure 3 , Figure 3 This is a schematic flowchart illustrating a data processing method provided in an embodiment of this application. The following embodiment is described with a server as the execution entity (although a terminal device can also be described as the execution entity). This data processing scheme includes the following steps:

[0052] Step S101: Obtain a set of settled transaction data. The set of settled transaction data includes N settled transaction data. Each settled transaction data includes transaction information and resource data type. The set of settled transaction data corresponds to K resource data types, where N is a positive integer.

[0053] Specifically, the server obtains a set of settled transaction data, which can be the transaction data in the aforementioned cross-border acquiring business. The set of settled transaction data includes N settled transaction data, each of which includes transaction information and resource data type. The transaction information includes transaction data volume, transaction ratio, and settlement ratio.

[0054] Among them, the transaction ratio can be considered as the exchange rate when consumers make transactions with overseas merchants; the settlement ratio can be considered as the exchange rate when participating entities settle with overseas merchants; and the resource data type refers to the resource data type for foreign exchange settlement, that is, the resource data type supported by overseas merchants.

[0055] For example, if a mainland consumer spends HKD 100 at merchant A in Hong Kong on day T, and the exchange rate of HKD to RMB at the time of the purchase is 0.8840, and the exchange rate of HKD to RMB at the time of settlement on day T+1 is 0.9540, then the corresponding settled transaction data can be represented as: (100, 0.8840, 0.9540, HKD).

[0056] The set of settled transaction data can correspond to K resource data types, where K is no greater than N.

[0057] It should be noted that, to reflect real-time performance, the set of settled transaction data can be retrieved every 10 minutes. For example, if the current date is February 24, 2021, the set of settled transaction data for today can be retrieved every 10 minutes, allowing for real-time determination of the profit and loss for each resource data type.

[0058] Of course, if the interval is shorter, the real-time performance will be stronger. For example, if the data set of settled transactions is obtained every minute, the profit and loss of each type of resource data can be determined more in real time using the solution of this application.

[0059] It should be noted that the above-mentioned settled transaction data refers to transaction data that has already been converted into foreign currency. In other words, it refers to transaction data that the participating entities have already settled with overseas merchants. However, the time of the transaction between the consumer and the overseas merchant is only required to be before the settlement, and there are no other restrictions.

[0060] Resource data types can refer to not only real currency types but also in-game currency types within game applications. For example, on a cross-game application player trading platform, one game application corresponds to one type of in-game currency. The transaction ratio can be considered the ratio at which players in game application A and players in game application B engage in transactions; the settlement ratio can be considered the ratio at which participating entities settle accounts with players in game application B. The resource data type refers to the in-game currency type corresponding to the game application.

[0061] Step S102: Perform profit and loss statistics processing on the transaction information in the settled transaction data set to obtain the profit and loss of each resource data type.

[0062] Specifically, the server can divide these N settled transaction data into multiple unit sets based on the resource data type in each settled transaction data, with the resource data type of the settled transaction data in each unit set being the same.

[0063] For any set of units, calculate the profit or loss per unit for each settled transaction in that set. Summing up all the profit or loss per unit yields the profit or loss for the resource data type corresponding to that set. The profit or loss for the resource data type can be positive or negative; a positive number indicates that the resource data type is currently profitable, and a negative number indicates that the resource data type is currently in a loss state.

[0064] For example, given four settled transaction data points: Settled Transaction Data 1: (100, 0.8, 0.9, HKD), Settled Transaction Data 2: (200, 0.7, 0.9, HKD), Settled Transaction Data 3: (100, 10, 9, USD), and Settled Transaction Data 4: (200, 10, 12, USD), these four settled transaction data points can be divided into two unit sets: Unit Set 1: {Settled Transaction Data 1: (100, 0.8, 0.9, HKD), Settled Transaction Data 2: (200, 0.7, 0.9, HKD)}, and Unit Set 2: {Settled Transaction Data 3: (100, 10, 9, USD), Settled Transaction Data 4: (200, 10, 12, USD)}. For unit set 1: the unit profit / loss of settled transaction data 1 is: 100×(0.8-0.9)=-10, and the unit profit / loss of settled transaction data 2 is: 200×(0.7-0.9)=-40. Therefore, the profit / loss of resource data type Hong Kong dollar is -50; the profit / loss of resource data type US dollar can be calculated in the same way as above, which is -30.

[0065] Optionally, the server can sum up the profits and losses of K types of resource data into a cumulative profit and loss, which can be considered as the overall profit and loss of the participating entities.

[0066] Optionally, each settled transaction data also includes a settlement timestamp. The set of settled transaction data corresponds to P settlement time periods. Except for the last settlement time period, the duration of the remaining settlement time periods is the same as the preset duration (the preset duration can be equal to 1 hour).

[0067] For example, if the set of settled transaction data is the data between 00:00 and 02:30 on 2021 / 2 / 24 / 00:00, and the preset duration is 1 hour, the set of settled transaction data can be divided into 3 settlement time periods: 00:00-00:59 on 2021 / 2 / 24 / 00:00, 01:00-01:59 on 2021 / 2 / 24 / 01:00, and 02:00-02:30 on 2021 / 2 / 24 / 02.

[0068] The server can further divide the aforementioned K unit sets into multiple sub-unit sets based on the settlement timestamp of each settled transaction. Within each sub-unit set, the settled transaction data shares the same resource data type, and their settlement timestamps fall within the same settlement time period. For each sub-unit set, the server calculates the profit or loss of the settled transaction data within that sub-unit set, using this profit or loss as the profit or loss for the corresponding resource data type and settlement time period. A profit or loss can be calculated for all sub-unit sets, yielding the profit or loss for each resource data type within each settlement time period.

[0069] The server can also sum up the profit and loss of all resource data types within each settlement period to form the total profit and loss for that settlement period.

[0070] In summary, the profit and loss for each type of resource data is determined at the level of the resource data type, the total profit and loss within each settlement period is determined at the level of the settlement period, and the cumulative profit and loss is the overall profit and loss of the participating entities.

[0071] Analyzing settled transaction data from different dimensions and at different granularities to determine profit and loss can better help participating entities analyze settled transaction data.

[0072] Please see Figure 4 , Figure 4 This is a schematic diagram of a profit and loss indicator system provided in an embodiment of this application. User transaction data and settlement data are combined to correspond to the settled transaction data in this application. The corresponding settled transaction data can be written to the corresponding channel based on the resource data type in the user transaction data. For settled transaction data occurring at night, the corresponding settled transaction data is written to the corresponding channel's nighttime transit. After the settled transaction data is divided, the daily cumulative profit and loss of the same resource data type can be obtained by statistically analyzing the profit and loss. The daily cumulative profit and loss of all resource data types are summed to form the overall profit and loss, which represents the total profit and loss of the participating entity on that day.

[0073] The server can display the total profit and loss, cumulative profit and loss, and profit and loss for each type of resource data for each time period on the display page; or, the server can display the total profit and loss for the longest settlement period among the P settlement time periods (if the duration of the settlement time period is 1 hour, the total profit and loss for all resource data types in the current 1 hour) and cumulative profit and loss on the display page.

[0074] Of course, when displaying the profit and loss of each resource data type, the server can display the profit and loss of K resource data types in descending (or ascending) order.

[0075] Please see Figure 5 , Figure 5 This is a schematic diagram of a display page provided in an embodiment of this application. The display page can show the hourly cumulative profit and loss (cumulative profit and loss within the current hour) and the daily cumulative profit and loss of the market. Figure 5 The black highlighted area represents the amount of loss. As you can see, different loss amounts correspond to different alarm levels, and naturally, different alarm levels generate different alarm messages. For example, a level 1 alarm level might be displayed in orange, while a level 2 alarm level might be displayed in red, and so on.

[0076] Step S103: When the profit or loss of M resource data types out of K resource data types is less than the profit or loss threshold, obtain the reasons for the profit or loss of the M resource data types, and generate profit or loss alarm information based on the reasons for the profit or loss of the M resource data types, where M is a positive integer.

[0077] Specifically, the server obtains a preset profit and loss threshold. When the profit and loss of M resource data types out of K resource data types is less than the profit and loss threshold, the server obtains the profit and loss reason for each of these M resource data types, where M is a positive integer.

[0078] The specific process for determining the profit and loss reasons for any one of the M resource data types (referred to as the target resource data type) is as follows:

[0079] As mentioned above, the set of settled transaction data corresponds to P settlement time periods. The longest settlement time period among the P settlement time periods can be called the second settlement time period, and the settlement time period adjacent to the second settlement time period among the P settlement time periods can be called the first settlement time period.

[0080] The server extracts settled transaction data of the target resource data type within a first settlement period (referred to as the first settled transaction dataset) from the settled transaction dataset, and extracts settled transaction data of the target resource data type within a second settlement period (referred to as the second settled transaction dataset). The server calculates the average transaction ratio of the settled transaction data in the first settled transaction dataset (referred to as the first average transaction ratio a1), calculates the average settlement ratio of the settled transaction data in the first settled transaction dataset (referred to as the first average settlement ratio b1), and calculates the sum of the transaction data volume of the settled transaction data in the first settled transaction dataset (referred to as the first total transaction volume c1).

[0081] Similarly, the server calculates the average transaction ratio of the settled transaction data in the second settled transaction data set (referred to as the second transaction ratio average a2), calculates the average settlement ratio of the settled transaction data in the second settled transaction data set (referred to as the second settlement ratio average b2), and calculates the sum of the transaction data volume of the settled transaction data in the second settled transaction data set (referred to as the second transaction total c2).

[0082] Calculate the first profit or loss s1 according to the following formula (2):

[0083] s1=c1*(a1-b1) (2)

[0084] Calculate the second profit / loss s2 according to the following formula (3):

[0085] s2=c2*(a2-b2) (3)

[0086] The server subtracts the first profit and loss s1 from the second profit and loss s2 to obtain the change in profit and loss. The formula for calculating the change in profit and loss is as follows: Formula (4):

[0087] Δs=s2-s1 (4)

[0088] Calculate the transaction volume contribution rate p1 according to the following formula (5):

[0089]

[0090] The contribution rate p2 is calculated according to the following formula (6):

[0091]

[0092] The server can combine the calculated transaction volume contribution rate p1 and ratio contribution rate p2 to determine the profit and loss factors for the target resource data type. Analyzing the calculation process of these profit and loss factors reveals that the profit and loss factors are determined by the sum of the transaction volume factor's contribution to overall profit and loss and the ratio between resource data types' contribution to overall profit and loss.

[0093] The server can use the same method to determine the profit or loss reasons for each of the M resource data types.

[0094] Once the server determines the cause of profit or loss for each of the M resource data types, it can generate profit / loss alarm information containing the causes of profit or loss for all M resource data types. This helps business personnel quickly locate the cause of the problem and take optimal action during the handling process. Alternatively, the server can generate corresponding profit / loss alarm information for each of the M resource data types, resulting in the generation of M profit / loss alarm messages.

[0095] Optionally, as described above, when the profit or loss of a resource data type is less than a profit or loss threshold, a profit or loss alarm message will be generated for that resource data type. As mentioned earlier, not only was the profit or loss of each resource data type calculated, but also the total profit or loss for each settlement period and the cumulative profit or loss. Similarly, if the total profit or loss of any of the P settlement periods is less than the first threshold, the resource data type (referred to as the auxiliary resource data type) that caused the total profit or loss of that settlement period to be less than the first threshold is analyzed. Then, the above method is used to determine the cause of the profit or loss of this auxiliary resource data type, and an alarm message is generated, which includes the cause of the profit or loss of the auxiliary resource data type.

[0096] Optionally, if the cumulative profit or loss is less than the second threshold, the resource data type (referred to as the auxiliary resource data type) that caused the cumulative profit or loss to be less than the second threshold is analyzed, and then the above method is used to determine the cause of the profit or loss of the auxiliary resource data type, and then an alarm message is generated, which contains the cause of the profit or loss of the auxiliary resource data type.

[0097] As we can see, when it is necessary to determine the reasons for cumulative profit and loss, or the reasons for profit and loss in each settlement period, we ultimately need to focus on a certain type of resource data.

[0098] Step S104: Output the loss and gain alarm information.

[0099] As described above, this application not only calculates the total profit and loss of the participating entities, but also the total profit and loss within each time period, as well as the profit and loss of each resource data type, providing finer-grained monitoring of transaction data. Furthermore, this application displays the total profit and loss, cumulative profit and loss, and profit and loss of each resource data type in descending order on the display page, which can improve the display effect of profit and loss. Moreover, the profit and loss reasons in this application are the contribution rate of transaction volume factors to the overall profit and loss + the contribution rate of the ratio between resource data types to the overall profit and loss, providing accurate analysis of the profit and loss reasons for resource data types.

[0100] Please see Figure 6 , Figure 6 This application provides a schematic diagram of a data processing process, which includes the following steps:

[0101] Step S201: Obtain a set of settled transaction data. The set of settled transaction data includes N settled transaction data. Each settled transaction data includes transaction information and resource data type. The set of settled transaction data corresponds to K resource data types. N and K are both positive integers.

[0102] Step S202: Perform profit and loss statistics processing on the transaction information in the settled transaction data set to obtain the profit and loss of each resource data type.

[0103] Step S203: When the profit or loss of M resource data types out of K resource data types is less than the profit or loss threshold, obtain the reasons for the profit or loss of the M resource data types, generate profit or loss alarm information based on the reasons for the profit or loss of the M resource data types, where M is a positive integer, and output the profit or loss alarm information.

[0104] The specific implementation methods of steps S201-S203 can be found above. Figure 3 Steps S101-S104 in the corresponding embodiment.

[0105] Step S204: Obtain the target transaction data of U transaction objects, where each target transaction data includes the transaction data volume.

[0106] Specifically, the server obtains the target transaction data of U transaction objects on day T. The previously mentioned settled transaction data can correspond to U transaction objects. Here, the transaction objects can refer to overseas merchants or game players. The current date is day T. In other words, the server obtains the transaction data of U transaction objects on that day.

[0107] Target transaction data refers to transaction data where a transaction has occurred between a consumer and a trading partner, but the trading partner has not yet settled the transaction with the participating entity. For example, on February 24, 2021, at 12:00, a user purchased product A from merchant A in Hong Kong for HK$100, and the transaction ratio was 0.8. However, the participating entity has not yet settled this transaction with merchant A. Therefore, the target transaction data can be represented as: (100, Hong Kong merchant A, 0.8, 2021 / 2 / 24 / 12:00). Since the transaction has not yet been settled, the corresponding settled transaction data cannot be generated at this time.

[0108] Step S205: Statistical processing is performed on the transaction data volume in the target transaction data of the U transaction objects to obtain abnormal transaction objects among the U transaction objects, and transaction alarm information is generated based on the abnormal transaction objects.

[0109] Specifically, the server can divide all target transaction data into U clusters according to different transaction objects, with the target transaction data in each cluster belonging to the same transaction object.

[0110] The total amount of transaction data in the target transaction data of each cluster is added together to obtain the total amount of transaction data of the corresponding transaction object of that cluster, which is to obtain the total amount of unsettled cumulative transaction data of each merchant.

[0111] The server then obtains the predicted transaction data volume for each transaction object. The predicted transaction data volume for the transaction object on day T is predicted from the actual transaction data for the transaction object on day T-1. The actual transaction data on day T-1 is the complete transaction data for day T-1.

[0112] The server will identify transactions whose predicted transaction data volume exceeds the cumulative transaction data volume as abnormal transactions.

[0113] This application categorizes all trading objects into three types (high-activity, medium-activity, and low-activity) based on five aspects: total number of historical trading days, time of the most recent transaction, average daily trading volume, trading stability, and trading activity. Each type is matched with a corresponding prediction scheme. Specifically, the Prophet algorithm is used for prediction of high-activity trading objects, the 3-SIGMA algorithm is used for medium-activity trading objects, and the dynamic industry average is used for low-activity trading objects.

[0114] The following sections explain these three prediction schemes. First, we will explain how to predict the predicted trading volume of a trading object on day T based on the Prophet algorithm and the complete historical trading data of a specific trading object on day T-1 (mainly utilizing the trading volume in the historical trading data):

[0115] Based on classical time series theory, Prophet breaks down time series data into trend, periodic, seasonal, and holiday components, and fits each component piecewise, which can be expressed as the following formula (7):

[0116] y(t)=g(t)+s(t)+h(t)+ε(t) (7)

[0117] In this algorithm, g(t) represents the trend term, indicating the non-periodic trend of the time series; s(t) represents the periodic term, or seasonal term, generally measured in weeks, years, or hours; h(t) represents the holiday term, indicating whether a holiday occurs on a given day; and ε(t) represents the error term, or residual term. The Prophet algorithm fits these terms and then sums them up to obtain the predicted value of the time series.

[0118] Trend model g(t): Piecewise linear or logistic growth curve trend:

[0119] The trend term based on logistic regression can be expressed as the following formula (8):

[0120]

[0121] Where C(t) is the upper limit of business experience; k is the initial growth rate; and α(t) is the indicator function. σ represents the change in the growth rate, and m is the deviation parameter;

[0122] The trend term based on linear regression can be expressed as the following formula (9):

[0123] g(t) = (k + α(t)σ*t + (m + α(t)) T γ)) (9)

[0124] The meaning of the parameter in formula (9) is the same as that of the parameter in formula (8).

[0125] Seasonal component model s(t): Simulating the seasonal components of each year using Fourier series:

[0126] Since the seasonal term describes periodic behavior, it can be comprehensively represented by sine and cosine functions (formula (10) below) as a Fourier series.

[0127]

[0128] Based on experience, for a sequence with a period of one year (P = 365.25), N = 10; for a sequence with a period of one week (P = 7), N = 3.

[0129] Equation (10) can be further abstracted as: s(t)=X(t)β, where β~N(0,σ 2 In the model, σ 2 Controlled by seasonal priority levels, σ 2 The larger the value, the more pronounced the seasonal effect.

[0130] The holiday term model h(t) uses dummy variables to simulate the weekly periodic components, and can be expressed by the following formula (11):

[0131]

[0132] Where, k~N(0,v) 2 v represents the holiday priority, with a default value of 10. The larger the value of v, the greater the impact of holidays on the model.

[0133] Next, we will explain how to predict the predicted trading volume of a trading object on day T based on the 3-SIGMA algorithm and the complete historical trading data of a certain trading object on day T-1 (mainly using the trading volume in the historical trading data):

[0134] The 3-SIGMA model data must follow a normal distribution. Under the 3σ principle, outliers exceeding three times the standard deviation are considered outliers. The probability of positive or negative 3σ is 99.7%. Therefore, the mean u and standard deviation σ are first calculated based on the amount of trading data in the complete historical trading data of day T-1, and u+3σ is used as the predicted amount of trading data for that trading object on day T.

[0135] Please see Figure 7 , Figure 7 Figure 7 is a schematic diagram of a 3-SIGMA algorithm provided in an embodiment of this application. As can be seen from Figure 7, an error equal to ±3σ is usually taken as the limiting error. For a normally distributed random error, the probability of falling outside ±3σ is only 0.27%, which is very small in a finite number of measurements, hence the existence of the 3σ criterion. If the absolute value νi of the residual error of a certain measurement value in a set of measurement data is greater than 3σ, then the measurement value is a bad value, which corresponds to the cumulative transaction data amount exceeding the predicted transaction volume in this application.

[0136] Finally, let's explain how to use dynamic industry averages to predict the volume of transaction data for a specific trading object on day T:

[0137] The server obtains the historical transaction data of other trading objects in the same industry as the trading object to be predicted on day T-1, and uses the average value of the transaction data in the obtained historical transaction data as the predicted transaction data volume of the trading object to be predicted on day T.

[0138] At this point, the server has identified the abnormal transaction objects from the U transaction objects. The server can directly generate transaction alarm information containing the abnormal transaction objects to remind business processing personnel to pay special attention to these abnormal transaction objects.

[0139] Optionally, after identifying the abnormal transaction object, it is possible to further determine whether to generate a transaction alarm message and the corresponding handling part of the transaction alarm message based on variables such as the transaction volume, the risk rating of the transaction object, the deviation of the cumulative transaction data volume and the predicted transaction data volume. The specific process is described by the following algorithm:

[0140]

[0141] Step S206: Output the transaction alarm information.

[0142] Optionally, the server can also sort the U transaction objects based on their cumulative transaction data volume, obtaining a sorting result. This result includes the U transaction objects and the cumulative transaction data volume for each object, arranged in descending (or ascending) order. The server displays the sorting result on the display page to allow business personnel to quickly locate transaction objects with larger cumulative transaction data volumes.

[0143] Please see Figure 8 , Figure 8 This is a schematic diagram of a display page provided in an embodiment of this application. By calculating the cumulative transaction volume of each transaction object (the cumulative transaction volume can correspond to the cumulative transaction data volume of this application), multiple transaction objects are displayed on the display page in descending order of size, along with the cumulative transaction volume of each transaction object.

[0144] Optionally, each target transaction data also includes a transaction timestamp and a transaction ratio. The server can divide all target transaction data into multiple target transaction data sets based on the transaction timestamps of the target transaction data for U transaction objects. Target transaction data within the same target transaction data set will have transaction timestamps within the same transaction time period. For example, one hour can be considered as a single transaction time period.

[0145] The server multiplies the transaction data volume of each target transaction by the transaction ratio to obtain the converted transaction volume for each target transaction. This is because different target transaction data correspond to different resource data types, and the resource data type of the converted transaction volume obtained by multiplying the transaction data volume and the transaction ratio is the same (for example, the resource data type of the converted transaction volume is RMB). The server can sum the converted transaction volumes of target transaction data in the same target transaction data set to obtain the transaction exposure for the corresponding transaction time period of that target transaction data set. The transaction exposure of all transaction time periods is summed into a cumulative transaction exposure, and the transaction exposure for each transaction time period and the cumulative transaction exposure are displayed on the display page. Alternatively, the display page can only display the cumulative transaction exposure and the transaction exposure of the largest transaction time period among multiple transaction time periods.

[0146] Transaction exposure refers to the amount of transaction data that a consumer has transacted with an overseas merchant but the transaction has not yet been settled with the overseas merchant. In simple terms, transaction exposure is the risk that the participating entity needs to bear. The larger the transaction exposure, the greater the risk that the participating entity needs to bear.

[0147] When the trading exposure in any of the multiple trading time periods exceeds the first exposure threshold, or when the cumulative trading exposure exceeds the second exposure threshold, the server generates an exposure alarm and outputs the exposure alarm to the corresponding business personnel.

[0148] like Figure 5 As shown, the display page shows the cumulative exposure of the market within the current hour, as well as the cumulative exposure of the market for the current day. When the cumulative exposure within the hour exceeds the corresponding threshold, or the cumulative exposure within the day exceeds the corresponding threshold, a corresponding alarm message can be generated. Different levels of alarm messages correspond to different degrees of exceeding the threshold, which can help business personnel prioritize the processing of alarm messages.

[0149] Please see Figure 9 , Figure 9 This is a schematic diagram of a display page provided in an embodiment of this application. The display page shows the exposure (which can be hourly cumulative exposure or daily cumulative exposure) corresponding to each resource data type (foreign currency type), as well as the profit and loss (which can be hourly cumulative profit and loss or daily cumulative profit and loss) for each resource data type. From the perspective of resource data types, the risks borne by each resource data type and the overall profit and loss are analyzed.

[0150] The exposure for each resource data type is obtained by summing up the conversion transaction volume of the target transaction data belonging to the same resource data type.

[0151] Optionally, when the server receives profit / loss alarm information, transaction alarm information, or exposure alarm information, the server can display the corresponding alarm information's business processing page, allowing business personnel to generate user processing instructions on the business processing page. Alternatively, the server can generate a business processing page and push it to the corresponding business processing personnel, enabling them to generate user processing instructions on the business processing page.

[0152] Please see Figure 10 , Figure 10 This is a schematic diagram of a business processing page provided in an embodiment of this application. The business processing page corresponding to Figure 10 is for processing transaction alarm information. The business processing page can display the identifier of the transaction object, the resource data type, the predicted transaction volume, and the real-time cumulative transaction volume. Of course, the real-time cumulative transaction volume is greater than the predicted transaction volume. Business personnel can select the corresponding processing operations based on the information displayed on the business processing page. Subsequently, user processing instructions can be generated based on the processing operations selected by the business personnel, and the server can execute the corresponding business operations based on the user processing instructions.

[0153] As mentioned above, the risk perception and assessment system for cross-border acquiring business can display the total profit and loss, cumulative profit and loss, and profit and loss for each resource data type for each settlement period, analyze the causes of profit and loss, generate alarm information, and redirect to the business processing page. The following explains the visualization implementation of the above system:

[0154] The front-end primarily handles data visualization, facilitating the analysis, alerting, and display of key data. A process is run in the Node.js middleware layer to handle data querying and API merging. The front-end uses the Vue framework and ECharts for data visualization, while the Node.js middleware layer uses the Egg framework. The relevant technical frameworks are shown in Table 1 below.

[0155] Table 1

[0156] Front-end framework Vue.js + Egg.js + elementUI Front-end chart library Ehcharts database MySQL

[0157] like Figure 11 As shown, the presentation uses the Vue framework based on the MVVM pattern, the common component library ElementUI, and the charting library Echarts. ElementUI's common components are integrated into larger-granularity business components. Leveraging Echarts' rich charting library, the data processed at the Node.js layer is displayed.

[0158] Data display and processing:

[0159] When it is necessary to display ratios (including transaction ratios and settlement ratios), since the ratio values ​​have relatively small deviations, it is necessary to clearly show the magnitude of the rise and fall in the display. The vertical axis displays the lowest and highest values ​​of the exchange rate, and the values ​​are arranged according to the deviation between the lowest and highest values, so as to achieve the effect of obvious fluctuations.

[0160] When displaying the total profit and loss for each settlement period or the trading exposure for each trading period, the horizontal axis data may be quite dense. In such cases, the points on the horizontal axis are spaced out. Specifically, some points on the horizontal axis are displayed first, while others are hidden. When the cursor moves to a hidden point, that hidden point is then displayed. This achieves an appropriate display density while maintaining the data trend.

[0161] To achieve better visualization, some interfaces in the Node.js layer have been merged. The merged interfaces are those that can retrieve the initial screen data, and these data are requested asynchronously. Non-initial screen data is lazy-loaded. Merging the interfaces reduces the number of HTTP requests to the initial screen, thus optimizing it.

[0162] like Figure 12As shown, the Node middleware layer uses the egg framework, which inherits from the koa framework. The middleware is written using the onion ring model, providing architectural capabilities to the upper layers. It features a highly extensible plugin mechanism; a plugin can contain extend (extending the context of the base object), middleware (adding one or more middleware), and config (configuring the plugin's default configuration items for various environments). Iterative development is carried out within a standardized directory structure, encompassing the routing layer, control layer, service layer, middleware, and scheduler.

[0163] In supporting the cross-border acquiring business, the risk perception and assessment system uses middleware to handle the security filtering and access control of interface requests. It configures the corresponding API paths at the routing layer, writes the business interfaces in the controller, writes SQL statements to call the MySQL database at the server layer, and relies on Egg's schedule to run scheduled tasks and perform periodic queries to monitor abnormal business data and determine whether to generate alarm information, thus shortening the time for business users to troubleshoot problems.

[0164] like Figure 13 As shown, the security and stability of the internal functions of the risk perception and assessment system for cross-border acquiring business are supported by four external components: the FiT permission system, mobile analytics tools, AEGIS front-end monitoring, and alarm generation. Real-time performance and effectiveness ensure the stability of the system and business data. In the risk perception and assessment system for cross-border acquiring business, a Node.js plugin is used within the Egg framework, without affecting the original business logic, providing external protection and monitoring for the system. This external protection allows the internal functions to operate smoothly.

[0165] Please see Figure 14 , Figure 14 This is a system architecture diagram of a blockchain provided in an embodiment of this application. The server in the foregoing embodiment can be... Figure 14 Node 1, Node 2, Node 3, or Node 4 can all be combined to form a blockchain system. Each node includes a hardware layer, a middleware layer, an operating system layer, and an application layer. From Figure 14 As can be seen, each node in the blockchain system stores the same blockchain data. It is understood that these nodes can include computer devices. The following embodiments are described using a target blockchain node as the execution subject. The target blockchain node is any one of multiple nodes in the blockchain system, and can correspond to the server in the aforementioned embodiments.

[0166] Please see also Figure 15 , Figure 15This is a schematic diagram of a data processing process provided in an embodiment of this application. This embodiment mainly describes the combination of monitoring target transaction data and settled transaction data with blockchain technology. The data processing includes the following steps:

[0167] Step 301: Obtain a set of settled transaction data from the blockchain. The set of settled transaction data includes N settled transaction data, each of which includes transaction information and resource data type. The set of settled transaction data corresponds to K resource data types, where N and K are both positive integers.

[0168] Specifically, when a monitoring request is received, the target blockchain node extracts the block height carried in the monitoring request, reads the block corresponding to that block height on the blockchain, and reads the data from the block body of that block as a set of settled transaction data.

[0169] When participating entities settle accounts with cross-border merchants, the target blockchain node can store the settled transaction data on the blockchain in real time and record the block height at which the settled transaction data is stored.

[0170] Step S302: Perform profit and loss statistics processing on the transaction information in the settled transaction data set to obtain the profit and loss of each resource data type;

[0171] Step S303: When the profit or loss of M resource data types out of K resource data types is less than the profit or loss threshold, obtain the reasons for the profit or loss of the M resource data types, generate profit or loss alarm information based on the reasons for the profit or loss of the M resource data types, where M is a positive integer, and output the profit or loss alarm information.

[0172] The specific processes of steps S302-S303 can be found above. Figure 3 The corresponding steps S102-S104 in the embodiment.

[0173] Step 304: Package the total profit and loss, cumulative profit and loss, profit and loss of each resource data type, reasons for profit and loss of M resource data types, and profit and loss alarm information into blocks for each settlement period, and store the blocks on the blockchain.

[0174] Specifically, the target blockchain node can store the total profit and loss, cumulative profit and loss, profit and loss for each resource data type, the reasons for profit and loss for M resource data types, and profit and loss alarm information for each settlement period calculated during the intermediate process of generating profit and loss alarm information into the block body. It then calculates the Merkle root of the total profit and loss, cumulative profit and loss, profit and loss for each resource data type, the reasons for profit and loss for M resource data types, and profit and loss alarm information for each settlement period, and obtains the hash value of the last block of the current blockchain. The target blockchain node stores the calculated Merkle root, the hash value of the last block of the current blockchain, and the current timestamp into the block header. The target blockchain node combines the block header and block body into a block, stores this block in the blockchain maintained by the target blockchain node, and broadcasts the block to other nodes so that the other nodes add the block to their respective maintained blockchains, thus synchronizing the blockchains maintained by all nodes.

[0175] Optionally, in addition to storing the total profit and loss, cumulative profit and loss, profit and loss of each resource data type, the reasons for profit and loss of M resource data types, and profit and loss alarm information on the blockchain for each settlement period, abnormal transaction objects, transaction alarm information, transaction exposure for each transaction period, cumulative transaction exposure, and exposure alarm information can also be stored on the blockchain.

[0176] As can be seen from the above, relying on the complete and immutable properties of blockchain, it can be guaranteed that the set of settled transaction data obtained by the target blockchain node is trustworthy and has not been tampered with. Therefore, the profit and loss of each type of resource data statistically based on the set of settled transaction data, as well as the generated profit and loss reasons and profit and loss alarm information, are also trustworthy, which can guarantee the accuracy of the statistical results of the set of settled transaction data.

[0177] For further details, please see Figure 16 This is a schematic diagram of the structure of a data processing device provided in an embodiment of this application. Figure 16 As shown, the data processing device 1 can be applied to the above-mentioned Figures 1-15 The corresponding embodiment refers to the server or target blockchain node. Specifically, the data processing device 1 can be a computer program (including program code) running on a computer device, such as an application software; the data processing device 1 can be used to execute the corresponding steps in the method provided in the embodiments of this application.

[0178] The data processing device 1 may include: an acquisition module 11, a statistics module 12, a generation module 13, and an output module 14.

[0179] The acquisition module 11 is used to acquire a set of settled transaction data, which includes N settled transaction data, each of which includes transaction information and resource data type. The set of settled transaction data corresponds to K resource data types, where N and K are both positive integers.

[0180] The statistics module 12 is used to perform profit and loss statistics processing on the transaction information in the settled transaction data set to obtain the profit and loss of each resource data type;

[0181] The generation module 13 is used to obtain the reasons for the loss or gain of M resource data types when the loss or gain of M resource data types among K resource data types is less than the loss or gain threshold, and generate loss or gain alarm information based on the reasons for the loss or gain of M resource data types, where M is a positive integer.

[0182] Output module 14 is used to output the loss and gain alarm information.

[0183] In one possible implementation, when the statistics module 12 performs profit and loss statistics processing on the transaction information in the settled transaction data set to obtain the profit and loss for each resource data type, it is specifically used for:

[0184] The set of settled transaction data is divided into K unit sets, and the resource data types in each unit set are the same.

[0185] Profit and loss statistics are performed on the transaction information in each unit set to obtain the profit and loss for each type of resource data.

[0186] In one possible implementation, each settled transaction data also includes a settlement timestamp, and the set of settled transaction data corresponds to P settlement time periods;

[0187] The data processing device 1 may further include: a first display module 15.

[0188] The first display module 15 is used to perform profit and loss statistics on the transaction information in the settled transaction data corresponding to each settlement time period, obtain the profit and loss of each resource data type in each settlement time period, sum up the profit and loss of all resource data types in each settlement time period to form the total profit and loss in each settlement time period, and sum up the profit and loss of each resource data type to form the cumulative profit and loss. The total profit and loss, cumulative profit and loss, and profit and loss of each resource data type are displayed on the display page for each settlement time period.

[0189] In one possible implementation, the target resource data type is any of the M resource data types, and the P settlement time periods include a first settlement time period and a second settlement time period, the first settlement time period and the second settlement time period are adjacent, and the second settlement time period is the longest settlement time period among the P settlement time periods.

[0190] When determining the reasons for gains and losses in the target resource data type, generation module 13 is specifically used for:

[0191] Extract the first set of settled transaction data containing the target resource data type within the first settlement time period;

[0192] Extract the target resource data type from the settled transaction data set and extract the second settled transaction data set within the second settlement time period;

[0193] The first and second settled transaction data sets are analyzed and processed to obtain the transaction volume contribution rate and the ratio contribution rate.

[0194] The transaction volume contribution rate and ratio contribution rate are combined to form the profit and loss reason for the target resource data type.

[0195] In one possible implementation, the transaction information includes transaction data volume, transaction ratio, and settlement ratio;

[0196] When generating module 13 analyzes and processes the first and second settled transaction data sets to obtain the transaction volume contribution rate and ratio contribution rate, it is specifically used for:

[0197] The first average transaction ratio is determined based on the transaction ratio in the first settled transaction data set; the first average settlement ratio is determined based on the settlement ratio in the first settled transaction data set; and the first total transaction volume is determined based on the transaction data volume in the first settled transaction data set.

[0198] The average second transaction ratio is determined based on the transaction ratio in the second settled transaction data set; the average second settlement ratio is determined based on the settlement ratio in the second settled transaction data set; and the total second transaction volume is determined based on the transaction data volume in the second settled transaction data set.

[0199] The first profit or loss is determined based on the average first transaction ratio, the average first settlement ratio, and the first total transaction volume. The second profit or loss is determined based on the average second transaction ratio, the average second settlement ratio, and the second total transaction volume. The change in profit or loss is obtained by subtracting the first profit or loss from the second profit or loss.

[0200] The trading volume contribution rate is determined based on the second total trading volume, the average first trading ratio, the average first settlement ratio, the first profit and loss, and the change in profit and loss. The ratio contribution rate is determined based on the second profit and loss and the trading volume contribution rate.

[0201] In one possible implementation, the acquisition module 11 is further configured to acquire target transaction data of U transaction objects, each target transaction data including transaction data volume;

[0202] The statistics module 12 is also used to perform statistical processing on the amount of transaction data in the target transaction data of the U transaction objects to obtain abnormal transaction objects in the U transaction objects, and generate transaction alarm information based on the abnormal transaction objects;

[0203] The output module 14 is also used to output the transaction alarm information.

[0204] In one possible implementation, each target transaction data also includes a transaction timestamp and a transaction ratio; the data processing device 1 also includes a second display module 16.

[0205] The second display module 16 is used to divide the target transaction data into multiple target transaction data sets according to the transaction timestamp of the target transaction data, and the transaction timestamps of the target transaction data in the same target transaction data set are within the same transaction time period;

[0206] The second display module 16 is also used to determine the conversion transaction volume of each target transaction data according to the transaction data volume and transaction ratio in each target transaction data, to superimpose the conversion transaction volumes of the target transaction data in the same target transaction data set to obtain the transaction exposure for each transaction time period, to superimpose the transaction exposure of all transaction time periods to obtain the cumulative transaction exposure, and to display the transaction exposure of each transaction time period and the cumulative transaction exposure on the display page.

[0207] In one possible implementation, the generation module 13 is further configured to generate an exposure alarm message when the trading exposure of any trading time period in a plurality of trading time periods is greater than a first exposure threshold, or when the cumulative trading exposure is greater than a second exposure threshold.

[0208] In one possible implementation, when the statistics module 12 performs statistical processing on the transaction data volume in the target transaction data of the U transaction objects to obtain the abnormal transaction objects among the U transaction objects, it is specifically used for:

[0209] The transaction data volume in the target transaction data of each transaction object is added together with the cumulative transaction data volume of each transaction object;

[0210] Obtain the predicted transaction data volume for each transaction object;

[0211] Transactions with a predicted transaction volume greater than the cumulative transaction volume are classified as abnormal transactions.

[0212] In one possible implementation, the data processing device 1 may further include a third display module 17.

[0213] The third display module 17 is used to sort the cumulative transaction data of U transaction objects, obtain the sorting result, and display the sorting result on the display page.

[0214] In one possible implementation, the data processing device 1 may further include a third display module 18.

[0215] The fourth display module 18 is used to display a business processing page when a profit and loss alarm message is received or a transaction alarm message is received. The business processing page is used to receive user processing instructions.

[0216] In one possible implementation, the set of settled transaction data is stored on a blockchain;

[0217] The data processing device 1 may further include: a storage module 19.

[0218] Storage module 19 is used to package the total profit and loss, cumulative profit and loss, profit and loss of each resource data type, profit and loss reasons of M resource data types, and profit and loss alarm information into blocks for each settlement period, and store the blocks on the blockchain.

[0219] According to one embodiment of the present invention, Figures 1-15 Each step involved in the method shown can be performed by... Figure 16 The data processing is performed by the various modules within the shown data processing device. For example, Figure 3 Steps S101-S104 shown can be respectively by Figure 16 The acquisition module 11, statistics module 12, generation module 13, output module 14, and first display module 15 shown are used for execution; for example, Figure 6 Steps S204-S206 shown can be performed by Figure 16 The acquisition module 11, statistics module 12, output module 14, second display module 16, third display module 17, and fourth display module 18 shown are used for execution; for example, Figure 15 Step S304 shown can be performed by Figure 16 The storage module 19 shown is used to perform this.

[0220] Further, please see Figure 17 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Figures 1-15In the corresponding embodiment, the server or target blockchain node can be a computer device 1000. For example... Figure 17 As shown, computer device 1000 may include a user interface 1002, a processor 1004, an encoder 1006, and a memory 1008. A signal receiver 1016 is used to receive or transmit data via a cellular interface 1010, a Wi-Fi interface 1012, ..., or an NFC interface 1014. The encoder 1006 encodes the received data into a data format that can be processed by a computer. The memory 1008 stores a computer program, and the processor 1004 is configured to execute the steps in any of the above method embodiments via the computer program. The memory 1008 may include volatile memory (e.g., dynamic random access memory DRAM) and may also include non-volatile memory (e.g., one-time programmable read-only memory OTPROM). In some instances, the memory 1008 may further include memory remotely located relative to the processor 1004, which can be connected to the computer device 1000 via a network. The user interface 1002 may include a keyboard 1018 and a display 1020.

[0221] exist Figure 17 In the computer device 1000 shown, the processor 1004 can be used to call computer programs stored in the memory 1008 to achieve:

[0222] Obtain a set of settled transaction data, which includes N settled transaction data, each of which includes transaction information and resource data type. The set of settled transaction data corresponds to K resource data types, where N and K are both positive integers.

[0223] Profit and loss statistics are performed on the transaction information in the settled transaction data set to obtain the profit and loss for each type of resource data;

[0224] When the profit or loss of M resource data types out of K resource data types is less than the profit or loss threshold, obtain the reasons for the profit or loss of the M resource data types, and generate profit or loss alarm information based on the reasons for the profit or loss of the M resource data types, where M is a positive integer;

[0225] Output the aforementioned profit and loss alarm information.

[0226] In one embodiment, when the processor 1004 performs profit and loss statistical processing on the transaction information in the settled transaction data set to obtain the profit and loss of each resource data type, it specifically performs the following steps: dividing the settled transaction data set into K unit sets, where the resource data types in each unit set are the same;

[0227] Profit and loss statistics are performed on the transaction information in each unit set to obtain the profit and loss for each type of resource data.

[0228] In one embodiment, each settled transaction data also includes a settlement timestamp, and the set of settled transaction data corresponds to P settlement time periods;

[0229] Processor 1004 also performs the following steps:

[0230] Profit and loss statistics are performed on the transaction information in the settled transaction data corresponding to each settlement period to obtain the profit and loss of each resource data type in each settlement period.

[0231] The profit and loss of all resource data types within each settlement period are summed up to form the total profit and loss for that settlement period.

[0232] The profit and loss of each resource data type are summed up to form a cumulative profit and loss;

[0233] The display page shows the total profit and loss, cumulative profit and loss, and profit and loss for each type of resource data for each settlement period.

[0234] In one embodiment, the target resource data type is any one of the M resource data types, and the P settlement time periods include a first settlement time period and a second settlement time period. The first settlement time period and the second settlement time period are adjacent, and the second settlement time period is the longest settlement time period among the P settlement time periods.

[0235] When processor 1004 is determining the cause of gain or loss for the target resource data type, it specifically performs the following steps:

[0236] Extract the first set of settled transaction data containing the target resource data type within the first settlement time period;

[0237] Extract the target resource data type from the settled transaction data set and extract the second settled transaction data set within the second settlement time period;

[0238] The first and second settled transaction data sets are analyzed and processed to obtain the transaction volume contribution rate and the ratio contribution rate.

[0239] The transaction volume contribution rate and ratio contribution rate are combined to form the profit and loss reason for the target resource data type.

[0240] In one embodiment, the transaction information includes transaction data volume, transaction ratio, and settlement ratio;

[0241] When processor 1004 analyzes and processes the first and second settled transaction data sets to obtain the transaction volume contribution rate and ratio contribution rate, it specifically performs the following steps:

[0242] The first average transaction ratio is determined based on the transaction ratio in the first settled transaction data set; the first average settlement ratio is determined based on the settlement ratio in the first settled transaction data set; and the first total transaction volume is determined based on the transaction data volume in the first settled transaction data set.

[0243] The average second transaction ratio is determined based on the transaction ratio in the second settled transaction data set; the average second settlement ratio is determined based on the settlement ratio in the second settled transaction data set; and the total second transaction volume is determined based on the transaction data volume in the second settled transaction data set.

[0244] The first profit or loss is determined based on the average first transaction ratio, the average first settlement ratio, and the first total transaction volume. The second profit or loss is determined based on the average second transaction ratio, the average second settlement ratio, and the second total transaction volume. The change in profit or loss is obtained by subtracting the first profit or loss from the second profit or loss.

[0245] The trading volume contribution rate is determined based on the second total trading volume, the average first trading ratio, the average first settlement ratio, the first profit and loss, and the change in profit and loss. The ratio contribution rate is determined based on the second profit and loss and the trading volume contribution rate.

[0246] In one embodiment, the processor 1004 further performs the following steps:

[0247] Obtain the target transaction data for U transaction objects, where each target transaction data includes the transaction data volume;

[0248] Statistical processing is performed on the transaction data volume in the target transaction data of the U transaction objects to obtain abnormal transaction objects among the U transaction objects, and transaction alarm information is generated based on the abnormal transaction objects;

[0249] Output the transaction alarm information.

[0250] In one embodiment, each target transaction data also includes a transaction timestamp and a transaction ratio;

[0251] Processor 1004 also performs the following steps:

[0252] Based on the transaction timestamps of the target transaction data, the target transaction data is divided into multiple target transaction data sets, and the transaction timestamps of the target transaction data in the same target transaction data set are within the same transaction time period;

[0253] The conversion transaction volume for each target transaction data is determined based on the transaction data volume and transaction ratio in each target transaction data.

[0254] The conversion transaction volumes of target transaction data in the same target transaction data set are superimposed to obtain the transaction exposure for each transaction time period;

[0255] The trading exposure across all trading periods is summed up to obtain the cumulative trading exposure;

[0256] The display page shows the trading exposure for each trading period and the cumulative trading exposure.

[0257] In one embodiment, the processor 1004 further performs the following steps:

[0258] An exposure alarm is generated when the trading exposure in any of the multiple trading time periods exceeds the first exposure threshold, or when the cumulative trading exposure exceeds the second exposure threshold.

[0259] In one embodiment, when the processor 1004 performs statistical processing on the transaction data volume of the target transaction data of the U transaction objects to obtain the abnormal transaction objects among the U transaction objects, it specifically performs the following steps:

[0260] The transaction data volume in the target transaction data of each transaction object is added together with the cumulative transaction data volume of each transaction object;

[0261] Obtain the predicted transaction data volume for each transaction object;

[0262] Transactions with a predicted transaction volume greater than the cumulative transaction volume are classified as abnormal transactions.

[0263] In one embodiment, the processor 1004 further performs the following steps:

[0264] Sort the cumulative transaction data of U transaction objects to obtain the sorting result;

[0265] The sorting results are displayed on the display page.

[0266] In one embodiment, the processor 1004 further performs the following steps:

[0267] When a profit / loss alarm is received, or when a transaction alarm is received, a business processing page is displayed, which is used to receive user processing instructions.

[0268] In one embodiment, the set of settled transaction data is stored on a blockchain;

[0269] Processor 1004 also performs the following steps:

[0270] The total profit and loss, cumulative profit and loss, profit and loss for each resource data type, the reasons for profit and loss for M resource data types, and profit and loss alarm information for each settlement period are packaged into blocks;

[0271] The block is stored on the blockchain.

[0272] It should be understood that the computer device 1000 described in the embodiments of this application can execute the foregoing text. Figures 1-15 The description of the data processing method in the corresponding embodiments can also be performed as described above. Figure 16 The description of the data processing device 1 in the corresponding embodiments will not be repeated here. Furthermore, the beneficial effects of using the same method will also not be repeated here.

[0273] Furthermore, it should be noted that this application embodiment also provides a computer storage medium, which stores a computer program executed by the aforementioned data processing device 1. The computer program includes program instructions, and when the processor executes the program instructions, it can execute the aforementioned... Figure 1 - The data processing method described in the embodiment corresponding to Figure 15 will not be repeated here. Furthermore, the beneficial effects of using the same method will also not be repeated. For technical details not disclosed in the embodiments of the computer storage medium involved in this application, please refer to the description of the method embodiments of this application. As an example, program instructions can be deployed and executed on a single computer device, or on multiple computer devices located in one location, or on multiple computer devices distributed across multiple locations and interconnected via a communication network. Multiple computer devices distributed across multiple locations and interconnected via a communication network can be combined to form a blockchain network.

[0274] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the aforementioned... Figures 1 to 15 The methods described in the corresponding embodiments are therefore not repeated here.

[0275] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0276] The above-disclosed embodiments are merely preferred embodiments of this application and should not be construed as limiting the scope of this application. Therefore, any equivalent variations made in accordance with the claims of this application shall still fall within the scope of this application.

Claims

1. A data processing method, characterized in that, include: Obtain a set of settled transaction data, which includes N settled transaction data, each of which includes transaction information and resource data type. The set of settled transaction data corresponds to K resource data types, where N and K are both positive integers. Profit and loss statistics are performed on the transaction information in the settled transaction data set to obtain the profit and loss for each type of resource data; When the profit or loss of M resource data types out of K resource data types is less than the profit or loss threshold, obtain the reasons for the profit or loss of the M resource data types, and generate profit or loss alarm information based on the reasons for the profit or loss of the M resource data types, where M is a positive integer; Output the aforementioned profit and loss alarm information; Retrieve target transaction data for U transaction objects. Each target transaction data includes the transaction data volume, transaction timestamp, and transaction ratio; U is a positive integer. Based on the transaction timestamps of the target transaction data, the target transaction data is divided into multiple target transaction data sets, with the transaction timestamps of the target transaction data within the same target transaction data set falling within the same transaction time period. The conversion transaction volume of each target transaction data set is determined based on the transaction data volume and transaction ratio within that set. The conversion transaction volumes of the target transaction data within the same target transaction data set are then summed to obtain the transaction exposure for each transaction time period. The transaction exposures for all transaction time periods are summed to obtain the cumulative transaction exposure. The transaction exposure for each transaction time period and the cumulative transaction exposure are displayed on the display page.

2. The method according to claim 1, characterized in that, The step of performing profit and loss statistical processing on the transaction information in the settled transaction data set to obtain the profit and loss for each resource data type includes: The set of settled transaction data is divided into K unit sets, and the resource data types in each unit set are the same. Profit and loss statistics are performed on the transaction information in each unit set to obtain the profit and loss for each type of resource data.

3. The method according to claim 1, characterized in that, Each settled transaction data also includes a settlement timestamp, and the set of settled transaction data corresponds to P settlement time periods; The method further includes: Profit and loss statistics are performed on the transaction information in the settled transaction data corresponding to each settlement period to obtain the profit and loss of each resource data type in each settlement period. The profit and loss of all resource data types within each settlement period are summed up to form the total profit and loss for that settlement period. The profit and loss of each resource data type are summed up to form a cumulative profit and loss; The display page shows the total profit and loss, cumulative profit and loss, and profit and loss for each type of resource data for each settlement period.

4. The method according to claim 3, characterized in that, The target resource data type is any of the M resource data types. The P settlement time periods include the first settlement time period and the second settlement time period. The first settlement time period and the second settlement time period are adjacent, and the second settlement time period is the longest settlement time period among the P settlement time periods. Determine the reasons for profit and loss in the target resource data type, including: Extract the first set of settled transaction data containing the target resource data type within the first settlement time period; Extract the target resource data type from the settled transaction data set and extract the second settled transaction data set within the second settlement time period; The first and second settled transaction data sets are analyzed and processed to obtain the transaction volume contribution rate and the ratio contribution rate. The transaction volume contribution rate and ratio contribution rate are combined to form the profit and loss reason for the target resource data type.

5. The method according to claim 4, characterized in that, The transaction information includes transaction data volume, transaction ratio, and settlement ratio; The analysis and processing of the first and second settled transaction data sets to obtain the transaction volume contribution rate and ratio contribution rate includes: The first average transaction ratio is determined based on the transaction ratio in the first settled transaction data set; the first average settlement ratio is determined based on the settlement ratio in the first settled transaction data set; and the first total transaction volume is determined based on the transaction data volume in the first settled transaction data set. The average second transaction ratio is determined based on the transaction ratio in the second settled transaction data set; the average second settlement ratio is determined based on the settlement ratio in the second settled transaction data set; and the total second transaction volume is determined based on the transaction data volume in the second settled transaction data set. The first profit or loss is determined based on the average first transaction ratio, the average first settlement ratio, and the first total transaction volume. The second profit or loss is determined based on the average second transaction ratio, the average second settlement ratio, and the second total transaction volume. The change in profit or loss is obtained by subtracting the first profit or loss from the second profit or loss. The trading volume contribution rate is determined based on the second total trading volume, the average first trading ratio, the average first settlement ratio, the first profit and loss, and the change in profit and loss. The ratio contribution rate is determined based on the second profit and loss and the trading volume contribution rate.

6. The method according to claim 1, characterized in that, Also includes: Statistical processing is performed on the transaction data volume in the target transaction data of the U transaction objects to obtain abnormal transaction objects among the U transaction objects, and transaction alarm information is generated based on the abnormal transaction objects; Output the transaction alarm information.

7. The method according to claim 1, characterized in that, Also includes: An exposure alarm is generated when the trading exposure in any of the multiple trading time periods exceeds the first exposure threshold, or when the cumulative trading exposure exceeds the second exposure threshold.

8. The method according to claim 6, characterized in that, The step of statistically processing the transaction data volume in the target transaction data of the U transaction objects to obtain the abnormal transaction objects among the U transaction objects includes: The transaction data volume in the target transaction data of each transaction object is added together with the cumulative transaction data volume of each transaction object; Obtain the predicted transaction data volume for each transaction object; Transactions with a predicted transaction volume greater than the cumulative transaction volume are classified as abnormal transactions.

9. The method according to claim 8, characterized in that, Also includes: Sort the cumulative transaction data of U transaction objects to obtain the sorting result; The sorting results are displayed on the display page.

10. The method according to claim 6, characterized in that, Also includes: When a profit / loss alarm is received, or when a transaction alarm is received, a business processing page is displayed, which is used to receive user processing instructions.

11. The method according to any one of claims 1-10, characterized in that, The set of settled transaction data is stored on the blockchain; The method further includes: The total profit and loss, cumulative profit and loss, profit and loss for each resource data type, the reasons for profit and loss for M resource data types, and profit and loss alarm information for each settlement period are packaged into blocks; The block is stored on the blockchain.

12. A data processing apparatus, characterized in that, include: The acquisition module is used to acquire a set of settled transaction data, which includes N settled transaction data, each of which includes transaction information and resource data type. The set of settled transaction data corresponds to K resource data types, where N and K are both positive integers. The statistics module is used to perform profit and loss statistics processing on the transaction information in the settled transaction data set to obtain the profit and loss of each resource data type; The generation module is used to obtain the reasons for the loss or gain of M resource data types when the loss or gain of M resource data types out of K resource data types is less than the loss or gain threshold, and generate loss or gain alarm information based on the reasons for the loss or gain of M resource data types, where M is a positive integer; The output module is used to output the loss and gain alarm information; The acquisition module is further configured to acquire target transaction data for U transaction objects, each target transaction data including the transaction data volume, and each target transaction data also including a transaction timestamp and a transaction ratio; U is a positive integer; The generation module is further configured to: divide the target transaction data into multiple target transaction data sets based on the transaction timestamps of the target transaction data, wherein the transaction timestamps of the target transaction data in the same target transaction data set are within the same transaction time period; determine the conversion transaction volume of each target transaction data based on the transaction data volume and transaction ratio in each target transaction data set; sum the conversion transaction volumes of the target transaction data in the same target transaction data set to obtain the transaction exposure for each transaction time period; sum the transaction exposures of all transaction time periods to obtain the cumulative transaction exposure; and display the transaction exposure for each transaction time period and the cumulative transaction exposure on the display page.

13. A computer device, characterized in that, It includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method according to any one of claims 1-11.

14. A computer storage medium, characterized in that, The computer storage medium stores a computer program, the computer program including program instructions, which, when executed by a processor, cause a computer device having the processor to perform the method according to any one of claims 1-11.

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