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

Abstract

Embodiments of the present application disclose a data processing method, device and equipment and a computer readable storage medium. The method comprises: obtaining simulation draw records of at least two first objects in at least two simulation draws; calculating a simulation cumulative winning rate of a target object in any simulation draw based on the number of the at least two first objects and the number of times the target object appears in each simulation draw of the at least two first objects; calculating an actual cumulative winning rate of the target object in any actual draw based on the number of at least two second objects and the number of times the target object appears in each actual draw of the at least two second objects; and detecting an abnormal situation of the actual cumulative winning rate based on the actual cumulative winning rate of the target object in any actual draw and the simulation cumulative winning rate of the corresponding simulation draw. The accuracy of actual winning rate anomaly detection can be improved by the method.

Description

Technical Field

[0001] This application relates to the field of computer technology, and more specifically to a data processing method, apparatus, device, and computer-readable storage medium. Background Technology

[0002] Online lotteries refer to the act of drawing prizes on a specific online platform. Each item has a set estimated winning rate to achieve the desired outcome.

[0003] In related technologies, when an item is added to a lottery, technicians need to manually configure the estimated winning rate threshold for that item. The platform provides the real-time winning rate for that item, and if this real-time winning rate exceeds the estimated winning rate threshold, an alert is triggered, notifying the technicians. However, the preset winning rate threshold configured by the technicians is a fixed value. If it is set too high, even if there are problems with the real-time winning rate, it may not reach the threshold, leading to missed anomaly detections. If it is set too low, even normal fluctuations may be detected as anomalies, resulting in false positives.

[0004] Therefore, improving the accuracy of detecting anomalies in the actual winning rate is an urgent problem to be solved. Summary of the Invention

[0005] To address the aforementioned technical problems, embodiments of this application provide a text generation method, apparatus, device, and computer-readable storage medium.

[0006] The technical solution adopted in this application is as follows:

[0007] A data processing method, comprising:

[0008] Obtain at least two simulated lottery records for each of the two first objects in at least two simulated lottery draws, wherein the simulated lottery record for each first object contains the items drawn by the first object in each simulated lottery draw.

[0009] Based on the number of the at least two first objects and the number of times the target item appears in the at least two first objects in each simulated lottery, calculate the simulated cumulative winning rate of the target item in any simulated lottery;

[0010] Based on the number of at least two second objects and the number of times the target item appears in each of the at least two second objects in each actual draw, calculate the actual cumulative winning rate of the target item in any actual draw;

[0011] Based on the actual cumulative winning rate of the target item in any actual lottery draw and the simulated cumulative winning rate in the corresponding simulated lottery draw, anomalies in the actual cumulative winning rate are detected.

[0012] A data processing apparatus, comprising:

[0013] The acquisition unit is used to acquire the simulated lottery records of at least two first objects in at least two simulated lottery draws, and the simulated lottery record corresponding to each first object contains the items drawn by the first object in each simulated lottery draw.

[0014] The calculation unit is used to calculate the simulated cumulative winning rate of the target item in any simulated lottery based on the number of the at least two first objects and the number of times the target item appears in the at least two first objects in each simulated lottery.

[0015] The calculation unit is also used to calculate the actual cumulative winning rate of the target item in any actual lottery based on the number of at least two second objects and the number of times the target item appears in each actual lottery in the at least two second objects;

[0016] The detection unit is used to detect abnormalities in the actual cumulative winning rate based on the actual cumulative winning rate of the target item in any actual lottery and the simulated cumulative winning rate in the corresponding simulated lottery.

[0017] In one embodiment of this application, based on the foregoing scheme, the acquisition unit is further configured to acquire first identification information corresponding to any simulated lottery draw, wherein the first identification information is used to characterize the position of any simulated lottery draw in the at least two simulated lottery draws; the calculation unit is further configured to calculate the first cumulative number of draws corresponding to any simulated lottery draw based on the quantity of the at least two first objects and the first identification information; and to calculate the simulated cumulative winning rate of the target item in any simulated lottery draw based on the first cumulative number of occurrences of the target item in any simulated lottery draw and the first cumulative number of draws.

[0018] In one embodiment of this application, based on the foregoing scheme, the acquisition unit is further configured to acquire second identification information corresponding to any actual lottery draw, the second identification information being used to characterize the position of any actual lottery draw in at least two actual lottery draws; based on the second identification information, acquire the second cumulative occurrence number of times the target item appears in any actual lottery draw, and acquire the second cumulative number of lottery draws corresponding to any actual lottery draw; the calculation unit is further configured to calculate the actual cumulative winning rate of the target item in any actual lottery draw based on the second cumulative occurrence number of times and the second cumulative number of lottery draws.

[0019] In one embodiment of this application, based on the foregoing scheme, the calculation unit is further configured to sum the actual number of times the target item appears in the lottery with a position less than or equal to the position represented by the second identification information to obtain the second cumulative number of occurrences; and to sum the actual number of times the target item appears in the lottery with a position less than or equal to the position represented by the second identification information to obtain the second cumulative number of lottery draws.

[0020] In one embodiment of this application, based on the foregoing scheme, the acquisition unit is further configured to acquire a first error fluctuation parameter if the number of second objects participating in any actual lottery is detected to be greater than a first quantity threshold; the device further includes a processing unit configured to determine that the actual cumulative winning rate is abnormal if the actual cumulative winning rate is detected to be greater than the sum of the simulated cumulative winning rate and the first error fluctuation parameter.

[0021] In one embodiment of this application, based on the foregoing scheme, the acquisition unit is further configured to acquire a second error fluctuation parameter if the number of second objects participating in the first actual lottery is less than or equal to a first quantity threshold and greater than a second quantity threshold; the processing unit is further configured to determine that the actual cumulative winning rate is abnormal if the actual cumulative winning rate of the first actual lottery is greater than the product of the first preset multiplier and the simulated cumulative winning rate.

[0022] In one embodiment of this application, based on the foregoing scheme, the acquisition unit is further configured to acquire a third error fluctuation parameter if it detects that the number of second objects participating in any actual lottery is less than or equal to a first quantity threshold and greater than a second quantity threshold, and that any actual lottery is not the first actual lottery; the processing unit is further configured to determine that the actual cumulative winning rate is abnormal if it detects that the actual cumulative winning rate of any actual lottery is greater than the product of the third error fluctuation parameter and the simulated cumulative winning rate.

[0023] In one embodiment of this application, based on the foregoing scheme, after detecting anomalies in the actual cumulative winning rate based on the actual cumulative winning rate of the target item in any actual lottery and the simulated cumulative winning rate in the corresponding simulated lottery, the processing unit is further configured to, if an anomaly is detected in the actual cumulative winning rate, jump to the step of calculating the actual cumulative winning rate of the target item in any actual lottery to obtain the current anomaly in the actual cumulative winning rate; the acquisition unit is further configured to, if the current actual cumulative winning rate is abnormal, acquire the first degree of abnormality of the previous actual cumulative winning rate and the second degree of abnormality of the current actual cumulative winning rate; the processing unit is further configured to generate an abnormal trend of the actual cumulative winning rate based on the first degree of abnormality and the second degree of abnormality.

[0024] In one embodiment of this application, based on the aforementioned scheme, the calculation unit is further configured to perform a division operation between the second degree of abnormality and the first degree of abnormality to obtain the abnormal trend; after generating the abnormal trend of the actual cumulative winning rate based on the first degree of abnormality and the second degree of abnormality, the processing unit is further configured to output alarm information to characterize the abnormality of the actual cumulative winning rate if the abnormal trend is greater than a preset trend threshold.

[0025] An electronic device includes a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the data processing method described above is implemented.

[0026] A computer-readable storage medium having stored computer-readable instructions thereon, which, when executed by a computer's processor, cause the computer to perform the data processing method described above.

[0027] A computer program product includes computer-readable instructions that, when executed by a processor, implement the data processing method described above.

[0028] In the above technical solutions:

[0029] It is possible to obtain simulated lottery records for at least two first objects in at least two simulated lotteries, with each first object corresponding to a specific simulated lottery record, including the item it won in each simulated lottery. Next, based on the quantity of the at least two first objects and the number of times the target item appeared in each simulated lottery for the at least two first objects, the simulated cumulative winning rate of the target item in any simulated lottery is calculated. Similarly, based on the quantity of at least two second objects and the number of times the target item appeared in each actual lottery for the at least two second objects, the actual cumulative winning rate of the target item in any actual lottery is calculated. Finally, by comparing the actual cumulative winning rate of the target item in any actual lottery with the simulated cumulative winning rate of the corresponding simulated lottery, anomalies in the actual cumulative winning rate are detected.

[0030] On the one hand, current technologies focus solely on the winning rate of the target item throughout the entire lottery process, resulting in an excessively long statistical period for the winning rate and the overlooking of some anomalies. This new method, however, refines anomaly detection down to each individual lottery draw, enabling more precise anomaly localization. For example, it identifies which actual lottery draw had an abnormal winning rate, preventing anomalies from being overlooked and thus improving the accuracy of anomaly detection for actual winning rates.

[0031] On the other hand, the actual cumulative winning rate of each actual draw is compared with the corresponding simulated cumulative winning rate. The simulated cumulative winning rate for different draw positions is calculated based on the simulated draw records for those positions, thus ensuring the accuracy of the comparison. Compared to fixing a uniform winning rate threshold, this application can more realistically reflect the changes in the winning rate of target items at different stages of the draw process, avoiding false positives due to simple threshold settings and improving the accuracy of detecting anomalies in the actual winning rate.

[0032] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0033] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0034] Figure 1 This is a flowchart illustrating the process of using related technologies to detect anomalies in real-time winning rates;

[0035] Figure 2 This is a schematic diagram of one implementation environment involved in this application;

[0036] Figure 3 This is a flowchart illustrating a data processing method according to an exemplary embodiment;

[0037] Figure 4 This is a flowchart illustrating a data processing method according to another exemplary embodiment;

[0038] Figure 5 This is a schematic diagram illustrating the relationship between the actual lottery position and the number of second objects involved in this application;

[0039] Figure 6 This is a flowchart of an anomaly detection process involved in this application;

[0040] Figure 7 This is a flowchart illustrating a data processing method according to another exemplary embodiment;

[0041] Figure 8 This is a schematic diagram of the overall process of an actual cumulative winning rate involved in this application;

[0042] Figure 9 This is a block diagram illustrating a data processing apparatus according to an exemplary embodiment;

[0043] Figure 10 This is a schematic diagram of the structure of a computer system for a data processing device according to an exemplary embodiment. Detailed Implementation

[0044] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments identical to those described in this application. Rather, they are merely examples of apparatuses and methods identical to some aspects of this application as detailed in the appended claims.

[0045] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in at least one hardware module or integrated circuit, or in different network and / or processor devices and / or microcontroller devices.

[0046] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily need to be performed in the described order. For example, some operations / steps can be broken down, while others can be integrated or partially integrated; therefore, the actual execution order may change depending on the specific circumstances.

[0047] It should be noted that "multiple" as mentioned in this application refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0048] Before introducing the technical solutions of the embodiments of this application, let's first introduce the technical terms involved in the embodiments of this application.

[0049] With the rise of internet technology, the ways in which online platforms interact with users have become increasingly diverse. Online lottery events are a common form of interaction. On some online platforms, users can use collected items to exchange for a certain number of lottery chances. Alternatively, some online platforms may offer a certain number of free lottery chances for users to participate in lottery events.

[0050] In related technologies, technicians typically need to pre-configure the winning rate for each item and add it to the data platform as an anomaly threshold for the real-time winning rate. After configuration, when an object starts participating in the lottery, the online real-time winning rate can be compared with this anomaly threshold to detect whether any real-time winning rate anomalies have occurred.

[0051] Specifically, such as Figure 1 The diagram illustrates the process of anomaly detection in real-time winning rates using relevant technologies. Figure 1 In the process, the data platform first obtains the lottery configuration information added or modified by technical personnel and publishes it to the server to support the launch of new activities or to correct problems in existing configurations. After the configuration information is published, since the lottery rules or winning probabilities may change, it is necessary to manually adjust the abnormal winning rate threshold for each item on the data platform to ensure that the subsequent anomaly detection mechanism can work properly.

[0052] When an object starts participating in the lottery, the data platform continuously collects lottery data, including the number of times each item is produced and the total number of draws, and calculates the real-time total winning rate for each item. Specifically, if the total number of times a target item is produced is N, and the total number of draws for the object is M, then the real-time total winning rate R of the target item can be expressed as R = N / M.

[0053] Finally, the data platform sends the calculated real-time total winning rate and the preset anomaly threshold to the anomaly detection platform. The anomaly detection platform can compare the two according to the preset anomaly detection strategy. When the anomaly detection platform detects that the real-time total winning rate exceeds the preset anomaly threshold, it will determine that the real-time total winning rate is abnormal, trigger an alarm, and notify the technical personnel of the alarm information so that the anomaly can be analyzed in a timely manner and necessary measures can be taken.

[0054] However, in the aforementioned technologies, anomaly detection relies solely on a single threshold for comparison, failing to provide a refined analysis of probability fluctuations during the lottery process. For instance, if the winning rate of an item fluctuates significantly across different lottery positions, or if anomalies only occur in specific positions while the overall winning rate remains as expected, the anomaly may go undetected, resulting in missed detections. Furthermore, if the lottery behavior exhibits random fluctuations, and the anomaly threshold fails to adequately account for these fluctuations, false alarms may occur, requiring technicians to frequently process and analyze numerous invalid alarms, increasing operational costs. Therefore, the accuracy of real-time anomaly detection in these technologies is low.

[0055] Based on this, embodiments of this application propose a data processing method, a data processing apparatus, a data processing device, a computer-readable storage medium, and a computer program product. In these embodiments, it is possible to obtain simulated lottery records for at least two first objects in at least two simulated lotteries, each first object corresponding to a specific simulated lottery record, including the item drawn in each simulated lottery. Next, based on the number of the at least two first objects and the number of times the target item appears in each simulated lottery for the at least two first objects, the simulated cumulative winning rate of the target item in any simulated lottery is calculated. Similarly, based on the number of at least two second objects and the number of times the target item appears in each actual lottery for the at least two second objects, the actual cumulative winning rate of the target item in any actual lottery is calculated. Finally, by comparing the actual cumulative winning rate of the target item in any actual lottery with the simulated cumulative winning rate of the corresponding simulated lottery, anomalies in the actual cumulative winning rate are detected. On the one hand, in related technologies, only the winning rate of the target item throughout the entire lottery process is considered, resulting in an excessively long statistical period for the winning rate, causing some anomalies to be overlooked. This method refines anomaly detection down to each individual draw, enabling more precise anomaly localization. For example, it identifies which actual draw's winning rate is abnormal, preventing anomalies from being overlooked and thus improving the accuracy of actual winning rate anomaly detection. Furthermore, the actual cumulative winning rate for each draw is compared to the corresponding simulated cumulative winning rate. Since the simulated cumulative winning rate for different draw positions is calculated based on the simulated draw records for those positions, the accuracy of the comparison is ensured. Compared to fixing a uniform winning rate threshold, this application more realistically reflects the changes in the winning rate of target items at different stages of the draw process, avoiding false detections due to simple threshold settings and improving the accuracy of actual winning rate anomaly detection.

[0056] Please see Figure 2 , Figure 2 This is a schematic diagram of one implementation environment involved in this application.

[0057] Figure 2 The implementation environment shown includes server 210, data platform 210 and anomaly detection platform 230.

[0058] Server 210 can be applied to various scenarios, such as gaming, e-commerce, and social media platforms. The specific type and function of server 210 vary depending on the application scenario. For example, in a gaming scenario, server 210 can act as a game server, responsible for running game logic, managing game data for object 240, and handling in-game lottery operations. In an e-commerce scenario, server 210 can act as a transaction server, managing user shopping behavior, such as points redemption and blind box draws involving random rewards. In a social media platform, server 210 can also serve as an activity management server, supporting various interactive lottery activities on the platform, such as giveaways and random red envelopes.

[0059] Taking a game scenario as an example, server 210 can be used to support in-game lottery activities. Typically, each object 240 can obtain multiple lottery opportunities during the game. For example, by completing tasks, challenging dungeons, participating in events, etc., they can collect game items (such as lottery tickets, points, fragments, etc.) and then exchange them for lottery opportunities; they can obtain additional lottery opportunities by directly recharging or purchasing specific gift packs; and they can receive free lottery chances through daily check-ins, friend invitations, watching advertisements, and other operational activities.

[0060] Before the lottery event begins, technician 250 needs to add configuration information to server 210 so that server 210 can generate the corresponding lottery logic based on the configuration information. This configuration information can vary depending on the scenario. In a game scenario, this configuration information may include the winning probability of each item in the prize pool, the limit on the number of draws, and the guaranteed minimum under specific conditions. At the same time, technician 250 also needs to generate simulated data using simulated lottery based on the added configuration information and calculate the simulated cumulative winning rate of each item in each draw. The simulated cumulative winning rate of each item in each draw can be automatically reported to data platform 210 as a threshold for the cumulative winning rate of each item in each draw, for use in subsequent anomaly detection.

[0061] Once the lottery officially begins, server 210 handles the lottery requests submitted by object 240. Object 240 can access server 210 through a terminal device, which can run applications such as games, shopping software, and social media platforms. When object 240 participates in the lottery, server 210 randomly allocates rewards based on preset lottery rules and records the results. The lottery logic can be as follows: server 210 receives the lottery request from object 240, confirms whether object 240 has enough chances to participate, and performs random calculations based on the set probability of winning each item in the prize pool, determines the winning item for object 240, and returns it to object 240. Server 210 records the raw data of each object 240, including the type of prize won, the time, and the lottery position, and synchronizes the relevant data to data platform 210 for storage and analysis.

[0062] Next, the data platform 210 can calculate the real-time cumulative winning rate for each item per draw based on the raw data sent by the server 210. Then, it sends the automatically reported simulated cumulative winning rate (cumulative winning rate threshold) for each item per draw and the real-time cumulative winning rate for each item per draw to the anomaly detection platform 230.

[0063] The anomaly detection platform 230 can detect whether the real-time cumulative winning rate of each item in each draw is greater than the cumulative winning rate threshold based on the anomaly detection strategy. If it is greater, it can be determined that the real-time cumulative winning rate is abnormal, which can trigger an alarm and send the alarm information to the technician 250. The technician 250 can then fine-tune the lottery logic by modifying the configuration information so that the real-time cumulative winning rate can be restored to normal as soon as possible.

[0064] It should be noted that the terminal device in the embodiments of this application can be a mobile phone, tablet computer, laptop computer, handheld computer, mobile internet device (MID), vehicle device, aircraft, wearable device (such as smart devices such as smartwatches, smart bracelets, pedometers, etc.), virtual reality device (such as VR (Virtual Reality) device, AR (Augmented Reality) device), etc.

[0065] The server in this application embodiment can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0066] In the specific implementation of this application, user-related data is involved, such as the pinyin sequence, target text, and input process data entered by the user. When the embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions.

[0067] Please see Figure 3 , Figure 3 This is a flowchart illustrating a data processing method according to an exemplary embodiment. The method can be applied to... Figure 2 The implementation environment is shown, and the method is executed by a computer. Of course, this method can also be applied to other implementation environments; there are no restrictions on the executing entity. Specifically, in... Figure 2 In this system, the data platform 220 and the anomaly detection platform 230 can be deployed on a computer, allowing the computer to obtain and analyze the raw lottery data from the server 210. Furthermore, the computer can also generate simulated lottery data to detect anomalies in the real-time cumulative winning rate.

[0068] The following section will use a computer as an example to illustrate this data processing method in detail. Figure 3 As shown, in an exemplary embodiment, the method includes at least the following steps:

[0069] S310. Obtain at least two simulated lottery records for each of the first objects in at least two simulated lottery draws. The simulated lottery record for each first object contains the items drawn by the first object in each simulated lottery draw.

[0070] Once technicians submit the new lottery configuration information, the computer automatically triggers a simulated lottery tool, initiating at least two first objects to perform a simulated lottery. These at least two first objects can be simulated first objects. The purpose of the simulated lottery is to calculate the cumulative winning rate of each item under different draw counts in advance, without affecting the actual lottery, providing reference data for subsequent anomaly detection. Optionally, the cumulative winning rate can also be called the cumulative drop rate. This allows for a preliminary assessment of the winning rate distribution of each item before the official launch of the lottery activity, providing reference data for subsequent anomaly detection.

[0071] In the simulated lottery process, assuming the total number of the first object in the simulated lottery is X, each first object will randomly draw one item in each simulated lottery. This item can be any item from the prize pool. Assume the prize pool contains R types of items, denoted as M1, M2, ..., MR. To accurately calculate the winning rate of items, the first object in the simulated lottery is usually set to a large value, such as tens of thousands or even hundreds of thousands. The specific value can be set by those skilled in the art and is not limited here. This ensures that the calculated cumulative winning rate in the simulation has high stability and reference value.

[0072] Specifically, after triggering the simulated lottery, the simulated lottery tool initializes the prize pool data based on the configuration information added by the technicians and starts X simulated first objects. Each first object will begin a simulated lottery. In each round of the simulated lottery, each first object will randomly draw an item and record the result. For example, after the first round of the lottery, each first object will obtain an item, forming the simulated lottery record for the first draw; after the second round of the lottery, it will obtain a new item, forming the simulated lottery record for the second draw. This process continues until the set number of rounds Y is reached. Assuming Y is 15, each first object will conduct 15 simulated lottery draws and record the item it draws in each draw.

[0073] Table 1 shows the simulated lottery record table for each first object.

[0074] First draw Second draw Third draw …… Draw Y First object 1 Item M1 Item M1 Item M2 …… Item MR First object 2 Item M2 Item MR Item M2 …… Item M1 First object 3 Item M2 Item M1 Item MR …… Item M1 …… …… Q… …… …… …… First object X Item MR Item M1 Item MR …… Item M2

[0075] Table 1

[0076] As shown in Table 1, in the first draw, object 1 drew item M1, object 2 drew item M2, object 3 drew item M2, and so on. Therefore, in the simulated draws at different positions, each object can generate the item it draws.

[0077] In one embodiment of this application, in a game scenario, the items obtained through a lottery are typically virtual items within the game. These items can be used to enhance character abilities, accelerate game progress, or personalize appearance. For example, in some games, lotteries may yield in-game trading resources such as "crystals," which players can use to exchange for other high-level items in the game's shop, such as equipment, character skins, or special skills. Furthermore, depending on the game, lotteries may also yield "fragments," which players can collect to synthesize complete characters, weapons, or special equipment.

[0078] Besides crystals and shards, lottery items may also include rare equipment, experience boosters, stamina recovery items, costumes, mount badges, and more. For example, in some adventure games, players can obtain weapons or armor of different qualities through lotteries; some weapons may have rare attributes that enhance their character's combat abilities. Different games typically set up multiple prize pools for their lottery systems, with different pools potentially containing different types of items. For instance, a basic prize pool might offer regular items such as experience potions and gold coins, while a higher-level prize pool might include limited-edition characters, legendary equipment, large amounts of gold coins or experience, and so on.

[0079] Optionally, some games may also introduce a pity system, where if a player does not obtain a high-value item in a certain number of draws, the system will automatically increase the probability of winning the target item to ensure that the player can eventually obtain a rare reward.

[0080] The process of simulating the lottery ensures that reference winning rate data is available before the lottery officially runs. This allows for the comparison of the real-time cumulative winning rate with the simulated cumulative winning rate during the actual lottery process, enabling the detection of any anomalies in the real-time cumulative winning rate. This helps improve the accuracy of anomaly detection and avoids false alarms and missed alarms.

[0081] S320. Based on the number of at least two first objects and the number of times the target item appears in each simulated lottery among the at least two first objects, calculate the simulated cumulative winning rate of the target item in any simulated lottery.

[0082] During the simulated lottery process, each first object will participate in the lottery, generating a corresponding simulated lottery record. To calculate the simulated cumulative winning rate of the target item in a particular simulated lottery, it is first necessary to obtain the first identifier information of that simulated lottery. This first identifier information is used to characterize the position of that simulated lottery in the entire simulated lottery process, such as whether it is the 1st draw, the 2nd draw, or the Yth draw.

[0083] After obtaining the first identifier information, the next step is to calculate the first cumulative number of draws corresponding to this simulated lottery. The first cumulative number of draws represents the total number of times all first objects have participated in the simulated lottery up to the current simulated lottery position. Assuming the total number of first objects in the simulated lottery is X, and the first identifier information for the Yth draw is Y, then the first cumulative number of draws for the Yth draw can be calculated as X*Y times.

[0084] Next, we need to calculate the first cumulative occurrence count of the target item in any simulated lottery draw. The target item is any one of at least two items configured by the technicians to be drawn. Assuming the occurrence counts of the target item in the 1st, 2nd, ..., Yth draws are denoted as N1, N2, ..., NY respectively, then in the Yth draw, the first cumulative occurrence count of the target item is N1 + N2 + ... + NY.

[0085] Finally, based on the first cumulative occurrence of the target item in draw Y and the first cumulative number of draws, the simulated cumulative winning rate TY of the target item in draw Y can be calculated, as shown below:

[0086] TY = (N1 + N2 + ... + NY) / (X * Y)

[0087] In this way, the simulated cumulative winning rate corresponding to any simulated lottery can be calculated. For example, T1 = N1 / X, T2 = (N1+N2) / 2X, T3 = (N1+N2+N3) / 3X, and so on.

[0088] This step ensures that the simulated cumulative winning rate of each target item during the simulated lottery process can be accurately recorded and calculated, thus providing a reference threshold for subsequent anomaly detection of the actual cumulative winning rate of the target item in the actual lottery.

[0089] S330. Based on the number of at least two second objects and the number of times the target item appears in each actual draw in at least two second objects, calculate the actual cumulative winning rate of the target item in any actual draw.

[0090] Specifically, the actual lottery data comes from the actual lottery records recorded by the server when at least two second objects participate in the lottery (i.e., Figure 2 The original data (in the database) is used to record each second object's lottery activity, which is then stored in the data platform. The second object can be a different role depending on the application scenario. In a game scenario, the second object is a real player; in a shopping scenario, the second object is a real buyer; in an investment scenario, the second object is a real investor, and so on.

[0091] like Figure 4 As shown, S330 may specifically include S410 to S430, which are described below:

[0092] S410. Obtain the second identifier information corresponding to any actual lottery draw. The second identifier information is used to represent the position of any actual lottery draw in at least two actual lottery draws.

[0093] First, the computer needs to obtain the second identifier information corresponding to any actual lottery draw. This second identifier information is used to characterize the ranking of that actual lottery draw among at least two actual lottery draws. For example, if an actual lottery draw is the Y-th draw, then the second identifier information for that draw is Y.

[0094] S420. Based on the second identification information, obtain the second cumulative number of times the target item appears in any actual lottery draw, and obtain the second cumulative number of lottery draws corresponding to any actual lottery draw.

[0095] Based on the second identifier information, the computer can obtain the second cumulative occurrence count of the target item in the actual lottery draw, and also obtain the second cumulative lottery draw count corresponding to that actual lottery draw. The second cumulative occurrence count refers to the total number of times the target item appears in actual lottery draws where the position is less than or equal to the position represented by the second identifier information, while the second cumulative lottery draw count refers to the total number of draws in actual lottery draws where the position is less than or equal to the position represented by the second identifier information.

[0096] Specifically, the computer can sum up the actual number of times the target item appears in the lottery when its position is less than or equal to the position represented by the second identification information to obtain the second cumulative number of appearances.

[0097] Then, the actual number of draws for which the rank is less than or equal to the rank represented by the second identifier information is summed to obtain the second cumulative number of draws.

[0098] In this embodiment, the number of times the target item appears in the first actual lottery draw is X1 (at least two second objects), and the number of times the target item appears is M1. In the second actual lottery draw, the number of draws is X2, and the number of times the target item appears is M2. In the third actual lottery draw, the number of draws is X3, and the number of times the target item appears is M3, and so on. In the Yth lottery draw, the number of draws is XY, and the number of times the target item appears is MY. Therefore, the second cumulative number of times the target item appears in the Yth actual lottery draw is M1 + M2 + ... + MY, and the second cumulative number of times the target item appears in the Yth actual lottery draw is X1 + X2 + ... + XY.

[0099] It should be noted that in the actual lottery process, as the lottery position increases, the number of draws with at least two second objects (i.e., the number of draws with at least two second objects) will gradually decrease. This is because not all second objects will participate in every lottery.

[0100] For example, some secondary participants might have already obtained their desired item in early draws and therefore stop participating in subsequent draws. Similarly, in a game scenario, some players might only want a specific item, and if they have already successfully obtained it in the previous draws, they might choose to stop drawing and not continue consuming their draw chances.

[0101] Furthermore, the decrease in the number of draws may also be related to the limited resources available to the second-party participants. In some lottery events, draw chances are typically obtained through specific methods, such as exchanging in-game items (e.g., lottery tickets, points, virtual trading resources) or earning free draw opportunities by completing in-game tasks. However, not all second-party participants can consistently obtain enough resources to support subsequent draws. As the draw ranking increases, some second-party participants may have to stop drawing due to a lack of sufficient items or resources. For example, some players may initially accumulate a large number of lottery items and participate frequently in the first few draws, but as their items are consumed, their draw chances will decrease, eventually leading to their withdrawal from subsequent draws.

[0102] Finally, the attractiveness of the lottery itself can also affect the continued participation of secondary participants. Some secondary participants may actively participate in the lottery at the beginning due to novelty or high popularity, but if they still do not get the desired item after multiple draws, or if they find that the winning rate of the desired item is not as expected, they may give up continuing to draw, resulting in a natural decrease in the number of draws.

[0103] This phenomenon of gradually decreasing lottery draws directly leads to a gradual decrease in the actual number of draws per draw as the ranking increases. Therefore, when calculating the actual cumulative winning rate of the target item in the Yth actual draw, it is not possible to simply assume that all second objects will participate in every draw. Instead, it is necessary to count the actual number of draws for each draw based on the actual situation to ensure the accuracy of the winning rate calculation.

[0104] For example, such as Figure 5 The diagram illustrates the relationship between the actual lottery draw position and the number of second-object participants, as described in this application. Figure 5 It can be seen that in the first actual draw, there were about 300,000 second-party participants. In the second actual draw, only 120,000 second-party participants participated. In the third actual draw, the number of participants further decreased to 60,000, and so on, until the 15th actual draw when only 4,000 second-party participants remained.

[0105] Therefore, in the process of calculating the actual cumulative winning rate of the target item, using the position identifier information to determine the cumulative winning rate of each actual draw can effectively reflect the number of second objects in different positions, making the calculation of the actual cumulative winning rate more accurate and improving the accuracy of anomaly detection.

[0106] S430. Based on the second cumulative occurrence count and the second cumulative number of draws, calculate the actual cumulative winning rate of the target item in any actual draw.

[0107] In this way, the computer can calculate the actual cumulative winning probability (denoted as RY) of the target item in the Yth actual lottery draw, using the following formula:

[0108] RY=(N1+N2+…+NY) / (X1+X2+…+XY)

[0109] The calculated actual cumulative winning rate of the target item can be used for subsequent anomaly detection. It can be compared with the corresponding simulated cumulative winning rate to identify anomalies. Since the number of second objects in the actual draw is not fixed, and the number of participants decreases as the actual draw position increases, this calculation method based on second identifier information ensures that the winning rate calculation for each actual draw accurately reflects the actual draw situation at the current position, rather than simply calculating the winning rate based on a fixed total, thereby improving the accuracy of anomaly detection.

[0110] S340. Based on the actual cumulative winning rate of the target item in any actual lottery and the simulated cumulative winning rate in the corresponding simulated lottery, detect abnormalities in the actual cumulative winning rate.

[0111] Here, "corresponding simulated lottery" refers to a simulated lottery with the same identifier information (i.e., the same rank) as the "any actual lottery". For example, if the current requirement is to detect the actual cumulative winning rate of the target item in the Yth actual lottery, then its corresponding simulated cumulative winning rate should be calculated in the Yth simulated lottery. By ensuring consistency in rank information, a strict correspondence can be guaranteed during anomaly detection, avoiding misjudgments due to data matching errors.

[0112] In one embodiment of this application, if the number of second objects participating in any actual lottery draw is detected to be greater than a first quantity threshold, a first error fluctuation parameter is obtained. If the actual cumulative winning rate is detected to be greater than the sum of the simulated cumulative winning rate and the first error fluctuation parameter, it is determined that the actual cumulative winning rate is abnormal.

[0113] Specifically, the computer needs to obtain the number of second objects participating in the current Y-th actual lottery draw, that is, to count the total number of second objects that submitted lottery requests in this round of lottery draws. If this number exceeds a first quantity threshold, a first error fluctuation parameter can be introduced to determine the judgment criteria for anomaly detection. This first preset threshold can be a preset reference value set by those skilled in the art, for example, 10000, indicating that when the number of actual participants in the current lottery position is greater than 10000, a more stringent anomaly detection strategy needs to be adopted.

[0114] At this point, the computer will further calculate the actual cumulative winning rate RY of the target item in the Y actual draws and obtain its simulated cumulative winning rate TY in the corresponding simulated draws. To determine if any anomalies have occurred, the computer will compare the magnitude of RY with TY + a first error fluctuation parameter. For example, when the first error fluctuation parameter is set to 0.05%, the computer will check if RY is greater than TY + 0.05%. If RY exceeds this threshold, it can be determined that the actual cumulative winning rate of the target item is abnormal, which may be due to abnormal drop rates, probability calculation errors, or other abnormal factors affecting the drop rate.

[0115] Taking a game scenario as an example, suppose in a certain lottery event, the simulated cumulative winning rate of a rare game item is calculated to be TY = 5.00% in the Yth lottery draw, while the actual cumulative winning rate in the current Yth lottery draw is RY = 5.10%. If the number of second objects in the actual lottery draw in the Yth lottery exceeds 10,000, and the set error fluctuation parameter is 0.05%, the computer will perform the following calculations:

[0116] Calculate the threshold: TY + first error fluctuation parameter = 5.00% + 0.05% = 5.05%. Next, check if the threshold is exceeded: RY(5.10%) > 5.05%, which meets the abnormal condition. Therefore, the computer will determine that the cumulative winning rate of the target item in the Yth actual lottery is abnormal.

[0117] In one embodiment of this application, if the number of second objects participating in the first actual lottery is detected to be less than or equal to a first quantity threshold and greater than a second quantity threshold, a second error fluctuation parameter is obtained. If the actual cumulative winning rate of the first actual lottery is detected to be greater than the product of a first preset multiplier and a simulated cumulative winning rate, it is determined that the actual cumulative winning rate of the target item in the Yth actual lottery is abnormal.

[0118] The computer first counts the number of second-party participants in the first actual draw. If this number is less than a first threshold (e.g., 10,000) and greater than a second threshold (e.g., 500), the computer introduces a second error fluctuation parameter. Since the number of second-party participants in the first actual draw is relatively small, the random fluctuation of the winning rate is large. Therefore, the computer relaxes the anomaly detection threshold to avoid false alarms due to normal probability fluctuations. The computer calculates the actual cumulative winning rate R1 (R1 = M1 / X1) of the first actual draw and obtains the simulated cumulative winning rate T1 (T1 = N1 / X) of the first simulated draw. The computer can calculate the product of a first preset multiplier and T1 and use it as the judgment criterion. For example, when the first preset multiplier is set to 2, the computer performs the following calculation:

[0119] Calculate the threshold: 2×T1. Then, if the detected R1>2×T1, it is determined that the actual cumulative winning rate of the target item in the first actual lottery is abnormal.

[0120] For example, in a game scenario, suppose the simulated cumulative winning rate T1 = 3.00% for a rare item in the first simulated lottery. If the actual number of participants in the first lottery is 800, then the condition "500 < number of participants ≤ 10000" is met. The computer will introduce a second error fluctuation parameter and use the doubling multiplier judgment standard, that is, calculate the threshold: 2 × 3.00% = 6.00%. The actual winning rate in the first lottery is assumed to be R1 = 6.50%. Then, it checks whether it exceeds the threshold: R1(6.50%) > 6.00%, which meets the abnormal condition. It is determined that the actual cumulative winning rate of the target item in the first actual lottery is abnormal.

[0121] In one embodiment of this application, if the number of second objects participating in any actual lottery is less than or equal to a first quantity threshold and greater than a second quantity threshold, and any actual lottery is not the first actual lottery, then a third error fluctuation parameter is obtained. If the actual cumulative winning rate of any actual lottery is greater than the product of the third error fluctuation parameter and the simulated cumulative winning rate, then it is determined that the actual cumulative winning rate is abnormal.

[0122] Specifically, when the computer detects that the number of second objects in any actual draw other than the first actual draw is less than or equal to a first quantity threshold (e.g., 10,000), the computer introduces a third error fluctuation parameter. In this case, the computer will not significantly relax the threshold as it would in the first actual draw, but will instead adopt a more moderate multiplier adjustment strategy to ensure that the calculation of the cumulative winning rate still accurately reflects the actual situation.

[0123] The computer first obtains the actual cumulative winning rate RY for any given lottery draw, and then obtains the simulated cumulative winning rate TY for that draw. To avoid false alarms due to a low number of draws, the computer multiplies TY by a third error fluctuation parameter (e.g., 1.2) and uses this as the threshold for anomaly detection. The calculation process is as follows:

[0124] Calculate the adjusted threshold: 1.2×TY. If RY>1.2×TY, then it is determined that the actual cumulative winning rate of the target item in the Yth actual lottery is abnormal.

[0125] For example, in a game scenario, suppose the simulated cumulative winning rate TY = 2.50% for a rare item in the Yth simulated lottery. If the actual number of objects in the Yth lottery is small (e.g., 9000), then the condition "Y > 1 and 500 < number of objects ≤ 10000" is met. The computer will then introduce a third error fluctuation parameter and adopt an adjusted judgment standard. That is, the computer will calculate the threshold: 1.2 × 2.50% = 3.00%, and the actual winning rate RY is assumed to be 3.20%. Since RY(3.20%) > 3.00%, the abnormal condition is met, and therefore an anomaly is determined.

[0126] In one embodiment of this application, if the number of second objects participating in the first actual lottery is less than a second quantity threshold (e.g., 500), it indicates that the number of people participating in the lottery is too small and the data error of the actual cumulative winning rate of each lottery is too large, so no anomaly detection is performed.

[0127] To more clearly illustrate the anomaly detection method corresponding to S340, the following is provided: Figure 6 The flowchart shown is for anomaly detection. Figure 6 In this context, the first quantity threshold is denoted as A, the second quantity threshold is denoted as B, and the first to third error fluctuation parameters are denoted as W1, W2, and W3, respectively.

[0128] First, the computer obtains the actual cumulative winning rate RY of the target item in the Yth actual lottery draw and the simulated cumulative winning rate TY in the Yth simulated lottery draw. Next, it needs to determine whether Y equals 1.

[0129] If Y equals 1, then there are 3 possibilities:

[0130] Case 1: If the number of second objects in the Yth actual lottery draw is less than the second quantity threshold B, it means that the number of second objects participating in the lottery is too small. No anomaly detection will be performed, and the result will be "No anomaly was found in R1 of the Yth actual lottery draw".

[0131] Case 2: If MY is less than or equal to A and greater than B, then we need to check if RY is greater than W2×TY. If so, we get the result "The RY of the target item in the Yth actual lottery draw is abnormal", otherwise we get the result "The RY of the target item in the Yth actual lottery draw is not abnormal".

[0132] Case 3: If MY is greater than A, then we need to check if RY is greater than W1+TY. If so, we get the result "The RY of the target item in the Yth actual lottery draw is abnormal", otherwise we get the result "The RY of the target item in the Yth actual lottery draw is not abnormal".

[0133] If Y is not equal to 1 (i.e., greater than 1), then there are 3 possibilities:

[0134] Case 1: If MY is less than the second quantity threshold B, it means that the number of second objects participating in the lottery is too small. No anomaly detection will be performed, and the result will be "the target item did not have an anomaly in R1 of the Yth actual lottery".

[0135] Scenario 2: If MY is greater than A, then we need to check if RY is greater than W1+TY. If so, we get the result "The target item's RY in the Yth actual lottery draw is abnormal", otherwise we get the result "The target item's RY in the Yth actual lottery draw is not abnormal".

[0136] Case 3: If MY is less than or equal to A and greater than B, then we need to check if RY is greater than W3×TY. If so, we get the result "The RY of the target item in the Yth actual lottery draw is abnormal", otherwise we get the result "The RY of the target item in the Yth actual lottery draw is not abnormal".

[0137] Firstly, in related technologies, winning rate detection is usually based on the cumulative winning rate of the entire lottery activity, which has a significant lag. Due to the long statistical period, certain periodic abnormal fluctuations may be masked, leading to the failure to detect and handle anomalies in a timely manner. The method in this application refines anomaly detection to each actual lottery draw, calculating and analyzing the actual winning rate of each draw position sequentially, enabling rapid identification of anomalies. For example, the computer can precisely pinpoint whether the actual cumulative winning rate of a target item is abnormal in a specific actual lottery draw, rather than relying solely on overall statistical data. This significantly reduces the risk of missing anomalies, thereby improving the accuracy of detecting anomalies in the actual cumulative winning rate.

[0138] Secondly, the actual cumulative winning rate of the target item in each actual draw is compared with the simulated cumulative winning rate of the corresponding position, which is calculated based on the simulated draw records of the corresponding position. Therefore, the simulated cumulative winning rates for different draw positions are different. Compared with related technologies that preset a fixed winning rate threshold, the method of this application can more realistically reflect the changes in the winning rate of the target item during the draw process, avoid misjudgments or omissions caused by fixed threshold settings, and improve the accuracy of anomaly detection in the actual cumulative winning rate.

[0139] In one embodiment of this application, another data processing method is provided, which can be executed by a computer. For example... Figure 7 As shown, the data processing method may include S310 to S340 and S710 to S730. That is, S710 to S730 are... Figure 3 The steps to be performed after S240 are shown.

[0140] The following describes S710 to S730:

[0141] S710. If an anomaly is detected in the actual cumulative winning rate, the process jumps to the step of calculating the actual cumulative winning rate of the target item in any actual lottery draw, and obtains the current anomaly in the actual cumulative winning rate.

[0142] If the computer detects an anomaly in the actual cumulative winning rate of the target item in a particular lottery, it will recalculate the actual cumulative winning rate of the target item in any given lottery to determine the anomaly in the current actual cumulative winning rate.

[0143] In addition, after detecting an anomaly in the actual cumulative winning rate in the actual lottery, the computer can also calculate the first degree of anomaly of the actual cumulative winning rate, denoted as S1, where S1 = current actual cumulative winning rate / simulated cumulative winning rate.

[0144] S720. If the current actual cumulative winning rate is abnormal, obtain the first degree of abnormality of the previous actual cumulative winning rate and the second degree of abnormality of the current actual cumulative winning rate.

[0145] If the actual cumulative winning rate becomes abnormal again in the current lottery, the second degree of abnormality S2 can be calculated, where S2 = current actual cumulative winning rate / simulated cumulative winning rate.

[0146] S730, Generate the abnormal trend of the actual cumulative winning rate based on the first and second abnormality levels.

[0147] Specifically, the computer can divide the second degree of anomaly by the first degree of anomaly to obtain the anomaly trend. This anomaly trend is used to characterize whether the anomaly of the actual cumulative winning rate is becoming more severe or more moderate.

[0148] If the abnormal trend exceeds a preset trend threshold, an alarm message indicating an abnormality in the actual cumulative winning rate is output. This preset trend threshold can be set by someone skilled in the art, for example, it can be set to 1. If S2 / S1 > 1, it means the abnormality in the actual cumulative winning rate is becoming increasingly severe. If S2 / S1 ≤ 1, it means the abnormality in the actual cumulative winning rate is not showing a worsening trend.

[0149] If the computer determines that the anomaly in the actual cumulative winning rate is becoming increasingly serious, it can output an alarm message and send it to the technicians so that the technicians can modify the configuration information to reduce the degree of anomaly in the actual cumulative winning rate until it is no longer abnormal.

[0150] This method allows alarm messages to be generated only when the actual cumulative winning rate becomes increasingly abnormal. This filters out duplicate alarm messages, improving the accuracy of alarms and reducing the workload of technical personnel.

[0151] In one embodiment of this application, such as Figure 8 The diagram shown illustrates the overall process of an actual cumulative winning rate involved in this application. Figure 8 First, technicians add configuration information. Based on this configuration, simulated lottery data can be generated. Then, based on the simulated lottery records, the simulated cumulative winning rate of the target item in each lottery draw can be generated. Next, at least two secondary entities can participate in the lottery, obtaining the actual cumulative winning rate of the target item in each lottery draw. The simulated and actual cumulative winning rates of the target item in each lottery draw are reported to the anomaly detection platform corresponding to the anomaly detection strategy. Anomaly detection can then be performed on the actual cumulative winning rate of each lottery draw according to the lottery ranking. This filtering mechanism represents... Figure 7 The corresponding method can filter out increasingly mild anomaly alerts by detecting abnormal trends, and instead generate alerts for increasingly severe anomalies and send them to technical personnel.

[0152] Figure 9 This is a block diagram illustrating a data processing apparatus according to an embodiment of this application. Figure 9 As shown, this data processing device can be applied to a computer, and the device includes:

[0153] A data processing apparatus, comprising:

[0154] The acquisition unit 910 is used to acquire at least two simulated lottery records of at least two first objects in at least two simulated lottery draws, and the simulated lottery record corresponding to each first object contains the items drawn by the first object in each simulated lottery draw.

[0155] The calculation unit 920 is used to calculate the simulated cumulative winning rate of the target item in any simulated lottery based on the number of at least two first objects and the number of times the target item appears in each simulated lottery in at least two first objects.

[0156] The calculation unit 920 is also used to calculate the actual cumulative winning rate of the target item in any actual draw based on the number of at least two second objects and the number of times the target item appears in each actual draw in at least two second objects;

[0157] The detection unit 930 is used to detect abnormalities in the actual cumulative winning rate based on the actual cumulative winning rate of the target item in any actual lottery and the simulated cumulative winning rate in the corresponding simulated lottery.

[0158] In one embodiment of this application, based on the aforementioned scheme, the acquisition unit 910 is further configured to acquire first identification information corresponding to any simulated lottery draw, the first identification information being used to characterize the position of any simulated lottery draw in at least two simulated lottery draws; the calculation unit 920 is further configured to calculate the first cumulative number of draws corresponding to any simulated lottery draw based on the quantity of at least two first objects and the first identification information; and to calculate the simulated cumulative winning rate of the target item in any simulated lottery draw based on the first cumulative number of occurrences of the target item in any simulated lottery draw and the first cumulative number of draws.

[0159] In one embodiment of this application, based on the aforementioned scheme, the acquisition unit 910 is further configured to acquire second identification information corresponding to any actual lottery draw, the second identification information being used to characterize the position of any actual lottery draw in at least two actual lottery draws; based on the second identification information, acquire the second cumulative occurrence number of the target item in any actual lottery draw, and acquire the second cumulative number of lottery draws corresponding to any actual lottery draw; the calculation unit 920 is further configured to calculate the actual cumulative winning rate of the target item in any actual lottery draw based on the second cumulative occurrence number and the second cumulative number of lottery draws.

[0160] In one embodiment of this application, based on the aforementioned scheme, the calculation unit 920 is further configured to sum the actual number of times the target item appears in the lottery with a position less than or equal to the position represented by the second identification information to obtain a second cumulative number of occurrences; and to sum the actual number of times the target item appears in the lottery with a position less than or equal to the position represented by the second identification information to obtain a second cumulative number of lottery occurrences.

[0161] In one embodiment of this application, based on the aforementioned scheme, the acquisition unit 910 is further configured to acquire a first error fluctuation parameter if the number of second objects participating in any actual lottery is detected to be greater than a first quantity threshold; the device further includes a processing unit 940 configured to determine that the actual cumulative winning rate is abnormal if the actual cumulative winning rate is detected to be greater than the sum of the simulated cumulative winning rate and the first error fluctuation parameter.

[0162] In one embodiment of this application, based on the aforementioned scheme, the acquisition unit 910 is further configured to acquire a second error fluctuation parameter if the number of second objects participating in the first actual lottery is less than or equal to a first quantity threshold and greater than a second quantity threshold; the processing unit 940 is further configured to determine that the actual cumulative winning rate is abnormal if the actual cumulative winning rate of the first actual lottery is greater than the product of the first preset multiplier and the simulated cumulative winning rate.

[0163] In one embodiment of this application, based on the aforementioned scheme, the acquisition unit 910 is further configured to acquire a third error fluctuation parameter if it is detected that the number of second objects participating in any actual lottery is less than or equal to a first quantity threshold, greater than a second quantity threshold, and any actual lottery is not the first actual lottery; the processing unit 940 is further configured to determine that the actual cumulative winning rate is abnormal if it is detected that the actual cumulative winning rate of any actual lottery is greater than the product of the third error fluctuation parameter and the simulated cumulative winning rate.

[0164] In one embodiment of this application, based on the aforementioned scheme, after detecting anomalies in the actual cumulative winning rate based on the actual cumulative winning rate of the target item in any actual lottery and the simulated cumulative winning rate in the corresponding simulated lottery, the processing unit 940 is further configured to, if an anomaly is detected in the actual cumulative winning rate, jump to the step of calculating the actual cumulative winning rate of the target item in any actual lottery to obtain the anomaly of the current actual cumulative winning rate; the acquisition unit 910 is further configured to, if an anomaly is detected in the current actual cumulative winning rate, acquire the first degree of anomaly of the previous actual cumulative winning rate and the second degree of anomaly of the current actual cumulative winning rate; the processing unit 940 is further configured to generate an anomaly trend of the actual cumulative winning rate based on the first degree of anomaly and the second degree of anomaly.

[0165] In one embodiment of this application, based on the aforementioned scheme, the calculation unit 920 is further configured to perform a division operation between the second degree of abnormality and the first degree of abnormality to obtain an abnormal trend; after generating an abnormal trend of the actual cumulative winning rate based on the first degree of abnormality and the second degree of abnormality, the processing unit 940 is further configured to output alarm information to characterize the abnormality of the actual cumulative winning rate if the abnormal trend is greater than a preset trend threshold.

[0166] It should be noted that the apparatus provided in the foregoing embodiments and the method provided in the foregoing embodiments belong to the same concept, and the specific way in which each module and unit performs operations has been described in detail in the method embodiments.

[0167] Embodiments of this application also provide an electronic device, including: at least one processor; and a memory for storing at least one program, which, when executed by the at least one processor, causes the electronic device to perform the aforementioned data processing method.

[0168] Figure 10 This is a schematic diagram of the structure of a computer system suitable for implementing the data processing apparatus of the present application.

[0169] It should be noted that, Figure 10 The computer system 1000 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0170] like Figure 10 As shown, the computer system 1000 includes a Central Processing Unit (CPU) 1001, which can perform various appropriate actions and processes, such as executing the methods described in the above embodiments, based on programs stored in Read-Only Memory (ROM) 1002 or programs loaded from storage portion 1008 into Random Access Memory (RAM) 1003. The RAM 1003 also stores various programs and data required for system operation. The CPU 1001, ROM 1002, and RAM 1003 are interconnected via a bus 1004. An Input / Output (I / O) interface 1005 is also connected to the bus 1004.

[0171] The following components are connected to I / O interface 1005: an input section 1006 including a keyboard, mouse, etc.; an output section 1007 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to I / O interface 1005 as needed. Removable media 1011, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 1010 as needed so that computer programs read from them can be installed into storage section 1008 as needed.

[0172] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 1009, and / or installed from removable medium 1011. When the computer program is executed by central processing unit (CPU) 1001, it performs various functions defined in the system of this application.

[0173] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. For example, a computer-readable medium can be an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable medium may include, but are not limited to: an electrical connection having at least one wire, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0174] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains at least one executable instruction for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0175] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0176] Another aspect of this application provides a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements the data processing method as described above. This computer-readable medium may be included in the electronic device described in the above embodiments, or it may exist independently and not assembled into the electronic device.

[0177] Another aspect of this application provides a computer program product or computer program including computer instructions stored in a computer-readable medium. A processor of a computer device reads the computer instructions from the computer-readable medium and executes the computer instructions, causing the computer device to perform the data processing methods provided in the various embodiments described above.

[0178] The above content is merely a preferred exemplary embodiment of this application and is not intended to limit the implementation of this application. Those skilled in the art can easily make corresponding modifications or alterations based on the main concept and spirit of this application. Therefore, the scope of protection of this application should be determined by the scope of protection claimed in the claims.

Claims

1. A data processing method, characterized in that, include: Obtain at least two simulated lottery records for each of the two first objects in at least two simulated lottery draws, wherein the simulated lottery record for each first object contains the items drawn by the first object in each simulated lottery draw. Based on the number of the at least two first objects and the number of times the target item appears in the at least two first objects in each simulated lottery, calculate the simulated cumulative winning rate of the target item in any simulated lottery; Based on the number of at least two second objects and the number of times the target item appears in each of the at least two second objects in each actual draw, calculate the actual cumulative winning rate of the target item in any actual draw; Based on the actual cumulative winning rate of the target item in any actual lottery draw and the simulated cumulative winning rate in the corresponding simulated lottery draw, anomalies in the actual cumulative winning rate are detected.

2. The method according to claim 1, characterized in that, The calculation of the simulated cumulative winning rate of the target item in any simulated lottery, based on the number of the at least two first objects and the number of times the target item appears in the at least two first objects in each simulated lottery, includes: Obtain the first identifier information corresponding to any one of the simulated lottery draws, wherein the first identifier information is used to characterize the position of any one of the simulated lottery draws in the at least two simulated lottery draws; Based on the number of the at least two first objects and the first identification information, calculate the first cumulative number of draws corresponding to any simulated lottery draw; Based on the first cumulative number of times the target item appears in any simulated lottery and the first cumulative number of lottery draws, calculate the simulated cumulative winning rate of the target item in any simulated lottery.

3. The method according to claim 1, characterized in that, The calculation of the actual cumulative winning rate of the target item in any actual draw, based on the number of at least two second objects and the number of times the target item appears in each of the at least two second objects, includes: Obtain the second identifier information corresponding to any one actual lottery draw, wherein the second identifier information is used to characterize the position of any one actual lottery draw in at least two actual lottery draws; Based on the second identification information, obtain the second cumulative number of times the target item appears in any actual lottery draw, and obtain the second cumulative number of lottery draws corresponding to any actual lottery draw; Based on the second cumulative occurrence count and the second cumulative number of draws, calculate the actual cumulative winning rate of the target item in any given actual draw.

4. The method according to claim 3, characterized in that, The step of obtaining the second cumulative number of times the target item appears in any actual lottery draw based on the second identification information, and obtaining the second cumulative number of draws corresponding to any actual lottery draw, includes: The second cumulative occurrence count is obtained by summing the actual number of times the target item appears in the lottery when its position is less than or equal to the position represented by the second identification information. The second cumulative number of draws is obtained by summing the actual number of draws for each draw whose rank is less than or equal to the rank represented by the second identifier information.

5. The method according to claim 1, characterized in that, The method of detecting abnormalities in the actual cumulative winning rate based on the actual cumulative winning rate of the target item in any actual lottery draw and the simulated cumulative winning rate in the corresponding simulated lottery draw includes: If the number of second objects participating in any of the actual draws is detected to be greater than the first quantity threshold, then the first error fluctuation parameter is obtained; If the actual cumulative winning rate is detected to be greater than the sum of the simulated cumulative winning rate and the first error fluctuation parameter, then it is determined that the actual cumulative winning rate is abnormal.

6. The method according to claim 1, characterized in that, The method of detecting abnormalities in the actual cumulative winning rate based on the actual cumulative winning rate of the target item in any actual lottery draw and the simulated cumulative winning rate in the corresponding simulated lottery draw includes: If the number of second objects participating in the first actual lottery is detected to be less than or equal to the first quantity threshold and greater than the second quantity threshold, then the second error fluctuation parameter is obtained. If the actual cumulative winning rate of the first actual lottery draw is detected to be greater than the product of the first preset multiplier and the simulated cumulative winning rate, then it is determined that the actual cumulative winning rate is abnormal.

7. The method according to claim 1, characterized in that, The method of detecting abnormalities in the actual cumulative winning rate based on the actual cumulative winning rate of the target item in any actual lottery draw and the simulated cumulative winning rate in the corresponding simulated lottery draw includes: If the number of second objects participating in any actual lottery is less than or equal to the first quantity threshold, greater than the second quantity threshold, and the actual lottery is not the first actual lottery, then the third error fluctuation parameter is obtained. If the actual cumulative winning rate of any given lottery draw is detected to be greater than the product of the third error fluctuation parameter and the simulated cumulative winning rate, then it is determined that the actual cumulative winning rate is abnormal.

8. The method according to claim 1, characterized in that, After detecting anomalies in the actual cumulative winning rate based on the actual cumulative winning rate of the target item in any actual lottery draw and the simulated cumulative winning rate in the corresponding simulated lottery draw, the method further includes: If an anomaly is detected in the actual cumulative winning rate, the process jumps to the step of calculating the actual cumulative winning rate of the target item in any actual lottery draw, and obtains the current anomaly in the actual cumulative winning rate. If the current actual cumulative winning rate is abnormal, then obtain the first degree of abnormality of the previous actual cumulative winning rate and the second degree of abnormality of the current actual cumulative winning rate; An abnormal trend in the actual cumulative winning rate is generated based on the first degree of abnormality and the second degree of abnormality.

9. The method according to claim 8, characterized in that, The abnormal trend in generating the actual cumulative winning rate based on the first degree of abnormality and the second degree of abnormality includes: The abnormal trend is obtained by dividing the second degree of abnormality by the first degree of abnormality. After generating the abnormal trend of the actual cumulative winning rate based on the first abnormality level and the second abnormality level, the method further includes: If the abnormal trend is greater than a preset trend threshold, an alarm message is output to indicate that the actual cumulative winning rate is abnormal.

10. A data processing apparatus, characterized in that, include: The acquisition unit is used to acquire the simulated lottery records of at least two first objects in at least two simulated lottery draws, and the simulated lottery record corresponding to each first object contains the items drawn by the first object in each simulated lottery draw. The calculation unit is used to calculate the simulated cumulative winning rate of the target item in any simulated lottery based on the number of the at least two first objects and the number of times the target item appears in the at least two first objects in each simulated lottery. The calculation unit is also used to calculate the actual cumulative winning rate of the target item in any actual lottery based on the number of at least two second objects and the number of times the target item appears in each actual lottery in the at least two second objects; The detection unit is used to detect abnormalities in the actual cumulative winning rate based on the actual cumulative winning rate of the target item in any actual lottery and the simulated cumulative winning rate in the corresponding simulated lottery.

11. An electronic device, characterized in that, include: Memory, which stores computer-readable instructions; A processor reads computer-readable instructions stored in memory to perform the method of any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that, It stores computer-readable instructions that, when executed by the computer's processor, cause the computer to perform the method of any one of claims 1 to 9.

13. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the method as described in any one of claims 1 to 9.

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