Data storage method and device, computer equipment and storage medium

Abstract

The invention relates to a data storage method and device, computer equipment and a storage medium. The method comprises: for different types of composition data in anti-cheating data, according to data request information corresponding to each type of composition data, determining a target database suitable for storing each type of composition data, the target database being a storage database, and different storage databases corresponding to different storage costs, different types of component data in the anti-cheating data are stored in different storage databases under the condition that the storage cost is reduced, so that the storage cost of the anti-cheating data is reduced, and the problem that the database storage cost is high due to the fact that a single database is adopted for storage in an existing anti-cheating process is solved.

Description

Technical Field

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

[0002] Anti-cheating data needs to be stored in the real-time anti-cheating process. In order to facilitate the maintenance of anti-cheating data, a single database is used. However, when the QPS (requests per second) of the anti-cheating business is too high, it will lead to an increase in data writing, an increase in log records, an increase in the frequency of index updates, and an increase in data backup pressure. This will significantly increase the storage cost of a single storage database. Therefore, a method that can reduce the storage cost of anti-cheating data is urgently needed. Summary of the invention

[0003] The present application provides a data storage method, apparatus, computer device and storage medium to solve the problem of high database storage cost caused by using a single database for storage in the existing anti-cheating process.

[0004] In a first aspect, the present application provides a data storage method, the method comprising:

[0005] Obtaining anti-cheating data corresponding to the anti-cheating service and data request information corresponding to the anti-cheating data, wherein the anti-cheating data includes multiple types of component data, and the data request information includes sub-request information corresponding to each type of component data;

[0006] Determining, according to the sub-request information corresponding to each type of component data in the anti-cheating data, a target database corresponding to each type of component data in the anti-cheating data, wherein the target database is a storage database with the lowest storage cost for storing the corresponding component data, and different storage databases correspond to different storage costs;

[0007] Each type of component data in the anti-cheating data is stored in a target database corresponding to each type of component data.

[0008] In a second aspect, the present application provides a data storage device, the device comprising:

[0009] An acquisition module, used to acquire anti-cheating data corresponding to the anti-cheating service and data request information corresponding to the anti-cheating data;

[0010] a processing module, configured to determine a target database corresponding to each type of component data in the anti-cheating data according to sub-request information corresponding to each type of component data in the anti-cheating data, wherein the target database is a storage database with the lowest storage cost for storing the corresponding component data, and different storage databases correspond to different storage costs;

[0011] The storage module is used to store various types of component data in the anti-cheating data in target databases corresponding to the various types of component data.

[0012] In a third aspect, the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned data storage method when executing the computer program.

[0013] In a fourth aspect, the present application also provides a computer storage medium storing computer executable instructions, wherein the computer executable instructions are used to execute the above-mentioned data storage method.

[0014] The above technical solution provided by the embodiment of the present application has the following advantages over the prior art: the method provided by the embodiment of the present application obtains anti-cheating data corresponding to the anti-cheating service and data request information corresponding to the anti-cheating data, wherein the anti-cheating data includes multiple types of component data, and the data request information includes sub-request information corresponding to each type of component data; according to the sub-request information corresponding to each type of component data in the anti-cheating data, a target database corresponding to each type of component data in the anti-cheating data is determined, wherein the target database is a storage database with the lowest storage cost for storing the corresponding component data, and different storage databases correspond to different storage costs; and each type of component data in the anti-cheating data is respectively stored in the target database corresponding to each type of component data.

[0015] Based on the above method, for different types of component data in the anti-cheating data, a target database suitable for storing each type of component data is determined according to the data request information corresponding to each type of component data. The target database is the storage database. Different storage databases correspond to different storage costs. Different types of component data in the anti-cheating data are stored in different storage databases while reducing the storage cost, thereby reducing the storage cost of the anti-cheating data and solving the problem of high database storage cost caused by using a single database for storage in the existing anti-cheating process. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0018] One or more embodiments are exemplarily described by pictures in the corresponding drawings, and these exemplified descriptions do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0019] Figure 1 An application environment diagram of a data storage method provided in an embodiment of the present application;

[0020] Figure 2 A schematic diagram of a data storage method provided in an embodiment of the present application;

[0021] Figure 3 A schematic diagram of a data storage method provided in an embodiment of the present application;

[0022] Figure 4 A structural block diagram of a data storage device provided in an embodiment of the present application;

[0023] Figure 5 A schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0025] The disclosure below provides many different embodiments or examples to implement different structures of the present invention. In order to simplify the disclosure of the present invention, the parts and settings of specific examples are described below. Of course, they are only examples, and the purpose is not to limit the present invention. In addition, the present invention can repeat reference numbers and / or letters in different examples. This repetition is for the purpose of simplification and clarity, and does not itself indicate the relationship between the various embodiments and / or settings discussed.

[0026] Figure 1 FIG. 1 is an application environment diagram of a data storage method in an embodiment. Figure 1, the data storage method is applied to a data storage system. The data storage system includes a server 110 and a database cluster 120. The server 110 and the database cluster 120 are connected via a network. The server 110 can be implemented with an independent server 110 or a server 110 cluster composed of multiple servers 110. The database cluster 120 includes multiple storage databases 121 with different storage costs for different types of data. The storage database 121 can specifically be a pegadb database or a couchbase database. The pegadb database is a distributed dictionary database product that is fully compatible with the Redis protocol. The advantage of the pegadb database is that it can store large amounts of data, but when the business qps (requests per second) is too high, more shards are required, which will increase the cost of use. Due to the anti-cheating blacklist, the storage structure is relatively simple, for example: id+label (label), and generally speaking, the number of blacklists is also small, but the qps of requesting blacklists is generally large, so the storage of the anti-cheating blacklist part uses the pegadb database, resulting in serious waste.

[0027] The couchbase database is an open source distributed database system and a better storage alternative. In scenarios with high QPS and small storage data volume, the cost of using the couchbase database is only 60% of the cost of using the pegadb database, so consider adding the pegadb database as another optional storage database121.

[0028] In one embodiment, Figure 2 A schematic diagram of a data storage method in an embodiment is shown in FIG. Figure 2 , provides a data storage method. This embodiment mainly applies this method to the above Figure 1 Taking the server 110 in the example as an example, the data storage method specifically includes the following steps:

[0029] Step S210, obtaining anti-cheating data corresponding to the anti-cheating service and data request information corresponding to the anti-cheating data, wherein the anti-cheating data includes multiple types of component data, and the data request information includes sub-request information corresponding to each type of component data.

[0030] Specifically, the anti-cheating business can be any different type of business that requires anti-cheating detection. The business types of the anti-cheating business can be game business, online education business, financial business, advertising business, social media business, workplace recruitment business, blockchain business, etc. Anti-cheating data is used to indicate the data flow corresponding to the anti-cheating business, specifically including various types of component data, including log data, anti-cheating blacklist, whitelist, rule data, model data, risk assessment data, third-party data, etc. The log data records in detail the time, participants, operation content and other characteristic data of various events and operations occurring in the system. Log data can help the anti-cheating team trace and analyze the user's behavior trajectory and find clues to cheating behavior. For example, the time and related parameters of user login, logout, and important operations (such as changing passwords, submitting orders, etc.) will be recorded in the log. When an abnormal situation is found, the order and specific situation of the operation can be understood by consulting the log data, so as to determine whether there is cheating behavior and the means and process of cheating.

[0031] The anti-cheating blacklist is used to record information such as users, devices, IP addresses, and operating behaviors that have cheated. On the contrary, the whitelist is used to record information such as users, devices, IP addresses, and operating behaviors that are recognized by the system as safe and trustworthy.

[0032] Model data is model-related data obtained through the analysis of a large amount of historical data and training of machine learning algorithms. These models can predict the possibility and risk level of user behavior, such as whether a user is likely to conduct fraudulent transactions or whether it is a malicious registration. Model data includes model parameters, training set features, evaluation indicators and other information.

[0033] Risk assessment data is the data obtained after risk assessment of users, devices, transactions and other objects. Risk assessment can take into account a variety of factors, such as the user's historical behavior, the abnormality of the current operation, the similarity with known cheating patterns, etc., and assign a risk score or level to each object. For example, if a newly registered user uses a device with multiple abnormal features and the registration behavior is similar to a known malicious registration pattern, then the user's risk assessment score may be higher, and the system will conduct more stringent monitoring and review of its subsequent operations.

[0034] Third-party data refers to data related to anti-cheating obtained from the outside, such as credit data, identity verification data, fraud data shared by the industry, etc. These data can supplement the system's own data and provide more comprehensive information for judging the credibility of users and the legality of their behavior. For example, by cooperating with credit reporting agencies to obtain users' credit scores, the system will be more vigilant and conduct more stringent reviews when users with low credit scores perform certain high-risk operations (such as large transactions); or share fraud data with other companies in the industry to keep abreast of emerging cheating methods and related information, and enhance its own anti-cheating capabilities.

[0035] The data request information includes sub-request information corresponding to each type of component data. The sub-request information specifically includes the number of requests per second (number of queries per second), number of concurrent connections, response time, throughput, CPU utilization, memory occupancy, etc. of the server 110 for the component data. Based on the sub-request information, the storage cost of the component data for the storage database 121 can be predicted.

[0036] The anti-cheating data corresponding to the anti-cheating service is obtained through the main thread. If the main thread fails to obtain the anti-cheating data, the backup thread is used to obtain the anti-cheating data, thereby ensuring the reliability of the anti-cheating data acquisition.

[0037] Step S220, determining a target database corresponding to each type of component data in the anti-cheating data according to the sub-request information corresponding to each type of component data in the anti-cheating data, wherein the target database is a storage database 121 with the lowest storage cost for storing the corresponding component data, and different storage databases 121 correspond to different storage costs.

[0038] Specifically, the storage cost of the component data for different storage databases 121 is determined based on the sub-request information of each type of component data, so as to screen out the target database that stores each type of component data at the lowest cost, so as to reduce the storage cost of the component data in the storage database 121 as much as possible.

[0039] Step S230, storing each type of component data in the anti-cheating data into a target database corresponding to each type of component data.

[0040] Specifically, each type of component data in the anti-cheating data is stored in a target database corresponding to each type of component data according to the lowest storage cost. Compared with storing the anti-cheating data in a single database, the overall storage cost of the anti-cheating data is reduced, and the problem of high database storage cost caused by using a single database for storage in the existing anti-cheating process is solved.

[0041] In one embodiment, the obtaining of anti-cheating data corresponding to the anti-cheating service and data request information corresponding to the anti-cheating data includes:

[0042] Acquire feature data corresponding to the anti-cheating service and feature sub-request information corresponding to the feature data;

[0043] Outputting an anti-cheating blacklist corresponding to the anti-cheating service according to a feature calculation result of the feature data;

[0044] Obtaining blacklist sub-request information corresponding to the anti-cheating blacklist, wherein the different types of component data in the anti-cheating data include the feature data and the anti-cheating blacklist, and the data request information includes the feature sub-request information and the blacklist sub-request information.

[0045] Specifically, feature extraction is performed from the log data corresponding to the anti-cheating service to obtain feature data, and feature sub-request information corresponding to the feature data is obtained. The feature sub-request information is used to indicate the request resources required for feature calculation of the feature data. The feature data includes at least one of time feature, space feature, element feature, and distribution feature. The time feature includes the moment when the object performs multiple different behavior operations, the behavior time difference of the same type of operation, and the behavior time difference of the natural order. Spatial features include the location where the behavior occurs, the location where the behavior takes effect, the geographical location switching frequency, the click location switching frequency, and the display location switching frequency. Element features include the behavior occurrence element, the display element, and the element operation frequency. For example, the behavior occurrence element is a click element, and the display element is an advertising element, a video element, an activity material element, etc. The element operation frequency includes the click frequency and the switching frequency. Distribution features include the number of clicks distribution within a preset time, the IP distribution, and the distribution of episodes watched. Based on the above feature data, it can be reflected whether the access behavior is cheating behavior.

[0046] Reference Figure 3 , perform feature calculation on the feature data to determine whether the access behavior is cheating, and then output the anti-cheating blacklist based on the objects with cheating behavior, refer to Figure 3 It can be seen that the anti-cheating blacklist is then used to clean the multiple anti-cheating data received by the server 110, that is, to respond to and filter the anti-cheating data, and only respond to the anti-cheating data that has successfully passed the cleaning. Then, the blacklist sub-request information corresponding to the anti-cheating blacklist is obtained, and the blacklist sub-request information is used to represent the request resource when accessing the anti-cheating blacklist.

[0047] The component data in the anti-cheating data that has a greater impact on the storage cost are the feature data and the anti-cheating blacklist. Therefore, the storage database 121 corresponding to the screening of these two types of data as the component data reduces the storage cost of the anti-cheating data while reducing the storage cost of the feature data and the storage cost of the anti-cheating blacklist respectively.

[0048] In one embodiment, determining the target database corresponding to each type of component data in the anti-cheating data according to the sub-request information corresponding to each type of component data in the anti-cheating data includes at least one of the following:

[0049] Determining a feature target database corresponding to the feature data according to the service type of the anti-cheating service and the feature sub-request information corresponding to the feature data;

[0050] According to the blacklist sub-request information corresponding to the anti-cheating blacklist and the number of list dimensions, a blacklist target database corresponding to the anti-cheating blacklist is determined.

[0051] Specifically, the target database with the lowest storage cost for storing the feature data is determined according to the business type of the anti-cheating business and the feature sub-request information. Different business types of anti-cheating business have different storage costs for different storage databases 121. Therefore, the business type of the anti-cheating business and the feature sub-request information are combined to comprehensively determine the feature target database with the lowest storage cost corresponding to the feature data.

[0052] The target database with the lowest storage cost for storing the anti-cheating blacklist is determined according to the blacklist sub-request information and the number of list dimensions of the anti-cheating blacklist. The number of list dimensions refers to the number of dimensions in the anti-cheating blacklist. The dimensions in the anti-cheating blacklist specifically include user ID dimension, device ID dimension, IP address dimension, drama ID dimension, etc. That is, the number of list dimensions of the anti-cheating blacklist will also affect the storage cost of the anti-cheating blacklist in the storage database 121. Therefore, the blacklist target database with the lowest storage cost corresponding to the anti-cheating blacklist is comprehensively determined in combination with the number of list dimensions and the blacklist sub-request information. The overall storage cost of the anti-cheating data is reduced by reducing the storage cost of the feature data and the storage cost of the anti-cheating blacklist respectively.

[0053] In one embodiment, determining the feature target database corresponding to the feature data according to the service type of the anti-cheating service and the feature sub-request information corresponding to the feature data includes:

[0054] Determining the feature calculation complexity of the feature data according to the service type of the anti-cheating service;

[0055] A feature target database corresponding to the feature data is determined according to the feature calculation complexity and the feature calculation request volume per second in the feature sub-request information.

[0056] Specifically, different types of anti-cheating services correspond to different feature calculation complexities. For example, the feature calculation complexity corresponding to the anti-cheating service of the game type or the feature calculation complexity corresponding to the anti-cheating service of the financial type is greater than the feature calculation complexity corresponding to the anti-cheating service of the advertising type. The feature calculation request volume per second refers to the number of requests per second (qps) of feature data during the feature calculation process. The feature calculation complexity and the feature calculation request volume per second are combined to determine the feature target database that can store feature data at the lowest storage cost.

[0057] In one embodiment, determining the feature target database corresponding to the feature data according to the feature calculation complexity and the feature calculation request volume per second in the feature sub-request information includes:

[0058] When the feature calculation complexity is greater than or equal to the preset complexity, and the feature calculation request volume per second is less than the first request volume per second, determining the first database as the feature target database corresponding to the feature data; or,

[0059] When the feature calculation complexity is less than the preset complexity and the feature calculation request volume per second is greater than or equal to the first request volume per second, the second database is determined as the feature target database corresponding to the feature data, wherein the second database and the first database are different storage databases 121.

[0060] Specifically, the feature calculation complexity is greater than or equal to the preset complexity, indicating that the feature calculation process of the feature data is relatively complex, the feature structure of the feature data is relatively complex, and the feature data is, for example, a distributed feature, but the feature calculation request volume per second is less than the first request volume per second, that is, the request volume per second in the feature calculation process of the feature data is not large. Therefore, in this scenario, the storage space required is larger, but the qps is not large, and the cost of using the PEGADB database is lower. Therefore, the first database is determined as the feature target database, that is, the first database is the PEGADB database.

[0061] The feature calculation complexity is less than the preset complexity, indicating that the feature calculation process of the feature data is relatively simple, and the feature structure of the feature data is relatively simple. The feature data may be, for example, a frequency feature or an aggregation feature, but the number of requests per second for feature calculation is greater than or equal to the first number of requests per second, that is, the number of requests per second for the feature data during feature calculation is large. Therefore, the storage space required in this scenario is not large, but the qps is large, and the cost of using the couchbase database is lower. Therefore, the second database is determined as the feature target database, that is, the second database is the couchbase database.

[0062] In one embodiment, determining the blacklist target database corresponding to the anti-cheating blacklist according to the blacklist sub-request information corresponding to the anti-cheating blacklist and the number of list dimensions includes:

[0063] When the blacklist request volume per second is less than the second request volume per second, and the number of list dimensions corresponding to the anti-cheating blacklist is greater than or equal to the preset number of dimensions, determining the first database as the blacklist target database corresponding to the anti-cheating blacklist; or,

[0064] When the blacklist request volume per second is greater than or equal to the second request volume per second, and the number of list dimensions corresponding to the anti-cheating blacklist is less than the preset number of dimensions, the second database is determined as the blacklist target database corresponding to the anti-cheating blacklist, wherein the second database and the first database are different storage databases 121.

[0065] Specifically, the number of requests per second for the blacklist is less than the second number of requests per second (qps), indicating that the number of requests per second for the anti-cheating blacklist is small, and the number of list dimensions corresponding to the anti-cheating blacklist is greater than or equal to the preset number of dimensions, indicating that the blacklist dimensions are relatively rich and the number of orders in the anti-cheating blacklist is large. In this scenario, the required storage space is larger, but the qps is smaller, and the cost of using the PEGADB database is lower. Therefore, the first database is determined as the feature target database, that is, the first database is the PEGADB database.

[0066] The number of requests per second for the blacklist is greater than or equal to the second number of requests per second, indicating that the number of requests per second for the anti-cheating blacklist is large, and the number of list dimensions corresponding to the anti-cheating blacklist is less than the preset number of dimensions, indicating that the blacklist dimension is relatively simple and the number of items in the anti-cheating blacklist is small. In this scenario, the required storage space is small, but the QPS is large, and the cost of using the Couchbase database is lower. Therefore, the second database is determined as the feature target database, that is, the second database is the Couchbase database.

[0067] In this way, a feature target database that can store feature data at the lowest storage cost and a blacklist target database that can store anti-cheating blacklists at the lowest storage cost are determined respectively, thereby reducing the overall storage cost of anti-cheating data.

[0068] In one embodiment, each type of component data in the anti-cheating data is stored in a target database corresponding to each type of component data, including:

[0069] The characteristic data is stored in the characteristic target database, and the anti-cheating blacklist is stored in the blacklist target database.

[0070] Specifically, refer to Figure 3, the feature data is stored in the feature target database with the lowest storage cost, and the anti-cheating blacklist is stored in the blacklist target database with the lowest storage cost, thereby reducing the overall storage cost of the anti-cheating data.

[0071] If a new storage database 121 is added, the existing database is used to synchronize data with the new database, and then the target database with the lowest storage cost is determined according to the aforementioned steps S210 to S230 and the anti-cheating data is stored.

[0072] Figure 2 FIG. 1 is a flow chart of a data storage method in one embodiment. It should be understood that although Figure 2 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 2 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0073] In one embodiment, Figure 4 As shown, a data storage device is provided, comprising:

[0074] An acquisition module 310, configured to acquire anti-cheating data corresponding to an anti-cheating service and data request information corresponding to the anti-cheating data;

[0075] The processing module 320 is used to determine the target database corresponding to each type of component data in the anti-cheating data according to the sub-request information corresponding to each type of component data in the anti-cheating data, wherein the target database is the storage database 121 with the lowest storage cost for storing the corresponding component data, and different storage databases 121 correspond to different storage costs;

[0076] The storage module 330 is used to store each type of component data in the anti-cheating data into a target database corresponding to each type of component data.

[0077] In one embodiment, the acquisition module 310 is further used for:

[0078] Acquire feature data corresponding to the anti-cheating service and feature sub-request information corresponding to the feature data;

[0079] Outputting an anti-cheating blacklist corresponding to the anti-cheating service according to a feature calculation result of the feature data;

[0080] Obtaining blacklist sub-request information corresponding to the anti-cheating blacklist, wherein the different types of component data in the anti-cheating data include the feature data and the anti-cheating blacklist, and the data request information includes the feature sub-request information and the blacklist sub-request information.

[0081] In one embodiment, the processing module 320 is further configured to implement at least one of the following:

[0082] Determining a feature target database corresponding to the feature data according to the service type of the anti-cheating service and the feature sub-request information corresponding to the feature data;

[0083] According to the blacklist sub-request information corresponding to the anti-cheating blacklist and the number of list dimensions, a blacklist target database corresponding to the anti-cheating blacklist is determined.

[0084] In one embodiment, the processing module 320 is further configured to:

[0085] Determining the feature calculation complexity of the feature data according to the service type of the anti-cheating service;

[0086] A feature target database corresponding to the feature data is determined according to the feature calculation complexity and the feature calculation request volume per second in the feature sub-request information.

[0087] In one embodiment, the processing module 320 is further configured to:

[0088] When the feature calculation complexity is greater than or equal to the preset complexity, and the feature calculation request volume per second is less than the first request volume per second, determining the first database as the feature target database corresponding to the feature data; or,

[0089] When the feature calculation complexity is less than the preset complexity and the feature calculation request volume per second is greater than or equal to the first request volume per second, the second database is determined as the feature target database corresponding to the feature data, wherein the second database and the first database are different storage databases 121.

[0090] In one embodiment, the processing module 320 is further configured to:

[0091] When the blacklist request volume per second is less than the second request volume per second, and the number of list dimensions corresponding to the anti-cheating blacklist is greater than or equal to the preset number of dimensions, determining the first database as the blacklist target database corresponding to the anti-cheating blacklist; or,

[0092] When the blacklist request volume per second is greater than or equal to the second request volume per second, and the number of list dimensions corresponding to the anti-cheating blacklist is less than the preset number of dimensions, the second database is determined as the blacklist target database corresponding to the anti-cheating blacklist, wherein the second database and the first database are different storage databases 121.

[0093] In one embodiment, the storage module 330 is further used for:

[0094] The characteristic data is stored in the characteristic target database, and the anti-cheating blacklist is stored in the blacklist target database.

[0095] like Figure 5 As shown, an embodiment of the present application provides a computer device, including a processor 711, a communication interface 712, a memory 713 and a communication bus 714, wherein the processor 711, the communication interface 712, and the memory 713 communicate with each other through the communication bus 714;

[0096] Memory 713, used for storing computer programs;

[0097] The processor 711 is used to implement the data storage method provided by any one of the aforementioned method embodiments when executing the program stored in the memory 713.

[0098] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0099] In one embodiment, the data storage device provided by the present application can be implemented in the form of a computer program. The computer program can be Figure 5 The computer device shown in the figure is run on the computer device. The memory of the computer device can store various program modules constituting the data storage device, such as: Figure 4 The acquisition module 310, processing module 320 and storage module 330 are shown. The computer program composed of various program modules enables the processor to execute the data storage method of each embodiment of the present application described in this specification.

[0100] Figure 5 The computer device shown can be Figure 4The acquisition module 310 in the data storage device shown acquires the anti-cheating data corresponding to the anti-cheating service and the data request information corresponding to the anti-cheating data. The computer device can determine the target database corresponding to each type of component data in the anti-cheating data through the processing module 320 according to the sub-request information corresponding to each type of component data in the anti-cheating data, wherein the target database is the storage database 121 with the lowest storage cost for storing the corresponding component data, and different storage databases 121 correspond to different storage costs. The computer device can store each type of component data in the anti-cheating data in the target database corresponding to each type of component data through the storage module 330.

[0101] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the data storage method provided by any one of the aforementioned method embodiments is implemented.

[0102] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0103] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a general hardware platform, and of course, by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the relevant technology can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0104] It should be understood that the terms used herein are only for the purpose of describing specific example embodiments and are not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms "one", "an" and "said" as used herein may also be meant to include plural forms. The terms "include", "comprise", "contain", and "have" are inclusive, and therefore specify the existence of stated features, steps, operations, elements and / or parts, but do not exclude the existence or addition of one or more other features, steps, operations, elements, parts, and / or combinations thereof. The method steps, processes, and operations described herein are not interpreted as necessarily requiring them to be performed in the specific order described or illustrated, unless the execution order is clearly indicated. It should also be understood that additional or alternative can be used.

[0105] The foregoing is merely a specific embodiment of the present invention, which enables those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A data storage method, characterized in that: The method comprises: Obtaining anti-cheating data corresponding to the anti-cheating service and data request information corresponding to the anti-cheating data, wherein the anti-cheating data includes multiple types of component data, and the data request information includes sub-request information corresponding to each type of component data; Determining, according to the sub-request information corresponding to each type of component data in the anti-cheating data, a target database corresponding to each type of component data in the anti-cheating data, wherein the target database is a storage database with the lowest storage cost for storing the corresponding component data, and different storage databases correspond to different storage costs; Each type of component data in the anti-cheating data is stored in a target database corresponding to each type of component data.

2. The method according to claim 1, characterized in that: The obtaining of anti-cheating data corresponding to the anti-cheating service and data request information corresponding to the anti-cheating data includes: Acquire feature data corresponding to the anti-cheating service and feature sub-request information corresponding to the feature data; Outputting an anti-cheating blacklist corresponding to the anti-cheating service according to a feature calculation result of the feature data; Obtaining blacklist sub-request information corresponding to the anti-cheating blacklist, wherein the different types of component data in the anti-cheating data include the feature data and the anti-cheating blacklist, and the data request information includes the feature sub-request information and the blacklist sub-request information.

3. The method according to claim 2, characterized in that The determining, according to the sub-request information corresponding to each type of component data in the anti-cheating data, a target database corresponding to each type of component data in the anti-cheating data comprises at least one of the following: Determining a feature target database corresponding to the feature data according to the service type of the anti-cheating service and the feature sub-request information corresponding to the feature data; According to the blacklist sub-request information corresponding to the anti-cheating blacklist and the number of list dimensions, a blacklist target database corresponding to the anti-cheating blacklist is determined.

4. The method according to claim 3, characterized in that The determining, according to the service type of the anti-cheating service and the feature sub-request information corresponding to the feature data, a feature target database corresponding to the feature data includes: Determining the feature calculation complexity of the feature data according to the service type of the anti-cheating service; A feature target database corresponding to the feature data is determined according to the feature calculation complexity and the feature calculation request volume per second in the feature sub-request information.

5. The method according to claim 4, characterized in that The determining, according to the feature calculation complexity and the feature calculation request volume per second in the feature sub-request information, a feature target database corresponding to the feature data includes: When the feature calculation complexity is greater than or equal to the preset complexity, and the feature calculation request volume per second is less than the first request volume per second, determining the first database as the feature target database corresponding to the feature data; or, When the feature calculation complexity is less than the preset complexity and the feature calculation request volume per second is greater than or equal to the first request volume per second, the second database is determined as the feature target database corresponding to the feature data, wherein the second database and the first database are different storage databases.

6. The method according to claim 3, characterized in that The step of determining a blacklist target database corresponding to the anti-cheating blacklist according to the blacklist sub-request information corresponding to the anti-cheating blacklist and the number of list dimensions includes: When the blacklist request volume per second is less than the second request volume per second, and the number of list dimensions corresponding to the anti-cheating blacklist is greater than or equal to the preset number of dimensions, determining the first database as the blacklist target database corresponding to the anti-cheating blacklist; or, When the blacklist request volume per second is greater than or equal to the second request volume per second, and the number of list dimensions corresponding to the anti-cheating blacklist is less than the preset number of dimensions, the second database is determined as the blacklist target database corresponding to the anti-cheating blacklist, wherein the second database and the first database are different storage databases.

7. The method according to claim 3, characterized in that Storing various types of component data in the anti-cheating data in target databases corresponding to the various types of component data, respectively, including: The characteristic data is stored in the characteristic target database, and the anti-cheating blacklist is stored in the blacklist target database.

8. A data storage device, characterized in that: The device comprises: An acquisition module, used to acquire anti-cheating data corresponding to the anti-cheating service and data request information corresponding to the anti-cheating data; a processing module, configured to determine a target database corresponding to each type of component data in the anti-cheating data according to sub-request information corresponding to each type of component data in the anti-cheating data, wherein the target database is a storage database with the lowest storage cost for storing the corresponding component data, and different storage databases correspond to different storage costs; The storage module is used to store various types of component data in the anti-cheating data in target databases corresponding to the various types of component data.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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