Method, system and device for processing ID in Redis in RTA scene and medium

Abstract

The invention discloses an ID processing method, system and device in Redis of an RTA scene and a medium, and the method comprises the steps: carrying out the barrel dividing processing of the devices, and enabling the devices to be uniformly distributed in a preset number of barrels; compressing and storing the equipment ID, processing the equipment ID, generating a hash value, and extracting 8-16 bits of the middle part as a compressed representation of the equipment ID for storage; enumerating and coding the bidding information, and representing elements such as whether the bidding information participates in the bidding, the bidding strategy type and the version number by adopting a uniform digital code; and data structure and storage: utilizing a Hash type data structure of Redis, taking the compressed representation of the equipment ID as a key, and taking the coded value of the bidding information as a field to be stored in Hash. Compared with the prior art, the compression storage technology of the equipment ID is adopted, the lengthy equipment ID is converted into short compression representation, and the storage data volume in the Redis is effectively reduced in combination with an equipment bucket processing strategy, so that the storage requirement of the Redis is remarkably reduced, the data retrieval and updating performance is optimized, and the overall response speed is improved.

Description

Technical Field

[0001] The present invention belongs to the field of big data processing and distributed systems, and relates to a method, system, device and medium for processing ID in Redis in an RTA scenario. Background Art

[0002] In the current digital advertising industry, real-time bidding advertising (RTA) has become an important technical means adopted by major advertising platforms and advertisers as an efficient advertising delivery method. RTA makes advertising delivery decisions in a very short time by analyzing multi-dimensional information such as user behavior and environment in real time to meet advertisers' needs for accurate advertising delivery and high conversion rates. However, with the popularization of mobile Internet and the continuous expansion of the advertising market, the number of device ID requests that the RTA system needs to process every day has reached tens of billions or even hundreds of billions, posing unprecedented challenges to the system's storage, search and decision-making capabilities.

[0003] In the RTA advertising scenario, the storage and search of device IDs is one of the core functions of the system. Device IDs, such as IMEI and IDFA, are unique identifiers for user devices. They vary in length and format, which not only takes up a lot of storage space, but also increases the complexity of data processing. In traditional solutions, high-performance cache systems such as Redis are often used to store device IDs and their corresponding advertising strategy information to speed up the query and decision-making process. However, when dealing with massive amounts of data, the following shortcomings often exist:

[0004] The storage space utilization is low. Since the device ID length is not uniform and long, direct storage will take up a lot of cache space, affecting the overall system performance. Although some solutions try to shorten the device ID length through hash algorithms, the shortened length is still not much different from the original length, and the storage space saving is limited.

[0005] Data overwrite risk: In order to further save storage space, some solutions have adopted the approach of shortening the length of device IDs to an extreme extent, but this increases the risk of hash collisions, which may cause data of different device IDs to be incorrectly overwritten, affecting the accuracy and efficiency of advertising.

[0006] The query and decision-making efficiency is low. During the query and decision-making process, traditional solutions often use JSON serialization or bit-by-bit parsing to process competitive information. When the amount of data is huge, it will significantly increase the CPU load, reduce the system's query and decision-making efficiency, and cannot meet the RTA system's requirements for low latency.

[0007] Differences in advertiser strategies. Since different advertisers may have different competitive strategies and competitive values, traditional solutions often ignore these differences when storing and querying, resulting in the inability to fully consider the specific needs of advertisers when making advertising decisions, affecting the effectiveness of advertising.

[0008] When faced with massive amounts of data, existing solutions have problems such as low storage space utilization, high data coverage risk, low query and decision-making efficiency, and failure to fully consider differences in advertiser strategies. Therefore, a more efficient, accurate, and flexible solution is urgently needed to address these challenges. Summary of the invention

[0009] The purpose of the present invention is to solve the problems of low storage space utilization, high data coverage risk, and low query and decision efficiency in the RTA system in the prior art, and to provide a method, system, device and medium for processing ID in Redis in an RTA scenario.

[0010] In order to achieve the above object, the present invention adopts the following technical solutions:

[0011] A method for processing IDs in Redis in an RTA scenario includes the following steps:

[0012] Device bucketing: bucketing is performed on devices so that the devices are evenly distributed in a preset number of buckets, and the number of elements in each bucket does not exceed the set value limit of the compression list;

[0013] Device ID compression storage: Perform MD5 hashing on the device ID to generate a 32-bit hexadecimal character hash value, extract the middle 8 to 16 bits as the compressed representation of the device ID for storage;

[0014] The bidding information enumeration code uses a unified digital code to represent whether to participate in the bidding, bidding strategy type, and version number elements in the bidding information;

[0015] Data structure and storage,Redis’s Hash type data structure is used, with the compressed representation of the device ID as the key, and the encoded value of the bidding information is stored as a field in the Hash.

[0016] The equipment performs bucket processing, specifically:

[0017] Initialize MD5 hash processing, create a MessageDigest instance, and specify the MD5 algorithm for hash processing;

[0018] Device ID hash calculation: Update the MessageDigest instance with the device ID of each device and perform the digest operation to get the original 32-bit MD5 hash value;

[0019] Determine the bucket parameters and set the bit parameters;

[0020] Calculate the bucket index and calculate the length of the result array r according to the bit parameter;

[0021] Calculate mask a and perform a bitwise AND operation on the last byte of the MD5 hash value and mask a.

[0022] Copy the first r.length bytes of the MD5 hash value into the result array r;

[0023] Traverse the r array and convert all negative numbers to positive numbers;

[0024] The mapping between device ID and bucket maps the device ID to the calculated bucket index to ensure that the devices are evenly distributed in each bucket.

[0025] The device ID is compressed and stored as follows:

[0026] Use the bucket index obtained through bucketing as the key in Redis;

[0027] Store the device information or data to be stored as values ​​in the corresponding bucket;

[0028] By utilizing the compression list feature of Redis, a compressed storage method is used when the data volume is low, and the device bidding information or data is stored in the Hash data structure of Redis.

[0029] When the bidding information is enumerated and coded, it is specifically:

[0030] Whether to participate in the competition is coded with a single number; the bidding strategy type is coded with another number, including direct bidding and bidding coefficient; the version number is assigned a continuous number code according to a predetermined version number list.

[0031] The data structure and storage are specifically as follows:

[0032] In Redis, a Hash data structure is created with the compressed representation of the device ID as the key;

[0033] The encoded value of the bidding information is stored as a field at a corresponding position of the Hash data structure, and each field may correspond to one or more attributes of the bidding information.

[0034] Data storage and retrieval,store the encoded bidding information in Redis Hash, and retrieve the bidding information of a specific device ID as needed.

[0035] A system for processing IDs in Redis in an RTA scenario includes the following modules:

[0036] A device bucket processing module, which is used to perform bucket processing on devices so that the devices are evenly distributed in a preset number of buckets, and the number of elements in each bucket does not exceed the set value limit of the compression list;

[0037] A device ID compression storage module, which is used to perform MD5 hash processing on the device ID, generate a hash value of 32 hexadecimal characters, extract 8 to 16 bits of the middle part as a compressed representation of the device ID for storage;

[0038] A bidding information enumeration coding module, which is used to represent whether to participate in the bidding, bidding strategy type, and version number elements in the bidding information using a unified digital code;

[0039] The data structure and storage module are used to utilize the Hash type data structure of Redis, use the compressed representation of the device ID as the key, and store the encoded value of the bidding information as a field in the Hash.

[0040] The system also includes a configuration and management module, which is used for the overall configuration of the system, including the preset number of buckets, the set value limit of the Redis compression list and the encoding rules of the bidding information, and provides a management interface or API to allow the system administrator to dynamically adjust and optimize the system configuration.

[0041] The system also includes a data verification module, which is used to verify the device ID and bidding information before data storage to ensure the integrity and accuracy of the data.

[0042] A device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method as described in any one of the preceding items when executing the computer program.

[0043] A computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the method described in any one of the preceding items.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The ID processing method in Redis of the RTA scenario in the present invention adopts the compression storage technology of device ID, converts the lengthy device ID into a short compressed representation, and combines the device bucket processing strategy to effectively reduce the amount of stored data in Redis, so that the storage demand of Redis is significantly reduced. It not only reduces the storage cost of the system, but also optimizes the performance of data retrieval and update, and improves the overall response speed.

[0046] Although the device ID is compressed, the system ensures that the query efficiency remains basically unchanged by designing a reasonable Redis Hash data structure and an efficient query algorithm. Users can quickly retrieve the corresponding bidding information based on the compressed representation of the device ID, meeting the real-time query requirements in the RTA scenario and ensuring the smoothness of the system and user experience.

[0047] Effectively solve the problem of high concurrency and low latency. In the RTA scenario, the system needs to cope with a large number of concurrent requests and real-time data processing. The present invention uses Redis's high-performance Hash data structure, enumeration encoding of bidding information, and equipment bucket processing and other technical means to effectively disperse the pressure of data access and improve the system's concurrent processing capability and data update speed. At the same time, the reduced amount of stored data further reduces the delay of data retrieval and update, meeting the strict requirements of the RTA scenario for low latency.

[0048] The present invention supports dynamic adjustment of Redis configuration, bucket number and other parameters to adapt to RTA scenarios and business needs of different scales. With the development of business and the growth of data volume, the system can be flexibly expanded and optimized to ensure long-term stable operation. In addition, the rich API interface and management interface provided by the system facilitate users to query, update and delete data, enhancing the flexibility and ease of use of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0050] Figure 1 This is a flow chart of a method for processing ID in Redis in the RTA scenario of the present invention;

[0051] Figure 2 It is a data storage flow chart of the present invention;

[0052] Figure 3 This is a data query flow chart of the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0054] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0056] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0057] See also Figure 1 , is a flow chart of a method for processing ID in Redis in the RTA scenario of the present invention, comprising the following steps:

[0058] S1, device bucketing, bucketing the devices so that the devices are evenly distributed in a preset number of buckets, and the number of elements in each bucket does not exceed the set value limit of the compression list.

[0059] The equipment performs bucket processing, specifically:

[0060] Initialize MD5 hash processing, create a MessageDigest instance, and specify the MD5 algorithm for hash processing;

[0061] Device ID hash calculation: Update the MessageDigest instance with the device ID of each device and perform the digest operation to get the original 32-bit MD5 hash value;

[0062] Determine the bucket parameters and set the bit parameters;

[0063] Calculate the bucket index and calculate the length of the result array r according to the bit parameter;

[0064] Calculate mask a and perform a bitwise AND operation on the last byte of the MD5 hash value and mask a.

[0065] Copy the first r.length bytes of the MD5 hash value into the result array r;

[0066] Traverse the r array and convert all negative numbers to positive numbers;

[0067] The mapping between device ID and bucket maps the device ID to the calculated bucket index to ensure that the devices are evenly distributed in each bucket.

[0068] S2, device ID compression storage, perform MD5 hash processing on the device ID, generate a 32-bit hexadecimal character hash value, extract 8 to 16 bits of the middle part as a compressed representation of the device ID for storage.

[0069] The device ID is compressed and stored as follows:

[0070] Use the bucket index obtained through bucketing as the key in Redis;

[0071] Store the device information or data to be stored as values ​​in the corresponding bucket;

[0072] By utilizing the compression list feature of Redis, a compressed storage method is used when the data volume is low, and the device bidding information or data is stored in the Hash data structure of Redis.

[0073] S3, bidding information enumeration code, uses a unified digital code to represent whether to participate in the bidding, bidding strategy type, and version number elements in the bidding information.

[0074] When the bidding information is enumerated and encoded, specifically: whether to participate in the bidding is encoded using a single number; the bidding strategy type is encoded using another number, including direct bidding and bidding coefficient; the version number is assigned a continuous number code according to a predetermined version number list.

[0075] S4, data structure and storage, utilizes the Hash type data structure of Redis, uses the compressed representation of the device ID as the key, and stores the encoded value of the bidding information as a field in the Hash.

[0076] Data structure and storage, specifically:

[0077] In Redis, a Hash data structure is created with the compressed representation of the device ID as the key;

[0078] The encoded value of the bidding information is stored as a field at a corresponding position of the Hash data structure, and each field may correspond to one or more attributes of the bidding information.

[0079] Data storage and retrieval,store the encoded bidding information in Redis Hash, and retrieve the bidding information of a specific device ID as needed.

[0080] Example

[0081] This embodiment uses the characteristics of redis compression list ziplist and bit encoding and decoding to reduce the data size of device ID and competition information, thereby saving a lot of storage costs. The specific scheme is as follows:

[0082] First, divide the devices into buckets using a bucketing algorithm so that the devices are stored evenly in certain buckets. The elements in each bucket do not exceed the limit of the redis ziplist setting value. This can take advantage of the characteristics of the redis compression list and save a lot of storage space.

[0083] Then perform MD5 (32-bit hexadecimal characters) on the device ID, and take the middle 8 to 16 bits of the 32-bit MD5 value as the device ID for storage. Due to the bucketing, the elements stored in each bucket are limited, generally not exceeding 128, and there is basically no hash collision. Therefore, this method can further save storage space on the basis of the compressed list, and there is no need to worry about data being overwritten.

[0084] Among the bidding factors, namely, whether to participate in the competition, bidding strategy type, bidding value, and version number, the bidding value represents the value or bid coefficient, which is variable and generally cannot be enumerated. Whether to participate in the competition, bidding strategy, and version number can all be enumerated and can be uniformly coded, such as 1 for not participating in the competition and 2 for participating in the competition. The bidding strategy includes two types, direct bidding and bidding coefficient, which can be represented by the number 1 or 2. The version number is 1.0.0, 1.0.1, 1.1.0, etc., and generally does not exceed 100, which can be represented by digital coding, such as 1 for 1.0.0, 2 for 1.0.1, 3 for 1.1.0, etc. In this way, all the bidding information can be enumerated with basically 3 bytes, which greatly reduces the storage of bidding information.

[0085] The steps of the bucketing algorithm are as follows:

[0086] (1) Create a MessageDigest instance and specify the MD5 algorithm.

[0087] (2) Update the MessageDigest instance using the provided device ID.

[0088] (3) Perform the summary operation to obtain the original MD5 hash value md.

[0089] (4) Calculate the length of the result array r based on the bit parameter. Note that each byte has 8 bits, but only 7 bits are used here, so the length calculated by (bit-1) / 7+1 ensures that r can accommodate the specified number of bits.

[0090] (5) Calculate the mask a to retain the low-order bits%7 in the last byte.

[0091] (6) Perform a bitwise AND operation on the last byte with mask a to ensure that the high bits are cleared.

[0092] (7) Copy the first r.length bytes of the MD5 digest into the r array.

[0093] (8) Traverse the r array and convert all negative numbers to positive numbers.

[0094] The value of the bit determines the number of buckets. If the bit is 30, 2^30 squares can be pre-generated, which is about 1 billion buckets. If a bucket stores 50 devices, the total number of devices that can be stored is 50 billion:

[0095] private static final Integer bit=30; / / 10 billion

[0096] public static String getBucketId(String key){

[0097] byte[]bytes=key.getBytes();

[0098] MessageDigest mdInst=null;;

[0099] try{

[0100] mdInst=MessageDigest.getInstance("MD5");

[0101] }catch(NoSuchAlgorithmException e){

[0102] return null;

[0103] }

[0104] mdInst.update(bytes);

[0105] byte[]md = mdInst.digest();

[0106] byte[]r=new byte[(bit-1) / 7+1];

[0107] int a=(int)Math.pow(2,bit%7)-2;

[0108] md[r.length-1]=(byte)(md[r.length-1]&a);

[0109] System.arraycopy(md,0,r,0,r.length);

[0110] for(int i=0; i <r.length;i++){

[0111] if(r[i]<0){

[0112] r[i]&=127;

[0113] }

[0114] }

[0115] CRC32 crc = new CRC32();

[0116] crc.update(r);

[0117] Long id = crc.getValue();

[0118] return id.toString();

[0119] }

[0120] An embodiment of the present invention provides a system for processing IDs in Redis in an RTA scenario, including the following modules:

[0121] The device bucket processing module is used to perform bucket processing on the devices so that the devices are evenly distributed in a preset number of buckets, and the number of elements in each bucket does not exceed the set value limit of the compression list, so as to reduce storage overhead and improve data retrieval efficiency.

[0122] The device ID compression storage module is used to perform MD5 hash processing on the device ID, generate a hash value of 32 hexadecimal characters, extract 8 to 16 bits of the middle part as a compressed representation of the device ID for storage to reduce the use of storage space.

[0123] The bidding information enumeration coding module is used to represent the bidding information elements such as whether to participate in the bidding, bidding strategy type, and version number using a unified digital code. These elements are represented by a unified digital code to reduce storage requirements and improve data processing efficiency.

[0124] The data structure and storage module are used to utilize the Hash type data structure of Redis, use the compressed representation of the device ID as the key, and store the encoded value of the bidding information as a field in the Hash. It also provides an efficient interface to support the fast storage, retrieval and update operations of the bidding information.

[0125] The configuration and management module is used for the overall configuration of the system, including the preset number of buckets, the set value limit of the Redis compression list and the encoding rules of the bidding information, and provides a management interface or API to allow the system administrator to dynamically adjust and optimize the system configuration.

[0126] The data verification module is used to verify the device ID and bidding information before data storage to ensure the integrity and accuracy of the data.

[0127] The system in the present invention can flexibly cope with RTA scenarios of different scales by adjusting the configuration parameters of Redis and the encoding rules of the bidding information to adapt to different data processing and storage requirements. The system supports high-concurrency access and uses the high-performance characteristics of Redis to ensure that stable data processing speed and response time can be maintained under a large number of concurrent requests. The system can integrate security measures such as data encryption and access control to ensure the security and privacy of bidding information during storage and transmission. Through the persistence mechanism of Redis, it is ensured that the bidding information can be restored in the event of system failure or data loss, thereby improving the reliability of the system and data protection capabilities.

[0128] An embodiment of the present invention provides a terminal device. The terminal device of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned method embodiments are implemented. Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are implemented.

[0129] The computer program may be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to accomplish the present invention.

[0130] The device / terminal equipment may be a computing device such as a desktop computer, a notebook, a palm computer, a cloud server, etc. The device / terminal equipment may include, but is not limited to, a processor and a memory.

[0131] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0132] The memory may be used to store the computer programs and / or modules, and the processor implements various functions of the apparatus / terminal device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory.

[0133] If the module / unit integrated in the device / terminal equipment is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0134] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for processing ID in Redis in an RTA scenario, characterized in that: The following steps are involved: Device bucketing: bucketing is performed on devices so that the devices are evenly distributed in a preset number of buckets, and the number of elements in each bucket does not exceed the set value limit of the compression list; Device ID compression storage: perform MD5 hash processing on the device ID to generate a 32-bit hexadecimal character hash value, extract 8 to 16 bits in the middle as a compressed representation of the device ID for storage; The bidding information enumeration code uses a unified digital code to represent whether to participate in the bidding, bidding strategy type, and version number elements in the bidding information; Data structure and storage,Redis’s Hash type data structure is used, with the compressed representation of the device ID as the key, and the encoded value of the bidding information is stored as a field in the Hash.

2. The method for processing ID in Redis in an RTA scenario according to claim 1, characterized in that: The equipment performs bucket processing, specifically: Initialize MD5 hash processing, create a MessageDigest instance, and specify the MD5 algorithm for hash processing; Device ID hash calculation: Update the MessageDigest instance with the device ID of each device and perform the digest operation to get the original 32-bit MD5 hash value; Determine the bucket parameters and set the bit parameters; Calculate the bucket index and calculate the length of the result array r according to the bit parameter; Calculate mask a and perform a bitwise AND operation on the last byte of the MD5 hash value and mask a; Copy the first r.length bytes of the MD5 hash value into the result array r; Traverse the r array and convert all negative numbers to positive numbers; The mapping between device ID and bucket maps the device ID to the calculated bucket index to ensure that the devices are evenly distributed in each bucket.

3. The method for processing ID in Redis in an RTA scenario according to claim 1, characterized in that: The device ID is compressed and stored as follows: Use the bucket index obtained through bucketing as the key in Redis; Store the device information or data to be stored as values ​​in the corresponding bucket; By utilizing the compression list feature of Redis, a compressed storage method is used when the data volume is low, and the device bidding information or data is stored in the Hash data structure of Redis.

4. The method for processing ID in Redis in an RTA scenario according to claim 1, characterized in that: When the bidding information is enumerated and coded, it is specifically: Whether to participate in the competition is coded with a single number; the bidding strategy type is coded with another number, including direct bidding and bidding coefficient; the version number is assigned a continuous number code according to a predetermined version number list.

5. The method for processing ID in Redis in an RTA scenario according to claim 1, characterized in that: The data structure and storage are specifically as follows: In Redis, a Hash data structure is created with the compressed representation of the device ID as the key; The encoded value of the bidding information is stored as a field in the corresponding position of the Hash data structure, and each field may correspond to one or more attributes of the bidding information; Data storage and retrieval,store the encoded bidding information in Redis Hash, and retrieve the bidding information of a specific device ID as needed.

6. A system for processing IDs in Redis in an RTA scenario, characterized in that: Includes the following modules: A device bucket processing module, which is used to perform bucket processing on devices so that the devices are evenly distributed in a preset number of buckets, and the number of elements in each bucket does not exceed the set value limit of the compression list; A device ID compression storage module, which is used to perform MD5 hash processing on the device ID, generate a hash value of 32 hexadecimal characters, extract 8 to 16 bits of the middle part as a compressed representation of the device ID for storage; A bidding information enumeration coding module, which is used to represent whether to participate in the bidding, bidding strategy type, and version number elements in the bidding information using a unified digital code; The data structure and storage module are used to utilize the Hash type data structure of Redis, use the compressed representation of the device ID as the key, and store the encoded value of the bidding information as a field in the Hash.

7. The ID processing system in Redis of the RTA scenario as claimed in claim 6, characterized in that: The system also includes a configuration and management module, which is used for the overall configuration of the system, including the preset number of buckets, the set value limit of the Redis compression list and the encoding rules of the bidding information, and provides a management interface or API to allow the system administrator to dynamically adjust and optimize the system configuration.

8. The ID processing system in Redis of a RTA scenario as claimed in claim 6, characterized in that: The system also includes a data verification module, which is used to verify the device ID and bidding information before data storage to ensure the integrity and accuracy of the data.

9. A 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 steps of the method according to any one of claims 1 to 5 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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