Data processing method, device and system

Abstract

The invention provides a data processing method, device and system, and the method is applied to a data tracking platform, and comprises the steps: obtaining the equipment information of a target client, and generating a target equipment identifier corresponding to the target client according to the equipment information; under the condition that the target client sends a data processing request to the at least one server, a data log record returned by the at least one server in response to the data processing request is obtained, and the data processing request and the data log record both carry a target device identifier; under the condition that the data log files corresponding to the at least two servers are obtained, associating the at least two data log files according to the target equipment identifier to obtain a target data log file; determining a target behavior link from the target client to the server according to the target data log file; the problem of log islands existing in a traditional log recording mode is effectively solved, a complete target behavior link can be restored, and a data basis is provided for troubleshooting and service analysis.

Description

Technical Field

[0001] This specification relates to the field of computer technology, and particularly to a data processing method. This specification also relates to a data processing device, a data processing system, a computing device, a computer-readable storage medium, and a computer program product. Background Art

[0002] With the increase in business complexity (such as multi-terminal collaboration, microservices architecture, multi-party service dependencies, etc.), traditional user behavior tracking solutions face the following problems: Identity fragmentation, that is, traditional device fingerprint technologies (such as Android ID, IDFA, etc.) have platform differences and cannot maintain consistency in cross-application scenarios. That is, when a user's operation involves multiple clients (such as a game launcher and the game itself), each system uses a different identity system, lacks a unified association mechanism, and each service provider independently provides log files, resulting in the problem of log islands, which can lead to the breakage of the behavior chain.

[0003] For example, the game login scenario involves multiple systems such as the game launcher client, account service, and game SDK (Software Development Kit). Traditional logging methods cannot restore the complete call chain. When a cross-service exception occurs, it is difficult to determine the fault propagation path. Existing solutions require manual comparison of the log timestamps and user information of each system, resulting in low troubleshooting efficiency. Summary of the Invention

[0004] In view of this, embodiments of this specification provide a data processing method. This specification also relates to a data processing device, a data processing system, a computing device, a computer-readable storage medium, and a computer program product to solve the above problems existing in the prior art.

[0005] According to a first aspect of the embodiments of this specification, a data processing method is provided, including: Obtain the device information of the target client, and generate a target device identifier corresponding to the target client according to the device information; When the target client sends a data processing request to at least one server, obtain the data log records returned by the at least one server in response to the data processing request, where the data processing request and the data log records both carry the target device identifier; When obtaining the data log files corresponding to at least two servers, associate the at least two data log files according to the target device identifier to obtain a target data log file, where the data log file includes data log records; Determine the target behavior link from the target client to the server according to the target data log file.

[0006] According to the second aspect of the embodiments of the present specification, there is provided a data processing device, including: An identification generation module, configured to obtain device information of a target client, and generate a target device identification corresponding to the target client according to the device information; A record acquisition module, configured to, when the target client sends a data processing request to at least one server, acquire a data log record returned by the at least one server in response to the data processing request, where the data processing request and the data log record both carry the target device identification; A file acquisition module, configured to, when obtaining data log files corresponding to at least two servers, associate at least two data log files according to the target device identification to obtain a target data log file, where the data log file includes data log records; A link determination module, configured to determine the target behavior link from the target client to the server according to the target data log file.

[0007] According to the third aspect of the embodiments of the present specification, there is provided a data processing system, including a data tracking platform, a target client, and at least one server, where The data tracking platform is configured to obtain device information of the target client, generate a target device identification corresponding to the target client according to the device information, and send the target device identification to the target client; The target client is configured to receive and store the target device identification, and send a data processing request to the at least one server, where the data processing request carries the target device identification; The at least one server is configured to respond to the data processing request, generate a data log record, and send the data log record to the data tracking platform, where the data tracking platform carries the target device identification; The data tracking platform is further configured to, when obtaining data log files corresponding to at least two servers, associate at least two data log files according to the target device identification to obtain a target data log file, and determine the target behavior link from the target client to the server according to the target data log file, where the data log file includes data log records.

[0008] According to a fourth aspect of the embodiments of the present specification, a computing device is provided, including a memory, a processor, and a computer program / instructions stored on the memory and executable on the processor. When the processor executes the computer program / instructions, the steps of the data processing method are implemented.

[0009] According to a fifth aspect of the embodiments of the present specification, a computer-readable storage medium is provided, which stores a computer program / instructions. When the computer program / instructions are executed by a processor, the steps of the data processing method are implemented.

[0010] According to a sixth aspect of the embodiments of the present specification, a computer program product is provided, including a computer program / instructions. When the computer program / instructions are executed by a processor, the steps of the above data processing method are implemented.

[0011] The data processing method provided in the present specification is applied to a data tracking platform, and includes obtaining device information of a target client and generating a target device identifier corresponding to the target client according to the device information; when the target client sends a data processing request to at least one server, obtaining data log records returned by the at least one server in response to the data processing request, where the data processing request and the data log records both carry the target device identifier; when obtaining data log files corresponding to at least two servers, associating the at least two data log files according to the target device identifier to obtain a target data log file, where the data log file includes data log records; and determining a target behavior link from the target client to the server according to the target data log file.

[0012] In an embodiment of the present specification, a target device identifier corresponding to a target client is determined through a data tracking platform, so that when the target client sends a data processing request to at least one server, the target device identifier is carried in the data processing request, and data log records obtained by the at least one server in response to the data processing request are obtained, and the target device identifier is also carried in the data log records. Based on this, when the data tracking platform obtains data log files (the data log file includes data log records) corresponding to at least two servers, at least two data log files can be associated through the target device identifier, so as to obtain a target data log file corresponding to the target client. The target data log file contains data log records of the interaction from the target client to the server. Therefore, based on the target data log file, the target behavior link from the target client to the server can be determined, effectively solving the problem of log islands existing in the traditional log recording method, and being able to restore the complete target behavior link from the target client to the server, providing a data basis for fault troubleshooting and business analysis. Description of the Drawings

[0013] Figure 1 It is a schematic diagram of the scenario of a data processing method provided by an embodiment of this specification; Figure 2 It is a flowchart of a data processing method provided by an embodiment of this specification; Figure 3 It is a schematic diagram of the processing flow of a data processing method in a data tracking platform provided by an embodiment of this specification; Figure 4 It is a schematic diagram of the structure of a data processing device provided by an embodiment of this specification; Figure 5 It is a structural block diagram of a computing device provided by an embodiment of this specification. Specific embodiments

[0014] Many specific details are set forth in the following description in order to provide a thorough understanding of this specification. However, this specification can be implemented in many other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the connotation of this specification. Therefore, this specification is not limited by the specific embodiments disclosed below.

[0015] The terms used in one or more embodiments of this specification are for the purpose of describing specific embodiments only and are not intended to limit one or more embodiments of this specification. The singular forms "a", "the", and "said" used in one or more embodiments of this specification and the appended claims are also intended to include the plural forms unless the context clearly dictates otherwise. It should also be understood that the term "and / or" used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more of the associated listed items.

[0016] It should be understood that although the terms first, second, etc. may be used in one or more embodiments of this specification to describe various information, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of one or more embodiments of this specification, the first may also be referred to as the second, and similarly, the second may also be referred to as the first. Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to determining".

[0017] First, the noun terms related to one or more embodiments of this specification are explained.

[0018] Android ID: It is a semi-permanent identifier that does not depend on hardware in Android (a mobile operating system) devices. It does not change during the system life cycle, but will change after the system is reset or flashed. Its scope of action is a group of related applications.

[0019] IDFA: Identifier for Advertising, which is a random device identifier corresponding to iOS (mobile operating system) devices and is mainly used for advertising tracking and marketing analysis. It is not directly associated with the device's hardware information, and users can reset the IDFA through the "Reset Location & Privacy" option in the device settings, and the system will generate a new identifier.

[0020] In the current Internet application ecosystem, user behavior tracking is a key technology for optimizing the user experience, improving service quality, and achieving precise operation. However, due to the platform differences of traditional device fingerprint technologies (such as Android ID, IDFA, etc.), different identification systems are adopted in each system, and each service provider independently records logs, so the traditional log recording method cannot restore the complete call chain.

[0021] For example, in the login scenario, currently, to track user behavior, device identification is required. For background services, most interfaces are stateless. The server returns a specified callback based on the parameters of the user request. Some user data will not be stored in the server database. Then, at most, the user device passed in will be written in the server's local log, rather than in the database. Then, it will be very difficult to troubleshoot when the user requested to log in and why the user's request failed, and it is not conducive to the client and the server to check the data. The form is often that the client gets the device identification and gives it to the server, and the server queries the logs one by one to view the user's behavior. And a service may involve multiple subsystems, such as client accounts, game account bindings, security verifications, etc. The traditional log query method simply cannot query, and it cannot construct a complete user conversion funnel and analyze the reasons for failure.

[0022] Therefore, in this specification, a data processing method is provided. This specification also relates to a data processing device, a data processing system, a computing device, a computer-readable storage medium, and a computer program product, which will be described in detail one by one in the following embodiments.

[0023] See Figure 1 , Figure 1 which shows a schematic diagram of a scenario of a data processing method provided according to an embodiment of this specification.

[0024] Specifically, this data processing method is applied to a data processing system, which includes a data tracking platform 102, a target client 104, and at least one server 106. Among them, the data tracking platform 102 is used to obtain the device information of the target client 104, generate a target device identifier corresponding to the target client 104 according to the device information, and send the target device identifier to the target client 104; the target client 104 is used to receive and store the target device identifier, and send a data processing request to the at least one server 106, where the data processing request carries the target device identifier; the server 106 is used to respond to the data processing request, generate a data log record and send the data log record to the data tracking platform 102, where the data log record carries the target device identifier; the data tracking platform 102 is further used to, when obtaining data log files corresponding to at least two servers 106, associate at least two data log files according to the target device identifier to obtain a target data log file, and determine a target behavior link from the target client 104 to the server 106 according to the target data log, where the data log file includes the data log record.

[0025] The target client 104 may include a browser, an APP (Application), or a web application such as an H5 (Hyper Text Markup Language 5) application, or a light application (also known as a mini program, a lightweight application program), or a cloud application, etc. The client can be developed based on a software development kit (SDK) provided by the server, such as developed based on a real-time communication (RTC) SDK. The client can be deployed in an electronic device and needs to rely on the device or certain APPs in the device to run. The electronic device can have a display screen and support information browsing, etc., such as a personal mobile terminal such as a mobile phone, a tablet computer, a personal computer, etc. Various other types of applications can usually be configured in the electronic device, such as human-computer dialogue applications, model training applications, data processing applications, web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0026] The server 106 can be understood as a server that provides various services, including physical servers and cloud servers. For example, it can be a server that provides communication services for multiple clients, or a server for background training that supports the models used on the clients, or a server that processes the data sent by the clients, etc. It should be noted that the server 106 can be implemented as a distributed server cluster composed of multiple servers, or as a single server. The server 106 can also be a server of a distributed system, or a server combined with a blockchain. The server 106 can also be a cloud server of basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs, Content Delivery Network), and big data and artificial intelligence platforms, or an intelligent cloud computing server or intelligent cloud host with artificial intelligence technology.

[0027] The data processing method provided in the embodiments of this specification determines a corresponding target device identifier for a target client through a data tracking platform. When the target client sends a data processing request to at least one server, the target device identifier is carried in the data processing request, and a data log record obtained by the at least one server in response to the data processing request is obtained. The target device identifier is also carried in the data log record. Based on this, when the data tracking platform obtains data log files (the data log files include data log records) corresponding to at least two servers, the at least two data log files can be associated through the target device identifier, so as to obtain a target data log file corresponding to the target client. The target data log file contains the data log records of the interaction from the target client to the server. Based on the target data log file, the target behavior link from the target client to the server can be determined, effectively solving the problem of log islands existing in the traditional log recording method, and being able to restore the complete target behavior link from the target client to the server, providing a data basis for fault troubleshooting and business analysis. For example, in the case of a fault, the problem source can be quickly locked based on the target behavior link, the fault link and propagation path can be accurately located, the fault troubleshooting time can be greatly shortened, the fault location efficiency is improved, the business interruption time is effectively reduced, and the reliability and stability of the system are improved.

[0028] Figure 2 The flowchart of a data processing method provided in an embodiment of this specification is shown, which specifically includes the following steps: Specifically, in this data processing method applied to a data tracking platform, the data tracking platform is usually deployed in the cloud and is used to generate device identifiers for clients and collect, manage, and analyze log data.

[0029] Step 202: Obtain the device information of the target client, and generate a target device identifier corresponding to the target client according to the device information.

[0030] Among them, in the embodiments of this specification, the target client can be understood as the client for which the target device identifier is to be determined. The target client can be a PC (Personal Computer) or a mobile device, which is not limited here; the device information can be understood as the hardware / software characteristic information collected from the target client that can be used to identify the device; the target device identifier can be understood as the data that uniquely identifies the target client, and the target device identifier can be represented in forms such as numbers and strings.

[0031] For example, the device information of the target client obtained may include information such as the MAC address (physical address of the network interface card), serial number, CPU (Central Processing Unit) model, and size. By processing the obtained device information, a unique target device identifier corresponding to the target client can be generated.

[0032] In one or more embodiments of this specification, a simple initial device identifier can be determined through the obtained device information. And to ensure the security of data matching, the initial device identifier is encrypted to generate a globally unique and secure target device identifier corresponding to the target client. The specific implementation is as follows: The generating the target device identifier corresponding to the target client according to the device information includes: Determine an initial device identifier according to the device information, and generate the target device identifier by encrypting the initial device identifier.

[0033] Among them, the initial device identifier can be understood as converting the device information into data in a unified format by processing the device information.

[0034] Specifically, in the case where the device information of different devices is the same, the determined initial device identifiers may be the same. Therefore, to ensure that a globally unique target device identifier is generated for the target client, a hierarchical encryption mechanism can be used to encrypt the initial device identifier on the basis of obtaining the initial device identifier. For example, symmetric encryption, asymmetric encryption, and salting base64 are used to encrypt the initial device identifier to obtain a globally unique and secure target device identifier corresponding to the target client.

[0035] In practical applications, when encrypting the initial device information, a dynamic encryption factor can also be added to the obtained initial device identifier. Here, the encryption factor generally refers to a key parameter or variable used to enhance security in an encryption algorithm or security protocol. For example, a timestamp encryption factor that is periodically updated can be added to the initial device identifier. That is, a dynamic encryption factor can be added to the initial device identifier so that the target device identifier can be dynamically updated along with the dynamic encryption factor.

[0036] The data processing method provided by the embodiments of this specification encrypts the initial device identifier on the basis of obtaining the initial device identifier according to the device information, so as to make it impossible to deduce the device identifier from the device information, and combines a dynamic update strategy to ensure the security and dynamics of the identifier.

[0037] In one or more embodiments of this specification, different types of target clients can obtain different types of device information according to their corresponding information acquisition rules, and perform encryption processing on different types of device information, so as to obtain the target device identifier corresponding to the target client. The specific implementation is as follows: Obtaining the device information of the target client includes: When the target client is a first-type client, obtain the first device information of the target client; When the target client is a second-type client, obtain the second device information of the target client.

[0038] Generating the target device identifier corresponding to the target client according to the device information includes: When the target client is a first-type client, determine a first initial device identifier according to the first device information, and generate a first target device identifier by encrypting the first initial device identifier. The first target device identifier includes a timestamp encryption factor that is periodically updated; When the target client is a second-type client, determine a second initial device identifier according to the second device information, and generate a second target device identifier by encrypting the second initial device identifier. The second target device identifier includes a timestamp encryption factor that is periodically updated.

[0039] Among them, the first-type client can be a PC, and the second-type client is a mobile device.

[0040] Due to the differences in platform characteristics and privacy compliance requirements between the PC side and the mobile device side, the device information that can be obtained by the PC side and the mobile device side is different; specifically, the PC side has strong openness and fewer privacy restrictions, that is, the collection of PC side hardware identifiers (such as MAC address, hard disk serial number) has fewer restrictions. Therefore, when the target client is the PC side, the first device information of the target client includes, but is not limited to, information such as MAC address, hard disk serial number, CPU model and serial number, GPU information, operating system version, etc. The mobile device side has more strict privacy restrictions. Generally, it is prohibited to directly obtain hardware identifiers such as IMEI (International Mobile Equipment Identity, that is, the mobile phone serial number usually mentioned). Therefore, on the mobile device side, other hardware identifiers that are not within the scope of privacy compliance and are not easily changed can be obtained. For example, the second device information includes, but is not limited to, CPU model, GPU size, storage capacity, device name, User-Agent string (including information such as device model, operating system), etc. For example, on an Android device, device information combinations such as "A processor, 8GB memory, 256GB storage, version 12 system" can be collected. In fact, by using a fuzzy matching algorithm mechanism, approximate initial device identifiers can be generated based on similar software and hardware characteristics.

[0041] It should be noted that under the condition of having permissions and conforming to the privacy agreement, on the mobile device side, advertising identifiers such as OAID (Open Anonymous Device Identifier) and IDFA (Identifier For Advertising, an advertising identifier unique to iOS devices) can be directly obtained as the initial device identifier, and the target device identifier is obtained by performing a layer of salted hash processing on these initial device identifiers.

[0042] Specifically, in the case of obtaining device information, device information in different formats is converted into a unified format and combined into a string (initial device identifier), and an irreversible hash value is generated based on the initial device identifier to obtain the target device identifier. For example, by using encryption methods such as symmetric encryption, asymmetric encryption, and salted Base64 to encrypt the initial device identifier, an irreversible hash value is generated, so that the obtained target device identifier can be safely transmitted, and the security can be enhanced by adding a periodically updated timestamp; for example, the target device identifier obtained after processing can be: "a1b2c3d4...xyz". By adopting a hierarchical encryption mechanism, first, the initial device identifier is generated based on the device information of the target client, and then the initial device identifier is encrypted. On this basis, a periodically updated timestamp encryption factor is added to obtain the unique target device identifier corresponding to the target client.

[0043] Of course, in actual applications, the PC side can directly determine the initial device ID through the generated random string, and generate the target device ID by encrypting the initial device ID, and store the target device ID persistently in the registry and / or the user's local path (for example, the user's local path is 'C:\Data\...\device_id'). In this way, the target device ID can be obtained by reading the same registry or file path.

[0044] The data processing method provided in the embodiments of this specification ensures the generation of a globally unique target device identifier for a client through an encryption algorithm, and adopts a layered encryption mechanism to generate a dynamic tracking identifier (target device identifier), thereby avoiding the privacy leakage risk caused by the plain text transmission of the target device identifier; and when the identifier generation process complies with the user agreement, there is no need to obtain excessive user permissions, thereby ensuring the security and privacy of user data. This privacy protection mechanism complies with international data protection regulations.

[0045] Step 204: When the target client sends a data processing request to at least one server, obtain a data log record returned by the at least one server in response to the data processing request, wherein both the data processing request and the data log record carry the target device identifier.

[0046] Among them, in this embodiment, the target client can be understood as a terminal device that initiates a data processing request (such as a user's mobile phone, computer, etc.), and the target client corresponds to a unique target device identifier; the server can be understood as a server that receives the target client request and returns data (such as a Web server, API server, etc.).

[0047] Data processing requests can be understood as requests (such as HTTP requests) sent by the target client to the server. In the game scenario, data processing requests can be login requests, verification requests, recharge requests, etc., which are not limited here. Data log records can be understood as log records generated by the server after processing data processing requests. Data log records can contain response data, processing status (success / failure), timestamp and other information.

[0048] Specifically, the target client (such as a mobile application or a browser) sends a data processing request for querying user data to at least one server (such as an API server), and uses the target device identifier of the target client as the request header or request parameter of the data processing request, so as to ensure that when the data processing request carrying the target device identifier is sent to the server, the server can accurately identify and track user behavior. Specifically, after receiving the data processing request, the server performs corresponding data processing and returns response data (such as user information) to the target client, and the server will generate a data log record, which also contains the target device identifier, that is, the target device identifier is passed in the data processing request and the data log record. The data tracking platform can obtain the data log record sent by the server, so that the behavior data of the same client can be tracked through the target device identifier and the data log record in the data tracking platform later.

[0049] That is, through the target device identifier of the target client, the entire process from the target client request to the server log can be penetrated to ensure data traceability.

[0050] In one or more embodiments of this specification, by injecting the target device identifier into the data processing request, it is ensured that the target device identifier can penetrate the entire request processing process, and the server records the data log record containing the target device identifier. Specifically, at the beginning of the request and when the request is called back, the request log record and the response log record containing the target device identifier are passed back to the data tracking platform. The specific implementation is as follows: When the target client sends a data processing request to at least one server, obtaining the data log record returned by the at least one server in response to the data processing request includes: When the target client sends a data processing request to at least one server, obtaining the request log record and the response log record returned by the at least one server in response to the data processing request, where the request log record and the response log record are stored in the data log file corresponding to the at least one server.

[0051] Among them, the request log record can be understood as the log record generated by the server when receiving the request, specifically recording the detailed information of the request. For example, the request log record includes but is not limited to information such as the request time, request method (GET / POST), request path, request header, and request parameters; the response log record can be understood as the log record generated by the server when returning the response, specifically recording the detailed information of the response. For example, the response log record includes but is not limited to information such as the response time, status code, response data, and processing time. And if there is an error, the response log record can also include error information.

[0052] A data log file can be understood as a file for the server to store log records, which is used to centrally manage request and response log records.

[0053] Specifically, when the server receives a data processing request, it can generate a request log record and store it in the data log file. When the data processing request is completed, it can return response data to the target client and generate a response log record. The data tracking platform can obtain the request log record and the response log record generated by the server in response to the data processing request, and store the request log record and the response log record obtained from the same server in the data log file corresponding to the server. Of course, in actual applications, the request log record and the response log record can also be stored in the same data log file on the server, and the data log file of the server can be sent to the data tracking platform, which is not limited here.

[0054] Subsequently, in the data tracking platform, the complete interaction record between the target client and the server can be retrieved through the data log file and the target device identifier.

[0055] The data processing method provided in the embodiments of this specification obtains a data log file for the entire link through two-way log records including request input and response output, ensuring that user behavior data can be completely traced. And when the data log records in the data log file contain the target device identifier, in the data tracking platform, all interactions of the target client can be associated through the target device identifier, supporting cross-request analysis, and providing data support for subsequent user behavior analysis.

[0056] Step 206: When obtaining data log files corresponding to at least two servers, according to the target device identifier, associate at least two data log files to obtain a target data log file, where the data log file includes data log records.

[0057] Among them, the data log files of at least two servers can be understood as data log files independently stored by different servers (for example, in a shopping scenario, different servers respectively provide order services and payment services), but the target device identifier is included in the data log files.

[0058] Specifically, when the data tracking platform obtains data log files corresponding to at least two servers, it can aggregate the data log files of multiple servers through the target device identifier to achieve cross-service user behavior tracking and analysis. Specifically, the data tracking platform matches the data log records in different data log files through the target device identifier, and aggregates the data log records of the same client into a target data log file, which contains the operation behavior records of the same client on multiple associated servers.

[0059] In practical applications, when obtaining the target data log file corresponding to the target client, the user behavior corresponding to the target client can be processed through behavior analysis, anomaly detection, etc. For example, through behavior analysis, the user conversion rate (such as the loss rate from placing an order to payment) can be statistically analyzed, and through anomaly detection, it can be determined that frequent payment requests from the same client within a short period of time may involve fraud, etc.

[0060] In the traditional solution, each service provider independently records logs, resulting in scattered log storage and complex management. In the embodiments of this specification, through the data tracking platform, the data log files from different service ends can be uniformly collected and managed, avoiding the redundant costs of repeated storage and management, and also solving the problem of log islands, that is, through the target device identifier, the data log files scattered on multiple service ends can be associated to obtain the target data log file.

[0061] In one or more embodiments of this specification, when associating at least two data log files, by associating the timestamp information and / or context information of the associated data logs and performing data processing on the associated data log records in at least two data log files, a relatively clear and sequential target data log file can be obtained. The specific implementation is as follows: Associating at least two data log files according to the target device identifier to obtain the target data log file includes: Associating the at least two data log files according to the target device identifier; Obtaining the associated data log records in the at least two data log files, and performing data processing on the associated data log records according to the timestamp information and / or context information of the associated data log records to obtain the target data log file.

[0062] Among them, each data log record in the independent data log files of at least two service ends includes a target device identifier, timestamp information, and other business fields (such as API path, status code, parameters, etc.).

[0063] Specifically, when there is the same target device identifier in at least two data log files, it is determined that the at least two data log files can be associated, and the data log records corresponding to the target device identifier in the at least two data log files that can be associated are determined as the associated data log records.

[0064] In practical applications, data log records belonging to the target client in different data log files are matched together according to the target device identifier, and the obtained associated data log records can be sorted based on the timestamp information to restore the user operation sequence. Through the context information of the associated data log records, such as request parameters (in a shopping scenario, the request parameters can be order identifiers, payment amounts, etc.), the log record fields of multiple services are merged to supplement the business logic relevance.

[0065] By performing sorting and / or merging processing on the associated data log records, an integrated target data log file is obtained. The target data log file contains all cross-service operations of the target client, and the data log records in the target data log file have a certain order relationship and / or logical relevance.

[0066] The data processing method provided by the embodiments of this specification can obtain the associated data log records corresponding to the target client by associating the data log files of multiple server sides. Through accurate timestamp matching and context association, the target data log file corresponding to the target client can be obtained, which is convenient for establishing an end-to-end behavior link based on the target data log file in the future. This process can effectively solve the problem of log islands existing in traditional log recording methods, can restore the complete user behavior trajectory, and provides a data basis for fault troubleshooting and business analysis.

[0067] Step 208: Determine the target behavior link from the target client to the server based on the target data log file.

[0068] Among them, the target behavior link can be understood as the complete operation path of the user when using the target client. Specifically, when the user operates on the interaction interface of the target client, corresponding events will be triggered, and these events will call the server interface and generate corresponding data logs. Therefore, the data log records in the target data log file actually reflect the user's operation behavior and the system's response process to these operations.

[0069] By analyzing the target data log file, the complete operation path of the interaction between the target client and the server can be obtained. Based on the time sorting, the complete user behavior link (i.e., the target behavior link) can be obtained. For example, the user behavior link is "browse products -> place an order -> make a payment".

[0070] In the implementation of this specification, based on obtaining the target behavior link, the target behavior link can be further visually processed to generate a more intuitive behavior trajectory map. The behavior trajectory map can support fault location and business attribution, and provide support for optimizing the user experience and improving the service quality.

[0071] After determining the target behavior link from the target client to the server, the method further includes: The target behavior link is visualized to generate a behavior trajectory map.

[0072] Specifically, the behavior trajectory map can be understood as converting the user behavior link into an intuitive graphical representation. The behavior trajectory map can contain nodes and edges, where nodes are used to represent single behaviors (such as "browse products" and "submit orders"), and edges are used to represent the transfer relationship between behaviors (such as "browse -> add to cart"). Of course, in actual applications, the behavior trajectory map can also be generated directly based on the obtained target data log files, which is not limited here.

[0073] In actual applications, on the data tracking platform, the multi-dimensional analysis module can be used to integrate and analyze user behavior data in different dimensions (such as time, operation type, user attributes, etc.) to build a complete behavior trajectory map for subsequent data analysis. For example, when building a behavior trajectory map, when combining multiple dimensions such as time, region, and user portrait, user behavior differences can be analyzed. For example, the purchase conversion rates of users in different regions can be compared, or the differences in operating habits of users of different operating systems can be analyzed.

[0074] In fact, based on obtaining the target data log file, the data tracking platform can also process the target data log file to create a visual analysis table and a business analysis funnel model to provide an intuitive basis for subsequent analysis and decision-making.

[0075] The data processing method provided in the embodiments of this specification generates a comprehensive and accurate behavior trajectory map, which can clearly show the user's behavior path in different terminals and different services, covering the complete link from client event triggering to server call and then to downstream response, providing strong support for the overall presentation of business processes, and based on the behavior trajectory map, it can support efficient fault location and business attribution.

[0076] In one or more embodiments of this specification, when a data processing request fails, the failure link and / or the failure time point can be obtained by analyzing the target behavior link and / or the behavior trajectory map. The specific implementation is as follows: After generating the behavior trajectory map, the method further includes: When it is determined that the data processing request fails, the target behavior link and / or the behavior trajectory map are analyzed to obtain a fault analysis result, wherein the fault analysis result includes a fault occurrence link and / or a fault occurrence time point.

[0077] Specifically, when it is determined that the data processing request fails, it can be determined that a failure has occurred. By analyzing the target behavior link and / or behavior trace map, the specific link where the failure occurs (i.e., the failure occurrence link, for example, if the failure occurrence link is the payment link, corresponding to a node in the behavior trace map) and / or the time when the failure occurs (i.e., the failure occurrence time point, for example, the failure occurrence time point is 12:05:00) can be quickly determined.

[0078] The data processing method provided by the embodiments of this specification can quickly lock the source of the problem based on the target behavior link and / or behavior trace map when a failure occurs, accurately locate the specific link and time point where the failure occurs, and analyze the failure propagation path through link tracing, greatly improving the cross-system failure troubleshooting efficiency and shortening the failure repair time.

[0079] In one or more embodiments of this specification, when the target client includes multiple target clients, the target data log files corresponding to each target client can be obtained. By performing data analysis on the multiple target data log files, a large amount of user behavior data can be obtained, thereby providing support for in-depth user insight and business analysis, and an alarm can be triggered for abnormal situations to ensure the stable operation of the business.

[0080] The obtaining of the target data log file includes: Obtaining the target data log files corresponding to each target client among the multiple target clients; After obtaining the target data log files corresponding to each target client among the multiple target clients, it further includes: Performing data analysis on the target data log files corresponding to each target client to obtain a data analysis result; When it is determined that the data is abnormal according to the data analysis result and a preset rule, a data alarm message is triggered.

[0081] Among them, the preset rule can be a rule for judging whether the data analysis result is abnormal. Usually, the preset rule includes a preset threshold. For example, when performing data analysis on the payment behavior of users, when the user payment behavior drops to 10% (for example, originally 30 people would pay after 100 users browsed product A, and now only 9 people pay after 100 users browse product A), it is determined that the index data of this payment service is lower than the preset threshold and the data is determined to be abnormal.

[0082] Specifically, in the data tracking platform, target data log files corresponding to different target clients can be obtained, so as to obtain the user behavior data of multiple users. By analyzing the target data log files corresponding to each target client, a behavior analysis result of analyzing the user behavior data and a business analysis result of key metrics in the business (such as key metrics including conversion rate, time-consuming analysis, etc.) can be obtained.

[0083] In practical applications, by integrating an intelligent alarm mechanism in the data tracking platform, the key metrics of the business and user behavior can be monitored. Specifically, data analysis can be directly performed on the target data log files corresponding to different target clients, or in the case of obtaining the target behavior link and / or behavior trajectory map, node analysis can be performed on the target behavior link and / or behavior trajectory map to obtain a node analysis result, so as to realize dynamic monitoring of nodes and business metrics, and obtain a behavior analysis result and a business analysis result.

[0084] When abnormal user behavior (such as an abnormal increase in the user churn rate, a decrease in the usage frequency of key functions, etc.) is detected according to the behavior analysis result and preset rules, or when it is determined according to the business analysis result and preset rules that the business metric is lower than the preset threshold, the system can automatically trigger an alarm to timely notify relevant personnel to take measures to ensure the continuous optimization of the user experience and the stable operation of the business.

[0085] For example, by performing data analysis on the target data log files corresponding to each target client, the payment conversion rate can be calculated, the step loss rate from browsing to payment can be analyzed (such as 100 views, 50 orders, 30 payments), time-consuming analysis can be performed to obtain the time interval between each step (such as the average time-consuming from "adding to cart to settlement" is 3 minutes), or an analysis result of a sudden increase in the request timeout rate can be obtained. In addition, abnormal detection can be performed on the user behavior of a single user, such as a user performing high-frequency operations in a short time (such as completing a payment within 1 second may be a script behavior), or frequent transactions of multiple accounts on the same device.

[0086] The data alarm information can be to send an email / sms alarm to the operation and maintenance team to timely notify relevant personnel to take measures, or it can be to automatically trigger defense measures (such as flow limiting, IP banning).

[0087] The data processing method provided in the embodiments of this specification can timely detect abnormal user behavior or abnormal business metrics through the intelligent alarm mechanism, so as to quickly respond to problems, realize optimizing product functions and service processes according to user behavior data, improve the user experience, and ensure the stable operation of the business, further improving the operation efficiency and market competitiveness of the enterprise.

[0088] In one or more embodiments of this specification, the data processing method can be applied to a marketing scenario. Thus, when obtaining the target data log files corresponding to each target client, the user behavior data therein can be analyzed for conversion behavior to obtain a conversion evaluation result, providing data support for optimizing the placement strategy.

[0089] After obtaining the target data log files corresponding to each target client among the multiple target clients, the following steps are further included: By analyzing the conversion behavior of the target data log files corresponding to each target client, a conversion evaluation result is obtained. Among them, the conversion evaluation result is used to optimize the initial placement strategy to obtain a target placement strategy.

[0090] Among them, the conversion evaluation result can be understood as the result of calculating marketing conversion metrics, and the marketing conversion metrics include, but are not limited to, metrics such as conversion rate, click-through rate, and add-to-cart rate.

[0091] Specifically, in a marketing scenario, conversion behavior analysis can be performed on the target data log files corresponding to each target client. At this time, the target data log file is the behavior log file of each target client in the marketing activity (including behavior data such as advertisement exposure, click, and purchase). By analyzing the user conversion behavior in the target data log file, key marketing conversion metrics can be calculated to locate high-value users and loss links.

[0092] Based on the calculated marketing conversion metrics, a better target placement strategy can be formulated. For example, when it is determined that the click-through rate is low (click-through rate < 10%), the advertisement material or placement time period can be changed, or the abstract advertisement picture can be changed to a real-person usage scenario, etc.; when it is determined that the add-to-cart rate is high but the payment rate is low, the settlement page process can be optimized or coupon incentives can be increased, and when it is determined through analysis that the conversion rate of a certain specific user group is high, targeted expansion can be carried out to increase the placement to similar users.

[0093] In fact, when obtaining the user behavior trajectory map, it is also possible to accurately restore the complete behavior path of the user based on the user behavior trajectory map and the multi-dimensional analysis module, including the whole process from advertisement reach, channel drainage to final conversion. By deeply analyzing the user behavior data, enterprises can more accurately evaluate the effectiveness of the buying channels, optimize the advertisement placement strategy, improve the utilization efficiency of marketing resources, and thus significantly increase the ROI (return on investment) of the buying channels.

[0094] The data processing method provided in the embodiments of this specification realizes two-way log association of client events, server calls and downstream responses through context-aware log collection and association, and accurately restores user behavior links through time-space alignment technology. The user behavior trajectory map can serve as an important basis for data-driven decision-making, supporting enterprises in making business decisions such as precision marketing, user portrait construction, and ROI analysis of buying channels, helping enterprises gain an advantage in the fierce market competition.

[0095] See also Figure 3 , Figure 3 A processing flow diagram of a data processing method in a data tracking platform provided in an embodiment of the present specification is shown.

[0096] Specifically, the data tracking platform generates a globally unique target device identifier for the target client. The target device identifier is generated using a layered encryption mechanism to ensure the uniqueness of the target device identifier. When a user initiates a service request (i.e., a data processing request in the above embodiment), the target device identifier is passed to all associated servers as a request header or request parameter to ensure that each server can accurately identify and track user behavior. For example, through a request interceptor, the target device identifier is automatically injected into the HTTP header to ensure that the target device identifier can run through the entire request processing process.

[0097] On the server side, data log records containing the target device identifier will be recorded. Specifically, when the request starts and the request is called back, the request log record and response log record containing the target device identifier will be sent back to the data tracking platform. If a service contains multiple subsystems, each subsystem must also record and send back logs separately to ensure the integrity and accuracy of the logs.

[0098] The data tracking platform processes the collected multi-source logs (data log files from different servers) and creates visual analysis tables and business analysis funnels to provide intuitive basis for subsequent analysis and decision-making. Specifically, the multi-source logs are aligned in time and space using a cross-system log aggregation engine, and an end-to-end behavior link is established through precise timestamp matching and context association. This process can effectively solve the log island problem existing in traditional log recording methods, restore the complete user behavior trajectory, provide a solid foundation for troubleshooting and business analysis, and build a user behavior analysis funnel to monitor service quality from the overall market.

[0099] Based on the aggregated log data (i.e., the target data log file in the above embodiment), a user behavior trajectory map is generated through a multi-dimensional analysis module. This map not only supports fault location and business attribution, but also provides strong support for optimizing user experience and improving service quality, helping enterprises to achieve precise operations in complex and changing business scenarios.

[0100] Specifically, through the multi-dimensional analysis module, the aggregated log data is deeply mined and visualized to generate a comprehensive and accurate user behavior trajectory map. This map can clearly show the user's behavior path in different terminals and different services, covering the complete link from client event triggering to server call, and then to downstream response, providing strong support for the overall presentation of business processes; based on the user behavior trajectory map, the system supports efficient fault location and business attribution. When a fault occurs, it can quickly lock the source of the problem, accurately locate the specific link and time point where the fault occurs, and analyze the fault propagation path through link tracking, greatly improving the efficiency of cross-system troubleshooting and shortening the fault repair time. At the same time, the system can perform multi-dimensional analysis of user behavior data, provide data support for business optimization, and help enterprises accurately grasp user needs and optimize product functions and service processes.

[0101] In addition, by integrating an intelligent alarm mechanism into the data tracking platform, key business indicators and abnormal user behavior can be monitored in real time. The system dynamically monitors key nodes and business indicators in the user behavior trajectory map through preset thresholds and rules. When abnormal behavior data is detected or business indicators are lower than the preset threshold, the system will automatically trigger an alarm and promptly notify relevant personnel to take measures to ensure the stable operation of the business and the continuous optimization of user experience.

[0102] The data processing method provided in the embodiments of this specification can achieve the spatiotemporal alignment of multi-source logs and the complete restoration of end-to-end behavior links by constructing a globally unique device identification system and a cross-system log aggregation engine. When a fault occurs, the system can quickly lock the source of the problem, accurately locate the fault link and propagation path, and greatly shorten the troubleshooting time. Compared with traditional solutions, the fault location efficiency is improved by more than 90%, which effectively reduces the business interruption time and improves the reliability and stability of the system. Moreover, through the log collection framework of the data tracking platform (user behavior logs and logs involving user behavior services are placed in a data tracking platform for data analysis) and the streaming processing process, the system can efficiently process billions of logs, reduce the occupancy of storage resources, and reduce log storage and operation and maintenance costs.

[0103] This data processing method is applicable not only to single-service scenarios but also to complex business scenarios such as multi-terminal collaboration, microservice architectures, and multi-party service dependencies. Through a distributed identity cluster (a system architecture for managing and verifying device identities, which disperses the target device identities across multiple nodes, creates unique identifiers for devices, and has a network composed of multiple nodes jointly maintaining the identity information and ensuring data consistency and reliability through a consensus mechanism) and a context awareness mechanism, the system can effectively solve the problem of cross-system behavior tracking, provide comprehensive analysis and decision-making support for complex businesses, help enterprises achieve precise operation and continuous optimization. By constructing a distributed identity cluster and a streaming processing engine, the data tracking platform supports real-time processing and analysis of billions of logs, meets high-concurrency requirements, and the generation of device identities strictly follows user agreements, using encryption technology to ensure privacy security and comply with regulatory requirements.

[0104] Corresponding to the above method embodiment, this specification also provides a data processing device embodiment. Figure 4 The following shows a schematic structural diagram of a data processing device provided by an embodiment of this specification. As Figure 4 shown, the device includes: An identity generation module 402, configured to obtain device information of a target client and generate a target device identity corresponding to the target client according to the device information; A record acquisition module 404, configured to, when the target client sends a data processing request to at least one server, acquire a data log record returned by the at least one server in response to the data processing request, where the data processing request and the data log record both carry the target device identity; A file acquisition module 406, configured to, when acquiring data log files corresponding to at least two servers, associate at least two data log files according to the target device identity to obtain a target data log file, where the data log file includes a data log record; A link determination module 408, configured to determine a target behavior link from the target client to the server according to the target data log file.

[0105] Optionally, the identity generation module 402 is further configured to: Determine an initial device identity according to the device information, and generate the target device identity by encrypting the initial device identity.

[0106] Optionally, the identity generation module 402 is further configured to: When the target client is a first - type client, obtain the first device information of the target client, determine a first initial device identifier according to the first device information, and generate a first target device identifier by encrypting the first initial device identifier, where the first target device identifier includes a timestamp encryption factor updated periodically; When the target client is a second - type client, obtain the second device information of the target client, determine a second initial device identifier according to the second device information, and generate a second target device identifier by encrypting the second initial device identifier, where the second target device identifier includes a timestamp encryption factor updated periodically.

[0107] Optionally, the record acquisition module 404 is further configured to: When the target client sends a data processing request to at least one server, obtain the request log record and the response log record returned by the at least one server in response to the data processing request, where the request log record and the response log record are stored in the data log file corresponding to the at least one server.

[0108] Optionally, the file acquisition module 406 is further configured to: Associate the at least two data log files according to the target device identifier; Obtain the associated data log records in the at least two data log files, and perform data processing on the associated data log records according to the timestamp information and / or context information of the associated data log records to obtain the target data log file.

[0109] The device further includes: A graph generation module, configured to perform visualization processing on the target behavior link to generate a behavior trajectory graph.

[0110] The device further includes: A fault analysis module, configured to analyze the target behavior link and / or the behavior trajectory graph when it is determined that the data processing request fails, to obtain a fault analysis result, where the fault analysis result includes the link where the fault occurs and / or the time point when the fault occurs.

[0111] Optionally, the file acquisition module 406 is further configured to: Obtain the target data log files corresponding to each target client among the multiple target clients.

[0112] The device further includes: An alarm module, configured to obtain a data analysis result by performing data analysis on the target data log files corresponding to the respective target clients; and trigger a data alarm message when it is determined that the data is abnormal according to the data analysis result and a preset rule.

[0113] The apparatus further includes: A conversion evaluation module, configured to obtain a conversion evaluation result by performing conversion behavior analysis on the target data log files corresponding to the respective target clients, where the conversion evaluation result is used to optimize an initial placement strategy to obtain a target placement strategy.

[0114] The data processing apparatus provided in the embodiments of this specification determines a corresponding target device identifier for a target client, so that when the target client sends a data processing request to at least one server, the target device identifier is carried in the data processing request, and a data log record obtained by at least one server in response to the data processing request is obtained, and the target device identifier is also carried in the data log record. Based on this, when data log files (the data log files include data log records) corresponding to at least two servers are obtained, at least two data log files can be associated through the target device identifier, so as to obtain a target data log file corresponding to the target client. The target data log file contains data log records of the interaction from the target client to the server. Therefore, based on this target data log, the target behavior link from the target client to the server can be determined, effectively solving the problem of log islands existing in the traditional log recording method, and being able to restore the complete target behavior link from the target client to the server, providing a data basis for fault troubleshooting and business analysis.

[0115] The above is a schematic solution of a data processing apparatus according to this embodiment. It should be noted that the technical solution of the data processing apparatus and the technical solution of the above data processing method belong to the same concept. For the details not described in the technical solution of the data processing apparatus, reference can be made to the description of the technical solution of the above data processing method.

[0116] Figure 5 FIG. shows a structural block diagram of a computing device 500 according to an embodiment of this specification. The components of the computing device 500 include, but are not limited to, a memory 510 and a processor 520. The processor 520 is connected to the memory 510 through a bus 530, and a database 550 is used to store data.

[0117] The computing device 500 also includes an access device 540, which enables the computing device 500 to communicate via one or more networks 560. Examples of such networks include the Public Switched Telephone Network (PSTN), Local Area Network (LAN), Wide Area Network (WAN), Personal Area Network (PAN), or a combination of communication networks such as the Internet. The access device 540 may include one or more of any type of wired or wireless network interfaces (e.g., a network interface card (NIC)), such as an IEEE 802.11 Wireless Local Area Network (WLAN) wireless interface, a Worldwide Interoperability for Microwave Access (Wi-MAX) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, a Near Field Communication (NFC) interface, and so on.

[0118] In one embodiment of the present specification, the above components of the computing device 500 and Figure 5 other components not shown therein may also be connected to each other, for example, via a bus. It should be understood that Figure 5 the block diagram of the computing device shown is for illustrative purposes only and is not a limitation on the scope of the present specification. Those skilled in the art can add or replace other components as needed.

[0119] The computing device 500 can be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, etc.), a mobile phone (e.g., a smartphone), a wearable computing device (e.g., a smartwatch, smart glasses, etc.), or other types of mobile devices, or a stationary computing device such as a desktop computer or a Personal Computer (PC). The computing device 500 can also be a mobile or stationary server.

[0120] Wherein, when the processor 520 executes the computer program / instructions, the steps of the data processing method are implemented.

[0121] The above is a schematic solution of a computing device according to this embodiment. It should be noted that the technical solution of this computing device and the technical solution of the above data processing method belong to the same concept. For the details not described in detail in the technical solution of the computing device, reference can be made to the description of the technical solution of the above data processing method.

[0122] An embodiment of this specification also provides a computer-readable storage medium, which stores computer programs / instructions. When the computer programs / instructions are executed by a processor, the steps of the data processing method described above are implemented.

[0123] The above is a schematic solution of a computer-readable storage medium according to this embodiment. It should be noted that the technical solution of this storage medium and the technical solution of the above data processing method belong to the same concept. For the details not described in detail in the technical solution of the storage medium, reference can be made to the description of the technical solution of the above data processing method.

[0124] An embodiment of this specification also provides a computer program product, including computer programs / instructions. When the computer programs / instructions are executed by a processor, the steps of the above data processing method are implemented.

[0125] The above is a schematic solution of a computer program product according to this embodiment. It should be noted that the technical solution of this computer program product and the technical solution of the above data processing method belong to the same concept. For the details not described in detail in the technical solution of the computer program product, reference can be made to the description of the technical solution of the above data processing method.

[0126] The above describes specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be executed in a different order than in the embodiments and still achieve the desired results. Additionally, the processes depicted in the figures do not necessarily require the particular order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0127] The computer programs / instructions include computer program code, and the computer program code can be in the form of source code, object code, executable files, or some intermediate forms, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0128] It should be noted that, for the foregoing method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this specification is not limited by the described action sequence, because according to this specification, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this specification.

[0129] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0130] The preferred embodiments of this specification disclosed above are only used to help explain this specification. The alternative embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of this specification, so that those skilled in the art can understand and utilize this specification well. This specification is only limited by the claims and their full scope and equivalents.

Claims

1. A data processing method, characterized in that: Applied to data tracking platforms, including: Acquire device information of a target client, and generate a target device identifier corresponding to the target client according to the device information; When the target client sends a data processing request to at least one server, obtain a data log record returned by the at least one server in response to the data processing request, wherein both the data processing request and the data log record carry the target device identifier; In the case of obtaining data log files corresponding to at least two servers, associating at least two data log files according to the target device identifier to obtain a target data log file, wherein the data log file includes a data log record; According to the target data log file, a target behavior link from the target client to the server is determined.

2. The data processing method according to claim 1, characterized in that: The generating a target device identifier corresponding to the target client according to the device information includes: An initial device identification is determined according to the device information, and the target device identification is generated by encrypting the initial device identification.

3. The data processing method according to claim 1 or 2, characterized in that: The step of obtaining device information of the target client includes: When the target client is a first type of client, obtaining first device information of the target client; When the target client is a second type of client, obtaining second device information of the target client; The generating a target device identifier corresponding to the target client according to the device information includes: In the case where the target client is a first type of client, determining a first initial device identifier according to the first device information, and generating a first target device identifier by encrypting the first initial device identifier, wherein the first target device identifier includes a periodically updated timestamp encryption factor; In the case where the target client is a second type of client, a second initial device identifier is determined based on the second device information, and a second target device identifier is generated by encrypting the second initial device identifier, wherein the second target device identifier includes a periodically updated timestamp encryption factor.

4. The data processing method according to claim 1, characterized in that: The data log includes request log records and response log records; When the target client sends a data processing request to at least one server, obtaining a data log record returned by the at least one server in response to the data processing request includes: In the case where the target client sends a data processing request to at least one server, the request log record and the response log record returned by the at least one server in response to the data processing request are obtained, wherein the request log record and the response log record are stored in a data log file corresponding to the at least one server.

5. The data processing method according to claim 1, characterized in that: The step of associating at least two data log files according to the target device identifier to obtain a target data log file includes: Associating the at least two data log files according to the target device identifier; The associated data log records in the at least two data log files are obtained, and data processing is performed on the associated data log records according to timestamp information and / or context information of the associated data log records to obtain the target data log file.

6. The data processing method according to any one of claims 1-2, 4-5, characterized in that: After determining the target behavior link from the target client to the server, the method further includes: The target behavior link is visualized to generate a behavior trajectory map.

7. The data processing method according to claim 6, characterized in that: After generating the behavior trajectory map, the method further includes: When it is determined that the data processing request fails, the target behavior link and / or the behavior trajectory map is analyzed to obtain a fault analysis result, wherein the fault analysis result includes a fault occurrence link and / or a fault occurrence time point.

8. The data processing method according to any one of claims 1-2, 4-5, characterized in that: The target client includes multiple target clients; The obtaining of the target data log file comprises: Obtaining a target data log file corresponding to each target client among the multiple target clients; After obtaining the target data log file corresponding to each target client among the multiple target clients, the method further includes: Obtaining data analysis results by performing data analysis on the target data log files corresponding to the target clients; When data anomalies are determined based on the data analysis results and preset rules, data alarm information is triggered.

9. The data processing method according to claim 8, characterized in that: Applied to the marketing scenario, after obtaining the target data log file corresponding to each target client among the multiple target clients, the method further includes: By performing conversion behavior analysis on the target data log files corresponding to the target clients, a conversion evaluation result is obtained, wherein the conversion evaluation result is used to optimize the initial delivery strategy to obtain the target delivery strategy.

10. A data processing device, characterized in that: include: An identification generating module is configured to obtain device information of a target client and generate a target device identification corresponding to the target client according to the device information; a record obtaining module, configured to obtain, when the target client sends a data processing request to at least one server, a data log record returned by the at least one server in response to the data processing request, wherein both the data processing request and the data log record carry the target device identifier; A file acquisition module is configured to, when obtaining data log files corresponding to at least two servers, associate the at least two data log files according to the target device identifier to obtain a target data log file, wherein the data log file includes a data log record; The link determination module is configured to determine the target behavior link from the target client to the server according to the target data log file.

11. A data processing system, characterized in that: It includes a data tracking platform, a target client and at least one server, wherein: The data tracking platform is used to obtain the device information of the target client, generate a target device identifier corresponding to the target client according to the device information, and send the target device identifier to the target client; The target client is used to receive and store the target device identifier, and send a data processing request to the at least one server, wherein the data processing request carries the target device identifier; The server is used to respond to the data processing request, generate a data log record and send the data log record to the data tracking platform, wherein the data log record carries the target device identifier; The data tracking platform is also used to, when obtaining data log files corresponding to at least two servers, associate at least two data log files according to the target device identifier to obtain a target data log file, and determine a target behavior link from the target client to the server according to the target data log file, wherein the data log file includes data log records.

12. A computing device comprising a memory, a processor, and a computer program / instruction stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program / instructions, the steps of the data processing method according to any one of claims 1 to 9 are implemented.

13. A computer-readable storage medium storing a computer program / instruction, characterized in that: When the computer program / instructions are executed by a processor, the steps of the data processing method according to any one of claims 1 to 9 are implemented.

14. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the data processing method according to any one of claims 1 to 9 are implemented.

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