Method and device for processing buried point data, electronic equipment and storage medium
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- DUXIAOMAN TECH (BEIJING) CO LTD
- Filing Date
- 2026-03-03
- Publication Date
- 2026-06-09
Smart Images
Figure CN122173535A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of data processing technology, and in particular to a method, apparatus, electronic device, and storage medium for processing embedded data. Background Technology
[0002] Tracked data refers to real-time data collected by embedding "data collection code (tracked points)" into the code of products such as apps, websites, mini-programs, and smart hardware. This data is triggered when users use the product and records user behavior and product status. It is a core data source for data analysis, product optimization, and business decision-making. After collecting tracked data, it needs to be processed, such as data cleaning, data format conversion, data integration, data processing, and data anonymization, to provide it for subsequent business scenarios. Summary of the Invention
[0003] This application provides a method, apparatus, electronic device, and storage medium for processing embedded data, which can improve the accuracy of embedded data processing. The technical solution is as follows: According to one aspect of this application, a method for processing embedded data is provided, the method comprising: Obtain event data from tracking points; Based on the data characteristics of the event data, applicable processing rules are determined from the rule configuration library; Determine the target event data that matches the applicable processing rules from the event data; Based on the applicable processing rules, the target embedded data is transformed to generate target output data.
[0004] In some possible implementations, determining the target event data that matches the applicable processing rule from the event data includes: determining the event type corresponding to the event data; determining the event data whose event type matches the applicable processing rule as candidate event data; and determining the target event data based on the applicable processing rule and the candidate event data.
[0005] In some possible implementations, determining the target tracking data based on the applicable processing rules and the candidate tracking data includes: obtaining the evaluation condition expression corresponding to the applicable processing rules; determining whether the candidate tracking data satisfies the evaluation condition expression; and determining the candidate tracking data that satisfies the evaluation condition expression as the target tracking data.
[0006] In some possible implementations, the data processing method is executed by a stream processing engine that includes an online processor, an observation processor, an isolation test processor, and an offline processor.
[0007] In some possible implementations, the method further includes: acquiring updated processing rules when a rule update operation is detected; and updating the applicable processing rules for each processing node in the stream processing engine through a broadcast state mode.
[0008] In some possible implementations, the step of performing data transformation processing on the target tracking data based on the applicable processing rules to generate target output data includes: if it is a deployment scenario, the deployment processor performs data transformation processing on the target tracking data based on the applicable processing rules to generate the target output data; if it is an observation scenario, the observation processor performs data transformation processing on the target tracking data based on the applicable processing rules to generate the target output data; if it is an isolation test scenario, the isolation test processor performs data transformation processing on the target tracking data based on the applicable processing rules to generate the target output data; if it is a shutdown scenario, the shutdown processor performs data transformation processing on the target tracking data based on the applicable processing rules to generate the target output data.
[0009] In some possible implementations, if it is the online scenario, the target output data is the online output topic; if it is the observation scenario, the target output data is the online output topic; if it is the isolation test scenario, the target output data is the test output topic; if it is the offline scenario, the target output data is dirty data.
[0010] According to another aspect of this application, a data processing apparatus for embedded points is provided, the apparatus comprising: The first acquisition module is used to acquire event data from embedded points; The first determining module is used to determine the applicable processing rules from the rule configuration library based on the data characteristics of the embedded event data; The second determining module is used to determine target embedded data that matches the applicable processing rules from the embedded event data; The generation module is used to perform data transformation processing on the target embedded data based on the applicable processing rules to generate target output data.
[0011] According to one aspect of this application, an electronic device is provided, comprising: a processor and a memory storing a program, the program including instructions that, when executed by the processor, cause the processor to perform the embedded data processing method as described above.
[0012] According to another aspect of this application, a non-transitory computer-readable storage medium is provided that stores computer instructions for causing the computer to perform the embedded data processing method as described above.
[0013] According to another aspect of this application, a computer program product is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the aforementioned embedded data processing method.
[0014] The beneficial effects of the technical solutions provided in this application include at least the following: After acquiring the event tracking data, the applicable processing rules are determined from the rule configuration library based on the data characteristics of the event tracking data. The event tracking data and the applicable processing rules are then matched to filter the required target event tracking data. Subsequently, the target event tracking data is processed based on the applicable processing rules to generate the target output data. By matching rules and filtering the required target event tracking data based on the rules, the appropriate data processing rules and the event tracking data required for subsequent business processing can be accurately obtained, thereby improving the accuracy of event tracking data processing. Attached Figure Description
[0015] Further details, features, and advantages of this application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which: Figure 1 A flowchart of a method for processing embedded data according to an exemplary embodiment of this application is shown; Figure 2 This is a complete flowchart of the data processing for embedded points provided in an exemplary embodiment of this application; Figure 3 This is an exemplary embodiment of the system architecture diagram for processing event data based on the present application. Figure 4 This is a schematic diagram of the structure of a data processing device for embedded points provided in an embodiment of this application; Figure 5 A structural block diagram of an exemplary electronic device that can be used to implement embodiments of this application is shown. Detailed Implementation
[0016] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While some embodiments of this application are shown in the drawings, it should be understood that this application can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this application. It should be understood that the drawings and embodiments of this application are for illustrative purposes only and are not intended to limit the scope of protection of this application.
[0017] It should be understood that the steps described in the method embodiments of this application may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this application is not limited in this respect.
[0018] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc., mentioned in this application are only used to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies. It should be noted that the modifications "a" and "a plurality" mentioned in this application are illustrative and not restrictive, and those skilled in the art should understand that unless explicitly indicated in the context, they should be understood as "one or more". The names of messages or information exchanged between multiple devices in the embodiments of this application are for illustrative purposes only and are not intended to limit the scope of these messages or information.
[0019] The present invention will now be described with reference to the accompanying drawings. The technical solutions provided by the embodiments of the present invention will be explained in detail through specific examples and application scenarios.
[0020] Please refer to Figure 1 The diagram illustrates a flowchart of a method for processing embedded data according to an exemplary embodiment of this application. The method is described using an example of its application in an electronic device. Figure 1 As shown, the method includes: Step 101: Obtain event tracking data; Step 102: Determine the applicable processing rules from the rule configuration library based on the data characteristics of the event tracking data; Step 103: Determine the target event data that matches the applicable processing rules from the event data; Step 104: Perform data transformation processing on the target embedded data based on applicable processing rules to generate target output data. In one possible implementation, event data from multiple data sources is collected by embedding points on the web front-end, mobile terminal, and server terminal. The collected event data is then transmitted to the data processing engine via an event data collection SDK, where the engine processes the acquired event data.
[0021] For example, the data processing engine can be the Flink stream processing engine (near real-time processing engine) to improve the timeliness of data processing for event tracking data.
[0022] After acquiring the event tracking data, the system first searches the rule configuration library for applicable processing rules based on the data characteristics of the event tracking data. These data characteristics can refer to the data type of the event tracking data, such as numeric, string, or time-based data; or the data structure type, such as structured, semi-structured, or unstructured data; or the business type to which the event tracking data belongs. This embodiment does not constitute a limitation in these respects.
[0023] After obtaining the applicable processing rules matching the event data, it is necessary to further confirm whether the applicable processing rules can process the event data. For example, whether the event type matches, or whether the event data meets the evaluation conditions of the applicable processing rules. Therefore, in one possible implementation, it is also necessary to determine the target event data that matches the applicable processing rules from the event data. That is, the selected target event data can be used to perform subsequent data processing, and other event data besides the target event data will be skipped and will not be processed.
[0024] In one exemplary example, step 103 may also include steps 103A to 103C.
[0025] Step 103A: Determine the event type corresponding to the event data.
[0026] Step 103B: Select the event data whose event type matches the applicable processing rules as candidate event data.
[0027] Step 103C: Determine the target data for the data based on the applicable processing rules and candidate data.
[0028] In one possible implementation, when matching applicable processing rules and event data, the event type corresponding to the event data is first obtained, and it is determined whether the event type matches the event type associated with the applicable processing rule. If they match, the event data whose event type matches the applicable processing rule is identified as candidate event data. Then, the candidate event data is evaluated for matching conditions, so as to continue to filter target event data based on the applicable processing rule and candidate event data. Otherwise, if the event type does not match, the event data is skipped (i.e., the event data is not processed).
[0029] For example, the event type is determined by the specific business type. For instance, if it is an alarm business, the event type can be CPU utilization, memory utilization, etc.; if it is a user profiling business, the event type can be user-related information such as age, address, and occupation.
[0030] Specifically, step 103C may further include: obtaining the evaluation condition expression corresponding to the applicable processing rule; determining whether the candidate tracking data satisfies the evaluation condition expression; if it does, then the candidate tracking data that satisfies the evaluation condition expression is determined as the target tracking data; otherwise, if it does not, then the processing of the candidate tracking data is skipped.
[0031] For example, the evaluation condition expression is also determined by the specific business type. For example, if it is an alarm business, the evaluation condition expression can be a CPU utilization rate greater than 80%, that is, only the data points with a CPU utilization rate greater than 80% will be processed later; if it is a user profiling business, the evaluation condition expression can be an age greater than 18 years old.
[0032] After the target data points are selected, the data can be transformed based on the applicable processing rules. The data transformation process can include data cleaning, data format conversion, data integration, data processing, data anonymization, etc., and generate the target output data.
[0033] Optionally, the generated target output data can be stored in a database, provided to a user profiling system for analyzing user characteristics, provided to a model system for model training and optimization, or provided to an operational system for application optimization, etc.
[0034] Please refer to Figure 2 This is a complete flowchart of the data processing for embedded points provided in an exemplary embodiment of this application. Figure 2 As shown, after receiving the event data, the applicable rules (i.e., the applicable processing rules) are obtained; first, it is determined whether the event type of the event data matches; if it does not match, the processing is skipped; if it matches, the evaluation condition expression is obtained, and it is determined whether the evaluation condition expression is satisfied; if it is not satisfied, the processing is also skipped; if it is satisfied, the event data is extracted and transformed based on the applicable rules, the output result is constructed, and the output result is sent, and the processing is completed.
[0035] Optionally, the data processing method provided in this application embodiment can be executed by a stream processing engine, which includes an online processor, an observation processor, an isolation test processor, and an offline processor. By distinguishing between online, observation, isolation test, and offline scenarios, the online, observation, isolation test, and offline functions of the event data processing can be realized.
[0036] Please refer to Figure 3This is a system architecture diagram of a data processing system for event tracking provided in an exemplary embodiment of this application. Figure 3 As shown, at the data source layer, there are front-end event data sources, back-end event data sources, and other event data sources. Event data pulled from multiple data sources is output to the Flink stream processing engine via a Kafka message queue cluster. At the rule management layer, there is a rule configuration database and a rule management service, where users can configure data processing rules online. When a data processing rule in the rule configuration database is updated, the Flink stream processing engine obtains the rule broadcast topic (i.e., the updated data processing rule) through a hot reloading mechanism. In the Flink stream processing engine, the rule state management stores the state of each data processing rule. After obtaining the updated data processing rule through the hot reloading mechanism, the broadcast state is executed. The system includes operations such as storage and rule caching. When processing event data, the rule loader loads the latest data processing rules, which are then executed by the rule execution engine. Additionally, in different scenarios, the online processor, observation processor, isolation test processor, and offline processor process the event data based on the data processing rules. Optionally, at the output layer, the online processor corresponds to the online output topic, the observation processor corresponds to the observation output topic, the isolation test processor corresponds to the test output topic, and the offline processor corresponds to the dirty data output. The monitoring and alerting layer includes rule status monitoring (monitoring the status of data processing rules in the Flink stream processing engine), performance monitoring, and an alerting system.
[0037] In one possible implementation, when a rule update operation is detected, the update processing rule is obtained, and the applicable processing rules (data processing rules) of each processing node in the stream processing engine are updated through the broadcast state mode.
[0038] By adding an online processor, an observation processor, an isolation test processor, and an offline processor to the Flink stream processing engine, the data processing procedures in scenarios such as online, observation, isolation test, and offline can be distinguished. Specifically, step 104 may further include: in the online scenario, the online processor performs data transformation processing on the target tracking data based on applicable processing rules to generate target output data; in the observation scenario, the observation processor performs data transformation processing on the target tracking data based on the applicable processing rules to generate target output data; in the isolation test scenario, the isolation test processor performs data transformation processing on the target tracking data based on applicable processing rules to generate target output data; and in the offline scenario, the offline processor performs data transformation processing on the target tracking data based on applicable processing rules to generate target output data.
[0039] For example, in a deployment scenario, the target output data is the online output topic; in an observation scenario, the target output data is the online output topic; in an isolation test scenario, the target output data is the test output topic; and in a decommissioning scenario, the target output data is dirty data.
[0040] In summary, this application provides a method for processing event tracking data: after acquiring event tracking data, applicable processing rules are determined from a rule configuration library based on the data characteristics of the event tracking data, and the event tracking data and applicable processing rules are further matched to filter the required target event tracking data. Then, the target event tracking data is processed by data transformation based on the applicable processing rules to generate target output data. By matching rules and filtering the required target event tracking data based on the rules, the appropriate data processing rules and the event tracking data required for subsequent business processing can be accurately obtained, improving the accuracy of the event tracking data. In addition, by introducing a stream processing engine to process the event tracking data, the timeliness of event tracking data processing is further improved.
[0041] Please refer to Figure 4 This is a schematic diagram of the structure of a data processing device for embedded data provided in an embodiment of this application. Figure 4 As shown, the device 400 includes: The first acquisition module 401 is used to acquire event data. The first determining module 402 is used to determine the applicable processing rules from the rule configuration library based on the data characteristics of the embedded event data; The second determining module 403 is used to determine target embedded data that matches the applicable processing rule from the embedded event data; The generation module 404 is used to perform data transformation processing on the target embedded data based on the applicable processing rules to generate target output data.
[0042] Optionally, the second determining module 403 is further configured to: Determine the event type corresponding to the event data of the embedded points; The event data whose event type matches the applicable processing rule are identified as candidate event data. Based on the applicable processing rules and the candidate embedding data, the target embedding data is determined.
[0043] Optionally, the second determining module 403 is further configured to: Obtain the evaluation condition expression corresponding to the applicable processing rule; Determine whether the candidate embedding data satisfies the evaluation condition expression; Candidate embedding data that satisfy the evaluation condition expression are determined as the target embedding data.
[0044] Optionally, the data processing method is executed by a stream processing engine, which includes an online processor, an observation processor, an isolation test processor, and an offline processor.
[0045] Optionally, the device further includes: The second acquisition module is used to acquire the update processing rules when a rule update operation is detected; The rule update module is used to update the applicable processing rules for each processing node in the stream processing engine through a broadcast state mode.
[0046] Optionally, the generation module 404404 is further configured to: If it is an online scenario, the online processor performs data transformation processing on the target embedded data based on the applicable processing rules to generate the target output data; If it is an observation scenario, the observation processor performs data transformation processing on the target embedded data based on the applicable processing rules to generate the target output data; If it is an isolation test scenario, the isolation test processor performs data transformation processing on the target embedded data based on the applicable processing rules to generate the target output data; In the case of an offline scenario, the offline processor performs data transformation processing on the target embedded data based on the applicable processing rules to generate the target output data.
[0047] Optionally, if it is the online scenario, the target output data is the online output topic; if it is the observation scenario, the target output data is the online output topic; if it is the isolation test scenario, the target output data is the test output topic; if it is the offline scenario, the target output data is dirty data.
[0048] In summary, this application provides a method for processing event tracking data: after obtaining event tracking data, applicable processing rules are determined from a rule configuration library based on the data characteristics of the event tracking data, and the event tracking data and applicable processing rules are further matched to filter the required target event tracking data. Then, the target event tracking data is processed by data transformation based on the applicable processing rules to generate target output data. By matching rules and filtering the required target event tracking data based on the rules, the appropriate data processing rules and the event tracking data required for subsequent business processing can be accurately obtained, thereby improving the accuracy of event tracking data.
[0049] An exemplary embodiment of this application also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, which, when executed by the at least one processor, causes the electronic device to perform a data embedding method according to an embodiment of this application.
[0050] An exemplary embodiment of this application also provides a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform a data processing method for embedded data according to an embodiment of this application.
[0051] An exemplary embodiment of this application also provides a computer program product, including a computer program, wherein, when executed by a computer's processor, the computer program is used to cause the computer to perform a data processing method for embedding data according to an embodiment of this application.
[0052] refer to Figure 5 The present invention describes a structural block diagram of an electronic device 500 that can serve as a server or client of this application, which is an example of a hardware device that can be applied to various aspects of this application. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the application described and / or claimed herein.
[0053] like Figure 5 As shown, the electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. The RAM 503 may also store various programs and data required for the operation of the electronic device 500. The computing unit 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.
[0054] Multiple components in electronic device 500 are connected to I / O interface 505, including: input unit 506, output unit 507, storage unit 508, and communication unit 509. Input unit 506 can be any type of device capable of inputting information to electronic device 500. Input unit 506 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of electronic device. Output unit 507 can be any type of device capable of presenting information and may include, but is not limited to, a display, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 508 may include, but is not limited to, disk and optical disk. Communication unit 509 allows electronic device 500 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and / or chipsets, such as Bluetooth devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.
[0055] The computing unit 501 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above. For example, in some embodiments, Figure 1 The method shown can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 500 via ROM 502 and / or communication unit 509. In some embodiments, computing unit 501 can be configured to execute by any other suitable means (e.g., by means of firmware). Figure 1 The method shown.
[0056] The program code used to implement the methods of this application may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.
[0057] In the context of this application, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.
[0058] As used in this application, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.
[0059] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).
[0060] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.
[0061] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other.
Claims
1. A method for processing embedded data, characterized in that, The method includes: Obtain event data from tracking points; Based on the data characteristics of the event data, applicable processing rules are determined from the rule configuration library; Determine the target event data that matches the applicable processing rules from the event data; Based on the applicable processing rules, the target embedded data is transformed to generate target output data.
2. The method according to claim 1, characterized in that, The step of determining the target event data that matches the applicable processing rule from the event data includes: Determine the event type corresponding to the event data of the embedded points; The event data whose event type matches the applicable processing rule are identified as candidate event data. Based on the applicable processing rules and the candidate embedding data, the target embedding data is determined.
3. The method according to claim 2, characterized in that, The step of determining the target data based on the applicable processing rules and the candidate data includes: Obtain the evaluation condition expression corresponding to the applicable processing rule; Determine whether the candidate embedding data satisfies the evaluation condition expression; Candidate embedding data that satisfy the evaluation condition expression are determined as the target embedding data.
4. The method according to any one of claims 1 to 3, characterized in that, The data processing method is executed by a stream processing engine, which includes an online processor, an observation processor, an isolation test processor, and an offline processor.
5. The method according to claim 4, characterized in that, The method further includes: When a rule update operation is detected, obtain the update processing rule; The applicable processing rules for each processing node in the stream processing engine are updated by broadcasting the state mode.
6. The method according to claim 4, characterized in that, The step of performing data transformation processing on the target embedded data based on the applicable processing rules to generate target output data includes: If it is an online scenario, the online processor performs data transformation processing on the target embedded data based on the applicable processing rules to generate the target output data; If it is an observation scenario, the observation processor performs data transformation processing on the target embedded data based on the applicable processing rules to generate the target output data; If it is an isolation test scenario, the isolation test processor performs data transformation processing on the target embedded data based on the applicable processing rules to generate the target output data; In the case of an offline scenario, the offline processor performs data transformation processing on the target embedded data based on the applicable processing rules to generate the target output data.
7. The method according to claim 6, characterized in that, If it is an online scenario, the target output data is the online output topic; if it is an observation scenario, the target output data is the online output topic; if it is an isolation test scenario, the target output data is the test output topic; if it is an offline scenario, the target output data is dirty data.
8. A device for processing embedded data, characterized in that, The device includes: The first acquisition module is used to acquire event data from embedded points; The first determining module is used to determine the applicable processing rules from the rule configuration library based on the data characteristics of the embedded event data; The second determining module is used to determine target embedded data that matches the applicable processing rules from the embedded event data; The generation module is used to perform data transformation processing on the target embedded data based on the applicable processing rules to generate target output data.
9. An electronic device, comprising: processor; as well as Stored program memory, The program includes instructions that, when executed by the processor, cause the processor to perform the data processing method for embedded data according to any one of claims 1-7.
10. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to execute the data processing method for embedded points according to any one of claims 1-7.