Intelligent analysis method for mobile application third-party component dependency and related device

Abstract

The application relates to the technical field of computer software, can be applied to the field of science and technology finance / digital medical treatment, and discloses an intelligent analysis method for mobile application third-party component dependence and related equipment. The method comprises the following steps: obtaining engineering information of a mobile application to be analyzed, analyzing basic dependence data of a third-party component based on engineering construction configuration, filtering the basic dependence data according to a preset dependence filtering rule to form a target dependence list, extracting basic attributes such as the name, version and source type of each dependence component and generating structured dependence description data, inputting a large model analysis engine by combining the dependence description data with a preset prompt word configuration, generating extended attributes associated with the dependence component, writing third-party component dependence information files according to a data structure specification, and outputting to a specified path or a target server according to a preset configuration. The method improves the accuracy and automation degree of third-party component dependence analysis in a mobile application.

Description

Technical Field

[0001] This application relates to the field of computer software technology and can be applied to the fields of fintech / digital healthcare, particularly to an intelligent analysis method and related equipment that mobile application third-party component dependencies rely on. Background Technology

[0002] With the development of mobile internet, mobile applications are playing an increasingly critical role in industries such as fintech and digital healthcare. These applications typically integrate numerous third-party SDKs to achieve capabilities such as network communication, statistical analysis, payment processing, identity authentication, and medical data transmission. Current technologies generally rely on the platform's build system (e.g., Gradle for Android, project configurations for iOS and HarmonyOS, and front-end package management tools for H5) to resolve dependencies. However, these methods can only obtain basic information such as dependency name, version, and source, and cannot automatically identify compliance attributes that regulators focus on, such as SDK license type, privacy permissions, official website information, and usage scenarios. In the fintech sector, if an SDK involves user privacy or transaction data, companies often need to manually retrieve its compliance information, a cumbersome process prone to omissions. In digital healthcare scenarios, some SDKs involve medical images and sensitive patient information, and their opaque privacy policies may pose data audit risks. Meanwhile, existing dependency analysis methods lack unified cross-platform management capabilities, with dependency lists being fragmented and difficult to centrally manage; they also lack flexible filtering mechanisms, failing to effectively exclude dependencies from internal development or debugging phases; and when handling large-scale projects, the performance and stability of existing tools are insufficient to meet the requirements of continuous integration environments. In summary, existing technologies are inadequate in terms of dependency identification depth, compliance attribute extraction capabilities, cross-platform uniformity, and engineering scalability, making it difficult to support the automated, precise, and intelligent SDK dependency management needs of the financial and healthcare industries. Summary of the Invention

[0003] The main technical problem addressed by the embodiments of this application is the scattered resolution of third-party component dependencies in existing mobile applications, which results in low technical management efficiency.

[0004] To address the aforementioned technical problems, the first technical solution adopted in this application is: providing an intelligent analysis method for third-party component dependencies in mobile applications, comprising: acquiring project information of the mobile application to be analyzed, and parsing basic dependency data of third-party components based on the project build configuration in the project information; filtering the basic dependency data according to preset dependency filtering rules to remove component dependencies that do not meet the filtering conditions from the filtering process, thereby obtaining a target dependency list; extracting basic attributes of each dependent component from the target dependency list, and generating structured dependency description data based on the basic attributes, wherein the basic attributes include at least name, version, and source type; inputting the dependency description data into a large model analysis engine based on the dependency description data and according to preset prompt word configuration, analyzing and processing each dependent component to obtain extended attributes associated with each dependent component; writing the basic attributes and the extended attributes into a third-party component dependency information file according to preset data structure specifications, and outputting the dependency information to a specified path or reporting it to a target server according to preset configuration.

[0005] Optionally, the step of filtering the basic dependency data according to preset dependency filtering rules includes: reading component name information in the basic dependency data item by item, and determining whether the component name information meets the filtering conditions based on preset name prefixes; reading component source type information in the basic dependency data item by item, and determining whether the component source type information meets the filtering conditions based on preset source types; reading component path information in the basic dependency data item by item, and determining whether the component path information meets the filtering conditions based on preset path rules.

[0006] Optionally, the step of parsing the basic dependency data of third-party components includes: calling a preset intelligent analysis tool to read the project configuration file of the mobile application to be analyzed to obtain the declaration information of each dependent component; parsing the dependency declaration field in the project build script of the mobile application to be analyzed through the intelligent analysis tool to extract the dependency name information, dependency version information and dependency source information of each dependent component; and writing the dependency name information, dependency version information and dependency source information into the basic dependency data according to preset parsing rules.

[0007] Optionally, the step of inputting the dependency description data into the large model analysis engine based on the dependency description data and according to a preset prompt word configuration, and analyzing and processing each dependency component to obtain extended attributes associated with each dependency component includes: generating prompt information for dependency component attribute analysis according to the preset prompt word configuration; combining the prompt information and the dependency description data to generate large model analysis request content corresponding to each dependency component; in the large model analysis engine, triggering the large model to search external knowledge bases or network resources according to the dependency component name information and dependency component version information in the analysis request content to obtain candidate text content of component information containing official website information, license information, privacy permission information, and usage scenario description; performing semantic parsing processing on the candidate text content of component information to extract the official website field, license field, privacy permission field, and usage scenario field to generate extended attribute data; and establishing the association relationship between the extended attribute data and the corresponding dependency components according to a preset field mapping relationship to obtain the extended attributes associated with each dependency component.

[0008] Optionally, the method further includes: receiving a running instruction input via a command-line interface, and parsing the running instruction according to a preset parameter format; extracting mobile application platform configuration parameters, fragment size parameters, concurrency control parameters, and retry mechanism parameters from the parsed running instruction to generate a set of running parameters for the dependency analysis process; initializing the execution environment of the dependency analysis task according to the set of running parameters, and controlling the execution behavior of the dependency analysis task according to the set of running parameters during the execution of the dependency analysis task.

[0009] Optionally, the method further includes: during the execution of the dependency analysis task, outputting console log information of the dependency analysis task according to preset log level parameters; when an exception occurs in the execution steps of the dependency analysis task, collecting the location of the exception, the exception type, and the exception description information to generate a dependency analysis error record; and performing error handling operations on the dependency analysis error record according to preset error handling rules to control the continued execution or safe termination of the dependency analysis task.

[0010] Optionally, the step of writing the basic attributes and the extended attributes to the third-party component dependency information file according to a preset data structure specification includes: performing field encoding processing on the basic attributes and the extended attributes according to the preset data structure specification to generate a dependency data field set that conforms to the target structure; converting the dependency data field set into a serialized target file format according to a preset serialization rule to generate dependency information content to be written; and writing the dependency information content to the third-party component dependency information file according to a preset file writing rule.

[0011] To address the aforementioned technical problems, the second technical solution adopted in this application is: providing an intelligent analysis device for third-party component dependencies in mobile applications, comprising: a basic dependency data module, used to acquire engineering information of the mobile application to be analyzed, and parse out the basic dependency data of the third-party components based on the engineering build configuration in the engineering information; a dependency list data module, used to filter the basic dependency data according to preset dependency filtering rules, so that component dependencies that do not meet the filtering conditions are removed from the filtering process, thereby obtaining a target dependency list; a dependency description data module, used to extract the basic attributes of each dependent component from the target dependency list, and generate structured dependency description data based on the basic attributes, wherein the basic attributes include at least name, version, and source type; an extended attribute data module, used to input the dependency description data into a large model analysis engine based on the dependency description data and according to preset prompt word configuration, analyze and process each dependent component, and obtain extended attributes associated with each dependent component; and a dependency information output module, used to write the basic attributes and the extended attributes to a third-party component dependency information file according to preset data structure specifications, and output the dependency information to a specified path or report it to a target server according to a preset configuration.

[0012] To solve the above-mentioned technical problems, the third technical solution adopted in the embodiments of this application is: to provide an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the intelligent analysis method that the third-party component of the mobile application depends on as described above.

[0013] To solve the above-mentioned technical problems, the fourth technical solution adopted in the embodiments of this application is: to provide a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by an electronic device, the electronic device executes the intelligent analysis method dependent on the third-party components of the mobile application as described above.

[0014] Unlike related technologies, this application obtains basic dependency data by parsing the project build configuration, generates a target dependency list based on filtering rules, produces structured dependency description data, and utilizes a large-scale model analysis engine driven by prompt word configuration to generate extended attributes. This enables automated collection, accurate identification, and attribute enhancement of third-party component dependency information. By outputting dependency information files through data structure specifications, the dependency information can have a consistent structural expression and reusability, thereby improving the automation, information completeness, and analysis efficiency of the dependency analysis process. Attached Figure Description

[0015] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements having the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0016] Figure 1 This is a schematic diagram of the operating environment of the intelligent analysis method for third-party components of mobile applications provided in this application embodiment.

[0017] Figure 2 This is a schematic diagram of the execution flow of the intelligent analysis method for third-party components of mobile applications provided in the embodiments of this application.

[0018] Figure 3 This is a schematic diagram of the execution flow of obtaining extended attributes in the intelligent analysis method for third-party components of mobile applications provided in the embodiments of this application.

[0019] Figure 4 This is a schematic diagram illustrating the execution flow of dependency analysis controlled by instructions in the intelligent analysis method for third-party component dependencies of mobile applications provided in this application embodiment.

[0020] Figure 5 This is a schematic diagram of the system structure of the intelligent analysis device on which the third-party components of the mobile application depend, as provided in the embodiments of this application.

[0021] Figure 6 This is a schematic diagram of the hardware structure of an electronic device that executes an intelligent analysis method dependent on third-party components of a mobile application, as provided in an embodiment of this application. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. Software tools, components, or servers not belonging to this company that appear in the embodiments of this application are merely illustrative examples and do not represent actual use.

[0023] It should be noted that, unless otherwise specified, the various features in the embodiments of this application can be combined with each other, all of which are within the protection scope of this application. Furthermore, although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device schematic diagram or the order in the flowchart.

[0024] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The term "and / or" as used in this specification includes any and all combinations of one or more of the associated listed items.

[0025] To facilitate understanding of this embodiment, a detailed description of the intelligent analysis method for third-party component dependencies in mobile applications disclosed in this application embodiment will be provided first. Please refer to [link to relevant documentation]. Figure 1 , Figure 1 This is a schematic diagram of the operating environment of the intelligent analysis method for third-party components of mobile applications provided in this application embodiment, such as... Figure 1 As shown, the execution subject of the intelligent analysis method for mobile application third-party components provided in this application embodiment is generally an electronic device with a certain computing power, such as a computer device. In some possible implementations, the intelligent analysis method for mobile application third-party components can be implemented by the processor calling computer-readable instructions stored in the memory. Figure 1 The computer equipment mentioned can be a server. A server can be a standalone server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. This can be understood as... Figure 1 The number of computer devices shown is merely illustrative and can be expanded in any number according to actual needs.

[0026] Please continue reading Figure 2 , Figure 2 This is a schematic diagram of the execution flow of the intelligent analysis method for third-party component dependencies in mobile applications provided in this application embodiment, such as... Figure 2 As shown, it includes the following steps: S1. Obtain the project information of the mobile application to be analyzed, and based on the project build configuration in the project information, parse out the basic dependency data of the third-party components.

[0027] Step S1 enables automated analysis of dependency structures at the source code level. By reading project configuration files and build scripts, the names, versions, and sources of third-party libraries actually referenced in business modules can be identified, describing the dependency chain. In fintech scenarios, some fund risk control mobile applications often integrate encryption modules, market data retrieval plugins, and graphics rendering tools. By parsing the project build configuration, the dependency sources and version combinations of these plugins can be automatically discovered, reducing the complexity of manual investigation. In digital healthcare scenarios, electronic medical record viewing applications typically integrate image rendering components, medical data parsing libraries, and privacy permission processing modules. By parsing project information, third-party libraries involving the processing of sensitive personal information can be quickly identified, providing foundational data for subsequent compliance checks and security assessments.

[0028] As an optional implementation, the process of parsing the basic dependency data of the third-party component in step S1 above may also specifically include the following steps S11 to S13.

[0029] S11. Call the preset intelligent analysis tool to read the project configuration file of the mobile application to be analyzed in order to obtain the declaration information of each dependent component.

[0030] S12. Use intelligent analysis tools to parse the dependency declaration fields in the project build script of the mobile application to be analyzed, so as to extract the dependency name information, dependency version information and dependency source information of each dependent component.

[0031] S13. According to the preset parsing rules, write the dependency name information, dependency version information and dependency source information to the basic dependency data.

[0032] By calling intelligent analysis tools to read project configuration files, the declaration information of each dependent component can be extracted. Combined with dependency fields in the build script, basic information such as dependency name, version, and source can be located. After parsing and writing these fields, structured basic dependency data can be formed for subsequent filtering and enhanced analysis. Intelligent analysis tools typically support multiple build methods, such as Gradle, CocoaPods, or npm, thus ensuring compatibility with mobile projects on different platforms. In the fintech sector, market data display applications often rely on several network transmission libraries, encryption / decryption toolkits, and data serialization libraries. Intelligent analysis tools can automatically identify these dependency characteristics, thus avoiding human error. In the digital healthcare sector, image processing mobile applications rely on numerous image computing plugins. Automatic parsing can quickly generate accurate basic dependency information, facilitating subsequent extended attribute identification.

[0033] Through steps S11 to S13, the automated collection and standardized organization of third-party component dependency information can be achieved at the project configuration file and project build script levels, thereby obtaining basic dependency data with complete fields and a unified format. This process, through the extraction and writing of dependency names, versions, and sources, effectively reduces omissions and inconsistencies that may occur when manually parsing build scripts, improving the accuracy and completeness of dependency identification. The structured basic dependency data can serve as standardized input for subsequent filtering analysis, attribute enhancement processing, and dependency information output, improving the scalability and processing efficiency of the overall dependency analysis process and providing a stable and reliable data foundation for subsequent analysis stages.

[0034] S2. Filter the basic dependency data according to the preset dependency filtering rules so that component dependencies that do not meet the filtering conditions are removed from the filtering process, and obtain the target dependency list.

[0035] The system filters basic dependency data according to preset dependency filtering rules, effectively eliminating dependencies that are not involved in actual business development or do not need to be included in management, such as local debugging libraries, self-developed internal libraries, or testing tool libraries. Filtering based on name prefixes, source types, or path patterns ensures that the selected target dependency list only covers third-party components required for actual business operations. In fintech scenarios, market data testing components and debugging libraries related to simulated trading typically do not need to be included in dependency management; filtering avoids meaningless security checks. In digital healthcare scenarios, some local image simulation plugins or internal diagnostic model libraries also do not need to be included in the dependency list; the filtering process reduces the complexity of the review process.

[0036] As an optional implementation, the process of filtering the basic dependency data according to the preset dependency filtering rules in step S2 above may also specifically include the following steps S21 to S23.

[0037] S21. Read the component name information in the basic dependency data item by item, and determine whether the component name information meets the filtering conditions based on the preset name prefix.

[0038] S22. Read the component source type information in the basic dependency data item by item, and determine whether the component source type information meets the filtering conditions based on the preset source type.

[0039] S23. Read the component path information in the basic dependency data item by item, and determine whether the component path information meets the filtering conditions according to the preset path rules.

[0040] This process involves filtering basic dependency data based on name prefixes, source types, and path rules, establishing a multi-dimensional filtering chain to ensure that only dependency components meeting business and compliance requirements proceed to the next stage of analysis. Name prefixes identify internally developed components, source types differentiate externally downloaded dependencies, and path rules identify debug components temporarily generated during the build process. In a fintech environment, this filtering process can automatically exclude internal risk control algorithm model libraries to avoid confusion with compliance dependency checks. In digital healthcare scenarios, filtering rules can eliminate unreleased image debugging plugins, facilitating subsequent permission attribute analysis.

[0041] Through steps S21 to S23, a multi-dimensional dependency filtering mechanism can be constructed based on component name information, component source type information, and component path information. This effectively removes dependency entries that do not meet the filtering conditions from the basic dependency data. Name prefix matching, source type judgment, and path rule verification complement each other, significantly improving the accuracy and fine-grained control of the filtering process, ensuring that the filtered target dependency list contains only valid dependency components that meet preset criteria. This filtering mechanism reduces the interference of invalid dependencies on subsequent attribute analysis and file generation processing, improves the efficiency and stability of the dependency analysis process, and provides more concise and reliable dependency data input for subsequent steps.

[0042] S3. Extract the basic attributes of each dependent component from the target dependency list, and generate structured dependency description data based on the basic attributes. The basic attributes include at least name, version and source type.

[0043] Extracting basic attributes from the target dependency list and generating structured dependency description data provides a unified input format for subsequent prompt word construction, large-scale model analysis, and extended attribute identification. Basic attributes, including name, version, and source type, constitute a minimal set describing the component's identity characteristics. Organizing these attributes in a structured manner improves the accuracy of model analysis. In fintech, financial data SDKs and encryption libraries often have strict version dependencies; structured data helps identify potential risks. In digital healthcare, the source type (open source, commercial, proprietary) of image computing components or medical data parsing components is usually directly related to data security levels; structured descriptions aid in subsequent permission and license analysis.

[0044] S4. Based on the dependency description data and according to the preset prompt words configuration, input the dependency description data into the large model analysis engine, analyze and process each dependency component, and obtain the extended attributes associated with each dependency component.

[0045] Based on dependency description data and configured prompts, descriptive data is input into the large-scale model analysis engine, which automatically generates extended attributes for third-party components. These extended attributes typically involve key information such as license type, official website link, privacy policy descriptions, and typical use cases, serving as crucial evidence for assessing third-party dependency risks. In the fintech sector, the large-scale model can identify whether a market data SDK includes permissions to access user device information or involves financial compliance licenses. In digital healthcare, the large-scale model can determine whether an image analysis plugin contains operations involving privacy data processing or whether its license permits deployment in commercial medical scenarios. Through model-driven augmented analytics, the cost of manually reviewing official website documentation can be significantly reduced.

[0046] As an alternative implementation method, please continue reading. Figure 3 , Figure 3 This is a schematic diagram illustrating the execution flow of the intelligent analysis method for third-party component dependencies in mobile applications provided in this application, where extended attributes are obtained. Figure 3 As shown, the process can specifically include the following steps S41 to S45.

[0047] S41. Generate prompt information for dependent component attribute analysis based on the preset prompt word configuration.

[0048] Step S41 provides clear analysis objectives and field extraction requirements for the large model analysis process. The prompt word configuration can include field identifiers, output format constraints, and analysis scope instructions. The generated prompt information guides the large model to perform targeted processing of the attribute information of dependent components in subsequent retrieval and parsing stages, thereby improving the accuracy and consistency of extended attribute extraction.

[0049] S42. Combine the prompt information and dependency description data to generate the large model analysis request content corresponding to each dependency component.

[0050] Specifically, combining prompts and dependency description data to generate corresponding large-scale model analysis request content allows the large model to simultaneously obtain instruction information and structured dependency description information during the input phase. The combined request content provides sufficient context, enabling the large model to perform more precise retrieval, analysis, and output format generation based on basic attributes such as the name, version, and source of dependent components, thus providing reliable input for subsequent expansion attribute construction.

[0051] S43. In the large model analysis engine, based on the dependent component name information and dependent component version information in the analysis request content, the large model is triggered to retrieve external knowledge bases or network resources to obtain candidate text content of component information containing official website information, license information, privacy permission information and usage scenario description.

[0052] Within the large model analysis engine, the search for dependent component names and versions within the analysis request triggers a retrieval process from external knowledge bases or online resources, enabling automated acquisition of textual content related to dependent component attributes. The dependent component names and versions can serve as search keywords, limiting the scope of candidate text extracted by the large model from public documents, technical specifications, software repository pages, license agreement pages, or other accessible resources. By providing explicit fields in the analysis request, the large model can leverage its internal language understanding and retrieval capabilities to identify text with a high degree of matching to the target dependent component, thereby reducing interference from irrelevant content and improving the accuracy of candidate content.

[0053] Retrieval processing typically includes steps such as keyword matching, semantic relevance assessment, and source credibility evaluation, ensuring high relevance throughout the retrieval process. Dependency component names, versions, and source attributes can be used to filter outdated documents, exclude semantically similar but non-identical content, and ensure that the search focuses on technical materials directly related to the target component. Through real-time access to external knowledge bases or online resources, the large model analysis engine can acquire candidate texts containing official website links, license information, privacy statements, and usage scenario descriptions, providing ample data for subsequent semantic parsing.

[0054] Furthermore, by analyzing the retrieval process triggered by the request content, automated information expansion can be achieved. This allows the generation of extended attributes to be independent of static rules or a single data table, instead enabling field extraction based on real-time content, thereby improving the timeliness and content coverage of extended attributes. The quality and diversity of candidate text content directly affect the field recognition performance in subsequent parsing steps. Therefore, a retrieval method driven by both name and version can ensure that candidate text has high relevance, accuracy, and parsability.

[0055] S44. Perform semantic parsing on the candidate text content of component information to extract the official website field, license field, privacy permission field, and usage scenario field to generate extended attribute data.

[0056] This process involves semantic parsing of candidate text content to extract fields such as official website, license, privacy permissions, and usage scenarios, transforming unstructured text into structured attribute data. Semantic parsing accurately identifies key fields related to dependent components, forming a clear set of fields to describe component extended attributes, ensuring consistency in expression and content structure.

[0057] S45. Based on the preset field mapping relationship, establish the association between the extended attribute data and the corresponding dependent components to obtain the extended attributes associated with each dependent component.

[0058] Among them, the field mapping relationship can ensure that the correspondence between extended attributes and dependent components is accurate, so that extended attributes can participate in subsequent data writing, display or analysis operations as part of the dependent component information, thereby realizing the automatic enhancement of dependent component attributes.

[0059] Through steps S41 to S45, the automated acquisition and structured generation of extended attributes for third-party components can be achieved. The prompts generated by configuring prompt words clearly define the field extraction targets, ensuring a consistent output format for the large model during the retrieval and analysis phases. By combining prompts with dependency description data to form the analysis request content, the large model can perform more accurate retrieval based on dependency names and versions. Searching external knowledge bases or online resources yields candidate text content containing component attribute information, providing sufficient data for subsequent parsing. Semantic parsing transforms the candidate text, extracting unstructured content into structured fields. Establishing a correspondence between extended attributes and dependent components through field mapping generates stable attribute results that can be used for subsequent writing and reporting, thereby improving the automation and accuracy of extended attribute generation.

[0060] S5. According to the preset data structure specifications, write the basic attributes and extended attributes to the third-party component dependency information file respectively, and output the dependency information to the specified path or report it to the target server according to the preset configuration.

[0061] This system, by writing basic and extended attributes according to a pre-defined data structure specification and outputting dependency information to a specified path or reporting to the target server, enables centralized management, automated updates, and cross-environment sharing of dependency information. In the fintech sector, dependency information can be reported to a compliance review platform for item-by-item checks of market data components, trading components, or encryption components. In the digital healthcare sector, dependency files can serve as part of the medical software review process, identifying third-party components involved in sensitive image processing or medical data parsing, thus meeting the requirements for medical software certification and security evaluation.

[0062] As an optional implementation, the process of writing the third-party component dependency information file in step S5 above may also specifically include the following steps S51 to S53.

[0063] S51. Based on the preset data structure specifications, perform field encoding processing on the basic attributes and extended attributes to generate a set of dependent data fields that conform to the target structure.

[0064] S52. According to the preset serialization rules, convert the set of dependency data fields into the serialized target file format to generate the dependency information content to be written.

[0065] S53. Write the dependency information to the third-party component dependency information file according to the preset file writing rules.

[0066] Encoding and serializing basic and extended attributes into a target format and writing them to a dependency information file ensures that dependency data can be transmitted, stored, and retrieved with a unified structure. Field encoding standardizes field names and formats, serialization guarantees compatibility across different platforms, and file writing creates a persistent dependency list. In fintech, this formatted output supports automated risk control scanning to identify high-risk dependencies. In digital healthcare, standardized file formats facilitate automated compliance checks by medical device adaptation programs or hospital IT platforms.

[0067] Through steps S51 to S53, encoding, serialization, and file writing operations are performed on basic and extended attributes to generate a third-party component dependency information file with a unified structure, standardized format, and persistent storage. Field encoding ensures that attribute content meets preset data structure requirements before writing, serialization rules ensure consistent parsing and transmission capabilities of dependency data across different operating environments, and the file writing process stably saves the processed dependency content as the target file, ensuring readability and reusability for subsequent access. These operations significantly improve the standardization, stability, and compatibility of dependency information, providing a reliable data carrier for subsequent data reporting, version management, or dependency review, and enhancing the continuity and maintainability of the overall dependency analysis process.

[0068] As another alternative implementation method, please continue reading. Figure 4 Figure 4 is a schematic diagram of the execution flow of dependency analysis controlled by instructions in the intelligent analysis method for third-party component dependencies of mobile applications provided in the embodiments of this application. Figure 4 As shown, it can specifically include the following steps S61 to S63.

[0069] S61. Receive the running command input through the command line interface and perform syntax parsing on the running command according to the preset parameter format.

[0070] S62. Extract mobile application platform configuration parameters, fragment size parameters, concurrency control parameters, and retry mechanism parameters from the parsed execution instructions to generate a set of execution parameters for the dependency analysis process.

[0071] S63. Based on the set of runtime parameters, initialize the execution environment of the dependency analysis task, and control the execution behavior of the dependency analysis task according to the set of runtime parameters during the execution of the dependency analysis task.

[0072] The command-line interface, by receiving and parsing execution commands, introduces flexible operation modes and highly adjustable parameter control to the dependency analysis process. Execution commands typically include key fields such as platform configuration, shard size, concurrency parameters, and retry strategies. These fields directly influence the scheduling strategy, processing speed, and resource consumption of the dependency analysis task. Syntax parsing of these commands transforms complex command combinations into structured parameters, enabling the dependency analysis process to run in the most suitable manner in different execution environments. For example, in the fintech sector, market data SDK analysis often involves hundreds of encryption libraries, network protocol libraries, and market data rendering libraries. The data volume of a single scan is large; a sharding strategy can process large-scale dependencies in batches, avoiding resource congestion. Concurrency control parameters allow the analysis task to fully utilize the processing power of high-concurrency servers, significantly shortening analysis time. Simultaneously, retry strategies prevent market data dependency parsing failures due to momentary network fluctuations, making the dependency review process more reliable in financial regulatory scenarios. In the field of digital healthcare, large-scale image processing applications typically include numerous image operator libraries, DICOM parsing libraries, and privacy data processing libraries. Through configurable parameters provided by the CLI, the analysis intensity can be automatically adjusted based on the performance of hospital equipment. For example, concurrency can be increased in image storage server environments, while concurrency can be reduced and the number of retries increased in medical device terminals to adapt to different hardware conditions and network environments. Therefore, the set of parameters parsed through the command-line interface can significantly improve the adaptability and execution efficiency of dependency analysis processes in different business scenarios.

[0073] As another optional implementation method, the intelligent analysis method for mobile application third-party component dependence provided in this application embodiment further includes the following steps S71 to S73.

[0074] S71. During the execution of the dependency analysis task, output the console log information of the dependency analysis task according to the preset log level parameters.

[0075] S72. When an exception occurs during the execution of the dependency analysis task, collect the location, type, and description of the exception to generate a dependency analysis error record.

[0076] S73. According to the preset error handling rules, perform error handling operations on dependency analysis error records to control the continued execution or safe termination of the dependency analysis task.

[0077] During dependency analysis, debugging information is output based on log levels, and the location, type, and description of any anomalies are collected when they occur, ensuring traceability and auditability throughout the process. Log information records dependency retrieval behavior, field parsing processes, model inputs and outputs, network request status, and file writing processes, enabling developers and auditors to accurately reproduce the analysis path through log replay. The anomaly collection mechanism further expands log content, recording the precise location, source, and runtime environment information of anomalies such as dependency field parsing failures, formatting errors in build scripts, large model response anomalies, or network retrieval timeouts, thus supporting rapid root cause identification. Error handling rules are used to process error records, enhancing the fault tolerance and recovery capabilities of the dependency analysis process, ensuring continuity in complex business scenarios. For example, in the fintech sector, failures in analyzing market data interface dependencies or encryption component dependencies may impact compliance review progress. Automatic retries and degradation handling maintain stable dependency scanning and, when necessary, prompt compliance auditors for manual confirmation, preventing interruptions to the entire financial software review process due to individual dependency anomalies. In the field of digital healthcare, the analysis of medical image-related dependencies typically involves large volumes of data and complex processing logic. If some image plugins fail to extract fields, error handling mechanisms can allow the task to continue analyzing other dependencies, thus maintaining the ability to continuously scan large-scale medical applications. Through these methods, logging and anomaly handling not only improve the transparency and interpretability of the dependency analysis process but also significantly enhance its robustness under high-intensity business scenarios.

[0078] The intelligent analysis method for third-party component dependencies in mobile applications provided in this application obtains basic dependency data by parsing the project construction configuration, filters effective dependency components using multi-dimensional filtering rules, constructs structured dependency description data, generates extended attributes using a large model analysis engine driven by prompt word configuration, and outputs dependency information files according to data structure specifications. This method achieves a high degree of automation and high-precision processing throughout the entire process of dependency collection, attribute identification, and information output. In the fintech field, this method can accurately identify and enhance the attributes of market data acquisition plugins, encrypted calculation plugins, and transaction-related dependencies, helping to improve the efficiency and accuracy of compliance review and risk assessment. In the digital healthcare field, this method can comprehensively identify and extract the attributes of medical image processing components, data parsing components, and privacy-related dependencies, providing a reliable data foundation for the quality audit and data security assurance of medical software, thereby improving the completeness, traceability, and usability of dependency analysis.

[0079] Please continue reading Figure 5 , Figure 5This is a schematic diagram of the system structure of the intelligent analysis device on which the third-party components of the mobile application depend, as provided in the embodiments of this application. Figure 5 As shown, the intelligent analysis device 50 for third-party components of the mobile application includes: a basic dependency data module 51, a dependency list data module 52, a dependency description data module 53, an extended attribute data module 54, and a dependency information output module 55.

[0080] The basic dependency data module 51 is specifically used to obtain the engineering information of the mobile application to be analyzed, and to parse out the basic dependency data of the third-party components based on the engineering build configuration in the engineering information. The dependency list data module 52 is specifically used to filter the basic dependency data according to the preset dependency filtering rules, so that the component dependencies that do not meet the filtering conditions are removed from the filtering process, and the target dependency list is obtained. The dependency description data module 53 is specifically used to extract the basic attributes of each dependency component from the target dependency list and generate structured dependency description data based on the basic attributes. The basic attributes include at least name, version and source type. The extended attribute data module 54 is specifically used to input the dependency description data into the large model analysis engine based on the dependency description data and according to the preset prompt word configuration, to analyze and process each dependency component, and obtain the extended attributes associated with each dependency component. The dependency information output module 55 is specifically used to write basic attributes and extended attributes to the third-party component dependency information file according to the preset data structure specifications, and output the dependency information to a specified path or report it to the target server according to the preset configuration.

[0081] As an optional implementation, the dependency list data module 52 is further configured to read the component name information in the basic dependency data item by item, and determine whether the component name information meets the filtering conditions based on the preset name prefix; read the component source type information in the basic dependency data item by item, and determine whether the component source type information meets the filtering conditions based on the preset source type; read the component path information in the basic dependency data item by item, and determine whether the component path information meets the filtering conditions based on the preset path rules.

[0082] As an optional implementation, the basic dependency data module 51 is further specifically used to call a preset intelligent analysis tool to read the project configuration file of the mobile application to be analyzed in order to obtain the declaration information of each dependent component; to parse the dependency declaration field in the project build script of the mobile application to be analyzed through the intelligent analysis tool in order to extract the dependency name information, dependency version information and dependency source information of each dependent component; and to write the dependency name information, dependency version information and dependency source information to the basic dependency data according to the preset parsing rules.

[0083] As an optional implementation, the extended attribute data module 54 is further configured to generate prompt information for dependent component attribute analysis based on a preset prompt word configuration; combine the prompt information and the dependent description data to generate large model analysis request content corresponding to each dependent component; in the large model analysis engine, based on the dependent component name information and dependent component version information in the analysis request content, trigger the large model to search for external knowledge bases or network resources to obtain candidate text content of component information containing official website information, license information, privacy permission information, and usage scenario description; perform semantic parsing processing on the candidate text content of component information to extract the official website field, license field, privacy permission field, and usage scenario field to generate extended attribute data; and establish the association relationship between the extended attribute data and the corresponding dependent components based on a preset field mapping relationship to obtain the extended attributes associated with each dependent component.

[0084] As an optional implementation, the intelligent analysis device 50 for mobile application third-party components further includes an environment configuration management module. This module is specifically used to receive running instructions input via a command-line interface, and to perform syntax parsing on the running instructions according to a preset parameter format; extract mobile application platform configuration parameters, fragment size parameters, concurrency control parameters, and retry mechanism parameters from the parsed running instructions to generate a set of running parameters for the dependency analysis process; initialize the execution environment of the dependency analysis task based on the set of running parameters, and control the execution behavior of the dependency analysis task according to the set of running parameters during the execution of the dependency analysis task.

[0085] As an optional implementation, the intelligent analysis device 50 for mobile application third-party components also includes a log and error handling module. Specifically, the log and error handling module is used to output console log information of the dependency analysis task according to preset log level parameters during the execution of the dependency analysis task; when an exception occurs in the execution step of the dependency analysis task, it collects the location of the exception, the exception type, and the exception description information to generate a dependency analysis error record; and performs error handling operations on the dependency analysis error record according to preset error handling rules to control the continued execution or safe termination of the dependency analysis task.

[0086] As an optional implementation, the dependency information output module 55 is further configured to perform field encoding processing on the basic attributes and the extended attributes according to a preset data structure specification to generate a dependency data field set that conforms to the target structure; convert the dependency data field set into a serialized target file format according to a preset serialization rule to generate dependency information content to be written; and write the dependency information content to the third-party component dependency information file according to a preset file writing rule.

[0087] It should be noted that the aforementioned intelligent analysis device for mobile application third-party component dependencies can execute the intelligent analysis method for mobile application third-party component dependencies provided in the embodiments of this application, and has the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in the embodiments of the intelligent analysis device for mobile application third-party component dependencies can be found in the intelligent analysis method for mobile application third-party component dependencies provided in the embodiments of this application.

[0088] Figure 6 This is a schematic diagram of the hardware structure of an electronic device that executes an intelligent analysis method for third-party components of a mobile application, as provided in an embodiment of this application. Figure 6 As shown, the electronic device 600 includes: One or more processors 610 and memory 620, Figure 6 Take the 610 processor as an example.

[0089] The processor 610 and the memory 620 can be connected via a bus or other means. Figure 6 Taking the example of a connection between China and Israel via a bus.

[0090] The memory 620, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the program instructions / modules corresponding to the intelligent analysis method dependent on the third-party components of the mobile application in the embodiments of this application. The processor 610 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions, and modules stored in the memory 620, thereby implementing the intelligent analysis method dependent on the third-party components of the mobile application in the above method embodiments.

[0091] The memory 620 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of intelligent analytics devices relied upon by third-party components of the mobile application. Furthermore, the memory 620 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 620 may optionally include memory remotely located relative to the processor 610, which can be connected via a network to intelligent analytics devices relied upon by third-party components of the mobile application. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0092] The one or more modules are stored in the memory 620. When executed by the one or more processors 610, they execute the intelligent analysis method that the mobile application third-party components depend on in any of the above method embodiments, for example, executing the methods described above. Figure 2 Method steps S1 to S5, Figure 3 Method steps S41 to S45, Figure 4 Method steps S61 to S63 are implemented. Figure 5 The functions of modules 51-55 in the document.

[0093] The above-described product can perform the methods provided in the embodiments of this application, and has the corresponding functional modules and beneficial effects for performing the methods. Technical details not described in detail in this embodiment can be found in the methods provided in the embodiments of this application.

[0094] This application provides a non-volatile computer-readable storage medium storing computer-executable instructions that are executed by one or more processors, for example... Figure 6 One of the processors 610 enables the above-described one or more processors to execute the intelligent analysis method dependent on the third-party components of the mobile application in any of the above method embodiments, for example, to execute the method described above. Figure 2Method steps S1 to S5, Figure 3 Method steps S41 to S45, Figure 4 Method steps S61 to S63 are implemented. Figure 5 The functions of modules 51-55 in the document.

[0095] This application provides a computer program product, which includes a computer program stored on a non-volatile computer-readable storage medium. The computer program includes program instructions that, when executed by an electronic device, enable the electronic device to execute the intelligent analysis method dependent on the third-party component of the mobile application in any of the above method embodiments, for example, to execute the method described above. Figure 2 Method steps S1 to S5, Figure 3 Method steps S41 to S45, Figure 4 Method steps S61 to S63 are implemented. Figure 5 The functions of modules 51-55 in the document.

[0096] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0097] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software and a general-purpose hardware platform, or of course, using hardware. Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and not to limit them; under the concept of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of this application as described above, which are not provided in detail for the sake of brevity; although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for intelligent analysis of third-party component dependencies in mobile applications, characterized in that, include: Obtain the project information of the mobile application to be analyzed, and based on the project build configuration in the project information, parse out the basic dependency data of the third-party components; The basic dependency data is filtered according to preset dependency filtering rules so that component dependencies that do not meet the filtering conditions are removed from the filtering process, resulting in a target dependency list. Extract the basic attributes of each dependency component from the target dependency list, and generate structured dependency description data based on the basic attributes, wherein the basic attributes include at least name, version and source type; Based on the dependency description data and according to the preset prompt word configuration, the dependency description data is input into the large model analysis engine to analyze and process each dependency component and obtain the extended attributes associated with each dependency component. According to the preset data structure specifications, the basic attributes and the extended attributes are written to the third-party component dependency information file respectively, and the dependency information is output to the specified path or reported to the target server according to the preset configuration.

2. The intelligent analysis method for third-party component dependencies in mobile applications according to claim 1, characterized in that, The step of filtering the basic dependency data according to preset dependency filtering rules includes: Read the component name information in the basic dependency data item by item, and determine whether the component name information meets the filtering conditions based on the preset name prefix; Read the component source type information in the basic dependency data item by item, and determine whether the component source type information meets the filtering conditions according to the preset source type. The component path information in the basic dependency data is read item by item, and the component path information is judged to meet the filtering conditions according to the preset path rules.

3. The intelligent analysis method for third-party component dependencies in mobile applications according to claim 1, characterized in that, The steps for parsing the basic dependency data of third-party components include: The preset intelligent analysis tool is invoked to read the project configuration file of the mobile application to be analyzed in order to obtain the declaration information of each dependent component; The intelligent analysis tool parses the dependency declaration fields in the project build script of the mobile application to be analyzed in order to extract the dependency name information, dependency version information and dependency source information of each dependent component; According to the preset parsing rules, the dependency name information, dependency version information, and dependency source information are written to the basic dependency data.

4. The intelligent analysis method for third-party component dependencies in mobile applications according to claim 1, characterized in that, The step of inputting the dependency description data into the large model analysis engine based on the dependency description data and according to the preset prompt word configuration, analyzing and processing each dependency component, and obtaining the extended attributes associated with each dependency component includes: Based on the preset prompt word configuration, generate prompt information for dependent component property analysis; Combine the prompt information and the dependency description data to generate the large model analysis request content corresponding to each dependency component; In the large model analysis engine, based on the dependent component name information and dependent component version information in the analysis request content, the large model is triggered to retrieve external knowledge bases or network resources to obtain candidate text content of component information containing official website information, license information, privacy permission information and usage scenario descriptions. Semantic parsing is performed on the candidate text content of the component information to extract the official website field, license field, privacy permission field and usage scenario field to generate extended attribute data; Based on the preset field mapping relationship, the association between the extended attribute data and the corresponding dependent components is established to obtain the extended attributes associated with each dependent component.

5. The intelligent analysis method for third-party component dependencies in mobile applications according to claim 1, characterized in that, The method further includes: It receives the execution command input through the command line interface and performs syntax parsing on the execution command according to the preset parameter format; The mobile application platform configuration parameters, fragment size parameters, concurrency control parameters, and retry mechanism parameters are extracted from the parsed execution instructions to generate a set of execution parameters for the dependency analysis process. The execution environment of the dependency analysis task is initialized based on the set of running parameters, and the execution behavior of the dependency analysis task is controlled according to the set of running parameters during the execution of the dependency analysis task.

6. The intelligent analysis method for third-party component dependencies in mobile applications according to claim 1, characterized in that, The method further includes: During the execution of the dependency analysis task, the console log information of the dependency analysis task is output according to the preset log level parameters; When an exception occurs during the execution of a dependency analysis task, the location, type, and description of the exception are collected to generate a dependency analysis error log. According to the preset error handling rules, error handling operations are performed on the dependency analysis error records to control the continued execution of the dependency analysis task or to safely terminate it.

7. The intelligent analysis method for third-party component dependencies in mobile applications according to claim 1, characterized in that, The step of writing the basic attributes and the extended attributes to the third-party component dependency information file according to the preset data structure specifications includes: According to the preset data structure specifications, the basic attributes and the extended attributes are processed by field encoding to generate a set of dependent data fields that conform to the target structure; According to the preset serialization rules, the set of dependent data fields is converted into the serialized target file format to generate the dependent information content to be written; According to the preset file writing rules, the dependency information content is written to the third-party component dependency information file.

8. A smart analysis device for third-party component dependencies in mobile applications, characterized in that, include: The basic dependency data module is used to obtain the project information of the mobile application to be analyzed, and based on the project build configuration in the project information, parse out the basic dependency data of the third-party components. The dependency list data module is used to filter the basic dependency data according to preset dependency filtering rules, so that component dependencies that do not meet the filtering conditions are removed from the filtering process to obtain the target dependency list. The dependency description data module is used to extract the basic attributes of each dependency component from the target dependency list and generate structured dependency description data based on the basic attributes, wherein the basic attributes include at least name, version and source type; The extended attribute data module is used to input the dependency description data into the large model analysis engine based on the dependency description data and according to the preset prompt word configuration, to analyze and process each dependency component and obtain the extended attributes associated with each dependency component. The dependency information output module is used to write the basic attributes and the extended attributes to the third-party component dependency information file according to the preset data structure specifications, and output the dependency information to a specified path or report it to the target server according to the preset configuration.

9. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the intelligent analysis method for mobile application third-party components as described in any one of claims 1-7.

10. A non-volatile computer-readable storage medium, characterized in that, The non-volatile computer-readable storage medium stores computer-executable instructions that, when executed by an electronic device, cause the electronic device to perform the intelligent analysis method for third-party components of a mobile application as described in any one of claims 1-7.

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