Cross-application service collaboration method and device, electronic equipment and computer storage medium

Abstract

The invention provides a cross-application service collaboration method and device, electronic equipment and a computer storage medium, and relates to the technical field of cross-application services. The method is applied to an intelligent cabin, and comprises the following steps: collecting original data from a plurality of heterogeneous data sources in the cabin, and packaging the original data into a context event stream in a unified format; performing semantic analysis on the context event flow to generate structured context information; storing the structured context information in a central storage area; and monitoring a state change of the context information in the central storage area, and actively sending a service scheduling instruction to one or more target applications based on the state change. Multi-source heterogeneous data in a cabin are integrated and uniformly packaged into a context event stream; structured context information is generated through semantic analysis, the intention of a user is understood, and cross-application intelligent cooperation is supported. Information is stored and shared in a centralized mode, services are dispatched actively according to context state changes, system-level collaboration is achieved, and service efficiency and convenience are improved.

Description

Technical Field

[0001] This application relates to the field of cross-application service technology, and more specifically, to a cross-application service collaboration method, apparatus, electronic device, and computer storage medium. Background Technology

[0002] The surge in the number of applications within smart cockpits has made data interaction and functional collaboration between applications crucial for enhancing the user experience. To address this, existing technologies primarily employ three modes: first, a point-to-point interface mode, where applications connect in pairs via private APIs; second, a system basic data sharing mode, where applications access limited general vehicle data through operating system interfaces; and third, a cloud synchronization mode, which synchronizes user settings and preferences via cloud servers.

[0003] Due to the lack of a unified context management and sharing mechanism, "data silos" often form between applications, leading to fragmented user experiences when using different applications. Especially when users switch to or add applications, existing behavioral habits and historical data are difficult for the new application to understand and inherit, causing service continuity interruptions. For example, in a point-to-point interface model, each pair of applications requiring collaboration needs to develop a custom interface separately. As the number of applications increases, the number of interfaces grows exponentially, resulting in a huge development and maintenance burden and making it difficult to adapt to a rapidly iterating application ecosystem. Furthermore, existing technologies pose significant risks to data privacy and security. On the one hand, the lack of a unified security control mechanism for data transmission between private interfaces means that sensitive user information may be transferred in plaintext between applications, increasing the risk of leakage. On the other hand, the reliance on cloud synchronization involves the storage and transmission of large amounts of personal data on remote servers, making it difficult to meet increasingly stringent regional data protection regulations. In weak or no network environments, cloud synchronization often experiences significant delays or even synchronization failures, hindering real-time interaction within the cabin and severely impacting the availability of functions and the consistency of the user experience. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a cross-application service collaboration method, apparatus, electronic device, and computer storage medium to improve the above-mentioned problems existing in the prior art.

[0005] In a first aspect, embodiments of this application provide a cross-application service collaboration method, which is applied to a smart cockpit. The method includes: collecting raw data from multiple heterogeneous data sources within the cockpit and encapsulating it into a context event stream in a unified format; performing semantic analysis on the context event stream to generate structured context information; storing the structured context information in a central storage area; wherein the central storage area is accessible to multiple applications within the cockpit; monitoring the state changes of the context information in the central storage area, and proactively sending service scheduling instructions to one or more target applications based on the state changes.

[0006] In the above implementation process, by constructing a unified event-driven and data-sharing architecture, multi-source heterogeneous data in the cockpit is encapsulated and semantically parsed into structured context information, which is then stored in the central storage area for access by various applications. This effectively decouples application services and improves system flexibility and scalability. At the same time, through multimodal data fusion and deep context awareness, the system shifts from passive response to proactive service, optimizing user experience. In addition, unified standardized interfaces reduce the complexity and cost of application development, while the central storage area, as the single source of facts, ensures the consistency of data throughout the vehicle and the real-time nature of service scheduling, thus constructing a perceptive, intelligent, collaborative, efficient, and easily evolving intelligent cockpit service ecosystem.

[0007] Optionally, the acquisition of raw data from multiple heterogeneous data sources within the cockpit includes: acquiring data from at least two of the following data sources: the vehicle bus, one or more applications within the cockpit, and the user interaction interface.

[0008] In the above implementation process, multiple data sources, including vehicle bus, application programs, and user interaction interfaces, are clearly covered, constructing a rich and three-dimensional data acquisition system. This ensures the comprehensiveness and accuracy of the generated contextual information, enabling the system to integrate real-time vehicle status, application service logic, and explicit / implicit user intent. This lays a solid data foundation for achieving deep, cross-domain scenario understanding and service collaboration, thereby avoiding scenario misjudgment or service limitations caused by a single data source and improving the perception dimension and decision-making intelligence level of the entire intelligent cockpit system.

[0009] Optionally, the step of performing semantic analysis on the context event stream to generate structured context information includes: performing named entity recognition on the context event stream to extract key entities; mapping the extracted entities to predefined semantic categories; and inferring user intent based on the mapped semantic categories and real-time context.

[0010] In the aforementioned implementation process, a hierarchical semantic analysis workflow, including named entity recognition, semantic mapping, and user intent inference, was introduced. This enabled a deep understanding and structured transformation from low-level raw event streams to high-level driving scenarios and user intents. This not only ensured the accuracy and machine readability of the generated contextual information but, more importantly, provided the ability to understand potential user needs. Subsequent service scheduling is no longer based on simple rule-based reflexes but on deep reasoning about scenario semantics and user goals. This drives a crucial leap in service collaboration from automation to true intelligence, laying a core foundation for providing accurate and forward-looking personalized services.

[0011] Optionally, storing the structured context information in the central storage area includes: mapping the physical address space of the central storage area to the virtual address space of multiple application processes using memory mapping technology, wherein the virtual address space is used for direct access by the multiple application processes.

[0012] In the above implementation process, memory mapping technology is introduced to directly map the physical address space of the central storage area to the virtual address space of each application process, realizing efficient data sharing and access across processes. This not only eliminates the data copying overhead present in traditional inter-process communication and reduces data access latency, providing ultimate real-time assurance for service collaboration; but also ensures that all applications access the same physical memory data, fundamentally maintaining the high consistency and atomicity of the entire system's context information, and providing a reliable synchronization foundation for concurrent read and write operations by multiple applications. Furthermore, this transparent access method, while maintaining a simple system architecture, further improves resource utilization efficiency, making high-performance, low-latency intelligent cockpit service collaboration possible.

[0013] Optionally, storing the structured context information in the central storage area includes: when the structured context information is used to identify sensitive data and security processing operations are performed on the identified sensitive data, the physical address space of the central storage area is mapped to the virtual address space of multiple application processes through memory mapping technology, wherein the virtual address space is used for direct access by the multiple application processes.

[0014] In the aforementioned implementation process, by introducing sensitive data identification and secure processing in the data storage stage and combining it with high-performance memory mapping technology, a collaborative framework that balances efficient sharing and privacy security was constructed. Firstly, sensitive data is identified and processed at the information source, reducing the risk of privacy information leakage during sharing. Simultaneously, direct access via memory mapping ensures extremely fast and efficient flow of critical, non-sensitive context information between applications. This eliminates the need for complex security checks on every subsequent access, thus achieving comprehensive protection of user privacy without sacrificing core performance, achieving an optimal balance between data security and access efficiency.

[0015] Optionally, monitoring the state changes of context information in the central storage area and actively sending service scheduling instructions to one or more target applications based on the state changes includes: identifying sensitive data in the structured context information and performing security processing operations on the identified sensitive data; wherein the service scheduling instructions are used to drive the target applications to perform operations, and the target applications include at least one of navigation applications, media playback applications, or vehicle environment control applications.

[0016] In the above implementation process, a sensitive data identification and processing step is added before the service scheduling command is generated, constructing a security protection system that runs through the entire data processing process. Privacy protection is deeply embedded into the core decision-making of service collaboration, ensuring that when the system performs proactive and intelligent service scheduling based on context state changes, it can filter sensitive information carried by the content. This fundamentally prevents unintentional leakage of user privacy through the service command link while driving target applications such as navigation, media, and environmental control to perform business operations. This achieves a unity of high-level intelligence in the cockpit system and strict data security compliance, ultimately establishing a convenient, proactive, secure, and reliable intelligent cockpit environment for users.

[0017] Optionally, the security processing operation includes at least one of the following: encrypting the sensitive data using a hardware security environment; and authenticating and authorizing requests to access the sensitive data based on a dynamic permission policy.

[0018] In the aforementioned implementation process, a defense-in-depth system was constructed by introducing hardware security environment encryption and dynamic permission policies. Hardware security environment encryption of sensitive data provides a high-security underlying protection, ensuring the confidentiality and integrity of sensitive information even if the system is accessed without authorization. Simultaneously, real-time authentication and authorization are achieved through dynamic permission policies, enabling granular and context-aware access control of data access requests. This not only ensures the security of static data storage at the physical level but also manages the dynamic data usage process at the logical level. Therefore, in the complex and ever-changing multi-application environment of in-vehicle systems, it provides a solid technical guarantee for the secure sharing and compliant use of data, ultimately enhancing system security capabilities and increasing user trust in intelligent cockpit services.

[0019] Secondly, embodiments of this application provide a cross-application service collaboration device, the device comprising: a data acquisition module, a semantic analysis module, a central storage module, and a service scheduling module; the data acquisition module is used to acquire raw data from multiple heterogeneous data sources within the cockpit and encapsulate it into a context event stream in a unified format; the semantic analysis module is used to perform semantic analysis on the context event stream to generate structured context information; the central storage module includes a central storage area; the central storage area is used to store the structured context information for access by multiple applications within the cockpit; the service scheduling module is used to monitor the state changes of the context information in the central storage module and proactively send service scheduling instructions to one or more target applications based on the state changes.

[0020] In the aforementioned implementation process, a highly cohesive and loosely coupled system architecture was constructed by dividing the complete process of cross-application service collaboration into four core functional modules: data acquisition, semantic analysis, central storage, and service scheduling. This not only clearly defines functional boundaries, greatly improving the system's maintainability and scalability, but also ensures that each module can form a well-defined and highly collaborative processing pipeline, from efficient acquisition and standardization of multi-source data to deep semantic parsing and structured storage of contextual information, and finally to intelligent service scheduling based on state changes. This gives the system high flexibility and reliability; each module can evolve independently and collaborate seamlessly through standardized interfaces, providing solid device-level support for intelligent cockpits to achieve precise, real-time, and proactive service collaboration in complex scenarios.

[0021] Thirdly, embodiments of this application also provide an electronic device, which includes a memory and a processor. The memory stores program instructions, and when the processor reads and runs the program instructions, it executes the steps in any of the above implementation methods.

[0022] Fourthly, embodiments of this application also provide a computer storage medium storing computer program instructions, which are read and executed by a processor to perform the steps in any of the above implementations. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a first schematic diagram of the cross-application service collaboration method provided in the embodiments of this application; Figure 2 This is a second schematic diagram of the cross-application service collaboration method provided in the embodiments of this application; Figure 3 This is a first schematic diagram of a cross-application service collaboration device provided in an embodiment of this application; Figure 4 This is a second schematic diagram of a cross-application service collaboration device provided in an embodiment of this application; Figure 5 This is a block diagram of an electronic device provided in an embodiment of this application.

[0025] Icons: 010-Data acquisition module; 020-Semantic analysis module; 030-Central storage module; 040-Service scheduling module; 100-Electronic device; 111-Memory; 112-Storage controller; 113-Processor; 114-Peripheral interface; 115-Input / output unit; 116-Display unit. Detailed Implementation

[0026] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of the embodiments of this application.

[0027] The existing problems with smart cockpits are as follows: Newly installed applications cannot understand users' historical behavior and data, leading to a fragmented user experience. Poor scalability and high collaboration costs: Custom interfaces need to be developed for each pair of applications that need to collaborate; for N applications, this becomes extremely expensive. The interface has extremely high expansion and maintenance costs. Privacy and data security risks: Sensitive user data (such as home address and travel habits) may be transmitted and stored in plaintext between applications or in the cloud, violating increasingly stringent data regulations (such as GDPR and CCPA). Poor real-time performance and network dependence: It heavily relies on the network; in weak or no-network environments such as tunnels or remote areas, synchronization latency is high (up to several seconds), making seamless real-time interaction impossible. Based on the above problems, this application provides a cross-application service collaboration method, apparatus, electronic device, and computer storage medium.

[0028] Firstly, this application provides a cross-application service collaboration method, please refer to [link to relevant documentation]. Figure 1 , Figure 1 This is a first schematic diagram of the cross-application service collaboration method provided in the embodiments of this application.

[0029] The cross-application service collaboration method provided in this application embodiment is applied to a smart cockpit. The method includes: collecting raw data from multiple heterogeneous data sources within the cockpit and encapsulating it into a context event stream in a unified format; performing semantic analysis on the context event stream to generate structured context information; storing the structured context information in a central storage area; wherein the central storage area is accessible to multiple applications within the cockpit; monitoring the state changes of the context information in the central storage area, and proactively sending service scheduling instructions to one or more target applications based on the state changes.

[0030] In the aforementioned implementation process, raw data from multiple heterogeneous data sources within the cockpit (such as vehicle status, application data, and user interactions) is collected and encapsulated into a unified-format context event stream. This solves the problems of scattered data sources and inconsistent formats in existing technologies, providing a standardized data foundation for subsequent processing. By performing semantic analysis on the context event stream to generate structured context information, the system can understand user behavior and scenario intent, improving the intelligence level of cross-application collaboration. The structured context information is stored in a central storage area and accessed by multiple applications within the cockpit, breaking down data silos between applications and achieving efficient information sharing. By monitoring the state changes of the context information in the central storage area and proactively sending service scheduling instructions to one or more target applications based on these changes, system-level proactive service collaboration is achieved, reducing manual user operations and improving the real-time performance and convenience of services.

[0031] Optionally, raw data may be collected from multiple heterogeneous data sources within the cockpit, including data from at least two of the following: the vehicle bus, one or more applications within the cockpit, and the user interaction interface.

[0032] In the above implementation process, multimodal contextual information is acquired by collecting data from at least two data sources, including the vehicle bus, one or more applications in the cockpit, and the user interaction interface. This method of collecting data from heterogeneous data sources breaks the limitations of a single data source and can more comprehensively capture multi-dimensional information such as vehicle status, application semantics, and user intent. This lays a rich data foundation for building a unified user scenario profile, thereby solving the problem of context fragmentation caused by a single data source in existing technologies.

[0033] In one embodiment of this application, a publish-subscribe model is adopted as the core data acquisition architecture, actively collecting raw data in real time from multiple heterogeneous data sources within the cockpit. This architecture successfully breaks down the silos between vehicle basic data, application semantic data, and user interaction data, providing a complete data foundation for subsequent scene understanding and intelligent services.

[0034] Specifically, firstly, a unified event dispatch center (or event bus) is established within the system. For each data source requiring data collection, an independent data publisher instance is created. Each publisher is responsible for monitoring and acquiring data from its associated single data source. A unified data acquisition module is created as a core subscriber, registering with the event dispatch center to receive messages from all data publishers. This method simultaneously collects data from at least two types of data sources: First, vehicle status data collection. This involves periodically or event-triggered reading of raw data from the bus by calling the vehicle bus interface. Based on predefined parsing rules, the raw data is converted into engineering values ​​with clear physical meaning. The collected data includes, but is not limited to, driving speed information, gear status information, and door lock status information. Second, cockpit application data collection. This involves listening for specific event broadcasts or messages from target applications through the system's inter-process communication mechanism. After detecting a target event, structured semantic information is extracted from the message payload. The collected data includes, but is not limited to, destination information set by navigation applications (including name, category, location coordinates, etc.). Third, user interaction data is collected by listening to system input event streams or application programming interface callbacks to capture user interactions with the cockpit system, recording the type of interaction, target object, and time of occurrence. The collected data includes, but is not limited to, commands issued to the voice assistant and operation sequences on the touchscreen.

[0035] Then, the collected data is standardized and encapsulated. Whenever a data publisher acquires new valid data, a precise and unified time sequence identifier is attached to the data unit, along with a data source identifier indicating its origin. The encapsulated data unit is then published to the event scheduling center as a complete event. The unified data acquisition module (i.e., the core subscriber) receives all data events from the event scheduling center. The module integrates and sorts these events from different sources according to the time sequence identifier, ultimately outputting a continuous, multimodal initial context event stream. This approach breaks down the technical barriers between different data sources, enabling heterogeneous data from underlying vehicles, upper-layer applications, and direct user interactions to be integrated into the same processing flow, laying a solid data foundation for subsequently building complete user scenario profiles.

[0036] Optionally, please refer to Figure 2 , Figure 2 This is a second schematic diagram of the cross-application service collaboration method provided in the embodiments of this application.

[0037] The process involves semantic analysis of the context event stream to generate structured context information, including: named entity recognition of the context event stream to extract key entities; mapping the extracted entities to predefined semantic categories; and inferring user intent based on the mapped semantic categories and real-time context.

[0038] In the above implementation process, key entities are extracted by named entity recognition of the context event stream, enabling accurate identification of semantically valuable core elements from raw data. By mapping the extracted entities to predefined semantic categories, specific instances are transformed into semantically meaningful concepts, establishing a standardized foundation for semantic understanding. User intent inference is performed based on the mapped semantic categories and real-time context, allowing the system to dynamically understand user needs in the current scenario. This achieves a deep semantic transformation from raw data to user intent, thus solving the problem of existing technologies lacking deep semantic understanding and proactive intent inference capabilities.

[0039] In one embodiment of this application, a contextual event stream aggregated from multiple data sources is received. Named entity recognition analysis is performed on the text information contained in the event stream to identify and extract key information entities. For example, when the navigation destination input is "Starbucks near the company," this step can accurately identify the two core entities, "company" and "Starbucks." The extracted key entities are compared with a predefined, standardized semantic category system. According to preset mapping rules, each entity is associated with its most relevant semantic category, thereby achieving the transformation from concrete instances to abstract concepts. For example, the identified entity "Starbucks" is mapped to the general category "coffee shop." The mapped semantic category information is integrated with the current real-time context information. Based on the integrated information, inference logic is applied to determine the user's potential, unexpressed behavioral intentions, and a confidence level assessment is assigned to the inference result. For example, when the semantic category is "coffee shop," combined with the time context of "weekday afternoon," the user's intention can be inferred to be "leisure and refreshment." The parsed semantic events, such as "arriving at a location"; device status, such as "change of operating mode"; and multi-dimensional information such as time context are dynamically correlated. Based on these correlations, a temporary event relationship network describing the current scenario is constructed. When a specific semantic event, such as "arriving at the gym," is triggered, the method retrieves historical correlation patterns found in this network, such as "closing unnecessary functions" or "starting a specific media playlist," and then generates context-aware rules with confidence applicable to the current scenario.

[0040] Optionally, the structured context information is stored in the central storage area, including: mapping the physical address space of the central storage area to the virtual address space of multiple application processes through memory mapping technology, wherein the virtual address space is used for direct access by multiple application processes.

[0041] In the above implementation process, memory mapping technology is used to map the physical address space of the central storage area to the virtual address space of multiple application processes, realizing efficient cross-process data sharing. This zero-copy data access mechanism avoids the kernel-mode data copying overhead required for traditional inter-process communication, enabling multiple applications to directly and concurrently access unified context information, reducing data access latency from milliseconds to microseconds, and significantly improving the real-time performance of cross-application collaboration and the overall system performance.

[0042] Optionally, storing structured context information in the central storage area includes: when identifying sensitive data in the structured context information and performing security processing operations on the identified sensitive data, mapping the physical address space of the central storage area to the virtual address space of multiple application processes through memory mapping technology, wherein the virtual address space is used for direct access by multiple application processes.

[0043] In the above implementation process, after identifying sensitive data in the structured context information and performing secure processing operations on the identified sensitive data, memory mapping technology is used to map the physical address space of the central storage area to the virtual address spaces of multiple application processes. This mechanism ensures the security and privacy protection of sensitive information while achieving efficient cross-process data sharing. By first identifying and securely processing sensitive data and then establishing a shared memory mapping, it ensures that multiple application processes can directly and with low latency access to unified context information, while effectively preventing the leakage of user privacy data, thus achieving a balance between data sharing efficiency and security protection.

[0044] In one embodiment of this application, a central storage area is created to uniformly store processed structured context information. Using memory mapping technology, the physical address space of this central storage area is directly mapped to the virtual address spaces of multiple application processes that need to access the data. Through this mapping, authorized application processes can directly read the latest context data from the shared memory address without data copying.

[0045] Specifically, at system startup, a fixed-size contiguous region is pre-allocated in physical memory as a central storage area. A unified data structure schema is defined for this area, specifying the storage format and memory offset address for various contextual information (such as vehicle speed, navigation destination, user intent, etc.), ensuring that all write and read operations conform to this specification. Using the memory mapping system call provided by the operating system, the physical address of the central storage area is mapped to the user-mode virtual address space of each application process that needs to consume data (such as air conditioning control, music playback, etc.). Through this mapping, each application process obtains a direct pointer to the shared data. When the data producer updates the shared area content, the consumer can directly read the latest data through this pointer without initiating a system call or performing data copying, achieving zero-copy inter-process communication. Furthermore, lightweight synchronization primitives (such as atomic operations and read-write locks) are used to manage concurrent access to shared memory, ensuring that dirty reads or data tearing do not occur during data updates. By defining a clear producer-consumer model and memory barriers, the visibility and timing consistency of data updates are guaranteed, allowing all consumer modules to see the same logically consistent snapshot of the entire system context.

[0046] Optionally, the system monitors the state changes of context information in the central storage area and proactively sends service scheduling instructions to one or more target applications based on the state changes. This includes: identifying sensitive data in the structured context information and performing security processing operations on the identified sensitive data. The service scheduling instructions are used to drive the target applications to perform operations, and the target applications include at least one of navigation applications, media playback applications, or vehicle environment control applications.

[0047] In the aforementioned implementation process, by identifying sensitive data in the structured context information and performing corresponding security processing operations, the system ensures the effective protection of user privacy data during service scheduling. Based on this, the system monitors changes in the state of context information in the central storage area and proactively sends service scheduling instructions to target applications such as navigation applications, media playback applications, or vehicle environment control applications based on these changes. This mechanism achieves intelligent cross-application linkage and proactive service while ensuring the security of sensitive information, enabling the system to balance user convenience and data processing compliance while driving target applications to perform corresponding operations.

[0048] In one embodiment of this application, a configurable sensitive data policy library is predefined, containing feature recognition rules for various sensitive data types (such as precise geographic location, address book, biometrics, etc.). Before data is written to the shared memory pool, a filtering engine scans all context data in real time, performs pattern matching based on the policy library, and automatically identifies and marks sensitive data fields. A trusted execution environment isolated from the main operating system is created using a chip-level hardware security zone. All operations on sensitive data (such as encryption, decryption, and access decisions) are forced to be executed within this environment. For identified sensitive data, the encryption engine within the hardware security zone is invoked to perform high-strength symmetric encryption (such as AES-256) using a hardware-protected key, and then the ciphertext is stored in the shared memory pool. The original plaintext data is never exposed to the ordinary operating system. A token-based access authorization mechanism is implemented. Before any application can access the shared memory pool, it must first apply to the access control component for an access token with time-limited and scope-restricted permissions. The access control component authenticates and authorizes the applicant based on the principle of least privilege and the user's preset privacy preferences. Applications requesting sensitive data will face stricter scrutiny regarding their legitimacy. Only applications holding valid tokens will be able to decrypt and access the authorized portion of the data.

[0049] Optionally, the security processing operations include at least one of the following: encrypting sensitive data using a hardware security environment; and authenticating and authorizing requests to access sensitive data based on dynamic permission policies.

[0050] In the above implementation process, by encrypting sensitive data using a hardware security environment and / or authenticating and authorizing requests to access sensitive data based on dynamic permission policies, multiple protection mechanisms are provided for sensitive data. Hardware-level encryption ensures the confidentiality and integrity of data during storage and transmission, while dynamic permission management enables fine-grained control over data access behavior. The use of these two security processing operations, individually or in combination, constructs a defense-in-depth system in the complex in-vehicle application environment, ensuring both the business needs of cross-application data sharing and effectively preventing unauthorized access and leakage of sensitive information, thus comprehensively improving the system's security level.

[0051] In one embodiment of this application, raw contextual information (such as navigation destination and vehicle status) collected from multiple data sources is processed into a unified, structured data format. Before the data is shared and used, it undergoes automated sensitive data scanning. Based on a predefined policy library, sensitive information such as home address and personal preferences is identified. Mandatory security processing is immediately performed on the identified sensitive data. This operation utilizes the isolation and encryption capabilities provided by hardware security zones to encrypt or de-identify the data, ensuring that its plaintext form is not exposed to the normal operating environment. The security-filtered contextual information is accessed, and semantic understanding results (such as user intent), real-time vehicle status, and environmental information are integrated for comprehensive analysis and scenario judgment. Based on pre-set intelligent rules or models, the current security context is mapped into specific, executable service scheduling instructions. The generated scheduling instructions are distributed to one or more target application executors through a reliable communication mechanism, driving them to perform corresponding operations. Target applications include, but are not limited to, navigation applications (such as route replanning), media playback applications (such as switching playlists), and vehicle environmental control applications (such as adjusting air conditioning and seats), thereby achieving cross-application collaborative services.

[0052] Secondly, this application provides a cross-application service collaboration device, please refer to... Figure 3 , Figure 3 This is a first schematic diagram of a cross-application service collaboration device provided in an embodiment of this application.

[0053] The cross-application service collaboration device provided in this application embodiment includes: a data acquisition module 010, a semantic analysis module 020, a central storage module 030, and a service scheduling module 040; the data acquisition module 010 is used to collect raw data from multiple heterogeneous data sources in the cockpit and encapsulate it into a context event stream in a unified format; the semantic analysis module 020 is used to perform semantic analysis on the context event stream to generate structured context information; the central storage module 030 includes a central storage area; the central storage area is used to store structured context information for access by multiple applications in the cockpit; the service scheduling module 040 is used to monitor the state changes of the context information in the central storage module 030 and actively send service scheduling instructions to one or more target applications based on the state changes.

[0054] In the above implementation process, the data acquisition module 010 collects raw data from multiple heterogeneous data sources within the cockpit and encapsulates it into a unified format context event stream, achieving standardized access to multi-source data. The semantic analysis module 020 performs semantic analysis on the context event stream and generates structured context information, transforming the raw data into semantic information that the system can understand. The central storage module 030 stores the structured context information in its central storage area, providing a unified data access center for multiple applications within the cockpit. The service scheduling module 040 monitors the state changes of the context information in the central storage area and proactively sends service scheduling instructions to target applications based on these state changes, achieving system-level intelligent service collaboration and automated scheduling.

[0055] In one embodiment of this application, please refer to Figure 4 , Figure 4 This is a second schematic diagram of a cross-application service collaboration device provided in an embodiment of this application.

[0056] Upon system startup, data acquisition module 010, acting as a unified subscriber, initiates subscriptions to all registered data source publishers. Each data source publisher, after preparing its data, waits for event triggers or periodic arrivals and broadcasts the data to all subscribers. The system simultaneously acquires data from at least two (all three in this example) heterogeneous data sources: First, data is acquired from the vehicle bus, specifically the in-vehicle CAN bus. A resident CAN bus listening service acts as the publisher, reading raw signals from the CAN bus at a fixed 100ms interval. This service uses a pre-loaded DBC file to parse the raw signals into meaningful engineering values. The acquired data can include vehicle speed, current gear, door open / close status, and GPS coordinates. Second, data is acquired from cockpit applications, specifically navigation applications running in the cockpit (such as Gaode Maps or Baidu Maps). The acquisition method involves an application event listening service acting as the publisher, using the Binder IPC mechanism to listen for system-level broadcasts from the navigation application, processing the data, parsing the additional information carried in the broadcast, and obtaining structured destination information. The acquired data can include navigation destination POIs. Third, data is collected from the user interaction interface, with data sources including the in-vehicle voice assistant and the central touchscreen. Specifically, the collection method involves a user interaction monitoring service acting as the publisher, listening to the voice assistant's command callback interface and the touchscreen's input event stream, processing the data, and recording the original interaction commands and operation sequences. The collected content can include voice commands and touch operations.

[0057] Each data source publisher, upon acquiring new valid data, assigns a high-precision, unified timestamp to the data packet to utilize the system's monotonic clock as much as possible, ensuring accurate timing. The encapsulated data (timestamp, source, original data body) is then published to a unified event bus. Data acquisition module 010, acting as a subscriber, receives all data packets from different data sources from the event bus. These data packets, tagged with unified timestamps and source labels, are then aggregated chronologically into a continuous, multimodal initial context event stream.

[0058] The semantic analysis module 020 receives a unified-format contextual event stream output by the data acquisition module 010. This event stream integrates heterogeneous data from multiple sources, including vehicle bus, cockpit applications, and user interactions. The core task of the module is to transform the raw event stream into structured, machine-understandable contextual information, and to achieve a deep understanding of user behavior and scene intent through semantic analysis technology.

[0059] Specifically, firstly, the module incorporates a natural language processing engine that performs real-time named entity recognition analysis on textual information (such as voice commands and navigation inputs) contained in the event stream. This engine, based on pre-trained deep learning models (such as BERT or BiLSTM-CRF), accurately identifies and extracts key information entities from unstructured text. For example, when a user inputs "I want to go to the Starbucks near my company" via voice, the engine can identify "company" and "Starbucks" as core entities and label them with their type (such as "location" or "brand").

[0060] Then, the module uses a predefined semantic category system (e.g., an ontology containing categories such as "coffee shop," "office," and "leisure venue") to map the extracted entities to the categories in the system. The mapping process is based on rule matching or vector similarity calculation, associating specific instances with standardized semantic concepts. For example, the entity "Starbucks" is mapped to the "coffee shop" category, and the entity "company" is mapped to the "office" category. This step realizes the transformation from raw data to abstract semantics, laying the foundation for intent inference. The module integrates the mapped semantic category information with real-time contextual information (such as vehicle status, time, location, etc.), applies inference logic based on rule engines or machine learning models, judges the user's potential, unexpressed behavioral intent, and assigns a confidence assessment to the inference results. For example, when the semantic category is "coffee shop," combined with the context of "the vehicle is traveling at low speed" and "the time is a weekday afternoon," the module can infer that the user's intent is "to go to a coffee shop for relaxation and refreshment," with a confidence level of 85%. At the same time, the module dynamically associates multi-dimensional information (such as semantic events, device status, and time context) to construct a temporary event relationship network. When a specific semantic event (such as "arriving at the gym") is triggered, the module generates context-aware rules with confidence by retrieving historical association patterns in the network (such as "disabling unnecessary functions" or "launching a specific media playlist"). The semantic analysis module 020 outputs structured contextual information, including extracted entities, mapped semantic categories, inferred user intent, and associated contextual rules. All of this information is encapsulated in a uniform format (such as JSON or Protocol Buffers) for use by the central storage module 030. This achieves the transformation from low-level data to high-level semantics, solving the problem of lacking deep semantic understanding and proactive intent inference capabilities.

[0061] The central storage module 030 is responsible for receiving and storing the structured context information generated by the semantic analysis module 020, and making it accessible to multiple applications within the cockpit through an efficient sharing mechanism. The core of the module is the creation of a central storage area, utilizing memory mapping technology to achieve zero-copy data access across processes, while integrating security mechanisms to protect sensitive data. Specifically, at system startup, a fixed-size contiguous region is pre-allocated in physical memory as the central storage area (e.g., a 1MB shared memory segment). A unified data structure schema (using C / C++ structures or a similar format) is defined for this area, specifying the storage format, data type, and memory offset address for various types of context information (such as vehicle speed, navigation destination, user intent, etc.), ensuring that all write and read operations conform to this specification. For example, the vehicle speed field might be defined as a floating-point type and stored at offset address 0x100.

[0062] The module achieves efficient data sharing through memory mapping technology. It uses operating system-provided memory mapping system calls (such as mmap in Linux or MapViewOfFile in Windows) to directly map the physical address space of the central storage area to the user-space virtual address space of each application process that needs to consume data (such as air conditioning control or music playback). Through this mapping, each application process obtains a direct pointer to the shared data, allowing it to read the latest context data without data copying. To manage concurrent access, the module employs lightweight synchronization primitives (such as atomic operations or read-write locks) to ensure that dirty reads or data tearing do not occur during data updates. By defining a clear producer-consumer model and memory barriers, the module guarantees the visibility and timing consistency of data updates, ensuring that all consuming modules see the same logically consistent snapshot of the entire system context.

[0063] In terms of data security, the module integrates a sensitive data processing submodule. This submodule predefines a configurable sensitive data policy library, containing feature recognition rules (e.g., regular expressions or keyword matching) for various sensitive data types (such as precise geographic locations, personal contacts, biometrics, etc.). Before data is written to the central storage area, the filtering engine scans all context data in real time, performs pattern matching based on the policy library, and automatically identifies and marks sensitive data fields. For identified sensitive data, the module utilizes a chip-level hardware security zone (such as ARM TrustZone or Intel SGX) to create a trusted execution environment isolated from the main operating system. All operations on sensitive data (such as encryption and decryption) are forced to be performed within this environment. Specifically, the encryption engine within the hardware security zone is invoked to perform high-strength symmetric encryption (such as AES-256) using a hardware-protected key, and then the ciphertext is stored in the central storage area. The original plaintext data is never exposed to the ordinary operating system. Simultaneously, the module implements a token-based access authorization mechanism: any application must apply for a time-limited and scope-restricted access token from the access control component before accessing the central storage area. The access control component authenticates and authorizes applicants based on the principle of least privilege and user-preset privacy preferences, ensuring that only applications with valid tokens can decrypt and access their authorized data. In other words, the central storage module 030 provides an efficient and secure data sharing center, breaking down data silos between applications, achieving efficient information sharing, and protecting user privacy.

[0064] The service scheduling module 040 continuously monitors the state changes of context information in the central storage module 030 and proactively sends service scheduling instructions to one or more target applications based on these changes, achieving system-level intelligent service collaboration. The module integrates functions such as state monitoring, intent parsing, and security control to ensure the real-time performance and security of the scheduling process. Specifically, the module uses a state monitoring engine to listen for update events of context information in the central storage area in real time. This engine uses a callback mechanism or a polling method (selected based on performance requirements) to detect changes in key context fields (such as user intent, vehicle location, and application state). When a state change is detected (e.g., the user intent changes from "navigating" to "arriving at destination," or the vehicle speed drops to 0), the engine triggers the scheduling logic.

[0065] The service scheduling module 040 first performs a security review of the structured context information to ensure that sensitive data is protected during the scheduling process. This review process references the sensitive data policy library of the central storage module 030, performing a secondary scan of the context data to identify fields that may involve privacy (such as home address and personal preferences). For the identified sensitive data, the module uses a hardware security environment to decrypt or anonymize it (only when necessary), and authenticates and authorizes data access related to scheduling commands based on dynamic permission policies. For example, if a scheduling command needs to obtain the user's current location, the module checks whether the target application has "location access" permissions and adjusts the authorization scope according to the user's real-time settings.

[0066] Then, the module integrates the security-verified context information (such as user intent, vehicle status, and environmental information), applies pre-built intelligent rules or machine learning models to determine the scenario, and maps the current context to specific, executable service scheduling instructions. The rule base uses a configurable IF-THEN format or a more complex decision tree, for example: IF "User intent is a coffee shop" AND "Vehicle is parked" THEN "Send instruction to media application to play relaxing music" AND "Send instruction to air conditioning application to adjust temperature". The module also generates metadata for each instruction, such as a list of target applications, execution priority, and timeout settings. The scheduling instructions are distributed to one or more target application executors through reliable communication mechanisms (such as inter-process communication (IPC) or message queues). Target applications include navigation applications (such as route replanning), media playback applications (such as switching playlists), and vehicle environmental control applications (such as adjusting air conditioning and seats). The distribution process ensures the atomicity and order of instructions, for example, by using transactional message passing to avoid instruction loss or duplication. The module also monitors the instruction execution status and dynamically adjusts the scheduling strategy (such as retrying or degrading) based on feedback, thereby achieving cross-application collaborative services.

[0067] Thirdly, embodiments of this application also provide an electronic device, which includes a memory and a processor. The memory stores program instructions, and when the processor reads and runs the program instructions, it executes the steps in any of the above implementation methods.

[0068] Fourthly, embodiments of this application also provide a computer storage medium storing computer program instructions, which are read and executed by a processor to perform the steps in any of the above implementations.

[0069] Optionally, please refer to Figure 5 , Figure 5 This is a block diagram illustrating an electronic device according to an embodiment of this application. The electronic device 100 may include a memory 111, a memory controller 112, a processor 113, a peripheral interface 114, an input / output unit 115, and a display unit 116. Those skilled in the art will understand that... Figure 5 The structure shown is for illustrative purposes only and does not limit the structure of the electronic device 100. For example, the electronic device 100 may also include components that are more... Figure 5 The more or fewer components shown, or having the same Figure 5 The different configurations shown.

[0070] The aforementioned memory 111, memory controller 112, processor 113, peripheral interface 114, input / output unit 115, and display unit 116 are electrically connected directly or indirectly to each other to achieve data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines. The aforementioned processor 113 is used to execute executable modules stored in the memory.

[0071] The memory 111 can be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc. The memory 111 stores programs, and the processor 113 executes these programs upon receiving execution instructions. The methods executed by the electronic device 100 as defined in any embodiment of this application can be applied to the processor 113, or implemented by the processor 113.

[0072] The aforementioned processor 113 may be an integrated circuit chip with signal processing capabilities. The processor 113 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a digital signal processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor.

[0073] The peripheral interface 114 described above couples various input / output devices to the processor 113 and the memory 111. In some embodiments, the peripheral interface 114, the processor 113, and the memory controller 112 can be implemented on a single chip. In other instances, they can be implemented on separate chips.

[0074] The input / output unit 115 described above is used to provide user input data. The input / output unit 115 may be, but is not limited to, a mouse and keyboard.

[0075] The aforementioned display unit 116 provides an interactive interface (e.g., a user interface) between the electronic device 100 and the user, or displays image data for the user's reference. In this embodiment, the display unit can be a liquid crystal display (LCD) or a touch display. If it is a touch display, it can be a capacitive touchscreen or a resistive touchscreen that supports single-point and multi-point touch operations. Supporting single-point and multi-point touch operations means that the touch display can sense touch operations generated simultaneously from one or more locations on the touch display and pass the sensed touch operations to the processor for calculation and processing.

[0076] It should be understood that the electronic device can be a personal computer (PC), tablet computer, smartphone, personal digital assistant (PDA), or other electronic device with logical computing capabilities.

[0077] This application also provides a computer-readable storage medium storing computer program instructions, which are read and executed by a processor to perform steps in a cross-application service collaboration method.

[0078] In summary, this application provides a cross-application service collaboration method applied to a smart cockpit. The method includes: collecting raw data from multiple heterogeneous data sources within the cockpit and encapsulating it into a unified format context event stream; performing semantic analysis on the context event stream to generate structured context information; storing the structured context information in a central storage area, accessible to multiple applications within the cockpit; monitoring state changes in the context information in the central storage area, and proactively sending service scheduling instructions to one or more target applications based on these state changes. By constructing a unified event-driven and data-sharing architecture, encapsulating and semantically parsing multi-source heterogeneous data within the cockpit into structured context information, and then storing it in a central storage area for access by various applications, effectively decoupling application services and improving system flexibility and scalability. Simultaneously, through multimodal data fusion and deep context awareness, the system shifts from passive response to proactive service, optimizing the user experience.

[0079] In the several embodiments provided in this application, it should be understood that the disclosed device can also be implemented in other ways. The device embodiments described above are merely illustrative; for example, the block diagrams in the accompanying drawings illustrate the possible architecture, functions, and operations of the device according to various embodiments of this application. In this regard, each block in the block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram, and combinations of block diagrams, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0080] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0081] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0082] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application. It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0083] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

[0084] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A cross-application service collaboration method, characterized in that, The method is applied to a smart cockpit, and the method includes: Collect raw data from multiple heterogeneous data sources within the cockpit and encapsulate it into a context event stream in a unified format; Semantic analysis is performed on the context event stream to generate structured context information; The structured context information is stored in a central storage area; wherein the central storage area is accessible to multiple applications within the cockpit; Monitor the state changes of context information in the central storage area, and proactively send service scheduling instructions to one or more target applications based on the state changes.

2. The method according to claim 1, characterized in that, The raw data collected from multiple heterogeneous data sources within the cockpit includes: Data is collected from at least two data sources, including the vehicle bus, one or more applications in the cockpit, and the user interface.

3. The method according to claim 1, characterized in that, The semantic analysis of the context event stream to generate structured context information includes: Named entity recognition is performed on the context event stream to extract key entities; The extracted entities are mapped to predefined semantic categories; User intent is inferred based on the mapped semantic categories and real-time context.

4. The method according to claim 1, characterized in that, The step of storing the structured context information in the central storage area includes: The physical address space of the central storage area is mapped to the virtual address space of multiple application processes using memory mapping technology. The virtual address space is used for direct access by the multiple application processes.

5. The method according to claim 1, characterized in that, The step of storing the structured context information in the central storage area includes: When sensitive data is identified from the structured context information and security processing is performed on the identified sensitive data, the physical address space of the central storage area is mapped to the virtual address space of multiple application processes through memory mapping technology. The virtual address space is used for direct access by the multiple application processes.

6. The method according to claim 1, characterized in that, The monitoring of state changes in context information in the central storage area, and the proactive sending of service scheduling instructions to one or more target applications based on the state changes, includes: Sensitive data identification is performed on the structured context information, and security processing operations are performed on the identified sensitive data; The service scheduling instruction is used to drive the target application to perform operations, and the target application includes at least one of a navigation application, a media playback application, or a vehicle environment control application.

7. The method according to claim 6, characterized in that, The security processing operation includes at least one of the following: The sensitive data is encrypted using a hardware security environment; Based on dynamic permission policies, requests to access the sensitive data are authenticated and authorized.

8. A cross-application service collaboration device, characterized in that, The device includes: a data acquisition module, a semantic analysis module, a central storage module, and a service scheduling module; The data acquisition module is used to collect raw data from multiple heterogeneous data sources in the cockpit and encapsulate it into a context event stream in a unified format. The semantic analysis module is used to perform semantic analysis on the context event stream and generate structured context information; The central storage module includes a central storage area; the central storage area is used to store the structured context information, which can be accessed by multiple applications within the cockpit. The service scheduling module is used to monitor the state changes of context information in the central storage module, and actively send service scheduling instructions to one or more target applications based on the state changes.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores program instructions, and when the processor executes the program instructions, it performs the steps of the method according to any one of claims 1-7.

10. A computer storage medium, characterized in that, The computer storage medium stores computer program instructions, which, when executed by a processor, perform the steps of the method according to any one of claims 1-7.

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