Vehicle cabin multi-screen cooperative control method and system based on interaction semantic model

Abstract

The application provides a vehicle cabin multi-screen cooperative control method and system based on an interactive semantic model, and relates to the technical field of vehicle cabin multi-screen control. The method comprises capturing original signal data and using a hash mapping algorithm to hierarchically search an HMI interactive event library to obtain a standardized event object; a scene arbitration thread is used to prioritize the standardized event object to output a target event object, a scene rule library is traversed to search for a condition trigger function corresponding to the target event object, a scene activation judgment is performed in combination with real-time state data of the vehicle, and a target activation scene is output; a target instruction template set is extracted based on the target activation scene, and the target event object is mapped to the target instruction template set to generate multiple target rendering instructions; the target rendering instructions are distributed to a rendering task queue of a corresponding target screen, the target rendering instructions are extracted, and the target rendering instructions are executed in parallel, so that multi-screen intelligent cooperative linkage is achieved, and the coherence and safety of human-computer interaction are improved.

Description

Technical Field

[0001] This invention relates to the field of vehicle cockpit multi-screen control technology, and in particular to a vehicle cockpit multi-screen collaborative control method and system based on an interactive semantic model. Background Technology

[0002] With the rapid development of automotive intelligence and connectivity, vehicle cabins are equipped with a variety of interactive displays, such as full LCD instrument panels, central control screens, passenger entertainment screens, and rear entertainment screens. Multi-screen collaboration has become a key technology for improving driving experience and interaction efficiency.

[0003] However, existing technologies still have significant shortcomings. On the one hand, there is a lack of unified event management and interaction logic among the various displays. Information updates on different displays often rely on independent control mechanisms, requiring drivers to frequently switch screens during operation, increasing the operational burden and interfering with driving safety. On the other hand, traditional interaction technologies mostly adopt fixed interface logic or simple event triggering mechanisms, making it difficult to dynamically adjust the display content and interaction methods of the screens according to the real-time status of the vehicle, driving scenarios, and user behavior. They lack a deep understanding and contextualized arrangement of the inherent logic of different interaction events, resulting in a fragmented multi-screen collaborative experience.

[0004] Therefore, it is necessary to provide a multi-screen collaborative control method and system for vehicle cockpits based on interactive semantic models to solve the above-mentioned technical problems. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a vehicle cockpit multi-screen collaborative control method and system based on an interactive semantic model. This method solves the problems of poor multi-screen collaboration, delayed scene response, and chaotic command logic in existing technologies, which prevent the intelligent linkage between multi-source interactive events and multi-source displays.

[0006] The present invention provides a multi-screen collaborative control method for a vehicle cockpit based on an interactive semantic model, the method comprising: The raw signal data is captured in real time by a background daemon thread and input into the interactive semantic model. A hash mapping algorithm is used to perform hierarchical retrieval and matching in the HMI interactive event library to obtain standardized event objects. The interactive semantic model includes the HMI interactive event library and the scene rule library. A scenario arbitration thread is used to perform priority arbitration on the standardized event object, output the target event object, and traverse the scenario rule base in the interaction semantic model to retrieve the condition trigger function corresponding to the target event object. The condition trigger function is used to determine the scene activation of the target event object and the real-time vehicle status data, and output the target activated scene. Based on the target activation scene, a target instruction template set is extracted from the scene rule base, and the target event object is mapped to the target instruction template set to generate multiple target rendering instructions; Using a process communication mechanism, multiple target rendering instructions are distributed to the rendering task queues of the corresponding target screens, and a blocking queue mechanism is used to extract the target rendering instructions. Based on multi-threaded scheduling technology, the target screens are controlled to execute the target rendering instructions in parallel, and a feedback closed-loop mechanism is used for real-time monitoring and adjustment.

[0007] Preferably, the step of capturing raw signal data in real time through a background daemon thread and inputting it into the interactive semantic model specifically includes: The background daemon thread is started by the cockpit domain controller. The background daemon thread continuously monitors the raw signal data based on a polling mechanism or an asynchronous notification mechanism. The raw signal data includes vehicle bus signals and inter-process communication signals within the vehicle cockpit. The original signal data is parsed using a data parsing algorithm to extract the signal type, signal value, and signal source. The raw signal data is input into the HMI interactive event library in the interactive semantic model.

[0008] Preferably, the step of using a hash mapping algorithm to perform hierarchical retrieval and matching in the HMI interaction event database to obtain standardized event objects specifically includes: Using the signal source as the retrieval key, the hash mapping algorithm is used to perform a first-level retrieval in the HMI interactive event database to obtain candidate event sources; Using the signal type as the retrieval key, the hash mapping algorithm is used to perform a secondary retrieval in the HMI interactive event database to obtain candidate event types. Obtain the predefined parameter formats of the candidate event source and the candidate event type, and use format conversion technology to convert the signal value into candidate event parameters; The candidate event source, candidate event type, candidate event parameters, and corresponding timestamp are encapsulated to generate multiple standardized event objects; Based on the queue data structure, multiple standardized event objects are sorted according to their corresponding timestamps to generate a standardized event queue.

[0009] Preferably, the step of using a scenario arbitration thread to perform priority arbitration on the standardized event object and output the target event object specifically includes: A fixed arbitration cycle is preset, and the scenario arbitration thread reads the standardized event objects sequentially from the standardized event queue according to the fixed arbitration cycle; A priority mapping table is preset, and the candidate event types of the standardized event objects are matched with the priority mapping table to obtain the priority level corresponding to the candidate event type; the priority level includes safety alarm P0 level, driving assistance P1 level, and comfort and entertainment P2 level; the priority levels of safety alarm P0 level, comfort and entertainment P2 level, and driving assistance P1 level decrease in sequence; Determine whether there is a standardized event object being processed. If not, use the current standardized event object as the target event object. If there is a standardized event object being processed, then the priority level of the current standardized event object is compared with the priority level of the standardized event object being processed. If the priority level of the current standardized event object is higher than the priority level of the standardized event object being processed, the processing flow of the standardized event object being processed will be interrupted through the arbitration control mechanism, and the current standardized event object will be used as the target event object. If the priority level of the current standardized event object is lower than or equal to the priority level of the standardized event object being processed, the current standardized event object is temporarily stored in the waiting queue. After the processing flow of the standardized event object being processed is completed, the current standardized event object is extracted from the waiting queue as the target event object.

[0010] Preferably, based on the target event object, the scenario rule base in the interaction semantic model is traversed to retrieve the conditional trigger function corresponding to the target event object. The conditional trigger function is then used to determine the scenario activation of the target event object and the real-time vehicle status data, and the target activated scenario is output. Specifically, this includes: The target event object is input into the interaction semantic model. The preset scenarios in the scene rule base are traversed according to the priority level of the target event object. The target event object is matched with the preset scenarios using the scene association interface. The correlation degree between the target event object and the preset scenarios is calculated using the cosine similarity algorithm. A preset correlation threshold is set. If the correlation is greater than or equal to the correlation threshold, it is determined that the target event object is associated with the preset scene, and the condition trigger function corresponding to the preset scene is extracted. Obtain the real-time vehicle status data that matches the timestamp of the target event object, input the target event object and the real-time vehicle status data into the condition trigger function for Boolean logic operation, and generate a scene determination result; If the scene determination result is true, the scene is determined to be successfully activated, and the preset scene is used as the target activation scene; if the scene determination result is false, the scene activation is determined to be unsuccessful, and the preset scenes in the scene rule base are traversed until the scene is determined to be successfully activated.

[0011] Preferably, the step of inputting the target event object and the real-time vehicle status data into the conditional trigger function for Boolean logic operations to generate a scene determination result specifically includes: Extract candidate event parameters from the target event object, and perform parameter alignment processing between the candidate event parameters and the real-time vehicle status data using a predefined parameter association rule base to establish a parameter mapping matrix; Based on the parameter mapping matrix, the conditional trigger function is structurally analyzed and split into multiple sub-condition judgment functions. These sub-condition judgment functions are then used to perform matching operations on the parameter mapping matrix to generate multiple sub-condition judgment results. These results are logically combined to generate the scene judgment result R. The corresponding calculation formula is as follows: In the formula, Represents the unit step function; Indicates the indicator function; n represents the number of subconditional functions; This represents the weight coefficient of the i-th sub-conditional judgment function; This represents the matching operation function of the i-th sub-condition judgment function, and the matching operation function is either a numerical matching operation function or a state matching operation function; This represents the parameter mapping matrix from the input to the i-th sub-conditional judgment function; This represents the criterion value for the i-th sub-conditional judgment function; This represents the decision tolerance of the i-th sub-conditional decision function; This indicates the threshold for determining scene activation.

[0012] Preferably, the step of extracting a target instruction template set from the scene rule base based on the target activation scene, and mapping the target event object to the target instruction template set to generate multiple target rendering instructions specifically includes: The scene rule base is retrieved based on the target activation scene, and the target instruction template set associated with the target activation scene is extracted using an association index algorithm; the target instruction template set contains multiple target instruction templates, and each target instruction template corresponds to a target screen; The template structure of each target instruction template is parsed, and the position of the instruction placeholder and the target screen information in each target instruction template are extracted. The parameter mapping function is used to convert the candidate event parameters in the target event object and fill them into the position of the instruction placeholder to generate multiple target rendering instructions.

[0013] Preferably, the step of distributing multiple target rendering instructions to the rendering task queues of corresponding target screens using inter-process communication mechanisms, retrieving the target rendering instructions using a blocking queue mechanism, and controlling the target screens to execute the target rendering instructions in parallel based on multi-threaded scheduling technology specifically includes: The multiple target rendering instructions are categorized and split according to the target screen information, and the multiple target rendering instructions are distributed to the rendering task queue of each corresponding target screen using the process communication mechanism. The blocking queue mechanism is used to verify each rendering task queue. When the rendering task queue is empty, it remains in a blocking and waiting state until the target rendering instruction is detected. When the target rendering instruction enters the rendering task queue, if the rendering task queue is not empty, the target rendering instruction is extracted from the rendering task queue. For the target rendering instructions extracted from multiple target screens, the multi-threaded scheduling technology is used to control the multiple target screens to execute the target rendering instructions in parallel.

[0014] A vehicle cockpit multi-screen collaborative control system based on an interactive semantic model, the system comprising: The data acquisition and standardization processing module is used to capture raw signal data in real time through a background daemon thread and input it into the interactive semantic model. It also uses a hash mapping algorithm to perform hierarchical retrieval and matching in the HMI interactive event library to obtain standardized event objects. The interactive semantic model includes the HMI interactive event library and the scene rule library. The scene arbitration and activation determination module is used to use a scene arbitration thread to perform priority arbitration on the standardized event object, output the target event object, traverse the scene rule base in the interaction semantic model, retrieve the condition trigger function corresponding to the target event object, and perform scene activation determination on the target event object and the real-time vehicle status data through the condition trigger function, and output the target activated scene. The template extraction and instruction generation module is used to extract a target instruction template set from the scene rule base based on the target activation scene, and map the target event object to the target instruction template set to generate multiple target rendering instructions. The instruction execution and feedback adjustment module is used to distribute multiple target rendering instructions to the rendering task queues of the corresponding target screens using a process communication mechanism, extract the target rendering instructions using a blocking queue mechanism, control the target screens to execute the target rendering instructions in parallel based on multi-threaded scheduling technology, and monitor and adjust in real time through a feedback closed-loop mechanism.

[0015] Compared with existing technologies, the vehicle cockpit multi-screen collaborative control method and system based on interactive semantic model provided by this invention has the following beneficial effects: This invention captures raw signal data in real time through a background daemon thread and inputs it into the interactive semantic model. A hash mapping algorithm is then used to perform hierarchical retrieval and matching in the HMI interactive event library to obtain standardized event objects. The interactive semantic model includes the HMI interactive event library and a scene rule library. A scene arbitration thread performs priority arbitration on the standardized event objects, outputs the target event object, and traverses the scene rule library in the interactive semantic model to retrieve the conditional trigger function corresponding to the target event object. The conditional trigger function is used to determine scene activation based on the target event object and real-time vehicle status data, outputting the target activated scene. Based on the target activated scene, a target instruction template set is extracted from the scene rule library, and the target event object is mapped to the target instruction template set to generate multiple target rendering instructions. Using a process communication mechanism, these multiple target rendering instructions are distributed to the rendering task queues of the corresponding target screens. A blocking queue mechanism is used to extract the target rendering instructions, and multi-threaded scheduling technology is used to control the target screens to execute the target rendering instructions in parallel. A feedback closed-loop mechanism is used for real-time monitoring and adjustment, thereby achieving intelligent multi-screen collaborative linkage and improving the continuity and security of human-computer interaction.

[0016] This invention constructs a unified interactive semantic model and introduces a hash mapping algorithm to convert scattered vehicle bus signals and inter-process communication signals within the vehicle cabin into standardized event objects. This effectively shields the differences in underlying hardware and signal protocols, improving event parsing efficiency and reducing signal processing complexity, thereby enhancing the overall system response speed and stability. Furthermore, by introducing a scene arbitration thread and a priority mapping mechanism, this invention classifies standardized event objects into priority levels: safety alarm (P0 level), driver assistance (P1 level), and comfort and entertainment (P2 level). This allows for orderly scheduling and dynamic arbitration of standardized event objects, ensuring that higher-priority standardized event objects are processed first in the event of multiple concurrent events, preventing lower-priority standardized event objects from consuming system resources, thus improving the real-time performance and reliability of the system. Finally, this invention relies on a scene rule base and conditional trigger functions, combining target event objects with real-time vehicle status data for scene determination. This allows for precise activation of actual scenes based on specific driving scenarios, avoiding false triggering of invalid scenes and enhancing the system's adaptability to specific driving scenarios. This invention achieves collaborative execution and parallel processing of various target screens through a blocking queue mechanism and multi-threaded scheduling technology. It ensures the orderliness of instruction execution for individual target screens through priority levels, and avoids mutual blocking of instruction execution between multiple independent target screens through independent parallel rendering threads. This effectively reduces task blocking and execution latency, ensures the consistency and stability of information display on each target screen, and realizes deep integration and collaborative linkage of multi-source interactive events and multi-source displays in the vehicle cabin, thereby improving the continuity of interaction and the level of intelligence. Attached Figure Description

[0017] Figure 1 A flowchart of a vehicle cockpit multi-screen collaborative control method based on an interactive semantic model provided in an embodiment of the present invention; Figure 2 A system block diagram of a vehicle cockpit multi-screen collaborative control system based on an interactive semantic model provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] like Figure 1The diagram shown is a flowchart of a vehicle cockpit multi-screen collaborative control method based on an interactive semantic model provided in an embodiment of the present invention. Figure 1 The execution entity of the method shown can be a software and / or hardware device. The execution entity of this application can include, but is not limited to, at least one of the following: user equipment, network equipment, etc. User equipment can include, but is not limited to, computers, smartphones, personal digital assistants (PDAs), and the aforementioned electronic devices. Network equipment can include, but is not limited to, a single network server, a server group consisting of multiple network servers, or a cloud based on cloud computing consisting of a large number of computers or network servers. Cloud computing is a type of distributed computing, consisting of a super virtual computer composed of a group of loosely coupled computers. This embodiment does not limit this. Steps S1 to S4 are detailed as follows: S1, the raw signal data is captured in real time by the background daemon thread and input into the interactive semantic model, and a hash mapping algorithm is used to perform hierarchical retrieval and matching in the HMI interactive event library to obtain standardized event objects; the interactive semantic model includes the HMI interactive event library and the scene rule library; The process of capturing raw signal data in real time through a background daemon thread and inputting it into the interactive semantic model specifically includes: The background daemon thread is started by the cockpit domain controller. The background daemon thread continuously monitors the raw signal data based on a polling mechanism or an asynchronous notification mechanism. The raw signal data includes vehicle bus signals and inter-process communication signals within the vehicle cockpit. The original signal data is parsed using a data parsing algorithm to extract the signal type, signal value, and signal source. The raw signal data is input into the HMI interactive event library in the interactive semantic model.

[0020] The background daemon thread refers to a resident task thread running at the bottom layer of the cockpit domain controller, used to continuously collect raw signal data during vehicle operation and prevent the loss of raw signal data. Vehicle bus signals include vehicle status data transmitted via CAN bus, LIN bus, and Ethernet bus, including door status, vehicle speed signal, gear signal, and light status. Inter-process communication signals within the vehicle cockpit refer to data transmitted between different applications or services within the cockpit domain controller. The interaction semantic model is a model used to coordinate multi-source interactive events and multi-source displays within the vehicle cockpit. The HMI interaction event library is a database used to define and retrieve standardized event objects, and the scene rule base is a knowledge base used for preset scene definitions, scene activation determination, and target rendering instruction generation, including preset scenes, conditional trigger functions, and target instruction template sets.

[0021] Next, the background daemon thread continuously reads vehicle bus signals through a polling mechanism, and simultaneously monitors inter-process communication signals within the vehicle cabin from the central control screen and passenger entertainment screen in real time through an asynchronous notification mechanism, feeding them back to the background daemon thread. These inter-process communication signals include touch operation signals and voice command signals. Both mechanisms run in parallel to capture raw signal data. A data parsing algorithm separates the identifier and data fields from the raw signal data, extracting the signal type, signal value, and signal source. The signal type distinguishes different interactive behaviors and vehicle states, including touch operation signals, voice command signals, vehicle speed status signals, and light control signals. The signal value reflects the actual values ​​of user-operated interactive parameters or vehicle status, including adjustment values ​​and vehicle speed values. The signal source indicates that the raw signal data originates from the vehicle bus or internal vehicle cabin applications, including the body domain controller, powertrain domain controller, chassis domain controller, and physical switch array.

[0022] The step of using a hash mapping algorithm to perform hierarchical retrieval and matching in the HMI interaction event database to obtain standardized event objects specifically includes: Using the signal source as the retrieval key, the hash mapping algorithm is used to perform a first-level retrieval in the HMI interactive event database to obtain candidate event sources; Using the signal type as the retrieval key, the hash mapping algorithm is used to perform a secondary retrieval in the HMI interactive event database to obtain candidate event types. Obtain the predefined parameter formats of the candidate event source and the candidate event type, and use format conversion technology to convert the signal value into candidate event parameters; The candidate event source, candidate event type, candidate event parameters, and corresponding timestamp are encapsulated to generate multiple standardized event objects; Based on the queue data structure, multiple standardized event objects are sorted according to their corresponding timestamps to generate a standardized event queue.

[0023] First, in the primary retrieval stage, the signal source is input into a hash function for calculation, generating a hash value corresponding to the signal source. This hash value is then used to locate the corresponding hash bucket in the HMI interactive event library. This hash bucket only stores candidate event sources that match the signal source. In the secondary retrieval stage, within the hash bucket located in the primary retrieval stage, the signal type is input into a hash function for calculation, obtaining a hash value corresponding to the signal type. The event type metadata corresponding to this hash value is then located in the HMI interactive event library, and the event type information stored in this metadata is retrieved to obtain the candidate event types.

[0024] Subsequently, the predefined parameter format corresponding to the candidate event source and candidate event type is read. This predefined parameter format is used to specify the data structure, field type and value rules. Based on the predefined parameter format, format conversion technology is used to perform data type conversion, range mapping calibration and parameter structure reorganization on the signal values ​​to generate candidate event parameters and improve the consistency and standardization of candidate event parameters.

[0025] Furthermore, the candidate event source, candidate event type, candidate event parameters, and timestamp information are uniformly encapsulated to generate structured standardized event objects. The timestamp information is used to identify the acquisition time of the original signal data, ensuring accurate reflection of the chronological relationship of the standardized event objects. Based on a first-in, first-out (FIFO) queue data structure, multiple standardized event objects are cached and sorted according to their timestamp order to generate a standardized event queue. This effectively achieves rapid location and accurate matching of multi-source original signal data, reduces the complexity of retrieval calculations, and improves the real-time performance and accuracy of standardized event object generation.

[0026] S2, the scene arbitration thread performs priority arbitration on the standardized event object, outputs the target event object, and traverses the scene rule base in the interaction semantic model to retrieve the condition trigger function corresponding to the target event object. The condition trigger function is used to determine the scene activation of the target event object and the real-time vehicle status data, and outputs the target activated scene. The step of using a scenario arbitration thread to perform priority arbitration on the standardized event object and output the target event object specifically includes: A fixed arbitration cycle is preset, and the scenario arbitration thread reads the standardized event objects sequentially from the standardized event queue according to the fixed arbitration cycle; A priority mapping table is preset, and the candidate event types of the standardized event objects are matched with the priority mapping table to obtain the priority level corresponding to the candidate event type; the priority level includes safety alarm P0 level, driving assistance P1 level, and comfort and entertainment P2 level; the priority levels of safety alarm P0 level, comfort and entertainment P2 level, and driving assistance P1 level decrease in sequence; Determine whether there is a standardized event object being processed. If not, use the current standardized event object as the target event object. If there is a standardized event object being processed, then the priority level of the current standardized event object is compared with the priority level of the standardized event object being processed. If the priority level of the current standardized event object is higher than the priority level of the standardized event object being processed, the processing flow of the standardized event object being processed will be interrupted through the arbitration control mechanism, and the current standardized event object will be used as the target event object. If the priority level of the current standardized event object is lower than or equal to the priority level of the standardized event object being processed, the current standardized event object is temporarily stored in the waiting queue. After the processing flow of the standardized event object being processed is completed, the current standardized event object is extracted from the waiting queue as the target event object.

[0027] The fixed arbitration period refers to the time interval parameter for scheduling standardized event objects by the scenario arbitration thread. It is used to balance system real-time performance and computational load. The fixed arbitration period can be set to 10ms-50ms depending on the performance of the cockpit domain controller. A fixed arbitration period that is too short will lead to frequent context switching and excessive consumption of CPU resources, while a fixed arbitration period that is too long will lead to delays and accumulation of standardized event objects. The target event object refers to the standardized event object that needs to be processed immediately after priority arbitration. It includes candidate event source, candidate event type, candidate event parameters, and timestamp information.

[0028] A priority mapping table is pre-built to correspond to candidate event types and priority levels, used to define the urgency of handling different standardized event objects. Safety alarms (P0 level) include driving safety events such as collision warnings, abnormal tire pressure warnings, door open warnings, and abnormal braking alerts; driver assistance events (P1 level) include navigation route prompts, lane keeping assist reminders, and gear shift changes; comfort and entertainment events (P2 level) include non-driving events such as media playback control, air conditioning temperature adjustment, and seat position memory, ensuring that the system prioritizes driving safety when resources are limited.

[0029] In practical applications, the standardized event object being processed refers to operations such as the scene arbitration thread performing scene activation determination or the instruction rendering thread performing target rendering instruction generation. Within a fixed arbitration cycle, the processing status flags of the scene arbitration thread and the instruction rendering thread can be queried sequentially to determine whether they are in an idle state, thereby deciding whether to process the currently read standardized event object.

[0030] If the processing status flag is idle, it means that there are no standardized event objects being processed. Then, the currently read standardized event objects are sorted in descending order of priority, and the standardized event object with the highest priority is selected as the current processing object and the target event object. At the same time, the processing status flag of the thread is updated to busy and the corresponding processing flow is started.

[0031] If the processing status flag is busy, it means that there are standardized event objects being processed. The priority level of the newly read standardized event object is compared with the priority level of the standardized event objects being processed one by one to determine whether the newly read standardized event object has the right to preempt processing resources. If the priority level of the newly read standardized event object is higher than the priority level of the standardized event object being processed, it means that the newly read standardized event object has the right to preempt processing resources, thus preventing low-priority standardized event objects from interfering with the processing process of high-priority standardized event objects.

[0032] If the priority level of a newly read standardized event object is higher than that of the standardized event object currently being processed, an arbitration control mechanism is triggered to preempt processing resources. First, the processing context of the currently processed standardized event object is saved to the thread's memory stack. Then, an interrupt signal is sent to terminate the processing flow of the currently processed standardized event object, and the thread's processing resources are released. Subsequently, the newly read standardized event object is used as the target event object, and the thread's processing status flag is updated. The processing flow of the newly read standardized event object is immediately started, ensuring the absolute priority response right of the higher-priority standardized event object. For example, when the system is processing a comfort and entertainment P2-level air conditioning temperature adjustment event, and a collision warning alarm event (a safety alarm P0-level standardized event object) is triggered, the processing flow of the comfort and entertainment P2-level air conditioning temperature adjustment event is immediately interrupted, and the safety alarm P0-level standardized event object is processed first, ensuring that the safety alarm information is pushed to the display screen in the vehicle cabin as soon as possible, ensuring driving safety.

[0033] If the priority level of a newly read standardized event object is lower than or equal to the priority level of the standardized event object currently being processed, the newly read standardized event object is temporarily stored in the processing queue. The processing queue is a buffer structure used to cache standardized event objects that are not immediately executed, and its storage order is sorted in descending order according to the priority level of the standardized event objects. When the standardized event object currently being processed completes its processing flow for that thread, the scene arbitration thread queries the processing queue. If the processing queue is not empty, it extracts the standardized event object with the highest priority level in the processing queue as the target event object and starts the processing flow, while removing the target event object from the processing queue. If the processing queue is empty, the processing status flag of the thread is updated to idle, waiting to read the standardized event object of the next fixed arbitration cycle, thus realizing an ordered conversion process from the input of multi-source standardized event objects to the output of the target event object.

[0034] Based on the target event object, the scene rule base in the interaction semantic model is traversed to retrieve the conditional trigger function corresponding to the target event object. The conditional trigger function is then used to determine scene activation based on the target event object and the real-time vehicle status data, and the target activated scene is output. Specifically, this includes: The target event object is input into the interaction semantic model. The preset scenarios in the scene rule base are traversed according to the priority level of the target event object. The target event object is matched with the preset scenarios using the scene association interface. The correlation degree between the target event object and the preset scenarios is calculated using the cosine similarity algorithm. A preset correlation threshold is set. If the correlation is greater than or equal to the correlation threshold, it is determined that the target event object is associated with the preset scene, and the condition trigger function corresponding to the preset scene is extracted. Obtain the real-time vehicle status data that matches the timestamp of the target event object, input the target event object and the real-time vehicle status data into the condition trigger function for Boolean logic operation, and generate a scene determination result; If the scene determination result is true, the scene is determined to be successfully activated, and the preset scene is used as the target activation scene; if the scene determination result is false, the scene activation is determined to be unsuccessful, and the preset scenes in the scene rule base are traversed until the scene is determined to be successfully activated.

[0035] Among them, the preset scenario refers to the actual interaction scenario that represents the target event object, such as the driver's door not closed warning scenario. The target activation scenario refers to the actual interaction scenario that is confirmed to be completely matched with the target event object and the vehicle's real-time status data through scenario activation determination.

[0036] It should be noted that existing technologies only trigger the generation and execution of target rendering instructions based on the target event object. This detachment from real-time vehicle status data can easily lead to a disconnect between the scene and the actual situation. For example, regarding the driver's door not closed warning event, if the vehicle speed is greater than 0 while driving, the corresponding driver's door not closed driving warning scenario is true, and a warning instruction is generated and a warning is issued. However, if the vehicle is stationary, the corresponding driver's door not closed driving warning scenario is false. If a warning instruction is still generated and a warning is issued, the scene triggering will be disconnected from the actual situation.

[0037] The step of inputting the target event object and the real-time vehicle status data into the conditional trigger function for Boolean logic operations to generate a scene determination result specifically includes: Extract candidate event parameters from the target event object, and perform parameter alignment processing between the candidate event parameters and the real-time vehicle status data using a predefined parameter association rule base to establish a parameter mapping matrix; Based on the parameter mapping matrix, the conditional trigger function is structurally analyzed and split into multiple sub-condition judgment functions. These sub-condition judgment functions are then used to perform matching operations on the parameter mapping matrix to generate multiple sub-condition judgment results. These results are logically combined to generate the scene judgment result R. The corresponding calculation formula is as follows: In the formula, Represents the unit step function; Indicates the indicator function; n represents the number of subconditional functions; This represents the weight coefficient of the i-th sub-conditional judgment function; This represents the matching operation function of the i-th sub-condition judgment function, and the matching operation function is either a numerical matching operation function or a state matching operation function; This represents the parameter mapping matrix from the input to the i-th sub-conditional judgment function; This represents the criterion value for the i-th sub-conditional judgment function; This represents the decision tolerance of the i-th sub-conditional decision function; This indicates the threshold for determining scene activation.

[0038] Specifically, candidate event parameters are extracted from the target event object, and the candidate event parameters are mapped one-to-one with the real-time vehicle status data that matches the timestamp information of the target event object. A two-dimensional parameter mapping matrix is ​​established to structurally represent the correspondence between candidate event parameters and real-time vehicle status data.

[0039] Furthermore, a conditional trigger function is a composite logic function that determines the activation of all dimensions of a scenario. It contains multiple independent sub-logic conditions. Directly performing overall calculations cannot adapt to the differentiated judgment requirements of different scenarios. By parsing and decomposing the conditional trigger function into multiple independent sub-condition judgment functions using a parameter mapping matrix, each sub-condition judgment function can be operated independently, allowing for parallel execution of multi-dimensional scenario activation judgments and reducing the computational complexity of the conditional trigger function. The sub-condition judgment functions determine the data type of the candidate event parameters. If the data type is analog or numerical, the parameter mapping matrix is ​​input to the numerical matching operation function; if the data type is switch or state, the parameter mapping matrix is ​​input to the state matching operation function. The numerical matching operation function determines whether the candidate event parameters in the parameter mapping matrix are within the valid range of a preset parameter judgment benchmark, and whether the real-time vehicle status data is within the valid range of a preset data judgment benchmark, outputting a Boolean sub-condition judgment result. The state matching operation function determines whether the candidate event parameters in the parameter mapping matrix are consistent with the preset parameter judgment benchmark state, and whether the real-time vehicle status data is consistent with the preset data judgment benchmark state, outputting a Boolean sub-condition judgment result.

[0040] The above formula is used to logically combine the results of multiple sub-conditions to generate a scene determination result R. If R=1, that is, the scene determination result is true, then the scene activation is successful. If R=0, that is, the scene determination result is false, then the scene activation is failed.

[0041] S3, based on the target activation scene, extract the target instruction template set from the scene rule base, and map the target event object to the target instruction template set to generate multiple target rendering instructions; Based on the target activation scene, the process involves extracting a target instruction template set from the scene rule base and mapping the target event object to the target instruction template set to generate multiple target rendering instructions, specifically including: The scene rule base is retrieved based on the target activation scene, and the target instruction template set associated with the target activation scene is extracted using an association index algorithm; the target instruction template set contains multiple target instruction templates, and each target instruction template corresponds to a target screen; The template structure of each target instruction template is parsed, and the position of the instruction placeholder and the target screen information in each target instruction template are extracted. The parameter mapping function is used to convert the candidate event parameters in the target event object and fill them into the position of the instruction placeholder to generate multiple target rendering instructions.

[0042] The target instruction template set refers to the set of instruction frameworks corresponding to the target activation scene. It consists of multiple independent target instruction templates and is used to transform the interaction requirements of the target activation scene into target rendering instructions that can be executed by various target screens in the vehicle cockpit. Instruction placeholder positions refer to the positions in the target instruction templates where dynamic parameters are reserved to fill blank fields, including numeric placeholder positions, status placeholder positions, and text placeholder positions. Target rendering instructions are instructions that can be executed by the target screen by filling in candidate event parameters.

[0043] Using the scene ID of the target activation scene as the search key, the association index algorithm is invoked to query the scene rule base, locate the storage address of the corresponding target instruction template set, and extract the target instruction template set. If the corresponding target instruction template set is not found in the scene rule base, an exception handling mechanism is triggered to train and update the interaction semantic model and iteratively update the scene rule base. The iteratively updated scene rule base is then searched again to extract the corresponding target instruction template set. For example, if the target activation scene is a driver's door not closed warning scene, the scene rule base is searched based on this target activation scene, and the target instruction template set corresponding to the driver's door not closed warning scene is extracted using the association index algorithm. This target instruction template set includes instrument panel rendering templates, central control screen rendering templates, and in-vehicle audio prompt templates, etc.

[0044] Each target instruction template is structured and parsed using a pre-defined data parser, transforming it into a tree-like data structure. The tree structure is traversed, and fields are scanned and identified. If a field matches the instruction placeholder identification rules, it is marked as an instruction placeholder, and its path position and parameter type within the tree structure are recorded. Simultaneously, the target screen ID corresponding to the target instruction template is identified, and a pre-defined screen configuration table is retrieved based on this ID to obtain the target screen information. For example, alarm icon placeholders, text prompt placeholders, and instrument panel information are extracted from the instrument panel template; warning pop-up placeholders and central control screen information are extracted from the central control screen template.

[0045] Based on the parameter type of the instruction placeholder in the tree data structure, the parameter mapping function is used to unify the format and calibrate the values ​​of the candidate event parameters in the target event object, and then fill them into the path positions of the corresponding instruction placeholders in the tree data structure, i.e., the instruction placeholder positions. The filled tree data structure is then serialized to generate the target rendering instruction corresponding to each target screen.

[0046] S4. Using a process communication mechanism, multiple target rendering instructions are distributed to the rendering task queues of the corresponding target screens, and a blocking queue mechanism is used to extract the target rendering instructions. Based on multi-threaded scheduling technology, the target screens are controlled to execute the target rendering instructions in parallel, and a feedback closed-loop mechanism is used to monitor and adjust in real time.

[0047] The process of distributing multiple target rendering instructions to the corresponding target screen's rendering task queue using inter-process communication mechanisms, retrieving the target rendering instructions using a blocking queue mechanism, and controlling the target screens to execute the target rendering instructions in parallel based on multi-threaded scheduling technology specifically includes: The multiple target rendering instructions are categorized and split according to the target screen information, and the multiple target rendering instructions are distributed to the rendering task queue of each corresponding target screen using the process communication mechanism. The blocking queue mechanism is used to verify each rendering task queue. When the rendering task queue is empty, it remains in a blocking and waiting state until the target rendering instruction is detected. When the target rendering instruction enters the rendering task queue, if the rendering task queue is not empty, the target rendering instruction is extracted from the rendering task queue. For the target rendering instructions extracted from multiple target screens, the multi-threaded scheduling technology is used to control the multiple target screens to execute the target rendering instructions in parallel.

[0048] The rendering task queue is a target rendering instruction cache queue independently allocated to each target screen, used to temporarily store target rendering instructions to be executed, thereby managing target rendering instructions in an orderly manner.

[0049] Specifically, multiple target rendering instructions are categorized and split according to the target screen information, the target screen information is identified through the process communication mechanism, and the target rendering instructions are distributed to the rendering task queue corresponding to the target screen according to the target screen information to prevent target rendering instructions from being confused or accidentally triggered.

[0050] Furthermore, each screen rendering task queue corresponds to a single independent instruction execution thread. A blocking queue mechanism acquires a mutex lock for each screen rendering task queue. A mutex lock is a thread synchronization mechanism for the rendering task queue of a single target screen, ensuring that only one instruction execution thread accesses the rendering task queue at any given time. The mutex locks of different target screens are independent and do not interfere with each other. The length of each rendering task queue is checked. If the length of the rendering task queue is 0, it means there are no target rendering instructions waiting to be executed in the queue. The mutex lock is released, and the thread automatically enters a blocking waiting state, ceasing to occupy CPU resources until a new target rendering instruction is detected entering the rendering task queue. If the length of the rendering task queue is greater than 0, the mutex lock is continuously acquired, and target rendering instructions are extracted from the queue and executed. After extraction, the length of the rendering task queue is updated, and the mutex lock is released, ensuring that new target rendering instructions can enter the queue. Multi-threaded scheduling technology is used to allocate independent scheduling resources to each target screen, ensuring that different target screens can run independently and synchronously, controlling multiple target screens to execute target rendering instructions in parallel, without interference or execution blocking between target screens.

[0051] During the execution of target rendering commands, a feedback closed-loop mechanism collects the command reception status, command execution progress, and command completion results of each target screen in real time. It monitors and logs abnormal situations such as command loss, execution failure, and execution timeout in real time, obtains abnormal execution signals, and feeds them back to the data acquisition and standardization processing module, the scene arbitration and activation determination module, and the template extraction and command generation module for processing. It generates abnormal adjustment commands and automatically adjusts for abnormal situations to ensure that target rendering commands can be executed completely, accurately, and stably, thereby improving the continuity, real-time performance, and reliability of multi-screen collaborative control in the vehicle cockpit.

[0052] like Figure 2 The diagram shown is a system block diagram of a vehicle cockpit multi-screen collaborative control system based on an interactive semantic model provided in an embodiment of the present invention. The system includes: The data acquisition and standardization processing module is used to capture raw signal data in real time through a background daemon thread and input it into the interactive semantic model. It also uses a hash mapping algorithm to perform hierarchical retrieval and matching in the HMI interactive event library to obtain standardized event objects. The interactive semantic model includes the HMI interactive event library and the scene rule library. The scene arbitration and activation determination module is used to use a scene arbitration thread to perform priority arbitration on the standardized event object, output the target event object, traverse the scene rule base in the interaction semantic model, retrieve the condition trigger function corresponding to the target event object, and perform scene activation determination on the target event object and the real-time vehicle status data through the condition trigger function, and output the target activated scene. The template extraction and instruction generation module is used to extract a target instruction template set from the scene rule base based on the target activation scene, and map the target event object to the target instruction template set to generate multiple target rendering instructions. The instruction execution and feedback adjustment module is used to distribute multiple target rendering instructions to the rendering task queues of the corresponding target screens using a process communication mechanism, extract the target rendering instructions using a blocking queue mechanism, control the target screens to execute the target rendering instructions in parallel based on multi-threaded scheduling technology, and monitor and adjust in real time through a feedback closed-loop mechanism.

[0053] Figure 2 The system of the illustrated embodiment can be used to perform corresponding operations. Figure 1 The steps in the method embodiments shown are implemented in a similar manner and have similar technical effects, and will not be repeated here.

[0054] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes the steps of the vehicle cockpit multi-screen collaborative control method based on the interactive semantic model as described above.

[0055] like Figure 3 The diagram shown is a hardware structure schematic of an electronic device according to an embodiment of the present invention. The electronic device 30 includes: a processor 31, a memory 32, and a computer program; wherein... The memory 32 is used to store the computer program, and the memory may also be flash memory. The computer program is, for example, an application program or functional module that implements the above method.

[0056] Processor 31 is configured to execute the computer program stored in the memory to implement the various steps performed by the device in the above method. For details, please refer to the relevant descriptions in the preceding method embodiments.

[0057] Alternatively, the memory 32 can be either standalone or integrated with the processor 31.

[0058] When the memory 32 is a device independent of the processor 31, the device may further include: Bus 33 is used to connect the memory 32 and the processor 31.

[0059] A readable storage medium storing a computer program, which, when executed by a processor, is used to implement the steps of the vehicle cockpit multi-screen collaborative control method based on an interactive semantic model as described above.

[0060] The readable storage medium can be a computer storage medium or a communication medium. A communication medium includes any medium that facilitates the transfer of computer programs from one location to another. A computer storage medium can be any available medium accessible to a general-purpose or special-purpose computer. For example, a readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application-Specific Integrated Circuit (ASIC). Alternatively, the ASIC can be located in a user equipment. Of course, the processor and the readable storage medium can also exist as discrete components in a communication device. The readable storage medium can be a read-only memory (ROM), random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0061] The present invention also provides a program product including executable instructions stored in a readable storage medium. At least one processor of the device can read the executable instructions from the readable storage medium, and the at least one processor executes the executable instructions to cause the device to implement the methods provided in the various embodiments described above.

[0062] In the embodiments of the above-described device, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0063] Through the above embodiments, this invention captures raw signal data in real time through a background daemon thread and inputs it into the interactive semantic model. A hash mapping algorithm is used to perform hierarchical retrieval and matching in the HMI interactive event library to obtain standardized event objects. The interactive semantic model includes an HMI interactive event library and a scene rule library. A scene arbitration thread performs priority arbitration on the standardized event objects, outputs target event objects, and traverses the scene rule library in the interactive semantic model to retrieve the conditional trigger function corresponding to the target event object. The conditional trigger function is used to determine scene activation based on the target event object and real-time vehicle status data, outputting the target activated scene. Based on the target activated scene, a target instruction template set is extracted from the scene rule library, and the target event object is mapped to the target instruction template set to generate multiple target rendering instructions. Using a process communication mechanism, multiple target rendering instructions are distributed to the rendering task queues of the corresponding target screens. A blocking queue mechanism is used to extract the target rendering instructions. Multi-threaded scheduling technology is used to control the target screens to execute the target rendering instructions in parallel. A feedback closed-loop mechanism is used for real-time monitoring and adjustment, thereby achieving intelligent multi-screen collaborative linkage and improving the continuity and security of human-computer interaction.

[0064] This invention constructs a unified interactive semantic model and introduces a hash mapping algorithm to convert scattered vehicle bus signals and inter-process communication signals within the vehicle cabin into standardized event objects. This effectively shields the differences in underlying hardware and signal protocols, improving event parsing efficiency and reducing signal processing complexity, thereby enhancing the overall system response speed and stability. Furthermore, by introducing a scene arbitration thread and a priority mapping mechanism, this invention classifies standardized event objects into priority levels: safety alarm (P0 level), driver assistance (P1 level), and comfort and entertainment (P2 level). This allows for orderly scheduling and dynamic arbitration of standardized event objects, ensuring that higher-priority standardized event objects are processed first in the event of multiple concurrent events, preventing lower-priority standardized event objects from consuming system resources, thus improving the real-time performance and reliability of the system. Finally, this invention relies on a scene rule base and conditional trigger functions, combining target event objects with real-time vehicle status data for scene determination. This allows for precise activation of actual scenes based on specific driving scenarios, avoiding false triggering of invalid scenes and enhancing the system's adaptability to specific driving scenarios. This invention achieves collaborative execution and parallel processing of various target screens through a blocking queue mechanism and multi-threaded scheduling technology. It ensures the orderliness of instruction execution for individual target screens through priority levels, and avoids mutual blocking of instruction execution between multiple independent target screens through independent parallel rendering threads. This effectively reduces task blocking and execution latency, ensures the consistency and stability of information display on each target screen, and realizes deep integration and collaborative linkage of multi-source interactive events and multi-source displays in the vehicle cabin, thereby improving the continuity of interaction and the level of intelligence.

[0065] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all 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 the present invention.

Claims

1. A multi-screen collaborative control method for vehicle cockpit based on an interactive semantic model, characterized in that, The method includes: The raw signal data is captured in real time by a background daemon thread and input into the interactive semantic model. A hash mapping algorithm is used to perform hierarchical retrieval and matching in the HMI interactive event library to obtain standardized event objects. The interactive semantic model includes the HMI interactive event library and the scene rule library. A scenario arbitration thread is used to perform priority arbitration on the standardized event object, output the target event object, and traverse the scenario rule base in the interaction semantic model to retrieve the condition trigger function corresponding to the target event object. The condition trigger function is used to determine the scene activation of the target event object and the real-time vehicle status data, and output the target activated scene. Based on the target activation scene, a target instruction template set is extracted from the scene rule base, and the target event object is mapped to the target instruction template set to generate multiple target rendering instructions; Using a process communication mechanism, multiple target rendering instructions are distributed to the rendering task queues of the corresponding target screens, and a blocking queue mechanism is used to extract the target rendering instructions. Based on multi-threaded scheduling technology, the target screens are controlled to execute the target rendering instructions in parallel, and a feedback closed-loop mechanism is used for real-time monitoring and adjustment.

2. The vehicle cockpit multi-screen collaborative control method based on an interactive semantic model according to claim 1, characterized in that, The process of capturing raw signal data in real time through a background daemon thread and inputting it into the interactive semantic model specifically includes: The background daemon thread is started by the cockpit domain controller. The background daemon thread continuously monitors the raw signal data based on a polling mechanism or an asynchronous notification mechanism. The raw signal data includes vehicle bus signals and inter-process communication signals within the vehicle cockpit. The original signal data is parsed using a data parsing algorithm to extract the signal type, signal value, and signal source. The raw signal data is input into the HMI interactive event library in the interactive semantic model.

3. The vehicle cockpit multi-screen collaborative control method based on an interactive semantic model according to claim 2, characterized in that, The step of using a hash mapping algorithm to perform hierarchical retrieval and matching in the HMI interaction event database to obtain standardized event objects specifically includes: Using the signal source as the retrieval key, the hash mapping algorithm is used to perform a first-level retrieval in the HMI interactive event database to obtain candidate event sources; Using the signal type as the retrieval key, the hash mapping algorithm is used to perform a secondary retrieval in the HMI interactive event database to obtain candidate event types. Obtain the predefined parameter formats of the candidate event source and the candidate event type, and use format conversion technology to convert the signal value into candidate event parameters; The candidate event source, candidate event type, candidate event parameters, and corresponding timestamp are encapsulated to generate multiple standardized event objects; Based on the queue data structure, multiple standardized event objects are sorted according to their corresponding timestamps to generate a standardized event queue.

4. The vehicle cockpit multi-screen collaborative control method based on an interactive semantic model according to claim 1, characterized in that, The step of using a scenario arbitration thread to perform priority arbitration on the standardized event object and output the target event object specifically includes: A fixed arbitration cycle is preset, and the scenario arbitration thread reads the standardized event objects sequentially from the standardized event queue according to the fixed arbitration cycle; A priority mapping table is preset, and the candidate event types of the standardized event objects are matched with the priority mapping table to obtain the priority level corresponding to the candidate event type; the priority level includes safety alarm P0 level, driving assistance P1 level, and comfort and entertainment P2 level; the priority levels of safety alarm P0 level, comfort and entertainment P2 level, and driving assistance P1 level decrease in sequence; Determine whether there is a standardized event object being processed. If not, use the current standardized event object as the target event object. If there is a standardized event object being processed, then the priority level of the current standardized event object is compared with the priority level of the standardized event object being processed. If the priority level of the current standardized event object is higher than the priority level of the standardized event object being processed, the processing flow of the standardized event object being processed will be interrupted through the arbitration control mechanism, and the current standardized event object will be used as the target event object. If the priority level of the current standardized event object is lower than or equal to the priority level of the standardized event object being processed, the current standardized event object is temporarily stored in the waiting queue. After the processing flow of the standardized event object being processed is completed, the current standardized event object is extracted from the waiting queue as the target event object.

5. The vehicle cockpit multi-screen collaborative control method based on an interactive semantic model according to claim 1, characterized in that, Based on the target event object, the scene rule base in the interaction semantic model is traversed to retrieve the conditional trigger function corresponding to the target event object. The conditional trigger function is then used to determine scene activation based on the target event object and the real-time vehicle status data, and the target activated scene is output. Specifically, this includes: The target event object is input into the interaction semantic model. The preset scenarios in the scene rule base are traversed according to the priority level of the target event object. The target event object is matched with the preset scenarios using the scene association interface. The correlation degree between the target event object and the preset scenarios is calculated using the cosine similarity algorithm. A preset correlation threshold is set. If the correlation is greater than or equal to the correlation threshold, it is determined that the target event object is associated with the preset scene, and the condition trigger function corresponding to the preset scene is extracted. Obtain the real-time vehicle status data that matches the timestamp of the target event object, input the target event object and the real-time vehicle status data into the condition trigger function for Boolean logic operation, and generate a scene determination result; If the scene determination result is true, the scene is determined to be successfully activated, and the preset scene is used as the target activation scene; if the scene determination result is false, the scene activation is determined to be unsuccessful, and the preset scenes in the scene rule base are traversed until the scene is determined to be successfully activated.

6. The vehicle cockpit multi-screen collaborative control method based on an interactive semantic model according to claim 5, characterized in that, The step of inputting the target event object and the real-time vehicle status data into the conditional trigger function for Boolean logic operations to generate a scene determination result specifically includes: Extract candidate event parameters from the target event object, and perform parameter alignment processing between the candidate event parameters and the real-time vehicle status data using a predefined parameter association rule base to establish a parameter mapping matrix; Based on the parameter mapping matrix, the conditional trigger function is structurally analyzed and split into multiple sub-condition judgment functions. These sub-condition judgment functions are then used to perform matching operations on the parameter mapping matrix to generate multiple sub-condition judgment results. These results are logically combined to generate the scene judgment result R. The corresponding calculation formula is as follows: In the formula, Represents the unit step function; Indicates the indicator function; n represents the number of subconditional functions; This represents the weight coefficient of the i-th sub-conditional judgment function; This represents the matching operation function of the i-th sub-condition judgment function, and the matching operation function is either a numerical matching operation function or a state matching operation function; This represents the parameter mapping matrix from the input to the i-th sub-conditional judgment function; This represents the criterion value for the i-th sub-conditional judgment function; This represents the decision tolerance of the i-th sub-conditional decision function; This indicates the threshold for determining scene activation.

7. The vehicle cockpit multi-screen collaborative control method based on an interactive semantic model according to claim 1, characterized in that, Based on the target activation scene, the process involves extracting a target instruction template set from the scene rule base and mapping the target event object to the target instruction template set to generate multiple target rendering instructions, specifically including: The scene rule base is retrieved based on the target activation scene, and the target instruction template set associated with the target activation scene is extracted using an association index algorithm; the target instruction template set contains multiple target instruction templates, and each target instruction template corresponds to a target screen; The template structure of each target instruction template is parsed, and the position of the instruction placeholder and the target screen information in each target instruction template are extracted. The parameter mapping function is used to convert the candidate event parameters in the target event object and fill them into the position of the instruction placeholder to generate multiple target rendering instructions.

8. The vehicle cockpit multi-screen collaborative control method based on an interactive semantic model according to claim 7, characterized in that, The process of distributing multiple target rendering instructions to the corresponding target screen's rendering task queue using inter-process communication mechanisms, retrieving the target rendering instructions using a blocking queue mechanism, and controlling the target screens to execute the target rendering instructions in parallel based on multi-threaded scheduling technology specifically includes: The multiple target rendering instructions are categorized and split according to the target screen information, and the multiple target rendering instructions are distributed to the rendering task queue of each corresponding target screen using the process communication mechanism. The blocking queue mechanism is used to verify each rendering task queue. When the rendering task queue is empty, it remains in a blocking and waiting state until the target rendering instruction is detected. When the target rendering instruction enters the rendering task queue, if the rendering task queue is not empty, the target rendering instruction is extracted from the rendering task queue. For the target rendering instructions extracted from multiple target screens, the multi-threaded scheduling technology is used to control the multiple target screens to execute the target rendering instructions in parallel.

9. A vehicle cockpit multi-screen collaborative control system based on an interactive semantic model, applied to the vehicle cockpit multi-screen collaborative control method based on an interactive semantic model as described in any one of claims 1-8, characterized in that, The system includes: The data acquisition and standardization processing module is used to capture raw signal data in real time through a background daemon thread and input it into the interactive semantic model. It also uses a hash mapping algorithm to perform hierarchical retrieval and matching in the HMI interactive event library to obtain standardized event objects. The interactive semantic model includes the HMI interactive event library and the scene rule library. The scene arbitration and activation determination module is used to use a scene arbitration thread to perform priority arbitration on the standardized event object, output the target event object, traverse the scene rule base in the interaction semantic model, retrieve the condition trigger function corresponding to the target event object, and perform scene activation determination on the target event object and the real-time vehicle status data through the condition trigger function, and output the target activated scene. The template extraction and instruction generation module is used to extract a target instruction template set from the scene rule base based on the target activation scene, and map the target event object to the target instruction template set to generate multiple target rendering instructions. The instruction execution and feedback adjustment module is used to distribute multiple target rendering instructions to the rendering task queues of the corresponding target screens using a process communication mechanism, extract the target rendering instructions using a blocking queue mechanism, control the target screens to execute the target rendering instructions in parallel based on multi-threaded scheduling technology, and monitor and adjust in real time through a feedback closed-loop mechanism.

Images