Multi-screen interaction method and multi-screen interaction system applied to intelligent automobile

Abstract

The invention relates to the technical field of automobile multi-screen interaction, and discloses a multi-screen interaction method and system applied to an intelligent automobile, and the method comprises the steps: capturing frame images of all display screens, calculating the difference, and selecting an interaction region; information entropy evaluation complexity is calculated, a high-efficiency or low-power-consumption encoder is dynamically allocated based on the complexity, and a matched compression algorithm is selected for encoding; and decoding and updating the received compressed data on the target display screen to realize collaborative response and real-time feedback among multiple screens, and the system comprises an image acquisition module, an interaction area selection module, an information entropy calculation module, an encoder task allocation module, a compressed encoding module, a data transmission module and a synchronous display module. According to the invention, a multi-region dynamic coding scheduling technical scheme based on image difference analysis and information entropy evaluation is adopted, and refined coding processing of image regions with different complexity is realized.

Description

Technical Field

[0001] The present invention relates to the field of automobile multi-screen interaction technology, and specifically to a multi-screen interaction method and a multi-screen interaction system applied to smart cars. Background Art

[0002] With the continuous development of smart cockpit technology, in-vehicle display systems have evolved from a single central control screen to a multi-screen collaboration. Multiple display terminals, including instrument clusters, heads-up displays (HUDs), passenger entertainment screens, and rear-seat displays, have gradually become standard features in smart cars. Multi-screen interaction not only enhances the richness of information presented but also increases the flexibility and immersiveness of human-computer interaction. In this multi-terminal collaborative scenario, achieving efficient, low-latency, and highly synchronized image processing and transmission mechanisms has become a key research direction in the design of human-computer interaction systems for smart cars.

[0003] Existing multi-screen interactive technologies for smart cars mostly employ an image transmission mechanism based on unified frame processing and centralized scheduling. This involves encoding the entire frame and pushing it to each display screen, which then decodes and displays it. This approach offers a certain degree of simplicity in fixed scenarios and was widely used in early applications for mirroring between central control and instrument panels. Furthermore, common compression methods typically do not differentiate between image content complexity, instead applying fixed parameters to the entire image and lacking dynamic adjustment capabilities.

[0004] However, the existing multi-screen interactive technology for smart cars, with its unified compression strategy, struggles to adapt to regional differences in images. Complex areas suffer from a significant degradation in image quality, while simple areas waste encoding resources. Furthermore, the centralized scheduling model suffers from low scheduling efficiency in multi-target parallel processing scenarios, and is prone to frame loss and display delays when bandwidth is limited. Therefore, the present invention provides a multi-screen interactive method and system for smart cars to address the shortcomings of the existing technology. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a multi-screen interaction method and a multi-screen interaction system for smart cars, which solve the problems of coarse image processing granularity, single compression strategy, inflexible transmission scheduling and asynchronous multi-screen display in the existing multi-screen interaction technology of smart cars.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A multi-screen interaction method applied to a smart car, comprising the following steps: By capturing the current and previous frame image data of each display screen in the smart car, calculating the image difference, and combining user input or external events, selecting the changed area that requires interaction; Calculate the information entropy of the selected change area that needs to be interacted, evaluate its image complexity, and divide it into high-complexity and low-complexity areas; Based on the complexity evaluation results, performance-adaptive encoders are assigned to different interactive areas, and corresponding compression algorithms are selected to optimize coding efficiency and response speed. The data in the interactive area is compressed and encoded, and the compressed data is transmitted in parallel to the target display screen via the in-vehicle network; the received compressed data is decoded and updated on the target display screen to achieve coordinated response and real-time feedback among multiple screens, and complete the visual presentation of the user's interaction intention.

[0007] Preferably, the step of selecting the change area requiring interaction is: Obtain current frame image data of each display screen through a camera or other sensors in the smart car; Obtain the previous frame of image data of each display screen through a camera or other sensors in the smart car; Compare the image data of the current frame with the previous frame, calculate the difference in each area, and determine the area that needs to be updated based on the difference. The area with a difference greater than a preset threshold is determined as the interactive area.

[0008] Preferably, the step of calculating information entropy for the selected change area requiring interaction includes: Get pixel data in the current frame for the selected interactive area; Calculating the probability of each pixel value appearing in the area according to the pixel values ​​in the interaction area; According to the probability distribution of pixel values, the entropy value of the interaction area is calculated to represent the complexity of the area.

[0009] Preferably, the formula for calculating the entropy value of the interaction area is: Among them, H(R i ) represents the information entropy of the interaction region; p(x) is the probability distribution of the pixel value x in the region; R i represents the i-th interaction area identified in the current image frame; x represents the pixel grayscale value or color value image element in the interaction area.

[0010] Preferably, the step of allocating performance-adapted encoders to different interaction areas comprises the following steps: The coding load of the interaction region is calculated based on the complexity and size of the interaction region; Select an appropriate encoder based on the load size, with high-load areas allocated to high-efficiency encoders and low-load areas allocated to low-power encoders; Assign each interactive area to the corresponding encoder to ensure load balancing between encoders.

[0011] Preferably, the coding load formula for calculating the interactive area is: L i =αH(R i )+β|R i |; Among them, L i represents the load of the i-th encoder; H(R i ) is the information entropy of the interaction area; |R i | is the size of the interaction region; α and β are adjustment factors; R i Represents the i-th interaction area identified in the current image frame.

[0012] Preferably, the compressing and encoding the data in the interactive area includes the following steps: According to the information entropy value of each interactive area, a suitable compression algorithm is selected, where a high-efficiency compression algorithm is selected for high-entropy areas and a low-power compression algorithm is selected for low-entropy areas; Applying a corresponding compression algorithm to each selected interactive area for compression processing; Convert the compressed regional data into encoded data in preparation for transmission over the in-vehicle network.

[0013] Preferably, the compression algorithm is: Among them, A i is the compression algorithm for the interactive area; H(R i ) is the information entropy of the interaction area; θ high is the high information entropy threshold; R i Indicates the i-th interaction area identified in the current image frame; HEVC is a highly efficient compression algorithm; JPEG is a compression method with lower computational cost.

[0014] Preferably, the step of transmitting the compressed data in parallel to the target display screen via the vehicle network is: Allocate the compressed coded data according to the target display screens in each interactive area, and allocate corresponding coded data to each display screen; Transmit the encoded data in parallel to multiple target displays via the vehicle network; During the data transmission process, a synchronization mechanism is used to ensure that the content displayed on each display screen remains consistent.

[0015] A multi-screen interactive system for smart cars is also provided, including: An image acquisition module is used to capture the image data of the current frame and the previous frame of each display screen in the smart car; The interactive region selection module is used to calculate the difference between the current frame and the previous frame, determine the changed area and select the display area that needs to be updated; An information entropy calculation module is used to calculate the information entropy of the selected update display area and evaluate the complexity of each area according to the information entropy value to select an appropriate encoding strategy; The encoder task allocation module assigns different encoders to process different interactive areas based on the size and information entropy value of each updated display area, and ensures load balance between encoders through a load balancing algorithm; The compression encoding module is used to compress and encode each interactive area using a compression algorithm to generate compressed encoded data. The data transmission module is used to transmit the compressed encoded data in parallel to multiple target display screens via the vehicle network and ensure the synchronization of content between the display screens. The synchronous display module is used to ensure that the content displayed on each display screen remains consistent through a synchronization mechanism during data transmission.

[0016] The present invention provides a multi-screen interaction method and system for smart cars, which have the following beneficial effects: 1. This invention utilizes a multi-region dynamic encoding scheduling solution based on image difference analysis and information entropy assessment, enabling refined encoding processing for image regions of varying complexity. By rationally allocating high-efficiency and low-power encoders, compared to the existing approach of uniformly processing the entire frame, this effectively reduces the waste of encoding resources and addresses the issues of high power consumption and slow system response caused by redundant encoding of the entire frame.

[0017] 2. This invention introduces an adaptive compression algorithm selection mechanism and combines it with regional entropy to adaptively set the compression strategy, achieving the goal of optimizing the compression ratio while maintaining image quality. Compared to the image distortion and transmission pressure caused by fixed compression modes in existing technologies, this effectively avoids the problems of insufficient compression in low-complexity areas and high distortion rates in high-complexity areas.

[0018] 3. This invention achieves parallel data delivery to multiple screens by building a mapping binding mechanism and a bandwidth-aware scheduling model. The introduction of scheduling weight factors ensures that data flows remain organized even under unstable network conditions. Compared to the bottlenecks and blocking issues inherent in traditional serial push architectures, this effectively solves the persistent issues of display asynchrony and inconsistent responses across multiple screens.

[0019] 4. This invention designs a data frame synchronization method based on frame-level timestamps and synchronization markers, combined with a flow control strategy to ensure multi-terminal decoding consistency, achieving stable content synchronization across displays. Existing technologies often rely on fixed refresh strategies or main screen-driven mechanisms, which are prone to display artifacts or latency at high frame rates or high resolutions. This invention significantly improves display consistency and system robustness in complex scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a flow chart of the method steps of the present invention; Figure 2 This is a system architecture diagram of the present invention. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the present specification. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0022] Please see the attached Figure 1 , an embodiment of the present invention provides a multi-screen interaction method applied to a smart car, comprising the following steps: S1. Capturing the current and previous frames of image data from each display screen in the smart car, calculating the image difference, and selecting the change area requiring interaction based on user input or external events. S2. Calculate the information entropy of the selected change area that needs to be interacted, evaluate its image complexity, and divide it into high-complexity and low-complexity areas; S3. Based on the complexity evaluation results, performance-adaptive encoders are assigned to different interactive areas, and corresponding compression algorithms are selected to optimize coding efficiency and response speed. S4, compressing and encoding the data in the interactive area, and transmitting the compressed data in parallel to the target display screen via the vehicle network; S5. Decode and update the received compressed data on the target display screen to achieve coordinated response and real-time feedback among multiple screens, and complete the visual presentation of the user's interaction intention.

[0023] In step S1, in this embodiment, the image data acquisition process is performed by a camera or other sensor device installed in the smart car. Specifically, the image data includes image information for the current frame and the previous frame. This image data can reflect the real-time status of each display screen in the smart car, including the static background and dynamically changing parts of the screen. The image data for each display screen is transmitted to the image acquisition module, which is responsible for capturing each display screen in real time.

[0024] In one possible implementation, an onboard sensor, such as a camera, infrared sensor, or other image capture device, captures images of each display screen. Each captured image is stored in a time-series format and compared and analyzed with the previous frame. This ensures synchronized operation across multiple screens, while allowing for precise processing of image changes in subsequent steps.

[0025] After the image acquisition module captures the current and previous frames of image data, it then uses a difference calculation to compare changes in image content. In some embodiments, the image data of the current and previous frames are compared using an image difference calculation algorithm to determine the amount of image change. This amount of change is typically calculated by calculating the pixel difference in each region. If the difference in a region exceeds a preset threshold, it is determined to be a changed region.

[0026] Specifically, in this step, by comparing the current frame with the previous frame of image data, the system can identify which areas of the image have changed significantly. For example, in a vehicle display system, when the navigation information is updated or the video screen in the entertainment system changes, only these areas have changed. The method for calculating the difference amount can be based on a variety of image processing technologies such as pixel contrast, edge detection, and color space changes. In some embodiments, the pixel difference value of the calculated image can be used to determine the changed area. If the pixel value difference in the changed area exceeds a preset threshold, these areas will be marked as interactive areas, which will then be processed in the information entropy calculation and encoding process.

[0027] In the calculation of the image change area, the following formula is usually used to calculate the difference of the change area: ΔI = ∑ x∈R |I1(x)-I2(x)|; Where ΔI represents the difference in the image change area; I1(x) is the pixel value of pixel x in the area in the current frame; I2(x) is the pixel value of pixel x in the area in the previous frame; R represents the display area being analyzed in the current frame; the difference ΔI is used to determine whether the area needs to be updated; x represents an image element such as pixel grayscale value or color value in the interactive area.

[0028] In this embodiment, the acquisition of image data and identification of changed regions provide the data foundation for subsequent data transmission and multi-screen synchronization. By using the acquired changed regions, the system can accurately determine which parts need to be transmitted, without having to transmit the entire image. This saves bandwidth and improves data transmission efficiency.

[0029] In subsequent steps, the selected interactive areas undergo further processing, including information entropy calculation, encoder assignment, and compression. These steps ensure data synchronization across multiple displays in the smart car, while also guaranteeing efficient and smooth display content updates.

[0030] In step S2, in this embodiment, information entropy is calculated for the changed regions to assess the information content and complexity of each region. Generally speaking, higher entropy indicates that the region contains more details and change information, and accordingly, these regions require higher coding efficiency and resources during image compression and encoding. Conversely, regions with low entropy have simpler content and are updated less frequently, so low-power encoding methods can be used.

[0031] Specifically, information entropy calculation is achieved through the following steps: Determine the changed regions: Based on the difference calculation results, select the changed regions. These regions are those that have undergone significant changes in the image. These regions have pixel values ​​that have changed compared to the previous image, and the difference exceeds a preset threshold.

[0032] Calculating information entropy: For each region of change, the system evaluates the complexity of the region by calculating the probability density function (PDF) of the pixel distribution within the region. The higher the information entropy, the richer and more complex the information content within the region. Specifically, the information entropy H can be calculated from the probability distribution of the pixel values ​​within the region. The formula is as follows: Among them, H(R i ) represents the information entropy of the interaction region; p(x) is the probability distribution of the pixel value x in the region; R i represents the i-th interaction area identified in the current image frame; x represents the image elements such as pixel grayscale value or color value in the interaction area, and the probability of each pixel in the area is determined by the frequency of its pixel value appearing in the area.

[0033] Regional complexity judgment: information entropy H(R i The result of the calculation of the entropy value determines the complexity of the region of change. A high entropy value indicates that the content of the region is complex and requires more computing resources for efficient compression. A low entropy value indicates that the region is simpler and the update frequency is low, so a more energy-efficient compression scheme can be used.

[0034] Alternatively, in some embodiments, information entropy calculations are not limited to single-pixel level calculations but can also incorporate the spatial distribution characteristics of the image. In this case, the information entropy calculation relies not only on the value of a single pixel but may also involve the spatial correlation between pixels. For example, by performing texture analysis or edge detection on an image region, the accuracy of the entropy calculation can be further improved, thereby better assessing the actual complexity of the region.

[0035] Specifically, some areas may contain rich textures and details, such as the windows of a vehicle, the fine fonts on an in-car entertainment screen, or the roads and icons in a navigation map. These areas will result in higher information entropy when they change. In contrast, changes in background color or simple color blocks will produce lower entropy values.

[0036] Based on this, the calculated information entropy directly influences the choice of compression algorithm in subsequent steps. Specifically, for high-entropy areas, the system may choose a more complex encoding algorithm (such as HEVC or H.264) for efficient compression to ensure compressed image quality while minimizing data volume. For low-entropy areas, a simpler compression algorithm (such as JPEG) may be selected to reduce power consumption and reduce the resources required for encoding.

[0037] As an implementation, the system may dynamically adjust compression algorithm parameters, such as compression ratio and quantization step size, to optimize image quality in high-entropy areas while ensuring coding efficiency in low-entropy areas. In this way, overall image processing efficiency is improved and resource usage during image updates is optimized.

[0038] In step S3, this embodiment dynamically assigns each region to different encoders for processing based on its information entropy and other relevant features, thereby optimizing the entire image processing process. The goal of this process is to ensure that each interactive region receives the most appropriate encoding processing, ultimately achieving efficient data transmission and display.

[0039] Generally speaking, the complexity of image content directly affects compression coding efficiency and image quality. For high-complexity areas, the system requires a higher-performance encoder to ensure image quality and compression ratio. For low-complexity areas, a lower-performance encoder can be used to reduce power consumption and improve system responsiveness. Therefore, the key to encoder task allocation lies in selecting the most appropriate encoder based on the complexity of each area.

[0040] In this embodiment, encoder task allocation is achieved in the following manner: Region Classification: Based on the entropy calculation results from the previous step, the system categorizes regions of change into high-complexity and low-complexity regions. Generally speaking, regions with higher entropy values ​​are characterized by rich detail and frequent changes, requiring more computing resources to maintain image quality. Regions with lower entropy values, on the other hand, exhibit less change and are easier to process.

[0041] Dynamic encoder allocation: The system dynamically selects the appropriate encoder based on the complexity of the region. Specifically, high-complexity regions are assigned to high-performance encoders (such as HEVC or H.264), which have stronger compression capabilities and are suitable for processing complex image content. Low-complexity regions can be assigned to low-power encoders (such as JPEG), which saves processing resources and improves overall processing speed.

[0042] Load Balancing: In some embodiments, to prevent overloading of a single encoder, the system balances the load across multiple encoders. Specifically, based on the entropy and size of each interactive region, the system adjusts the encoder task allocation to ensure load balancing across encoders, avoiding performance bottlenecks caused by over-reliance on a single encoder.

[0043] Specifically, encoder task allocation not only considers the complexity of the region, but also adjusts based on the size of each region. In some embodiments, the size of the region and its entropy value together determine the encoding load of the region. Regions with larger loads are assigned to high-efficiency encoders, while regions with smaller loads are assigned to low-power encoders. The load can be calculated using the following formula: L i =αH(R i )+β|R i |; Among them, L i represents the load of the i-th encoder; H(R i ) is the information entropy of the interaction area; |R i | is the size of the interaction region; α and β are adjustment factors; R i represents the i-th interaction region identified in the current image frame. This load formula can help the system more accurately control the task allocation of each encoder, ensuring that the system can effectively allocate computing resources when processing regions of different complexities.

[0044] Regarding step S4, in this embodiment, the process of performing compression coding on the selected area mainly includes two aspects: selection of a compression algorithm and execution of coding compression.

[0045] Generally speaking, using a unified compression algorithm for image areas of different complexity may cause resource waste or image quality degradation. Therefore, an adaptive compression strategy is introduced in this step. According to the image information entropy value of each interactive area, different types of compression algorithms are selected to achieve reasonable scheduling of coding resources.

[0046] Specifically, the information entropy of an image region, a measure of image complexity, has been calculated in the previous steps. As an option, when image information entropy is high, high-efficiency compression algorithms such as HEVC (High Efficiency Video Coding) are preferred to achieve better compression ratios and visual fidelity. In scenarios with lower image information entropy, more resource-efficient compression algorithms such as JPEG are used to reduce system load.

[0047] In one possible implementation, the choice of compression algorithm can be given by the following discriminant function: Among them, A i is the compression algorithm for the interactive region, H(R i ) is the information entropy of the interaction area, θ high is the high information entropy threshold; R i Indicates the i-th interaction area identified in the current image frame; HEVC is a highly efficient compression algorithm; JPEG is a compression method with lower computational cost.

[0048] As a further extension, some embodiments also introduce a weighted compression model to quantitatively evaluate the compression load to assist encoder allocation decisions or perform fine-grained tuning of compression algorithm strategies.

[0049] In some embodiments, in order to further improve the efficiency of multi-region parallel coding, a block-level splitting strategy is adopted to divide the large interactive area into several sub-blocks R ij , and calculate their information entropy values ​​respectively: Among them, H(R ij ) represents the information entropy of the sub-block, p(x) represents the probability of occurrence of pixel value x in the sub-block; R ij is the jth sub-block obtained by further dividing the large interactive area; x is the sub-block R ij The pixel values ​​of the image in .

[0050] In this way, more targeted compression processing can be performed on the details in high-complexity areas, optimizing the overall coding resource allocation while ensuring image fidelity.

[0051] As an optimization solution, the compression coding module can preset different compression parameter combination strategies. For example, in HEVC encoding, different quantization parameter (QP) values, prediction modes, and transform block sizes can be used to adapt to the texture characteristics and intensity of the image. In low-entropy areas, the sampling rate can be appropriately reduced or a higher compression factor can be used to reduce bandwidth consumption.

[0052] In step S5, this embodiment transmits the generated encoded data in parallel over the vehicle network to ensure that multiple target displays receive their corresponding image updates and achieve synchronized control of image display. Taking into account objective conditions such as limited network resources and uneven distribution of display devices in the vehicle environment, the network allocation strategy is optimized during the data transmission process, and a parallel scheduling mechanism is introduced to improve transmission stability and overall transmission efficiency.

[0053] To meet the data needs of multiple target displays, we first complete the mapping and binding of each interactive area to the display terminal based on the mapping relationship of each interactive area in the original display frame. Through this mapping and binding table, the compressed encoded data can be assigned to the display target, ensuring a clear data transmission path and reducing intermediate processing delays.

[0054] In general, to adapt to bandwidth fluctuations in the in-vehicle network, this embodiment introduces a bandwidth-aware scheduling mechanism during the transmission process. This mechanism dynamically adjusts data fragmentation and scheduling order based on factors such as the current available bandwidth, transmission queue status, and display screen update priority, achieving parallel delivery of compressed data.

[0055] As an option, in the case of multi-target display, the data packets of each interactive area can be divided into independent transmission units, and an independent cache queue and synchronization control module can be configured for each target display screen to achieve efficient data reception and image reconstruction.

[0056] Specifically, the data transmission module uses a priority queue algorithm with a scheduling weight factor for scheduling, and the scheduling priority is calculated by the following function: Among them, P k Indicates the scheduling priority of the current transmission task of the k-th display; U k Indicates the average frame difference index of the interactive area of ​​the display screen; B k Indicates the current available bandwidth of the channel; δ1 and δ2 are priority weighting coefficients; it can be dynamically adjusted based on the real-time status of the system.

[0057] In one possible implementation, to further ensure the stability of parallel transmission, this embodiment adopts a flow control mechanism to adjust the transmission rate of the encoded data based on the cache status feedback of each display screen, thereby avoiding image desynchronization caused by cache overflow or network congestion.

[0058] In some embodiments, frame-level timestamps and synchronization markers are embedded during data transmission to align the target display's timing reference and improve display synchronization accuracy. These markers are embedded in the packet header at a low bit rate and do not affect the core content of the encoded data.

[0059] As an extended optimization measure, this embodiment also introduces transmission redundancy control logic, enables an error detection mechanism for key frame data, ensures the data integrity of key image areas through verification redundancy and fast retransmission mechanisms, and adapts to possible interference factors in the vehicle environment.

[0060] The multi-screen interaction system applied to a smart car described below and the multi-screen interaction method applied to a smart car described above can be referenced to each other.

[0061] Please see the attached Figure 2 The present invention also provides a multi-screen interactive system for a smart car, comprising: An image acquisition module is used to capture the image data of the current frame and the previous frame of each display screen in the smart car; The interactive region selection module is used to calculate the difference between the current frame and the previous frame, determine the changed area and select the display area that needs to be updated; An information entropy calculation module is used to calculate the information entropy of the selected update display area and evaluate the complexity of each area according to the information entropy value to select an appropriate encoding strategy; The encoder task allocation module assigns different encoders to process different interactive areas based on the size and information entropy value of each updated display area, and ensures load balance between encoders through a load balancing algorithm; The compression encoding module is used to compress and encode each interactive area using a compression algorithm to generate compressed encoded data. The data transmission module is used to transmit the compressed encoded data in parallel to multiple target display screens via the vehicle network and ensure the synchronization of content between the display screens. The synchronous display module is used to ensure that the content displayed on each display screen remains consistent through a synchronization mechanism during data transmission.

[0062] The image acquisition module typically includes an image acquisition unit and a buffer processing unit, enabling real-time acquisition of the current and previous frames of image data from each display screen. Optionally, this module can also support multi-channel concurrent acquisition and differential buffering, improving the system's processing capabilities for high-refresh-rate displays. In some embodiments, this module further integrates a frame timestamp mechanism to provide a time reference for subsequent display synchronization.

[0063] The interactive region selection module compares the difference values ​​of corresponding pixels in two frames of image data to identify regions of inter-frame change. Specifically, various methods can be used to determine interactive regions, including pixel difference algorithms, edge detection, or motion estimation techniques. One possible implementation also incorporates a dynamic region clustering algorithm to merge discontinuous but visually related regions, optimizing the granularity of subsequent encoding resource allocation.

[0064] The information entropy calculation module calculates the information entropy of each interaction region based on the probability distribution of pixel values ​​in the image, serving as a quantitative reference for regional complexity. Generally, the module includes a histogram statistics unit and an entropy calculation unit, supporting processing of various formats such as 8-bit grayscale images and RGB three-channel images. In some embodiments, it also supports an incremental entropy update strategy to reduce redundant calculations in scenarios with small changes.

[0065] The encoder task allocation module dynamically schedules encoding tasks based on the complexity and size of the interaction region and the current task status of each encoder. Specifically, the module incorporates a load assessment function based on an entropy-area weight model and implements strategies such as multi-encoder polling and priority scheduling. Optionally, the module also integrates a load feedback interface to enable collaborative management with the system scheduler and dynamically allocate computing resources.

[0066] The compression encoding module performs compression encoding on designated regions based on instructions output by the encoder task allocation module. It supports adaptive selection of multiple compression standards, such as HEVC, AV1, JPEG, and JPEG2000. In one possible implementation, the module can configure compression parameter sets (such as quantization step size and intra-frame prediction mode) to customize the characteristics of different regions. The module also provides a mechanism for reusing compression information between regions to improve coding efficiency and compression ratio.

[0067] The data transmission module transmits the compressed encoded data in parallel streams to each target display. Typically, this module integrates multiple buffer queues and bandwidth management logic. In some embodiments, a concurrent transmission control mechanism based on scheduling priorities is introduced to adjust the transmission order of data packets in different areas as needed to adapt to fluctuations in the vehicle network bandwidth.

[0068] The synchronous display module, which controls the display terminal to load and render received image content based on the same time base, includes subunits such as a synchronization signal trigger and a display refresh controller. Optionally, it can achieve timing alignment of displayed frames by comparing frame numbers with timestamps. In some embodiments, redundant checksums are used to verify consistency across screen reconstructions, ensuring the integrity and uniformity of displayed content.

[0069] The system of this embodiment can be used to execute the above method embodiments, and its principles and technical effects are similar, so they will not be repeated here.

[0070] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A multi-screen interaction method applied to a smart car, characterized in that: The following steps are involved: By capturing the current and previous frame image data of each display screen in the smart car, calculating the image difference, and combining user input or external events, selecting the changed area that requires interaction; Calculate the information entropy of the selected change area that needs to be interacted, evaluate its image complexity, and divide it into high-complexity and low-complexity areas; Based on the complexity evaluation results, performance-adaptive encoders are assigned to different interactive areas, and corresponding compression algorithms are selected to optimize coding efficiency and response speed. Compress and encode the data in the interactive area, and transmit the compressed data in parallel to the target display screen through the vehicle network; The received compressed data is decoded and updated on the target display screen to achieve coordinated response and real-time feedback among multiple screens, and complete the visual presentation of the user's interaction intention.

2. The multi-screen interaction method for a smart car according to claim 1, characterized in that: The step of selecting the change area requiring interaction is as follows: Obtain current frame image data of each display screen through a camera or other sensors in the smart car; Obtain the previous frame of image data of each display screen through a camera or other sensors in the smart car; Compare the image data of the current frame with the previous frame, calculate the difference in each area, and determine the area that needs to be updated based on the difference. The area with a difference greater than a preset threshold is determined as the interactive area.

3. The multi-screen interaction method for smart cars according to claim 1, characterized in that: The step of calculating information entropy for the selected change area requiring interaction includes: Get pixel data in the current frame for the selected interactive area; Calculating the probability of each pixel value appearing in the area according to the pixel values ​​in the interaction area; According to the probability distribution of pixel values, the entropy value of the interaction area is calculated to represent the complexity of the area.

4. The multi-screen interaction method for smart cars according to claim 3, characterized in that: The formula for calculating the entropy value of the interaction area is: H(R i )=-∑ x∈Ri p(x)logp(x); Among them, H(R i ) represents the information entropy of the interaction region; p(x) is the probability distribution of the pixel value x in the region; R i represents the i-th interaction area identified in the current image frame; x represents the pixel grayscale value or color value image element in the interaction area.

5. The multi-screen interaction method for smart cars according to claim 1, characterized in that: The method of allocating performance-adapted encoders to different interactive areas comprises the following steps: The coding load of the interaction region is calculated based on the complexity and size of the interaction region; Select an appropriate encoder based on the load size, with high-load areas allocated to high-efficiency encoders and low-load areas allocated to low-power encoders; Assign each interactive area to the corresponding encoder to ensure load balancing between encoders.

6. The multi-screen interaction method for smart cars according to claim 5, characterized in that: The formula for calculating the coding load of the interactive area is: L i =αH(R i )+β|R i |; Among them, L i represents the load of the i-th encoder; H(R i ) is the information entropy of the interaction area; |R i | is the size of the interaction region; α and β are adjustment factors; R i Represents the i-th interaction area identified in the current image frame.

7. The multi-screen interaction method for smart cars according to claim 1, characterized in that: The compression encoding of the data in the interactive area comprises the following steps: According to the information entropy value of each interactive area, a suitable compression algorithm is selected, where a high-efficiency compression algorithm is selected for high-entropy areas and a low-power compression algorithm is selected for low-entropy areas; Applying a corresponding compression algorithm to each selected interactive area for compression processing; Convert the compressed regional data into encoded data in preparation for transmission over the in-vehicle network.

8. The multi-screen interaction method for smart cars according to claim 7, characterized in that: The formula of the compression algorithm is: Among them, A i is the compression algorithm for the interactive region, H(R i ) is the information entropy of the interaction area, θ high is the high information entropy threshold; R i Indicates the i-th interaction area identified in the current image frame; HEVC is a highly efficient compression algorithm; JPEG is a compression method with lower computational cost.

9. The multi-screen interaction method for smart cars according to claim 1, characterized in that: The steps of transmitting the compressed data in parallel to the target display screen via the vehicle network are as follows: Allocate the compressed coded data according to the target display screens in each interactive area, and allocate corresponding coded data to each display screen; Transmit the encoded data in parallel to multiple target displays via the vehicle network; During the data transmission process, a synchronization mechanism is used to ensure that the content displayed on each display screen remains consistent.

10. A multi-screen interactive system for a smart car, applied to a multi-screen interactive method for a smart car according to any one of claims 1 to 9, characterized in that: include: An image acquisition module is used to capture the image data of the current frame and the previous frame of each display screen in the smart car; The interactive area selection module is used to calculate the difference between the current frame and the previous frame, determine the changed area and select the display area that needs to be updated; An information entropy calculation module is used to calculate the information entropy of the selected update display area and evaluate the complexity of each area according to the information entropy value to select an appropriate encoding strategy; The encoder task allocation module assigns different encoders to process different interactive areas based on the size and information entropy value of each updated display area, and ensures load balance between encoders through a load balancing algorithm; A compression coding module is used to compress and encode each interactive area using a compression algorithm to generate compressed coded data; A data transmission module is used to transmit the compressed encoded data in parallel to multiple target display screens through the vehicle network and ensure the synchronization of content between the display screens; The synchronous display module is used to ensure that the content displayed on each display screen remains consistent through a synchronization mechanism during data transmission.

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