Multi-mode car window system based on generative AI and adaptive phase change material and car window changing method

Abstract

The invention discloses a multi-mode car window system and a car window changing method based on generative AI and adaptive phase change materials, the system comprises a car window, a multi-mode information sensing module, a central processing unit and a driving module, the car window comprises multiple layers of composite adaptive phase change materials, the multi-mode information sensing module comprises a plurality of sensors, and the central processing unit is connected with the central processing unit. The central processing unit is used for acquiring real-time sensing information, and the central processing unit comprises a generative AI decision engine and is used for processing the real-time sensing information through the generative AI decision engine and outputting physical field information of the multi-layer composite adaptive phase change material; and the driving module is used for driving the multi-layer composite adaptive phase change material to realize multi-mode display based on the physical field information. According to the invention, the vehicle window can cope with complex and changeable real scenes, individual differences of different passengers in the vehicle can be sensed, and man-machine interaction and multi-mode display can be realized.

Description

Technical Field

[0001] This invention relates to the field of automotive window technology, specifically to a multimodal automotive window system and a method for changing the shape of the window based on generative AI and adaptive phase change materials. Background Technology

[0002] With the development of automotive intelligence, the traditional functions of car windows can no longer meet people's higher demands for safety, comfort, and interactive experience. Currently, car window technology mainly focuses on electrochromic glass, which can adjust light transmittance through manual or simple light-sensing control to achieve basic sunshade and heat insulation effects. However, existing technology has significant limitations: 1. Simple control logic: Most are based on a single light intensity sensor, which cannot cope with complex and ever-changing real-world scenarios.

[0003] 2. Lack of personalization: It fails to perceive the individual differences among different occupants in the vehicle, providing a "one-size-fits-all" dimming solution.

[0004] 3. Limited functionality: It can only achieve "light-dark" changes, lacks deep integration with other in-vehicle systems, and cannot be expanded to more modalities such as information display, privacy protection, and human-computer interaction.

[0005] 4. Unresolved conflict: When the driver needs a clear view while the passenger needs to rest and be protected from the sun, a system based on simple logic cannot handle such conflicting needs.

[0006] Therefore, a vehicle window system that can comprehensively perceive, make intelligent decisions, and adaptively switch between multimodal functions is needed. Summary of the Invention

[0007] To address the problems existing in the prior art, this invention provides a multimodal vehicle window system and a method for changing vehicle windows based on generative AI and adaptive phase change materials, enabling vehicle windows to cope with complex and ever-changing real-world scenarios, perceive individual differences among different occupants inside the vehicle, and achieve human-computer interaction and multimodal display.

[0008] According to a first aspect of this application, this embodiment provides a multimodal vehicle window system based on generative AI and adaptive phase change materials, comprising: Car windows, including multi-layered composite adaptive phase change materials; The multimodal information sensing module includes several sensors for acquiring real-time sensing information; The central processing unit includes a generative AI decision engine, which processes the real-time perceived information through the generative AI decision engine to obtain display information; The driving module includes a driver and a controller, which are used to generate physical field information of the multilayer composite adaptive phase change material based on the display information through the controller, and drive the multilayer composite adaptive phase change material to achieve multi-mode display based on the physical field information through the driver.

[0009] In some embodiments of this application, based on the aforementioned scheme, the vehicle window includes an inner transparent substrate, an inner transparent conductive layer, an electrochromic layer, an ion conductor layer, an ion storage layer, an outer transparent conductive layer, and an outer transparent substrate arranged sequentially from the inside of the vehicle to the outside of the vehicle. The inner transparent substrate is used for inner layer protection; The inner transparent conductive layer and the outer transparent conductive layer are used to apply a uniform electric field to the electrochromic layer, the ion conductor layer and the ion storage layer; The electrochromic layer is used to change or fade color based on ions and electrons applied by a uniform electric field; The ion conductor layer serves as an ion channel; The ion storage layer is used for outputting and receiving ions; The outer transparent substrate is used for outer protection.

[0010] In some embodiments of this application, based on the aforementioned scheme, the vehicle window includes an inner transparent substrate, an inner transparent conductive layer, a polymer-dispersed liquid crystal, an outer transparent conductive layer, and an outer transparent substrate arranged sequentially from the inside of the vehicle to the outside. The inner transparent substrate is used for inner layer protection; The inner transparent conductive layer and the outer transparent conductive layer are used to apply a uniform electric field to the polymer-dispersed liquid crystal; The polymer-dispersed liquid crystal is used for atomization based on the uniform electric field; The outer transparent substrate is used for outer protection.

[0011] According to a second aspect of this application, this embodiment provides a method for changing the window of a multimodal vehicle window system based on generative AI and adaptive phase change materials, applied to the aforementioned multimodal vehicle window system based on generative AI and adaptive phase change materials, including: Collect real-time sensing information, including environmental data stream, occupant data stream and vehicle data stream; Extract the environmental features, passenger features, and vehicle features corresponding to the environmental data stream, the passenger data stream, and the vehicle data stream, respectively; The environmental features, passenger features, and vehicle features are input into the Transformer multimodal encoder, and the fused features are output. The fused features are input into a trained classifier, which maps the fused features to the transformation scene and generates a target transformation rate related to the transformation scene. The trained classifier is trained from multiple historical fused features. A conversion model is established for the vehicle window. The target conversion rate is input into the conversion model, and a voltage is output. The voltage is the physical field information of the multilayer composite adaptive phase change material. Voltage is applied to the inner transparent conductive layer and the outer transparent conductive layer to enable the vehicle window to achieve the target conversion rate.

[0012] In some embodiments of this application, based on the foregoing scheme, the target conversion rate is the target transmittance, and the charge is obtained based on the target transmittance using the following calculation formula:

[0013] in, For the target light transmittance, In dark state, It is in a transparent state. The attenuation coefficient is... It represents the amount of charge; The voltage applied to the inner and outer transparent conductive layers is obtained based on the amount of charge. The calculation formula is as follows:

[0014] in, For electrode area, The electrolyte conductivity, To apply time, It represents voltage.

[0015] In some embodiments of this application, based on the foregoing scheme, the step of establishing a conversion model for the vehicle window, inputting the target conversion rate into the conversion model, and outputting a voltage, wherein the voltage is the physical field information of the multilayer composite adaptive phase change material, includes: The target conversion rate is the target atomization rate. Based on the target atomization rate, the target scattering coefficient is obtained, and the calculation formula is as follows:

[0016] in, For the target atomization rate, The target scattering coefficient, The thickness of the polymer-dispersed liquid crystal; The uniform electric field is obtained based on the target scattering coefficient, and the calculation formula is as follows:

[0017] in, The characteristic electric field reflects the sensitivity of the polymer-dispersed liquid crystal to the electric field. This represents the scattering coefficient of the polymer-dispersed liquid crystal in the absence of an electric field. It is a non-linear exponent; The voltage is obtained based on a uniform electric field, and the calculation formula is as follows:

[0018] in, It represents voltage.

[0019] In some embodiments of this application, based on the foregoing scheme, the environmental data stream includes optical data, image data, point cloud and radio frequency data, and location and navigation data; The occupant data stream includes visual data, physiological data, and acoustic data; The vehicle data stream includes vehicle status data acquired via the CAN bus.

[0020] In some embodiments of this application, based on the aforementioned scheme, data preprocessing, time stamp synchronization, and unified spatial coordinate system are performed on the data in the environmental data stream, the occupant data stream, and the vehicle data stream. The data preprocessing includes image preprocessing, point cloud preprocessing, and signal filtering. The image preprocessing is used to denoise, correct distortion, and white balance the camera images. The point cloud preprocessing is used to downsample, filter out outliers, and segment the ground in the lidar point cloud. The signal filtering is used to apply Kalman filtering to the time-series signal, smooth instantaneous fluctuations, and eliminate glitches.

[0021] According to a fourth aspect of this application, this embodiment provides a computer-readable storage medium having a computer program stored thereon, the computer program including executable instructions that, when executed by a processor, implement the method described above.

[0022] According to a fifth aspect of this application, an electronic device according to this embodiment includes: One or more processors; A memory for storing executable instructions of the processor, which, when executed by the one or more processors, cause the one or more processors to implement the method described above.

[0023] The beneficial effects of this application are as follows: (1) This application provides a multimodal window system and window change method based on generative AI and adaptive phase change material. Through generative AI, the system can intelligently understand and make decisions on complex working conditions, drive the phase change material to adaptively switch between multiple modes, realize the intelligent and personalized nature of the window in terms of safety, comfort, privacy and information interaction, and significantly improve the driving experience.

[0024] (2) This application provides a multimodal window system and window changing method based on generative AI and adaptive phase change material, which can understand complex scenes and occupant intentions. By changing the molecular arrangement of the adaptive phase change material layer under external excitation, the transmittance, color and fogging degree can be controlled to drive the window to seamlessly switch between multiple modes such as color change or fogging, and realize the intelligent unity of safety, comfort, privacy and interaction.

[0025] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and do not limit this application. Attached Figure Description

[0026] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and are intended to explain the invention, but do not constitute an undue limitation thereof. In the drawings: Figure 1 This is a schematic diagram of a multimodal vehicle window system based on generative AI and adaptive phase change materials according to the present invention; Figure 2 This is a schematic diagram of a vehicle window in the first multimodal vehicle window system based on generative AI and adaptive phase change materials according to the present invention. Figure 3 This is a schematic diagram of a vehicle window in the second type of multimodal vehicle window system based on generative AI and adaptive phase change materials according to the present invention. Figure 4 This is a flowchart of a method for changing the window in a multimodal window system based on generative AI and adaptive phase change materials, according to the present invention. Figure 5 This is a schematic diagram of an electronic device according to the present invention. Detailed Implementation

[0027] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0028] It should be understood that the terms "comprising" and other similar expressions in the specification, claims, and accompanying drawings of this invention are intended to cover a non-exclusive inclusion, such as a process, method, system, or apparatus that includes a series of steps or units and is not limited to the listed steps or units. Furthermore, "first" and "second" are used to distinguish different objects and are not intended to describe a specific order.

[0029] According to the first aspect of this application, Figure 1 As shown, this embodiment provides a multimodal vehicle window system based on generative AI and adaptive phase change materials, including a vehicle window, a multimodal information perception module, a central processing unit, and a drive module.

[0030] The car windows incorporate multi-layered composite adaptive phase change materials.

[0031] The multimodal information sensing module includes several sensors used to acquire real-time sensing information.

[0032] The central processing unit includes a generative AI decision engine, which processes the real-time perceived information and outputs the physical field information of the multilayer composite adaptive phase change material.

[0033] The driving module is used to drive the multilayer composite adaptive phase change material to achieve multi-mode display based on the physical field information.

[0034] This embodiment provides a multimodal vehicle window system based on generative AI and adaptive phase change materials, which enables the vehicle window to cope with complex and ever-changing real-world scenarios, perceive the individual differences of different occupants inside the vehicle, and realize human-computer interaction and multi-mode display.

[0035] In some implementations of this embodiment, such as Figure 2 As shown, the vehicle window includes, in sequence from the inside of the vehicle to the outside, an inner transparent substrate, an inner transparent conductive layer, an electrochromic layer, an ion conductor layer, an ion storage layer, an outer transparent conductive layer, and an outer transparent substrate.

[0036] The inner transparent substrate is used for inner layer protection.

[0037] The inner transparent conductive layer and the outer transparent conductive layer are used to apply a uniform electric field to the electrochromic layer, the ion conductor layer and the ion storage layer.

[0038] The electrochromic layer is used to change or fade color based on ions and electrons applied by a uniform electric field.

[0039] The ion conductor layer is an ion channel.

[0040] The ion storage layer is used for outputting and receiving ions.

[0041] The outer transparent substrate is used for outer protection.

[0042] In this embodiment, both the inner and outer transparent conductive layers are indium tin oxide thin films located on both sides, serving as electrodes to apply a uniform electric field to the electrochromic layer, the ion conductor layer, and the ion storage layer, while ensuring high light transmittance.

[0043] In this embodiment, the electrochromic layer is composed of electrochromic materials, such as WO3 for coloring and NiO for complementary coloring.

[0044] In this embodiment, the ion conductor layer is a solid or gel-state electrolyte layer (such as Ta2O3, LiPON), which serves as a channel for ion migration while blocking electron conduction to prevent short circuits.

[0045] In this embodiment, the ion storage layer provides or accepts corresponding ions to the electrode layer (such as IrO2, V2O3) when the electrochromic layer changes color, so as to maintain the charge balance of the entire system.

[0046] During the color-changing process, the drive module outputs a positive DC voltage signal, and the electric field direction is from the outside to the inside. Lithium ions (Li+) are injected into the electrochromic layer through the electrolyte from the ion storage layer, and electrons (e+) are injected into the electrochromic layer. - Synchronously injected via external circuit, an electrochemical change occurs: WO3 + xLi + +xe - →LixWO3, the material's color changes from transparent to blue.

[0047] During the fading process, the drive module outputs a reverse DC voltage signal, with the electric field direction from the inside out. Lithium ions (Li+) are extracted from the electrochromic layer and returned to the ion storage layer, while electrons (e+) are released. - Synchronously extracted from the external circuit, an electrochemical change occurs: LixWO3 → WO3 + xLi + +xe - The material's color changed from blue to transparent.

[0048] In some implementations of this embodiment, such as Figure 3 As shown, the vehicle window includes an inner transparent substrate, an inner transparent conductive layer, a polymer dispersed liquid crystal (PDLC) layer, an outer transparent conductive layer, and an outer transparent substrate arranged sequentially from the inside of the vehicle to the outside.

[0049] The inner transparent substrate is used for inner layer protection.

[0050] The inner transparent conductive layer and the outer transparent conductive layer are used to apply a uniform electric field to the polymer-dispersed liquid crystal.

[0051] The polymer-dispersed liquid crystal is used for atomization based on the uniform electric field.

[0052] The outer transparent substrate is used for outer protection.

[0053] In this embodiment, in the power-off state: without an external electric field, the liquid crystal molecules dispersed in the polymer-dispersed liquid crystal are randomly and disordered in their arrangement direction. When incident light passes through the liquid crystal microdroplets, the disordered arrangement of the liquid crystal molecules causes its refractive index to mismatch with the surrounding polymer matrix, resulting in strong scattering of light on each liquid crystal surface. The polymer-dispersed liquid crystal presents a milky white, opaque, fogged state, achieving light-shielding and privacy effects.

[0054] In this embodiment, under energized conditions: the driving module outputs an AC electric field signal, and the liquid crystal molecules, as dipoles, are subjected to the electric field force. The long axes of all liquid crystal molecules will be uniformly and orderly arranged along the direction of the electric field. Therefore, the ordinary light refractive index of the liquid crystal molecules becomes very close to the refractive index of the polymer matrix. At this time, the incident light can pass through the entire PDLC layer with approximately no scattering, and the material changes from a milky white atomized state to a transparent state.

[0055] Thus, this embodiment can understand complex scenarios and occupant intentions, and by changing the molecular arrangement of the adaptive phase change material layer under external excitation, it can achieve controllable changes in light transmittance, color, and degree of fogging, driving the window to seamlessly switch between multiple modes such as color change or fogging, and achieving intelligent unity of safety, comfort, privacy and interaction.

[0056] In some embodiments of this example, the outer transparent substrate is an outer glass layer, and the inner transparent substrate integrates a transparent display layer and a touch sensing layer, making the car window an information display and interactive interface. Specifically, the outer transparent substrate includes a transparent display layer, a touch sensing layer, and an inner glass layer.

[0057] According to the second aspect of this application, such as Figure 4 As shown, this embodiment provides a method for changing vehicle windows in a multimodal vehicle window system based on generative AI and adaptive phase change materials, including: Step S1: Collect real-time sensing information, which includes environmental data stream, occupant data stream and vehicle data stream.

[0058] Step S2: Extract the environmental features, passenger features, and vehicle features corresponding to the environmental data stream, the passenger data stream, and the vehicle data stream, respectively.

[0059] Step S3: Input the environmental features, passenger features, and vehicle features into the Transformer multimodal encoder and output the fused features.

[0060] Step S4: Input the fused features into the trained classifier, so that the fused features are mapped to the transformation scene, and generate a target transformation rate related to the transformation scene, wherein the trained classifier is trained from multiple historical fused features.

[0061] Step S5: Establish a conversion model for the vehicle window, input the target conversion rate into the conversion model, and output a voltage. The voltage is the physical field information of the multilayer composite adaptive phase change material. Apply voltage to the inner transparent conductive layer and the outer transparent conductive layer to enable the vehicle window to achieve the target conversion rate.

[0062] In some embodiments of this example, the target conversion rate is the target transmittance, and the charge is obtained based on the target transmittance using the following formula:

[0063] in, For the target light transmittance, In dark state, It is in a transparent state. The attenuation coefficient is... It represents the amount of charge; The voltage applied to the inner and outer transparent conductive layers is obtained based on the amount of charge. The calculation formula is as follows:

[0064] in, For electrode area, The electrolyte conductivity, To apply time, It represents voltage.

[0065] In some embodiments of this example, the target conversion rate is the target atomization rate, and the target scattering coefficient is obtained based on the target atomization rate, calculated using the following formula:

[0066] in, For the target atomization rate, The target scattering coefficient, The thickness of the polymer-dispersed liquid crystal; The uniform electric field is obtained based on the target scattering coefficient, and the calculation formula is as follows:

[0067] in, The characteristic electric field reflects the sensitivity of the polymer-dispersed liquid crystal to the electric field. This represents the scattering coefficient of the polymer-dispersed liquid crystal in the absence of an electric field. It is a non-linear exponent; The voltage is obtained based on a uniform electric field, and the calculation formula is as follows:

[0068] in, It represents voltage.

[0069] The following specific examples illustrate the scene transition and the target transmittance related to the scene transition. When the scene transition is to reduce light transmission and glare, the target transmittance is 10%-20%; when the scene transition is to increase light transmission and enhance the field of vision, the target transmittance is 60%-70%; when the scene transition is to darken a part of the side window, the target transmittance is 5%-10% of the area. This example does not limit this.

[0070] In some embodiments of this example, real-time sensing information is collected through a multimodal information sensing module, including environmental data stream, occupant data stream, and vehicle data stream.

[0071] Specifically, the environmental data stream includes optical data, image data, point cloud and radio frequency (RF) data, and location and navigation data. Optical data includes ambient light and ultraviolet light intensity sampled at high frequencies (e.g., 10Hz) by illumination and ultraviolet sensors; image data includes visible light and infrared images captured by surround-view cameras at a fixed frame rate (e.g., 30fps); point cloud and RF data includes high-precision 3D point clouds generated by lidar; and a target list (distance, speed, angle) output by millimeter-wave radar. Location and navigation data includes GPS providing the vehicle's precise position, speed, and heading angle, and the navigation system providing predicted path information.

[0072] The occupant data stream includes visual data, physiological data, and acoustic data. The visual data includes images of occupant posture, facial expressions, and eye movements captured by in-vehicle cameras. The physiological data includes non-contact measurements of body surface temperature obtained by infrared sensors and heart rate and / or respiratory rate detected by seat sensors. The acoustic data includes voice commands collected by a microphone array and ambient sounds inside the vehicle.

[0073] Vehicle data streams include vehicle status data acquired via the CAN bus, such as vehicle speed, turn signal status, and windshield wiper status.

[0074] This embodiment comprehensively utilizes multiple sensors, including optical, acoustic, and imaging sensors, to capture information such as in-vehicle lighting, weather, road conditions, surrounding vehicles, as well as in-vehicle temperature, humidity, and sound. This facilitates the acquisition of environmental information and passenger intentions. By leveraging the understanding and reasoning capabilities of generative AI, it achieves cross-domain processing from "perception-reaction" to "authentication-decision," enabling it to handle complex and contradictory scenarios that traditional rule engines cannot resolve, and providing a customized light and heat environment for each passenger.

[0075] In some embodiments of this example, before extracting the environmental features, passenger features, and vehicle features corresponding to the environmental data stream, passenger data stream, and vehicle data stream, respectively, it is necessary to perform data preprocessing, synchronize timestamps, and unify spatial coordinate systems on the data in the environmental data stream, passenger data stream, and vehicle data stream, since the data in these streams usually contains noise and has different formats. Data preprocessing includes image preprocessing, point cloud preprocessing, and signal filtering. Image preprocessing is used to denoise, correct distortion, and white balance the camera images. Point cloud preprocessing is used to downsample, filter outliers, and segment the ground in the lidar point cloud. Signal filtering is used to apply Kalman filtering to time-series signals such as illumination and temperature to smooth instantaneous fluctuations and eliminate glitches.

[0076] Synchronization timestamps involve all sensors using a unified high-precision clock source (PTP protocol) to accurately timestamp their respective data.

[0077] A unified spatial coordinate system includes transforming the coordinates of all sensors to a vehicle coordinate system with the vehicle's center of mass as the origin.

[0078] In some implementations of this embodiment, environmental features, passenger features, and vehicle features corresponding to the environmental data stream, passenger data stream, and vehicle data stream are extracted respectively, and the synchronized low-level data is transformed into high-level, semantically rich features.

[0079] In some implementations of this embodiment, environmental features, passenger features, and vehicle features are fused at the feature level, and complementary features of different modalities are spliced ​​together. For example, the feature of "strong light detected in the image" can be fused with the feature of "extremely high light sensor reading" to jointly strengthen the judgment of "the presence of glare source" so as to generate a unified scene description: a structured "scene description file" is generated, mapped to the relevant color-changing scene or fogging scene, and transmitted to the central processing unit in real time with low latency through a high-speed data bus.

[0080] In some implementations of this embodiment, the generative AI decision engine includes a Transformer multimodal encoder, a classifier, and a transformation model. The Transformer multimodal encoder receives fused features, constructs a transformation scenario, and integrates historical state data (such as environmental change trends in the previous few seconds) and preset rules and prior knowledge (such as security protocols) to form a decision foundation with richer information that includes spatiotemporal dimensions.

[0081] The Transformer multimodal encoder is used to perform in-depth analysis of the constructed transformation scene. Through a self-attention mechanism, the engine calculates and weighs the intrinsic relationships between all input information, ultimately understanding the current overall situation and generating a highly condensed color-changing or fog-like scene.

[0082] In some embodiments of this example, the vehicle window is divided into multiple areas. The physical field information of the multi-layered composite adaptive phase change material corresponding to each area is obtained by the central processing unit. The driving module drives the multi-layered composite adaptive phase change material corresponding to each area to achieve multi-mode display, further leading to a composite scheme of "shielding a specific area and triggering a gradual change and displaying a prompt message at a specific time point." Subsequently, the generative AI decision engine calls upon built-in conflict resolution strategies (safety first principle), personalization strategies (based on occupant habits), and scene prediction strategies (based on navigation data) to verify, adjust, and optimize this preliminary scheme, ensuring that the final decision can collaboratively meet multiple potentially competing goals such as safety, comfort, and personalization.

[0083] The optimized intelligent decisions are translated into structured control instructions that downstream drive modules can precisely execute. These instructions specify the concrete objects to be operated on, actions, parameters, and timing, thus transforming abstract intelligent decisions into physically executable commands.

[0084] It continuously listens for and receives structured control commands from the central processing unit, and parses and verifies the commands to ensure that the command format is correct and the parameters are within a safe and valid range.

[0085] Based on the parsed instruction parameters, the control algorithm inside the module starts working, converting the target atomization rate or target transmittance in the instruction into a precise electrical signal to drive the adaptive phase change material layer.

[0086] The generated control electrical signal is applied to the transparent conductive layer of the target area through the output circuit. The voltage, current and other parameters of the output circuit are monitored in real time to ensure the stability and absence of abnormalities in the driving process.

[0087] Once the drive signal is output stably and the status feedback is normal, the module will return a "command execution successful" confirmation signal to the central processing unit.

[0088] If an anomaly is detected (such as short circuit, overload, or drive timeout), the module immediately takes protective measures (cuts off the output) and reports "execution failure" and error code to the central processing unit, thereby triggering a re-decision or safety handling mechanism.

[0089] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0090] According to a fourth aspect of this application, this embodiment provides a computer-readable storage medium having a computer program stored thereon, the computer program including executable instructions that, when executed by a processor, implement the method described above.

[0091] The present invention can implement all or part of the processes in the above methods, or it can be accomplished by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or system capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0092] According to a fifth aspect of this application, an electronic device is provided, such as... Figure 5 As shown, it includes: One or more processors; Memory is used to store executable instructions for the processor, which, when executed by one or more processors, cause one or more processors to implement the methods described above.

[0093] Electronic devices are manifested in the form of general-purpose computing devices. Components of an electronic device may include, but are not limited to: at least one processor, at least one memory, and a bus connecting different system components (including memory and processor).

[0094] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of a computer system, connecting all parts of the computer system through various interfaces and lines.

[0095] Memory can be used to store computer programs and / or modules. The processor implements various functions of the computer system by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory. Memory can mainly include a program storage area and a data storage area. The program storage area can store the operating system and at least one application program required for a function (e.g., sound playback, image playback, etc.); the data storage area can store data created based on the use of the mobile phone (e.g., audio data, video data, etc.). Furthermore, memory can include high-speed random access memory, and can also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, SmartMedia Cards (SMC), Secure Digital (SD) cards, Flash Cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0096] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, servers, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and memory) containing computer-usable program code.

[0097] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), servers, and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0098] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction set implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0099] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0100] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0101] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0102] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. 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 of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multimodal vehicle window system based on generative AI and adaptive phase change materials, characterized in that, include: Car windows, including multi-layered composite adaptive phase change materials; The multimodal information sensing module includes several sensors for acquiring real-time sensing information; The central processing unit includes a generative AI decision engine, which processes the real-time perceived information through the generative AI decision engine and outputs the physical field information of the multilayer composite adaptive phase change material. The driving module is used to drive the multilayer composite adaptive phase change material to achieve multi-mode display based on the physical field information.

2. The system according to claim 1, characterized in that: The vehicle window comprises, from the inside of the vehicle to the outside, an inner transparent substrate, an inner transparent conductive layer, an electrochromic layer, an ion conductor layer, an ion storage layer, an outer transparent conductive layer, and an outer transparent substrate. The inner transparent substrate is used for inner layer protection; The inner transparent conductive layer and the outer transparent conductive layer are used to apply a uniform electric field to the electrochromic layer, the ion conductor layer and the ion storage layer; The electrochromic layer is used to change or fade color based on ions and electrons applied by a uniform electric field; The ion conductor layer serves as an ion channel; The ion storage layer is used for outputting and receiving ions; The outer transparent substrate is used for outer protection.

3. The system according to claim 1, characterized in that: The vehicle window comprises, in sequence from the inside of the vehicle to the outside, an inner transparent substrate, an inner transparent conductive layer, a polymer-dispersed liquid crystal, an outer transparent conductive layer, and an outer transparent substrate; The inner transparent substrate is used for inner layer protection; The inner transparent conductive layer and the outer transparent conductive layer are used to apply a uniform electric field to the polymer-dispersed liquid crystal; The polymer-dispersed liquid crystal is used for atomization based on the uniform electric field; The outer transparent substrate is used for outer protection.

4. A method for changing the window of a multimodal vehicle window system based on generative AI and adaptive phase change materials, applied to the multimodal vehicle window system based on generative AI and adaptive phase change materials as described in any one of claims 1-3, characterized in that, include: Collect real-time sensing information, including environmental data stream, occupant data stream and vehicle data stream; Extract the environmental features, passenger features, and vehicle features corresponding to the environmental data stream, the passenger data stream, and the vehicle data stream, respectively; The environmental features, passenger features, and vehicle features are input into the Transformer multimodal encoder, and the fused features are output. The fused features are input into a trained classifier, which maps the fused features to the transformation scene and generates a target transformation rate related to the transformation scene. The trained classifier is trained from multiple historical fused features. A conversion model is established for the vehicle window. The target conversion rate is input into the conversion model, and a voltage is output. The voltage is the physical field information of the multilayer composite adaptive phase change material. Voltage is applied to the inner transparent conductive layer and the outer transparent conductive layer to enable the vehicle window to achieve the target conversion rate.

5. The method according to claim 4, characterized in that, The conversion model for the vehicle window is established, the target conversion rate is input into the conversion model, and the output voltage is the physical field information of the multilayer composite adaptive phase change material, including: The target conversion rate is the target transmittance. The charge is obtained based on the target transmittance, and the calculation formula is as follows: in, For the target light transmittance, In dark state, It is in a transparent state. The attenuation coefficient is... It represents the amount of charge; The voltage applied to the inner and outer transparent conductive layers is obtained based on the amount of charge. The calculation formula is as follows: in, For electrode area, The electrolyte conductivity, To apply time, It represents voltage.

6. The method according to claim 4, characterized in that, The conversion model for the vehicle window is established, the target conversion rate is input into the conversion model, and the output voltage is the physical field information of the multilayer composite adaptive phase change material, including: The target conversion rate is the target atomization rate. Based on the target atomization rate, the target scattering coefficient is obtained, and the calculation formula is as follows: in, For the target atomization rate, The target scattering coefficient, The thickness of the polymer-dispersed liquid crystal; The uniform electric field is obtained based on the target scattering coefficient, and the calculation formula is as follows: in, The characteristic electric field reflects the sensitivity of the polymer-dispersed liquid crystal to the electric field. This represents the scattering coefficient of the polymer-dispersed liquid crystal in the absence of an electric field. It is a non-linear exponent; The voltage is obtained based on a uniform electric field, and the calculation formula is as follows: in, It represents voltage.

7. The method according to claim 4, characterized in that: The environmental data stream includes optical data, image data, point cloud and radio frequency data, and location and navigation data; The occupant data stream includes visual data, physiological data, and acoustic data; The vehicle data stream includes vehicle status data acquired via the CAN bus.

8. The method according to claim 7, characterized in that: The data in the environmental data stream, the occupant data stream, and the vehicle data stream are preprocessed, and timestamps are synchronized and a unified spatial coordinate system is established. The data preprocessing includes image preprocessing, point cloud preprocessing, and signal filtering. The image preprocessing is used to denoise, correct distortion, and adjust white balance for camera images. The point cloud preprocessing is used to downsample, filter out outliers, and segment the ground for lidar point clouds. The signal filtering is used to apply Kalman filtering to time-series signals, smooth instantaneous fluctuations, and eliminate glitches.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program includes executable instructions that, when executed by a processor, implement the method described in any one of claims 4-8.

10. An electronic device, characterized in that, include: One or more processors; A memory for storing executable instructions of the processor, which, when executed by the one or more processors, cause the one or more processors to perform the method according to any one of claims 4-8.

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