Electronic and electrical architecture, function backup method, seat adjusting method and vehicle

Abstract

The invention relates to the technical field of intelligent sports products, and discloses an electronic and electrical architecture, a function backup method, a seat adjusting method and a vehicle. Edge nodes are connected with first sensing and interaction components in the same distribution area, and a power distribution and data transmission module is connected with a first edge node corresponding to the same power distribution and communication area; ordered association of components according to areas, power distribution and communication is realized, the complexity and length of wiring harnesses in the whole equipment are greatly reduced, and the cost is saved. And the power distribution and data transmission module is simultaneously connected with the central computing platform and the power supply part, so that data interaction between the central computing platform and the sensing and interaction part can be completed by relying on the edge nodes, and power supply distribution can be realized. The problems of disordered component connection and complex data transmission path in a traditional architecture are avoided, data interaction is more efficient, power distribution is more accurate, a foundation is laid for subsequent software reuse, and the cost of repeated development of different AI sports equipment architectures is reduced.

Description

Technical Field

[0001] This invention relates to the field of intelligent sports product technology, specifically to electronic and electrical architecture, function backup methods, seat adjustment methods, and vehicles. Background Technology

[0002] Traditional electronic and electrical architectures are solutions designed for single product types, such as the electronic and electrical architectures of intelligent electric vehicles and intelligent robots. With the development of intelligent technologies, the ecosystem of AI-powered sports products is becoming increasingly rich, and traditional automotive companies are gradually transforming into comprehensive technology companies with diverse ecosystems. Various products, empowered by AI, exhibit richer and more intelligent functionalities, and behind these products, software accounts for an increasingly larger proportion of product value. Previous architectural solutions required the repeated design and development of electronic and electrical architectures for each type of AI-powered sports product, hindering the reuse of high-value software and inevitably leading to significant redundant development investments. Summary of the Invention

[0003] This invention provides an electronic and electrical architecture, a function backup method, a seat adjustment method, and a vehicle to solve the problem that the electronic and electrical architectures in related technologies are solutions proposed for single types of products. Each type of AI sports product requires repeated design and development of electronic and electrical architectures, resulting in a large amount of repetitive development investment.

[0004] In a first aspect, the present invention provides an electronic and electrical architecture for use in artificial intelligence sports equipment. The electronic and electrical architecture includes: a central computing platform, a power supply component, several power distribution and data transmission modules, several edge nodes, and several sensing and interaction components. The edge nodes are connected to at least one first sensing and interaction component, which is a sensing and interaction component located in the same distribution area in the device. The power distribution and data transmission module is connected to at least one first edge node, which is the edge node connected to the sensing and interaction components in the same power distribution and communication area of ​​the device. The power distribution and data transmission module is also connected to the central computing platform and the power supply component, respectively, and is used to realize data interaction between the central computing platform and each of the sensing and interaction components through the edge node, and to distribute power in the electronic and electrical architecture of the power supply component.

[0005] This invention achieves orderly association of components by region and power distribution / communication by connecting edge nodes to first sensing and interaction components in the same distribution area, and connecting the power distribution and data transmission module to the first edge node corresponding to the same power distribution and communication area. This significantly reduces the complexity and length of wiring harnesses in the entire device, saving costs. Furthermore, the power distribution and data transmission module simultaneously connects the central computing platform and the power supply component, enabling data interaction between the central computing platform and the sensing and interaction components via edge nodes, while also achieving power distribution. This structure avoids the problems of chaotic component connections and complex data transmission paths in traditional architectures, making data interaction more efficient and power allocation more precise. It also lays the foundation for subsequent software reuse, reducing the cost of redundant development for different AI motion device architectures.

[0006] In a second aspect, the present invention provides a functional backup method, the method being applied to a vehicle, the vehicle employing the electronic and electrical architecture provided by the first aspect above or any corresponding embodiment thereof, the method comprising: Based on the driving functions of the vehicle, the vehicle control function software is divided into software function modules to obtain multiple software function modules. Each software function module and its corresponding redundant backup module are deployed in different computing centers of the central computing platform. The computing centers include: a main computing center and at least one auxiliary computing center. Based on the calling relationship between the software functions contained in each software functional module, a software scheduling table is generated. The software scheduling table includes: the deployment address information of the redundant backup module corresponding to the software functional module, and the scheduling link used to characterize the scheduling order between the software functions contained in the software functional module, so that each software functional module is scheduled to run according to the scheduling link. When any software function module is detected to have an anomaly during operation, the scheduling link is updated based on the software function associated with the anomaly and the deployment address information to obtain the updated scheduling link. The software function modules that malfunction are scheduled to run according to the updated scheduling link.

[0007] This invention divides software functional modules according to driving functions based on a specific electronic and electrical architecture, and deploys them along with corresponding redundant backup modules in different computing centers of a central computing platform. A scheduling link is generated based on software function call relationships. In case of anomalies, the scheduling link is updated based on the function associated with the anomaly and the deployment address of the redundant backup module. The software functional module experiencing the anomaly is then scheduled to run according to the updated scheduling link. This redundancy implementation scheme for vehicle control functions eliminates the need for full data reprocessing; it significantly reduces control command output lag by dynamically adjusting and updating the scheduling link, ensuring the continuity of functions in advanced driver assistance scenarios. Simultaneously, the deployment in different computing centers achieves distributed backup of each software functional module at the hardware level. Combined with the dynamic adjustment of the scheduling link, this ensures the integrity and reliability of the vehicle control functions. While achieving safety redundancy for vehicle control functions, it also meets the real-time requirements of advanced driver assistance, further ensuring vehicle driving safety.

[0008] Thirdly, the present invention provides a seat adjustment method, the method being applied to a vehicle, the vehicle employing the electronic and electrical architecture provided in the first aspect above or any corresponding embodiment thereof, the method comprising: Obtain the current signal of the vehicle's power steering motor; Extract feature data related to road surface roughness from the current signal; The feature data is classified to determine the road surface type of the current road surface on which the vehicle is driving; In response to the road surface type being an abnormally rough road surface, the relative positional relationship between the abnormally rough road surface and the vehicle is determined; Based on the relative positional relationship, at least one seat in the vehicle is adjusted to increase the support force of the seat in the direction of the abnormally rough road surface.

[0009] This invention utilizes a specific electronic and electrical architecture to acquire the power steering motor current signal, extract road surface roughness-related feature data, and classify and determine road surface types. When an abnormally rough road surface is identified, the seat support force is adjusted based on its relative position to the vehicle, achieving coordinated adjustment across different control domains of the vehicle. This solution does not rely on steering operation, can actively sense various types of rough road surfaces, and can also specifically adjust the seat support force in the corresponding direction, effectively alleviating discomfort for occupants on the rough road side, significantly improving the riding experience and comfort under different road conditions, and enhancing the user's driving experience.

[0010] Fourthly, the present invention provides a vehicle that employs the electronic and electrical architecture provided in the first aspect or any corresponding embodiment thereof.

[0011] In some alternative implementations, the vehicle includes: The memory and the processor are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the method provided by the second aspect above or any corresponding embodiment thereof, or to perform the method provided by the third aspect above or any corresponding embodiment thereof.

[0012] Fifthly, the present invention provides a computer-readable storage medium storing computer instructions, which are used to cause a computer to perform the method provided in the second aspect or any of the corresponding embodiments thereof, or to perform the method provided in the third aspect or any of the corresponding embodiments thereof.

[0013] In a sixth aspect, the present invention provides a computer program product, including computer instructions, which are used to cause a computer to perform the method provided in the second aspect or any of its corresponding embodiments, or to perform the method provided in the third aspect or any of its corresponding embodiments.

[0014] The beneficial effects of this invention are: This invention achieves orderly association of components by region and power distribution / communication by connecting edge nodes to first sensing and interaction components in the same distribution area, and connecting the power distribution and data transmission module to the first edge node corresponding to the same power distribution and communication area. This significantly reduces the complexity and length of wiring harnesses in the entire device, saving costs. Furthermore, the power distribution and data transmission module simultaneously connects the central computing platform and the power supply component, enabling data interaction between the central computing platform and the sensing and interaction components via edge nodes, while also achieving power distribution. This structure avoids the problems of chaotic component connections and complex data transmission paths in traditional architectures, making data interaction more efficient and power allocation more precise. It also lays the foundation for subsequent software reuse, reducing the cost of redundant development for different AI motion device architectures. Attached Figure Description

[0015] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the electronic and electrical architecture according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the control logic business flow of the electronic and electrical architecture according to an embodiment of the present invention; Figure 3This is a schematic diagram of the structure of the central computing platform according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of the electrical and digital hub in an embodiment of the present invention; Figure 5 This is a schematic diagram illustrating the working principle of the data exchange module according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the chip power distribution module according to an embodiment of the present invention; Figure 7 This is a schematic diagram of the data and power distribution coupling module according to an embodiment of the present invention; Figure 8 This is a schematic diagram of the edge node structure according to an embodiment of the present invention; Figure 9 This is a schematic diagram of the integrated structure of the electrical and digital hub and edge nodes according to an embodiment of the present invention; Figure 10 This is a schematic diagram of the communication network of the electronic and electrical architecture according to an embodiment of the present invention; Figure 11A This is a schematic diagram of the power supply network of the electronic and electrical architecture according to an embodiment of the present invention; Figure 11B This is a schematic diagram of the power supply system of the electronic and electrical architecture according to an embodiment of the present invention; Figure 12 This is a schematic flowchart of the first type of functional backup method according to an embodiment of the present invention; Figure 13 This is a schematic diagram of a second process for a functional backup method according to an embodiment of the present invention; Figure 14 This is a schematic diagram of the third process of the functional backup method according to an embodiment of the present invention; Figure 15 This is a schematic flowchart of a first embodiment of a seat adjustment method according to the present invention; Figure 16 This is a schematic diagram of a second process for a seat adjustment method according to an embodiment of the present invention; Figure 17 This is a schematic diagram of the third process of the seat adjustment method according to an embodiment of the present invention; Figure 18 This is a schematic diagram of the hardware structure of a vehicle according to an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0019] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0020] Furthermore, in the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0021] In related technologies, electronic and electrical architectures are solutions proposed for single-type products. Each type of AI motion product requires repeated design and development of its electronic and electrical architecture, leading to significant repetitive development costs. This invention addresses this issue by considering the common characteristics of motion-based AI intelligent products such as smart electric vehicles, robots, drones, and unmanned agricultural machinery. Taking vehicles as an example, it designs an integrated electronic and electrical architecture solution centered around AI intelligence, based on a top-level architectural concept. All motion-based AI intelligent products can be considered to consist of the following components: 1) A computing and control center, i.e., a high-performance central computing platform, similar to the brain, and may also include communication modules for external connections; 2) A power distribution and data hub, composed of several power and data hubs, responsible for power distribution to all electrical equipment in the vehicle and the exchange of different types of data; 3) Sensing and interaction components, such as cameras, radar, and other sensing devices, and screens and speakers, and other interactive components. 4) Power supply components, including high-voltage and low-voltage power supplies. The high-voltage power supply provides power to the entire AI product, while the low-voltage power supply powers other controllers, sensing / actuating components in the vehicle. 5) Edge nodes, which connect to various motors, lights, and other drive modules, as well as sensors related to diagnostics and data acquisition, and automotive electric drive, braking, and steering components, drone propellers, and humanoid robot limbs, are responsible for data interaction. 6) Power network, which provides power to various computing, control, sensing, and execution components, ensuring power supply safety based on redundant power supply. 7) Communication network, which transmits data from cameras, radar, and various sensors to the central computing platform, and then feeds back the computing and control results to the drive module, ensuring the safety and redundancy of data exchange based on a data loop.

[0022] The aforementioned architectural solution employs a layered design for computing and control, sensing components, and drive execution components, achieving a high degree of separation between drive and control. Control algorithms are highly centralized on a central computing platform, leveraging high-performance chips to implement AI, thereby improving software development and OTA upgrade efficiency. Edge nodes, through MCU-less technology, are used solely for data transmission, simplifying hardware configuration, reducing costs, and increasing component standardization. This Electrical / Electronic Architecture (EE) solution is not only suitable for intelligent electric vehicles but also for various AI-driven motion products such as robots, drones, and unmanned agricultural machinery.

[0023] This embodiment provides an electronic and electrical architecture for use in artificial intelligence-based motion devices, such as... Figure 1 As shown, the electronic and electrical architecture includes: a central computing platform 101, a power supply unit 102, and several power distribution and data transmission modules 103 (in... Figure 1The device includes an electronic and digital hub, several edge nodes 104 and several sensing and interaction components 105, wherein the edge nodes 104 are connected to at least one first sensing and interaction component, and the first sensing and interaction component is a sensing and interaction component 105 located in the same distribution area in the device. The power distribution and data transmission module 103 is connected to at least one first edge node, which is the edge node 104 connected to the sensing and interaction component 105 in the same power distribution and communication area in the device. The power distribution and data transmission module 103 is also connected to the central computing platform 101 and the power supply component 102 respectively, and is used to realize data interaction between the central computing platform 101 and each sensing and interaction component 105 through the edge node 104, and to distribute power in the electronic and electrical architecture of the power supply component 102.

[0024] The AI-powered sports equipment can be intelligent electric vehicles, robots, drones, unmanned agricultural machinery, or other AI-powered sports products. In this embodiment of the invention, an AI-powered sports equipment is used as an example of a vehicle integrated with artificial intelligence; this is merely an example and the invention is not limited thereto. The aforementioned sensing and interaction components include: sensing components and execution components. Sensing components include: radar, sensors, and other components with data acquisition functions; execution components include: microphones, speakers, electric seats, and other components that perform the functions of each device.

[0025] It should be noted that, based on the communication and power distribution component requirements at different locations within the AI ​​motion device, the entire device can be divided into several power distribution and communication zones. Each power distribution and communication zone is configured with a power distribution and data transmission module. For example, the entire vehicle can be divided into three power distribution and communication zones: the front, the body, and the rear. Each power distribution and communication zone can be further divided into multiple distribution zones according to the specific distribution locations of the sensing and interaction components within the zone. Each distribution zone corresponds to an edge node. For example, the vehicle body can be divided into the interior of the doors, the front, middle, and rear passenger compartments, the roof, and the rear door area. This edge node connects to the various sensing and interaction components within the corresponding distribution zone, thereby maximizing the simplification of the data and power distribution wiring harness complexity and length in the AI ​​device, saving costs while improving the efficiency of data transmission and power distribution.

[0026] This embodiment connects edge nodes to the first sensing and interaction components in the same distribution area, and the power distribution and data transmission module to the first edge node corresponding to the same power distribution and communication area. This achieves an orderly association of components by region and power distribution and communication, significantly reducing the complexity and length of wiring harnesses in the entire device and saving costs. Furthermore, the power distribution and data transmission module simultaneously connects the central computing platform and the power supply component, enabling data interaction between the central computing platform and the sensing and interaction components via edge nodes, while also achieving power distribution. This structure avoids the problems of chaotic component connections and complex data transmission paths in traditional architectures, making data interaction more efficient and power allocation more precise. It also lays the foundation for subsequent software reuse, reducing the cost of redundant development for different AI motion device architectures.

[0027] Specifically, the control logic for the electronic and electrical results of the liquid level flow provided in this embodiment is as follows: Figure 2 As shown, the control logic is simplified into two levels: a central computing platform and edge nodes. Data is converted through a data gateway, i.e., the aforementioned power distribution and data transmission module. The gateway only forwards data and does not perform logical processing. Edge nodes are responsible for collecting sensor data and sending it back to the central computing platform for processing. After centralized processing, the central computing platform sends execution instructions to the relevant edge nodes to drive the actuators to complete the command actions.

[0028] In some alternative implementations, the sensing and interaction components include: sensing components and execution components; Each edge node collects the operating data of the sensing components in the same distribution area and transmits the operating data to the corresponding power distribution and data transmission module; The power distribution and data transmission module transmits operational data to the central computing platform; The central computing platform processes the operating data transmitted by each power distribution and data transmission module, generates control instructions corresponding to the first execution unit, and transmits the control instructions to the first edge node through the first power distribution and data transmission module connected to the first edge node. The first edge node is the edge node connected to the first execution unit.

[0029] This embodiment collects operational data from sensing components in the same area via edge nodes and transmits it to the central computing platform through the power distribution and data transmission module. The central computing platform processes the data and generates control commands, which are then sent back to the edge nodes to drive the execution components. This process achieves a complete closed loop from sensor data acquisition and processing to command execution. The edge nodes are only responsible for data acquisition and command execution, while the control logic is centralized on the central computing platform. This not only improves data processing efficiency and command execution accuracy but also simplifies the edge node structure, reduces hardware costs, and facilitates unified algorithm optimization by the central computing platform, improving software development and OTA upgrade efficiency.

[0030] In some alternative implementations, the central computing platform is connected to at least one sensing and interaction component to enable redundant data interaction between the central computing platform and the sensing and interaction component.

[0031] Specifically, for the aforementioned AI-powered motion devices, such as important sensing and interaction components in vehicles, such as radar, cameras, screens, microphones, and speakers, direct connection between these components and the central computing platform enables redundant data exchange between the central computing platform and these important sensing and interaction components. This avoids problems such as missing important data or the inability to use important functions due to communication failures in the power distribution and data transmission modules.

[0032] This embodiment connects directly to at least one sensing and interaction component via a central computing platform, adding a direct connection path to the existing connection via power distribution and data transmission modules and edge nodes. This dual connection method forms a redundant data interaction channel. When one path fails, the other path can ensure normal data interaction, avoiding the problem of sensing and interaction interruption caused by the failure of a single connection path. This significantly improves the reliability and security of the architecture's data interaction and meets the high requirements of AI motion devices for data transmission stability.

[0033] In some alternative implementations, the central computing platform and each power distribution and data transmission module form a ring Ethernet communication connection structure via Ethernet communication.

[0034] Specifically, each power distribution and data transmission module communicates via Ethernet, forming an Ethernet communication link between the power distribution and data transmission modules. Then, the central computing platform and each power distribution and data transmission module are also connected via Ethernet communication, thus forming one or more Ethernet communication loops between the central computing platform and each power distribution and data transmission module, realizing redundant communication functions between the central computing platform and each power distribution and data transmission module.

[0035] This embodiment uses Ethernet to form a ring communication structure connecting the central computing platform and each power distribution and data transmission module. The ring structure provides each node with two communication links. Compared to traditional linear structures, when a segment of the Ethernet fails, data can immediately switch to another path for transmission, ensuring uninterrupted communication and improving redundancy and security. Simultaneously, Ethernet's high-speed transmission capabilities meet the efficient transmission requirements of large amounts of data between the central computing platform and the power distribution and data transmission modules. Furthermore, the ring structure offers flexibility, allowing nodes to be added or removed as needed without affecting existing node hardware, adapting to the architectural scale of different AI motion devices.

[0036] In some alternative implementations, the central computing platform includes a main computing center and at least one secondary computing center, with the main computing center and the secondary computing center serving as redundant backups for each other.

[0037] It should be noted that the main computing center and the secondary computing center can be fully redundantly backed up, meaning that when the main computing center fails, the operation is switched to the secondary computing center. Alternatively, they can be partially redundantly backed up, meaning that when some functions of the main computing center fail, the corresponding functions are switched to the secondary computing center. In addition, based on the computing resource requirements and importance of each function of the device, only some functions can be redundantly backed up in the secondary computing center. This is just an example, and the present invention is not limited thereto.

[0038] This embodiment divides the central computing platform into a main computing center and at least one secondary computing center, with redundancy and backup for each other. The main computing center is responsible for computing and control under normal circumstances. When the main computing center fails, the secondary computing center can immediately take over the work, ensuring that the core computing functions of the architecture are not interrupted. This redundancy design improves the reliability of the central computing platform, avoids the paralysis of the entire architecture due to the failure of a single computing center, meets the high availability requirements of AI motion devices for core computing units, and is especially suitable for scenarios with extremely high safety requirements, such as unmanned operation, providing core assurance for the stable operation of the equipment.

[0039] For example, such as Figure 3 As shown, the aforementioned central computing platform consists of a "brain" (main computing center, also known as the main brain) and a "cerebellum" (secondary computing center, also known as the secondary brain), as well as a communication module. Each of the brain, cerebellum, and communication module is equipped with a high-performance SOC chip. Compared to the traditional central + regional architecture, this embodiment's central computing platform has a higher degree of control logic integration. All control logic and algorithms are handled by the central computing platform, while other modules are only responsible for sensing, execution, and necessary diagnostics. The main brain uses a high-performance chip as its processor, responsible for the calculation and control of all vehicle functions. The secondary brain uses a relatively high-performance chip as its processor, providing basic product functions and redundancy backup in case of main brain failure, enabling limp-down operation and ensuring basic safety. The main and secondary brains have independent networks, power supplies, and chips, allowing them to operate independently. The communication module communicates with external facilities via 4G, 5G, or Wi-Fi, enabling interconnection between the AI ​​motion product and other AI motion products, the cloud, and other devices, thereby achieving remote control and monitoring, autonomous networking, and other functions. Sensing and interactive components such as radar, cameras, screens, microphones, and speakers can be directly connected to the central computing platform, or they can be connected via power distribution and data transmission modules to transmit information to the central computing platform, enabling sensing data access and HMI interaction. This architecture, by adding a redundant central computing platform in series with the backbone network and implementing redundant combinations of necessary sensing and execution modules, along with redundant communication and power supply designs, achieves architecture-level redundancy and safety to meet the high safety requirements of unmanned operation.

[0040] In some optional implementations, the power distribution and data transmission module is also connected to the electronic control unit that has the same function as the device and other second sensing and interaction components that are not connected to the edge node, in order to realize data interaction between the electronic control unit and the second sensing and interaction components and the central computing platform; The power distribution and data transmission module is also used to enable data exchange between various electronic control units connected to the power distribution and data transmission module.

[0041] Specifically, in practical applications, based on the design principle of simplified wiring harnesses, if the distance between certain sensing and interaction components and the power distribution and data transmission module is less than the distance between the edge nodes of their respective areas, these sensing and interaction components can be directly connected to the power distribution and data transmission module without going through the edge nodes. The specific configuration can be flexibly set according to actual needs, and this invention is not limited thereto.

[0042] It should be noted that, taking vehicles as an example, some electronic control units (ECUs) in existing vehicles cannot yet evolve into edge nodes at the current stage. ECUs still need to exchange a small amount of data. These nodes can also be directly connected to the power distribution and data transmission module to realize the data exchange function, so as to ensure the integrity of the entire artificial intelligence motion device architecture and function.

[0043] This embodiment connects the power distribution and data transmission module to an electronic control unit with the same device function and a second sensing and interaction component that is not connected to the edge node. This enables data interaction between these components and the central computing platform, and also supports data exchange between connected electronic control units. This design covers different types of components in the architecture, ensuring efficient transmission of various types of data and avoiding the data silo problem caused by some components being unable to access the central computing platform. It also meets the data interaction needs between electronic control units, improving the architecture's compatibility and the integrity of data flow, and adapting to scenarios with diverse component types in different AI motion devices.

[0044] In some alternative implementations, the power supply components include: a main power supply and a redundant power supply; Each power distribution and data transmission module is connected in sequence to form a ring network, with the main power supply and redundant power supply set on the ring network respectively; The power distribution and data transmission module distributes the main power or redundant power to various structures connected to the power distribution and data transmission module. The structures include: a central computing platform, edge nodes, electronic control units, and second sensing and interaction components. The edge node distributes the power allocated by the power distribution and data transmission module to the various sensing and interaction components connected to the edge node.

[0045] The power supply components, including both main and redundant power supplies, can meet the power requirements of different devices in the AI-powered motion equipment, providing both high-voltage and low-voltage options to achieve redundant backup for all power sources. The power distribution and data transmission module, together with the edge nodes, implements two-level power distribution, achieving an integrated design for power distribution and data transmission, further reducing the complexity and cost of wiring harnesses in the AI-powered motion equipment.

[0046] This embodiment divides the power supply components into a main power supply and a redundant power supply. Each power distribution and data transmission module forms a ring network, with the main and redundant power supplies respectively located on the ring network. Power distribution is achieved hierarchically through the power distribution and data transmission modules and edge nodes. This dual-power supply design, combined with the ring power distribution network, forms a redundant power supply system. When the main power supply or a segment of the power distribution link fails, the redundant power supply and other power distribution links can ensure normal power supply to each structure. The hierarchical power distribution method of the power distribution and data transmission modules and edge nodes allows for more precise power allocation, avoids power waste, and improves the stability and security of the power supply, thus solving the problem of operational interruptions caused by power supply failures in AI motion equipment.

[0047] In some optional implementations, the power distribution and data transmission module includes: a power supply and data coupling processing circuit, which is used to couple the power supplied by the power supply component and the data received by the power distribution and data transmission module, and output the coupling processing result to the corresponding connection object through a twisted pair cable. The connection object includes: a central computing platform, an edge node, an electronic control unit, and a second sensing and interaction component. The connection object is equipped with a power and data decoupling circuit. The power and data decoupling circuit decouples the coupling processing result to obtain the power and transmission data.

[0048] This embodiment incorporates a power and data coupling processing circuit within the power distribution and data transmission module. The coupling result is transmitted to the connected object via twisted-pair cable, and the connected object then separates the power and data through a decoupling circuit. This design achieves co-linear transmission of power and data, reducing the number of power and data cabling harnesses and lowering hardware costs and internal wiring complexity. Furthermore, twisted-pair transmission offers strong anti-interference capabilities, ensuring the stability of power transmission and the accuracy of data transmission. It is particularly suitable for low-power components such as radar and cameras, enhancing the integration and practicality of the architecture.

[0049] Furthermore, such as Figure 4As shown, the aforementioned power distribution and data transmission module, also known as the electrical and data hub, can be composed of a data exchange module and a chip power distribution module. In practical applications, it can also be composed of a data and power distribution coupling module. The data exchange module enables data routing between different communication types or different communication nodes, and also transmits signal data from edge nodes or data transmitted by ECU nodes connected to the electrical and data hub to the central computing platform via Ethernet. The chip power distribution module uses electronic fuses (E-Fuse) or high-side drive switching chips (HSD) to distribute power to electrical appliances. The electrical and data hub modules are deployed in zones within the product, maximizing power savings and data cabling efficiency. The number of electrical and data hubs can be set to one or more depending on equipment requirements.

[0050] The working principle of the aforementioned data exchange module is as follows: Figure 5 As shown, the data exchange module mainly performs two functions. First, it facilitates data exchange between all network nodes connected to the module. Due to limitations in hardware and software technology, some Electronic Control Units (ECUs) cannot yet evolve into edge nodes, requiring some data exchange between them. The data exchange module connects to these ECUs via interfaces such as LIN, CAN, CANFD, and CANXL to enable data exchange between them. The second function is to transmit data from edge nodes and ECUs connected to the electronic and data hub to the central computing platform. For edge nodes, a 10-BaseT1 Ethernet data interface with a speed of 10Mbps is used, employing the RCP (Remote Control Protocol) or other similar remote control protocols. This protocol standardizes data transmission between edge nodes, the data exchange module of the electronic and data hub, and the central computing platform. For other LIN / CAN ECU nodes, the data needs to be converted to Ethernet before being forwarded to the central computing platform. The data exchange module can also connect to audio devices such as speakers and microphones via a dedicated DSP interface, converting the audio data to Ethernet before transmitting it to the central computing platform.

[0051] Among them, such as Figure 6As shown, the chip-based power distribution module provides power to the ECU, sensing components, and actuators. For safety reasons, the power output is a low-voltage power supply below 60V, which can be any combination of 48V, 24V, 12V, and 5V. The power supply from the vehicle's power components is supplied to the electrical and data control center. The electrical and data control center is configured via chips, including electronic fuses and high-side drive switches, to supply power to the electrical loads. Furthermore, the intelligent power management program on the central computing platform can control the opening and closing of power supplies to various power components according to different functional scenarios, thus achieving intelligent power distribution. E-Fuse is used for power distribution to appliances in customizable scenarios, while HSD is used for power distribution to appliances in fixed scenarios. Compared to traditional relays + fuses, the chip-based power distribution technology using E-Fuse and HSD offers more flexible and energy-efficient power distribution, higher reliability, and more precise overcurrent protection. It should be noted that the power distribution technology used in the chip-based power distribution module is existing technology, and its specific implementation process will not be elaborated here.

[0052] In addition, such as Figure 7 As shown, the aforementioned data and power distribution coupling module is an Ethernet data transmission and power distribution co-line solution based on PoDL technology. It can support the maximum power output of 5W (12V) or 50W (48V) systems. For low-power loads such as radar and cameras, it can achieve power lines and data lines co-lined, further saving costs. Specifically, the module performs power and data coupling processing on the power and data harnesses at the output end of the board, and outputs them through a pair of twisted-pair cables. Then, at the device end, power and data decoupling processing is performed to realize power distribution and data exchange for the device.

[0053] For example, such as Figure 8As shown, each edge node includes a power supply module and a communication module. The power supply module redistributes the power provided by the electrical and data hub according to the modules of the edge node, distributing power to the components and parts connected to the edge node through power supply module chips such as LDO / DCDC. The communication module of the edge node adopts RCP or other similar remote control protocols. The central computing platform sends the remote control protocol, which is transmitted through the electrical and data hub to the edge node. The remote protocol directly implements the drive commands for various loads such as motors, valves, and lights. Compared with traditional controllers, the edge node has moved the logic algorithm to the central computing platform. It does not have the function of logic operation and uses an RCP Server module to replace the MCU of the traditional controller, thereby reducing hardware costs. The edge node can be a car seat motor drive module, a drone rotor motor drive module, a humanoid robot simulated limb servo motor drive module, or an unmanned agricultural machine harvesting device drive module, etc. But they all have a common structure, including drive circuits and sensor feedback circuits. By using edge nodes to perform analog-to-digital conversion, various types of signals can be directly transmitted and received with the central brain via the RCP protocol, greatly improving efficiency.

[0054] Furthermore, in practical applications, the aforementioned electronic and digital hubs and edge nodes can also exist in an integrated form, for example... Figure 9 The integrated form shown possesses both the data exchange and power distribution capabilities of the electrical and data hub, as well as the ability of edge nodes to drive actuators and collect sensor information. If the sensor actuator modules connected to the edge nodes are basic and standardized, they can be integrated with the electrical and data hub to form a larger standardized component, achieving cost savings.

[0055] like Figure 10As shown, the aforementioned central computing platform and the data and electrical hub form a ring network with a high-speed Ethernet (ETH) backbone. This can be a single Ethernet ring network or multiple Ethernet ring networks, connected via a switch on the central computing platform. The Ethernet ring network is a high-speed communication backbone with a bandwidth of 100 Mbps or higher. Both the central computing platform and the data and electrical hub have Ethernet communication chips, using a switch + transceiver or bridge + transceiver configuration to support Ethernet ring network transmission. The central computing platform can consist of a single high-performance SOC chip or n SOC chips, where n is greater than or equal to 2. If it uses a single SOC chip, it directly connects to the two nearest data and electrical hub nodes via Ethernet to form a ring network. The data and electrical hub can also have one or more nodes, connected via Ethernet. All the central computing platform and data and electrical hub nodes form a ring Ethernet network. Ring network architecture offers flexibility, allowing the number of nodes to be increased or decreased based on actual needs without affecting existing node hardware. The topology always remains ring-shaped, meeting the varying node requirements of different AI motion-related products.

[0056] The central computing platform and the electrical and data hub form a high-speed Ethernet ring network. Based on the redundancy of the ring network, each node has two communication link transmission paths to other nodes. If a problem occurs in one of the Ethernet segments, it will immediately switch to the other transmission path to ensure communication security.

[0057] For example, such as Figure 11A As shown, the power network of the electronic and electrical architecture provided in this embodiment adopts a redundant power supply network. It consists of a main power supply, redundant power supplies, and an electrical and data processing hub. The main power supply and redundant power supplies form two independent power supply systems for the power-consuming modules. Each independent power supply system can be a DC-DC converter (DCDC) or a low-voltage battery; it can also be a combination of both. Figure 11BAs shown, redundant power supplies can take the following forms: DC-DC1 and low-voltage battery 1, "DCDC1 + low-voltage battery 2" and low-voltage battery 2, "DCDC1 + low-voltage battery 2" and DC-DC2, "DCDC1 + low-voltage battery 2" and "DCDC2 + low-voltage battery 1". The two power supply sets and power networks are isolated by a power isolation module. Each electrical and data processing hub has two power interfaces. A MOS circuit is used between these two interfaces to monitor and quickly shut down the power supply. Specifically, under normal conditions, the two power interfaces of the electrical and data processing hub are connected, allowing the DC-DC converter to supply power to the low-voltage battery and the electrical and data processing hub. When an undervoltage or overvoltage problem occurs in any part of the power grid, the electrical and data processing hub quickly disconnects the two power networks using its internal MOS device, achieving microsecond-level shutdown. The other power interface of the electrical and data processing hub can also ensure continuous normal power supply. Simultaneously, the current and voltage monitoring characteristics of the MOS device quickly pinpoint the link in the power grid where the abnormal power supply problem occurred.

[0058] For electrical devices with particularly high power supply safety requirements, the power supply can be connected to two electrical and data centers at the same time. This solution can solve the problem of power supply being maintained immediately by the other electrical and data center if one electrical and data center malfunctions.

[0059] Combining the two solutions above can ensure normal power supply even if any link in the power supply network—whether it's the power source, power wiring harness, or electrical and data control center—provides a highly secure power supply solution and diagnostic approach.

[0060] For example, the aforementioned electronic and electrical architecture in a vehicle can support both intelligent driving safety redundancy and human-driver safety redundancy. The brain, with a high-performance SOC chip as the main chip, deploys the control redundancy strategies required for high-performance autonomous driving, while the cerebellum implements redundant control for intelligent driving. Simultaneously, basic body, powertrain, and chassis functional logic related to human-driver functions can be deployed in the cerebellum, forming a backup redundancy with the brain, achieving safer redundant control for human-driver interaction. Based on this software deployment strategy, backup redundancy is formed between the brain and cerebellum in the central computing platform.

[0061] According to an embodiment of the present invention, a functional backup method embodiment is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0062] This embodiment provides a functional backup method that can be used in the software functional safety redundancy strategy module of a vehicle, wherein the vehicle adopts such as Figure 1 The electronic and electrical architecture shown is as follows:Figure 12 This is a flowchart of a method for implementing redundancy in vehicle control functions according to an embodiment of the present invention, such as... Figure 12 As shown, the process includes the following steps: Step S201: Based on the driving functions of the whole vehicle, the vehicle control function software is divided into software function modules to obtain multiple software function modules, and each software function module and its corresponding redundant backup module are deployed in different computing centers of the central computing platform.

[0063] Specifically, the computing power center refers to the hardware with computing power in the vehicle, including a main computing power center and at least one auxiliary computing power center. For example, taking two computing power centers as an example, they can be configured as follows: Figure 3 The brain and cerebellum shown in the diagram can be further illustrated in practical applications by using a computing platform consisting of three or more computing centers. Each computing center can deploy one or more software function modules for different driving functions. Each software function module includes a software function module that corresponds one-to-one with each driving function. For example, driving functions can be functionally divided according to the vehicle system, such as the power system, steering system, braking system, body system, and some sensing systems. Each system corresponds to a type of driving function; for example, the steering system corresponds to the steering function, and the braking system corresponds to the braking function. This is merely an example and is not intended to limit the invention. Correspondingly, the vehicle control function software is divided, and the software related to each driving function is encapsulated into a software function module. The specific process of dividing the vehicle control function software into multiple software function modules is existing technology and will not be elaborated upon here.

[0064] Specifically, by deploying each software functional module and its corresponding redundant backup module in different computing centers, redundant backup of each software functional module is achieved.

[0065] Step S202: Generate a software scheduling table based on the calling relationships between software functions contained in each software functional module.

[0066] The software scheduling table includes: deployment address information of the redundant backup modules corresponding to the software functional modules, and scheduling links that characterize the scheduling order among the software functions contained in the software functional modules, so that each software functional module runs according to the scheduling links. This scheduling link is the calling order of the corresponding call interface addresses among the software functions contained in the software functional modules. The working order of each software function can be controlled by the calling order of the corresponding call interface addresses among the software functions, thereby realizing the software functions of the software functional modules.

[0067] Specifically, the deployment address information includes: the call interface addresses of different backup sub-software functional modules contained in the redundant backup module, and the call interface addresses corresponding to different software functions. Each redundant backup module consists of multiple backup sub-software functional modules, each used to implement a sub-function related to driving functions. For example, the steering function includes: parameter acquisition sub-function for obtaining steering parameters, calculation sub-function for processing steering parameters, etc. Each backup sub-software functional module consists of multiple software functions. As the smallest unit of software implementation, the specific software function type and number can be set according to the sub-functions corresponding to different backup sub-software functional modules, which will not be elaborated here.

[0068] Step S203: When any software function module is detected to have an abnormality during operation, the scheduling link is updated based on the software function and deployment address information associated with the abnormality, and the updated scheduling link is obtained.

[0069] Specifically, by replacing the call interface address corresponding to the abnormal software function in the scheduling link with the call interface address of the corresponding redundant software function, an updated scheduling link is obtained, so as to ensure that the abnormal software function can be backed up and run normally.

[0070] Step S204: Control the abnormal software function modules to run according to the updated scheduling link.

[0071] Specifically, since the updated scheduling link has redundantly replaced the abnormal software functions, the updated scheduling link can be scheduled and run normally, thus ensuring the normal and stable operation of the entire software functional module.

[0072] The functional backup method provided in this embodiment divides software functional modules according to driving functions based on a specific electronic and electrical architecture, and deploys them and corresponding redundant backup modules in different computing centers of a central computing platform. A scheduling link is generated based on software function call relationships. In case of an anomaly, the scheduling link is updated based on the function associated with the anomaly and the deployment address of the redundant backup module. The software functional module experiencing the anomaly is then scheduled to run according to the updated scheduling link. This vehicle control function redundancy implementation scheme does not require full data reprocessing; it only dynamically adjusts and updates the scheduling link, significantly reducing control command output lag and ensuring the continuity of functions in advanced driver assistance scenarios. Simultaneously, the deployment in different computing centers achieves distributed backup of each software functional module at the hardware level. Combined with the dynamic adjustment of the scheduling link, this ensures the integrity and reliability of the vehicle control functions. While achieving safety redundancy for the vehicle control functions, it also meets the real-time requirements of advanced driver assistance, further ensuring vehicle driving safety.

[0073] This embodiment provides a functional backup method that can be used in the software functional safety redundancy strategy module of a vehicle. Figure 13 This is a flowchart of a functional backup method according to an embodiment of the present invention, such as... Figure 13 As shown, the process includes the following steps: Step S301: Based on the driving functions of the whole vehicle, the vehicle control function software is divided into software function modules to obtain multiple software function modules, and each software function module and its corresponding redundant backup module are deployed in different computing centers of the central computing platform.

[0074] Specifically, the computing power center includes: a main computing power center and at least one secondary computing power center. In step S301 above, each software functional module and its corresponding redundant backup module are deployed in different computing power centers throughout the vehicle, including: For example, software A functional modules can be deployed in a main computing center and a secondary computing center to achieve redundancy of software A functional modules; in addition, more redundancy can be set according to the importance of driving functions, and the scheduling priority of different redundant backup modules can be set accordingly.

[0075] The scheduling priority can be set according to the computing power resources of different computing centers. The richer the computing power resources, the higher the scheduling priority. For example, since the main computing center has the richest computing power and can support the synchronous operation of multiple software functional modules, the scheduling priority of the redundant backup modules deployed in the main computing center can be set to the highest.

[0076] Step S302: Based on the call relationships between software functions contained in each software functional module, a software scheduling table is generated. The software scheduling table includes: deployment address information of the redundant backup module corresponding to the software functional module, and a scheduling link used to characterize the scheduling order between software functions contained in the software functional module, so that each software functional module runs according to the scheduling link. See details below. Figure 12 The relevant descriptions of step S202 shown will not be repeated here.

[0077] Step S303: When any software function module is detected to have an abnormality during operation, the scheduling link is updated based on the software function and deployment address information associated with the abnormality, and the updated scheduling link is obtained.

[0078] Specifically, step S303 includes: Step S3031: When an anomaly is detected in any software function module during operation, the scheduling link update mode is determined based on the function type of the software function associated with the anomaly, and / or the relationship between the first number of software functions associated with the anomaly and the total number of software functions contained in the software function module that has an anomaly.

[0079] Specifically, step S3031 includes: Step a1: When the function type of the software function associated with the exception is a critical function, and / or when the first number of functions exceeds the first preset ratio of the total number of software functions, the scheduling link update mode is determined to be the function update mode; the critical function is the function that implements the core function corresponding to the software function module that has the exception.

[0080] Step a2: When the function type of the software function associated with the exception is not a critical function, and / or when the first number and the first proportion of the total number of software functions do not exceed the first preset proportion, the scheduling link update mode is determined to be the function update mode.

[0081] Specifically, through the atomized layered design of the vehicle control software, critical and non-critical functions in each software module are identified. Critical functions are those that directly implement the core control logic of the software module. For example, the motor torque precision control function in the powertrain software module (which directly determines the vehicle's power output accuracy and affects driving stability) and the brake pressure adjustment function in the braking system software module (which directly relates to vehicle deceleration and braking effects and is crucial for driving safety) are both critical functions. Non-critical functions are those that do not directly affect the core control logic of the software module and only perform auxiliary tasks. Examples include the "sensor data backup function" in the sensor system software module (used only for data storage and does not affect real-time perception output) and the "window control log function" in the body system software module (which only records operation logs and does not affect window lifting control).

[0082] Furthermore, upon detecting an anomaly in the operation of a software function module, the software functions associated with the anomaly are first identified: on the one hand, it is determined whether these functions belong to the aforementioned key function functions; on the other hand, the first number of software functions associated with the anomaly is counted, and its first proportion to the total number of software functions contained in the software function module is calculated, while a first proportion value is preset (e.g., typically set to 50% based on the safety requirements of advanced driver assistance functions, which can be fine-tuned according to the importance of different function modules). The scheduling link update mode is determined to be a function update mode when either of the following conditions is met: Condition 1, at least one key function function exists among the software functions associated with the anomaly; Condition 2, the first proportion of software functions associated with the anomaly exceeds the first preset proportion value (e.g., 50%). If neither of the above two conditions is met, the scheduling link update mode is determined to be a function update mode.

[0083] This embodiment achieves precise matching of scheduling update methods by distinguishing between critical and non-critical functions and whether the proportion of abnormal functions exceeds a preset value. When critical functions are abnormal and / or multiple functions are abnormal, a function update mode is used, which can completely avoid the risk of core function failure and quickly curb the spread of abnormalities. When non-critical functions or a small number of functions are abnormal, a function update mode is used, which can minimize the impact of updates on the operation of software functional modules, ensuring continuous operation of functions without interrupting the overall functionality while reducing the time consumed by software function processing. This ensures both the safety of the core functions of advanced driver assistance systems and the efficiency of software function operation.

[0084] Step S3032: Based on the scheduling link update mode and deployment address information, update the scheduling link to obtain the updated scheduling link.

[0085] This embodiment determines the update method of the scheduling link by analyzing the type and / or quantity ratio of the software functions associated with the anomaly, making the scheduling update more targeted. It avoids the problem of untimely processing caused by replacing the entire functional software regardless of the severity of the anomaly, and further improves the flexibility and effectiveness of the vehicle control redundancy strategy, which can better adapt to the complex software operation requirements in advanced driver assistance scenarios.

[0086] Furthermore, when the scheduling link update mode is function update mode, the above step S3032 includes: Step b1: Based on the deployment address information, determine the call interface address of the redundant software function corresponding to the software function associated with the exception.

[0087] Step b2: Replace the call interface address of the software function associated with the exception in the scheduling link with the call interface address of the corresponding redundant software function to obtain the updated scheduling link.

[0088] Specifically, during the software deployment phase, the atomic functions (including normal and redundant functions) of each software functional module are deployed in different computing centers (such as the main computing center and the secondary computing center), and a unique call interface address (such as an IP address + port identifier based on the Ethernet protocol) is assigned to each function. Simultaneously, the correspondence between functions, redundant functions, and their corresponding call interface addresses is stored in the address mapping library of the vehicle control software. After locating the software function associated with the anomaly, the call interface address of the redundant software function is extracted through a matching query in the address mapping library. After obtaining the call interface address of the redundant software function, there is no need to reconstruct the entire scheduling chain; only the interface address field corresponding to the software function associated with the anomaly in the scheduling table is replaced: the call interface address of the original abnormal function is deleted, and the determined call interface address of the redundant function is filled in, while the calling order and interface address of other normal functions in the scheduling chain remain unchanged. After the replacement is completed, the scheduling table automatically generates an updated scheduling chain. This chain only corrects the abnormal node; the rest of the logic is completely consistent with the original chain and can be directly called and executed by the vehicle control software.

[0089] In this embodiment, under function update mode, the call interface address of redundant software functions is located by deploying address information, and the interface address of the abnormal function is directly replaced to complete the scheduling link update. This method does not require replacing the entire software functional module, but only performs precise replacement of the abnormal function, preserving the original normally operating software logic and data processing results to the greatest extent, avoiding the delay caused by full data recalculation, and ensuring the real-time output of control commands. At the same time, the direct replacement of interface addresses simplifies the scheduling link update process, reduces system resource consumption, and makes the fault-tolerant response of software functions in advanced driver assistance scenarios faster, ensuring the continuity and safety of the driving process.

[0090] Furthermore, when the scheduling link update mode is the function update mode, the above step S3032 includes: Step c1: Determine the target sub-software function module corresponding to the software function associated with the exception.

[0091] The software function module includes multiple sub-software function modules, and the target sub-software function module is at least one of the sub-software function modules that has malfunctioned. Each software function module for vehicle control is decomposed into multiple sub-software function modules according to functional subdivision logic. Each sub-software function module consists of a set of associated software functions, and the mapping relationship between software functions and sub-software function modules is pre-stored (such as the sub-module identifier to which the function belongs, the core function definition of the sub-module, etc.).

[0092] Step c2: Determine the second number of corresponding sub-software functional modules in the target sub-software functional module; Step c3: When the second ratio of the second quantity to the total number of sub-software functional modules included in the abnormal software functional module exceeds the second preset ratio value, if the current driving mode of the whole vehicle is intelligent driving mode, control the current driving mode to switch from intelligent driving mode to human driving mode, and switch to the corresponding redundant backup module to run based on the deployment address information.

[0093] Specifically, based on the safety requirements for human-driver takeover in the event of an anomaly in the advanced driver assistance system, a second preset ratio value can be pre-set for each software functional module (this ratio is set according to the impact of the sub-module on the overall function; the second preset ratio value for core functional modules is usually 50%, which can be flexibly adjusted). If the second ratio exceeds the second preset ratio value, it indicates that the anomaly range of the software functional module has affected the core control logic, and driving safety must be prioritized. At this time, the current driving mode (intelligent driving mode or human-driver mode) is obtained through the vehicle control bus: if the current mode is intelligent driving mode, a driving mode switching command is immediately triggered, prompting the driver to take over the vehicle through the cockpit interaction system. Simultaneously, based on the pre-stored deployment address information of the sub-software functional modules and redundant backup modules, the system switches to the corresponding redundant backup module to avoid safety risks caused by the expansion of the anomaly; if the current mode is human-driver mode, only the redundant backup module is activated to ensure the basic control functions of the vehicle.

[0094] Step c4: If the second ratio of the second quantity to the total number of sub-software functional modules contained in the abnormal software functional module does not exceed the second preset ratio value, then proceed to step c5.

[0095] Specifically, if the calculated second proportion does not exceed the second preset proportion value, it indicates that the software function module anomaly is limited to a small number of non-core sub-modules. There is no need to interrupt the current driving mode or switch redundant modules entirely; function recovery can be achieved by precisely replacing the redundant backup of the abnormal sub-module. At this point, the system automatically jumps to step c5 to perform the address location operation of the target backup sub-software function module, ensuring that the anomaly is resolved at minimal cost.

[0096] This embodiment, after identifying the target sub-software functional modules, performs differentiated processing based on their proportion and the current driving mode: if the proportion exceeds a preset value and the system is in intelligent driving mode, it switches to human driving mode and activates redundant backup modules to ensure driving safety; if the proportion does not exceed the preset value, sub-module replacement continues. This design considers both the severity of sub-module anomalies and the safety requirements of the driving mode, avoiding dangers caused by the escalation of anomalies. Timely switching to human driving in intelligent driving mode meets the safety requirements of human takeover in case of anomalies in advanced driver assistance scenarios; while sub-module replacement in cases of minor anomalies ensures functional continuity, achieving a balance between safety and practicality.

[0097] Step c5: Based on the deployment address information, determine the call interface address of the target backup sub-software functional module corresponding to the target sub-software functional module.

[0098] Step c6: Replace the call interface address of the target sub-software function module in the scheduling link with the call interface address of the corresponding target backup sub-software function module to obtain the updated scheduling link.

[0099] Specifically, redundant backups of each sub-software functional module (i.e., target backup sub-software functional modules) are pre-deployed in computing centers different from the original modules, and the association information between the target sub-software functional modules and their calling interface addresses is stored in an address mapping library. By calling the address mapping library, based on the determined target sub-software functional module identifier and the deployment address information of the original module, the deployment location of the corresponding target backup sub-software functional module is queried, and its unique calling interface address is extracted. After obtaining the calling interface address of the target backup sub-software functional module, there is no need to reconstruct the entire scheduling link; only the interface address field corresponding to the target sub-software functional module in the link table is replaced: the calling interface address of the original target sub-module is deleted, and the determined backup sub-module interface address is filled in, while the calling order and interface addresses of other normal sub-modules remain unchanged. After the replacement is completed, the link table automatically generates an updated scheduling link, which can be immediately called by the vehicle control software to achieve functional recovery.

[0100] In this embodiment, under the function update mode, the target sub-software function module corresponding to the anomaly association function is used as the replacement unit. The interface address of the target backup sub-software function module is located and replaced by deploying address information. Compared with replacing the entire function module, sub-function module-level replacement is more precise, reduces unnecessary software switching, and lowers software operation fluctuations. This allows for the rapid resolution of anomalies while maintaining the stable operation of the advanced driver assistance system, avoiding a decline in driving experience or safety risks caused by replacing the entire function.

[0101] Step S304: Control the software function modules that malfunction to run according to the updated scheduling link. See details below. Figure 12 The relevant description of step S204 shown will not be repeated here.

[0102] Step S305: During the process of the software function module that malfunctioned being scheduled to run according to the updated scheduling link, in response to the software function module that malfunctioned returning to normal, the updated scheduling link is restored to its initial state, and the software function module continues to run according to the scheduling link in the initial state.

[0103] Specifically, after monitoring that all software functions of the original abnormal software module are running normally and its deployed hardware is stable, the module is determined to have recovered. At this time, the system automatically triggers the scheduling link reset mechanism: it calls the pre-stored initial scheduling link backup (containing information such as the function call order and interface addresses when the original abnormal module was running normally); it replaces the currently updated redundant scheduling link with the initial scheduling link, deletes the calling logic of the redundant module, and restores the original abnormal module to its normal position in the scheduling link; after the reset is completed, the vehicle control software immediately runs according to the initial scheduling link, the original abnormal module resumes its corresponding control function, and the redundant module returns to standby status, waiting for the next abnormal trigger.

[0104] In this embodiment, after the abnormal software functional module recovers, the scheduling link is restored to its initial state and continues to run. This achieves dynamic and reversible adjustment of the scheduling link, avoiding resource waste caused by continuing to run redundant links after anomaly recovery, and ensuring that each software functional module returns to its optimal operating state. At the same time, the automated execution of the recovery mechanism requires no manual intervention, ensuring the continuity of vehicle control functions, enabling the advanced driver assistance system to quickly restore optimal performance after anomaly resolution, and improving system efficiency and user experience.

[0105] This embodiment provides a method for implementing redundancy in vehicle control functions, which can be used in the vehicle's software functional safety redundancy strategy module. Figure 14 This is a flowchart of a method for implementing redundancy in vehicle control functions according to an embodiment of the present invention, such as... Figure 14 As shown, the process includes the following steps: Step S401: Based on the vehicle's driving functions, the vehicle control function software is divided into multiple software function modules. Each software function module and its corresponding redundant backup module are then deployed to different computing centers on the central computing platform. For details, please refer to... Figure 13 The relevant descriptions of step S301 shown will not be repeated here.

[0106] Step S402: Based on the call relationships between software functions contained in each software functional module, a software scheduling table is generated. The software scheduling table includes: deployment address information of the redundant backup module corresponding to the software functional module, and a scheduling link used to characterize the scheduling order between software functions contained in the software functional module, so that each software functional module runs according to the scheduling link. See details below. Figure 13 The relevant description of step S302 shown will not be repeated here.

[0107] Step S403: During the operation of each software functional module, monitor the running status of the software functions contained in that software functional module.

[0108] The runtime status includes at least one of the following: stack usage, static variable and / or global variable usage, array access out of bounds, function runtime, and multiple calls.

[0109] For example, by embedding stack monitoring code during function execution, the used, remaining, and peak stack space of the function can be statistically analyzed in real time to determine whether there is a risk of stack overflow (such as stack space usage continuously approaching the upper limit); the number of assignments, value range, and illegal modifications of variables can be recorded (such as static variables being tampered with by unauthorized functions) to avoid calculation errors caused by abnormal variables; boundary checking logic can be added to array read and write operations to monitor whether the array index exceeds the preset index range (such as whether the index is greater than or equal to 10 if the array length is 10) to prevent out-of-bounds access from damaging memory data; the time from function startup to execution completion can be recorded by a timer and compared with a preset normal operation duration threshold (set according to the function's functional complexity) to identify execution lag or infinite loops; the number of times the function is called per unit time and the source of the call (such as which parent functions call the function) can be statistically analyzed to determine whether there are abnormal calls (such as call frequency far exceeding normal business needs, or from unauthorized callers). This is just an example, and the present invention is not limited thereto.

[0110] Step S404: Based on the functional logic corresponding to the software functional module, determine whether there are any abnormalities in the running status of each software function.

[0111] Specifically, by combining the specific functional logic of the software functional modules (such as the core control objectives of the modules and the collaborative relationships between functions), multi-dimensional anomaly judgment rules are established to comprehensively judge the running status of each function. When the running status data of a function violates the judgment rules based on functional logic, it is preliminarily judged that the function has an anomaly. The specific judgment rules can be flexibly set according to the actual situation, and will not be elaborated here.

[0112] Step S405: When the running state of at least one software function is abnormal, determine that the software function module is abnormal, mark the abnormal software function, and obtain the software function associated with the abnormality.

[0113] For example, if at least one software function in a certain software functional module is determined to be abnormal, and the abnormal function belongs to the core operation link of the module, then the entire software functional module is determined to be abnormal (if the abnormal function is an independent auxiliary function and does not affect the core function, the module can be temporarily not determined to be abnormal, and only the function can be marked as abnormal). For all software functions determined to be abnormal, an abnormal marker is added to the function identifier (such as function ID, function name), and information such as the abnormal type (such as execution timeout, array out of bounds, etc.), the time of occurrence of the abnormality, and the abnormal data (such as timeout duration, out-of-bounds index value) is recorded to form an abnormal function list, which is stored in the database to provide a clear target object for subsequent updates to the scheduling link based on abnormal functions.

[0114] This embodiment monitors the runtime status of software functions, including stack usage, variable usage, array access, runtime, and call patterns. Combined with functional logic, it determines whether software functional modules are abnormal and marks abnormal functions. This achieves early identification and accurate location of anomalies, preventing functional failures or system crashes caused by the continuous execution of abnormal functions. Anomaly determination based on functional logic ensures the accuracy of anomaly identification, reducing unnecessary updates due to misjudgments. Timely marking of abnormal functions provides a clear basis for subsequent scheduling link updates, enabling faster response of redundancy strategies and ensuring the stable operation of the advanced driver assistance system.

[0115] Step S406: When an anomaly is detected in any software function module during operation, the scheduling link is updated based on the software function and deployment address information associated with the anomaly, resulting in the updated scheduling link. See details below. Figure 13 The relevant description of step S303 shown will not be repeated here.

[0116] Step S407: Control the software function modules that malfunction to run according to the updated scheduling link. See details below. Figure 13 The relevant description of step S304 shown will not be repeated here.

[0117] Step S408: During the process of controlling the software function module that has malfunctioned to run according to the updated scheduling link, if the software function module is detected to malfunction again, determine whether the software function associated with the second malfunction is the same as the software function associated with the previous malfunction.

[0118] Specifically, the system can retrieve the list of exception functions from the previous exception and the list of exception functions for the current exception from the database. Then, it can compare the two lists of exception functions using their unique identifiers (such as function ID and function name) to determine if there are any completely identical functions. If the function identifiers for the two exceptions are exactly the same, it is determined that the function associated with the current exception is the same as the previous one; if the identifiers are different, it is determined that the function associated with the current exception is different from the previous one.

[0119] Step S409: If the software function associated with the recurring exception is the same as the software function associated with the previous exception, determine whether the current driving mode of the vehicle is intelligent driving mode.

[0120] Specifically, if the function that is determined to be abnormal again is the same as the one that was abnormal before, it indicates that the function (including the original function and the redundant function) may have a persistent fault (such as a hardware failure causing the function to repeatedly fail). The driving mode should be verified first to ensure safety.

[0121] Step S410: When the current driving mode of the vehicle is intelligent driving mode, control the current driving mode to switch from intelligent driving mode to human driving mode.

[0122] Specifically, when the current mode is determined to be intelligent driving mode, the system immediately triggers the driving mode switching process and issues a takeover prompt: it issues a takeover prompt to the driver through the cockpit interaction system (such as a pop-up window on the instrument panel or a voice broadcast saying "The driver assistance function is abnormal, please take over the vehicle immediately"), and at the same time illuminates the hazard warning lights to alert surrounding vehicles; simultaneously, it sends an intelligent driving mode exit command to the vehicle control bus, cutting off the control authority of the advanced driver assistance system over the power, steering, and braking systems, and transferring control to the driver; and it synchronizes the mode switching result to all area controllers to ensure that all systems respond to the driver's operation in human-driven mode (such as when the driver presses the accelerator, the power system directly executes the accelerator command without going through the driver assistance algorithm).

[0123] In this embodiment, when a software function module malfunctions again, it determines whether the correlation functions of the two malfunctions are the same to identify persistent faults. If they are the same and the system is in intelligent driving mode, it switches to human driving mode. Therefore, switching to human driving mode for recurring malfunction scenarios completely avoids the safety risks caused by intelligent driving function failures, further enhancing driving safety in advanced driver assistance scenarios.

[0124] Step S411: When the software function associated with the recurring anomaly is different from the software function associated with the previous anomaly, determine whether the updated scheduling link contains redundant software functions corresponding to the recurring software function module anomaly.

[0125] Specifically, when the function that causes the recurrence of an anomaly is different from the previous one, it indicates that the anomaly has spread to a new function. It is necessary to first verify whether the updated scheduling link already includes redundant resources for the new anomaly function. This can be achieved by determining the function identifier and its module, calling the address mapping library, and checking whether the new anomaly function has a pre-configured redundant software function and whether the redundant function is connected to the current updated scheduling link (e.g., whether the link contains the call interface address of the redundant function). If the link already contains the corresponding redundant function, it is determined that the redundant software function corresponding to the anomaly is included; if it is not included (e.g., the redundant function is not configured, or the redundant function is not connected to the current link), it is determined that the redundant software function corresponding to the anomaly is not included.

[0126] In step S412, if no redundant software function corresponding to the software function module abnormality reappears in the updated scheduling link, return to step S406.

[0127] Specifically, when it is determined that the updated link does not contain the redundant function of the new abnormal function, it indicates that the current link cannot cope with the new abnormality and the link update process needs to be re-executed: the system automatically returns to step S406, takes the new abnormal function as the abnormal associated function, and performs a second update on the scheduling link in combination with its deployment address information (the deployment location of the redundant function); update the link storage: replace the original updated link with the second updated link and store it in the scheduling table to ensure that the vehicle control software runs according to the new link.

[0128] In this embodiment, when the associated functions for two anomalies differ, it determines whether a corresponding redundant software function exists in the updated link. If no corresponding redundant software function exists, the scheduling link update is re-executed. Thus, for complex scenarios with multiple different anomalies, the existence of redundant software functions is checked to ensure an effective response to each anomaly: when no corresponding redundant software function exists, the link is updated promptly to avoid functional interruption due to a lack of redundant software function support; simultaneously, instead of blindly switching driving modes, the issue is resolved primarily through scheduling updates, ensuring maximum availability of advanced driver assistance functions and balancing safety and functional continuity.

[0129] Step S413: If the updated scheduling link contains redundant software functions corresponding to the recurrence of software function module anomalies, then execute step S409.

[0130] Specifically, when it is determined that the updated link contains a redundant function of the new abnormal function, it indicates that the current link can deal with the new abnormality by activating the redundant function. However, it is necessary to determine whether a safety backup is required in combination with the driving mode. The logic of step S409 is executed directly, that is, the current driving mode (intelligent driving / human driving) is obtained through the vehicle control bus. If it is intelligent driving mode, step S410 is executed to switch to human driving mode. If it is human driving mode, a function abnormality reminder is issued and software and hardware maintenance is performed in a timely manner.

[0131] This embodiment executes a driving mode determination step when the updated link contains redundant software functions corresponding to further anomalies. This design addresses scenarios where redundant software functions exist but different anomalies still occur. By determining the driving mode, it decides whether to switch drivers, avoiding the risk that redundant software functions may exist but anomalies could still affect safety. This makes the execution of the vehicle control redundancy strategy more closely aligned with actual driving scenarios, improving the safety and flexibility of vehicle control.

[0132] This embodiment achieves a vehicle control function redundancy scheme through a structured design of software functional safety redundancy strategy modules. In practical applications, the software functional safety redundancy strategy software is not a software module independent of the existing vehicle software system, but rather a complete set of software development rules (including function definitions and, of course, independent software code). It is hierarchically divided, from system to module to function, defining and integrating it layer by layer into each software functional module. Specifically, it includes the following layers: (1) Function hierarchy: In the development of code for a single function, it is necessary to simultaneously develop code with the following functionalities: Monitor stack usage; Monitor the usage of static / global variables; Monitor array access out of bounds.

[0133] Monitor the runtime of the function and the number of times it is called.

[0134] By monitoring the data access and computation during function runtime, we can characterize whether the function is functioning correctly.

[0135] (2) Module hierarchy: The module hierarchy summarizes the running status of each function under it, and after checking its own functional logic, determines whether the module is performing a certain task normally.

[0136] When this module determines that its own function is not performing correctly, it will send a notification to the next higher-level monitoring module, informing it of the level and cause of the error.

[0137] (3) System hierarchy: The system summarizes the operating status of each subordinate module at the system level, and after checking its own functional logic, determines whether the overall operating status of the system is normal.

[0138] When the system detects an anomaly notification from the module level, it will decide whether to continue using the module or request a redundant module to be called based on the anomaly level and cause.

[0139] This embodiment borrows concepts from hardware hot-swappable technology and automatic network node identification technology to register, invoke, suspend, and deregister each software module, and allows setting access permissions. First, each software module is tagged according to its functional logic to determine its operating mode and upstream / downstream data transmission links. The content of these tags is determined during the detailed software design phase. In the initial state (assuming the entire vehicle software system is normal), the system generates a software scheduling table based on the software tags and status feedback. The scheduling table specifies the software module scheduling links and redundant backup modules under normal conditions. When a software module malfunctions, the system modifies the scheduling table, re-organizes the software module scheduling links, and records and reports the malfunction. After the malfunctioning software module is repaired, the system modifies the scheduling table again, re-organizing the software module scheduling links.

[0140] In actual operation, the system constantly monitors the running status of software modules, managing them hierarchically from functions to modules and then to the system. Information from each layer is summarized and integrated to provide a basis for modifying the scheduling table. A software module is merely a carrier for implementing a certain function or executing a task; the execution of a function or task is essentially the transfer and computation of data. Therefore, when the system detects an anomaly in a software module, it modifies the scheduling table—that is, the data transmission path—in real time according to the cause and severity of the anomaly, thus ensuring that the function or task continues to execute.

[0141] This embodiment fully exposes the vehicle's control capabilities (dynamic and static) to the surface, leveraging the powerful computing power and large AI model capabilities of the SOC to achieve more powerful and superior advanced driver assistance systems (ADAS). Even in the event of a fault in the vehicle network or an anomaly in a specific area controller, the ADAS maintains its functional integrity and performance reliability. Furthermore, in the event of a system-wide malfunction, the driver can fully take over vehicle control to ensure driving safety.

[0142] According to an embodiment of the present invention, a seat adjustment method embodiment is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0143] This embodiment provides a seat adjustment method applied to a vehicle, wherein the vehicle adopts, as shown in the example below. Figure 1 The illustrated electronic and electrical architecture demonstrates a method that can be specifically applied to the vehicle's central computing platform. Figure 15 This is a flowchart of a seat adjustment method according to an embodiment of the present invention, such as... Figure 15 As shown, the process includes the following steps: Step S1501: Obtain the current signal of the vehicle's power steering motor.

[0144] Specifically, step S1501 includes: receiving a current signal from the power steering motor sent via Ethernet by the target power distribution and data transmission module. The current signal is a current signal collected from the power steering motor driver by a target sensing component corresponding to the power steering motor. The current signal is uploaded to the target power distribution and data transmission module via a target edge node connected to the target sensing component. The target sensing component is a current acquisition device such as a current sensor. The target sensing component is installed on the power steering motor driver, the target edge node is located in the area where the power steering motor is located, and the target power distribution and data transmission module and the power steering motor are located in the same power distribution and communication area. The current signal is transmitted via Ethernet communication through the target power distribution and data transmission module.

[0145] For example, during vehicle operation, the sensing components collect current signals from the power steering motor driver in real time and transmit them to the electrical and data center via the corresponding edge node. The electrical and data center then uploads these signals to the central computing platform via Ethernet for processing. Considering that changes in road surface roughness directly cause fluctuations in the motor load and consequently generate minute current fluctuations during power steering motor operation, the sampling period can be flexibly set, such as to 30 cycles, to accurately capture these fluctuations. The object of data acquisition is the three-phase current of the motor; if the vehicle control system has already converted the three-phase current into d / q-axis current (a commonly used current form for subsequent control and calculation), the d / q-axis current can also be acquired directly.

[0146] In practical applications, after acquiring the current signal, it is necessary to preprocess it: for example, by using a low-pass filter to filter high-frequency noise (such as noise generated by electromagnetic interference from motors) in the current signal, and then by using a moving average algorithm to smooth the filtered signal, eliminating the influence of instantaneous pulse interference on subsequent analysis, and ensuring that the current signal finally used for feature extraction accurately reflects the changes brought about by road surface roughness.

[0147] Step S1502: Extract feature data related to road surface roughness from the current signal.

[0148] Specifically, feature data related to road surface roughness can be extracted from multiple dimensions such as time domain, frequency domain, and envelope based on the preprocessed current signal. By capturing the influence of road surface roughness on the current signal from multiple dimensions, key information can be avoided by missing features from a single dimension.

[0149] Step S1503: Classify the feature data to determine the road surface type of the current road surface on which the vehicle is driving.

[0150] Specifically, the road type can be determined by comparing historical feature data extracted from the vehicle's historical driving process on different road types with the current feature data. The road type corresponding to the historical feature data with the highest feature similarity can be determined as the road type of the current driving road. Alternatively, a model can be trained on the historical feature data using deep learning, and the trained model can be used to classify the current feature data.

[0151] Step S1504: In response to the road surface type being an abnormal roughness road surface, determine the relative positional relationship between the abnormal roughness road surface and the vehicle.

[0152] Specifically, when the road surface type is determined to be an abnormally rough road surface such as a shoulder or lane line, the position determination module is activated. By comparing the vibration signals collected by the vibration sensors on the left and right sides of the vehicle, the relative position relationship between the abnormal road surface and the vehicle is determined. In addition, the vehicle's inertial measurement unit can also collect the vehicle's pose. By utilizing the fact that the vehicle's pose will show obvious fluctuations when the roughness is abnormal, and the direction of the pose fluctuation is consistent with the relative position relationship between the abnormally rough road surface and the vehicle, the relative position relationship between the abnormally rough road surface and the vehicle can be determined by analyzing the changes in the vehicle's pose. The specific analysis process is existing technology and will not be described in detail here.

[0153] Step S1505: Based on the relative positional relationship, at least one seat in the vehicle is adjusted to increase the support force of the seat in the direction of the abnormally rough road surface.

[0154] Specifically, the central computing platform, based on the determined relative positional relationship, activates the seat adjustment module to generate corresponding seat adjustment commands. These commands are then transmitted to the electrical and data control center via Ethernet. The electrical and data control center transmits the commands to the corresponding motors on the seat through edge nodes, driving and controlling these motors, such as the seat cushion motor, lumbar support motor, and side wing motor. This motor-driven mechanical structure adjusts the seat support, increasing support in the direction of abnormal road surfaces and alleviating driver discomfort caused by road bumps. The adjustable seats can be flexibly configured according to the vehicle's specifications. For example, in a low-spec vehicle, only the driver's seat can be adjusted; in a mid-spec vehicle, both the driver and passenger seats can be adjusted if someone is in the passenger seat; and in a high-spec vehicle, all occupied seats can be adjusted simultaneously. This is merely an example and is not a limitation of the invention.

[0155] Among them, the seat cushion motor is a seat cushion support air pump motor, the lumbar support motor is a seat lumbar support air pump motor, and the side wing motor is a side wing support air pump motor. Each seat has two side wing support air pump motors on the left and right sides. These support motors can make the entire seat actively conform to the driver's body posture, allowing the driver to feel the vehicle's protective action after the wheels run over the curb or lane line, forming an intuitive tactile care.

[0156] This embodiment acquires the power steering motor current signal, extracts road surface roughness-related feature data, and classifies and determines the road surface type. When an abnormally rough road surface is identified, the seat support force is adjusted according to its relative position to the vehicle. This solution does not rely on steering operation, can actively sense various rough road surfaces, and can also adjust the seat support force in the corresponding direction, effectively alleviating the discomfort of passengers on the rough road side, significantly improving the riding experience and comfort under different road conditions, and enhancing the user's driving experience.

[0157] This embodiment provides a seat adjustment method applied to a vehicle, wherein the vehicle adopts, as shown in the example below. Figure 1 The illustrated electronic and electrical architecture demonstrates a method that can be specifically applied to the vehicle's central computing platform. Figure 16 This is a flowchart of a seat adjustment method according to an embodiment of the present invention, such as... Figure 16 As shown, the process includes the following steps: Step S1601: Obtain the current signal of the vehicle's power steering motor. See details below. Figure 15 The relevant description of step S1501 shown will not be repeated here.

[0158] Step S1602: Extract feature data related to road surface roughness from the current signal.

[0159] Specifically, step S1602 includes: Step S16021: Calculate the root mean square value, peak value, and peak-to-peak value of the current signal to obtain the time-domain characteristics.

[0160] Specifically, the root mean square (RMS), peak value, and peak-to-peak value of the current signal are calculated within a preset time window (e.g., 50ms). The RMS value reflects the average energy of the current signal; the rougher the road surface and the more severe the motor load fluctuation, the larger the RMS value. The peak value is the maximum value of the current signal within the time window, reflecting the instantaneous load impact intensity. The peak-to-peak value is the difference between the peak and valley values, which directly reflects the fluctuation amplitude of the current signal and is also positively correlated with the road surface roughness.

[0161] Step S16022: Convert the current signal into the frequency domain and extract the energy of the preset frequency band to obtain the frequency domain characteristics.

[0162] The preset frequency band is the frequency range corresponding to the road surface with abnormal roughness, which can be determined through a large number of experiments.

[0163] Specifically, the preprocessed current signal is converted from a time-domain signal to a frequency-domain signal by performing a short-time Fourier transform (STFT). To accurately capture impact events when a vehicle drives over rough surfaces such as road shoulders or lane lines, the STFT analysis period is set to 100ms (which fully covers the duration of a typical impact event), with adjacent analysis periods overlapping by 50% to avoid missing impact signals. Simultaneously, the frequency resolution is set to 25Hz to ensure clear differentiation of signal components at different frequencies. Experiments determine the frequency range corresponding to vibrations caused by rough surfaces such as road shoulders and lane lines, such as 50-200Hz. Then, the signal energy within this frequency band is extracted as a frequency domain feature; the higher the energy value, the more likely the vehicle is to be traveling on rough surfaces such as road shoulders or lane lines.

[0164] Step S16023: Perform envelope analysis on the current signal, extract the amplitude envelope, and obtain the envelope features.

[0165] Specifically, Hilbert transform can be used to perform envelope analysis on the current signal. First, the analytic signal is obtained through Hilbert transform, and then the magnitude of the analytic signal is calculated to obtain the amplitude envelope of the current signal. The amplitude envelope can highlight the impact component in the current signal. When a wheel runs over a road surface protrusion (such as the edge of the shoulder or the raised part of the lane line), the amplitude envelope will show obvious peaks. By identifying these peaks, the impact event can be accurately located, further assisting in the judgment of road surface roughness.

[0166] This embodiment extracts time-domain features (root mean square value, peak value, peak-to-peak value), frequency-domain features (preset frequency band energy), and envelope features (amplitude envelope) from the current signal to capture signal information related to road surface roughness from multiple dimensions. The time-domain features directly reflect the intensity changes of the current signal, the frequency-domain features accurately pinpoint the frequency range corresponding to abnormal road surface roughness, and the envelope features highlight impact events in the current signal. The combination of these three features makes the extracted feature data more comprehensive and accurate, providing reliable data support for subsequent accurate classification of road surface types, reducing misjudgments of road surface types due to single features, and ensuring the accuracy of subsequent seat adjustments.

[0167] Step S1603: Classify the feature data to determine the road surface type of the current road surface on which the vehicle is driving.

[0168] Specifically, step S1603 above obtains the road type of the road surface where the vehicle is currently traveling by inputting feature data into a pre-trained road surface type classification model.

[0169] Among them, the road surface type classification model is used to predict the road surface type based on feature data related to road surface roughness. The road surface type includes at least: shoulder, lane line and normal road surface.

[0170] Furthermore, the aforementioned road surface type classification model can be trained using Support Vector Machines (SVM), decision trees, or simple neural networks. The specific training process includes: first, collecting a large amount of feature data from different road surface scenarios (including normal asphalt pavement, cement pavement, road shoulders, worn lane markings, and intact lane markings). After labeling the data with road surface types, 70% of the data is used as the training set and 30% as the test set. The training set is used to train a model such as the SVM model. The model performance is optimized by adjusting the kernel function parameters (e.g., using a radial basis function kernel) until the model's classification accuracy on the test set reaches over 95%. The model's output includes three categories: normal road surface, road shoulder, and lane markings, accurately distinguishing different road surface types and providing a basis for subsequent judgments on whether seat adjustments are necessary.

[0171] This embodiment determines the road surface type by inputting feature data into a pre-trained road surface type classification model, which can distinguish between road shoulders, lane lines, and normal road surfaces. After being trained on a large amount of data, the pre-trained model has stronger pattern recognition capabilities, can more accurately identify different types of roughness abnormal road surfaces, avoids confusing road shoulders, lane lines, and normal road surfaces, and can accurately distinguish different abnormal road surface types. This provides a basis for subsequently developing more suitable seat adjustment strategies based on different road surface types, further improving the targeting and rationality of seat adjustments.

[0172] Step S1604: In response to the road surface type being an abnormal roughness road surface, determine the relative positional relationship between the abnormal roughness road surface and the vehicle.

[0173] Specifically, step S1604 includes: Step S16041: Obtain vibration signals collected by two vibration sensors on the vehicle.

[0174] Two vibration sensors are installed on the left and right sides of the vehicle, respectively. For example, vibration sensors are installed on the left and right sides of the front suspension of the vehicle to collect vibration signals and obtain vibration acceleration.

[0175] Step S16042: Based on the magnitude and difference of the vibration signals collected by the two vibration sensors on the vehicle, determine the relative positional relationship between the roughness abnormal road surface and the vehicle.

[0176] The relative positional relationship includes: the road surface with abnormal roughness is located on the left, right, or both sides of the vehicle.

[0177] Specifically, when the left front wheel travels over an abnormally rough road surface, the peak vibration acceleration collected by the left vibration sensor is 30%-50% higher than that of the right, and the signal arrival time is 5-10ms earlier. Conversely, when the right front wheel travels over an abnormally rough road surface, the peak vibration acceleration collected by the right vibration sensor is higher, and the signal arrival time is earlier. By comparing the peak values ​​and differences between the two vibration sensor signals, it is possible to accurately determine whether the abnormal road surface is located on the left or right side of the vehicle. Of course, if the arrival time and magnitude of the peak values ​​on both sides are similar, then the abnormally rough road surface is considered to be located on either the left or right side of the vehicle.

[0178] For example, after determining that the road surface type is a shoulder, the peak vibration acceleration collected by the vibration sensor on the left is 2.5 m / s², while that on the right is 1.2 m / s². The signal on the left arrives 8 ms earlier than that on the right. Based on this, it is determined that the shoulder is located on the left side of the vehicle.

[0179] This embodiment determines the relative position (left, right, and both sides) of the vehicle to the abnormally rough road surface by analyzing the magnitude and differences of vibration signals from vibration sensors on the left and right sides of the vehicle. This method can accurately locate the abnormal road surface. Once the location is clear, subsequent seat adjustments become more directional, adjusting the seat support only in the direction of the abnormal road surface. This avoids resource waste and poor adjustment results caused by indiscriminate adjustments, improving the accuracy and efficiency of seat adjustments.

[0180] Step S1605: Based on the relative positional relationship, at least one seat in the vehicle is adjusted to increase the seat's support in the direction of the abnormally rough road surface.

[0181] Specifically, step S1605 includes: Step S16051: When the relative positional relationship is that the road surface with abnormal roughness is located on the left side of the vehicle, activate the seat cushion, lumbar support and right side wing motor.

[0182] In step S16052, when the relative positional relationship is that the road surface with abnormal roughness is located on the right side of the vehicle, the seat cushion, lumbar support and left side wing motor are activated.

[0183] Step S16053: When the relative positional relationship is that the road surface with abnormal roughness is located on the left and right sides of the vehicle, activate the seat cushion, lumbar support, left side wing and right side wing motors.

[0184] For example, if the abnormal road surface is located on the left side of the vehicle (the left front wheel has passed over the abnormal road surface), the seat cushion motor, lumbar support motor, and right side wing motor are activated. The seat cushion motor causes the right side of the seat cushion to protrude upwards by 5-8mm, increasing support for the right hip; the lumbar support motor causes the right side of the lumbar support to push forward by 3-5mm, conforming to the driver's right waist; the right side wing motor causes the left side wing to tighten inwards by 2-3cm, wrapping around the driver's right side and reducing body roll caused by bumps. If the abnormal road surface is located on the right side of the vehicle (the right front wheel has passed over the abnormal road surface), the seat cushion motor, lumbar support motor, and left side wing motor are activated, adjusting the left side support structure of the seat by the same amount to increase left-side support. If the abnormal road surface is located on both sides of the vehicle, the seat cushion motor, lumbar support motor, left side wing motor, and right side wing motor are activated simultaneously to adjust the overall seat support structure, increasing support on both sides.

[0185] It should be noted that in practical applications, in order to avoid changes in the relative position of the vehicle and the road surface with abnormal roughness in a short period of time, such as the vehicle changing from the left wheel to the right wheel on the lane line, the seat cushion, lumbar support, left side wing and right side wing motors can be activated directly after the above positional relationship is determined, so as to provide the driver and passengers with the maximum protection and reduce their bumpy feeling.

[0186] This embodiment activates motors in different parts of the seat (seat cushion, lumbar support, left and / or right side wing motors) according to the different relative positions of the vehicle and the abnormally rough road surface (left, right, left and right sides). This precise adjustment allows the seat to provide more ergonomic support in the direction of the abnormal road surface. For example, activating the right side wing motor when the left side is abnormal can effectively balance the discomfort caused by the rough road surface on the left, providing targeted support and protection for the occupant. Compared to indiscriminate adjustment, it can more accurately alleviate the occupant's discomfort in a specific direction, further improving the comfort and safety of the ride.

[0187] This embodiment provides a seat adjustment method applied to a vehicle, wherein the vehicle adopts, as shown in the example below. Figure 1The illustrated electronic and electrical architecture demonstrates a method that can be specifically applied to the vehicle's central computing platform. Figure 17 This is a flowchart of a seat adjustment method according to an embodiment of the present invention, such as... Figure 17 As shown, the process includes the following steps: Step S1701: Monitor the vehicle's current speed and / or the activation status of the turn signals.

[0188] Specifically, vehicle speed signals can be acquired via the vehicle's CAN bus. The acquisition period for the speed signals can be set as needed, such as 10ms, to ensure real-time reflection of vehicle speed changes without increasing the system's computational load due to excessive acquisition frequency. The turn signal activation status is determined by reading the turn signal control signal output from the body control module. The signal sampling period can be set as needed, such as 50ms, to quickly capture the on / off changes of the turn signals without increasing system resource consumption due to excessive sampling frequency.

[0189] Step S1702: When the current vehicle speed is detected to be higher than the preset vehicle speed threshold, and / or the turn signal is not activated, step S1704 is executed.

[0190] For example, the preset speed threshold can be set according to the impact of road surface roughness on the driving experience. In this embodiment, the preset speed threshold of 40 km / h is used as an example. When driving at low speeds (such as below 40 km / h), the road surface roughness has little impact on the driver's riding experience, and the driver can control the vehicle more precisely, so there is no need to activate the seat adjustment function. When the speed is higher than 40 km / h, abnormal road surface conditions can easily cause significant bumps, requiring system intervention. If the turn signal is activated, it indicates that the driver has a clear intention to operate. Crossing the lane line is a normal operation, and the seat adjustment function under abnormal road surface roughness is not activated. However, the existing seat adjustment function during the turning process in the vehicle can be triggered normally. If the turn signal is not activated, it indicates that the driver has no intention to turn or change lanes, and the seat adjustment function needs to be activated in this case.

[0191] In practical applications, to ensure the accuracy of seat adjustment function activation, vehicle speed and turn signal activation status usually need to be detected together. The seat adjustment function will only be activated when both of them meet the trigger conditions.

[0192] Step S1703: When it is detected that the current vehicle speed is not higher than the preset vehicle speed threshold and / or the turn signal is in the active state, return to step S1701.

[0193] For example, when the vehicle speed is ≤40km / h, regardless of the turn signal status, the system returns to step S1701 to continue monitoring. At this time, the impact of the road surface is small, and there is no need to start the subsequent process. When the turn signal is on, regardless of whether the vehicle speed is higher than 40km / h, the system returns to step S1701. When the driver actively changes lanes, crossing the lane line is a normal operation, so the system needs to temporarily suppress further processing and re-evaluate after the turn signal is turned off.

[0194] This embodiment controls seat adjustment by monitoring vehicle speed and turn signal status. The seat adjustment logic is only executed when the vehicle speed exceeds a preset threshold and / or the turn signal is not activated; otherwise, it returns to the monitoring step. At low speeds (such as when parked), road surface roughness has little impact on occupants, eliminating the need for seat adjustment. This condition prevents the system from operating ineffectively at low speeds, saving energy. When the turn signal is activated, the driver actively changes lanes, crossing lane lines, etc., which are normal operations and do not trigger seat adjustment. This setting ensures that seat adjustment only operates when truly needed, improving the rationality and efficiency of system operation while avoiding unnecessary adjustments that affect the driving and riding experience.

[0195] Step S1704: Obtain the current signal of the vehicle's power steering motor. See details below. Figure 16 The relevant description of step S1601 shown will not be repeated here.

[0196] Step S1705: Extract feature data related to road surface roughness from the current signal. See details below. Figure 16 The relevant description of step S1602 shown will not be repeated here.

[0197] Step S1706: Detect whether the feature values ​​of the time domain feature, frequency domain feature, and envelope feature exceed the feature threshold of the corresponding feature.

[0198] The feature threshold is the minimum feature value that indicates a vehicle is traveling on a road surface with abnormal roughness. Specifically, each feature dimension has a corresponding feature threshold, which is the critical value for determining whether an abnormal road surface event has occurred. When the feature value exceeds the threshold, it indicates that the change in the current signal has reached the judgment standard for road surface abnormality, and further confirmation of the event is required; when the feature value does not exceed the threshold, it is judged as a normal road surface, and no further action is required. The specific settings can be adaptively adjusted based on the statistical measures of historical data (mean + 3 times the standard deviation) to avoid false alarms under different road conditions with a fixed threshold.

[0199] For example, the time-domain characteristic threshold can be based on statistics of current signals from a large number of normal road driving conditions, such as setting the root mean square value threshold to 1.2A, the peak value threshold to 1.8A, and the peak-to-peak value threshold to 1.5A. The frequency-domain characteristic threshold can be set to 25dB by referencing the energy value of the 50-200Hz frequency band on a normal road surface (typically ≤25dB). When a wheel runs over a shoulder / lane line, the energy in this frequency band will increase significantly, exceeding 25dB. The envelope characteristic threshold can be based on the peak value of the amplitude envelope of a normal road surface (typically ≤0.8V), setting the envelope characteristic threshold to 0.8V. Impact events caused by road bumps will cause the peak value to exceed this threshold.

[0200] Specifically, the methods for determining whether the feature values ​​of the aforementioned time-domain features, frequency-domain features, and envelope features exceed the feature thresholds of the corresponding features include: Step d1: Obtain the vehicle's current speed.

[0201] Step d2: Based on the current vehicle speed, determine the feature thresholds corresponding to the time domain features, frequency domain features, and envelope features respectively. The feature thresholds corresponding to the time domain features, frequency domain features, and envelope features are negatively correlated with the current vehicle speed.

[0202] Specifically, the higher the vehicle speed, the more sensitive the motor is to road surface roughness. Even slight changes in the road surface can cause significant fluctuations in the characteristic value. Therefore, the threshold needs to be lowered (e.g., at a vehicle speed of 60 km / h, the root mean square value threshold is reduced to 1.1A). The lower the vehicle speed, the lower the motor sensitivity, and the threshold needs to be increased (e.g., at a vehicle speed of 45 km / h, the root mean square value threshold is increased to 1.3A) to avoid misjudgments at different vehicle speeds.

[0203] This embodiment determines the feature thresholds corresponding to each feature by combining the vehicle's current speed, and the thresholds are negatively correlated with the vehicle speed. Since the degree to which the power steering motor current signal is affected by road surface roughness varies at different vehicle speeds, higher speeds mean that even slight changes in road surface roughness can cause significant fluctuations in the current signal. In this case, setting a lower threshold can promptly detect anomalies. Conversely, lower speeds mean that only severe changes in road surface roughness will cause significant fluctuations in the current signal, and a higher threshold can avoid false positives. This dynamic threshold setting method makes feature detection more closely reflect actual vehicle speed conditions, improving the accuracy of abnormal road surface detection.

[0204] Step S1707: When any feature value is detected to exceed the feature threshold of the corresponding feature, it is determined that a vehicle is traveling on a road surface with abnormal roughness, and step S1709 is executed.

[0205] Specifically, if any feature value in the three dimensions of time domain, frequency domain, and envelope exceeds the corresponding threshold, it is determined that a vehicle is traveling on a road surface with abnormal roughness. Since some road surface anomalies (such as slight lane line wear) may only cause the frequency domain feature to exceed the threshold, while the time domain and envelope features may not change significantly; and some road surface anomalies (such as shoulder protrusions) may cause all three dimensions to exceed the threshold simultaneously, this method can cover various abnormal scenarios to the greatest extent possible, ensuring no omissions. After determining that an abnormal event has occurred, there is no need to wait for the detection results of other feature values; step S1709 is executed directly to enter the road surface type classification stage, shortening the event response time and allowing more time for subsequent seat adjustment.

[0206] In practical applications, before performing step S1709, the seat adjustment method further includes: Step e1: When it is determined that a vehicle is traveling on a rough road surface, return to step S1704 until the cumulative duration or number of consecutively determined vehicle travel on a rough road surface reaches a preset duration or number of times, then execute step S1709.

[0207] Specifically, after determining in step S1707 that a vehicle is traveling on a rough road surface, step S1709 is not executed directly. Instead, the abnormal event accumulation verification process is initiated first, returning to step S1704 to continuously collect the current signal of the power steering motor, and repeating steps S1705, S1706, and S1707 until the cumulative duration of the abnormal road surface event is determined to reach a preset duration or the cumulative number of events reaches a preset number, and then step S1709 is executed.

[0208] For example, the preset duration can be set to 0.5s, and the preset number of times can be set to 3. A single abnormal event determination may originate from instantaneous interference (such as a brief current fluctuation caused by a wheel running over a small stone), rather than a continuous roughness abnormal road surface. The cumulative duration of 0.5s can cover the typical time for a wheel to continuously run over lane lines / shoulders and other roughness abnormal road surfaces, ensuring the capture of continuous abnormalities. At the same time, considering the current signal acquisition and processing cycle, the preset number of times can complement the preset duration, avoiding verification errors caused by deviations in the single determination cycle. In practical applications, a single parameter verification (such as only the cumulative duration) can also be selected according to the actual application scenario, and this invention is not limited to this.

[0209] In this embodiment, after determining that a rough road surface event has occurred, the road surface classification is only performed after the cumulative duration or number of such events reaches a certain threshold. This setting effectively filters out false judgments caused by instantaneous, occasional fluctuations in electrical signals, such as signal fluctuations caused by a vehicle briefly driving over a small stone, which are not truly continuous rough road surface conditions. Through cumulative detection, the subsequent process is only initiated when there is indeed a continuous abnormal rough road surface, avoiding unnecessary seat adjustments due to a single false judgment, improving the reliability and stability of the system, and ensuring that the passenger's riding experience is not disturbed.

[0210] Step S1708: If none of the feature values ​​exceed the feature threshold of the corresponding feature, return to step S1704.

[0211] Specifically, if the feature values ​​of the above three dimensions do not exceed the corresponding thresholds, it indicates that the current road surface is a normal road surface, and there is no need to initiate the subsequent road surface classification and seat adjustment process. At this time, return to step S1704 to continue collecting the current signal of the power steering motor and enter the next round of signal analysis loop. This avoids the system performing invalid calculations under normal road surface conditions, reduces CPU resource consumption and power consumption, and ensures that the system continuously monitors the road surface status. Once an abnormality occurs on the road surface, the subsequent process can be triggered in a timely manner.

[0212] Before classifying road surface types, this embodiment first checks whether the time-domain, frequency-domain, and envelope feature values ​​exceed the corresponding feature thresholds. The classification step continues only if any feature value exceeds the threshold; otherwise, it returns to the current signal acquisition step. This step can pre-filter out signal data without anomalies, avoiding unnecessary subsequent classification processing of normal road surface signals, reducing system computation and improving processing efficiency. Simultaneously, by initially judging abnormal events through feature thresholds, some interference signals can be eliminated in advance, reducing the probability of misjudgment in subsequent classification processes. This ensures that seat adjustments are only made when there is a genuine road surface with abnormal roughness, avoiding accidental adjustments that could affect the riding experience.

[0213] Step S1709: Classify the feature data to determine the road surface type of the current road surface the vehicle is traveling on. See details below. Figure 16 The relevant description of step S1603 shown will not be repeated here.

[0214] Step S1710: In response to a road surface type of abnormal roughness, determine the relative positional relationship between the abnormal roughness road surface and the vehicle. See details below. Figure 16 The relevant description of step S1604 shown will not be repeated here.

[0215] Step S1711: Based on the relative positional relationship, adjust at least one seat in the vehicle to increase the seat's support force in the direction of the abnormally rough road surface. See details below. Figure 16The relevant description of step S1605 shown will not be repeated here.

[0216] Figure 18 This is a structural schematic diagram of a vehicle provided in an embodiment of the present invention.

[0217] The following is a detailed reference. Figure 18 The diagram illustrates a structural schematic suitable for implementing a vehicle according to an embodiment of the present invention. The vehicle may include a processor (e.g., a central processing unit, graphics processor, etc.) 1801, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 1802 or a program loaded from memory 1808 into random access memory (RAM) 1803. The RAM 1803 also stores various programs and data required for vehicle operation. The processor 1801, ROM 1802, and RAM 1803 are interconnected via a bus 1804. An input / output (I / O) interface 1805 is also connected to the bus 1804.

[0218] Typically, the following devices can be connected to the I / O interface 1805: input devices 1806 including, for example, a touchscreen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 1807 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; memory devices 1808 including, for example, magnetic tape, hard disk, etc.; and communication devices 1809. Communication device 1809 allows the vehicle to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 18 Vehicles with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0219] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 1809, or installed from a memory 1808, or installed from a ROM 1802. When the computer program is executed by the processor 1801, it performs the functions defined in the function backup method or seat adjustment method of the embodiments of the present invention.

[0220] Figure 18 The vehicle shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention.

[0221] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the function backup method or seat adjustment method shown in the above embodiments is implemented.

[0222] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0223] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. An electronic and electrical architecture applied to artificial intelligence motion equipment, characterized in that, The electronic and electrical architecture includes: a central computing platform, power supply components, several power distribution and data transmission modules, several edge nodes, and several sensing and interaction components, wherein... The edge node is connected to at least one first sensing and interaction component, which is a sensing and interaction component located in the same distribution area of ​​the device; The power distribution and data transmission module is connected to at least one first edge node, which is the edge node connected to the sensing and interaction components in the same power distribution and communication area of ​​the device. The power distribution and data transmission module is also connected to the central computing platform and the power supply component, respectively, and is used to realize data interaction between the central computing platform and each of the sensing and interaction components through the edge node, and to distribute power in the electronic and electrical architecture of the power supply component.

2. The electronic and electrical architecture according to claim 1, characterized in that, The sensing and interaction component includes: a sensing component and an execution component; Each edge node collects operating data from the sensing components in the same distribution area and transmits the operating data to the corresponding power distribution and data transmission module; The power distribution and data transmission module transmits the operational data to the central computing platform; The central computing platform processes the operating data transmitted by each of the power distribution and data transmission modules, generates control instructions corresponding to the first execution component, and transmits the control instructions to the first edge node through the first power distribution and data transmission module connected to the first edge node, wherein the first edge node is an edge node connected to the first execution component.

3. The electronic and electrical architecture according to claim 1, characterized in that, The power distribution and data transmission module is also connected to the electronic control unit, which has the same function as the device, and other second sensing and interaction components that are not connected to the edge node, in order to realize data interaction between the electronic control unit and the second sensing and interaction components and the central computing platform; The power distribution and data transmission module is also used to realize data exchange between the various electronic control units connected to the power distribution and data transmission module.

4. The electronic and electrical architecture according to claim 1, characterized in that, The central computing platform is connected to at least one sensing and interaction component to enable redundant data interaction between the central computing platform and the sensing and interaction component.

5. The electronic and electrical architecture according to claim 1, characterized in that, The central computing platform and each of the power distribution and data transmission modules form a ring Ethernet communication connection structure via Ethernet communication.

6. The electronic and electrical architecture according to claim 3, characterized in that, The power supply components include: a main power supply and a redundant power supply; Each power distribution and data transmission module is connected in sequence to form a ring network, and the main power supply and the redundant power supply are respectively set on the ring network; The power distribution and data transmission module distributes the main power supply or the redundant power supply to various structures connected to the power distribution and data transmission module. The structures include: a central computing platform, edge nodes, electronic control units, and a second sensing and interaction component. The edge node distributes the power allocated by the power distribution and data transmission module to each sensing and interaction component connected to the edge node.

7. The electronic and electrical architecture according to claim 3, characterized in that, The power distribution and data transmission module includes a power supply and data coupling processing circuit. The power supply and data coupling processing circuit is used to couple the power supply provided by the power supply component and the data received by the power distribution and data transmission module, and output the coupling processing result to the corresponding connection object through a twisted pair cable. The connection object includes a central computing platform, an edge node, an electronic control unit, and a second sensing and interaction component. The connection object is equipped with a power and data decoupling circuit. The power and data decoupling circuit decouples the coupling processing result to obtain power and transmission data.

8. The electronic and electrical architecture according to any one of claims 1-6, characterized in that, The central computing platform includes a main computing center and at least one secondary computing center, wherein the main computing center and the secondary computing center are redundant backups of each other.

9. A method for functional backup, characterized in that, The method is applied to a vehicle employing the electronic and electrical architecture as described in claim 8, and the method includes: Based on the driving functions of the vehicle, the vehicle control function software is divided into software function modules to obtain multiple software function modules. Each software function module and its corresponding redundant backup module are deployed in different computing centers of the central computing platform. The computing centers include: a main computing center and at least one auxiliary computing center. Based on the calling relationship between the software functions contained in each software functional module, a software scheduling table is generated. The software scheduling table includes: the deployment address information of the redundant backup module corresponding to the software functional module, and the scheduling link used to characterize the scheduling order between the software functions contained in the software functional module, so that each software functional module is scheduled to run according to the scheduling link. When any software function module is detected to have an anomaly during operation, the scheduling link is updated based on the software function associated with the anomaly and the deployment address information to obtain the updated scheduling link. The software function modules that malfunction are scheduled to run according to the updated scheduling link.

10. The method according to claim 9, characterized in that, The step of updating the scheduling link based on the software function associated with the anomaly and the deployment address information to obtain the updated scheduling link includes: Based on the function type of the software function associated with the anomaly, and / or the relationship between the first number of software functions associated with the anomaly and the total number of software functions contained in the software functional module where the anomaly occurred, the scheduling link update mode is determined. Based on the scheduling link update mode and the deployment address information, the scheduling link is updated to obtain the updated scheduling link.

11. The method according to claim 10, characterized in that, The determination of the scheduling link update mode based on the function type of the software function associated with the anomaly, and / or the relationship between the first number of software functions associated with the anomaly and the total number of software functions contained in the software functional module where the anomaly occurred, includes: When the function type of the software function associated with the anomaly is a critical function, and / or when the first ratio of the first quantity to the total number of software functions exceeds a first preset ratio, the scheduling link update mode is determined to be a function update mode; the critical function is a function that implements the core function corresponding to the software function module that has an anomaly. When the function type of the software function associated with the anomaly is not a critical function, and / or when the first quantity to the first proportion of the total number of software functions does not exceed a first preset proportion, the scheduling link update mode is determined to be the function update mode.

12. The method according to claim 11, characterized in that, When the scheduling link update mode is a function update mode, updating the scheduling link based on the scheduling link update mode and the deployment address information to obtain the updated scheduling link includes: Based on the deployment address information, determine the call interface address of the redundant software function corresponding to the software function associated with the anomaly; The call interface address of the software function associated with the exception in the scheduling link is replaced with the call interface address of the corresponding redundant software function to obtain the updated scheduling link.

13. The method according to claim 11, characterized in that, When the scheduling link update mode is a function update mode, updating the scheduling link based on the scheduling link update mode and the deployment address information to obtain the updated scheduling link includes: Determine the target sub-software function module corresponding to the software function associated with the anomaly. The software function module includes multiple sub-software function modules, and the target sub-software function module is at least one of the sub-software function modules in which the anomaly occurred. Based on the deployment address information, determine the call interface address of the target backup sub-software functional module corresponding to the target sub-software functional module; The updated scheduling link is obtained by replacing the call interface address of the target sub-software function module in the scheduling link with the call interface address of the corresponding target backup sub-software function module.

14. The method according to claim 13, characterized in that, After determining the target sub-software functional module corresponding to the software function associated with the anomaly, the method further includes: Determine the second number of corresponding sub-software functional modules in the target sub-software functional module; When the second ratio of the second quantity to the total number of sub-software functional modules included in the abnormal software functional module exceeds the second preset ratio value, if the current driving mode of the whole vehicle is intelligent driving mode, control the current driving mode to switch from intelligent driving mode to human driving mode, and switch to the corresponding redundant backup module for operation based on the deployment address information. When the second ratio of the second quantity to the total number of sub-software function modules included in the abnormal software function module does not exceed the second preset ratio value, the step of determining the call interface address of the target backup sub-software function module corresponding to the target sub-software function module based on the deployment address information is executed.

15. The method according to claim 9, characterized in that, The method further includes: During the operation of each software functional module, the running status of the software functions contained in that software functional module is monitored. The running status includes at least one of the following: stack usage, static variable and / or global variable usage, array access out of bounds, function runtime, and multiple calls. Based on the functional logic corresponding to the software functional module, determine whether there are any abnormalities in the running status of each software function; When the execution state of at least one software function is abnormal, it is determined that the software function module is abnormal, and the abnormal software function is marked to obtain the software function associated with the abnormality.

16. The method according to claim 9, characterized in that, The method further includes: During the process of controlling a software function module that malfunctions and running according to the updated scheduling link, once the malfunctioning software function module recovers, the updated scheduling link is restored to its initial state, and the software function module continues to run according to the initial scheduling link.

17. The method according to any one of claims 9-16, characterized in that, The method further includes: During the process of controlling the software function module that malfunctions and running according to the updated scheduling link, if the software function module is detected to malfunction again, it is determined whether the software function associated with the second malfunction is the same as the software function associated with the previous malfunction. If the software function associated with the second exception is the same as the software function associated with the previous exception, determine whether the current driving mode of the vehicle is intelligent driving mode. When the current driving mode of the vehicle is Intelligent Driving Mode, control the switching of the current driving mode from Intelligent Driving Mode to Human Driving Mode.

18. The method according to claim 17, characterized in that, When the software function associated with the subsequent exception is different from the software function associated with the previous exception, the method further includes: Determine whether the updated scheduling link contains redundant software functions corresponding to the recurrence of software function module anomalies. If no redundant software function corresponding to the software function module exception reappears in the updated scheduling link, the process returns to the step of updating the scheduling link based on the software function associated with the exception and the deployment address to obtain the updated scheduling link.

19. The method according to claim 18, characterized in that, The method further includes: When the updated scheduling link contains redundant software functions corresponding to the recurrence of software function module anomalies, the step of determining whether the current driving mode of the vehicle is intelligent driving mode is executed.

20. A method for adjusting a seat, characterized in that, The method is applied to a vehicle employing the electronic and electrical architecture as described in any one of claims 1-8, the method comprising: Obtain the current signal of the vehicle's power steering motor; Extract feature data related to road surface roughness from the current signal; The feature data is classified to determine the road surface type of the current road surface on which the vehicle is driving; In response to the road surface type being an abnormally rough road surface, the relative positional relationship between the abnormally rough road surface and the vehicle is determined; Based on the relative positional relationship, at least one seat in the vehicle is adjusted to increase the support force of the seat in the direction of the abnormally rough road surface.

21. The method according to claim 20, characterized in that, The extraction of feature data related to road surface roughness from the current signal includes: Calculate the root mean square value, peak value, and peak-to-peak value of the current signal to obtain its time-domain characteristics; The current signal is converted into the frequency domain, and the energy of a preset frequency band is extracted to obtain frequency domain features. The preset frequency band is the frequency range corresponding to the road surface with abnormal roughness. Envelope analysis is performed on the current signal to extract the amplitude envelope and obtain the envelope features.

22. The method according to claim 20, characterized in that, The feature data is classified to determine the road surface type of the current road surface on which the vehicle is traveling, including: The feature data is input into a pre-trained road surface type classification model to obtain the road surface type of the road surface on which the vehicle is currently driving. The road surface type classification model is used to predict the road surface type based on feature data related to road surface roughness. The road surface type includes at least: shoulder, lane line and normal road surface.

23. The method according to claim 20, characterized in that, Determining the relative positional relationship between the road surface with abnormal roughness and the vehicle includes: The vibration signals collected by two vibration sensors on the vehicle are acquired, and the two vibration sensors are respectively located in the left and right regions of the vehicle. Based on the magnitude and difference of the vibration signals collected by the two vibration sensors on the vehicle, the relative positional relationship between the roughness abnormal road surface and the vehicle is determined. The relative positional relationship includes: the roughness abnormal road surface is located on the left, right, or both sides of the vehicle.

24. The method according to claim 23, characterized in that, The adjustment of at least one seat in the vehicle based on the relative positional relationship includes: When the relative positional relationship is such that the road surface with abnormal roughness is located on the left side of the vehicle, the seat cushion, lumbar support, and right side wing motor of the seat are activated; When the relative positional relationship is such that the road surface with abnormal roughness is located on the right side of the vehicle, the seat cushion, lumbar support, and left side wing motor of the seat are activated; When the relative positional relationship is such that the abnormally rough road surface is located on the left and right sides of the vehicle, the seat cushion, lumbar support, left side wing and right side wing motors of the seat are activated.

25. The method according to claim 21, characterized in that, Before classifying the feature data and determining the road surface type of the current road surface the vehicle is traveling on, the method further includes: The feature values ​​of the time-domain feature, the frequency-domain feature, and the envelope feature are respectively detected to see if they exceed the feature threshold of the corresponding feature. The feature threshold is the minimum feature value that characterizes the occurrence of a vehicle traveling on a road surface with abnormal roughness. When any feature value is detected to exceed the feature threshold of the corresponding feature, it is determined that a vehicle is traveling on a road surface with abnormal roughness, and the step of classifying the feature data and determining the road surface type of the road surface on which the vehicle is currently traveling is performed. If none of the feature values ​​exceed the feature threshold of the corresponding feature, return to the step of obtaining the current signal of the vehicle's power steering motor.

26. The method according to claim 25, characterized in that, The method further includes: Obtain the current speed of the vehicle; Based on the current vehicle speed, the feature thresholds corresponding to the time-domain feature, the frequency-domain feature, and the envelope feature are determined respectively, and the feature thresholds corresponding to the time-domain feature, the frequency-domain feature, and the envelope feature are negatively correlated with the current vehicle speed.

27. The method according to claim 25, characterized in that, The method further includes: When it is determined that a vehicle is traveling on a rough road surface, the process returns to the step of acquiring the current signal of the vehicle's power steering motor until the cumulative duration or number of times the vehicle is traveling on a rough road surface reaches a preset duration or number of times. Then, the process of classifying the feature data and determining the road surface type of the current road surface of the vehicle is executed.

28. The method according to claim 20, characterized in that, The acquisition of the current signal of the vehicle's power steering motor includes: The system receives a current signal from the power steering motor sent via Ethernet by the target power distribution and data transmission module. The current signal is a current signal collected from the power steering motor driver by the target sensing component corresponding to the power steering motor. The current signal is uploaded to the target power distribution and data transmission module through the target edge node connected to the target sensing component.

29. The method according to any one of claims 20-28, characterized in that, The method further includes: Monitor the vehicle's current speed and / or the activation status of its turn signals; When the current vehicle speed is detected to be higher than a preset vehicle speed threshold, and / or the turn signal is not activated, the step of obtaining the current signal of the vehicle's power steering motor is executed. When it is detected that the current vehicle speed is not higher than a preset vehicle speed threshold, and / or the turn signal is in the activated state, return to the step of monitoring the current vehicle speed and / or the activation state of the turn signal.

30. A vehicle, characterized in that, The vehicle employs the electronic and electrical architecture as described in any one of claims 1-8.

31. The vehicle according to claim 30, characterized in that, The vehicles include: A memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, the processor executing the computer instructions to perform the method of any one of claims 9 to 29.

32. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a computer to perform the method of any one of claims 9 to 29.

33. A computer program product, characterized in that, Includes computer instructions for causing a computer to perform the method of any one of claims 9 to 29.

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