Method for configuring a nonvolatile memory of a vehicle, memory device and vehicle

Abstract

The invention provides a method for configuring a non-volatile memory of a vehicle. The method comprises the following steps: acquiring an expected grading result of vehicle data to be written into the non-volatile memory in the aspects of reliability requirement and data volume; according to the expected grading result of the vehicle data, determining a storage strategy of the vehicle data in the nonvolatile memory, the storage strategy defining the category and the number of storage units distributed by the vehicle data in the nonvolatile memory; a pre-configured partition is performed on the non-volatile memory according to a storage policy determined for the vehicle data. The application also proposes a memory device for a vehicle, a vehicle and a computer program product. According to the method and the device, the partition configuration of the nonvolatile memory is customized in advance, so that the safety and the stability of the key vehicle data are improved, and the overall performance and the cost benefit of the nonvolatile memory are also improved.

Description

Technical Field

[0001] The present application relates to a method for configuring a non-volatile memory of a vehicle. The present application also relates to a memory device for a vehicle, a vehicle and a computer program product. Background Art

[0002] As the automotive industry rapidly develops towards intelligence and electrification, the amount of data generated by vehicles has increased dramatically. This data is essential for the normal operation and intelligent functions of vehicles. In order to effectively manage and store this data, non-volatile memory (NVM) is widely used in vehicles.

[0003] However, the diversity of vehicle data requires different reliability and storage density of memory. At present, vehicles generally adopt a unified and static memory configuration method, which fails to flexibly adapt to the specific storage requirements of various types of data. This causes critical data to be lost due to insufficient storage space or unreliable memory, while regular data may occupy too many storage resources. This imbalance in resource allocation not only affects storage efficiency, but also may endanger data security and the stability of vehicle systems, hindering the progress of vehicle intelligence.

[0004] Therefore, there is still room for improvement in the configuration of vehicle non-volatile memory in the prior art. Summary of the invention

[0005] The object of the present application is to provide a method for configuring a non-volatile memory of a vehicle, a memory device for a vehicle, a vehicle and a computer program product, so as to at least solve some of the problems in the prior art.

[0006] According to a first aspect of the present application, a method for configuring a non-volatile memory of a vehicle is provided, the method comprising the following steps:

[0007] Step S1: obtaining expected classification results of vehicle data to be written into the non-volatile memory in terms of reliability requirements and data volume;

[0008] Step S2: determining a storage strategy for the vehicle data in the non-volatile memory according to the expected classification result of the vehicle data, wherein the storage strategy defines the type and number of storage units allocated to the vehicle data in the non-volatile memory; and

[0009] Step S3: Perform pre-configured partitioning of the non-volatile memory according to the storage strategy determined for the vehicle data.

[0010] The present application particularly includes the following technical concepts: the above method allows pre-allocation of storage units for each type of vehicle data according to its specific requirements for reliability and storage density, and pre-customization of the partition configuration of the non-volatile memory accordingly. In this way, not only the security and stability of key data are improved, but also the overall performance and cost-effectiveness of the non-volatile memory are improved by optimizing the storage resource configuration.

[0011] In an exemplary embodiment, vehicle data includes at least first-level data and second-level data, the reliability requirement of the first-level data is higher than that of the second-level data, and the data volume of the first-level data is lower than that of the second-level data, wherein in step S2, SLC storage units are allocated to the first-level data, and MLC / TLC / QLC storage units are allocated to the second-level data, wherein: the first-level data includes: vehicle information, driver preference setting data, system software and firmware data, log files and / or at least part of the data required by regulations; the second-level data includes: map and route data, sensor data, operation record data, OEM customization data and / or at least part of the data required by regulations.

[0012] In an exemplary embodiment, the method further includes the following steps: obtaining real-time classification results of vehicle data in terms of reliability requirements and data volume; determining an updated storage strategy for at least part of the vehicle data in a non-volatile memory based on the real-time classification results of the vehicle data; and dynamically adjusting preconfigured partitions of the non-volatile memory based on the updated storage strategy determined for at least part of the vehicle data.

[0013] In an exemplary embodiment, the preconfigured partitions of the nonvolatile memory include SLC partitions, MLC partitions, TLC partitions and / or QLC partitions, and dynamically adjusting the preconfigured partitions of the nonvolatile memory includes: resizing, removing, or class converting storage units in the partitions.

[0014] In an exemplary embodiment, the characteristic fixed data, whose real-time classification results remain unchanged during the use of the vehicle, and the pre-configured partition of the non-volatile memory is not dynamically adjusted for the characteristic fixed data, and the characteristic fixed data includes: vehicle information, driver preference setting data, system software and firmware data, log files and / or map and route data; the characteristic variable data, whose real-time classification results dynamically change during the use of the vehicle, and the pre-configured partition of the non-volatile memory is dynamically adjusted for the characteristic variable data, and the characteristic variable data includes: operation record data, sensor data, regulatory requirement data and / or OEM customized data.

[0015] In an exemplary embodiment, based on the vehicle usage target, the installed applications, the vehicle driving area and / or the vehicle usage habits, the expected classification result and / or the real-time classification result of the vehicle data to be written into the non-volatile memory is determined, wherein:

[0016] Determine the expected classification of map and route data based on the map pre-load method and / or vehicle travel area,

[0017] Determine real-time grading results for regulatory data and / or OEM customized data according to the latest laws, regulations and regulatory requirements, and / or

[0018] Determine real-time classification results of sensor data and / or operation record data according to the frequency and / or duration of use of the vehicle and / or vehicle functions;

[0019] The method further includes the following steps before step S1: pre-configuring the hardware storage capacity of the non-volatile memory according to the life cycle and vehicle usage objectives of the vehicle.

[0020] In an exemplary embodiment, the method further includes the following steps: monitoring the health of the non-volatile memory, and if the health of the non-volatile memory is detected to be lower than a preset standard, notifying the original equipment manufacturer or the vehicle owner, wherein the health is reflected by the following indicators: the remaining life of the non-volatile memory, the error rate, the number of bad sectors and / or the remaining storage space.

[0021] According to a second aspect of the present application, a memory device for a vehicle is provided, the memory device comprising: a volatile memory; a non-volatile memory; and a controller configured to be able to perform pre-configured partitioning on the non-volatile memory according to the method described in the first aspect of the present application.

[0022] According to a third aspect of the present application, a vehicle is provided, comprising the memory device according to the second aspect of the present application.

[0023] According to a fourth aspect of the present application, a computer program product is provided, comprising computer program instructions, wherein when the computer program instructions are executed by a processor, the processor is enabled to perform the method according to the first aspect of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The present application will be described in more detail below with reference to the accompanying drawings, so that the principles, features and advantages of the present application can be better understood. The accompanying drawings include:

[0025] Figure 1 A block diagram of a memory device for a vehicle according to an exemplary embodiment of the present application is shown;

[0026] Figure 2 A flow chart showing a method for configuring a non-volatile memory of a vehicle according to an exemplary embodiment of the present application is shown;

[0027] Figure 3 A flow chart showing a method for configuring a non-volatile memory of a vehicle according to another exemplary embodiment of the present application is shown;

[0028] Figure 4 A schematic diagram showing the allocation of storage units in a memory for different types of vehicle data according to expected classification results of the vehicle data;

[0029] Figure 5A A schematic diagram of a non-volatile memory in a conventional configuration is shown;

[0030] Figure 5B A schematic diagram showing a non-volatile memory after pre-configured partitioning is performed using the method according to the present application; and

[0031] Figure 5C A schematic diagram of a non-volatile memory after dynamic adjustment of pre-configured partitions is performed using the method according to the present application is shown. DETAILED DESCRIPTION

[0032] In order to make the technical problems, technical solutions and beneficial technical effects to be solved by the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and multiple exemplary embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, rather than to limit the scope of protection of the present application.

[0033] Figure 1 A block diagram of a memory device for a vehicle according to an exemplary embodiment of the present application is shown.

[0034] The memory device is, for example, a memory device 10 of an ECU (Electronic Control Unit) 1 of a vehicle, which includes a controller 11 , a nonvolatile memory 12 , and a volatile memory 13 .

[0035] The non-volatile memory 12 has the characteristic that data remains unchanged after power failure, and can adopt NAND (such as SLC, MLC, TLC, QLC, etc.) storage technology. This technology can be applied to various types of non-volatile memories, such as SSD, eMMC, PCM and UFS. Figure 1 As shown, the nonvolatile memory 12 may include multiple storage areas of different types (having different storage densities), such as an SLC storage area 111 and a TLC storage area 112. The types of these storage areas 111 and 112 and the number of storage cells included therein may be configured by the controller 11.

[0036] The volatile memory 13 relies on power to maintain data, and power failure will cause data loss, so it is suitable for storing temporary data. For example, the volatile memory 13 can be used to cache vehicle data and to temporarily store runtime data and instructions of the controller 11 to improve processing performance. In addition, when it is necessary to change the category of the storage unit in the non-volatile memory 12, the data pre-stored in these storage units can be temporarily extracted to the volatile memory 13 and stored again in the corresponding storage unit of the non-volatile memory 12 after the category change is completed. In this way, it can be ensured that the data in the original storage unit is not affected during the conversion process.

[0037] The controller 11 may be a general-purpose microprocessor or a dedicated microcontroller, and may have a processor and an internal memory integrated therein (not specifically shown for simplicity). The internal memory may include, for example, SRAM, DRAM or flash memory, for storing instructions and the classification standard or expected classification result of vehicle data. The processor is responsible for reading the classification result in the internal memory and executing related instructions. In addition, the controller 11 may also access other memories (such as EEPROM) other than the non-volatile memory 12 and the volatile memory 13, and obtain the expected classification result therefrom.

[0038] The memory device 10 may also be externally coupled to a host device 20 (e.g., a main controller 20 of the ECU 1). The controller 11 of the memory device 10 may, for example, perform write and read operations of vehicle data in response to a request of the host device 20. In addition, the controller 11 of the memory device 10 may also automatically perform write and read operations of data in response to an internal event.

[0039] Figure 2 A flowchart of a method for configuring a non-volatile memory of a vehicle according to an exemplary embodiment of the present application is shown. The method exemplarily includes steps S1 to S3, and can be used, for example, in Figure 1 The memory device 10 shown is implemented in the context of FIG.

[0040] In step S1 , expected classification results of vehicle data to be written into a non-volatile memory in terms of reliability requirements and data volume are obtained.

[0041] Exemplarily, vehicle data can be divided into multiple types of data, and each type of data is assigned different expected classification results according to its data volume and reliability requirements, thereby determining the type of storage unit to be stored in the non-volatile memory. Vehicle data is exemplarily classified into:

[0042] Vehicle information (Data1): including vehicle model, vehicle identification number (VIN), manufacturing date, mileage, maintenance records, and vehicle configuration information.

[0043] Driver preferences (Data2): including seat position, mirror settings, audio settings and navigation destination.

[0044] Operation record (Data3): including data such as vehicle speed, acceleration, braking force, steering wheel angle, etc., which can be used for diagnosis and troubleshooting.

[0045] Sensor data (Data4): includes data generated by sensors such as speed sensors, brake sensors, accelerometers, GPS locators, cameras and radars. This data can be used for functions such as autonomous driving, vehicle safety and navigation.

[0046] Map and route data (Data5): This includes real-time traffic information, road speed limits, road grades, and road curvature, which can be used for autonomous driving, navigation, and vehicle safety.

[0047] System software and firmware data (Data 6): including software and firmware for driver infotainment systems, vehicle control systems, safety control systems, autonomous driving systems, etc., which can be updated or upgraded to improve system performance or fix vulnerabilities.

[0048] Log file data (Data7): includes the vehicle’s operation log, fault log, maintenance log and other information, which can be used to track the vehicle’s history and analyze the vehicle’s performance.

[0049] Regulatory data (Data8): including vehicle operating status, safety-related data, emission data, etc. This type of data is vehicle information that must be collected and recorded in accordance with relevant laws and regulations.

[0050] OEM Customized Data 9: This includes map learning and update data (i.e., data collected to help update maps when sensor perception results are inconsistent with map information). This type of data refers to data collected and customized by OEMs based on specific business goals and user needs.

[0051] Based on the different characteristics of the above data, the expected classification results are obtained as shown in Table 1:

[0052] Table 1

[0053] Data Data volume Data reliability requirements Expected grading results 1 Small Very high 1 2 Small Very high 1 3 Medium / Large high 2,3 4 Large / Extremely Large high 2,3 5 Medium / Large / Extremely Large Medium / High 4 6 Small Very high 1 7 Small Very high 1 8 Small / Medium / Large / Extremely Large High / Very High 1,2,4 9 Small / Medium / Large / Extremely Large Low / Medium / High / Very High 2,3 …

[0054] Exemplarily, the classification results are represented by numbers 1 to 4, and the smaller the number, the higher the reliability requirement for the data and the lower the storage density requirement. Each type of vehicle data can correspond to a single or multiple classification results. When data corresponds to multiple classification results, it means that the data of this type can be further subdivided, and each data type after the subdivision will correspond to a specific classification result. In the above example, each classification result is represented by a single numerical value, but in fact, a more complex representation of the classification results (such as a multivariate representation or a composite representation of numbers and letters, etc.) can also be considered to more accurately guide the required storage unit categories and quantities.

[0055] In one embodiment, in step S1, the expected classification results of the vehicle data to be written into the non-volatile memory can be determined based on the vehicle's usage objectives, the applications carried, the vehicle's driving area and / or the vehicle's usage habits. This expected classification operation itself can usually be completed in the design stage, for example, with the help of machine learning models, big data analysis, or based on professional knowledge and experience. The expected classification results can be pre-stored in the built-in memory or external memory of the controller of the memory device so that the built-in processor of the controller can quickly access it and dynamically adjust it as needed during the use of the vehicle. The standards for data classification can be determined based on experience, laws, regulations and / or regulatory requirements, and can also be customized according to the driving functions and / or user preferences of the vehicle.

[0056] In one embodiment, the expected ranking results of the map and route data may be determined according to the map preloading method and / or the vehicle driving area.

[0057] Here, the map preloading methods include, for example: preloading all maps, preloading all map elements, and preloading some map elements. When the OEM chooses to preload all map data of the area where the vehicle will travel, this will require a large storage space, thereby increasing the data volume of Data5. In contrast, if the OEM chooses to dynamically load the required maps based on the real-time location of the vehicle, the estimated data volume of Data5 may be reduced because only the map data currently and recently required by the vehicle will be stored, reducing the demand for storage space. "Preloading all map elements" means that the OEM decides to preload all map elements, including roads, points of interest, traffic signals, etc. This method will also increase the data volume of Data5 because each element of the map requires storage space. "Preloading some map elements" means preloading only key map elements or map elements that users often use. Compared with "preloading all map elements", "preloading some map elements" can relatively reduce the data volume of Data5 because it only focuses on the map information that users are most likely to need.

[0058] The amount of map and route data (Data5) varies when a vehicle is traveling in different geographic areas. In urban environments, map data is usually more detailed due to the complexity of roads and geographical features, resulting in an increase in the amount of Data5 data. In rural or open areas, map data is relatively simplified, so the amount of data to be stored in Data5 is smaller. In addition, the driving range of the vehicle also affects the storage requirements for map and route data. Vehicles traveling in a smaller area may only need to store maps for that area, while vehicles traveling over a larger area require more map data to cover a wider geographic area.

[0059] In an optional embodiment, the method may further include the following steps before step S1: pre-configuring the hardware storage capacity of the non-volatile memory according to the life cycle and vehicle usage objectives of the vehicle. Here, "hardware storage capacity" refers to the physical storage capacity of the memory, which is an inherent property of the memory and is usually determined during the device manufacturing process. This hardware storage capacity defines the maximum physical space in which the memory can store data, usually in bytes. In contrast, the present application also relates to the concept of optimizing storage efficiency at the software or firmware level through technical means. This optimization can be achieved, for example, by changing the category of the storage unit, and is achieved through adjustments to the software or firmware without changing the physical hardware of the memory.

[0060] For example, for operational vehicles, if it is expected that there will be a high frequency of use of the vehicle and its functions and a long life cycle, a larger hardware storage space can be pre-configured to meet the data storage needs in long-term operations. For test vehicles in the development process, although the frequency of use of vehicle functions may be high, due to their relatively short life cycle, a relatively low hardware storage space can be configured. Such initial hardware configuration can both meet testing needs and control costs.

[0061] In step S2, a storage strategy for the vehicle data in the non-volatile memory is determined according to the expected classification result of the vehicle data, wherein the storage strategy defines the type and number of storage units allocated to the vehicle data in the non-volatile memory.

[0062] For example, SLC storage cells can be assigned to data types that require the highest reliability but have smaller data volumes, while QLC storage cells can be assigned to data types that have lower reliability requirements but larger data volumes.

[0063] In one embodiment, the vehicle data includes at least first-level data and second-level data, the reliability requirement of the first-level data is higher than that of the second-level data, and the data volume of the first-level data is lower than that of the second-level data. In step S2, for example, the first-level data may be assigned an SLC storage unit, and the second-level data may be assigned an MLC / TLC / QLC storage unit. The first-level data may include, for example, vehicle information Data1, driver preference setting data Data2, system software and firmware data Data6, log file data Data7, and / or at least part of the regulatory data Data8. The second-level data may include, for example, map and route data Data5, sensor data Data4, operation record data Data3, OEM customization data Data9, and / or at least part of the regulatory data Data8.

[0064] In one embodiment, the number of storage units to be allocated may also be determined according to the expected classification result of the vehicle data. For example, a 2MB SLC storage unit may be allocated to vehicle data with an expected classification result of "1-1", a 6MB SLC storage unit may be allocated to vehicle data with an expected classification result of "1-3", and a 6GB QLC storage unit may be allocated to vehicle data with an expected classification result of "4-3".

[0065] In addition, storage policies may also include specific requirements for read and write speeds, single write volume, and write intervals to ensure the performance and reliability of data storage.

[0066] In step S3, pre-configured partitioning is performed on the non-volatile memory according to the storage strategy determined for the vehicle data. Here, different partitions may have different storage densities or be based on different storage technologies (such as SLC, MLC, TLC or QLC).

[0067] In one embodiment, the storage units pre-allocated for each type of vehicle data may be aggregated to form corresponding partitions. For example, all SLC storage units allocated to vehicle data may be aggregated to determine the total size of the required SLC partition.

[0068] In one embodiment, after all types of storage units are aggregated to determine the storage space required for each partition, the sizes of all partitions may be accumulated to estimate the total storage requirement. During the pre-configuration phase of the memory, if the total capacity of the partition exceeds the hardware limit, fine-tuning may be performed by reducing the number of storage units of certain data types or adjusting the storage density to accommodate the hardware limit.

[0069] In one embodiment, when pre-configuring partitions, a certain amount of space may be reserved for each type of vehicle data to cope with future growth in data volume or new storage requirements, thereby ensuring the flexibility and scalability of the storage system.

[0070] Figure 3 A flow chart of a method for configuring a non-volatile memory of a vehicle according to another exemplary embodiment of the present application is shown. Figure 3 In the embodiment shown, Figure 2 The method shown also additionally includes sub-steps S4 to S12.

[0071] In step S4, the vehicle data is stored in pre-configured partitions of the non-volatile memory by means of the storage strategy determined for the vehicle data. For example, if 2MB of SLC storage cells are pre-allocated for the log file data Data7 in the design phase, the generated log file data can be stored in this part of the storage cells during the actual use of the vehicle.

[0072] In step S5, the real-time classification results of the vehicle data in terms of reliability requirements and data volume are obtained. Although step S5 is shown to be performed after step S4, in fact, step S5 can also be performed in parallel or alternately with step S4.

[0073] The real-time classification result of the vehicle data to be written into the non-volatile memory may be determined based on the vehicle's usage objectives, installed applications, vehicle driving areas, and / or vehicle usage habits.

[0074] For example, the real-time classification results of regulatory data and / or OEM customized data can be determined according to the latest laws, regulations and regulatory requirements. For example, vehicle data such as traffic flow data and driving behavior data may not be required to be recorded in the early design stage, but as policies pay more attention to intelligent connected vehicle data, they may become data required to be recorded by regulations in the future.

[0075] For example, the real-time classification results of sensor data Data3 and / or operation record data Data4 can also be determined according to the frequency and / or duration of use of the vehicle and / or vehicle functions. The data volume per unit time of Data3 and 4 generally does not fluctuate much during the use of the vehicle, but the total amount of data written will change significantly with the increase or decrease in the frequency / duration of the driver's use of the vehicle and its functions, and even show an obvious linear relationship. Therefore, if it is found during the use of the vehicle that the frequency and / or duration of use of the vehicle and / or vehicle functions is significantly greater than the preset value in the design stage, the real-time classification results for Data3 and Data4 can be adjusted accordingly to correspond to the larger data storage space requirements. The frequency and / or duration of use of vehicles and / or vehicle functions can be obtained in a variety of ways. For example, the owner's driving frequency / duration and vehicle model (ADS / ADAS) configuration can be understood in advance by analyzing customer needs before sales. The frequency / duration of actual use of the vehicle and vehicle functions can also be understood after sales (through return visits). In addition, you can also understand the actual frequency / duration of use of the vehicle and its functions by binding the car owner's mobile terminal and initiating inquiries or reading personal information through the mobile terminal.

[0076] In step S6, it is checked whether the real-time classification result of the vehicle data is consistent with the expected classification result.

[0077] If inconsistency is determined in step S6, an updated storage strategy for the inconsistent vehicle data in the non-volatile memory may be determined in step S7, and the pre-configured partitions of the non-volatile memory may be dynamically adjusted according to the updated storage strategy in the following step S8. Such vehicle data for which real-time classification results deviate from expected classification results, for example, involve characteristic change data in vehicle data, and their real-time classification results change dynamically during vehicle use. Such data include, for example: operation record data, sensor data, regulatory requirement data, and / or OEM customized data. For characteristic change data, the pre-configured partitions of the non-volatile memory may need to be dynamically adjusted during vehicle use.

[0078] For example, some regulatory requirements data Data8 may have been previously allocated MLC units, but due to changes in regulatory requirements, they may now be reconfigured as SLC units. For another example, because the use of vehicles or vehicle functions is more frequent than in the design stage, the expected data volume of the corresponding sensor data Data4 or operation record data Data3 will increase significantly, thereby placing higher requirements on the number of storage units or storage density. In addition, factors such as driving behavior patterns, different climatic conditions, geographical environments, vehicle software updates, and new requirements for data security and privacy protection may also more or less cause deviations between the expected classification results of vehicle data and the real-time classification results. By continuously monitoring and analyzing the usage patterns and trends of vehicle data, storage strategies can be adjusted in a timely manner, so that the memory can be used more reasonably and effectively.

[0079] In one embodiment, the preconfigured partitions of the non-volatile memory include SLC partitions, MLC partitions, TLC partitions and / or QLC partitions. In order to dynamically adapt to changes in data management, the following measures may be taken to adjust the preconfigured partitions of the memory:

[0080] - Resize a partition: This may involve extending a partition to increase its capacity, or shrinking a partition to free up space for other uses;

[0081] -Remove partition: This operation involves completely deleting a storage partition so that the space originally occupied by the partition can be reallocated to create a new partition or for other storage purposes. When removing a partition, all data stored on the partition will be migrated or deleted;

[0082] - Storage cell class conversion: involves reconfiguring storage cells based on a specific storage technology (such as MLC) in a partition to storage cells based on another storage technology (such as TLC).

[0083] When the storage strategy is adjusted according to the new classification results, the partition configuration of the memory may change relative to the initial preset configuration. However, this is not absolute, and the partition configuration after dynamic adjustment may also remain consistent with the initial preset configuration. For example, if the demand for SLC storage units for certain data types decreases, while the demand for SLC storage units for other types of vehicle data increases, the total amount of SLC storage units may remain unchanged in the end.

[0084] If it is determined in step S6 that the real-time classification result is consistent with the expected classification result, the previous storage strategy can be kept unchanged in step S9. This type of vehicle data involves fixed-characteristic data, and their real-time classification results remain unchanged during the use of the vehicle and are consistent with the expected classification results. This usually includes: vehicle information, driver preference setting data, system software and firmware data, log files and / or map and route data. For fixed-characteristic data, dynamic adjustment of the pre-configured partition of the non-volatile memory may not be performed during the use of the vehicle.

[0085] like Figure 3 As shown, the method may further include sub-steps S10, S11 and S12. In step S10, the health of the non-volatile memory is monitored, and it is determined whether the health is lower than a preset standard.

[0086] The health status can be reflected, for example, by the following indicators: the remaining life of the non-volatile memory, the error rate (the frequency of errors occurring in the memory during data reading and writing), the number of bad sectors (the number of defective physical areas in the memory) and / or the remaining storage space (the size of the unused space in the memory).

[0087] For example, when the remaining life of the non-volatile memory is lower than a certain percentage (e.g., 10%) of its design life; or the error rate is significantly higher than the normal operation threshold specified by the manufacturer; or if the number of bad sectors in the non-volatile memory begins to increase so that it exceeds the set fault tolerance limit; or if the remaining storage space cannot meet the minimum storage requirements required for normal operation, these all indicate that the health of the non-volatile memory is lower than the preset standard.

[0088] When it is determined that the health of the non-volatile memory is lower than a preset standard, a notification may be issued to the original equipment manufacturer or the vehicle owner in step S11 (eg, requesting data migration, optimizing the grading standard or hardware upgrade to prevent the risk of data loss).

[0089] On the contrary, if the monitoring result in step S10 indicates that the health of the non-volatile memory is good, the current storage partition configuration can be maintained unchanged in step S12 without adjustment.

[0090] Figure 4 A schematic diagram is shown for allocating storage units in a memory to different types of vehicle data according to expected classification results of the vehicle data.

[0091] like Figure 4As shown, according to the expected classification results of vehicle data Data1 to Data9 shown in Table 1, four categories of storage cells are assigned to them: SLC storage cell (Single Level Cell), MLC storage cell (Multi-Level Cell), TLC storage cell (Triple Level Cell) and QLC storage cell (Quad Level Cell). The categories of these storage cells are distinguished according to the number of data bits they store: SLC storage cells store a single information bit, while MLC, TLC and QLC storage cells store two, three and four information bits respectively.

[0092] From Table 1, we can see that Data1, Data2, Data6, and Data7 have a grading result of "1", indicating that these data have extremely high requirements for reliability and a small amount of data, so they are allocated SLC storage units in the memory to ensure data security and stability. The grading results of Data3, Data4, and Data9 are "2" and "3", so they are allocated MLC storage units and TLC storage units respectively. These data have a large amount of data and have lower reliability requirements than the data stored in the SLC storage unit. The grading result of Data5 is "4", which indicates that its data volume may be large and its reliability requirements are relatively low, so it is allocated QLC storage units with the highest storage density. Data8 has multiple grading results of "1", "2", and "4", so it is allocated SLC storage units, MLC storage units, and QLC storage units accordingly to meet the storage requirements of different segmented data.

[0093] By summarizing and integrating the storage units allocated to various types of vehicle data, the corresponding partition configuration scheme of the non-volatile memory 12 can be obtained. In this embodiment, the non-volatile memory 12 can be pre-divided into four partitions based on the above classification results of the vehicle data, corresponding to SLC, MLC, TLC and QLC partitions respectively. The storage capacity of each partition is calculated by superimposing all the allocated storage units of the same type, ensuring that each partition can meet the storage requirements of the corresponding data type.

[0094] Figure 5A FIG. 1 shows a schematic diagram of a non-volatile memory in a conventional configuration. Figure 5A In the conventional configuration example shown, the non-volatile memories 12, 12' of all electronic control units (ECUs) 1, 2 are configured with a uniform fixed partition. When shipped from the factory, a preset number of storage cells are allocated as SLC storage cells and QLC storage cells, forming SLC partitions and QLC partitions with predetermined capacities. For example, Figure 5AThe storage capacity of the SLC partition is set to 12MB, while the storage capacity of the QLC partition is 128GB. This configuration is static during use and will not be adjusted according to the actual needs of the data.

[0095] In contrast, Figure 5B In the illustrated embodiment, the partition configuration of the non-volatile memory 12, 12' is based on the expected classification result of the vehicle data. Specifically, the non-volatile memory 12 of ECU1 is divided into three storage areas with different storage densities: the initial capacity of the SLC partition is 12MB, the initial capacity of the TLC partition is 32GB, and the initial capacity of the QLC partition is 64GB. The non-volatile memory 12' of ECU2 is subdivided into four storage areas: the initial capacity of the SLC partition is 12MB, the initial capacity of the MLC partition is 1GB, the initial capacity of the TLC partition is 16GB, and the initial capacity of the QLC partition is also 64GB. This configuration difference reflects the difference in the type of vehicle data to be stored in different ECUs 1 and 2 and their expected classification results. For example, ECU1 may need to store more map and route data than ECU2. The data volume of these data is large and the requirements for storage reliability are not particularly high, so more TLC storage units are occupied.

[0096] contrast Figure 5A ,exist Figure 5B In the illustrated embodiment, the partition configuration of the non-volatile memory 12, 12' adopts a more flexible and dynamic approach that is customized according to the expected classification results of the vehicle data. This approach takes into account the specific requirements of different data types for reliability and storage density, thereby achieving an optimized allocation of storage resources.

[0097] exist Figure 5C FIG. 1 shows the configuration of the non-volatile memory 12 and 12' after a period of use. It can be observed that the partition configuration of the non-volatile memory 12 and 12' of ECU1 and ECU2 has undergone adaptive adjustment. The specific changes are as follows:

[0098] In the non-volatile memory 12 of ECU1, the original QLC partition has been completely removed, and a 1GB MLC partition has been added. The capacity of the SLC partition has been expanded from the original 12MB to 64MB; the capacity of the TLC partition has been adjusted from 32GB to 40GB. These adjustments are achieved, for example, by reconfiguring the QLC storage cells, where some storage cells are converted to SLC storage cells, some storage cells are converted to TLC storage cells, and other storage cells are converted to MLC storage cells to balance storage density and cost-effectiveness.

[0099] For ECU1, changes in regulatory requirements may be encountered during use, which may result in the need to store more regulatory data than initially expected. Although the total amount of this data may not be large, it contains critical information such as accident judgment and accident scene restoration, which have extremely high requirements for data reliability. This increase in demand for high-reliability data storage directly leads to the conversion of low-density storage units to higher-density storage units. Although this conversion means sacrificing some storage space in exchange for higher data retention and durability, it ensures that critical data can be stored more securely and reliably.

[0100] In the non-volatile memory 12' of ECU2, the original MLC partition is removed, and the capacity of the SLC partition is increased from the original 12MB to 32MB to meet the growing demand for high-reliability storage space. These changes are achieved, for example, by reconfiguring the MLC storage unit to the SLC storage unit, ensuring the security and stability of key data.

[0101] For ECU2, the frequency and duration of vehicle use in actual applications may be much lower than expected, resulting in the MLC storage units reserved for sensor and operation record data exceeding actual needs. In view of the potential changes in regulatory policies that may require the storage of more critical data, a storage unit conversion strategy from MLC to SLC is implemented. This adjustment not only improves the utilization efficiency of storage resources, but also provides a flexible solution to meet current storage needs and future regulatory requirements.

[0102] Although the specific embodiments of the present application are described in detail herein, they are provided for the purpose of explanation only and should not be considered to limit the scope of the present application. Various substitutions, changes and modifications may be conceived without departing from the spirit and scope of the present application.

Claims

1. A method for configuring a non-volatile memory of a vehicle, the method comprising the steps of: Step S1: obtaining expected classification results of vehicle data to be written into the non-volatile memory in terms of reliability requirements and data volume; Step S2: determining a storage strategy for the vehicle data in the non-volatile memory according to the expected classification result of the vehicle data, wherein the storage strategy defines the category and quantity of storage units allocated to the vehicle data in the non-volatile memory; as well as Step S3: Perform pre-configured partitioning of the non-volatile memory according to the storage strategy determined for the vehicle data.

2. The method according to claim 1, wherein: The vehicle data includes at least first-level data and second-level data, the reliability requirement of the first-level data is higher than that of the second-level data, and the data volume of the first-level data is lower than that of the second-level data, wherein in step S2, an SLC storage unit is allocated to the first-level data, and an MLC / TLC / QLC storage unit is allocated to the second-level data, wherein: The first level of data includes: vehicle information, driver preference data, system software and firmware data, log files and / or at least part of regulatory requirement data; The second level data includes: map and route data, sensor data, operation record data, OEM customization data and / or at least part of regulatory requirement data.

3. The method according to claim 1 or 2, wherein: The method further comprises the following steps: Obtain real-time classification results of vehicle data in terms of reliability requirements and data volume; Determining an updated storage strategy for at least part of the vehicle data in a non-volatile memory according to the real-time classification result of the vehicle data; and The preconfigured partitions of the non-volatile memory are dynamically adjusted based on an updated storage strategy determined for at least a portion of the vehicle data.

4. The method according to claim 3, wherein: The preconfigured partitions of the nonvolatile memory include SLC partitions, MLC partitions, TLC partitions and / or QLC partitions, and dynamically adjusting the preconfigured partitions of the nonvolatile memory includes: resizing, removing, or class-converting storage units in the partitions of the preconfigured partitions of the nonvolatile memory.

5. The method according to any one of claims 1 to 4, wherein: The vehicle data includes: Fixed characteristic data, whose real-time classification results remain unchanged during vehicle use, for which no dynamic adjustment of the pre-configured partitions of the non-volatile memory is performed, the fixed characteristic data including: vehicle information, driver preference setting data, system software and firmware data, log files and / or map and route data; Characteristic change data, whose real-time classification results change dynamically during vehicle use, and the pre-configured partitions of the non-volatile memory are dynamically adjusted according to the characteristic change data, and the characteristic change data includes: operation record data, sensor data, regulatory requirement data and / or OEM customized data.

6. The method according to any one of claims 1 to 5, wherein: Based on the vehicle usage objectives, installed applications, vehicle driving areas and / or vehicle usage habits, an expected classification result and / or a real-time classification result of the vehicle data to be written into the non-volatile memory is determined, wherein: Determine the expected classification of map and route data based on the map pre-load method and / or vehicle travel area, Determine real-time grading results for regulatory data and / or OEM customized data according to the latest laws, regulations and regulatory requirements, and / or Determine real-time classification results of sensor data and / or operation record data according to the frequency and / or duration of use of the vehicle and / or vehicle functions; The method further includes the following steps before step S1: pre-configuring the hardware storage capacity of the non-volatile memory according to the life cycle and vehicle usage objectives of the vehicle.

7. The method according to any one of claims 1 to 6, wherein: The method further comprises the following steps: Monitor the health of the non-volatile memory and send a notification to the original equipment manufacturer or the vehicle owner if the health of the non-volatile memory is monitored to be lower than a preset standard, wherein the health is reflected by the following indicators: the remaining life of the non-volatile memory, the error rate, the number of bad sectors and / or the remaining storage space.

8. A memory device for a vehicle, the memory device comprising: Volatile memory; Non-volatile memory; as well as A controller configured to perform pre-configured partitioning of a non-volatile memory according to the method of any one of the preceding claims.

9. A vehicle comprising a memory device according to claim 8.

10. A computer program product comprising computer program instructions, wherein: The computer program instructions, when executed by a processor, enable the processor to perform a method according to any one of claims 1 to 7.

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