Whole vehicle software flashing method, equipment, device and storage medium

Abstract

A whole vehicle software flashing method, apparatus and device, and a computer readable storage medium, the method comprising: obtaining basic data and health detection data of a target vehicle, the basic data comprising configuration information, historical fault information and regional information of an ECU, and the health detection data comprising a driving condition type, environmental data and a real-time fault code; based on a preset software upgrade package type model, according to the basic data and the health detection data, obtaining a software upgrade package type predicted by the preset software upgrade package type model; and generating a corresponding software upgrade package according to the software upgrade package type, and upgrading and flashing the target vehicle, thereby solving the technical problems in the prior art that upgrade content cannot be dynamically adjusted according to real-time data and a prediction mechanism for future demands of the vehicle is lacked, realizing dynamic generation of a customized upgrade package, and improving the upgrade efficiency. And in combination with the real-time state of the vehicle, historical data and future demand pre-judgment, separation of upgrading and reality is avoided.

Description

Technical Field

[0001] This application relates to the field of vehicle software flashing technology, specifically to a method, apparatus, device, and computer-readable storage medium for flashing vehicle software. Background Technology

[0002] With the increasing complexity of automotive electronic control systems (ECUs), vehicle software upgrades have become a crucial step in ensuring driving safety and optimizing performance. Current mainstream vehicle upgrade solutions are mostly based on pre-set fixed program packages, relying on a unified push mechanism, which makes it difficult to adapt to the dynamic operating status of vehicles, and their technical limitations are becoming increasingly apparent. At the real-time adaptation level, existing technologies cannot establish a dynamic correlation between upgrade packages and vehicle status. As a core control unit, the failure rate of the vehicle ECU dynamically changes with usage time and operating load, while external data such as ambient temperature, humidity, and road conditions also directly affect hardware adaptation requirements. However, current upgrade technologies lack the ability to collect and analyze this real-time data, and can only push upgrade content according to standardized templates. This forces some ECUs in good health to undergo redundant upgrades, while modules with higher failure risks cannot receive targeted repairs. This not only wastes system resources but may also cause new control logic conflicts due to improper upgrades. In terms of proactive upgrades, passive response mechanisms severely restrict efficiency improvements. Current solutions are generally based on "problem-triggered" upgrades, initiating upgrades only when a vehicle displays a clear fault code or a manufacturer releases a general patch, completely lacking the ability to predict future vehicle needs. For example, they cannot optimize energy management algorithms in advance based on battery degradation trends, nor can they predict the adaptation needs of the transmission system based on user driving habits. This lag not only leads to untimely vehicle performance optimization but also increases later repair costs. Especially in the field of new energy vehicles, passive upgrades of the battery management system (BMS) have become a significant factor affecting driving range and battery life, failing to meet users' needs for performance assurance throughout the vehicle's entire lifecycle. Summary of the Invention

[0003] This application provides a method, apparatus, device, and computer-readable storage medium for flashing vehicle software, which can solve the technical problems existing in the prior art that the upgrade content cannot be dynamically adjusted according to real-time data and that there is a lack of a predictive mechanism for future vehicle needs.

[0004] In a first aspect, embodiments of this application provide a method for flashing vehicle software, comprising: Acquire basic data and health monitoring data of the target vehicle. The basic data includes configuration information, historical fault information of the ECU, and regional information. The health monitoring data includes driving condition type, environmental data, and real-time fault codes. Based on the pre-set software upgrade package type model, the software upgrade package type predicted by the pre-set software upgrade package type model is obtained according to the basic data and the health detection data; A corresponding software upgrade package is generated based on the software upgrade package type, and the target vehicle is upgraded and flashed.

[0005] In conjunction with the first aspect, in one implementation, obtaining the software upgrade package type predicted by the pre-set software upgrade package type model based on the basic data and the health detection data includes: The acquired basic data and health detection data are input into the preset software upgrade package type model; The pre-built software upgrade package type model is used to analyze and predict based on the basic data and the health detection data; If the pre-set software upgrade package type model analyzes the basic data and the health detection data to identify an extremely cold region, then the software upgrade package type predicted by the pre-set software upgrade package type model is obtained as the battery thermal management type. If the preset software upgrade package type model analyzes motor loss from the basic data and the health detection data, then the software upgrade package type predicted by the preset software upgrade package type model is determined to be the wear compensation type.

[0006] In conjunction with the first aspect, in one implementation, the software upgrade package type includes battery thermal management type and wear compensation type, and the step of generating a corresponding software upgrade package according to the software upgrade package type and upgrading and flashing the target vehicle includes... If the software upgrade package type is determined to be battery thermal management type, a battery thermal management strategy upgrade package is generated and the target vehicle is upgraded and flashed. If the software upgrade package type is determined to be wear compensation type, a wear compensation algorithm update upgrade package is generated, and the target vehicle is upgraded and flashed.

[0007] In conjunction with the first aspect, in one implementation, acquiring the basic data and health monitoring data of the target vehicle includes: The target vehicle's model number is read by the manufacturing system to obtain the target vehicle's configuration information, which includes model information and configuration information of the electronic control unit list. The geographical location information of the target vehicle is obtained through the sales system, wherein the geographical location information is used to adjust the type of upgrade package; By extracting historical repair records of the target vehicle from the after-sales system to form a past case database, historical fault information of the target vehicle's ECU can be obtained; The target vehicle's operating status is monitored in real time through a cloud platform, and the vehicle's driving condition type, environmental data, and real-time fault codes are obtained.

[0008] In conjunction with the first aspect, in one implementation, before obtaining the software upgrade package type output by the pre-set software upgrade package type model based on the basic data and the health detection data, the method further includes: Obtain the training set, wherein the training set data includes multiple sets of training data, and each set of training data includes basic training data, health detection data, and labeled software upgrade package types; The preset neural network is trained based on the training set to generate a software upgrade package type model, and the software upgrade package type model is used as the preset software upgrade package type model.

[0009] In conjunction with the first aspect, in one implementation, prior to generating the software upgrade package type model, the method further includes: The preset neural network is trained based on the training set, and it is determined whether the trained preset neural network is in a convergent state. If the pre-built neural network is determined to be in a convergent state, a software upgrade package type model is generated.

[0010] In conjunction with the first aspect, in one implementation, determining whether the trained preset neural network is in a convergent state includes: Based on the loss value of the pre-trained neural network, it is determined that the pre-trained neural network is in a convergent state. If the loss value is less than or equal to the preset loss value, then it is determined whether the preset neural network after training has converged. Alternatively, based on the number of training iterations of the pre-trained neural network, it can be determined whether the pre-trained neural network is in a convergent state. If the number of training iterations is greater than or equal to the preset number of training iterations, then the preset neural network after training is determined to be in a convergent state.

[0011] Secondly, embodiments of this application provide a vehicle software flashing device, the vehicle software flashing device comprising: The acquisition module is used to acquire basic data and health detection data of the target vehicle. The basic data includes configuration information, historical fault information of the ECU, and regional information. The health detection data includes driving condition type, environmental data, and real-time fault codes. The prediction module is used to obtain the software upgrade package type predicted by the pre-set software upgrade package type model based on the basic data and the health detection data. The generation and upgrade module is used to generate a corresponding software upgrade package according to the software upgrade package type, and to upgrade and flash the target vehicle.

[0012] Thirdly, embodiments of this application provide a vehicle software flashing device, which includes a processor, a memory, and a vehicle software flashing program stored in the memory and executable by the processor. When the vehicle software flashing program is executed by the processor, it implements the steps of the vehicle software flashing method described above.

[0013] Fourthly, embodiments of this application provide a computer-readable storage medium storing a vehicle software flashing program, wherein when the vehicle software flashing program is executed by a processor, it implements the steps of the vehicle software flashing method described above.

[0014] The beneficial effects of the technical solutions provided in this application include: By acquiring basic data and health monitoring data of the target vehicle, wherein the basic data includes configuration information, historical fault information of the ECU, and regional information, and the health monitoring data includes driving condition type, environmental data, and real-time fault codes; based on a pre-set software upgrade package type model, the software upgrade package type predicted by the pre-set software upgrade package type model is obtained according to the basic data and the health monitoring data; a corresponding software upgrade package is generated according to the software upgrade package type, and the target vehicle is upgraded and flashed, solving the technical problems of existing technologies that cannot dynamically adjust upgrade content based on real-time data and lack a mechanism for predicting future vehicle needs, realizing the dynamic generation of customized upgrade packages, and combining real-time vehicle status, historical data, and future demand predictions to avoid the upgrade being out of touch with reality. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the first embodiment of the vehicle software flashing method of this application; Figure 2 This is a flowchart illustrating the second embodiment of the vehicle software flashing method of this application; Figure 3 This is a schematic diagram of the functional modules of an embodiment of the vehicle software flashing device of this application; Figure 4 This is a schematic diagram of the hardware structure of the vehicle software flashing device involved in the embodiments of this application. Detailed Implementation

[0016] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0017] First, some of the technical terms used in this application will be explained to help those skilled in the art understand this application.

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0019] In a first aspect, embodiments of this application provide a method for flashing vehicle software.

[0020] In one embodiment, reference is made to Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the vehicle software flashing method of this application. Figure 1 As shown, the methods for flashing the vehicle software include: Step S10: Obtain basic data and health detection data of the target vehicle. The basic data includes configuration information, historical fault information of the ECU, and regional information. The health detection data includes driving condition type, environmental data, and real-time fault codes. As an example, the target vehicle's model is read through the manufacturing system to obtain its configuration information, including model information and configuration information for the electronic control unit (ECU) list; the target vehicle's geographical location is obtained through the sales system, which is used to adjust the type of upgrade package; historical repair records of the target vehicle are extracted from the after-sales system to form a past case database, thereby obtaining historical fault information of the target vehicle's ECU; and the target vehicle's operating status is monitored in real time through a cloud platform to obtain the target vehicle's driving condition type, environmental data, and real-time fault codes. For example, the manufacturing system reads the vehicle-specific configuration string information and generates specific vehicle configuration information based on it. This configuration information includes vehicle model information and electronic control unit software version. For this configuration information, the system combines the sales system with information about the vehicle's after-sales sales region regulations to automatically match regional emission standards and adjust upgrade content according to the sales location. If the sales location is a cold region, a privacy protection module is added; if the sales location is a tropical region, heating-related function packages are removed. The system extracts historical maintenance and repair records, which include vehicle identification numbers and fault diagnostic codes. Analysis of these records identifies high-frequency problems and creates a past case database. This database is then linked to the configuration information to generate basic data. A flexible data acquisition system combined with a cloud platform monitors the vehicle's operating status in real time, acquiring driving condition data, including congestion or highway modes. Based on this driving condition data and environmental data, potential software upgrade actions are identified. Environmental data includes temperature and altitude information. If the temperature is below a preset threshold, battery management monitoring is triggered; if the altitude is above a preset threshold, power adjustment monitoring is activated. Potential software upgrade actions are integrated with real-time fault codes to generate health check data.

[0021] Step S20: Based on the pre-set software upgrade package type model, obtain the software upgrade package type predicted by the pre-set software upgrade package type model according to the basic data and the health detection data; As an example, the acquired basic data and the health monitoring data are input into a pre-set software upgrade package type model. The pre-set software upgrade package type model analyzes and predicts based on the basic data and the health monitoring data. If the pre-set software upgrade package type model analyzes and identifies extremely cold regions from the basic data and health monitoring data, the predicted software upgrade package type is determined to be battery thermal management. If the pre-set software upgrade package type model analyzes and identifies motor wear from the basic data and health monitoring data, the predicted software upgrade package type is determined to be wear compensation. For example, by fusing vehicle basic data and health monitoring data, and processing the fused information through a machine learning model, the future needs of the vehicle are predicted, including the probability of outputting the electronic control unit module that needs to be upgraded within a preset timeframe. Preventive upgrade actions are generated based on the probability; if the vehicle is identified as about to enter an extremely cold region, a battery thermal management strategy package is pre-downloaded; if a high-mileage vehicle is analyzed, a wear compensation algorithm update is generated in advance. The preventive upgrade actions are transformed into customized upgrade packages, and software flashing is performed to solve the problem of the upgrade package being out of sync with the vehicle's status, ensuring that the model output matches the real-time data dynamic adjustment.

[0022] Step S30: Generate the corresponding software upgrade package according to the software upgrade package type, and upgrade and flash the target vehicle.

[0023] For example, if the software upgrade package type is determined to be battery thermal management type, a battery thermal management strategy upgrade package is generated and the target vehicle is upgraded and flashed; if the software upgrade package type is determined to be wear compensation type, a wear compensation algorithm update upgrade package is generated and the target vehicle is upgraded and flashed. For instance, the system aggregates configuration strings provided by the manufacturing system and electronic control unit version data provided by the engineering database from a dynamic upgrade decision tree; it integrates regional information provided by the sales system and historical fault records provided by the after-sales system for rule matching; based on the rule matching results, the upgrade package content is determined, and if it targets a specific vehicle group, taxis are prioritized for upgrades; the software flashing of the upgrade package is executed, ensuring that the process reduces invalid transmission and improves preventative maintenance capabilities.

[0024] In this embodiment, basic data and health monitoring data of the target vehicle are acquired. The basic data includes configuration information, historical fault information of the ECU, and regional information. The health monitoring data includes driving condition type, environmental data, and real-time fault codes. Based on a pre-set software upgrade package type model, the software upgrade package type predicted by the pre-set software upgrade package type model is obtained according to the basic data and the health monitoring data. A corresponding software upgrade package is generated according to the software upgrade package type, and the target vehicle is upgraded and flashed. This solves the technical problems of existing technologies that cannot dynamically adjust the upgrade content based on real-time data and lack a mechanism for predicting future vehicle needs. It enables the dynamic generation of customized upgrade packages, combining real-time vehicle status, historical data, and future demand predictions to avoid the upgrade being out of touch with reality.

[0025] In one embodiment, reference is made to Figure 2 , Figure 1 This is a flowchart illustrating the second embodiment of the vehicle software flashing method of this application. Figure 2 As shown, the methods for flashing the vehicle software include: Step S11: Obtain the training set, wherein the training set data includes multiple sets of training data, and each set of training data includes the basic data to be trained, health detection data, and labeled software upgrade package types; Step S12: Train the preset neural network based on the training set to generate a software upgrade package type model, and use the software upgrade package type model as the preset software upgrade package type model.

[0026] As an example, a pre-defined neural network is trained based on the training set to determine if it is in a convergent state. If convergence is confirmed, a software upgrade package type model is generated. The convergence of the pre-defined neural network is determined by obtaining its loss value; if the loss value is less than or equal to the preset loss value, it is considered convergent. For example, the loss value of the pre-defined neural network (0.2) is compared to the preset loss value (0.3). If the loss value 0.2 is less than or equal to the preset loss value 0.3, convergence is confirmed. Otherwise, training continues.

[0027] Alternatively, the training iterations of the pre-trained neural network can be used to determine whether the pre-trained neural network is in a convergent state. If the training iterations are greater than or equal to the preset training iterations, then the pre-trained neural network is considered to be in a convergent state. For example, if the training iterations of the pre-trained neural network (10001) are greater than the preset training iterations (10000), then the pre-trained neural network is considered to be in a convergent state. Or, if the training iterations of the pre-trained neural network (10000) are equal to the preset training iterations (10000), then the pre-trained neural network is considered to be in a convergent state. Otherwise, training of the pre-trained neural network continues.

[0028] Step S13: Obtain basic data and health detection data of the target vehicle. The basic data includes configuration information, historical fault information of the ECU, and regional information. The health detection data includes driving condition type, environmental data, and real-time fault codes. As an example, the target vehicle's model is read through the manufacturing system to obtain its configuration information, including model information and configuration information for the electronic control unit (ECU) list; the target vehicle's geographical location is obtained through the sales system, which is used to adjust the type of upgrade package; historical repair records of the target vehicle are extracted from the after-sales system to form a past case database, thereby obtaining historical fault information of the target vehicle's ECU; and the target vehicle's operating status is monitored in real time through a cloud platform to obtain the target vehicle's driving condition type, environmental data, and real-time fault codes. For example, the manufacturing system reads the vehicle-specific configuration string information and generates specific vehicle configuration information based on it. This configuration information includes vehicle model information and electronic control unit software version. For this configuration information, the system combines the sales system with information about the vehicle's after-sales sales region regulations to automatically match regional emission standards and adjust upgrade content according to the sales location. If the sales location is a cold region, a privacy protection module is added; if the sales location is a tropical region, heating-related function packages are removed. The system extracts historical maintenance and repair records, which include vehicle identification numbers and fault diagnostic codes. Analysis of these records identifies high-frequency problems and creates a past case database. This database is then linked to the configuration information to generate basic data. A flexible data acquisition system combined with a cloud platform monitors the vehicle's operating status in real time, acquiring driving condition data, including congestion or highway modes. Based on this driving condition data and environmental data, potential software upgrade actions are identified. Environmental data includes temperature and altitude information. If the temperature is below a preset threshold, battery management monitoring is triggered; if the altitude is above a preset threshold, power adjustment monitoring is activated. Potential software upgrade actions are integrated with real-time fault codes to generate health check data.

[0029] Step S14: Based on the pre-set software upgrade package type model, obtain the software upgrade package type predicted by the pre-set software upgrade package type model according to the basic data and the health detection data; As an example, the acquired basic data and the health monitoring data are input into a pre-set software upgrade package type model. The pre-set software upgrade package type model analyzes and predicts based on the basic data and the health monitoring data. If the pre-set software upgrade package type model analyzes and identifies extremely cold regions from the basic data and health monitoring data, the predicted software upgrade package type is determined to be battery thermal management. If the pre-set software upgrade package type model analyzes and identifies motor wear from the basic data and health monitoring data, the predicted software upgrade package type is determined to be wear compensation. For example, by fusing vehicle basic data and health monitoring data, and processing the fused information through a machine learning model, the future needs of the vehicle are predicted, including the probability of outputting the electronic control unit module that needs to be upgraded within a preset timeframe. Preventive upgrade actions are generated based on the probability; if the vehicle is identified as about to enter an extremely cold region, a battery thermal management strategy package is pre-downloaded; if a high-mileage vehicle is analyzed, a wear compensation algorithm update is generated in advance. The preventive upgrade actions are transformed into customized upgrade packages, and software flashing is performed to solve the problem of the upgrade package being out of sync with the vehicle's status, ensuring that the model output matches the real-time data dynamic adjustment.

[0030] Step S15: Generate the corresponding software upgrade package according to the software upgrade package type, and upgrade and flash the target vehicle.

[0031] For example, if the software upgrade package type is determined to be battery thermal management type, a battery thermal management strategy upgrade package is generated and the target vehicle is upgraded and flashed; if the software upgrade package type is determined to be wear compensation type, a wear compensation algorithm update upgrade package is generated and the target vehicle is upgraded and flashed. For instance, the system aggregates configuration strings provided by the manufacturing system and electronic control unit version data provided by the engineering database from a dynamic upgrade decision tree; it integrates regional information provided by the sales system and historical fault records provided by the after-sales system for rule matching; based on the rule matching results, the upgrade package content is determined, and if it targets a specific vehicle group, taxis are prioritized for upgrades; the software flashing of the upgrade package is executed, ensuring that the process reduces invalid transmission and improves preventative maintenance capabilities.

[0032] In this embodiment, a pre-set neural network is trained using a training set to generate a software upgrade package type model. Then, basic data and health monitoring data of the target vehicle are acquired. The basic data includes configuration information, historical fault information of the ECU, and regional information. The health monitoring data includes driving condition type, environmental data, and real-time fault codes. Based on the pre-set software upgrade package type model, the software upgrade package type predicted by the model is obtained according to the basic data and the health monitoring data. A corresponding software upgrade package is generated based on the software upgrade package type, and the target vehicle is upgraded and flashed. This solves the technical problems of existing technologies that cannot dynamically adjust upgrade content based on real-time data and lack a mechanism for predicting future vehicle needs. It achieves dynamic generation of customized upgrade packages, combining real-time vehicle status, historical data, and future demand predictions, avoiding a disconnect between upgrades and actual needs, and solving the problem of whole-vehicle software flashing in related technologies.

[0033] Secondly, embodiments of this application also provide a device for flashing vehicle software.

[0034] In one embodiment, reference is made to Figure 3 , Figure 3 This is a functional module diagram of an embodiment of the vehicle software flashing device of this application. Figure 3 As shown, the vehicle software flashing device includes: The acquisition module 10 is used to acquire basic data and health detection data of the target vehicle. The basic data includes configuration information, historical fault information of the ECU and regional information. The health detection data includes driving condition type, environmental data and real-time fault codes. Prediction module 20 is used to obtain the software upgrade package type predicted by the preset software upgrade package type model based on the basic data and the health detection data; The generation and upgrade module 30 is used to generate a corresponding software upgrade package according to the software upgrade package type and to upgrade and flash the target vehicle.

[0035] Furthermore, in one embodiment, the prediction module 20 is used for: The acquired basic data and health detection data are input into the preset software upgrade package type model; The pre-built software upgrade package type model is used to analyze and predict based on the basic data and the health detection data; If the pre-set software upgrade package type model analyzes the basic data and the health detection data to identify an extremely cold region, then the software upgrade package type predicted by the pre-set software upgrade package type model is obtained as the battery thermal management type. If the preset software upgrade package type model analyzes motor loss from the basic data and the health detection data, then the software upgrade package type predicted by the preset software upgrade package type model is determined to be the wear compensation type.

[0036] Furthermore, in one embodiment, the generation and upgrade module 30 is used for: If the software upgrade package type is determined to be battery thermal management type, a battery thermal management strategy upgrade package is generated and the target vehicle is upgraded and flashed. If the software upgrade package type is determined to be wear compensation type, a wear compensation algorithm update upgrade package is generated, and the target vehicle is upgraded and flashed.

[0037] Furthermore, in one embodiment, the acquisition module 10 is used for: The target vehicle's model number is read by the manufacturing system to obtain the target vehicle's configuration information, which includes model information and configuration information of the electronic control unit list. The geographical location information of the target vehicle is obtained through the sales system, wherein the geographical location information is used to adjust the type of upgrade package; By extracting historical repair records of the target vehicle from the after-sales system to form a past case database, historical fault information of the target vehicle's ECU can be obtained; The target vehicle's operating status is monitored in real time through a cloud platform, and the vehicle's driving condition type, environmental data, and real-time fault codes are obtained.

[0038] Furthermore, in one embodiment, the vehicle software flashing device further includes a new module for: Obtain the training set, wherein the training set data includes multiple sets of training data, and each set of training data includes basic training data, health detection data, and labeled software upgrade package types; The preset neural network is trained based on the training set to generate a software upgrade package type model, and the software upgrade package type model is used as the preset software upgrade package type model.

[0039] Furthermore, in one embodiment, the vehicle software flashing device further includes a new module for: The preset neural network is trained based on the training set, and it is determined whether the trained preset neural network is in a convergent state. If the pre-built neural network is determined to be in a convergent state, a software upgrade package type model is generated.

[0040] Furthermore, in one embodiment, the vehicle software flashing device further includes a new module for: Based on the loss value of the pre-trained neural network, determine whether the pre-trained neural network is in a convergent state. If the loss value is less than or equal to the preset loss value, then the trained preset neural network is determined to be in a convergent state. Alternatively, based on the number of training iterations of the pre-trained neural network, it can be determined whether the pre-trained neural network is in a convergent state. If the number of training iterations is greater than or equal to the preset number of training iterations, then the preset neural network after training is determined to be in a convergent state.

[0041] The functions of each module in the above-mentioned vehicle software flashing device correspond to the steps in the above-mentioned vehicle software flashing method embodiment, and their functions and implementation processes will not be described in detail here.

[0042] Thirdly, embodiments of this application provide a vehicle software flashing device, which can be a personal computer (PC), laptop computer, server, or other device with data processing capabilities.

[0043] Reference Figure 4 , Figure 4 This is a schematic diagram of the hardware structure of the vehicle software flashing device involved in the embodiments of this application. In the embodiments of this application, the vehicle software flashing device may include a processor, a memory, a communication interface, and a communication bus.

[0044] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0045] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting internal components of the vehicle software flashing device, as well as interfaces used for interconnecting the vehicle software flashing device with other devices (such as other computing devices or user devices). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user devices can be displays, keyboards, etc.

[0046] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0047] The processor can be a general-purpose processor, which can call the vehicle software flashing program stored in the memory and execute the vehicle software flashing method provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the vehicle software flashing program is called can be referred to the various embodiments of the vehicle software flashing method of this application, and will not be repeated here.

[0048] Those skilled in the art will understand that Figure 4 The hardware structure shown does not constitute a limitation of this application and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0049] Fourthly, embodiments of this application also provide a computer-readable storage medium.

[0050] The present application provides a computer-readable storage medium storing a vehicle software flashing program, wherein when the vehicle software flashing program is executed by a processor, it implements the steps of the vehicle software flashing method described above.

[0051] The method implemented when the vehicle software flashing program is executed can be referred to in the various embodiments of the vehicle software flashing method of this application, and will not be repeated here.

[0052] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0053] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0054] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0055] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0056] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0057] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0058] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for flashing vehicle software, characterized in that, include: Acquire basic data and health monitoring data of the target vehicle. The basic data includes configuration information, historical fault information of the ECU, and regional information. The health monitoring data includes driving condition type, environmental data, and real-time fault codes. Based on the pre-set software upgrade package type model, the software upgrade package type predicted by the pre-set software upgrade package type model is obtained according to the basic data and the health detection data; A corresponding software upgrade package is generated based on the software upgrade package type, and the target vehicle is upgraded and flashed.

2. The method for flashing vehicle software as described in claim 1, characterized in that, The method based on the pre-set software upgrade package type model, according to the basic data and the health detection data, obtains the software upgrade package type predicted by the pre-set software upgrade package type model, including: The acquired basic data and health detection data are input into the preset software upgrade package type model; The pre-built software upgrade package type model is used to analyze and predict based on the basic data and the health detection data; If the pre-set software upgrade package type model analyzes the basic data and the health detection data to identify an extremely cold region, then the software upgrade package type predicted by the pre-set software upgrade package type model is obtained as the battery thermal management type. If the preset software upgrade package type model analyzes motor loss from the basic data and the health detection data, then the software upgrade package type predicted by the preset software upgrade package type model is determined to be the wear compensation type.

3. The method for flashing vehicle software as described in claim 1, characterized in that, in, The software upgrade package types include battery thermal management type and wear compensation type. The process involves generating a corresponding software upgrade package based on the software upgrade package type and then upgrading and flashing the target vehicle, including... If the software upgrade package type is determined to be battery thermal management type, a battery thermal management strategy upgrade package is generated and the target vehicle is upgraded and flashed. If the software upgrade package type is determined to be wear compensation type, a wear compensation algorithm update upgrade package is generated, and the target vehicle is upgraded and flashed.

4. The method for flashing vehicle software as described in claim 1, characterized in that, The acquisition of basic data and health monitoring data of the target vehicle includes: The target vehicle's model number is read by the manufacturing system to obtain the target vehicle's configuration information, which includes model information and configuration information of the electronic control unit list. The geographical location information of the target vehicle is obtained through the sales system, wherein the geographical location information is used to adjust the type of upgrade package; By extracting historical repair records of the target vehicle from the after-sales system to form a past case database, historical fault information of the target vehicle's ECU can be obtained; The target vehicle's operating status is monitored in real time through a cloud platform, and the vehicle's driving condition type, environmental data, and real-time fault codes are obtained.

5. The method for flashing vehicle software as described in claim 1, characterized in that, Before obtaining the software upgrade package type output by the pre-set software upgrade package type model based on the basic data and the health detection data, the method further includes: Obtain the training set, wherein the training set data includes multiple sets of training data, and each set of training data includes basic training data, health detection data, and labeled software upgrade package types; The pre-set neural network is trained based on the training set to generate a software upgrade package type model, and the software upgrade package type model is used as the pre-set software upgrade package type model.

6. The method for flashing vehicle software as described in claim 5, characterized in that, Before generating the software upgrade package type model, the following is also included: The preset neural network is trained based on the training set, and it is determined whether the trained preset neural network is in a convergent state. If the pre-built neural network is determined to be in a convergent state, a software upgrade package type model is generated.

7. The method for flashing vehicle software as described in claim 6, characterized in that, Determining whether the pre-trained neural network is in a convergent state includes: Based on the loss value of the pre-trained neural network, determine whether the pre-trained neural network is in a convergent state. If the loss value is less than or equal to the preset loss value, then the trained preset neural network is determined to be in a convergent state. Alternatively, based on the number of training iterations of the pre-trained neural network, it can be determined whether the pre-trained neural network is in a convergent state. If the number of training iterations is greater than or equal to the preset number of training iterations, then the preset neural network after training is determined to be in a convergent state.

8. A device for flashing vehicle software, characterized in that, The vehicle software flashing device includes: The acquisition module is used to acquire basic data and health detection data of the target vehicle. The basic data includes configuration information, historical fault information of the ECU, and regional information. The health detection data includes driving condition type, environmental data, and real-time fault codes. The prediction module is used to obtain the software upgrade package type predicted by the pre-set software upgrade package type model based on the basic data and the health detection data. The generation and upgrade module is used to generate a corresponding software upgrade package according to the software upgrade package type, and to upgrade and flash the target vehicle.

9. A device for flashing vehicle software, characterized in that, The vehicle software flashing device includes a processor, a memory, and a vehicle software flashing program stored in the memory and executable by the processor, wherein when the vehicle software flashing program is executed by the processor, it implements the steps of the vehicle software flashing method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a vehicle software flashing program, wherein when the vehicle software flashing program is executed by a processor, it implements the steps of the vehicle software flashing method as described in any one of claims 1 to 7.

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