Vehicle performance simulation method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of Internet of Vehicles, and discloses a vehicle performance simulation method and device, computer equipment and a storage medium, and the method applied to a cloud end comprises the steps that user configuration information sent by a vehicle end is received, and the user configuration information comprises a plurality of function items and configuration parameters corresponding to each function item; matching a corresponding simulation model cluster according to the function items, and inputting the function items and the configuration parameters corresponding to each function item into the simulation model cluster to obtain a simulation result; and sending the simulation result to the vehicle end, so that the vehicle end generates a visual icon and a parameter adjustment suggestion according to the simulation result. The problems that in the prior art, a user cannot visually know the influence of vehicle setting on various performances and optimization suggestions are lacked are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet of Vehicles, and in particular to a vehicle performance simulation method and device, a computer device and a storage medium. BACKGROUND

[0002] With the continuous improvement of the intelligent degree of automobiles, the functions of human-computer interaction systems are increasingly diversified, and the interaction between the human-computer interaction systems and other control systems of vehicles is increasingly close. At present, many vehicle models tend to open the settings of various functions and performances to drivers, and use human-computer interaction interfaces to enable drivers to customize their own styles to meet the individualized needs of different users for vehicle use.

[0003] However, the existing technology only simply provides a setting interface, which cannot enable users to intuitively know the specific contribution of each setting to the performance of the vehicle (such as acceleration performance, low-speed power response, economy, charging time, battery service life, etc.) in the order of magnitude. For example, although the vehicle mode selection includes multiple modes such as power mode and comfort mode, each mode lacks detailed analysis and presentation of the power consumption, endurance, power performance, etc. in all scenarios, which leads to the fact that users do not understand the influence of their own settings on the use of the vehicle, and may mistakenly turn off or set related functions due to the lack of understanding of the functions, thereby causing the lack of vehicle performance and even safety hazards, and the user cannot be provided with targeted optimization suggestions, and it is difficult to truly exert the advantages of user customization. SUMMARY

[0004] Therefore, the embodiments of the present application provide a vehicle performance simulation method and device, a computer device and a storage medium to solve the problem that users cannot intuitively understand the influence of vehicle settings on various performances and lack optimization suggestions in the prior art.

[0005] In a first aspect, the embodiments of the present application provide a vehicle performance simulation method applied to the cloud, which comprises the following steps:

[0006] receiving user configuration information sent by a vehicle end, wherein the user configuration information comprises a plurality of function items and configuration parameters corresponding to each function item;

[0007] matching a corresponding simulation model cluster according to the function items, and inputting the function items and the configuration parameters corresponding to each function item into the simulation model cluster to obtain a simulation result;

[0008] sending the simulation result to the vehicle end to enable the vehicle end to generate a visual icon and a parameter adjustment suggestion according to the simulation result.

[0009] Further, the step of inputting the function items and the configuration parameters corresponding to each function item into the simulation model cluster to obtain a simulation result comprises the following steps:

[0010] generating a simulation task according to the function item and the configuration parameter corresponding to each function item;

[0011] based on the calculation characteristics of each simulation sub-model in the simulation model cluster, decomposing the simulation task into multiple simulation sub-tasks;

[0012] parallelly executing the corresponding simulation sub-tasks by using the simulation sub-models to obtain multiple calculation results;

[0013] weighting and fusing each calculation result according to the performance index to obtain a simulation result.

[0014] Further, the parallel execution of the corresponding simulation sub-tasks by using the simulation sub-models to obtain multiple simulation sub-results comprises:

[0015] obtaining the historical input parameters of each simulation sub-model;

[0016] determining a version state according to the configuration parameter in the simulation sub-task and the historical input parameter;

[0017] if the version state is an original state, obtaining a historical calculation result of the simulation sub-model and taking the historical calculation result as the calculation result of the simulation sub-model, or if the version state is an updated state, performing incremental calculation on the simulation sub-model according to the simulation sub-task to obtain the calculation result of the simulation sub-model.

[0018] Further, before inputting the function item and the configuration parameter corresponding to each function item into the simulation model cluster to obtain a simulation result, the method further comprises:

[0019] obtaining scene prediction data of a current position of a vehicle;

[0020] inputting the scene prediction data as a boundary condition into the simulation model cluster to enable each simulation sub-model in the simulation model cluster to perform a calculation operation based on the boundary condition.

[0021] In a second aspect, an embodiment of the present application provides a vehicle performance simulation method applied to a vehicle end, and the method comprises:

[0022] obtaining a function item triggered by a user and a configuration parameter input for the function item;

[0023] generating user configuration information according to the function item and the corresponding configuration parameter, sending the user configuration information to a cloud end, enabling the cloud end to perform a simulation operation according to the user configuration information to obtain a simulation result, and feeding back the simulation result to the vehicle end;

[0024] receive the simulation result sent by the cloud, generate a visual icon and a parameter adjustment suggestion according to the simulation result, and display the visual icon and the parameter adjustment suggestion to an interactive interface.

[0025] Further, the method further comprises:

[0026] detecting an operation instruction of a user on the interactive interface;

[0027] performing a corresponding response operation according to a type of the operation instruction;

[0028] The performing of the corresponding response operation according to the type of the operation instruction comprises:

[0029] If the operation instruction is a configuration rollback instruction, corresponding target configuration information is extracted from a preset configuration version according to the configuration rollback instruction; or, if the operation instruction is a configuration save instruction, current user configuration information is saved as the preset configuration version; or, if the operation instruction is a parameter adjustment instruction, adjusted user configuration information is generated according to the parameter adjustment instruction, and the adjusted user configuration information is sent to the cloud.

[0030] Further, the generating of the visual icon and the parameter adjustment suggestion according to the simulation result comprises:

[0031] parsing the simulation result to obtain a plurality of performance index data and an influence weight of each configuration parameter on the performance index data;

[0032] comparing and analyzing the performance index data with preset benchmark data to obtain performance deviation data;

[0033] filtering out target configuration parameters from the configuration parameters based on the influence weight;

[0034] generating a visual icon according to the performance index data, and generating a parameter adjustment suggestion for the target configuration parameters.

[0035] In a third aspect, an embodiment of the present application provides a vehicle performance simulation device applied to a cloud, the device comprising:

[0036] a receiving module configured to receive user configuration information sent by a vehicle end, wherein the user configuration information comprises a plurality of function items and configuration parameters corresponding to each of the function items;

[0037] a matching module configured to match a corresponding simulation model cluster according to the function items, and input the function items and the configuration parameters corresponding to each of the function items into the simulation model cluster to obtain a simulation result;

[0038] The feedback module is configured to send the simulation result to the vehicle end, so that the vehicle end generates a visual icon and parameter adjustment suggestion according to the simulation result.

[0039] In a fourth aspect, an embodiment of the present application provides a vehicle performance simulation device applied to a vehicle end, the device comprising:

[0040] The acquisition module is configured to acquire a function item triggered by a user and a configuration parameter input for the function item.

[0041] The sending module is configured to generate user configuration information according to the function item and the corresponding configuration parameter, send the user configuration information to a cloud end, so that the cloud end performs a simulation operation according to the user configuration information, obtains a simulation result, and feeds back the simulation result to the vehicle end.

[0042] The generation module is configured to receive the simulation result sent by the cloud end, generate a visual icon and a parameter adjustment suggestion according to the simulation result, and display the visual icon and the parameter adjustment suggestion to an interactive interface.

[0043] In a fifth aspect, an embodiment of the present application provides a computer device, comprising a memory and a processor, which are communicatively connected, and the memory stores computer instructions, and the processor executes the computer instructions to perform the method in the first aspect or any corresponding implementation manner thereof.

[0044] In a sixth aspect, an embodiment of the present application provides a computer readable storage medium, which stores computer instructions, and the computer instructions are used to make a computer execute the method in the first aspect or any corresponding implementation manner thereof.

[0045] The method provided by the embodiment of the present application has the following beneficial effects:

[0046] The method provided by the embodiment of the present application receives user configuration information sent by the vehicle end, so that the cloud end can acquire detailed data containing multiple function items and corresponding configuration parameters, and accurately capture the personalized needs of the user. The cloud end can use professional simulation models to perform efficient and accurate calculation by matching the corresponding simulation model cluster according to the function item and inputting the function item and the configuration parameter into the model cluster, so as to obtain reliable simulation results. The simulation result is sent to the vehicle end, and the vehicle end can generate a visual icon and a parameter adjustment suggestion based on the simulation result, thereby providing intuitive feedback and optimization direction for the user.

[0047] The method provided by the embodiment of the application can accurately collect the operation intention and specific demand of the user by acquiring the function item triggered by the user and the configuration parameter input for the function item. The user configuration information is generated according to the function item and the corresponding configuration parameter, and is sent to the cloud end, so that the cloud end can perform simulation operation by using the powerful computing capability of the cloud end, and the simulation efficiency and accuracy can be improved by fully utilizing the cloud end resources. The simulation result sent by the cloud end is received, and the visual icon and parameter adjustment suggestion are generated according to the simulation result, and are displayed to the interactive interface, so that the user can be provided with intuitive and understandable feedback information, and the user can make decision and adjustment conveniently. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort.

[0049] Figure 1 is a flowchart of a vehicle performance simulation method according to an embodiment of the present application;

[0050] Figure 2 is a flowchart of another vehicle performance simulation method according to an embodiment of the present application;

[0051] Figure 3 is an example architecture diagram of a vehicle performance simulation and interaction system according to an embodiment of the present application;

[0052] Figure 4 is a structural block diagram of a vehicle performance simulation device according to an embodiment of the present application;

[0053] Figure 5 is a structural block diagram of another vehicle performance simulation device according to an embodiment of the present application;

[0054] Figure 6 is a hardware structure diagram of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0055] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present application.

[0056] According to embodiments of the present invention, a vehicle performance simulation method, apparatus, computer device, and storage medium are provided. It should be noted that the steps shown in the flowcharts in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowcharts, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0057] This embodiment provides a vehicle performance simulation method applied in the cloud. Figure 1 This is a flowchart of a vehicle performance simulation method according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps:

[0058] Step S101: Receive user configuration information sent by the vehicle terminal, wherein the user configuration information includes multiple function items and configuration parameters corresponding to each function item.

[0059] It should be noted that the vehicle-side includes the vehicle and the bound mobile phone, including the vehicle-side human-machine interaction device (vehicle system, display screen), mobile APP, vehicle configuration list in the vehicle control unit (VCU), user interface conversion module, and data transceiver module; the user configuration information is the information set by the user on the vehicle-side, including multiple function items (such as power mode switching, etc.) and corresponding configuration parameters (such as selecting economy mode, etc.).

[0060] In this embodiment, the vehicle-side uses a data transceiver module (a module in the vehicle and mobile phone used for data transmission and exchange with the vehicle-cloud interface) to send the function items and corresponding configuration parameters set by the user on the vehicle-side human-machine interaction device (vehicle system, display screen) or mobile APP personalized settings interface, based on the vehicle configuration list (stored in the vehicle controller), to the remote information processor (TCAM) of the vehicle-cloud interface. The communication module in the cloud receives the user configuration information.

[0061] Step S102: Match the corresponding simulation model cluster according to the function item, and input the function item and the configuration parameters corresponding to each function item into the simulation model cluster to obtain the simulation results.

[0062] It should be noted that: function items refer to specific items that users set for the vehicle, such as switching power modes and adjusting suspension stiffness; simulation model clusters refer to a set of models stored in the cloud that correspond to different function items, such as power performance models, economy models, and thermal management models; configuration parameters refer to the specific values ​​or selections set by the user for function items, such as selecting economy mode for power mode and suspension height percentage; simulation results are itemized results obtained by the cloud based on the user's configuration information, which include various vehicle performance data (such as comprehensive fuel consumption prediction data).

[0063] In this embodiment, firstly, based on the user-configured function items, the corresponding simulation model cluster is matched from the cloud storage module. Next, the user-configured parameters are normalized (e.g., converting suspension height percentage into a specific travel value), while parameter conflicts are detected (if there are mutually exclusive cases such as "track mode" and "pure electric priority," an alarm is triggered). Then, combining the computational characteristics of each simulation sub-model in the simulation model cluster (such as motor efficiency calculation, air drag coefficient update, battery SOC prediction, etc.), a simulation task is generated based on the function items and corresponding configuration parameters. This simulation task is then decomposed into multiple simulation sub-tasks, and the corresponding simulation sub-models are executed in parallel to obtain multiple calculation results. These calculation results are then weighted and fused according to performance indicators (e.g., the driving range is fused based on 70% electrochemical model + 30% historical data regression model) to finally obtain the simulation result. In this process, if a simulation task is to be executed, the scene prediction data of the vehicle's current location (such as altitude, temperature, road condition prediction, etc.) will be obtained first as boundary conditions input to the simulation model cluster, allowing each simulation sub-model to perform calculation operations based on the boundary conditions. For each simulation sub-model to execute a simulation sub-task, the historical input parameters of each simulation sub-model will be obtained first. The version status will be determined based on the configuration parameters and historical input parameters in the simulation sub-task. If it is the original state, the historical calculation results of the simulation sub-model will be obtained as the current result. If it is the updated state, the simulation sub-model will be incrementally calculated to obtain the result.

[0064] Step S103: Send the simulation results to the vehicle terminal so that the vehicle terminal can generate visual icons and parameter adjustment suggestions based on the simulation results.

[0065] In this embodiment, the cloud-based communication module outputs the calculated simulation results, processes them through the data conversion and packaging module of the vehicle-cloud interface, and then interacts with the vehicle-side data transceiver module via the vehicle-cloud interface to transmit the simulation results back to the data transceiver modules in the vehicle and mobile phone. Upon receiving the data, the vehicle-side data transceiver module passes it to the user interface conversion module. This module parses and converts the simulation results, generating visual icons. Simultaneously, based on the comparison between performance index data and preset benchmark data in the simulation results (such as comparing the estimated combined fuel consumption with the vehicle model's test values), and the influence weight of configuration parameters on performance index data, it filters out target configuration parameters and generates parameter adjustment suggestions for these parameters (such as identifying the three settings with the greatest impact on fuel consumption and providing adjustment prompts). Finally, the visual icons and parameter adjustment suggestions are displayed on the vehicle-side human-machine interaction device (vehicle system, display screen) or the interactive interface of the mobile APP.

[0066] In this embodiment of the application, the functional items and the configuration parameters corresponding to each functional item are input into the simulation model cluster to obtain the simulation results, including the following steps A1-A4:

[0067] Step A1: Generate simulation tasks based on the function items and the configuration parameters corresponding to each function item.

[0068] It should be noted that the simulation task is a task instruction generated based on the user-configured function items and corresponding configuration parameters for conducting vehicle performance simulation calculations.

[0069] Specifically, the process first identifies the various functions configured by the user (e.g., whether it is a power mode switch or a suspension stiffness adjustment function), then extracts the configuration parameters corresponding to each function (e.g., if the power mode is set to economy mode, the suspension height is set to a certain percentage), integrates these functions and configuration parameters, and organizes them into task content that can be executed by the simulation model cluster according to the needs of simulation calculation, thereby generating simulation tasks.

[0070] Step A2: Based on the computational characteristics of each simulation sub-model in the simulation model cluster, the simulation task is decomposed into multiple simulation sub-tasks.

[0071] It should be noted that a simulation sub-model is an independent computational unit separated from a cluster of simulation models, such as a motor efficiency calculation model, an air resistance coefficient update model, and a battery SOC prediction model; computational characteristics refer to the inherent attributes of a simulation sub-model in terms of computational logic, required input data, and computation time.

[0072] Specifically, the computational characteristics of each simulation sub-model in the simulation model cluster are first clarified. For example, different sub-models are good at handling calculations of different dimensions such as motor efficiency, air resistance, and battery state, as well as their respective suitable data types and computational processes. Then, the generated simulation tasks (composed of user function items and configuration parameters) are analyzed. Based on the computational characteristics of the simulation sub-models, the simulation tasks are decomposed into multiple independently executable parts. Each part is assigned to a corresponding simulation sub-model, forming multiple simulation sub-tasks, enabling each sub-model to process its corresponding task in parallel based on its own characteristics.

[0073] Step A3: Execute the corresponding simulation sub-tasks in parallel using the simulation sub-model to obtain multiple calculation results.

[0074] Specifically, after obtaining multiple simulation sub-tasks, distributed computing nodes in the cloud are used to allow each simulation sub-model to execute its corresponding simulation sub-task in parallel. During execution, the historical input parameters of each simulation sub-model are first obtained. Then, the version status is determined based on the configuration parameters in the simulation sub-task and the historical input parameters. If the version status is "original," it means that the configuration parameters of the simulation sub-model have not been updated, so the historical calculation results of the simulation sub-model are obtained and used as the calculation result of the current simulation sub-model. If the version status is "updated," it indicates that the configuration parameters have been updated, so incremental calculations are performed on the simulation sub-model according to the simulation sub-task to obtain the calculation result of the simulation sub-model, ultimately obtaining multiple calculation results.

[0075] Step A4: Weight and fuse the calculation results according to the performance indicators to obtain the simulation results.

[0076] It should be noted that performance indicators are specific parameters used to measure vehicle performance, such as driving range, fuel consumption, and power output; calculation results are data on different performance dimensions of the vehicle obtained after each simulation sub-model executes simulation sub-tasks in parallel; weighted fusion is a method that assigns corresponding weights to the performance indicators according to the degree of influence of different calculation results, and then performs comprehensive calculations to obtain the final result.

[0077] Specifically, for the same performance index, the calculation results output by each simulation sub-model are collected. For example, for the performance index of driving range, there are calculation results based on the electrochemical model and calculation results based on the historical data regression model. Then, according to a pre-set weight ratio, these calculation results are weighted. For example, the driving range can be weighted with 70% based on the calculation results based on the electrochemical model and 30% based on the calculation results based on the historical data regression model. Finally, the weighted results are merged to obtain the final simulation result of the performance index.

[0078] As an example, the simulation results are presented in an itemized structure: parameter adjustment suggestions can also be itemized and displayed in a table. Settings such as driving mode, recovery level, and air conditioning temperature are set. Each setting corresponds to a suggested adjustment (e.g., "Proposal001" for driving mode) and a target adjustment value (e.g., "Target001"). The description column explains the triggering logic (Proposal: 1 - triggers the adjustment suggestion, 0 - does not trigger) and the meaning of the target value (e.g., a target value of 0 for driving mode represents economy mode, 1 represents comfort mode, etc.). The specific format is as follows:

[0079]

[0080] In this embodiment of the application, the corresponding simulation sub-tasks are executed in parallel using simulation sub-models to obtain multiple simulation sub-results, including the following steps B1-B3:

[0081] Step B1: Obtain the historical input parameters for each simulation sub-model.

[0082] Specifically, historical input parameters are the user configuration parameters and other input data received by the simulation sub-model during previous computational tasks. When using simulation sub-models to execute corresponding simulation sub-tasks in parallel, the input parameters recorded by each simulation sub-model during historical computation are first retrieved from the cloud data storage module. These parameters include the user configuration parameters set for functional items that the simulation sub-model previously processed, which are used to compare with the configuration parameters in the current simulation sub-task to determine the version status.

[0083] Step B2: Determine the version status based on the configuration parameters and historical input parameters in the simulation subtask.

[0084] Specifically, the version status characterizes the update status of the input parameters of the simulation sub-model, including the original state and the updated state. After obtaining the historical input parameters of each simulation sub-model, the configuration parameters in the current simulation sub-task are compared with the corresponding historical input parameters. Specifically, it checks whether the configuration parameters corresponding to each function item have changed. If all parameters are consistent with the historical input parameters, the version status is determined to be the original state; if any parameter is different from the historical input parameters, the version status is determined to be the updated state, thus providing a basis for subsequent selection of calculation methods.

[0085] Step B3: If the version status is the original state, obtain the historical calculation results of the simulation sub-model and use the historical calculation results as the calculation results of the simulation sub-model; or, if the version status is the updated state, perform incremental calculations on the simulation sub-model according to the simulation sub-tasks to obtain the calculation results of the simulation sub-model.

[0086] It should be noted that the original state refers to the current configuration parameters in the simulation subtask being consistent with the historical input parameters of the simulation submodel, without any updates; the updated state refers to the current configuration parameters differing from the historical input parameters, indicating an update. Historical calculation results are the results obtained by performing calculation tasks when the simulation submodel was in the original state, and are stored in the cloud data storage module. Incremental calculation is performed on the configuration parameters in the updated state, recalculating only the affected portion of the simulation submodel, rather than performing a complete calculation on the entire model.

[0087] Specifically, after determining the version status, if the version status is in the original state, it means that the configuration parameters have not been updated. In this case, the historical calculation results corresponding to the simulation sub-model are directly obtained from the cloud data storage module and used as the calculation results of the current simulation sub-model without recalculation. If the version status is in the updated state, it means that the configuration parameters have been updated. In this case, the simulation sub-model is incrementally calculated according to the updated configuration parameters in the simulation sub-task, that is, only the part affected by the configuration parameter update is calculated, so as to obtain the calculation results of the simulation sub-model.

[0088] In this embodiment of the application, before inputting the functional items and the configuration parameters corresponding to each functional item into the simulation model cluster to obtain the simulation results, the method further includes: obtaining scene prediction data of the current position of the vehicle; and inputting the scene prediction data as boundary conditions into the simulation model cluster so that each simulation sub-model in the simulation model cluster performs calculation operations based on the boundary conditions.

[0089] Specifically, scene prediction data refers to environmental data such as altitude, temperature, and road condition predictions for the vehicle's current location; boundary conditions are input parameters used to limit the simulation calculation range and affect the calculation logic of the simulation sub-models. First, scene prediction data for the vehicle's current location is obtained through the communication module of the Vehicle-to-Cloud Interface (TCAM). This data is transmitted from the vehicle to TCAM via the data transceiver module, or actively obtained by TCAM from relevant data sources. After acquisition, the data is standardized through TCAM's data conversion and packaging module, and then transmitted to the cloud via TCAM's communication module. Upon receiving the scene prediction data, the cloud uses it as boundary conditions input into the simulation model cluster. Each simulation sub-model in the simulation model cluster (such as the motor efficiency calculation model and the air drag coefficient update model) performs calculations based on these boundary conditions, making the simulation results more closely reflect the vehicle's actual operating environment.

[0090] This embodiment provides a vehicle performance simulation method, applied to the vehicle side. Figure 2 This is a flowchart of a vehicle performance simulation method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps:

[0091] Step S201: Obtain the function item triggered by the user and the configuration parameters input for the function item.

[0092] In this embodiment, the vehicle's human-machine interface (vehicle infotainment system, display screen) or the interface of a mobile app linked to the vehicle provides an entry point for vehicle performance settings. When a user opens the vehicle performance settings interface via touch, click, or other operations, the system detects the function selected by the user (e.g., clicking the "Power Mode" option) and acquires the specific configuration parameters entered by the user for that function (e.g., selecting "Sport Mode" in the Power Mode). Through the input detection module and data acquisition module of the interface, the user-triggered function and corresponding configuration parameters are collected and recorded in real time, preparing for the subsequent generation of user configuration information.

[0093] Step S202: Generate user configuration information based on the function items and corresponding configuration parameters, and send the user configuration information to the cloud. The cloud then performs simulation operations based on the user configuration information, obtains simulation results, and feeds these results back to the vehicle. The simulation results are vehicle performance-related data obtained by the cloud after performing simulation operations based on the user configuration information.

[0094] In this embodiment, the cloud is a remote server for simulation calculations, comprising a communication module, a data storage module, and a simulation calculation module. After acquiring the user-triggered function items and the configuration parameters input for those function items, the vehicle integrates this information according to a preset data format to generate user configuration information containing multiple function items and their corresponding configuration parameters. Then, the vehicle sends the generated user configuration information to the Vehicle-to-Cloud Interface (TCAM) via the data transceiver modules in the vehicle and mobile phone. Upon receiving this information, the communication module of the TCAM standardizes and compresses the information using a data conversion and packaging module. Then, based on the current network conditions, it transmits the processed user configuration information to the cloud via the communication module. Upon receiving the information, the cloud's communication module transmits it to the simulation calculation module, enabling the cloud to perform simulation operations based on the user configuration information, obtain simulation results, and feed these results back to the vehicle.

[0095] Step S203: Receive the simulation results sent from the cloud, generate visualization icons and parameter adjustment suggestions based on the simulation results, and display the visualization icons and parameter adjustment suggestions on the interactive interface.

[0096] In this embodiment, the visual icons are the display content of the simulation results converted into intuitive forms such as graphics and charts by the vehicle end; the parameter adjustment suggestions are the optimization setting suggestions provided by the vehicle end to the user for configuration parameters that affect vehicle performance based on the simulation results; the interactive interface is the interface on the vehicle end human-machine interaction device (vehicle system, display screen) or the bound mobile APP used to display information and user operations.

[0097] After receiving the simulation results transmitted from the cloud communication module via the vehicle-cloud interface, the vehicle-side data transceiver module passes them to the user interface conversion module. The user interface conversion module analyzes the simulation results, first extracting multiple performance index data (such as fuel consumption and range) and the influence weight of each configuration parameter on these performance index data. Then, it compares and analyzes the performance index data with preset benchmark data (such as test values ​​for this vehicle model) to obtain performance deviation data. Based on the influence weights, it then filters out the target configuration parameters that have a significant impact on performance. Next, it generates visual charts (such as fuel consumption comparison bar charts and range trend charts) based on the performance index data and generates specific parameter adjustment suggestions for the target configuration parameters (such as "It is recommended to adjust the power mode to economy mode to reduce fuel consumption"). Finally, the visual charts and parameter adjustment suggestions are displayed on the vehicle-side human-machine interface or the mobile app's interactive interface.

[0098] As an example, such as Figure 3 As shown, the example architecture of the vehicle performance simulation and interaction system consists of three parts: the vehicle-side, the vehicle-cloud interface, and the cloud. The vehicle-side displays the vehicle configuration list through a UI settings interface and presents vehicle-related data through result visualization. The vehicle-cloud interface acts as a communication hub, ensuring data interaction between the vehicle-side and the cloud through communication services, packaging and transferring information, and optimizing data transmission efficiency through data caching. The cloud integrates multiple key functions: first, it processes the received data using simulation algorithms; then, it prints out the analysis results after simulation; and finally, it stores various types of data on a storage platform. All these links work together to realize the entire process of vehicle performance simulation and data interaction.

[0099] In this embodiment of the application, the method further includes: detecting user operation instructions on the interactive interface; executing corresponding response operations according to the instruction type of the operation instruction; wherein, executing corresponding response operations according to the instruction type of the operation instruction includes: if the operation instruction is a configuration rollback instruction, extracting the corresponding target configuration information from a preset configuration version according to the configuration rollback instruction; or, if the operation instruction is a configuration save instruction, saving the current user configuration information as a preset configuration version; or, if the operation instruction is a parameter adjustment instruction, generating adjusted user configuration information according to the parameter adjustment instruction, and sending the adjusted user configuration information to the cloud.

[0100] It should be noted that operation commands refer to instructions captured by the system when the user performs operations on the interactive interface (such as the vehicle-side U8 scenario interface), such as configuration rollback, configuration save, and parameter adjustment commands. The configuration rollback command triggers the system to revert to a previous configuration version. The preset configuration version is a collection of configuration information stored by the system at different times or under different states. The target configuration information is the configuration content of the application to be rolled back from the preset configuration version, selected based on the configuration rollback command. The configuration save command triggers the system to save the current configuration as the preset configuration version. The parameter adjustment command triggers the system to adjust vehicle configuration parameters. The adjusted user configuration information is the new configuration content obtained by modifying the original user configuration information based on the parameter adjustment command.

[0101] Specifically, the system first detects user commands on the interactive interface, capturing commands issued by the user on the vehicle-side interface (e.g., when setting vehicle configuration). Then, it executes the corresponding response based on the command type: if the command is a configuration rollback command, it extracts the corresponding target configuration information from a pre-saved preset configuration version, finding a previously saved configuration version for rollback. If the command is a configuration save command, it saves the current user configuration information (the user's current vehicle configuration settings) as a preset configuration version and stores it in the saved configuration set. If the command is a parameter adjustment command, it modifies the current user configuration information according to the command, generating adjusted user configuration information, which is then sent to the cloud for subsequent processing (such as simulation calculations).

[0102] In this embodiment of the application, the generation of visualization icons and parameter adjustment suggestions based on simulation results includes the following steps C1-C4:

[0103] Step C1: Analyze the simulation results to obtain multiple performance index data and the influence weight of each configuration parameter on the performance index data.

[0104] Specifically, the simulation results are itemized results containing various vehicle performance data, obtained from simulation calculations performed in the cloud based on user configuration information. Performance index data refers to specific data used to measure vehicle performance, such as combined fuel consumption, driving range, and acceleration time. Influence weights are quantified values ​​representing the degree of influence of each configuration parameter on the performance index data. The process includes: After receiving the simulation results from the cloud, the vehicle's user interface conversion module performs structured analysis. First, it extracts various performance index data describing vehicle performance from the simulation results, such as fuel consumption and driving range data. Simultaneously, it analyzes the influence weight of each configuration parameter (such as power mode selection and suspension height setting) on ​​different performance index data. These weights are pre-determined through simulation model training or historical data statistics, representing the degree of influence of changes in the configuration parameter on the corresponding performance index. Through this analysis process, multiple performance index data and the influence weight of each configuration parameter on the performance index data are obtained, providing a data foundation for subsequently generating parameter adjustment suggestions and visualization charts.

[0105] Step C2 involves comparing and analyzing the performance index data with the preset benchmark data to obtain the performance deviation data.

[0106] Specifically, the preset benchmark data are pre-set standard data used for comparison, such as the test values ​​for this vehicle model; performance deviation data are deviation information obtained by comparing and analyzing performance index data with the preset benchmark data, used to reflect the degree of difference between the current vehicle performance and the standard state. The process is as follows: After analyzing the simulation results to obtain the performance index data, each performance index data is compared with the corresponding preset benchmark data. For example, the combined fuel consumption data in the simulation results is compared with the preset test fuel consumption value for this vehicle model, and the difference or ratio between the two is calculated. Through this comparative analysis, the deviation of each performance index data from the preset benchmark data is determined, thus obtaining the performance deviation data, which serves as the basis for subsequent selection of target configuration parameters and generation of adjustment suggestions.

[0107] Step C3: Filter out the target configuration parameters from the configuration parameters based on the influence weight.

[0108] Specifically, target configuration parameters are those that have a significant impact on performance metrics, selected from the existing configuration parameters. Adjustment suggestions will be generated for these parameters subsequently. After obtaining the impact weight of each configuration parameter on the performance metrics, a threshold for the impact weight is set. The impact weight of each configuration parameter is compared with this threshold, and configuration parameters with impact weights exceeding the threshold are selected as target configuration parameters. For example, if the impact weight threshold is set to 20%, then configuration parameters with impact weights higher than 20% will be selected as target configuration parameters, allowing for targeted adjustment suggestions to be generated for these parameters that have a significant impact on performance.

[0109] Step C4: Generate visualization charts based on performance metric data and generate parameter adjustment suggestions for the target configuration parameters.

[0110] Specifically, based on the analyzed performance index data, appropriate visualization formats, such as bar charts, line charts, and pie charts, are selected to transform the performance index data into intuitive visual icons, allowing users to more clearly understand the vehicle's performance status. Simultaneously, for the selected target configuration parameters, specific parameter adjustment suggestions are generated by combining performance deviation data and vehicle performance optimization logic. For example, if the target configuration parameters are power mode and tire pressure, and simulation results show high fuel consumption, a suggestion can be made to "adjust the power mode to economy mode and adjust the tire pressure to the standard value to reduce fuel consumption." Finally, the generated visual icons and parameter adjustment suggestions are integrated for display on the vehicle's interactive interface.

[0111] As an example, parameter adjustment suggestions can also be presented in an itemized structure: setting driving mode, recovery level, air conditioning temperature, etc. Each setting item corresponds to a suggested adjustment (e.g., driving mode corresponds to "Proposal001") and an adjustment target value (e.g., "Target001"). The description bar explains the triggering logic (Proposal: 1 - triggers the adjustment suggestion, 0 does not trigger) and the meaning of the target value (e.g., the target value for driving mode is 0 for economy mode, 1 for comfort mode, etc.). The specific format is as follows:

[0112]

[0113]

[0114] This embodiment also provides a vehicle performance simulation system, which includes: a vehicle terminal and a cloud terminal, wherein the cloud terminal and the vehicle terminal are connected by communication.

[0115] On the vehicle side, it is used to obtain the functions triggered by the user and the configuration parameters entered, generate user configuration information, and send it to the cloud;

[0116] In the cloud, the system receives user configuration information (containing multiple function items and configuration parameters corresponding to each function item) sent from the vehicle. It matches the corresponding simulation model cluster based on the function item, obtains scene prediction data for the vehicle's current location, and inputs this data as boundary conditions into the simulation model cluster. Simulation tasks are generated based on the function items and configuration parameters, and are decomposed into multiple simulation sub-tasks according to the computational characteristics of each simulation sub-model in the simulation model cluster. The corresponding sub-tasks are executed in parallel using the simulation sub-models (during which historical input parameters of each sub-model are obtained, and the version status is determined based on the sub-task configuration parameters and historical input parameters; for the original state, historical calculation results are called, and for the updated state, incremental calculations are performed), resulting in multiple calculation results. The calculation results are weighted and fused according to performance indicators to obtain the simulation result, which is then sent to the vehicle.

[0117] On the vehicle side, simulation results are received from the cloud, and multiple performance index data and the influence weight of each configuration parameter on the performance index data are parsed. The performance index data is compared and analyzed with preset benchmark data to obtain performance deviation data, and target configuration parameters are selected based on the influence weight. Visual icons are generated based on the performance index data, parameter adjustment suggestions are generated for the target configuration parameters, and both are displayed on the interactive interface.

[0118] Specifically, the vehicle acquires the user's input function items and configuration parameters, generates user configuration information, and sends it to the cloud. After receiving the user configuration information, the cloud matches the simulation model cluster, obtains scene prediction data as boundary conditions, generates and decomposes simulation tasks, obtains simulation results through parallel computation of sub-models in the simulation model cluster, and feeds them back to the vehicle. After receiving the simulation results, the vehicle generates and displays visual icons and parameter adjustment suggestions. If the user triggers an operation command on the vehicle's interactive interface, the vehicle executes the corresponding operation (rollback, saving, or resending the adjusted configuration information) according to the command type. If it is a parameter adjustment, the interaction process between the vehicle and the cloud is repeated.

[0119] This embodiment also provides a vehicle performance simulation device for implementing the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0120] This embodiment provides a vehicle performance simulation device applied in the cloud, such as... Figure 4 As shown, it includes:

[0121] The receiving module 41 is used to receive user configuration information sent by the vehicle terminal, wherein the user configuration information includes multiple function items and configuration parameters corresponding to each function item;

[0122] Matching module 42 is used to match the corresponding simulation model cluster according to the function item, and input the function item and the configuration parameters corresponding to each function item into the simulation model cluster to obtain the simulation result;

[0123] Feedback module 43 is used to send the simulation results to the vehicle terminal so that the vehicle terminal can generate visual icons and parameter adjustment suggestions based on the simulation results.

[0124] Furthermore, the matching module 42 also includes: a generation submodule, a disassembly submodule, an execution submodule, and a fusion submodule;

[0125] The generation submodule is used to generate simulation tasks based on the functional items and the configuration parameters corresponding to each functional item.

[0126] The sub-module is used to decompose the simulation task into multiple simulation sub-tasks based on the computational characteristics of each simulation sub-model in the simulation model cluster.

[0127] The execution submodule is used to execute the corresponding simulation subtasks in parallel using the simulation submodel to obtain multiple calculation results;

[0128] The fusion submodule is used to weight and fuse the various calculation results according to performance indicators to obtain the simulation results.

[0129] Furthermore, the execution submodule is used to obtain the historical input parameters of each simulation sub-model; determine the version status based on the configuration parameters in the simulation sub-task and the historical input parameters; if the version status is the original state, obtain the historical calculation results of the simulation sub-model and use the historical calculation results as the calculation results of the simulation sub-model; or, if the version status is the updated state, perform incremental calculations on the simulation sub-model according to the simulation sub-task to obtain the calculation results of the simulation sub-model.

[0130] Furthermore, the device also includes: an input module for acquiring scene prediction data of the vehicle's current position; and inputting the scene prediction data as boundary conditions into the simulation model cluster so that each simulation sub-model in the simulation model cluster performs calculation operations based on the boundary conditions.

[0131] This embodiment provides a vehicle performance simulation device, applied to the vehicle end, such as... Figure 5 As shown, it includes:

[0132] The acquisition module 51 is used to acquire the function items triggered by the user and the configuration parameters input for the function items;

[0133] The sending module 52 is used to generate user configuration information based on the function items and corresponding configuration parameters, send the user configuration information to the cloud, so that the cloud can perform simulation operations based on the user configuration information, obtain simulation results, and feed back the simulation results to the vehicle.

[0134] The generation module 53 is used to receive simulation results sent from the cloud, generate visualization icons and parameter adjustment suggestions based on the simulation results, and display the visualization icons and parameter adjustment suggestions on the interactive interface.

[0135] Furthermore, the device also includes: a detection module, used to detect user operation commands on the interactive interface; and to execute corresponding response operations according to the command type of the operation command; wherein, executing corresponding response operations according to the command type of the operation command includes: if the operation command is a configuration rollback command, then extracting the corresponding target configuration information from the preset configuration version according to the configuration rollback command; or, if the operation command is a configuration save command, then saving the current user configuration information as the preset configuration version; or, if the operation command is a parameter adjustment command, then generating the adjusted user configuration information according to the parameter adjustment command, and sending the adjusted user configuration information to the cloud.

[0136] Furthermore, the generation module 53 is used to parse the simulation results, obtain multiple performance index data and the influence weight of each configuration parameter on the performance index data; compare and analyze the performance index data with the preset benchmark data to obtain performance deviation data; filter out the target configuration parameters from the configuration parameters based on the influence weight; generate a visualization chart based on the performance index data, and generate parameter adjustment suggestions for the target configuration parameters.

[0137] Please see Figure 6 , Figure 6 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 6 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system).

[0138] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0139] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0140] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device as shown by a landing page for an app. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, which can be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0141] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0142] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0143] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0144] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A vehicle performance simulation method, applied in the cloud, characterized in that, The method includes: Receive user configuration information sent by the vehicle terminal, wherein the user configuration information includes multiple function items and configuration parameters corresponding to each function item; The simulation model cluster is matched according to the function item, and the function item and the configuration parameters corresponding to each function item are input into the simulation model cluster to obtain the simulation result. The simulation results are sent to the vehicle terminal, so that the vehicle terminal can generate visual icons and parameter adjustment suggestions based on the simulation results.

2. The method according to claim 1, characterized in that, The step of inputting the functional items and the configuration parameters corresponding to each functional item into the simulation model cluster to obtain simulation results includes: A simulation task is generated based on the functional items and the configuration parameters corresponding to each functional item. Based on the computational characteristics of each simulation sub-model in the simulation model cluster, the simulation task is decomposed into multiple simulation sub-tasks; The corresponding simulation sub-tasks are executed in parallel using the simulation sub-model to obtain multiple calculation results; The calculation results are weighted and fused according to the performance indicators to obtain the simulation results.

3. The method according to claim 2, characterized in that, The simulation sub-model is used to execute the corresponding simulation sub-tasks in parallel to obtain multiple simulation sub-results, including: Obtain the historical input parameters for each of the simulation sub-models; The version status is determined based on the configuration parameters in the simulation subtask and the historical input parameters. If the version status is the original status, the historical calculation results of the simulation sub-model are obtained and used as the calculation results of the simulation sub-model; or, if the version status is the updated status, the simulation sub-model is incrementally calculated according to the simulation sub-task to obtain the calculation results of the simulation sub-model.

4. The method according to claim 1, characterized in that, Before inputting the functional items and the configuration parameters corresponding to each functional item into the simulation model cluster to obtain the simulation results, the method further includes: Obtain scene prediction data of the vehicle's current location; The scene prediction data is input as boundary conditions into the simulation model cluster, so that each simulation sub-model in the simulation model cluster performs calculation operations based on the boundary conditions.

5. A vehicle performance simulation method, applied to the vehicle end, characterized in that, The method includes: Obtain the user-triggered function item and the configuration parameters input for the function item; User configuration information is generated based on the function items and corresponding configuration parameters, and the user configuration information is sent to the cloud so that the cloud can perform simulation operations based on the user configuration information, obtain simulation results, and feed back the simulation results to the vehicle. The system receives simulation results sent from the cloud, generates visualization icons and parameter adjustment suggestions based on the simulation results, and displays the visualization icons and parameter adjustment suggestions on the interactive interface.

6. The method according to claim 5, characterized in that, The method further includes: Detect user commands on the interactive interface; Execute the corresponding response operation according to the instruction type of the operation instruction; The step of executing the corresponding response operation according to the instruction type of the operation instruction includes: If the operation instruction is a configuration rollback instruction, the corresponding target configuration information is extracted from the preset configuration version according to the configuration rollback instruction; or, if the operation instruction is a configuration save instruction, the current user configuration information is saved as the preset configuration version; or, if the operation instruction is a parameter adjustment instruction, the adjusted user configuration information is generated according to the parameter adjustment instruction, and the adjusted user configuration information is sent to the cloud.

7. The method according to claim 5, characterized in that, The step of generating visualizations and parameter adjustment suggestions based on the simulation results includes: The simulation results are analyzed to obtain multiple performance index data and the influence weight of each configuration parameter on the performance index data; The performance index data is compared and analyzed with the preset benchmark data to obtain the performance deviation data; Target configuration parameters are selected from the configuration parameters based on the influence weights. Visualization charts are generated based on the performance metric data, and parameter adjustment suggestions are generated for the target configuration parameters.

8. A vehicle performance simulation device, applied in the cloud, characterized in that, The device includes: The receiving module is used to receive user configuration information sent by the vehicle terminal, wherein the user configuration information includes multiple function items and configuration parameters corresponding to each function item; The matching module is used to match the corresponding simulation model cluster according to the function item, and input the function item and the configuration parameters corresponding to each function item into the simulation model cluster to obtain the simulation result; The feedback module is used to send the simulation results to the vehicle terminal, so that the vehicle terminal can generate visual icons and parameter adjustment suggestions based on the simulation results.

9. A vehicle performance simulation device, applied to a vehicle, characterized in that, The device includes: The acquisition module is used to acquire the function items triggered by the user and the configuration parameters input for the function items; The sending module is used to generate user configuration information based on the function items and corresponding configuration parameters, send the user configuration information to the cloud, so that the cloud can perform simulation operations based on the user configuration information, obtain simulation results, and feed back the simulation results to the vehicle terminal; The generation module is used to receive the simulation results sent from the cloud, generate visualization icons and parameter adjustment suggestions based on the simulation results, and display the visualization icons and parameter adjustment suggestions on the interactive interface.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the method of any one of claims 1 to 7.

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