A test method and device for an intelligent cockpit, an electronic device, and a storage medium

Abstract

The application discloses a kind of test method, device, electronic equipment and storage medium of intelligent cabin.Its characteristics include: response simulation intelligent cabin corresponding test target, based on simulation intelligent cabin and test target initialization simulation intelligent cabin scene;Based on simulation intelligent cabin scene and test target, model test is carried out on the pre-established to-be-tested cabin object, and the intelligent cabin control instruction is determined;Based on equipment intelligent agent and environment intelligent agent, the cabin scene evolution is carried out on the intelligent cabin control instruction, and the target intelligent cabin scene is determined;Based on cabin occupant intelligent agent, the cabin evolution simulation is carried out on the target intelligent cabin scene, the test cabin occupant comfort degree of to-be-tested cabin object is determined, and the cabin test result is determined based on cabin occupant comfort degree.Through multi-agent, real intelligent cabin is accurately simulated, the judgment standard of test simulation is taken as the angle of cabin occupant, the high cost of real vehicle test is avoided, the intelligent cabin test of full scene is covered, and the test efficiency and test effect are improved.

Description

Technical Field

[0001] This invention relates to the field of smart cockpit technology, and more particularly to a testing method, apparatus, electronic device, and storage medium for a smart cockpit. Background Technology

[0002] With the rapid development of automotive intelligence and connectivity, the intelligent cockpit has become the core carrier for vehicle-user interaction. The intelligent cockpit integrates various complex systems such as multimedia control, voice interaction, occupant monitoring, environmental perception, lighting ambiance, and temperature control. Through artificial intelligence technology, it proactively perceives and responds to occupant status, providing users with a personalized and intelligent driving experience. Due to the highly integrated functions and complex and varied interaction scenarios of the intelligent cockpit's in-vehicle system, its development process places stringent requirements on testing and verification. Existing intelligent cockpit testing methods mainly suffer from the following four technical shortcomings: First, real-vehicle testing is costly and time-consuming. Traditional real-vehicle testing relies on real vehicles and test personnel, requiring on-site deployment and execution for each test scenario. This consumes significant testing resources and poses safety risks. For example, extreme scenarios such as high temperatures, extreme cold, and strong light exposure are difficult to reproduce stably, severely limiting testing efficiency and coverage. Second, existing simulation test scenarios are limited and lack dynamism. Existing cockpit simulation environments typically use pre-set fixed scenarios for testing, evaluating the intelligent cockpit's response performance through various static indicators. This type of testing method has limited scope and cannot simulate the complex interactions, dynamic interplay, and device linkages among multiple roles such as drivers, passengers, and children in a real cockpit. Consequently, the test results fail to accurately reflect the user experience. Third, it suffers from high human involvement and low automation. Some testing solutions utilize integrated development environments (IDEs) to remotely evaluate the smart cockpit through manual operation. This approach is highly dependent on human intervention, resulting in high testing costs and inconsistent, subjective conclusions, lacking objective and consistent evaluation standards, and failing to support large-scale automated testing needs. Fourth, it lacks an experience evaluation mechanism centered on passenger comfort. Existing testing methods typically use functional indicators such as response latency and command execution accuracy as evaluation criteria, neglecting the decisive influence of passenger physiological perception and emotional state on the test results. This fails to provide an objective and quantitative evaluation of the smart cockpit's overall performance from the perspective of passenger experience. In view of this, there is an urgent need for an automated testing method for intelligent cockpits that can dynamically simulate and evolve complex intelligent cockpit scenarios through multi-agent collaboration without the need for actual vehicles, and that uses passenger comfort as the core criterion, thereby achieving efficient, comprehensive, and repeatable testing. Summary of the Invention

[0003] This invention provides a testing method, apparatus, electronic device, and storage medium for intelligent cockpits, to solve the technical problem that existing intelligent cockpit testing processes cannot simulate complex intelligent cockpit environments and have poor testing results.

[0004] According to one aspect of the present invention, a testing method for an intelligent cockpit is provided, comprising: In response to the test target corresponding to the simulated intelligent cockpit, initialize the simulated intelligent cockpit scenario based on the simulated intelligent cockpit and the test target; Based on the simulated smart cockpit scenario and the test target, a model test is performed on the pre-established cockpit object to determine the smart cockpit control commands. Based on device intelligence agents and environmental intelligence agents, the cockpit control commands are used to evolve the cockpit scenario and determine the target intelligent cockpit scenario. Based on the cockpit occupant intelligent agent, the target intelligent cockpit scenario is simulated to determine the test cockpit occupant comfort of the test cockpit object, and the cockpit test results are determined based on the cockpit occupant comfort.

[0005] According to another aspect of the present invention, a testing apparatus for a smart cockpit is provided, comprising: The cockpit initialization module is used to respond to the test target corresponding to the simulated intelligent cockpit and initialize the simulated intelligent cockpit scenario based on the simulated intelligent cockpit and the test target; The cockpit simulation module is used to perform model testing on a pre-established cockpit object based on the simulated intelligent cockpit scenario and the test target, and to determine the intelligent cockpit control commands. The cockpit evolution module is used to evolve the cockpit scene based on the intelligent cockpit control commands of the device intelligent agent and the environmental intelligent agent, and to determine the target intelligent cockpit scene; The test analysis module is used to simulate the cabin evolution of the target intelligent cabin scenario based on the cabin occupant intelligent agent, determine the test cabin occupant comfort of the cabin object under test, and determine the cabin test results based on the cabin occupant comfort.

[0006] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the smart cockpit testing method according to any embodiment of the present invention.

[0007] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the intelligent cockpit testing method according to any embodiment of the present invention.

[0008] The technical solution of this invention, through dynamic simulation of simulated intelligent cockpit scenarios, can effectively simulate intelligent cockpit scenarios in real vehicle conditions, effectively avoiding the high testing costs and reducing the testing cycle in real vehicle scenarios. Based on the dynamically simulated intelligent cockpit scenario, the test cockpit object is simulated using multi-agent combined structured simulation to simulate cockpit equipment linkage and environmental interaction, thereby realizing the dynamic evolution of the simulated intelligent cockpit scenario, improving the comprehensive coverage of intelligent cockpit scenarios, simulating various extreme and edge scenarios, and the structured state ensures the repeatability of the testing process and the consistency of results. The cockpit occupant agent simulates the physiological and emotional comfort state of the cockpit occupant from the perspective of the cockpit occupant, simulating the comfort of the cockpit occupant in the current intelligent cockpit scenario, making the test results closer to the actual user experience, and further improving the accuracy of intelligent cockpit testing. This solves the technical problem in the prior art that the intelligent cockpit testing process cannot simulate complex intelligent cockpit environments and the test results are too poor.

[0009] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A flowchart of a testing method for an intelligent cockpit is provided for an embodiment of the present invention; Figure 2 A flowchart of another intelligent cockpit testing method provided in an embodiment of the present invention; Figure 3 A flowchart of another intelligent cockpit testing method provided in an embodiment of the present invention; Figure 4 A schematic diagram of the structure of a testing device for an intelligent cockpit provided in an embodiment of the present invention; Figure 5 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. Detailed Implementation

[0012] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0013] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0014] Figure 1 This invention provides a flowchart of a testing method for a smart cockpit, applicable to situations where a smart cockpit model is tested by simulating the cockpit environment. This method can be executed by a smart cockpit testing device, which can be implemented in hardware and / or software and can be configured in any electronic device. Figure 1 As shown, the method includes: S110. Respond to the test target corresponding to the simulated intelligent cockpit, and initialize the simulated intelligent cockpit scenario based on the simulated intelligent cockpit and the test target.

[0015] The test objectives can be test items that simulate a smart cockpit. It should be noted that the test process corresponding to the test objectives is executed on the simulated smart cockpit to evaluate the comfort level of the simulated smart cockpit's response to the current test items and its intelligent decision-making. For example, test objectives could be inputting the following into the smart cockpit: turn on the air conditioning to a suitable temperature; I need to take a nap, so the vehicle enters nap mode; the vehicle enters camping mode; the smell inside the car is too strong, so ventilation is needed, etc.

[0016] Optionally, the test targets, in addition to the simulated intelligent cockpit, are test items input into the simulated intelligent cockpit. These can typically be input automatically or manually. For automatic input, a Large Language Model (LLM) can be used to automatically simulate test items for input into the simulated intelligent cockpit. For manual input, a test engineer can select a test item for input into the simulated intelligent cockpit.

[0017] Optionally, the simulated smart cockpit can be a structured virtual cockpit system that simulates the smart cockpit of a real vehicle. It should be noted that the simulated smart cockpit consists of multiple agents and the cockpit object to be tested. Through multiple agents, the device state and the environment state inside the vehicle cockpit in the real world are simulated and evolved to build a dynamic virtual cockpit system with evolution capabilities.

[0018] Optionally, the simulated smart cockpit scenario can be a structured cockpit scenario state that matches the simulated smart cockpit to the test target. It should be noted that the device states and the vehicle cockpit environment states in the simulated smart cockpit are represented by structured classes. By modeling each device in the real vehicle smart cockpit as a structured class, the independent device module classes of the vehicle cockpit are obtained, and the structured cockpit scenario state is established based on each independent module class.

[0019] Optionally, in this invention, the various devices within a real vehicle's intelligent cockpit are abstracted into independent device module classes; a unique set of attributes is defined for each device module class; a global singleton class is designed to uniformly manage shared cockpit resources; and the structured object states of each device module class and the global singleton class are converted into a standardized dictionary format to obtain a structured simulated intelligent cockpit. The devices may include in-cabin multimedia control, touch operation, vehicle control, lighting systems, driver monitoring, occupant monitoring, voice microphones, temperature control, ambient lighting, cockpit seat functions, and air purification and filtration, etc.

[0020] Optionally, for each device module class, a unique set of attributes is defined for that device module class. This unique set includes device status parameters, control interfaces, and methods to describe the device status of the actual device corresponding to the device module class, the methods for controlling and changing the device status, and the methods that can be called. For example, taking a vehicle-controlled air conditioner as an example, the air conditioner's device status parameters include the outside ambient temperature, the inside ambient temperature, the front row temperature, the rear row temperature, the target temperature, the fan speed setting, the airflow mode, the external and internal circulation status, and the status of each air outlet. The control interface can include setting the target temperature, the fan speed setting, switching the airflow mode, switching between external and internal circulation, controlling the airflow at the front and rear air outlets. Methods can include rapid cooling or heating, automatic switching between external and internal circulation, setting the fan speed to maximum or minimum, automatically adjusting the comfort temperature, automatically adjusting the airflow direction, and automatically adjusting the inside ambient temperature based on the outside ambient temperature.

[0021] Optionally, a global singleton class provides unified scheduling management for each device module class, ensuring consistency in resource scheduling across multiple devices at the scheduling level. Within the global singleton class, scheduling control is performed on each device module class based on its device state parameters. Simultaneously, when a device's state changes, it checks whether the change in the device module class's state parameters meets scheduling requirements. If it does, the device module class's state parameters are synchronized. For example, the scheduling control of a multimedia control device module class could involve, while the audio device is playing music, during navigation voice broadcast, the global singleton class schedules the device module class's state parameters to a paused or lowered state. After the navigation voice broadcast completes, the global singleton class schedules the device module class's state parameters to return to the playback state.

[0022] Optionally, the global singleton class and the corresponding device module classes can be converted into JSON strings through a JSON serialization mechanism, and the data mapping of each device module class corresponding to the global singleton class can be converted into JSON format to establish a global state dictionary. In the global state dictionary, type definition and description information are added to the JSON value of each device module class to achieve interpretability, operability and traceability of the state.

[0023] Optionally, upon receiving a test target, the simulated intelligent cockpit initializes a global state dictionary based on the test item corresponding to the test target to form a simulated intelligent cockpit scene, which serves as the initial intelligent cockpit state for the current test. For example, the test item could be set as a quick nap mode test at 35 degrees Celsius outdoors at midday. The test target could be adjusting the vehicle cabin to nap mode. Based on the test item, the simulated intelligent cockpit automatically simulates a scenario where the external ambient temperature is 35 degrees Celsius, the vehicle cabin time is 12:30, and the vehicle cabin temperature is 45 degrees Celsius. By adjusting the global state dictionary, it simulates a real cockpit scene, initializing the simulated intelligent cockpit scene for the test item.

[0024] Specifically, in response to the test target corresponding to the simulated intelligent cockpit, a simulated intelligent cockpit scenario is initialized based on the simulated intelligent cockpit and the test target.

[0025] S120. Based on the simulated intelligent cockpit scenario and the test target, perform model testing on the pre-established cockpit object to determine the intelligent cockpit control commands.

[0026] Optionally, the cockpit object under test can be understood as a pre-set digital interactive object within a real vehicle cockpit that interacts with the occupants. The cockpit object under test has the ability to proactively communicate with the occupants or respond to their commands. It can perceive the smart cockpit scenario and automatically recommend adjustment methods to the occupants to improve their comfort within the smart cockpit; simultaneously, it can respond to occupant commands communicated in any manner and adjust the smart cockpit based on these commands. For example, the cockpit object under test could be a smart cockpit assistant, smart cockpit voice system, or cockpit companion trained using a large language model.

[0027] Optionally, the intelligent cockpit control commands can be generated by the test cockpit object to meet the test objectives. These commands are used to control various devices in the simulated intelligent cockpit and can be directly used as control commands for adjusting vehicle equipment in a real intelligent cockpit. It should be noted that the test cockpit object controls the simulated intelligent cockpit based on the intelligent cockpit control commands to change the simulated intelligent cockpit scenario. For example, taking adjusting the vehicle's air conditioning in nap mode as an example, the intelligent cockpit control commands could be: set the air conditioning system's fan speed to maximum and the air conditioning temperature to 24 degrees Celsius; gradually reduce the fan speed as the cabin temperature approaches 24 degrees Celsius; and maintain a fixed fan speed to keep the cabin temperature at 24 degrees Celsius when the cabin temperature reaches 24 degrees Celsius.

[0028] Specifically, based on simulated intelligent cockpit scenarios and test objectives, a model test is conducted on a pre-established cockpit object to determine the intelligent cockpit control commands.

[0029] S130. Based on the device intelligent agent and the environmental intelligent agent, the cockpit control commands are used to evolve the cockpit scene and determine the target intelligent cockpit scene.

[0030] Optionally, the device agent can be an independent Agent instance set up for each device based on a large language model. It should be noted that for each device, a dedicated device agent is set up, with one device corresponding to multiple device agents. Each device agent encapsulates the interface and state modification logic of a device. The device agent can respond to intelligent cockpit control commands and complete the device's state switching and function execution.

[0031] Optionally, the environmental agent can be an Agent instance configured for the global resources of the intelligent cockpit based on a large language model. The environmental agent is used to maintain the shared attributes of the global resources of the intelligent cockpit and provide interfaces for querying and modifying the status parameters of each device. By accessing the modification interface, the device status parameters of each device can be obtained; the modification interface also supports the modification of device status parameters, and the simulated intelligent cockpit scenario is composed of the device status parameters of all devices.

[0032] Optionally, the number of environmental agents is not limited and can be one or more. Each environmental agent obtains the same device state parameters of all devices in the global resources to ensure consistency in the simulated intelligent cockpit scenario.

[0033] Optionally, the target intelligent cockpit scenario can be a simulated intelligent cockpit scenario obtained by responding to intelligent cockpit control commands based on a virtual cockpit scenario that simulates an intelligent cockpit scenario. It should be noted that the target intelligent cockpit scenario is a dynamic virtual cockpit scenario. The environmental agent simulates the device state parameters of each device in real time to fully reproduce the real dynamic intelligent cockpit scenario. In this invention, based on the simulated intelligent cockpit scenario, each device agent and the environmental agent respond to and execute the scene evolution corresponding to the intelligent cockpit control commands to obtain the target intelligent cockpit scenario.

[0034] Optionally, upon receiving the intelligent cockpit control command, each device agent and each environmental agent identifies the command content and determines the corresponding device agent and environmental agent. The device agents then virtually adjust the devices based on the intelligent cockpit control command, and each environmental agent responds to the device agent's adjustment process by updating the device status parameters of each device. This process simulates the cockpit scene evolution of the entire intelligent cockpit scenario, mimicking the changes in the real intelligent cockpit during the operation of each device. For example, when the intelligent cockpit control command is to cool down to 26 degrees Celsius, the intelligent agent corresponding to the air conditioning equipment receives the intelligent cockpit control command: cool down to 26 degrees Celsius. In response to the intelligent cockpit control command, it executes the air conditioning cooling process. The intelligent agent corresponding to the cabin temperature sensor simulates the air conditioning cooling process and gradually updates the cabin temperature it senses. Meanwhile, the environmental intelligent agent senses the air conditioning cooling process and synchronously executes the air conditioning cooling process based on the outdoor ambient temperature, including changes in wind speed, temperature, cabin temperature, air outlet status, and cabin sound decibel level, to fully evolve the entire process of the real intelligent cockpit turning on the air conditioning for cooling.

[0035] Specifically, based on the intelligent agents of the device and the environment, the control commands for the intelligent cockpit are used to evolve the cockpit scenario and determine the target intelligent cockpit scenario.

[0036] S140. Based on the cockpit occupant intelligent agent, perform cockpit evolution simulation on the target intelligent cockpit scenario, determine the test cockpit occupant comfort of the test cockpit object, and determine the cockpit test result based on the cockpit occupant comfort.

[0037] Optionally, the cabin occupant intelligent agent can be an intelligent agent that simulates the physiology and emotions of cabin occupants. It should be noted that the cabin occupant intelligent agent can be a passenger intelligent agent and a driver intelligent agent; the passenger intelligent agent can be set to passengers of different genders and different ages; the driver intelligent agent can be set to drivers of different genders; for example, the passenger intelligent agent can be a child intelligent agent and an adult intelligent agent, and the driver intelligent agent can be a female driver and a male driver.

[0038] Optionally, the test cabin occupant comfort can be based on the comfort data of the cabin occupant intelligent agent in the target intelligent cabin scenario. It should be noted that the test cabin occupant comfort is used to evaluate the physiological and emotional states of the cabin occupant intelligent agent in the target intelligent cabin scenario. The cabin occupant intelligent agent reads the state data of various devices in the target intelligent cabin scenario, simulates the physiological perception state of the occupant in a real intelligent cabin, and thus determines the occupant's emotional state. For example, in a scenario where air conditioning is used to cool the vehicle cabin, the target intelligent cabin scenario perceives the noise, wind speed, and temperature of the air conditioning equipment. If the physiological perception state of the cabin occupant intelligent agent is low noise, moderate wind speed, and suitable temperature, then the test cabin occupant comfort is rated as comfortable; if the physiological perception state of the cabin occupant intelligent agent is high noise, high wind speed with direct airflow, and very low temperature, then the test cabin occupant comfort is rated as uncomfortable.

[0039] Optionally, the cockpit occupant intelligent agent can simulate the physiological and emotional feedback of real occupants and continuously update and test the comfort of cockpit occupants in the target intelligent cockpit scenario.

[0040] Optionally, the cockpit test results can be understood as the performance test results of the cockpit under test. It should be noted that after the intelligent cockpit control commands issued by the cockpit under test are processed through cockpit evolution, the target intelligent cockpit scenario is obtained. The occupant comfort of the target intelligent cockpit scenario is obtained through the cockpit occupant intelligent agent to evaluate the performance of the cockpit under test, and this is taken as the cockpit test result.

[0041] Specifically, based on the intelligent agent of the cockpit occupants, the cockpit evolution simulation of the target intelligent cockpit scenario is carried out to determine the occupant comfort of the test cockpit object, and the cockpit test results are determined based on the occupant comfort.

[0042] The technical solution of this invention, through dynamic simulation of simulated intelligent cockpit scenarios, can effectively simulate intelligent cockpit scenarios in real vehicle conditions, effectively avoiding the high testing costs and reducing the testing cycle in real vehicle scenarios. Based on the dynamically simulated intelligent cockpit scenario, the test cockpit object is simulated using multi-agent combined structured simulation to simulate cockpit equipment linkage and environmental interaction, thereby realizing the dynamic evolution of the simulated intelligent cockpit scenario, improving the comprehensive coverage of intelligent cockpit scenarios, simulating various extreme and edge scenarios, and the structured state ensures the repeatability of the testing process and the consistency of results. The cockpit occupant agent simulates the physiological and emotional comfort state of the cockpit occupant from the perspective of the cockpit occupant, simulating the comfort of the cockpit occupant in the current intelligent cockpit scenario, making the test results closer to the actual user experience, and further improving the accuracy of intelligent cockpit testing. This solves the technical problem in the prior art that the intelligent cockpit testing process cannot simulate complex intelligent cockpit environments and the test results are too poor.

[0043] Figure 2 This is a flowchart illustrating another intelligent cockpit testing method provided by an embodiment of the present invention. The relationship between this embodiment and the above embodiments is that this specifically describes the process of handling conflicts arising in the simulated intelligent cockpit scenario during cockpit evolution simulation. For example... Figure 2 As shown, the method includes: S210. Respond to the test target corresponding to the simulated intelligent cockpit, and initialize the simulated intelligent cockpit scenario based on the simulated intelligent cockpit and the test target.

[0044] S220. Based on the simulated smart cockpit scenario and the test target, perform model testing on the pre-established cockpit object to determine the smart cockpit control commands.

[0045] S230. Based on the device intelligent agent and the environmental intelligent agent, the cockpit control commands are used to evolve the cockpit scene and determine the target intelligent cockpit scene.

[0046] Optionally, the cockpit scene can be evolved in the device agent and the environment agent, and a Prompt project can be configured for the device agent and the environment agent. The Prompt project can be used to configure each device agent and the environment agent in the current test process.

[0047] Optionally, the method of configuring device agents in this invention is as follows: Configure a Prompt project for each device agent in the intelligent cockpit. In the Prompt project, assemble the simulated intelligent cockpit scene, intelligent cockpit control commands and device tool definitions corresponding to the current device agent to inject into the large language model context. Require the current device agent to output executable code snippets to form a complete Prompt. The executable code snippets modify the device state parameters and global state dictionary to realize the operation of the simulated intelligent cockpit device.

[0048] Optionally, the present invention configures the environmental agents as follows: A Prompt project is configured for each environmental agent in the intelligent cockpit. Within the Prompt project, the simulated intelligent cockpit scenario corresponding to the environmental agent, the device API documentation for all intelligent cockpit devices, intelligent cockpit control commands, and the tool definitions of the environmental agents are assembled and injected into the large language model context. The environmental agents are then required to output executable code snippets to modify device state parameters and the global state dictionary, forming a complete Prompt. Through the cooperation of each device agent and each environmental agent, at least one executable code snippet is executed to evolve the cockpit scenario and determine the target intelligent cockpit scenario. The executable code snippets can be executable Python code snippets.

[0049] S240. If the scheduling agent identifies a cockpit simulation conflict in the target intelligent cockpit scenario, then it acquires at least one conflict state program corresponding to the cockpit simulation conflict.

[0050] Optionally, cockpit simulation conflict can be a logical conflict between the states of various devices during the evolution of the cockpit scene by the device agent and the environmental agent in response to the intelligent cockpit control commands. It should be noted that during the execution of intelligent cockpit control commands by the device agent and the environmental agent, the scheduling agent perceives the state parameters of each device in the target intelligent cockpit scene to identify conflicting decisions among the devices and determine the cockpit simulation conflict.

[0051] Optionally, the conflict state procedure can be the device execution procedure that causes the cockpit simulation conflict; it should be noted that when the scheduling agent identifies a cockpit simulation conflict, it analyzes and traces the source of the cockpit simulation conflict, finds the device that caused the cockpit simulation conflict, and then determines the conflict state procedure of the simulation device.

[0052] Optionally, the conflict state procedure is an executable code snippet output by the device agent or the environmental agent based on the intelligent cockpit control commands.

[0053] Optionally, the present invention configures the scheduling agent as follows: a Prompt project is configured for the scheduling agent of the intelligent cockpit. In the Prompt project, the simulated intelligent cockpit scenario, intelligent cockpit control commands, device logic analysis rules, and global state dictionary corresponding to the current scheduling agent are configured. A large language model context is injected, requiring the current scheduling agent to detect cockpit simulation conflicts and output executable task scheduling code snippets to resolve cockpit simulation conflicts. The task scheduling code snippets are used to update the device state parameters of the device agent or environment agent to eliminate cockpit simulation conflicts and realize the update of the target intelligent cockpit scenario.

[0054] Specifically, during the evolution of the target intelligent cockpit scenario, the scheduling agent performs real-time conflict detection on the simulated evolution process of the target intelligent cockpit scenario. If a cockpit simulation conflict is detected in the target intelligent cockpit scenario, at least one conflict state program corresponding to the cockpit simulation conflict is obtained; if no cockpit simulation conflict is detected in the target intelligent cockpit scenario, real-time conflict detection continues.

[0055] S250. For each conflict state procedure, the scheduling agent performs conflict decision on the conflict state procedure and determines the conflict decision execution command.

[0056] Optionally, the conflict decision execution command can be an executable task scheduling code snippet generated by the scheduling agent for adjusting the conflict state procedure. It should be noted that when the scheduling agent identifies a cockpit simulation conflict, it analyzes the various conflict state procedures corresponding to the cockpit simulation conflict, makes conflict handling decisions for each conflict state procedure of the cockpit simulation conflict, and obtains the conflict decision execution command.

[0057] Optionally, for conflicts existing in the cockpit simulation, the procedure may involve multiple executable code snippets generated by the same device agent that conflict. In this case, the conflict decision execution command adjusts the device agent to regenerate executable code snippets that do not conflict in the cockpit simulation, thereby resolving the cockpit simulation conflict. For example, the air conditioning agent generates two executable code snippets: cooling to 24 degrees Celsius at maximum wind speed and setting the wind speed level to minimum. When the scheduling agent identifies the cooling to 24 degrees Celsius at maximum wind speed and the wind speed level being set to minimum, it considers that when the wind speed level is set to minimum, the ambient temperature inside the cockpit is 30 degrees Celsius, which conflicts with the goal of cooling to 24 degrees Celsius. Therefore, the conflict decision execution command is generated: the air conditioning agent needs to update the instruction to cool to 24 degrees Celsius.

[0058] Optionally, the conflict state procedure between the cockpit simulation conflict can be a conflict between the executable code segment of the device agent and the executable code segment of the environment agent; in this case, the conflict decision execution command adjusts the device agent and / or the environment agent so that the device agent and / or the environment agent regenerates the executable code segment that does not have a cockpit simulation conflict in order to resolve the cockpit simulation conflict.

[0059] Specifically, for each conflict state procedure, the scheduling agent performs conflict decision-making on the conflict state procedure and determines the conflict decision execution command.

[0060] S260. The target intelligent cockpit scene is updated by the device intelligent agent and / or the environment intelligent agent based on the conflict decision to execute commands.

[0061] Optionally, the scheduling agent sends the conflict decision execution command to the device agent and / or the environment agent respectively. After receiving the conflict decision execution command, the device agent and / or the environment agent regenerates the executable code fragment based on the conflict decision execution command to replace the conflict state program, thereby updating the scene state of the target intelligent cockpit scene and continuing the cockpit scene evolution of the target intelligent cockpit scene.

[0062] Optionally, when the scheduling agent identifies that the conflict state program corresponding to the cockpit simulation conflict will affect the efficiency of the cockpit scene evolution, the scheduling agent can issue an instruction to stop the cockpit scene evolution of the target intelligent cockpit scene. After the device agent and / or the environment agent update the executable code segment, the scheduling agent issues an instruction to continue the cockpit scene evolution of the target intelligent cockpit scene.

[0063] Optionally, when the scheduling agent recognizes that the cockpit simulation conflict has caused the cockpit scenario evolution to fail to meet the test objectives, the scheduling agent can issue an instruction to roll back the cockpit scenario evolution of the target intelligent cockpit scenario, so that the cockpit scenario evolution process rolls back to the time point before the conflict state program was executed. After the device agent and / or the environment intelligently update the executable code segment, the scheduling agent issues an instruction to continue the cockpit scenario evolution of the target intelligent cockpit scenario.

[0064] Specifically, the target intelligent cockpit scenario is updated by executing commands based on conflict decision-making through device intelligent agents and / or environmental intelligent agents.

[0065] S270. Based on the cockpit occupant intelligent agent, simulate the cockpit evolution of the target intelligent cockpit scenario, determine the test cockpit occupant comfort of the test cockpit object, and determine the cockpit test results based on the cockpit occupant comfort.

[0066] The automatic correction mechanism of the scheduling agent in the technical solution of this invention schedules and controls the testing process of the test cockpit object, detects conflicts in the evolution process of the virtual cockpit, and makes decisions and performs state corrections for conflicts, so as to ensure the correctness of strong logical interaction and ensure the logical consistency and behavioral rationality of multi-agent collaboration.

[0067] Figure 3 This is a flowchart illustrating another intelligent cockpit testing method provided by an embodiment of the present invention. The relationship between this embodiment and the above embodiments is that this specifically describes the process of simulating passenger experience for the cockpit occupant intelligent agent and generating test results for the cockpit occupant during the testing process. For example... Figure 3 As shown, the method includes: S310. Respond to the test target corresponding to the simulated intelligent cockpit, and initialize the simulated intelligent cockpit scenario based on the simulated intelligent cockpit and the test target.

[0068] S320. Based on the simulated smart cockpit scenario and the test target, perform model testing on the pre-established cockpit object to determine the smart cockpit control commands.

[0069] S330: Based on the device intelligent agent and the environmental intelligent agent, the cockpit control commands are used to evolve the cockpit scene and determine the target intelligent cockpit scene.

[0070] S340. If the scheduling agent identifies a cockpit simulation conflict in the target intelligent cockpit scene, it obtains at least one conflict state program corresponding to the cockpit simulation conflict. For each conflict state program, the scheduling agent makes a conflict decision on the conflict state program and determines the conflict decision execution command. The device agent and / or the environment agent update the scene state of the target intelligent cockpit scene based on the conflict decision execution command.

[0071] S350. Update the cockpit occupant prompt words of the cockpit occupant intelligent agent based on the target intelligent cockpit scene.

[0072] The cockpit occupant prompts can be a Prompt project consisting of a target intelligent cockpit scene and fixed prompts. It should be noted that the prompts for the cockpit occupant agent are configured with fixed occupant identities, occupant identity preference data, tool definitions, and a global state dictionary. During testing, the target intelligent cockpit scene and its corresponding global state dictionary are injected as dynamic data to form dynamic prompts corresponding to the target intelligent cockpit scene, i.e., the cockpit occupant prompts.

[0073] Optionally, the cockpit occupant intelligent agent can be configured as follows: Configure a Prompt project for the cockpit occupant intelligent agent of the intelligent cockpit. In the Prompt project, configure the target intelligent cockpit scene, occupant identity, occupant identity preference data, tool definition and global state dictionary corresponding to the current occupant intelligent agent, inject the large language model context, and obtain the cockpit occupant prompt words. The cockpit occupant prompt words require the cockpit occupant intelligent agent to perform comfort analysis based on the target intelligent cockpit scene and the global state dictionary corresponding to the target intelligent cockpit scene, and output the test cockpit occupant comfort level.

[0074] Specifically, the cockpit occupant prompts of the cockpit occupant intelligent agent are updated based on the target intelligent cockpit scenario.

[0075] S360 updates the cabin occupant agent based on cabin occupant prompts, updates the cabin occupant status through the cabin occupant agent, determines the test cabin occupant comfort, and determines the cabin test results based on cabin occupant comfort.

[0076] Optionally, based on the updated cockpit occupant prompts, configure the cockpit occupant intelligent agent corresponding to the target intelligent cockpit scenario. The cockpit occupant intelligent agent performs a real cockpit experience in the target intelligent cockpit scenario and outputs the test cockpit occupant comfort level.

[0077] Specifically, the cabin occupant agent is updated based on the occupant prompts, the occupant status is updated through the occupant agent, the comfort level of the test cabin occupants is determined, and the cabin test results are determined based on the occupant comfort level.

[0078] Optionally, in another optional embodiment of the present invention, determining the cabin test results of the test cabin object based on the occupant comfort of the test cabin includes: If the occupant comfort level of the test cabin is comfortable, the test result is considered that the test cabin is qualified; if the occupant comfort level of the test cabin is uncomfortable, the test result is considered that the test cabin is unqualified.

[0079] Optionally, in this invention, comfort and discomfort can be understood as the comfort level evaluation output by the cabin occupant intelligent agent. The cabin occupant intelligent agent experiences the target intelligent cabin scenario to analyze the physiological and emotional experience of the cabin occupant in the target intelligent cabin scenario, and then outputs comfort and discomfort. For example, if the cabin occupant intelligent agent feels cold due to the low cabin temperature in the air conditioning cooling scenario of the target intelligent cabin scenario, it will output discomfort; if the cabin occupant intelligent agent outputs discomfort because the volume of the music played by the intelligent cabin is louder than the navigation notification sound in the normal driving scenario of the target intelligent cabin scenario; if the cabin occupant intelligent agent outputs comfort because the air conditioning cooling scenario of the target intelligent cabin scenario initially cools down rapidly, resulting in relatively loud cooling noise, but the air conditioning noise gradually decreases after the temperature drops later.

[0080] Specifically, if the test cabin occupant comfort level is rated as comfortable, the cabin test result is considered qualified; if the test cabin occupant comfort level is rated as uncomfortable, the cabin test result is considered unqualified.

[0081] Optionally, in this invention, the test cabin occupant comfort is used as the target to complete the test closed loop. The differences between the test cabin object and the actual system state in the current real intelligent cabin are compared, and an evaluation report and optimization suggestions for the test cabin object in the test target test process are output.

[0082] Optionally, in this invention, the process of testing the cockpit object under test is carried out in a sandbox environment for safety testing. The sandbox isolation technology and command execution capability are used to run in an isolated environment to ensure the safety of the test.

[0083] Optionally, in another optional embodiment of the present invention, after determining that the cabin test result is unqualified if the occupant comfort level of the test cabin is uncomfortable, the method further includes: If the scheduling agent identifies that the occupant comfort level of the test cabin corresponding to the target intelligent cockpit scene is uncomfortable, then the test cabin object is invoked to generate a comfort adjustment command; the device agent and / or the environment agent update the scene state of the target intelligent cockpit scene based on the comfort adjustment command, and return to execute the step of performing a cabin evolution simulation of the target intelligent cockpit scene based on the cabin occupant agent to determine the test cabin occupant comfort level of the test cabin object.

[0084] Optionally, after outputting the test cabin occupant comfort level, the cabin occupant intelligent agent updates the test cabin occupant comfort level to the target intelligent cabin scene, schedules the intelligent agent to perceive the target intelligent cabin scene, and if it recognizes that the test cabin occupant comfort level is uncomfortable, it generates a comfort adjustment command and uses the comfort adjustment command to conduct the next round of testing on the test cabin object.

[0085] Optionally, the comfort adjustment command can be a cockpit control instruction for the test cockpit to adjust the target intelligent cockpit scenario to meet the test objectives. It should be noted that the comfort adjustment command is used to adjust the current target intelligent cockpit scenario so that it evolves in a direction that satisfies the comfort needs of the cockpit occupant intelligent agent.

[0086] Specifically, when the scheduling agent identifies that the occupant comfort level of the test cabin corresponding to the target intelligent cockpit scene is uncomfortable, it calls the test cabin object to generate a comfort adjustment command; the device agent and / or environment agent update the scene state of the target intelligent cockpit scene based on the comfort adjustment command, and return to execute the step of performing a cabin evolution simulation of the target intelligent cockpit scene based on the cabin occupant agent to determine the test cabin occupant comfort level of the test cabin object.

[0087] Optionally, after evaluating the cabin test results of the cabin to be tested, the current round of testing of the cabin to be tested is ended, and if the cabin to be tested fails the current round of testing, the comfort adjustment command is used to conduct the next round of testing on the cabin to be tested.

[0088] Optionally, this invention also includes: Based on the target intelligent cockpit scenario, the test cockpit occupant comfort, the cockpit test results, and the comfort adjustment commands, a state evolution log for the target intelligent cockpit scenario is established; the state evolution log is stored in a preset scenario database.

[0089] Optionally, the state evolution log can be a log file recording the testing of the cockpit under test. The state evolution log supports traceability analysis of the testing process; it records the entire process of each round of testing of the cockpit under test, saving the initialization scenario before the test begins, the intelligent cockpit control commands during the test, every update of the global state dictionary, every update of the target intelligent cockpit scenario, the test cockpit occupant comfort, the cockpit test results, and comfort adjustment commands. The state evolution log supports traceability analysis of the testing process.

[0090] Optionally, the scenario database can be a pre-configured database for storing state evolution logs. In the scenario database, the test target, the cockpit object under test, and the state evolution log are stored accordingly. It supports querying any data in the test target, the cockpit object under test, and the state evolution log to locate the test scenario of the entire smart cockpit.

[0091] Optionally, during a test run of the test target, a state evolution log is established based on the test target and the test target, and the test target, the test target, the test target, and the state evolution log are stored in the scenario database.

[0092] Optionally, the scenario database of the present invention can also be used for scenario initialization during the testing process; specifically: at the start of the test, responding to the test target corresponding to the simulated intelligent cockpit, querying the scenario database based on the test target to analyze whether the scenario database has a state evolution log that matches the test target; if a matching state evolution log exists, then obtaining the initial cockpit scenario based on the state evolution log to serve as the simulated intelligent cockpit scenario.

[0093] Specifically, a state evolution log for the target intelligent cockpit scenario is established based on the target intelligent cockpit scenario, the comfort of test cockpit occupants, cockpit test results, and comfort adjustment commands; the state evolution log is then stored in a pre-defined scenario database.

[0094] Optionally, in another optional embodiment of the present invention, when the scheduling agent identifies that the occupant comfort level of the test cabin corresponding to the target intelligent cockpit scenario is uncomfortable, the step of invoking the test cabin object to generate a comfort adjustment command includes: The scheduling agent queries historical smart cockpit scenarios associated with the target smart cockpit scenario. If at least one historical smart cockpit scenario is found, the scheduling agent calls the test cockpit object to perform comfort adjustment analysis based on the state evolution log of each historical smart cockpit scenario and the occupant comfort of the test cockpit, and determines the comfort adjustment command. If no historical smart cockpit scenario is found, the scheduling agent calls the test cockpit object to perform comfort adjustment analysis based on the occupant comfort of the test cockpit, and determines the comfort adjustment command.

[0095] Optionally, the historical intelligent cockpit scenario can be a cockpit scenario recorded in the state evolution log that matches the target intelligent cockpit scenario in the scenario database. It should be noted that the historical intelligent cockpit scenario can be any cockpit scenario recorded in the state evolution log. For example, it could be the target intelligent cockpit scenario after an update in the state evolution log, it could be the initialization scenario, or it could be an updated global state dictionary.

[0096] Optionally, the scheduling agent performs a similarity query to the scenario database based on the target smart cockpit scenario to analyze whether there are any historical smart cockpit scenarios in the scenario database that are similar to the target smart cockpit scenario. If a historical smart cockpit scenario is found in the scenario database, the state evolution log corresponding to the historical smart cockpit scenario is referenced during the generation of the comfort adjustment command. If no historical smart cockpit scenario is found in the scenario database, the comfort adjustment command is generated based on the comfort of the test cockpit occupants.

[0097] Optionally, when there is at least one related historical intelligent cockpit scenario to the target intelligent cockpit scenario, the state evolution log corresponding to each historical intelligent cockpit scenario is read from the scenario database, and each state evolution log is recorded as reference data for generating comfort adjustment commands. Comfort adjustment analysis is performed in conjunction with the comfort of the test cockpit occupants to generate comfort adjustment commands.

[0098] Optionally, if the number of related historical smart cockpit scenarios for the target smart cockpit scenario exceeds a preset data threshold, in order to improve testing efficiency and reduce the amount of context data, the similarity of each historical smart cockpit scenario is identified, the scenarios are sorted based on their similarity, and the state evolution log records of historical smart cockpit scenarios with higher similarity are selected as reference data for generating comfort adjustment commands. Comfort adjustment analysis is then performed in conjunction with the comfort of the test cockpit occupants to generate comfort adjustment commands.

[0099] Specifically, the scheduling agent queries historical intelligent cockpit scenarios associated with the target intelligent cockpit scenario. If at least one historical intelligent cockpit scenario is found, the scheduling agent calls the cockpit object under test to perform comfort adjustment analysis based on the state evolution log of each historical intelligent cockpit scenario and the occupant comfort of the test cockpit, and determines the comfort adjustment command. If no historical intelligent cockpit scenario is found, the scheduling agent calls the cockpit object under test to perform comfort adjustment analysis based on the occupant comfort of the test cockpit, and determines the comfort adjustment command.

[0100] The scheduling agent of the technical solution of this invention can establish a state evolution log for each test process during the testing process, support the traceability analysis of the test process, and effectively ensure the repeatability of the test process and the consistency of the results. In the candidate test, it selects the data in the state evolution log as test reference data to perform more accurate comfort adjustment analysis, which can further improve the testing efficiency and accuracy, and improve the comprehensiveness of the test scenario coverage.

[0101] Figure 4 This is a schematic diagram of a testing device for an intelligent cockpit provided in an embodiment of the present invention. Figure 4 As shown, the device includes: a cockpit initialization module 410, a cockpit simulation module 420, a cockpit evolution module 430, and a test and analysis module 440; wherein, The cockpit initialization module 410 is used to respond to the test target corresponding to the simulated intelligent cockpit and initialize the simulated intelligent cockpit scenario based on the simulated intelligent cockpit and the test target; The cockpit simulation module 420 is used to perform model testing on a pre-established cockpit object based on the simulated intelligent cockpit scenario and the test target, and to determine the intelligent cockpit control commands. The cockpit evolution module 430 is used to perform cockpit scene evolution based on the intelligent cockpit control commands of the device intelligent agent and the environmental intelligent agent, and to determine the target intelligent cockpit scene. The test analysis module 440 is used to simulate the cabin evolution of the target intelligent cabin scenario based on the cabin occupant intelligent agent, determine the test cabin occupant comfort of the cabin object under test, and determine the cabin test results based on the cabin occupant comfort.

[0102] The technical solution of this invention, through dynamic simulation of simulated intelligent cockpit scenarios, can effectively simulate intelligent cockpit scenarios in real vehicle conditions, effectively avoiding the high testing costs and reducing the testing cycle in real vehicle scenarios. Based on the dynamically simulated intelligent cockpit scenario, the test cockpit object is simulated using multi-agent combined structured simulation to simulate cockpit equipment linkage and environmental interaction, thereby realizing the dynamic evolution of the simulated intelligent cockpit scenario, improving the comprehensive coverage of intelligent cockpit scenarios, simulating various extreme and edge scenarios, and the structured state ensures the repeatability of the testing process and the consistency of results. The cockpit occupant agent simulates the physiological and emotional comfort state of the cockpit occupant from the perspective of the cockpit occupant, simulating the comfort of the cockpit occupant in the current intelligent cockpit scenario, making the test results closer to the actual user experience, and further improving the accuracy of intelligent cockpit testing. This solves the technical problem in the prior art that the intelligent cockpit testing process cannot simulate complex intelligent cockpit environments and the test results are too poor.

[0103] Optionally, the device further includes a cockpit scheduling module; the cockpit scheduling module is used for: If the scheduling agent identifies a cockpit simulation conflict in the target intelligent cockpit scenario, it acquires at least one conflict state program corresponding to the cockpit simulation conflict. For each conflict state procedure, the scheduling agent performs conflict decision-making on the conflict state procedure and determines the conflict decision execution command. The device agent and / or the environment agent execute commands based on the conflict decision to update the scene state of the target intelligent cockpit scene.

[0104] Optionally, the test analysis module 440 is specifically used for: Update the cockpit occupant prompts for the cockpit occupant agent based on the target intelligent cockpit scenario; The cabin occupant agent is updated based on the occupant prompts, and the occupant status is updated through the occupant agent to determine the comfort level of the test cabin occupants.

[0105] Optionally, the test analysis module 440 is further used for: If the occupant comfort level of the test cabin is rated as comfortable, then the cabin test result is considered to indicate that the test cabin is qualified. If the occupant comfort level of the test cabin is deemed uncomfortable, then the cabin test result is considered unqualified.

[0106] Optionally, the device further includes a cabin comfort adjustment module; the cabin comfort adjustment module is used for: If the scheduling agent identifies that the occupant comfort level of the test cabin corresponding to the target intelligent cockpit scenario is uncomfortable, then the test cabin object is invoked to generate a comfort adjustment command. The device agent and / or the environment agent update the scene state of the target intelligent cockpit scene based on the comfort adjustment command, and return to execute the step of performing a cockpit evolution simulation of the target intelligent cockpit scene based on the cockpit occupant agent to determine the test cockpit occupant comfort of the test cockpit object.

[0107] Optionally, the cabin comfort adjustment module is specifically used for: Based on the scheduling agent, query the historical smart cockpit scenarios associated with the target smart cockpit scenario; If at least one of the aforementioned historical smart cockpit scenarios is found, the scheduling agent calls the test cockpit object to perform comfort adjustment analysis based on the state evolution log of each of the aforementioned historical smart cockpit scenarios and the occupant comfort of the test cockpit, and determines the comfort adjustment command. If the historical smart cockpit scenario is not found, the scheduling agent calls the test cockpit object to perform comfort adjustment analysis based on the occupant comfort of the test cockpit, and determines the comfort adjustment command.

[0108] Optionally, the device further includes a test log module; the test log module is used for: A state evolution log for the target intelligent cockpit scenario is established based on the target intelligent cockpit scenario, the test cockpit occupant comfort, the cockpit test results, and the comfort adjustment commands. The state evolution log is stored in a preset scenario database.

[0109] The intelligent cockpit testing device provided in this embodiment of the invention can execute the intelligent cockpit testing method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0110] Figure 5 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their patterns are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0111] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0112] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0113] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the testing methods for a smart cockpit.

[0114] In some embodiments, the smart cockpit testing method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the smart cockpit testing method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the smart cockpit testing method by any other suitable means (e.g., by means of firmware).

[0115] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0116] Computer programs used to implement the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the patterns / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs can be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0117] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0118] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0119] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0120] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS servers, such as high management difficulty and weak business scalability.

[0121] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0122] This embodiment provides a computer-readable storage medium storing a computer program thereon. When executed by a processor, the program implements the testing method steps of the intelligent cockpit provided in any embodiment of the present invention. The method includes: In response to the test target corresponding to the simulated intelligent cockpit, initialize the simulated intelligent cockpit scenario based on the simulated intelligent cockpit and the test target; Based on the simulated smart cockpit scenario and the test target, a model test is performed on the pre-established cockpit object to determine the smart cockpit control commands. Based on device intelligence agents and environmental intelligence agents, the cockpit control commands are used to evolve the cockpit scenario and determine the target intelligent cockpit scenario. Based on the cockpit occupant intelligent agent, the target intelligent cockpit scenario is simulated to determine the test cockpit occupant comfort of the test cockpit object, and the cockpit test results are determined based on the cockpit occupant comfort.

[0123] The computer storage medium of this invention can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0124] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0125] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0126] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0127] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computing device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0128] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0129] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A testing method for an intelligent cockpit, characterized in that, include: In response to the test target corresponding to the simulated intelligent cockpit, initialize the simulated intelligent cockpit scenario based on the simulated intelligent cockpit and the test target; Based on the simulated smart cockpit scenario and the test target, a model test is performed on the pre-established cockpit object to determine the smart cockpit control commands. Based on device intelligence agents and environmental intelligence agents, the cockpit control commands are used to evolve the cockpit scenario and determine the target intelligent cockpit scenario. Based on the cockpit occupant intelligent agent, the target intelligent cockpit scenario is simulated to determine the test cockpit occupant comfort of the test cockpit object, and the cockpit test results are determined based on the cockpit occupant comfort.

2. The method according to claim 1, characterized in that, Also includes: If the scheduling agent identifies a cockpit simulation conflict in the target intelligent cockpit scenario, it acquires at least one conflict state program corresponding to the cockpit simulation conflict. For each conflict state procedure, the scheduling agent performs conflict decision-making on the conflict state procedure and determines the conflict decision execution command. The device agent and / or the environment agent execute commands based on the conflict decision to update the scene state of the target intelligent cockpit scene.

3. The method according to claim 1, characterized in that, The step of simulating the cabin evolution of the target intelligent cabin scenario based on the cabin occupant intelligent agent to determine the occupant comfort of the test cabin object includes: Update the cockpit occupant prompts for the cockpit occupant agent based on the target intelligent cockpit scenario; The cabin occupant agent is updated based on the occupant prompts, and the occupant status is updated through the occupant agent to determine the comfort level of the test cabin occupants.

4. The method according to claim 1, characterized in that, The determination of the cabin test results based on the occupant comfort of the test cabin includes: If the occupant comfort level of the test cabin is rated as comfortable, then the cabin test result is considered to indicate that the test cabin is qualified. If the occupant comfort level of the test cabin is deemed uncomfortable, then the cabin test result is considered unqualified.

5. The method according to claim 4, characterized in that, After determining that the cabin test result is unqualified if the occupant comfort level of the test cabin is deemed uncomfortable, the method further includes: If the scheduling agent identifies that the occupant comfort level of the test cabin corresponding to the target intelligent cockpit scenario is uncomfortable, then the test cabin object is invoked to generate a comfort adjustment command. The device agent and / or the environment agent update the scene state of the target intelligent cockpit scene based on the comfort adjustment command, and return to execute the step of performing a cockpit evolution simulation of the target intelligent cockpit scene based on the cockpit occupant agent to determine the test cockpit occupant comfort of the test cockpit object.

6. The method according to claim 5, characterized in that, When the scheduling agent identifies that the occupant comfort level of the test cabin corresponding to the target intelligent cockpit scenario is uncomfortable, it invokes the test cabin object to generate a comfort adjustment command, including: Based on the scheduling agent, query the historical smart cockpit scenarios associated with the target smart cockpit scenario; If at least one of the aforementioned historical smart cockpit scenarios is found, the scheduling agent calls the test cockpit object to perform comfort adjustment analysis based on the state evolution log of each of the aforementioned historical smart cockpit scenarios and the occupant comfort of the test cockpit, and determines the comfort adjustment command. If the historical smart cockpit scenario is not found, the scheduling agent calls the test cockpit object to perform comfort adjustment analysis based on the occupant comfort of the test cockpit, and determines the comfort adjustment command.

7. The method according to claim 5, characterized in that, Also includes: A state evolution log for the target intelligent cockpit scenario is established based on the target intelligent cockpit scenario, the test cockpit occupant comfort, the cockpit test results, and the comfort adjustment commands. The state evolution log is stored in a preset scenario database.

8. A testing device for an intelligent cockpit, characterized in that, include: The cockpit initialization module is used to respond to the test target corresponding to the simulated intelligent cockpit and initialize the simulated intelligent cockpit scenario based on the simulated intelligent cockpit and the test target; The cockpit simulation module is used to perform model testing on a pre-established cockpit object based on the simulated intelligent cockpit scenario and the test target, and to determine the intelligent cockpit control commands. The cockpit evolution module is used to evolve the cockpit scene based on the intelligent cockpit control commands of the device intelligent agent and the environmental intelligent agent, and to determine the target intelligent cockpit scene; The test analysis module is used to simulate the cabin evolution of the target intelligent cabin scenario based on the cabin occupant intelligent agent, determine the test cabin occupant comfort of the cabin object under test, and determine the cabin test results based on the cabin occupant comfort.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the test method for the smart cockpit according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the test method for the intelligent cockpit according to any one of claims 1-7.

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