Vehicle window control method, device, equipment, vehicle, storage medium and product

Abstract

The application discloses a vehicle window control method, device, equipment, vehicle, storage medium and product, and relates to the technical field of vehicle control. The method comprises the following steps: in response to a vehicle window control trigger event, obtaining a vehicle window control operation to be executed, and collecting a vehicle window opening degree data sequence in a historical time window; performing feature extraction on the vehicle window opening degree data sequence to obtain historical operation features; identifying a man-machine confrontation intention based on the historical operation features and the vehicle window control operation, and obtaining an identification result; and executing a corresponding response strategy based on the identification result. The application can control the vehicle window based on the man-machine confrontation intention, so as to improve the adaptability of the vehicle window automatic control function to different use scenarios and the user experience.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to a method, device, equipment, vehicle, storage medium and product for controlling vehicle windows. Background Technology

[0002] With the development of intelligent vehicle technology, more and more vehicles are equipped with automated control functions or perception and control functions utilizing artificial intelligence. Among them, automatic window control, as a common intelligent feature, can improve user convenience to a certain extent.

[0003] Currently, automatic window control functions typically employ a single-condition trigger mechanism. This means that when a preset condition is met, the window automatically opens or closes. Typical scenarios include automatically closing the window when a rain sensor detects rainfall, and automatically opening the window when the air quality inside the vehicle deteriorates or the temperature is high. However, this single-condition control method fails to consider the actual needs of users in different usage scenarios, potentially leading to a "human-machine conflict"—the window may automatically close when the user doesn't want it to, or automatically open when the user doesn't want it to, resulting in a poor user experience.

[0004] Therefore, how to control car windows based on human-machine interaction intentions in order to improve the adaptability of automatic window control functions to different usage scenarios and user experience is a problem that urgently needs to be solved. Summary of the Invention

[0005] The main purpose of this application is to provide a method, device, equipment, vehicle, storage medium and product for controlling vehicle windows, which aims to improve the adaptability of automatic window control function to different usage scenarios and user experience based on human-machine interaction intention.

[0006] To achieve the above objectives, this application provides a vehicle window control method, the vehicle window control method comprising: In response to a window control trigger event, the window control operation to be executed is obtained, and the window opening data sequence within the historical time window is collected; Feature extraction is performed on the window opening data sequence to obtain historical operation features; Based on the historical operation characteristics and the window control operation, the human-machine confrontation intention is identified, the identification result is obtained, and the corresponding response strategy is executed based on the identification result.

[0007] In one embodiment, the historical operation features include the amount of change in window opening and the direction of the change in window opening. The step of extracting features from the window opening data sequence to obtain the historical operation features includes: Based on the initial and final openings in the window opening data sequence, the amount and direction of opening change within the historical time window are determined.

[0008] In one embodiment, the step of identifying human-machine confrontation intent based on the historical operation characteristics and the window control operation, and obtaining the identification result, includes: When the opening change is zero and the window control operation is automatic window closing, determine whether the final opening is greater than a first preset threshold and whether the most recent set time of the final opening is within a preset historical time period. If the end opening is greater than the first preset threshold and the most recent set time is within the preset historical time period, then the identification result is determined to be an intention to oppose. If the end opening is less than or equal to the first preset threshold and / or the most recent set time is not within the preset historical time period, then the identification result is determined to be no adversarial intent.

[0009] In one embodiment, the step of identifying human-machine confrontation intent based on the historical operation characteristics and the window control operation, and obtaining the identification result, includes: When the opening change is zero and the window control operation is automatic window opening, determine whether the final opening is less than a second preset threshold and whether the most recent set time of the final opening is within a preset historical time period. If the end opening is less than the second preset threshold and the most recent set time is within the preset historical time period, then the identification result is determined to be an intention to oppose. If the end opening is greater than or equal to the second preset threshold and / or the most recent set time is not within the preset historical time period, then the identification result is determined to be no adversarial intent.

[0010] In one embodiment, the step of identifying human-machine confrontation intent based on the historical operation characteristics and the window control operation, and obtaining the identification result, includes: If the opening change is not zero, determine whether the direction of the opening change is the same as the window control direction of the window control operation. If so, the identification result is determined to be without adversarial intent; If not, then the identification result is determined to be a strong adversarial intent.

[0011] In one embodiment, the step of identifying human-machine confrontation intent based on the historical operation characteristics and the window control operation, and obtaining the identification result, includes: Based on the historical operation characteristics and the window control operation, the first resistance strength is determined; Obtain historical behavior data corresponding to the window control trigger event, and determine the second resistance strength based on the historical behavior data, wherein the historical behavior data is the probability that the user agrees or refuses to perform the window control operation after the historical window control trigger event is triggered; The vehicle status data of the current vehicle is obtained, and the third resistance strength is determined based on the vehicle status data and the window control operation. The vehicle status data includes at least one of the following: vehicle interior temperature, vehicle interior air quality, seat pressure, door status, and vehicle speed. The first confrontation strength, the second confrontation strength, and the third confrontation strength are weighted and summed to obtain the comprehensive confrontation strength. The identification result is determined based on the comprehensive adversarial strength.

[0012] In one embodiment, the step of executing the corresponding response strategy based on the identification result includes: If the identification result indicates no confrontational intent, the window control operation is performed. If the identification result indicates that there is an intention to resist, a preset prompt message will be output to remind the user to choose whether to perform the window control operation; If the identification result indicates a strong intention to resist, the window control operation will be cancelled. Record information about this event to update the historical behavior data.

[0013] Furthermore, to achieve the above objectives, this application also provides a vehicle window control device, the vehicle window control device comprising: The data acquisition module is used to respond to window control trigger events, obtain the window control operation to be executed, and collect the window opening data sequence within the historical time window; The feature extraction module is used to extract features from the window opening data sequence to obtain historical operation features; The intent recognition module is used to identify human-machine confrontation intent based on the historical operation features and the window control operation, obtain the recognition result, and execute the corresponding response strategy based on the recognition result.

[0014] In addition, to achieve the above objectives, this application also proposes an electronic device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the window control method described above.

[0015] In addition, to achieve the above objectives, this application also proposes a vehicle that includes the electronic equipment described above.

[0016] In addition, to achieve the above objectives, this application also provides a storage medium, which is a computer-readable storage medium, on which a program implementing a window control method is stored, and the program implementing the window control method is executed by a processor to implement the steps of the window control method as described above.

[0017] In addition, to achieve the above objectives, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the window control method described above.

[0018] This application provides a method for controlling vehicle windows. In response to an automatic window control event, this application acquires the window control operation to be executed and collects a sequence of window opening data within a historical time window. Then, it extracts features from the window opening data sequence to obtain historical operation features. Finally, based on the historical operation features and the window control operation, it identifies human-machine confrontation intentions and obtains the identification result, and executes the corresponding response strategy based on the identification result.

[0019] In summary, this application, in response to a window control trigger event, first acquires the window control operation to be executed and collects a sequence of window opening data within a historical time window; then, it extracts features from the data sequence to obtain historical operation features that characterize the user's recent window operation habits; based on this, it combines the window control operation to be executed with the extracted historical operation features to jointly identify human-machine confrontation intent, thereby obtaining an identification result; finally, it executes a corresponding response strategy based on the identification result. Compared to the traditional "one-size-fits-all" approach of directly opening or closing windows based on a single environmental condition (such as rain or temperature), this application proactively introduces analysis of the user's recent window operation behavior before automatic control execution. By judging whether the user's operating habits conflict with the upcoming automatic operation, it effectively identifies human-machine confrontation intent and then adopts differentiated response strategies based on the identification results. Therefore, this application can prevent the windows from being forcibly closed when the user does not want to close them, and prevent the windows from being forcibly opened when the user does not want to open them. This reduces the conflict between automatic control and user wishes, thereby improving the adaptability of the automatic window control function to different usage scenarios and enhancing the user experience, achieving more intelligent and user-friendly window control. At the same time, reducing the conflict between automatic control and user wishes can, to some extent, prevent additional wear and tear on the window motor.

[0020] In addition, by reducing the conflict between automatic control and user will, this application avoids counter-operations caused by the user manually preventing the automatic closing or opening of windows, thereby reducing additional wear on the window motor to a certain extent. Attached Figure Description

[0021] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0022] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating the first embodiment of the window control method of this application; Figure 2 This is a schematic diagram of window opening data sampling according to an embodiment of the window control method of this application; Figure 3 This is a schematic diagram of the adversarial intent recognition process involved in an embodiment of the window control method of this application; Figure 4 This is a schematic diagram of the comprehensive determination process involved in one embodiment of the window control method of this application; Figure 5 This is a schematic diagram of the module structure of the window control device of this application; Figure 6 This is a schematic diagram of the device structure of the hardware operating environment involved in the window control method in the embodiments of this application.

[0024] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0025] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0026] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0027] Currently, automatic window control functions typically employ a single-condition trigger mechanism. This means that when a preset condition is met, the window automatically opens or closes. Typical scenarios include automatically closing the window when a rain sensor detects rainfall, and automatically opening the window when the air quality inside the vehicle deteriorates or the temperature is high. However, this single-condition control method fails to consider the actual needs of users in different usage scenarios, potentially leading to a "human-machine conflict"—the window may automatically close when the user doesn't want it to, or automatically open when the user doesn't want it to, resulting in a poor user experience.

[0028] Therefore, how to control vehicle windows based on human-machine adversarial intent in order to improve the adaptability of automatic window control functions to different usage scenarios and user experience is a problem that urgently needs to be solved.

[0029] The main solution of this application is as follows: in response to a window control trigger event, the window control operation to be executed is obtained, and a window opening data sequence within a historical time window is collected; features are extracted from the window opening data sequence to obtain historical operation features; based on the historical operation features and the window control operation, human-machine confrontation intention is identified to obtain the identification result, and a corresponding response strategy is executed based on the identification result.

[0030] This application, in response to a window control trigger event, first acquires the window control operation to be executed and collects a sequence of window opening data within a historical time window. Then, it extracts features from the data sequence to obtain historical operation features that characterize the user's recent window operation habits. Based on this, it combines the window control operation to be executed with the extracted historical operation features to jointly identify human-machine confrontational intent, thereby obtaining an identification result. Finally, it executes a corresponding response strategy based on this identification result. Compared to the traditional "one-size-fits-all" approach of directly opening or closing windows based on a single environmental condition (such as rain or temperature), this application proactively introduces analysis of the user's recent window operation behavior before automatic control execution. By judging whether the user's operating habits conflict with the upcoming automatic operation, it effectively identifies human-machine confrontational intent and then adopts differentiated response strategies based on the identification results. Therefore, this application can avoid forcibly closing windows when the user does not want to close them, and avoid forcibly opening windows when the user does not want to open them, thus reducing the conflict between automatic control and user wishes. This improves the adaptability of the automatic window control function to different usage scenarios and the user experience, achieving more intelligent and user-friendly window control. At the same time, by reducing the conflict between automatic control and user wishes, additional wear and tear on the window motor can be avoided to some extent.

[0031] In addition, by reducing the conflict between automatic control and user will, this application avoids counter-operations caused by the user manually preventing the automatic closing or opening of windows, thereby reducing additional wear on the window motor to a certain extent.

[0032] It should be noted that the execution subject of the method in each embodiment of the window control method of this application can be the vehicle control system. The following uses the vehicle control system as the execution subject to describe this embodiment and the following embodiments.

[0033] Based on this, this application proposes a window control method according to a first embodiment, please refer to... Figure 1 The window control method includes steps S10 to S30: Step S10: In response to the window control trigger event, obtain the window control operation to be executed and collect the window opening data sequence within the historical time window; It should be noted that automatic window control refers to conditions triggered by vehicle sensors or system logic, such as a rain sensor detecting rainfall, an in-vehicle temperature sensor detecting high temperatures, an air quality sensor detecting excessive levels of harmful gases, the vehicle about to enter a tollbooth (manual toll collection), or the vehicle reaching and maintaining a preset speed after exiting a tollbooth for a certain period. The window control operation to be executed refers to the default window control command corresponding to the triggering event, such as automatically closing or opening the windows. For example, if a rain sensor detects rainfall, or the vehicle reaches and maintains a preset speed after exiting a tollbooth for a certain period, the corresponding window control operation is automatic window closing; if an in-vehicle temperature sensor detects high temperatures, an air quality sensor detects excessive levels of harmful gases, or the vehicle is about to enter a tollbooth (manual toll collection), the corresponding window control operation is automatic window opening. A historical time window refers to a preset time period (e.g., 3 seconds) prior to the current moment. A window opening data sequence refers to a series of window opening values ​​(usually expressed as a percentage) collected chronologically within this time window.

[0034] After detecting a window control trigger event (such as rain), the system first determines the default operation corresponding to the event (such as automatically closing the window), and at the same time, it traces back the window opening data over a period of time to form a time-series data sequence.

[0035] In one feasible implementation, a rain sensor detects rainfall, the system determines that an automatic window closing event has been triggered, and identifies the operation to be performed as "closing the window." Simultaneously, taking the collection of window opening data for a specific vehicle window as an example, the system reads window opening records collected at a frequency of 1Hz over the past 3 seconds from the memory, obtaining four sampling points: =60% =60% =60% =60%, forming a window opening data sequence. This sequence reflects that the user did not operate the window in the 3 seconds prior to the current moment, and the current opening is 60%.

[0036] For example, such as Figure 2 The diagram shows a sampling pattern for vehicle window opening data. The horizontal axis represents time, and the vertical axis represents the percentage of window opening. When the rain sensor... and When an automatic window closing event is triggered, the system backtracks and collects the window opening data sequence within a preset monitoring window (historical time window) T before the trigger time. Figure 2 middle, to The window opening remained stable at 60% at all times, indicating that the user did not perform any active operation before the trigger; after the trigger, the user... to Constantly and actively raising the car window opening to 100% indicates that the user is engaging in window-opening behavior that contradicts the automatic window-closing function. By analyzing the opening trend within the historical window before the trigger, combined with the user's real-time actions after the trigger, the system can comprehensively determine whether there is a human-machine confrontational intent. It should be noted that the data collection for the actual confrontational intent identification only occurred within the historical time window before the trigger moment; the data after the trigger is used for verification and feedback and is not included in the current intent identification.

[0037] Step S20: Extract features from the window opening data sequence to obtain historical operation features; It should be noted that historical operation characteristics refer to parameters that reflect the user's window operation habits within a historical time window, including but not limited to the amount of change in opening degree, the direction of the change in opening degree, the number of window operations, and the direction of the change in opening degree corresponding to each window operation.

[0038] In this embodiment, the historical operation features include the amount of change in opening degree and the direction of change in opening degree. Step S20 may include: Step S201: Based on the initial and final openings in the window opening data sequence, determine the amount and direction of opening change within the historical time window.

[0039] It should be noted that the initial opening refers to the window opening value corresponding to the earliest collection time within the historical time window, while the final opening refers to the window opening value corresponding to the latest collection time (i.e., the current time).

[0040] The opening value of the first data frame is directly read from the acquired opening data sequence as the starting opening value, and the opening value of the last data frame is read as the ending opening value. Then, the difference between the ending opening value and the starting opening value is calculated to obtain the opening change amount, and the direction of opening change is determined according to the sign of the difference: a positive difference indicates the opening direction, a negative difference indicates the closing direction, and a zero difference indicates no change.

[0041] Step S30: Based on the historical operation features and the window control operation, identify the human-machine confrontation intention, obtain the identification result, and execute the corresponding response strategy based on the identification result.

[0042] It should be noted that adversarial intent refers to a conflict between a user's intention to actively operate the car window and the system's impending automatic window control operation. For example, if a user is opening the window (historical operation characteristics show the opening direction) while the system is preparing to automatically close the window, then adversarial intent exists. The identification result refers to the quantitative conclusion of the adversarial intent, typically categorized into different levels such as no adversarial intent, presence of adversarial intent, and strong adversarial intent. The response strategy refers to the corresponding actions taken based on the identification result, including executing the automatic window control operation, canceling the operation, outputting prompts, or updating the user behavior model.

[0043] When the change in opening degree is zero, the system determines whether there is any intention to resist based on the current opening degree and the time of the most recent user adjustment. When the change in opening degree is not zero, the system determines whether there is no intention to resist or a strong intention to resist based on whether the direction of the opening degree change is the same as the direction of the operation to be performed. Based on the determination result, the system executes the corresponding response strategy, such as performing automatic operation, canceling the operation and prompting the user, or directly canceling the operation and updating historical behavior data.

[0044] In this embodiment, step S30 may include: Step S301: If the identification result indicates no intention to resist, perform the window control operation.

[0045] It should be noted that "no opposing intent" means that the identification results indicate that the user's recent window operation behavior does not conflict with the automatic window control operation that the system is about to perform; that is, the user has not actively expressed a willingness to oppose the automatic operation. For example, when the system is preparing to automatically close the window, the user has not recently opened the window, or the opening degree has changed to zero and the current opening degree is small; when the system is preparing to automatically open the window, the user has not recently opened the window. In this case, the system's execution of the automatic operation will not interfere with the user's actual needs.

[0046] When the system determines that there is no intention to antagonize the user by comprehensively analyzing historical operation characteristics and the operation to be performed, it indicates that the current user's vehicle usage status and operating habits are consistent with the goal of automatic control, and there is no risk of conflict. The system then sends an execution command to the window motor to complete the closing or opening operation of the window according to the preset speed and distance, so as to achieve the functions of environmental protection (closing the window when it rains) or comfort adjustment (opening the window for ventilation). This process does not require user intervention, ensuring the efficiency and timeliness of automatic control.

[0047] For example, when a rain sensor detects rainfall, the system prepares to automatically close the windows. The system collects a sequence of window opening data over the past 3 seconds as [10%, 10%, 10%, 10%], with no change in opening, a current opening of 10%, and the most recent user adjustment not occurring within the historical time window. The system determines there is no intention to obstruct the flow of traffic and then executes the automatic window closing operation, completely closing the windows to prevent rainwater from entering the vehicle.

[0048] Step S302: If the identification result indicates an intention to resist, a preset prompt message is output to remind the user to choose whether to perform the window control operation.

[0049] It should be noted that the presence of adversarial intent refers to situations where the identification results indicate that the user has recently engaged in actions or intentions contrary to the automatic operation the system is about to execute, but the degree of adversarial intent has not yet reached a level of strong conflict (e.g., the user has actively opened the car window to a large degree and kept it open, while the system is preparing to automatically close the window). In this case, the system does not directly execute the automatic operation but instead confirms with the user through human-computer interaction. Pre-set prompts refer to pre-designed visual or voice prompts, such as a pop-up window on the in-vehicle display saying "Rain detected, do you want to close the car window?" accompanied by a confirmation / cancel button, or a voice prompt to the user.

[0050] When the system detects an intention to resist (not a strong one), to avoid forcibly executing automatic operations and causing user dissatisfaction, the system pauses the automatic control process and instead displays a prompt to the user via the in-vehicle display, voice assistant, or dashboard icon, asking whether the user agrees to perform the window control operation. The user can confirm or refuse via the touchscreen, physical button, or voice command. The system determines the subsequent action based on the user's choice: if the user confirms, the operation is executed; if the user refuses or there is no response within the time limit, the operation is canceled. This interaction strategy respects the user's wishes while also providing the user with an opportunity for active control.

[0051] For example, a user parks their car in a parking lot with the windows open to 60% for ventilation, and it starts to drizzle. The system prepares to automatically close the windows, but the collected window opening data sequence shows no change in the past 3 seconds (opening change is 0), the current opening of 60% is relatively large, and the user actively adjusted the windows from 20% to 60% 8 seconds ago. The system determines that there is an intention to obstruct (potential obstruction), so it temporarily suspends the automatic window closing and instead displays a prompt on the vehicle's screen: "Rain detected, do you want to close the windows?", providing three options: "Yes," "No," and "Will remind you later." If the user selects "Yes," the windows will close; if they select "No" or the 30-second timeout period expires, the window closing will be canceled.

[0052] Step S303: If the identification result indicates a strong intention to resist, cancel the execution of the window control operation.

[0053] It should be noted that strong adversarial intent refers to situations where the identification results indicate that the user is currently actively performing a window control operation opposite to the system's automatic operation, and the adversarial intent is very clear and urgent. For example, the user is continuously opening the window while the system is preparing to automatically close it. In this case, the system judges that the user's real-time operational intention is far stronger than the automatic control requirement, and the user's wishes should be fully respected. "Cancel the execution of the window control operation" means directly terminating the current automatic control process, without performing any window opening or closing actions, and without outputting any prompt information (to avoid disturbing the user).

[0054] When the system detects that the user is actively performing window control in the opposite direction to the automatic operation (such as a non-zero change in window opening and a change in the opposite direction to the automatic operation), it is determined to be a strong counter-attack intention. The system immediately cancels the automatic control operation, does not perform any window actions, and does not display any prompts to ensure that it does not interfere with the user's current operation.

[0055] Step S304: Record the event information to update the historical behavior data.

[0056] It should be noted that regardless of whether the identification result indicates no adversarial intent, adversarial intent, or strong adversarial intent, the system will record relevant information about this automatic window control event to update historical behavior data. Event information includes, but is not limited to: trigger event type (e.g., rainfall level), identification result, user operation characteristics (e.g., direction and magnitude of opening change), user response to prompts (if any), final execution result, and vehicle status data (e.g., vehicle speed, interior temperature, etc.). "Update the historical behavior data" means recording this adversarial event (including triggering conditions, user operation, scene context, etc.) in the user behavior log for subsequent personalized learning, such as adjusting the adversarial probability model for the user in different scenarios.

[0057] The system stores relevant data about the event (such as the type of the triggering event, the direction of the user's operation, the current opening degree, and the vehicle status) in the historical behavior database to update the user's personalized behavior model. For example, if a user repeatedly refuses to automatically close the window in similar scenarios, the system will increase the adversarial confidence in that scenario and will be more inclined to use prompts or cancellation strategies when the event is triggered in the future.

[0058] For example, a user is actively opening a window (the opening degree has increased from 30% to 55% in the past 3 seconds, a change of +25%, with the direction being towards the window). At this moment, the rain sensor detects rainfall, and the system prepares to automatically close the window. The system determines this to be a strong act of defiance and immediately cancels the automatic window-closing operation without issuing any notification to avoid disturbing the user's ongoing window-opening action. Simultaneously, the system records this event information in the user behavior log, including rainfall level, user's operation direction, operation magnitude, and vehicle status (e.g., parked / driving). This historical data will be used to subsequently train a user preference model. For instance, if the user encounters light rain again, the system will lower the priority of automatic window-closing or directly adopt a notification strategy. In this way, the system achieves continuous learning and adaptive optimization of the user's personalized habits.

[0059] like Figure 3 The diagram illustrates the adversarial intent recognition process. Upon detecting a window control trigger event, the system determines the window control operation to be executed (automatic window closing or automatic window opening) and collects a sequence of window opening data within a historical time window. Historical operation features (including the amount and direction of opening change) are extracted from this data sequence. Based on these historical features and the window control operation, adversarial intent recognition is performed, yielding a result. If the recognition result indicates no adversarial intent, the system executes the window control operation and updates the user model (also known as the user behavior model) based on the event information including the execution result. If the recognition result indicates adversarial intent, the system outputs a prompt for the user to confirm whether to execute the window control operation and updates the user model based on the event information including the user's selection. If the recognition result indicates strong adversarial intent, the window control operation is canceled, and the user model is updated based on the event information including the execution result.

[0060] This application embodiment, in response to a window control trigger event, first acquires the window control operation to be executed and collects a sequence of window opening data within a historical time window; then, it extracts features from the data sequence to obtain historical operation features that characterize the user's recent window operation habits; based on this, it combines the window control operation to be executed with the extracted historical operation features to jointly identify human-machine confrontation intent, thereby obtaining an identification result; finally, it executes a corresponding response strategy based on the identification result. Compared to the traditional "one-size-fits-all" approach of directly executing window opening or closing based on a single environmental condition (such as rain or temperature), this application proactively introduces the analysis of the user's recent window operation behavior before automatic control execution. By judging whether the user's operating habits conflict with the upcoming automatic operation, it effectively identifies human-machine confrontation intent and then adopts a differentiated response strategy based on the identification result. Therefore, the embodiments of this application can avoid forcibly closing windows when the user does not wish to close them, and avoid forcibly opening windows when the user does not wish to open them. This reduces the conflict between automatic control and user intent, thereby improving the adaptability of the automatic window control function to different usage scenarios and enhancing the user experience, achieving more intelligent and user-friendly window control. Simultaneously, reducing the conflict between automatic control and user intent can, to some extent, prevent additional wear and tear on the window motor. Furthermore, by reducing the conflict between automatic control and user intent, the embodiments of this application avoid confrontational operations caused by the user manually preventing the automatic window from closing or opening, thus further reducing additional wear and tear on the window motor.

[0061] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description and will not be repeated hereafter. Based on this, step S30 may include: Step A10: When the opening change is zero and the window control operation is automatic window closing, determine whether the final opening is greater than a first preset threshold and whether the most recent set time of the final opening is within a preset historical time period. Step A20: If the end opening is greater than the first preset threshold and the most recent set time is within the preset historical time period, then the identification result is determined to be that there is an adversarial intent. Step A30: If the end opening is less than or equal to the first preset threshold and / or the most recent set time is not within the preset historical time period, then the identification result is determined to be no adversarial intent.

[0062] It should be noted that the first preset threshold is a critical value used to determine whether the current opening degree belongs to the "large opening degree", such as 30%. The most recent set time refers to the time point when the user last actively adjusted the window opening degree, that is, the time point from the most recent adjustment of the window opening degree to the current opening degree (the last opening degree). The preset historical period refers to a backtracking period (such as 10 seconds) that is longer than the historical time window, used to determine whether the user's most recent adjustment occurred recently.

[0063] For the specific scenario where the window opening change is zero and the pending operation is automatic window closing, the system first checks whether the current window opening (final opening) is greater than a first preset threshold. If it is, it indicates that the window is at a large opening. Simultaneously, it checks whether the user's most recent active window adjustment occurred within a preset historical time period. If it did, it indicates that the current opening was a state the user recently set and wished to maintain. When both conditions are met, the system determines there is an intention to resist, meaning the user does not wish for the window to close automatically. If either condition is not met (opening less than or equal to the threshold, or the most recent adjustment time has passed), it determines there is no intention to resist, and automatic window closing can proceed.

[0064] For example, taking a scenario where a user parks their vehicle in a parking lot, the rain sensor detects rainfall, and the system prepares to automatically close the windows. The system collects a sequence of window opening data over the past 3 seconds: [60%, 60%, 60%, 60%], with zero change in opening. The system reads the last opening as 60%, determining it to be greater than a first preset threshold of 30%. Simultaneously, it finds that the user adjusted the window from 20% to 60% 8 seconds ago (within a preset historical timeframe of 10 seconds), and the most recent setting time is within this preset historical timeframe. Therefore, both conditions are met, and the system determines there is an intention to resist, temporarily halting automatic window closing and instead outputting a prompt message. Conversely, if the current opening is only 15% (less than the threshold), or the user's most recent adjustment occurred 15 seconds ago (outside the preset historical timeframe), then there is no intention to resist, and automatic window closing is executed directly. Through this rule, the system can accurately identify the user's desire to maintain full ventilation, avoiding human-machine conflict caused by forcibly closing the windows.

[0065] In this embodiment, step S30 may include: Step B10: When the opening change is zero and the window control operation is automatic window opening, determine whether the final opening is less than the second preset threshold and whether the most recent set time of the final opening is within a preset historical time period. Step B20: If the end opening is less than the second preset threshold and the most recent set time is within the preset historical time period, then the identification result is determined to be an intention to oppose. Step B30: If the end opening is greater than or equal to the second preset threshold and / or the most recent set time is not within the preset historical time period, then the identification result is determined to be no adversarial intent.

[0066] It should be noted that the second preset threshold is a critical value used to determine whether the current opening degree belongs to the "small opening degree", such as 20%.

[0067] For the specific scenario where the window opening change is zero and the operation to be performed is automatic window opening, the system first checks whether the current window opening (final opening) is less than a second preset threshold. If it is less, it indicates that the window is in a small opening state (closed to being closed). Simultaneously, it checks whether the user's most recent active window adjustment occurred within a preset historical time period. If it did, it indicates that the current small opening state was recently set by the user and is something they wish to maintain (e.g., the user actively closed the window to block noise or dust). When both conditions are met, the system determines that there is an intention to resist, i.e., the user does not wish for automatic window opening. If either condition is not met (opening greater than or equal to the threshold, or the most recent adjustment time has passed), it determines that there is no intention to resist, and automatic window opening can be performed.

[0068] For example, when the vehicle's interior temperature sensor detects a high temperature, the system prepares to automatically open the windows for ventilation. The system collects a sequence of window opening data over the past 3 seconds: [15%, 15%, 15%, 15%], with zero change in opening. The system reads the last opening as 15%, determining it to be less than the second preset threshold of 20%. Simultaneously, it finds that the user adjusted the window from 50% to 15% 8 seconds prior (within a preset historical timeframe of 10 seconds), and the most recent setting time falls within this timeframe. Therefore, both conditions are met, and the system determines there is an intention to resist, temporarily halting automatic window opening and instead outputting a prompt message (e.g., "The interior temperature is high, do you want to open the windows?"). If the current opening is 25% (greater than or equal to the threshold), or the user's most recent adjustment occurred 15 seconds prior (outside the preset historical timeframe), then there is no intention to resist, and automatic window opening is executed directly. Through this rule, the system can accurately identify the user's desire to keep the windows closed (e.g., due to outside noise, dust, or air conditioning), avoiding forced window opening that could trigger human-machine conflict, thus improving the intelligence and user experience of the automatic window opening function.

[0069] In this embodiment, step S30 may include: Step C10: If the opening change is not zero, determine whether the opening change direction is the same as the window control direction of the window control operation. Step C20: If yes, then determine that the identification result is that there is no adversarial intent; Step C30: If not, then the identification result is determined to be a strong adversarial intent.

[0070] For scenarios where the window opening change is not zero (i.e., the user has recently actively operated the window), the direction of the opening change in historical operation characteristics is obtained and compared with the control direction of the window control operation to be performed. If the two directions are the same (e.g., the user is opening the window and the system is preparing to open it automatically; or the user is closing the window and the system is preparing to close it automatically), it means that the user's operation intention is consistent with the system's automatic control target and there is no conflict, so it is judged as having no antagonistic intent. If the two directions are opposite (e.g., the user is opening the window and the system is preparing to close it automatically, or the user is closing the window and the system is preparing to open it automatically), it means that the user is actively performing an action opposite to the system's automatic operation, and the antagonistic intent is very clear and strong, so it is judged as having a strong antagonistic intent.

[0071] For example, if the window opening increases from 30% to 55% within the past 3 seconds, a change of +25%, and the direction of the change is the opening direction (positive direction), and the rain sensor detects rainfall, the system is preparing to perform an automatic operation to close the window (negative direction). Since the two directions are opposite, the system determines that there is a strong intention to resist, immediately cancels the automatic window closing operation, does not disturb the user's current operation, and records this event. Conversely, if the system is preparing to perform an automatic operation to open the window in the same scenario (such as ventilation in high temperature), then the directions are the same, the system determines that there is no intention to resist, and directly performs the automatic window opening.

[0072] Alternatively, if the user is actively closing a window (the opening direction is negative), and the system is preparing to automatically close the window (also in a negative direction), it determines there is no intention to obstruct the process and executes the automatic window closing. If the system is preparing to automatically open the window, it determines there is a strong intention to obstruct the process and cancels the automatic window opening. In this way, the system can quickly identify when the user is performing an operation that contradicts the intention of automatic operation and prioritize respecting the user's real-time control.

[0073] In one feasible implementation, this application also provides a gradual window closing strategy: when the system detects a possible adversarial intent, it does not directly cancel the automatic window closing, but first closes the window to a preset position (e.g., leaving a 5cm gap), and then observes whether the user performs any further actions. If the user does not perform any actions within a certain delay, it is determined that the user has tacitly consented to closing the window, and the system continues to completely close the window; if the user actively reopens the window during this period, the system cancels the automatic window closing operation, fully respecting the user's intent. This gradual strategy avoids strong adversarial actions while also taking into account environmental protection needs, reducing the complete abandonment of window closing due to misjudgment.

[0074] In another feasible implementation, this application also provides a voice interaction confirmation method: before automatically closing the window, the system asks the user through the in-vehicle voice assistant, for example, "It's raining outside, do you need me to close the window for you?", and decides whether to perform the window closing operation based on the user's voice response. If the user answers "yes" or confirms, the window is closed; if the user answers "no" or cancels, the window closing is abandoned. Furthermore, the system also supports users setting personalized preferences via voice, for example, a user can say "Don't close the window when it's drizzling," and the system records this preference and automatically suppresses the window closing prompt or directly cancels the window closing in subsequent drizzling weather. This solution improves the naturalness of human-computer interaction and the user's personalized experience.

[0075] In summary, in scenarios where the opening change is zero, this application's embodiments set different threshold judgment logics for automatic window closing and automatic window opening, effectively distinguishing between the two states of "the user intentionally maintaining the current opening" and "no special intention," thus avoiding misjudgments caused by a single condition triggering the system. In scenarios where the opening change is not zero, by comparing whether the direction of the opening change is consistent with the direction of automatic operation, the direct conflict between the user's active operation and the system's automatic control can be quickly identified, and a strong counter-intention can be decisively determined, thereby immediately canceling the automatic operation. This completely avoids interference with the user's current operation and also eliminates the repeated starting and stopping of the window motor and additional wear caused by the user manually stopping the automatic operation. Through the aforementioned hierarchical identification mechanism, this application embodiment achieves refined intent assessment from "no resistance" to "resistance" and then to "strong resistance," and executes differentiated response strategies (direct execution, output prompts, cancellation operation) accordingly. This ensures efficient execution of the automatic control function when the user has no resistance, while fully respecting the user's wishes when the user has resistance. This improves the scenario adaptability and user experience of the automatic window control function, and effectively extends the service life of the window motor.

[0076] Based on the first and second embodiments of this application, in the third embodiment of this application, the content that is the same as or similar to the first and second embodiments described above can be referred to the above description and will not be repeated hereafter. Based on this, step S30 may include: Step D10: Determine the first resistance strength based on the historical operation characteristics and the window control operation.

[0077] It should be noted that the first level of resistance strength is a numerical value quantified solely based on the degree of conflict between the user's recent window operation behavior (i.e., historical operation characteristics) and the window control operation to be performed. This strength reflects the magnitude of the user's willingness to resist through actual operation; for example, the greater the change in opening degree and the more opposite the direction of change to the automatic operation direction, the higher the first level of resistance strength. This value is usually normalized to between 0 and 1.

[0078] Based on historical operational characteristics (including the amount of window opening change, the direction of the opening change, and the current window opening) and the window control operation to be executed (automatic window opening or automatic window closing), an adversarial strength value is calculated according to preset rules or functions. For example, a higher strength is assigned when the direction of the opening change is opposite to the direction of the automatic operation, and a lower strength is assigned when they are the same; the larger the amount of opening change, the higher the strength. This calculation can be done through rule mapping, table lookup, or lightweight model inference. The output first adversarial strength serves as an input dimension for subsequent multi-source fusion.

[0079] In one feasible implementation, if the operation to be performed is automatic window closing, and the historical operation characteristics show that the opening direction is window opening (opposite), with an opening change of 25%, the system determines the first resistance strength as 0.6 based on a preset mapping rule: if the direction is opposite, the base strength is 0.6, and a change coefficient (increasing by 0.1 for every 10%) is added, ultimately determining the first resistance strength to be 0.6 + 0.25 = 0.85. If the direction is the same, the base strength is 0.1, and the added change coefficient does not exceed 0.3. In this way, the system quantifies the degree of user operation conflict into a numerical value.

[0080] Step D20: Obtain historical behavior data corresponding to the window control trigger event, and determine the second resistance strength based on the historical behavior data, wherein the historical behavior data is the probability that the user agrees to perform the window control operation after the historical window control trigger event is triggered.

[0081] It should be noted that historical behavior data refers to the user's response records to system automatic operation requests in the past when the same or similar window control trigger events occurred. Specifically, it is quantified as the probability that the user agrees to or refuses to execute the window control operation (e.g., number of agrees / total triggers, number of refusals / total triggers). The second dimension, adversarial strength, is an adversarial strength value derived from this probability. The lower the probability (i.e., the more frequently the user refuses), the higher the adversarial strength. This dimension reflects the personalized characteristics of the user's long-term preferences.

[0082] The system queries the user's historical behavior database based on the type of the current window control trigger event (such as closing the window in rain or opening the window in high temperature) and the scene context (such as parking / driving or interior temperature). This database retrieves the probability that the user will agree to or refuse to perform the automatic operation in the corresponding scenario. Then, a preset mapping function is used to convert this probability into a secondary resistance strength. For example, if the user only agreed to close the window once out of five previous requests in light rain, the probability of agreement is 0.2, and the secondary resistance strength is 0.8. This strength value reflects the user's tendency to resist automatic control in that scenario.

[0083] For example, if the system finds that the current triggering event is automatic window closing in light rain, then it obtains that the user's historical rejection rate in light rain is 80% (agreement probability 0.2), and the second adversarial strength is 0.8. In this way, by introducing historical behavioral data, the system can achieve personalized adversarial strength assessment for different users.

[0084] In one feasible implementation, the system establishes and maintains a user behavior model for each vehicle occupant to record user responses to different window control trigger events. The model is stored in JSON format and includes the statistical period, total number of events in each scenario, number of user acceptances, number of rejections, and rejection rate. Example data is as follows: { "userId":"USER_001", "statisticsPeriod":"30days", "scenarios":{ "lightRain":{ "totalEvents":5, "accepted":1, "rejected":4, "rejectRate": 0.80 }, "moderateRain":{ "totalEvents":3, "accepted":3, "rejected":0, "rejectRate": 0.00 } } } In this model, "lightRain" represents a light rain scenario, "totalEvents" represents the total number of times the automatic window closing was triggered in this scenario, "accepted" represents the number of times the user agreed to close the window, "rejected" represents the number of times the user refused to close the window, and "rejectRate" represents the rejection rate. When the system detects a current triggering event (such as rain), it first queries the model based on the scenario identifier (such as rainfall level) to obtain the corresponding rejection rate and converts it into a second adversarial strength. After each user response (whether acceptance or rejection), the system updates the "totalEvents," "accepted," or "rejected" fields for the corresponding scenario and recalculates the "rejectRate," thus updating the current user's historical behavior data. Through continuous recording and updating, the model can accurately reflect the user's long-term preferences and achieve personalized adaptive decision-making. When the identification result is a strong adversarial intent, the system records the user's active cancellation of the automatic operation in the model, increasing the rejection count in the corresponding scenario, thereby improving the sensitivity of adversarial judgment in subsequent similar scenarios. This dynamically updated user behavior model is an important technical foundation for the continuous learning and personalized services implemented in this application embodiment.

[0085] Step D30: Obtain the current vehicle status data, and determine the third resistance strength based on the vehicle status data and the window control operation. The vehicle status data includes at least one of the following: vehicle interior temperature, vehicle interior air quality, seat pressure, door status, and vehicle speed.

[0086] It should be noted that vehicle status data refers to the current status parameters of the vehicle and its environment, including interior temperature, interior air quality, seat pressure (to determine if there are passengers), door status (whether it is open), and vehicle speed. The third type of adversarial strength is a quantified value based on the logical relationship between these status data and the window control operation to be performed. For example, if the interior temperature is high and the automatic operation is to close the window, the user may have a need for ventilation, resulting in a higher adversarial strength; if the vehicle speed is high and the automatic operation is to open the window, wind noise will increase the likelihood of adversarial action.

[0087] The system collects various vehicle status data, generates a sub-strength for each preset judgment rule, and then obtains the third countermeasure strength by weighting or taking the maximum value. For example, if the interior temperature is higher than the comfort threshold, the automatic operation to close the windows increases the countermeasure strength; if there is no one in the car (the seat pressure is zero), the countermeasure strength of automatically closing the windows decreases (property protection takes priority).

[0088] For example, the current scenario is: interior temperature 32℃ (high temperature), good air quality (PM2.5=30), a passenger in the driver's seat, closed doors, vehicle speed 50km / h, and the operation to be performed is automatic window closing. Then, the sub-intensities are: temperature 0.9, air quality 0.2 (low intensity for automatic window closing when good), seat pressure 0.6, door status 0, and vehicle speed 0.2; their weights are 0.4, 0.2, 0.1, 0.2, and 0.1 respectively, resulting in a calculated third-party resistance strength of 0.48. These weights can be dynamically adjusted based on actual conditions.

[0089] Step D40: The first confrontation strength, the second confrontation strength, and the third confrontation strength are weighted and summed to obtain the comprehensive confrontation strength.

[0090] It should be noted that weighted summation refers to multiplying each of the three adversarial intensities by its respective weight coefficient and then summing them to obtain a comprehensive value. The weight coefficients reflect the importance of each dimension of adversarial intensity in the final decision and can be dynamically adjusted according to the scenario (e.g., increasing the weight of historical behavior in a parking scenario and increasing the weight of vehicle state in a driving scenario). The comprehensive adversarial intensity can be a value between 0 and 1, representing the system's overall assessment of the current human-machine adversarial probability.

[0091] The three adversarial strengths are weighted and summed according to preset weighting coefficients (e.g., 0.5, 0.3, 0.2) to obtain the comprehensive adversarial strength. These weighting coefficients can be set empirically or optimized by learning from historical data using machine learning methods. This comprehensive value integrates real-time user actions, long-term preferences, and the current environmental state, making it more reliable than a single-dimensional judgment.

[0092] It is worth emphasizing that the weight of the first resistance strength can be dynamically adjusted based on the number of operation behaviors extracted from the window opening data sequence and the direction of the opening change for each operation, so as to more precisely reflect the user's determination of operation. Specifically, the number of times the user operates the window (i.e., the number of operation behaviors) is determined based on the window opening data sequence collected within the historical time window. Each operation corresponds to one user instruction (such as short press or long press of the open / close window button), and it is counted as one operation regardless of whether the opening change exceeds a preset threshold; at the same time, the opening change direction (open or close window) corresponding to each operation is recorded. When the number of operations is low (e.g., 1 time) and the direction of each operation is consistent, it indicates that the user's intention is clear and decisive. In this case, a higher weight (e.g., 0.7) is assigned to the first level of adversarial strength to enhance its influence on the overall adversarial strength. When the number of operations is high (e.g., more than 3 times) and the direction of the operation changes repeatedly (e.g., alternating between opening and closing the window), it indicates that the user may be in a state of hesitation, probing, or accidental touch. In this case, a lower weight (e.g., 0.3) is assigned to the first level of adversarial strength to reduce its contribution and avoid misjudging strong adversarial behavior due to the user's hesitation. Through this dynamic weight adjustment, the system can adaptively assess the firmness of the user's intention based on the number of user operations and the consistency of the direction, thereby more accurately identifying adversarial intentions.

[0093] Step D50: Determine the identification result based on the comprehensive adversarial strength.

[0094] The system compares the calculated overall adversarial strength with two preset thresholds: if the overall adversarial strength is less than the first threshold (e.g., 0.3), it is determined that there is no adversarial intent; if it is between the first and second thresholds (e.g., 0.7), it is determined that there is adversarial intent; if it is greater than or equal to the second threshold, it is determined that there is strong adversarial intent. The thresholds can be adjusted according to the actual application scenario, for example, the thresholds can be lowered in security-sensitive scenarios.

[0095] For example, such as Figure 4The diagram shows the comprehensive judgment process. The multimodal signal sources include: a rain sensor (used to detect rain triggering automatic window closing events), a positioning module (used to determine whether the vehicle is in a parking lot, toll station, or other specific scenario), a window opening detection module (used to collect window opening data sequences within a historical time window), an in-vehicle temperature sensor (used to determine whether ventilation is needed due to stuffiness), an in-vehicle air quality sensor (used to determine whether air exchange is needed due to air quality issues), a seat pressure sensor (used to determine whether there are passengers in the vehicle), a door status sensor (used to determine whether someone is getting on or off the vehicle), and a vehicle speed detection module (used to distinguish between parked and moving states). After being collected, the multi-source signals are input into a multi-dimensional signal fusion processing module. This module performs comprehensive analysis of the signals based on preset rules or weighted fusion algorithms, and finally outputs the adversarial intent judgment result (no adversarial intent, presence of adversarial intent, or strong adversarial intent), providing a decision-making basis for the response strategy of automatic window control.

[0096] This application embodiment achieves a multi-dimensional and multi-level comprehensive evaluation of human-machine confrontational intent in window control by fusing a first confrontational strength based on historical operation characteristics, a second confrontational strength based on user historical behavior data, and a third confrontational strength based on vehicle status data, and obtaining a comprehensive confrontational strength through a weighted summation method. Compared to schemes that rely solely on a single historical operation characteristic or environmental triggering condition, this application embodiment fully considers the synergistic influence between the user's real-time operation habits, long-term behavioral preferences, and the current vehicle environment status, enabling a more comprehensive and accurate identification of whether the user has confrontational intent. Through the weighted fusion mechanism, the system can dynamically balance the confidence levels of information from each dimension, avoiding erroneous responses caused by misjudgments of a single dimension. This allows for efficient execution of automatic window control when the user is not confrontational, and timely cancellation or prompting when the user is confrontational, significantly improving the intelligence level, scenario adaptability, and user satisfaction of the automatic window control function, while effectively reducing additional wear on the window motor caused by confrontational operations.

[0097] Furthermore, embodiments of this application can also adopt differentiated judgment strategies based on different driving scenarios: In parking scenarios, the system reduces the priority of automatic window closing to increase respect for the user's intentions and avoid interfering with the user's ventilation needs while parking; in driving scenarios, especially at high speeds, for safety reasons, the system increases the priority of automatic window closing to prevent safety hazards caused by opening windows at high speeds; in unmanned scenarios, when no passengers are detected in the vehicle by seat pressure sensors or other means, the system prioritizes automatic window closing to protect property inside the vehicle and avoid property loss due to open windows. Through the above-mentioned scenario-adaptive strategies, embodiments of this application can further improve the intelligence level and scenario adaptability of window control.

[0098] This application also provides a vehicle window control device; please refer to... Figure 5 The window control device includes: The data acquisition module 10 is used to respond to the window control trigger event, acquire the window control operation to be executed, and collect the window opening data sequence within the historical time window; Feature extraction module 20 is used to extract features from the window opening data sequence to obtain historical operation features; The intent recognition module 30 is used to recognize human-machine confrontation intent based on the historical operation features and the window control operation, obtain the recognition result, and execute the corresponding response strategy based on the recognition result.

[0099] Optionally, the historical operation features include the amount of change in opening degree and the direction of the change in opening degree, and the feature extraction module 20 is further used for: Based on the initial and final openings in the window opening data sequence, the amount and direction of opening change within the historical time window are determined.

[0100] Optionally, the intent recognition module 30 is further configured to: When the opening change is zero and the window control operation is automatic window closing, determine whether the final opening is greater than a first preset threshold and whether the most recent set time of the final opening is within a preset historical time period. If the end opening is greater than the first preset threshold and the most recent set time is within the preset historical time period, then the identification result is determined to be an intention to oppose. If the end opening is less than or equal to the first preset threshold and / or the most recent set time is not within the preset historical time period, then the identification result is determined to be no adversarial intent.

[0101] Optionally, the intent recognition module 30 is further configured to: When the opening change is zero and the window control operation is automatic window opening, determine whether the final opening is less than a second preset threshold and whether the most recent set time of the final opening is within a preset historical time period. If the end opening is less than the second preset threshold and the most recent set time is within the preset historical time period, then the identification result is determined to be an intention to oppose. If the end opening is greater than or equal to the second preset threshold and / or the most recent set time is not within the preset historical time period, then the identification result is determined to be no adversarial intent.

[0102] Optionally, the intent recognition module 30 is further configured to: If the opening change is not zero, determine whether the direction of the opening change is the same as the window control direction of the window control operation. If so, the identification result is determined to be without adversarial intent; If not, then the identification result is determined to be a strong adversarial intent.

[0103] Optionally, the intent recognition module 30 is further configured to: Based on the historical operation characteristics and the window control operation, the first resistance strength is determined; Obtain historical behavior data corresponding to the window control trigger event, and determine the second resistance strength based on the historical behavior data, wherein the historical behavior data is the probability that the user agrees or refuses to perform the window control operation after the historical window control trigger event is triggered; The vehicle status data of the current vehicle is obtained, and the third resistance strength is determined based on the vehicle status data and the window control operation. The vehicle status data includes at least one of the following: vehicle interior temperature, vehicle interior air quality, seat pressure, door status, and vehicle speed. The first confrontation strength, the second confrontation strength, and the third confrontation strength are weighted and summed to obtain the comprehensive confrontation strength. The identification result is determined based on the comprehensive adversarial strength.

[0104] Optionally, the intent recognition module 30 is further configured to: If the identification result indicates no confrontational intent, the window control operation is performed. If the identification result indicates that there is an intention to resist, a preset prompt message will be output to remind the user to choose whether to perform the window control operation; If the identification result indicates a strong intention to resist, the window control operation will be cancelled. Record information about this event to update the historical behavior data.

[0105] The window control device provided in this application, employing the window control method described in the above embodiments, can solve the technical problem of how to control windows based on human-machine interaction intent, thereby improving the adaptability of automatic window control functions to different usage scenarios and enhancing user experience. Compared with the prior art, the beneficial effects of the window control device provided in this application are the same as those of the window control method provided in the above embodiments, and other technical features in the window control device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0106] This application provides a vehicle window control device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the vehicle window control method in the above embodiment 1.

[0107] The following is for reference. Figure 6 It shows a structural schematic diagram of a window control device suitable for implementing the embodiments of this application. Figure 6 The window control device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of this application.

[0108] like Figure 6 As shown, the window control device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the window control device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. The communication device 1009 allows the window control device to communicate wirelessly or wiredly with other devices to exchange data. Although the figures show window control devices with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented alternatively.

[0109] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0110] The window control device provided in this application, employing the window control method described in the above embodiments, solves the technical problem of how to control windows based on human-machine interaction intent, thereby improving the adaptability of automatic window control functions to different usage scenarios and enhancing user experience. Compared with the prior art, the beneficial effects of the window control device provided in this application are the same as those of the window control method provided in the above embodiments, and other technical features of this window control device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0111] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0112] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0113] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the window control method in the above embodiments.

[0114] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having 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 fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0115] The aforementioned computer-readable storage medium may be included in the window control device; or it may exist independently and not be installed in the window control device.

[0116] The aforementioned computer-readable storage medium carries one or more programs. When the aforementioned one or more programs are executed by the window control device, the window control device causes the following to occur: in response to a window control trigger event, it acquires a window control operation to be executed and collects a window opening data sequence within a historical time window; it performs feature extraction on the window opening data sequence to obtain historical operation features; based on the historical operation features and the window control operation, it identifies human-machine confrontation intent, obtains an identification result, and executes a corresponding response strategy based on the identification result.

[0117] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language 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 can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0118] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0119] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0120] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described window control method. This solves the technical problem of how to control windows based on human-machine adversarial intent, thereby improving the adaptability of automatic window control functions to different usage scenarios and enhancing user experience. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the window control method provided in the above embodiments, and will not be repeated here.

[0121] This application provides a vehicle having the electronic device described above, the electronic device being used to perform the window control method in the above embodiments.

[0122] This application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the window control method described above.

[0123] The computer program product provided in this application can control vehicle windows based on human-machine adversarial intent, thereby improving the adaptability of automatic window control functions to different usage scenarios and the user experience. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the window control method provided in the above embodiments, and will not be repeated here.

[0124] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent scope of this application.

Claims

1. A method for controlling vehicle windows, characterized in that, The window control method includes: In response to a window control trigger event, the window control operation to be executed is obtained, and the window opening data sequence within the historical time window is collected; Feature extraction is performed on the window opening data sequence to obtain historical operation features; Based on the historical operation characteristics and the window control operation, the human-machine confrontation intention is identified, the identification result is obtained, and the corresponding response strategy is executed based on the identification result.

2. The window control method as described in claim 1, characterized in that, The historical operation features include the amount and direction of window opening change. The step of extracting features from the window opening data sequence to obtain the historical operation features includes: Based on the initial and final openings in the window opening data sequence, the amount and direction of opening change within the historical time window are determined.

3. The window control method as described in claim 2, characterized in that, The step of identifying human-machine confrontation intent based on the historical operation characteristics and the window control operation, and obtaining the identification result, includes: When the opening change is zero and the window control operation is automatic window closing, determine whether the final opening is greater than a first preset threshold and whether the most recent set time of the final opening is within a preset historical time period. If the end opening is greater than the first preset threshold and the most recent set time is within the preset historical time period, then the identification result is determined to be an intention to oppose. If the end opening is less than or equal to the first preset threshold and / or the most recent set time is not within the preset historical time period, then the identification result is determined to be no adversarial intent.

4. The window control method as described in claim 2, characterized in that, The step of identifying human-machine confrontation intent based on the historical operation characteristics and the window control operation, and obtaining the identification result, includes: When the opening change is zero and the window control operation is automatic window opening, determine whether the final opening is less than a second preset threshold and whether the most recent set time of the final opening is within a preset historical time period. If the end opening is less than the second preset threshold and the most recent set time is within the preset historical time period, then the identification result is determined to be an intention to oppose. If the end opening is greater than or equal to the second preset threshold and / or the most recent set time is not within the preset historical time period, then the identification result is determined to be no adversarial intent.

5. The window control method as described in claim 2, characterized in that, The step of identifying human-machine confrontation intent based on the historical operation characteristics and the window control operation, and obtaining the identification result, includes: If the opening change is not zero, determine whether the direction of the opening change is the same as the window control direction of the window control operation. If so, the identification result is determined to be without adversarial intent; If not, then the identification result is determined to be a strong adversarial intent.

6. The vehicle window control method as described in claim 1, characterized in that, The step of identifying human-machine confrontation intent based on the historical operation characteristics and the window control operation, and obtaining the identification result, includes: Based on the historical operation characteristics and the window control operation, the first resistance strength is determined; Obtain historical behavior data corresponding to the window control trigger event, and determine the second resistance strength based on the historical behavior data, wherein the historical behavior data is the probability that the user agrees or refuses to perform the window control operation after the historical window control trigger event is triggered; Obtain the current vehicle status data, and determine the third resistance strength based on the vehicle status data and the window control operation, wherein the vehicle status data includes at least one of the following: vehicle interior temperature, vehicle interior air quality, seat pressure, door status, and vehicle speed. The first confrontation strength, the second confrontation strength, and the third confrontation strength are weighted and summed to obtain the comprehensive confrontation strength. The identification result is determined based on the comprehensive adversarial strength.

7. The vehicle window control method as described in claim 6, characterized in that, The step of executing the corresponding response strategy based on the recognition result includes: If the identification result indicates no confrontational intent, the window control operation is performed. If the identification result indicates that there is an intention to resist, a preset prompt message will be output to remind the user to choose whether to perform the window control operation; If the identification result indicates a strong intention to resist, the window control operation will be cancelled. Record information about this event to update the historical behavior data.

8. A vehicle window control device, characterized in that, The window control device includes: The data acquisition module is used to respond to window control trigger events, obtain the window control operation to be executed, and collect window opening data sequences within historical time windows; The feature extraction module is used to extract features from the window opening data sequence to obtain historical operation features; The intent recognition module is used to identify human-machine confrontation intent based on the historical operation features and the window control operation, obtain the recognition result, and execute the corresponding response strategy based on the recognition result.

9. An electronic device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the window control method as described in any one of claims 1 to 7.

10. A vehicle, characterized in that, The vehicle includes the electronic equipment as described in claim 9.

11. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the window control method as described in any one of claims 1 to 7.

12. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the window control method as described in any one of claims 1 to 7.

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