An anti-fog control method, storage medium, program product, electronic device and vehicle

Abstract

This invention proposes an anti-fog control method, storage medium, program product, electronic device, and vehicle. In this solution, after the vehicle is powered on, a dataset of fogging-related parameters is acquired. These parameters include at least: windshield temperature, windshield humidity, ambient temperature, and sunlight intensity. A target fogging probability is predicted based on the dataset of fogging-related parameters. The air conditioning system is then controlled to perform corresponding operations based on the target fogging probability. This invention eliminates the need to add or upgrade vehicle hardware, thus solving the problems of high cost and performance degradation over long periods in related technologies.

Description

Technical Field

[0001] This invention relates to the field of intelligent vehicle technology, and in particular to an anti-fog control method, storage medium, program product, electronic device, and vehicle. Background Technology

[0002] Windshield fogging is one of the factors that lead to traffic accidents, especially in adverse weather conditions, where fog can severely impair a driver's vision and increase the risk of traffic accidents.

[0003] In related technologies, the anti-fog function is mainly achieved by improving the hardware. However, this method is costly and its performance degrades over a long period of time, making it difficult to achieve a high cost-performance ratio for the anti-fog function. Summary of the Invention

[0004] This invention aims to at least solve one of the technical problems existing in the prior art. To this end, this invention proposes a steering feel mode switching method, a storage medium, a program product, an electronic device, and a vehicle. In this solution, after the vehicle is powered on, a dataset of fogging-related parameters is acquired; these fogging-related parameters include at least: windshield temperature, windshield humidity, ambient temperature, and sunlight intensity; a target fogging probability is predicted based on the dataset of fogging-related parameters; and the air conditioning system is controlled to perform corresponding operations based on the target fogging probability. Thus, this solution can acquire fogging-related parameters to predict the target fogging probability, thereby controlling the air conditioning system to perform corresponding operations based on the target fogging probability. The software-based control method improves accuracy and reliability while enhancing the user's intelligent experience and improving passenger cabin comfort, without requiring additional or upgraded vehicle hardware, thus solving the problems of high cost and performance degradation over long periods in related technologies.

[0005] To achieve the above objectives, this application adopts the following technical solution:

[0006] In a first aspect, this application provides an anti-fog control method, comprising: after the vehicle is powered on, acquiring a dataset of fogging-related parameters; the fogging-related parameters include at least: windshield temperature, windshield humidity, ambient temperature, and solar radiation intensity; predicting a target fogging probability based on the dataset of fogging-related parameters; and controlling the air conditioning system to perform corresponding operations based on the target fogging probability.

[0007] Thus, this solution can acquire fogging-related parameters to predict the target fogging probability, and then control the air conditioning system to perform corresponding operations based on the target fogging probability. The software-based control method improves accuracy and reliability while enhancing the user's intelligent experience and improving passenger cabin comfort, without requiring additional or upgraded vehicle hardware. This solves the problems of high cost and performance degradation over long periods associated with related technologies.

[0008] In some embodiments of this application, predicting the target fog probability based on the dataset of fog-related parameters includes: determining a basic fog probability based on the dataset of fog-related parameters; and correcting the basic fog probability based on scene information to obtain the target fog probability, wherein the scene information includes at least one or more of the following: number of people in the vehicle, weather conditions, and altitude information.

[0009] In some embodiments of this application, the step of correcting the basic fogging probability to obtain the target fogging probability based on scene information includes: when the number of people in the vehicle is a first set value, the target fogging probability is the basic probability plus a first threshold; and / or, when the weather condition is a target weather condition, the target fogging probability is the basic probability plus a second threshold; the target weather condition includes, but is not limited to: rainy weather, foggy weather, and snowy weather; and / or, when the altitude is a second set value, the target fogging probability is the basic probability plus a third threshold.

[0010] In some embodiments of this application, determining the basic fogging probability based on the fogging-related parameter dataset includes: calculating the current dew point temperature difference based on the fogging-related parameter dataset; and determining the basic fogging probability based on the current dew point temperature difference.

[0011] In some embodiments of this application, determining the basic fogging probability based on the current dew point temperature difference includes: determining the basic fogging probability based on the current dew point temperature difference and the mapping relationship between the dew point temperature difference and the fogging probability.

[0012] In some embodiments of this application, calculating the current dew point temperature difference based on the dataset of fogging-related parameters includes: performing linear regression fitting on the fogging-related parameters to calculate the minimum glass temperature and calculating the dew point temperature based on the windshield temperature and windshield humidity in the fogging-related parameters; and calculating the current dew point temperature difference based on the minimum glass temperature and the dew point temperature.

[0013] In some embodiments of this application, the step of determining the minimum windshield temperature by performing linear regression fitting calculation on the fogging-related parameters includes: substituting the fogging-related parameters into the linear regression fitting formula to obtain the minimum windshield temperature; the linear regression fitting formula is: MIN=a*x1+b*x2+c*x3+d*x5+e*x6+f*x7+G, where a,b,c,d,e,f are correction coefficients for the fogging-related parameters, x1,x2,x3,x4,x5,x6,x7 are fogging-related parameters, G is a constant, and MIN is the minimum windshield temperature.

[0014] In some embodiments of this application, the method further includes: obtaining the operating status of the air conditioning system; controlling the air conditioning system to perform corresponding operations according to the target fogging probability includes: when the target fogging probability is less than or equal to a fourth threshold and the air conditioning system is in operation, adjusting the air circulation damper ratio, evaporation temperature, and passenger cabin operating mode of the air conditioning system; and / or, when the target fogging probability is greater than or equal to a fifth threshold and less than a sixth threshold, and the air conditioning system is in non-operational state, controlling the air conditioning system to be in anti-fog mode.

[0015] In some embodiments of this application, after controlling the air conditioning system to perform corresponding operations based on the target fogging probability, the method further includes: when the target fogging probability is less than a seventh threshold, controlling the air conditioning system to exit the anti-fog mode or controlling the air conditioning system to maintain the automatic mode.

[0016] In some embodiments of this application, the method further includes: when the air conditioning system is in anti-fog mode, the vehicle interface displays an anti-fog mode mark; or, when the air conditioning system exits anti-fog mode, the vehicle interface displays an anti-fog mode exit mark.

[0017] Secondly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, causes the computer to perform the method described in the first aspect.

[0018] Thirdly, this application provides a computer program product that stores instructions which, when executed by a computer, cause the computer to perform the method described in the first aspect.

[0019] Fourthly, this application provides an electronic device, comprising: a memory having a computer program stored thereon; and a processor for executing the computer program in the memory to implement the method as described in the first aspect.

[0020] Fifthly, this application provides a vehicle comprising: an electronic device as described in the fourth aspect; or, a processor configured to perform the method as described in the first aspect.

[0021] The advantages and control methods of the vehicle and electronic equipment compared to the prior art are the same, and will not be elaborated here.

[0022] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] To gain a more complete understanding of this application and its beneficial effects, the following description will be provided in conjunction with the accompanying drawings, wherein the same reference numerals in the following description denote the same parts.

[0025] Figure 1 This is a schematic flowchart of an anti-fog control method according to an embodiment of the present invention;

[0026] Figure 2 This is a schematic diagram of glass temperature correction logic provided in an embodiment of the present invention;

[0027] Figure 3 This is a schematic diagram of the anti-fog control process provided in an embodiment of the present invention;

[0028] Figure 4 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present invention. Detailed Implementation

[0029] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the protection scope of this application.

[0030] In related technologies, the anti-fog function is mainly achieved by improving the hardware. However, this method is costly and its performance degrades over a long period of time, making it difficult to achieve a high cost-performance ratio for the anti-fog function.

[0031] To address this issue, this invention proposes an anti-fog control method, storage medium, program product, electronic device, and vehicle. In this solution, after the vehicle is powered on, a dataset of fogging-related parameters is acquired. These parameters include at least: windshield temperature, windshield humidity, ambient temperature, and sunlight intensity. A target fogging probability is predicted based on the dataset of fogging-related parameters. The air conditioning system is then controlled to perform corresponding operations based on the target fogging probability. Thus, this solution can acquire fogging-related parameters to predict the target fogging probability, thereby controlling the air conditioning system to perform corresponding operations based on the target fogging probability. This software-based control method improves accuracy and reliability while enhancing the user's intelligent experience and improving passenger cabin comfort, without requiring additional or upgraded vehicle hardware. This solves the problems of high cost and performance degradation over long periods associated with related technologies.

[0032] The present invention will now be described in further detail with reference to the embodiments.

[0033] like Figure 1 The diagram shown is a flowchart of an anti-fog control method provided in an embodiment of the present invention. The method includes:

[0034] 101. After the vehicle is powered on, obtain a dataset of fogging-related parameters.

[0035] The fogging-related parameters include at least: windshield temperature, windshield humidity, ambient temperature, and solar radiation intensity.

[0036] Specifically, embodiments of the present invention can collect relevant parameters of the windshield through a four-in-one sensor, including glass temperature, glass humidity, sunlight intensity, rainfall, and ambient temperature information detected by in-vehicle and out-of-vehicle temperature sensors. The co-simulation provides glass temperature point data for various combinations of ambient temperature, sunlight intensity, vehicle speed, and airflow mode / speed, and extracts the lowest temperature value of each glass temperature zone and the equivalent temperature value from the four-in-one sensor.

[0037] The embodiments of the present invention fully consider the heat exchange of the windshield of the passenger cabin flow field simulator under multiple scenarios by collecting relevant parameters from sensors and relevant parameters inside the passenger cabin. It can accurately identify the glass temperature distribution and predict the probability of fogging based on the change of dew point temperature to perform corresponding operations, thereby reducing the system's defogging energy consumption and improving anti-fogging efficiency.

[0038] 102. Predict the probability of fog formation on a target based on a dataset of fog-related parameters.

[0039] For example, step 102 above specifically includes the following:

[0040] 102a. Determine the basic fogging probability based on the dataset of fogging-related parameters.

[0041] For example, step 102a above specifically includes the following:

[0042] 102a1. Calculate the current dew point temperature difference based on the dataset of fogging-related parameters.

[0043] For example, step 102a1 above specifically includes the following:

[0044] 102a11. Perform linear regression fitting on fogging-related parameters to calculate the minimum glass temperature and calculate the dew point temperature based on the windshield temperature and windshield humidity in the fogging-related parameters.

[0045] Specifically, based on the fogging-related parameter dataset obtained above, the minimum glass temperature and the temperature from the four-in-one sensor are extracted. After data normalization, multiple linear regression analysis is performed. The correlation between variables is determined based on the coefficients and t-values ​​of the regression model, and signals with low correlation are removed. The statistical confidence level of each variable is determined based on the p-value of the regression model, and data with low confidence are removed. The prediction accuracy of the fitted parameters is determined based on the coefficient of determination (R-squared), and the fitting algorithm is modified to ensure that the R-squared remains above 0.85. For detailed procedures, please refer to [reference needed]. Figure 2 The content shown is used to determine the linear regression fitting formula through a fitting algorithm.

[0046] Furthermore, in step 102a11 above, the minimum windshield temperature is calculated by performing linear regression fitting on the fogging-related parameters. Specifically, this includes: substituting the fogging-related parameters into the linear regression fitting formula to obtain the minimum windshield temperature; the linear regression fitting formula is: MIN=a*x1+b*x2+c*x3+d*x5+e*x6+f*x7+G, where a, b, c, d, e, and f are correction coefficients for the fogging-related parameters, x1, x2, x3, x4, x5, x6, and x7 are the fogging-related parameters, x1 is the ambient temperature, x2 is the vehicle speed, x3 is the blower voltage, x4 is the defrosting ratio, x5 is the glass temperature, x6 is the solar radiation intensity, and x7 is the wind temperature. G is a constant, and MIN is the minimum windshield temperature.

[0047] Furthermore, in step 102a11 above, the dew point temperature is calculated based on the windshield temperature and windshield humidity among the fogging-related parameters, specifically including the following:

[0048] Substituting the windshield temperature and windshield humidity from the fogging-related parameters into the calculation formula, we obtain the dew point temperature. The calculation formula is as follows: .in, T is the filtered windshield temperature, RH is the filtered windshield humidity, and a and b are constants.

[0049] 102a12. Calculate the current dew point temperature difference based on the lowest glass temperature and the dew point temperature.

[0050] For example, substituting the minimum glass temperature and dew point temperature mentioned above into the calculation formula yields the current dew point temperature difference, as shown in the following formula: ,in, This is the lowest temperature for glass. This is the current dew point temperature difference. This is the dew point temperature.

[0051] 102a2. Determine the basic fogging probability based on the current dew point temperature difference.

[0052] For example, step 102a2 above specifically includes the following: determining the basic fogging probability based on the current dew point temperature difference and the mapping relationship between the dew point temperature difference and the fogging probability.

[0053] Optionally, the mapping relationship between the dew point temperature difference and the probability of fogging described above can be represented by a linear table. For example, the content of the linear table is as follows:

[0054] Dew point temperature difference -1 0 2 4 6 10 Baseline probability of fogging (%) 100 95 80 50 20 0

[0055] In this table, the endpoint values ​​represent upper and lower limits. For example, a dew point temperature difference less than -1°C corresponds to a basic fogging probability of 100%, and a dew point temperature difference greater than 10°C corresponds to a basic fogging probability of 0°C. Specifically, in algorithm or model calculations, the two axis endpoints (dew point temperature difference values) can be set as A0 and A1, and the value endpoints (basic fogging probabilities) as B0 and B1. If the input value (current dew point temperature difference value) is C, and C is between A0 and A1, then the lookup value is D = B0 + [(C-A0)*(B1-B0)] / (A1-A0), where D is the basic fogging probability corresponding to the current dew point temperature difference. For example, if the current dew point temperature difference C is -0.5, then A0=-1, A1=0, B0=100, B1=95. Then, by looking up the table above, the basic probability of fogging is D=100+[(-0.5+1)(95-100)] / (0+1)=97.5.

[0056] 102b. The target fogging probability is obtained by correcting the basic fogging probability based on the scene information.

[0057] The aforementioned scenario information includes at least one or more of the following: the number of people in the vehicle, weather conditions, and altitude information.

[0058] For example, step 102b above specifically includes the following:

[0059] 102b1. When the number of people in the vehicle is a first preset value, the target fogging probability is the base probability plus a first threshold; and / or,

[0060] 102b2. When the weather condition is the target weather, the target fog probability is the base probability plus a second threshold; the target weather includes, but is not limited to: rainy weather, foggy weather, and snowy weather; and / or,

[0061] 102b3. When the altitude is the second set value, the probability of fogging of the target is the base probability plus a third threshold.

[0062] Furthermore, step 102b can include any one of steps 102b1-102b3, or it can include two of steps 102b1-102b3, or it can include three of steps 102b1-102b3.

[0063] Specifically, the system uses in-vehicle and external sensors to deduce fogging event scenarios that affect the probability of fogging. For example, it uses rain sensor values ​​and wiper speed to determine rainy scenarios; fog light status and weather forecasts to determine foggy scenarios; seat sensor status to determine the number of people in the vehicle; and altitude to determine high-altitude scenarios. The base fogging probability is then adjusted based on these scenarios. For example, the fogging probability increases by 10% when the number of people increases by 4; and the fogging probability increases by 5% for every 1000m increase in altitude.

[0064] By combining user scenarios, the embodiments of the present invention can achieve seamless intervention in a more intelligent and user-friendly way, which greatly improves the comfort and noise of the passenger cabin.

[0065] 103. Control the air conditioning system to perform corresponding operations based on the target fogging probability.

[0066] For example, the above method further includes: 104. Obtaining the operating status of the air conditioning system, wherein step 103 specifically includes the following:

[0067] 103a. When the target fogging probability is less than or equal to the fourth threshold and the air conditioning system is in operation, adjust the air conditioning system's recirculation damper ratio, evaporation temperature, and passenger cabin operating mode; and / or, 103b. When the target fogging probability is greater than or equal to the fifth threshold and less than the sixth threshold, and the air conditioning system is in non-operational state, control the air conditioning system to be in anti-fog mode.

[0068] This invention utilizes existing systems and hardware, without requiring additional hardware devices, and solves the problems of low anti-fog recognition rate and low reliability through software algorithms and control.

[0069] Further optionally, after step 103 above, the method further includes: 103c, when the target fogging probability is less than the seventh threshold, controlling the air conditioning system to exit the anti-fog mode or controlling the air conditioning system to maintain the automatic mode.

[0070] The embodiments of the present invention achieve anti-fogging by determining that the probability of fogging of the target meets a certain threshold and by controlling the air conditioning system according to the different states of the air conditioner, which can improve the user experience.

[0071] Further optionally, the above method also includes: when the air conditioning system is in anti-fog mode, the vehicle interface displays an anti-fog mode mark; or, when the air conditioning system exits anti-fog mode, the vehicle interface displays an anti-fog mode exit mark.

[0072] This invention, by displaying mode markers on the vehicle's infotainment interface, can promptly inform users of the current operating status or mode of the air conditioning system, thereby further enhancing the user experience.

[0073] Further optionally, the above method also includes: 104. Continuously monitoring the changing trend of fogging on the target, and when the probability of fogging on the target continues to rise, controlling the air conditioning system to perform corresponding operations.

[0074] For example, when the probability of fogging of the target continues to rise, the target temperature of the evaporator is dynamically adjusted, the target air outlet temperature is dynamically adjusted, the air volume is dynamically adjusted, the air outlet mode is dynamically adjusted, and the circulation ratio is dynamically adjusted.

[0075] This invention, through continuous monitoring of the target fogging trend, controls the air conditioning system to dynamically adjust relevant actuators, thereby preventing glass fogging and meeting user needs.

[0076] For details, please refer to Figure 3Based on the changing trend of fogging probability, when the target fogging probability T is less than or equal to the fourth threshold (e.g., T≤50%), when the air conditioner is working, the system dynamically adjusts relevant actuators according to operating conditions. Specifically, to achieve optimal economy and user experience, priority is given to controlling the recirculation damper ratio, evaporation temperature, and passenger compartment operating mode, followed by controlling airflow, air temperature, and air outlet mode. The air conditioner UI displays the anti-fog mode. When the fogging probability in the passenger compartment decreases to a safe zone T less than the seventh threshold (e.g., T<20%), actuator control is discontinued, and the UI displays "anti-fog mode exited," with continuous dynamic monitoring. When the target fogging probability is greater than or equal to the fifth threshold but less than the sixth threshold (e.g., 75%≤T<85%), and the air conditioner is not turned on, the multimedia PAD pop-up / voice prompts the driver to switch the air conditioner to intelligent anti-fog mode, and the system dynamically adjusts relevant actuators according to operating conditions. When the probability of fogging in the passenger cabin decreases to a safe zone T that is less than the seventh threshold (e.g., T<20), actuator control is disengaged, and the air conditioning maintains automatic mode and continuously monitors the system.

[0077] The human-computer interaction system also features a master switch, and reminds the user only once during the power-on cycle, allowing for user-defined settings. Simultaneously, to minimize energy consumption, the air conditioning system's execution end is configured with optimal control priorities based on driving scenarios (priority decisions depend on the most efficient control mode currently available), ensuring both comfort and anti-fog functionality while minimizing energy consumption.

[0078] The embodiments of this invention can accurately obtain the temperature values ​​of various areas of the glass, significantly improving the accuracy of dew point temperature calculation and control accuracy; it can generally improve the anti-fogging efficiency in both complex and ordinary scenarios; it can improve anti-fogging efficiency and reduce energy consumption; it can enhance the comfort and intelligent experience of the passenger cabin; it can avoid the shortcomings of traditional defogging methods, such as poor comfort and high wind noise; it has the advantage of not increasing costs, and has strong anti-interference capabilities, with scalability and extensibility; it can prevent problems in advance and improve the air conditioning response speed; it can achieve the effects of precise control, rapid response, and early prevention.

[0079] like Figure 4 The above is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. The electronic device 700 includes a processor 701 with one or more processing cores, a memory 702 with one or more computer-readable storage media, and a computer program stored in the memory 702 and executable on the processor. The processor 701 and the memory 702 are electrically connected.

[0080] The processor 701 is the control center of the electronic device 700. It connects various parts of the electronic device 700 via various interfaces and lines. By running or loading software programs and / or units stored in the memory 702, and by calling data stored in the memory 702, it executes various functions and processes data of the electronic device 700, thereby providing overall monitoring of the electronic device 700. The processor 701 can be a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a Network Processor (NP), etc., and can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application.

[0081] In this embodiment of the application, the processor 701 in the electronic device 700 loads the computer program corresponding to the process of one or more applications into the memory 702 according to the method or steps of the above embodiment, and the processor 701 runs the applications stored in the memory 702 to execute the above method.

[0082] The electronic device according to embodiments of the present invention predicts the target fogging probability by acquiring fogging-related parameters through the above-described method, and then controls the air conditioning system to perform corresponding operations based on the target fogging probability. The software-based control method improves accuracy and reliability while enhancing the user's intelligent experience and improving passenger cabin comfort, without requiring additional or upgraded vehicle hardware, thus solving the problems of high cost and performance degradation over long periods in related technologies.

[0083] Embodiments of the present invention also provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, enables the computer to implement the vehicle control method described above. For example, the computer-readable storage medium may be the aforementioned memory including program instructions, which may be executed by a processor of an electronic device to implement or execute the methods, steps, and logic diagrams disclosed in the embodiments of this application.

[0084] This invention also provides a computer program product storing instructions that, when executed by a computer, cause the computer to implement the vehicle control method described above. For example, when executed by a computer, the instructions implement or execute the methods, steps, and logic diagrams disclosed in the embodiments of this application.

[0085] Embodiments of the present invention also provide a vehicle comprising the system described above, or an electronic device, or a processor, the processor being configured to execute the methods described above. The vehicle may be a gasoline-powered vehicle, a plug-in hybrid electric vehicle, or a new energy vehicle, etc., and this specification does not specifically limit it.

[0086] According to embodiments of the present invention, a vehicle executes the above-described method via electronic devices, a control system, or a controller to predict a target fogging probability by acquiring fogging-related parameters, and then controls the air conditioning system to perform corresponding operations based on the target fogging probability. This software-based control method improves accuracy and reliability while enhancing the user's intelligent experience and improving passenger cabin comfort, without requiring additional or upgraded vehicle hardware. This solves the problems of high cost and performance degradation over long periods in related technologies.

[0087] The above-described embodiments are only used to illustrate the technical solutions of applying the above methods to vehicles, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the method can also be used in motor vehicles, trains, and ships, etc., without causing the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0088] In one embodiment, the vehicle can be configured for fully or partially autonomous driving. For example, the vehicle can control itself while in autonomous driving mode, and can determine the current state of the vehicle and its surrounding environment through human intervention, determine the possible behaviors of at least one other vehicle in the surrounding environment, and determine the confidence level corresponding to the probability of that other vehicle performing a possible behavior, and control the vehicle based on the determined information. When the vehicle is in autonomous driving mode, it can be configured to operate without human interaction.

[0089] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0090] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," "optional example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0091] The embodiments, implementation methods, and related technical features of this application can be combined and substituted for each other without conflict.

[0092] The above are merely preferred embodiments of this application and are not intended to limit this application in any way. Although the descriptions of each embodiment in this application have different focuses, and the parts not described in detail in a certain embodiment can be referred to the relevant embodiments of other embodiments, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of this application without departing from the content of the technical solution of this application shall still fall within the scope of the technical solution of this application.

Claims

1. A method for controlling fog, characterized in that, include: After the vehicle is powered on, a dataset of fogging-related parameters is acquired; The fogging-related parameters include at least: windshield temperature, windshield humidity, ambient temperature, and solar radiation intensity; Predict the target fogging probability based on the dataset of fogging-related parameters; The air conditioning system is controlled to perform corresponding operations based on the target fogging probability.

2. The method according to claim 1, characterized in that, The step of predicting the target fog probability based on the dataset of fog-related parameters includes: The basic fogging probability is determined based on the dataset of the fogging-related parameters. The target fogging probability is obtained by correcting the basic fogging probability based on the scene information, wherein the scene information includes at least one or more of the following: number of people in the vehicle, weather conditions, and altitude information.

3. The method according to claim 2, characterized in that, The step of correcting the basic fogging probability based on scene information to obtain the target fogging probability includes: When the number of people in the vehicle is a first preset value, the target fogging probability is the base probability plus a first threshold; and / or, When the weather condition is the target weather, the target fog probability is the base probability plus a second threshold; the target weather includes, but is not limited to: rainy weather, foggy weather, and snowy weather; and / or, When the altitude is the second set value, the probability of fogging of the target is the base probability plus a third threshold.

4. The method according to claim 2, characterized in that, The determination of the basic fogging probability based on the fogging-related parameter dataset includes: Calculate the current dew point temperature difference based on the dataset of fogging-related parameters; The basic probability of fogging is determined based on the current dew point temperature difference.

5. The method according to claim 4, characterized in that, The step of determining the basic fogging probability based on the current dew point temperature difference includes: The basic fogging probability is determined based on the current dew point temperature difference and the mapping relationship between the dew point temperature difference and the fogging probability.

6. The method according to claim 4, characterized in that, The step of calculating the current dew point temperature difference based on the dataset of fogging-related parameters includes: The minimum glass temperature is calculated by performing linear regression fitting on the fogging-related parameters, and the dew point temperature is calculated based on the windshield temperature and windshield humidity in the fogging-related parameters. The current dew point temperature difference is calculated based on the lowest glass temperature and the dew point temperature.

7. The method according to claim 6, characterized in that, The step of determining the minimum windshield temperature by performing linear regression fitting calculations on the fogging-related parameters includes: Substituting the fogging-related parameters into the linear regression fitting formula yields the minimum temperature of the windshield; the linear regression fitting formula is: MIN = a*x1 + b*x2 + c*x3 + d*x5 + e*x6 + f*x7 + G, where a, b, c, d, e, f are correction coefficients for fogging-related parameters, x1, x2, x3, x4, x5, x6, x7 are fogging-related parameters, G is a constant, and MIN is the lowest temperature of the windshield.

8. The method according to claim 1, characterized in that, The method further includes: acquiring the operating status of the air conditioning system; and controlling the air conditioning system to perform corresponding operations based on the target fogging probability, including: When the target fogging probability is less than or equal to the fourth threshold, and the air conditioning system is in operation, adjust the air conditioning system's recirculation damper ratio, evaporation temperature, and passenger compartment operating mode; and / or, When the target fogging probability is greater than or equal to the fifth threshold and less than the sixth threshold, and the air conditioning system is in a non-working state, the air conditioning system is controlled to enter the anti-fog mode.

9. The method according to claim 8, characterized in that, After controlling the air conditioning system to perform corresponding operations based on the target fogging probability, the method further includes: When the probability of fogging at the target is less than the seventh threshold, control the air conditioning system to exit the anti-fog mode or control the air conditioning system to maintain the automatic mode.

10. The method according to claim 9, characterized in that, The method further includes: When the air conditioning system is in anti-fog mode, the vehicle's infotainment system displays an anti-fog mode indicator; or, when the air conditioning system exits anti-fog mode, the vehicle's infotainment system displays an anti-fog mode exit indicator.

11. The method according to any one of claims 1-10, characterized in that, The method further includes: The system continuously monitors the changing trend of the target's fogging probability. When the target's fogging probability continues to rise, the system controls the air conditioning system to perform corresponding operations.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it causes the computer to perform the method described in any one of claims 1-11.

13. A computer program product, characterized in that, The computer program product stores instructions that, when executed by a computer, cause the computer to perform the method described in any one of claims 1-11.

14. An electronic device, characterized in that, include: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the method of any one of claims 1-11.

15. A vehicle, characterized in that, include: The electronic device according to claim 14; Alternatively, a processor, the processor being configured to perform the method according to any one of claims 1-11.

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