Vehicle front windshield fogging risk identification method and device, vehicle and medium

Abstract

The invention discloses a fogging risk identification method and device for a vehicle front windshield, a vehicle and a medium, and belongs to the technical field of vehicles. The method comprises the steps that the glass temperature and humidity of a front windshield of a vehicle are collected to determine a basic fogging risk value; performing interval correction on the basic fogging risk value based on the multi-source sensing data to generate a target fogging risk value; and judging a target risk level corresponding to the target fogging risk value, and controlling an anti-fogging actuator to execute corresponding anti-fogging operation according to the target risk level. According to the method, through anti-fog identification and control before fogging of the front windshield, the accuracy and robustness of identifying the fogging trend of the front windshield of the vehicle are improved, so that the driving safety and the intelligent level of cabin environment control are improved.

Description

Technical Field

[0001] The present application belongs to the field of vehicle technology, and in particular relates to a method, device, vehicle, and medium for identifying fogging risks of a vehicle windshield. Background Art

[0002] During vehicle operation, windshield fogging significantly affects the driver's field of vision, especially in rain, snow, cold temperatures, or high humidity, where visibility is often obstructed, posing a safety hazard. Therefore, the ability to effectively identify and control windshield fogging risks is crucial for improving driving safety and ride comfort, and has become a key feature of intelligent cockpit environmental awareness.

[0003] Conventional fogging detection methods typically rely on a few parameters, such as vehicle interior and exterior temperature and humidity. Some solutions employ fixed thresholds or empirical formulas to estimate fogging trends, then leverage the air conditioning system for anti-fogging control. However, due to complex environmental conditions and volatile vehicle states, existing solutions often suffer from delayed responses or inaccurate identification, making it difficult to achieve a balanced balance between accuracy and applicability.

[0004] Therefore, how to improve the accuracy and robustness of fogging recognition and control to enhance the overall performance of the vehicle windshield anti-fog system remains an urgent problem to be solved. Summary of the Invention

[0005] This application aims to address at least one of the technical problems existing in the related art. To this end, this application proposes a method, device, vehicle, and medium for identifying the fogging risk of a vehicle windshield, thereby improving the accuracy and robustness of identifying the fogging trend of the vehicle windshield, thereby enhancing driving safety and the level of intelligent cabin environment control.

[0006] In a first aspect, the present application provides a method for identifying fogging risk of a vehicle windshield, the method comprising:

[0007] Collect the temperature and humidity of the vehicle's windshield to determine the basic fogging risk value;

[0008] Performing interval correction on the basic fogging risk value based on multi-source sensor data to generate a target fogging risk value;

[0009] A target risk level corresponding to the target fogging risk value is determined, and an anti-fog actuator is controlled to perform a corresponding anti-fog operation according to the target risk level.

[0010] In a second aspect, the present application provides a vehicle windshield fogging risk identification device, the device comprising:

[0011] Collect the temperature and humidity of the vehicle's windshield to determine the basic fogging risk value;

[0012] Performing interval correction on the basic fogging risk value based on multi-source sensor data to generate a target fogging risk value;

[0013] A target risk level corresponding to the target fogging risk value is determined, and an anti-fog actuator is controlled to perform a corresponding anti-fog operation according to the target risk level.

[0014] In a third aspect, the present application provides a vehicle comprising the fogging risk identification device for the vehicle windshield as described in the second aspect.

[0015] In a fourth aspect, the present application provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for identifying fogging risks of a vehicle windshield as described in the first aspect above is implemented.

[0016] In a fifth aspect, the present application provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for identifying fogging risks of a vehicle windshield as described in the first aspect above.

[0017] In a sixth aspect, the present application provides a chip comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run a computer program or instruction to implement the method for identifying fogging risks of a vehicle windshield as described in the first aspect above.

[0018] In a seventh aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the method for identifying fogging risks of a vehicle windshield as described in the first aspect above.

[0019] According to the vehicle windshield fogging risk identification method, device, vehicle, computer equipment, non-transitory computer-readable storage medium, chip and computer program product provided by the present application, by collecting the glass temperature and humidity of the vehicle windshield, a basic fogging risk value is constructed, which can evaluate the deviation trend between the thermal and humidity conditions of the glass surface and the air saturation state in real time, providing a basis for judging the possibility of fogging; based on multi-source sensor data, the basic fogging risk value is interval-corrected to generate a target fogging risk value, which can perceive and compensate for the impact of different environmental changes on the thermal and humidity behavior of the glass, and realize dynamic correction of the basic risk value. Based on the strategy of interval division and graded adjustment, the system's adaptability to risk evolution in complex scenarios is improved. It reduces the judgment bias caused by the fluctuation of a single variable; then determines the target risk level corresponding to the target fogging risk value, and controls the anti-fog actuator to perform the corresponding anti-fog operation according to the target risk level. It can output differentiated control strategies according to the risk level to ensure that the system can achieve early suppression of the fogging trend with minimum power consumption, taking into account both energy efficiency and responsiveness. Therefore, the risk identification method based on the fusion of initial glass temperature and humidity evaluation and multi-source dynamic correction, combined with the graded response control strategy, can actively trigger intervention actions before fogging is formed, realize true anti-fog control, avoid driving safety hazards caused by obstructed vision, improve the accuracy and stability of windshield fogging trend identification, and help improve the safety and comfort of the entire vehicle.

[0020] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become obvious from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0022] Figure 1 is a schematic structural diagram of a vehicle windshield fogging risk identification system provided in some embodiments of the present application;

[0023] Figure 2 1 is a flow chart of a method for identifying fogging risk of a vehicle windshield provided in some embodiments of the present application;

[0024] Figure 3 is a flow chart of a method for identifying fogging risk of a vehicle windshield provided in some other embodiments of the present application;

[0025] Figure 4 1 is a schematic structural diagram of a vehicle windshield fogging risk identification device provided in some embodiments of the present application;

[0026] Figure 5It is a schematic diagram of the structure of a computer device provided in some embodiments of the present application. DETAILED DESCRIPTION

[0027] The following will be combined with the accompanying drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the embodiments described are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of this application.

[0028] Unless otherwise defined, all technical and scientific terms used in this application have the same meanings as commonly understood by those skilled in the art to which this application belongs. The terms used in the specification of this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first" and "second" in the specification and claims of this application or the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order or a primary-secondary relationship.

[0029] References to "embodiments" in this application mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described in this application may be combined with other embodiments.

[0030] In the description of this application, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," "connected," and "attached" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to direct connections, indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms in this application based on specific circumstances.

[0031] The term "and / or" in this application simply describes an association between related objects, indicating that three possible relationships exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this application generally indicates that the related objects are in an "or" relationship.

[0032] The term "multiple" in this application refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0033] Among related technologies, automotive windshield anti-fogging technology has the following major limitations: First, traditional methods often determine fogging risk based on a single temperature and humidity threshold, performing linear calculations based solely on the temperature difference between the inside and outside of the vehicle or humidity sensor data, ignoring the dynamic coupling effects of environmental parameters. Second, traditional solutions lack compensation mechanisms for interfering factors such as the vehicle's real-time status. Consequently, traditional methods are prone to misjudgments due to dynamic environmental changes or changes in vehicle status. For example, if they fail to account for the heating effect of sunlight on the glass surface, they may initiate defogger prematurely, increasing air conditioning energy consumption by over 20%. Alternatively, they may ignore airflow cooling during high-speed driving, delaying warnings and causing partial fogging of the glass, creating a safety hazard of a sudden decrease in the driver's field of view.

[0034] In addition, most current solutions lack robust sensor fault-tolerant processing strategies. Once the sensor fluctuates or fails, it is very easy to cause system failure or miscontrol.

[0035] In view of this, the present application provides a method and system for determining the risk of fogging of automobile windshields based on multi-sensor fusion. Through dynamic environmental compensation and hierarchical decision-making logic, it achieves high-precision, low-complexity fogging risk determination and has strong anti-interference capabilities. Specifically, by fusing glass temperature and humidity data with multi-source environmental and vehicle status parameters, a hierarchical interval correction mechanism is constructed to achieve dynamic perception and precise quantification of fogging risks. It can perform effective anti-fogging operations before the windshield actually fogs, effectively avoiding the safety risks of windshield fogging to driving. It also has the ability to replace risk calculations in the event of sensor failure, thereby improving the accuracy of risk identification and the robustness of the system.

[0036] It should be noted that the vehicles mentioned in the embodiments of this application include but are not limited to fuel vehicles, plug-in hybrid vehicles or new energy vehicles, etc., and this application does not make specific limitations on this.

[0037] Below, in conjunction with the accompanying drawings, the method, device, vehicle and medium for identifying the fogging risk of the vehicle windshield provided in the embodiments of the present application are described in detail through specific embodiments and their application scenarios.

[0038] Figure 1 The structure diagram of the vehicle windshield fogging risk identification system provided in some embodiments of the present application is as follows. The vehicle windshield fogging risk identification method provided in the embodiments of the present application can be applied to Figure 1 In the fogging risk identification system shown, the fogging risk identification system includes a multi-source sensor unit, a controller and an anti-fog actuator unit.

[0039] Multi-source sensor data refers to a collection of data collected and uploaded by multiple different types of sensors onboard the vehicle, used to describe the thermal and humid boundary conditions of the glass and their changing trends during vehicle operation. Multi-source sensor units can be deployed in locations such as the vehicle's front cabin, around the dashboard, or in the air conditioning system ducts to collect real-time information on the vehicle's internal and external thermal and humid environment, as well as its operating status. These sensors include at least an interior temperature sensor, an exterior temperature sensor, a glass temperature sensor, a humidity sensor, a sunlight intensity sensor, a vehicle speed sensor, and a rain sensor.

[0040] The controller can be an on-board computing module with computing and storage capabilities, capable of receiving output data from multiple sensor units, executing a fogging risk identification algorithm, and outputting control instructions to the anti-fog actuator unit. For example, the controller includes a data processing unit, a fault-tolerant control unit, and a hierarchical decision-making unit. The data processing unit is used to perform a fusion analysis of the aforementioned sensor data and calculate the anti-fog risk value of the windshield. The hierarchical decision-making unit is used to determine the current fogging risk level based on the identification result of the anti-fog risk value and generate control instructions based on the fogging risk level. The fault-tolerant control unit is used to perform risk substitution reasoning when it detects that some sensors are abnormal or have failed.

[0041] The controller can communicate with the multi-source sensor unit and anti-fog actuator through the vehicle's CAN (Controller Area Network) bus, LIN (Local Interconnect Network) bus or Ethernet, and supports OTA (Over-The-Air) online updates.

[0042] The anti-fog actuator unit is a device component that can adjust the thermal and humid conditions of the air inside the vehicle and is used to suppress the tendency of glass fogging. It can be a component related to the thermal and humid environment regulation of the glass, such as the air-conditioning system, fan module, ventilation module or air circulation channel control mechanism. It can actively adjust the wind direction, air volume or circulation mode according to the control instructions generated by the controller, thereby effectively suppressing the fogging tendency before the windshield actually fogs.

[0043] In the present application, the controller can be an independent vehicle domain controller, an air-conditioning control unit, an intelligent cockpit controller, or a computing module integrated in a central computing platform (such as a central gateway, a fusion computing unit), which includes at least one processor and memory for running the vehicle windshield fogging risk identification method provided in the present application.

[0044] The embodiment of the present application provides a method for identifying the fogging risk of a vehicle windshield. The executor of the method can be a controller or a functional module or functional entity in the controller that can implement the method for identifying the fogging risk of a vehicle windshield.

[0045] The following describes the method for identifying fogging risks of a vehicle windshield provided in an embodiment of the present application, taking the controller as an example of the execution entity.

[0046] Figure 2 FIG. 1 is a flow chart of a method for identifying fogging risk of a vehicle windshield provided in some embodiments of the present application. Figure 2 As shown, the method for identifying fogging risk of a vehicle windshield includes steps 210 to 230.

[0047] Step 210: Collect the glass temperature and humidity of the vehicle's windshield to determine a basic fogging risk value.

[0048] The controller obtains the temperature and humidity of the vehicle's windshield. For example, this information can be collected and output by a glass temperature sensor and an in-vehicle air humidity sensor located near the windshield. The controller uses these glass temperature and humidity as inputs to assess the dew point of the air near the glass. For example, the controller calculates a baseline fogging risk value based on the temperature difference between the glass temperature and the dew point temperature, reflecting the potential for condensation on the glass surface under the current conditions.

[0049] Step 220: Perform interval correction on the basic fogging risk value based on the multi-source sensor data to generate a target fogging risk value.

[0050] Based on the basic fogging risk value, the controller further acquires multiple sensory parameters related to the vehicle's environmental changes and operating status, including but not limited to external climate information and vehicle status information. Based on the position of each parameter within a preset influencing factor range, the controller searches for or calculates the corresponding correction factor, thereby weighting the basic risk value.

[0051] The interval correction strategy uses a mapping relationship to establish the direction and intensity of each parameter's adjustment for fogging risk. For example, when a parameter is in a fogging-increasing range (such as high humidity, low speed, and low temperature), the corresponding correction coefficient is a positive enhancement coefficient, which is used to increase the basic risk value. Conversely, when the fogging trend is weakened (such as high temperature and strong sunlight), the suppression coefficient is used to reduce the risk value.

[0052] The controller executes a multi-factor combination correction strategy based on the interval coefficients corresponding to all parameters to generate a target fogging risk value that can reflect the current overall fogging risk level, which is used to support subsequent risk level classification and anti-fog control decision-making.

[0053] In some embodiments, the controller implements a hierarchical correction process for the basic fog risk value through a multi-level risk correction model based on parameter intervals. This model divides and compensates environmental sensor data and vehicle status sensor data into intervals, effectively capturing the dynamic process of fog formation caused by the superposition of multiple factors. This model enables feedforward perception of fog trends and precise dynamic adjustment of risk values, improving overall recognition accuracy and adaptability. Specifically, by combining the interval information of environmental parameters and vehicle status parameters, compensation and dynamic adjustment operations are performed separately to generate a more scenario-adaptive target fog risk value.

[0054] With the help of the interval-driven multi-level compensation mechanism, a more engineering-feasible risk value correction path can be constructed without increasing the computational complexity, realizing the refinement, dynamicization and adaptability of fog risk identification, which is effectively different from related identification methods that only rely on static models or empirical thresholds.

[0055] Step 230: Determine a target risk level corresponding to the target fogging risk value, and control an anti-fog actuator to perform a corresponding anti-fog operation according to the target risk level.

[0056] The target risk level refers to the risk level corresponding to the target fog risk value within the preset risk range. The risk level can be set based on actual circumstances, for example, to "low risk," "medium risk," and "high risk," or to multiple levels such as "10% risk," "50% risk," and so on, up to "90% risk."

[0057] The controller matches the target fogging risk value with a pre-set multi-level risk range to identify the target risk level. For example, a risk value above a first threshold can be considered high risk, while a value below a second threshold can be considered low risk. At a high risk level, the controller issues control instructions to the anti-fog actuator, adjusting parameters such as air circulation status, air volume, and air temperature to proactively control the thermal and humidity conditions of the glass and suppress fogging. At a low risk level, the controller maintains its current operating state and continuously monitors the glass.

[0058] The vehicle windshield fogging risk identification method provided in the embodiments of the present application collects the temperature and humidity of the vehicle windshield to construct a basic fogging risk value. This method can evaluate the deviation trend between the thermal and humid conditions of the glass surface and the air saturation state in real time, providing a basis for judging the possibility of fogging. The basic fogging risk value is interval-corrected based on multi-source sensor data to generate a target fogging risk value. This method can perceive and compensate for the impact of different environmental changes on the thermal and humid behavior of the glass, and realize dynamic correction of the basic risk value. Based on the strategy of interval division and graded adjustment, the system improves its adaptability to risk evolution in complex scenarios and reduces judgment bias caused by fluctuations in a single variable. Afterwards, the target risk level hit by the target fogging risk value is determined, and the anti-fog actuator is controlled to perform the corresponding anti-fog operation according to the target risk level. Differentiated control strategies can be output according to the risk level to ensure that the system can achieve early suppression of the fogging trend with minimum power consumption, taking into account both energy efficiency and responsiveness. Therefore, the risk identification method based on the fusion of initial glass temperature and humidity evaluation and multi-source dynamic correction, combined with the graded response control strategy, can actively trigger intervention actions before fogging is formed, realize true anti-fog control, avoid driving safety hazards caused by obstructed vision, improve the accuracy and stability of windshield fogging trend identification, and help improve the safety and comfort of the entire vehicle.

[0059] Due to the diversity and dynamics of vehicle operating environments, different types of influencing factors have distinct mechanisms of action in the fog formation process. Without hierarchical identification and differentiated processing of parameter types, the risk correction model may be overly simplistic and unable to accurately reflect the dynamic evolution of fogging trends.

[0060] To this end, in some embodiments, the basic fogging risk value is interval-corrected through multi-source sensor data to generate a target fogging risk value, including: performing environmental compensation on the basic fogging risk value based on the interval of the environmental sensor data to obtain a corrected risk value; and dynamically adjusting the corrected risk value based on the interval of the vehicle status sensor data to reflect the influence of the vehicle's aerodynamic cooling effect on the fogging trend to obtain a target fogging risk value.

[0061] When the controller corrects the basic fogging risk value, it first performs risk compensation correction based on a type of environmental sensor data that is used to characterize the relationship between external climate conditions and the thermal and humid environment of the glass.

[0062] Among them, environmental sensor data can be used to reflect the direct impact of the external environment on the potential condensation environment of the glass surface, including but not limited to sunlight radiation parameters reflecting the heat accumulation capacity of the glass surface and rainfall parameters reflecting the air humidity environmental load, which are used to perform negative and positive bidirectional compensation on the initial fogging risk value to correct the risk error caused by external thermal and humidity disturbances.

[0063] Environmental sensor data includes, but is not limited to, external radiation heat flux, precipitation intensity, and external air humidity. Based on the position of this data within a preset interval, the controller searches for the corresponding risk adjustment coefficient and performs a weighted correction on it with the basic fogging risk value to obtain a modified risk value.

[0064] Subsequently, the controller further combines a type of vehicle status sensor data used to reflect the vehicle's operating status and its impact on air flow and heat exchange efficiency to perform dynamic adjustment operations on the corrected risk value.

[0065] Vehicle status sensor data refers to parameters that indirectly reflect changes in the vehicle's aerodynamic cooling effect or ventilation efficiency, such as vehicle speed and air conditioning mode. This data incorporates the indirect regulatory effects of aerodynamic heat exchange and internal regulation on the thermal and moisture conditions of the glass. Based on the distribution of these parameters within their respective intervals, the controller introduces dynamic correction coefficients and performs secondary adjustments to the corrected risk value, ultimately yielding the final target fogging risk value.

[0066] In this embodiment, by dividing multi-source sensor data into environmental and vehicle status categories, and performing compensation and dynamic correction in stages, a risk evolution path that is more in line with the actual heat and moisture migration mechanism is formed. This can accurately identify fogging risk trends in different environments and operating scenarios, providing a more reliable basis for anti-fog control strategies.

[0067] In order to effectively reflect the real impact of the external environment on the fogging tendency of the glass, in some embodiments, the basic fogging risk value is environmentally compensated based on the interval in which the environmental sensor data is located to obtain a corrected risk value, including: collecting the current sunlight radiation intensity and rainfall, and respectively determining the target radiation intensity interval and target rainfall interval hit by the sunlight radiation intensity and rainfall respectively; obtaining a first compensation coefficient matching the target radiation intensity interval, and a second compensation coefficient matching the target rainfall interval; wherein the first compensation coefficient is negatively correlated with the fogging trend, and the second compensation coefficient is positively correlated with the fogging trend; based on the first compensation coefficient and the second compensation coefficient, the basic fogging risk value is corrected to obtain a corrected risk value.

[0068] When performing fog risk compensation based on environmental sensor data, the controller can start with two typical environmental parameters: sunlight radiation intensity and rainfall.

[0069] The stronger the sunlight, the more heat the windshield absorbs, raising its surface temperature. When the glass temperature is significantly above the dew point, condensation is less likely to form, preventing fogging. Therefore, the greater the sunlight intensity, the lower the actual fogging risk. Furthermore, solar heating can introduce a risk of misjudgment: even when humidity is high, the glass temperature may rise due to sunlight, preventing fogging. Therefore, the original risk value needs to be compensated.

[0070] When it rains, the humidity outside the vehicle rises significantly, reducing the humidity difference between inside and outside the vehicle and reducing the dehumidification efficiency of ventilation. Especially when recirculation is activated, rainy days can easily cause humidity to accumulate inside the vehicle, accelerating condensation on the glass surface. Furthermore, rainwater itself can cool the glass surface, bringing its temperature closer to the dew point and increasing the risk of condensation.

[0071] Therefore, the controller collects the current values ​​from the radiation sensor and the rainfall sensor, respectively, and determines the target range within which these two values ​​fall based on a pre-set multi-level interval standard. The controller then queries the corresponding compensation coefficients. The first compensation coefficient, corresponding to sunlight intensity, reflects its ability to suppress fogging and is therefore negatively correlated with fogging risk. The second compensation coefficient, corresponding to rainfall, reflects its role in promoting glass cooling and increasing external humidity, and is therefore positively correlated with fogging risk.

[0072] For example, the controller compensates the base risk value R1 at a preset ratio based on the solar radiation level range. The radiation intensity is divided into four levels: weak, moderate, strong, and extremely strong, with corresponding compensation coefficients of 1.0, 0.8-1.0, 0.6-0.8, and 0.6, respectively. The controller also applies overlay compensation based on the precipitation intensity range detected by the rain sensor, categorizing rainfall into four levels: no rain, light rain, moderate rain, and heavy rain, with corresponding risk coefficients of 1, 1.1, 1.2, and 1.3, respectively.

[0073] The controller uses the compensation coefficients of the two as weight factors to perform weighted correction on the current basic fogging risk value to obtain the corrected risk value after environmental compensation to support subsequent more accurate fogging risk assessment.

[0074] For example, the compensation coefficient values ​​associated with the irradiance may be as shown in Table 1:

[0075] Table 1

[0076] Irradiation intensity ＜300 300～600 600～900 ＞900 Compensation coefficient 1.0 0.8～1.0 0.6～0.8 0.6

[0077] For example, the risk coefficient values ​​associated with rainfall may be shown in Table 2:

[0078] Table 2

[0079] rainfall No rain light rain moderate rain rainstorm Compensation coefficient 1 1.1 1.2 1.3

[0080] In this embodiment, by clearly distinguishing the correlation direction between different environmental data and fogging trends, and using it as the basis for constructing the compensation coefficient, on the one hand, the fogging risk value is negatively compensated by the effect of sunlight radiation intensity on significantly suppressing fogging in increasing the glass temperature. On the other hand, the fogging risk value is positively compensated by the humidity effect brought by rainfall. By introducing a positive and negative correlation control mechanism, the quantitative regulation of the fogging trend can be preliminarily realized, thereby enhancing the response accuracy and dynamic adaptability of fogging risk identification.

[0081] In order to reflect the regulating ability of the vehicle's operating state on heat and moisture conduction and further enhance the adaptability of the overall recognition algorithm to actual dynamic driving scenarios, in some embodiments, the corrected risk value is dynamically adjusted in combination with the interval in which the vehicle status sensor data is located to reflect the influence of the vehicle's aerodynamic cooling effect on the fogging trend, and a target fogging risk value is obtained, including: obtaining the current vehicle speed and determining the target speed interval hit by the vehicle speed; determining a third compensation coefficient that matches the target speed interval; wherein the third compensation coefficient is positively correlated with the fogging trend; based on the third compensation coefficient, the corrected risk value is adjusted to obtain a target fogging risk value.

[0082] When performing risk adjustment based on vehicle status sensor data, the controller can start with the current vehicle speed.

[0083] The controller first obtains the current vehicle speed, which is collected in real time by the speed sensor and transmitted to the controller. Based on this value, the controller searches for a preset speed range, such as "low speed zone (<30 km / h)", "medium speed zone (30-80 km / h)", or "high speed zone (>80 km / h)".

[0084] The controller then searches the vehicle speed-risk adjustment coefficient mapping table to find the third compensation coefficient for the corresponding vehicle speed range. This coefficient is used to reflect the effect of the vehicle's speed on the heat exchange conditions on the windshield surface.

[0085] As vehicle speed increases, the aerodynamic cooling effect of the vehicle strengthens, causing the temperature of the outer glass to drop faster, increasing the risk of condensation fogging on the inner glass. Therefore, the controller sets a third compensation coefficient that is positively correlated with the fogging trend. This is used to dynamically adjust the corrected risk value after environmental compensation to obtain the final target fogging risk value.

[0086] For example, the controller performs segmented corrections based on the vehicle speed range, setting three ranges: low speed (0-30km / h), medium speed (30-80km / h), and high speed (above 80km / h). Correction coefficients of 0.8, 1.0, and 1.2 are used to multiply the basic fogging risk value to obtain the target fogging risk value.

[0087] For example, the correction coefficient values ​​associated with the vehicle speed may be as shown in Table 3:

[0088] Table 3

[0089] Speed <30km / h 30~80km / h >80km / h Correction factor 0.8 0.8～1.2 1.2

[0090] In this embodiment, by taking the vehicle speed as a dynamic adjustment factor, the positive correlation between the vehicle speed and the fogging trend is clarified, and mapped to the correction logic in the form of a compensation coefficient, quantitative modeling of the change in condensation risk under the actual operating state of the vehicle is achieved. This enables the system to timely increase the risk value under high-risk conditions such as high-speed driving, improve the ability to identify fogging trends, and provide a reliable basis for subsequent anti-fog control, effectively enhancing the intelligent response level of the vehicle's anti-fog system and the safety of vehicle operation.

[0091] In some embodiments, the collecting of the glass temperature and humidity of the vehicle's windshield to determine a basic fogging risk value includes: collecting the glass temperature and air humidity of the vehicle's windshield to evaluate the fogging tendency of the windshield; evaluating the dew point temperature of the air near the glass based on the glass temperature and air humidity, and determining the current degree of thermal and humidity deviation in combination with the dew point temperature and the glass temperature to construct an initial fogging risk value; determining a glass temperature correction coefficient reflecting the deviation of the glass temperature from the baseline based on the ambient temperature, and determining an in-vehicle steady-state correction coefficient reflecting the deviation of the in-vehicle temperature from the baseline; and performing a weighted correction on the initial fogging risk value based on the glass temperature correction coefficient and the in-vehicle steady-state correction coefficient to obtain a basic fogging risk value.

[0092] The controller first collects the vehicle's windshield temperature and the relative humidity (RH) of the air near the windshield. Based on this data, the controller calculates the dew point temperature (Td) of the air near the windshield by table lookup or interpolation. The dew point temperature is the critical temperature at which water vapor in the air condenses into liquid and is an important indicator for assessing fogging trends.

[0093] The controller then sets the dew point temperature T d With the current glass temperature T g Comparison is performed to determine the degree of thermal and humidity deviation between the two, and to construct the initial fogging risk value R0 at the current moment. This value represents the degree to which the glass surface approaches the dew point. The smaller the deviation, the higher the fogging risk. For example, the initial fogging risk value R0 can be expressed as:

[0094] R0=T d -T g

[0095] Then, the controller uses the current ambient temperature T aAs a reference, calculate the glass temperature T g The difference between the temperature of the glass and the ambient temperature is used to determine the glass temperature correction coefficient α.

[0096] Furthermore, the controller calculates the vehicle interior temperature T using the same reference. c and ambient temperature T a The difference between the two values ​​is used to determine the vehicle's steady-state correction coefficient β. These two coefficients can reflect the correction weights of internal and external heat exchange and thermal inertia.

[0097] Finally, the controller performs a linear weighted combination or function synthesis on the initial fogging risk value R0 with the glass temperature correction coefficient α and the vehicle interior steady-state correction coefficient β to obtain the basic fogging risk value R1, which is used for subsequent risk level identification and anti-fog control decision-making.

[0098] For example, the basic fogging risk value R1 can be expressed as:

[0099] R1=R0*α*β

[0100] This embodiment incorporates a dew point calculation and temperature difference correction model to elevate the condensation risk on the glass surface from a static value to a dynamic response value. This correction is based on the deviation of both the glass and interior temperature from the ambient baseline. This not only enhances the model's responsiveness to actual fogging conditions but also improves recognition accuracy and timeliness. Compared to traditional methods that rely solely on temperature and humidity, this embodiment incorporates more detailed corrections for both temperature and humidity, better reflecting the complex interactions at the glass's thermal and humidity boundaries. This helps the system provide a more reliable anti-fogging response under varying climates and driving conditions.

[0101] Among them, in some embodiments, the method of determining a glass temperature correction coefficient reflecting the deviation of the glass temperature from the baseline based on the ambient temperature, and determining an in-vehicle steady-state correction coefficient reflecting the deviation of the in-vehicle temperature from the baseline, includes: determining a first temperature difference between the glass temperature and the ambient temperature, and correcting the first temperature difference based on a first correction factor to obtain a glass temperature correction coefficient reflecting the deviation of the glass temperature from the baseline; wherein the first temperature difference is positively correlated with the glass temperature correction coefficient; determining a second temperature difference between the in-vehicle temperature and the ambient temperature, and correcting the second temperature difference based on a second correction factor and air humidity to obtain an in-vehicle steady-state correction coefficient reflecting the deviation of the in-vehicle temperature from the baseline; wherein the second temperature difference is positively correlated with the in-vehicle steady-state correction coefficient.

[0102] In the process of constructing the basic fogging risk value, the controller first takes the ambient temperature T a As a unified reference benchmark, the deviation between the glass temperature and the vehicle interior temperature is evaluated.

[0103] On the one hand, the controller calculates the current glass temperature T g and ambient temperature Ta The first temperature difference between the two is used to measure the degree of deviation between the glass surface temperature and the ambient thermal background, and usually reflects the cooling trend after heat exchange between the inside and outside of the glass.

[0104] For example, the first temperature difference Δ1 can be expressed as:

[0105] Δ1=T a -T g

[0106] Subsequently, the controller performs linear or curvilinear mapping on Δ1 based on the first correction factor C1 to obtain the glass temperature correction coefficient α, wherein the first correction factor is a preset coefficient for mapping the temperature difference between the glass temperature and the ambient temperature into the sensitivity of the glass surface to external thermal interference.

[0107] For example, the glass temperature correction coefficient α can be expressed as:

[0108] α=1+C1*e (Ta-Tg)

[0109] As the first temperature difference increases, the glass temperature correction coefficient α shows a positive growth trend, indicating that the greater the temperature difference between the glass and the ambient temperature, the higher the risk of fogging, and the basic risk value needs to be increased.

[0110] On the other hand, the controller calculates the current interior temperature T c and ambient temperature T a The second temperature difference between the two is used to judge the steady-state deviation trend of the thermal environment in the car, and indirectly reflects the intensity of the internal control systems such as heating / air conditioning.

[0111] Exemplarily, the second temperature difference Δ2 can be expressed as:

[0112] Δ2=T c -T a

[0113] To improve accuracy, the controller nonlinearly adjusts the second temperature difference Δ2 by combining the second correction factor C2 and the air humidity RH to generate the in-vehicle steady-state correction factor β. The second correction factor is a preset coefficient used to map the temperature difference between the in-vehicle temperature and the ambient temperature to the in-vehicle thermal and humidity steady-state response capability.

[0114] For example, the in-vehicle steady-state correction coefficient β can be expressed as:

[0115] β=1+C2*(T c -T a )*ln(RH)

[0116] As the second temperature difference increases or the humidity rises (more humid), the steady-state correction coefficient β in the vehicle interior tends to increase, indicating that fogging is more likely to occur.

[0117] In this embodiment, two types of correction coefficients are established by combining the two temperature indicators of glass temperature difference and vehicle interior temperature difference with correction factors and humidity parameters. This has good physical interpretability and can flexibly adjust the risk value under various working conditions, significantly improving the anti-fog judgment accuracy and response robustness of the system under variable climatic conditions.

[0118] In some embodiments, the anti-fog actuator is controlled to perform corresponding anti-fog operations based on the target risk level, including: when the target fogging risk value hits a high risk level, controlling the air-conditioning system to enter a defog mode and increasing the air volume and air temperature; when the target fogging risk value hits a medium risk level, controlling the air-conditioning system to switch to an external circulation mode; when the target fogging risk value hits a low risk level, maintaining the current state of the air-conditioning system.

[0119] After obtaining the target fogging risk value, the controller classifies it into corresponding risk levels based on pre-set grading rules. Risk levels are typically categorized as high, medium, and low, each corresponding to a different anti-fog strategy intensity. Consequently, the controller implements a hierarchical anti-fog control strategy based on the target risk level. Specifically, a mapping relationship is established between fogging risk levels and control schemes. Based on the determined fogging risk level, anti-fog control operations with differentiated intensity are executed, thereby achieving dynamic response and resource optimization in different risk scenarios.

[0120] If the target fogging risk value is at a high risk level, indicating a high likelihood and severity of glass fogging, the controller will directly control the air conditioning system to enter defogger mode, forcibly switching to the front windshield air outlet, and simultaneously increasing air volume and temperature to accelerate heat exchange and drying of the glass inner surface. For example, the controller can activate maximum power defogger mode and trigger a warning icon on the instrument panel as an early warning.

[0121] If the target fogging risk value is at a medium risk level, indicating that the fogging trend is just beginning to emerge but has not yet formed into fog, the controller controls the air conditioning system to switch to external circulation mode, introducing drier air from outside to reduce the humidity in the cabin, thereby achieving active prevention.

[0122] If the target fogging risk value is at a low risk level, indicating that the fogging risk is low, the controller maintains the current operating state of the air conditioner, retains only the continuous monitoring mechanism, and does not trigger actual intervention operations, thereby avoiding resource waste and user perception interference.

[0123] In this embodiment, a hierarchical control mapping mechanism is established, enabling targeted anti-fog operations at the earliest stages of fogging. Different levels of air conditioning intervention are matched to different risk levels, enabling rapid response to high-risk situations while avoiding unnecessary energy consumption in low-risk situations. This significantly improves the precision, efficiency, and energy utilization of anti-fog control, effectively ensuring driving safety and comfort.

[0124] During actual driving, some sensor data relied upon for fog risk assessment may fail or become abnormal due to sensor aging, communication anomalies, or environmental interference. Without fault-tolerance processing, this will directly affect the accuracy of the basic risk value assessment, leading to misjudgment or failure of the anti-fog control strategy. Therefore, it is necessary to introduce a fault-tolerant control process to ensure that the system maintains basic identification and anti-fog control capabilities even when key sensors fail.

[0125] In some embodiments, the fault-tolerant control process includes: when it is determined that the collected humidity is an invalid value within a preset time period, a fixed humidity value is used to replace the actual collected humidity for calculating the initial fogging risk value; when it is determined that the collected glass temperature is an invalid value within a preset time period, referring to the steady-state historical ambient temperature difference table to estimate the current glass temperature of the windshield for calculating the initial fogging risk value.

[0126] Among them, the collected humidity or glass temperature are invalid values ​​within the preset time period, which means that when the controller performs validity detection on the humidity sensor data or temperature sensor data, if the humidity or glass temperature value is monitored to be constant for more than the preset detection time (for example, 5 minutes), the humidity or glass temperature value exceeds the reasonable physical range, or is a missing mark (NaN, etc.), then it can be determined that the humidity data or glass temperature value during this period is continuously invalid and is insufficient to support the normal calculation of the fogging risk.

[0127] The controller has a fault-tolerant control process in the fog risk identification process, which is used to perform alternative calculations when key sensor data fails. This process includes the following two typical scenarios:

[0128] When it is detected that the humidity sensor continuously reports invalid values ​​for more than a preset time threshold (such as 5 minutes), the controller determines that the current humidity data is unavailable and automatically uses a fixed humidity value (such as 70% RH) instead of the actual humidity data to participate in the calculation of the dew point temperature and the initial risk value.

[0129] When the glass temperature sensor data is abnormal (such as a constant dead value, no update, etc.), the controller estimates the current glass temperature value based on the empirical temperature difference between the current ambient temperature and the glass temperature by consulting the historical steady-state data table, and continues to calculate the initial fogging risk value accordingly.

[0130] For example, when the vehicle is restarted after being stopped, it is found that the humidity sensor reports a value of NaN and does not recover for 5 minutes. The controller activates the fallback logic and sets the humidity value to 70% RH to continue the dew point temperature calculation.

[0131] If the glass temperature sensor displays a value of 0°C and remains unchanged for more than 10 minutes, the system deems it to be in a failed state. The controller then checks the table to determine the current ambient temperature is 25°C. The corresponding empirical glass temperature deviation is –5°C, so the estimated glass temperature is 20°C. The fogging deviation and risk level are then calculated.

[0132] This fault-tolerant control process can maintain system functionality continuity in the absence of real-time data.

[0133] In this embodiment, the introduction of a fault-tolerant control process significantly improves the system's robustness and fault tolerance under sensor anomalies. Even in extreme scenarios where sensors fail, the system can still complete fogging risk assessment based on preset reference values ​​or historical steady-state deviation data, avoiding driving safety hazards caused by anti-fog recognition failure and ensuring the continuity and practicality of the anti-fog function.

[0134] In some embodiments, the above method further includes: performing validity detection on at least the humidity sensor and glass temperature sensor carried by the vehicle according to a preset period; if the validity detection result is abnormal, triggering a fault warning mechanism and switching to a fault-tolerant control process.

[0135] The controller is equipped with a sensor validity detection mechanism to periodically check the validity of key sensor data. For example, the controller polls the output data characteristics of the humidity sensor and glass temperature sensor at a preset interval (e.g., every 2 minutes). The judgment criteria include but are not limited to: whether the data is continuously constant, whether it is an invalid value (such as NaN), and whether it exceeds the reasonable physical range.

[0136] If a sensor exhibits an anomaly for multiple consecutive cycles, the controller deems it invalid, triggering the fault warning mechanism and switching to a fault-tolerant control process to implement alternative risk assessments. This mechanism ensures the system can respond before a sensor actually fails, improving the real-time and intelligent nature of the anti-fog recognition system.

[0137] In addition, the controller can also record the fault code in the vehicle diagnostic system when any sensor fails for subsequent analysis and reporting.

[0138] In this embodiment, by introducing a periodic validity check mechanism, potential sensor malfunctions can be identified in advance, enabling fault prediction and early warning, helping to reduce safety risks caused by misjudgments. Furthermore, by integrating with the fault-tolerant control process, smooth switching is achieved, ensuring the continuity, stability, and accuracy of windshield fogging detection, making it suitable for vehicle applications under complex operating conditions.

[0139] Figure 3 FIG. 1 is a flow chart of a method for identifying fogging risk of a vehicle windshield provided in some other embodiments of the present application. Figure 3 As shown, the controller executes the fogging risk identification and anti-fog control process during the operation of the air-conditioning system, which is used to determine in real time whether there is a fogging trend on the windshield during vehicle operation and decide whether to start the anti-fog control action.

[0140] First, during air conditioning system operation, the controller periodically acquires multi-source sensor data related to fogging, including but not limited to interior and exterior temperature, window temperature, interior humidity, solar radiation intensity, vehicle speed, and rainfall. This data is provided by temperature, humidity, light, speed, and rainfall sensors deployed at various locations and transmitted to the controller via the vehicle bus system.

[0141] After collecting data, the controller first determines whether the current operating status of key sensors is normal. If an anomaly is found in the data, such as a constant value, a value outside the reasonable physical range, or an invalid flag (such as NaN), the controller triggers the fault-tolerant control mechanism and obtains alternative data from the preset value library or the historical environmental steady-state difference table for subsequent risk assessment.

[0142] When the sensor data is valid, the controller estimates the dew point temperature based on the collected glass temperature and in-vehicle humidity, and corrects the initial fogging risk value based on multi-source environment and vehicle status parameters to generate the target fogging risk value at the current moment.

[0143] Subsequently, the controller matches the risk value with the preset risk level range: if the current value hits the high risk or medium risk level, the controller will execute the corresponding anti-fog control action, such as opening the front windshield defogger vents, switching the air conditioner to external circulation, increasing the air volume or air temperature, etc.; if it is judged to be a low risk level, the current air conditioner operating status will remain unchanged, and only the continuous monitoring mechanism will be maintained.

[0144] When it is confirmed that there is no fogging trend and the current system status does not need to be adjusted, this round of judgment process ends, and the controller enters the next cycle to wait and continue to perform the next round of anti-fog identification task.

[0145] The process has a complete closed-loop structure of multi-source data collection, fault-tolerant judgment, risk assessment and control execution, which can realize feedforward identification and dynamic prevention and control of vehicle glass fogging trends, significantly improving the vehicle's operating safety and air-conditioning control intelligence level in complex climatic environments.

[0146] The vehicle windshield fogging risk identification method provided in the embodiments of the present application may be executed by a vehicle windshield fogging risk identification device. In the embodiments of the present application, the vehicle windshield fogging risk identification method is executed by the vehicle windshield fogging risk identification device as an example to illustrate the vehicle windshield fogging risk identification device provided in the embodiments of the present application.

[0147] An embodiment of the present application also provides a fogging risk identification device for a vehicle windshield, which is applied to a controller.

[0148] Figure 4 Schematic diagram of the structure of the fogging risk identification device for the vehicle windshield provided in some embodiments of the present application. Figure 4 As shown, the fogging risk identification device for the vehicle windshield includes a collection module 401, a correction module 402 and a control module 403.

[0149] The collection module 401 is used to collect the glass temperature and humidity of the vehicle's windshield to determine a basic fogging risk value.

[0150] The correction module 402 is configured to perform interval correction on the basic fogging risk value based on multi-source sensor data to generate a target fogging risk value.

[0151] The control module 403 is configured to determine a target risk level corresponding to the target fogging risk value, and control the anti-fog actuator to perform a corresponding anti-fog operation according to the target risk level.

[0152] According to the fogging risk identification device for the vehicle windshield provided in the embodiment of the present application, by collecting the glass temperature and humidity of the vehicle windshield, a basic fogging risk value is constructed, which can evaluate the deviation trend between the thermal and humidity conditions of the glass surface and the air saturation state in real time, and provide a basis for judging the possibility of fogging; based on multi-source sensor data, the basic fogging risk value is interval-corrected to generate a target fogging risk value, which can perceive and compensate for the impact of different environmental changes on the thermal and humidity behavior of the glass, and realize dynamic correction of the basic risk value. Based on the strategy of interval division and graded adjustment, the system's adaptability to risk evolution in complex scenarios is improved, and the judgment deviation caused by fluctuations in a single variable is reduced. ; Then, the target risk level corresponding to the target fogging risk value is determined, and the anti-fog actuator is controlled to perform the corresponding anti-fog operation according to the target risk level. Differentiated control strategies can be output according to the risk level to ensure that the system can suppress the fogging trend in advance with minimum power consumption, taking into account energy efficiency and responsiveness. Therefore, the risk identification method based on the fusion of initial glass temperature and humidity evaluation and multi-source dynamic correction, combined with the graded response control strategy, can actively trigger intervention actions before fogging is formed, realize true anti-fog control, avoid driving safety hazards caused by obstructed vision, improve the accuracy and stability of windshield fogging trend identification, and help improve the safety and comfort of the entire vehicle.

[0153] In some embodiments, the correction module is also used to perform environmental compensation on the basic fogging risk value based on the interval of the environmental sensor data to obtain a corrected risk value; and dynamically adjust the corrected risk value based on the interval of the vehicle status sensor data to reflect the impact of the vehicle's aerodynamic cooling effect on the fogging trend to obtain a target fogging risk value.

[0154] In some embodiments, the correction module is also used to collect the current sunlight radiation intensity and rainfall, and respectively determine the target radiation intensity interval and target rainfall interval hit by the sunlight radiation intensity and rainfall respectively; obtain a first compensation coefficient matching the target radiation intensity interval, and a second compensation coefficient matching the target rainfall interval; wherein, the first compensation coefficient is negatively correlated with the fogging trend, and the second compensation coefficient is positively correlated with the fogging trend; based on the first compensation coefficient and the second compensation coefficient, the basic fogging risk value is corrected to obtain a corrected risk value.

[0155] In some embodiments, the correction module is also used to obtain the current vehicle speed and determine the target vehicle speed range hit by the vehicle speed; determine a third compensation coefficient that matches the target vehicle speed range; wherein the third compensation coefficient is positively correlated with the fogging trend; based on the third compensation coefficient, the corrected risk value is adjusted to obtain the target fogging risk value.

[0156] In some embodiments, the acquisition module is also used to collect the glass temperature and air humidity of the vehicle's windshield for evaluating the fogging tendency of the windshield; evaluate the dew point temperature of the air near the glass based on the glass temperature and air humidity, and determine the current degree of thermal and humidity deviation in combination with the dew point temperature and the glass temperature to construct an initial fogging risk value; based on the ambient temperature, determine a glass temperature correction coefficient reflecting the deviation of the glass temperature from the baseline, and determine an in-vehicle steady-state correction coefficient reflecting the deviation of the in-vehicle temperature from the baseline; based on the glass temperature correction coefficient and the in-vehicle steady-state correction coefficient, perform a weighted correction on the initial fogging risk value to obtain a basic fogging risk value.

[0157] In some embodiments, the acquisition module is also used to determine a first temperature difference between the glass temperature and the ambient temperature, and correct the first temperature difference based on a first correction factor to obtain a glass temperature correction coefficient reflecting the deviation of the glass temperature from a reference; wherein the first temperature difference is positively correlated with the glass temperature correction coefficient; determine a second temperature difference between the interior temperature and the ambient temperature, and correct the second temperature difference based on a second correction factor and air humidity to obtain an interior steady-state correction coefficient reflecting the deviation of the interior temperature from the reference; wherein the second temperature difference is positively correlated with the interior steady-state correction coefficient.

[0158] In some embodiments, the control module is also used to control the air-conditioning system to enter the defog mode and increase the air volume and air temperature when the target fogging risk value hits the high risk level; control the air-conditioning system to switch to the external circulation mode when the target fogging risk value hits the medium risk level; and maintain the current state of the air-conditioning system when the target fogging risk value hits the low risk level.

[0159] In some embodiments, the above-mentioned device also includes a fault-tolerant module, which is used to use a fixed humidity value to replace the actual collected humidity when it is determined that the collected humidity is an invalid value within a preset time period, so as to calculate the initial fogging risk value; when it is determined that the collected glass temperature is an invalid value within a preset time period, refer to the steady-state historical ambient temperature difference table to estimate the current glass temperature of the windshield, so as to calculate the initial fogging risk value.

[0160] In some embodiments, the fault-tolerant module is also used to perform validity detection on at least the humidity sensor and glass temperature sensor installed in the vehicle according to a preset cycle; if the validity detection result is abnormal, the fault warning mechanism is triggered and the fault-tolerant control process is switched to.

[0161] The fogging risk identification device for the vehicle windshield in the embodiment of the present application can be an on-board computing device or its components, such as a domain controller, an electronic control unit (ECU), an integrated circuit or a functional chip module. The device can be deployed in the vehicle's air-conditioning control system, intelligent cockpit control system, central gateway, central computing platform or other on-board electronic architecture with computing capabilities. Exemplarily, the fogging risk identification device can be a vehicle body domain controller, an air-conditioning controller, an intelligent cockpit domain controller, a central processing platform, or a processor and memory module embedded therein. The present application does not limit the specific type and deployment structure of the controller. As long as it has data acquisition, logical operation and control output capabilities, it can be applied to the anti-fog identification method described in the present application.

[0162] The embodiment of the present application further provides a vehicle, which includes: Figure 4 The fogging risk identification device for the vehicle windshield is shown.

[0163] The fogging risk identification device for the vehicle windshield provided in the embodiment of the present application can implement each process implemented in each method embodiment. To avoid repetition, it will not be described here.

[0164] Figure 5 Schematic diagram of the structure of the computer device provided in some embodiments of the present application. Figure 5 As shown, an embodiment of the present application also provides a computer device 500, including a processor 501, a memory 502, and a computer program stored in the memory 502 and executable on the processor 501. When the program is executed by the processor 501, the various processes of the above-mentioned method embodiments are implemented, and the same technical effects can be achieved. To avoid repetition, they will not be described here.

[0165] It should be noted that the computer devices in the embodiments of the present application include the mobile computer devices and non-mobile computer devices mentioned above.

[0166] An embodiment of the present application also provides a non-transitory computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the various processes of the above-mentioned vehicle windshield fogging risk identification method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0167] The processor is the processor in the computer device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0168] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned method for identifying fogging risks of a vehicle windshield.

[0169] The processor is the processor in the computer device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0170] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned embodiment of the method for identifying the risk of fogging of the vehicle windshield, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0171] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0172] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the statement "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In addition, it should be noted that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the opposite order according to the functions involved. For example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0173] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the relevant technology, can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0174] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms without departing from the purpose of this application and the scope of protection of the claims, all of which are within the protection of this application.

[0175] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0176] Unless otherwise specified, all embodiments and optional embodiments of the present application can be combined with each other to form a new technical solution.

[0177] Unless otherwise specified, all technical features and optional technical features of this application can be combined with each other to form a new technical solution.

[0178] Unless otherwise specified, all steps of the present application may be performed sequentially or randomly, preferably sequentially. For example, the method includes steps (a) and (b), which means that the method may include steps (a) and (b) performed sequentially, or may include steps (b) and (a) performed sequentially. For example, it is mentioned that the method may also include step (c), which means that step (c) may be added to the method in any order, for example, the method may include steps (a), (b) and (c), or may include steps (a), (c) and (b), or may include steps (c), (a) and (b), etc.

[0179] The above are merely preferred embodiments of the present application and are not intended to limit the present application. Those skilled in the art will readily appreciate that various modifications and variations are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present application shall be included within the scope of protection of the present application.

Claims

1. A method for identifying fogging risk of a vehicle windshield, characterized in that: The method comprises: Collect the temperature and humidity of the vehicle's windshield to determine the basic fogging risk value; Performing interval correction on the basic fogging risk value based on multi-source sensor data to generate a target fogging risk value; A target risk level corresponding to the target fogging risk value is determined, and an anti-fog actuator is controlled to perform a corresponding anti-fog operation according to the target risk level.

2. The method according to claim 1, characterized in that The performing interval correction on the basic fogging risk value based on the multi-source sensor data to generate a target fogging risk value includes: Based on the interval of the environmental sensor data, the basic fogging risk value is subjected to environmental compensation to obtain a modified risk value; Based on the interval of the vehicle state sensor data, the corrected risk value is dynamically adjusted to reflect the influence of the vehicle aerodynamic cooling effect on the fogging trend, thereby obtaining a target fogging risk value.

3. The method according to claim 2, characterized in that The performing environmental compensation on the basic fogging risk value based on the interval of the environmental sensor data to obtain a modified risk value includes: Collecting the current sunlight irradiance and rainfall, and determining the target irradiance interval and target rainfall interval that the sunlight irradiance and rainfall respectively hit; Obtaining a first compensation coefficient that matches the target irradiance intensity range and a second compensation coefficient that matches the target rainfall range; wherein the first compensation coefficient is negatively correlated with the fogging trend, and the second compensation coefficient is positively correlated with the fogging trend; The basic fogging risk value is corrected based on the first compensation coefficient and the second compensation coefficient to obtain a corrected risk value.

4. The method according to claim 2, characterized in that The modified risk value is dynamically adjusted based on the interval of the vehicle state sensor data to reflect the influence of the vehicle aerodynamic cooling effect on the fogging trend to obtain a target fogging risk value, including: Obtaining the current vehicle speed and determining the target vehicle speed range within which the vehicle speed falls; determining a third compensation coefficient that matches the target vehicle speed range; wherein the third compensation coefficient is positively correlated with the fogging tendency; The modified risk value is adjusted based on the third compensation coefficient to obtain a target fogging risk value.

5. The method according to any one of claims 1 to 4, characterized in that The collecting of the temperature and humidity of the vehicle windshield to determine the basic fogging risk value includes: Collect the glass temperature and air humidity of the vehicle's windshield to assess the fogging tendency of the windshield; estimating the dew point temperature of the air near the glass according to the glass temperature and the air humidity, and determining the current degree of thermal and humidity deviation in combination with the dew point temperature and the glass temperature to construct an initial fogging risk value; Determining, based on the ambient temperature, a glass temperature correction coefficient reflecting the deviation of the glass temperature from the reference, and determining an in-vehicle steady-state correction coefficient reflecting the deviation of the in-vehicle temperature from the reference; Based on the glass temperature correction coefficient and the vehicle interior steady-state correction coefficient, a weighted correction is performed on the initial fogging risk value to obtain a basic fogging risk value.

6. The method according to claim 5, characterized in that The determining of the glass temperature correction coefficient reflecting the deviation of the glass temperature from the reference based on the ambient temperature, and the determining of the vehicle interior steady-state correction coefficient reflecting the deviation of the vehicle interior temperature from the reference, includes: Determining a first temperature difference between the glass temperature and the ambient temperature, and correcting the first temperature difference based on a first correction factor to obtain a glass temperature correction coefficient reflecting the deviation of the glass temperature from a reference; wherein the first temperature difference is positively correlated with the glass temperature correction coefficient; A second temperature difference between the interior temperature and the ambient temperature is determined, and the second temperature difference is corrected based on a second correction factor and air humidity to obtain an interior steady-state correction coefficient reflecting the deviation of the interior temperature from the reference; wherein the second temperature difference is positively correlated with the interior steady-state correction coefficient.

7. The method according to claim 1, characterized in that The controlling the anti-fog actuator to perform a corresponding anti-fog operation based on the target risk level includes: When the target fogging risk value reaches a high risk level, the air conditioning system is controlled to enter a defog mode and the air volume and air temperature are increased; When the target fogging risk value reaches a medium risk level, controlling the air conditioning system to switch to an external circulation mode; When the target fogging risk value reaches a low risk level, the current state of the air-conditioning system is maintained.

8. The method according to claim 1, characterized in that The method further includes a fault-tolerant control process, which includes: If the collected humidity is determined to be invalid within the preset time period, a fixed humidity value is used to replace the actual collected humidity for calculating the initial fogging risk value; When it is determined that the collected glass temperatures are all invalid values ​​within a preset time period, the current glass temperature of the windshield is estimated by referring to the steady-state historical ambient temperature difference table to calculate the initial fogging risk value.

9. The method according to claim 1 or 8, characterized in that The method further comprises: At least the vehicle's humidity sensor and glass temperature sensor shall be tested for effectiveness according to the preset period; In the event of an abnormal validity test result, the fault warning mechanism is triggered and the control process switches to fault-tolerant control.

10. A vehicle windshield fogging risk identification device, characterized in that: The device comprises: A collection module is used to collect the glass temperature and humidity of the vehicle's windshield to determine the basic fogging risk value; a correction module, configured to perform interval correction on the basic fogging risk value based on multi-source sensor data to generate a target fogging risk value; The control module is used to determine the target risk level corresponding to the target fogging risk value, and control the anti-fog actuator to perform a corresponding anti-fog operation according to the target risk level.

11. A vehicle, characterized in that: The vehicle includes the fogging risk identification device for a vehicle windshield according to claim 10 .

12. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for identifying fogging risk of a vehicle windshield is implemented as claimed in any one of claims 1 to 9.

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