Intelligent anti-fog automatic control method and system

Abstract

The invention relates to the technical field of vehicles, and belongs to an intelligent anti-fog automatic control method and system. According to the method, judgment of the cold start anti-fog locking state is added, so that the situation of fogging and frosting during low-temperature cold start of the vehicle is avoided, and fogging of the front windshield is restrained through anti-fog locking at the moment. And the driving safety of passengers is better ensured. By judging the fogging risk level, the intelligent defogging system is started in time before the high-risk fog begins to influence the front windshield, so that the glass can be prevented from fogging, and the driving safety is ensured. The automatic demisting system not only protects the safety of passengers, but also contributes to reducing energy consumption and reducing oil consumption. Based on different fogging risk levels, the air inlet circulation proportion, the air outlet mode, the air volume and the target air outlet temperature are corrected to different degrees, so that the automatic anti-fogging system is more intelligent and closer to the real requirements of passengers, and the physical examination of the passengers is improved. Compared with a non-on-and-off mode of the demisting system in the prior art, more levels are increased, the actuator is prevented from working in a high-load state all the time, and energy consumption can be reduced.

Description

Technical Field

[0001] The invention relates to the technical field of vehicles, and belongs to an intelligent anti-fog automatic control method and system. Background Art

[0002] With the rapid development of the automobile industry, automobile safety performance has received more and more attention. Windshield fogging is one of the factors that lead to traffic accidents. Especially in bad weather conditions, fog will seriously affect the driver's vision and increase the risk of traffic accidents. In the prior art, the defogging function of the vehicle air conditioner mostly requires manual operation by the driver, and there is a lack of automated solutions to prevent fogging. Some air conditioning control methods based on image acquisition do not clearly define which specific areas of the front windshield are collected and analyzed, which may cause the defogging operation to be started when non-critical areas are fogged, resulting in a waste of resources. The defogging method based on machine vision may not reflect the actual fogging situation of the front windshield well due to different driver perspectives and image distortion. Even for the existing automatic defogging control systems on the market, most of them start working after the front windshield has fogged. It takes several seconds or even longer from the appearance of fog to the system detecting the fog and starting to defog. During this period of time, visibility is extremely low, the risk factor is high, and traffic accidents are easy to cause. Moreover, the existing technology may not fully take into account the habits and preferences of different drivers, as well as the adaptability under different environmental conditions, for example, the impact of humidity and temperature changes under different weather conditions on the defogging effect. Summary of the invention

[0003] In view of the above technical problems, the present invention provides an intelligent anti-fog automatic control method and system.

[0004] The present invention aims to overcome the technical defects of the prior art. The purpose of the present invention is to provide an intelligent anti-fog automatic control method and system. Before the front windshield fogs up, the anti-fog system is turned on in advance to avoid fogging of the front windshield and ensure driving safety. At the same time, during the operation of the anti-fog system, environmental parameters are collected at all times, and the actions of actuators such as the circulating damper, mode damper and blower are corrected to make the entire anti-fog and defog process more comfortable and accurate.

[0005] In order to achieve the above object, the present invention provides the following technical solutions: In the first aspect, the present application provides an intelligent anti-fog automatic control method, including multiple sensors for collecting and monitoring real-time parameters of corresponding positions, wherein the sensors include a defog sensor, a sunlight sensor, an indoor temperature sensor, an outdoor temperature sensor and a vehicle speed sensor, so as to more accurately determine the glass air dew point temperature and the dancing risk level under different vehicle usage scenarios.

[0006] Absolute humidity indicates the mass of water vapor contained in each cubic meter of moist air, and its unit is kilograms per cubic meter (kg / m³).

[0007] Relative humidity is the ratio of the absolute humidity in the air to the saturated absolute humidity at the same temperature. The result is a percentage. The higher the air temperature, the more water vapor it can bear. That is, under the same relative humidity conditions, the higher the air temperature, the greater the absolute humidity in the air.

[0008] Dew point temperature, under a certain air pressure, gradually lowers the air temperature, when the water vapor in the air reaches saturation and begins to condense to form water droplets, it is called the dew point temperature of the air under the air pressure. That is, when the temperature drops below the dew point temperature, water droplets will precipitate in the air.

[0009] The specific steps include: S1: Determine the effective state of automatic defogger. If effective, collect data near the glass through the defogger sensor. If not effective, exit the anti-fog function. S2: Collecting corresponding data through the sunlight sensor, indoor temperature sensor, outdoor temperature sensor and vehicle speed sensor; S3: Filter and correct the data collected in S1 and S2 to obtain preprocessing values; S4: Calculate the air dew point temperature Td according to the pre-processed value obtained in S3, and determine the sunlight intensity level and the cold start anti-fog lock state; S5: judging the fogging risk level RiskLvl calculation execution status according to the sunlight intensity level and the cold start anti-fog lock state described in S4, and calculating the fogging risk level RiskLvl according to the air dew point temperature Td described in S4, wherein the fogging risk level RiskLvl is divided into four levels from low risk to high risk: Lvl1, Lvl2, Lvl3, and Lvl4; S6: Modify the relevant actuator response action according to the fogging risk level described in S5.

[0010] Furthermore, the data in S1 includes a relative humidity value of the air near the glass, an original value of the glass surface temperature and a value of the air temperature near the glass, and after preprocessing, the relative humidity value RH_air, the original value of the glass temperature T_gl_raw and the air temperature value T_glair near the glass are obtained respectively.

[0011] Furthermore, the data in S2 includes light intensity, original value of interior temperature, original value of ambient temperature and original value of vehicle speed, and after processing, the maximum light intensity Solar_Max, interior temperature correction value T_ict, exterior temperature correction value T_oat and vehicle speed slow filter value V_spd are obtained respectively, and the temperature difference correction weighting coefficient α is obtained according to the temperature difference T_dif between the interior temperature correction value T_ict and the exterior temperature correction value T_oat and the vehicle speed slow filter value V_spd.

[0012] Furthermore, the air dew point temperature Td is calculated according to the dry-bulb air temperature T_da and the air relative humidity value RH_air, and the dry-bulb air temperature T_da = T_ict * α + T_glair * (1 - α), wherein α is a temperature difference correction weighting coefficient.

[0013] Furthermore, the water vapor partial pressure Pqb2 corresponding to the relative humidity RH_air at the dry-bulb air temperature T_da is obtained by the moisture content calculation formula, and the air dew point temperature Td is obtained according to the relative humidity RH_air corresponding to Pqb2. Pqb2 = RH_air * Pqb1 / 100%, wherein Pqb1 is the saturated water vapor partial pressure corresponding to the dry-bulb air temperature T_da.

[0014] Furthermore, the fogging risk level RiskLvl is determined based on the vehicle exterior temperature correction value T_oat and the dew point temperature difference value Td_Diff, wherein the dew point temperature difference value Td_Diff = Td - T_gl, wherein T_gl is the glass surface temperature correction value.

[0015] Furthermore, the relevant actuator response actions include air intake circulation ratio, air outlet mode, air volume and target air temperature.

[0016] On the second aspect, the present application also discloses an intelligent anti-fogging automatic control system, which provides a solution including a measurement acquisition module, an analysis and processing module, a condition judgment module and an execution monitoring module; the measurement acquisition module is used to arrange a variety of sensors to collect and monitor the environmental parameters around the vehicle glass, including the glass surface temperature, the air humidity near the glass and the air temperature on the glass surface, and to pre-process and correct the data in combination with the vehicle driving conditions and the temperature inside and outside the vehicle, and then calculate the air dew point temperature near the front windshield. The analysis and processing module is used to build a fogging prediction model based on the pre-processed environmental parameters; the condition judgment module is used to determine whether the monitored glass is about to fog when it is determined that the glass needs to be defogged, trigger the anti-fogging control mechanism, dynamically determine whether to execute defogging according to the real-time environmental parameters, and the specific execution mode, and adjust the defogging intensity and frequency; the execution monitoring module is used to monitor the defogging state of the glass in real time, adjust the defogging control strategy, and correct the actuator response action to optimize the anti-fogging control strategy.

[0017] Compared with the prior art, the present invention provides an intelligent anti-fog automatic control method and system, which has the following beneficial effects: 1. The present invention adds the judgment of the cold start anti-fog lock state, the purpose is to avoid fogging and frosting when the vehicle is cold started at low temperature. At this time, the anti-fog lock is used to suppress the fogging of the windshield, so as to better ensure the driving safety of the passengers.

[0018] 2. The present invention can prevent the glass from fogging and ensure driving safety by judging the fogging risk level and activating the intelligent defogger system in time before the high-risk fog starts to affect the front windshield. The automatic defogger system not only protects the safety of passengers, but also helps to reduce energy consumption and fuel consumption.

[0019] 3. Based on different fogging risk levels, the present invention makes different degrees of corrections to the air intake circulation ratio, air outlet mode, air volume and target air outlet temperature, making the automatic anti-fogging system more intelligent, closer to the real needs of passengers, and improving passenger health. Compared with the on-off mode of the defog system in the prior art, more levels are added to avoid the actuator working under high load all the time, which also helps to reduce energy consumption.

[0020] The features and advantages of the present invention will be described in detail through embodiments in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is an intelligent anti-fog automatic control method and system overall flow chart of the present invention; Figure 2 Switch graph for light intensity levels; Figure 3 Toggle graph for fogging risk level. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below through the accompanying drawings and embodiments. However, it should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the scope of the present invention. In addition, in the following description, the description of known structures and technologies is omitted to avoid unnecessary confusion of the concept of the present invention.

[0023] The present invention provides an intelligent anti-fog automatic control method and system, which is specifically implemented as follows Figure 1 As shown, the following steps are included: Step 1: Automatic defog effectiveness status judgment; Step 2: Sensor data collection and data processing; Step 3: Determine the fogging risk level; Step 4: Correct the response action of the relevant actuator.

[0024] As a further improvement of the present invention, the specific content of step 1 is as follows: If there are no faults in the following situations, the anti-fog sensor is effective; otherwise, the anti-fog sensor is invalid and the anti-fog function logic is exited: If there is no defog sensor-air humidity output terminal short-circuited to ground, and the defog sensor-air humidity output terminal is open-circuited / short-circuited to power, the defog sensor air humidity sensor is not faulty; If there is no short circuit to ground of the defog sensor-glass temperature output terminal, and the defog sensor-glass temperature output terminal is open circuit / short circuit to power supply, the defog sensor glass temperature sensor is not faulty; If there is no short circuit to ground of the defog sensor-air temperature output terminal, and the defog sensor-air temperature output terminal is open circuit / short circuit to power supply, the defog sensor air temperature sensor is not faulty.

[0025] As a further improvement of the present invention, the specific content of step 2 is as follows: 2.1 The left / right sunlight sensor collects the light intensity on the left / right side, and takes the maximum light intensity on the left / right side as Solar_Max; according to the maximum light intensity on the left / right side Solar_Max, Figure 2 The switching curve shown determines the light intensity level. The specific contents are as follows: If Solar_Max ≥ S2 and lasts for more than 5 minutes, it is judged as a high sunlight intensity level; If Solar_Max ≤ S1 for more than 5 minutes, it is judged as low sunlight intensity level; Among them, S1 = 200W / m2, S2 = 450W / m2.

[0026] 2.2 The indoor temperature sensor collects the original value of the indoor temperature, and after correction, obtains the indoor temperature correction value T_ict; 2.3 The outdoor temperature sensor collects the original value of the ambient temperature and obtains the external temperature correction value T_oat after correction; 2.4 The vehicle speed sensor collects the original value of the vehicle speed, and after slow filtering at 1kph / 5s, obtains the vehicle speed slow filtering value V_spd; 2.5 The defog sensor collects the relative humidity value of the air near the glass, and after slow filtering at 0.1% / s, the relative humidity value RH_air is obtained; 2.6 The defogging sensor collects the original value of the glass surface temperature, and after slow filtering at 0.05℃ / s, the original value of the glass temperature T_gl_raw is obtained; the original value of the glass temperature T_gl_raw is further corrected to obtain the corrected value of the glass surface temperature T_gl, the specific contents are as follows: According to the temperature difference T_dif between the interior temperature T_ict and the ambient temperature T_oat and the vehicle speed V_spd, the compensation amount T_glOft is obtained by linear interpolation from Table 1, where the temperature difference T_dif = T_ict - T_oat; Therefore, the glass surface temperature correction value T_gl = T_gl_raw + T_glOft. Table 1: Glass surface temperature compensation calibration table 2.7 The defog sensor collects the air temperature value near the glass, and after slow filtering at 0.05℃ / s, the air temperature value T_glair near the glass is obtained; the air temperature value T_glair near the glass is further corrected to obtain the dry bulb air temperature T_da, the specific contents are as follows: According to the temperature difference T_dif between the interior temperature T_ict and the ambient temperature T_oat and the vehicle speed V_spd, the temperature difference correction weighting coefficient α is obtained by interpolation from Table 2, where the temperature difference T_dif = T_ict - T_oat, and the value range of the temperature difference correction weighting coefficient α is [0,1]. Table 2 is a nonlinear table, and the boundary value is taken when it is greater than the boundary value, which can be calibrated; Calculate the dry-bulb air temperature T_da = T_ict * α + T_glair * (1 - α), that is, the greater the temperature difference, the greater the α value, the closer T_da is to the interior temperature; the greater the vehicle speed, the smaller the α value, the closer T_da is to T_glair.

[0027] Table 2: Temperature difference correction weighted coefficient calibration table 2.8 Calculation of air dew point temperature Td, the details are as follows: Get the dry-bulb air temperature T_da and the relative humidity RH_air; obtain the saturated water vapor partial pressure Pqb1 corresponding to the dry-bulb air temperature T_da by linear interpolation according to Table 3 (Air temperature-saturated water vapor partial pressure table); calculate the water vapor partial pressure Pqb2 corresponding to the relative humidity RH_air at the dry-bulb air temperature T_da by using the moisture content calculation formula f2; where f2 is: Pqb2 = RH_air * Pqb1 / 100%; obtain the air dew point temperature Td corresponding to the relative humidity RH_air of Pqb2 by linear interpolation according to Table 3 (Air temperature-saturated water vapor partial pressure table).

[0028] Table 3: Air temperature-saturated water vapor partial pressure table As a further improvement of the present invention, the specific content of step 3 is as follows: 3.1 In order to avoid fogging and frost when the vehicle is cold started at low temperature, the anti-fog lock will be used to suppress the fogging of the windshield. The specific conditions for entering and exiting the cold start anti-fog lock state (CoolStartDefLockSt) are as follows: If the following conditions are met: (Condition 1 && (Condition 2 || (Condition 3 && Condition 4))), the cold start anti-fog lock state (CoolStartDefLockSt = Lock) will be entered, and the lock timing Time_Lock will start: Condition 1: The air conditioner status changes from off to on; Condition 2: The cold start air outlet mode restriction function is met; Condition 3: Glass surface temperature correction value T_gl ≤ 15°C (quantitative calibration possible); Condition 4: Air humidity RH_air ≥ RH_ref + 5% (RH_ref and 5% are both calibrable).

[0029] If the following conditions are met: (Condition 1 || Condition 2), exit the cold start anti-fog lock state (CoolStartDefLockSt = Unlock): Condition 1: Lock timing Time_Lock = 150s timeout (timeout can be calibrated); Condition 2: Air humidity RH_air ≤ RH_ref - 10% (RH_ref and 10% are both calibrable).

[0030] 3.2 Fogging risk level RiskLvl is divided into four levels from low risk to high risk: Lvl1, Lvl2, Lvl3, Lvl4. The calculation logic of fogging risk level must meet the following conditions to be executed: (Condition 1 && Condition 2), Condition 1: Exit the cold start anti-fog lock state (CoolStartDefLockSt = Unlock); Condition 2: The light intensity is judged to be a low sunlight intensity level.

[0031] 3.3 In particular, when the following conditions are met: (Condition 1 && Condition 2), the fogging risk level RiskLvl = Lvl1, Condition 1: The light intensity is judged to be a high sunlight intensity level; Condition 2: Glass surface temperature correction value T_gl > external temperature correction value T_oat.

[0032] 3.4 In general, the specific contents of the calculation logic of the steady-state fogging risk level are as follows: According to the calculated air dew point temperature Td and the glass surface temperature correction value T_gl, calculate the dew point temperature difference Td_Diff = Td - T_gl; According to the external temperature correction value T_oat and the dew point temperature difference Td_Diff, the fogging risk level RiskLvl is obtained by looking up Table 4 (risk level dew point temperature difference calibration table). The switching curve is as follows: Figure 3 shown.

[0033] Table 4: Risk level dew point temperature difference calibration table As a further improvement of the present invention, the specific content of step 4 is as follows: 4.1 Control the intake circulation ratio according to the fogging risk level and correct its response action. The specific contents are as follows: If the following conditions are met: (condition 1 && condition 2), the intake circulation ratio calculation logic of the cold start anti-fog lock state is entered. At this time, the intake circulation ratio Rec_Pct = 0% (calibrable), Condition 1: Anti-fog lock state CoolStartDefLockSt = Lock; Condition 2: The intake air circulation control state is the automatic control state or the manual external circulation control state.

[0034] If the following conditions are met: (condition 1 && condition 2 && condition 3), look up Table 5 (risk level-intake cycle ratio increment calibration table) to obtain the intake cycle ratio increment ΔRec_Pct under different fogging risk levels. This increment will be added to the current intake cycle base ratio Rec_Pct_base to obtain a new ratio value Rec_Pct_new. Otherwise, maintain the current ratio value, ΔRec_Pct = 0%. That is: Rec_Pct_new = Rec_Pct_base + ΔRec_Pct, where ΔRec_Pct is only negative. If Rec_Pct_new ≤ 0%, Rec_Pct_new = 0%. Condition 1: The intake air circulation control state is the automatic control state or the manual external circulation control state; Condition 2: The intake air cycle temperature zone is not equal to Temp.Zone4; Condition 3: Anti-fog lock state CoolStartDefLockSt = Unlock.

[0035] Table 5: Risk level-intake cycle ratio increment calibration table 4.2 Control the air outlet mode according to the fogging risk level and modify its response action. The specific contents are as follows: If the following conditions are met 1, enter the anti-fog level control of the air outlet mode, Condition 1: The air outlet mode control state is the automatic control state.

[0036] After entering the anti-fog level control of the air outlet mode, the following logic is executed: When RiskLvl = Lvl1, the air outlet mode is not modified; When RiskLvl = Lvl2, the air outlet mode is not modified; When RiskLvl = Lvl3, If the current air outlet mode = window blowing|| foot blowing+window blowing, the air outlet mode will not be corrected; If the current air outlet mode = blowing on the face && (actual air outlet temperature > glass temperature – 5°C), the air outlet mode is switched to blowing on the face + blowing on the window; If the current air outlet mode = blowing face + blowing feet && (actual air outlet temperature > glass temperature – 5°C), the air outlet mode is switched to blowing face + blowing feet + blowing windows; If the current air outlet mode = foot blowing, the air outlet mode switches to foot blowing + window blowing; When RiskLvl = Lvl4, If the current air outlet mode = window blowing|| foot blowing+window blowing, the air outlet mode will not be corrected; If the current air outlet mode = blowing on the face && (actual air outlet temperature > glass temperature – 5°C), the air outlet mode is switched to blowing on the face + blowing on the window; If the current air outlet mode = face blowing + foot blowing && (actual air outlet temperature > glass temperature – 5°C), the air outlet mode is switched to foot blowing + window blowing; If the current air outlet mode = Foot Blowing, the air outlet mode switches to Window Blowing.

[0037] 4.3 Control the air volume according to the fogging risk level and correct its response action. The specific contents are as follows: Check Table 6 (Risk Level-Air Volume Compensation Level Calibration Table) to obtain the air volume compensation level BlwLvl_defOft under different fogging risk levels. The air volume compensation amount will be added to the current air volume display level BlwLvl_Cur to obtain the new air volume output value BlwLvl_New. That is: BlwLvl_New = BlwLvl_Cur + BlwLvl_defOft, where BlwLvl_defOft is only a positive value. If BlwLvl_New ≥ BlwLvlMax (maximum air volume level), BlwLvl_New = BlwLvlMax. Table 6: Risk level-air volume compensation level calibration table 4.4 Control and correct the target wind temperature according to the fogging risk level. The specific contents are as follows: If the following conditions are met 1, enter the target wind temperature anti-fog level control, Condition 1: The air conditioning system mode is not equal to shutdown.

[0038] After entering the target wind temperature anti-fog level control, the following logic is executed: based on the fogging risk level RiskLvl and the ambient temperature T_oat, Table 7 (Risk Level - Target Wind Temperature Compensation Calibration Table) is used to obtain the target outlet air temperature compensation Ttot_defOft under different fogging risk levels. The target outlet air temperature compensation will be added based on the current target outlet air temperature Ttrg to obtain a new target outlet air temperature output value TtrgNew. That is: TtrgNew = Ttrg + Ttot_defOft. The upper limit value of TtrgNew is 60°C and the lower limit value is 3°C.

[0039] Table 7: Risk level-target wind temperature compensation calibration table Specifically, an intelligent anti-fog automatic control system includes a measurement and acquisition module, an analysis and processing module, a condition judgment module and an execution monitoring module; The measurement and acquisition module is used to arrange multiple sensors to collect and monitor the environmental parameters around the vehicle glass, including the glass surface temperature, the air humidity near the glass, and the air temperature on the glass surface. The data is pre-processed and corrected in combination with the vehicle driving conditions and the temperature inside and outside the vehicle, and then the dew point temperature of the air near the windshield is calculated; An analysis and processing module is used to construct a fogging prediction model based on the pre-processed environmental parameters; The condition judgment module is used to determine whether the monitored glass is about to fog when it is determined that glass defogger is needed, trigger the anti-fogging control mechanism, dynamically determine whether to perform defogger according to real-time environmental parameters, and the specific execution mode, and adjust the defogger intensity and frequency; The execution monitoring module is used to monitor the defog status of the glass in real time, adjust the defog control strategy, and correct the actuator response action to optimize the anti-fogging control strategy.

[0040] The main reason for the fogging of the vehicle windshield is that the water vapor produced by the breathing of the driver and passengers in the car causes the humidity content in the car to rise rapidly. In low temperature weather, the dehumidification effect of the air conditioning system is weakened and the surface temperature of the windshield is much lower than the air temperature in the car, which increases the risk of air condensation on the glass surface. By applying this system to new energy vehicles, the proportion of air in the air intake cycle of the air conditioner can be further increased, the air intake temperature of the air conditioner in low temperature environments can be increased, and the cooling load demand of the air conditioning system can be reduced, thereby achieving better energy-saving and consumption-reducing effects and significantly increasing the vehicle's driving range.

[0041] The principle of windshield defogger is to defog by reducing the relative humidity of the air in the car cabin or heating the windshield.

[0042] a. Turn on the compressor to dehumidify → the absolute humidity of the air in the car interior decreases → the relative humidity of the air in the car interior decreases; b. Turn on the external circulation mode to introduce outdoor dry air → the absolute humidity of the air in the car interior is reduced → the relative humidity of the air in the car interior is reduced; c. Turn on the defrost mode and blow hot air to the windshield → the windshield temperature rises → the windshield temperature is greater than the dew point temperature For the embodiments of the intelligent anti-fog automatic control method and system of the present invention, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are only illustrative, and ordinary technicians in this field can understand and implement them without creative work.

[0043] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modification, equivalent substitution or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent anti-fog automatic control method, arranging multiple sensors to collect corresponding parameters around the window, the sensors include a defog sensor, a sunlight sensor, an indoor temperature sensor, an outdoor temperature sensor and a vehicle speed sensor, characterized in that: The following steps are involved: S1: Determine the effective state of automatic defogger. If effective, collect data near the glass through the defogger sensor. If not effective, exit the anti-fog function. S2: Collecting corresponding data through the sunlight sensor, indoor temperature sensor, outdoor temperature sensor and vehicle speed sensor; S3: Filter and correct the data collected in S1 and S2 to obtain preprocessing values; S4: Calculate the air dew point temperature Td according to the pre-processed value obtained in S3, and determine the sunlight intensity level and the cold start anti-fog lock state; S5: judging the fogging risk level RiskLvl calculation execution status according to the sunlight intensity level and the cold start anti-fog lock state described in S4, and calculating the fogging risk level RiskLvl according to the air dew point temperature Td described in S4, wherein the fogging risk level RiskLvl is divided into four levels from low risk to high risk: Lvl1, Lvl2, Lvl3, and Lvl4; S6: Modify the relevant actuator response action according to the fogging risk level described in S5.

2. An intelligent anti-fog automatic control method as claimed in claim 1, characterized in that: The data in S1 include the relative humidity value of the air near the glass, the original value of the glass surface temperature and the air temperature value near the glass. After preprocessing, the relative humidity value RH_air, the original value of the glass temperature T_gl_raw and the air temperature value T_glair near the glass are obtained respectively.

3. An intelligent anti-fog automatic control method as claimed in claim 2, characterized in that: The data in S2 include light intensity, original value of interior temperature, original value of ambient temperature and original value of vehicle speed. After processing, the maximum light intensity Solar_Max, interior temperature correction value T_ict, exterior temperature correction value T_oat and vehicle speed slow filtering value V_spd are obtained respectively. The temperature difference correction weighting coefficient α is obtained according to the temperature difference T_dif between the interior temperature correction value T_ict and the exterior temperature correction value T_oat and the vehicle speed slow filtering value V_spd.

4. An intelligent anti-fog automatic control method as claimed in claim 3, characterized in that: The air dew point temperature Td is calculated according to the dry-bulb air temperature T_da and the air relative humidity value RH_air, wherein the dry-bulb air temperature T_da = T_ict * α + T_glair * (1 - α), wherein α is a temperature difference correction weighting coefficient.

5. An intelligent anti-fog automatic control method as claimed in claim 4, characterized in that: The water vapor partial pressure Pqb2 corresponding to the relative humidity RH_air at the dry-bulb air temperature T_da is obtained by the moisture content calculation formula. The air dew point temperature Td is obtained according to the relative humidity RH_air corresponding to Pqb2. Pqb2 = RH_air * Pqb1 / 100%, where Pqb1 is the saturated water vapor partial pressure corresponding to the dry-bulb air temperature T_da.

6. An intelligent anti-fog automatic control method as claimed in claim 3, characterized in that: The fogging risk level RiskLvl is determined according to the vehicle exterior temperature correction value T_oat and the dew point temperature difference value Td_Diff, wherein the dew point temperature difference value Td_Diff = Td - T_gl, where T_gl is the glass surface temperature correction value.

7. The intelligent anti-fog automatic control method according to claim 1, characterized in that: The relevant actuator response actions include air intake cycle ratio, air outlet mode, air volume and target air temperature.

8. An intelligent anti-fog automatic control system, based on the intelligent anti-fog automatic control system according to any one of claims 1 to 7, characterized in that: It includes a measurement and acquisition module, an analysis and processing module, a condition judgment module and an execution monitoring module; the measurement and acquisition module is used to arrange multiple sensors to collect and monitor the environmental parameters around the vehicle glass, including the glass surface temperature, the air humidity near the glass and the air temperature on the glass surface, and to perform data preprocessing and correction in combination with the vehicle driving conditions and the temperature inside and outside the vehicle, and then calculate the air dew point temperature near the windshield; The analysis and processing module is used to build a fogging prediction model based on the pre-processed environmental parameters. The condition judgment module is used to determine whether the monitored glass is about to fog when it is determined that glass defogging is necessary, trigger the anti-fogging control mechanism, dynamically decide whether to perform defogging and the specific execution mode according to the real-time environmental parameters, and adjust the defogging intensity and frequency. The execution monitoring module is used to monitor the defogging status of the glass in real time, adjust the defogging control strategy, and correct the actuator response action to optimize the anti-fogging control strategy.

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