Cabin comfort intelligent control system and method

Abstract

The application provides a cockpit comfort intelligent control system and method, comprising: step S1: obtaining a user's direct setting value for a cockpit environment as a basic target value; obtaining one or more external comfort interference source signals and passenger human body characteristic signals; step S2: obtaining a corresponding dynamic compensation correction amount based on the external comfort interference source signals and the passenger human body characteristic signals; step S3: superimposing the basic target value and the dynamic compensation correction amount to obtain a final execution target value; and step S4: generating a driving signal to drive a corresponding cockpit actuator to execute compensation according to the difference between the current cockpit environment parameter and the final execution target value. The application aims to solve the problem that the existing cockpit comfort control system cannot accurately respond to local physical interference and the adjustment process experience is poor.

Description

Technical Field

[0001] This invention belongs to the field of vehicle intelligent control technology, specifically relating to an intelligent control system and method for cabin comfort. More specifically, it is an intelligent control system and method for cabin comfort based on the synthesis of concrete physical input and dynamic compensation. In particular, it is an intelligent control system and method for cabin comfort that senses specific external interference sources and occupant body characteristics, performs dynamic compensation superposition based on user settings, and simultaneously optimizes the dynamic quality of the compensation process. Background Technology

[0002] Currently, most vehicle cabin automatic comfort control systems employ closed-loop feedback architectures based on thermal comfort mathematical models, such as the predictive average voting model. These systems collect macroscopic environmental parameters such as average temperature and humidity within the cabin, and combine these with fixed estimates of parameters like occupant clothing and metabolic rate to calculate an abstract comfort index, such as the PMV value. Subsequently, the system uses feedback control algorithms, such as proportional-integral-derivative (PI-DE) adjustment, to adjust actuators like the air conditioning system to bring the index closer to its theoretical optimal value.

[0003] However, the aforementioned mainstream technical solutions have inherent flaws. First, their input parameters are too general, failing to perceive and respond to specific, localized physical disturbances, such as strong sunlight from a particular direction or direct airflow from an air conditioning vent, resulting in a lack of targeted adjustment. Second, their control objective is an abstract score; when the score is poor, the system cannot trace the specific physical source of discomfort, and its compensatory actions, such as global cooling, are often indiscriminate and may negatively impact undisturbed areas. Third, existing technologies typically focus only on the final steady-state comfort, neglecting the transient experience during adjustment. They lack proactive constraints on dynamic qualities affecting occupants, such as the rate of change of parameters like temperature and wind speed, and overshoot, easily causing discomfort such as "thermal shock" for passengers.

[0004] In addition, although some advanced control methods take into account external disturbances, such as the impact of predicted future vehicle speed on heat load, and use complex algorithms such as model predictive control for optimization, they are still essentially system-dominated fully automatic control, which may completely override or violate the user's manual settings, causing the user to feel out of control and resulting in a poor human-computer interaction experience.

[0005] Therefore, existing technologies urgently need a new cabin comfort control solution that can accurately sense specific sources of interference and individual characteristics, respect user settings, and perform fine-grained quality control on the adjustment process. Summary of the Invention

[0006] In view of the deficiencies in the prior art, the purpose of this invention is to provide an intelligent control system and method for cabin comfort.

[0007] A cockpit comfort intelligent control method according to the present invention includes: Step S1: Obtain the user's direct settings for the cabin environment as the basic target values; obtain one or more external comfort interference source signals and occupant human body characteristic signals; Step S2: Based on the external comfort interference source signal and the occupant human body characteristic signal, obtain the corresponding dynamic compensation correction amount; Step S3: Superimpose the basic target value and the dynamic compensation correction amount to obtain the final execution target value; Step S4: Based on the difference between the current cockpit environment parameters and the final execution target value, generate a drive signal to drive the corresponding cockpit actuator to perform compensation.

[0008] Preferably, in step S1, the external comfort interference source signal includes a signal representing directional sunlight; the occupant human body characteristic signal includes a signal representing the occupant's clothing type.

[0009] Preferably, the signal representing directional sunlight is acquired through a photodiode array; the signal representing the occupant's clothing type is obtained through a visual sensor and a neural network model.

[0010] Preferably, in step S2, the dynamic compensation correction amount is the output result based on the external comfort interference source signal and the occupant human body characteristic signal as input, and then based on the pre-established compensation mapping relationship.

[0011] Preferably, at least one control quality parameter is determined to achieve the final execution target value; The control quality parameters include the approximation rate, the allowable overshoot, or the overshoot regression rate.

[0012] According to the present invention, a cabin comfort intelligent control system is provided for implementing a cabin comfort intelligent control method, comprising: an image perception module: acquiring the user's direct set value for the cabin environment as a basic target value; acquiring one or more external comfort interference source signals and occupant human body characteristic signals; Intelligent decision-making and synthesis module: Based on the external comfort interference source signal and the occupant human body characteristic signal, obtain the corresponding dynamic compensation correction amount; superimpose the basic target value and the dynamic compensation correction amount to obtain the final execution target value; Execution control module: Based on the difference between the current cockpit environment parameters and the final execution target value, it generates a drive signal to drive the corresponding cockpit actuators to perform compensation.

[0013] Preferably, in the image perception module, the external comfort interference source signal includes a signal representing directional sunlight; the occupant human body characteristic signal includes a signal representing the occupant's clothing type.

[0014] Preferably, the signal representing directional sunlight is acquired through a photodiode array; the signal representing the occupant's clothing type is obtained through a visual sensor and a neural network model.

[0015] Preferably, in the intelligent decision-making and synthesis module, the dynamic compensation correction amount is the output result based on the external comfort interference source signal and the occupant human body characteristic signal as input, and then based on the pre-established compensation mapping relationship.

[0016] Preferably, at least one control quality parameter is determined to achieve the final execution target value; The control quality parameters include the approximation rate, the allowable overshoot, or the overshoot regression rate.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. By sensing specific physical interference and human characteristics, this invention can achieve targeted, precise, and localized compensation, avoiding the side effects and unnecessary energy consumption caused by global adjustment, and achieving source control.

[0018] 2. This invention provides a "basic setting + dynamic correction" composite architecture, which ensures that all intelligent compensations are fine-tuned on the benchmark clearly set by the user. This fundamentally solves the problem of conflict between traditional fully automatic systems and user intentions, which leads to the user's "sense of loss of control", and achieves a harmonious unity between intelligent assistance and user autonomy.

[0019] 3. The present invention adopts feedforward-driven control logic, which can pre-compensate before discomfort occurs on a large scale, and ensures smooth and shock-free adjustment process by actively constraining dynamic processes such as approximation rate and overshoot, thus significantly improving dynamic comfort.

[0020] 4. This invention abandons the abstract comfort model with complex and difficult-to-obtain parameters, and instead adopts compensation logic based on specific and measurable physical parameters, making the system effect easier to objectively and directly verify and optimize, and reducing the complexity of system debugging and iteration.

[0021] 5. By using user settings as a benchmark and superimposing dynamic compensation, this invention achieves accurate response to local disturbances and ensures the smoothness of the adjustment process, thereby improving the dynamic comfort experience while respecting user autonomy. Attached Figure Description

[0022] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 The system architecture and data flow diagram provided for this invention; Figure 2 This is a schematic diagram of the method flow provided by the present invention; Figure 3 This is a schematic diagram illustrating the synthesis of the final execution target value from user-defined basic values ​​and dynamic compensation corrections provided by the present invention. Figure 4 This diagram illustrates a comparison between the feedforward-feedback hybrid control structure provided by this invention and the traditional closed-loop control structure; from top to bottom, the diagram shows the traditional closed-loop control structure and the feedforward-feedback hybrid control structure. Detailed Implementation

[0023] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0024] This invention aims to overcome the aforementioned shortcomings of existing technologies and propose a novel technological paradigm. Its purpose is to directly perceive specific physical disturbances and human characteristics, intelligently superimpose dynamic compensation amounts while absolutely respecting the user's explicit settings, and constrain the dynamic quality of the compensation process, ultimately achieving precise, stable, and user-friendly intelligent control of cabin comfort. In other words, this invention aims to solve the problems of existing cabin comfort control systems, which rely on abstract models and macroscopic parameters, failing to accurately respond to specific local physical disturbances and dynamic individual occupant states, lacking dynamic quality optimization in their control processes, and often conflicting with the user's direct intentions.

[0025] According to the present invention, a cabin comfort intelligent control method includes: S1: acquiring the user's direct set value for the cabin environment as a basic target value; S2: acquiring at least one specific external comfort interference source signal and at least one occupant human body characteristic signal in real time; the specific external comfort interference source signal includes: vehicle external ambient temperature, solar radiation intensity including orientation and angle, and real-time wind direction of each electric air vent; the occupant human body characteristic signal includes: clothing type estimated by a visual sensor, and age and weight information, i.e., BMI information, obtained from a pre-stored file; S3: calculating one or more dynamic compensation correction amounts based on a pre-established compensation mapping relationship, according to the external interference source signal and the human body characteristic signal; S4: superimposing the basic target value with the one or more dynamic compensation correction amounts to generate a final execution target value, and determining the control quality parameters required to achieve the target, the control quality parameters including at least one of approximation rate, allowable overshoot, and overshoot return rate; S5: driving the corresponding actuator to perform compensation according to the requirements of the control quality parameters so that the environmental parameters approach the final execution target value.

[0026] Specifically, at least one of the control quality parameters is dynamically adjusted based on the age or weight information in the occupant's human characteristic signal.

[0027] Specifically, the dynamic compensation correction includes at least one of the following: temperature compensation for offsetting local thermal interference, air volume compensation for adjusting perceived wind speed, and wind direction compensation for avoiding direct airflow to the human body.

[0028] According to the present invention, a cabin comfort intelligent control system for implementing a cabin comfort intelligent control method includes: a concrete perception module, an intelligent decision-making and synthesis module, and a multi-channel collaborative execution module.

[0029] According to the present invention, a vehicle includes the aforementioned intelligent control system for cabin comfort.

[0030] Example 1: This invention provides a cabin comfort intelligent control method and system, which constructs an innovative feedforward-feedback hybrid control architecture, a "predictive" hybrid control. Specifically, it uses direct user settings and concrete real-time perception signals as feedforward inputs, enabling predictive compensation decisions before discomfort develops on a large scale. Correspondingly, its control target is a specific physical quantity, and the entire adjustment process is strictly constrained by preset control quality parameters, thereby achieving a fundamental shift from reactive to proactive prediction.

[0031] The method provided by the present invention includes: obtaining the user's direct setpoint for the cabin environment as a basic target value; acquiring specific external comfort disturbance source signals and occupant human body characteristic signals in real time; calculating dynamic compensation correction amount; superimposing the basic target value and the dynamic compensation correction amount to generate a final execution target value; determining the control quality parameters required to achieve the target, such as approximation rate and allowable overshoot; and finally, driving the actuator to perform smooth adjustment by adjusting the parameters of the control algorithm according to the final execution target value and control quality parameters.

[0032] In this embodiment, the system can be integrated into a vehicle, such as a car, truck, or any vehicle with an enclosed cabin. The system includes: a visualization perception module, an intelligent decision-making and synthesis module, and a multi-channel collaborative execution module.

[0033] Among them, the concrete perception module is used to replace the general measurement of the environment in traditional technology, and instead accurately and in real time acquires specific physical signals that have a direct impact on passenger comfort.

[0034] These signals are divided into two categories: one is external comfort interference source signals, such as the intensity and direction of solar radiation; the other is occupant human characteristic signals, such as the occupant's clothing and physiological characteristics.

[0035] The intelligent decision-making and synthesis module, as the core control unit of the system, receives basic setting values ​​input by the user, such as temperature values ​​set via the vehicle's central control screen or physical buttons, and uses these setting values ​​as the control reference. This module also receives various specific signals from the visualization sensing module and, based on internally stored compensation mapping relationships, calculates dynamic compensation correction amounts to counteract interference or improve comfort. Furthermore, this module algebraically superimposes the user's basic setting values ​​and the calculated dynamic compensation correction amounts to synthesize the final, specific execution target value.

[0036] At the same time, the module also determines the control quality parameters required to achieve this goal, in order to regulate the dynamic characteristics of the regulation process.

[0037] The multi-channel collaborative execution module serves as the system's execution unit. It receives the final execution target value and control quality parameters from the intelligent decision-making and synthesis module, and accordingly drives one or more actuators in the vehicle, such as the air conditioning system and seat temperature control system, to collaboratively and smoothly complete the adjustment task in accordance with the manner specified by the quality parameters.

[0038] User baseline setting A represents the user's sovereignty and basic intent, serving as the control anchor. Dynamic compensation and correction B represents the system's intelligence, representing dynamic and precise fine-tuning based on real-time, specific perception. By superimposing and calculating these two values, a unique final execution target value C can be achieved.

[0039] This "basic settings + dynamic correction" architecture fundamentally ensures that all intelligent behaviors of the system are assisted and enhanced within the framework of the user's intentions, rather than overriding or violating them, thus effectively solving the "sense of loss of control" problem that may be brought about by traditional fully automatic systems.

[0040] The technical solution of this application will be described in more detail below through several specific embodiments.

[0041] Example 1: This example aims to describe in detail how the intelligent cabin comfort control system and method provided in this application can perform precise and efficient dynamic compensation in a typical scenario where a vehicle is exposed to strong unilateral sunlight on a summer afternoon, in order to support a solution to cope with local sunlight interference.

[0042] In a specific application scenario, a vehicle is traveling westward at 3 PM in summer, resulting in strong sunlight hitting the driver's side window. After entering the vehicle, the driver sets the air conditioning temperature to 26°C via the touchscreen of the in-vehicle infotainment system.

[0043] During the process of acquiring user settings, the intelligent decision-making and synthesis module receives the user-set temperature value via the vehicle's bus system, such as the controller area network, and defines this value T_base = 26℃ as the user's basic setting value A, which serves as the benchmark for all subsequent calculations and controls.

[0044] In parallel, the system acquires sensing signals, at which point the visualization sensing module begins to operate. In this embodiment, the visualization sensing module may include multiple sensor subunits: One component is the solar radiation sensing unit, which can consist of an array of photodiodes mounted above the dashboard or inside the A-pillar. By comparing the signal strength differences generated by different diodes and combining this with the vehicle's GPS data and time information to calculate the solar azimuth and altitude angles, the system can not only accurately measure the total solar radiation intensity but also determine its primary source direction. In this scenario, the unit detected a solar radiation intensity of up to 800 W / m² from the left side of the vehicle, i.e., the driver's side, while the solar radiation intensity from the right side, i.e., the passenger's side, was only 150 W / m². This is a typical, specific, and non-uniform external comfort interference source signal.

[0045] The second component is the in-cabin visual perception unit, typically a wide-angle camera mounted near the rearview mirror or on the overhead control panel. The cabin images captured by the camera are fed in real-time into a pre-trained convolutional neural network model. This model analyzes the images to identify the occupant's clothing type. In this scenario, the model identifies the driver as wearing a short-sleeved shirt. This is a specific occupant anatomy signal. These two signals—directional sunlight and clothing type—together constitute the key inputs for subsequent decision-making.

[0046] Subsequently, the process proceeds to the intelligent decision-making and synthesis module, which performs the compensation correction calculation. The intelligent decision-making and synthesis module internally stores a set of compensation mapping relationships, which can take the form of a multidimensional lookup table, a set of fuzzy logic rules, or a complex machine learning model trained on data, such as a gradient-boosting decision tree or a neural network. The input to this mapping relationship is a vector containing multiple concrete perceptual signals, such as [Sunlight Intensity_Left: 800, Sunlight Intensity_Right: 150, Clothing Type: Short Sleeve, Occupant Position: Driver's Seat, ...].

[0047] In the calculation of the compensation correction, the intelligent decision-making and synthesis module performs calculations based on this compensation mapping relationship. The rule base or model determines that "high-intensity sunlight of 800W / m²" acting on the "left side of the body" of an "occupant wearing short sleeves" will generate a significant local heat load, resulting in a perceived temperature much higher than the set 26℃. To counteract this discomfort, the system calculates a temperature compensation correction for the driver's zone, i.e., the dynamic compensation correction B. In this example, this value is Δ_temp = -3℃, serving as a temperature compensation amount to offset local thermal interference.

[0048] Meanwhile, in the intelligent decision-making and synthesis module, which determines control quality parameters, the system obtains the driver's age information from the user profile or analyzes their preferences based on historical driving behavior. Assuming the profile indicates the driver is young, such as 25 years old, and highly adaptable to environmental changes, the system will match a control strategy for them, setting the temperature approach rate to 2.0℃ / minute to quickly eliminate discomfort.

[0049] In the intelligent decision-making and synthesis module, the user's basic setting value A is superimposed with the calculated dynamic compensation correction amount B to generate the final execution target value C for the driver's left-side partition, i.e.: T_final_left = T_base + Δ_temp = 26℃ + (-3℃) = 23℃ Where T_final_left represents the final execution target value, T_base represents the user's basic setting value, and Δ_temp represents the dynamic compensation correction amount.

[0050] Correspondingly, for the passenger side, which is not affected by strong sunlight, the compensation correction can be 0 or a small value, so its final target value remains around 26°C.

[0051] Finally, the intelligent decision-making and synthesis module sends the calculated final execution target values, namely 23°C on the left and 26°C on the right, and the control quality parameters, namely at an approximation rate of 2.0°C / minute, to the multi-channel collaborative execution module. In this embodiment, the multi-channel collaborative execution module collaboratively controls at least two actuators: the zoned air conditioning system and the area radiant panel on the door panel.

[0052] Specifically, the multi-channel collaborative execution module first adjusts the air damper mixing ratio of the left air conditioning vent to deliver cooler air at a lower temperature and appropriately increases the air volume; and then activates the radiant cooling plate under the interior panel of the driver's side door panel, such as a semiconductor cooling chip array based on the Peltier effect. This cooling plate absorbs the heat emitted by the driver's body through non-contact radiative heat exchange by lowering its own surface temperature, thereby providing a gentle cooling experience without the feeling of being blown by a draft.

[0053] This combined convective and radiative cooling mechanism of the air conditioner creates a more efficient and comfortable local microclimate. Throughout the adjustment process, the control algorithms within the multi-channel collaborative execution module, such as the parameters of the proportional-integral-derivative controller, are adjusted based on the received quality parameter of "approach rate 2.0℃ / minute." To achieve a faster approach rate, the controller's proportional and integral gains can be set relatively high. Furthermore, the module continuously monitors the readings of micro-environmental temperature sensors installed near the driver, such as those located on the B-pillar or the side of the seat, and compares them with the final target value of 23℃. This comparison then controls the actuator's output power to ensure that the actual temperature drop rate in that area is precisely constrained to approximately 2.0℃ / minute, ultimately reaching and maintaining a stable temperature of 23℃.

[0054] Through this embodiment, the system accurately identifies and resolves the local overheating problem caused by western exposure while fully respecting the user's global setting of 26°C. The adjustment process is fast and stable, without causing excessive cooling interference to the front passenger, fully demonstrating the beneficial effects of this application in terms of accurate compensation, user sovereignty, and smooth experience.

[0055] Example 2 aims to demonstrate how the system adaptively adjusts control quality parameters based on the physiological characteristics of the occupants, specifically their age, thereby providing a more humane and caring adjustment experience, and particularly reflects the improvements of this application in dynamic control quality.

[0056] The scenario is set on a winter morning with an outside temperature of -5°C. A 45-year-old middle-aged user enters the car, starts the vehicle, and sets the target temperature of the automatic air conditioning to 24°C.

[0057] In obtaining user settings, the intelligent decision-making and synthesis module obtains the user's basic setting value A, namely T_base = 24℃.

[0058] During the acquisition of sensory signals, the visualization perception module begins operation. The onboard external temperature sensor reports the current outside temperature as -5°C. Simultaneously, the cabin visual perception unit, using a camera and image recognition algorithms, determines that the user is wearing a heavy coat. Crucially, the visualization perception module accesses the vehicle's locally stored user profile and obtains the user's age information as 45 years old. The occupant's anthropometric signals here include clothing type and age. This profile may be created when the user first uses the vehicle or synchronized via a mobile application.

[0059] Subsequently, the intelligent decision-making and synthesis module performs analysis. On one hand, considering the user is wearing a heavy coat, if the interior temperature is rapidly heated to 24°C, the user's body heat generation and the coat's insulation effect will likely result in stuffiness. To proactively avoid this foreseeable discomfort, the system calculates a predictive temperature compensation correction B based on its compensation mapping relationship, for example, Δ_temp = -1°C. In terms of control quality parameters, the intelligent decision-making and synthesis module performs a crucial adaptive adjustment. Internally, it contains an adaptive rule base related to control quality parameters, which correlates the occupant's physiological characteristics with the dynamic characteristics of the adjustment process. For example, one rule could be set as: "When the occupant's age is between 40 and 60 years old, the upper limit of the temperature approach rate should be set to 1.2°C / minute." It is understandable that middle-aged people may be more sensitive to drastic changes in ambient temperature than younger people, and excessively rapid temperature increases may lead to physiological discomfort such as rapid vasodilation. Based on this, the system determined the control quality parameters for this round of adjustment based on the acquired signal of "age 45 years old": temperature approach rate ≤ 1.2℃ / minute.

[0060] In step S40, the final execution target value is synthesized: T_final = T_base + Δ_temp = 24℃ + (-1℃) = 23℃.

[0061] Finally, in step S60 of driving the actuator, the multi-channel collaborative execution module receives the final execution target value of 23°C and the quality parameter "approach rate ≤ 1.2°C / minute". To meet this gradual temperature rise requirement, the multi-channel collaborative execution module adjusts the parameters of its internal heating control algorithm.

[0062] For example, it can use a smaller proportional gain to avoid excessive power consumption at the initial heating stage, which could lead to excessively high outlet temperatures. Simultaneously, it may begin reducing heating power earlier, using a "slow simmer" rather than a "stir-fry" approach to raise the cabin temperature. Actuators, such as the heaters and fans of the air conditioning system, are driven to operate in a very gentle manner, allowing the cabin temperature to slowly and linearly rise to 23°C at a rate not exceeding 1.2°C / minute.

[0063] Compared to heating rates of 2.5°C / minute or faster that might be used for younger users, this embodiment provides middle-aged users with a virtually imperceptible and extremely smooth heating process. This effectively avoids physiological discomfort and a "baking" sensation that may be caused by drastic temperature changes, demonstrating the system's deep understanding of the needs of different user groups and its personalized care, elevating comfort control to a level of human-centered care.

[0064] Example 3 further demonstrates how the system prioritizes the health and safety of occupants by strictly constraining another key control quality parameter—"allowable overshoot"—when it senses a special health condition of the occupants.

[0065] Suppose a passenger has a health condition requiring monitoring of their blood sugar levels and, through authorization, allows their smartwatch or health monitoring device to interact with the vehicle's system on a limited basis. One day, the passenger enters the vehicle and sets the air conditioning to 22°C, while the actual interior temperature is 25°C. The system then needs to perform a cooling operation.

[0066] In acquiring user settings and collecting sensing signals, the system obtained the basic target value T_base = 22℃.

[0067] Furthermore, the visualization sensing module obtains an anonymous health status signal from the occupant's wearable device interface via wireless communication methods such as Bluetooth Low Energy. This signal may not be a specific blood glucose value, but rather a processed trend indicator or risk level, such as "recent large fluctuations in blood glucose" or "high sensitivity to body temperature regulation".

[0068] Next, in steps S30 and S50 executed by the intelligent decision-making and synthesis module, the system prioritizes this health factor. The compensation mapping relationship within the intelligent decision-making and synthesis module includes a set of "health and safety" rules. This rule set is activated when a health-related input signal is detected. The rules determine that for individuals with unstable blood glucose levels, rapid fluctuations or overshoots in ambient temperature (i.e., temperatures below the target value) may induce physiological stress responses and should be strictly avoided.

[0069] Therefore, the system generates an extremely stringent control quality parameter: an allowable overshoot of ≤ 0.2℃. This means that throughout the entire cooling process, the temperature at any point inside the cabin cannot fall below 22℃ - 0.2℃ = 21.8℃ at any given time. This value is far smaller than the overshoot of 0.5℃ or even 1.0℃ that might be allowed under normal circumstances. Furthermore, in the compensation correction calculation, the system calculates a small positive compensation correction, such as Δ_temp = +0.2℃, setting the final target at 22.2℃, thus reserving more margin for the control process.

[0070] The final execution target value is synthesized as T_final = 22℃ + 0.2℃ = 22.2℃.

[0071] In the actuator driving step S60, the multi-channel cooperative execution module 30 faces a control task with strict constraints. To ensure that the overshoot does not exceed 0.2℃, it adopts a more conservative and precise control strategy. For example, the parameters of its internal proportional-integral-derivative control algorithm are specially adjusted to significantly increase the weight of the derivative (D) term, thereby enhancing the ability to predict and suppress temperature change trends and thus "brake" in advance when the temperature approaches the target value. In addition, the system may activate a logic called "dead-zone control," which stops the operation of the refrigeration compressor after the temperature enters a very narrow range, such as 22.2℃ to 22.5℃, and relies solely on the fan for air circulation, allowing the temperature to naturally drop to the target point. In this way, the system can effectively avoid temperature overshoot caused by the inertia of the refrigeration system.

[0072] The result of this embodiment is that the cabin temperature steadily decreased from 25°C, and the cooling rate slowed significantly when approaching 22.2°C, eventually stabilizing near the target value. Throughout the process, the temperature curve never fell below 21.8°C. By actively and strictly constraining the quality parameter of "allowable overshoot," the system successfully provided a safer and more reliable cabin environment for occupants with special health needs. This fully demonstrates that the proposed solution can extend the scope of comfort control to proactive health care.

[0073] Example 4 focuses on how the system utilizes precise control of the actuator to achieve a more advanced and seamless comfort adjustment by compensating for the dimension of "wind direction" rather than just the traditional "temperature" or "airflow".

[0074] The scenario is as follows: A user is feeling slightly fatigued after a long drive and is very sensitive to direct airflow from the air conditioner. He sets the air conditioner to 23°C with the fan speed on "automatic".

[0075] In the steps of acquiring user settings and collecting sensing signals, the system obtains the basic target value T_base = 23℃. The concrete sensing module, by analyzing the user's historical behavior data or directly from the user's preference settings, determines that the user has the characteristic of being "sensitive to the feeling of wind". In addition, this module can obtain the current wind direction information with precision down to the angle in real time by reading the encoder values ​​of the stepper motors inside each electric air outlet. This can also be regarded as a kind of external comfort interference source signal in a broad sense, that is, an unsuitable wind direction constitutes a kind of interference.

[0076] After a period of time, due to the rise in outside temperature or heat generated by the occupants' metabolism, the system detects a slight upward trend in temperature through the cabin temperature sensor, requiring an increase in cooling capacity to maintain the target of 23°C.

[0077] At this point, the intelligent decision-making and synthesis module is activated during the calculation of the compensation correction. Traditional automatic air conditioning systems typically lower the outlet air temperature or increase the fan speed, but both methods enhance the user's "windy feeling," contradicting known user preferences. The decision module provided by this invention selects a better strategy, namely, calculating a wind direction compensation correction B. This correction is not a simple scalar, but rather a vector of one or more target angles. For example, if system analysis reveals that the air vent currently facing the driver's chest is the primary source of the windy feeling, the calculated wind direction compensation correction is: deflecting the vertical guide vane of the air vent upwards by 30 degrees and the horizontal guide vane to the left by 20 degrees to guide the airflow to the lower edge of the windshield.

[0078] In determining the synthesized target value and control quality parameters, the final target value is no longer just a temperature value, but a state vector containing multiple dimensions, such as: [target temperature: 23℃, target airflow: level 2, target wind direction at the driver's side air outlet: {vertical angle: 50°, horizontal angle: 10°}]. Furthermore, the determined control quality parameters may include the angular velocity of the wind direction servo motor to ensure that changes in wind direction are slow and imperceptible.

[0079] Finally, in the drive actuator, the multi-channel collaborative execution module precisely controls the stepper motor of the corresponding air outlet, causing its air guide plate to slowly rotate to the calculated target angle. In this way, the cool air is blown towards the windshield, flows along the glass surface and exchanges heat with the interior surfaces of the roof, and then mixes with the air throughout the cabin. The overall environment is cooled by utilizing the air circulation throughout the cabin and the indirect heat exchange with the interior surfaces.

[0080] Ultimately, passengers enjoyed a consistently comfortable 23°C environment with virtually no direct airflow. The system's refined compensation for "airflow direction" makes the adjustment process more invisible and imperceptible, significantly enhancing the premium feel and comfort of the ride. This fully demonstrates that the dynamic compensation correction in this application can be multi-dimensional, including but not limited to temperature, airflow, and airflow direction, thereby achieving more flexible and user-friendly control.

[0081] Example 5: This invention provides a method for intelligent control of cabin comfort, comprising: acquiring a user's direct setpoint for the cabin environment as a basic target value; acquiring at least one specific external comfort disturbance source signal and at least one occupant human body characteristic signal in real time; calculating one or more dynamic compensation correction amounts based on a pre-established compensation mapping relationship and according to the external comfort disturbance source signal and the occupant human body characteristic signal; superimposing the basic target value with the one or more dynamic compensation correction amounts to generate a final execution target value; determining at least one control quality parameter required to achieve the final execution target value, the control quality parameter including at least one of approximation rate, allowable overshoot, and overshoot regression rate; acquiring current cabin environment parameters in real time; adjusting the parameters of a control algorithm based on the difference between the current cabin environment parameters and the final execution target value, and according to the control quality parameter; generating a drive signal through the control algorithm to drive the corresponding actuator to perform compensation, so that the current cabin environment parameters approach the final execution target value.

[0082] The external comfort interference source signal includes a signal representing directional sunlight, and the occupant human body characteristic signal includes a signal representing the occupant's clothing type.

[0083] The signal representing directional sunlight is acquired through a photodiode array, and the signal representing the occupant's clothing type is identified through a visual sensor and a neural network model.

[0084] The specific values ​​of the control quality parameters are dynamically and adaptively adjusted based on the occupant's human body characteristic signals.

[0085] The occupant human characteristic signal includes a signal representing the occupant's age and / or a signal representing the occupant's health data, and the value of the approximation rate is adjusted according to the signal representing the occupant's age, and / or the value of the allowable overshoot is adjusted according to the signal representing the occupant's health data.

[0086] Specifically, the dynamic compensation correction includes at least one of the following: temperature compensation for offsetting local thermal interference, air volume compensation for adjusting perceived wind speed, and wind direction compensation for avoiding direct airflow to the human body.

[0087] Specifically, the actuator includes at least two selected from the group consisting of an air conditioning system, a seat temperature control system, and a zone radiant panel, and drives the at least two to perform compensation.

[0088] The present invention also provides a cabin comfort intelligent control system, comprising: a figurative sensing module for acquiring in real time at least one specific external comfort disturbance source signal, at least one occupant human body characteristic signal, and current cabin environment parameters; An intelligent decision-making and synthesis module is configured to: receive the user's direct set value for the cabin environment as the basic target value; calculate one or more dynamic compensation corrections based on the external comfort interference source signal and the occupant human body characteristic signal; superimpose the basic target value and the dynamic compensation corrections to generate the final execution target value; and determine at least one control quality parameter required to achieve the final execution target value. An execution control module is configured to: receive the final execution target value and the control quality parameters, adjust the parameters of a control algorithm according to the control quality parameters, and then generate a drive signal through the control algorithm based on the difference between the current cabin environment parameters and the final execution target value, so as to drive the corresponding actuator to perform compensation.

[0089] Specifically, the intelligent decision-making and synthesis module is further configured to dynamically and adaptively adjust the specific values ​​of the control quality parameters based on the occupant's human body characteristic signals.

[0090] The present invention also provides a cabin comfort intelligent control system, which can be implemented by executing the process steps of the cabin comfort intelligent control method. That is, those skilled in the art can understand the cabin comfort intelligent control method as a preferred embodiment of the cabin comfort intelligent control system.

[0091] According to the present invention, a cabin comfort intelligent control system is provided for implementing a cabin comfort intelligent control method, comprising: an image perception module: acquiring the user's direct set value for the cabin environment as a basic target value; acquiring one or more external comfort interference source signals and occupant human body characteristic signals; Intelligent decision-making and synthesis module: Based on the external comfort interference source signal and the occupant human body characteristic signal, obtain the corresponding dynamic compensation correction amount; superimpose the basic target value and the dynamic compensation correction amount to obtain the final execution target value; Execution control module: Based on the difference between the current cockpit environment parameters and the final execution target value, it generates a drive signal to drive the corresponding cockpit actuators to perform compensation.

[0092] Those skilled in the art will understand that, besides implementing the system and its various devices, modules, and units provided by this invention in the form of purely computer-readable program code, the same functions can be achieved entirely through logical programming of the method steps, making the system and its various devices, modules, and units of this invention function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, the system and its various devices, modules, and units provided by this invention can be considered as a hardware component, and the devices, modules, and units included therein for implementing various functions can also be considered as structures within the hardware component; alternatively, the devices, modules, and units for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0093] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A method for intelligent control of cabin comfort, characterized in that, include: Step S1: Obtain the user's direct settings for the cockpit environment as the basic target values; Acquire signals from one or more external comfort disturbance sources and occupant human body characteristic signals; Step S2: Based on the external comfort interference source signal and the occupant human body characteristic signal, obtain the corresponding dynamic compensation correction amount; Step S3: Superimpose the basic target value and the dynamic compensation correction amount to obtain the final execution target value; Step S4: Based on the difference between the current cockpit environment parameters and the final execution target value, generate a drive signal to drive the corresponding cockpit actuator to perform compensation.

2. The intelligent cabin comfort control method according to claim 1, characterized in that, In step S1, the external comfort interference source signal includes a signal representing directional sunlight; the occupant human body characteristic signal includes a signal representing the occupant's clothing type.

3. The intelligent cabin comfort control method according to claim 2, characterized in that, The signal representing directional sunlight is acquired through a photodiode array; the signal representing the type of occupant clothing is obtained through a visual sensor and a neural network model.

4. The intelligent control method for cabin comfort according to claim 1, characterized in that, In step S2, the dynamic compensation correction amount is the output result based on the external comfort interference source signal and the occupant human body characteristic signal as input, and then according to the pre-established compensation mapping relationship.

5. The intelligent cabin comfort control method according to claim 1, characterized in that, Determine at least one control quality parameter required to achieve the final execution target value; The control quality parameters include the approximation rate, the allowable overshoot, or the overshoot regression rate.

6. A cabin comfort intelligent control system, used to implement the cabin comfort intelligent control method according to any one of claims 1 to 5, characterized in that, include: Image perception module: Acquires the user's direct settings for the cabin environment as the basic target values; Acquire signals from one or more external comfort disturbance sources and occupant human body characteristic signals; Intelligent decision-making and synthesis module: Based on the external comfort interference source signal and the occupant human body characteristic signal, obtain the corresponding dynamic compensation correction amount; superimpose the basic target value and the dynamic compensation correction amount to obtain the final execution target value; Execution control module: Based on the difference between the current cockpit environment parameters and the final execution target value, it generates a drive signal to drive the corresponding cockpit actuators to perform compensation.

7. The intelligent control system for cabin comfort according to claim 6, characterized in that, In the image perception module, the external comfort interference source signal includes a signal representing directional sunlight; the occupant human body characteristic signal includes a signal representing the occupant's clothing type.

8. The intelligent control system for cabin comfort according to claim 7, characterized in that, The signal representing directional sunlight is acquired through a photodiode array; the signal representing the type of occupant clothing is obtained through a visual sensor and a neural network model.

9. The intelligent control system for cabin comfort according to claim 6, characterized in that, In the intelligent decision-making and synthesis module, the dynamic compensation correction amount is the output result based on the external comfort interference source signal and the occupant human body characteristic signal as input, and then according to the pre-established compensation mapping relationship.

10. The intelligent control system for cabin comfort according to claim 6, characterized in that, Determine at least one control quality parameter required to achieve the final execution target value; The control quality parameters include the approximation rate, the allowable overshoot, or the overshoot regression rate.

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