Intelligent control method and system for automobile air conditioner

Abstract

The invention discloses an intelligent control method and system for an automobile air conditioner, and relates to the technical field of intelligent sensing control of air conditioners. The intelligent control method and system for the automobile air conditioner comprise the steps that S1, multi-source data are collected and preprocessed, and a standardized data set is constructed; s2, multi-path temperature difference coupling characteristics are evaluated based on the standardized data set, and the cold and hot air mixing proportion and the air door opening degree are dynamically adjusted; s3, the adjustment adaptability of the multi-source temperature difference and the air outlet speed is analyzed, and opening and closing of an air door and the air supply flow are adjusted; s4, evaluating the thermal response coordination and execution stability of the channels, and adjusting the proportion and control priority of the cold and hot channels; and S5, air door adjustment and response consistency is monitored, the airflow adjustment track is corrected, and mismatch response is analyzed. The problems that most current automobile air conditioning systems cannot dynamically adjust the mixed airflow proportion according to the air inlet temperature change, the evaporator outlet temperature difference and the air blowing opening real-time temperature gradient, so that frequent manual intervention of a user is caused, and comfort and energy efficiency are affected are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent sensing control of air conditioners, and in particular to an intelligent control method and system for an automobile air conditioner. Background Art

[0002] With the continuous advancement of automotive intelligence and connectivity, the control model of automotive air conditioning systems has gradually evolved from traditional temperature settings to multi-parameter fusion sensing and dynamic adjustment. Under complex operating conditions such as urban congestion, highway driving, tunnel ventilation, and direct sunlight, the vehicle's in-vehicle thermal environment is influenced by multiple factors, including inlet air temperature, evaporator outlet temperature difference, air velocity at the air outlet, cabin heat capacity, and light radiation intensity, creating a dynamic and complex temperature control environment. The structured physical data and unstructured disturbance information collected by various sensors form the key foundation for intelligent control of automotive air conditioning, while also placing higher demands on temperature difference recognition, airflow mixing control, and dynamic response strategies.

[0003] For example, patent application CN104375532B discloses a vehicle interior temperature control device and method. The device includes a vehicle air conditioner, an engine ECU, a central processing unit, a storage unit, a wireless communication module, a first temperature sensor located outside the vehicle, and a second temperature sensor located inside the vehicle. The central processing unit is electrically connected to the storage unit, the wireless communication module, the first temperature sensor, the second temperature sensor, the vehicle air conditioner, and the engine ECU. The device analyzes the vehicle's outdoor temperature, the vehicle's indoor temperature, the user-set target temperature for the vehicle's interior, and the time it takes to enter the vehicle. The device determines the optimal time to activate the vehicle air conditioner, ensuring that the vehicle's indoor temperature reaches the target temperature upon entry, thus saving fuel.

[0004] For example, the invention patent with announcement number CN115016574B discloses a method, system, device, and electronic device for controlling in-vehicle temperature, wherein the method includes: obtaining environmental detection information and air conditioning control mode corresponding to a target vehicle; obtaining seat information in the target vehicle, wherein the seat information includes at least: the current occupancy status of multiple vehicle seats in the target vehicle, and position information corresponding to the multiple vehicle seats; determining the initial heating power corresponding to the multiple vehicle seats based on the environmental detection information and the air conditioning control mode; determining the target heating power corresponding to the multiple vehicle seats based on the seat information and the initial heating power corresponding to the multiple vehicle seats; and heating the multiple vehicle seats using the corresponding target heating power. The present invention solves the technical problems of low heating efficiency and low heating targeting existing in the related art.

[0005] However, existing intelligent control strategies, often based on static partitioning modeling and single adjustment rules, struggle to cope with rapidly changing inlet air temperature disturbances, evaporator heat exchange lag, and uneven cabin temperature zones. This is particularly true under transient operating conditions such as sunlight exposure and intermittent start-stop cycles. These factors often lead to lags in the ratio adjustment of hot and cold airflows, mismatched temperature differential responses, and damper actuation oscillation. Furthermore, most existing damper control strategies lack a fusion mechanism for sensing outlet air temperature trends and cabin thermal inertia, making it impossible to implement real-time damper step size correction and airflow compensation adjustments in the face of sudden temperature changes across multiple paths. This severely hinders the continued improvement of passenger comfort and air conditioning energy efficiency.

[0006] In view of the above problems, there is an urgent need for an intelligent control method and system for automobile air conditioning. Summary of the Invention

[0007] Technical problems solved

[0008] In response to the shortcomings of the existing technology, the present invention provides an intelligent control method and system for automobile air conditioning, which solves the problem that most current automobile air conditioning systems are unable to dynamically adjust the mixed airflow ratio according to changes in inlet air temperature, evaporator outlet temperature difference and real-time temperature gradient of the air outlet, resulting in frequent manual intervention by users, affecting comfort and energy efficiency.

[0009] Technical Solution

[0010] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent control method and system for automobile air conditioners, including S1, collecting multi-point temperature data, air volume and air flow data and vehicle exterior environment data during the operation of the automobile air conditioner, and pre-processing the collected multi-point temperature data, air volume and air flow data and vehicle exterior environment data to construct a standardized vehicle status data set; S2, based on the standardized vehicle status data set, evaluating the composite temperature difference characteristics from the multi-path temperature difference coupling relationship, and dynamically adjusting the cold and hot air mixing ratio and the air door opening adjustment strategy based on the evaluation results; S3, based on the standard The vehicle status data set is optimized, and the adaptability of airflow regulation is analyzed from the perspective of multi-source temperature difference paths and air outlet wind speed, and the damper opening and closing ratio and air supply flow rate are dynamically adjusted based on the analysis results; S4, with the composite temperature difference feature evaluation results and the airflow regulation adaptability analysis results as input, a comprehensive evaluation is performed on the channel thermal response coordination and execution stability, and based on the evaluation results, the adaptive adjustment of the cold and hot channel ratio distribution and control priority is completed; S5, the damper adjustment step value and the consistency of the multi-temperature difference path response are jointly monitored, and the airflow regulation trajectory is dynamically and hierarchically corrected and the hot and cold response mismatch analysis process is triggered.

[0011] Furthermore, the steps of collecting multi-point temperature data, air volume and air flow data, and vehicle exterior environment data during the operation of the automobile air conditioner, and pre-processing the collected multi-point temperature data, air volume and air flow data, and vehicle exterior environment data to construct a standardized vehicle status data set are as follows:

[0012] Collect multi-point temperature data during the operation of automobile air conditioning, including: intake path temperature response amplitude value, duct heat transfer hysteresis value, composite temperature difference balance reference value, ambient temperature distribution of multiple areas in the cabin, temperature difference adjustment threshold and cold air residual temperature in the floor area; collect temperature-related air volume and airflow data, including: target outlet temperature, current outlet temperature, hot air temperature, cold air temperature, duct heat loss value, outlet wind speed, current deviation value of damper, air path switching frequency and adjustment response time under damper state; collect external environment data, including: external air temperature change trend, daily The system collects the illumination intensity index, the overall cabin warm and cold offset value, the current cabin heat capacity value, and the local temperature difference amplitude, while recording the standard heat capacity reference value and temperature response delay. It also standardizes and normalizes the collected multi-point temperature data, air volume and airflow data, and the external environment data to correct the data scale inconsistency caused by the sensor distribution height, response delay difference, and air velocity difference. It also archives and stores the standardized and normalized multi-point temperature data, air volume and airflow data, and the external environment data with unified time series coding, spatial location identification, and functional attribute labels to construct a standardized vehicle status dataset.

[0013] Furthermore, the specific steps of evaluating the composite temperature difference characteristics from the multi-path temperature difference coupling relationship based on the standardized vehicle status data set are as follows: extracting the intake path temperature response amplitude value, the air duct heat transfer hysteresis value and the overall cabin warm and cold offset value in the standardized vehicle status data set, squaring them respectively and then accumulating them to obtain the heat exchange fluctuation value; extracting the local temperature difference amplitude and the composite temperature difference balance reference value, dividing the local temperature difference amplitude by the sum of the composite temperature difference balance reference value and the small adjustment coefficient and then adding one to obtain the temperature path balance degree; multiplying the heat exchange fluctuation value by the temperature path balance degree and then taking the logarithm to obtain the temperature difference dynamic evaluation value.

[0014] Furthermore, the specific steps of the strategy for dynamically adjusting the hot and cold air mixing ratio and the damper opening based on the evaluation results are as follows: real-time comparison of the current temperature difference dynamic evaluation value and the temperature control response threshold: when the temperature difference dynamic evaluation value is less than or equal to the temperature control response threshold, it is determined to be an air-conditioning response stable section, the current hot and cold air mixing ratio and the damper opening are maintained unchanged, and the temperature difference stability evaluation, supply air temperature tracking and regional temperature gradient monitoring based on the standard time window are continued; when the temperature difference dynamic evaluation value is greater than the temperature control response threshold, it is determined to be an air-conditioning response offset section, the regulation enhancement mechanism is started, the temperature analysis window duration is shortened, the time resolution of the regulation strategy is improved, the damper opening is step-by-step fine-tuned, a dynamic correction factor is applied to the hot and cold mixing ratio, and all temperature difference path data, historical adjustment records and multi-point temperature synchronization deviation values ​​in this stage are cached, the current response is marked as a high-sensitivity state and the control sensitivity level is updated.

[0015] Furthermore, the specific steps of analyzing the adaptability of airflow regulation from the multi-source temperature difference path and the outlet wind speed based on the standardized vehicle status data set are as follows: extracting the target outlet air temperature, hot air temperature, cold air temperature, duct heat loss value and outlet wind speed of the current cabin, calculating the difference between the target outlet air temperature and the hot air temperature, and then calculating the product of the heat loss compensation factor and the duct heat loss value and the product of the wind speed sensitivity adjustment factor and the outlet wind speed; adding the product of the heat loss compensation factor and the duct heat loss value to the difference between the target outlet air temperature and the hot air temperature, and then subtracting the wind speed sensitivity adjustment factor from the product of the heat loss compensation factor and the duct heat loss value. The product of the temperature adjustment factor and the outlet wind speed is used as the numerator, and the difference between the cold air temperature and the hot air temperature is used as the denominator to obtain the air supply path response value; the temperature difference dynamic evaluation value, the current cabin heat capacity value, the standard heat capacity reference value and the temperature difference adjustment threshold are extracted, the absolute value of the difference between the temperature difference dynamic evaluation value and the temperature difference adjustment threshold is calculated and divided by the temperature difference adjustment threshold, and then multiplied by the ratio of the current cabin heat capacity value to the standard heat capacity reference value, and then added one to take the logarithm to obtain the temperature difference impact amplification value; the air supply path response value is multiplied by the temperature difference impact amplification value to obtain the cold and hot air flow mixing value.

[0016] Furthermore, the specific steps of dynamically adjusting the damper opening and closing ratio and the air supply flow rate based on the analysis results are as follows: comparing the current cold and hot air flow mixture value with the mixture adjustment level threshold in real time, the mixture adjustment level threshold includes a first mixture threshold and a second mixture threshold: when the cold and hot air flow mixture value is less than or equal to the second mixture threshold, it is determined to be a cold and warm balance stable section, the original damper opening and the compressor power output are maintained unchanged, and the current mixture state is used as a reference for stable control, and the current cabin response data is cached; when the cold and hot air flow mixture value is greater than the second mixture threshold and less than or equal to the first mixture threshold When the cold and hot air flow mixture value is greater than the first mixing threshold, it is determined to be a strong deviation from the response section, the damper adjustment frequency and the air duct response level are increased, the emergency temperature control compensation logic is triggered, the compressor cooling and heating channel output intensity is instantaneously adjusted, and the abnormal state of the mixing ratio, the cabin temperature deviation trajectory and the environmental interference factor during this period are recorded.

[0017] Furthermore, the specific steps of comprehensively evaluating the channel thermal response coordination and execution stability using the composite temperature difference feature evaluation results and the airflow regulation adaptability analysis results as input are as follows: obtaining the cold and hot air flow mixing value, subtracting the current damper deviation value from one, and then multiplying it by the cold and hot air flow mixing value to obtain the cold and hot air ratio correction value; extracting the current air outlet temperature and the target air outlet temperature, calculating the absolute value of the difference between the current air outlet temperature and the target air outlet temperature, adding one, and taking the logarithm to obtain the cold and hot deviation suppression value; dividing the cold and hot air ratio correction value by the cold and hot deviation suppression value to obtain the temperature regulation intensity value; extracting the temperature difference dynamic evaluation value and the temperature response delay, calculating the ratio between the temperature difference dynamic evaluation value and the temperature response delay, and then multiplying it by the temperature difference dynamic gain coefficient and adding one to obtain the complex influence correction value; multiplying the temperature regulation intensity value by the complex influence correction value to obtain the current damper adjustment step value.

[0018] Furthermore, the specific steps of completing the adaptive adjustment of the cold and hot channel proportion distribution and control priority based on the evaluation results are: real-time comparison of the current damper adjustment step value and the damper adjustment level threshold, the damper adjustment level threshold includes a first adjustment threshold and a second adjustment threshold: when the damper adjustment step value is less than or equal to the second adjustment threshold, it is determined to be a stable adjustment section, the current damper position and mixing ratio are maintained unchanged, only the low-frequency position confirmation instruction is executed, and the temperature collection data, wind speed trend and adjustment error records of the period are archived; when the damper adjustment step value is greater than the second adjustment threshold and less than or equal to the first adjustment threshold, it is determined to be a slow-changing adjustment section, and the real-time start A phased step control strategy is used to start the damper position tracking and compressor response synchronization correction mechanism, while enhancing the sampling density of the outlet air temperature data. The adjustment efficiency is evaluated in combination with the current adjustment rate and deviation value, and the damper action interval of the next cycle is dynamically corrected. When the damper adjustment step value is greater than the first adjustment threshold, it is determined to be a sudden change adjustment section, entering the fast response mode, increasing the adjustment frequency and control priority, suspending all non-critical control tasks, and caching all current adjustment inputs and execution feedback data. The cycle is isolated as an abnormal response section for dynamic performance analysis and abnormal trend prediction, and the temperature difference dynamic value, hot and cold mixing ratio and outlet air status trajectory of this time period are recorded.

[0019] Furthermore, the damper adjustment step value and the consistency of the multi-temperature difference path response are jointly monitored, and the specific steps of the dynamic graded correction of the airflow adjustment trajectory and the triggering of the hot and cold response mismatch analysis process are as follows: when the damper adjustment step value shows an abnormal trend in the local control window, including repeated switching of the adjustment direction, execution deviation amplification and imbalance of the temperature control response, the damper state feedback channel is immediately called to upload the damper action record, the outlet temperature change trajectory and the temperature difference evaluation data of this stage; the response baseline correction strategy of the corresponding type is matched according to the abnormal adjustment mode and the temperature control offset amplitude: when it is identified as a stable adjustment section, the damper action inertia baseline is constructed, and the damper offset buffer zone is extended with the current damper adjustment step value as a reference to stabilize the control frequency and optimize the subsequent rhythm alignment; when it is identified as a slowly changing adjustment section, an adjustment amplitude elastic baseline is generated, the throttle adjustment step peak and period characteristics are extracted, the acceptable deviation range is calibrated and a sensitivity suppression zone is constructed; when it is identified as a drastic change adjustment section, a control behavior reconstruction baseline is formed, and based on the current execution lag and temperature response misalignment, the hot and cold channel instruction streams are recombined to construct an alternative execution trajectory and a dynamic correction window; after the baseline is constructed, the output hot and cold adjustment rhythm and execution compensation strategy recommendations are generated. When the adjustment baseline reconstruction fails, the multi-point temperature data, air volume and airflow data and vehicle external environment data within the time window are re-collected to execute a closed-loop hot and cold response mismatch identification-proportional control reconstruction-adjustment strategy regeneration process.

[0020] The second aspect of the present invention provides an automobile air-conditioning intelligent control system, comprising: a data acquisition and preprocessing module for collecting multi-point temperature data, air volume and air flow data and vehicle exterior environment data during the operation of the automobile air-conditioning, and preprocessing the collected multi-point temperature data, air volume and air flow data and vehicle exterior environment data to construct a standardized vehicle-mounted state data set; a temperature difference dynamic calculation module for evaluating the composite temperature difference characteristics from the multi-path temperature difference coupling relationship based on the standardized vehicle-mounted state data set, and dynamically adjusting the cold and hot air mixing ratio and the air door opening adjustment strategy based on the evaluation result; a mixed airflow evaluation module for evaluating the composite temperature difference characteristics from the multi-path temperature difference coupling relationship based on the standardized vehicle-mounted state data set The state data set analyzes the adaptability of airflow regulation from the perspective of multi-source temperature difference paths and air outlet wind speed, and dynamically adjusts the damper opening and closing ratio and air supply flow rate based on the analysis results; the closed-loop execution correction module uses the composite temperature difference feature evaluation results and the airflow regulation adaptability analysis results as input to comprehensively evaluate the channel thermal response coordination and execution stability, and completes the adaptive adjustment of the cold and hot channel ratio distribution and control priority based on the evaluation results; the real-time comfort adjustment module is used to jointly monitor the damper adjustment step value and the consistency of the multi-temperature difference path response, and dynamically and hierarchically correct the airflow regulation trajectory and trigger the cold and hot response mismatch analysis process.

[0021] Beneficial effects

[0022] The present invention has the following beneficial effects:

[0023] (1) This intelligent control method and system for automobile air conditioners introduces a damper adjustment step value calculation mechanism, integrates the cold and hot air flow mixing value and the temperature difference dynamic evaluation value, and quantifies the degree of damper action's adaptability to multi-path temperature difference changes, effectively solving the problems of disconnection between cold and hot channel regulation and delayed mixing ratio response in the prior art.

[0024] (2) This intelligent control method and system for automobile air conditioners constructs a joint correction model of air duct heat loss delay and air outlet temperature deviation, dynamically corrects the damper response trajectory and adjustment frequency, and effectively improves the stable control capability of the air conditioning system under the scenarios of air inlet disturbance and wind speed sudden change.

[0025] (3) This intelligent control method and system for automobile air conditioning establishes an adaptive fusion model of the cabin target air outlet temperature and the dynamic offset of the heat and cold sources, thereby realizing continuous correction of the ratio of hot and cold air flows when the cabin has strong thermal inertia and uneven temperature zones, effectively enhancing the comfort maintenance and energy efficiency stability during the adjustment process.

[0026] (4) This intelligent control method and system for automobile air conditioners, by setting a cold and hot adjustment rhythm recognition module, jointly monitors the change in the direction of the damper movement and the consistency of the temperature difference trend, supports dynamic correction of control path oscillation and adjustment deviation amplification, and effectively solves the problems of cold and hot response mismatch and frequent damper oscillation under complex working conditions.

[0027] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a flow chart of an intelligent control method for automobile air conditioner of the present invention;

[0029] Figure 2 This is a structural diagram of an automobile air-conditioning intelligent control system of the present invention;

[0030] Figure 3 This is a diagram of the intelligent control architecture of the automobile air conditioner involved in the present invention;

[0031] Figure 4 This is a bar graph of the damper adjustment step values ​​involved in the present invention. DETAILED DESCRIPTION

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0033] See also Figure 1-Figure 4 The embodiment of the present invention provides a technical solution: an intelligent control method and system for automobile air conditioners, including S1, collecting multi-point temperature data, air volume and air flow data and vehicle exterior environment data during the operation of the automobile air conditioner, and pre-processing the collected multi-point temperature data, air volume and air flow data and vehicle exterior environment data to construct a standardized vehicle status data set; S2, based on the standardized vehicle status data set, evaluating the composite temperature difference characteristics from the multi-path temperature difference coupling relationship, and dynamically adjusting the cold and hot air mixing ratio and the air door opening adjustment strategy based on the evaluation result; S3, based on the standardized vehicle status data set, Dynamic data set, analyzes the adaptability of airflow regulation from multi-source temperature difference paths and outlet wind speed, and dynamically adjusts the damper opening and closing ratio and air supply flow rate based on the analysis results; S4, takes the composite temperature difference feature evaluation results and airflow regulation adaptability analysis results as input, comprehensively evaluates the channel thermal response coordination and execution stability, and completes the adaptive adjustment of the cold and hot channel ratio distribution and control priority based on the evaluation results; S5, jointly monitors the damper adjustment step value and the consistency of the multi-temperature difference path response, and dynamically and hierarchically corrects the airflow regulation trajectory and triggers the cold and hot response mismatch analysis process.

[0034] Specifically, multi-point temperature data, air volume and air flow data, and external environment data are collected during the operation of the automobile air conditioner, and the collected multi-point temperature data, air volume and air flow data, and external environment data are preprocessed to construct a standardized vehicle status data set. The specific steps are as follows: multi-point temperature data are collected during the operation of the automobile air conditioner. The multi-point temperature data include: intake path temperature response amplitude value, duct heat transfer hysteresis value, composite temperature difference balance reference value, ambient temperature distribution in multiple areas of the cabin, temperature difference adjustment threshold, and cold air residual temperature in the floor area. At the same time, the temperature fluctuation frequency, sensor response delay, and temperature difference gradient between measuring points are annotated in real time based on the sampling time to construct the multi-path temperature difference evolution relationship and regional thermal inertia characteristic model;

[0035] Collect temperature-related air volume and airflow data, including target outlet temperature, current outlet temperature, hot air temperature, cold air temperature, duct heat loss value, outlet air speed, current damper deviation value, air path switching frequency and adjustment response time under damper status. At the same time, monitor the time delay and adjustment amplitude error between damper command and execution feedback to evaluate damper execution accuracy and duct dynamic response capability, and assist in subsequent damper step correction and adjustment rhythm matching.

[0036] Collecting external environmental data, including outside air temperature trends, sunlight intensity index, overall cabin temperature offset, current cabin heat capacity, and local temperature difference. The system also records the standard heat capacity reference value and temperature response delay to quantify the indirect impact of external disturbances and build a coupled response model between the internal and external heat exchange relationship and the environment.

[0037] Standardize and normalize the collected multi-point temperature data, air volume and airflow data, and vehicle exterior environment data. This corrects data scale inconsistencies caused by differences in sensor distribution height, response delay, and air velocity. It also unifies the physical semantics and value accuracy of various input data types, ensuring stable and reliable algorithm input for subsequent adjustment strategies.

[0038] The standardized and normalized multi-point temperature data, air volume and airflow data, and vehicle exterior environment data are archived and stored with unified time series coding, spatial location identification, and functional attribute labels to construct a standardized vehicle status data set, providing complete, continuous, and traceable input support for modules such as cold and hot air flow mixing ratio adjustment, damper adjustment step length calculation, and adjustment rhythm control.

[0039] In this implementation plan, by standardizing and normalizing the multi-point temperature data, air volume and airflow data, and external environment data collected during the operation of the automobile air conditioner, a unified, continuous, and traceable vehicle status data foundation is established, effectively solving the data fusion obstacles caused by uneven sensor distribution, response delay differences, and inconsistent physical quantity scales. Through archiving processing of time series coding, spatial location identification, and functional attribute labels, each type of data is given clear spatiotemporal semantics and functional roles, making it highly consistent and comparable. This standardized vehicle status data set not only improves the input accuracy of subsequent control modules in the calculation of hot and cold airflow mixing values, damper adjustment step evaluation, and airflow ratio adjustment, but also provides data support for dynamic adjustment rhythm control, comfort stability maintenance, and air duct abnormal response analysis, comprehensively strengthening the perception, decision-making, and execution closed-loop capabilities under complex working conditions.

[0040] Specifically, based on the standardized vehicle status data set, the specific steps for evaluating the composite temperature difference characteristics from the multi-path temperature difference coupling relationship are as follows: extract the intake path temperature response amplitude value, the air duct heat transfer hysteresis value and the overall cabin warm and cold offset value in the standardized vehicle status data set, square them respectively and then accumulate them to obtain the heat exchange fluctuation value; extract the local temperature difference amplitude and the composite temperature difference balance reference value, divide the local temperature difference amplitude by the sum of the composite temperature difference balance reference value and the small adjustment coefficient, and then add one to obtain the temperature path balance degree; multiply the heat exchange fluctuation value by the temperature path balance degree and then take the logarithm to obtain the temperature difference dynamic evaluation value.

[0041] The calculation formula for the dynamic evaluation value of temperature difference is:

[0042] ;

[0043] Where A represents the intake path temperature response amplitude, which is used to reflect the temperature difference and fluctuation between the inlet air temperature and the evaporator outlet temperature. It comprehensively considers the inlet air temperature change amplitude and fluctuation frequency, and is often used to evaluate the transient heat exchange capacity of the evaporator and the inlet end temperature disturbance level. B represents the duct heat transfer hysteresis value, which is used to measure the insulation effect and response delay of the evaporator outlet temperature during the air supply process. Combined with the wind speed change and sensor response delay, it is used to determine the problem of heat loss or slow conduction in the duct, reflecting the thermal inertia characteristics of the duct structure. C represents the overall cabin cooling and heating offset value, which is used to describe the overall cabin average temperature relative to the air outlet temperature. The degree of deviation is measured, and the standard deviation of temperature differences in multiple areas of the cabin is introduced to identify the imbalance of temperature distribution and reflect the wind efficiency and uniformity of cold and warm coverage. D represents the amplitude of local temperature difference, which is used to measure the maximum deviation between the three key temperature paths. It is a reflection of the degree of imbalance in internal cold and hot control. The larger the value, the worse the response consistency of the adjustment path, and the need to trigger the synchronous optimization mechanism. E represents the composite temperature difference balance reference value, which is used to provide the average level of the current thermal regulation path and is used as a normalized reference benchmark to determine whether the temperature difference fluctuation deviates from the overall trend and to assist in determining whether the adjustment intensity or frequency needs to be increased or decreased. ε represents the small adjustment coefficient, which ranges from The value is between 0.001 and 0.01, which is used to prevent the zero division error when the temperature difference equilibrium value is close to zero, ensuring that all types of structures still have numerical stability and control consistency in the thermal equilibrium state. As a numerical protection factor in the calculation formula, in the specific setting, when calculating the dynamic response of heat exchange, the minimum absolute deviation in the multi-region temperature difference sequence is extracted to determine whether there is a temperature convergence trend or a steady-state equilibrium stage; in this stage, if the temperature difference term approaches zero or is far below the actual adjustment resolution lower limit, it will cause numerical distortion or logic jump of the dynamic amplification term. In order to suppress this abnormal phenomenon, a small adjustment coefficient is introduced as a division and logarithmic calculation process. The stable anchor point is used as the control point, and its effectiveness is verified by historical comparison of the adjustment sensitivity distribution map; at the end of the temperature control stage, the outlet temperature is basically close to the cabin target temperature, the temperature difference fluctuation is small and the change rhythm is smooth, and the adjustment accuracy requirement is high, the small adjustment coefficient should be taken as a small value to avoid frequent jitter or misjudgment of the adjustment behavior due to numerical fluctuations, so as to ensure the continuity and stability of fine-grained adjustment; and when in the rapid response stage after the damper action, the temperature difference is approaching zero but there are signs of mutation, the small adjustment coefficient should be appropriately amplified to enhance the absorption and buffering capacity of near-zero errors and prevent the calculation logic from being broken or the control parameters from being distorted.

[0044] In this implementation, during the operation of the automotive air conditioner, by integrating temperature data from multiple key locations such as the air inlet, evaporator outlet, air outlet and cabin area, the multiple temperature difference states in the cold and hot transmission link are comprehensively evaluated, and exponential functions, logarithmic functions, weighted ratios and nonlinear correction terms are introduced to form a composite temperature difference index with temperature difference directionality, amplitude intensity and adjustment delay sensitivity, thereby dynamically reflecting the current degree of cold and hot balance and adjustment response efficiency; the calculation result serves as the core judgment basis in the temperature control chain, which can support the intelligent correction of the cold and hot air flow mixing ratio, real-time adjustment of the air door adjustment step and feedback control of the cabin comfort stability, effectively solving the problem that traditional systems cannot jointly drive the adjustment strategy based on the change of inlet temperature, evaporator outlet temperature difference and air outlet temperature gradient, thereby improving the adjustment accuracy, response sensitivity and energy utilization efficiency of automotive air conditioners in complex thermal environments.

[0045] Specifically, the strategy for dynamically adjusting the hot and cold air mixing ratio and the damper opening degree based on the evaluation results is as follows: Real-time comparison of the current temperature difference dynamic evaluation value with the temperature control response threshold, which serves as a benchmark limit for judging whether the response deviates from the ideal state:

[0046] When the temperature difference dynamic assessment value is less than or equal to the temperature control response threshold, and the current temperature adjustment frequency, hot and cold airflow adjustment amplitude, and cabin area temperature difference are all within the historical stable range, the air conditioning response is determined to be stable. The current hot and cold airflow mixing ratio and damper opening settings are maintained unchanged. Temperature difference stability trend assessment, outlet air temperature tracking, and regional heat distribution monitoring continue to be performed within the standard time window to avoid excessive resource consumption or comfort fluctuations caused by excessive adjustment.

[0047] When the dynamic temperature difference evaluation value exceeds the temperature control response threshold, and is accompanied by an increase in the temperature difference fluctuation rate for multiple consecutive cycles and frequent fine-tuning of the wind speed output, it is determined to be an air conditioning response deviation section, triggering the adjustment enhancement mechanism, shortening the analysis window, increasing the strategy update frequency, and executing the damper opening step fine-tuning and dynamic correction of the hot and cold air flow ratio. At the same time, the outlet air temperature change trajectory and regional synchronization offset within the current window are synchronously recorded to form a reference for subsequent adjustment behavior;

[0048] When the dynamic evaluation value of the temperature difference is higher than the temperature control response threshold, and is accompanied by abnormal adjustment lag, regional temperature control imbalance and a sharp increase in the frequency of damper adjustment, the air conditioner is judged to be in a high-sensitivity state, and the system immediately switches to high-sensitivity control logic, activates the fast response algorithm, reconstructs the air duct heat transfer control parameters, and temporarily freezes non-critical function adjustments. Resources are concentrated on executing damper adjustment and outlet temperature compensation control to ensure the timeliness and accuracy of the core adjustment link.

[0049] This implementation incorporates a real-time comparison mechanism between the dynamic temperature difference assessment and a single temperature control response threshold, enabling continuous monitoring and dynamic identification of the air conditioning response state. This clearly distinguishes between stable, drifting, and highly sensitive states, effectively addressing the inability of traditional air conditioning control to precisely stratify adjustment levels based on temperature difference fluctuation trends. This hierarchical determination of assessment values ​​not only ensures the timeliness and adaptability of hot and cold airflow adjustment strategies, but also provides a basis for subsequent decision-making regarding damper control, airflow ratio correction, and multi-point temperature consistency optimization, further enhancing the intelligent level and stable operation of vehicle thermal comfort control.

[0050] Specifically, based on the standardized vehicle status data set, the adaptability of airflow regulation is analyzed from the multi-source temperature difference path and the outlet wind speed. The specific steps are: extract the target outlet air temperature, hot air temperature, cold air temperature, duct heat loss value and outlet wind speed of the current cabin, calculate the difference between the target outlet air temperature and the hot air temperature, and then calculate the product of the heat loss compensation factor and the duct heat loss value and the product of the wind speed sensitivity adjustment factor and the outlet wind speed; add the difference between the target outlet air temperature and the hot air temperature to the product of the heat loss compensation factor and the duct heat loss value, and then subtract the wind speed sensitivity adjustment factor from the product of the wind speed sensitivity adjustment factor and the outlet wind speed. The product of the adjustment factor and the outlet wind speed is used as the numerator, and the difference between the cold air temperature and the hot air temperature is used as the denominator to obtain the air supply path response value; the temperature difference dynamic evaluation value, the current cabin heat capacity value, the standard heat capacity reference value and the temperature difference adjustment threshold are extracted, the absolute value of the difference between the temperature difference dynamic evaluation value and the temperature difference adjustment threshold is calculated and divided by the temperature difference adjustment threshold, and then multiplied by the ratio of the current cabin heat capacity value and the standard heat capacity reference value, and then added one to take the logarithm to obtain the temperature difference impact amplification value; the air supply path response value is multiplied by the temperature difference impact amplification value to obtain the cold and hot air flow mixing value.

[0051] The calculation formula for the mixed value of cold and hot air flow is:

[0052] ;

[0053] Where, Indicates the target outlet air temperature as a temperature regulation benchmark for achieving comfort goals; Indicates the hot air temperature, which is used to characterize the current output capacity of the high-temperature channel; Indicates the cold air temperature, representing the current cooling status of the low-temperature channel; Indicates the heat loss value of the air duct, comprehensively evaluating the heat attenuation of the ventilation duct, the air path length and the thermal insulation of the structure; Indicates the wind speed at the air outlet, used to indicate the effect of air volume on temperature perception, and can actively suppress overcooling when delivering air at high speed; Indicates the dynamic evaluation value of temperature difference, reflecting the current cold and hot response delay, path imbalance and regulation efficiency; Indicates the current cabin heat capacity value, which is estimated by combining the vehicle interior volume, interior material thermal conductivity, and occupant heat load to influence the temperature control response inertia; Represents the standard heat capacity reference value, which is used to normalize the effect of heat capacity on the adjustment rhythm under different cabin models; Indicates the temperature difference adjustment threshold, which is used to distinguish whether it is in the overcooling, overheating or steady-state response state, and is the reference baseline for automatic adjustment sensitivity; The heat loss compensation factor, with a value ranging from 0.05 to 0.5, is used to adjust temperature control deviations caused by thermal attenuation of the duct structure. It dynamically calculates the proportion of non-target heat loss during heat transfer, combining parameters such as the heat capacity density in the current heat flow path, the thermal conductivity of the duct wrapping material, and the insulation capacity of the cabin. This factor is then used to generate a correction factor to compensate for the target outlet air temperature error. When the heat loss level is high, the heat loss compensation factor is set to a larger value, thereby increasing the proportion of hot air to maintain target temperature stability. Conversely, when the heat transfer efficiency is good or the heat capacity gradient is stable, the factor is reduced accordingly to avoid excessive heating adjustments that may cause comfort disturbances. This represents the wind speed sensitivity adjustment factor, ranging from 0.1 to 1. It characterizes the impact of air volume changes on the control response. This factor is derived from the coupling relationship between the real-time wind speed value at the air outlet and the delay in the control action. It takes into account the wind speed change rate, the frequency of air volume adjustment, and the damper opening response time, and measures the sensitivity of the wind speed change to the cooling and heating mixing efficiency under the current state. If the wind speed rises sharply and the damper adjustment response lags, this factor is increased, actively increasing the adjustment amount to respond to dynamic load changes. If the wind speed fluctuations are slight and the response rhythm is consistent, the factor is minimized, ensuring control stability and suppressing overcorrection, thereby improving energy efficiency and comfort control consistency.

[0054] This implementation dynamically calculates the outlet air temperature adjustment rate, comprehensively reflecting the combined impact of multiple factors on the air conditioning adjustment rate during the current cabin temperature adjustment process, including the hot and cold air mixture ratio, temperature deviation, wind speed disturbance, and response delay. By incorporating key parameters such as the target outlet air temperature, hot air temperature, cold air temperature, duct heat loss, and outlet air speed, and combining them with a dynamic temperature differential assessment and the difference between the current cabin thermal capacity and a standard reference, the formula enables adaptive control of the adjustment rate. This allows for rapid correction and sensitive response to temperature control strategies under varying operating conditions, thereby improving the air conditioning's comfort adjustment efficiency and energy-saving steady-state control capabilities in complex environments.

[0055] Specifically, the specific steps for dynamically adjusting the damper opening and closing ratio and air flow rate based on the analysis results are as follows:

[0056] Compare the current cold and hot air flow mixing value with the mixing adjustment level threshold in real time. The mixing adjustment level threshold includes the first mixing threshold and the second mixing threshold:

[0057] When the hot and cold airflow mixture value is less than or equal to the second mixture threshold, the system is determined to be in a stable cooling and heating balance range. The current damper opening and compressor power output are maintained to avoid energy consumption disturbances caused by frequent adjustments. This state is used as a stable control benchmark for subsequent cooling and heating offset judgments. The system also synchronously caches the current cabin response data, thermal capacity assessment results, and duct heat loss status to construct a stable state parameter template.

[0058] When the hot and cold airflow mixture value is greater than the second mixture threshold and less than or equal to the first mixture threshold, it is determined that the air conditioning control has slightly deviated from the transition section, and the dynamic fine-tuning control strategy is activated. The mixture ratio is step-corrected according to the actual adjustment sensitivity of the hot and cold airflow channels, and a correction action with a limited step length is applied to the damper actuator. At the same time, in order to avoid local physical delay caused by duct transmission lag during the adjustment response process, a local air supply delay compensation mechanism is introduced. The local air supply delay compensation mechanism includes combining the current wind speed, duct structure and heat loss to dynamically compensate for the time difference error between the adjustment target and the actual cabin response. The wind speed and heat loss factor weights are adaptively updated according to the latest temperature trend and disturbance level to improve the matching degree and response stability of the adjustment process.

[0059] When the mixture value of the cold and hot air flows is greater than the first mixture threshold, it is determined to be a strong deviation section of the air conditioning response, triggering the emergency temperature control compensation logic, increasing the damper adjustment frequency and the air duct response level, executing high-frequency damper adjustment and hot and cold channel switching strategies, enhancing the output intensity of the compressor cooling and heating channels in real time, and quickly recovering the offset temperature difference. At the same time, this state is marked as a mixing abnormality period, and the abnormal mixing ratio characteristics, cabin temperature offset trajectory, wind speed mutation frequency and external environmental interference factors in this stage are recorded as feedback basis for subsequent adjustment strategy optimization and error modeling.

[0060] In this implementation, hierarchical identification and dynamic adjustment control of the mixed state of hot and cold airflows in automobile air conditioners are realized. By comparing the hot and cold airflow mixing values ​​with multi-level adjustment thresholds in real time, the cabin environment can be accurately determined to be in different response states of stability, slight deviation or strong deviation, and corresponding adjustment strategies and compensation mechanisms are triggered for different states, thereby improving the adjustment accuracy and response stability under complex working conditions; at the same time, the control parameters are adaptively adjusted based on multiple factors including wind speed and heat loss to enhance the robustness of the adjustment strategy to dynamic disturbances, avoid fluctuations in physical sensation caused by excessive adjustment or response delay, and ensure the consistency of occupant comfort and energy efficiency control.

[0061] Specifically, with the composite temperature difference characteristic evaluation results and the airflow regulation adaptability analysis results as input, a comprehensive evaluation of the channel thermal response coordination and execution stability is carried out. The specific steps are: obtain the cold and hot air flow mixing value, subtract the current damper deviation value from one, and then multiply it by the cold and hot air flow mixing value to obtain the cold and hot air ratio correction value; extract the current air outlet temperature and the target air outlet temperature, calculate the absolute value of the difference between the current air outlet temperature and the target air outlet temperature, add one, and then take the logarithm to obtain the cold and hot deviation suppression value; divide the cold and hot air ratio correction value by the cold and hot deviation suppression value to obtain the temperature regulation intensity value; extract the temperature difference dynamic evaluation value and the temperature response delay, calculate the ratio between the temperature difference dynamic evaluation value and the temperature response delay, multiply it by the temperature difference dynamic gain coefficient, and then add one to obtain the complex influence correction value; multiply the temperature regulation intensity value by the complex influence correction value to obtain the current damper adjustment step value.

[0062] The calculation formula for the damper adjustment step value is:

[0063] ;

[0064] Where, Indicates the mixing value of cold and hot air flows, which is used to reflect the current ideal cold and hot air distribution ratio; Indicates the current deviation value of the damper, which is used to determine the adjustment direction and correction degree; Indicates the current air outlet temperature, used to evaluate the actual air supply status after hot and cold air are mixed; Indicates the target air outlet temperature as the current desired thermal output; represents the dynamic evaluation value of the temperature difference, which is calculated from the temperature difference coupling path and its distribution characteristics and is used to evaluate the complexity of the current thermal regulation response. G represents the temperature response delay, which is dynamically measured by combining the average temperature difference duration between the air inlet and outlet, the air velocity variation amplitude, and the actual air duct length. It is used to reflect the temperature hysteresis trend and airflow buffering effect that occur during the transmission of cold and hot air flows in the air duct. It is the actual feedback quantity that reasonably constrains the temperature difference response amplitude during the regulation action. It represents the temperature difference dynamic gain coefficient, with a value range of 1 to 3. This coefficient is derived from the dynamic deviation between the instantaneous gradient of the temperature difference change in the current air outlet path and the reference curve under the historical temperature control stable state. It aims to enhance the adjustment adaptability and compensation stability for scenarios with drastic temperature difference fluctuations. In the specific calculation process, the temperature difference sequence in the sliding time window is first extracted from the hot and cold air mixing path in real time, and the dynamic characteristic indicators of the slope change, fluctuation intensity and number of zero crossings of the sequence are calculated to characterize the current temperature difference fluctuation trend; then the temperature difference response reference curve under steady-state conditions is extracted to form a benchmark control template, and the fitting score is performed through multi-dimensional indicators such as gradient difference, frequency band consistency and amplitude offset rate; then, combined with the current adjustment strategy level and the cabin temperature control target state, a comprehensive sensitivity mapping function is constructed to finally generate the temperature difference dynamic gain coefficient. When the temperature difference fluctuates violently, the adjustment lag is obvious and the fluctuation frequency increases, the gain coefficient increases to amplify the damper adjustment response amplitude; when the temperature difference curve is stable, the adjustment response is timely and the fluctuation degree is small, the coefficient automatically drops back to avoid overcompensation causing disturbance amplification, thereby achieving dynamic and stable gain control of the damper step adjustment process.

[0065] In this embodiment, Example 1 is set to a cold and hot air flow mixing value of 0.85, a damper current deviation value of 0.10, a current outlet temperature of 27, a target outlet temperature of 25, a temperature difference dynamic evaluation value of 2.1, a temperature response delay of 1.2, and a temperature difference dynamic gain coefficient of 1.5; Example 2 is set to a cold and hot air flow mixing value of 0.90, a damper current deviation value of 0.15, a current outlet temperature of 26, a target outlet temperature of 24, and a temperature difference dynamic evaluation value of 1 .8, the temperature response delay is 1.0, and the temperature difference dynamic gain coefficient is 1.3; set the example 3 to the cold and hot air flow mixing value of 0.78, the damper current deviation value of 0.08, the current outlet temperature of 28, the target outlet temperature of 25, the temperature difference dynamic evaluation value of 2.5, the temperature response delay is 1.5, and the temperature difference dynamic gain coefficient is 1.7; set the example 4 to the cold and hot air flow mixing value of 0.92, the damper current deviation value of 0.20, the current outlet temperature of 2 5, the target outlet temperature is 23, the temperature difference dynamic evaluation value is 1.6, the temperature response delay is 0.9, and the temperature difference dynamic gain coefficient is 1.4; set instance 5 to the cold and hot air flow mixture value of 0.88, the damper current deviation value is 0.12, the current outlet temperature is 27, the target outlet temperature is 26, the temperature difference dynamic evaluation value is 2.0, the temperature response delay is 1.1, and the temperature difference dynamic gain coefficient is 1.6; set instance 6 to the cold and hot air flow mixture value of 0.83, the damper current deviation value is 0.12, the current outlet temperature is 27, the target outlet temperature is 26, the temperature difference dynamic evaluation value is 2.0, the temperature response delay is 1.1, and the temperature difference dynamic gain coefficient is 1.6 The current door deviation value is 0.09, the current outlet temperature is 26, the target outlet temperature is 24, the temperature difference dynamic evaluation value is 1.9, the temperature response delay is 1.0, and the temperature difference dynamic gain coefficient is 1.5. Example 7 is set to a cold and hot air flow mixing value of 0.87, the current door deviation value is 0.11, the current outlet temperature is 28, the target outlet temperature is 26, the temperature difference dynamic evaluation value is 2.2, the temperature response delay is 1.3, and the temperature difference dynamic gain coefficient is 1.2. The door adjustment step values ​​for each example are calculated, as shown in Table 1.

[0066] Table 1 Data table of air door adjustment step value

[0067] Instance number Hot and cold air flow mixing value Current deviation value of the damper Current air outlet temperature Target air outlet temperature Temperature difference dynamic evaluation value Temperature response delay Temperature difference dynamic gain coefficient Air door adjustment step value Example 1 0.85 0.10 27 25 2.1 1.2 1.5 0.6892 Example 2 0.90 0.15 26 24 1.8 1.0 1.3 0.7276 Example 3 0.78 0.08 28 25 2.5 1.5 1.7 0.6357 Example 4 0.92 0.20 25 23 1.6 0.9 1.4 0.7014 Example 5 0.88 0.12 27 26 2.0 1.1 1.6 0.7548 Example 6 0.83 0.09 26 24 1.9 1.0 1.5 0.6945 Example 7 0.87 0.11 28 26 2.2 1.3 1.2 0.7221

[0068] like Figure 4 As shown in Table 1 and Figure 4As can be seen, case 5 has the highest damper adjustment step value, indicating that the hot and cold airflow mixture in this case is in an unbalanced state and there is a significant deviation between the current outlet temperature and the target outlet temperature. Furthermore, the temperature difference dynamic evaluation value and temperature response delay are both at medium-to-high levels, indicating that the current thermal regulation response path is complex and has significant hysteresis. Therefore, a larger adjustment step value is required to achieve rapid temperature control response and airflow compensation. Case 3 has the lowest damper adjustment step value. Although the hot and cold airflow mixture in this case is low, the damper position deviation is small and the temperature target is close. Although the temperature difference dynamic response is high, the delay is large and the gain coefficient is high, resulting in a limited actual adjustment range, a compressed overall step value, and a conservative control rhythm. The distribution characteristics of the damper adjustment step value intuitively reflect the comprehensive response of each case to the coupling of hot and cold airflow deviation, airflow delay, and temperature difference during the current air conditioning regulation process. Higher values ​​indicate that rapid correction of control deviation is required, while lower values ​​indicate that the current state is close to equilibrium or there is regulation hysteresis.

[0069] Specifically, the steps for adaptively adjusting the hot and cold aisle ratio and control priority based on the evaluation results are as follows:

[0070] Compare the current damper adjustment step value with the damper adjustment level threshold in real time. The damper adjustment level threshold includes the first adjustment threshold and the second adjustment threshold:

[0071] When the damper adjustment step value is less than or equal to the second adjustment threshold, it is determined to be a stable adjustment section, and the current damper position and the mixing ratio of the hot and cold air flows are maintained unchanged. Only low-frequency position confirmation instructions are executed. Through periodic damper position feedback verification and static filtering of the outlet air temperature deviation, the adjustment action is ensured to be in a convergence state. At the same time, the temperature collection data, wind speed trend and adjustment error records of this period are archived;

[0072] When the damper adjustment step value is greater than the second adjustment threshold and less than or equal to the first adjustment threshold, it is determined to be a slowly changing adjustment section, and a phased step control strategy is activated in real time. This strategy is based on the minimum adjustment unit, splits the damper opening adjustment process into multiple sub-adjustment intervals with continuous time and progressive amplitude, and determines the step direction and amplitude of each step based on the difference between the current damper position and the target position; at the same time, the damper position tracking and compressor response synchronization correction mechanism are started, and the compressor load change rate and refrigerant flow adjustment delay are used to construct a compressor response time window, and dynamically feedforward correction is performed on the damper action; in addition, the sampling density of the outlet air temperature data is enhanced, and the trend slope is extracted based on the continuous temperature gradient curve and combined with the current adjustment rate and the residual deviation value to comprehensively evaluate the current adjustment efficiency, and then dynamically correct the damper action interval and fine-tuning step size for the next cycle;

[0073] When the damper adjustment step value is greater than the first adjustment threshold, it is determined to be a sudden change adjustment section and enters the fast response mode. It is immediately switched to the high-frequency adjustment state, the control task scheduling priority is increased, and the maximum amplitude step action is executed through the fast damper position closed-loop control logic, and the damper action execution interval is compressed to the minimum allowed time unit. At the same time, all non-critical control tasks are suspended to release scheduling resources; in this stage, all adjustment input signals and execution feedback values ​​in the current cycle are synchronously cached, including target temperature offset, actual wind speed mutation, damper execution deviation and compressor response delay, and the cycle is isolated as an abnormal response section for constructing a dynamic performance analysis model and predicting the adjustment lag trend in the damper control process. The temperature difference dynamic value, hot and cold mixing ratio fluctuation and air outlet state trajectory in this time period are recorded in the data archive to provide a data basis for the subsequent adaptive adjustment of the adjustment strategy.

[0074] This implementation constructs a zoning control strategy based on stable, slowly changing, and drastic change zones by comparing the current damper adjustment step size with the damper adjustment level threshold in real time. This strategy enables precise damper control and optimized dynamic response under different adjustment scenarios. When the damper adjustment step size is in the lower range (less than or equal to the second adjustment threshold), the system is identified as in the stable adjustment zone. The current damper position and hot / cold mixture ratio remain unchanged, position confirmation commands are executed only at a low frequency, and temperature, air speed, and error data are archived. When the adjustment step size is in the medium range (greater than the second adjustment threshold but less than or equal to the first adjustment threshold), the system is identified as in the slowly changing adjustment zone, triggering a phased step control strategy. This strategy activates damper position tracking and compressor response synchronization correction mechanisms, while increasing the frequency of outlet air temperature sampling and dynamically adjusting the damper adjustment rhythm for the next cycle. When the step size exceeds the first adjustment threshold, the system is identified as in the drastic change adjustment zone. Rapid response mode is entered, increasing the adjustment frequency and suspending non-critical control tasks. The cycle's adjustment input and feedback data are isolated and cached for abnormal behavior analysis and performance trend prediction. Overall, this step realizes the dynamic classification of damper adjustment modes and intelligent switching of response rhythms, improving the temperature control robustness, execution efficiency and environmental adaptability of the air conditioner under variable heat load conditions.

[0075] Specifically, the damper adjustment step value and the consistency of the multi-temperature difference path response are jointly monitored, and the airflow adjustment trajectory and the triggering of the hot and cold response mismatch analysis process are dynamically corrected in stages. The specific steps are as follows:

[0076] When the damper adjustment step value shows a clear abnormal trend within the local control window, such as frequent positive and negative switching of the damper adjustment direction, continuous amplification of the deviation amplitude between consecutive execution actions, and significant misalignment between the temperature control response of the hot and cold adjustment results and the target, the abnormal trend identification process is initiated. The specific identification method includes: within a limited sliding control cycle, counting the number of switching times of the damper position change direction, the slope change trend of the continuous step value, and the response slope and fluctuation index of the outlet air temperature relative to the target temperature; if the number of switching times exceeds the threshold, the step fluctuation growth rate is continuously positive and exceeds the deviation control threshold, and the outlet air temperature delayed response exceeds the drift time threshold, then the section is marked as the period of adjustment imbalance trend, and the current damper action sequence, outlet air temperature response trajectory, cold and hot air flow mixing ratio evolution and temperature difference dynamic evaluation value are recorded.

[0077] To address the above abnormal trends, we no longer rely on the static control method of dividing the hot and cold air mixing ratio by preset gears in traditional automotive air conditioners. Instead, we introduce a dynamic response baseline construction mechanism based on adjustment behavior to support fine-grained correction of offset patterns, hysteresis, and response rhythm during the adjustment process. The specific measures are as follows: When identifying a stable adjustment section, under the premise that the current step change range and temperature error do not fluctuate significantly, the adjustment inertia curve is fitted based on the damper movement trend of the past three cycles to construct a damper offset buffer zone as a stable beat reference for subsequent adjustment steps, and the damper control frequency is adjusted to prevent unnecessary repeated adjustments from interfering with temperature control stability; when identifying a slowly changing adjustment section, in the case where the temperature response rhythm has slight fluctuations and a stable adjustment rhythm has not been formed, the amplitude value, cycle span, and adjustment rate change trend of the damper adjustment step in this stage are extracted to construct an adjustment elasticity baseline. This baseline will smooth out small adjustments that do not have actual response gains through the sensitivity suppression interval, prevent short-term disturbances from being misjudged as deviations from the adjustment target, and avoid excessive intervention; when it is identified as a sudden change adjustment section, when the damper action and the hot and cold responses are highly misaligned, the current compressor cooling or heating channel output is severely delayed, and the cabin temperature deviates from the target value rapidly, the control behavior reconstruction process will be executed: the damper adjustment lag, the abnormal trajectory of the hot and cold mixing ratio, and the duct thermal response delay time will be comprehensively considered to reconstruct the adjustment execution sequence, stretch and compress the original control signal stream on the time axis, and set multi-point response calibration nodes to enhance the synchronization and dynamic compensation capabilities of the hot and cold channel instructions.

[0078] This implementation, through dynamic identification of abnormal trends during damper adjustment and the construction of a multi-modal response baseline, enables refined identification and control correction of repeated switching of adjustment direction, response imbalance, and amplified execution deviations during hot and cold air mixing. This effectively overcomes the coarse adjustment granularity, delayed response, and frequent user intervention inherent in traditional automotive air conditioning systems using fixed-gear adjustment. By sequentially analyzing adjustment behavior within a sliding window, the characteristic patterns of adjustment imbalance are precisely identified. Differentiated response strategies are then applied based on the adjustment state. This multi-level control rhythm correction mechanism, spanning inertia buffering, sensitivity suppression, and behavior reconstruction, dynamically aligns damper adjustment with cooling and heating output, improving both efficiency and comfort. This approach provides more continuous and adaptive decision-making support for air conditioning control and is a crucial step in addressing the needs of high-precision thermal management. By incorporating baseline construction and abnormal trend identification into dynamic control, the hot and cold air mixing ratio can be flexibly adjusted in real time based on inlet air temperature fluctuations, evaporator outlet temperature differences, and real-time outlet temperature gradients. This improves the response speed and accuracy of in-vehicle temperature control, reduces the frequency of user intervention, and ultimately achieves the coordinated optimization of comfort and energy efficiency.

[0079] A second aspect of the present invention provides an intelligent control system for an automobile air conditioner, comprising: a data acquisition and preprocessing module for deploying multiple types of on-board sensor terminals to collect multi-point temperature data, air volume and airflow data, and external environmental data during the operation of the automobile air conditioner, wherein the multi-point temperature data includes the intake path temperature response amplitude value, the duct heat transfer hysteresis value, the cabin area temperature distribution, and the floor cold residual temperature; the air volume and airflow data includes the current outlet temperature, the target outlet temperature, the damper status, and the wind speed channel response frequency; the environmental data includes the external temperature change trend, the sunlight intensity, and the local heat capacity offset amplitude; after the acquisition is completed, each data channel is normalized and calibrated to correct the scale inconsistency problem caused by sensor distribution differences, sampling delay, and duct structure complexity, and complete unified time series coding and spatial position labeling, and finally construct a standardized on-board status data set.

[0080] The temperature difference dynamic calculation module is used to extract the response lag characteristics between the air inlet and outlet, the temperature difference jump value in the cold and hot interface area, and the heat balance offset characteristics from the multi-path temperature difference coupling relationship based on the standardized vehicle status data set, calculate the composite temperature difference dynamic evaluation value, and comprehensively reflect the complexity of the cold and hot air mixing response and the adjustment lag trend. Based on the evaluation value, the cold and hot air mixing ratio and the damper opening adjustment strategy are dynamically adjusted to achieve precise control of the adjustment frequency, response granularity and feedback delay.

[0081] The mixed airflow assessment module is used to integrate the current wind speed change trend, temperature difference path distribution, hot and cold mixing ratio and damper status indicators based on the standardized vehicle status data set, and conduct a multi-dimensional analysis of the adaptability of airflow adjustment from multiple source temperature difference paths and outlet wind speeds. It further identifies the hysteresis and over-adjustment risks in the damper adjustment behavior based on the adjustment hysteresis sensitivity and the degree of interference from the hot and cold channels, and dynamically corrects the damper opening and closing ratio and air supply flow configuration to ensure the coordination of the cold and hot air channel responses and the stability of the adjustment rhythm.

[0082] The closed-loop execution correction module is used to take the composite temperature difference feature evaluation results and the airflow adjustment adaptability analysis results as input, integrate the damper adjustment step change trend, the degree of hot and cold response alignment and the temperature difference response delay information, and conduct a comprehensive evaluation of the channel thermal response coordination and execution stability; and construct a hot and cold adjustment priority model based on the evaluation results, dynamically adjust the hot and cold channel ratio allocation strategy, reasonably allocate compressor energy consumption resources, reconstruct the control rhythm, and realize adaptive optimization of hot and cold output control under different adjustment sections.

[0083] The real-time comfort adjustment module is used to jointly monitor the damper adjustment step value and the consistency of multiple temperature difference path responses, and identify abnormal trends such as repeated switching of adjustment direction, fluctuations in adjustment accuracy, and imbalance in temperature control response. Once an adjustment abnormality is identified, the graded correction mechanism is triggered to adjust the adjustment trajectory section by section, introducing a dynamic response delay compensation mechanism, a damper inertia adjustment template, and a hot and cold channel command buffering strategy. At the same time, the hot and cold response mismatch analysis process is synchronously triggered to perform closed-loop optimization and reconstruction of the current adjustment behavior, thereby improving cabin comfort and adjustment stability.

[0084] In this implementation, the data acquisition and preprocessing module deploys high-resolution sensor terminals to collect multi-source data, including cabin temperature at key points, air outlet velocity, duct pressure differential, and external climate parameters, throughout the entire air conditioning operation process. The module then performs multi-dimensional synchronization, outlier removal, and time-window aggregation on the raw data. By constructing a standardized onboard status dataset in a unified format, it addresses input instability caused by environmental disturbances, inconsistent sampling frequencies, and interface protocol differences, providing reliable support for temperature difference calculation and control logic execution.

[0085] The temperature difference dynamic calculation module focuses on exploring the multi-path temperature difference change trend between the cold and hot air interface area and the target cabin temperature zone. It establishes a dynamic evolution model based on time delay, local gain and path gradient change characteristics, and dynamically updates the hot and cold adjustment priority parameters based on the external temperature change rate and the internal and external heat exchange delay, providing a theoretical basis for achieving precise mixing ratio adjustment and temperature control response rhythm.

[0086] The mixed airflow assessment module uses the damper status history, actual outlet temperature trajectory, and wind speed mutation index as core inputs to evaluate the combined effects of hot and cold airflows on the duct structure. By extracting the airflow deviation amplitude in key sections, the temperature response hysteresis in boundary areas, and the damper operation frequency characteristics, it determines whether the current regulation mechanism is sufficiently sensitive and stable, thereby providing quantitative feedback on the real-time adaptability of the damper strategy.

[0087] The closed-loop execution correction module comprehensively evaluates the execution quality of the cooling and heating control chain, constructing a control error mapping relationship based on target temperature difference achievement, command-response delay, and execution action backtracking information. By analyzing action redundancy, overshoot, and rhythm mismatches within the control path, it adjusts the damper adjustment amplitude threshold, compressor output level, and response timing parameters to achieve logical reconstruction and coordinated correction of cooling and heating execution behaviors.

[0088] The real-time comfort adjustment module focuses on user-perceived stability and air conditioning response flexibility, continuously monitoring the consistency between adjustment actions and environmental feedback during operation. When potential imbalances such as overshoot, repeated wind speed fluctuations, or abnormal temperature drift are detected, the module automatically assesses the source of control rhythm interference and, based on the variation in adjustment step size, determines whether to trigger rhythm suppression, adjustment lag compensation, and damper action limiting mechanisms. This enables feedforward intervention to mitigate the risk of sudden comfort drops and stabilize the rhythm.

[0089] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0090] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method and system for intelligent control of automobile air conditioning, characterized by: include: S1, collects multi-point temperature data, air volume and air flow data, and vehicle external environment data during the operation of the vehicle air conditioner, and pre-processes the collected multi-point temperature data, air volume and air flow data, and vehicle external environment data to construct a standardized vehicle status data set; S2, based on a standardized vehicle status dataset, evaluates the composite temperature difference characteristics from the multi-path temperature difference coupling relationship, and dynamically adjusts the cold and hot air mixing ratio and air damper opening adjustment strategy based on the evaluation results; S3, based on a standardized vehicle status data set, analyzes the adaptability of airflow regulation from multiple temperature difference paths and outlet wind speeds, and dynamically adjusts the damper opening and closing ratio and air flow rate based on the analysis results; S4, using the composite temperature difference characteristic assessment results and airflow adjustment adaptability analysis results as input, comprehensively evaluates the channel thermal response coordination and execution stability, and adaptively adjusts the hot and cold aisle ratio and control priority based on the evaluation results; S5, jointly monitor the consistency of the damper adjustment step value and the multi-temperature difference path response, and dynamically grade and correct the airflow adjustment trajectory and trigger the hot and cold response mismatch analysis process.

2. The intelligent control method for automobile air conditioner according to claim 1, characterized in that: The specific steps of collecting multi-point temperature data, air volume and air flow data, and vehicle exterior environment data during the operation of the automobile air conditioner, and pre-processing the collected multi-point temperature data, air volume and air flow data, and vehicle exterior environment data to construct a standardized vehicle status data set are as follows: Collect multi-point temperature data during vehicle air conditioning operation, including: intake path temperature response amplitude, duct heat transfer hysteresis, composite temperature difference balance reference value, ambient temperature distribution in multiple cabin areas, temperature difference adjustment threshold, and cold air residual temperature in the floor area; Collect temperature-related air volume and airflow data, including: target outlet temperature, current outlet temperature, hot air temperature, cold air temperature, duct heat loss value, outlet air speed, damper current deviation value, air path switching frequency and adjustment response time under damper status; Collects external environmental data, including: outside air temperature trends, sunlight intensity index, cabin overall temperature offset, current cabin heat capacity, and local temperature difference amplitude. It also records the standard heat capacity reference value and temperature response delay. Standardize and normalize the collected multi-point temperature data, air volume and air flow data, and vehicle exterior environment data to correct for data scale inconsistencies caused by differences in sensor distribution height, response delay, and air velocity. The standardized and normalized multi-point temperature data, air volume and airflow data, and vehicle exterior environment data are archived and stored with unified time series coding, spatial location identification, and functional attribute labels to construct a standardized vehicle status data set.

3. The intelligent control method for automobile air conditioner according to claim 1, characterized in that: The specific steps of evaluating the composite temperature difference feature from the multi-path temperature difference coupling relationship based on the standardized vehicle status data set are as follows: The intake path temperature response amplitude, duct heat transfer hysteresis, and cabin overall warm and cold offset values ​​are extracted from the standardized vehicle status dataset, squared, and then added together to obtain the heat exchange fluctuation value. Extract the local temperature difference amplitude and the composite temperature difference balance reference value, divide the local temperature difference amplitude by the sum of the composite temperature difference balance reference value and the small adjustment coefficient, and then add one to obtain the temperature path balance degree; The heat exchange fluctuation value is multiplied by the temperature path balance and then the logarithm is taken to obtain the temperature difference dynamic evaluation value.

4. The method for intelligently controlling an automobile air conditioner according to claim 1, wherein: The specific steps of the strategy for dynamically adjusting the cold and hot air mixing ratio and the damper opening based on the evaluation results are as follows: Real-time comparison of the current temperature difference dynamic assessment value with the temperature control response threshold: When the temperature difference dynamic assessment value is less than or equal to the temperature control response threshold, the air conditioning response is determined to be stable. The current cold and hot air mixing ratio and damper opening are maintained unchanged. Temperature difference stability assessment, supply air temperature tracking, and regional temperature gradient monitoring are continued based on the standard time window. When the dynamic evaluation value of the temperature difference is greater than the temperature control response threshold, it is determined to be the air conditioning response offset section, the adjustment enhancement mechanism is activated, the temperature analysis window duration is shortened, the time resolution of the adjustment strategy is improved, the damper opening is step-by-step fine-tuned, and a dynamic correction factor is applied to the hot and cold mixing ratio. At the same time, all temperature difference path data, historical adjustment records and multi-point temperature synchronization deviation values ​​in this stage are cached, the current response is marked as a high-sensitivity state, and the control sensitivity level is updated.

5. The intelligent control method for automobile air conditioner according to claim 1, characterized in that: The specific steps for analyzing the adaptability of airflow regulation from the perspective of multi-source temperature difference paths and outlet wind speed based on the standardized vehicle status data set are as follows: Extract the current cabin's target air outlet temperature, hot air temperature, cold air temperature, duct heat loss value, and air outlet wind speed, calculate the difference between the target air outlet temperature and the hot air temperature, and then calculate the product of the heat loss compensation factor and the duct heat loss value, as well as the product of the wind speed sensitivity adjustment factor and the air outlet wind speed; The difference between the target outlet air temperature and the hot air temperature is added to the product of the heat loss compensation factor and the duct heat loss value, and then the product of the wind speed sensitivity adjustment factor and the outlet air speed is subtracted as the numerator. The difference between the cold air temperature and the hot air temperature is used as the denominator to obtain the air supply path response value. Extract the temperature difference dynamic evaluation value, the current cabin heat capacity value, the standard heat capacity reference value, and the temperature difference adjustment threshold. Calculate the absolute value of the difference between the temperature difference dynamic evaluation value and the temperature difference adjustment threshold, divide it by the temperature difference adjustment threshold, and then multiply it by the ratio of the current cabin heat capacity value to the standard heat capacity reference value, then add one and take the logarithm to obtain the temperature difference impact amplification value. Multiply the air supply path response value by the temperature difference influence amplification value to obtain the cold and hot air flow mixing value.

6. The intelligent control method for automobile air conditioner according to claim 1, characterized in that: The specific steps of dynamically adjusting the damper opening and closing ratio and the air supply flow rate based on the analysis results are as follows: Compare the current cold and hot air flow mixing value with the mixing adjustment level threshold in real time. The mixing adjustment level threshold includes the first mixing threshold and the second mixing threshold: When the hot and cold airflow mixture value is less than or equal to the second mixture threshold, the system is determined to be in a stable cooling and heating balance range. The original damper opening and compressor power output are maintained unchanged. The current mixture state is used as a reference for stable control, and the current cabin response data is cached. When the hot and cold airflow mixing value is greater than the second mixing threshold and less than or equal to the first mixing threshold, it is judged as a slight deviation from the transition section, and the hot and cold airflow channel ratio is dynamically fine-tuned. Step correction control is applied to the damper actuator. At the same time, a local air supply delay compensation mechanism is introduced to buffer the adjustment fluctuations, and the current wind speed and heat loss factor weights are appropriately updated. When the mixture value of the cold and hot air flows is greater than the first mixture threshold, it is judged as a strong deviation from the response section, the damper adjustment frequency and the air duct response level are increased, the emergency temperature control compensation logic is triggered, the compressor cooling and heating channel output intensity are instantaneously adjusted, and the abnormal mixing ratio status, cabin temperature deviation trajectory and environmental interference factors during this period are recorded.

7. The automobile air conditioner intelligent control method according to claim 1, characterized in that: The specific steps of comprehensively evaluating the channel thermal response coordination and execution stability using the composite temperature difference characteristic evaluation results and the airflow adjustment adaptability analysis results as input are as follows: Get the mixed value of hot and cold airflow, subtract the current deviation value of the damper from 1, and then multiply it by the mixed value of hot and cold airflow to get the correction value of the hot and cold air ratio; Extract the current air outlet temperature and the target air outlet temperature, calculate the absolute value of the difference between the current air outlet temperature and the target air outlet temperature, add one, and then take the logarithm to obtain the hot and cold deviation suppression value; Divide the cold and hot air ratio correction value by the cold and hot deviation suppression value to obtain the temperature adjustment intensity value; Extract the temperature difference dynamic evaluation value and the temperature response delay, calculate the ratio between the temperature difference dynamic evaluation value and the temperature response delay, multiply it by the temperature difference dynamic gain coefficient and add one to obtain the complex impact correction value; Multiply the temperature adjustment intensity value by the complex influence correction value to obtain the current damper adjustment step value.

8. The automobile air conditioner intelligent control method according to claim 1, characterized in that: The specific steps for completing the adaptive adjustment of the hot and cold aisle ratio distribution and control priority based on the evaluation results are as follows: Compare the current damper adjustment step value with the damper adjustment level threshold in real time. The damper adjustment level threshold includes the first adjustment threshold and the second adjustment threshold: When the damper adjustment step value is less than or equal to the second adjustment threshold, it is determined to be a stable adjustment section, the current damper position and mixing ratio are maintained unchanged, only the low-frequency position confirmation command is executed, and the temperature collection data, wind speed trend and adjustment error of this period are recorded and archived; When the damper adjustment step value is greater than the second adjustment threshold and less than or equal to the first adjustment threshold, it is determined to be a slowly changing adjustment section, and the staged step control strategy is activated in real time. The damper position tracking and compressor response synchronization correction mechanism are started. At the same time, the sampling density of the air outlet temperature data is increased, and the adjustment efficiency is evaluated by combining the current adjustment rate and deviation value. The damper action interval of the next cycle is dynamically corrected; When the damper adjustment step value is greater than the first adjustment threshold, it is determined to be a sudden change adjustment section, and the system enters the fast response mode, increases the adjustment frequency and control priority, suspends all non-critical control tasks, and caches all current adjustment inputs and execution feedback data. The cycle is isolated as an abnormal response section for dynamic performance analysis and abnormal trend prediction, and the dynamic temperature difference value, hot and cold mixing ratio and air outlet status trajectory of the time period are recorded.

9. The automobile air conditioner intelligent control method according to claim 1, characterized in that: The specific steps of the joint monitoring of the damper adjustment step value and the consistency of the multi-temperature difference path response, and the dynamic hierarchical correction of the airflow adjustment trajectory and the triggering of the hot and cold response mismatch analysis process are as follows: When the damper adjustment step value shows abnormal trends in the local control window, including repeated switching of adjustment direction, execution deviation amplification, and imbalanced temperature control response, the damper status feedback channel is immediately called to upload the damper action record, outlet temperature change trajectory, and temperature difference evaluation data of that stage; The response baseline correction strategy is matched to the corresponding type of abnormal adjustment mode and temperature control offset amplitude: when it is identified as a stable adjustment section, the damper movement inertia baseline is constructed, and the damper offset buffer zone is extended with the current damper adjustment step value as a reference to stabilize the control frequency and optimize subsequent rhythm alignment; when it is identified as a slowly changing adjustment section, the adjustment amplitude elastic baseline is generated, the damper adjustment step peak and period characteristics are extracted, the acceptable deviation range is calibrated, and the sensitivity suppression zone is constructed; When a sudden change regulation section is identified, a control behavior reconstruction baseline is formed. Based on the current execution lag and temperature response misalignment, the hot and cold channel instruction flows are recombined to construct an alternative execution trajectory and a dynamic correction window. After the baseline is constructed, the output hot and cold adjustment rhythm and execution compensation strategy recommendations are generated. When the adjustment baseline reconstruction fails, the multi-point temperature data, air volume and airflow data and external environment data within the time window are recollected to execute the closed-loop hot and cold response mismatch identification - proportional control reconstruction - adjustment strategy regeneration process.

10. A system using the automobile air-conditioning intelligent control method according to any one of claims 1 to 9, characterized in that: include: The data acquisition and preprocessing module is used to collect multi-point temperature data, air volume and air flow data, and external environment data during the operation of the automobile air conditioner, and preprocess the collected multi-point temperature data, air volume and air flow data, and external environment data to construct a standardized vehicle status data set; The temperature difference dynamic calculation module is used to evaluate the composite temperature difference characteristics from the multi-path temperature difference coupling relationship based on the standardized vehicle status data set, and dynamically adjust the cold and hot air mixing ratio and damper opening adjustment strategy based on the evaluation results; A mixed airflow assessment module analyzes the adaptability of airflow regulation based on a standardized vehicle status dataset, including multi-source temperature difference paths and outlet air speeds, and dynamically adjusts the damper opening and closing ratio and air flow rate based on the analysis results. The closed-loop execution correction module uses the composite temperature difference characteristic assessment results and airflow adjustment adaptability analysis results as input to comprehensively evaluate the channel thermal response coordination and execution stability, and adaptively adjusts the hot and cold aisle ratio and control priority based on the assessment results. The real-time comfort adjustment module is used to jointly monitor the damper adjustment step value and the consistency of the multi-temperature difference path response, and dynamically grade and correct the airflow adjustment trajectory and trigger the hot and cold response mismatch analysis process.

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