Intelligent diagnosis and assessment method and system for automobile air conditioner

Abstract

The invention discloses an automobile air conditioner intelligent diagnosis and assessment method and system, and relates to the technical field of automobile air conditioner intelligent manufacturing. The intelligent diagnosis and assessment method and system for the automobile air conditioner comprises the steps that S1, multi-point temperature, air volume, airflow and environment data in the operation process of the air conditioner are collected and preprocessed, and a standardized vehicle-mounted state data set is constructed; s2, evaluating the comfort adjusting capacity and dynamically adjusting the opening degree of the air door in combination with the temperature and humidity air change and the stability approaching time; s3, the parameter recovery speed and the thermal load intensity are analyzed, and the air door switching rhythm is adjusted; s4, integrating comfort and environment adaptability evaluation results, and executing regulation and control resource redirection; and S5, jointly monitoring the environmental pressure and the regulation and control capability value, constructing a cross-scene scoring system, and marking strategy switching nodes. The problems that an existing air conditioning system lacks an assessment index with cooperation of a use scene and an environment as a core, and the control performance cannot be dynamically assessed in a cross-environment mode are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent manufacturing of automobile air conditioners, in particular to an intelligent diagnosis and evaluation method and system for automobile air conditioners. BACKGROUND

[0002] With the rapid development of automobile electrification and intelligent driving technology, the vehicle air conditioning system gradually evolves from the traditional manual control mode to automatic adjustment, scene perception and intelligent optimization. The air conditioning system is affected by various factors during actual operation, including the distribution of indoor temperature and humidity, the thermal comfort needs of passengers, the external climate environment and the change of driving conditions, etc., which constitutes a dynamic coupling, nonlinear disturbance significant thermal regulation control problem. A large amount of collection and processing of multi-source data such as temperature, wind speed, humidity, sunlight intensity, vehicle speed, etc. becomes the key basis for realizing accurate regulation and control.

[0003] For example, the invention patent with the announcement number CN106502148B discloses an automobile air conditioner cloud control system, which comprises: a manipulator for converting the user-set automobile air conditioner operation parameters into air conditioner setting CAN bus message output, and receiving and displaying working state information and remote control information in Chinese; a top control module for receiving air conditioner setting CAN bus message and sensor signals, performing logical judgment and processing, and then outputting corresponding control signals, while remotely sending air conditioner setting CAN bus message, and receiving remote control data and outputting corresponding remote control signals after analysis, and transmitting remote control information to the manipulator; a working state detection module for detecting the working state of the top electric control box, the fan and the clutch, and converting them into corresponding working state CAN bus message for remote sending through the top control module; a cloud control service platform for remotely receiving air conditioner setting and working state CAN bus message transmitted by a 3G mobile communication base station through the Internet, and sending remote control data.

[0004] For example, the invention patent with the announcement number CN104597787B discloses a method and system for remotely controlling the air conditioner of an electric vehicle, which mainly includes: the application software in the mobile terminal sends the air conditioner control instruction of the electric vehicle to the server through the wireless communication network, the server sends the air conditioner control instruction to the vehicle-mounted data terminal of the electric vehicle through the wireless communication network; the vehicle-mounted data terminal sends the air conditioner control instruction to the vehicle controller of the electric vehicle through wired communication, and the vehicle controller controls the electronic temperature control controller to control the air conditioner of the electric vehicle according to the state of charge of the battery of the electric vehicle. The embodiment of the present application can realize the opening and ending of the air conditioner control process of the electric vehicle by using the application software in the mobile terminal in combination with the server, so that the user can pre-cool or pre-heat the cockpit before going out, and improve the driving experience of the electric vehicle.

[0005] However, the existing automobile air conditioning control strategy is mainly based on static threshold judgment and experience rule driving, lacks joint modeling mechanism of high-dimensional heterogeneous environment data and system response state, and is difficult to reflect the actual adaptability of the system under complex scenes. Especially under extreme environmental conditions such as high temperature direct exposure, heavy rain, night low temperature and high humidity, the air conditioning adjustment behavior is prone to adjustment lag, air door mismatch or comfort imbalance, and the current evaluation system generally ignores the dynamic feedback of system regulation flexibility and cross-scene robustness, and cannot quantitatively judge the real and effective state change trend of the system running.

[0006] Therefore, there is an urgent need for an automobile air conditioner intelligent diagnosis and evaluation method and system. SUMMARY

[0007] Technical problems solved

[0008] In view of the defects of the prior art, the present application provides an automobile air conditioner intelligent diagnosis and evaluation method and system, which solves the problem that the current air conditioning system lacks evaluation indexes with the use scene and environment as the core, and cannot dynamically evaluate the control performance across the environment.

[0009] Technical scheme

[0010] In order to achieve the above purpose, the present application is implemented by the following technical scheme: an automobile air conditioner intelligent diagnosis and evaluation method and system, comprising S1, collecting multi-point temperature data, air volume and airflow data and environment data in the running process of the automobile air conditioner, and preprocessing the collected multi-point temperature data, air volume and airflow data and environment data to construct a standardized vehicle-mounted state data set; S2, based on the standardized vehicle-mounted state data set, the comfort regulation ability is evaluated in combination with the multi-point temperature and humidity wind change and the regulation trend stable time, and the air door opening degree is dynamically adjusted based on the evaluation result; S3, based on the standardized vehicle-mounted state data set, the environmental adaptability is analyzed in combination with the cabin multi-point parameter recovery speed and the external heat load intensity, and the air door switching rhythm is dynamically adjusted based on the analysis result; S4, taking the comfort regulation ability evaluation result and the environmental adaptability analysis result as input, the vehicle cabin air conditioner adaptive regulation ability is comprehensively evaluated, and resource recombination redirection is executed based on the evaluation result; S5, the multi-source environment pressure index and the adaptive regulation ability value are jointly monitored, a cross-scene evolution scoring system is constructed, and a strategy switching node is marked.

[0011] Further, the multi-point temperature data, air flow data and environmental data collected during the operation of the automobile air conditioner are preprocessed to construct a standardized vehicle state data set. The specific steps are as follows: collecting multi-point temperature data during the operation of the automobile air conditioner, including the maximum temperature, humidity variation amplitude, average temperature, average wind speed variation amplitude, temperature residual mean value, and heat distribution imbalance degree of the front and rear areas of each monitoring point, recording the total number of monitoring points in the cabin, the temperature stabilization time, humidity stabilization time and wind speed stabilization time of each monitoring point from the start of adjustment to the stable state, and the maximum delay time experienced by the last monitoring point to enter the stable state; collecting air flow data related to the heat load, including the coordinated recovery speed of the temperature, humidity and wind parameters in the cabin, the frequency of air door path switching, the fluctuation amplitude of multi-point comfort parameters, the wind speed disturbance index, and the heat exchange delay, and recording the wind speed error value between the actual wind speed and the theoretical air supply speed; collecting the scene environmental data inside and outside the vehicle, including the current period sunlight intensity gradient, the current cabin temperature mean value, the target set temperature, the heat load pressure index, the external wind speed disturbance number and the solar incident angle; standardizing and normalizing the collected multi-point temperature data, air flow data and environmental data to correct the data scale inconsistency caused by the differences in sensor spatial arrangement, thermal inertia response delay, air flow disturbance interference intensity and meteorological fluctuation periodicity; uniformly encoding and archiving the multi-point temperature data, air flow data and environmental data that have completed standardization and normalization to construct a standardized vehicle state data set for dynamic environmental perception.

[0012] Further, based on the standardized vehicle state data set, the comfort adjustment capability is evaluated by combining the multi-point temperature, humidity and wind variation and adjustment stabilization time. The specific steps are as follows: traversing all the monitoring points in the cabin, extracting the maximum temperature and average temperature of each monitoring point in the adjustment process from the standardized vehicle state data set, calculating the difference between the maximum temperature and the average temperature and dividing it by the temperature stabilization time of the corresponding monitoring point to obtain the temperature deviation amplitude of each monitoring point; extracting the humidity variation amplitude and humidity stabilization time of each monitoring point, calculating the ratio of the humidity variation amplitude and the humidity stabilization time to obtain the humidity deviation amplitude of each monitoring point; extracting the average wind speed variation amplitude and wind speed stabilization time of each monitoring point, calculating the ratio of the average wind speed variation amplitude and the wind speed stabilization time to obtain the wind speed deviation amplitude of each monitoring point; adding the temperature deviation amplitude, humidity deviation amplitude and wind speed deviation amplitude of each monitoring point to obtain the overall deviation amplitude value of each monitoring point; adding the overall deviation amplitude values of all monitoring points, dividing by the total number of monitoring points in the cabin, and then subtracting the maximum delay time experienced by the last monitoring point to enter the stable state after the start of adjustment to obtain the comfort response trend value.

[0013] Further, the step of dynamically adjusting the damper opening degree based on the evaluation result is: comparing the current comfort response trend value with the comfort adjustment reference threshold value in real time: when the comfort response trend value is greater than or equal to the comfort adjustment reference threshold value, it is determined that the air conditioner response is in the normal section, and the current air outlet air speed, damper opening degree and cold-heat mixing ratio are maintained unchanged, and the local comfort stability evaluation, space heat balance tracking and fluctuation detection of each monitoring point comfort parameter are continued to be performed based on the standard time sliding window; when the comfort response trend value is less than the comfort adjustment reference threshold value, it is determined that the air conditioner response is in the inefficient section, and the adaptive enhancement control strategy is triggered, including shortening the comfort analysis window length, increasing the cabin state refresh frequency, implementing micro-step adjustment on the key damper area, applying a time weighted correction term to the air outlet temperature, dynamically adjusting the multi-point monitoring priority and caching the wind speed variation trajectory, thermal feeling offset characteristics and response lag record in the current period.

[0014] Further, the step of analyzing the environmental adaptability based on the standardized vehicle state data set, combining the cabin multi-point parameter recovery speed and external heat load intensity is: extracting the cooperative recovery speed of the cabin internal temperature, humidity and air parameters per unit time in the standardized vehicle state data set and the damper path switching frequency, taking the natural logarithm of the cooperative recovery speed plus one, and multiplying the square value of the difference between the damper path switching frequency minus one, to obtain the response synchronization correction value; extracting the multi-point comfort parameter fluctuation amplitude and temperature residual mean value, taking the natural logarithm of the product of the multi-point comfort parameter fluctuation amplitude and the temperature residual mean value plus one, to obtain the adjustment fluctuation value; extracting the wind speed disturbance index, dividing the wind speed disturbance index by the value of the wind speed disturbance index plus one, and adding the adjustment fluctuation value to obtain the comprehensive fluctuation change number; extracting the heat load pressure index, taking the response synchronization correction value as the numerator, and taking the product of the heat load pressure index and the comprehensive fluctuation change number as the denominator to obtain the environmental adaptability elasticity value.

[0015] Further, the dynamic adjustment of the damper switching rhythm based on the analysis result comprises the following steps: comparing the environmental adaptation elasticity value with the environmental adaptation control threshold value in real time, the environmental adaptation control threshold value comprising a first elasticity threshold value and a second elasticity threshold value; when the environmental adaptation elasticity value is greater than or equal to the first elasticity threshold value, determining that the air conditioner is in a high adaptability section, entering a steady-state regulation mode, locking the current cold air and hot air ratio, the air outlet temperature and the damper position, and suspending the data refresh cycle of the local sensor, while starting a periodic elasticity fluctuation monitoring mechanism, maintaining the global response structure unchanged in a low control resource occupation state, and ensuring continuous operation under the minimum regulation intervention; when the environmental adaptation elasticity value is greater than or equal to the second elasticity threshold value and less than the first elasticity threshold value, determining that the air conditioner is in a critical adaptability section, resetting the damper switching threshold value and the air speed adjustment step parameter, shortening the temperature control execution cycle and increasing the air direction correction frequency, actively adjusting the control sensitivity level, binding the comfort factor change to the heat load change trend in priority, and starting a regional priority sorting mechanism to dynamically switch the key response points in the cabin, and improving the local response strength of the core area; when the environmental adaptation elasticity value is less than the second elasticity threshold value, determining that the air conditioner is in a low adaptability section, forcibly cutting off the current flexible control channel, reconstructing the regulation priority sequence, starting a full-parameter control convergence mode, canceling the redundant feedback items in the dynamic adjustment path, compressing the control resolution of the non-key area in the cabin, and converting the damper action mode to a fixed interval switching mode.

[0016] Further, the comprehensive evaluation of the adaptive regulation capacity of the vehicle cabin air conditioner based on the comfort adjustment capacity evaluation result and the environmental adaptation capacity analysis result comprises the following steps: obtaining the comfort response trend value and the environmental adaptation elasticity value, multiplying the two values to obtain a comprehensive adjustment capacity index; extracting the current cabin temperature mean value and the target set temperature, calculating the absolute value of the difference between the current cabin temperature mean value and the target set temperature, taking the logarithm of the value plus one to obtain a response error suppression value; extracting the external wind speed disturbance number, dividing the external wind speed disturbance number by the sum of the heat exchange delay and the wind speed error value, and then adding one to obtain a regulation lag correction number; dividing the comprehensive adjustment capacity index by the response error suppression value, multiplying the regulation lag correction number to obtain an adaptive regulation capacity value.

[0017] Further, the specific step of performing resource recombination redirection based on the evaluation result is: comparing the current adaptive regulation capacity value with the regulation capacity classification threshold in real time, the regulation capacity classification threshold including a first regulation threshold and a second regulation threshold: when the adaptive regulation capacity value is greater than or equal to the first regulation threshold, it is determined that it is in a high-response matching section, a steady-state control freezing mode is entered, all automatic adjustment instructions related to the air duct switching are suspended, the current air direction output configuration is locked, the path selection space is further compressed, the wind speed error limit is reduced to within the fine adjustment threshold, the parameter disturbance interval in the control period is fixed, and unnecessary rapid disturbance convergence actions are reduced; when the adaptive regulation capacity value is greater than or equal to the second regulation threshold and less than the first regulation threshold, it is determined that it is in a medium-response coordination section, a dynamic path buffer mechanism is started, the current unfinished register queue of the air door instruction is delayed for execution, the wind speed and temperature regulation are decoupled for execution, and the regulation stability priority is improved; when the adaptive regulation capacity value is less than the second regulation threshold, it is determined that it is in a low-response imbalance section, a regulation resource recombination mechanism is triggered, the current multi-zone linkage control strategy is cancelled, all regional comfort regulation tasks are redirected to the main pilot region, and a self-adaptive slope correction process of the outlet air speed is started, the difference distribution diagram of the current actual wind speed curve and the reference regulation curve is calculated point by point, and the controller step size is dynamically adjusted.

[0018] Further, the specific step of monitoring the multi-source environmental pressure index and the adaptive regulation capacity value, constructing a cross-scene evolution scoring system, and marking a strategy switching node is: fusing and analyzing the thermal load pressure index, the environmental adaptation elasticity curve, and the regulation capacity value, constructing a dynamic evolution trajectory, marking a response section, and feeding back the regulation stability and adaptive capacity of the air conditioner control under multiple environmental situations in real time; taking the thermal load pressure index, the environmental adaptation elasticity curve, and the adaptive regulation capacity value as joint inputs, constructing a multi-dimensional environmental adaptation factor group, binding the regulation behavior and the external scene state in each time period, and extracting a cross-scene performance response feature vector; according to the dynamic weight of the environmental factor, reconstructing the scene label of typical working conditions including high temperature and strong radiation, low temperature and high humidity, and heavy rain in a closed environment, marking the regulation path structure, the response lag characteristic, and the regulation convergence efficiency under different environmental categories; based on the trend distribution of the adaptive regulation capacity value and the environmental adaptation elasticity curve offset characteristics, identifying the current environmental characteristics, and adding a disturbance sensitivity index and a control link compression ratio in each state for control resource allocation optimization; constructing a scene-regulation bidirectional evolution trajectory matrix, dynamically archiving the response change mode and the control strategy adjustment behavior in various extreme environments, and identifying the regulation strategy switching boundary conditions through the marked switching points.

[0019] The second aspect of the application provides an automobile air conditioner intelligent diagnosis and evaluation system, comprising: a data acquisition and preprocessing module, used for acquiring multi-point temperature data, air volume and airflow data and environmental data in the running process of the automobile air conditioner, and preprocessing the acquired multi-point temperature data, air volume and airflow data and environmental data to construct a standardized vehicle-mounted state data set; an in-cabin state monitoring module, used for evaluating the comfort adjustment capability based on the standardized vehicle-mounted state data set, combining multi-point temperature, humidity and wind changes and adjustment trend stabilization time, and dynamically adjusting the damper opening degree based on the evaluation result; a regulation flexibility evaluation module, used for analyzing the environmental adaptability based on the standardized vehicle-mounted state data set, combining the in-cabin multi-point parameter recovery speed and the external thermal load intensity, and dynamically adjusting the damper switching rhythm based on the analysis result; a cross-scene performance comparative analysis module, used for taking the comfort adjustment capability evaluation result and the environmental adaptability analysis result as input, comprehensively evaluating the adaptive regulation capability of the vehicle cabin air conditioner, and performing resource reorganization and redirection based on the evaluation result; a comprehensive evaluation and diagnosis feedback module, used for jointly monitoring multi-source environmental pressure indicators and adaptive regulation capability values, constructing a cross-scene evolution scoring system and marking a strategy switching node.

[0020] Advantages

[0021] The application has the following advantages:

[0022] (1) The automobile air conditioner intelligent diagnosis and evaluation method and system, by constructing a thermal load pressure index and introducing a temperature-air speed coupling response dynamic calculation mechanism, quantize the load sensitivity of the air conditioning system under high temperature and strong radiation and other typical scenes, effectively solve the problem that the environmental change cannot be mapped to the regulation performance in the prior art, and the control strategy lacks situational adaptability.

[0023] (2) The automobile air conditioner intelligent diagnosis and evaluation method and system, by constructing an environmental adaptability elasticity curve and fusing multi-source disturbance and regulation path information, realize the modeling of the adjustment stability and robustness of the system under different external conditions, effectively improve the accuracy and dynamic adaptability evaluation ability of the air conditioner performance diagnosis under complex environment.

[0024] (3) The automobile air conditioner intelligent diagnosis and evaluation method and system, by introducing an adaptive regulation capability value, comprehensively analyzing the comfort response trend and environmental elasticity characteristics, supporting the collaborative diagnosis of the system regulation path, damper control behavior and cold and hot airflow dynamics, effectively solving the problem of coarse evaluation granularity and inaccurate strategy adjustment of the existing system.

[0025] (4), the intelligent diagnosis and examination method and system of the automobile air conditioner, through the construction of the scene-regulation bidirectional evolution track, the response change mode and the control strategy switching behavior under multiple environments can be automatically archived, the strategy switching boundary and the regulation ability degradation node can be clearly identified, the accurate criterion for the OTA upgrade and the adaptive control reconstruction is provided, and the intelligent adjustment ability and the continuous optimization ability of the system are effectively enhanced.

[0026] Of course, implementing any product of the present application does not necessarily require all the advantages described above to be achieved at the same time. BRIEF DESCRIPTION OF DRAWINGS

[0027] Fig. 1 The flow chart of the intelligent diagnosis and examination method of the automobile air conditioner of the present application;

[0028] Fig. 2 The structure diagram of the intelligent diagnosis and examination system of the automobile air conditioner of the present application;

[0029] Fig. 3 The columnar graph of the adaptive regulation ability value involved in the present application;

[0030] Fig. 4 The environment adaptive elasticity curve instance diagram involved in the present application. DETAILED DESCRIPTION

[0031] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0032] Please refer to Fig. 1-Fig. 4The embodiment of the application provides a technical scheme: a car air conditioner intelligent diagnosis and evaluation method and system, comprising S1, collecting multi-point temperature data, air volume airflow data and environmental data in the running process of the car air conditioner, and preprocessing the collected multi-point temperature data, air volume airflow data and environmental data to construct a standardized vehicle-mounted state data set; S2, based on the standardized vehicle-mounted state data set, the comfort regulation ability is evaluated in combination with the multi-point temperature and humidity wind changes and the adjustment trend stability time, and the damper opening degree is dynamically adjusted based on the evaluation result; S3, based on the standardized vehicle-mounted state data set, the environmental adaptability is analyzed in combination with the cabin multi-point parameter recovery speed and the external thermal load intensity, and the damper switching rhythm is dynamically adjusted based on the analysis result; S4, taking the comfort regulation ability evaluation result and the environmental adaptability analysis result as inputs, the vehicle cabin air conditioner adaptive control ability is comprehensively evaluated, and resource reorganization redirection is performed based on the evaluation result; S5, the multi-source environmental pressure index and the adaptive control ability value are jointly monitored, a cross-scene evolution scoring system is constructed, and a strategy switching node is marked.

[0033] Specifically, the multi-point temperature data, air volume airflow data and environmental data in the running process of the car air conditioner are collected, and the collected multi-point temperature data, air volume airflow data and environmental data are preprocessed to construct a standardized vehicle-mounted state data set. The specific steps are as follows:

[0034] The multi-point temperature data in the running process of the car air conditioner is collected, and the multi-point temperature data includes: the maximum temperature of each monitoring point, the humidity change amplitude, the average temperature, the average change amplitude of wind speed, the average residual error of temperature, and the heat distribution imbalance degree of front and rear areas; wherein, the maximum temperature is used to identify the peak value of thermal load, the humidity change amplitude reflects the stability of humidity control ability, and the wind speed change amplitude is used to evaluate the instantaneous fluctuation in the air flow regulation process; the total number of monitoring points in the cabin, the temperature stabilization time, the humidity stabilization time and the wind speed stabilization time of each monitoring point from the start of regulation to the stable state are recorded, which are used to judge the response time of the regulation process in different physical parameter dimensions; and the maximum delay time experienced by the last monitoring point entering the stable state is additionally extracted, which is used to represent the response boundary of the completion of the global regulation closed loop;

[0035] The air volume airflow data related to the thermal load are collected, specifically including: the cooperative recovery speed of the temperature and humidity wind parameters in the cabin is used to evaluate the regulation synchronization coordination among multiple parameters, the damper path switching frequency is used to reflect the stability of the damper regulation strategy, the multi-point comfort parameter fluctuation amplitude is used to perceive the disturbance situation of the body feeling consistency in the cabin, and the wind speed disturbance index and the heat exchange delay amount are used to identify the air flow stability and energy conversion lag problem; the wind speed error value between the actual wind speed and the theoretical air supply speed is recorded as an important parameter for judging the accuracy of the wind control target;

[0036] Collecting the in-vehicle and external scene environment data, the environment data includes: the current period sunlight irradiation intensity gradient representing the light heat interference change intensity, the current vehicle cabin temperature average value for depicting the overall heat field level, the target set temperature as the control reference, the heat load pressure index reflecting the total heat disturbance load currently borne, the external wind speed disturbance number reflecting the external airflow disturbance frequency and the solar incident angle affecting the local uneven heating distribution in the cabin;

[0037] Standardizing and normalizing the collected multi-point temperature data, airflow data and environment data, respectively constructing temperature normalization factor, airflow disturbance normalization factor and heat load normalization factor, to correct the data scale inconsistency problems caused by sensor spatial layout difference, thermal inertia response delay, airflow disturbance interference intensity and meteorological fluctuation periodicity, to ensure the comparability and fusion of various types of data;

[0038] Uniformly encoding and archiving the multi-point temperature data, airflow data and external environment data after standardization and normalization processing, using a hierarchical tag structure to identify sampling time, control stage, vehicle cabin area and environment scenario, and finally constructing a standardized vehicle state data set for dynamic environment perception, providing a structured data foundation for subsequent intelligent diagnosis and control capability evaluation.

[0039] In this embodiment, the purpose is to build a high-quality, fusible vehicle state data foundation to support intelligent diagnosis and control capability evaluation of automobile air conditioners in a variable environment. By comprehensively collecting multi-point temperature data, airflow data and in-vehicle and external environment data during operation, not only the key features of temperature maximum value, average value, humidity change amplitude, wind speed change amplitude, residual mean value and heat imbalance degree are covered, but also the trend stabilization time and maximum delay time of each monitoring point reaching a stable state are recorded, fully reflecting the response efficiency and steady state establishment capability during the adjustment process. In terms of airflow, the collected content includes cooperative recovery speed, air door path switching frequency, comfort parameter fluctuation amplitude, wind speed disturbance index and heat exchange delay, and the adjustment capability and disturbance response degree are quantified by combining wind speed error value. The environment data level covers sunlight irradiation intensity gradient, vehicle cabin average temperature, target set temperature, heat load pressure index, external wind speed disturbance value and solar incident angle, accurately describing the influence of external disturbance factors on the in-vehicle microenvironment. All collected data are standardized and normalized under a unified standard, correcting the data scale inconsistency problems caused by sensor layout difference, thermal inertia response delay, airflow disturbance intensity and meteorological fluctuation periodicity, and finally constructing a standardized vehicle state data set with unified structure, clear semantics and time sequence, providing reliable data support for subsequent environmental adaptability analysis, control capability evaluation and state recognition.

[0040] Specifically, based on the standardized vehicle state data set, the comfort adjustment ability is evaluated in combination with the multi-point temperature, humidity and wind changes and the adjustment trend stability time. The specific steps are as follows: traversing all the in-cabin monitoring points, extracting the maximum temperature and the average temperature of each monitoring point in the adjustment process in the standardized vehicle state data set, calculating the difference between the maximum temperature and the average temperature and dividing by the temperature stabilization time of the corresponding monitoring point to obtain the temperature deviation amplitude of each monitoring point; extracting the humidity change amplitude and the humidity stabilization time of each monitoring point, calculating the ratio of the humidity change amplitude and the humidity stabilization time to obtain the humidity deviation amplitude of each monitoring point; extracting the average wind speed change amplitude and the wind speed stabilization time of each monitoring point, calculating the ratio of the average wind speed change amplitude and the wind speed stabilization time to obtain the wind speed deviation amplitude of each monitoring point; adding the temperature deviation amplitude, the humidity deviation amplitude and the wind speed deviation amplitude of each monitoring point to obtain the overall deviation amplitude value of each monitoring point; adding the overall deviation amplitude values of all the monitoring points, dividing by the total number of in-cabin monitoring points, and then subtracting the maximum delay time experienced by the last monitoring point entering the stable state after the adjustment starts to obtain the comfort response trend value.

[0041] The comfort response trend value calculation formula is as follows:

[0042] ;

[0043] In the formula, indicates the total number of in-cabin monitoring points participating in the calculation, which is used to ensure comprehensive coverage of the vehicle cabin space distribution, and usually includes sensor points in the front row, the middle row, the rear row and key heat-sensitive areas; indicates the maximum temperature of the i-th monitoring point in the adjustment process, which is used to reflect the thermal peak load during the adjustment of the region and identify the hot spot fluctuation in the temperature control process; indicates the average temperature of the i-th monitoring point in the adjustment period, which is used to be subtracted from the maximum value to characterize the temperature adjustment amplitude and evaluate the thermal equilibrium establishment rate; indicates the humidity change amplitude of the i-th monitoring point, which is used to measure the humidity volatility during the adjustment process, and the comfort is often reduced when the humidity is unstable; indicates the temperature stabilization time of the i-th monitoring point from the start of the adjustment to the entry into the stable interval, which is used as a time quantitative basis for the temperature control response speed; indicates the humidity stabilization time required for the i-th monitoring point to reach a stable state, which is used to evaluate the stability and response delay of humidity adjustment; indicates the average wind speed change amplitude of the i-th monitoring point in the adjustment process, which is used to reflect the amplitude of the air flow intensity adjustment and is an important reference for the air volume control ability; indicates the wind speed stabilization time of the i-th monitoring point, which is used to reflect the response delay and stability performance of the air conditioner air volume; The maximum time of the delay experienced by the last one of all the monitoring points entering the steady state after the start of the adjustment, used to quantify the response delay of the worst area.

[0044] In this embodiment, the comfort response trend value of the vehicle air conditioner in the adjustment process is calculated, that is, the overall regulation efficiency of the air conditioner in achieving temperature stability and wind speed coordination in the dynamic adjustment process is quantified. Specifically, the formula jointly analyzes multiple dimensions of the maximum value, average value, fluctuation amplitude, steady state establishment time of the temperature of multiple monitoring points in the cabin during the adjustment process, as well as the wind speed change amplitude and steady state delay time, and comprehensively evaluates the response timeliness and adjustment stability of the air conditioner when responding to changes in heat load, thereby depicting the temperature control robustness and execution coordination of the air conditioner under complex working conditions. This index can be used as a key reference value for subsequent judgment of whether the thermal control regulation performance is excellent.

[0045] Specifically, and based on the evaluation results, the damper opening degree is dynamically adjusted as follows:

[0046] Real-time comparison of the current comfort response trend value and the comfort regulation reference threshold: when the comfort response trend value is greater than or equal to the reference threshold, it is determined that the air conditioner response is in the normal section, and the current air outlet wind speed, damper opening degree and cold-heat mixing ratio are maintained unchanged, while the three types of regulation tasks are continuously carried out within the standard time sliding window: one is to dynamically evaluate the local comfort stability of each area; two is to track the balance degree of space heat distribution in real time, and identify the heat deviation trend between the front and rear rows; three is to monitor the short-period fluctuation of all temperature, humidity and wind parameters, and record the abnormal jump points;

[0047] When the comfort response trend value is less than the reference threshold, it indicates that the current regulation effect has decayed, and the adaptive enhancement control mechanism needs to be triggered. This mechanism comprehensively guides multi-dimensional regulation correction: on the one hand, by shortening the comfort analysis window and increasing the cabin state refresh frequency, the comfort judgment is more timely; on the other hand, small-step fine-tuning is implemented for the damper and air outlet path to avoid secondary fluctuations caused by drastic disturbances; at the same time, a time-weighted correction term is introduced for the air outlet temperature to emphasize the temperature inertia influence of the historical stable section; in addition, the multi-point monitoring priority needs to be dynamically adjusted, and the sensing resources are tilted to the area with significant heat load changes, and the wind speed variation trajectory, heat feeling offset characteristics and response delay record in the current period are completely cached to build the perception input basis for auxiliary regulation path selection and behavior prediction.

[0048] In this embodiment, the response efficiency and comfort adjustment accuracy of the air conditioner are dynamically evaluated and regulated, and by comparing the comfort response trend value with the comfort adjustment reference threshold value in real time, it is determined whether the current air conditioning adjustment state is in the effective section. If the response efficiency is low, a series of adaptive enhancement mechanisms including window shortening, adjustment granularity refinement, and monitoring priority rearrangement are actively triggered, so as to more quickly and accurately identify and correct the cabin comfort deviation problem, and enhance the regulation flexibility and scene adaptability under complex thermal environment changes.

[0049] Specifically, based on the standardized vehicle state data set, the environmental adaptability is analyzed in combination with the recovery speed of the multi-point parameters in the cabin and the external thermal load intensity. The specific steps are as follows: the cooperative recovery speed of the temperature, humidity and wind parameters in the cabin per unit time and the frequency of switching the air door path in the standardized vehicle state data set are extracted, the natural logarithm of the cooperative recovery speed plus one is multiplied by the square value of the difference between the frequency of switching the air door path minus one, to obtain a response synchronization correction value; the fluctuation amplitude of the multi-point comfort parameters and the mean value of the temperature residual error are extracted, the natural logarithm of the product of the fluctuation amplitude of the multi-point comfort parameters and the mean value of the temperature residual error plus one is obtained, to obtain an adjustment fluctuation value; the wind speed disturbance index is extracted, the wind speed disturbance index is divided by the value of the wind speed disturbance index plus one, and then added to the adjustment fluctuation value, to obtain a comprehensive fluctuation change number; the response synchronization correction value is taken as the numerator, and the product of the thermal load pressure index and the comprehensive fluctuation change number is taken as the denominator, to obtain an environmental adaptability elasticity value.

[0050] The environmental adaptability elasticity value calculation formula is:

[0051] ;

[0052] In the formula, represents the cooperative recovery speed of the temperature, humidity and wind parameters in the cabin per unit time, which is used to comprehensively measure the synchronization stabilization ability of temperature, humidity and wind speed in the adjustment process, and is calculated by averaging the parameter recovery rates of multiple key areas in the cabin. The larger the value is, the more coordinated the adjustment reaction is; represents the frequency of switching the air door path, which refers to the number of times of state conversion of the internal air door components of the automobile air conditioner including the mode air door, the mixed air door and the circulating air door due to the change of control instructions per unit time. The state conversion includes the transition of the air door from full opening to full closing, half opening to other angles, and the transition from one ventilation mode to another ventilation mode such as from face blowing to foot blowing, defrosting, which is used to reflect the number of times of air door state change per unit time, and to determine whether the air door actuator exists high-frequency adjustment and control instability problem; represents the thermal load pressure index, which is used to represent the thermal load intensity caused by environmental factors including the current outdoor temperature, solar radiation, air humidity and vehicle speed on the vehicle cabin, and is an important indicator for measuring the complexity of external scenes; Indicates the fluctuation amplitude of the multi-point comfort parameter, which is used to reflect the temperature, humidity and wind speed variation range of multiple measurement points in the cabin during the adjustment process. The larger the value, the more unstable the regulation. Indicates the temperature residual mean value, which is used to represent the average deviation between the temperature of each monitoring point in the cabin and the target set temperature, and to evaluate whether the current adjustment state meets the thermal comfort demand. Indicates the wind speed disturbance index, which is used to reflect the intensity of wind speed fluctuation during the wind process. The value is obtained by the ratio of short-time fluctuation amplitude to steady-state wind speed. The larger the value, the more unstable the air volume adjustment.

[0053] In this embodiment, the environmental adaptation resilience value of the vehicle air conditioner under the condition of complex regulation of multiple cabin areas is calculated, that is, the recovery ability of the coordinated regulation in the multi-factor coupling background of responding to multi-source heat load, damper switching and temperature and humidity fluctuation disturbance is measured. By weighting and fusing multiple dimensions including the coordinated recovery speed of wind speed and temperature adjustment parameters, the frequency of damper switching, the intensity of external environmental heat load, the amplitude of cabin temperature variation, the mean value of temperature control deviation, and the intensity of wind speed disturbance, it is dynamically evaluated whether there is regulation lag, wind control instability and environmental disadaptation phenomenon, so as to realize the quantitative judgment of the overall regulation robustness and cross-scene adaptability.

[0054] Specifically, and based on the analysis results, the damper switching rhythm is dynamically adjusted. The specific steps are as follows: comparing the current environmental adaptation resilience value with the environmental adaptation control threshold value in real time. The environmental adaptation control threshold value includes a first resilience threshold value and a second resilience threshold value.

[0055] When the environmental adaptation resilience value is greater than or equal to the first resilience threshold value, it is determined that the air conditioner is in a high adaptability section, and the adjustment enters a steady-state operation stage. The current cold and hot air mixing ratio, outlet air temperature and damper opening degree remain frozen state, and each adjustment instruction is temporarily suspended. At the same time, the data refresh period of non-critical sensors is extended to reduce the computational load. Periodic resilience stability observation logic is started to periodically detect resilience fluctuation trend and ensure comfort stability on the basis of minimizing adjustment intervention.

[0056] When the environmental adaptation resilience value is greater than or equal to the second resilience threshold value and less than the first resilience threshold value, it is determined that the air conditioner is running in a critical adaptability section. The regulation parameter structure is actively adjusted, the action threshold of damper path switching and the step size of wind speed change are re-set, the temperature control adjustment period of each round is compressed, and the number of wind direction fine tuning is increased. In this stage, the comfort change is strongly bound to the main axis of heat load change, and the priority of each area in the cabin is updated according to the real-time comfort offset value to guide the adjustment resources to the key areas with response lag and improve the overall regulation efficiency.

[0057] When the environmental adaptation elasticity value is less than the second elasticity threshold value, it is determined that the air conditioner is in a low adaptability section, and a global regulation reconstruction process is triggered. The flexible adjustment path is temporarily interrupted, the core regulation variable enters a forced convergence state, the reorganization adjustment parameter executes a priority sequence, the redundant feedback path is eliminated, the cabin air door adjustment instruction is unified and switched to a fixed rhythm operation mode. The adjustment granularity of the non-core area is compressed, and the released resources are used for high-intensity scene adjustment to accumulate response space for subsequent adaptability recovery and strategy reconstruction.

[0058] In the embodiment, by comparing the current environmental adaptation elasticity value with the environmental adaptation control threshold value in real time, the air conditioning regulation strategy is dynamically adjusted according to the section of the elasticity value, and the fine classification control of the adjustment behavior under the variable environment is realized. The classification strategy effectively enhances the response ability of the air conditioning adjustment to the extreme external environment, improves the continuity of comfort maintenance and the rationality of resource allocation, and also provides the basis for the behavior of the elasticity fluctuation monitoring and the strategy switching boundary for the subsequent strategy diagnosis and adaptive control model.

[0059] Specifically, taking the comfort adjustment ability evaluation result and the environmental adaptation ability analysis result as inputs, the comprehensive evaluation of the adaptive regulation ability of the vehicle cabin air conditioner is carried out. The specific steps are: obtaining the comfort response trend value and the environmental adaptation elasticity value, multiplying the two to obtain a comprehensive adjustment ability index; extracting the current vehicle cabin temperature mean value and the target set temperature, calculating the absolute value of the difference between the current vehicle cabin temperature mean value and the target set temperature, taking the logarithm of the result plus one to obtain a response error suppression value; extracting the external wind speed disturbance number, dividing the external wind speed disturbance number by the sum of the heat exchange delay and the wind speed error value, and then adding one to obtain a regulation lag correction number; dividing the comprehensive adjustment ability index by the response error suppression value, and then multiplying the regulation lag correction number to obtain an adaptive regulation ability value.

[0060] The adaptive regulation ability value calculation formula is:

[0061] ;

[0062] In the formula, The comfort response trend value is used to describe the adjustment response speed and synchronization of the temperature, humidity and wind speed parameters in the cabin, and reflects the overall comfort recovery ability in the fine adjustment state; The environmental adaptation elasticity value is used to reflect the ability to maintain adjustment stability under different external environmental load intensities; The current vehicle cabin temperature mean value is used to represent the average temperature level of multiple key thermal sensing areas in the cabin at the current time, and is compared with the set temperature to judge whether there is an overall temperature control deviation at the current time; The target set temperature is used to reflect the ideal cabin temperature reference value, and is the basis for regulation deviation calculation; Indicates the external wind speed disturbance number, which is used to identify whether the external wind speed environment fluctuates frequently; Indicates the heat exchange delay, which is used to indicate the time required for the hot and cold air modules to reach a significant outlet air temperature difference for the first time after the adjustment command is issued, reflecting the hysteresis of the thermal response; The wind speed error value between the actual wind speed and the theoretical air supply speed is used to indicate the degree of deviation between the outlet wind speed and the set value, and is the basis for determining the stability of air volume control.

[0063] In this embodiment, the comfort response trend value of scenario 1 is set to 0.78, the environmental adaptation elasticity value is 0.62, the temperature mean is 34.2, the target set temperature is 25.0, the external wind speed disturbance number is 0.15, the heat exchange delay is 3.2, and the wind speed error is 0.28; the comfort response trend value of scenario 2 is set to 0.91, the environmental adaptation elasticity value is 0.88, the temperature mean is 26.7, the target set temperature is 25.0, the external wind speed disturbance number is 0.04, the heat exchange delay is 1.8, and the wind speed error is 0.07; the comfort response trend value of scenario 3 is set to 0.74, the environmental adaptation elasticity value is 0.53, and the temperature mean is 27. 9, the target set temperature is 25.0, the external wind speed disturbance number is 0.22, the heat exchange delay is 3.6, and the wind speed error is 0.36. The comfort response trend value of scenario 4 is set to 0.62, the environmental adaptability elasticity value is 0.49, the temperature mean is 21.8, the target set temperature is 25.0, the external wind speed disturbance number is 0.12, the heat exchange delay is 4.1, and the wind speed error is 0.31. The comfort response trend value of scenario 5 is set to 0.81, the environmental adaptability elasticity value is 0.74, the temperature mean is 25.5, the target set temperature is 25.0, the external wind speed disturbance number is 0.07, the heat exchange delay is 2.1, and the wind speed error is 0.14. The adaptive control capability values ​​of each scenario are calculated, as shown in Table 1.

[0064] Table 1 Adaptability and control capability data table

[0065] Working condition number Comfort response trend value Environment adaptation elasticity value Temperature average value Target setting temperature External wind speed disturbance number Heat exchange delay amount Wind speed error value Adaptation regulation ability value Scene 1 0.78 0.62 34.2 25.0 0.15 3.2 0.28 0.412 Scene 2 0.91 0.88 26.7 25.0 0.04 1.8 0.07 0.827 Scene 3 0.74 0.53 27.9 25.0 0.22 3.6 0.36 0.352 Scene 4 0.62 0.49 21.8 25.0 0.12 4.1 0.31 0.295 Scene 5 0.81 0.74 25.5 25.0 0.07 2.1 0.14 0.683

[0066] like Fig. 3 As shown in Table 1 and Fig. 3It can be seen that the highest adaptive control ability value is scene 2, the comfort response trend value and the environmental adaptation flexibility value are maintained at a high level, and the temperature deviation is small, the wind speed disturbance and the error are controlled at a very low level, which shows that the response stability and environmental adaptation ability are good under this working condition, which can be used as the optimal matching scene under the current strategy to support entering the steady state control area and maintaining high efficiency and comfort output. The lowest adaptive control ability value is scene 4, the environmental adaptation flexibility value and the comfort response trend value are at a low level, the heat exchange delay and the wind speed error are significantly high, which reflects that the current regulation strategy has obvious hysteresis and convergence imbalance problem, and the control priority of this scene should be improved in subsequent control to adjust the controller parameters to improve the regulation lag performance and avoid continuous deviation of comfort; the adaptive control ability value column chart directly shows the comprehensive regulation efficiency and adaptation performance distribution characteristics of the air conditioner under each typical working condition, the higher the evaluation value, the more stable the control and the more efficient the response in this environment, and the more priority should be given to this strategy structure to realize the cross-environment adaptive performance optimization.

[0067] Specifically, and based on the evaluation results, the resource reorganization redirection specific steps are: comparing the current adaptive control ability value with the control ability classification threshold value in real time, the control ability classification threshold value includes a first control threshold value and a second control threshold value:

[0068] When the adaptive control ability value is greater than or equal to the first control threshold value, it is determined to be in a high response matching section, a steady state control freezing mode is entered, all automatic regulation instructions related to the air duct switching are suspended, and the current air direction output configuration is locked, further compressing the path selection space to prevent transient disturbance caused by path reconstruction; at the same time, the wind speed error is limited to within the fine adjustment threshold value, interval constraints are implemented on all comfort factors, the parameter disturbance amplitude in the control period is fixed, and the controller is prevented from being repeatedly triggered by micro-amplitude fluctuations; in addition, a low fluctuation buffer band mechanism is started, and disturbance behaviors that do not meet the adjustment threshold value in the short term are automatically shielded, reducing unnecessary rapid disturbance convergence actions;

[0069] When the adaptive control ability value is greater than or equal to the second control threshold value and less than the first control threshold value, it is determined to be in a medium response coordination section, a dynamic path buffer mechanism is started, the current unfinished air door instruction queue is delayed for execution, regulation conflicts caused by multiple instructions overlapping are avoided, and time stamp sorting is implemented on the instruction queue to prioritize time-sensitive instructions; and the wind speed and temperature regulation are decoupled for execution, the overall response stability is improved by converging the control through the temperature channel first; in this process, an adjustment window adaptive mechanism based on response inertia is loaded, a longer buffer period is set for high inertia adjustment paths to improve the regulation stability priority and avoid frequent path oscillation triggered by short-term fluctuations;

[0070] When the adaptive regulation capability value is less than the second regulation threshold, it is determined to be in a low response imbalance section, a regulation resource reorganization mechanism is triggered immediately, the current multi-zone linkage control strategy is revoked, the edge area control load is released, all zone comfort adjustment tasks are redirected to the main driving area, and the key passenger area is prioritized to ensure; At the same time, the adaptive slope correction process of the air outlet speed is started, the response weight attenuation is applied to the high deviation area; The controller step size is dynamically adjusted, the output frequency is reduced and the parameter adjustment amplitude is reduced to prevent over-adjustment of the controller in the imbalance state.

[0071] In the embodiment, the control strategy of the automobile air conditioner is managed according to the real-time change of the adaptive regulation capability value, so as to realize the dynamic balance of stability, adaptability and core area priority. In the high response matching section, the regulation instruction and error limit reduction strategy are frozen to maximize the disturbance frequency and path switching redundancy, and the running stability is improved; In the medium response coordination section, the buffer queue management and parameter channel decoupling are used to strengthen the adjustment delay tolerance and execution instruction timing, and the coherence and adaptive adjustment capability of the control logic are ensured; In the low response imbalance section, the resource allocation structure is reconstructed, the control granularity of the non-key area is compressed, and the error slope correction mechanism is introduced to improve the comfort guarantee level of the main area, and to avoid control precision deterioration and energy waste. Overall, the hierarchical regulation mechanism effectively supports the elastic recovery capability and priority dynamic reconstruction capability of the air conditioner control under complex thermal load disturbance and environmental fluctuation conditions.

[0072] Specifically, the multi-source environmental pressure index and the adaptive regulation capability value are jointly monitored, and a cross-scenario evolution scoring system is constructed to mark the strategy switching node. The specific steps are as follows: The thermal load pressure index, environmental adaptability elasticity curve and regulation capability value are analyzed. First, the environmental disturbance intensity, thermal load change slope and comfort response trend in each control period are standardized, and a preliminary thermal load pressure score vector is formed by weighted linear combination; Then, based on the fluctuation degree and stable duration of each state variable in the elastic response window, the average change rate of the sliding time window is used to construct the environmental adaptability elasticity curve, wherein the higher the elasticity curve value, the stronger the recovery capability of the air conditioner to the disturbance; The regulation path convergence speed and wind speed error residual mean are introduced as constraint factors based on the regulation capability value to form a fused dynamic evolution trajectory matrix, and the local extreme section is identified by the first derivative change trend, which is marked as high response area, lag area and adaptive loss area, and the air conditioner adjustment stability and environmental matching capability are fed back in real time.

[0073] With the thermal load pressure index, environmental adaptation elasticity curve and adaptive regulation capacity value as the joint input, a multi-dimensional environmental adaptation factor group is constructed, the key factors are extracted by principal component analysis and self-adaptive weighted clustering method, and the scene vector representation is formed; In each time period, according to the vehicle geographic location information, the sunlight incidence angle, the external wind speed disturbance and the indoor comfort parameter feedback, the adjustment behavior and the external scene state are explicitly bound, the adjustment response characteristics of different typical environmental sections are identified by matching, and the cross-scene performance response feature vector is formed, which supports the subsequent strategy adaptation;

[0074] According to the dynamic weight of environmental factors, the typical working conditions including high temperature and strong radiation, low temperature and high humidity, and heavy rain in a closed environment are labeled and reconstructed. First, set the disturbance reference value and comfort response index threshold of each type of working condition, then get the matching degree score by comparing with the real-time monitoring data, and complete the automatic identification of scene category. Then, track the adjustment path selection, execution response lag and control convergence step in each type of scene, record the regulation efficiency and strategy switching time consumption, and mark the response path structure characteristics, lag range and control stability.

[0075] Based on the trend distribution of adaptive regulation capacity value and the offset characteristics of environmental adaptation elasticity curve, the multi-index joint smoothing filter and trend clustering algorithm are used to judge the environmental classification of the current regulation state. In each identified state, the disturbance sensitivity index, which measures the response intensity change rate of environmental variables on comfort, and the control link compression ratio, which measures the compression of regulation path and the improvement degree of resource utilization, are calculated respectively, and are used as additional parameters for strategy scheduling, realizing the priority allocation of control resources and the minimization of control complexity reconstruction.

[0076] A scene-regulation bidirectional evolution trajectory matrix is constructed, which considers the elastic response trajectory and adjustment behavior evolution path in various extreme environments, records the environmental disturbance level, strategy response mode, adaptive state transfer and control path change in each strategy period according to time sequence; Then, the response mutation point and regulation boundary condition are marked by difference evolution and transfer probability modeling, which helps to identify the strategy failure precursor and switching boundary, output the strategy update suggestion and controller rollback path, and support the subsequent OTA control strategy dynamic reconstruction and historical evolution process tracking.

[0077] In this embodiment, a set of multi-environment situation-oriented regulation performance identification and strategy adaptation mechanism is constructed, the regulation stability and response trend characteristics under different external disturbance scenarios in the air conditioning control process are extracted by jointly modeling the heat load pressure index, the environmental adaptation elasticity curve and the adaptation regulation capacity value; further, the control behavior and the actual environment state are accurately matched by building a multi-dimensional environmental factor group and reconstructing a typical scene label, the strategy discrimination ability in complex scenarios is improved; the dynamic optimization of control resource allocation and response efficiency is completed by combining the disturbance sensitivity index and the control link compression ratio index, and finally the visualization archiving of strategy evolution process and the identification of switching boundary are realized through the scene-regulation two-way evolution trajectory matrix, which provides data support and decision basis for the continuous optimization, rapid switching and failure warning of regulation strategy.

[0078] The second aspect of the application provides an automobile air conditioner intelligent diagnosis and evaluation system, comprising:

[0079] A data acquisition and preprocessing module is used for acquiring multi-point temperature data, air volume airflow data and environmental data in the running process of the automobile air conditioner, the multi-point temperature data including temperature mean value, temperature maximum value, humidity change amplitude and residual fluctuation characteristics of each region in the cabin, the air volume airflow data including wind speed disturbance index, air door path switching frequency and wind speed error value, and the environmental data including sunlight irradiation intensity, heat load pressure index and external wind speed change; the acquired multi-source data is subjected to synchronous standardization processing and normalization correction, signal deviation caused by sensor layout difference, airflow uneven disturbance and thermal inertia delay is eliminated, a standardized vehicle-mounted state data set with unified coding is constructed, and a clear-structured data basis is provided for subsequent analysis;

[0080] A cabin state monitoring module is used for extracting temperature, humidity and wind speed change trend of each monitoring point in the control instruction response process based on the standardized vehicle-mounted state data set, calculating trend stability time, thermal feeling distribution imbalance degree and wind speed recovery stability index, and quantifying comfort adjustment capability; when the comfort index deviates from the reference threshold, a fine adjustment strategy is started to automatically adjust the air door opening degree, wind speed grade and cold-heat mixing ratio, and high refresh weight is preferentially given to key areas in the comfort low response section, so as to realize balanced correction of space response;

[0081] A regulation elasticity evaluation module is used for constructing a wind volume disturbance-response delay joint distribution model based on the standardized vehicle-mounted state data set, dynamically calculating the environmental adaptation elasticity curve value in combination with the cabin multi-point parameter recovery speed and the external heat load intensity change; when the external environment changes dramatically such as strong radiation and heavy rain, the module will reset the air door switching period, adjust the execution rhythm and shorten the control step interval, so as to improve the absorption and buffering capacity of the regulation path to environmental disturbance;

[0082] The cross-scene performance comparison and analysis module takes the comfort adjustment capability evaluation result and the environment adaptation capability analysis result as inputs, fuses the heat load pressure index, the wind speed error change rate, and the cabin temperature difference distribution trend, constructs a regulation and control capability vector, and comprehensively evaluates the control stability of the vehicle cabin in the process of high temperature, low temperature, high humidity, and sharp scene switching. When a performance decline trend is identified, the module will automatically execute resource reorganization and adjustment task redirection mechanism, preferentially guaranteeing the stability output of the primary driving area, and executing low-frequency strategy compression on the secondary area;

[0083] The comprehensive examination and diagnosis feedback module is used for joint monitoring of multi-source environmental pressure indicators and adaptive regulation capability values, fusing the heat load pressure index, the regulation including the response intensity trend and the environment adaptation elasticity curve, constructing a cross-scene evolution scoring system, and identifying the response mode, regulation efficiency and strategy convergence behavior of the air conditioner control in real time. The module triggers OTA regulation logic correction suggestions and execution path compression optimization instructions by marking the regulation strategy switching node and the performance degradation critical point, realizing closed-loop evolution and adaptive adjustment of the control strategy.

[0084] In the embodiment, the data acquisition and preprocessing module acquires multi-point temperature data, air volume airflow data and indoor and outdoor environment data during the operation of the automobile air conditioner, and performs standardization and normalization processing, solves the data consistency problem caused by sensor layout difference, signal response delay and environmental disturbance interference, and thus constructs a standardized vehicle state data set that can be used for dynamic analysis;

[0085] The cabin state monitoring module calculates the comfort response index by identifying the change trend and stabilization time of the temperature, humidity and wind parameters in the control response process, and automatically adjusts the damper opening, cold and hot air mixing ratio and weight of each monitoring point according to the comfort deviation level, realizes dynamic tracking and intervention of the comfort adjustment capability. The regulation and control elasticity evaluation module constructs the joint feature expression of air volume disturbance and response delay, evaluates the environmental adaptation elasticity level of the cabin regulation structure under different scenes in combination with the heat load intensity change, and then drives the adaptive adjustment of the damper execution rhythm and regulation granularity;

[0086] The cross-scene performance comparison and analysis module fuses the comfort adjustment capability evaluation result and the environment adaptation analysis result, extracts the response characteristics of the key control behavior in multiple typical environmental situations such as high temperature and strong radiation, night high humidity, reorganizes the resources and redirects the priorities of the control tasks in each area of the cabin, to ensure the response efficiency of the key area;

[0087] The comprehensive evaluation and diagnosis feedback module constructs a cross-scenario evolution scoring system by jointly analyzing the thermal load pressure index, adaptive regulation capacity value and environmental adaptation elasticity curve, performs overall evaluation on the control response stability, marks the switching nodes and performance boundaries of the regulation strategy, and provides feedback support for strategy optimization and dynamic adjustment.

[0088] It is to be understood that the terminology used herein such as first and second, and the like, is only used to distinguish one entity or action from another entity or action, and does not necessarily require or imply any such actual relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0089] The preferred embodiments of the application disclosed above are only used to help explain the application. The preferred embodiments do not describe all of the details of the application, nor limit the application to the specific embodiments described. Obviously, many modifications and variations of the application can be made in light of the teachings above. The embodiments are chosen and described in order to best explain the principles of the application and its practical application, thus enabling others skilled in the art to best utilize the application. The application is limited only by the claims and their full scope and equivalents.

Claims

1. An intelligent diagnosis and assessment method for automobile air conditioners, characterized by: include: S1, collects multi-point temperature data, air volume and air flow data, and environmental data during the operation of the automobile air conditioner, and pre-processes the collected multi-point temperature data, air volume and air flow data, and environmental data to construct a standardized vehicle status data set; S2, based on a standardized vehicle status data set, combines multi-point temperature, humidity, and wind changes with the adjustment stabilization time to evaluate the comfort adjustment capability, and dynamically adjusts the air damper opening based on the evaluation results; S3, based on a standardized vehicle status data set, analyzes environmental adaptability by combining the recovery speed of multiple points in the cabin with the intensity of the external heat load, and dynamically adjusts the damper switching rhythm based on the analysis results; S4, using the comfort adjustment capability assessment results and environmental adaptability analysis results as input, conducts a comprehensive assessment of the cabin air conditioning adaptability and control capabilities, and performs resource reorganization and redirection based on the assessment results; S5, jointly monitor multi-source environmental pressure indicators and adaptive control capability values, build a cross-scenario evolution scoring system and mark strategy switching nodes.

2. The intelligent diagnosis and assessment method for automobile air conditioners according to claim 1, characterized in that: The specific steps of collecting multi-point temperature data, air volume and air flow data, and environmental 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 environmental data to construct a standardized vehicle status data set are as follows: Collect multi-point temperature data during vehicle air conditioning operation. This data includes the maximum temperature, humidity change, temperature average, wind speed average change, temperature residual mean, and heat distribution imbalance between the front and rear rows at each monitoring point. The system also records the total number of monitoring points in the cabin, the temperature stabilization time, humidity stabilization time, and wind speed stabilization time for each monitoring point from the start of adjustment to the stable state, as well as the maximum delay experienced by the last monitoring point to reach a stable state. Collect air volume and airflow data related to heat load, including: the coordinated recovery speed of cabin temperature, humidity and wind parameters, damper path switching frequency, multi-point comfort parameter fluctuation amplitude, wind speed disturbance index and heat exchange delay, and record the wind speed error between actual wind speed and theoretical air supply speed; Collecting environmental data inside and outside the vehicle, including: sunlight intensity gradient during the current period, average cabin temperature, target set temperature, heat load pressure index, external wind speed disturbance number, and solar incidence angle; Standardize and normalize the collected multi-point temperature data, air volume and air flow data, and environmental data to correct data scale inconsistencies caused by differences in sensor spatial layout, thermal inertia response delay, air flow disturbance intensity, and meteorological fluctuation periodicity; The standardized and normalized multi-point temperature data, air volume and airflow data, and vehicle exterior environment data are uniformly coded and archived to construct a standardized vehicle status data set for dynamic environment perception.

3. The intelligent diagnosis and assessment method for automobile air conditioners according to claim 1, characterized in that: The specific steps for evaluating the comfort adjustment capability based on the standardized vehicle status data set and combining the multi-point temperature, humidity and wind changes and the adjustment stabilization time are as follows: Traverse all monitoring points in the cabin, extract the maximum temperature and average temperature of each monitoring point in the standardized vehicle status data set during the adjustment process, calculate the difference between the maximum temperature and the average temperature, and divide it by the temperature stabilization time of the corresponding monitoring point to obtain the temperature deviation amplitude of each monitoring point; Extract the humidity change amplitude and humidity stabilization time of each monitoring point, calculate the ratio of humidity change amplitude and humidity stabilization time, and obtain the humidity deviation amplitude of each monitoring point; The average wind speed variation and wind speed stabilization time of each monitoring point were extracted, and the ratio of the average wind speed variation and wind speed stabilization time was calculated to obtain the wind speed deviation amplitude of each monitoring point. The temperature deviation amplitude, humidity deviation amplitude and wind speed deviation amplitude of each monitoring point are added together to obtain the overall deviation amplitude value of each monitoring point; The comfort response trend value is obtained by adding up the total deviation amplitude values ​​of all monitoring points and dividing it by the total number of monitoring points in the cabin, and then subtracting the maximum delay time experienced by the last monitoring point to enter the stable state after the start of adjustment.

4. The intelligent diagnosis and assessment method for automobile air conditioners according to claim 1, characterized in that: The specific steps of dynamically adjusting the damper opening based on the evaluation results are as follows: Real-time comparison of the current comfort response trend value and the comfort adjustment benchmark threshold: When the comfort response trend value is greater than or equal to the comfort adjustment benchmark threshold, the air conditioning response is determined to be normal. The current air outlet speed, damper opening, and hot and cold mixing ratio are maintained unchanged. The local comfort stability assessment, spatial heat balance tracking, and fluctuation detection of comfort parameters at each monitoring point are continued based on the standard time sliding window. When the comfort response trend value is less than the comfort adjustment benchmark threshold, it is determined to be an inefficient air conditioning response section, triggering the adaptive enhancement control strategy, including shortening the comfort analysis window length, increasing the cabin status refresh frequency, implementing micro-step adjustments to key air door areas, applying a time-weighted correction term to the outlet air temperature, and dynamically adjusting the multi-point monitoring priority and caching the wind speed change trajectory, thermal offset characteristics and response hysteresis records within the current cycle.

5. The intelligent diagnosis and assessment method for automobile air conditioners according to claim 1, characterized in that: The specific steps for analyzing the environmental adaptability based on the standardized vehicle status data set and combining the recovery speed of multiple points in the cabin and the external heat load intensity are as follows: The coordinated recovery speed of the cabin's absolute temperature, humidity, and wind parameters per unit time and the damper path switching frequency are extracted from the standardized vehicle status data set. The coordinated recovery speed is added by one, the natural logarithm is taken, and then multiplied by the square of the difference between one and the damper path switching frequency to obtain the response synchronization correction value. Extract the fluctuation amplitude of the multi-point comfort parameter and the mean of the temperature residual, multiply the fluctuation amplitude of the multi-point comfort parameter and the mean of the temperature residual, add one, and take the natural logarithm to obtain the adjustment fluctuation value; Extract the wind speed disturbance index, divide the wind speed disturbance index by the wind speed disturbance index plus one, and then add it to the adjustment fluctuation value to obtain the comprehensive fluctuation change number; The heat load pressure index is extracted, and the response synchronization correction value is used as the numerator, and the product of the heat load pressure index and the comprehensive fluctuation change number is used as the denominator to obtain the environmental adaptation elasticity value.

6. The intelligent diagnosis and assessment method for automobile air conditioners according to claim 1, characterized in that: The specific steps of dynamically adjusting the damper switching rhythm based on the analysis results are as follows: Compare the current environment adaptation elasticity value with the environment adaptation control threshold in real time. The environment adaptation control threshold includes the first elasticity threshold and the second elasticity threshold: When the environmental adaptability elasticity value is greater than or equal to the first elasticity threshold, the air conditioner is determined to be in a high adaptability zone and enters the steady-state control mode. The current cold and hot air ratio, air outlet temperature, and damper position are locked, and the data refresh cycle of local sensors is temporarily suspended. At the same time, a periodic elastic fluctuation monitoring mechanism is activated to maintain the global response structure unchanged while controlling low resource usage, ensuring continuous operation with minimal control intervention. When the environmental adaptability elasticity value is greater than or equal to the second elasticity threshold and less than the first elasticity threshold, the air conditioner is determined to be in a critical adaptability section. The damper switching threshold and wind speed adjustment step parameters are reset, the temperature control execution cycle is shortened, the wind direction correction frequency is increased, the control sensitivity level is actively adjusted, the comfort factor change is prioritized to the heat load change trend, and the regional priority sorting mechanism is activated in real time to dynamically switch key response points in the cabin, improving the local response strength of the core area. When the environmental adaptability elasticity value is less than the second elasticity threshold, it is determined to be a low-adaptability section of the air conditioning. The current flexible control channel is forcibly cut off, the regulation priority sequence is reconstructed, the full-parameter control convergence mode is started, the redundant feedback items in the dynamic adjustment path are canceled, the control resolution of non-critical areas in the cabin is compressed, and the damper action mode is changed to a fixed-interval switching mode.

7. The intelligent diagnosis and assessment method for automobile air conditioners according to claim 1, characterized in that: The specific steps of comprehensively evaluating the cabin air conditioning adaptability and control capability using the comfort adjustment capability evaluation results and the environmental adaptability analysis results as input are as follows: Obtain the comfort response trend value and the environmental adaptation elasticity value, and multiply them together to obtain the comprehensive adjustment ability index; Extract the current cabin temperature mean and the target set temperature, calculate the absolute value of the difference between the current cabin temperature mean and the target set temperature, add one, take the logarithm, and add one again to obtain the response error suppression value; Extract the external wind speed disturbance number, divide the external wind speed disturbance number by the sum of the heat exchange delay and the wind speed error value, and then add one to obtain the control lag correction number; The comprehensive regulation capability index is divided by the response error suppression value and then multiplied by the regulation lag correction number to obtain the adaptive regulation capability value.

8. The intelligent diagnosis and assessment method for automobile air conditioners according to claim 1, characterized in that: The specific steps of performing resource reorganization and redirection based on the evaluation results are as follows: Compare the current adaptive control capability value with the control capability grading threshold in real time. The control capability grading threshold includes the first control threshold and the second control threshold: When the adaptive control capability value is greater than or equal to the first control threshold, it is determined to be a high-response matching section and enters the steady-state control freeze mode. All automatic adjustment instructions related to duct switching are suspended, and the current wind direction output configuration is locked, further compressing the path selection space. At the same time, the wind speed error is limited to within the fine-tuning threshold, and the parameter disturbance range within the control cycle is fixed to reduce unnecessary rapid disturbance convergence actions. When the adaptive control capability value is greater than or equal to the second control threshold and less than the first control threshold, it is determined to be in the medium response coordination section, and the dynamic path buffer mechanism is activated to delay the execution of the currently unfinished damper command queue. At the same time, the wind speed and temperature control are decoupled to improve the control stability priority; When the adaptive control capability value is less than the second control threshold, it is determined to be in a low-response imbalance section, triggering the control resource reorganization mechanism, canceling the current multi-zone linkage control strategy, redirecting all regional comfort adjustment tasks to the main driving area, and starting the air outlet speed adaptive slope correction process at the same time. The difference distribution map between the current actual wind speed curve and the reference adjustment curve is calculated point by point, and the controller step granularity is dynamically adjusted.

9. The intelligent diagnosis and assessment method for automobile air conditioners according to claim 1, characterized in that: The specific steps of jointly monitoring multi-source environmental pressure indicators and adaptive control capability values, building a cross-scenario evolution scoring system, and marking strategy switching nodes are as follows: Fusion analysis of the heat load pressure index, environmental adaptability curve, and control capability values ​​constructs a dynamic evolution trajectory and marks response segments, providing real-time feedback on the control stability and adaptability of air conditioning control in multiple environmental scenarios. Using the heat load pressure index, environmental adaptation elasticity curve, and adaptive control capability as joint input, a multi-dimensional environmental adaptation factor group is constructed. The adjustment behavior in each time period is bound to the external scene state to extract the cross-scene performance response feature vector. Based on the dynamic weights of environmental factors, the scene labels of typical working conditions, including high temperature and strong radiation, low temperature and high humidity, and heavy rainfall and confinement, are reconstructed to mark the regulation path structure, response hysteresis characteristics, and control convergence efficiency under different environmental categories; Based on the trend distribution of adaptive control capability values ​​and the offset characteristics of the environmental adaptability elasticity curve, the current environmental characteristics are identified, and a disturbance sensitivity index and control link compression ratio are added to each state to optimize control resource allocation. A scenario-control bidirectional evolution trajectory matrix is ​​constructed to dynamically archive the response change patterns and control strategy adjustment behaviors in various extreme environments, and the control strategy switching boundary conditions are identified by marking the switching points.

10. A system using the automotive air conditioner intelligent diagnosis and assessment 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 environmental data during the operation of the automobile air conditioner, and preprocess the collected multi-point temperature data, air volume and air flow data, and environmental data to construct a standardized vehicle status data set; The cabin status monitoring module is used to evaluate the comfort adjustment capability based on a standardized vehicle status data set, combining multi-point temperature, humidity and wind changes and adjustment stabilization time, and dynamically adjust the air damper opening based on the evaluation results; A control flexibility assessment module analyzes environmental adaptability based on a standardized onboard status dataset, combining the recovery speed of multiple in-cabin parameters with the intensity of external heat loads, and dynamically adjusts the damper switching rhythm based on the analysis results. A cross-scenario performance comparison and analysis module, which uses the comfort adjustment capability assessment and environmental adaptability analysis results as input to comprehensively evaluate the cabin air conditioning adaptability and control capabilities, and then performs resource reorganization and redirection based on the assessment results; The comprehensive assessment and diagnostic feedback module is used to jointly monitor multi-source environmental pressure indicators and adaptive control capability values, build a cross-scenario evolution scoring system, and mark strategy switching nodes.

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