Automated window ventilation temperature regulation method and system based on fuzzy control

Abstract

This application discloses an automated vehicle window ventilation and temperature regulation method and system based on fuzzy control, belonging to the field of automated vehicle window control technology. The method includes: real-time acquisition of in-vehicle and out-of-vehicle environmental data, preprocessing to obtain preprocessed in-vehicle and out-of-vehicle environmental data; inputting the preprocessed in-vehicle and out-of-vehicle environmental data into a fuzzy control system to construct a fuzzy rule base for the fuzzy control system; optimizing the fuzzy control system based on the Zebra optimization algorithm and the preprocessed in-vehicle and out-of-vehicle environmental data to obtain an optimized fuzzy control system; and dynamically adjusting the opening and closing degree of the vehicle window through a window motor actuator using the optimized fuzzy control system based on subsequently obtained in-vehicle and out-of-vehicle environmental data. This application, based on in-vehicle and out-of-vehicle environmental data and combined with a fuzzy control system for optimized control, overcomes the shortcomings of existing technologies in complex environment adaptability, multi-objective optimization capability, and global convergence performance, achieving efficient and precise vehicle window ventilation and temperature regulation.

Description

Technical Field

[0001] This application relates to the field of automated vehicle window control technology, specifically to an automated vehicle window ventilation and temperature regulation method and system based on fuzzy control. Background Technology

[0002] With the development of intelligent and automated technologies, the automotive industry is gradually introducing intelligent in-vehicle systems to improve the driving experience and in-vehicle comfort. Window ventilation and temperature regulation are important components of in-vehicle environment control, and their effects directly affect passenger comfort, air quality, and vehicle energy efficiency.

[0003] In existing technologies, window ventilation and temperature regulation are mostly based on simple sensor triggers or preset rules. For example, some vehicles trigger the opening and closing of windows by a single variable such as interior temperature or humidity. Although this method can achieve a certain environmental regulation effect under specific conditions, it has significant limitations: First, single-variable control cannot comprehensively consider the multi-dimensional changes in the interior and exterior environment, such as the deterioration of external air quality or the impact of vehicle speed on ventilation, resulting in poor accuracy and adaptability of window regulation; Second, existing methods cannot dynamically respond to the interaction of multiple variables in complex environments, making it difficult to adjust the opening and closing status of windows in a timely manner in rapidly changing interior and exterior environments.

[0004] Furthermore, some existing intelligent vehicle systems have begun to introduce simple rule bases or automatic control methods based on traditional optimization algorithms, attempting to achieve window control through rule logic or model prediction. However, in practical applications, the following problems have been exposed: On the one hand, the rule base design relies on fixed logic, making it difficult to adapt to dynamic changes in environmental variables and complex interactions, and thus failing to meet the need for precise window adjustment; on the other hand, traditional optimization algorithms have limited efficiency in solving multi-objective optimization problems, and often fail to obtain the global optimal solution when it comes to multi-objective collaborative optimization involving energy consumption optimization, user comfort, and air quality.

[0005] In summary, existing technologies have significant shortcomings in terms of multi-dimensional comprehensive consideration, dynamic response, and optimization efficiency of window ventilation and temperature regulation. They cannot efficiently cope with the dynamic changes of multiple variables in complex environments, and they are difficult to meet users' multiple needs for comfort, air quality, and energy consumption optimization.

[0006] Therefore, in order to address the above problems and meet practical needs, an automated window ventilation temperature regulation technology based on fuzzy control is proposed. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this application aims to provide an automated vehicle window ventilation and temperature regulation method and system based on fuzzy control. Based on data of the vehicle's internal and external environment, and combined with a fuzzy control system for optimized control, this method overcomes the deficiencies of existing technologies in terms of adaptability to complex environments, multi-objective optimization capabilities, and global convergence performance, thereby enabling efficient and precise regulation of vehicle window ventilation and temperature.

[0008] To achieve the above objectives, the technical solution adopted in this application is as follows: In a first aspect, this application provides an automated method for regulating the temperature of vehicle window ventilation based on fuzzy control, the method comprising the following steps: Real-time collection of environmental data inside and outside the vehicle; The vehicle interior and exterior environmental data are preprocessed to obtain preprocessed vehicle interior and exterior environmental data; The preprocessed vehicle interior and exterior environmental data are input into a preset fuzzy control system to construct the fuzzy rule base of the fuzzy control system. Based on the Zebra optimization algorithm, and combined with the preprocessed vehicle interior and exterior environmental data, the fuzzy control system is optimized, and the optimal fuzzy rule base and optimal membership function parameters are output to obtain the optimized fuzzy control system. Based on the subsequently obtained data on the vehicle's internal and external environment, the optimized fuzzy control system dynamically adjusts the opening and closing degree of the vehicle windows via the window motor actuator; wherein, The preprocessing includes normalization and outlier removal.

[0009] Based on the above technical solution, the in-vehicle and out-of-vehicle environmental data are preprocessed to obtain preprocessed in-vehicle and out-of-vehicle environmental data, including the following steps: Based on a pre-set vehicle sensor network, real-time data of the internal and external environment of the vehicle is collected; Establish a unified expression corresponding to the vehicle's internal and external environmental data, and construct a vehicle internal and external environment dataset: The in-vehicle and out-of-vehicle environmental data include in-vehicle temperature data, in-vehicle humidity data, in-vehicle air quality data, out-of-vehicle temperature data, out-of-vehicle humidity data, out-of-vehicle air quality data, and vehicle operating speed data.

[0010] Based on the above technical solution, the in-vehicle and out-of-vehicle environmental data are preprocessed to obtain preprocessed in-vehicle and out-of-vehicle environmental data, including the following steps: The in-vehicle and out-of-vehicle environment dataset is normalized to obtain a normalized in-vehicle and out-of-vehicle environment dataset. The normalized in-vehicle environment dataset is subjected to outlier detection using the three-standard-deviation method. The detected outliers are filtered out and replaced with the historical moving average of the same variable to obtain the outlier-filtered in-vehicle environment dataset, which is denoted as the preprocessed in-vehicle environment dataset.

[0011] Based on the above technical solution, the preprocessed vehicle interior and exterior environmental data are input into a preset fuzzy control system to construct the fuzzy rule base of the fuzzy control system, including the following steps: The preprocessed vehicle interior and exterior environment dataset is input into the fuzzy control system, and the preprocessed vehicle interior and exterior environment dataset is transformed into a fuzzy set. Based on the logical relationship between the various data types in the vehicle's internal and external environmental data and the degree of window opening, a fuzzy rule base including multiple rule conditions and corresponding rules is constructed. The fuzzy set is input into the fuzzy rule base, and the degree of conformity with each rule condition is calculated based on the minimum value method. The weighted average method is used to synthesize the outputs of all the rules to obtain the fuzzy output set. The fuzzy output set is converted into the initial opening angle of the car window based on the centroid method.

[0012] Based on the above technical solution, and using the Zebra optimization algorithm combined with the preprocessed vehicle interior and exterior environmental data, the fuzzy control system is optimized to output the optimal fuzzy rule base and optimal membership function parameters, thus obtaining the optimized fuzzy control system. This includes the following steps: The population size and maximum number of iterations of the Zebra optimization algorithm are dynamically set based on the real-time fluctuations of the preprocessed in-vehicle and out-of-vehicle environment dataset. A time decay factor is dynamically introduced when defining the fitness function; The search strategy of the Zebra Optimization Algorithm is adjusted according to the dynamic changes in population fitness. The environmental fluctuation coefficient is calculated in real time. When the environmental fluctuation coefficient exceeds the corresponding preset threshold, the current optimal solution is adjusted based on a preset algorithm. When the maximum number of iterations is reached or the fitness value meets the convergence condition, a multi-objective collaborative evaluation is performed on the current optimal solution based on the preset evaluation function and the optimization objective. Based on the dynamic fluctuations of environmental variables inside and outside the vehicle, a multi-objective optimization decision function based on entropy weight allocation is constructed, and the optimization priority among temperature control, air quality and energy consumption is dynamically adjusted through information entropy. Based on consideration of entropy weight allocation and multi-objective collaboration, the optimal fuzzy rule base and the optimal membership function parameters are defined. Output the optimal fuzzy rule base and the optimal membership function parameters to obtain the optimized fuzzy control system.

[0013] Based on the above technical solution, the method further includes the following steps: Based on the subsequently obtained in-vehicle and out-of-vehicle environmental data, the degree of data change is calculated to obtain a comprehensive environmental change value; When the value of the comprehensive environmental change exceeds a preset warning threshold but is not less than a preset value, the optimized fuzzy control system is dynamically optimized based on the subsequently obtained data on the vehicle's internal and external environment.

[0014] Based on the above technical solution, and using the subsequently obtained in-vehicle and out-of-vehicle environmental data, the degree of data change is calculated to obtain a comprehensive environmental change value, including the following steps: Based on the subsequently obtained vehicle interior and exterior environmental data, multi-dimensional analysis is performed to extract the dynamic characteristics of the vehicle interior and exterior environmental data and record the rate of change. A comprehensive environmental change index is constructed by weighting the dynamic characteristics of the in-vehicle and out-of-vehicle environmental data to obtain a comprehensive environmental change value that reflects the overall degree of change.

[0015] Based on the above technical solution, and using the subsequently obtained in-vehicle and out-of-vehicle environmental data, the degree of data change is calculated to obtain a comprehensive environmental change value, including the following steps: When the value of the comprehensive environmental change exceeds the preset warning threshold but is not less than the preset value, the preprocessed vehicle interior and exterior environmental data is input into the optimized fuzzy control system to construct the optimized fuzzy rule base corresponding to the current optimization cycle. Based on the Zebra optimization algorithm, and combined with the preprocessed vehicle interior and exterior environment data corresponding to the subsequently obtained vehicle interior and exterior environment data, the fuzzy control system is optimized, and the optimal fuzzy rule base and optimal membership function parameters corresponding to the current optimization cycle are output to obtain the optimized fuzzy control system corresponding to the current optimization cycle.

[0016] Secondly, this application provides an automated vehicle window ventilation and temperature control system based on fuzzy control, the system comprising: Onboard sensor networks are used to collect real-time data on the environment inside and outside the vehicle. The preprocessing module is used to preprocess the vehicle interior and exterior environmental data to obtain preprocessed vehicle interior and exterior environmental data. The rule base construction module is used to input the preprocessed vehicle interior and exterior environmental data into a preset fuzzy control system to construct the fuzzy rule base of the fuzzy control system. The control system optimization module, based on the Zebra optimization algorithm and combined with the preprocessed vehicle interior and exterior environmental data, optimizes the fuzzy control system, outputs the optimal fuzzy rule base and the optimal membership function parameters, and obtains the optimized fuzzy control system. A dynamic adjustment module is used to dynamically adjust the opening and closing degree of the vehicle window through the window motor actuator, based on subsequently acquired data of the vehicle's internal and external environment and utilizing the optimized fuzzy control system; wherein, The preprocessing includes normalization and outlier removal.

[0017] Based on the above technical solution, the preprocessing module is also used to collect the vehicle interior and exterior environmental data in real time based on a preset vehicle sensor network; The preprocessing module is also used to establish a unified expression corresponding to the vehicle interior and exterior environment data, and to construct a vehicle interior and exterior environment dataset: The in-vehicle and out-of-vehicle environmental data include in-vehicle temperature data, in-vehicle humidity data, in-vehicle air quality data, out-of-vehicle temperature data, out-of-vehicle humidity data, out-of-vehicle air quality data, and vehicle operating speed data.

[0018] Based on the above technical solution, the preprocessing module is also used to normalize the in-vehicle and out-of-vehicle environment dataset to obtain a normalized in-vehicle and out-of-vehicle environment dataset. The preprocessing module is also used to perform outlier detection on the normalized in-vehicle environment dataset using the three-standard-deviation method, filter out the detected outliers, and replace the outliers with the historical moving average of the same variable to obtain the in-vehicle environment dataset after outlier filtering, which is denoted as the preprocessed in-vehicle environment dataset.

[0019] Based on the above technical solution, the rule base construction module is also used to input the preprocessed vehicle interior and exterior environment dataset into the fuzzy control system and convert the preprocessed vehicle interior and exterior environment dataset into a fuzzy set. The rule base construction module is also used to construct a fuzzy rule base that includes multiple rule conditions and corresponding rules based on the logical relationship between the data types in the vehicle interior and exterior environment data and the degree of window opening and closing. The rule base construction module is also used to input the fuzzy set into the fuzzy rule base, calculate the degree of conformity with each rule condition based on the minimum value method, and use the weighted average method to integrate the outputs of all the rules to obtain a fuzzy output set; The rule base construction module is also used to convert the fuzzy output set into the initial opening angle of the car window based on the centroid method.

[0020] Based on the above technical solution, the control system optimization module is also used to dynamically set the population size and maximum number of iterations of the Zebra optimization algorithm according to the real-time fluctuation of the preprocessed vehicle interior and exterior environment dataset; The control system optimization module is also used to dynamically introduce a time decay factor when defining the fitness function. The control system optimization module is also used to adjust the search strategy of the zebra optimization algorithm according to the dynamic changes in population fitness. The control system optimization module is also used to calculate the environmental fluctuation coefficient in real time. When the environmental fluctuation coefficient exceeds the corresponding preset threshold, the current optimal solution is adjusted based on the preset algorithm. The control system optimization module is also used to perform multi-objective collaborative evaluation of the current optimal solution based on a preset evaluation function and according to the optimization objective when the maximum number of iterations is reached or the fitness value meets the convergence condition. The control system optimization module is also used to construct a multi-objective optimization decision function based on entropy weight allocation according to the dynamic fluctuation of environmental variables inside and outside the vehicle, and dynamically adjust the optimization priority between temperature control, air quality and energy consumption through information entropy. The control system optimization module is also used to define the optimal fuzzy rule base and the optimal membership function parameters based on entropy weight allocation and multi-objective cooperation. The control system optimization module is also used to output the optimal fuzzy rule base and the optimal membership function parameters to obtain the optimized fuzzy control system.

[0021] Based on the above technical solution, the system further includes: The dynamic optimization module is used to calculate the degree of data change based on the subsequently obtained in-vehicle and out-of-vehicle environmental data, and obtain a comprehensive environmental change value. The dynamic optimization module is also used to dynamically optimize the optimized fuzzy control system based on the subsequently obtained vehicle interior and exterior environmental data when the comprehensive environmental change value exceeds a preset warning threshold but is not less than a preset value.

[0022] Based on the above technical solution, the dynamic optimization module is also used to perform multi-dimensional analysis based on the subsequently obtained vehicle interior and exterior environmental data, extract the dynamic features of the vehicle interior and exterior environmental data and record the rate of change. The dynamic optimization module is also used to construct a comprehensive environmental change index, which obtains a comprehensive environmental change value that comprehensively reflects the overall degree of change by weighting the dynamic characteristics of the in-vehicle and out-of-vehicle environmental data.

[0023] Based on the above technical solution, the dynamic optimization module is also used to input the preprocessed vehicle interior and exterior environment data into the optimized fuzzy control system when the comprehensive environmental change value exceeds a preset warning threshold or is not less than a preset value, so as to construct the optimized fuzzy rule library corresponding to the current optimization cycle. The dynamic optimization module is also used to optimize the fuzzy control system based on the Zebra optimization algorithm and the preprocessed vehicle interior and exterior environment data corresponding to the subsequently obtained vehicle interior and exterior environment data, and output the optimal fuzzy rule base and optimal membership function parameters corresponding to the current optimization cycle, so as to obtain the optimized fuzzy control system corresponding to the current optimization cycle.

[0024] Compared with the prior art, the advantages of this application are: This application uses data on the vehicle's internal and external environment as a basis and combines it with a fuzzy control system for optimized control. It overcomes the shortcomings of existing technologies in terms of adaptability to complex environments, multi-objective optimization capabilities, and global convergence performance, and efficiently and accurately performs window ventilation and temperature regulation.

[0025] This application introduces an improved Zebra optimization algorithm, which achieves multi-objective collaborative optimization of the window opening angle by dynamically adjusting the population size, fitness function weights, and search strategy. It can simultaneously take into account the three major objectives of optimizing in-vehicle temperature, air quality, and energy consumption, and dynamically adjusts the priority of optimization objectives based on the entropy weight allocation mechanism to ensure control performance under complex environmental conditions.

[0026] This application constructs a feedback adjustment mechanism by combining real-time environmental changes to dynamically monitor and comprehensively analyze in-vehicle and out-of-vehicle environmental data. In response to rapid fluctuations in environmental variables, a dynamic adjustment model is constructed using a change index based on information entropy. When the comprehensive change exceeds a set threshold, the system can automatically re-execute fuzzy inference and optimization calculation, thereby achieving rapid response to complex dynamic environments. The dynamic adjustment mechanism significantly improves the response speed and stability of the control system, reducing the window adjustment lag rate to 1.2%, ensuring passenger comfort and system robustness.

[0027] The horse optimization algorithm proposed in this application introduces a phased search strategy at the algorithm level, which is divided into three phases: exploration, development, and convergence based on the dynamic changes in the population fitness. By strengthening the global search capability in the exploration phase, refining the local optimization in the development phase, and introducing an entropy constraint mechanism in the convergence phase to improve the diversity of solutions, the algorithm's global convergence capability and resistance to getting trapped in local optima are greatly enhanced. Combined with the improved fitness function and time decay mechanism, it can achieve the global optimum in complex multi-objective optimization problems. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a flowchart illustrating the steps of an automated vehicle window ventilation temperature control method based on fuzzy control, according to an embodiment of this application. Figure 2 This is a flowchart illustrating the principle of the automated vehicle window ventilation temperature regulation method based on fuzzy control according to an embodiment of this application. Figure 3 This application presents a structural block diagram of an automated vehicle window ventilation and temperature control system based on fuzzy control. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0031] The embodiments of this application will be further described in detail below with reference to the accompanying drawings.

[0032] This application provides an automated vehicle window ventilation temperature regulation method and system based on fuzzy control. Based on data of the vehicle's internal and external environment, it combines fuzzy control system for optimized control, overcoming the shortcomings of existing technologies in terms of adaptability to complex environments, multi-objective optimization capabilities, and global convergence performance, and efficiently and accurately regulating vehicle window ventilation temperature.

[0033] To achieve the aforementioned technical effects, the overall concept of this application is as follows: An automated method for regulating vehicle window ventilation temperature based on fuzzy control, the method comprising the following steps: A1. Real-time collection of environmental data inside and outside the vehicle; A2. Preprocess the vehicle's internal and external environmental data to obtain preprocessed vehicle internal and external environmental data; A3. Input the pre-processed vehicle interior and exterior environmental data into the preset fuzzy control system to construct the fuzzy rule base of the fuzzy control system; A4. Based on the Zebra optimization algorithm, combined with preprocessed vehicle interior and exterior environmental data, the fuzzy control system is optimized, and the optimal fuzzy rule base and optimal membership function parameters are output to obtain the optimized fuzzy control system. A5. Based on subsequently acquired data on the vehicle's internal and external environment, an optimized fuzzy control system is used to dynamically adjust the opening and closing degree of the windows via the window motor actuator; among which, Preprocessing includes normalization and outlier removal.

[0034] The embodiments of this application will be further described in detail below with reference to the accompanying drawings.

[0035] Firstly, see [the following] Figures 1-2 As shown in the figure, this application provides an automated vehicle window ventilation temperature regulation method based on fuzzy control, which includes the following steps: A1. Real-time collection of environmental data inside and outside the vehicle; A2. Preprocess the vehicle's internal and external environmental data to obtain preprocessed vehicle internal and external environmental data; A3. Input the pre-processed vehicle interior and exterior environmental data into the preset fuzzy control system to construct the fuzzy rule base of the fuzzy control system; A4. Based on the Zebra optimization algorithm, combined with preprocessed vehicle interior and exterior environmental data, the fuzzy control system is optimized, and the optimal fuzzy rule base and optimal membership function parameters are output to obtain the optimized fuzzy control system. A5. Based on subsequently acquired data on the vehicle's internal and external environment, an optimized fuzzy control system is used to dynamically adjust the opening and closing degree of the windows via the window motor actuator; among which, Preprocessing includes normalization and outlier removal.

[0036] In this embodiment, based on data of the vehicle's internal and external environment, a fuzzy control system is used for optimized control, overcoming the shortcomings of existing technologies in terms of adaptability to complex environments, multi-objective optimization capabilities, and global convergence performance, thereby efficiently and accurately regulating the vehicle window ventilation and temperature.

[0037] Furthermore, the vehicle interior and exterior environmental data are preprocessed to obtain preprocessed vehicle interior and exterior environmental data, including the following steps: Based on a pre-set vehicle sensor network, real-time data of the internal and external environment of the vehicle is collected; Establish a unified expression corresponding to the vehicle's internal and external environmental data, and construct a vehicle internal and external environment dataset: The in-vehicle and out-of-vehicle environmental data include in-vehicle temperature data, in-vehicle humidity data, in-vehicle air quality data, out-of-vehicle temperature data, out-of-vehicle humidity data, out-of-vehicle air quality data, and vehicle operating speed data.

[0038] Furthermore, the vehicle interior and exterior environmental data are preprocessed to obtain preprocessed vehicle interior and exterior environmental data, including the following steps: The in-vehicle and out-of-vehicle environment dataset is normalized to obtain a normalized in-vehicle and out-of-vehicle environment dataset. The normalized in-vehicle environment dataset is subjected to outlier detection using the three-standard-deviation method. The detected outliers are filtered out and replaced with the historical moving average of the same variable to obtain the outlier-filtered in-vehicle environment dataset, which is denoted as the preprocessed in-vehicle environment dataset.

[0039] Furthermore, the preprocessed vehicle interior and exterior environmental data are input into a preset fuzzy control system to construct a fuzzy rule base for the fuzzy control system, including the following steps: The preprocessed vehicle interior and exterior environment dataset is input into the fuzzy control system, and the preprocessed vehicle interior and exterior environment dataset is transformed into a fuzzy set. Based on the logical relationship between the various data types in the vehicle's internal and external environmental data and the degree of window opening, a fuzzy rule base including multiple rule conditions and corresponding rules is constructed. The fuzzy set is input into the fuzzy rule base, and the degree of conformity with each rule condition is calculated based on the minimum value method. The weighted average method is used to synthesize the outputs of all the rules to obtain the fuzzy output set. The fuzzy output set is converted into the initial opening angle of the car window based on the centroid method.

[0040] Furthermore, based on the Zebra optimization algorithm and combined with the preprocessed vehicle interior and exterior environmental data, the fuzzy control system is optimized to output the optimal fuzzy rule base and optimal membership function parameters, thus obtaining the optimized fuzzy control system. This includes the following steps: The population size and maximum number of iterations of the Zebra optimization algorithm are dynamically set based on the real-time fluctuations of the preprocessed in-vehicle and out-of-vehicle environment dataset. A time decay factor is dynamically introduced when defining the fitness function; The search strategy of the Zebra Optimization Algorithm is adjusted according to the dynamic changes in population fitness. The environmental fluctuation coefficient is calculated in real time. When the environmental fluctuation coefficient exceeds the corresponding preset threshold, the current optimal solution is adjusted based on a preset algorithm. When the maximum number of iterations is reached or the fitness value meets the convergence condition, a multi-objective collaborative evaluation is performed on the current optimal solution based on the preset evaluation function and the optimization objective. Based on the dynamic fluctuations of environmental variables inside and outside the vehicle, a multi-objective optimization decision function based on entropy weight allocation is constructed, and the optimization priority among temperature control, air quality and energy consumption is dynamically adjusted through information entropy. Based on consideration of entropy weight allocation and multi-objective collaboration, the optimal fuzzy rule base and the optimal membership function parameters are defined. Output the optimal fuzzy rule base and the optimal membership function parameters to obtain the optimized fuzzy control system.

[0041] Furthermore, the method also includes the following steps: Based on the subsequently obtained in-vehicle and out-of-vehicle environmental data, the degree of data change is calculated to obtain a comprehensive environmental change value; When the value of the comprehensive environmental change exceeds a preset warning threshold but is not less than a preset value, the optimized fuzzy control system is dynamically optimized based on the subsequently obtained data on the vehicle's internal and external environment.

[0042] Furthermore, based on the subsequently obtained in-vehicle and out-of-vehicle environmental data, the degree of data change is calculated to obtain a comprehensive environmental change value, including the following steps: Based on the subsequently obtained vehicle interior and exterior environmental data, multi-dimensional analysis is performed to extract the dynamic characteristics of the vehicle interior and exterior environmental data and record the rate of change. A comprehensive environmental change index is constructed by weighting the dynamic characteristics of the in-vehicle and out-of-vehicle environmental data to obtain a comprehensive environmental change value that reflects the overall degree of change.

[0043] Furthermore, based on the subsequently obtained in-vehicle and out-of-vehicle environmental data, the degree of data change is calculated to obtain a comprehensive environmental change value, including the following steps: When the value of the comprehensive environmental change exceeds the preset warning threshold but is not less than the preset value, the preprocessed vehicle interior and exterior environmental data is input into the optimized fuzzy control system to construct the optimized fuzzy rule base corresponding to the current optimization cycle. Based on the Zebra optimization algorithm, and combined with the preprocessed vehicle interior and exterior environment data corresponding to the subsequently obtained vehicle interior and exterior environment data, the fuzzy control system is optimized, and the optimal fuzzy rule base and optimal membership function parameters corresponding to the current optimization cycle are output to obtain the optimized fuzzy control system corresponding to the current optimization cycle.

[0044] Based on the technical solution of this application embodiment, the operation process is as follows during specific implementation: S1. Real-time collection of environmental data inside and outside the vehicle via onboard sensor network; S2. Normalize the vehicle interior and exterior environmental data collected by the vehicle sensor network, and filter out outliers in the vehicle interior and exterior environmental data; S3. Input the preprocessed vehicle interior and exterior environmental data into the fuzzy control system; S4. Optimize the fuzzy rule base and membership function parameters in the fuzzy control system based on the Zebra optimization algorithm, initialize the parameters of the Zebra optimization algorithm, input the vehicle's internal and external environmental data into the Zebra optimization algorithm, and obtain the optimal fuzzy rule base and membership function parameters; S5. The fuzzy control system optimized by the Zebra optimization algorithm is applied to the window ventilation and temperature regulation. The environmental data inside and outside the vehicle are input into the optimized fuzzy control system. The optimized fuzzy control system outputs the window opening angle signal. According to the window opening angle signal, the window motor actuator dynamically adjusts the opening degree of the window. S6. Monitor the environmental data inside and outside the vehicle in real time. When the changes in the environmental data inside and outside the vehicle exceed the preset value, repeat steps S3 to S5 to dynamically adjust the opening degree of the windows. S7. Dynamically optimize window opening and closing based on different vehicle operating conditions. When the vehicle is traveling at high speed, reduce the opening angle of the windows to reduce wind noise. When the vehicle is traveling at low speed or parked, increase the opening angle of the windows to prioritize natural ventilation to regulate the interior temperature and reduce the load on the air conditioning system.

[0045] Specifically, step S1 above includes the following steps: S11. Real-time data collection of in-vehicle temperature, humidity, air quality, outside temperature, humidity, and speed is achieved through an on-board sensor network. S12. Establish a unified expression for in-vehicle and out-of-vehicle environmental data, and construct the in-vehicle and out-of-vehicle environmental dataset D: ; in, For the vehicle interior temperature data, For vehicle interior humidity data, For in-vehicle air quality data, For outside temperature data, For the humidity outside the vehicle, V represents the air quality outside the vehicle, and V represents the vehicle's operating speed.

[0046] Specifically, step S2 above includes the following steps: S21. Normalize the vehicle interior and exterior environment dataset D collected by the vehicle sensor network to obtain the normalized vehicle interior and exterior environment dataset; S22. Outlier detection is performed on the normalized in-vehicle and out-of-vehicle environment dataset using the three-standard-deviation method. Detected outliers are filtered out and replaced with the historical moving average of the same variable, resulting in the outlier-filtered in-vehicle and out-of-vehicle environment dataset. ; in, The data includes in-vehicle temperature, in-vehicle humidity, in-vehicle air quality, outside temperature, outside humidity, outside air quality, and vehicle speed after normalization and filtering out outliers.

[0047] Specifically, step S3 above includes the following steps: S31. The normalized and outlier-filtered dataset of the vehicle's interior and exterior environments. The data is input into the fuzzy control system, and the fuzzification module of the fuzzy control system processes the data of the vehicle's internal and external environments. Convert to fuzzy set : ; ; Where x represents The value of a single variable in the text. The membership degree of the fuzzy set is represented by a and b, which are the boundaries of the definition interval of the fuzzy set and are set according to empirical rules. S33. Based on the logical relationship between the vehicle's internal and external environmental data and the degree of window opening / closing, construct a fuzzy rule base R: ; These conditions include that car windows should be opened wide when the temperature is high, opened moderately when the humidity is low, and opened less when the outside air quality is poor. S34. In the fuzzy inference module, fuzzy sets... Input a fuzzy rule base R, calculate the compliance degree of the rule conditions based on the minimum value method, and use the weighted average method to synthesize the output of all rules to obtain a fuzzy output set. ; S35. In the deblurring module, the fuzzy output set is fuzzed based on the centroid method. Converted to the initial opening angle of the car window : ; Where y represents the output value indicating the degree of window opening / closing. Represents the fuzzy output set membership function, This represents the initial opening angle of the car window.

[0048] Specifically, step S4 above includes the following steps: S41. Based on the vehicle interior and exterior environment dataset The real-time volatility affects the population size N and maximum number of iterations in the Zebra Optimization Algorithm. The population size N is dynamically set and defined as follows: ; in, The initial population size is given by k, where k is the population size adjustment coefficient. Data set of in-vehicle and out-of-vehicle environments The overall degree of fluctuation of each variable; The dimensions of the initial zebra individual are the total number of fuzzy rule base and membership function parameters, and the initial position is... and speed Settings are based on current environmental data and historical optimization data; S42. Dynamically introduce a time decay factor when defining the fitness function f(X). The fitness function is in the form of: ; in, , The time decay coefficient represents the change in the priority of the optimization objective over time, and is dynamically adjusted. The weights are matched to the main optimization objectives of the current environment. For temperature error, To the extent of air quality improvement, For energy efficiency optimization; S43. The behavior of the Zebra Optimization Algorithm is divided into three stages, and the search strategy is adjusted according to the dynamic changes in population fitness: During the exploration phase, when the fitness variance of the population exceeds a preset value, the global search capability is enhanced, and the update rule is as follows: ; in, This indicates that high-amplitude random perturbations are used to enhance exploration capabilities. For the current individual position, This is the optimal solution; During the development phase, when the population fitness variance decreases, the local search is refined: ; in, For individual learning factor weights, For the group learning factor weights, and It is a random number. As the population center; During the convergence phase, when the population fitness is stable, an entropy constraint mechanism is introduced to determine the diversity of the current solution through information entropy: ; in, This represents the entropy value of the i-th zebra solution. The entropy weights are the entropy values ​​of the solution. It indicates an individual's performance in terms of diversity; S44. Construct a feedback adjustment mechanism based on real-time environmental changes to calculate the environmental fluctuation coefficient in real time. : ; like If the preset threshold is exceeded, then the current optimal solution... Adjustments will be made: ; S45. Reaching the maximum number of iterations Or, if the fitness value satisfies the convergence condition, then the optimal solution is determined according to the optimization objective. To conduct multi-objective collaborative evaluation, the evaluation function is defined as follows: ; in, To optimize the i-th in-vehicle temperature, The target interior temperature, These represent the optimization of the in-vehicle air quality index before and after optimization. These are the baseline and optimized air conditioning energy consumption figures, respectively. The number of samples for each target. , and These are the weighting coefficients for the corresponding targets; S46. Construct a multi-objective optimization decision function based on entropy weight allocation according to the dynamic fluctuations of in-vehicle and out-of-vehicle environmental variables, and dynamically adjust the optimization priority among temperature control, air quality and energy consumption through information entropy: ; ; in, The weight of the i-th objective is used to dynamically adjust the objective priority during the optimization process. The relative contribution of the i-th target in the j-th sample. To optimize the deviation values ​​of each target before and after; S47. Based on entropy weight allocation and multi-objective collaboration, define the final optimized fuzzy rule base and membership function parameter set: ; Where W is the target weight matrix after entropy weight allocation. The final optimized solution after feedback adjustment. The optimization offset based on environmental fluctuations and fitness evaluation feedback is defined as: ; S48. Output the optimized fuzzy rule base. and membership function parameter set .

[0049] Specifically, step S6 above includes the following steps: S61. Real-time dynamic monitoring of changes in in-vehicle and out-of-vehicle environmental data; using the changing trends of various variables in the in-vehicle and out-of-vehicle environmental dataset D'' to perform multi-dimensional analysis of temperature, humidity, air quality and vehicle speed; extracting dynamic features and recording the rate of change; and identifying fluctuations in the current environment. S62. Construct a comprehensive environmental change index, which comprehensively reflects the overall degree of change in vehicle interior and exterior temperature, humidity, air quality, and vehicle operating status by weighting the dynamic characteristics of environmental data; S63. Determine whether the changes in the overall environment exceed the warning threshold set by the system. When the overall environmental change index reaches or exceeds the preset value, it is determined that there has been a significant change in the environment inside and outside the vehicle, and the dynamic adjustment process needs to be initiated. If the preset value is not reached, the current window control status is maintained and monitoring continues. S64. Trigger the fuzzy control system to re-execute steps S3 to S5, update the fuzzy rule base and membership function parameters, combine the latest environmental data to perform fuzzy inference and Zebra optimization algorithm to recalculate the opening angle of the car window, and generate a new car window adjustment signal; S65. After the windows are dynamically adjusted, the adjustment effect data is collected in real time to evaluate the actual improvement of the adjustment on the interior temperature, humidity, air quality and vehicle operating status, and generate performance feedback evaluation. S66. Based on the performance feedback evaluation results, dynamically update the optimization priority and parameter configuration of the fuzzy control system, continuously monitor environmental changes, and initiate the next round of dynamic adjustment when the conditions are met, so that the window ventilation and temperature regulation system always keeps in sync with environmental changes.

[0050] Based on the technical solution of this application, a practical implementation embodiment is given as follows: In this example, the test scenario was selected on a summer afternoon. The test location was a typical urban road in Beijing. The test vehicle was a new energy vehicle equipped with in-vehicle and out-of-vehicle environmental sensors and an automated window control system. The vehicle was equipped with temperature and humidity sensors, air quality sensors, vehicle speed sensors, and a real-time environmental data recording module. The data sampling interval of all sensors was set to 5 seconds.

[0051] At the start of the test, the vehicle was stationary with an initial interior temperature of 38°C, humidity of 55%, and an air quality index (AQI) of 120. The outside temperature was 34°C, humidity of 60%, and AQI of 90. The target interior temperature was set at 25°C, the target humidity range at 40%-50%, and the target AQI below 100. The vehicle then entered urban roads for dynamic testing, covering various driving conditions including low-speed driving (below 20 km / h), medium-speed driving (20-40 km / h), and parking / waiting. The total test time was one hour.

[0052] Throughout the process, the method of this application was used to adjust the opening and closing state of the car window, and a comparative analysis was conducted with the traditional car window adjustment method based on single temperature control.

[0053] In summer, urban roads are characterized by high ambient temperatures and low humidity, making it easy for heat to accumulate inside vehicles. This results in interior temperatures significantly exceeding outside temperatures. Traditional window control methods in such situations typically rely on simple temperature threshold logic: when the interior temperature exceeds a set value, the windows open at a certain angle for ventilation. However, in actual operation, this simple temperature control approach ignores the fact that changes in external air quality and humidity can further worsen interior air quality in heavily polluted areas. Furthermore, single-variable control cannot respond to complex multi-variable interactions in dynamic environments, such as the impact of driving speed on wind noise and ventilation efficiency, leading to a significant decrease in passenger comfort and system efficiency.

[0054] At the start of the test, the vehicle was stationary in a parking lot. Sensors collected real-time data on the internal and external environment of the vehicle and input it into the fuzzy control system. The fuzzification module converted the temperature, humidity, and air quality data into fuzzy sets and applied a dynamically adjusted fuzzy rule base for preliminary inference based on the environmental characteristics. The initial Zebra optimization algorithm was activated to optimize the fuzzy rule base and membership function parameters, setting the initial window opening angle to 5 degrees. At this time, the vehicle achieved natural ventilation in the parking lot by slightly opening the window. The interior temperature dropped from 38°C to 33°C within 10 minutes, and the air quality index remained at 120 without deterioration.

[0055] The vehicle then entered urban roads and began driving. At a low speed (approximately 15 km / h), the external air quality index rose to 110. Meanwhile, the external temperature and humidity remained relatively stable. Traditional methods, prioritizing temperature, would further open the windows to 10 degrees Celsius. After detecting a deteriorating air quality trend, the Zebra algorithm would dynamically optimize the calculations and determine that the window opening angle should be reduced to 3 degrees Celsius. The system would then prompt passengers to turn on the air conditioning with recirculation mode to maintain air quality. After 10 minutes of driving, the interior temperature stabilized at 28°C, and the air quality index dropped to 95. In contrast, the traditional method showed that the interior air quality index continued to rise to 135. Although the interior temperature remained at 29°C, the air quality problem was significant.

[0056] At a medium speed (approximately 30 km / h), the vehicle enters a highly polluted area where the external air quality index (AQI) rapidly rises to 150, the outside temperature remains at 34°C, and the humidity is 55%. Traditional methods, failing to detect changes in air quality under these conditions, maintain the window opening angle at 10 degrees, allowing polluted air to enter the vehicle and worsening the AQI to 140. The method proposed in this application, however, monitors the comprehensive environmental change index in real time, rapidly adjusts the fuzzy rule base, outputs a control signal to completely close the windows, and reduces system response time with dynamic feedback from the Zebra optimization algorithm. It also suggests activating the air conditioning's recirculation mode. Within 5 minutes, the AQI inside the vehicle drops to 90, and the interior temperature stabilizes at 27°C.

[0057] In the final stage of the test, the vehicle entered a low-pollution area and stopped to wait. The outside temperature was still 34°C, and the air quality index dropped to 85. The traditional method would continue to maintain the window opening angle at 10 degrees under these conditions. However, the method of this application reassessed the environmental changes and found that the optimal window opening angle was 7 degrees. Natural ventilation reduced the reliance on air conditioning, while the interior temperature was further reduced to 25°C, the humidity was stabilized at 45%, and the air quality index remained excellent.

[0058] Specifically, the performance comparison data of the method involved in this embodiment and the traditional method under different test scenarios are shown in Table 1 below.

[0059]

[0060] Table 1 This embodiment verifies the superior performance of the present invention in various scenarios. By combining fuzzy control with the Zebra optimization algorithm, it can achieve efficient and precise window ventilation temperature regulation in complex environments, significantly improving passenger comfort and energy utilization efficiency. Comparative results show that the method of the present invention is superior to traditional methods in terms of air quality optimization and temperature control accuracy, and energy consumption is significantly reduced.

[0061] Secondly, see Figure 3As shown in the figure, this application provides an automated vehicle window ventilation and temperature control system based on fuzzy control. The system includes: Onboard sensor networks are used to collect real-time data on the environment inside and outside the vehicle. The preprocessing module is used to preprocess the vehicle interior and exterior environmental data to obtain preprocessed vehicle interior and exterior environmental data. The rule base construction module is used to input the preprocessed vehicle interior and exterior environmental data into a preset fuzzy control system to construct the fuzzy rule base of the fuzzy control system. The control system optimization module, based on the Zebra optimization algorithm and combined with the preprocessed vehicle interior and exterior environmental data, optimizes the fuzzy control system, outputs the optimal fuzzy rule base and the optimal membership function parameters, and obtains the optimized fuzzy control system. A dynamic adjustment module is used to dynamically adjust the opening and closing degree of the vehicle window through the window motor actuator, based on subsequently acquired data of the vehicle's internal and external environment and utilizing the optimized fuzzy control system; wherein, The preprocessing includes normalization and outlier removal.

[0062] In this embodiment, based on data of the vehicle's internal and external environment, a fuzzy control system is used for optimized control, overcoming the shortcomings of existing technologies in terms of adaptability to complex environments, multi-objective optimization capabilities, and global convergence performance, thereby efficiently and accurately regulating the vehicle window ventilation and temperature.

[0063] Furthermore, the preprocessing module is also used to collect the vehicle's internal and external environmental data in real time based on a preset vehicle sensor network; The preprocessing module is also used to establish a unified expression corresponding to the vehicle interior and exterior environment data, and to construct a vehicle interior and exterior environment dataset: The in-vehicle and out-of-vehicle environmental data include in-vehicle temperature data, in-vehicle humidity data, in-vehicle air quality data, out-of-vehicle temperature data, out-of-vehicle humidity data, out-of-vehicle air quality data, and vehicle operating speed data.

[0064] Furthermore, the preprocessing module is also used to normalize the in-vehicle and out-of-vehicle environment dataset to obtain a normalized in-vehicle and out-of-vehicle environment dataset. The preprocessing module is also used to perform outlier detection on the normalized in-vehicle environment dataset using the three-standard-deviation method, filter out the detected outliers, and replace the outliers with the historical moving average of the same variable to obtain the in-vehicle environment dataset after outlier filtering, which is denoted as the preprocessed in-vehicle environment dataset.

[0065] Furthermore, the rule base construction module is also used to input the preprocessed vehicle interior and exterior environment dataset into the fuzzy control system and convert the preprocessed vehicle interior and exterior environment dataset into a fuzzy set. The rule base construction module is also used to construct a fuzzy rule base that includes multiple rule conditions and corresponding rules based on the logical relationship between the data types in the vehicle interior and exterior environment data and the degree of window opening and closing. The rule base construction module is also used to input the fuzzy set into the fuzzy rule base, calculate the degree of conformity with each rule condition based on the minimum value method, and use the weighted average method to integrate the outputs of all the rules to obtain a fuzzy output set; The rule base construction module is also used to convert the fuzzy output set into the initial opening angle of the car window based on the centroid method.

[0066] Furthermore, the control system optimization module is also used to dynamically set the population size and maximum number of iterations of the Zebra optimization algorithm based on the real-time fluctuations of the preprocessed in-vehicle and out-of-vehicle environment dataset. The control system optimization module is also used to dynamically introduce a time decay factor when defining the fitness function. The control system optimization module is also used to adjust the search strategy of the zebra optimization algorithm according to the dynamic changes in population fitness. The control system optimization module is also used to calculate the environmental fluctuation coefficient in real time. When the environmental fluctuation coefficient exceeds the corresponding preset threshold, the current optimal solution is adjusted based on the preset algorithm. The control system optimization module is also used to perform multi-objective collaborative evaluation of the current optimal solution based on a preset evaluation function and according to the optimization objective when the maximum number of iterations is reached or the fitness value meets the convergence condition. The control system optimization module is also used to construct a multi-objective optimization decision function based on entropy weight allocation according to the dynamic fluctuation of environmental variables inside and outside the vehicle, and dynamically adjust the optimization priority between temperature control, air quality and energy consumption through information entropy. The control system optimization module is also used to define the optimal fuzzy rule base and the optimal membership function parameters based on entropy weight allocation and multi-objective cooperation. The control system optimization module is also used to output the optimal fuzzy rule base and the optimal membership function parameters to obtain the optimized fuzzy control system.

[0067] Furthermore, the system also includes: The dynamic optimization module is used to calculate the degree of data change based on the subsequently obtained in-vehicle and out-of-vehicle environmental data, and obtain a comprehensive environmental change value. The dynamic optimization module is also used to dynamically optimize the optimized fuzzy control system based on the subsequently obtained vehicle interior and exterior environmental data when the comprehensive environmental change value exceeds a preset warning threshold but is not less than a preset value.

[0068] Furthermore, the dynamic optimization module is also used to perform multi-dimensional analysis based on the subsequently obtained in-vehicle and out-of-vehicle environmental data, extract the dynamic features of the in-vehicle and out-of-vehicle environmental data, and record the rate of change. The dynamic optimization module is also used to construct a comprehensive environmental change index, which obtains a comprehensive environmental change value that comprehensively reflects the overall degree of change by weighting the dynamic characteristics of the in-vehicle and out-of-vehicle environmental data.

[0069] Furthermore, the dynamic optimization module is also used to input the preprocessed vehicle interior and exterior environmental data into the optimized fuzzy control system when the comprehensive environmental change value exceeds a preset warning threshold or is not less than a preset value, so as to construct the optimized fuzzy rule base corresponding to the current optimization cycle. The dynamic optimization module is also used to optimize the fuzzy control system based on the Zebra optimization algorithm and the preprocessed vehicle interior and exterior environment data corresponding to the subsequently obtained vehicle interior and exterior environment data, and output the optimal fuzzy rule base and optimal membership function parameters corresponding to the current optimization cycle, so as to obtain the optimized fuzzy control system corresponding to the current optimization cycle.

[0070] In summary, the automated window ventilation and temperature control system based on fuzzy control provided in this application embodiment has the same technical principle as the automated window ventilation and temperature control method based on fuzzy control provided in the first aspect in terms of technical problems, technical solutions, and technical effects, so it will not be described in detail here.

[0071] In the description of this application, it should be noted that the terms "upper," "lower," etc., indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the system or component referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application. Unless otherwise expressly specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication between two components. For those skilled in the art, the specific meaning of the above terms in this application can be understood according to the specific circumstances.

[0072] It should be noted that in this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0073] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for automatically regulating the temperature of a vehicle window based on fuzzy control, characterized in that, The method includes the following steps: Real-time collection of environmental data inside and outside the vehicle; The vehicle interior and exterior environmental data are preprocessed to obtain preprocessed vehicle interior and exterior environmental data; The preprocessed vehicle interior and exterior environmental data are input into a preset fuzzy control system to construct the fuzzy rule base of the fuzzy control system. Based on the Zebra optimization algorithm, and combined with the preprocessed vehicle interior and exterior environmental data, the fuzzy control system is optimized, and the optimal fuzzy rule base and optimal membership function parameters are output to obtain the optimized fuzzy control system. Based on the subsequently obtained data on the vehicle's internal and external environment, the optimized fuzzy control system dynamically adjusts the opening and closing degree of the vehicle windows via the window motor actuator; wherein, The preprocessing includes normalization and outlier removal.

2. The fuzzy control based automated window vent temperature regulation method of claim 1, wherein, The process of preprocessing the vehicle's internal and external environmental data to obtain preprocessed vehicle internal and external environmental data includes the following steps: Based on a pre-set vehicle sensor network, the vehicle's internal and external environmental data are collected in real time. Establish a unified expression corresponding to the vehicle's internal and external environmental data, and construct a vehicle internal and external environment dataset: The in-vehicle and out-of-vehicle environmental data include in-vehicle temperature data, in-vehicle humidity data, in-vehicle air quality data, out-of-vehicle temperature data, out-of-vehicle humidity data, out-of-vehicle air quality data, and vehicle operating speed data.

3. The fuzzy control based automated window vent temperature regulation method of claim 1, wherein, The process of preprocessing the vehicle's internal and external environmental data to obtain preprocessed vehicle internal and external environmental data includes the following steps: The in-vehicle and out-of-vehicle environment dataset is normalized to obtain a normalized in-vehicle and out-of-vehicle environment dataset. The normalized in-vehicle environment dataset is subjected to outlier detection using the three-standard-deviation method. The detected outliers are filtered out and replaced with the historical moving average of the same variable to obtain the outlier-filtered in-vehicle environment dataset, which is denoted as the preprocessed in-vehicle environment dataset.

4. The automated vehicle window ventilation temperature control method based on fuzzy control as described in claim 3, characterized in that, The preprocessed vehicle interior and exterior environmental data are input into a preset fuzzy control system to construct the fuzzy rule base of the fuzzy control system, including the following steps: The preprocessed vehicle interior and exterior environment dataset is input into the fuzzy control system, and the preprocessed vehicle interior and exterior environment dataset is transformed into a fuzzy set. Based on the logical relationship between the various data types in the vehicle's internal and external environmental data and the degree of window opening, a fuzzy rule library is constructed, which includes multiple rule conditions and corresponding rules. The fuzzy set is input into the fuzzy rule base, and the degree of conformity with each rule condition is calculated based on the minimum value method. The weighted average method is used to synthesize the outputs of all the rules to obtain the fuzzy output set. The fuzzy output set is converted into the initial opening angle of the car window based on the centroid method.

5. The automated vehicle window ventilation temperature control method based on fuzzy control as described in claim 4, characterized in that, Based on the Zebra optimization algorithm and combined with the preprocessed vehicle interior and exterior environmental data, the fuzzy control system is optimized to output the optimal fuzzy rule base and optimal membership function parameters, thus obtaining the optimized fuzzy control system. The optimization process includes the following steps: The population size and maximum number of iterations of the Zebra optimization algorithm are dynamically set based on the real-time fluctuations of the preprocessed in-vehicle and out-of-vehicle environment dataset. A time decay factor is dynamically introduced when defining the fitness function; The search strategy of the Zebra Optimization Algorithm is adjusted according to the dynamic changes in population fitness. The environmental fluctuation coefficient is calculated in real time. When the environmental fluctuation coefficient exceeds the corresponding preset threshold, the current optimal solution is adjusted based on a preset algorithm. When the maximum number of iterations is reached or the fitness value meets the convergence condition, a multi-objective collaborative evaluation is performed on the current optimal solution based on the preset evaluation function and the optimization objective. Based on the dynamic fluctuations of environmental variables inside and outside the vehicle, a multi-objective optimization decision function based on entropy weight allocation is constructed, and the optimization priority among temperature control, air quality and energy consumption is dynamically adjusted through information entropy. Based on consideration of entropy weight allocation and multi-objective collaboration, the optimal fuzzy rule base and the optimal membership function parameters are defined. Output the optimal fuzzy rule base and the optimal membership function parameters to obtain the optimized fuzzy control system.

6. The automated vehicle window ventilation temperature control method based on fuzzy control as described in claim 1, characterized in that, The method further includes the following steps: Based on the subsequently obtained in-vehicle and out-of-vehicle environmental data, the degree of data change is calculated to obtain a comprehensive environmental change value; When the value of the comprehensive environmental change exceeds a preset warning threshold but is not less than a preset value, the optimized fuzzy control system is dynamically optimized based on the subsequently obtained data on the vehicle's internal and external environment.

7. The automated vehicle window ventilation temperature control method based on fuzzy control as described in claim 6, characterized in that, Based on the subsequently obtained in-vehicle and out-of-vehicle environmental data, the degree of data change is calculated to obtain a comprehensive environmental change value, including the following steps: Based on the subsequently obtained vehicle interior and exterior environmental data, multi-dimensional analysis is performed to extract the dynamic characteristics of the vehicle interior and exterior environmental data and record the rate of change. A comprehensive environmental change index is constructed by weighting the dynamic characteristics of the in-vehicle and out-of-vehicle environmental data to obtain a comprehensive environmental change value that reflects the overall degree of change.

8. The fuzzy control based automated window vent temperature regulation method of claim 7, wherein, Based on the subsequently obtained in-vehicle and out-of-vehicle environmental data, the degree of data change is calculated to obtain a comprehensive environmental change value, including the following steps: When the value of the comprehensive environmental change exceeds the preset warning threshold but is not less than the preset value, the preprocessed vehicle interior and exterior environmental data is input into the optimized fuzzy control system to construct the optimized fuzzy rule base corresponding to the current optimization cycle. Based on the Zebra optimization algorithm, and combined with the preprocessed vehicle interior and exterior environment data corresponding to the subsequently obtained vehicle interior and exterior environment data, the fuzzy control system is optimized, and the optimal fuzzy rule base and optimal membership function parameters corresponding to the current optimization cycle are output to obtain the optimized fuzzy control system corresponding to the current optimization cycle.

9. An automated window defrosting temperature regulation system based on fuzzy control, characterized by, The system includes: Onboard sensor networks are used to collect real-time data on the environment inside and outside the vehicle. The preprocessing module is used to preprocess the vehicle interior and exterior environmental data to obtain preprocessed vehicle interior and exterior environmental data. The rule base construction module is used to input the preprocessed vehicle interior and exterior environmental data into a preset fuzzy control system to construct the fuzzy rule base of the fuzzy control system. The control system optimization module, based on the Zebra optimization algorithm and combined with the preprocessed vehicle interior and exterior environmental data, optimizes the fuzzy control system, outputs the optimal fuzzy rule base and the optimal membership function parameters, and obtains the optimized fuzzy control system. A dynamic adjustment module is used to dynamically adjust the opening and closing degree of the vehicle window through the window motor actuator, based on subsequently acquired data of the vehicle's internal and external environment and utilizing the optimized fuzzy control system; wherein, The preprocessing includes normalization and outlier removal.

10. The fuzzy control based automated window vent temperature regulation system of claim 9, wherein, The system also includes: The dynamic optimization module is used to calculate the degree of data change based on the subsequently obtained in-vehicle and out-of-vehicle environmental data, and obtain a comprehensive environmental change value. The dynamic optimization module is also used to dynamically optimize the optimized fuzzy control system based on the subsequently obtained vehicle interior and exterior environmental data when the comprehensive environmental change value exceeds a preset warning threshold but is not less than a preset value.

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