A method, device, electronic device and storage medium for controlling vehicle air conditioner

Abstract

The present invention discloses a control method, device, electronic device and storage medium for vehicle air conditioning. The method comprises obtaining the current vehicle status data of the target vehicle, the current vehicle status data comprising the current human body characteristic data and the current in-vehicle environmental parameter data; inputting the current human body characteristic data and the current in-vehicle environmental parameter data into a pre-trained adaptive support vector machine model, and outputting the current human body thermal comfort evaluation value; calculating the current working parameters of the air conditioning to be controlled in the target vehicle according to the current human body thermal comfort evaluation value; when the current working parameters are adjusted by the passengers, recording the adjusted current working parameters into a learning data set, and using the learning data set to correct the pre-trained adaptive support vector machine model. The technical solution of the present invention makes the control method of the vehicle air conditioning more intelligent and personalized.

Description

Technical Field

[0001] The present invention relates to the field of vehicle control technology, and in particular to a control method, device, electronic equipment and storage medium for a vehicle air conditioner. Background Art

[0002] The internal temperature control of the vehicle is an important part of the vehicle comfort design. At present, the intelligent control technology of the vehicle air conditioner detects parameters such as the temperature and humidity in the vehicle through sensors, and feeds these parameters back to the control system. The control system adjusts the operation of the air conditioning system according to the preset program and parameters, and automatically controls the temperature and humidity in the vehicle. PMV (Predicted Mean Vote) is an indicator to measure the human body's comfort. In the existing automatic temperature control technology, the PMV value is used as the temperature and humidity control parameter of the vehicle to meet the comfort requirements of passengers.

[0003] However, the method of relying on PMV value to realize intelligent control of air conditioner still has the problems of poor adaptability and insufficient intelligence among different groups of people. Summary of the invention

[0004] The present invention provides a control method, device, electronic equipment and storage medium for a vehicle air conditioner, so as to make the control method for the vehicle air conditioner more intelligent and personalized.

[0005] According to one aspect of the present invention, a method for controlling a vehicle air conditioner is provided, the method comprising:

[0006] Acquire current vehicle status data of the target vehicle, wherein the current vehicle status data includes current human body feature data and current in-vehicle environment parameter data;

[0007] Inputting the current human body feature data and the current in-vehicle environment parameter data into a pre-trained adaptive support vector machine model, and outputting a current human body thermal comfort evaluation value; the adaptive support vector machine model is trained based on the correspondence between the vehicle state data and the human body thermal comfort evaluation value;

[0008] Calculating the current operating parameters of the air conditioner to be controlled in the target vehicle according to the current human thermal comfort evaluation value;

[0009] In the case where the current operating parameters are adjusted by the passenger, the adjusted current operating parameters are recorded in a learning data set, and the pre-trained adaptive support vector machine model is corrected using the learning data set.

[0010] According to another aspect of the present invention, there is provided a control device for a vehicle air conditioner, the device comprising:

[0011] A vehicle status data acquisition module is used to acquire the current vehicle status data of the target vehicle, wherein the current vehicle status data includes current human body feature data and current in-vehicle environment parameter data;

[0012] A thermal comfort evaluation value acquisition module is used to input the current human body feature data and the current in-vehicle environment parameter data into a pre-trained adaptive support vector machine model, and output the current human body thermal comfort evaluation value; the adaptive support vector machine model is trained based on the correspondence between the vehicle state data and the human body thermal comfort evaluation value;

[0013] An air conditioning operating parameter calculation module, used to calculate the current operating parameters of the air conditioning to be controlled in the target vehicle according to the current human thermal comfort evaluation value;

[0014] The model correction module is used to record the adjusted current working parameters into a learning data set when the current working parameters are adjusted by the passenger, and to correct the pre-trained adaptive support vector machine model using the learning data set.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the vehicle air conditioner control method described in any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the vehicle air conditioner control method described in any embodiment of the present invention when executed.

[0020] The technical solution of the embodiment of the present invention obtains the current vehicle status data of the target vehicle, the current vehicle status data includes the current human body characteristic data and the current in-vehicle environmental parameter data; inputs the current human body characteristic data and the current in-vehicle environmental parameter data into a pre-trained adaptive support vector machine model, and outputs the current human body thermal comfort evaluation value; calculates the current working parameters of the air conditioner to be controlled in the target vehicle according to the current human body thermal comfort evaluation value; when the current working parameters are adjusted by the passengers, records the adjusted current working parameters into a learning data set, and uses the learning data set to correct the pre-trained adaptive support vector machine model, and adopts the method of using The pre-trained adaptive support vector machine model outputs the current human thermal comfort evaluation value in the car according to the current vehicle status data, and reversely calculates the current working parameters of the air conditioner based on the current human thermal comfort evaluation value in the car to realize air conditioning control. If the passenger manually adjusts the current working parameters of the air conditioner, the pre-trained adaptive support vector machine model is continuously corrected according to the adjusted working parameters to ensure the technical means of continuous learning of the model. This solves the problem that the method of relying on PMV value to realize intelligent control of air conditioner in the existing technology still has poor adaptability and insufficient intelligence among different groups of people, making the control method of vehicle air conditioning more intelligent and personalized.

[0021] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 A flow chart of a vehicle air conditioner control method provided in Embodiment 1 of the present invention;

[0024] Figure 2 A schematic diagram of a vehicle status data acquisition process provided in the first embodiment of the present invention;

[0025] Figure 3 A schematic diagram of a pre-training process of an SVM model provided in the first embodiment of the present invention;

[0026] Figure 4 A complete flow chart of a vehicle air conditioning control method provided in the first embodiment;

[0027] Figure 5A flowchart of another vehicle air conditioning control method provided in Embodiment 2 of the present invention;

[0028] Figure 6 A schematic diagram of the principle of data fault tolerance processing provided in the second embodiment of the present invention;

[0029] Figure 7 A schematic diagram of an update process of an SVM model provided in the second embodiment of the present invention;

[0030] Figure 8 A schematic diagram of the structure of a vehicle air conditioner control device provided in Embodiment 3 of the present invention;

[0031] Fig. 9 It is a schematic diagram of the structure of an electronic device for implementing the vehicle air conditioner control method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0034] Embodiment 1

[0035] Figure 1 This is a flow chart of a vehicle air conditioner control method provided by the first embodiment of the present invention. This embodiment is applicable to the case of intelligently controlling a vehicle air conditioner. The method can be executed by a vehicle air conditioner control device. The vehicle air conditioner control device can be implemented in the form of hardware and / or software. The vehicle air conditioner control device can be configured in the main controller of the vehicle. Figure 1 As shown, the method includes:

[0036] S110, obtaining current vehicle status data of the target vehicle, where the current vehicle status data includes current human body feature data and current in-vehicle environment parameter data.

[0037] The whole vehicle state data of the target vehicle may refer to the in-vehicle state data of the target vehicle, which may include in-vehicle passenger state data and in-vehicle environmental parameter data. The current human characteristic data (i.e., in-vehicle passenger state data) may include characteristic data such as gender, height, weight, clothing, etc. The current in-vehicle environmental parameter data may include parameter data such as ambient humidity and ambient temperature.

[0038] In an optional implementation, obtaining the current vehicle status data of the target vehicle may include: using the driver monitoring system of the target vehicle to collect the original current human body characteristic data of the target vehicle, and preprocessing and credibility judging the original current human body characteristic data to obtain the current human body characteristic data; using the humidity sensor of the target vehicle to collect the current in-vehicle humidity data of the target vehicle; using the in-vehicle temperature estimation model of the target vehicle to calculate the current in-vehicle heat load data of the target vehicle to obtain the current in-vehicle temperature data of the target vehicle.

[0039] In this embodiment, human feature data can be obtained through the vehicle's DMS (Driver Monitor System), and humidity data can be directly obtained by the humidity sensor. Since the in-vehicle temperature sensor cannot accurately reflect the actual temperature change in the vehicle, the current in-vehicle temperature data can be obtained through a pre-deployed temperature estimation model.

[0040] Specifically, based on the above optional implementation, the original current human feature data is preprocessed and the credibility is judged to obtain the current human feature data, which may include: for each dimension, the original current human feature data is identified multiple times to obtain the individual human feature physical values ​​obtained by the multiple identifications corresponding to the dimension; the dimensions include at least gender, height, clothing, age and BMI (Body Mass Index); the data error of the human feature physical value is calculated, and if the data error meets the data credibility requirement, the original current human feature data is used as the current human feature data; if the data error does not meet the data credibility requirement, the operation of performing multiple identifications on the original current human feature data is returned. Among them, the preprocessing includes identifying the original current human feature data and calculating the error of the identified human feature physical value.

[0041] Exemplary, reference Figure 2, the vehicle's DMS system can be used to obtain the in-vehicle image and identify the in-vehicle image to obtain the original current human feature data, and the original current human feature data can be further classified and identified. By presetting the identification parameters, the physical values ​​of various human features, such as gender, height, clothing, age, BMI (Body Mass Index, body mass index) and other values, are obtained. By comparing the errors between the gender results, height results, clothing results, age results, and BMI results of multiple identifications, if the error meets the credibility requirements (for example, the mean error is less than the preset error threshold), the signal preprocessing result can be directly output. If the error does not meet the credibility requirements (for example, the mean error is greater than the preset error threshold), the signal preprocessing is returned.

[0042] Based on the above optional implementation manner, the current in-car thermal load data of the target vehicle is calculated using the in-car temperature estimation model of the target vehicle to obtain the current in-car temperature data of the target vehicle, which may include: obtaining the initial thermal load, current passive thermal load, and current active thermal load of the target vehicle; taking the difference between the current passive thermal load and the current active thermal load as the current actual in-car thermal load change rate; taking the product of the current in-car actual in-car thermal load change rate multiplied by the current startup duration of the target vehicle as the actual thermal load change; taking the sum of the initial thermal load and the actual thermal load change as the current in-car actual thermal load; and obtaining the current in-car actual temperature through a thermal calculation method based on the current in-car actual thermal load, and taking the current in-car actual temperature as the current in-car temperature data.

[0043] The initial heat load may refer to the heat load in the static state of the vehicle, that is, the heat load when the vehicle enters but the vehicle is not started. The current passive heat load may include the heat load of the window glass, the heat load of solar radiation, the heat load of the vehicle structure, the heat load of the human body and the heat load of electrical equipment, etc. The current active heat load may refer to the heat load of the fresh air of the target vehicle, etc.

[0044] In this embodiment, the difference between the current passive heat load and the current active heat load can be used as the current actual heat load change rate in the vehicle; then the current startup time of the target vehicle from the vehicle stationary state to the current time is obtained; then the current actual heat load change rate in the vehicle and the current startup time of the target vehicle are multiplied to obtain the actual heat load change amount, that is, the cumulative value of the actual heat load change rate of the target vehicle from the vehicle stationary state to the current time; further, the initial heat load is added to the actual heat load change amount to obtain the current actual heat load in the vehicle; thus, the current actual temperature in the vehicle (that is, the current temperature data in the vehicle) is obtained by a thermal calculation method for the current actual heat load in the vehicle.

[0045] S120, inputting current human body feature data and current in-vehicle environment parameter data into a pre-trained adaptive support vector machine model, and outputting a current human body thermal comfort evaluation value.

[0046] In this embodiment, the pre-trained adaptive support vector machine model can be trained based on the correspondence between the vehicle state data and the human thermal comfort evaluation value. It should be noted that the human comfort influencing factors can be analyzed through weight processing, so that human characteristics, vehicle temperature and vehicle humidity can be selected as input features of the support vector machine model, and the human thermal comfort evaluation value can be used as the output label of the support vector machine model.

[0047] In an optional implementation, the following is a pre-training method for an adaptive support vector machine model provided in this embodiment, which may include: obtaining human feature sample data and in-vehicle environment parameter sample data; generating a learning data set through a human thermal comfort evaluation model based on the human feature sample data and the in-vehicle environment parameter sample data; dividing the learning data set into a training set and a test set; training the original support vector machine model based on the training set to obtain a support vector machine training model; when the accuracy of the output result of the support vector machine training model is verified to be passed, testing the support vector machine training model based on the test set to obtain a support vector machine test model; when the accuracy of the output result of the support vector machine test model is verified to be passed, using the support vector machine test model as a pre-trained adaptive support vector machine model; when the accuracy of the output result of the support vector machine training model fails to be verified, obtaining the number of times the accuracy verification of the output result of the support vector machine training model fails; when the number of times the verification fails is less than or equal to a first preset number threshold, continuing to adjust the parameters of the support vector machine training model; when the number of times the verification fails is greater than the first preset number threshold, or, when the accuracy verification of the output result of the support vector machine test model fails, re-dividing the training set and the test set.

[0048] In this embodiment, the human body feature sample data and the vehicle interior environment parameter sample data can be obtained based on the same method as the above-mentioned method of obtaining the current vehicle status data. Figure 3, based on the human thermal comfort evaluation model, a learning data set for machine learning can be generated according to human feature sample data and in-vehicle environmental parameter sample data; the learning data set is divided into a training set and a test set, and 90% of the data can be randomly selected as the training set and 10% of the data can be selected as the test set; the original support vector machine model is created, and the training set is learned by using the original support vector machine model to obtain a support vector machine training model, which can verify the accuracy of the model output result by means of the root mean square value, that is, to determine whether the root mean square value of the training model output result is less than the first preset root mean square threshold. If it is greater than or equal to the first preset root mean square threshold, it is determined that the SVM training model result verification has not passed, and the SVM training model can be adjusted again; if the number of parameter adjustments of the SVM model (equivalent to the number of times the SVM training model verification has failed) exceeds the first preset number threshold (for example, 5 times), it can be considered that the previously divided training set is inaccurate, and the training set can be re-divided. If it is less than the first preset root mean square threshold, it is determined that the SVM training model has been verified, and the test set learning phase can be directly entered to obtain the SVM test model. Similarly, the output result of the SVM test model is verified for accuracy, that is, whether the root mean square value of the output result of the SVM test model is greater than the second preset root mean square threshold. If it is greater than the second preset root mean square threshold, it is determined that the SVM test model has not been verified, and the training set and test set are re-divided from the learning data set of machine learning; if it is less than or equal to the second preset root mean square threshold, it is determined that the SVM test model has been verified, and the SVM initial model can be directly output, and the SVM initial model is activated to obtain a pre-trained adaptive support vector machine model.

[0049] S130. Calculate current operating parameters of the air conditioner to be controlled in the target vehicle according to the current human thermal comfort evaluation value.

[0050] In this embodiment, the current human thermal comfort evaluation value output by the pre-trained adaptive support vector machine model according to the current vehicle status data can be used to reversely calculate the current working parameters of the air conditioner to be controlled (i.e., the current working temperature and the current working air volume, etc.), thereby controlling the operation of the air conditioner to be controlled according to the current working parameters to optimize the current comfort of the passengers in the target vehicle.

[0051] S140. When the current operating parameters are adjusted by the passenger, the adjusted current operating parameters are recorded in a learning data set, and the pre-trained adaptive support vector machine model is corrected using the learning data set.

[0052] In this embodiment, if the air conditioner to be controlled works according to the current working parameters, it is also possible to monitor whether the passengers in the car have made personalized adjustments to the working parameters of the air conditioner. If it is monitored that the passengers in the car have adjusted the working temperature and / or the working air volume of the air conditioner, the air conditioner working parameters adjusted by the passengers can be recorded in the learning data set, and then the updated learning data set can be used to correct the adaptive support vector machine model, so that the adaptive support vector machine model provides a basis for more intelligent and personalized calculation of the air conditioner working parameters.

[0053] The technical solution of this embodiment is to obtain the current vehicle status data of the target vehicle, which includes the current human body feature data and the current in-vehicle environmental parameter data; input the current human body feature data and the current in-vehicle environmental parameter data into a pre-trained adaptive support vector machine model, and output the current human body thermal comfort evaluation value; calculate the current working parameters of the air conditioner to be controlled in the target vehicle according to the current human body thermal comfort evaluation value; when the current working parameters are adjusted by the passengers, record the adjusted current working parameters into the learning data set, and use the learning data set to correct the pre-trained adaptive support vector machine model, and adopt the method of using the pre-trained adaptive support vector machine model to obtain the current human body thermal comfort evaluation value; calculate ... The trained adaptive support vector machine model outputs the current human thermal comfort evaluation value in the car according to the current vehicle status data, and reversely calculates the current working parameters of the air conditioner based on the current human thermal comfort evaluation value in the car to realize air conditioning control. If the passenger manually adjusts the current working parameters of the air conditioner, the pre-trained adaptive support vector machine model is continuously corrected according to the adjusted working parameters to ensure the technical means of continuous learning of the model. This solves the problem that the method of relying on PMV value to realize intelligent control of air conditioner in the existing technology still has poor adaptability and insufficient intelligence among different groups of people, making the control method of vehicle air conditioning more intelligent and personalized.

[0054] In order to enable those skilled in the art to better understand the vehicle air conditioning control method of the first embodiment, Figure 4 A complete flow chart of a vehicle air conditioning control method is provided for this embodiment 1. Human body characteristic data and passenger compartment status (i.e., in-vehicle environment parameter status) are acquired through real-time vehicle status monitoring, and the real-time status of the vehicle is input into the adaptive SVM model to output the PMV value. The PMV value is calculated through the inverse PMV model to output the air conditioning target (i.e., air conditioning working parameters): comfortable temperature and comfortable air volume. The air conditioning system feeds back the passenger adjustment results. If there is passenger adjustment, the learning data set can be updated through data fault-tolerant processing, so that the adaptive SVM model learns the updated learning data set. At this time, the controller needs to have a self-learning function, and uses the optimization method to learn offline after the vehicle is powered off to find the optimal SVM hyperparameters.

[0055] Embodiment 2

[0056] Figure 5 This is a flow chart of another vehicle air conditioning control method provided by the second embodiment of the present invention. Based on the above embodiment, this embodiment refines the operation of S140. Figure 5 As shown, the method includes:

[0057] S210, obtaining current vehicle status data of the target vehicle, where the current vehicle status data includes current human body feature data and current in-vehicle environment parameter data.

[0058] S220, inputting current human body feature data and current in-vehicle environment parameter data into a pre-trained adaptive support vector machine model, and outputting a current human body thermal comfort evaluation value.

[0059] S230. Calculate current operating parameters of the air conditioner to be controlled in the target vehicle according to the current human thermal comfort evaluation value.

[0060] In this embodiment, in order to convert the thermal sensation into the actual engineering target physical value, the target value of PMV may be defuzzified to calculate the actual target temperature and the actual required air volume of the air conditioning thermal comfort requirement.

[0061] In an optional embodiment, calculating the current operating parameters of the air conditioner to be controlled in the target vehicle based on the current human thermal comfort evaluation value may include: obtaining a preset initial human thermal comfort evaluation value corresponding to the target vehicle, and obtaining multiple preset segmented intervals of the human thermal comfort evaluation value; calculating the initial comfort requirement temperature and the initial comfort requirement air volume of the air conditioner to be controlled based on the preset initial human thermal comfort evaluation value; calculating the sum of the products of the current human thermal comfort evaluation value and the influencing factors of multiple preset segmented intervals, to obtain a first quantitative value; using the sum of the initial comfort requirement temperature and the first quantitative value as the current operating temperature of the air conditioner to be controlled, and using the sum of the initial comfort requirement air volume and the first quantitative value as the current working air volume of the air conditioner to be controlled.

[0062] The preset initial human thermal comfort evaluation value may refer to a universal initial value of the human thermal comfort evaluation value set for the target vehicle. The preset segmented interval may refer to a segmented interval of the conventional human thermal comfort evaluation value. For example, the range of PMV values ​​is between -10 and 10, which may represent the passenger's feeling of the ambient temperature from very cold to very hot. The PMV value is divided into 7 intervals: PMV<-10, -10 <PMV<-5,-5<PMV<-0.5,-0.5<PMV<0.5,0.5<PMV<5,5<PMV<10, PMV> 10. The influence factors of the multiple preset segment intervals may refer to the influence weight values ​​corresponding to each PMV value segment interval.

[0063] In this embodiment, the preset initial human thermal comfort evaluation value can be defuzzified to convert it into an actual engineering target physical value. Specifically, the preset initial human thermal comfort evaluation value is reversely calculated through the adaptive support vector machine model, that is, the preset initial human thermal comfort evaluation value is used as the output data of the adaptive support vector machine model, and the input data of the adaptive support vector machine model (i.e., the in-car temperature and the in-car humidity) is reversely calculated. According to the in-car temperature and the in-car humidity, the initial comfort demand temperature T of the air conditioner to be controlled is further calculated through the general air conditioner working parameter formula. 初 and initial comfort demand air volume L 初 . The current human thermal comfort evaluation value output by the adaptive support vector machine model can be further multiplied by the influence factors of each preset segment interval and then summed to obtain a first quantity value P. Thus, T 初 +P is the current operating temperature T of the air conditioner to be controlled 当前 , L 初 +P is the current working air volume L of the air conditioner to be controlled 当前 .

[0064] S240. When the current operating temperature in the current operating parameters is adjusted by the passenger and it is determined that the adjusted operating temperature is suspicious, the adjusted operating temperature is corrected according to the number of occurrences of the adjusted operating temperature in the suspicious database, and the corrected adjusted operating temperature is recorded in the learning data set.

[0065] In this embodiment, if the working temperature of the air conditioner to be controlled is adjusted, it can be determined whether there is a suspicious situation in the adjustment. For example, if the current ambient temperature is high, and the air conditioner working temperature after adjustment is consistent with the current ambient temperature or higher than the current ambient temperature or different from the average temperature that most people adapt to under the current ambient temperature, etc.; for another example, if the current ambient temperature is low, and the air conditioner seat temperature after adjustment is consistent with the current ambient temperature or lower than the current ambient temperature or obviously different from the average temperature that most people adapt to under the current ambient temperature, etc.

[0066] In an optional implementation, determining whether to record the adjusted working temperature into the learning data set based on the number of occurrences of the adjusted working temperature may include: if the adjusted working temperature appears for the first time in the suspicious database, correcting the adjusted working temperature using an initial suspicious factor, storing the correspondence between the initial suspicious factor and the adjusted working temperature in the suspicious database, and recording the first corrected working temperature into the learning data set; if the adjusted working temperature appears for the second time in the suspicious database, adjusting the initial suspicious factor to a secondary suspicious factor, correcting the adjusted working temperature using the secondary suspicious factor, and recording the second corrected working temperature into the learning data set, the second corrected working temperature being closer to the adjusted working temperature than the first corrected working temperature; if the adjusted working temperature appears for the third time in the suspicious database, directly recording the adjusted working temperature into the learning data set.

[0067] Exemplary, reference Figure 6 , collect the ambient temperature and the in-car air conditioning setting temperature (i.e., the adjusted working temperature), and make a suspicious data judgment on the in-car air conditioning setting temperature. If the in-car air conditioning setting temperature appears 0 times in the suspicious database (i.e., it appears for the first time at present), for example, if the ambient temperature is greater than 28 degrees and the in-car air conditioning setting temperature is greater than 28 degrees, or the ambient temperature is less than 20 degrees and the in-car air conditioning setting temperature is less than 20 degrees, then the in-car air conditioning setting temperature is considered suspicious, and the in-car air conditioning setting temperature is corrected by using the suspicious factor 1 (i.e., the initial suspicious factor) (suspicious factor 1*in-car air conditioning setting temperature), and the corrected air conditioning temperature is further stored in the learning data set, and the suspicious factor 1 and the suspicious in-car air conditioning setting temperature are stored in the suspicious database. If the in-car air conditioning setting temperature appears 1 times in the suspicious database (i.e., it appears for the second time at present), the historically stored suspicious factor 1 can be corrected to the suspicious factor 2, and the in-car air conditioning setting temperature can be corrected by using the suspicious factor 2, and the corrected air conditioning temperature is stored in the learning data set. If the in-car air conditioning temperature appears 2 times in the suspicious database (i.e., it appears for the third time at present), it can be considered that the special air conditioning temperature setting is a personalized demand of the passenger, and the in-car air conditioning setting temperature is directly recorded in the learning data set.

[0068] S250. When the current working air volume in the current working parameters is adjusted by the passenger, the adjusted working air volume is directly recorded in the learning data set.

[0069] In this embodiment, considering that most people pay more attention to the air-conditioning temperature and less attention to the air-conditioning air volume, when the air-conditioning working air volume is adjusted by the passenger, it can be considered that the adjusted air-conditioning air volume is the passenger's personalized demand, which is directly recorded in the learning data set.

[0070] S260: Use the updated learning data set to modify the pre-trained adaptive support vector machine model.

[0071] The technical solution of this embodiment adopts a pre-trained adaptive support vector machine model to output the current human thermal comfort evaluation value in the car according to the current vehicle status data, and reversely calculate the current working parameters of the air conditioner based on the current human thermal comfort evaluation value in the car to achieve air conditioning control. If the passenger manually adjusts the current working parameters of the air conditioner, the pre-trained adaptive support vector machine model is continuously corrected according to the adjusted working parameters to ensure the continuous learning of the model. This solves the problem that the method of relying on PMV value to achieve intelligent control of air conditioner in the prior art still has poor adaptability and insufficient intelligence among different groups of people, making the control method of vehicle air conditioning more intelligent and personalized.

[0072] Optional, Figure 7 A schematic diagram of the update process of the SVM model is provided for the second embodiment. Determine whether to activate the update SVM model. When the new personalized data input by the passenger reaches a threshold (for example Figure 6 The vehicle is powered off. To avoid affecting the vehicle's operating functions, offline learning is used for SVM learning. When the vehicle is powered off, the passengers have left the vehicle, and the door lock state is activated, a new data set can be imported for offline learning of SVM. After learning is completed, the controller sleep command is sent. The learning process uses an optimization method to globally optimize the hyperparameters. When the learning error reaches the minimum value, the hyperparameters of SVM machine learning are finally locked.

[0073] Embodiment 3

[0074] Figure 8 This is a schematic diagram of the structure of a vehicle air conditioner control device provided by Embodiment 3 of the present invention. Figure 8 As shown, the device includes: a vehicle state data acquisition module 310, a thermal comfort evaluation value acquisition module 320, an air conditioning operating parameter calculation module 330 and a model correction module 340. Among them:

[0075] The vehicle status data acquisition module 310 is used to acquire the current vehicle status data of the target vehicle, wherein the current vehicle status data includes current human body feature data and current in-vehicle environment parameter data;

[0076] The thermal comfort evaluation value acquisition module 320 is used to input the current human body feature data and the current in-vehicle environment parameter data into a pre-trained adaptive support vector machine model, and output the current human body thermal comfort evaluation value; the adaptive support vector machine model is trained based on the correspondence between the vehicle state data and the human body thermal comfort evaluation value;

[0077] An air conditioning operating parameter calculation module 330, used to calculate the current operating parameters of the air conditioning to be controlled in the target vehicle according to the current human thermal comfort evaluation value;

[0078] The model correction module 340 is used to record the adjusted current operating parameters into a learning data set when the current operating parameters are adjusted by the passenger, and to correct the pre-trained adaptive support vector machine model using the learning data set.

[0079] The technical solution of this embodiment is to obtain the current vehicle status data of the target vehicle, which includes the current human body feature data and the current in-vehicle environmental parameter data; input the current human body feature data and the current in-vehicle environmental parameter data into a pre-trained adaptive support vector machine model, and output the current human body thermal comfort evaluation value; calculate the current working parameters of the air conditioner to be controlled in the target vehicle according to the current human body thermal comfort evaluation value; when the current working parameters are adjusted by the passengers, record the adjusted current working parameters into the learning data set, and use the learning data set to correct the pre-trained adaptive support vector machine model, and adopt the method of using the pre-trained adaptive support vector machine model to obtain the current human body thermal comfort evaluation value; calculate ... The trained adaptive support vector machine model outputs the current human thermal comfort evaluation value in the car according to the current vehicle status data, and reversely calculates the current working parameters of the air conditioner based on the current human thermal comfort evaluation value in the car to realize air conditioning control. If the passenger manually adjusts the current working parameters of the air conditioner, the pre-trained adaptive support vector machine model is continuously corrected according to the adjusted working parameters to ensure the technical means of continuous learning of the model. This solves the problem that the method of relying on PMV value to realize intelligent control of air conditioner in the existing technology still has poor adaptability and insufficient intelligence among different groups of people, making the control method of vehicle air conditioning more intelligent and personalized.

[0080] Optionally, the vehicle status data acquisition module 310 may include:

[0081] A current human body feature data acquisition unit, used to collect original current human body feature data of the target vehicle using the driver monitoring system of the target vehicle, and preprocess and determine the credibility of the original current human body feature data to obtain the current human body feature data;

[0082] A current in-vehicle humidity data acquisition unit, used to acquire current in-vehicle humidity data of the target vehicle using a humidity sensor of the target vehicle;

[0083] The current in-vehicle temperature data acquisition unit is used to calculate the current in-vehicle heat load data of the target vehicle using the in-vehicle temperature estimation model of the target vehicle to obtain the current in-vehicle temperature data of the target vehicle.

[0084] Optionally, the current human body feature data acquisition unit may be specifically used for:

[0085] For each dimension, the original current human body characteristic data is identified multiple times to obtain the physical value of the individual human body characteristic obtained by the multiple identifications corresponding to the dimension; the dimension at least includes gender, height, clothing, age and body mass index BMI;

[0086] Calculate the data error of the human body feature physical value, and if the data error meets the data credibility requirement, use the original current human body feature data as the current human body feature data;

[0087] If the data error does not meet the data credibility requirement, the process returns to executing the operation of performing multiple recognitions on the original current human body feature data.

[0088] Optionally, the current vehicle interior temperature data acquisition unit may be used to:

[0089] Obtaining an initial heat load, a current passive heat load, and a current active heat load of the target vehicle;

[0090] Taking the difference between the current passive heat load and the current active heat load as the current actual heat load change rate in the vehicle;

[0091] The product of the current actual thermal load change rate in the vehicle and the current startup time of the target vehicle is used as the actual thermal load change amount;

[0092] The sum of the initial heat load and the actual heat load change is used as the current actual heat load in the vehicle;

[0093] According to the current actual heat load in the vehicle, the current actual temperature in the vehicle is obtained by a heat calculation method, and the current actual temperature in the vehicle is used as the current temperature data in the vehicle.

[0094] Optionally, the vehicle air conditioner control device further includes a model pre-training module for:

[0095] Obtaining human body feature sample data and in-vehicle environment parameter sample data;

[0096] Generate the learning data set through a human thermal comfort evaluation model according to the human feature sample data and the in-vehicle environment parameter sample data;

[0097] Dividing the learning data set into a training set and a test set;

[0098] The original support vector machine model is trained according to the training set to obtain a support vector machine training model;

[0099] When the accuracy of the output result of the support vector machine training model is verified, the support vector machine training model is tested according to the test set to obtain a support vector machine test model;

[0100] When the accuracy of the output result of the support vector machine test model is verified, the support vector machine test model is output as an initial support vector machine model, and the initial support vector machine model is activated to obtain the pre-trained adaptive support vector machine model.

[0101] Optionally, the model pre-training module can also be used for:

[0102] In the case that the accuracy verification of the output result of the support vector machine training model fails, obtaining the number of times the accuracy verification of the output result of the support vector machine training model fails;

[0103] When the number of verification failures is less than or equal to a first preset number threshold, the support vector machine training model continues to be adjusted.

[0104] Optionally, the model pre-training module can also be used for:

[0105] When the number of verification failures is greater than a first preset number threshold, or when the accuracy verification of the output result of the support vector machine test model fails, the training set and the test set are re-divided.

[0106] Optionally, the air conditioning operating parameter calculation module 330 may be specifically used for:

[0107] Obtaining a preset initial human thermal comfort evaluation value corresponding to the target vehicle, and obtaining a plurality of preset segmented intervals of the human thermal comfort evaluation value;

[0108] The preset initial human thermal comfort evaluation value is reversely calculated by the adaptive support vector machine model to obtain the initial comfort demand temperature and the initial comfort demand air volume of the air conditioner to be controlled;

[0109] Calculating the sum of the products of the current human thermal comfort evaluation value and the influencing factors of the plurality of preset segment intervals to obtain a first quantity value;

[0110] The sum of the initial comfort demand temperature and the first quantity value is used as the current working temperature of the air conditioner to be controlled, and the sum of the initial comfort demand air volume and the first quantity value is used as the current working air volume of the air conditioner to be controlled.

[0111] Optionally, the model modification module 340 may include:

[0112] a first data recording unit, configured to, when the current operating temperature in the current operating parameters is adjusted by a passenger and the adjusted operating temperature is determined to be suspicious, correct the adjusted operating temperature according to the number of occurrences of the adjusted operating temperature in the suspicious database, and record the corrected adjusted operating temperature into the learning data set;

[0113] The second data recording unit is used to directly record the adjusted working air volume into the learning data set when the current working air volume in the current working parameters is adjusted by the passenger.

[0114] Optionally, the first data recording unit may be specifically used for:

[0115] If the adjusted working temperature appears for the first time in the suspicious database, the adjusted working temperature is corrected using the initial suspicious factor, the corresponding relationship between the initial suspicious factor and the adjusted working temperature is stored in the suspicious database, and the first corrected working temperature is recorded in the learning data set;

[0116] If the adjusted working temperature appears for the second time in the suspicious database, the initial suspicious factor is adjusted to a secondary suspicious factor, the adjusted working temperature is corrected using the secondary suspicious factor, and the second corrected working temperature is recorded in the learning data set, and the second corrected working temperature is closer to the adjusted working temperature than the first corrected working temperature;

[0117] If the adjusted working temperature appears for the third time in the suspicious database, the adjusted working temperature is directly recorded in the learning data set.

[0118] The control device for the vehicle air conditioner provided in the embodiment of the present invention can execute the control method for the vehicle air conditioner provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0119] Embodiment 4

[0120] Fig. 9 A schematic diagram of an electronic device 400 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers or various forms of mobile devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0121] like Fig. 9As shown, the electronic device 400 includes at least one processor 401, and a memory connected to the at least one processor 401 in communication, such as a read-only memory (ROM) 402, a random access memory (RAM) 403, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 401 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 402 or the computer program loaded from the storage unit 408 to the random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device 400 can also be stored. The processor 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0122] Multiple components in the electronic device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a disk, an optical disk, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the electronic device 400 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0123] The processor 401 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 401 executes the various methods and processes described above, such as a method for controlling a vehicle air conditioner.

[0124] In some embodiments, the control method of the vehicle air conditioner may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the processor 401, one or more steps of the control method of the vehicle air conditioner described above may be performed. Alternatively, in other embodiments, the processor 401 may be configured to execute the control method of the vehicle air conditioner in any other appropriate manner (e.g., by means of firmware).

[0125] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0126] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0127] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0128] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0129] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0130] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0131] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0132] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for controlling a vehicle air conditioner, characterized in that: include: Acquire current vehicle status data of the target vehicle, wherein the current vehicle status data includes current human body feature data and current in-vehicle environment parameter data; Inputting the current human body feature data and the current in-vehicle environment parameter data into a pre-trained adaptive support vector machine model, and outputting a current human body thermal comfort evaluation value; The adaptive support vector machine model is trained based on the correspondence between vehicle status data and human thermal comfort evaluation values; Calculating the current operating parameters of the air conditioner to be controlled in the target vehicle according to the current human thermal comfort evaluation value; When the current operating parameters are adjusted by the passenger, the adjusted current operating parameters are recorded in a learning data set, and the pre-trained adaptive support vector machine model is modified using the learning data set; Wherein, when the current working parameter is adjusted by the passenger, recording the adjusted current working parameter into the learning data set includes: In the case where the current operating temperature in the current operating parameters is adjusted by a passenger and it is determined that the adjusted operating temperature is suspicious, the adjusted operating temperature is corrected according to the number of occurrences of the adjusted operating temperature in the suspicious database, and the corrected adjusted operating temperature is recorded in the learning data set; In the case where the current working air volume in the current working parameters is adjusted by a passenger, the adjusted working air volume is directly recorded in the learning data set.

2. The method according to claim 1, characterized in that: Get the current vehicle status data of the target vehicle, including: Using the driver monitoring system of the target vehicle to collect the original current human body feature data of the target vehicle, and preprocessing and credibility determination of the original current human body feature data to obtain the current human body feature data; Using a humidity sensor of the target vehicle to collect current humidity data inside the target vehicle; The current in-vehicle heat load data of the target vehicle is calculated using the in-vehicle temperature estimation model of the target vehicle to obtain the current in-vehicle temperature data of the target vehicle.

3. The method according to claim 2, characterized in that Preprocessing and credibility determination are performed on the original current human body feature data to obtain the current human body feature data, including: For each dimension, the original current human body characteristic data is identified multiple times to obtain the physical value of the individual human body characteristic obtained by the multiple identifications corresponding to the dimension; the dimension at least includes gender, height, clothing, age and body mass index BMI; Calculate the data error of the human body feature physical value, and if the data error meets the data credibility requirement, use the original current human body feature data as the current human body feature data; If the data error does not meet the data credibility requirement, the process returns to executing the operation of performing multiple recognitions on the original current human body feature data.

4. The method according to claim 2, characterized in that: The current in-vehicle heat load data of the target vehicle is calculated using the in-vehicle temperature estimation model of the target vehicle to obtain the current in-vehicle temperature data of the target vehicle, including: Obtaining an initial heat load, a current passive heat load, and a current active heat load of the target vehicle; Taking the difference between the current passive heat load and the current active heat load as the current actual heat load change rate in the vehicle; The product of the current actual thermal load change rate in the vehicle and the current startup time of the target vehicle is used as the actual thermal load change amount; The sum of the initial heat load and the actual heat load change is used as the current actual heat load in the vehicle; According to the current actual heat load in the vehicle, the current actual temperature in the vehicle is obtained by a heat calculation method, and the current actual temperature in the vehicle is used as the current temperature data in the vehicle.

5. The method according to claim 1, characterized in that The pre-trained adaptive support vector machine model is pre-trained in the following manner: Obtaining human body feature sample data and in-vehicle environment parameter sample data; Generate the learning data set through a human thermal comfort evaluation model according to the human feature sample data and the in-vehicle environment parameter sample data; Dividing the learning data set into a training set and a test set; The original support vector machine model is trained according to the training set to obtain a support vector machine training model; When the accuracy of the output result of the support vector machine training model is verified, the support vector machine training model is tested according to the test set to obtain a support vector machine test model; When the accuracy of the output result of the support vector machine test model is verified, the support vector machine test model is output as an initial support vector machine model, and the initial support vector machine model is activated to obtain the pre-trained adaptive support vector machine model.

6. The method according to claim 5, characterized in that Also includes: In the case that the accuracy verification of the output result of the support vector machine training model fails, obtaining the number of times the accuracy verification of the output result of the support vector machine training model fails; When the number of verification failures is less than or equal to a first preset number threshold, the support vector machine training model continues to be adjusted.

7. The method according to claim 6, characterized in that Also includes: When the number of verification failures is greater than a first preset number threshold, or when the accuracy verification of the output result of the support vector machine test model fails, the training set and the test set are re-divided.

8. The method according to claim 1, characterized in that Calculating the current operating parameters of the air conditioner to be controlled in the target vehicle according to the current human thermal comfort evaluation value includes: Obtaining a preset initial human thermal comfort evaluation value corresponding to the target vehicle, and obtaining a plurality of preset segmented intervals of the human thermal comfort evaluation value; The preset initial human thermal comfort evaluation value is reversely calculated by the adaptive support vector machine model to obtain the initial comfort demand temperature and the initial comfort demand air volume of the air conditioner to be controlled; Calculating the sum of the products of the current human thermal comfort evaluation value and the influencing factors of the plurality of preset segment intervals to obtain a first quantity value; The sum of the initial comfort demand temperature and the first quantity value is used as the current working temperature of the air conditioner to be controlled, and the sum of the initial comfort demand air volume and the first quantity value is used as the current working air volume of the air conditioner to be controlled.

9. The method according to claim 1, characterized in that: The method further comprises: modifying the adjusted working temperature according to the number of occurrences of the adjusted working temperature in the suspicious database, and recording the modified adjusted working temperature into the learning data set, including: If the adjusted working temperature appears for the first time in the suspicious database, the adjusted working temperature is corrected using the initial suspicious factor, the corresponding relationship between the initial suspicious factor and the adjusted working temperature is stored in the suspicious database, and the first corrected working temperature is recorded in the learning data set; If the adjusted working temperature appears for the second time in the suspicious database, the initial suspicious factor is adjusted to a secondary suspicious factor, the adjusted working temperature is corrected using the secondary suspicious factor, and the second corrected working temperature is recorded in the learning data set, and the second corrected working temperature is closer to the adjusted working temperature than the first corrected working temperature; If the adjusted working temperature appears for the third time in the suspicious database, the adjusted working temperature is directly recorded in the learning data set.

10. A vehicle air conditioner control device, characterized in that: include: A vehicle status data acquisition module is used to acquire the current vehicle status data of the target vehicle, wherein the current vehicle status data includes current human body feature data and current in-vehicle environment parameter data; A thermal comfort evaluation value acquisition module, used for inputting the current human body feature data and the current in-vehicle environment parameter data into a pre-trained adaptive support vector machine model, and outputting the current human body thermal comfort evaluation value; The adaptive support vector machine model is trained based on the correspondence between vehicle status data and human thermal comfort evaluation values; An air conditioning operating parameter calculation module, used to calculate the current operating parameters of the air conditioning to be controlled in the target vehicle according to the current human thermal comfort evaluation value; A model correction module, used for recording the adjusted current working parameters into a learning data set when the current working parameters are adjusted by the passenger, and correcting the pre-trained adaptive support vector machine model using the learning data set; The model correction module includes: a first data recording unit, configured to, when the current operating temperature in the current operating parameters is adjusted by a passenger and the adjusted operating temperature is determined to be suspicious, correct the adjusted operating temperature according to the number of occurrences of the adjusted operating temperature in the suspicious database, and record the corrected adjusted operating temperature into the learning data set; The second data recording unit is used to directly record the adjusted working air volume into the learning data set when the current working air volume in the current working parameters is adjusted by the passenger.

11. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the vehicle air conditioner control method according to any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the vehicle air conditioner control method according to any one of claims 1 to 9 when executed.

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