Vehicle-mounted air conditioner control method and device, electronic equipment and medium

Abstract

The invention belongs to the technical field of artificial intelligence, and relates to a vehicle-mounted air conditioner control method and device, electronic equipment and a medium. The method comprises the following steps: acquiring vehicle driving data related to the vehicle-mounted air conditioner; and the vehicle driving data is input into a preset temperature prediction model, the target temperature of the target time after the vehicle-mounted air conditioner acts is obtained, the target temperature is the temperature of the designated seat in the designated air blowing direction, and the vehicle driving data is a variable-length sequence. The invention provides a mode for predicting the temperature by using the vehicle driving data, the vehicle driving data is a variable-length sequence, and the situation that the acquired data are missing is allowed to occur, so that the dependence of the temperature prediction on the data integrity is reduced, the effect of predicting the temperature in real time in a vehicle-mounted real-time scene is realized, and the user experience is improved. According to the method, the target temperatures of multiple moments in the future can be predicted in parallel, the low-delay prediction requirement of a vehicle-mounted scene is met, the prediction efficiency is improved, and the automation and intelligence degree of the intelligent cabin is also improved.

Description

Technical Field

[0001] This disclosure belongs to the field of artificial intelligence technology, and in particular relates to a vehicle air conditioning control method, a vehicle air conditioning control device, an electronic device, and a computer-readable storage medium. Background Technology

[0002] A smart cockpit is a vehicle cabin that integrates multiple intelligent technologies, aiming to improve the passenger experience, safety, and comfort. With the rapid development of smart cockpit technology, people's demands for driving comfort are increasing, and correspondingly, the ability to predict and automatically control vehicle air conditioning temperatures is becoming increasingly important.

[0003] However, the usage scenarios of car air conditioners are more complex and varied than those of household air conditioners, and conventional temperature prediction methods are not applicable. Summary of the Invention

[0004] To overcome the problems existing in related technologies, this disclosure provides a vehicle air conditioning control method, a vehicle air conditioning control device, an electronic device, and a computer-readable storage medium.

[0005] According to a first aspect of the present disclosure, a vehicle air conditioning control method is provided, comprising: Obtain vehicle driving data related to the vehicle's air conditioning system; The vehicle driving data is input into a preset temperature prediction model to obtain the target temperature at a target time after the vehicle air conditioner is activated. The target temperature is the temperature at a specified seat in a specified airflow direction. The vehicle driving data is a variable-length sequence.

[0006] Optionally, the method further includes: Multiple segments of vehicle driving data within a preset unit time period are determined, and the vehicle driving data is aggregated based on the preset unit time period to obtain aggregated vehicle driving data.

[0007] Optionally, inputting the vehicle driving data into a preset temperature prediction model includes: The sliding window length and sliding window step size are determined to be the first time interval and the second time interval, respectively. Based on the sliding window length of the first time interval, the sliding window step of the second time interval is slid across the aggregated vehicle driving data to obtain sliding window data, and the sliding window data is input into a preset temperature prediction model.

[0008] Optionally, the target temperature is input into a preset energy consumption prediction model to obtain the air conditioning energy consumption for a target time after the vehicle air conditioner is activated.

[0009] Optionally, the method further includes: The air conditioning energy consumption at the target time is averaged to obtain the average energy consumption, and the average energy consumption is then converted to obtain the converted average energy consumption. The vehicle's driving energy consumption and remaining battery power are obtained, and the remaining driving range is determined based on the average energy consumption, the driving energy consumption, and the remaining battery power.

[0010] Optionally, the step of averaging the air conditioning energy consumption at the target time to obtain the average energy consumption includes: Obtain the navigation duration of the vehicle and the predicted duration of air conditioning energy consumption at the target time; When the navigation duration is less than the predicted duration, the air conditioning energy consumption at the target time is truncated based on the navigation duration to obtain the truncated air conditioning energy consumption, and the truncated air conditioning energy consumption is averaged to obtain the average energy consumption.

[0011] Optionally, the method further includes: The adjustment strategy is determined based on the target temperature, and the vehicle air conditioner is controlled according to the adjustment strategy.

[0012] Optionally, the temperature in the specified airflow direction of the specified seat includes one or more of the following: driver's seat face temperature, driver's seat foot temperature, passenger's seat face temperature, and passenger's seat foot temperature; The control of the vehicle air conditioner according to the adjustment strategy includes any of the following methods: When the temperature difference between the driver's side airflow temperature and the preset temperature is greater than the preset value, the vehicle air conditioning is controlled to restore the driver's side airflow temperature to the preset temperature. When the temperature difference between the driver's foot warmer temperature and the preset temperature is greater than the preset value, the vehicle air conditioner is controlled to restore the driver's foot warmer temperature to the preset temperature. When the temperature difference between the passenger-side airflow temperature and the preset temperature is greater than the preset value, the vehicle air conditioning is controlled to restore the passenger-side airflow temperature to the preset temperature. When the temperature difference between the passenger-side footwell temperature and the preset temperature is greater than the preset value, the vehicle air conditioning is controlled to restore the passenger-side footwell temperature to the preset temperature. When the temperature difference between the target temperature and the preset temperature is less than or equal to the preset value, the blower of the vehicle air conditioner is controlled to adjust to the first duty cycle. When the temperature difference between the target temperature and the preset temperature is greater than the preset value, the blower of the vehicle air conditioner is controlled to adjust to the second duty cycle, which is greater than the first duty cycle.

[0013] According to a second aspect of the present disclosure, a vehicle air conditioning control device is provided, comprising: The data acquisition module is configured to acquire vehicle driving data related to the vehicle's air conditioning system. The temperature prediction module is configured to input the vehicle driving data into a preset temperature prediction model to obtain the target temperature at a target time after the vehicle air conditioner is activated. The target temperature is the temperature at a specified seat in a specified airflow direction, and the vehicle driving data is a variable-length sequence.

[0014] Optionally, the vehicle air conditioning control device is further configured to: The target temperature is input into a preset energy consumption prediction model to obtain the air conditioning energy consumption for the target time after the vehicle air conditioner is activated.

[0015] Optionally, the vehicle air conditioning control device is further configured to: The adjustment strategy is determined based on the target temperature, and the vehicle air conditioner is controlled according to the adjustment strategy.

[0016] Optionally, the temperature in the specified airflow direction of the specified seat includes one or more of the following: driver's seat face temperature, driver's seat foot temperature, passenger's seat face temperature, and passenger's seat foot temperature; The vehicle air conditioning control device is also configured to: When the temperature difference between the driver's side airflow temperature and the preset temperature is greater than the preset value, the vehicle air conditioning is controlled to restore the driver's side airflow temperature to the preset temperature. When the temperature difference between the driver's foot warmer temperature and the preset temperature is greater than the preset value, the vehicle air conditioner is controlled to restore the driver's foot warmer temperature to the preset temperature. When the temperature difference between the passenger-side airflow temperature and the preset temperature is greater than the preset value, the vehicle air conditioning is controlled to restore the passenger-side airflow temperature to the preset temperature. When the temperature difference between the passenger-side footwell temperature and the preset temperature is greater than the preset value, the vehicle air conditioning is controlled to restore the passenger-side footwell temperature to the preset temperature. When the temperature difference between the target temperature and the preset temperature is less than or equal to the preset value, the blower of the vehicle air conditioner is controlled to adjust to the first duty cycle. When the temperature difference between the target temperature and the preset temperature is greater than the preset value, the blower of the vehicle air conditioner is controlled to adjust to the second duty cycle, which is greater than the first duty cycle.

[0017] According to a third aspect of the present disclosure, an electronic device is provided, comprising: processor; Memory used to store processor-executable instructions; The processor is configured to execute executable instructions stored in the memory to implement the steps of the vehicle air conditioning control method provided in the first aspect.

[0018] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided that stores computer program instructions thereon, which, when executed by a processor, implement the steps of the vehicle air conditioning control method provided in any of the first aspects of the present disclosure.

[0019] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects: The methods and apparatus provided in the exemplary embodiments of this disclosure provide a way to predict temperature using vehicle driving data. Since the vehicle driving data is a variable-length sequence, it allows for the occurrence of missing data, reducing the dependence of temperature prediction on data integrity. It achieves the effect of real-time temperature prediction in real-time vehicle scenarios and can also predict the target temperature at multiple future moments in parallel, meeting the low-latency prediction requirements of vehicle scenarios, improving prediction efficiency, and enhancing the automation and intelligence of smart cockpits.

[0020] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0021] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0022] Figure 1 The schematic diagram illustrates a flow chart of an in-vehicle air conditioning control method according to an exemplary embodiment of the present disclosure; Figure 2 The schematic diagram illustrates a flowchart of a sliding window processing method in an exemplary embodiment of the present disclosure; Figure 3 The illustration schematically shows a flowchart of a method for determining the remaining driving range based on a target temperature in an exemplary embodiment of the present disclosure; Figure 4 The schematic diagram illustrates a process flow diagram of a method for further averaging in an exemplary embodiment of the present disclosure; Figure 5 The schematic diagram illustrates a flowchart of a method for controlling a vehicle air conditioner in an exemplary embodiment of the present disclosure; Figure 6 The schematic diagram illustrates a flowchart of an in-vehicle air conditioning control method in an application scenario of an exemplary embodiment of this disclosure; Figure 7This schematic diagram illustrates the structure of an in-vehicle air conditioning control device according to an exemplary embodiment of the present disclosure; Figure 8 This schematic diagram illustrates the structure of another vehicle air conditioning control device according to an exemplary embodiment of the present disclosure; Figure 9 The schematic diagram illustrates the structure of another vehicle air conditioning control device according to an exemplary embodiment of the present disclosure. Detailed Implementation

[0023] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0024] It should be noted that all actions involving the acquisition of signals, information, or data in this disclosure are carried out in compliance with the relevant data protection laws and policies of the country where the location is situated, and with authorization from the owner of the relevant device.

[0025] To address the problems existing in related technologies, this disclosure provides a method for controlling vehicle air conditioning. Figure 1 This is a flowchart illustrating an in-vehicle air conditioning control method according to an exemplary embodiment, such as... Figure 1 As shown, the method may include at least the following steps: Step S110. Obtain vehicle driving data related to the vehicle air conditioning.

[0026] Step S120. Input the vehicle driving data into the preset temperature prediction model to obtain the target temperature at the target time after the vehicle air conditioner is activated. The target temperature is the temperature at the specified seat in the specified airflow direction. The vehicle driving data is a variable length sequence.

[0027] In an exemplary embodiment of this disclosure, a method for temperature prediction using vehicle driving data is provided. This method allows for the occurrence of missing data, reduces the dependence of temperature prediction on data, achieves real-time temperature prediction in real-time vehicle scenarios, and can also predict target temperatures at multiple future moments in parallel. This meets the low-latency prediction requirements of vehicle scenarios, improves prediction efficiency, and enhances the automation and intelligence of the smart cockpit.

[0028] The following is a detailed explanation of each step in the vehicle air conditioning control method.

[0029] In step S110, vehicle driving data related to the vehicle air conditioning is acquired.

[0030] In the exemplary embodiments disclosed herein, the collection of vehicle driving data can be divided into three categories: user preference data, air conditioning setting data, and external environment data.

[0031] Among them, user preference signals include vehicle speed, driver's side window position, passenger side window position, rear left side window position, rear right side window position, driver's side door opening status, passenger side door opening status, rear right side door opening status, rear left side door opening status, whether there is someone in the passenger seat, whether there is someone in the rear left side, whether there is someone in the rear middle, whether there is someone in the rear right side, whether the passenger's seat belt is fastened, and driving mode.

[0032] Air conditioning setting signals include: manual / automatic air conditioning mode, internal / external circulation setting type, front air conditioning mode on / off, manual defrost / defog setting on / off, front air conditioning fan speed mode (automatic or manual), air conditioning fan speed setting, air conditioning self-drying on / off, passenger side temperature setting, driver side temperature setting, driver side temperature setting mode (automatic or manual), driver side air conditioning operating mode, front air conditioning operating mode (ventilation / heating / cooling), air conditioning switch status (on / off), AC (Air Conditioning, air conditioning compressor switch) status (on / off), driver side foot temperature, blower duty cycle, passenger side face temperature, and passenger side foot temperature.

[0033] External environmental signals include ambient temperature, light intensity (left side), light intensity (right side), and windshield humidity.

[0034] In step S120, the vehicle driving data is input into a preset temperature prediction model to obtain the target temperature at the target time after the vehicle air conditioner is activated. The target temperature is the temperature at the specified seat in the specified airflow direction, and the vehicle driving data is a variable-length sequence.

[0035] In an exemplary embodiment of this disclosure, after obtaining vehicle driving data, the vehicle driving data can be preprocessed.

[0036] In an optional embodiment, multiple segments of vehicle driving data within a preset unit time are determined, and the vehicle driving data are aggregated based on the preset unit time to obtain aggregated vehicle driving data.

[0037] When the acquired vehicle driving data is not sorted in chronological order, the vehicle driving data can be sorted first according to the driving time from oldest to newest. Then, the sorted vehicle driving data can be divided into multiple segments of vehicle driving data according to a preset unit time.

[0038] For example, when the preset unit time is 2 seconds, the time interval between the start time of the current segment of vehicle driving data and the end time of the previous segment of vehicle driving data is greater than or equal to 2 seconds.

[0039] In one embodiment of this disclosure, the preset unit of time is minutes. For example, if the vehicle signal is transmitted per second, after obtaining second-level vehicle driving data, N segments of second-level vehicle driving data can be aggregated using minutes as the preset unit of time.

[0040] In one embodiment of this disclosure, the aggregation processing method may include at least one of the following: taking the maximum value of vehicle driving data within a preset unit time, taking the minimum value of vehicle driving data within a preset unit time, taking the average value of vehicle driving data within a preset unit time, the set value at the last moment, and the proportion of each state, etc.

[0041] In one embodiment of this disclosure, the aggregation method can be singular or combined. For example, different aggregation methods can be selected and combined based on different types of vehicle driving data.

[0042] For example, taking vehicle driving data related to user preferences as an example, for vehicle driving data related to vehicle speed, the aggregation method can be set to average vehicle speed; for vehicle driving data related to driver's side window position preference, the aggregation method can be set to average driver's side window position; for vehicle driving data related to passenger side window position preference, the aggregation method can be set to average passenger side window position; for vehicle driving data related to rear left side window position preference, the aggregation method can be set to average rear left side window position; and for vehicle driving data related to rear right side window position preference, the aggregation method can be set to average rear right side window position. Methods: For vehicle driving data related to driver's side door opening preferences, you can choose to aggregate the percentage of each driver's side door opening state, including both open and closed states. For vehicle driving data related to passenger side door opening preferences, you can choose to aggregate the percentage of each passenger side door opening state, including both open and closed states. For vehicle driving data related to rear right-side door opening preferences, you can choose to aggregate the percentage of each rear right-side door opening state, including both open and closed states. For vehicle driving data related to rear left-side door opening preferences, you can choose to aggregate the percentage of each rear left-side door opening state. The aggregation method for the percentage of each state in the "on" state includes "on" or "off". For vehicle driving data related to whether the front passenger seat is occupied, the aggregation method can be selected based on the percentage of each state, including "yes" or "no". For vehicle driving data related to whether the rear left seat is occupied, the aggregation method can be selected based on the percentage of each state, including "yes" or "no". For vehicle driving data related to whether the rear middle seat is occupied, the aggregation method can be selected based on the percentage of each state, including "yes" or "no". For vehicle driving data related to whether the rear right seat is occupied, the aggregation method can be selected based on the percentage of each state, including "yes" or "no". The right side of the data can be aggregated based on the percentage of each status indicating whether or not a passenger is present. For vehicle driving data related to passenger seatbelt fastening preferences, the aggregation method can be selected based on the percentage of each status indicating whether or not a passenger seatbelt is fastened. For vehicle driving data related to driving mode preferences, the aggregation method can be selected based on the percentage of each driving mode status, including Comfort mode, Sport mode, Personalized mode 1, Personalized mode 2, Personalized mode 3, Snow mode, Mountain mode, Track mode, Extreme Eco mode, Super Sport mode, Off-road mode, Economy mode, Standard mode, and Anti-motion Sickness mode.

[0043] Taking vehicle driving data related to air conditioning settings as an example, for air conditioning mode settings, you can choose to aggregate the percentage of each air conditioning mode state, including manual or automatic; for internal / external circulation settings, you can choose to aggregate the percentage of each internal / external circulation setting state, including internal circulation, external circulation, and not on; for front air conditioning mode settings, you can choose to aggregate the percentage of each front air conditioning mode state, including on or off; for manual defrost settings, you can choose to aggregate the percentage of each manual defrost setting state, including on or off; and for air conditioning front... For vehicle driving data related to exhaust fan speed mode settings, you can choose to aggregate the percentage of each air conditioning front fan speed mode state, including automatic and manual. For vehicle driving data related to air conditioning fan speed setting (e.g., which fan level), you can choose to aggregate the average value. For vehicle driving data related to air conditioning self-drying settings, you can choose to aggregate the percentage of each self-drying state, including on and off. For vehicle driving data related to passenger side air conditioning temperature settings, you can choose to aggregate the average value of the passenger side air conditioning temperature setting. For vehicle driving data related to driver side air conditioning temperature settings, you can choose to aggregate the average value of the driver side air conditioning temperature setting. For vehicle driving data related to driver side temperature setting mode settings... The data can be aggregated in several ways: First, for driver's side temperature setting settings, the aggregation method can be selected based on the percentage of each state (automatic or manual). Second, for driver's side air conditioning operating mode settings, the aggregation method can be selected based on the percentage of each state (ventilation, heating, or cooling). Third, for front-seat air conditioning operating mode settings, the aggregation method can be selected based on the percentage of each state (ventilation, heating, or cooling). Fourth, for rear-seat air conditioning operating mode settings, the aggregation method can be selected based on the percentage of each state (ventilation, heating, or cooling). Fifth, for air conditioning on / off status settings, the aggregation method can be selected based on the percentage of each state (ventilation, heating, or cooling). The aggregation method for vehicle driving data related to air conditioning switch status settings can be selected from the percentage of each status, including on or off. For vehicle driving data related to driver's side footwell temperature settings, the aggregation method can be selected from the average driver's side footwell temperature. For vehicle driving data related to blower duty cycle settings, the aggregation method can be selected from the average blower duty cycle. For vehicle driving data related to passenger side face temperature settings, the aggregation method can be selected from the average passenger side face temperature. For vehicle driving data related to passenger side footwell temperature settings, the aggregation method can be selected from the average passenger side footwell temperature, etc.

[0044] Taking vehicle driving data related to external environment as an example, for environmental vehicle driving data such as ambient temperature, you can choose to aggregate the data by taking the average ambient temperature; for environmental vehicle driving data such as ambient temperature difference, you can choose to aggregate the data by taking the maximum value minus the minimum value of the ambient temperature difference; for environmental vehicle driving data such as light intensity to the left, you can choose to aggregate the data by taking the average light intensity to the left; for environmental vehicle driving data such as light intensity to the right, you can choose to aggregate the data by taking the average light intensity to the right; for environmental vehicle driving data such as windshield humidity, you can choose to aggregate the data by taking the average windshield humidity, and so on.

[0045] In addition, data such as air conditioning fan speed or light intensity can be aggregated using the last set value within that minute, and this exemplary embodiment does not impose any special limitations on this.

[0046] In an optional embodiment, Figure 2 A flowchart illustrating the sliding window processing method is shown, such as... Figure 2 As shown, the method may include at least the following steps: In step S210, the sliding window length and the sliding window step size are determined to be the first time interval and the second time interval, respectively.

[0047] When the first time interval is determined to be the sliding window length, the first time interval can be 40 minutes, 60 minutes, etc., and this exemplary embodiment does not make any special limitation on it; when the second time interval is determined to be the sliding window step size, the second time interval can be 1 minute, or other durations can be set according to the actual situation, and this exemplary embodiment does not make any special limitation on it.

[0048] In step S220, according to the sliding window length of the first time interval, the sliding window step of the second time interval is slid on the aggregated vehicle driving data to obtain sliding window data, and the sliding window data is input into the preset temperature prediction model.

[0049] Since the temperature prediction model aims to use the characteristics of the past X minutes to predict the temperature in the next Y minutes, the total duration of each sample needs to be greater than or equal to Z (X+Y, 1<=X<=30, 1<=Y<=30, but 30<=X+Y<=60) minutes. This can cover the extreme case where the input is only one minute and the output is the next 30 minutes.

[0050] For example, if a vehicle's journey lasts 40 minutes, based on the duration Z, the aggregated vehicle driving data is divided into segments with a first time interval of 40 minutes and a second time interval of 1 minute. 1) When X<=10, X=1, Y=30; 2) When X>10, X=11, Y=29; X=12, Y=28;…, X=38, Y=2;

[0051] After obtaining the sliding window data, it can be input into the temperature prediction model so that the temperature prediction model can output the driver's face temperature (equivalent to the passenger compartment temperature), driver's foot temperature, passenger's face temperature, and passenger's foot temperature for the next 30 minutes.

[0052] As can be seen, the vehicle driving data input into the temperature prediction model or the processed sliding window data is a variable-length sequence. This variable-length sequence means that the data input into the temperature prediction model can be one minute's worth of data, five minutes' worth of data, or thirty minutes' worth of data; this exemplary embodiment does not impose any special limitations on this. Furthermore, the variable-length sequence can also be reflected in the fact that the one-minute data includes sixty sets of data corresponding to sixty seconds, and the length of each set of data is variable; for example, the first set may include ten data points, the second set may include fifteen data points, etc., this exemplary embodiment does not impose any special limitations on this.

[0053] Based on this, a variable-length sequence is a sequence whose length is not fixed in the time dimension, or a sequence with different numbers of data over the same time length.

[0054] After obtaining the four target temperatures, there are two applications.

[0055] In an optional embodiment, the target temperature is input into a preset energy consumption prediction model to obtain the air conditioning energy consumption for the target time after the vehicle air conditioner is activated.

[0056] These four values ​​are used as input to the second energy consumption prediction model to predict the air conditioning energy consumption for the next 30 minutes. The output is an array of 30 elements, corresponding to the air conditioning energy consumption for the next 30 minutes.

[0057] In an optional embodiment, Figure 3 A flowchart illustrating the method for determining remaining driving range based on a target temperature is shown, such as... Figure 3 As shown, the method may include at least the following steps: in step S310, the air conditioning energy consumption at the target time is averaged to obtain the average energy consumption, and the average energy consumption is converted to obtain the converted average energy consumption.

[0058] Since the model output is an array of length 30, representing the energy consumption over the next 30 minutes, it is usually averaged out and then the battery life is calculated.

[0059] Since the model predicts air conditioning energy consumption in watts, after averaging to obtain the average energy consumption, the average energy consumption can be converted into kilowatt-hours per 100 kilometers by dividing by 100 and multiplying by 60.

[0060] In an optional embodiment, Figure 4 A flowchart illustrating the method for further averaging is shown, such as... Figure 4 As shown, the method may include at least the following steps: In step S410, the navigation duration of the vehicle and the predicted duration of air conditioning energy consumption at the target time are obtained.

[0061] In step S420, when the navigation duration is less than the predicted duration, the air conditioning energy consumption at the target time is truncated based on the navigation duration to obtain the truncated air conditioning energy consumption, and the truncated air conditioning energy consumption is averaged to obtain the average energy consumption.

[0062] If the user's remaining navigation time is less than the predicted 30 minutes, the model will still output the energy consumption for 30 minutes, but this will be truncated. For example, if the remaining navigation time is only 20 minutes, the model will only take the first 20 values ​​out of the 30 output values, and then average these 20 values ​​to obtain the future average energy consumption.

[0063] In step S320, the vehicle's driving energy consumption and remaining battery power are obtained, and the remaining driving range is determined based on the average energy consumption, driving energy consumption and remaining battery power.

[0064] Furthermore, the vehicle obtains the driving energy consumption and battery energy consumption as the overall vehicle driving energy consumption, and obtains the remaining power, for example, by dividing 90% to get the remaining driving range.

[0065] Specifically, the method for determining the remaining driving range is shown in formula (1): (1) in, Range (unit: km). The current remaining electricity (unit: kWh, kilowatt-hours) (In kilowatt-hours), the remaining charge of an electric vehicle is equal to its battery capacity multiplied by its State of Charge (SOC). If an electric vehicle has a battery capacity of 100 kWh and its SOC is 50%, then the current remaining charge is 50 kWh. The energy consumption per 100 kilometers (unit: kWh / 100km) is obtained by summing the average energy consumption and the driving energy consumption.

[0066] For example, when the remaining power kWh, energy consumption kWh / 100km, then the remaining driving range km.

[0067] Therefore, the first use is to transmit the energy consumption of the next 30 minutes to the downstream module on the vehicle for the calculation of the subsequent driving range.

[0068] In an optional embodiment, an adjustment strategy is determined based on the target temperature, and the vehicle air conditioner is controlled according to the adjustment strategy.

[0069] Therefore, the second application is to input four target temperatures for the next 30 minutes into the crew cabin control system to generate different adaptive adjustment strategies.

[0070] In an optional embodiment, the temperature in a specified airflow direction for a specified seat includes one or more of the following: driver's seat face temperature, driver's seat foot temperature, passenger's seat face temperature, and passenger's seat foot temperature; Figure 5 A flowchart illustrating a method for controlling a vehicle's air conditioning system is shown, such as... Figure 5 As shown, the method may include at least the following steps: In step S510, when the temperature difference between the driver's side airflow temperature and the preset temperature is greater than the preset value, the vehicle air conditioning is controlled to restore the driver's side airflow temperature to the preset temperature.

[0071] Since the temperature of the driver's side airflow can be used to characterize the temperature of the driver's cabin, the human body is much more sensitive to temperature fluctuations than to absolute temperature fluctuations (for example, a fluctuation of ±2°C will be noticeably uncomfortable). If the temperature of the driver's cabin is predicted to drop by 2°C in 30 minutes, the system can activate heating in advance or reduce the cooling intensity to allow the temperature to gradually transition to the target value, avoiding sudden temperature changes. Conversely, if the temperature is predicted to rise, the system can reduce the heating intensity or activate weak cooling in advance to maintain a stable temperature.

[0072] In step S520, when the temperature difference between the driver's footwell temperature and the preset temperature is greater than the preset value, the vehicle air conditioner is controlled to restore the driver's footwell temperature to the preset temperature.

[0073] Since the temperature of the driver's footwell can also be used to characterize the temperature of the driver's cabin, the human body is much more sensitive to temperature fluctuations than to absolute temperature (for example, a fluctuation of ±2°C will be noticeably uncomfortable). If the temperature of the driver's cabin is predicted to drop by 2°C in 30 minutes, the system can activate heating in advance or reduce the cooling intensity to allow the temperature to gradually transition to the target value, avoiding sudden temperature changes. Conversely, if the temperature is predicted to rise, the system can reduce the heating intensity or activate weak cooling in advance to maintain a stable temperature.

[0074] In step S530, when the temperature difference between the passenger side airflow temperature and the preset temperature is greater than the preset value, the vehicle air conditioning is controlled to restore the passenger side airflow temperature to the preset temperature.

[0075] Regarding the temperature of the air blowing on the passenger's face, different parts of the human body have different temperature requirements. For example, the face is more sensitive to cold air. Therefore, if it is predicted that the temperature of the air blowing on the passenger's face will rise by 2°C after 30 minutes, the cooling intensity of the air vents can be reduced in advance to avoid the face feeling stuffy due to hot air.

[0076] In step S540, when the temperature difference between the passenger footwell temperature and the preset temperature is greater than the preset value, the vehicle air conditioner is controlled to restore the passenger footwell temperature to the preset temperature.

[0077] Regarding the temperature of the passenger's feet, different parts of the body have different temperature requirements; for example, the feet need a higher temperature in winter. If it is predicted that the temperature of the passenger's feet will drop by 3°C after 30 minutes, the heat output of the foot warmer can be increased in advance to prevent the feet from getting cold.

[0078] In step S550, when the temperature difference between the target temperature and the preset temperature is less than or equal to the preset value, the blower of the vehicle air conditioner is controlled to adjust to the first duty cycle.

[0079] The airflow of the blower is directly related to energy consumption (a high duty cycle means high power). If it is predicted that the temperature in the main cabin will be close to the preset temperature in 30 minutes, such as 24°C, there is no need to maintain a high airflow (such as 80% duty cycle). It can be gradually reduced to 30% in advance, and the temperature can be maintained by "continuous circulation of small airflow" to reduce the energy consumption of the blower.

[0080] In step S560, when the temperature difference between the target temperature and the preset temperature is greater than the preset value, the blower of the vehicle air conditioner is controlled to adjust to the second duty cycle, which is greater than the first duty cycle.

[0081] If it is predicted that the future temperature will deviate from the preset temperature, such as a sudden drop in temperature, it can be pre-adjusted with a medium airflow (e.g., 50% duty cycle) in advance (to avoid the need for high power and high airflow to quickly heat up later) and reduce total energy consumption.

[0082] The vehicle air conditioning control method in this embodiment will be described in detail below with reference to an application scenario.

[0083] Vehicle air conditioning energy consumption prediction is an important research area in the automotive industry and energy management. With the popularization of electric vehicles and rapid technological development, this field has attracted increasing attention and research. Vehicle air conditioning energy consumption prediction helps optimize compressor control strategies, reduce ineffective energy consumption, improve energy efficiency, and extend vehicle driving range.

[0084] The related technology provides a single-vehicle air conditioning energy consumption prediction method and device based on operating condition identification. First, the current ambient temperature information is obtained, and the average speed, average acceleration and average idling time of the current operating condition are calculated to obtain the characteristic information of the current operating condition. The cold start energy consumption f1 of the current operating condition is obtained; the air conditioning energy consumption f2 of the idling segment is obtained; the air conditioning energy consumption f3 of the current operating condition during the motion segment is obtained; finally, the temperature correction factor k is obtained; and the comprehensive energy consumption of the air conditioning under the current operating condition is obtained by weighting f1, f2 and f3.

[0085] This approach relies on predefined operating condition classifications (such as average speed and idling duration), making it difficult to cover complex and ever-changing real-world scenarios, such as sudden traffic congestion and extreme weather. Furthermore, the large amount of manually labeled operating condition data required for idling segment segmentation results in excessively high labor costs.

[0086] The related technology also provides another chassis dynamometer that can simulate the actual operating conditions of a car, and places a whole vehicle equipped with an air conditioning system to be tested on the chassis dynamometer; the chassis dynamometer is used to measure the power absorption value when the whole vehicle is running with the air conditioning system on and when the whole vehicle is running with the air conditioning system off; the difference between the two power absorption values ​​measured by the chassis dynamometer is calculated, and the absolute value of the difference is the energy consumption of the air conditioning system.

[0087] This method is based on laboratory fixed-condition testing, which cannot reflect the impact of dynamic driving behavior on energy consumption, and it also relies on expensive laboratory equipment.

[0088] Therefore, when using related technologies to predict the energy consumption of vehicle air conditioning, the dynamic adaptability is not high, and it is difficult to cover complex and ever-changing real-world scenarios; the data utilization efficiency is low, requiring manual annotation or relying on laboratory equipment.

[0089] Figure 6 A flowchart illustrating the vehicle air conditioning control method in an application scenario is shown, such as... Figure 6 As shown, in step S610, the input is a second-level signal.

[0090] Enter vehicle driving data related to the vehicle's air conditioning system.

[0091] In step S620, minute-level features are obtained by aggregation.

[0092] The vehicle driving data is divided into segments to obtain segmented vehicle driving data, and then the segmented vehicle driving data is aggregated to obtain aggregated vehicle driving data.

[0093] The aggregated vehicle driving data is processed by a sliding window to obtain sliding window data, which is then input into a preset temperature prediction model.

[0094] In step S630, the neural network makes a prediction.

[0095] The neural network can be any time-series model, such as LTSM (Long Short-Term Memory) network, Transformer model, etc., and this exemplary embodiment does not impose any special limitations on it.

[0096] The sliding window data is input into Neural Network 1, which outputs the driver's face temperature (equivalent to the passenger compartment temperature), driver's foot temperature, passenger's face temperature, and passenger's foot temperature for the next 30 minutes.

[0097] In step S640, the neural network performs a second prediction.

[0098] The second neural network can be any time series model, such as LTSM, Transformer model, etc., and this exemplary embodiment does not impose any special limitations on it.

[0099] These four values ​​are used as input to the second neural network to predict the air conditioner energy consumption for the next 30 minutes. The output is an array of 30 elements, corresponding to the air conditioner energy consumption for the next 30 minutes.

[0100] Furthermore, the energy consumption for the next 30 minutes is fed to the downstream modules on the vehicle for calculation of the subsequent driving range.

[0101] In step S650, the crew cabin control system takes control.

[0102] The four target temperatures for the next 30 minutes are input into the crew cabin control system to generate different adaptive adjustment strategies for control.

[0103] In the exemplary embodiments of this disclosure, a more dynamic and adaptive effect can be achieved even in the energy consumption prediction of vehicle air conditioning: the neural network can automatically learn the nonlinear coupling relationship between the environment and driving behavior (such as the impact of wind temperature transients on energy consumption during rapid acceleration) and adapt to new operating conditions that have not been seen before in real time.

[0104] Furthermore, it boasts superior data utilization efficiency and greater interpretability: trained on a large amount of data rather than targeting a specific vehicle model, the model exhibits strong generalization capabilities. Utilizing two neural networks for joint learning—first predicting wind temperature, then predicting energy consumption—further enhances interpretability.

[0105] In addition, it has wider applicability: the output of the first model can not only be input into the second model for energy consumption prediction, but also into the crew cabin control system for adjusting factors such as air volume, temperature, and heating / cooling intensity.

[0106] Furthermore, in an exemplary embodiment of this disclosure, a first type of vehicle air conditioning control device is also provided. Figure 7 A schematic diagram of the vehicle air conditioning control device is shown, such as... Figure 7 As shown, the vehicle air conditioning control device 700 may include: a data acquisition module 710 and a temperature prediction module 720. Wherein: The data acquisition module 710 is configured to acquire vehicle driving data related to the vehicle air conditioning system. The temperature prediction module 720 is configured to input the vehicle driving data into a preset temperature prediction model to obtain the target temperature at a target time after the vehicle air conditioner is activated. The target temperature is the temperature at a specified seat in a specified airflow direction, and the vehicle driving data is a variable-length sequence.

[0107] In some embodiments of this disclosure, the vehicle air conditioning control device 700 is further configured to: Multiple segments of vehicle driving data within a preset unit time period are determined, and the vehicle driving data is aggregated based on the preset unit time period to obtain aggregated vehicle driving data.

[0108] In some embodiments of this disclosure, the temperature prediction module 720 is configured to: The sliding window length and sliding window step size are determined to be the first time interval and the second time interval, respectively. Based on the sliding window length of the first time interval, the sliding window step of the second time interval is slid across the aggregated vehicle driving data to obtain sliding window data, and the sliding window data is input into a preset temperature prediction model.

[0109] In some embodiments of this disclosure, the vehicle air conditioning control device 700 is further configured to: The target temperature is input into a preset energy consumption prediction model to obtain the air conditioning energy consumption for the target time after the vehicle air conditioner is activated.

[0110] In some embodiments of this disclosure, the vehicle air conditioning control device 700 is further configured to: The air conditioning energy consumption at the target time is averaged to obtain the average energy consumption, and the average energy consumption is then converted to obtain the converted average energy consumption. The vehicle's driving energy consumption and remaining battery power are obtained, and the remaining driving range is determined based on the average energy consumption, the driving energy consumption, and the remaining battery power.

[0111] In some embodiments of this disclosure, the vehicle air conditioning control device 700 is further configured to: Obtain the navigation duration of the vehicle and the predicted duration of air conditioning energy consumption at the target time; When the navigation duration is less than the predicted duration, the air conditioning energy consumption at the target time is truncated based on the navigation duration to obtain the truncated air conditioning energy consumption, and the truncated air conditioning energy consumption is averaged to obtain the average energy consumption.

[0112] In some embodiments of this disclosure, the vehicle air conditioning control device 700 is further configured to: The adjustment strategy is determined based on the target temperature, and the vehicle air conditioner is controlled according to the adjustment strategy.

[0113] In some embodiments of this disclosure, the temperature in the specified airflow direction of the specified seat includes one or more of the following: driver's seat face temperature, driver's seat foot temperature, passenger's seat face temperature, and passenger's seat foot temperature; The vehicle air conditioning control device 700 is also configured to: When the temperature difference between the driver's side airflow temperature and the preset temperature is greater than the preset value, the vehicle air conditioning is controlled to restore the driver's side airflow temperature to the preset temperature. When the temperature difference between the driver's foot warmer temperature and the preset temperature is greater than the preset value, the vehicle air conditioner is controlled to restore the driver's foot warmer temperature to the preset temperature. When the temperature difference between the passenger-side airflow temperature and the preset temperature is greater than the preset value, the vehicle air conditioning is controlled to restore the passenger-side airflow temperature to the preset temperature. When the temperature difference between the passenger-side footwell temperature and the preset temperature is greater than the preset value, the vehicle air conditioning is controlled to restore the passenger-side footwell temperature to the preset temperature. When the temperature difference between the target temperature and the preset temperature is less than or equal to the preset value, the blower of the vehicle air conditioner is controlled to adjust to the first duty cycle. When the temperature difference between the target temperature and the preset temperature is greater than the preset value, the blower of the vehicle air conditioner is controlled to adjust to the second duty cycle, which is greater than the first duty cycle.

[0114] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0115] This disclosure also provides an electronic device, including: processor; Memory used to store processor-executable instructions; The processor is configured to execute executable instructions stored in the memory to implement any of the vehicle air conditioning control methods provided in this disclosure.

[0116] The electronic device may be a vehicle, a mobile terminal, or a standalone computing device, etc., and this exemplary embodiment does not impose any special limitations on it.

[0117] This disclosure also provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the steps of the vehicle air conditioning control method provided in this disclosure.

[0118] Figure 8 This is a block diagram illustrating another vehicle air conditioning control device 800 according to an exemplary embodiment. For example, device 800 may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.

[0119] Reference Figure 8 The device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input / output interface 812, a sensor component 814, and a communication component 816.

[0120] Processing component 802 typically controls the overall operation of device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0121] Memory 804 is configured to store various types of data to support the operation of device 800. Examples of this data include instructions for any application or method operating on device 800, contact data, phonebook data, messages, pictures, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0122] Power supply component 806 provides power to various components of device 800. Power supply component 806 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power to device 800.

[0123] Multimedia component 808 includes a screen that provides an output interface between the device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When the device 800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0124] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0125] Input / output interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0126] Sensor assembly 814 includes one or more sensors for providing status assessments of various aspects of device 800. For example, sensor assembly 814 may detect the on / off state of device 800, the relative positioning of components such as the display and keypad of device 800, changes in the position of device 800 or a component of device 800, the presence or absence of user contact with device 800, the orientation or acceleration / deceleration of device 800, and temperature changes of device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.

[0127] Communication component 816 is configured to facilitate wired or wireless communication between device 800 and other devices. Device 800 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0128] In an exemplary embodiment, the apparatus 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0129] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, which can be executed by a processor 820 of the device 800 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0130] The aforementioned device can be a standalone electronic device or a part of a standalone electronic device. For example, in one embodiment, the device can be an integrated circuit (IC) or a chip, wherein the integrated circuit can be a single IC or a collection of multiple ICs. The chip can include, but is not limited to, the following types: GPU (Graphics Processing Unit), CPU (Central Processing Unit), FPGA (Field Programmable Gate Array), DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), and SoC (System on Chip). The aforementioned integrated circuit or chip can be used to execute executable instructions (or code) to implement the aforementioned vehicle air conditioning control method. The executable instructions can be stored in the integrated circuit or chip or obtained from other devices or equipment. For example, the integrated circuit or chip includes a processor, memory, and an interface for communicating with other devices. The executable instructions can be stored in the memory, and when the executable instructions are executed by the processor, the above-mentioned vehicle air conditioning control method is implemented; or, the integrated circuit or chip can receive the executable instructions through the interface and transmit them to the processor for execution to implement the above-mentioned method.

[0131] In another exemplary embodiment, a computer program product is also provided, the computer program product comprising a computer program executable by a programmable device, the computer program having a code portion for performing the above-described vehicle air conditioning control method when executed by the programmable device.

[0132] Figure 9 This is a block diagram illustrating another vehicle air conditioning control device 900 according to an exemplary embodiment. For example, device 900 may be provided as a server. (Refer to...) Figure 9 The device 900 includes a processing component 922, which further includes one or more processors, and memory resources represented by a memory 932 for storing instructions, such as application programs, that can be executed by the processing component 922. The application programs stored in the memory 932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 922 is configured to execute instructions to perform the aforementioned vehicle air conditioning control method.

[0133] The device 900 may also include a power supply component 926 configured to perform power management of the device 900, a wired or wireless network interface 950 configured to connect the device 900 to a network, and an input / output interface 958. The device 900 can operate on an operating system stored in memory 932.

[0134] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of this disclosure. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0135] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for controlling a vehicle air conditioner, characterized in that, include: Obtain vehicle driving data related to the vehicle's air conditioning system; The vehicle driving data is input into a preset temperature prediction model to obtain the target temperature at a target time after the vehicle air conditioner is activated. The target temperature is the temperature at a specified seat in a specified airflow direction. The vehicle driving data is a variable-length sequence.

2. The vehicle air conditioning control method according to claim 1, characterized in that, The method further includes: Multiple segments of vehicle driving data are determined within a preset unit time period, and the vehicle driving data is aggregated based on the preset unit time period to obtain aggregated vehicle driving data.

3. The energy consumption prediction method for vehicle air conditioning according to claim 2, characterized in that, The step of inputting the vehicle driving data into a preset temperature prediction model includes: The sliding window length and sliding window step size are determined to be the first time interval and the second time interval, respectively. Based on the sliding window length of the first time interval, the sliding window step of the second time interval is slid across the aggregated vehicle driving data to obtain sliding window data, and the sliding window data is input into a preset temperature prediction model.

4. The vehicle air conditioning control method according to claim 1, characterized in that, The method further includes: The target temperature is input into a preset energy consumption prediction model to obtain the air conditioning energy consumption for the target time after the vehicle air conditioner is activated.

5. The vehicle air conditioning control method according to claim 4, characterized in that, The method further includes: The air conditioning energy consumption at the target time is averaged to obtain the average energy consumption, and the average energy consumption is then converted to obtain the converted average energy consumption. The vehicle's driving energy consumption and remaining battery power are obtained, and the remaining driving range is determined based on the average energy consumption, the driving energy consumption, and the remaining battery power.

6. The vehicle air conditioning control method according to claim 5, characterized in that, The process of averaging the air conditioning energy consumption at the target time to obtain the average energy consumption includes: Obtain the navigation duration of the vehicle and the predicted duration of air conditioning energy consumption at the target time; When the navigation duration is less than the predicted duration, the air conditioning energy consumption at the target time is truncated based on the navigation duration to obtain the truncated air conditioning energy consumption, and the truncated air conditioning energy consumption is averaged to obtain the average energy consumption.

7. The vehicle air conditioning control method according to claim 1, characterized in that, The method further includes: The adjustment strategy is determined based on the target temperature, and the vehicle air conditioner is controlled according to the adjustment strategy.

8. The vehicle air conditioning control method according to claim 7, characterized in that, The temperature in the specified airflow direction of the specified seat includes one or more of the following: driver's seat face temperature, driver's seat foot temperature, passenger's seat face temperature, and passenger's seat foot temperature; The control of the vehicle air conditioner according to the adjustment strategy includes any of the following methods: When the temperature difference between the driver's side airflow temperature and the preset temperature is greater than the preset value, the vehicle air conditioning is controlled to restore the driver's side airflow temperature to the preset temperature. When the temperature difference between the driver's foot warmer temperature and the preset temperature is greater than the preset value, the vehicle air conditioner is controlled to restore the driver's foot warmer temperature to the preset temperature. When the temperature difference between the passenger-side airflow temperature and the preset temperature is greater than the preset value, the vehicle air conditioning is controlled to restore the passenger-side airflow temperature to the preset temperature. When the temperature difference between the passenger-side footwell temperature and the preset temperature is greater than the preset value, the vehicle air conditioning is controlled to restore the passenger-side footwell temperature to the preset temperature. When the temperature difference between the target temperature and the preset temperature is less than or equal to the preset value, the blower of the vehicle air conditioner is controlled to adjust to the first duty cycle. When the temperature difference between the target temperature and the preset temperature is greater than the preset value, the blower of the vehicle air conditioner is controlled to adjust to the second duty cycle, which is greater than the first duty cycle.

9. A vehicle air conditioning control device, characterized in that, include: The data acquisition module is configured to acquire vehicle driving data related to the vehicle's air conditioning system. The temperature prediction module is configured to input the vehicle driving data into a preset temperature prediction model to obtain the target temperature at a target time after the vehicle air conditioner is activated. The target temperature is the temperature at a specified seat in a specified airflow direction, and the vehicle driving data is a variable-length sequence.

10. The vehicle air conditioning control device according to claim 9, characterized in that, The vehicle air conditioning control device is also configured to: The target temperature is input into a preset energy consumption prediction model to obtain the air conditioning energy consumption for the target time after the vehicle air conditioner is activated.

11. The vehicle air conditioning control device according to claim 9, characterized in that, The vehicle air conditioning control device is also configured to: The adjustment strategy is determined based on the target temperature, and the vehicle air conditioner is controlled according to the adjustment strategy.

12. The vehicle air conditioning control device according to claim 11, characterized in that, The temperature in the specified airflow direction of the specified seat includes one or more of the following: driver's seat face temperature, driver's seat foot temperature, passenger's seat face temperature, and passenger's seat foot temperature; The vehicle air conditioning control device is also configured to: When the temperature difference between the driver's side airflow temperature and the preset temperature is greater than the preset value, the vehicle air conditioning is controlled to restore the driver's side airflow temperature to the preset temperature. When the temperature difference between the driver's foot warmer temperature and the preset temperature is greater than the preset value, the vehicle air conditioner is controlled to restore the driver's foot warmer temperature to the preset temperature. When the temperature difference between the passenger-side airflow temperature and the preset temperature is greater than the preset value, the vehicle air conditioning is controlled to restore the passenger-side airflow temperature to the preset temperature. When the temperature difference between the passenger-side footwell temperature and the preset temperature is greater than the preset value, the vehicle air conditioning is controlled to restore the passenger-side footwell temperature to the preset temperature. When the temperature difference between the target temperature and the preset temperature is less than or equal to the preset value, the blower of the vehicle air conditioner is controlled to adjust to the first duty cycle. When the temperature difference between the target temperature and the preset temperature is greater than the preset value, the blower of the vehicle air conditioner is controlled to adjust to the second duty cycle, which is greater than the first duty cycle.

13. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to execute executable instructions stored in the memory to implement the steps of the method according to any one of claims 1 to 8.

14. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When executed by a processor, the program instructions implement the steps of the method described in any one of claims 1 to 8.

Images