Vehicle air conditioning control method, device, equipment, medium and program product

Abstract

The application provides an in-vehicle air conditioner control method, device, equipment, medium and program product, and relates to the technical field of intelligent automobiles. The method comprises the following steps: acquiring real-time data; the real-time data comprises at least one of in-vehicle environment data, out-of-vehicle environment data and navigation data of a vehicle; performing data processing on the real-time data to obtain target data; inputting the target data into an identification model, identifying the target data through the identification model, and obtaining adjustment parameters for an in-vehicle air conditioner output by the identification model; and controlling operation of the in-vehicle air conditioner based on the adjustment parameters. The in-vehicle air conditioner control method, device, equipment, medium and program product provided by the application can improve the intelligent degree of in-vehicle air conditioner control.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent automobiles, and in particular to an in-vehicle air conditioner control method, device, equipment, medium and program product. BACKGROUND

[0002] In-vehicle air conditioner intelligent control is one of the important manifestations of the development of modern automobile intelligence, which uses advanced sensors, controllers and actuators and other technologies to realize accurate and automatic adjustment of the in-vehicle air conditioner system, and provides a comfortable in-vehicle environment for passengers. There are many ways to control the in-vehicle air conditioner, but the accuracy of the current in-vehicle air conditioner control method is still defective, and the intelligent degree is not high enough.

[0003] Therefore, it is urgent to provide a new in-vehicle air conditioner intelligent control method. SUMMARY

[0004] The present application provides an in-vehicle air conditioner control method, device, equipment, medium and program product to solve the defect of low intelligent degree of in-vehicle air conditioner control in the prior art and improve the intelligent degree of in-vehicle air conditioner control.

[0005] In a first aspect, the present application provides an in-vehicle air conditioner control method, comprising:

[0006] Obtaining real-time data; the real-time data includes at least one of in-vehicle environment data, out-of-vehicle environment data and navigation data of the vehicle;

[0007] Data processing the real-time data to obtain target data;

[0008] Inputting the target data into a recognition model, identifying the target data through the recognition model, and obtaining the adjustment parameters of the in-vehicle air conditioner output by the recognition model;

[0009] Based on the adjustment parameters, controlling the operation of the in-vehicle air conditioner.

[0010] Optionally, the in-vehicle environment data includes at least one of in-vehicle temperature data and in-vehicle humidity data; the out-of-vehicle environment data includes at least one of out-of-vehicle temperature data, out-of-vehicle humidity data and out-of-vehicle weather data; and the navigation data includes at least one of road condition information of a road section where the vehicle is located, driving destination information, driving time length and altitude information.

[0011] Optionally, the data processing the real-time data to obtain target data comprises:

[0012] Spacetime calibration is performed on the real-time data to obtain calibrated data;

[0013] verify whether the calibration data is accurate based on a preset verification rule; the preset verification rule is established based on a logical relationship among the in-vehicle environment data, the out-of-vehicle environment data and the navigation data;

[0014] If the calibration data is accurate, feature extraction is performed on the calibration data to obtain the target data.

[0015] If the calibration data is not accurate, data correction is performed on the calibration data, and feature extraction is performed on the corrected calibration data to obtain the target data; the data correction mode includes at least one of a cubic spline interpolation repair, Kalman filter dynamic compensation and data replacement.

[0016] Optionally, the identification model is obtained by training in the following manner:

[0017] Obtain a training sample set; the training sample set includes a plurality of groups of historical target data and historical adjustment parameters corresponding to each group of historical target data.

[0018] Train an initial model through the training sample set to obtain the identification model.

[0019] Optionally, the in-vehicle air conditioner control method further comprises:

[0020] Obtain a first number of times that the in-vehicle air conditioner is controlled based on the adjustment parameter within a preset time period, and a second number of times that the in-vehicle air conditioner is adjusted by a user within the preset time period.

[0021] If the ratio of the second number of times to the first number of times is greater than a preset threshold, the parameters of the identification model are optimized.

[0022] Optionally, the trigger condition for obtaining the first number of times and the second number of times is any one of the following conditions:

[0023] Every interval of a preset time length;

[0024] The cumulative distance traveled by the vehicle reaches a preset distance, and the cumulative distance traveled is the distance traveled since the last time the identification model was optimized.

[0025] An instruction to optimize the identification model is received.

[0026] In a second aspect, the application also provides an in-vehicle air conditioner control device, comprising:

[0027] A collection module for obtaining real-time data; the real-time data includes at least one of in-vehicle environment data, out-of-vehicle environment data and vehicle navigation data;

[0028] A verification module for performing data processing on the real-time data to obtain target data.

[0029] an identification module, configured to input the target data into an identification model, and identify the target data through the identification model to obtain an adjustment parameter for the in-vehicle air conditioner output by the identification model;

[0030] a control module, configured to control operation of the in-vehicle air conditioner based on the adjustment parameter.

[0031] In a third aspect, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the method according to the first aspect when executing the computer program.

[0032] In a fourth aspect, the present application also provides a non-transitory computer-readable storage medium, which stores a computer program executable by a processor to implement the method according to the first aspect.

[0033] In a fifth aspect, the present application also provides a computer program product, including a computer program executable by a processor to implement the method according to the first aspect.

[0034] The in-vehicle air conditioner control method, device, equipment, medium and program product provided by the present application can obtain the adjustment parameter for the in-vehicle air conditioner by collecting real-time data such as in-vehicle environment data, out-of-vehicle environment data and navigation data, and then processing the real-time data to obtain target data, so that the operation of the in-vehicle air conditioner can be automatically controlled without manual intervention of the user, the accuracy and intelligent degree of in-vehicle air conditioner adjustment can be improved, the air conditioner adjustment can be more in line with the actual situation, the passenger comfort can be improved, and the user experience can be enhanced. BRIEF DESCRIPTION OF DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the present application or prior art, the following will briefly introduce the drawings needed in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0036] Figure 1 is a flowchart of the in-vehicle air conditioner control method provided by the embodiments of the present application;

[0037] Figure 2 is a structural schematic diagram of the in-vehicle air conditioner control device provided by the embodiments of the present application;

[0038] Figure 3 is a structural schematic diagram of the electronic device provided by the embodiments of the present application. DETAILED DESCRIPTION

[0039] The technical solutions and advantages of the present application will be described clearly and completely below with reference to the drawings in the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0040] The present application provides a vehicle air conditioner control method, and the execution subject of the method can be an electronic device, for example, a controller. The execution subject of the method is taken as an example for description. Figure 1 is a flowchart of the vehicle air conditioner control method provided by the embodiments of the present application. Referring to Figure 1 , the method can include:

[0041] Step 110, acquiring real-time data; the real-time data includes at least one of vehicle interior environment data, vehicle exterior environment data and vehicle navigation data;

[0042] Step 120, data processing on the real-time data to obtain target data;

[0043] Step 130, inputting the target data into a recognition model, identifying the target data through the recognition model to obtain adjustment parameters for the vehicle air conditioner output by the recognition model;

[0044] Step 140, controlling the operation of the vehicle air conditioner based on the adjustment parameters.

[0045] Considering that the prior art in the control of the vehicle air conditioner is not comprehensive enough in data collection, and cannot comprehensively consider the influence of multi-source data on the demand of the vehicle interior environment, resulting in deviation between the air conditioner adjustment and the actual demand. In step 110, the controller can acquire real-time data. The real-time data can specifically include at least one of vehicle interior environment data, vehicle exterior environment data and vehicle navigation data. Among them, the vehicle interior environment data is the environment data inside the vehicle, such as the temperature inside the vehicle. The vehicle exterior environment data is the environment data outside the vehicle, such as the temperature outside the vehicle. The navigation data can reflect the travel information of the vehicle, and can assist in determining the adjustment parameters of the air conditioner to a certain extent.

[0046] In step 120, the controller can perform data processing on the real-time data to obtain target data. The real-time data may have deviation, and by performing data processing (such as cross-validation) on the real-time data, abnormal data can be eliminated or repaired, and more accurate target data can be obtained.

[0047] In step 130, the controller can input the target data into the identification model, identify the target data through the identification model, and obtain the adjustment parameter for the in-vehicle air conditioner. The identification model can be a deep learning model. The adjustment parameter for the in-vehicle air conditioner can include temperature, wind speed, wind volume, wind direction, mode, and the like. When the identification model determines that the vehicle is in a congested road section and the outside temperature is high and the humidity is high according to the current target data, the adjustment parameter of reducing the temperature setting value, increasing the wind speed and the wind volume, and strengthening the cooling and dehumidifying function can be output, so that the in-vehicle environment is kept comfortable; when the vehicle travels to the vicinity of the destination, the model can adjust the adjustment parameter of the air conditioner in advance in combination with the navigation data, so that the in-vehicle environment reaches a more suitable state when reaching the destination.

[0048] In step 140, the controller can control the in-vehicle air conditioner to be automatically and intelligently adjusted according to the adjustment parameter output by the identification model.

[0049] The in-vehicle air conditioner control method provided by the embodiments of the present application can collect real-time data such as in-vehicle environment data, outside environment data, and navigation data, then process the real-time data to obtain target data, input the target data into an identification model, and obtain the adjustment parameter for the in-vehicle air conditioner, so that the operation of the in-vehicle air conditioner can be automatically controlled without manual intervention, the accuracy and the intelligent degree of the in-vehicle air conditioner adjustment can be improved, the air conditioner adjustment can be more in line with the actual situation, the passenger comfort can be improved, and the user experience can be enhanced.

[0050] In some embodiments, the in-vehicle environment data includes at least one of in-vehicle temperature data and in-vehicle humidity data; the outside environment data includes at least one of outside temperature data, outside humidity data, and outside weather data; and the navigation data includes at least one of road condition information of a road section where the vehicle is located, travel destination information, travel time length, and altitude information. The real-time data can further include air pressure data.

[0051] Specifically, the in-vehicle temperature data and the in-vehicle humidity data can be collected by installing high-precision temperature and humidity sensors at different positions in the vehicle (such as the driver's seat, the front passenger's seat, the rear seats, and the like). Collecting the temperature and humidity data of each area in the vehicle in real time can comprehensively reflect the temperature and humidity conditions of the in-vehicle environment.

[0052] The outside weather data can include rainfall data, weather conditions, and the like. The outside temperature data and the outside humidity data can be collected by installing temperature and humidity sensors at appropriate positions outside the vehicle (such as the rearview mirror, the roof, and the like). The rainfall data can be collected by installing a rainfall sensor to monitor the outside rainfall information in real time. The weather conditions can be collected by obtaining weather forecasts and the like. The temperature and humidity of the outside environment and the weather conditions will affect the in-vehicle environment demand, such as the generation of fog in the vehicle in rainy days, which requires the air conditioner to perform defogging and the like.

[0053] The controller can obtain real-time navigation data, including road condition information of a road segment where the vehicle is located, travel destination information, and travel duration, etc., by connecting with a vehicle navigation system. Different travel information (such as long-distance driving, short-distance commuting, driving to a high-temperature or high-humidity area, etc.) has different requirements for the air conditioner in the vehicle. For example, passengers have higher requirements for the comfort of the vehicle interior during long-distance driving, and the requirements can change over time, and the air conditioner needs stronger cooling capacity when driving to a high-temperature area.

[0054] The vehicle interior air conditioner control method provided by the embodiments of the present application comprehensively perceives the internal and external environments and travel information of the vehicle by collecting multi-source data such as vehicle interior environment data, vehicle exterior environment data, and navigation data, compared with the prior art which only relies on part of the environment data, can more accurately grasp the vehicle interior environment demand, improve the accuracy and intelligent degree of the vehicle interior air conditioner adjustment, make the air conditioner adjustment more in line with the actual situation, and improve the passenger comfort.

[0055] In some embodiments, the real-time data is processed to obtain target data, including: performing space-time calibration on the real-time data to obtain calibrated data; verifying whether the calibrated data is accurate based on a preset verification rule; the preset verification rule is established based on the logical relationship of the vehicle interior environment data, the vehicle exterior environment data, and the navigation data; if the calibrated data is accurate, performing feature extraction on the calibrated data to obtain the target data; if the calibrated data is inaccurate, performing data correction on the calibrated data, and performing feature extraction on the corrected calibrated data to obtain the target data; the data correction method includes at least one of cubic spline interpolation repair, Kalman filter dynamic compensation, and data replacement.

[0056] The controller can establish a unified time stamp and space coordinate system, perform space-time calibration on the vehicle interior environment data (0.1℃ / 0.5%RH precision), the vehicle exterior environment data (0.2℃ / 1%RH precision, rainfall classification 0-5 levels), and the navigation data (GPS positioning accuracy ±2.5m, time stamp synchronization error <10ms) to eliminate the difference in sampling frequency of different sensors (such as temperature and humidity sensor 10Hz vs. rainfall sensor 5Hz), and obtain calibrated data.

[0057] The controller can verify whether the calibrated data is accurate based on a preset verification rule, and perform data correction on the calibrated data if the calibrated data is inaccurate. Specifically, the preset verification rule is established based on the logical relationship of the vehicle interior environment data, the vehicle exterior environment data, and the navigation data. The preset verification rule and the corresponding data correction example are as follows:

[0058]

[0059] Table 1 Data verification logic example

[0060] The controller can also determine whether the calibration data is accurate through the Bayesian inference engine and data confidence evaluation. When the calibration data is inaccurate, data correction is performed according to different situations. For example, single-point anomalies can be repaired by a cubic spline interpolation method (such as a sudden change in rainfall data at a certain time), and systematic deviations can be compensated dynamically by establishing a sensor drift model through Kalman filtering (experiments have shown that the humidity measurement error can be reduced from ±3% RH to ±1.2% RH).

[0061] If the calibration data is accurate, the controller can directly extract features from the calibration data to obtain target data; if the calibration data is inaccurate, the controller can correct the calibration data and extract features from the corrected calibration data to obtain target data. Specifically, feature extraction can be performed through principal component analysis, linear discriminant analysis, K-Means clustering, etc. Environmental features include, for example, an indoor-outdoor temperature difference ΔT (|T indoor-T outdoor |>5℃ triggers a warning), a humidity gradient ΔH (H indoor-H outdoor >15% RH), and a rain intensity index (calculated based on a pulse signal of a rainfall sensor). Navigation features include, for example, an altitude change rate (combined with navigation data), a remaining range (associated with air conditioning energy consumption prediction), and a congestion index (from real-time traffic data). After considering the influence of different features on the indoor air conditioner, an adaptive particle swarm optimization algorithm (APSO) can be used to dynamically adjust the weights of each data source, for example, the weight of rainfall data is increased to 0.4 in rainy weather, and the weight of outdoor temperature is increased to 0.35 in high-temperature warning.

[0062] The controller can also integrate and analyze the collected indoor environmental data, outdoor environmental data, and navigation data. By establishing a data correlation model, different sources of data are cross-verified. For example, when the outdoor rainfall increases, it is determined whether the outdoor temperature and humidity data indicate that the vehicle glass may fog up, and the navigation data is referenced to determine the driving direction and destination environment information, and the indoor air conditioner is adjusted comprehensively. If the vehicle is driving towards a high-humidity area and it has started to rain outside, the air conditioning mode can be adjusted in advance to enhance the dehumidification function and prevent the vehicle from fogging up and maintaining a comfortable humidity. Through data cross-verification, the data accuracy and reliability are improved, and a more solid data foundation is provided for subsequent control strategy formulation.

[0063] The indoor air conditioner control method provided by the embodiments of the present application effectively eliminates possible data errors or anomalies of a single data source through integration and verification of multi-source real-time data, improves the quality and reliability of real-time data, and determines the adjustment parameters of the air conditioner based on more reliable data, thereby enhancing the accuracy and stability of the indoor air conditioner control strategy and reducing the air conditioner misadjustment caused by inaccurate data.

[0064] In some embodiments, the identification model is trained by: obtaining a training sample set, the training sample set including a plurality of sets of historical target data and historical adjustment parameters corresponding to each set of historical target data; and training an initial model based on the training sample set to obtain the identification model.

[0065] The controller can obtain a plurality of sets of historical target data and historical adjustment parameters of the in-vehicle air conditioner corresponding to each set of historical target data as a training sample set, and train an initial model based on the training sample set to obtain the identification model. Specifically, the controller can input the verified historical target data as training data into the model. The model uses a deep learning algorithm, such as a neural network model, to continuously optimize the model parameters through learning and training of a large amount of sample data. During the training process, the model learns the multi-source data corresponding to different driving scenarios (such as high-temperature sunny days, rainy days, long-distance driving, and congested road sections) and the corresponding optimal air conditioner adjustment parameters, so that the model can accurately identify the mapping relationship between the multi-source data features and the air conditioner adjustment requirements in different scenarios, thereby training a model that can accurately adapt to various complex actual driving scenarios.

[0066] The in-vehicle air conditioner control method provided by the embodiments of the present application uses comprehensive and rich target data to train the identification model, so that the identification model can learn the relationship between data features and air conditioner adjustment requirements in various complex driving scenarios. Compared with the single training data of the prior art model, the identification model trained by the present method has stronger adaptability to actual driving scenarios, can more accurately output appropriate air conditioner adjustment parameters based on real-time data, achieve more efficient and intelligent air conditioner adjustment, further improve the comfort of the in-vehicle environment, and at the same time, under the premise of meeting the comfort, can optimize the air conditioner energy consumption to achieve the purpose of energy saving.

[0067] In some embodiments, the in-vehicle air conditioner control method further includes: obtaining a first number of times of controlling the in-vehicle air conditioner based on the adjustment parameters in a preset time period and a second number of times of adjusting the in-vehicle air conditioner by a user in the preset time period; and optimizing the parameters of the identification model if a ratio of the second number of times to the first number of times is greater than a preset threshold.

[0068] The controller can obtain a first number of times of controlling the in-vehicle air conditioner based on the adjustment parameters in a preset time period and a second number of times of adjusting the in-vehicle air conditioner by a user in the preset time period. When the automatic adjustment of the air conditioner is not in place, the user will manually adjust the in-vehicle air conditioner, and therefore the second number of times can reflect the user's satisfaction with the automatic adjustment of the in-vehicle air conditioner. If the ratio of the second number of times to the first number of times is greater than a preset threshold, it indicates that the effect of the automatic adjustment of the in-vehicle air conditioner does not meet the customer's expectations, and the controller can further optimize the parameters of the identification model.

[0069] The vehicle air conditioner control method provided in the embodiments of the present application can further improve the effect of the identification model, improve the accuracy of the vehicle air conditioner adjustment, and improve the user experience by optimizing the parameters of the identification model.

[0070] In some embodiments, the trigger conditions for the first number of times and the second number of times are any one of the following conditions: every interval of a preset time length; the cumulative driving distance of the vehicle reaches a preset distance, and the cumulative driving distance is the driving distance accumulated since the last time the identification model is optimized; an instruction to optimize the identification model is received.

[0071] The vehicle air conditioner control method provided in the embodiments of the present application can further improve the effect of the identification model, improve the accuracy of the vehicle air conditioner adjustment, and improve the user experience by triggering the optimization logic of the identification model when the trigger condition is met.

[0072] The vehicle air conditioner control device provided in the embodiments of the present application is described below, and the vehicle air conditioner control device described below can be referred to in correspondence with the vehicle air conditioner control method described above.

[0073] Figure 2 FIG. 1 is a structural schematic diagram of the vehicle air conditioner control device provided in the embodiments of the present application. Referring to FIG. 1, Figure 2 The vehicle air conditioner control device provided in the embodiments of the present application can include:

[0074] The collection module 210 is configured to acquire real-time data, and the real-time data includes at least one of in-vehicle environment data, out-of-vehicle environment data, and navigation data of the vehicle.

[0075] The verification module 220 is configured to perform data processing on the real-time data to obtain target data.

[0076] The identification module 230 is configured to input the target data into an identification model, identify the target data through the identification model, and obtain adjustment parameters for the vehicle air conditioner output by the identification model.

[0077] The control module 240 is configured to control the operation of the vehicle air conditioner based on the adjustment parameters.

[0078] The vehicle air conditioner control device provided in the embodiments of the present application can improve the accuracy of the vehicle air conditioner adjustment and the intelligent degree, make the air conditioner adjustment more in line with the actual situation, improve the passenger comfort, and enhance the user experience by collecting real-time data such as in-vehicle environment data, out-of-vehicle environment data, and navigation data, performing data processing on the real-time data to obtain target data, and inputting the target data into an identification model to obtain adjustment parameters for the vehicle air conditioner, thereby automatically controlling the operation of the vehicle air conditioner without manual intervention of the user.

[0079] In some embodiments, the in-vehicle environment data comprises at least one of in-vehicle temperature data and in-vehicle humidity data; the out-of-vehicle environment data comprises at least one of out-of-vehicle temperature data, out-of-vehicle humidity data, and out-of-vehicle weather data; and the navigation data comprises at least one of road condition information of a road segment where the vehicle is located, travel destination information, travel duration, and altitude information.

[0080] In some embodiments, the verification module is specifically configured to:

[0081] spatiotemporally calibrate the real-time data to obtain calibrated data;

[0082] verify whether the calibrated data is accurate based on a preset verification rule; the preset verification rule is established based on a logical relationship among the in-vehicle environment data, the out-of-vehicle environment data, and the navigation data;

[0083] if the calibrated data is accurate, extract features of the calibrated data to obtain the target data;

[0084] if the calibrated data is not accurate, correct the calibrated data, extract features of the corrected calibrated data to obtain the target data; and the data correction manner comprises at least one of a cubic spline interpolation method repair, Kalman filter dynamic compensation, and data replacement.

[0085] In some embodiments, the identification model is obtained by training in the following manner:

[0086] obtain a training sample set; the training sample set comprises a plurality of groups of historical target data and historical adjustment parameters corresponding to each group of historical target data;

[0087] train an initial model based on the training sample set to obtain the identification model.

[0088] In some embodiments, the identification module is further configured to:

[0089] obtain a first number of times that the in-vehicle air conditioner is controlled based on the adjustment parameter within a preset time period and a second number of times that the in-vehicle air conditioner is adjusted by a user within the preset time period;

[0090] if a ratio of the second number of times to the first number of times is greater than a preset threshold, optimize parameters of the identification model.

[0091] In some embodiments, a triggering condition for obtaining the first number of times and the second number of times is any one of the following conditions:

[0092] every interval of a preset time length;

[0093] The cumulative driving distance of the vehicle reaches a preset distance, and the cumulative driving distance is a driving distance accumulated since the last time the identification model is optimized.

[0094] An instruction to optimize the identification model is received.

[0095] Specifically, the above-mentioned in-vehicle air conditioner control device provided by the embodiments of the present application can realize all the method steps realized by the method embodiments of the above-mentioned execution subject being the controller, and can achieve the same technical effects. Here, the same parts and beneficial effects in the method embodiments will not be described in detail.

[0096] Figure 3 is a structural schematic diagram of an electronic device provided by the embodiments of the present application. As shown in the figure, Figure 3 The electronic device can include a processor 310, a communications interface 320, a memory 330, and a communications bus 340, wherein the processor 310, the communications interface 320, and the memory 330 communicate with each other through the communications bus 340. The processor 310 can invoke the logical instructions in the memory 330 to execute the in-vehicle air conditioner control method, for example, including:

[0097] Obtaining real-time data; the real-time data includes at least one of in-vehicle environment data, out-of-vehicle environment data, and navigation data of the vehicle;

[0098] Data processing is performed on the real-time data to obtain target data;

[0099] The target data is input into an identification model, and the target data is identified by the identification model to obtain adjustment parameters for the in-vehicle air conditioner output by the identification model;

[0100] Based on the adjustment parameters, the operation of the in-vehicle air conditioner is controlled.

[0101] Further, the logic instructions in the memory 330 described above can be implemented in the form of software functional units and sold or used as independent products, and can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the parts that make contributions to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0102] In another aspect, the present application also provides a non-transitory computer readable storage medium having stored thereon a computer program, which, when executed by a processor, implements the steps of the in-vehicle air conditioning control method provided by the above-mentioned methods, for example, including:

[0103] obtaining real-time data; the real-time data includes at least one of in-vehicle environment data, out-of-vehicle environment data, and navigation data of the vehicle;

[0104] performing data processing on the real-time data to obtain target data;

[0105] inputting the target data into an identification model, identifying the target data through the identification model, and obtaining adjustment parameters for the in-vehicle air conditioner output by the identification model;

[0106] controlling the operation of the in-vehicle air conditioner based on the adjustment parameters.

[0107] In yet another aspect, the present application also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer readable storage medium, and when the computer program is executed by a processor, the computer can execute the steps of the in-vehicle air conditioning control method provided by the above-mentioned methods, for example, including:

[0108] obtaining real-time data; the real-time data includes at least one of in-vehicle environment data, out-of-vehicle environment data, and navigation data of the vehicle;

[0109] performing data processing on the real-time data to obtain target data;

[0110] inputting the target data into an identification model, identifying the target data through the identification model, and obtaining adjustment parameters for the in-vehicle air conditioner output by the identification model;

[0111] Based on the adjustment parameter, control operation of the in-vehicle air conditioner.

[0112] The apparatus embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, i.e., may be located in one place, or may be distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0113] Through the description of the above embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software plus necessary universal hardware platforms, and of course can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of software products, and the computer software products can be stored in a computer readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and include a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment or some parts of the embodiments.

[0114] In addition, it should be noted that: in the embodiments of the present application, the terms "first", "second" and the like are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than that illustrated or described herein, and the objects distinguished by "first", "second" are generally a class, and do not limit the number of objects, for example, the first object can be one or more.

[0115] In the embodiments of the present application, the term "and / or" describes the association relationship between the associated objects, which means that there can be three kinds of relationships, for example, A and / or B, which can represent the following three cases: A exists alone, A and B exist together, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after it.

[0116] The "determining B based on A" in the embodiments of the present application means that A is considered as a factor when determining B. It is not limited to "determining B based on A only", but also includes "determining B based on A and C", "determining B based on A, C and E", "determining C based on A, and determining B based on C further", and the like. In addition, it can also include taking A as a condition for determining B, for example, "when A meets a first condition, determining B by using a first method"; for example, "when A meets a second condition, determining B"; and the like; for example, "when A meets a third condition, determining B based on a first parameter", and the like. Of course, it can also be that A is taken as a condition for determining B, for example, "when A meets a first condition, determining C by using a first method, and determining B further based on C", and the like.

[0117] In the embodiments of the present application, the term "a plurality of" refers to two or more, and other quantifiers are similar.

[0118] In the embodiments of the present application, the terms "center", "longitudinal", "transverse", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, which is only for the convenience of describing the embodiments of the present application and simplifying the description, and does not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the embodiments of the present application.

[0119] In the embodiments of the present application, unless otherwise explicitly specified and limited, the terms "connected", "connected" should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium. For those skilled in the art, the specific meaning of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0120] In the embodiments of the present application, unless otherwise explicitly specified and limited, the first feature is "on" or "under" the second feature, which can be that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, the first feature "above", "above" and "above" the second feature can be that the first feature is directly above or obliquely above the second feature, or only indicates that the horizontal height of the first feature is higher than that of the second feature. The first feature "below", "below" and "below" the second feature can be that the first feature is directly below or obliquely below the second feature, or only indicates that the horizontal height of the first feature is less than that of the second feature.

[0121] In the embodiments of the present application, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the embodiments of the present application. In the embodiments of the present application, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine the different embodiments or examples described in the specification and the features of the different embodiments or examples without contradiction, and the spirit and scope of the technical solutions of the embodiments of the present application are not deviated.

[0122] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An in-vehicle air conditioning control method characterized by comprising: The method comprises the following steps: acquiring real-time data; the real-time data comprises at least one of in-vehicle environment data, out-of-vehicle environment data and navigation data of the vehicle; performing data processing on the real-time data to obtain target data; inputting the target data into an identification model, identifying the target data through the identification model, and obtaining adjustment parameters for an in-vehicle air conditioner output by the identification model; controlling operation of the in-vehicle air conditioner based on the adjustment parameters; the data processing on the real-time data to obtain target data comprises: performing space-time calibration on the real-time data to obtain calibrated data; verifying whether the calibrated data is accurate based on a preset verification rule; the preset verification rule is established based on a logical relationship among the in-vehicle environment data, the out-of-vehicle environment data and the navigation data; if the calibrated data is accurate, performing feature extraction on the calibrated data to obtain the target data; if the calibrated data is inaccurate, performing data correction on the calibrated data, performing feature extraction on the corrected calibrated data, and obtaining the target data; the data correction mode comprises at least one of a cubic spline interpolation method repair, Kalman filter dynamic compensation and data replacement.

2. The in-vehicle air conditioning control method according to claim 1, characterized by, The in-vehicle environment data comprises at least one of in-vehicle temperature data and in-vehicle humidity data; the out-of-vehicle environment data comprises at least one of out-of-vehicle temperature data, out-of-vehicle humidity data and out-of-vehicle weather data; and the navigation data comprises at least one of road condition information of a road section where the vehicle is located, driving destination information, driving time length and altitude information.

3. The in-vehicle air conditioning control method according to claim 1, characterized by, The identification model is trained in the following manner: obtaining a training sample set; the training sample set comprises a plurality of groups of historical target data and historical adjustment parameters corresponding to each group of historical target data; training an initial model through the training sample set to obtain the identification model.

4. The in-vehicle air conditioning control method according to any one of claims 1 to 3, characterized by, The method further comprises the following steps: obtaining a first number of times that the in-vehicle air conditioner is controlled based on the adjustment parameters within a preset time period and a second number of times that the in-vehicle air conditioner is adjusted by a user within the preset time period; if a ratio of the second number of times to the first number of times is greater than a preset threshold, optimizing parameters of the identification model.

5. The in-vehicle air conditioning control method according to claim 4, characterized by, Triggering conditions for obtaining the first number of times and the second number of times are any of the following conditions: every interval of a preset time length; cumulative driving distance of the vehicle reaches a preset distance; the cumulative driving distance is a driving distance accumulated since the last time the identification model is optimized; receiving an instruction to optimize the identification model.

6. An in-vehicle air conditioning control device characterized by comprising: The method comprises the following steps: a collection module is configured to acquire real-time data; the real-time data comprises at least one of in-vehicle environment data, out-of-vehicle environment data and navigation data of the vehicle; a verification module is configured to perform data processing on the real-time data to obtain target data; an identification module is configured to input the target data into an identification model, identify the target data through the identification model, and obtain adjustment parameters for an in-vehicle air conditioner output by the identification model; a control module is configured to control operation of the in-vehicle air conditioner based on the adjustment parameters; the verification module is specifically configured to: perform space-time calibration on the real-time data to obtain calibrated data; verifying whether the calibration data is accurate based on a preset verification rule; the preset verification rule is established based on a logical relationship among the in-vehicle environment data, the out-of-vehicle environment data and the navigation data; if the calibration data is accurate, performing feature extraction on the calibration data to obtain the target data; if the calibration data is not accurate, performing data correction on the calibration data, and performing feature extraction on the corrected calibration data to obtain the target data; the data correction mode includes at least one of a cubic spline interpolation repair, Kalman filter dynamic compensation and data replacement.

7. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the in-vehicle air conditioning control method according to any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the in-vehicle air conditioning control method according to any one of claims 1 to 5.

9. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the in-vehicle air conditioning control method according to any one of claims 1 to 5.

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