In-vehicle air conditioner control method, device, equipment, medium and program product

Abstract

The invention provides an in-vehicle air conditioner control method, device and equipment, a medium and a program product, and relates to the technical field of intelligent vehicles. The method comprises the following steps: acquiring real-time data; the real-time data comprises at least one of in-vehicle environment data, out-vehicle environment data and vehicle navigation data; performing data processing on the real-time data to obtain target data; the target data are input into a recognition model, the target data are recognized through the recognition model, and adjustment parameters, output by the recognition model, for the in-vehicle air conditioner are obtained; and on the basis of the adjusting parameters, operation of the in-vehicle air conditioner is controlled. According to the in-vehicle air conditioner control method, device and equipment, the medium and the program product, the intelligent degree of in-vehicle air conditioner control can be improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent automobile technology, and in particular to a method, device, equipment, medium and program product for controlling in-vehicle air conditioning. Background Art

[0002] Intelligent control of in-car air conditioning is one of the important manifestations of the development of modern automobile intelligence. It uses advanced sensors, controllers, actuators and other technologies to achieve accurate and automatic adjustment of the in-car air conditioning system to provide passengers with a comfortable in-car environment. There are many ways to control in-car air conditioning, but the accuracy of the current in-car air conditioning control methods is still flawed and the degree of intelligence is not high enough.

[0003] Therefore, there is an urgent need to provide a new intelligent control method for in-vehicle air conditioning. Summary of the invention

[0004] The present application provides a method, device, equipment, medium and program product for controlling in-vehicle air conditioning, which are used to solve the defect of low intelligence level of in-vehicle air conditioning control in the prior art and improve the intelligence level of in-vehicle air conditioning control.

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

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

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

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

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

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

[0011] Optionally, the performing data processing on the real-time data to obtain target data includes:

[0012] Performing time-space calibration on the real-time data to obtain calibration data;

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

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

[0015] If the calibration data is inaccurate, the calibration data is corrected, and features are extracted from the corrected calibration 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.

[0016] Optionally, the recognition model is trained in the following manner:

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

[0018] The initial model is trained using the training sample set to obtain the recognition model.

[0019] Optionally, the in-vehicle air conditioning control method further includes:

[0020] Acquire a first number of times the in-vehicle air conditioner is controlled based on the adjustment parameter within a preset time period, and a second number of times the in-vehicle air conditioner is adjusted by the 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 recognition model are optimized.

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

[0023] Preset duration for each interval;

[0024] The accumulated driving distance of the vehicle reaches a preset distance, and the accumulated driving distance is the accumulated driving distance since the last time the recognition model was optimized;

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

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

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

[0028] A verification module, used for processing the real-time data to obtain target data;

[0029] an identification module, used for 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;

[0030] A control module is used to control the operation of the in-vehicle air conditioner based on the adjustment parameters.

[0031] In a third aspect, the present application further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the first aspect when executing the computer program.

[0032] In a fourth aspect, the present application further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect.

[0033] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which implements the method described in the first aspect when executed by a processor.

[0034] The in-vehicle air-conditioning control method, device, equipment, medium and program product provided by the present application collects real-time data such as in-vehicle environmental data, outdoor environmental data and navigation data, and then processes the real-time data to obtain target data. By inputting the target data into a recognition model, adjustment parameters for the in-vehicle air-conditioning can be obtained, thereby automatically controlling the operation of the in-vehicle air-conditioning without manual intervention by the user, thereby improving the accuracy and intelligence of the in-vehicle air-conditioning adjustment, making the air-conditioning adjustment more in line with the actual situation, improving passenger comfort, and enhancing user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the present application or the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0036] Figure 1 is a flow chart of a method for controlling an in-vehicle air conditioner provided in an embodiment of the present application;

[0037] Figure 2 is a structural schematic diagram of an in-vehicle air conditioning control device provided in an embodiment of the present application;

[0038] Figure 3 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0040] An embodiment of the present application provides a method for controlling an in-vehicle air conditioner, the execution subject of which may be an electronic device, for example, a controller. The following description will be made using an example in which the execution subject of the method is a controller. Figure 1 is a flow chart of the in-vehicle air conditioning control method provided by the embodiment of the present application. Figure 1 , the method may include:

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

[0042] Step 120: Process the real-time data to obtain target data;

[0043] Step 130: input the target data into the recognition model, identify the target data through the recognition model, and obtain the adjustment parameters for the in-vehicle air conditioner output by the recognition model;

[0044] Step 140: Control the operation of the air conditioner in the vehicle based on the adjustment parameters.

[0045] Considering that the existing technology in the in-vehicle air conditioning control is not comprehensive enough in data collection, it is impossible to integrate the impact of multi-source data on the in-vehicle environmental requirements, resulting in a deviation between the air conditioning adjustment and the actual requirements. In step 110, the controller can obtain real-time data. The real-time data can specifically include at least one of the in-vehicle environmental data, the out-vehicle environmental data and the vehicle's navigation data. Among them, the in-vehicle environmental data is the environmental data inside the vehicle, such as the temperature inside the vehicle. The out-vehicle environmental data is the environmental data outside the vehicle, such as the temperature outside the vehicle. The navigation data can reflect the vehicle's travel information and can assist in determining the adjustment parameters of the air conditioner to a certain extent.

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

[0047] In step 130, the controller may input the target data into the recognition model, recognize the target data through the recognition model, and obtain the adjustment parameters for the in-vehicle air conditioner. The recognition model may specifically be a deep learning model. The adjustment parameters of the in-vehicle air conditioner may include temperature, wind speed, wind volume, wind direction, mode, etc. When the recognition model determines that the vehicle is in a congested section and the temperature outside the vehicle is high and the humidity is high based on the current target data, it may output adjustment parameters for lowering the temperature setting value, increasing the wind speed and wind volume, and strengthening the cooling and dehumidification functions to maintain a comfortable environment in the vehicle; when the vehicle is near the destination, combined with the navigation data, the model may adjust the adjustment parameters of the air conditioner in advance so that the in-vehicle environment reaches a more suitable state when arriving at the destination.

[0048] In step 140 , the controller may control the vehicle air conditioning system to automatically perform intelligent adjustment on the air conditioning in the vehicle according to the adjustment parameters output by the recognition model.

[0049] The in-vehicle air-conditioning control method provided in the embodiment of the present application collects real-time data such as in-vehicle environmental data, external environmental data, and navigation data, and then processes the real-time data to obtain target data. By inputting the target data into a recognition model, adjustment parameters for the in-vehicle air-conditioning can be obtained, thereby automatically controlling the operation of the in-vehicle air-conditioning without manual intervention by the user, thereby improving the accuracy and intelligence of the in-vehicle air-conditioning adjustment, making the air-conditioning adjustment more in line with the actual situation, improving passenger comfort, and enhancing user experience.

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

[0051] Specifically, the temperature and humidity data inside the car can be collected by installing high-precision temperature and humidity sensors at different locations in the car (such as the driver's seat, the front passenger seat, the back seat, etc.). Real-time collection of temperature and humidity data in various areas of the car can fully reflect the temperature and humidity conditions of the car environment.

[0052] The weather data outside the vehicle may include rainfall data, weather conditions, etc. The temperature data outside the vehicle and the humidity data outside the vehicle can be collected by installing temperature and humidity sensors at appropriate locations outside the vehicle (such as rearview mirrors, roofs, etc.). Rainfall data can be obtained by installing rain sensors to monitor the rainfall information outside the vehicle in real time. Weather conditions can be collected by obtaining weather forecasts, etc. The temperature, humidity and weather conditions of the environment outside the vehicle will affect the requirements of the environment inside the vehicle. For example, fog is easily generated inside the vehicle on rainy days, and air conditioning is required for defogger and other adjustments.

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

[0054] The in-vehicle air-conditioning control method provided in the embodiment of the present application comprehensively senses the internal and external environment and travel information of the vehicle by collecting multi-source data such as in-vehicle environmental data, external environmental data and navigation data. Compared with the existing technology that only relies on part of the environmental data, it can more accurately grasp the in-vehicle environmental requirements, improve the accuracy and intelligence of the in-vehicle air-conditioning adjustment, make the air-conditioning adjustment more in line with the actual situation, and improve the passenger comfort.

[0055] In some embodiments, real-time data is processed to obtain target data, including: performing time and space calibration on real-time data to obtain calibration data; verifying whether the calibration data is accurate based on preset verification rules; the preset verification rules are established based on the logical relationship between in-vehicle environment data, out-of-vehicle environment data and navigation data; if the calibration data is accurate, performing feature extraction on the calibration data to obtain target data; if the calibration data is inaccurate, performing data correction on the calibration data, and performing feature extraction on the corrected calibration data to obtain 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 timestamp and spatial coordinate system, perform time and space calibration on the in-vehicle environmental data (0.1℃ / 0.5%RH accuracy), the outdoor environmental data (0.2℃ / 1%RH accuracy, rainfall classification 0-5), and the navigation data (GPS positioning accuracy ±2.5m, timestamp synchronization error <10ms), eliminate the sampling frequency differences of different sensors (such as temperature and humidity sensor 10Hz vs rainfall sensor 5Hz), and obtain calibration data.

[0057] The controller can verify whether the calibration data is accurate based on the preset verification rules, and correct the calibration data if the calibration data is inaccurate. Specifically, the preset verification rules are established based on the logical relationship between the in-vehicle environment data, the out-of-vehicle environment data and the navigation data. The preset verification rules and corresponding data correction examples are shown in the following table:

[0058]

[0059] Table 1 Data validation logic example

[0060] The controller can also determine whether the calibration data is accurate through the Bayesian inference engine and data confidence assessment. When the calibration data is inaccurate, the data can be corrected according to different situations. For example, single-point anomalies can be repaired by cubic spline interpolation (such as a jump in rainfall data at a certain moment), and systematic deviations can be compensated 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 the target data; if the calibration data is inaccurate, the controller can perform data correction on the calibration data and extract features from the corrected calibration data to obtain the target data. Specifically, feature extraction can be performed by principal component analysis, linear discriminant analysis, K-Means clustering, etc. Environmental features include: temperature difference ΔT inside and outside the car (|T inside - T outside |> 5℃ triggers warning), humidity gradient ΔH (H inside - H outside > 15% RH), and rain intensity index (calculated based on the pulse signal of the rain sensor). Navigation features include: altitude change rate (combined with navigation data), remaining cruising range (associated with air conditioning energy consumption prediction), and congestion index (from real-time traffic data). After considering the impact of different features on the air conditioning in the car, the 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 on rainy days, and the weight of the outside temperature is increased to 0.35 during high temperature warnings.

[0062] The controller can also integrate and analyze the collected in-vehicle environment data, out-vehicle environment data, and navigation data. By establishing a data association model, data from different sources can be cross-validated. For example, when the amount of rain outside the car increases, the temperature and humidity data outside the car can be combined to determine whether it may cause the glass inside the car to fog up. At the same time, the vehicle's driving direction and destination environment information in the navigation data can be referred to to comprehensively determine what kind of adjustment the air conditioner in the car should make. If the vehicle is heading towards a high humidity area and it has begun to rain outside the car, the air conditioning mode can be adjusted in advance to strengthen the dehumidification function, prevent fogging in the car, and maintain a comfortable humidity. Through data cross-validation, the accuracy and reliability of the data can be improved, providing a more solid data foundation for the formulation of subsequent control strategies.

[0063] The in-vehicle air conditioning control method provided in the embodiment of the present application effectively eliminates data errors or anomalies that may exist in a single data source by integrating, analyzing and verifying multi-source real-time data, thereby improving the quality and reliability of real-time data. In addition, the adjustment parameters of the air conditioner are determined based on more reliable data, thereby enhancing the accuracy and stability of the in-vehicle air conditioning control strategy and reducing the misadjustment of the air conditioner caused by inaccurate data.

[0064] In some embodiments, the recognition model is trained in the following manner: obtaining a training sample set; the training sample set includes multiple groups of historical target data and historical adjustment parameters corresponding to each group of historical target data; and training the initial model with the training sample set to obtain the recognition model.

[0065] The controller can obtain multiple groups of historical target data and the historical adjustment parameters of the in-vehicle air conditioner corresponding to each group of historical target data as training sample sets, and train the initial model through the training sample sets. After reaching a preset number of training times or a preset model accuracy, a recognition model is obtained. Specifically, the controller can input the verified historical target data into the model as the data to be trained. The model uses a deep learning algorithm, such as a neural network model, to continuously optimize the model parameters through learning and training with a large amount of sample data. During the training process, multi-source data corresponding to different driving scenarios (such as hot sunny days, rainy days, long-distance driving, congested roads, etc.) and the corresponding optimal air-conditioning adjustment parameters are learned, so that the model can accurately identify the mapping relationship between multi-source data features and air-conditioning adjustment requirements in different scenarios, thereby training a model that can accurately adapt to various complex actual driving scenarios.

[0066] The in-vehicle air-conditioning control method provided in the embodiment of the present application uses comprehensive and rich target data to train the recognition model, so that the recognition model can learn the relationship between data features and air-conditioning adjustment requirements in various complex driving scenarios. Compared with the single model training data in the prior art, the recognition model trained by this method is more adaptable to the actual driving scene, and can more accurately output appropriate air-conditioning adjustment parameters according to real-time data, thereby achieving more efficient and intelligent air-conditioning adjustment, further improving the comfort level of the in-vehicle environment, and at the same time, optimizing the air-conditioning energy consumption and achieving energy saving while meeting the comfort level.

[0067] In some embodiments, the in-vehicle air conditioning control method also includes: obtaining the first number of times the in-vehicle air conditioning is controlled based on the adjustment parameters within a preset time period, and the second number of times the user adjusts the in-vehicle air conditioning within the preset time period; if the ratio of the second number to the first number is greater than a preset threshold, optimizing the parameters of the recognition model.

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

[0069] The in-vehicle air conditioning control method provided in the embodiment of the present application can further improve the effect of the recognition model, improve the accuracy of the in-vehicle air conditioning adjustment, and enhance the user experience by optimizing the parameters of the recognition model.

[0070] In some embodiments, the triggering condition for obtaining the first number and the second number is any one of the following conditions: a preset duration for each interval; the cumulative driving distance of the vehicle reaches a preset distance, and the cumulative driving distance is the accumulated driving distance since the last time the recognition model was optimized; an instruction to optimize the recognition model is received.

[0071] The in-vehicle air conditioning control method provided in the embodiment of the present application triggers the optimization logic of the recognition model when the trigger conditions are met, which can further improve the effect of the recognition model, improve the accuracy of the in-vehicle air conditioning adjustment, and enhance the user experience.

[0072] The in-vehicle air conditioning control device provided by the present application is described below. The in-vehicle air conditioning control device described below and the in-vehicle air conditioning control method described above can be referenced to each other.

[0073] Figure 2 Schematic diagram of the structure of the in-vehicle air conditioning control device provided by the embodiment of the present application. Figure 2 The in-vehicle air conditioning control device provided in the embodiment of the present application may include:

[0074] The acquisition module 210 is used to acquire real-time data; the real-time data includes at least one of the vehicle interior environment data, vehicle exterior environment data and vehicle navigation data;

[0075] The verification module 220 is used to process the real-time data to obtain target data;

[0076] The recognition module 230 is used to input the target data into a recognition model, recognize the target data through the recognition model, and obtain the adjustment parameters for the in-vehicle air conditioner output by the recognition model;

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

[0078] The in-vehicle air-conditioning control device provided in the embodiment of the present application collects real-time data such as in-vehicle environmental data, outdoor environmental data, and navigation data, and then processes the real-time data to obtain target data. By inputting the target data into a recognition model, adjustment parameters for the in-vehicle air-conditioning can be obtained, thereby automatically controlling the operation of the in-vehicle air-conditioning without manual intervention by the user, thereby improving the accuracy and intelligence of the in-vehicle air-conditioning adjustment, making the air-conditioning adjustment more in line with the actual situation, improving passenger comfort, and enhancing user experience.

[0079] In some embodiments, the in-vehicle environment data includes at least one of in-vehicle temperature data and in-vehicle humidity data; the out-vehicle environment data includes at least one of out-vehicle temperature data, out-vehicle humidity data and out-vehicle weather data; the navigation data includes at least one of road condition information of the road section where the vehicle is located, driving destination information, driving time and altitude information.

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

[0081] Performing time-space calibration on the real-time data to obtain calibration data;

[0082] Verifying whether the calibration data is accurate based on a preset verification rule; the preset verification rule is established based on a logical relationship between the in-vehicle environment data, the out-vehicle environment data and the navigation data;

[0083] If the calibration data is accurate, performing feature extraction on the calibration data to obtain the target data;

[0084] If the calibration data is inaccurate, the calibration data is corrected, and features are extracted from the corrected calibration 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.

[0085] In some embodiments, the recognition model is trained in the following manner:

[0086] Acquire a training sample set; the training sample set includes multiple groups of historical target data and historical adjustment parameters corresponding to each group of historical target data;

[0087] The initial model is trained using the training sample set to obtain the recognition model.

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

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

[0090] 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 recognition model are optimized.

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

[0092] Preset duration for each interval;

[0093] The accumulated driving distance of the vehicle reaches a preset distance, and the accumulated driving distance is the accumulated driving distance since the last time the recognition model was optimized;

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

[0095] Specifically, the above-mentioned in-vehicle air-conditioning control device provided in the embodiment of the present application can implement all the method steps implemented by the method embodiment in which the above-mentioned execution subject is the controller, and can achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as those of the method embodiment will not be described in detail here.

[0096] Figure 3 Schematic diagram of the structure of the electronic device provided in the embodiment of the present application. Figure 3 As shown, the electronic device may include: a processor 310, a communication interface 320, a memory 330 and a communication bus 340, wherein the processor 310, the communication interface 320 and the memory 330 communicate with each other through the communication bus 340. The processor 310 may call the logic instructions in the memory 330 to execute the in-vehicle air conditioning control method, for example, including:

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

[0098] Performing data processing on the real-time data to obtain target data;

[0099] Inputting the target data into a recognition model, recognizing the target data through the recognition model, and obtaining adjustment parameters for the in-vehicle air conditioner output by the recognition model;

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

[0101] In addition, the logic instructions in the above-mentioned memory 330 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-On l yMemory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0102] On the other hand, the present application also provides a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the in-vehicle air conditioning control method provided by the above methods are implemented, for example, including:

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

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

[0105] Inputting the target data into a recognition model, recognizing the target data through the recognition model, and obtaining adjustment parameters for the in-vehicle air conditioner output by the recognition model;

[0106] Based on the adjustment parameters, the operation of the in-vehicle air conditioner is controlled.

[0107] In another aspect, the present application further provides a computer program product, the computer program product comprising a computer program, the computer program may 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 methods, for example, including:

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

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

[0110] Inputting the target data into a recognition model, recognizing the target data through the recognition model, and obtaining adjustment parameters for the in-vehicle air conditioner output by the recognition model;

[0111] Based on the adjustment parameters, the operation of the in-vehicle air conditioner is controlled.

[0112] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0113] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0114] It should also be noted that in the embodiments of the present application, the terms "first", "second", etc. 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 interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first" and "second" are generally of the same type, and the number of objects is not limited. 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 of the associated objects, indicating that three relationships may exist. For example, A and / or B may represent three situations: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects before and after are in an "or" relationship.

[0116] "Determine B based on A" in the embodiments of the present application means that the factor A should be considered when determining B. It is not limited to "B can be determined based on A alone", but should also include: "Determine B based on A and C", "Determine B based on A, C and E", "Determine C based on A, and further determine B based on C", etc. In addition, it can also include taking A as a condition for determining B, for example, "When A meets the first condition, use the first method to determine B"; for another example, "When A meets the second condition, determine B", etc.; for another example, "When A meets the third condition, determine B based on the first parameter", etc. Of course, it can also be a condition that takes A as a factor for determining B, for example, "When A meets the first condition, use the first method to determine C, and further determine B based on C", etc.

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

[0118] In the embodiments of the present application, the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the embodiments of the present application and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they should not be understood as limiting the embodiments of the present application.

[0119] In the embodiments of the present application, unless otherwise clearly specified and limited, the terms "connected" and "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in the embodiments of the present application can be understood according to specific circumstances.

[0120] In the embodiments of the present application, unless otherwise clearly specified and limited, the first feature being "above" or "below" the second feature may mean 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 being "above", "above" and "above" the second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. The first feature being "below", "below" and "below" the second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0121] In the embodiments of the present application, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction 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 schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradicting each other.

[0122] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for controlling an in-vehicle air conditioner, characterized in that: include: Acquiring real-time data; the real-time data includes at least one of in-vehicle environment data, out-vehicle environment data and vehicle navigation data; Processing the real-time data to obtain target data; Inputting the target data into a recognition model, recognizing the target data through the recognition model, and obtaining adjustment parameters for the in-vehicle air conditioner output by the recognition model; Based on the adjustment parameters, the operation of the in-vehicle air conditioner is controlled.

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

3. The in-vehicle air conditioning control method according to claim 1, characterized in that: The processing of the real-time data to obtain target data includes: Performing spatiotemporal calibration on the real-time data to obtain calibration 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 between the in-vehicle environment data, the out-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 inaccurate, the calibration data is corrected, and features are extracted from the corrected calibration 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.

4. The in-vehicle air conditioning control method according to claim 1, characterized in that: The recognition model is trained in the following way: Acquire a training sample set; the training sample set includes multiple groups of historical target data and historical adjustment parameters corresponding to each group of historical target data; The initial model is trained using the training sample set to obtain the recognition model.

5. The in-vehicle air conditioning control method according to any one of claims 1 to 4, characterized in that: Also includes: Acquire a first number of times the in-vehicle air conditioner is controlled based on the adjustment parameter within a preset time period, and a second number of times the in-vehicle air conditioner is adjusted by the user within the preset time period; 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 recognition model are optimized.

6. The in-vehicle air conditioning control method according to claim 5, characterized in that: The triggering condition for obtaining the first number of times and the second number of times is any one of the following conditions: Preset duration for each interval; The accumulated driving distance of the vehicle reaches a preset distance, and the accumulated driving distance is the accumulated driving distance since the last time the recognition model was optimized; An instruction to optimize the recognition model is received.

7. An in-vehicle air conditioning control device, characterized in that: include: A collection module, used to obtain real-time data; the real-time data includes at least one of in-vehicle environment data, out-vehicle environment data and vehicle navigation data; A verification module, used for processing the real-time data to obtain target data; an identification module, used for 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; A control module is used to control the operation of the in-vehicle air conditioner based on the adjustment parameters.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the in-vehicle air conditioning control method as claimed in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the in-vehicle air conditioning control method as claimed in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the in-vehicle air conditioning control method as claimed in any one of claims 1 to 6 is implemented.

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