Automobile air conditioner AI pre-control method and system and electronic equipment

Abstract

The invention provides an automobile air conditioner AI pre-control method and system and electronic device.The method comprises the steps that environment data inside and outside an automobile are obtained in real time based on a sensor, and meanwhile historical air conditioner temperature adjustment data of a user are called; comprehensively analyzing the data of the environment inside and outside the vehicle and the historical air conditioner temperature adjustment data based on an AI model, predicting the temperature requirement of a user, and monitoring the relative distance between the user and the vehicle based on a vehicle-mounted communication system; and a pre-control scheme for the automobile air conditioner is determined based on the relative distance and the temperature requirement, and the automobile air conditioner is controlled based on the pre-control scheme. The intelligence and accuracy of control over the automobile air conditioner are improved, meanwhile, the driving experience feeling of a user is improved, and the temperature adjusting effect of the air conditioner is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of equipment control technology, and in particular to an automotive air-conditioning AI pre-control method, system, and electronic equipment. Background Art

[0002] At present, cars have become an essential tool for everyone's travel, and air conditioners provide a more comfortable environment for everyone's travel. Effective temperature control of air conditioners can enhance the user's driving experience. Therefore, effective temperature control of air conditioners is particularly important.

[0003] However, traditional car air conditioners typically require users to manually adjust parameters such as temperature and air speed. When a user enters the car, the interior temperature may be too high or too low, requiring time to reach a comfortable state. Furthermore, users often need to repeatedly adjust the air conditioner settings in different weather conditions and driving scenarios. While some cars currently have remote air conditioning start functions, they lack intelligent pre-control and cannot accurately adjust based on various environmental factors and user habits, significantly reducing the air conditioner's intelligent temperature control and effectiveness.

[0004] Therefore, in order to overcome the above-mentioned defects, the present invention provides an automobile air-conditioning AI pre-control method, system and electronic equipment. Summary of the Invention

[0005] The present invention provides an automobile air-conditioning AI pre-control method, system and electronic equipment, which are used to provide data support for predicting the user's temperature demand by obtaining the in-vehicle and out-of-vehicle environmental data and the user's historical air-conditioning temperature adjustment data. Secondly, the in-vehicle and out-of-vehicle environmental data and the historical air-conditioning temperature adjustment data are comprehensively analyzed by an AI model to achieve accurate and reliable prediction of the user's temperature demand. At the same time, the relative distance between the user and the vehicle is determined, which provides a reference for determining the pre-control plan for the air-conditioning. Finally, the pre-control plan for the automobile air-conditioning is reliably formulated according to the relative distance and temperature demand, thereby realizing the control of the automobile air-conditioning, improving the intelligence and accuracy of the automobile air-conditioning control, and at the same time enhancing the user's driving experience and ensuring the air-conditioning temperature adjustment effect.

[0006] The present invention provides an AI pre-control method for an automobile air conditioner, comprising:

[0007] Step 1: Use sensors to obtain real-time data about the vehicle's internal and external environments, and retrieve the user's historical air conditioning temperature data.

[0008] Step 2: The AI model comprehensively analyzes the vehicle's internal and external environmental data and historical air conditioning temperature data to predict the user's temperature needs. The vehicle's in-vehicle communication system also monitors the user's relative distance from the vehicle.

[0009] Step 3: Determine a pre-control plan for the automobile air conditioner based on the relative distance and temperature requirements, and control the automobile air conditioner based on the pre-control plan.

[0010] Preferably, an AI pre-control method for automobile air conditioning, in step 1, real-time acquisition of vehicle interior and exterior environmental data based on sensors, includes:

[0011] Determine the configuration requirements for sensor performance based on control needs, and configure sensor parameters based on the configuration requirements, where the configuration requirements include data collection cycle and device sensitivity;

[0012] Performing time-series synchronization association on each sensor based on the parameter configuration result, and performing synchronous control on each sensor based on the time-series synchronization association result;

[0013] Based on the synchronous control results, the sensors collect the vehicle's internal and external environmental data in real time, and manage the data separately.

[0014] Preferably, an AI pre-control method for automobile air conditioning, which manages the environmental data inside and outside the vehicle separately, includes:

[0015] Acquire the obtained vehicle interior and exterior environment data, and perform a first differentiation on the vehicle interior and exterior environment data to obtain vehicle exterior environment data and vehicle interior environment data;

[0016] Clustering the external vehicle environment data and the internal vehicle environment data respectively, and performing a second differentiation on the external vehicle environment data and the internal vehicle environment data based on the clustering results to obtain corresponding environment category data sets;

[0017] The same environment category data sets under the vehicle exterior environment data and vehicle interior environment data are mapped and associated, and a regional category data comparison table is constructed based on the mapping association.

[0018] Preferably, an AI pre-control method for automobile air conditioning, in step 1, retrieves the user's historical air conditioning temperature adjustment data, including:

[0019] Log in to the backend server of the user's vehicle and traverse data in the backend server based on the unique vehicle identity of the user's vehicle;

[0020] Filter out the air conditioning temperature control related data from the data traversal results, and analyze the air conditioning temperature control related data to obtain the actual temperature control data of the automobile air conditioning and the environmental data in the same space and time;

[0021] The actual temperature control data of the car air conditioner and the environmental data in the same time and space are retrieved, and the actual temperature control data of the car air conditioner and the environmental data in the same time and space are bound based on the time series to obtain the user's historical air conditioner temperature control data.

[0022] Preferably, in a method for AI pre-control of automobile air conditioning, in step 2, a comprehensive analysis of the vehicle interior and exterior environmental data and historical air conditioning temperature adjustment data is performed based on an AI model to predict the user's temperature demand, including:

[0023] Obtaining the obtained vehicle interior and exterior environment data and historical air conditioning temperature control data, and parsing the historical air conditioning temperature control data to obtain a correspondence between the historical air conditioning temperature control data and the historical vehicle exterior environment data and the historical vehicle interior environment data;

[0024] Based on the corresponding relationship, the historical vehicle exterior environment data is divided into a first interval using a preset parameter value interval, and based on the time series relationship between the historical vehicle exterior environment data and the historical vehicle interior environment data, the historical vehicle interior environment data is divided into a second interval according to the first interval division result, and the historical air-conditioning temperature adjustment data is divided into a third interval based on the second interval division result;

[0025] Based on the first interval division result and the second interval division result, the historical vehicle exterior environment data and the historical vehicle interior environment data within the same preset parameter value interval are quantified by the first parameter, and based on the first parameter quantification result, a change trend of the historical vehicle interior environment data relative to the historical vehicle exterior environment data is determined;

[0026] performing second parameter quantization on the historical in-vehicle environment data and the historical air-conditioning temperature adjustment data within the same preset parameter value interval based on the second interval division result and the third interval division result, and determining a functional relationship between the historical automobile air-conditioning temperature unit change value and the historical in-vehicle environment data value unit change value based on the second parameter quantization result;

[0027] At the same time, based on industry implementation standards, standard air conditioning temperature data under different in-vehicle environmental data is determined and used as auxiliary training samples;

[0028] The AI model is trained based on the changing trend of historical in-vehicle environmental data relative to historical external vehicle environmental data, the functional relationship between historical vehicle air conditioning temperature unit change values and historical in-vehicle environmental data unit change values, and auxiliary training samples;

[0029] Based on the training results, the obtained in-vehicle and out-of-vehicle environmental data are predicted and analyzed to obtain the user's temperature requirements under the current real-time in-vehicle and out-of-vehicle environmental data.

[0030] Preferably, in a method for AI pre-control of an automobile air conditioner, in step 2, monitoring the relative distance between the user and the vehicle based on the vehicle communication system includes:

[0031] Build a communication link between the car and the user's smart terminal, and send a continuous wireless signal of a specific frequency to the user's smart terminal based on the vehicle communication system;

[0032] Feedback a response signal to the car based on the user's smart terminal, and determine the signal strength of the feedback response signal;

[0033] The determined signal strength is matched with a table of mappings between signal strength and distance, and the relative distance between the user and the vehicle is obtained based on the matching result.

[0034] Preferably, an AI pre-control method for automobile air conditioning, in step 3, determining a pre-control scheme for the automobile air conditioning based on the relative distance and temperature requirements, and controlling the automobile air conditioning based on the pre-control scheme, includes:

[0035] Obtaining the relative distance between the user and the vehicle, and continuously monitoring the relative distance between the user and the vehicle;

[0036] Determine the change trend of the relative distance based on the monitoring results, and when the relative distance gradually decreases, determine that the pre-control start condition of the automobile air conditioner is met;

[0037] Determine the user's walking speed based on the change trend of the relative distance based on the determination result, and determine the remaining time for the user to reach the vehicle based on the relative distance and the walking speed;

[0038] Extracting the performance curve of the automobile air conditioner and determining pre-control conditions for the automobile air conditioner, wherein the pre-control conditions include maximizing energy utilization;

[0039] The temperature demand and remaining time are analyzed based on the performance curve and pre-control conditions to obtain the advance start time and step power change coefficient of the automobile air conditioner;

[0040] The wind speed of the automobile air conditioner at different times after startup is obtained based on the step power variation coefficient, and a pre-control scheme for the automobile air conditioner is obtained based on the wind speed at different times after startup and the advance startup time;

[0041] Control the automobile air conditioner based on the pre-control scheme.

[0042] Preferably, in a method for AI pre-control of an automobile air conditioner, in step 3, the automobile air conditioner is controlled based on the pre-control scheme, including:

[0043] When controlling the car air conditioner, real-time monitoring of the user's operating behavior data on the car air conditioner;

[0044] Determine local adjustment parameters in the AI model based on operational behavior data, and determine new operational habit characteristics of users for car air conditioners based on operational behavior data;

[0045] Adjust local adjustment parameters based on newly added operation habit features, and verify the AI model as a whole based on the adjustment results;

[0046] After verification, the optimization of the AI model is completed.

[0047] The present invention provides an automotive air-conditioning AI pre-control system, comprising:

[0048] The data acquisition module is used to obtain real-time data on the vehicle's internal and external environments based on sensors, and at the same time, retrieve the user's historical air conditioning temperature data;

[0049] The parameter determination module is used to comprehensively analyze the in-vehicle and out-of-vehicle environmental data and historical air conditioning temperature data based on the AI model to predict the user's temperature requirements and monitor the relative distance between the user and the vehicle based on the in-vehicle communication system;

[0050] The control module is used to determine a pre-control plan for the automobile air conditioner based on the relative distance and temperature requirements, and control the automobile air conditioner based on the pre-control plan.

[0051] The present invention provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and capable of running on the processor;

[0052] When the computer program is executed by a processor, the steps of any one of the automobile air-conditioning AI pre-control methods are implemented.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] By obtaining the environmental data inside and outside the car and the user's historical air-conditioning temperature control data, data support is provided for predicting the user's temperature needs. Secondly, the AI model is used to conduct a comprehensive analysis of the environmental data inside and outside the car and the historical air-conditioning temperature control data to achieve accurate and reliable prediction of the user's temperature needs. At the same time, the relative distance between the user and the vehicle is determined, which provides a reference for determining the pre-control plan for the air-conditioning. Finally, the pre-control plan for the car air-conditioning is reliably formulated according to the relative distance and temperature requirements, thereby realizing the control of the car air-conditioning, improving the intelligence and accuracy of the car air-conditioning control, while improving the user's driving experience and ensuring the air-conditioning temperature control effect.

[0055] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be achieved and obtained through the structures specifically pointed out in this application document.

[0056] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0058] Figure 1 This is a flow chart of an automotive air-conditioning AI pre-control method according to an embodiment of the present invention;

[0059] Figure 2 This is a flowchart of step 1 in an automotive air-conditioning AI pre-control method according to an embodiment of the present invention;

[0060] Figure 3 This is a structural diagram of an automotive air-conditioning AI pre-control system in an embodiment of the present invention. DETAILED DESCRIPTION

[0061] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0062] Example 1:

[0063] This embodiment provides an AI pre-control method for automobile air conditioner, such as Figure 1 As shown, including:

[0064] Step 1: Use sensors to obtain real-time data about the vehicle's internal and external environments, and retrieve the user's historical air conditioning temperature data.

[0065] Step 2: The AI model comprehensively analyzes the vehicle's internal and external environmental data and historical air conditioning temperature data to predict the user's temperature needs. The vehicle's in-vehicle communication system also monitors the user's relative distance from the vehicle.

[0066] Step 3: Determine a pre-control plan for the automobile air conditioner based on the relative distance and temperature requirements, and control the automobile air conditioner based on the pre-control plan.

[0067] In this embodiment, the vehicle interior and exterior environmental data include temperature data, humidity data, and light intensity inside and outside the vehicle.

[0068] In this embodiment, the historical air-conditioning temperature adjustment data refers to the actual temperature values previously set by the user for the air-conditioning under different environmental data.

[0069] In this embodiment, the AI model is trained based on industry standards (i.e., the optimal air-conditioning temperature data corresponding to different environmental data) and the user's air-conditioning temperature setting habits, and is a key tool for determining the user's temperature requirements.

[0070] In this embodiment, the vehicle communication system refers to a system that can communicate with a user's smart terminal, thereby facilitating determination of the relative distance between the user and the vehicle through the connection relationship between the vehicle and the smart terminal.

[0071] In this embodiment, the pre-control scheme refers to specific measures for pre-controlling the automobile air conditioner, including setting the temperature and wind speed, etc.

[0072] The beneficial effects of the above technical solution are: by obtaining the environmental data inside and outside the car and the user's historical air-conditioning temperature control data, data support is provided for predicting the user's temperature requirements. Secondly, the environmental data inside and outside the car and the historical air-conditioning temperature control data are comprehensively analyzed through the AI model to achieve accurate and reliable prediction of the user's temperature requirements. At the same time, the relative distance between the user and the vehicle is determined, which provides a reference for determining the pre-control plan for the air-conditioning. Finally, the pre-control plan for the car air-conditioning is reliably formulated according to the relative distance and temperature requirements, thereby realizing the control of the car air-conditioning, improving the intelligence and accuracy of the car air-conditioning control, and at the same time improving the user's driving experience and ensuring the air-conditioning temperature control effect.

[0073] Example 2:

[0074] Based on Example 1, this embodiment provides an AI pre-control method for automobile air conditioner, such as Figure 2 As shown, in step 1, real-time acquisition of vehicle interior and exterior environmental data is performed based on sensors, including:

[0075] Step 101: Determine the configuration requirements for sensor performance based on the control requirements, and configure the sensor parameters based on the configuration requirements, wherein the configuration requirements include data collection period and device sensitivity;

[0076] Step 102: performing time-series synchronization association on each sensor based on the parameter configuration result, and performing synchronization control on each sensor based on the time-series synchronization association result;

[0077] Step 103: Based on the synchronous control result, the sensors collect the vehicle interior and exterior environment data in real time, and manage the vehicle interior and exterior environment data separately.

[0078] In this embodiment, the control requirement refers to the sensitivity and accuracy of controlling the automobile air conditioner, which are known in advance.

[0079] In this embodiment, the time-series synchronization association refers to associating the sensors, that is, starting and executing the data monitoring task at the same time, in order to determine the vehicle interior and exterior environment data in the same time and space.

[0080] In this embodiment, differentiated management refers to dividing the monitored vehicle interior and exterior environment data.

[0081] The beneficial effect of the above technical solution is: by configuring the sensor performance, the sensors can be time-synchronized and associated according to the configuration results, thereby ensuring that the sensors can effectively collect the environmental data inside and outside the vehicle in the same time and space, providing reliable data guarantee for the pre-control of automobile air conditioning.

[0082] Example 3:

[0083] Based on Example 2, this example provides an AI pre-control method for automobile air conditioning, which manages the environmental data inside and outside the vehicle separately, including:

[0084] Acquire the obtained vehicle interior and exterior environment data, and perform a first differentiation on the vehicle interior and exterior environment data to obtain vehicle exterior environment data and vehicle interior environment data;

[0085] Clustering the external vehicle environment data and the internal vehicle environment data respectively, and performing a second differentiation on the external vehicle environment data and the internal vehicle environment data based on the clustering results to obtain corresponding environment category data sets;

[0086] The same environment category data sets under the vehicle exterior environment data and vehicle interior environment data are mapped and associated, and a regional category data comparison table is constructed based on the mapping association.

[0087] In this embodiment, the first distinction refers to distinguishing between the in-vehicle environment data and the out-vehicle environment data.

[0088] In this embodiment, the second distinction refers to distinguishing the data types in the in-vehicle environment data and the out-vehicle environment data, that is, obtaining corresponding environment category data sets.

[0089] In this embodiment, mapping association refers to associating data of the same environment category inside and outside the vehicle.

[0090] In this embodiment, the area category data comparison table refers to a data set corresponding to different areas inside and outside the vehicle.

[0091] The beneficial effect of the above technical solution is: by performing the first and second distinctions on the environmental data inside and outside the vehicle, effective differentiated management of the environmental data inside and outside the vehicle is achieved, thereby facilitating the effective determination of the specific environmental data corresponding to different areas inside and outside the vehicle, thereby providing convenience for the pre-control of the automobile air conditioner.

[0092] Example 4:

[0093] Based on Example 1, this embodiment provides an AI pre-control method for automobile air conditioning. In step 1, the user's historical air conditioning temperature adjustment data is retrieved, including:

[0094] Log in to the backend server of the user's vehicle and traverse data in the backend server based on the unique vehicle identity of the user's vehicle;

[0095] Filter out the air conditioning temperature control related data from the data traversal results, and analyze the air conditioning temperature control related data to obtain the actual temperature control data of the automobile air conditioning and the environmental data in the same space and time;

[0096] The actual temperature control data of the car air conditioner and the environmental data in the same time and space are retrieved, and the actual temperature control data of the car air conditioner and the environmental data in the same time and space are bound based on the time series to obtain the user's historical air conditioner temperature control data.

[0097] In this embodiment, the unique vehicle identity refers to unique information that can represent the identity of the vehicle, such as the vehicle frame number.

[0098] In this embodiment, the air-conditioning temperature control related data refers to data related to automobile air-conditioning temperature control extracted from the data traversal results, that is, the air-conditioning temperature control data is screened out.

[0099] The beneficial effect of the above technical solution is: by logging into the background server of the user's vehicle, the user's historical air-conditioning temperature control data can be accurately and effectively retrieved from the background server, which provides convenience for determining the user's air-conditioning temperature control habits, thereby ensuring the reliability of the car's air-conditioning pre-control.

[0100] Example 5:

[0101] Based on Example 1, this embodiment provides an AI pre-control method for automobile air conditioning. In step 2, a comprehensive analysis of the vehicle's internal and external environmental data and historical air conditioning temperature adjustment data is performed based on the AI model to predict the user's temperature requirements, including:

[0102] Obtaining the obtained vehicle interior and exterior environment data and historical air conditioning temperature control data, and parsing the historical air conditioning temperature control data to obtain a correspondence between the historical air conditioning temperature control data and the historical vehicle exterior environment data and the historical vehicle interior environment data;

[0103] Based on the corresponding relationship, the historical vehicle exterior environment data is divided into a first interval using a preset parameter value interval, and based on the time series relationship between the historical vehicle exterior environment data and the historical vehicle interior environment data, the historical vehicle interior environment data is divided into a second interval according to the first interval division result, and the historical air-conditioning temperature adjustment data is divided into a third interval based on the second interval division result;

[0104] Based on the first interval division result and the second interval division result, the historical vehicle exterior environment data and the historical vehicle interior environment data within the same preset parameter value interval are quantified by the first parameter, and based on the first parameter quantification result, a change trend of the historical vehicle interior environment data relative to the historical vehicle exterior environment data is determined;

[0105] performing second parameter quantization on the historical in-vehicle environment data and the historical air-conditioning temperature adjustment data within the same preset parameter value interval based on the second interval division result and the third interval division result, and determining a functional relationship between the historical automobile air-conditioning temperature unit change value and the historical in-vehicle environment data value unit change value based on the second parameter quantization result;

[0106] At the same time, based on industry implementation standards, standard air conditioning temperature data under different in-vehicle environmental data is determined and used as auxiliary training samples;

[0107] The AI model is trained based on the changing trend of historical in-vehicle environmental data relative to historical external vehicle environmental data, the functional relationship between historical vehicle air conditioning temperature unit change values and historical in-vehicle environmental data unit change values, and auxiliary training samples;

[0108] Based on the training results, the obtained in-vehicle and out-of-vehicle environmental data are predicted and analyzed to obtain the user's temperature requirements under the current real-time in-vehicle and out-of-vehicle environmental data.

[0109] In this embodiment, the correspondence between historical air-conditioning temperature adjustment data and historical vehicle exterior environment data and historical vehicle interior environment data refers to the correspondence between actual temperature adjustment values of the vehicle air-conditioning and vehicle interior and exterior environment data under different circumstances.

[0110] In this embodiment, the preset parameter value interval is set in advance and is used to divide the historical vehicle exterior environment data into different values.

[0111] In this embodiment, the first interval division refers to dividing the data in the historical vehicle exterior environment data that meets different value intervals according to the preset parameter value intervals.

[0112] In this embodiment, the second division refers to dividing the historical in-vehicle environment data at the same time according to the first division result, that is, dividing the historical in-vehicle environment data into intervals according to the parameter value intervals corresponding to the historical out-vehicle environment data.

[0113] In this embodiment, the third interval division refers to dividing the historical air-conditioning temperature adjustment data.

[0114] In this embodiment, the first parameter quantization refers to determining the values of the historical vehicle exterior environment data and the historical vehicle interior environment data within the same preset parameter value range.

[0115] In this embodiment, the second parameter quantization refers to determining the values of the historical in-vehicle environment data and the historical air-conditioning temperature adjustment data within the same preset parameter value range.

[0116] In this embodiment, the unit change value of the historical automobile air-conditioning temperature refers to the unit change amount of the historical automobile air-conditioning temperature, that is, one degree is one unit change value.

[0117] In this embodiment, the unit change value of the historical in-vehicle environment data refers to the unit change amount of the historical in-vehicle environment data.

[0118] In this embodiment, the functional relationship is used to represent the relative change relationship between the historical unit change value of the automobile air-conditioning temperature and the historical unit change value of the vehicle interior environment data value.

[0119] In this embodiment, the industry implementation standards are known in advance.

[0120] In this embodiment, the standard air-conditioning temperature data refers to specific air-conditioning temperature values corresponding to when a human body feels comfortable under different in-vehicle environment data determined according to industry implementation standards.

[0121] The beneficial effects of the above technical solution are: by analyzing the indoor and outdoor environmental data and the historical air-conditioning temperature control data, the correspondence between the historical air-conditioning temperature control data and the historical outdoor environmental data and the historical indoor environmental data is determined; secondly, the historical outdoor environmental data, the historical indoor environmental data and the historical air-conditioning temperature control data are divided into intervals and parameter quantified according to the correspondence, so as to determine the change trend of the historical indoor environmental data relative to the historical outdoor environmental data, the functional relationship between the historical automobile air-conditioning temperature unit change value and the historical indoor environmental data value unit change value; at the same time, considering the industry implementation standards, the standard air-conditioning temperature data under different indoor environmental data is determined; finally, the AI model is trained according to the change trend, functional relationship and standard air-conditioning temperature data, and the trained AI model is used to predict and analyze the indoor and outdoor environmental data, thereby achieving accurate and effective determination of the temperature requirements under the current real-time indoor and outdoor environmental data, thereby ensuring the accuracy of automobile air-conditioning pre-control.

[0122] Example 6:

[0123] Based on Example 1, this embodiment provides an AI pre-control method for automobile air conditioning. In step 2, the relative distance between the user and the vehicle is monitored based on the vehicle communication system, including:

[0124] Build a communication link between the car and the user's smart terminal, and send a continuous wireless signal of a specific frequency to the user's smart terminal based on the vehicle communication system;

[0125] Feedback a response signal to the car based on the user's smart terminal, and determine the signal strength of the feedback response signal;

[0126] The determined signal strength is matched with a table of mappings between signal strength and distance, and the relative distance between the user and the vehicle is obtained based on the matching result.

[0127] In this embodiment, the specific frequency is set in advance and is only used for communication between the car and the user's smart terminal.

[0128] In this embodiment, the mapping table between signal strength and distance is known in advance and is used to record the distances corresponding to different signal strengths.

[0129] The beneficial effect of the above technical solution is: by building a communication link between the car and the user's smart terminal, the distance between the user and the car can be determined based on the signal strength of the communication signal between the car and the user's smart terminal, thereby facilitating the timing of starting the car's air conditioner.

[0130] Example 7:

[0131] Based on Example 1, this embodiment provides an AI pre-control method for an automobile air conditioner. In step 3, a pre-control scheme for the automobile air conditioner is determined based on the relative distance and temperature requirement, and the automobile air conditioner is controlled based on the pre-control scheme, including:

[0132] Obtaining the relative distance between the user and the vehicle, and continuously monitoring the relative distance between the user and the vehicle;

[0133] Determine the change trend of the relative distance based on the monitoring results, and when the relative distance gradually decreases, determine that the pre-control start condition of the automobile air conditioner is met;

[0134] Determine the user's walking speed based on the change trend of the relative distance based on the determination result, and determine the remaining time for the user to reach the vehicle based on the relative distance and the walking speed;

[0135] Extracting the performance curve of the automobile air conditioner and determining pre-control conditions for the automobile air conditioner, wherein the pre-control conditions include maximizing energy utilization;

[0136] The temperature demand and remaining time are analyzed based on the performance curve and pre-control conditions to obtain the advance start time and step power change coefficient of the automobile air conditioner;

[0137] The wind speed of the automobile air conditioner at different times after startup is obtained based on the step power variation coefficient, and a pre-control scheme for the automobile air conditioner is obtained based on the wind speed at different times after startup and the advance startup time;

[0138] Control the automobile air conditioner based on the pre-control scheme.

[0139] In this embodiment, the changing trend of the relative distance refers to whether the relative distance between the user and the vehicle is gradually decreasing or gradually increasing.

[0140] In this embodiment, the performance curve refers to the temperature adjustment capability of the automobile air conditioner at different powers.

[0141] In this embodiment, the step power variation coefficient refers to the power control results of the automobile air conditioner at different times, that is, the power is larger in the early stage to achieve the purpose of rapid temperature adjustment, and the power is reduced in the later stage to achieve the purpose of temperature control.

[0142] The beneficial effect of the above technical solution is: by considering the relative distance between the user and the vehicle, the user's walking speed and the performance curve of the car air conditioner, the start-up time of the car air conditioner and the wind speed at different times can be determined, thereby realizing accurate and effective determination of the pre-control scheme of the car air conditioner, ensuring the accuracy and reliability of the pre-control of the car air conditioner.

[0143] Example 8:

[0144] Based on Example 1, this embodiment provides an AI pre-control method for an automobile air conditioner. In step 3, the automobile air conditioner is controlled based on the pre-control scheme, including:

[0145] When controlling the car air conditioner, real-time monitoring of the user's operating behavior data on the car air conditioner;

[0146] Determine local adjustment parameters in the AI model based on operational behavior data, and determine new operational habit characteristics of users for car air conditioners based on operational behavior data;

[0147] Adjust local adjustment parameters based on newly added operation habit features, and verify the AI model as a whole based on the adjustment results;

[0148] After verification, the optimization of the AI model is completed.

[0149] In this embodiment, the operation behavior data refers to the operation data applied by the user to the automobile air conditioner, that is, the operation data actively applied by the user on the basis of automobile pre-control.

[0150] In this embodiment, the local adjustment parameters refer to the specific data that need to be adjusted in the AI model.

[0151] In this embodiment, the newly added operation habit feature refers to the user's latest operation behavior habit determined based on the operation behavior data.

[0152] The beneficial effect of the above technical solution is: by real-time monitoring of the user's operating behavior data on the car air conditioner, the corresponding data in the AI model can be adjusted according to the operating behavior data, ensuring that the AI model can dynamically update the pre-control plan of the car air conditioner according to the user's behavioral habits, thereby ensuring the effect of the pre-control of the car air conditioner.

[0153] Example 9:

[0154] This embodiment provides an AI pre-control system for automobile air conditioners, such as Figure 3 Shown, including:

[0155] The data acquisition module is used to obtain real-time data on the vehicle's internal and external environments based on sensors, and at the same time, retrieve the user's historical air conditioning temperature data;

[0156] The parameter determination module is used to comprehensively analyze the in-vehicle and out-of-vehicle environmental data and historical air conditioning temperature data based on the AI model to predict the user's temperature requirements and monitor the relative distance between the user and the vehicle based on the in-vehicle communication system;

[0157] The control module is used to determine a pre-control plan for the automobile air conditioner based on the relative distance and temperature requirements, and control the automobile air conditioner based on the pre-control plan.

[0158] The beneficial effects of the above technical solution are: by obtaining the environmental data inside and outside the car and the user's historical air-conditioning temperature control data, data support is provided for predicting the user's temperature requirements. Secondly, the environmental data inside and outside the car and the historical air-conditioning temperature control data are comprehensively analyzed through the AI model to achieve accurate and reliable prediction of the user's temperature requirements. At the same time, the relative distance between the user and the vehicle is determined, which provides a reference for determining the pre-control plan for the air-conditioning. Finally, the pre-control plan for the car air-conditioning is reliably formulated according to the relative distance and temperature requirements, thereby realizing the control of the car air-conditioning, improving the intelligence and accuracy of the car air-conditioning control, and at the same time improving the user's driving experience and ensuring the air-conditioning temperature control effect.

[0159] Example 10:

[0160] This embodiment provides an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor;

[0161] When the computer program is executed by a processor, the steps of the automobile air-conditioning AI pre-control method as described in any one of embodiments 1 to 8 are implemented.

[0162] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. An AI pre-control method for automobile air conditioning, characterized in that: include: Step 1: Use sensors to obtain real-time data about the vehicle's internal and external environments, and retrieve the user's historical air conditioning temperature data. Step 2: The AI model comprehensively analyzes the vehicle's internal and external environmental data and historical air conditioning temperature data to predict the user's temperature needs. The vehicle's in-vehicle communication system also monitors the user's relative distance from the vehicle. Step 3: Determine a pre-control plan for the automobile air conditioner based on the relative distance and temperature requirements, and control the automobile air conditioner based on the pre-control plan.

2. The automobile air-conditioning AI pre-control method according to claim 1, characterized in that: In step 1, real-time data about the vehicle's internal and external environments is acquired using sensors, including: Determine the configuration requirements for sensor performance based on control needs, and configure sensor parameters based on the configuration requirements, where the configuration requirements include data collection cycle and device sensitivity; Performing time-series synchronization association on each sensor based on the parameter configuration result, and performing synchronous control on each sensor based on the time-series synchronization association result; Based on the synchronous control results, the sensors collect the vehicle's internal and external environmental data in real time, and manage the data separately.

3. The AI pre-control method for automobile air conditioner according to claim 2, characterized in that: Differentiate and manage vehicle and interior and exterior environmental data, including: Acquire the obtained vehicle interior and exterior environment data, and perform a first differentiation on the vehicle interior and exterior environment data to obtain vehicle exterior environment data and vehicle interior environment data; Clustering the external vehicle environment data and the internal vehicle environment data respectively, and performing a second differentiation on the external vehicle environment data and the internal vehicle environment data based on the clustering results to obtain corresponding environment category data sets; The same environment category data sets under the vehicle exterior environment data and vehicle interior environment data are mapped and associated, and a regional category data comparison table is constructed based on the mapping association.

4. The AI pre-control method for automobile air conditioner according to claim 1, characterized in that: In step 1, retrieve the user's historical air conditioning temperature data, including: Log in to the backend server of the user's vehicle and traverse data in the backend server based on the unique vehicle identity of the user's vehicle; Filter out the air conditioning temperature control related data from the data traversal results, and analyze the air conditioning temperature control related data to obtain the actual temperature control data of the automobile air conditioning and the environmental data in the same space and time; The actual temperature control data of the car air conditioner and the environmental data in the same time and space are retrieved, and the actual temperature control data of the car air conditioner and the environmental data in the same time and space are bound based on the time series to obtain the user's historical air conditioner temperature control data.

5. The automobile air-conditioning AI pre-control method according to claim 1, characterized in that: In step 2, the AI model comprehensively analyzes the vehicle's internal and external environmental data and historical air conditioning temperature data to predict the user's temperature needs, including: Obtaining the obtained vehicle interior and exterior environment data and historical air conditioning temperature control data, and parsing the historical air conditioning temperature control data to obtain a correspondence between the historical air conditioning temperature control data and the historical vehicle exterior environment data and the historical vehicle interior environment data; Based on the corresponding relationship, the historical vehicle exterior environment data is divided into a first interval using a preset parameter value interval, and based on the time series relationship between the historical vehicle exterior environment data and the historical vehicle interior environment data, the historical vehicle interior environment data is divided into a second interval according to the first interval division result, and the historical air-conditioning temperature adjustment data is divided into a third interval based on the second interval division result; Based on the first interval division result and the second interval division result, the historical vehicle exterior environment data and the historical vehicle interior environment data within the same preset parameter value interval are quantified by the first parameter, and based on the first parameter quantification result, a change trend of the historical vehicle interior environment data relative to the historical vehicle exterior environment data is determined; performing second parameter quantization on the historical in-vehicle environment data and the historical air-conditioning temperature adjustment data within the same preset parameter value interval based on the second interval division result and the third interval division result, and determining a functional relationship between the historical automobile air-conditioning temperature unit change value and the historical in-vehicle environment data value unit change value based on the second parameter quantization result; At the same time, based on industry implementation standards, standard air conditioning temperature data under different in-vehicle environmental data is determined and used as auxiliary training samples; The AI model is trained based on the changing trend of historical in-vehicle environmental data relative to historical external vehicle environmental data, the functional relationship between historical vehicle air conditioning temperature unit change values and historical in-vehicle environmental data unit change values, and auxiliary training samples; Based on the training results, the obtained in-vehicle and out-of-vehicle environmental data are predicted and analyzed to obtain the user's temperature requirements under the current real-time in-vehicle and out-of-vehicle environmental data.

6. The automobile air-conditioning AI pre-control method according to claim 1, characterized in that: In step 2, the relative distance between the user and the vehicle is monitored based on the vehicle communication system, including: Build a communication link between the car and the user's smart terminal, and send a continuous wireless signal of a specific frequency to the user's smart terminal based on the vehicle communication system; Feedback a response signal to the car based on the user's smart terminal, and determine the signal strength of the feedback response signal; The determined signal strength is matched with a table of mappings between signal strength and distance, and the relative distance between the user and the vehicle is obtained based on the matching result.

7. The AI pre-control method for automobile air conditioner according to claim 1, characterized in that: In step 3, a pre-control scheme for the automobile air conditioner is determined based on the relative distance and the temperature requirement, and the automobile air conditioner is controlled based on the pre-control scheme, including: Obtaining the relative distance between the user and the vehicle, and continuously monitoring the relative distance between the user and the vehicle; Determine the change trend of the relative distance based on the monitoring results, and when the relative distance gradually decreases, determine that the pre-control start condition of the automobile air conditioner is met; Determine the user's walking speed based on the change trend of the relative distance based on the determination result, and determine the remaining time for the user to reach the vehicle based on the relative distance and the walking speed; Extracting the performance curve of the automobile air conditioner and determining pre-control conditions for the automobile air conditioner, wherein the pre-control conditions include maximizing energy utilization; The temperature demand and remaining time are analyzed based on the performance curve and pre-control conditions to obtain the advance start time and step power change coefficient of the automobile air conditioner; The wind speed of the automobile air conditioner at different times after startup is obtained based on the step power variation coefficient, and a pre-control scheme for the automobile air conditioner is obtained based on the wind speed at different times after startup and the advance startup time; Control the automobile air conditioner based on the pre-control scheme.

8. The automobile air-conditioning AI pre-control method according to claim 1, characterized in that: In step 3, the automobile air conditioner is controlled based on the pre-control scheme, including: When controlling the car air conditioner, real-time monitoring of the user's operating behavior data on the car air conditioner; Determine local adjustment parameters in the AI model based on operational behavior data, and determine new operational habit characteristics of users for car air conditioners based on operational behavior data; Adjust local adjustment parameters based on newly added operation habit features, and verify the AI model as a whole based on the adjustment results; After verification, the optimization of the AI model is completed.

9. An AI pre-control system for automobile air conditioning, characterized in that: include: The data acquisition module is used to obtain real-time data on the vehicle's internal and external environments based on sensors, and at the same time, retrieve the user's historical air conditioning temperature data; The parameter determination module is used to comprehensively analyze the in-vehicle and out-of-vehicle environmental data and historical air conditioning temperature data based on the AI model to predict the user's temperature requirements and monitor the relative distance between the user and the vehicle based on the in-vehicle communication system; The control module is used to determine a pre-control plan for the automobile air conditioner based on the relative distance and temperature requirements, and control the automobile air conditioner based on the pre-control plan.

10. An electronic device, characterized in that: comprising a processor, a memory, and a computer program stored in the memory and operable to run on the processor; When the computer program is executed by a processor, the steps of the automobile air-conditioning AI pre-control method according to any one of claims 1 to 8 are implemented.

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