A method, device, medium and equipment for sun protection of an automobile

Abstract

The present invention discloses a car sun protection method, device, medium and equipment. The method includes: obtaining weather information and information of a target vehicle; wherein, the weather information includes an ultraviolet index, and the information of the target vehicle includes the temperature inside the vehicle and the humidity inside the vehicle; predicting the sun protection requirement of the target vehicle based on the weather information, the information of the target vehicle and a pre-configured machine learning model to obtain a prediction result; and controlling the opening and closing degree of the window of the target vehicle according to the prediction result to adjust the temperature inside the vehicle and the humidity inside the vehicle. Embodiments of the present invention can predict the sun protection requirement of a vehicle according to weather and vehicle information, and correspondingly control the opening and closing of the window, so as to improve the temperature and humidity inside the vehicle, enabling the driver and passengers not to endure the sultriness when just getting into the car in summer, realizing automatic sun protection adjustment of the car. In addition, since there is no need to use a refrigeration device, energy conservation and emission reduction can be effectively achieved.

Description

Technical Field

[0001] The present invention relates to the field of automotive sun protection, and particularly to an automotive sun protection method, device, medium and equipment. Background Art

[0002] In summer, when a car is parked in an open area and exposed to direct sunlight, it is easy to cause changes in the temperature and humidity inside the car, resulting in stuffiness inside the car, thus bringing discomfort to the driver and passengers who just get into the car. Therefore, the most important purpose of automotive sun protection is to adjust the temperature and humidity inside the car. In the prior art, traditional automotive sun protection methods usually use sun visors or sunshade films to prevent sunlight from shining into the car, thereby physically isolating the sunlight.

[0003] However, in the prior art, these methods require the use of additional physical devices and need to be manually operated and set, resulting in inconvenience in use. Summary of the Invention

[0004] In order to solve the above technical problems, embodiments of the present invention propose an automotive sun protection method, device, medium and equipment. By using a machine learning model to predict the sun protection requirements of a target vehicle according to weather information and target vehicle information, the opening and closing of the vehicle window are controlled, so as to adjust the temperature and humidity inside the car and realize the automatic sun protection function of the car.

[0005] In order to achieve the above object, an embodiment of the present invention provides an automotive sun protection method, including:

[0006] Obtain weather information and information of the target vehicle; wherein, the weather information includes the ultraviolet index, and the information of the target vehicle includes the temperature inside the car and the humidity inside the car;

[0007] Based on the weather information, the information of the target vehicle and a pre-configured machine learning model, predict the sun protection requirements of the target vehicle to obtain a prediction result;

[0008] According to the prediction result, control the opening and closing degree of the window of the target vehicle to adjust the temperature and humidity inside the car.

[0009] Further, the machine learning model includes an input layer, a hidden layer and an output layer; then, the step of predicting the sun protection requirements of the target vehicle based on the weather information, the information of the target vehicle and a pre-configured machine learning model to obtain a prediction result specifically includes: taking the weather information and the information of the target vehicle as input variables, inputting them into the hidden layer through the input layer, enabling the hidden layer to perform calculations and inputting the calculated result into the output layer, so as to obtain the prediction result calculated and output by the output layer.

[0010] Further, the weather information further includes the outside temperature, outside humidity, and wind speed, the information of the target vehicle further includes the vehicle model, vehicle color, and vehicle parking position, the machine learning model is configured using the multi-layer perceptron algorithm, the hidden layer includes 10 neurons, and the output layer includes 1 neuron;

[0011] Both the hidden layer and the output layer are calculated by Equation (1):

[0012]

[0013] where y k represents the output of the k-th neuron, w kj is the weight between the j-th input layer and the k-th neuron, x j is the j-th input variable, b k is the bias of the k-th neuron, and f k is the activation function of the k-th neuron.

[0014] Further, the error function of the multi-layer perceptron algorithm includes the mean square error, the calculation method of the error gradient of the multi-layer perceptron algorithm includes the backpropagation algorithm, and the activation function includes the Sigmoid function.

[0015] Further, the opening degree of the window of the target vehicle is given by Equation (2):

[0016]

[0017] where x is the opening degree of the window of the target vehicle, D max is the maximum value of the preset prediction result, and D is the prediction result.

[0018] Further, after adjusting the inside temperature and inside humidity, the method further includes: re-monitoring the current inside temperature and inside humidity, and inputting the re-monitored inside temperature and inside humidity into the machine learning model to iterate and optimize the machine learning model.

[0019] An embodiment of the present invention further provides an automobile sun protection device, including:

[0020] An information acquisition module, configured to acquire weather information and information of a target vehicle; wherein, the weather information includes the ultraviolet index, and the information of the target vehicle includes the inside temperature and inside humidity;

[0021] A prediction module, configured to predict the sun protection requirement of the target vehicle based on the weather information, the information of the target vehicle, and a pre-configured machine learning model, and obtain a prediction result;

[0022] A control module for controlling the opening and closing degree of the window of the target vehicle according to the prediction result to adjust the temperature and humidity inside the vehicle.

[0023] Further, the machine learning model includes an input layer, a hidden layer, and an output layer. Then, predicting the sun protection requirement of the target vehicle based on the weather information, the information of the target vehicle, and a pre-configured machine learning model to obtain a prediction result specifically includes: using the weather information and the information of the target vehicle as input variables, inputting them into the hidden layer through the input layer, enabling the hidden layer to perform calculations and inputting the calculated result into the output layer to obtain the prediction result calculated and output by the output layer.

[0024] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the vehicle sun protection method described in any one of the above are implemented.

[0025] An embodiment of the present invention further provides a computer device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the steps of the vehicle sun protection method described in any one of the above are implemented.

[0026] In summary, the present invention has the following beneficial effects:

[0027] By adopting the embodiment of the present invention, it is possible to predict the sun protection requirement of the vehicle according to the weather and vehicle information, correspondingly control the opening and closing of the window, thereby improving the temperature and humidity inside the vehicle, enabling the driver and passengers not to endure the stuffiness when just getting into the car in summer, realizing automatic sun protection adjustment of the vehicle. In addition, since there is no need to use refrigeration equipment, energy conservation and emission reduction can be effectively achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 is a flowchart of an embodiment of a vehicle sun protection method provided by the present invention;

[0029] Figure 2 is a structural diagram of an embodiment of a vehicle sun protection device provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0031] SeeFigure 1 , which is a schematic flowchart of an embodiment of the car sun protection method provided by the present invention. The method includes steps S1 to S3, which are specifically as follows:

[0032] S1. Obtain weather information and information of the target vehicle; wherein, the weather information includes the ultraviolet index, and the information of the target vehicle includes the temperature and humidity inside the vehicle;

[0033] It should be noted that the ultraviolet index is used to measure the possible damage degree of ultraviolet radiation in the sun rays reaching the earth's surface to the human skin under the current weather, so as to represent the urgency of sun protection under the current weather.

[0034] S2. Based on the weather information, the information of the target vehicle, and a pre-configured machine learning model, predict the sun protection requirement of the target vehicle to obtain a prediction result;

[0035] Preferably, the machine learning model includes an input layer, a hidden layer, and an output layer; then, the predicting the sun protection requirement of the target vehicle based on the weather information, the information of the target vehicle, and a pre-configured machine learning model to obtain a prediction result specifically includes: using the weather information and the information of the target vehicle as input variables, inputting them into the hidden layer through the input layer, enabling the hidden layer to perform calculations and inputting the calculated result into the output layer to obtain the prediction result calculated and output by the output layer.

[0036] As an improvement of the above solution, the weather information further includes the temperature outside the vehicle, the humidity outside the vehicle, and the wind speed, the information of the target vehicle further includes the vehicle model, the vehicle color, and the vehicle parking position, the machine learning model is configured using the multi-layer perceptron algorithm, the hidden layer includes 10 neurons, and the output layer includes 1 neuron;

[0037] Both the hidden layer and the output layer perform calculations through formula (1):

[0038]

[0039] where y k represents the output of the kth neuron, w kj is the weight between the jth input layer and the kth neuron, x j is the jth input variable, b k is the bias of the kth neuron, and f k is the activation function of the kth neuron.

[0040] As a further improvement of the above solution, the error function of the multi-layer perceptron algorithm includes the mean square error, the calculation method of the error gradient of the multi-layer perceptron algorithm includes the backpropagation algorithm, and the activation function includes the Sigmoid function.

[0041] Exemplarily, the multi-layer perceptron (MLP) algorithm is used to predict the sun protection requirements of vehicles. Assume that the input variables include weather data and vehicle information, where the weather data includes temperature T, humidity H, and wind speed V, and the vehicle information includes vehicle model M, vehicle color C, and vehicle parking position P. The MLP algorithm adopts a three-layer structure, where the hidden layer has 10 neurons and the output layer has 1 neuron. The activation function of the MLP algorithm uses the Sigmoid function, the error function is the mean square error (MSE), and the calculation of the error gradient uses the backpropagation algorithm (BP). The output of the MLP algorithm is the sun protection requirement D of the vehicle.

[0042] S3. According to the prediction result, control the opening and closing degree of the window of the target vehicle to adjust the temperature and humidity inside the vehicle.

[0043] Preferably, the opening and closing degree of the window of the target vehicle is in Equation (2):

[0044]

[0045] where x is the opening and closing degree of the window of the target vehicle, D max is the maximum value of the preset prediction result, and D is the prediction result.

[0046] It should be noted that the maximum value of the prediction result can be set by the user. For example, the user can adjust it according to the current season, or it can be set as the maximum value of the prediction results output by the machine learning model in previous times.

[0047] Preferably, after adjusting the temperature and humidity inside the vehicle, it further includes: re-monitoring the current temperature and humidity inside the vehicle, and inputting the re-monitored temperature and humidity inside the vehicle into the machine learning model to iterate and optimize the machine learning model.

[0048] It should be noted that by monitoring the temperature and humidity inside the vehicle and feeding the real-time data back into the machine learning model. For example, assume that the monitored temperature inside the vehicle is T1 and the humidity is H1, then T1 and H1 can be used as input variables and fed back into the machine learning model for iterating and optimizing the model; and according to the iteration and optimization results of the model, further optimize the prediction model for the sun protection requirements of the vehicle, improve the accuracy and precision of the model, so as to achieve more effective vehicle sun protection. The real-time monitored temperature and humidity data inside the vehicle can also be stored and processed for future data analysis and maintenance.

[0049] See Figure 2 , which is a schematic structural diagram of an embodiment of the car sun protection device provided by the present invention.

[0050] An information acquisition module 101 is configured to acquire weather information and information of a target vehicle; wherein, the weather information includes an ultraviolet index, and the information of the target vehicle includes the temperature and humidity inside the vehicle;

[0051] It should be noted that the ultraviolet index is used to measure the possible damage degree of ultraviolet radiation in the sun rays reaching the earth's surface to the human skin under the current weather, so as to represent the urgency of sun protection under the current weather.

[0052] A prediction module 102 is configured to predict the sun protection requirement of the target vehicle based on the weather information, the information of the target vehicle, and a pre-configured machine learning model, and obtain a prediction result;

[0053] Preferably, the machine learning model includes an input layer, a hidden layer, and an output layer; then, predicting the sun protection requirement of the target vehicle based on the weather information, the information of the target vehicle, and a pre-configured machine learning model to obtain a prediction result specifically includes: using the weather information and the information of the target vehicle as input variables, inputting them into the hidden layer through the input layer, enabling the hidden layer to perform calculations and inputting the calculated result into the output layer, so as to obtain the prediction result calculated and output by the output layer.

[0054] As an improvement of the above solution, the weather information further includes the temperature outside the vehicle, the humidity outside the vehicle, and the wind speed, the information of the target vehicle further includes the vehicle model, the vehicle color, and the vehicle parking position, the machine learning model is configured using a multi-layer perceptron algorithm, the hidden layer includes 10 neurons, and the output layer includes 1 neuron;

[0055] Both the hidden layer and the output layer perform calculations through Equation (1):

[0056]

[0057] where y k represents the output of the kth neuron, w kj is the weight between the jth input layer and the kth neuron, x j is the jth input variable, b k is the bias of the kth neuron, and f k is the activation function of the kth neuron.

[0058] As a further improvement of the above solution, the error function of the multi-layer perceptron algorithm includes the mean square error, the calculation method of the error gradient of the multi-layer perceptron algorithm includes the backpropagation algorithm, and the activation function includes the Sigmoid function.

[0059] Exemplarily, the multi-layer perceptron (MLP) algorithm is used to predict the sun protection requirements of vehicles. Assume that the input variables include weather data and vehicle information, where the weather data includes temperature T, humidity H, and wind speed V, and the vehicle information includes vehicle model M, vehicle color C, and vehicle parking position P. The MLP algorithm adopts a three-layer structure, where the hidden layer has 10 neurons and the output layer has 1 neuron. The activation function of the MLP algorithm uses the Sigmoid function, the error function is the mean square error (MSE), and the calculation of the error gradient uses the backpropagation algorithm (BP). The output of the MLP algorithm is the sun protection requirement D of the vehicle.

[0060] The control module 103 is configured to control the opening and closing degree of the window of the target vehicle according to the prediction result to adjust the temperature and humidity inside the vehicle.

[0061] Preferably, the opening and closing degree of the window of the target vehicle is in formula (2):

[0062]

[0063] where x is the opening and closing degree of the window of the target vehicle, D max is the maximum value of the preset prediction result, and D is the prediction result.

[0064] It should be noted that the maximum value of the prediction result can be set by the user. For example, the user can adjust it according to the current season, or it can be set as the maximum value of the prediction results output by the machine learning model in previous times.

[0065] Preferably, after adjusting the temperature and humidity inside the vehicle, it further includes: re-monitoring the current temperature and humidity inside the vehicle, and inputting the re-monitored temperature and humidity inside the vehicle into the machine learning model to iterate and optimize the machine learning model.

[0066] It should be noted that by monitoring the temperature and humidity inside the vehicle and feeding the real-time data back into the machine learning model. For example, assuming the monitored temperature inside the vehicle is T1 and the humidity is H1, then T1 and H1 can be used as input variables and fed back into the machine learning model for iterative and optimization of the model. And based on the iterative and optimization results of the model, further optimize the prediction model for the vehicle's sun protection requirements, improve the accuracy and precision of the model, so as to achieve more effective vehicle sun protection. The real-time monitored temperature and humidity data inside the vehicle can also be stored and processed for future data analysis and maintenance.

[0067] The embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the steps of the automotive sun protection method described in any one of the above.

[0068] The embodiment of the present invention also provides a computer device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps of the automotive sun protection method described in any one of the above.

[0069] The computer device of this embodiment includes: a processor, a memory, and a computer program stored in the memory and operable on the processor, such as an automotive sun protection program. When the processor executes the computer program, it implements the steps in each of the above automotive sun protection method embodiments, such as Figure 1 the steps S1 to S3 shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in each of the above device embodiments, such as 101 to 103.

[0070] Exemplarily, the computer program can be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program in the computer device.

[0071] The computer device can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The computer device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the schematic diagram is only an example of the computer device, and does not constitute a limitation on the computer device. It may include more or fewer components than shown, or combine certain components, or different components. For example, the computer device may also include input / output devices, network access devices, a bus, etc.

[0072] The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the computer device and connects various parts of the entire computer device through various interfaces and circuits.

[0073] The memory may be used to store the computer programs and / or modules. The processor realizes various functions of the computer device by running or executing the computer programs and / or modules stored in the memory, and by calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. Among them, the program storage area may store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area may store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0074] Among them, if the modules / units integrated in the computer device are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0075] In summary, the present invention has the following beneficial effects:

[0076] By adopting the embodiment of the present invention, it is possible to predict the sun protection requirements of the vehicle according to weather and vehicle information, and correspondingly control the opening and closing of the vehicle window, thereby improving the temperature and humidity inside the vehicle, so that the driver and passengers do not need to endure the sultriness when just getting into the car in summer, realizing automatic sun protection adjustment of the vehicle. In addition, since there is no need to use refrigeration equipment, it can effectively save energy and reduce emissions.

[0077] Through the description of the above embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus a necessary hardware platform, and of course, it can also be implemented entirely through hardware. Based on such an understanding, all or part of the technical solutions of the present invention that contribute to the background technology can be embodied in the form of a software product. The computer software product can be stored in a storage medium such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present invention.

[0078] The above is the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art in the technical field of the present invention, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements are also regarded as the protection scope of the present invention.

Claims

1. An automobile sun protection method, characterized in that, Including: Obtain weather information and information of the target vehicle; wherein, the weather information includes wind speed and the ultraviolet index representing the urgency of sun protection under the current weather, and the information of the target vehicle includes vehicle model, vehicle color, interior temperature and interior humidity; Based on the weather information, the information of the target vehicle and a pre-configured machine learning model, predict the sun protection requirement of the target vehicle to obtain a prediction result; According to the prediction result, control the opening and closing degree of the window of the target vehicle to adjust the interior temperature and interior humidity; The opening and closing degree of the window of the target vehicle is in formula (2): where x is the opening degree of the window of the target vehicle, D max is the maximum value of the preset prediction result, and D is the prediction result; wherein, the maximum value of the prediction result is the maximum value of the prediction results output by the machine learning model in previous times; wherein, after adjusting the interior temperature and interior humidity, it further includes: Re-monitor to obtain the current interior temperature and interior humidity, and input the re-monitored interior temperature and interior humidity into the machine learning model to iterate and optimize the machine learning model.

2. The automotive sunscreen method according to claim 1, characterized in that, The machine learning model includes an input layer, a hidden layer and an output layer; Then, the predicting the sun protection requirement of the target vehicle based on the weather information, the information of the target vehicle and a pre-configured machine learning model to obtain a prediction result specifically includes: Take the weather information and the information of the target vehicle as input variables, input them into the hidden layer through the input layer, so that the hidden layer performs calculations and inputs the calculated result into the output layer to obtain the prediction result calculated and output by the output layer.

3. The automotive sunscreen method according to claim 2, characterized in that, The weather information further includes exterior temperature and exterior humidity, the information of the target vehicle further includes the vehicle parking position, the machine learning model is configured by using a multi-layer perceptron algorithm, the hidden layer includes 10 neurons, and the output layer includes 1 neuron; Both the hidden layer and the output layer perform calculations through formula (1): where, y k represents the output of the k-th neuron, w kj is the weight between the j-th input layer and the k-th neuron, x j is the j-th input variable, b k is the bias of the k-th neuron, f k is the activation function of the k-th neuron.

4. The automotive sun protection method according to claim 3, characterized in that, The error function of the multi-layer perceptron algorithm includes mean square error, the calculation method of the error gradient of the multi-layer perceptron algorithm includes backpropagation algorithm, and the activation function includes Sigmoid function.

5. An automobile sun protection device, characterized in that, Including: An information acquisition module, configured to obtain weather information and information of the target vehicle; wherein, the weather information includes wind speed and the ultraviolet index representing the urgency of sun protection under the current weather, and the information of the target vehicle includes vehicle model, vehicle color, interior temperature and interior humidity; A prediction module, configured to predict the sun protection requirement of the target vehicle based on the weather information, the information of the target vehicle and a pre-configured machine learning model to obtain a prediction result; A control module, configured to control the opening and closing degree of the window of the target vehicle according to the prediction result to adjust the interior temperature and interior humidity; The opening and closing degree of the window of the target vehicle is in formula (2): where x is the opening degree of the window of the target vehicle, D max is the maximum value of the preset prediction result, and D is the prediction result; wherein, the maximum value of the prediction result is the maximum value of the prediction results output by the machine learning model in previous times; wherein, after adjusting the interior temperature and interior humidity, it further includes: Re - monitor to obtain the current in - vehicle temperature and in - vehicle humidity, and input the re - monitored in - vehicle temperature and in - vehicle humidity into the machine learning model to iterate and optimize the machine learning model.

6. The automobile sun protection device according to claim 5, characterized in that, The machine learning model includes an input layer, a hidden layer, and an output layer; Then, predicting the sun protection requirement of the target vehicle based on the weather information, the information of the target vehicle, and a pre - configured machine learning model to obtain a prediction result specifically includes: Taking the weather information and the information of the target vehicle as input variables, inputting them into the hidden layer through the input layer, enabling the hidden layer to perform calculations and inputting the calculated result into the output layer to obtain the prediction result calculated and output by the output layer.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, it implements the vehicle sun protection method according to any one of claims 1 to 4.

8. A computer device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the vehicle sun protection method according to any one of claims 1 to 4.

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