Induction system based on vehicle-mounted electric defroster
By installing an induction system based on the on-board electric defroster on the vehicle, using the LSTM network to predict the window temperature, and automatically adjusting the working status of the electric defroster with humidity data, the problem of frequent operation of the defroster caused by distraction caused by drivers during peak winters is solved, and the automatic defrost of the vehicle is realized, improving the defrost efficiency and safety.
Patent Information
- Application Number
- CN202411738329.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-29
- Publication Date
- 2025-02-28
AI Technical Summary
During winter rush hour, drivers need to frequently operate the defroster, which leads to distraction and increases the risk of traffic accidents. How to achieve automatic defrost of vehicles has become an urgent problem.
By installing an induction system based on the vehicle on-board electric defroster, the system includes an environmental collection module, a humidity collection module, a comprehensive prediction module and a PLC control module, the window temperature prediction is predicted using the LSTM network, and the working status of the electric defroster is automatically adjusted in combination with the humidity data to realize automatic defroster defroster.
The system can predict the fogging moment of the car window in advance, automatically turn on the electric defroster, improve defrost efficiency, ensure clear field of view, reduce energy consumption, and reduce safety hazards through the overheating protector.
Smart Images
Figure CN119527231A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the technical field of vehicle defrosting, and more particularly to an induction system based on a vehicle-mounted electric defroster. Background Art
[0002] At present, there are three types of bus defrosters: water heating, electric heating, and integrated water and electricity heating. Water heating is generally used in tourist buses, while electric heating and integrated water and electricity heating are used in city buses. At present, there are two types of defrost control methods. One is the rocker switch control, which is generally used for water heating. It can be divided into the control of the fan high and low gear switch and the control of the internal and external circulation switch; the other is the knob switch, which controls electric heating and water and electricity heating, and can be divided into 1, 2, 3, 4, and 5 gears to control the wind speed, the button controls the heating on and off, and the button controls the internal and external circulation.
[0003] During the winter rush hour, there is a large passenger flow and complex road conditions. Drivers have to drive and deal with the front windshield at the same time. Frequent operation of the defroster is distracting and can easily cause traffic accidents.
[0004] Therefore, how to automatically defrost a vehicle is a problem that those skilled in the art need to solve urgently. Summary of the invention
[0005] In view of this, the present invention provides a sensing system based on a vehicle-mounted electric defroster, which realizes early prediction of the time when the vehicle fogs up by using the number of people in the vehicle and the temperature and humidity data around the windows based on a window temperature prediction model, thereby activating the corresponding function of the electric defroster in advance and realizing automatic defrosting of the vehicle.
[0006] In order to achieve the above object, the present invention adopts the following technical solution:
[0007] A sensing system based on a vehicle-mounted electric defroster comprises: an environment collection module, a humidity collection module, a humidity determination module, a comprehensive prediction module, a PLC control module, and a vehicle-mounted electric defroster, wherein the environment collection module collects external environment information and internal vehicle data and transmits the information to the comprehensive prediction module; the humidity collection module collects external humidity data and sends the information to the humidity determination module, and when the humidity is greater than a preset humidity threshold, sends a humidity signal to the PLC control module; the comprehensive prediction module predicts the temperature of the vehicle window based on the external environment information and the internal vehicle data through a preset window temperature prediction model, obtains a temperature prediction value after a fixed time period, generates different control signals according to the temperature prediction values, and sends the signals to the PLC control module, and the PLC control module generates different control instructions according to the control signals and the humidity signals, and controls the vehicle-mounted electric defroster according to the control instructions.
[0008] Preferably, the vehicle-mounted electric defroster includes: a fan, a high-voltage PTC heating component, an external air inlet device, and an overheating protector. The fan is responsible for blowing air, the high-voltage PTC heating component is responsible for generating heat, the external air inlet device is responsible for selecting external circulation and internal circulation, and the overheating protector controls the heating to stop when the high-voltage PTC heating component heats up to a preset temperature threshold.
[0009] Preferably, the environment collection module collects temperature data around the vehicle windows, as well as passenger number data and vehicle interior temperature data.
[0010] Preferably, the window temperature prediction model preset in the comprehensive prediction module specifically includes: using LSTM network to establish the window temperature prediction model, the LSTM layer includes memory unit c t , input gate i t , output gate o t and forget gate f t ; Memory unit c t Used to remember the state value within any time interval; the gate is used to manage and determine whether the information flow needs to enter and exit the unit, and is used to select and reject information transmitted through the network; the input gate i t Determines the amount of new information added to the cell state; forget gate f t Determines what information is discarded from the cell state; output gate o t Select the output information to be regarded as the current state; use the input gate i t and forget gate f t The filtered input data updates the memory cell state and outputs the gate o t And the memory unit c after the tanh activation function t Multiply together to form the final output of the network.
[0011] Preferably, the comprehensive prediction module generates different control signals according to the temperature prediction value, specifically including:
[0012] When the temperature prediction value is ≤5°C, a first temperature control signal is generated;
[0013] When 5°C<temperature prediction value≤25°C, a second temperature control signal is generated;
[0014] When 25°C < the predicted temperature value, a third temperature control signal is generated.
[0015] Preferably, the PLC control module generates different control instructions specifically including:
[0016] The first control instruction: receiving the first temperature control signal and the humidity signal, controlling the vehicle-mounted electric defroster to turn on the heating function and the external circulation function;
[0017] The second control instruction: only receiving the first temperature control signal, controls the vehicle-mounted electric defroster to turn on the heating function;
[0018] The third control instruction: after receiving the second temperature control signal and the humidity signal, control the vehicle-mounted electric defroster to turn on the heating function for 60 seconds and turn on the external circulation function;
[0019] Fourth control instruction: upon receiving only the second temperature control signal, control the vehicle-mounted electric defroster to turn on the heating function for 60 seconds;
[0020] A fifth control instruction: upon receiving the third temperature control signal and the humidity signal, controlling the vehicle-mounted electric defroster to turn on the blowing function and the external circulation function;
[0021] Sixth control instruction: upon receiving only the third temperature control signal, control the vehicle-mounted electric defroster to turn on the blowing function.
[0022] Preferably, it also includes a knob main switch, which includes an AUTO button, a fan knob, an external air inlet knob, and a heating button. The AUTO button turns on the sensing system or manual defrosting as needed. During manual defrosting, the air volume, the heating button on and off, and the internal and external circulation functions need to be manually controlled according to the fan knob.
[0023] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a sensing system based on a vehicle-mounted electric defroster, which obtains real-time indoor and outdoor environmental data through an environment collection module and a humidity collection module, and intelligently predicts the window temperature based on a comprehensive prediction module, and can automatically adjust the working state of the vehicle-mounted electric defroster to improve the defrosting efficiency; by adjusting the control instructions according to the dynamic changes of humidity and temperature, the system can optimize the window defrosting effect under different environmental conditions to ensure that a good field of vision is always maintained; the PLC control module issues control instructions according to actual needs, and turns on or off functions such as heating and fans in a timely manner, thereby reducing energy consumption and improving the energy efficiency of the system; the high-voltage PTC heating component is equipped with an overheating protector, which can automatically stop heating when a temperature abnormality is identified, reducing the safety hazards caused by overheating; the use of an LSTM network to predict the window temperature can effectively capture the time dependency in environmental data, improve the accuracy of temperature prediction, and provide a more reliable basis for control instructions. BRIEF DESCRIPTION OF THE DRAWINGS
[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.
[0025] Figure 1 A system structure diagram provided by the present invention;
[0026] Figure 2 A control instruction generation diagram provided by the present invention. DETAILED DESCRIPTION
[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0028] The embodiment of the present invention discloses a sensing system based on a vehicle-mounted electric defroster, such as Figure 1 As shown, it includes: an environment collection module, a humidity collection module, a humidity determination module, a comprehensive prediction module, a PLC control module, and an on-board electric defroster. The environment collection module collects outdoor environment information and in-vehicle data and transmits them to the comprehensive prediction module; the humidity collection module collects outdoor humidity data and sends it to the humidity determination module. When the humidity is greater than a preset humidity threshold, a humidity signal is sent to the PLC control module; the comprehensive prediction module predicts based on the outdoor environment information and the in-vehicle data through a preset window temperature prediction model, obtains a temperature prediction value after a fixed time, generates different control signals according to the temperature prediction value, and sends them to the PLC control module. The PLC control module generates different control instructions according to the control signal and the humidity signal, and controls the on-board electric defroster according to the control instructions.
[0029] In a specific embodiment, the vehicle-mounted electric defroster includes: a fan, a high-voltage PTC heating component, an external air inlet device, and an overheating protector. The fan is responsible for blowing air, the high-voltage PTC heating component is responsible for generating heat, the external air inlet device is responsible for selecting external circulation and internal circulation, and the overheating protector controls the heating to stop when the high-voltage PTC heating component reaches a preset temperature threshold.
[0030] In a specific embodiment, the environment collection module collects temperature data around the car windows, as well as passenger number data and in-car temperature data, wherein the temperature data around the car windows is the external environment information, and the in-car data is the passenger number data and in-car temperature data.
[0031] In a specific embodiment, the window temperature prediction model preset in the comprehensive prediction module specifically includes: using an LSTM network to establish a window temperature prediction model, the LSTM layer includes a memory unit c t , input gate i t , output gate o t and forget gate f t; Memory unit c t Used to remember the state value within any time interval; the gate is used to manage and determine whether the information flow needs to enter and exit the unit, and is used to select and reject information transmitted through the network; the input gate i t Determines the amount of new information added to the cell state; forget gate f t Determines what information is discarded from the cell state; output gate o t Select the output information to be regarded as the current state; use the input gate i t and forget gate f t The filtered input data updates the memory cell state and outputs the gate o t And the memory unit c after the tanh activation function t Multiply together to form the final output of the network.
[0032] In a specific embodiment, the model parameters of the window temperature prediction model are adjusted by a loss function, specifically including: sample data of N different vehicles at time t and the first τ time steps are sequentially sent to the established window temperature prediction model, the model obtains N temperature prediction values at time t through N forward calculations, and adjusts the parameters of the window temperature prediction model through the gradient descent method to back propagate the error. When the model is updated at time t by reverse gradient, the total error of each parameter is the sum of the errors at each time within the time step required for prediction, specifically as follows:
[0033]
[0034] in, is the error of each parameter at time t, and τ is the time step required for prediction;
[0035] The gradient of each parameter at time t is updated to the mean of the N vehicle temperature prediction values, calculated as follows:
[0036]
[0037] Among them, Loss is the error value of network back propagation, p represents the input gate, forget gate, output gate and memory state, W p,t , U p,t is the LSTM network weight matrix at time t, N means that there are N sample data corresponding to different vehicles participating in the model training, is the input gate, forget gate, output gate and memory state output value of the LSTM network at time t, B p,t is the input gate, forget gate, output gate and memory state bias term of the LSTM network at time t;
[0038] The error value Loss of the network back propagation is used to predict the temperature value E p With the actual value E rCalculate the loss function L, specifically:
[0039] Loss=L(E r ,E p );
[0040] Finally, the parameter adjustment methods of the window temperature prediction model training process are as follows:
[0041]
[0042] Among them, γ is the learning rate of neural network training; p = i,f,o,c represents the input gate, forget gate, output gate and memory state.
[0043] Repeat the above training process until the model converges and completes the model training.
[0044] In a specific embodiment, generating different control signals according to the temperature prediction value in the comprehensive prediction module specifically includes:
[0045] When the temperature prediction value is ≤5°C, a first temperature control signal is generated;
[0046] When 5°C<temperature prediction value≤25°C, a second temperature control signal is generated;
[0047] When 25°C < the predicted temperature value, a third temperature control signal is generated.
[0048] In a specific embodiment, Figure 2 As shown, the PLC control module generates different control instructions, including:
[0049] The first control instruction: receiving the first temperature control signal and the humidity signal, controlling the vehicle-mounted electric defroster to turn on the heating function and the external circulation function;
[0050] The second control instruction: only receiving the first temperature control signal, controls the vehicle-mounted electric defroster to turn on the heating function;
[0051] The third control instruction: after receiving the second temperature control signal and the humidity signal, the vehicle-mounted electric defroster is controlled to turn on the heating function for 60 seconds and turn on the external circulation function;
[0052] Fourth control instruction: when only the second temperature control signal is received, the vehicle-mounted electric defroster is controlled to turn on the heating function for 60 seconds;
[0053] The fifth control instruction: upon receiving the third temperature control signal and the humidity signal, controlling the vehicle-mounted electric defroster to turn on the blowing function and the external circulation function;
[0054] Sixth control instruction: only receiving the third temperature control signal, controlling the vehicle-mounted electric defroster to turn on the blowing function.
[0055] In a specific embodiment, it also includes a knob main switch, which includes an AUTO button, a fan knob, an external air inlet knob, and a heating button. The AUTO button turns on the sensing system or manual defrosting as needed. During manual defrosting, the air volume, the heating button on and off, and the internal and external circulation functions need to be manually controlled according to the fan knob.
[0056] The present invention uses the collected humidity data (when the car windows are frosted, the humidity change in the air is usually relatively slow. This is because frost is formed when the water vapor in the air drops below the dew point when it contacts the cooled car window surface, causing the water vapor to condense into solid frost. This process takes a certain amount of time, especially in a low temperature environment. When the car windows are frosted, the air humidity inside and outside the car may be different, but the overall humidity change will not be very rapid), passenger number data and temperature data (window surrounding temperature data and interior temperature data), and combines the collected temperature data with the passenger number data to perform real-time prediction of window temperature changes, compares the obtained temperature prediction data with a preset temperature range, and thus generates different control signals, and combines the humidity signal generated by the humidity data to perform functional control on the vehicle-mounted electric defroster according to preset control instructions, thereby achieving real-time prediction and removal of window fogging.
[0057] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.
[0058] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A sensing system based on a vehicle-mounted electric defroster, characterized in that: include: An environment collection module, a humidity collection module, a humidity determination module, a comprehensive prediction module, a PLC control module, and an on-board electric defroster. The environment collection module collects external environment information and internal data and transmits them to the comprehensive prediction module; the humidity collection module collects external humidity data and sends it to the humidity determination module. When the humidity is greater than a preset humidity threshold, a humidity signal is sent to the PLC control module; the comprehensive prediction module predicts based on the external environment information and the internal data through a preset window temperature prediction model, obtains a temperature prediction value after a fixed period of time, generates different control signals according to the temperature prediction value, and sends them to the PLC control module. The PLC control module generates different control instructions according to the control signal and the humidity signal, and controls the on-board electric defroster according to the control instruction.
2. The induction system based on the vehicle electric defroster according to claim 1, characterized in that: The vehicle-mounted electric defroster includes: a fan, a high-voltage PTC heating component, an external air inlet device, and an overheating protector. The fan is responsible for blowing air, the high-voltage PTC heating component is responsible for generating heat, the external air inlet device is responsible for selecting external circulation and internal circulation, and the overheating protector controls the heating to stop when the high-voltage PTC heating component heats up to a preset temperature threshold.
3. The induction system based on the vehicle electric defroster according to claim 1, characterized in that: The environment collection module collects temperature data around the vehicle windows, as well as passenger number data and vehicle interior temperature data.
4. The induction system based on the vehicle electric defroster according to claim 3, characterized in that: The window temperature prediction model preset in the comprehensive prediction module specifically includes: using LSTM network to establish a window temperature prediction model, the LSTM layer includes memory units c t , input gate i t , output gate o t and forget gate f t ; Memory unit c t Used to remember the state value within any time interval; the gate is used to manage and determine whether the information flow needs to enter and exit the unit, and is used to select and reject information transmitted through the network; the input gate i t Determines the amount of new information added to the cell state; forget gate f t Determines what information is discarded from the cell state; output gate o t Select the output information to be regarded as the current state; use the input gate i t and forget gate f t The filtered input data updates the memory cell state and outputs the gate o t And the memory unit c after the tanh activation function t Multiply together to form the final output of the network.
5. The induction system based on the vehicle electric defroster according to claim 1, characterized in that: The comprehensive prediction module generates different control signals according to the temperature prediction value, specifically including: When the temperature prediction value is ≤5°C, a first temperature control signal is generated; When 5°C<temperature prediction value≤25°C, a second temperature control signal is generated; When 25°C < the predicted temperature value, a third temperature control signal is generated.
6. The induction system based on the vehicle electric defroster according to claim 5, characterized in that: The PLC control module generates different control instructions specifically including: The first control instruction: receiving the first temperature control signal and the humidity signal, controlling the vehicle-mounted electric defroster to turn on the heating function and the external circulation function; The second control instruction: only receiving the first temperature control signal, controls the vehicle-mounted electric defroster to turn on the heating function; The third control instruction: after receiving the second temperature control signal and the humidity signal, control the vehicle-mounted electric defroster to turn on the heating function for 60 seconds and turn on the external circulation function; Fourth control instruction: upon receiving only the second temperature control signal, control the vehicle-mounted electric defroster to turn on the heating function for 60 seconds; A fifth control instruction: upon receiving the third temperature control signal and the humidity signal, controlling the vehicle-mounted electric defroster to turn on the blowing function and the external circulation function; Sixth control instruction: upon receiving only the third temperature control signal, control the vehicle-mounted electric defroster to turn on the blowing function.
7. The induction system based on the vehicle electric defroster according to claim 2, characterized in that: It also includes a knob main switch, which includes an AUTO button, a fan knob, an external air inlet knob, and a heating button. The AUTO button turns on the sensing system or manual defrosting as needed. During manual defrosting, the air volume, the heating button on and off, and the internal and external circulation functions need to be manually controlled according to the fan knob.