Multi-parameter monitor for air environment of automobile cabin

Abstract

The utility model discloses a multi-parameter monitor for the air environment of an automobile cabin, which belongs to the technical field of monitors and is intelligently controlled according to the air environment corresponding to different automobile conditions so as to achieve the purposes of accurate monitoring and energy conservation. The device is composed of a heat insulation shell, an aperture-like valve (a movable rim, a fixed rim, a blade connecting rod and aperture blades), a sliding block valve, a button, a connecting part, a detachable small gas chamber, a one-way valve, a rubber gasket, a locking structure, a fixed sensor module, an air pipe and an air pump. The system consists of a human-computer interaction interface module, a constant current source module, an automatic resistance voltage and current acquisition and sampling module, a communication module and a calculation module, can be applied to various fields, realizes on-line measurement of various parameters of an air environment in a vehicle, is based on electric signals and an intelligent algorithm, and is short in measurement time and high in measurement accuracy. In addition, the device is high in integration level, multiple in function, small in size and low in manufacturing cost, and the measurement accuracy and precision can meet general requirements.

Description

TECHNICAL FIELD

[0001] The utility model relates to a kind of automobile cabin air environment multi-parameter monitor, belong to monitor technical field. BACKGROUND

[0002] Vehicle gas sensor plays a key role in automobile exhaust treatment system, is the "eyes and ears" of automobile electronic control system, directly determines the control effect of automobile emissions, with the tide of automobile intelligentization and automation, vehicle gas sensor is ushering in unprecedented development opportunity.

[0003] Types and applications: vehicle gas sensor is various, mainly including oxygen sensor, nitrogen oxide sensor, ammonia sensor, particulate matter sensor and hydrogen sensor for new energy vehicles, etc.;

[0004] These sensors bear the task of accurately collecting gas temperature, pressure, flow, component concentration and other information in automobile engine, which helps to reduce emission pollution and improve energy use efficiency.

[0005] Market demand: with the increasing attention of consumers to indoor air quality and the improvement of environmental protection consciousness, the market demand of vehicle gas sensor is growing;

[0006] At the same time, the development of new energy vehicles also brings more development opportunities for vehicle gas sensor industry.

[0007] The existing vehicle air sensor still has deficiencies in precision and stability, the response time is slow, real-time monitoring and prediction cannot be realized, which leads to measurement error

[0008] The prior art cannot monitor all the indoor air environment parameters with one sensor, and the long-term stability and repeatability of traditional sensor also need to be further improved.

[0009] Connector wear and life: the connector of vehicle gas sensor wears fast, which may affect the service life and reliability of sensor.

[0010] The data processing system of existing sensor has little association with vehicle system, and cannot realize overall control.

[0011] Therefore, a kind of automobile cabin air environment multi-parameter monitor is needed to improve the above deficiencies. INVENTION CONTENTS

[0012] The main purpose of the utility model is to provide a kind of automobile cabin air environment multi-parameter monitor.

[0013] The purpose of the utility model can be achieved by adopting the following technical scheme:

[0014] A kind of automobile cabin air environment multi-parameter monitor, including heat insulation and insulation shell for limiting, heat insulation and insulation shell is connected with small gas chamber around, the outer end of small gas chamber is installed with one-way valve in oblique;

[0015] The side of heat insulation and insulation shell is equipped with rotating disc, the side of rotating disc is covered with diaphragm valve, the side of diaphragm valve is installed with connecting component, the other end of connecting component is swing-connected with heat insulation and insulation shell;

[0016] The inner side of heat insulation and insulation shell is installed with slider valve around.

[0017] Preferably, the outer end of rotating disc is installed with locking structure, the side of locking structure is installed with button.

[0018] Preferably, heat insulation and insulation shell is covered with rubber gasket inside.

[0019] Preferably, one-way valve is one-way in and out structure.

[0020] Preferably, diaphragm valve and connecting component are at least one group structure.

[0021] Preferably, diaphragm valve is mutually adhering structure.

[0022] The beneficial technical effects of the present application are as follows:

[0023] The automobile cabin air environment multi-parameter monitor provided by the present application integrates multiple sensors in one device, and can measure multiple air parameters simultaneously.

[0024] 1. The present application has a self-cleaning function, which is realized by using a gas pump and a mechanical structure, prolonging the service life of the device.

[0025] 2. The present application can automatically select the opening frequency of the valve according to different air environments, achieving accurate monitoring and energy saving.

[0026] 3. The mechanical device of the present application is composed of diaphragm valves and multiple valves, which has good sealing performance and quick opening and closing response.

[0027] 4. The present application can realize single-valve adjustment independently, and can be reasonably dormant in a relatively safe driving environment to improve the power efficiency of the automobile and the service life of the sensor.

[0028] 5. The sensor module of the present application can be freely disassembled, assembled and replaced.

[0029] 6. Multiple steps are performed for data preprocessing to ensure the quality of data.

[0030] 7. Based on the BP neural network model, the data can be quickly and accurately classified and converted, realizing high-precision monitoring and prediction. Based on the simulated annealing algorithm, the optimal valve opening frequency regulation can be realized. BRIEF DESCRIPTION OF DRAWINGS

[0031] Figure 1 A valve closing schematic view of a preferred embodiment of the automobile cabin air environment multi-parameter monitor according to the utility model;

[0032] Figure 2 A valve opening schematic view of a preferred embodiment of the automobile cabin air environment multi-parameter monitor according to the utility model;

[0033] Figure 3 A device bottom class aperture structure schematic view of a preferred embodiment of the automobile cabin air environment multi-parameter monitor according to the utility model;

[0034] Figure 4 A device use and algorithm diagram of a preferred embodiment of the automobile cabin air environment multi-parameter monitor according to the utility model;

[0035] Figure 5 A simulated annealing solution process diagram of a preferred embodiment of the automobile cabin air environment multi-parameter monitor according to the utility model.

[0036] In the figure: 1 - heat insulation shell, 2 - aperture valve, 3 - slider valve, 4 - button, 5 - connecting part, 6 - small gas chamber, 7 - one-way valve, 8 - rubber gasket, 9 - locking structure, 10 - fixed sensor module. DETAILED DESCRIPTION

[0037] In order to make the technical personnel in the art more clear and clear the technical scheme of the utility model, the utility model is described in further detail below in combination with examples and drawings, but the implementation manner of the utility model is not limited thereto.

[0038] As shown in Figure 1 - Figure 4 The automobile cabin air environment multi-parameter monitor provided by the embodiment includes a heat insulation shell 1 for limiting, the heat insulation shell 1 is circumnavigated and communicated with a small gas chamber 6, and a one-way valve 7 is installed at the outer end of the small gas chamber 6 in an oblique manner.

[0039] A turntable 10 is sleeved on one side of the heat insulation shell 1, the turntable 10 is opened and closed and covered with an aperture valve 2 on one side, the aperture valve 2 is installed with a connecting part 5 on one side, and the other end of the connecting part 5 is swing-connected with the heat insulation shell 1.

[0040] The slider valve 3 is installed on the inner side of the heat insulation shell 1.

[0041] The outer end of the rotary disc 10 is provided with a locking structure 9, and one side of the locking structure 9 is provided with a button 4.

[0042] The heat-insulating and insulating shell 1 is covered with rubber pads 8.

[0043] The one-way valve 7 is a one-way in-out structure.

[0044] The aperture valve 2 and the connecting component 5 are at least one set of structures.

[0045] The aperture valve 2 is a mutual fitting structure.

[0046] As shown in Figure 1 - Figure 4 The working process of the automobile cabin air environment multi-parameter monitor provided by the embodiment is as follows:

[0047] Step 1. Place the device in the automobile cabin, turn on the switch, power on, and connect the vehicle system.

[0048] Step 2. When working normally, the slider valves of each gas sensor module are all automatically opened every 3 minutes.

[0049] Step 3. Open the aperture valve, start the air pump to suck the measured gas, and then close the aperture valve. The sensors in the air chamber output data in the form of electrical signals.

[0050] Step 4. The data is transmitted to the vehicle system, preprocessed, and then input into the trained BP neural network model.

[0051] Step 5. The model outputs the corresponding monitoring results in real time, predicts the gas concentration of the next time (within 3 minutes in the future), and updates the database.

[0052] Example 1

[0053] As shown in Figure 4The basic gas species to be measured include CO2, CO, PM2.5, O2 and VOCS, as shown. The collected sample data includes the concentration of the known gas measured by the sensor and the corresponding electrical signal. The data preprocessing is performed on the collected data to reduce noise, clean data, remove abnormal data and fill in missing values, so as to ensure the reliability of the training result. The abnormal value may be an outlier, i.e. a data point deviating greatly from the overall data situation (common 3σ criterion), or a data point exceeding an unreasonable range. In the analysis, the abnormal value should be excluded first. The abnormal value detection logic is to determine the data set of the variable according to the set or value, to filter out the data within the abnormal value detection range, and to null or fill in other valid values for the abnormal value.

[0054] 70% of the data samples are input to the BP neural network model for training. The process of the BP neural network mainly includes two stages. The first stage is the forward propagation of the signal, from the input layer to the hidden layer and finally to the output layer. The second stage is the backward propagation of the error, from the output layer to the hidden layer and finally to the input layer, to adjust the weights and biases of the hidden layer to the output layer and the weights and biases of the input layer to the hidden layer.

[0055] 30% of the data samples are used to test the accuracy of the model and evaluate the performance of the training result, such as MSE, MAE index, etc. The smaller the value, the higher the accuracy of the model. RMSE (Root Mean Square Error) is the square root of MSE. The smaller the value, the higher the accuracy of the model. MAE (Mean Absolute Error) reflects the actual situation of the prediction error. The smaller the value, the higher the accuracy of the model. MAPE (Mean Absolute Percentage Error) is a percentage value of MAE. The smaller the value, the higher the accuracy of the model. R 2 : Compared with the case of using only the mean value, the closer the result is to 1, the higher the accuracy of the model.

[0056] The device is placed in the car cabin, the switch is turned on, the power is turned on, and the car system is connected.

[0057] When working normally, the slider valves of each gas sensor module are automatically opened every 3 minutes.

[0058] The aperture valve is opened, the gas pump is started to inhale the gas to be measured, the gas is introduced into each gas chamber, and then the aperture valve is closed. The sensors in the gas chamber output data in the form of electrical signals representing the characteristics of the gas.

[0059] The data is transmitted to the car system, preprocessed, and then input into the trained BP neural network model.

[0060] The model outputs corresponding monitoring results in real time, predicts the gas concentration for the next time (within 3 minutes in the future), and updates the database.

[0061] Since the car encounters different vehicle conditions during driving, corresponding to different air environments in the car, for a more severe air environment, the measurement frequency needs to be increased, that is, the opening frequency of the slide valve and the air pump is increased, and for a healthier air environment, the air environment does not need to be measured frequently.

[0062] According to the comprehensive analysis of the real-time data and the predicted data output by the model, the comprehensive analysis of the gas parameter data is obtained.

[0063] Input the data into the set control formula:

[0064] Objective function: min E1+E2

[0065] Constraint condition:

[0066]

[0067] β1x1+β2x2+β3x3+β4x4+β5x5+β6x6=E2

[0068] E k >0;α k ≥0;β k >0;x k ∈(0 or 1)

[0069] Wherein E1 represents the comprehensive concentration of air that is harmful to the human body, and E2 represents the loss corresponding to the opening of the valve (such as sensor loss, power loss);

[0070] K k represents the comprehensive concentration of the gas in the kth air chamber (real-time concentration + predicted concentration), α k represents the influence factor of the harmful degree of the gas in the kth air chamber to the human body, β k represents the loss factor when the kth air chamber valve is opened, (the influence factors α k , β k For different gas types and sensors, there are different corresponding values, for example, CO2 gas and CO gas, the harmful degree of CO gas to the human body is higher, so the influence factor of CO is larger than that of CO2; and the loss factor is determined according to the price and service life of the sensor.

[0071] x k represents whether to increase the opening frequency of the valve of the kth air chamber (30s once), 1 represents opening, and 0 represents closing.

[0072] The formula is solved using a simulated annealing algorithm:

[0073] Simulated annealing algorithm: Simulated annealing algorithm (SA) is a heuristic search algorithm that simulates the annealing process in physics to solve optimization problems. This algorithm is particularly suitable for solving complex optimization problems and can jump out of local optimal solution to find global optimal solution. The name of the simulated annealing algorithm comes from the principle of solid annealing, in which materials change their crystalline state by heating and cooling to eliminate defects and improve crystalline quality. In optimization problems, the algorithm controls temperature and randomness to jump out of local optimal solution in search space in order to find global optimal solution.

[0074] Simulated annealing algorithm principle: The core principle of simulated annealing algorithm is to simulate the annealing process in physics, regarding the solution state of the problem as the state of the physical system and the objective function value as the energy of the system. The algorithm starts from a higher initial temperature, accompanied by the continuous decrease of temperature parameter, and combines the probability of jumping to randomly search for the global optimal solution of the objective function in the solution space. In the process of solid annealing, when heated, the particles inside the solid become disordered with the increase of temperature; when cooled slowly, the particles tend to be ordered, and at each temperature, the system reaches equilibrium state, and finally at room temperature, the system reaches ground state with minimum internal energy. Simulated annealing algorithm gives the search process a time-varying probability of jumping that tends to zero, so as to effectively avoid falling into local minimum and finally tend to global optimum.

[0075] Example two

[0076] The objective function in this paper is the profit function min E1+E2, which contains 0-1 decision variables for each monitoring, representing the selection of whether the current valve is open or not.

[0077] In the solving process, first generate a random initial solution, set the temperature to 1000°C, the maximum number of iterations to 500, and the temperature decay coefficient a = 0.98. In each iteration, generate a new solution by randomly changing a variable of the current solution. According to the difference between the new solution and the current solution and the current temperature, decide whether to accept the new solution. Use the following criteria to determine whether to accept the new solution

[0078] If the new solution is better, directly accept the new solution;

[0079] If the new solution is worse, the following probability accepts the new solution

[0080]

[0081] Update the best solution: compare the current solution with the best solution found in history, and if the current solution is better than the best value found, update the best solution and other parameters

[0082] The temperature update formula is as follows:

[0083] T = a · T

[0084] Wherein a is a temperature attenuation coefficient (usually between 0.8 and 0.99), this process gradually reduces the probability of the algorithm accepting a poor solution, so that the algorithm converges to the global optimal solution.

[0085] When the maximum number of iterations is reached or the temperature drops to a certain set threshold, the algorithm terminates and returns the best solution When the car system is turned off (i.e. when the car is not in use), the internal power supply of the self-starting device is powered on All sensor valves are fully open, the aperture valve is opened, the air pump is turned on to ventilate and clean for 30s, all valves are closed, and the power is turned off.

[0086] The above is only a further embodiment of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art within the scope disclosed by the present application, according to the technical scheme and concept of the present application, makes equivalent replacement or change, all belong to the protection scope of the present application.

Claims

1. A multi-parameter monitor for the air environment of an automotive cabin, characterized by: It includes a heat insulation casing (1) for limiting, a small gas chamber (6) is communicated around the heat insulation casing (1), a one-way valve (7) is installed at the outer end of the small gas chamber (6) with an inclined cut; A rotating disc (10) is sleeved on one side of the heat insulation casing (1), a diaphragm valve (2) is opened and closed on one side of the rotating disc (10), a connecting part (5) is installed on one side of the diaphragm valve (2), and the other end of the connecting part (5) is swingly connected with the heat insulation casing (1); A sliding block valve (3) is installed around the inner side of the heat insulation casing (1).

2. The multi-parameter monitor of claim 1, wherein: A locking structure (9) is installed at the outer end of the rotating disc (10), and a button (4) is installed on one side of the locking structure (9).

3. The multi-parameter monitor of claim 2, wherein: the air quality sensor is a photoionization detector (PID) sensor. A rubber gasket (8) is covered in the heat insulation casing (1).

4. The multi-parameter monitor of claim 3, wherein: The one-way valve (7) is a one-way in-out structure.

5. The multi-parameter monitor of claim 4, wherein: The diaphragm valve (2) and the connecting part (5) are at least one set of structure.

6. The multi-parameter monitor of claim 5, wherein: The diaphragm valve (2) is a mutual adhering structure.

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