Leakage current type particulate matter sensor, working curve calibration method and measuring device

Abstract

The invention discloses a leakage current type particulate matter sensor, relates to the field of sensors, and solves the technical problem of detection defects caused by lack of a calibration mode of an existing leakage current type particulate matter sensor. The sensor comprises a shell, a high-voltage electrode and a conductive shell, a cavity is formed in the shell, a fixing plate is fixedly installed in the cavity, the high-voltage electrode is fixedly installed at the bottom of the fixing plate, the conductive shell is fixedly installed at the position, located on the periphery of the high-voltage electrode, of the cavity, and a concentration testing area is formed between the conductive shell and the high-voltage electrode. And an outer side protection area is formed between the conductive shell and the inner wall of the cavity. The invention further discloses a calibration method and a measurement device for the working curve of the leakage current type particulate matter sensor. According to the method, the leakage current value of the particulate matter is calculated, and the influence of the flow velocity and temperature of the airflow on the particulate matter sensor is considered, so that the fitted calibration curve better conforms to the actual working condition.

Description

Technical Field

[0001] The present invention relates to the field of sensors, and more specifically, to a leakage current type particle sensor and a working curve calibration method and a measuring device. Background Art

[0002] Automobile emission pollution is becoming increasingly serious, among which particulate matter emissions from automobile exhaust have become an important source of suspended particulate matter in the air. Diesel particulate filter (DPF) has a significant effect on capturing exhaust particulate matter, so it is widely used to remove vehicle exhaust particulate matter emissions. If the DPF fails, the emission of particulate matter will far exceed the emission regulations, so we usually use sensors to monitor the DPF. Among them, the leakage current particulate matter sensor can monitor the particle concentration of DPF exhaust in real time and accurately in harsh exhaust environments.

[0003] The leakage current type particulate matter sensor determines the carbon content by the electrical signal output by the sensor. However, the measurement accuracy of the existing leakage current type particulate matter sensor is general and there is a lack of accurate and effective particulate matter sensor measurement devices and measurement methods, which makes the measurement of the leakage current type particulate matter sensor have certain defects. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a leakage current type particle matter sensor and a working curve calibration method and a measuring device in view of the deficiencies in the prior art, so as to solve the technical problem that the existing leakage current type particle matter sensor lacks a calibration method and causes detection defects.

[0005] A leakage current type particulate matter sensor described in the present invention comprises a shell, a high-voltage electrode and a conductive shell, wherein a cavity is provided in the shell, a fixing plate is fixedly installed in the cavity, a high-voltage electrode is fixedly installed at the bottom of the fixing plate, a conductive shell is fixedly installed at the periphery of the high-voltage electrode in the cavity, a concentration test area is formed between the conductive shell and the high-voltage electrode, an outer protection area is formed between the conductive shell and the inner wall of the cavity, a total exhaust outlet is opened at the bottom of the conductive shell, and a total exhaust inlet is formed by the bottom of the conductive shell and the bottom of the cavity.

[0006] As a further improvement, L-shaped plates are fixedly installed on both sides of the inner wall of the cavity, an exhaust inlet of the concentration test area is formed between the L-shaped plate and the conductive shell, an exhaust outlet of the concentration test area is formed between the conductive shell and the high-voltage electrode, and a boss is formed between the high-voltage electrode and the outer side of the fixed plate.

[0007] A method for calibrating a working curve of a leakage current type particle sensor, the method comprising placing the leakage current type particle sensor as described in any one of claims 1-2 in a standard particle smoke environment, obtaining a voltage value of the leakage current type particle sensor, calibrating a curve of the leakage current type particle sensor using the particle data and the voltage value, and correcting the curve using a sparrow search algorithm.

[0008] As a further improvement, the voltage value is obtained by converting the current value of the leakage current type particulate matter sensor, and the expression for calculating the current value of the leakage current type particulate matter sensor is:

[0009]

[0010] Among them, I r is the leakage current value measured between the two capacitor plates of the high voltage electrode, a is the leakage current constant; ε r is the relative dielectric constant of the dielectric; S is the area of ​​the capacitor plates, U is the voltage between the two capacitor plates, k is the electrostatic force constant, and d is the distance between the two capacitor plates.

[0011] Furthermore, the method for calibrating the curve of the leakage current particulate matter sensor is that the particulate matter data includes particulate matter concentration, airflow velocity and airflow temperature, and the calibration curve is fitted by a multivariate linear fitting method and based on the particulate matter concentration, airflow velocity, airflow temperature and voltage value.

[0012] Furthermore, the expression for fitting the calibration curve is:

[0013] f(x 1 , x 2 , x 3 )=α 1 x 1 +α 2 x 2 +α 3 x 3 +β;

[0014] Among them, x 1 is the particle concentration, x 2 is the air velocity, x 3 is the air flow temperature, f(x 1 ,x 2 ,x 3 ) is the voltage value, α 1 is the first coefficient to be fitted, α 2 is the second coefficient to be fitted, α 3 is the third coefficient to be fitted, and β represents the constant to be fitted.

[0015] Furthermore, the method of correcting the curve by using the sparrow search algorithm is:

[0016] Step 1: Setting the maximum number of iterations of the sparrow search algorithm, taking all the data points on the curve as the population, and taking each data point as an individual;

[0017] Step 2: Randomly initialize the positions of individuals in the population;

[0018] Step 3: Calculate the initial fitness value of the individual at the initial position, sort the individuals into the discoverer identity, follower identity and warning identity according to the initial fitness value, and iterate the position of the individuals in the population;

[0019] Step 4: Calculate the updated fitness value of the individual after iteration, update and sort the identity of the individual according to the updated fitness value, and when the updated fitness value of the individual is greater than the initial fitness value, update the identity of the individual;

[0020] Step 5: When the updated fitness value of the individual is equal to the preset fitness threshold, the optimization is stopped and the updated fitness value and individual position are output; when the updated fitness value of the individual is not equal to the preset fitness threshold, the above fourth step is returned to continue iteration;

[0021] When the number of iterations is equal to the maximum number of iterations, the updated fitness closest to the fitness threshold in the iteration cycle and the individual position corresponding to the updated fitness are output.

[0022] A measuring device for implementing the working curve calibration method of the above-mentioned leakage current type particle sensor, the measuring device includes a flue gas pretreatment box, a flue gas generator and a DPF, the first air inlet of the flue gas pretreatment box is connected to an external air source through the flue gas generator, the DPF is installed in the exhaust port of the flue gas pretreatment box, the flue gas pretreatment box is connected to a circulation unit, the circulation unit is installed with a detection module and a leakage current type particle sensor, and the DPF, the flue gas generator and the circulation unit are all connected to the flue gas pretreatment box.

[0023] As a further improvement, the circulation unit includes a circulation pump and a heating device, the first circulation port of the flue gas pretreatment box is connected to the second circulation port of the flue gas pretreatment box through the circulation pump and the heating device connected in sequence, the third circulation port is connected to the second circulation port through a pipe, a detection module is installed on the pipe connecting the first circulation port of the flue gas pretreatment box and the second circulation port of the flue gas pretreatment box, a leakage particle sensor is installed on the pipe connecting the second circulation port of the flue gas pretreatment box and the third circulation port of the flue gas pretreatment box, and the first circulation port, the second circulation port and the third circulation port are all connected.

[0024] Furthermore, the detection module includes a flow meter, a temperature sensor and a flue gas transient meter, a pressure relief port is provided on the flue gas pretreatment box, a pressure relief valve is installed on the pressure relief port, and a pressure gauge is installed on the flue gas pretreatment box.

[0025] Beneficial Effects

[0026] The advantages of the present invention are:

[0027] 1. The present invention provides a leakage current type particle sensor, which forms a concentration test area between a high voltage electrode and a conductive shell. The particles release electric charges in the concentration test area to form a leakage current phenomenon, thereby calculating the leakage current value and voltage value of the particles, thereby realizing the calculation of the leakage current value of the particles.

[0028] 2. The present invention uses a multivariate linear fitting method to fit the calibration curve. The fitting curve not only takes into account the influence of the particle concentration in the airflow on the sensor output voltage, but also takes into account the influence of the airflow velocity and temperature on the particle sensor, so that the fitted calibration curve is more in line with the actual working conditions.

[0029] 3. The present invention introduces a sparrow search algorithm to optimize the calibration curve, which reduces the errors caused by defects in the detection method itself, insufficient precision of instruments and equipment, and improper and non-standard operation of calibration personnel during the calibration process to a certain extent, and improves the accuracy of the calibration curve. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a schematic diagram of the internal structure of the leakage current type particle sensor of the present invention;

[0031] Figure 2 It is a schematic diagram of the structure of the sensor measuring device of the present invention;

[0032] Figure 3 The present invention is a flow chart of optimizing the sparrow search algorithm.

[0033] Wherein: 1-leakage current particle sensor, 101-high voltage electrode, 102-conductive shell, 103-cavity, 104-fixed plate, 105-concentration test area, 106-outer protection area, 107-total exhaust outlet, 108-total exhaust inlet, 109-L-shaped plate, 110-concentration test area exhaust inlet, 111-concentration test area exhaust outlet, 112-shoulder, 2-air pump, 3-smoke generator, 4-smoke pretreatment box, 5-pressure gauge, 6-pressure relief port, 7-DPF, 8-smoke outlet, 9-circulation pump, 10-heating device, 11-flow meter, 12-temperature sensor, 13-smoke transient meter, 14-first circulation port, 15-second circulation port, 16-third circulation port, F1-first control valve, F2-second control valve, F3-third control valve, F4-pressure relief valve, F5-fifth control valve, F6-sixth control valve. DETAILED DESCRIPTION

[0034] The present invention will be further described below in conjunction with the embodiments, but it does not constitute any limitation to the present invention. Any limited number of modifications made by anyone within the scope of the claims of the present invention are still within the scope of the claims of the present invention.

[0035] See also Figure 1-Figure 3 The present invention provides a leakage current type particle sensor and a working curve calibration method and a measuring device, such as Figure 1 As shown, a leakage current type particle sensor has the advantages of strong anti-interference ability and real-time detection. Its internal structure is as follows Figure 1 The sensor comprises a shell, a high-voltage electrode 101 and a conductive shell 102, a cavity 103 is provided in the shell, a fixing plate 104 is fixedly installed in the cavity 103, a high-voltage electrode 101 is fixedly installed at the bottom of the fixing plate 104, the cavity 103 is located at the periphery of the high-voltage electrode 101 and a conductive shell 102 is fixedly installed, a concentration test area 105 is formed between the conductive shell 102 and the high-voltage electrode 101, an outer protection area 106 is formed between the conductive shell 102 and the inner wall of the cavity 103, a total exhaust outlet 107 is opened at the bottom of the conductive shell 102, and a total exhaust inlet 108 is formed between the bottom of the conductive shell 102 and the bottom of the cavity 103.

[0036] L-shaped plates 109 are fixedly installed on both sides of the inner wall of the cavity 103 , an exhaust inlet 110 of the concentration test area is formed between the L-shaped plate 109 and the conductive shell 102 , and an exhaust outlet 111 of the concentration test area is formed between the conductive shell 102 and the high voltage electrode 101 .

[0037] The outer sides of the high voltage electrode 101 and the fixing plate 104 form a convex shoulder 112. The labyrinth-like convex shoulder 112 design can effectively prevent particulate matter from entering the sensor sealing space with the exhaust gas and corroding the wires and other components at the tail end.

[0038] The sensor head is installed vertically in the exhaust pipe behind the DPF7, and the exhaust gas flows into the sensor based on the Venturi principle. The cup-shaped high-voltage electrode 101 and the cylindrical conductive shell 102 form a concentration test area 105. During operation, the high-voltage electrode of the concentration test area 105 will be applied with a high voltage of 800V-1000V, thereby forming an electric field between the high-voltage electrode 101 and the conductive shell 102. When the exhaust gas particles enter the electric field, they will carry charges. The charged particles will move toward the grounding electrode of the high-voltage electrode 101, and release the charge to it when touching the grounding electrode of the high-voltage electrode 101, thereby forming a leakage current phenomenon. Then, by establishing a relationship between the leakage current value and the concentration of exhaust gas particles, the measured leakage current value can be converted into the concentration of exhaust gas particles.

[0039] The leakage current calculation formula is:

[0040]

[0041] Where: I r is the leakage current value measured between the plates of the leakage current type particle sensor, in A; a is the leakage current constant; ε r is the relative dielectric constant of the dielectric, which refers to the equivalent dielectric constant of the mixture of gas components and particulate matter in the exhaust gas; S is the area facing the capacitor plate, which is a constant for mass-produced leakage current particulate matter sensors, and the unit is m 2 ; U is the voltage between the plates, the sensor housing is grounded, so U (V) here refers to the central electrode voltage, the unit is V; k is the electrostatic force constant; d is the distance between the capacitor plates (constant), the unit is m.

[0042] In the formula, ε r The relative dielectric constant is the independent variable, I r The leakage current value is the dependent variable. In an ideal measurement environment, we hope that ε r It will only change with the number of particles in the airflow, and then calculate the leakage current value and amplify it into a voltage value. According to the voltage value, the concentration of particles can be calculated. r The measurement results will also change due to the temperature of the measured gas and the speed of the airflow. r The value and voltage value cannot accurately reflect the particle concentration in the airflow, so the impact of these two uncontrollable external factors on the results needs to be considered during the calibration process.

[0043] Sensor measurement device diagram Figure 2 As shown:

[0044] The measuring device includes a flue gas pretreatment box 4, a flue gas generator 3 and a DPF 7. The first air inlet of the flue gas pretreatment box 4 is connected to an external air source through the flue gas generator 3. The exhaust port of the flue gas pretreatment box 4 is installed with a DPF 7. The flue gas pretreatment box 4 is connected to a circulation unit. The circulation unit is installed with a detection module and a leakage particle sensor 1. The DPF 7, the flue gas generator 3 and the circulation unit are all connected to the flue gas pretreatment box 4.

[0045] The circulation unit includes a circulation pump 9 and a heating device 10. The first circulation port 14 of the flue gas pretreatment box 4 is connected to the second circulation port 15 of the flue gas pretreatment box 4 through the circulation pump 9 and the heating device 10 connected in sequence. The third circulation port 16 is connected to the second circulation port 15 through a pipeline. A detection module is installed on the pipeline connecting the first circulation port 14 of the flue gas pretreatment box 4 and the second circulation port 15 of the flue gas pretreatment box 4. A leakage particle sensor 1 is installed on the pipeline connecting the second circulation port 15 of the flue gas pretreatment box 4 and the third circulation port 16 of the flue gas pretreatment box 4. The first circulation port 14, the second circulation port 15 and the third circulation port 16 are all connected.

[0046] The detection module includes a flow meter 11 , a temperature sensor 12 and a flue gas transient meter 13 . A pressure relief port 6 is provided on the flue gas pretreatment box 4 , a pressure relief valve is installed on the pressure relief port, and a pressure gauge 5 is installed on the flue gas pretreatment box 4 .

[0047] The smoke generator 3 is connected to the external air source through the air pump 2 and the first control valve F1, the smoke pretreatment box is connected to the external air source through the second control valve F2, the third control valve F3 is installed in the pipeline between the exhaust port of the smoke pretreatment box 4 and the DPF7, the fifth control valve F5 is installed on the second circulation port 15, and the sixth control valve F6 is installed on the pipeline between the second circulation port 15 and the third circulation port 16.

[0048] Composition and function of the sensor measuring device: Channel 1: "First control valve F1-air pump 2-smoke pretreatment box 4" is responsible for filling the outside air into the smoke pretreatment box 4. The size of the pressure gauge 5 is used to determine whether the air in the smoke pretreatment box 4 is sufficient. When the pressure in the smoke pretreatment box 4 reaches the preset pressure range, the inflation is stopped. If the pressure in the smoke pretreatment box 4 exceeds the pressure range, the pressure relief valve F4 releases the gas. When the internal pressure of the gas in the smoke pretreatment box 4 is too high due to excessive temperature, the pressure relief valve F4 can also be used to relieve the pressure.

[0049] Channel 2: "Air pump 2-smoke generator 3-smoke pretreatment box 4-second control valve F2" is responsible for adjusting the concentration of particulate matter in the smoke pretreatment box 4. The smoke generator 3 releases smoke, and the air pump 2 drives the gas in the smoke pretreatment box 4 to continuously bring the smoke generated by the smoke generator 3 into the smoke pretreatment box 4, thereby continuously increasing the smoke concentration in the smoke pretreatment box 4.

[0050] Channel 3: "First control valve F1-air pump 2-smoke pretreatment box 4-third control valve F3-DPF7" is responsible for removing the smoke in the smoke pretreatment box 4 and adjusting the smoke concentration. The air pump 2 continuously fills the outside air into the smoke pretreatment box 4 from the first control valve F1, so that the smoke originally in the smoke pretreatment box 4 is discharged to the outside after being processed by DPF7, until the smoke in the smoke pretreatment box 4 is completely removed. When it is necessary to reduce the smoke concentration in the smoke pretreatment box 4, part of the gas in the smoke pretreatment box 4 can be discharged, and at the same time, air can be inhaled from the outside to dilute the smoke concentration.

[0051] Channel 4: "Flue gas pretreatment box 4-circulation pump 9-heating device 10-flow meter 11-temperature sensor 12-flue gas transient meter 13-fifth control valve F5" is responsible for adjusting the temperature and flow rate of flue gas and monitoring the particle concentration in the pretreatment box. The circulation pump 9 can make the gas in the flue gas pretreatment box 4 circulate in the ventilation duct and adjust the flow rate of the gas according to demand. The gas flows from the flue gas pretreatment box 4 into the heating device 10 for heating, and then flows back to the flue gas pretreatment box 4 after heating, and circulates continuously to meet the requirements for heating the gas in the flue gas pretreatment box 4. The flue gas transient meter 13 is responsible for monitoring the particle concentration of the gas in the channel. When the change in the concentration of the flue gas flowing through it is less than a certain value within a period of time, it means that the particle concentration in the flue gas pretreatment box 4 is stable.

[0052] Channel 5: "Flue gas pretreatment box 4-circulation pump 9-heating device 10-flow meter 11-temperature sensor 12-flue gas transient meter 13-sixth control valve F6-leakage current type particle sensor 1" is responsible for calibrating the leakage current type particle sensor 1. When the airflow temperature, particle concentration and flow rate in the flue gas pretreatment box 4 are stable, channel 5 is opened, and the stable measurement airflow flows through the leakage current type particle sensor 1. The measured leakage current value is amplified and converted into a voltage value, and the voltage value, the particle concentration of the flue gas transient meter, the airflow temperature and flow rate are recorded.

[0053] The expression for calculating the current value of the leakage current particulate matter sensor is:

[0054]

[0055] Among them, I r is the leakage current value measured between the two capacitor plates of the high voltage electrode 101, a is the leakage current constant; ε r is the relative dielectric constant of the dielectric; S is the area of ​​the capacitor plates, U is the voltage between the two capacitor plates, k is the electrostatic force constant, and d is the distance between the two capacitor plates.

[0056] Calibration method: In the actual measurement environment, the leakage current value will be affected not only by the particle concentration but also by the airflow speed and temperature. Therefore, multivariate linear fitting is used to fit the calibration curve.

[0057] The expression of fitting calibration curve is:

[0058] f(x 1 , x 2 , x 3 )=α 1 x 1 +α 2 x2 + α 3 x 3 +β;

[0059] Among them, x 1 is the particle concentration, x 2 is the air velocity, x 3 is the air flow temperature, f(x 1 ,x 2 ,x 3 ) is the voltage value, α 1 is the first coefficient to be fitted, α 2 is the second coefficient to be fitted, α 3 is the third coefficient to be fitted, and β represents the constant to be fitted.

[0060] Fitting data collection process:

[0061] Step 1: Turn on the air pump 2 and the DPF 7 to clean the gas in the flue gas pretreatment box 4 , that is, operate the channel 3 to clean the gas in the flue gas pretreatment box 4 .

[0062] Step 2: close DPF 7 and turn on the air pump 2 to allow air to flow into the flue gas pretreatment box 4, that is, operate the channel 1 to allow a sufficient amount of air to flow into the flue gas pretreatment box 4.

[0063] Step three: Turn on the air pump 2 and the smoke generator 3 to increase the smoke in the smoke pretreatment box 4 , that is, operate the channel 2 to adjust the concentration of the gas in the smoke pretreatment box 4 .

[0064] Step 4: Turn on the circulation pump 9 and the heating device 10 to adjust the temperature and flow rate of the flue gas in the flue gas pretreatment box, and obtain the particle data of the flue gas, that is, operate the channel 4 to adjust the gas temperature and flow rate in the flue gas pretreatment box 4 and monitor the particle concentration in the flue gas pretreatment box 4. Wait until the three independent variables of gas particle concentration, gas temperature and gas flow rate in the flue gas pretreatment box 4 reach the expected adjustment value and stabilize.

[0065] Step 5: When the particle data is equal to the preset data adjustment value, obtain the voltage value of the leakage current particle sensor, and calibrate the leakage current particle sensor curve through the particle data and voltage value. That is, run channel 5 to measure the current value in the leakage current particle sensor 1 and convert it into the corresponding voltage value. Record the four measured values ​​of voltage value, gas particle concentration, gas temperature and gas flow rate obtained in the above experiment. These four data are called a set of data. According to the above method, by continuously adjusting the size of the three independent variables of gas particle concentration, gas temperature and gas flow rate, and then measuring the voltage value corresponding to the particle sensor, several sets of data are obtained as samples.

[0066] The mathematical model is solved using the sample data collected above, and the coefficient α is preliminarily obtained. 1 , α 2 , α 3 And the value of the fitting constant β. Then use the goodness of fit R 2 , the F test of the overall significance test of the equation, the p-value and the estimated value of the error variance s2 test the statistics of the regression model. If the test meets the corresponding requirements, it is considered that the fitting curve can be used.

[0067] Correction of calibration curve: In the calibration process, due to the defects of the detection method itself, the lack of precision of the instrument and equipment, and the errors caused by improper and non-standard operation of the calibration personnel, there will still be a certain error between the actual calibration curve of the leakage current particulate matter sensor 1 and the above-mentioned fitted calibration curve. In order to further improve the measurement accuracy of the leakage current particulate matter sensor 1, the sparrow search algorithm (SSA) is introduced to optimize the calibration curve. The SSA algorithm is an optimization algorithm based on swarm intelligence. It has the advantages of strong search ability, simple and easy implementation, and no need to adjust parameters too much. It has been proven to be very effective in finding high-quality solutions. Taking the minimization of measurement error as the algorithm optimization objective function, the calibration curve is optimized by using the SSA algorithm to obtain the optimal calibration curve.

[0068] The optimization steps are as follows Figure 3 As shown:

[0069] The abstract representation of the sparrow search algorithm is:

[0070] Set the sparrow search algorithm related parameters in the hybrid vehicle energy management strategy based on optimized fuzzy control, such as the initialization group, the size of the sparrow group, the maximum number of iterations, and the population size.

[0071] Randomly initialize the positions of individuals in the sparrow population.

[0072] Calculate the fitness value of the sparrow at the initial position, and sort the identities of discoverers, followers, and warning persons according to the fitness value. Discoverers with high fitness values ​​mainly provide food sources and foraging ranges for the entire sparrow population. Followers correspond to sparrows with medium fitness values. The identity of sparrows will change with the position of followers. When warning persons sense danger, they will warn the group members to flee the dangerous area.

[0073] Calculate the new fitness value and update the individual identity of the sparrow. Reorder the calculated fitness values. If the fitness value of the sparrow individual in the new position is higher than the fitness value of the sparrow individual in the old position, update the identity of the sparrow individual.

[0074] Determine whether the termination condition of the optimization is met. If the best fitness value after updating meets the specified threshold, stop the optimization and output the best fitness value and individual position of the sparrow at this time. Otherwise, jump to the fourth step to continue iterating until the maximum number of iterations is reached, and output the best fitness value and individual position of the sparrow in the entire cycle.

[0075] In the present invention, the optimization of the curve by the sparrow search algorithm is specifically as follows:

[0076] The curve is corrected by sparrow search algorithm.

[0077] The method of correcting the curve by sparrow search algorithm is:

[0078] Step 1: Set the maximum number of iterations of the sparrow search algorithm, taking all data points on the curve as the population and each data point as an individual.

[0079] Step 2: Randomly initialize the positions of individuals in the population.

[0080] Step 3: Get the initial fitness value of the individual at the initial position, sort the individuals into discoverer identity, follower identity and warning identity according to the initial fitness value, and iterate the position of the individuals in the population.

[0081] In this embodiment, the fitness value is calculated using a technical method in the prior art, which is briefly introduced below.

[0082] In the optimization objective function of the hybrid vehicle energy management strategy, the inverse of the equivalent fuel consumption is used as the fitness function of the sparrow search algorithm optimization direction. Its fitness function is as follows:

[0083]

[0084] In the formula, J min is the minimum equivalent fuel consumption; m engis the engine fuel consumption; s(t) is the fuel-electric equivalent coefficient (null); mbat is the equivalent fuel consumed by the motor per unit time. The value of the fuel-electric equivalent coefficient s(t) is shown in the following formula:

[0085]

[0086] Where η chg is the battery charging efficiency (%), η dis is the battery discharge efficiency (%), η e , η m They are the average efficiency (%) of the engine and motor respectively.

[0087] Set the parameters related to the sparrow search algorithm in the hybrid vehicle energy management strategy based on optimized fuzzy control, such as the initialization population, the size of the sparrow population, the maximum number of iterations, etc., including the population size N = 30, the number of iterations T = 100, and the variable dimension D = 36. At the same time, the number of discoverers accounts for 20% of the sparrow population, the number of dangerous sparrow individuals accounts for 10% of the population, and ST = 0.8.

[0088] Suppose that in a 36-dimensional space, a population of 30 sparrows is foraging. The sparrow population can be represented by (X, f(x)), where X is the position of the individual sparrow, each individual position of the sparrow in the population corresponds to a solution, and f(x) is the fitness function of the sparrow. The size of the fitness value determines the identity of the individual sparrow in the population. The specific representation of the position information of the sparrow population is shown in the following formula:

[0089]

[0090] The fitness function corresponding to each individual in the sparrow population can be expressed as follows:

[0091]

[0092] Randomly initialize the position of individuals in the sparrow population. Calculate the fitness value of the sparrow at the initialized position, calculate the fitness value of the individual sparrow according to the fitness function of formula (4-3), and sort the identities of discoverers, followers, and warners according to the fitness value. In the sparrow search algorithm, discoverers with high fitness values ​​mainly provide food sources and foraging ranges for the entire sparrow population. Followers correspond to sparrows with medium fitness. The identity of the sparrow will change with the position of the followers. When the warner senses danger, it will warn the members of the group to escape from the dangerous area.

[0093] The individual position of the sparrow in the fuzzy control membership function is updated using the following formula in each dimension of the individual sparrow position.

[0094] The discoverer position is updated as follows:

[0095]

[0096] Where \(t\) is the current iteration number; iter max is the maximum number of iterations; is the position of the \(i\)-th sparrow in the \(j\)-th dimension; both \(R\) and \(\alpha\) are random numbers in \((0, 1)\), \(R\) represents the alarm value; \(ST\) is a random number in the interval \((0.5, 1)\) representing the safety threshold; \(L\) j is a \(1\times D\) matrix; \(Q\) is a random number. When \(R \lt ST\), there is no predator near the foraging environment at this time, and the sparrow individuals can search widely within the marginal range; when \(R \gt ST\), some early warning sparrows in the sparrow group have detected the presence of predators and issued warnings, and the sparrows nearby flee from the current area.

[0097] The position update formula for followers is shown as follows:

[0098]

[0099] Where is the position of the sparrow with the highest fitness in the population after the \(t\)-th iteration; is the position of the sparrow with the lowest fitness in the population after the \(t\)-th iteration; \(A\) is a \(1\times D\) all-ones matrix and \(A\) + \(= A^T(A A^T)^{-1}\). When \(i \gt n / 2\), it indicates that the follower has a lower fitness and loses the qualification to compete for food, and has to fly to other areas to forage; otherwise, the follower has a higher fitness and moves towards the position of the discoverer to forage.

[0100] The position update of the early warning sparrows is shown as follows:

[0101]

[0102] Where \(\beta\) is the solution step size; \(K\) is in the interval \((-1, 1)\); \(f\) i is the fitness value of the current sparrow individual; \(f\) g and \(f\) w represent the current global optimal and worst fitness values; \(\varepsilon\) is the smallest constant to avoid a zero denominator. When \(f\) i＝ \(f\) g , the early warning sparrows in the sparrow group detect danger and immediately move to other positions in the group; when \(f\) i≠ \(f\) g , this sparrow individual is at the edge of the group and is vulnerable to predator attacks.

[0103] Calculate the fitness value of the sparrow individual after position update according to formula (4 - 3), re - sort the calculated fitness values. If the fitness value of the sparrow individual at the new position is higher than that of the sparrow individual at the old position, then update the identity of the sparrow individual.

[0104] Step 4: Calculate the updated fitness value of the individual after iteration, update and sort the identity of the individual by updating the fitness value, and update the identity of the individual when the updated fitness value of the individual is greater than the initial fitness value.

[0105] Step 5: When the updated fitness value of the individual is equal to the preset fitness threshold, the optimization is stopped and the updated fitness value and individual position are output; when the updated fitness value of the individual is not equal to the preset fitness threshold, return to the above fourth step to continue iteration.

[0106] When the number of iterations is equal to the maximum number of iterations, the updated fitness closest to the fitness threshold in the iteration cycle and the individual position corresponding to the updated fitness are output.

[0107] The relationship between the fitness value of the discoverer, the fitness value of the follower and the fitness value of the warner is: the fitness value of the discoverer> the fitness value of the follower> the fitness value of the warner.

[0108] The above is only a preferred embodiment of the present invention. It should be pointed out that for those skilled in the art, several modifications and improvements can be made without departing from the structure of the present invention, which will not affect the effect of the implementation of the present invention and the practicality of the patent.

Claims

1. A leakage current type particle sensor, characterized in that: The sensor comprises a shell, a high-voltage electrode (101) and a conductive shell (102); a cavity (103) is provided in the shell; a fixing plate (104) is fixedly installed in the cavity (103); a high-voltage electrode (101) is fixedly installed at the bottom of the fixing plate (104); a conductive shell (102) is fixedly installed on the periphery of the high-voltage electrode (101) in the cavity (103); a concentration test area (105) is formed between the conductive shell (102) and the high-voltage electrode (101); an outer protection area (106) is formed between the conductive shell (102) and the inner wall of the cavity (103); a total exhaust outlet (107) is provided at the bottom of the conductive shell (102); and a total exhaust inlet (108) is formed between the bottom of the conductive shell (102) and the bottom of the cavity (103).

2. A leakage current type particle sensor according to claim 1, characterized in that: L-shaped plates (109) are fixedly mounted on both sides of the inner wall of the cavity (103); an exhaust inlet (110) of the concentration test area is formed between the L-shaped plate (109) and the conductive shell (102); an exhaust outlet (111) of the concentration test area is formed between the conductive shell (102) and the high-voltage electrode (101); and a boss (112) is formed between the high-voltage electrode (101) and the outer side of the fixed plate (104).

3. A method for calibrating a working curve of a leakage current type particle sensor, characterized in that: The method includes placing the leakage current particulate matter sensor as described in any one of claims 1-2 in a standard particulate matter flue gas environment, obtaining the voltage value of the leakage current particulate matter sensor, calibrating the leakage current particulate matter sensor curve through the particulate matter data and the voltage value, and correcting the curve through a sparrow search algorithm.

4. The working curve calibration method of a leakage current type particle sensor according to claim 3, characterized in that: The voltage value is obtained by converting the current value of the leakage current type particulate matter sensor. The expression for calculating the current value of the leakage current type particulate matter sensor is: Among them, I r is the leakage current value measured between the two capacitor plates of the high voltage electrode (101), a is the leakage current constant; ε r is the relative dielectric constant of the dielectric; S is the area of ​​the capacitor plates, U is the voltage between the two capacitor plates, k is the electrostatic force constant, and d is the distance between the two capacitor plates.

5. The working curve calibration method of a leakage current type particle sensor according to claim 3, characterized in that: The method for calibrating the curve of the leakage current particulate matter sensor is as follows: the particulate matter data includes particulate matter concentration, airflow velocity and airflow temperature, and a calibration curve is fitted according to the particulate matter concentration, airflow velocity, airflow temperature and voltage value by a multivariate linear fitting method.

6. A method for calibrating a working curve of a leakage current type particle sensor according to claim 5, characterized in that: The expression for fitting the calibration curve is: f(x1,x2,x3)=α1x1+α2x2+α3x3+β; Wherein, x1 is the particle concentration, x2 is the airflow velocity, x3 is the airflow temperature, f(x1, x2, x3) is the voltage value, α1 is the first coefficient to be fitted, α2 is the second coefficient to be fitted, α3 is the third coefficient to be fitted, and β represents the constant to be fitted.

7. The method for calibrating a working curve of a leakage current type particle sensor according to claim 3, characterized in that: The method for correcting the curve by using the sparrow search algorithm is: Step 1: setting the maximum number of iterations of the sparrow search algorithm, taking all the data points on the curve as the population, and taking each data point as an individual; Step 2: Randomly initialize the positions of individuals in the population; Step 3: Obtain the initial fitness value of the individual at the initial position, sort the individuals into the discoverer identity, the follower identity and the warning identity according to the initial fitness value, and iterate the position of the individuals in the population; Step 4: Obtain the updated fitness value of the individual after iteration, update and sort the identity of the individual according to the updated fitness value, and when the updated fitness value of the individual is greater than the initial fitness value, update the identity of the individual; Step 5: When the updated fitness value of the individual is equal to the preset fitness threshold, the optimization is stopped and the updated fitness value and individual position are output; when the updated fitness value of the individual is not equal to the preset fitness threshold, the above fourth step is returned to continue iteration; When the number of iterations is equal to the maximum number of iterations, the updated fitness closest to the fitness threshold in the iteration cycle and the individual position corresponding to the updated fitness are output.

8. A measuring device for implementing the working curve calibration method of a leakage current type particulate matter sensor as claimed in any one of claims 3 to 7, characterized in that: The measuring device comprises a flue gas pretreatment box (4), a flue gas generator (3) and a DPF (7); a first air inlet of the flue gas pretreatment box (4) is connected to an external air source via the flue gas generator (3); a DPF (7) is installed in an exhaust port of the flue gas pretreatment box (4); the flue gas pretreatment box (4) is connected to a circulation unit; the circulation unit is installed with a detection module and a leakage type particle sensor (1); and the DPF (7), the flue gas generator (3) and the circulation unit are all in communication with the flue gas pretreatment box (4).

9. A measuring device according to claim 8, characterized in that: The circulation unit comprises a circulation pump (9) and a heating device (10); the first circulation port (14) of the flue gas pretreatment box (4) is connected to the second circulation port (15) of the flue gas pretreatment box (4) through the circulation pump (9) and the heating device (10) connected in sequence; the third circulation port (16) is connected to the second circulation port (15) through a pipeline; a detection module is installed on the pipeline connecting the first circulation port (14) of the flue gas pretreatment box (4) and the second circulation port (15) of the flue gas pretreatment box (4); a leakage particle sensor (1) is installed on the pipeline connecting the second circulation port (15) of the flue gas pretreatment box (4) and the third circulation port (16) of the flue gas pretreatment box (4); the first circulation port (14), the second circulation port (15) and the third circulation port (16) are all connected.

10. A measuring device according to claim 8, characterized in that: The detection module comprises a flow meter (11), a temperature sensor (12) and a flue gas transient meter (13); a pressure relief port (6) is provided on the flue gas pretreatment box (4); a pressure relief valve (F4) is installed on the pressure relief port (6); and a pressure gauge (5) is installed on the flue gas pretreatment box (4).

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