CN114018326B - Low-voltage transformer area environment multi-parameter detection method based on microsystem sensor array - Google Patents
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Abstract
The invention discloses a low-voltage station environment multi-parameter detection method based on a microsystem sensor array, which comprises the following steps: a structure mapping algorithm is adopted, a one-to-one correspondence relation between the output current of the micro-system sensor array and each parameter is established, cross interference among the parameters is eliminated, and an accurate model for simultaneous detection of multiple parameters is obtained; wherein the multiple parameters include temperature, humidity, gas concentration, current and magnetic field; the microsystem sensor array comprises a plurality of three-electrode structure ionization sensors; wherein the cathodes of the ionization sensors with the three-electrode structures are manufactured on the same polar plate, and the leading-out electrode and the collecting electrode have the same structure but different polar distances; and obtaining the output current of the micro-system sensor array, and obtaining the environment multi-parameter value of the low-voltage station area through the accurate model matching. The invention has the advantages of high integration level, small volume, high level of intellectualization and digitalization, low cost and the like.
Description
Technical Field
The invention mainly relates to the technical field of environmental parameter measurement, in particular to a low-voltage station area environment multi-parameter detection method based on a microsystem sensor array.
Background
With the leap development of microelectronic technology, the micro-nano technology aiming at processing micro-nano structures and systems has been developed, and a new revolution is started in the field of small-sized mechanical manufacturing, resulting in the birth of micro-electromechanical systems (MEMS). The MEMS is the widening and extension of the microelectronic technology, integrates the microelectronic technology and the precision machining technology, realizes the system integrating the microelectronic technology and the machinery, can process the morphology structure of micron or even below micron, and ensures that the MEMS device has the characteristics which cannot be achieved by the traditional sensor, thus having wide application prospect.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: aiming at the problems existing in the prior art, the invention provides a low-voltage transformer area environment multi-parameter detection method based on a microsystem sensor array, which has high automation degree and low cost.
In order to solve the technical problems, the technical scheme provided by the invention is as follows:
a low-voltage transformer area environment multi-parameter detection method based on a microsystem sensor array comprises the following steps:
a structure mapping algorithm is adopted, a one-to-one correspondence relation between the output current of the micro-system sensor array and each parameter is established, cross interference among the parameters is eliminated, and an accurate model for simultaneous detection of multiple parameters is obtained; wherein the multiple parameters include temperature, humidity, gas concentration, current and magnetic field; the microsystem sensor array comprises a plurality of three-electrode structure ionization sensors; wherein the cathodes of the ionization sensors with the three-electrode structures are manufactured on the same polar plate, and the leading-out electrode and the collecting electrode have the same structure but different polar distances;
and obtaining the output current of the micro-system sensor array, and obtaining the environment multi-parameter value of the low-voltage station area through the accurate model matching.
Preferably, wherein the gas comprises H 2 、C 2 H 2 And CH (CH) 4 The data fusion process of the mixed gas detection under the corresponding temperature interference is as follows: the structure mapping algorithm is a support vector machine, wherein the support vector machine model of the mixed gas and the temperature comprises an input layer, a kernel function layer and an output layer; output of a current vector I= [ I ] with a sensor array H2 ,I C2H2 ,I C2H4 ,I T ]Support vector I k ＝[I kH2 ,I kC2H2 ,I kC2H4 ,I kT ]As an input layer, the concentration and the temperature of three components in the mixed gas are calculated to be phi' H2 、φ' C2H2 、φ' C2H4 T' is a model output layer; the middle of the input layer and the output layer is a kernel function layer, and kernel functions have various forms, including linear kernel functions, polynomial kernel functions, RBF kernel functions and tensor kernel functions; the support vector machine structure is constructed by a kernel function K (I, I k ) The operation maps the data of the input space to a high-dimensional feature space, then by multiplying a by Lagrange 1k –a 4k And the linear regression function determined by the offset (b 1-b 4) achieves data fitting.
Preferably, NO/SO under temperature interference 2 /O 2 The data fusion process of the mixed gas of PM1/PM2.5/PM4/PM10 and the detection of particulate matters comprises the following steps: detection of NO/SO for microsystem sensor array 2 /O 2 The support vector machine for the mixed gas, the particulate matters and the temperature of the PM1/PM2.5/PM4/PM10 is composed of an input layer, a kernel function layer and an output layer; the input layer comprises a current vector I= [ I ] formed by collecting electrode currents of sensors with different electrode pitches in a silicon micron column three-electrode sensor array T ,I NO ,I SO2 ,I O2 ,I PM1 ,I PM2.5 ,I PM4 ,I PM10 ]And a support vector I composed of sensor array calibration experimental data as training samples k ＝[I kT ,I kNO ,I kSO2 ,I kO2 ,I kPM1 ,I kPM2.5 ,I kPM4 ,I kPM10 ]The method comprises the steps of carrying out a first treatment on the surface of the The kernel function layer includes kernel functions K (I, I k ) Lagrangian multiplier alpha 1k -α 8k And threshold b k N is the number of training samples; kernel function layer pass kernel functionMapping the input layer data to a high-dimensional feature space, and determining a linear function through Lagrangian multipliers and a threshold value to realize data fusion; the output layer outputs the temperature, NO concentration and SO corresponding to the current vector I of the collector electrode of the sensor array 2 Concentration, O 2 Concentration, PM1 concentration, PM2.5 concentration, PM4 concentration, and PM10 concentration.
Preferably, the temperature T ' and the NO concentration phi ' are obtained according to a structural model of the support vector machine ' NO 、SO 2 Concentration phi' SO2 、O 2 Concentration phi' O2 PM1 concentration phi' PM1 PM2.5 concentration phi' PM2.5 PM4 concentration phi' PM4 PM10 concentration phi' PM10 The relation with the current collected by the microsystem sensor array is:
wherein: alpha 1k -α 8k K=1, 2,3 … 570, lagrange multiplier; i j Collector current values for a silicon micron column three electrode sensor array, j=1, 2,3 …; i kj For support vector machine training sample data, k=1, 2,3 … 570; j=1, 2,3 ….
Preferably, the structural algorithm comprises a neural network and a support vector machine.
The invention also discloses a low-voltage transformer area environment multi-parameter detection system based on the microsystem sensor array, which comprises:
the first program module is used for establishing a one-to-one correspondence between the output current of the micro-system sensor array and each parameter by adopting a structure mapping algorithm, eliminating cross interference among the parameters and obtaining an accurate model for simultaneous detection of multiple parameters; wherein the multiple parameters include temperature, humidity, gas concentration, current and magnetic field; the microsystem sensor array comprises a plurality of three-electrode structure ionization sensors; wherein the cathodes of the ionization sensors with the three-electrode structures are manufactured on the same polar plate, and the leading-out electrode and the collecting electrode have the same structure but different polar distances;
and the second program module is used for obtaining the output current of the micro-system sensor array, and obtaining the environment multi-parameter value of the low-voltage station area through the accurate model matching.
The invention further discloses a computer readable storage medium having stored thereon a computer program which, when run by a processor, performs the steps of the low-voltage zone environment multi-parameter detection method based on a microsystem sensor array as described above.
The invention also discloses a computer device comprising a memory and a processor, wherein the memory stores a computer program which, when being run by the processor, executes the steps of the low-voltage area environment multi-parameter detection method based on the microsystem sensor array.
Compared with the prior art, the invention has the advantages that:
the invention can detect the ambient temperature, humidity, smoke dust and weak current through the micro-system sensor array, can judge whether fire accident happens or not through detecting the concentration of the ambient smoke, and has wide measuring range, high resolution, high response speed, high integration level and small volume; the current situation of on-line monitoring false alarm of the power grid is positively improved, the intellectualization and the digitalization of the power system are promoted, the updating of the detection standard of the power industry is promoted, the comprehensive intelligent perception material and equipment cost of the domestic power system are greatly reduced, and the maximization of the equipment state monitoring benefit is realized.
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FIG. 1 is a schematic diagram of a micro-system sensor array of the present invention in an embodiment.
Fig. 2 is a graph of the spatial point location relationship of a long straight wire according to the present invention.
FIG. 3 is a schematic diagram showing the deflection of a charged particle according to the present invention when it moves in a magnetic field.
Fig. 4 is a flow chart of an embodiment of the method of the present invention.
Detailed Description
The invention is further described below with reference to the drawings and specific examples.
As shown in fig. 4, the method for detecting multiple parameters of a low-voltage platform area environment based on a microsystem sensor array according to an embodiment of the present invention includes the steps of:
a structure mapping algorithm is adopted, a one-to-one correspondence relation between the output current of the micro-system sensor array and each parameter is established, cross interference among the parameters is eliminated, and an accurate model for simultaneous detection of multiple parameters is obtained; wherein the multiple parameters include temperature, humidity, gas concentration, current and magnetic field; the microsystem sensor array comprises a plurality of three-electrode structure ionization sensors; wherein the cathodes 1 of the ionization sensors with three-electrode structures are manufactured on the same polar plate, and the leading-out electrode 2 and the collecting electrode 3 have the same structure but different polar distances, as shown in figure 1;
and obtaining the output current of the micro-system sensor array, and obtaining the environment multi-parameter value of the low-voltage station area through the accurate model matching.
The microsystem sensor array can detect the ambient temperature, the humidity, the smoke dust and the weak current, can judge whether fire accidents occur or not through detecting the concentration of the ambient smoke, and has the advantages of wide range, high resolution, high response speed, high integration level and small volume; the current situation of on-line monitoring false alarm of the power grid is positively improved, the intellectualization and the digitalization of the power system are promoted, the updating of the detection standard of the power industry is promoted, the comprehensive intelligent perception material and equipment cost of the domestic power system are greatly reduced, and the maximization of the equipment state monitoring benefit is realized.
Specifically, the theoretical basis of temperature detection is: three-electrode ionization sensors are extremely temperature sensitive (formulas (1) - (3)), and have the characteristics of thermal emission and thermal ionization, and can detect temperature, in particular:
I＝I 0 e αd (1)
α＝APe -BPE (2)
wherein I is the sensor output current; i 0 Is the initial discharge current; j (j) e Is the cathode emission current density; alpha is the first ionization coefficient of the gas; d is the distance of the alpha process; e is the electric field strength of the cathode nanotip; p is the gas pressure; a and B are constants related to gas species and temperature; epsilon 0 Is the absolute permittivity; phi is the electron work function before the electric field is applied; e is the charge carried by an electron; b (B) E Is the emission constant, the value of which is related to the material and surface conditions; k is the boltzmann constant; collecting current I c Is a part of I, then I c =f (T); temperature rise, current I c And (3) increasing.
The theoretical basis of humidity detection is as follows: the collector current and the relative humidity of the three-electrode ionization type sensor show a monotonically rising trend, and the collector current of the sensor gradually increases with the increase of the relative humidity. Wherein the relative humidity measurement formula is as follows:
P w ＝RH×P S (4)
wherein P is w Is the current air pressure of the water vapor to be measured, P s Is the air pressure generated by saturated steam, P s The calculation of (c) uses the Wexler formula:
wherein T is the absolute temperature of water vapor, C 0 ＝-6.044×10 3 ；C 1 ＝1.893×10 1 ；C 2 ＝-2.824×10 -2 ；C 3 ＝1.724×10 -5 ；C 4 ＝2.858。
In gas impact ionization, when the temperature and the excitation voltage are fixed, the water vapor impact ionization coefficient alpha mainly depends on the water vapor pressure P w The expression is:
the theoretical basis of smoke detection is as follows: first ionization coefficient alpha and gas species and concentrationIn connection with collecting current I c The change in (2) can reflect the change in the type and concentration of the gas between the sensor electrodes, and can be used to detect the gas concentration. In addition, when particulate matter exists between the sensor electrodes, a part of positive ions collide with the particulate matter, and charge is transferred to charge the particulate matter. The charged particles and other positive ions move towards the extraction electrode through diffusion under the action of concentration gradient; after passing through the leading-out hole, the collector is accelerated to move under the action of a reverse electric field. After reaching the collector, the collector collects the charged particles and positive ions to generate a collector current I c 。
Known gas discharge current I c ＝I 0 e αd α=ape-BP/E, and ideal gas state equation pv=nrt, resulting in equations (7), (8), (9), indicating that the change in particulate matter concentration causes a change in discharge current. Wherein P is the gas pressure; v is the gas volume; r is the ideal gas constant.
The theoretical basis of current detection is as follows: according to the law of electromagnetic induction, the current is measured indirectly. For the induction magnetic field micro-element dB direction generated by any micro-element current d l of the current I in the same direction in the current-carrying long straight wire, according to the Piaosavart law, the induction magnetic field at the A point is known as follows:
wherein θ is the angle between the line connecting the point A and any point on the long straight wire and the long straight wire. Perpendicular line AO is drawn along the point A, and the AO length is r 0 The lead trace d l is separated from the O point l, and the following steps are obtained:
wherein θ is 1 、θ 2 Is L 1 、L 2 The corresponding angle value of θ at both ends is used as the lead L 1 、L 2 When the distance between two points is infinitely long, theta 1 ＝0、θ 2 Let pi, take into equation (11) then:
equation (13) shows that when a current I passes through the conductor, a magnetic induction B is generated around the conductor, and the magnetic induction B is proportional to the current I. It follows that the basic principle of the current sensor is: the magnetic induction intensity B of the fixed position inside the current sensor is accurately measured, and the magnitude and the direction of the measured current are obtained through signal processing, as shown in fig. 2.
The theoretical basis of the magnetic field detection is as follows: the three-electrode ionization sensor is based on a discharge principle, when placed in a magnetic field, free electrons generate rotation and migration motion, namely, a lamo motion due to the action of magnetic field force near a discharge area, the motion path of the free electrons is prolonged, the residence time between polar plates is prolonged, the collision frequency of the free electrons and gas molecules is increased, the ionization of the gas molecules near the discharge electrode is effectively enhanced, the concentration of charged particles is increased, and the discharge current density j is increased (formula 14). Meanwhile, according to the electron motion equation and electron energy equation (equations 15 and 16) in the magnetic field, the electron energy Q will also increase with the magnetic field. The increase in electron energy increases the probability of electron impact ionization, thereby generating more positive ions and increasing the collection current density jc.
Wherein μ is the charged particle mobility and N is the charged particle mobilityDensity, v is charged particle velocity, E is electric field strength, D MGF The diffusion coefficient of the plasma under the action of a magnetic field, and B is the magnetic induction intensity; m is electron mass, e is electron charge, v e Is the electron movement speed.
Meanwhile, electrons and positive ions generated by interelectrode discharge are acted by a magnetic field, as shown in fig. 3; for example, deflected inwards or outwards by the magnetic field Bx, thereby influencing the collecting current Ic.
The theoretical basis of simultaneous detection of multiple parameters is as follows: because of the nonlinear relation between the collected current Ic and the pole spacing d of the sensors, the output current values Ic of the sensors with different pole spacing are different, and the sensors with different pole spacing can be used for detecting multiple parameters simultaneously. The cathodes of the sensors are manufactured on the same polar plate, and the leading-out electrode and the collecting electrode have the same structure but different polar distances.
Wherein d 1 、d 2 、…、d n Is the different pole spacing of the sensor; function f 0 、f 1 、…、f n Respectively describe d 1 、And temperature, humidity, gas to sensor output I 1 Is a function of (1); a, a 0 、a 1 、…、a n Respectively f 0 、f 1 、…、f n Coefficients of (2); b 0 、b 1 、…、b n G is respectively 0 、g 1 …、g n Coefficients of (2); c 0 、c 1 、…、c n H is respectively 0 、h 1 、…、h n Is a coefficient of (a).
Through experimental calibration, a model of multiple parameters to be measured and the output current of the sensor can be established, and measurement is realized.
In a specific embodiment, a structural mapping algorithm such as a neural network and a support vector machine is adopted to establish a one-to-one correspondence between the output current of the sensor array and each parameter, eliminate cross interference between the parameters and obtain an accurate model for simultaneous detection of multiple parameters.
In one embodiment, the three electrode ionization sensor implements a single CH 4 、SO 2 、C 2 H 2 、H 2 、NO、CH 2 Detection of O gas concentration, sensor collection current decreases with increasing gas concentration. The lowest detection values are CH respectively 4 (2ppt)、SO 2 (15ppt)、C 2 H 2 (1ppm)、H 2 (20ppt)、NO(70ppt)、CH 2 O (50 ppt). The pulse power supply is adopted as the excitation of the sensor, so that the problem of hysteresis of the sensor is solved, and the breakthrough of the sensor in the aspect of gas detection is realized. Further improvement of the detection accuracy of the sensor can be achieved by optimizing the pulse excitation conditions. Wherein the sensor array is composed of three electrode ionization sensors with different polar pitches, and three components H are realized 2 /C 2 H 2 /CH 4 Gas, NO/SO 2 Temperature and CH 4 /H 2 /C 2 H 2 /C 2 H 4 and/CO/temperature simultaneous detection.
In one embodiment, a support vector machine model for a sensor array to detect three component gas mixtures and temperatures during multi-sensor data fusion includes an input layer, a kernel function layer, and an output layer. Output of a current vector I= [ I ] with a sensor array H2 ,I C2H2 ,I C2H4 ,I T ]Support vector I k ＝[I kH2 ,I kC2H2 ,I kC2H4 ,I kT ](k=1, 2, …, n) as an input layer, and the calculated values phi 'of the concentrations and temperatures of the three components in the mixed gas' H2 、φ' C2H2 、φ' C2H4 T' is the model output layer. Intermediate the input and output layers is a kernel function layer, kernel function K (I, I k ) There are many forms such as linear kernel functions, polynomial kernel functions, RBF kernel functions, tensor kernel functions, etc. The support vector machine structure is constructed by a kernel function K (I, I k ) The operation maps the data of the input space to the high-dimensional feature space, then through the data of the input spaceLangmuir multiplier a 1k –a 4k (k=1, 2, …, 1248) and offset b 1 –b 4 The determined linear regression function achieves data fitting.
In one embodiment, the NO/SO is under temperature interference 2 /O 2 The data fusion process of the mixed gas of PM1/PM2.5/PM4/PM10 and the detection of particulate matters comprises the following steps:
for detecting NO/SO by sensor array 2 /O 2 The support vector machine for the mixed gas, the particulate matters and the temperature of the PM1/PM2.5/PM4/PM10 is composed of an input layer, a kernel function layer and an output layer. The input layer comprises a current vector I= [ I ] formed by collecting electrode currents of sensors with different electrode pitches in a silicon micron column three-electrode sensor array T ,I NO ,I SO2 ,I O2 ,I PM1 ,I PM2.5 ,I PM4 ,I PM10 ]And a support vector I composed of sensor array calibration experimental data as training samples k ＝[I kT ,I kNO ,I kSO2 ,I kO2 ,I kPM1 ,I kPM2.5 ,I kPM4 ,I kPM10 ]. The kernel function layer includes kernel functions K (I, I k ) Lagrangian multiplier alpha 1k -α 8k (k=1, 2 … n) and a threshold b k (k=1, 2 …), n being the number of training samples; the kernel function layer maps the input layer data to a high-dimensional feature space through a kernel function, and determines a linear function through Lagrangian multipliers and a threshold value to realize data flux fusion. The output layer outputs the temperature, NO concentration and SO corresponding to the current vector I of the collector electrode of the sensor array 2 Concentration, O 2 Concentration, PM1 concentration, PM2.5 concentration, PM4 concentration, and PM10 concentration.
Obtaining the temperature T ' and the NO concentration phi ' according to the structural model of the support vector machine ' NO 、SO 2 Concentration phi' SO2 、O 2 Concentration phi' O2 PM1 concentration phi' PM1 PM2.5 concentration phi' PM2.5 PM4 concentration phi' PM4 PM10 concentration phi' PM10 The relation with the sensor array collection current is:
wherein: alpha 1k -α 8k (k=1, 2,3 … 570) is a lagrange multiplier; i j (j=1, 2,3 … 8) is a silicon micro-pillar three-electrode sensor array collector current value; i kj (k=1, 2,3 … 570; j=1, 2,3 …) is support vector machine training sample data. After the structural parameters of the support vector machine are optimized through a particle swarm algorithm, the model is used for controlling the temperature and O 2 The concentration, PM1 concentration, PM4 concentration, and PM10 concentration have large errors. And the errors of the concentration of NO, the concentration of SO2 and the concentration of PM2.5 are smaller, and the maximum reference error is smaller than 5%.
The embodiment of the invention also discloses a low-voltage transformer area environment multi-parameter detection system based on the microsystem sensor array, which comprises the following steps:
the first program module is used for establishing a one-to-one correspondence between the output current of the micro-system sensor array and each parameter by adopting a structure mapping algorithm, eliminating cross interference among the parameters and obtaining an accurate model for simultaneous detection of multiple parameters; wherein the multiple parameters include temperature, humidity, gas concentration, current and magnetic field; the microsystem sensor array comprises a plurality of three-electrode structure ionization sensors; wherein the cathodes of the ionization sensors with the three-electrode structures are manufactured on the same polar plate, and the leading-out electrode and the collecting electrode have the same structure but different polar distances;
and the second program module is used for obtaining the output current of the micro-system sensor array, and obtaining the environment multi-parameter value of the low-voltage station area through the accurate model matching.
The detection system of the invention corresponds to the detection method and has the advantages as described in the method.
The embodiments of the present invention further disclose a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the low-voltage zone environment multi-parameter detection method based on a microsystem sensor array as described above. The embodiment of the invention also discloses a computer device which comprises a memory and a processor, wherein the memory is stored with a computer program which executes the steps of the low-voltage area environment multi-parameter detection method based on the microsystem sensor array when being run by the processor.
The present invention may be implemented by implementing all or part of the procedures in the methods of the embodiments described above, or by instructing the relevant hardware by a computer program, which may be stored in a computer readable storage medium, and which when executed by a processor, may implement the steps of the embodiments of the methods described above. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. The memory may be used to store computer programs and/or modules, and the processor performs various functions by executing or executing the computer programs and/or modules stored in the memory, and invoking data stored in the memory. The memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid state storage device, etc.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above examples, and all technical solutions belonging to the concept of the present invention belong to the protection scope of the present invention. It should be noted that modifications and adaptations to the invention without departing from the principles thereof are intended to be within the scope of the invention as set forth in the following claims.
Claims (6)
1. The low-voltage station environment multi-parameter detection method based on the microsystem sensor array is characterized by comprising the following steps:
a structure mapping algorithm is adopted, a one-to-one correspondence relation between the output current of the micro-system sensor array and each parameter is established, cross interference among the parameters is eliminated, and an accurate model for simultaneous detection of multiple parameters is obtained; wherein the multiple parameters include temperature, humidity, gas concentration, current and magnetic field; the microsystem sensor array comprises a plurality of three-electrode structure ionization sensors; wherein the cathodes of the ionization sensors with the three-electrode structures are manufactured on the same polar plate, and the leading-out electrode and the collecting electrode have the same structure but different polar distances;
acquiring the output current of a microsystem sensor array, and then obtaining a multi-parameter value of the low-voltage area environment through the accurate model matching;
a pulse power supply is adopted as excitation of a sensor;
wherein the gas comprises H 2 、C 2 H 2 And CH (CH) 4 The data fusion process of the mixed gas detection under the corresponding temperature interference is as follows: the structure mapping algorithm is a support vector machine, wherein the support vector machine model of the mixed gas and the temperature comprises an input layer, a kernel function layer and an output layer; output of a current vector I= [ I ] with a sensor array H2 ,I C2H2 ,I C2H4 ,I T ]Support vector I k ＝[I kH2 ,I kC2H2 ,I kC2H4 ,I kT ]As an input layer, the concentration and the temperature of three components in the mixed gas are calculated to be phi' H2 、φ' C2H2 、φ' C2H4 T' is a model output layer; the middle of the input layer and the output layer is a kernel function layer, and kernel functions have various forms, including linear kernel functions, polynomial kernel functions, RBF kernel functions and tensor kernel functions; the support vector machine structure is constructed by a kernel function K (I, I k ) The operation maps the data of the input space to a high-dimensional feature space, then by multiplying a by Lagrange 1k –a 4k And the linear regression function determined by the offset (b 1-b 4) realizes data fitting;
NO/SO under temperature interference 2 /O 2 The data fusion process of the mixed gas of PM1/PM2.5/PM4/PM10 and the detection of particulate matters comprises the following steps: detection of NO/SO for microsystem sensor array 2 /O 2 The support vector machine for the mixed gas, the particulate matters and the temperature of the PM1/PM2.5/PM4/PM10 is composed of an input layer, a kernel function layer and an output layer; the input layer comprises a current vector I= [ I ] formed by collecting electrode currents of sensors with different electrode pitches in a silicon micron column three-electrode sensor array T ,I NO ,I SO2 ,I O2 ,I PM1 ,I PM2.5 ,I PM4 ,I PM10 ]And a support vector I composed of sensor array calibration experimental data as training samples k ＝[I kT ,I kNO ,I kSO2 ,I kO2 ,I kPM1 ,I kPM2.5 ,I kPM4 ,I kPM10 ]The method comprises the steps of carrying out a first treatment on the surface of the The kernel function layer includes kernel functions K (I, I k ) Lagrangian multiplier alpha 1k -α 8k And threshold b k N is the number of training samples; the kernel function layer maps the input layer data to a high-dimensional feature space through a kernel function, and determines a linear function through a Lagrangian multiplier and a threshold value to realize data flux fusion; the output layer outputs the temperature, NO concentration and SO corresponding to the current vector I of the collector electrode of the sensor array 2 Concentration, O 2 Concentration, PM1 concentration, PM2.5 concentration, PM4 concentration, and PM10 concentration;
the theoretical basis of temperature detection is: three-electrode ionization type sensor is extremely sensitive to temperature, has characteristics of thermal emission and thermal ionization, and can detect temperature, in particular:
I＝I 0 e αd (1)
α＝APe -BPE (2)
wherein I is the sensor output current; i 0 Is the initial discharge current; j (j) e Is the cathode emission current density; alpha is the first ionization coefficient of the gas; d is the distance of the alpha process; e is the electric field strength of the cathode nanotip; p is the gas pressure; a and B are constants related to gas species and temperature; epsilon 0 Is the absolute permittivity; phi is the electron work function before the electric field is applied; e is the charge carried by an electron; b (B) E Is the emission constant, the value of which is related to the material and surface conditions; k is the boltzmann constant; collecting current I c Is a part of I, then I c =f (T); temperature rise, current I c An increase;
the theoretical basis of humidity detection is as follows: the collector current and the relative humidity of the three-electrode ionization type sensor show a monotonic rising trend, and the collector current of the sensor gradually increases along with the increase of the relative humidity; wherein the relative humidity measurement formula is as follows:
P w ＝RH×P S (4)
wherein P is w Is the current water vapor to be measuredAir pressure of air, P s Is the air pressure generated by saturated steam, P s The calculation of (c) uses the Wexler formula:
wherein T is the absolute temperature of water vapor, C 0 ＝-6.044×10 3 ；C 1 ＝1.893×10 1 ；C 2 ＝-2.824×10 -2 ；C 3 ＝1.724×10 -5 ；C 4 ＝2.858；
In gas impact ionization, when the temperature and the excitation voltage are fixed, the water vapor impact ionization coefficient alpha mainly depends on the water vapor pressure P w The expression is:
the theoretical basis of smoke detection is as follows: first ionization coefficient alpha and gas species and concentrationIn connection with collecting current I c The change of (2) can reflect the change of the gas type and concentration between the sensor electrodes, and can be used for detecting the gas concentration; in addition, when the particles exist between the sensor electrodes, part of positive ions collide with the particles to transfer charges, so that the particles are charged; the charged particles and other positive ions move towards the extraction electrode through diffusion under the action of concentration gradient; after passing through the leading-out hole, the collector moves in an accelerating way under the action of a reverse electric field; after reaching the collector, the collector collects the charged particles and positive ions to generate a collector current I c ；
Known gas discharge current I c ＝I 0 e αd α=ape-BP/E, and ideal gas state equation pv=nrt, resulting in formulas (7), (8), (9), indicating that the change in particulate matter concentration causes a change in discharge current; wherein P is the gas pressure;v is the gas volume; r is the ideal gas constant;
the current detection theory basis is as follows: indirectly measuring the current according to the law of electromagnetic induction; for the induction magnetic field generated by any micro-element current dl of the current I in the same direction in the current-carrying long straight wire, the direction of the micro-element dB is the same, and according to the Pioshal law, the induction magnetic field at the A point is known as follows:
wherein θ is the included angle between the connecting line of the point A and any point on the long straight wire and the long straight wire; perpendicular line AO is drawn along the point A, and the AO length is r 0 And (3) the distance l between the conducting wire infinitesimal dl and the O point l is obtained:
wherein θ is 1 、θ 2 Is L 1 、L 2 The corresponding angle value of θ at both ends is used as the lead L 1 、L 2 When the distance between two points is infinitely long, theta 1 ＝0、θ 2 Let pi, take into equation (11) then:
formula (13) shows that when a current I passes through the conductor, magnetic induction B is generated around the conductor, and the magnetic induction B is proportional to the current I; it follows that the basic principle of the current sensor is: accurately measuring the magnetic induction intensity B of a fixed position inside the current sensor, and obtaining the magnitude and the direction of the measured current through signal processing;
the theoretical basis of the magnetic field detection is as follows: the three-electrode ionization type sensor is based on a discharge principle, when placed in a magnetic field, free electrons generate rotation and migration motion, namely, a lamo motion due to the action of magnetic field force near a discharge area, the motion path of the free electrons is prolonged, the residence time between polar plates is prolonged, the collision frequency of the free electrons and gas molecules is increased, the ionization of the gas molecules near the discharge electrode is effectively enhanced, the concentration of charged particles is increased, and the discharge current density j is increased; meanwhile, according to an electron motion equation and an electron energy equation in a magnetic field, the probability of electron collision ionization is increased by increasing electron energy, so that more positive ions are generated, and the collection current density jc is increased;
wherein μ is the charged particle mobility, N is the charged particle density, v is the charged particle velocity, E is the electric field strength, D MGF For the diffusion coefficient of the plasma under the action of a magnetic fieldB is magnetic induction intensity; m is electron mass, e is electron charge, v e Is the electron movement speed; q is electron energy;
meanwhile, electrons and positive ions generated by interelectrode discharge are acted by a magnetic field;
the theoretical basis of simultaneous detection of multiple parameters is as follows: because of the nonlinear relation between the collected current Ic and the pole spacing d of the sensors, the output current values Ic of the sensors with different pole spacing are different, and the sensors with different pole spacing can be used for detecting multiple parameters simultaneously.
2. The method for detecting the environment multiple parameters of the low-voltage transformer area based on the microsystem sensor array according to claim 1, wherein the temperature T ' and the NO concentration phi ' are obtained according to a support vector machine structural model ' NO 、SO 2 Concentration phi' SO2 、O 2 Concentration phi' O2 PM1 concentration phi' PM1 PM2.5 concentration phi' PM2.5 PM4 concentration phi' PM4 PM10 concentration phi' PM10 The relation with the current collected by the microsystem sensor array is:
wherein: alpha 1k -α 8k K=1, 2,3 … 570, lagrange multiplier; i j Collector current values for a silicon micron column three electrode sensor array, j=1, 2,3 …; i kj For support vector machine training sample data, k=1, 2,3 … 570; j=1, 2,3 ….
3. The low-voltage transformer area environment multi-parameter detection method based on the microsystem sensor array according to any one of claims 1-2, wherein the structure mapping algorithm comprises a neural network and a support vector machine.
4. A low-voltage area environment multi-parameter detection system based on a microsystem sensor array, for performing the steps of the low-voltage area environment multi-parameter detection method based on a microsystem sensor array as claimed in any one of claims 1 to 3, characterized by comprising:
the first program module is used for establishing a one-to-one correspondence between the output current of the micro-system sensor array and each parameter by adopting a structure mapping algorithm, eliminating cross interference among the parameters and obtaining an accurate model for simultaneous detection of multiple parameters; wherein the multiple parameters include temperature, humidity, gas concentration, current and magnetic field; the microsystem sensor array comprises a plurality of three-electrode structure ionization sensors; wherein the cathodes of the ionization sensors with the three-electrode structures are manufactured on the same polar plate, and the leading-out electrode and the collecting electrode have the same structure but different polar distances;
and the second program module is used for obtaining the output current of the micro-system sensor array, and obtaining the environment multi-parameter value of the low-voltage station area through the accurate model matching.
5. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being run by a processor, performs the steps of the low-voltage zone environment multi-parameter detection method based on a microsystem sensor array as claimed in any one of claims 1 to 3.
6. A computer device comprising a memory and a processor, the memory having stored thereon a computer program, characterized in that the computer program, when run by the processor, performs the steps of the low-voltage zone environment multi-parameter detection method based on a microsystem sensor array as claimed in any one of claims 1-3.
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