CN110174127B - Sensor model identification method and device based on parasitic capacitance - Google Patents
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Abstract
The invention discloses a sensor model identification method and device based on parasitic capacitance, which are used for identifying the model of a sensor by utilizing the difference of electrical characteristics of the sensor at the moment of power-on and the moment of power-off caused by different types of sensors and different types of sensors with the same type and different types of parasitic capacitance. The method comprises the following steps: determining a first electrical signal at a power-on moment and a second electrical signal at a power-off moment of each sample sensor; determining a first electrical eigenvector and a second electrical eigenvector of each sample sensor; determining a classification decision surface of each sensor type; determining a first electrical signal and a second electrical signal of a sensor to be identified; determining a first electrical characteristic and a second electrical characteristic of a sensor to be identified; and determining the type of the sensor to be identified and calculating the physical quantity represented by the output signal of the sensor according to the first electrical characteristic and the second electrical characteristic of the sensor to be identified and the classification decision surface of each sensor type.
Description
Technical Field
The invention relates to the field of sensor model identification, in particular to a sensor model identification method and device based on parasitic capacitance.
Background
In industrial production and test sites, various sensors are widely applied to monitoring various parameters and indexes of a production link. Since the signals output by the sensors of the same type and different models represent different physical quantities, the models of the sensors need to be identified. The traditional sensor model identification method adopts ID to identify the sensor, for example, the patent document CN201210593313.3 adopts 485 communication sensor identification signals to complete the sensor identification. However, the sensor that directly outputs the analog signal cannot recognize the model of the sensor because the sensor does not have a digital communication function.
In many production and test sites, sensors that directly output analog signals are used in large quantities due to cost limitations, such as pressure sensors, temperature sensors, pedal force sensors, etc. in automotive brake tests and suspension products. For the sensor directly outputting the analog signal, the model of the sensor needs to be manually selected, and the physical quantity corresponding to the analog signal output by the sensor is determined according to the selected model of the sensor, so that the efficiency is low. In addition, some sensors are difficult to determine the model of their equipment due to narrow installation locations.
Disclosure of Invention
In order to overcome the problems in the related art, the invention provides a sensor model identification method and device based on parasitic capacitance.
According to a first aspect of the embodiments of the present invention, there is provided a sensor model identification method based on parasitic capacitance, the method having the following principle: different types of sensors and sensors of the same type and different types have different parasitic capacitances due to different processing technologies, circuit board manufacturing, component welding and other factors. The processing technology and the element welding of the sensors of the same model are basically the same, and the parasitic capacitance is also approximately the same. The different parasitic capacitances cause the electrical characteristics of the sensor to change at the moment of power-on and the moment of power-off, so that the electrical characteristics at the moment of power-on and the moment of power-off can be used as the characteristics for identifying the type of the sensor. The method comprises the following steps:
determining a first electrical signal at the power-on moment and a second electrical signal at the power-off moment of each sample sensor, wherein the first electrical signal represents a digital quantity corresponding to analog-to-digital conversion of an output signal from the power-on moment of the sensor to the output stability of the sensor, and the second electrical signal represents a digital quantity corresponding to analog-to-digital conversion of an output signal from the power-off moment of the sensor to the output stability of the sensor;
determining a first electrical feature vector and a second electrical feature vector of each sample sensor according to the first electrical signal and the second electrical signal of each sample sensor, wherein the first electrical feature vector represents the features of the first electrical signal, and the second electrical feature vector represents the features of the second electrical signal;
determining a classification decision surface of each sensor type according to the first electrical characteristic vector and the second electrical characteristic vector of each sample sensor;
determining a first electrical signal at the power-on moment and a second electrical signal at the power-off moment of a sensor to be identified; determining a first electrical characteristic and a second electrical characteristic of a sensor to be identified;
determining the type of the sensor to be identified according to the first electrical characteristic and the second electrical characteristic of the sensor to be identified and the classification decision surface of each sensor type;
and calculating the physical quantity represented by the output signal of the sensor according to the type of the sensor to be identified.
In one possible implementation, determining the first electrical signal at the moment of power-up and the second electrical signal at the moment of power-down of each sample sensor includes:
acquiring an electrical signal of the sensor, wherein the electrical signal comprises an analog signal output by the sensor and converted into a digital signal, and the analog signal comprises a voltage signal or a current signal;
and determining a first electrical signal corresponding to the power-on moment and a second electrical signal corresponding to the power-off moment of each sample sensor according to the electrical signals.
In one possible implementation, determining the first electrical feature vector and the second electrical feature vector of each sample sensor according to the first electrical signal and the second electrical signal of each sample sensor includes:
the first electrical characteristic vector comprises signal rising time of the first electrical signal, and a frequency domain distribution vector and an amplitude distribution vector which are obtained by the first electrical signal through discrete Fourier transform;
the second electrical characteristic vector comprises a signal falling time of the second electrical signal, and a frequency domain distribution vector and an amplitude distribution vector which are obtained by the second electrical signal through discrete Fourier transform.
In a possible implementation manner, determining a classification decision surface of each sensor type according to the first electrical feature vector and the second electrical feature vector of each sample sensor includes:
and establishing a classification decision surface of each sensor type by adopting a nonlinear support vector machine method according to the first electrical characteristic vector and the second electrical characteristic vector of each sample sensor.
In a possible implementation manner, the sample sensor and the sensor to be identified are sensors that output analog signals, and the analog signals include voltage signals and current signals.
According to a second aspect of the embodiments of the present invention, there is provided a sensor model identification device based on parasitic capacitance, including:
the first determining module is used for determining a first electrical signal and a second electrical signal of each sample sensor and the sensor to be identified;
the second determining module is used for determining the first electrical characteristics and the second electrical characteristics of each sample sensor and the sensor to be identified;
the third determining module is used for determining a classification decision surface of each sensor type according to the first electrical characteristics and the second electrical characteristics of each sample sensor;
and the fourth determination module is used for determining the type of the sensor to be identified and the physical quantity corresponding to the output signal.
In a possible implementation manner, the first determining module includes:
the first acquisition submodule is used for acquiring the electrical signals of the sensor, acquiring analog signals output by the sensor and converting the analog signals into digital signals, wherein the analog signals comprise voltage signals or current signals;
and the first determining submodule is used for determining a first electrical signal corresponding to the power-on moment and a second electrical signal corresponding to the power-off moment of each sample sensor according to the electrical signals.
In a possible implementation manner, the second determining module includes:
the second determining submodule is used for determining frequency domain distribution vectors of the first electrical signals and the second electrical signals of each sample sensor and the sensor to be identified through discrete Fourier transform;
the third determining submodule is used for determining the amplitude distribution vectors of the first electrical signal and the second electrical signal of each sample sensor and the sensor to be identified;
and the fourth determining submodule is used for determining the rising time of the first electrical signal and the falling time of the second electrical signal of each sample sensor and the sensor to be identified.
In a possible implementation manner, the fourth determining module includes:
the fifth determining submodule is used for determining the type of the sensor to be identified according to the first electrical characteristic and the second electrical characteristic of the sensor to be identified and the classification decision surface of each sensor type;
and the sixth determining submodule is used for calculating the physical quantity represented by the sensor output signal according to the type of the sensor to be identified.
The invention has the beneficial effects that:
the identification of the sensor model is completed according to the difference of signal characteristics at the power-on moment and the power-off moment caused by the difference of parasitic capacitances of different sensors;
and after the identification of the model of the sensor is finished, calculating the corresponding physical quantity according to the corresponding relation between different sensor output signals and the physical quantity.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
FIG. 1 is a flow chart illustrating a sensor signal identification according to an exemplary embodiment.
FIG. 2 is a diagram illustrating an equivalent circuit of a sensor according to an exemplary embodiment.
Fig. 3 is a flowchart illustrating step 100 according to an exemplary embodiment.
FIG. 4 is a flowchart illustrating step 101 according to an exemplary embodiment.
Fig. 5 is a block diagram illustrating a sensor model identification apparatus based on parasitic capacitance according to an exemplary embodiment.
Fig. 6 is a block diagram illustrating a sensor model identification apparatus based on parasitic capacitance according to an exemplary embodiment.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to be consistent with all embodiments of the present disclosure.
Fig. 1 is a flow chart illustrating a sensor model identification method based on parasitic capacitance according to an exemplary embodiment. The method may be applied to a controller implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors, or other electronic devices.
As shown in fig. 1, the method may include:
102, determining a classification decision surface of each sensor type according to the first electrical characteristic vector and the second electrical characteristic vector of each sample sensor;
103, determining a first electrical signal at the power-on moment and a second electrical signal at the power-off moment of the sensor to be identified;
104, determining a first electrical characteristic vector and a second electrical characteristic vector of a sensor to be identified;
105, determining the type of the sensor to be identified according to the first electrical characteristic vector and the second electrical characteristic vector of the sensor to be identified and the classification decision surface of each sensor type;
and 106, calculating the physical quantity represented by the output signal of the sensor according to the type of the sensor to be identified.
In this example, the sensor is a sensor that outputs an analog signal, which may include a voltage signal and a current signal. The sensor may comprise one or more of a temperature sensor, a pressure sensor, and a humidity sensor.
As an example of this embodiment, if the sensor includes a temperature sensor, step 100 may include: the controller determines that a digital quantity corresponding to analog-to-digital conversion of a sensor output signal between the moment of electrifying the temperature sensors of different models and the output stability of the sensor is a first electrical signal, and determines that a digital quantity corresponding to analog-to-digital conversion of a sensor output signal between the moment of deenergizing the temperature sensors and the moment of outputting the sensor to zero is a second electrical signal.
Step 101 may include: the controller conducts discrete Fourier change on the first electric signals and the second electric signals of a large number of temperature sensors of different models, and signal features are extracted.
Step 102 may include: the high-performance computer classifies the signal characteristics of the temperature sensors according to the sensor types by adopting a nonlinear support vector machine method, and determines the classification decision surface of each type of sensor.
Step 103 may include: the controller collects a first electrical signal and a second electrical signal of a temperature sensor of unknown model.
Step 104 may include: the controller determines a first electrical characteristic and a second electrical characteristic of the unknown type of temperature sensor.
Step 105 may include: and the controller determines the model of the temperature sensor according to the first electrical characteristic and the second electrical characteristic of the temperature sensor and the classification decision surface of each model of sensor calculated by the high-performance computer in step 102.
Step 106 may include the controller calculating a physical quantity represented by the sensor output signal based on the determined model of the temperature sensor.
FIG. 2 is a diagram illustrating an equivalent circuit of a sensor according to an exemplary embodiment. As shown in fig. 2, the equivalent circuit may include:
Different types of sensors and sensors of the same type and different types have different parasitic capacitances due to different processing technologies, circuit board manufacturing, component welding and other factors. The processing technology and the element welding of the sensors of the same model are basically the same, and the parasitic capacitance is also approximately the same. The different parasitic capacitances cause the electrical characteristics of the sensor to change at the moment of power-on and the moment of power-off, so that the electrical characteristics at the moment of power-on and the moment of power-off can be used as the characteristics for identifying the type of the sensor.
As an example of this embodiment, if the sensor comprises a temperature sensor, the temperature change causes the resistance of the element 205 to change, which in turn causes the output voltage of the element 207 to change; component 206 is a parasitic capacitance that develops between the output component and power ground due to circuit board fabrication, component soldering, and other factors.
Fig. 3 is a flowchart illustrating step 100 according to an exemplary embodiment. As shown in fig. 3, step 100 may include:
1001, acquiring an electrical signal of a sensor, wherein the electrical signal includes acquiring an analog signal output by the sensor and converting the analog signal into a digital signal, and the analog signal includes a voltage signal or a current signal;
For example, the controller may collect an analog signal output by the sensor and convert the analog signal into a digital signal as an electrical signal, and obtain a first electrical signal output by the sensor at the moment of power-on of the sensor, and obtain a second electrical signal output by the sensor at the moment of power-off of the sensor.
FIG. 4 is a flowchart illustrating step 101 according to an exemplary embodiment. As shown in fig. 4, step 101 may include:
for example, the controller is used for performing discrete fourier transform on the first electrical signal and the second electrical signal of each sample sensor, and a frequency domain distribution vector and an amplitude distribution vector obtained through fourier transform and a signal rising time and a signal falling time obtained through measurement are used as feature vectors of the type of sensor.
Fig. 5 is a block diagram illustrating a sensor model identification apparatus based on parasitic capacitance according to an exemplary embodiment, the apparatus including:
a first determining module 41, configured to determine a first electrical signal and a second electrical signal of each sample sensor and the sensor to be identified;
a second determination module 42 for determining the first and second electrical characteristics of each sample sensor and sensor to be identified;
a third determining module 43, configured to determine a classification decision surface of each sensor type according to the first electrical characteristic and the second electrical characteristic of each sample sensor;
and a fourth determination module 44, configured to determine the type of the sensor to be identified and the physical quantity corresponding to the output signal.
In a possible implementation manner, the first determining module includes:
the first acquisition submodule 411 is configured to acquire an electrical signal of the sensor, where the electrical signal includes an analog signal output by the sensor and is converted into a digital signal, and the analog signal includes a voltage signal or a current signal;
the first determining submodule 412 is configured to determine, according to the electrical signal, a first electrical signal corresponding to a power-on instant and a second electrical signal corresponding to a power-off instant of each sample sensor.
In a possible implementation manner, the second determining module includes:
the second determining submodule 421 is configured to determine frequency domain distribution vectors of the first electrical signal and the second electrical signal of each sample sensor and the sensor to be identified through discrete fourier transform;
the third determining submodule 422 is configured to determine amplitude distribution vectors of the first electrical signal and the second electrical signal of each sample sensor and the sensor to be identified;
the fourth determining submodule 423 is configured to determine a rise time of the first electrical signal and a fall time of the second electrical signal of each of the sample sensor and the sensor to be identified.
In a possible implementation manner, the fourth determining module includes:
the fifth determining submodule 441 is configured to determine the type of the sensor to be identified according to the first electrical characteristic and the second electrical characteristic of the sensor to be identified and the classification decision surface of each sensor type;
a sixth determining submodule 442 is configured to calculate the physical quantity represented by the sensor output signal according to the type of the sensor to be identified.
Fig. 6 is a block diagram illustrating a sensor model identification apparatus based on parasitic capacitance according to an exemplary embodiment. Referring to fig. 6, the apparatus 500 may include one or more of the following components: a power supply component 501, an analog-to-digital conversion component 502, a signal input component 503, a processing component 504, a communication component 505 and a storage component 506.
The processing component 504 generally controls the overall operation of the apparatus 500, such as analog-to-digital conversion of the signal, signal feature extraction. The processing components 504 may include one or more processors 510 to execute instructions. Further, processing component 504 may include one or more modules that facilitate interaction between processing component 504 and other components.
The signal input component 503 outputs the analog signal output by the sensor to the analog-to-digital conversion component after conditioning, wherein the conditioning process includes one or more of filtering, amplifying and attenuating. For example, when the sensor input signal is a current signal, the current signal is converted into a voltage signal to be output.
The analog-to-digital conversion component 502 converts the voltage signal output by the signal input component into a digital signal;
the communication component 505 is configured to facilitate communications between the apparatus 500 and other devices, both wired and wireless, and the apparatus 500 may access a wireless network based on a communication standard, such as Wi-Fi, 2G, 3G, or 4G, or a combination thereof. In an exemplary embodiment, the communication component 505 CAN also be implemented using USB communication technology, ethernet communication technology, CAN communication technology, and other technologies.
In an exemplary embodiment, the apparatus 500 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic devices for performing the above-described methods.
In an exemplary embodiment, a non-transitory computer-readable storage medium comprising instructions, such as the memory 506 comprising instructions, executable by the processor 510 of the apparatus 500 to perform the above-described method is also provided. For example, the non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (9)
1. A sensor model identification method based on parasitic capacitance is characterized by comprising the following steps:
(1) determining a first electrical signal at the power-on moment and a second electrical signal at the power-off moment of each sample sensor, wherein the first electrical signal represents a digital quantity corresponding to analog-to-digital conversion of an output signal from the power-on moment of the sensor to the output stability of the sensor, and the second electrical signal represents a digital quantity corresponding to analog-to-digital conversion of an output signal from the power-off moment of the sensor to the output stability of the sensor;
(2) determining a first electrical characteristic vector and a second electrical characteristic vector of each sample sensor according to the first electrical signal and the second electrical signal of each sample sensor;
determining a classification decision surface of each sensor type according to the first electrical characteristic vector and the second electrical characteristic vector of each sample sensor;
(3) determining a first electrical signal at the power-on moment and a second electrical signal at the power-off moment of a sensor to be identified; determining a first electrical characteristic vector and a second electrical characteristic vector of a sensor to be identified;
(4) determining the type of the sensor to be identified according to the first electrical characteristic vector and the second electrical characteristic vector of the sensor to be identified and the classification decision surface of each sensor type;
(5) and calculating the physical quantity represented by the output signal of the sensor according to the type of the sensor to be identified.
2. The method of claim 1, wherein determining the first electrical signal at a power-up instant and the second electrical signal at a power-down instant for each sample sensor comprises:
acquiring an electrical signal of the sensor, wherein the electrical signal comprises an analog signal output by the sensor and converted into a digital signal, and the analog signal comprises a voltage signal or a current signal;
and determining a first electric signal at the power-on moment and a second electric signal at the power-off moment of each sample sensor according to the electric signals.
3. The method of claim 1, wherein determining the first and second electrical eigenvectors for each sample sensor from the first and second electrical signals for each sample sensor comprises:
the first electrical characteristic vector comprises signal rising time of the first electrical signal, and a frequency domain distribution vector and an amplitude distribution vector which are obtained by the first electrical signal through discrete Fourier transform;
the second electrical characteristic vector comprises a signal falling time of the second electrical signal, and a frequency domain distribution vector and an amplitude distribution vector which are obtained by the second electrical signal through discrete Fourier transform.
4. The method of claim 1, wherein determining a classification decision surface for each sensor type based on the first and second electrical eigenvectors for each sample sensor comprises:
and establishing a classification decision surface of each sensor type by adopting a nonlinear support vector machine method according to the first electrical characteristic vector and the second electrical characteristic vector of each sample sensor.
5. The method of claim 1, wherein the sample sensor and the sensor to be identified are sensors that output analog signals, the analog signals including voltage signals and current signals.
6. A sensor model identification device based on parasitic capacitance, comprising:
the first determining module is used for determining a first electrical signal and a second electrical signal of each sample sensor and the sensor to be identified; the method specifically comprises the following steps: the electrical characteristics of the sensor at the moment of power-on and the moment of power-off are changed due to different parasitic capacitances, so that the electrical characteristics at the moment of power-on and the moment of power-off are used as characteristics for identifying the type of the sensor; the controller collects analog signals output by the sensor and converts the analog signals into digital signals serving as electrical signals, first electrical signals output by the sensor are obtained at the moment of electrifying the sensor, and second electrical signals output by the sensor are obtained at the moment of power failure of the sensor;
the second determination module is used for determining a first electrical characteristic vector and a second electrical characteristic vector of each sample sensor and the sensor to be identified;
the third determining module is used for determining a classification decision surface of each sensor type according to the first electrical characteristic vector and the second electrical characteristic vector of each sample sensor;
and the fourth determination module is used for determining the type of the sensor to be identified and the physical quantity corresponding to the output signal.
7. The apparatus of claim 6, wherein the first determining module comprises:
the first acquisition submodule is used for acquiring the electrical signals of the sensor, acquiring analog signals output by the sensor and converting the analog signals into digital signals, wherein the analog signals comprise voltage signals or current signals;
and the first determining submodule determines a first electrical signal at the moment of power-on and a second electrical signal at the moment of power-off of each sample sensor according to the electrical signals.
8. The apparatus of claim 6, wherein the second determining module comprises:
the second determining submodule is used for determining frequency domain vectors of the first electrical signals and the second electrical signals of each sample sensor and the sensor to be identified after discrete Fourier transform;
the third determining submodule is used for determining the amplitude vectors of the first electrical signal and the second electrical signal of each sample sensor and the sensor to be identified;
and the fourth determining submodule is used for determining the rising time of the first electrical signal and the falling time of the second electrical signal of each sample sensor and the sensor to be identified.
9. The apparatus of claim 6, wherein the fourth determining module comprises:
the fifth determining submodule is used for determining the type of the sensor to be identified according to the first electrical characteristic vector and the second electrical characteristic vector of the sensor to be identified and the classification decision surface of each sensor type;
and the sixth determining submodule is used for calculating the physical quantity represented by the sensor output signal according to the type of the sensor to be identified.
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| KR20180127118A (en) * | 2017-05-19 | 2018-11-28 | 삼성전자주식회사 | Power on/off reset circuit and reset signal generating circuit including the same |
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