CN115728276A - Explosive detection method and detection system - Google Patents
Explosive detection method and detection system Download PDFInfo
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Abstract
The embodiment of the application discloses an explosive detection method and an explosive detection system, wherein after multichannel fluorescent signals of explosives are obtained, the interference of fluorescent backgrounds is eliminated, then signal preprocessing is carried out to reduce the interference mixed in the fluorescent signals, then the fluorescent signals are processed by adopting a fluorescent signal compensation algorithm, a K value solving algorithm and a characteristic value representing algorithm to obtain characteristic values of the explosives, and then the characteristic values are compared with characteristic values in a material characteristic library, so that the types of the explosives are judged in advance. The method eliminates the interference of fluorescence background, miscellaneous signals and the like, and improves the accuracy of explosive detection.
Description
Technical Field
The application relates to the field of explosive fluorescence detection, in particular to an explosive detection method and an explosive detection system.
Background
The fluorescent explosive detector can detect whether the substance to be detected contains explosives by using a Mass Spectrometry (MS), and the detection principle is as follows. The fluorescent explosive detector is internally provided with a fluorescent material, and when the fluorescent material is excited by exciting light with a certain wavelength, an original fluorescent signal with a certain wavelength and intensity can be generated. When a substance to be detected is detected by a fluorescent explosive detector, if the substance to be detected contains an explosive, the explosive contacts a fluorescent material, and the fluorescent material is excited by excitation light with the same wavelength, the fluorescent intensity, wavelength, peak position, and the like of the fluorescent material change to generate a new fluorescent signal, for example, the fluorescent intensity is quenched or enhanced relative to the original fluorescent signal. These new fluorescent signals can be captured by the detection device of the fluorescent explosives detector to detect the corresponding class of explosives. The above process involves the analysis of fluorescence signals, however, the noise of fluorescence signals obtained by the current fluorescence explosive detector is too large, the interference of fluorescence signals is strong, and the judgment of explosive types is not accurate.
Disclosure of Invention
The embodiment of the application provides an explosive detection method and an explosive detection system, which are used for solving the problem that the judgment of the existing fluorescent explosive detector on the types of explosives is inaccurate.
To achieve the above object, the present application provides a method for detecting explosives, which comprises:
collecting fluorescence intensity signals of a plurality of fluorescence channels of the fluorescent explosives, converting the fluorescence intensity signals into digital fluorescence signals, and calculating fluorescence response values of the fluorescent explosives;
eliminating the fluorescence background of the fluorescence response value to obtain a net fluorescence signal;
performing signal pretreatment on the net fluorescence signal to obtain a pretreated signal;
and (3) carrying out fluorescence signal compensation algorithm, K value solving algorithm and characteristic value representation algorithm processing on the preprocessed signals, and pre-judging the type of the fluorescence explosive according to the characteristic value comparison algorithm.
In some embodiments, multiple ADS1255 sampling chips are alternately driven by an SPI chip selection driving manner to collect fluorescence intensity signals of multiple fluorescence channels.
In some embodiments, the process of eliminating comprises:
smoothing the fluorescence response value to eliminate high-frequency white noise and obtain a smooth fluorescence signal;
obtaining the minimum value in each window range in the smooth fluorescence signal, obtaining the position of the maximum value between two minimum values according to the minimum value of two adjacent window ranges, and dividing the signal segment covered by the two adjacent window ranges in the smooth fluorescence signal into a left signal segment and a right signal segment according to the position of the maximum value;
subtracting the minimum value from the left signal section to obtain a left return-to-zero signal section, and subtracting the minimum value from the right signal section to obtain a right return-to-zero signal section;
and (3) selecting a higher signal segment in the left return-to-zero signal segment and the right return-to-zero signal segment to be multiplied by a proportionality coefficient f, and then respectively carrying out self-adaptive signal scaling to obtain a net fluorescence signal.
In some embodiments, the scaling factor f is formulated as:
wherein, f max The signal segment is a left signal segment and a right signal segment, and a and b are maximum values in the left return-to-zero signal segment and the right return-to-zero signal segment.
In some embodiments, the adaptive signal scaling is: after the left return-to-zero signal segment or the right return-to-zero signal segment is multiplied by the scaling factor f, the following calculation is respectively carried out:
wherein x is i For each data value in the left return-to-zero signal segment or the right return-to-zero signal segment, n is the size of the data set of the left return-to-zero signal segment or the right return-to-zero signal segment.
In some embodiments, the signal pre-processing includes a zero-averaging process and a normalization process.
In some embodiments, the method of zero-averaging processing includes:
number of data num and each data value x based on the net fluorescence signal i Obtaining a net fluorescence signalMean value of (a) = (x) 1 +x 2 +…+x i )/num；
Calculating the standard deviation
Performing zero equalization processing
A zero-averaged signal is obtained.
In some embodiments, the method of normalization processing comprises:
obtaining the maximum value min (x) of the zero-mean signal i ) And minimum value min (x) i ) Passing each data value of the zero-averaged signal
And scaling the signal to be between 0 and 1 to obtain a preprocessed signal.
In some embodiments, the fluorescence signal compensation algorithm comprises: each data value x of the preprocessed signal ii Substituted into the formula for compensating the fluorescent signal
Obtaining a compensated signal x ii '; t is the ambient temperature at the time of detection.
In some embodiments, the K-value solving algorithm is based on the following relationship
Solving a K value; wherein, I F Representing the original fluorescence signal, I O Represents the fluorescence reference signal, A T Represents the wavelength of fluorescence at the current temperature, τ represents the absorbance of fluorescence, # represents the multiplication,
the fluorescence quenching rate is shown. The original fluorescence signal refers to the fluorescence signal obtained when no explosive is introduced, i.e. when the explosive is not in contact with the fluorescent material. The reference signal of fluorescence being presetAnd the fluorescence signal value is used for eliminating system errors such as measurement errors. The fluorescence wavelength at the current temperature refers to the wavelength of fluorescence emitted by a fluorescent explosive when the explosive comes into contact with the fluorescent material at the current measurement temperature. The fluorescence absorbance is the device performance of the fluorescence detection instrument, and can be set to different values according to different measurement conditions or set empirically. The fluorescence quenching rate may be set to a different value depending on different measurement conditions or may be set empirically. Of course, there are other setting methods for the fluorescence absorbance and the fluorescence quenching rate.
In some embodiments, the eigenvalue characterization algorithm is based on the compensated signal x ii ' calculation of the value of K and the value of Z characteristic of the fluorescent explosive i ', the calculation formula is as follows: | z i ′*E[K*x ii ′]-A |, where E represents the mean value and A represents the compensated signal x ii ' an inverse of the formed signal matrix. Because the compensated signal x ii ' is large in number, so a signal matrix can be formed, and A is the inverse of the signal matrix.
In some embodiments, the eigenvalue comparison algorithm compares the eigenvalues z i Comparing the obtained data with the characteristic values Z in a pre-established material characteristic library one by one, if a comparison result is obtained, obtaining the type of the fluorescent explosive, and if the comparison result is not obtained, indicating that no fluorescent explosive exists.
In some embodiments, the method for establishing the substance characteristic library comprises the step of acquiring a room temperature signal characteristic value Z r Step (2), obtaining a high temperature signal characteristic value Z h Obtaining a low temperature signal characteristic value Z l And a fitting step.
In some embodiments, room temperature signal characteristic value Z is obtained r Comprises the following steps:
respectively collecting multiple fluorescent explosives at room temperature T r Converting the fluorescence intensity signals of the plurality of fluorescence channels into digital fluorescence signals;
eliminating the fluorescence background of the digital fluorescence signal to obtain a net fluorescence signal;
and performing signal preprocessing on the net fluorescence signal to obtain a preprocessed signal. The substance characteristic library is constructed according to the standard temperature value, the fluorescence data is the standard value, and deviation does not occur, so that the pretreatment process of fluorescence signals such as a fluorescence signal compensation algorithm, a K value solving algorithm and the like is not needed in the construction of the substance characteristic library.
Calculating the room temperature signal characteristic value Z corresponding to various fluorescent explosives according to the following formula r ：
Wherein Z is r Is a room temperature signal characteristic value, i is a primary data set of the preprocessed signal, j is another secondary data set of the preprocessed signal remeasured after i, s is the time for obtaining the primary data set, the unit of s is min, T r Is the temperature at room temperature, T r The unit of (b) is [ deg. ] C.
In some embodiments, a high temperature signal characteristic value Z is obtained h Comprises the following steps:
respectively collecting multiple fluorescent explosives at high temperature T h Converting the fluorescence intensity signals of the plurality of fluorescence channels into digital fluorescence signals;
eliminating the fluorescence background of the digital fluorescence signal to obtain a net fluorescence signal;
performing signal pretreatment on the net fluorescent signal to obtain a pretreated signal;
calculating the high-temperature signal characteristic value Z corresponding to various fluorescent explosives according to the following formula h ：
Wherein Z is h Is the high temperature signal characteristic value, i is a primary dataset of the preprocessed signal, j is another secondary dataset of the preprocessed signal remeasured after i, s is the time to obtain the primary dataset, s is in units of min, T h Is the temperature at high temperature, T h The unit of (A) is [ deg. ] C.
In some embodiments, a low temperature signal characteristic value Z is obtained l Comprises the following steps:
respectively collecting multiple fluorescent explosives at low temperature T l Converting the fluorescence intensity signals of the plurality of fluorescence channels into digital fluorescence signals;
eliminating the fluorescence background of the digital fluorescence signal to obtain a net fluorescence signal;
performing signal pretreatment on the net fluorescent signal to obtain a pretreated signal;
calculating the low-temperature signal characteristic value Z corresponding to various fluorescent explosives according to the following formula l ：
Wherein, Z l Is the low temperature signal characteristic value, i is a primary dataset of the preprocessed signal, j is another secondary dataset of the preprocessed signal remeasured after i, s is the time to obtain the primary dataset, s is in units of min, T l At a low temperature, T l The unit of (A) is [ deg. ] C.
In some embodiments, the fitting step is to fit the room temperature signal characteristic value Z r Characteristic value Z of high temperature signal h Low temperature signal characteristic value Z l Fit to a library of material characteristics.
An embodiment of the present application further provides an explosive detection system, including:
the multi-channel signal acquisition unit is used for acquiring fluorescence intensity signals of a plurality of fluorescence channels of the fluorescence explosives, converting the fluorescence intensity signals into digital fluorescence signals and calculating fluorescence response values of the fluorescence explosives;
the signal preprocessing unit is used for eliminating the fluorescence background of the fluorescence response value to obtain a net fluorescence signal; performing signal pretreatment on the net fluorescence signal to obtain a pretreated signal;
a signal characterization unit for performing a fluorescent signal compensation algorithm on the preprocessed signals,K value solving algorithm and characteristic value representation algorithm processing are carried out to obtain characteristic value z of the fluorescent explosive i ′；
A substance detection algorithm unit for comparing the characteristic value z with the characteristic value i Comparing the obtained data with the characteristic values Z in a pre-established material characteristic library one by one, if a comparison result is obtained, obtaining the type of the fluorescent explosive, and if the comparison result is not obtained, indicating that no fluorescent explosive exists.
In some embodiments, the explosives detection system further comprises a central processing unit, and the central processing unit is respectively connected with the multi-channel signal acquisition unit, the signal preprocessing unit, the signal characterization unit and the material detection algorithm unit in a communication manner.
The explosives detection system further includes: and the acousto-optic alarm unit generates an acousto-optic alarm signal when the type of the fluorescent explosive is obtained.
In some embodiments, the explosives detection system further includes a build materials library unit that includes:
the room temperature characteristic value acquisition module is used for acquiring room temperature signal characteristic values Z according to the acquired fluorescence intensity signals of the multiple fluorescence channels of the multiple fluorescence explosives at the room temperature Tr r ；
A high temperature characteristic value acquisition module for respectively acquiring multiple fluorescent explosives at high temperature T h Obtaining the high temperature signal characteristic value Z from the fluorescence intensity signals of a plurality of fluorescence channels h ；
A low-temperature characteristic value acquisition module for acquiring multiple fluorescent explosives at low temperature T l Obtaining the characteristic value Z of the low-temperature signal from the fluorescence intensity signals of a plurality of fluorescence channels l ；
A fitting module for fitting the room temperature signal characteristic value Z r High temperature signal characteristic value Z h Characteristic value Z of low temperature signal l Fit to a library of material characteristics.
Due to the adoption of the technical scheme, the application has the following technical effects:
the application discloses an explosive detection method and an explosive detection system, which eliminate the interference of a fluorescent background after acquiring a multichannel fluorescent signal of an explosive, then carry out signal preprocessing to reduce the interference mixed in the fluorescent signal, then process the fluorescent signal by adopting a fluorescent signal compensation algorithm, a K value solving algorithm and a characteristic value representing algorithm to obtain a characteristic value of the explosive, and then compare the characteristic value with the characteristic value in a material characteristic library, thereby judging the type of the explosive in advance. The method eliminates the interference of fluorescence background, miscellaneous signals and the like, and improves the accuracy of explosive detection.
Drawings
The present application is further described below with reference to the accompanying drawings.
Fig. 1 is a block diagram of an explosives detection system in accordance with an embodiment of the application.
Fig. 2 is a flowchart of a detection method according to an embodiment of the present application.
Fig. 3 is a library creation flowchart according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application. In the description of the present application, it is to be understood that the terms "center," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer" (if any), and the like are used in the orientations and positional relationships indicated in the drawings, which are based on the orientation or positional relationship shown in the drawings, and are used for convenience of description and simplicity of description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and therefore should not be construed as limiting the present application.
The embodiment of the application provides a method for detecting explosives, which comprises the following steps:
(1) Collecting fluorescence intensity signals of a plurality of fluorescence channels of the fluorescent explosives, converting the fluorescence intensity signals into digital fluorescence signals and calculating fluorescence response values of the fluorescent explosives;
(2) Eliminating the fluorescence background of the fluorescence response value to obtain a net fluorescence signal;
(3) Performing signal pretreatment on the net fluorescence signal to obtain a pretreated signal;
(4) And performing fluorescence signal compensation algorithm, K value solving algorithm and characteristic value characterization algorithm processing on the preprocessed signals, and pre-judging the type of the fluorescent explosive according to the characteristic value comparison algorithm.
Wherein, in the step (1), the fluorescent explosive is a substance formed after the fluorescent material in the fluorescent explosive detecting instrument contacts with the explosive. The fluorescent material emits a fluorescent signal after contacting with an explosive, and the fluorescent signal is collected by adopting an ADS1255 sampling chip. The ADS1255 sampling chip is in an SPI driving mode of a single chip microcomputer. Specifically, multiple ADS1255 sampling chips are alternately driven in an SPI chip selection driving mode to collect fluorescence intensity signals of multiple fluorescence channels, and the fluorescence intensity signals are converted from analog fluorescence signals to digital fluorescence signals. Step (1) may be performed with a multi-channel signal acquisition unit. The multiple channels comprise two or more than two channels, which can be three channels, four channels, five channels, and the highest is eight channels.
In the step (1), the multichannel acquisition principle is that an ADS1255 high-precision chip is used as a medium for acquiring digital fluorescence signals of fluorescent explosives, fluorescence intensity signals (i.e., multichannel fluorescence signal data) of a plurality of fluorescence channels are acquired in an SPI alternating driving manner, and after the fluorescence intensity signals of the fluorescent explosives of the plurality of fluorescence channels are acquired, the fluorescence intensity signals are calculated according to the corresponding characteristics of each fluorescent explosive to acquire the fluorescence response value of the corresponding fluorescent explosive. The calculation process is calculated according to the sampling voltage value and the intensity of the digital fluorescence signal.
In the step (2), the fluorescence background elimination is to find spectral characteristic peaks of the fluorescence intensity signals of the explosives, eliminate some larger fluorescence background signals superposed by the characteristic peaks, and can reduce and obtain better fluorescence spectrum data, especially for some weaker fluorescence intensity reaction peaks, so that the signal change is more obvious. The process of fluorescence background elimination comprises:
(2-1) smoothing the fluorescence response value to eliminate high-frequency white noise and obtain a smooth fluorescence signal;
(2-2) obtaining a minimum value in each window range in the smooth fluorescence signal, obtaining the position of a maximum value between two minimum values according to the minimum value of two adjacent window ranges, and dividing signal segments covered by two adjacent window ranges in the smooth fluorescence signal into a left signal segment and a right signal segment according to the position of the maximum value;
(2-3) subtracting the minimum value from the left signal section to obtain a left return-to-zero signal section, and subtracting the minimum value from the right signal section to obtain a right return-to-zero signal section;
and (2-4) selecting a higher signal segment in the left return-to-zero signal segment and the right return-to-zero signal segment to be multiplied by a proportionality coefficient f, and then respectively carrying out self-adaptive signal scaling to obtain a net fluorescence signal.
Wherein, the main purpose of step (2) is to remove the fluorescence background signal, which adopts the fluorescence background elimination algorithm (adaptive wavelet). The fluorescence background elimination algorithm is used for eliminating fluorescence background signals superposed by fluorescence characteristic peaks, so that better fluorescence spectrum data can be obtained, the change of the fluorescence signals is more obvious, and the accuracy is higher when signal characterization is carried out subsequently. Because the signal of the fluorescent explosive needs to have the characteristics of small interference, single signal characteristic and the like, after the fluorescent response values of the fluorescent explosives of a plurality of fluorescent channels are obtained, the adaptive wavelet is adopted to filter the fluorescent response values so as to reduce the interference of background noise signals.
In step (2-2), the size of the coverage of each window range in the smoothed fluorescence signal may be arbitrarily specified for the sake of calculation convenience. In addition, the left signal segment and the right signal segment are screened according to two adjacent window ranges, so that the signal segments covered by the left signal segment and the right signal segment together are the window range of each signal peak. The algorithm is processed by adopting a variable-length window cutting method, data in different window ranges are subdivided according to the minimum value, and finally calculation is carried out according to the subdivided window ranges, so that the algorithm is favorable for eliminating the signals which are irrelevant to substance analysis and influence the substance analysis, such as the substrate of a fluorescence signal, a change process signal, and a mutation during time sequence switching, and the like, and the accuracy and the sensitivity of analysis are improved.
In step (2-3), since the minimum value is subtracted from the value, two discontinuous data segments are obtained, i.e., the left return-to-zero signal segment and the right return-to-zero signal segment respectively exhibit the characteristics of the discontinuous data segments.
In step (2-4), the formula of the proportionality coefficient f is:
wherein f is max The maximum values of the left signal segment and the right signal segment are obtained, and a and b are the maximum values of the left return-to-zero signal segment and the right return-to-zero signal segment.
In step (2-4), the adaptive signal is scaled to: after the left return-to-zero signal segment or the right return-to-zero signal segment is multiplied by the scaling factor f, the following calculation is respectively carried out:
wherein x is i For each data value in a left-return-to-zero signal segment or a right-return-to-zero signal segment, n is the size of the data set of the left-return-to-zero signal segment or the right-return-to-zero signal segment (i.e., the number of data contained in each signal segment).
In step (3), the signal preprocessing includes a zero-averaging process and a normalization process. The signal preprocessing aims to reduce interference signals included in the fluorescence signals, can reduce errors of the fluorescence intensity signals (for example, errors caused by large difference between the self difference of the fluorescence intensity signals and an array can be reduced), enables the fluorescence intensity signals to be more accurate, and mainly comprises a centering algorithm and a standardization algorithm.
In step (3), the method of zero-averaging processing (centering algorithm) includes:
number of data num and each data value x based on the net fluorescence signal i Obtaining the mean value u = (x) of the net fluorescent signal 1 +x 2 +…+x i )/num；
Calculating the standard deviation
Performing zero equalization processing
A zero-averaged signal (i.e., a normalization algorithm) is obtained.
Because each channel signal acquired by the fluorescent explosive detecting instrument does not only have a fluorescent signal of a reaction explosive, but also is mixed with a substrate signal, a change process signal and a time sequence switching mutation signal (because the fluorescent signal acquired by the fluorescent explosive detecting instrument is the same as the driving time sequence of a light source), the fluorescent signal is processed by adopting a zero mean value and a standard deviation so as to remove the substrate of the fluorescent signal, the change process signal, the mutation during time sequence switching and other signals which are irrelevant to material analysis and influence the material analysis, thereby improving the accuracy and the sensitivity of the analysis.
In step (3), the normalization processing method includes:
obtaining the maximum value min (x) of the zero-mean signal i ) And minimum value min (x) i ) Passing each data value of the zero-averaged signal
And scaling the signal to be between 0 and 1 to obtain a preprocessed signal.
And (4) judging the types of the explosives by adopting a material detection algorithm, wherein the method comprises a fluorescence signal compensation algorithm, a K value solving algorithm, a characteristic value characterization algorithm, a characteristic value comparison algorithm and the like, and finally pre-judging the types of the fluorescence explosives. The material detection algorithm is a comprehensive algorithm of the detection state of the fluorescent explosive detector and the detection logic of explosive species, and can perform comprehensive logic judgment according to the state of the fluorescent explosive detector and the response of fluorescent signals to different explosives, so that the detection of the fluorescent explosive detector on the explosive species is completed.
In step (4), the fluorescence signal compensation algorithm includes: each data value x of the preprocessed signal ii Substitution into the formula for compensating the fluorescent signal
Obtaining a compensated signal x ii '; t is the ambient temperature at the time of detection.
In step (4), the K value solving algorithm is based on the following relation
Solving a K value; wherein, I F Representing the original fluorescence signal, I O Represents the fluorescence reference signal, A T Represents the fluorescence wavelength at the current temperature, tau represents the fluorescence absorbance, x represents the multiplication,
the fluorescence quenching rate is indicated. The original fluorescence signal refers to the fluorescence signal obtained without passing an explosive, i.e. without contacting the explosive with a fluorescent material. The fluorescence reference signal is a preset fluorescence signal value and is used for eliminating system errors such as measurement errors. The fluorescence wavelength at the current temperature refers to the wavelength of fluorescence emitted by a fluorescent explosive when the explosive comes into contact with a fluorescent material at the current measurement temperature. The fluorescence absorbance is the device performance of the fluorescence detection instrument, and can be set to different values according to different measurement conditions or set according to experience. The fluorescence quenching rate may be set to a different value depending on different measurement conditions or may be set empirically. Of course, there are other setting methods for the fluorescence absorbance and the fluorescence quenching rate.
In step (4), the eigenvalue characterization algorithm is based on the compensated signal x ii ' calculation of the value of K and the value of Z characteristic of the fluorescent explosive i ', the calculation formula is as follows: | z i ′*E[K*x ii ′]-A |, where E represents the mean value and A represents the compensated signal x ii ' an inverse of the formed signal matrix. Because of the compensated signal x ii ' is so many that a signal matrix can be formed, and a is the inverse of the signal matrix.
In step (4), the eigenvalue comparison algorithm compares the eigenvalue z with the eigenvalue z i Comparing the obtained data with the characteristic values Z in a pre-established material characteristic library one by one, if a comparison result is obtained, obtaining the type of the fluorescent explosive, and if the comparison result is not obtained, indicating that no fluorescent explosive exists. And (4) judging the type of the fluorescent explosive as a pre-judgment, and carrying out final judgment by combining other algorithms. Because the temperature value is deviated from the temperature point when the library is built in the actual detection, the type of the explosive is pre-judged in the material detection.
In the substance detection algorithm, the substance detection algorithm carries out algorithm processing aiming at the reactions of different explosives and fluorescent materials, fixes the detection limit priority and the identification time of the explosives, and increases the signal characteristic differentiation of the explosives through the fluorescent materials, so that a fluorescent explosive detection instrument can accurately and quickly detect the result.
In step (4), the substance detection algorithm further comprises a multi-channel signal alternating response identification logic algorithm. The algorithm is used for carrying out final judgment by combining the pre-judgment result of the type of the fluorescent explosive. The judgment principle of the multi-channel signal alternating response identification logic algorithm is as follows: the method comprises the following steps of locking the type range of the explosive according to the pre-judgment result of the type of the fluorescent explosive, and finally judging the explosive through a fluorescent signal cross response identification algorithm of each channel, wherein the method mainly comprises the following steps:
1. and setting the value of the threshold value P, and judging whether each channel reaches the response limit or not according to the threshold value P. The threshold value P is compared with the magnitude of the fluorescence response value. If the magnitude of the fluorescence response value exceeds the threshold value P, the fluorescence response value acquired by the channel is incorrect and needs to be discarded or re-measured.
2. And acquiring channels which do not reach the response limit, and acquiring the range of explosive species through the peak output time T of each channel and the signal peak intensity of each channel.
3. Binding eigenvalues z i ' library comparison is performed to narrow the range of explosive species.
4. And comprehensively judging the result of the characteristic value comparison and the response priority of various explosives to obtain a detection result.
The characteristic value comparison algorithm in the step (4) further comprises a step (5) of establishing a substance characteristic library. The method for establishing the substance characteristic library comprises the steps of obtaining a room temperature signal characteristic value Z r Obtaining a high temperature signal characteristic value Z h Obtaining a low temperature signal characteristic value Z l And a fitting step.
In the step (5), the conventional material library only considers the signal intensity corresponding to different materials, and does not consider the response time of the material signal and the parameters of the fluorescent explosive detector, and the material characteristic library established by the method combines the parameters of the fluorescent explosive detector such as high temperature (+ 40 ℃), low temperature (-20 ℃), flow, signal response time and the like, fully considers the working environment of the fluorescent explosive detector and the aging degree of the fluorescent explosives, and can better improve the accuracy of material identification.
Wherein, (5-1) acquiring a room temperature signal characteristic value Z r Comprises the following steps:
respectively collecting multiple fluorescent explosives at room temperature T r Converting the fluorescence intensity signals of the plurality of fluorescence channels into digital fluorescence signals;
eliminating the fluorescence background of the digital fluorescence signal to obtain a net fluorescence signal;
performing signal pretreatment on the net fluorescence signal to obtain a pretreated signal;
calculating the room temperature signal characteristic value Z corresponding to various fluorescent explosives according to the following formula r ：
Wherein, Z r Is a room temperature signal characteristic value, i is a primary data set of the preprocessed signal, j is another secondary data set of the preprocessed signal re-measured after i, s is a time to obtain the primary data set, s is a unit of min, T r Is the temperature at room temperature (i.e., ambient temperature), T r The unit of (A) is [ deg. ] C. In the present application, the data set may be collected twice (generally even number of times) to avoid errors caused by collecting the data set only once. The same applies below.
(5-2) obtaining a high-temperature signal characteristic value Z h Comprises the following steps:
respectively collecting multiple fluorescent explosives at high temperature T h Converting the fluorescence intensity signals of the plurality of fluorescence channels into digital fluorescence signals;
eliminating the fluorescence background of the digital fluorescence signal to obtain a net fluorescence signal;
performing signal pretreatment on the net fluorescence signal to obtain a pretreated signal;
calculating the high-temperature signal characteristic value Z corresponding to various fluorescent explosives according to the following formula h ：
Wherein Z is h Is the high temperature signal characteristic value, i is a primary dataset of the preprocessed signal, j is another secondary dataset of the preprocessed signal remeasured after i, s is the time to obtain the primary dataset, s is in units of min, T h The temperature at elevated temperature (room temperature +40 ℃ C.), T h The unit of (b) is [ deg. ] C.
(5-3) obtaining a low-temperature signal characteristic value Z l Comprises the following steps:
respectively collecting multiple fluorescent explosives at low temperature T l Converting the fluorescence intensity signals of the plurality of fluorescence channels into digital fluorescence signals;
eliminating the fluorescence background of the digital fluorescence signal to obtain a net fluorescence signal;
performing signal pretreatment on the net fluorescence signal to obtain a pretreated signal;
calculating the low-temperature signal characteristic value Z corresponding to various fluorescent explosives according to the following formula l ：
Wherein Z is l Is the low temperature signal characteristic value, i is one time data set of the preprocessed signal, j is another time data set of the preprocessed signal re-measured after i, s is the time to obtain one time data set, s is the unit of min, T l At a low temperature (room temperature-20 ℃), T l The unit of (A) is [ deg. ] C.
(5-4) fitting the room temperature signal characteristic value Z r Characteristic value Z of high temperature signal h Characteristic value Z of low temperature signal l Fit to a library of material characteristics. The fitting process is as follows: and aiming at each fluorescent explosive, respectively acquiring three characteristic values of the fluorescent explosive at room temperature, high temperature and low temperature, and then fitting a smooth characteristic curve based on the corresponding relation between the three characteristic values and the three temperatures, wherein the characteristic curve can cover the characteristic values of each fluorescent explosive at different temperatures. Aiming at multiple fluorescent explosives, one-to-one corresponding characteristic curve can be fitted, so that the material characteristic library comprises multiple characteristic curves, and the types of the explosives corresponding to the fluorescent signals can be obtained from the material characteristic library according to the ambient temperature value and the calculated characteristic value when the fluorescent signals are measured during characteristic comparison.
The substance detection algorithm of the embodiment of the application comprises signal compensation, K value solving, substance pre-judgment and library comparison and the like. This application compensates fluorescence signal according to the temperature of the environment that the instrument is located, humidity and atmospheric pressure isoparametric, prevents that the instrument is under different environment, and signal response and material characteristic storehouse exist great deviation when detecting the explosive.
In addition, in order to be capable of rapidly and accurately identifying various explosives, a material characteristic library is established for the explosives to be tested under different environmental conditions, meanwhile, when the explosives are identified, different explosives are pre-judged through an algorithm, a primary identification result of the detected explosives is given, and then specific explosive types are identified through an alternate response identification algorithm among channels, so that the explosives are prevented from being judged by mistake.
As shown in fig. 1, the present application further provides an explosives detection system, which includes the following units: the device comprises a multi-channel signal acquisition unit, a signal preprocessing unit, a signal characterization unit and a substance detection algorithm unit.
The multi-channel signal acquisition unit is used for acquiring fluorescence intensity signals of a plurality of fluorescence channels of the fluorescent explosives, converting the fluorescence intensity signals into digital fluorescence signals and calculating fluorescence response values of the fluorescent explosives. The fluorescence intensity signal drives the 12-bit high-precision ADS acquisition chip through chip selection in an alternating mode to complete conversion from analog signals of the fluorescence signals of the multiple channels to digital signals respectively.
The signal preprocessing unit is used for eliminating the fluorescence background of the fluorescence response value to obtain a net fluorescence signal; and performing signal preprocessing on the net fluorescence signal to obtain a preprocessed signal. The fluorescence background elimination is to eliminate the fluorescence background signal superposed by the characteristic peak of the fluorescence of the explosive, and can obtain better fluorescence spectrum data, so that the change of the fluorescence signal is more obvious, and the subsequent detection is more accurate.
The signal characterization unit is used for carrying out fluorescence signal compensation algorithm, K value solving algorithm and characteristic value characterization algorithm processing on the preprocessed signals to obtain a characteristic value z of the fluorescent explosive i ′。
The substance detection algorithm unit is used for comparing the characteristic value z with the characteristic value i Comparing the obtained data with the characteristic values Z in a pre-established material characteristic library one by one, if a comparison result is obtained, obtaining the type of the fluorescent explosive, and if the comparison result is not obtained, indicating that no fluorescent explosive exists.
In some embodiments, the explosives detection system further includes a central processing unit, and the central processing unit is respectively in communication with the multi-channel signal acquisition unit, the signal preprocessing unit, the signal characterization unit, and the material detection algorithm unit.
In some embodiments, the explosives detection system further comprises: the acousto-optic alarm unit generates an acousto-optic alarm signal when the type of the fluorescent explosive is obtained, and the alarm module gives out sound or light alarm according to the acousto-optic alarm signal so as to prompt a user that the explosive exists and facilitate subsequent explosive removal.
In some embodiments, the explosives detection system further includes a build materials library unit that includes: the device comprises a room temperature characteristic value acquisition module, a high temperature characteristic value acquisition module, a low temperature characteristic value acquisition module and a fitting module.
The room temperature characteristic value acquisition module is used for acquiring multiple fluorescent explosives at room temperature T according to the acquired multiple fluorescent explosives respectively r Obtaining room temperature signal characteristic value Z from fluorescence intensity signals of a plurality of fluorescence channels r 。
The high-temperature characteristic value acquisition module is used for acquiring multiple fluorescent explosives at high temperature T h Obtaining the high temperature signal characteristic value Z from the fluorescence intensity signals of a plurality of fluorescence channels h 。
The low-temperature characteristic value acquisition module is used for acquiring various fluorescent explosives at low temperature T l Obtaining the characteristic value Z of the low-temperature signal by the fluorescence intensity signals of a plurality of fluorescence channels l 。
The fitting module is used for fitting the room temperature signal characteristic value Z r Characteristic value Z of high temperature signal h Characteristic value Z of low temperature signal l Fit to a library of material characteristics.
Algorithms or steps used by each unit or module in the above embodiments have been described in the above detection method embodiments, and for the same features, reference may be made to the description in the related embodiments of the detection method, and details are not repeated here.
A brief description of the method of explosives detection and the workflow of the explosives detection system follows.
The method for detecting explosives and the explosive detection system provided by the embodiment of the application adopt a fluorescent explosive detection instrument (also called a fluorescent explosive detection instrument) to detect whether explosives exist in a substance to be detected on site in real time.
As shown in fig. 2, before data is collected, a working platform needs to be built and a working environment needs to be built. The working platform comprises a fluorescent explosive detector, a temperature tester, an electronic computer and the like. Establishing the working environment comprises connecting the components by adopting a data line and opening algorithm software generated according to the detection method on the electronic computer. And then opening the fluorescent explosive detector and the temperature tester, and recording the parameters of the two instruments. Because the present application considers the effect of environmental factors (e.g., temperature conditions, etc.) on the test results, it is desirable to record instrument parameters.
Subsequently, the substance to be detected is brought into contact with the fluorescent material in the fluorescent explosive detection instrument, and then the detection process for the explosive is started. If the substance to be detected does not contain explosives, the fluorescence of the fluorescent material is not changed. If the substance to be detected contains explosives, the explosives can contact with the fluorescent material, so that the intensity of fluorescence emitted by the fluorescent material is changed, for example, the fluorescence is quenched or enhanced, whether the quenching or the enhancement of the fluorescence is different according to different types of the explosives, and a corresponding relationship exists between the quenching or the enhancement of the fluorescence and the different types of the explosives, whether the substance to be detected contains the explosives and which types of the explosives can be inferred according to the change of the fluorescence intensity.
When data are collected, the ADS1255 sampling chip is driven by the SPI of the single chip microcomputer, the ADS1255 sampling chip is driven alternately in a mode that the SPI chip selects a plurality of sampling chips, multi-channel collection is carried out on fluorescent signals generated by fluorescent explosives, and analog signals of the fluorescent signals are converted into digital fluorescent signals.
After the digital fluorescence signal is obtained, the change value of the digital fluorescence signal after the explosive is introduced relative to the change value before the explosive is introduced is the fluorescence response value when the explosive is introduced. That is, the fluorescence response value is the absolute value of the difference between the intensity of the fluorescence signal after the introduction of the explosive and the intensity of the fluorescence signal before the introduction of the explosive.
And then eliminating the background noise of the fluorescence response value by a minimum value, a maximum value and an adaptive scaling window method. Determining the maximum value and the minimum value of the signal to determine the window range of the characteristic peak of the signal so as to find the characteristic peak of the spectrum signal of the fluorescent explosive, then carrying out zero-setting processing on the signal, and reducing the influence of leading peak by self-adapting signal scaling after zero-setting. After the explosive is introduced and combined with the fluorescent material, the characteristic peak of the fluorescent signal is coincided with some background peaks to form a plurality of characteristic peaks, the characteristic peaks are also mutually overlapped, and the leading peak refers to the characteristic peak adjacent to the characteristic peak.
And then, signal preprocessing is carried out on the signal without the background noise, and after the zero mean value of the signal set is obtained by using a zero mean value processing and normalization processing method, the signal data is normalized to be in a range of 0-1, so that the preprocessed signal is obtained. The method comprises the steps of reducing the dimension and the variation range of signals, reducing the interference of circuits or natural noise to the signals, and finally judging the substances by adopting a substance detection algorithm according to the preprocessed signals.
The embodiment of the application compensates the fluorescent signal according to the temperature, humidity or air pressure and other conditions (mainly temperature) of the environment where the fluorescent explosive detecting instrument is located, prevents the fluorescent explosive detecting instrument from being located in different environments, and prevents the signal response and the material library from having larger deviation when detecting the explosive, thereby improving the detection precision.
In addition, in order to be capable of rapidly and accurately identifying various explosives, a material library is established for the explosives to be tested under different environmental conditions, meanwhile, when the explosives are identified, different explosives are pre-judged through a related algorithm, a primary identification result of the detected explosives is given, and then specific explosives are identified through an inter-channel fluorescent signal alternate response identification algorithm, so that the explosives are prevented from being misjudged.
The embodiment of the application further provides a method for establishing a material characteristic library based on multi-channel fluorescent explosive detection, all environments in which the fluorescent explosive detection instrument needs to work are established by establishing a library establishing platform the same as the parameters of the fluorescent explosive detection instrument, parameters of the instrument (such as low temperature, high temperature, room temperature and the like) in each environment are recorded, and the environmental parameters (such as low temperature, high temperature, room temperature and the like) are substituted into a formula of a characteristic curve formed by fitting. The characteristic values of the explosives under the environment are obtained by introducing different types of explosives in advance, then the environment where the instrument is located is changed, and the library building operation is repeated, so that a material characteristic library for material identification is obtained finally, wherein the material characteristic library contains corresponding relations between different environment parameters and the characteristic values of the different types of explosives, and therefore the types of the corresponding explosives can be found in the material characteristic library according to the newly-detected characteristic values of the explosives and the corresponding environment parameters, and detection of the types of the explosives is achieved.
In fig. 2, the analyte library can obtain a prediction result of the explosive species, but the prediction result may deviate from the actual situation, so that the final determination of the explosive species can be performed according to the cross-response identification algorithm of the fluorescence signals of each channel, mainly by fixing the priority of explosive response and the time of explosive peak occurrence, and then by performing comprehensive determination on the result of comparing the characteristic value with the priority within a fixed time, the detection result is obtained. The anti-misjudgment algorithm can improve the accuracy of judging the types of the explosives. If the final judgment result is the same as the pre-judgment result, the detection process is finished. And if the final judgment result is different from the pre-judgment result, adjusting the working environment and carrying out detection again.
The process of establishing the material characteristic library in the embodiment of the application is as shown in fig. 3, all environments in which the fluorescent explosive detector needs to work are established by establishing a library establishing platform which is the same as the parameters of the fluorescent explosive detector, each environment parameter is recorded, and the environment parameters are substituted into the formula of the fitted characteristic curve. Aiming at different types of explosives, the characteristic value of the explosives under a certain specific environment needs to be acquired, then the environment of a fluorescent explosive detection instrument is changed, the library building operation is repeated, and finally a material characteristic library for material identification is obtained. In some embodiments of the present application, the environmental factor may include room temperature (T) r ) Low temperature (T) l ＝T r -20 ℃ and high temperature (T) h ＝T r +40℃)。
In addition, the environmental factors may also include an air pressure value or humidity, and the like. The atmospheric pressure value is divided into atmospheric pressure (room temperature pressure P) r ) Low voltage (P) l ) Or high pressure (P) h ) Then the corresponding calculation formula is as follows:
characteristic value Z of normal pressure signal pr ：
Low voltage signal characteristic value Z pl ：
High voltage signal characteristic value Z ph ：
The humidity value is divided into a current ambient humidity (H) r ) Low humidity (H) l ) Or high humidity (H) h ) Then the corresponding calculation formula is as follows:
current ambient humidity signal characteristic value Z hr ：
Low humidity signal characteristic value Z hl ：
High humidity signal characteristic value Z ph ：
In this case, each of the above equations may be multiplied by the reciprocal of the corresponding parameter, and for example, if the temperature value (room temperature) and the pressure value (room pressure) are considered at the same time, the corresponding calculation formula is as follows:
normal temperature and pressure signal characteristic value Z tpr ：
The calculation of the rest parameters is the same.
In this case, the respective equations represent the reciprocal values of the three parameters at the same time, and for example, the corresponding calculation formula is as follows when the temperature value (room temperature), the air pressure value (room pressure), and the humidity value (ambient humidity) are taken into account at the same time:
normal temperature normal pressure normal humidity signal characteristic value Z tphr ：
And the calculation of other parameters of high temperature, low temperature, high pressure, low pressure, current environment humidity, low humidity and high humidity is the same. If there are more parameters, the same process is performed.
In summary, the present application provides an explosive detection method and detection system for a fluorescent explosive detection instrument, which adopts a method of adaptively scaling a window to eliminate background noise of a fluorescence spectrum, and then performs zero-mean and normalization preprocessing on the fluorescence spectrum to reduce interference caused by a signal. By adopting a comprehensive judgment method of characteristic value comparison, material pre-judgment and multi-channel signal alternate judgment, the fluorescent explosive detection instrument can detect and alarm the types of explosives more accurately and quickly. The material characteristic library is established and used for responding to the fluorescent explosive signal, the influence of environmental factors is considered, the response of different types of explosives under different environmental parameters can be identified, the accuracy of judging the types of the explosives is greatly improved, and the probability of misjudging the types of the explosives is reduced.
The particular features, structures, or characteristics may be combined in any suitable manner in any one or more embodiments or examples.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method of detecting explosives, comprising:
collecting fluorescence intensity signals of a plurality of fluorescence channels of a fluorescence explosive, converting the fluorescence intensity signals into digital fluorescence signals, and calculating a fluorescence response value of the fluorescence explosive;
eliminating the fluorescence background of the fluorescence response value to obtain a net fluorescence signal;
performing signal preprocessing on the net fluorescence signal to obtain a preprocessed signal;
and carrying out fluorescence signal compensation algorithm, K value solving algorithm and characteristic value characterization algorithm processing on the preprocessed signals, and pre-judging the type of the fluorescence explosive according to a characteristic value comparison algorithm.
2. The method of claim 1, wherein the fluorescence intensity signals of the plurality of fluorescence channels are collected by alternately driving a plurality of ADS1255 sampling chips in a SPI chip selection driving manner.
3. The method of claim 1, wherein the eliminating comprises:
smoothing the fluorescence response value to eliminate high-frequency white noise and obtain a smooth fluorescence signal;
obtaining the minimum value in each window range in the smooth fluorescence signal, obtaining the position of the maximum value between two minimum values according to the minimum value of two adjacent window ranges, and dividing the signal segment covered by the two adjacent window ranges in the smooth fluorescence signal into a left signal segment and a right signal segment according to the position of the maximum value;
subtracting the minimum value from the left signal section to obtain a left return-to-zero signal section, and subtracting the minimum value from the right signal section to obtain a right return-to-zero signal section;
and selecting a higher signal segment in the left return-to-zero signal segment and the right return-to-zero signal segment to be multiplied by a proportionality coefficient f, and then respectively carrying out self-adaptive signal scaling to obtain the net fluorescence signal.
4. The method of claim 3, wherein the scaling factor f is expressed by the formula:
wherein, f max The maximum values in the left signal section and the right signal section are obtained, and a and b are the maximum values in the left return-to-zero signal section and the right return-to-zero signal section; and/or
The adaptive signal scaling is: after the left return-to-zero signal segment or the right return-to-zero signal segment is multiplied by the scaling coefficient f, the following calculation is respectively carried out:
wherein x is i For each data value in the left return-to-zero signal segment or the right return-to-zero signal segment, n is the size of the data set of the left return-to-zero signal segment or the right return-to-zero signal segment.
5. The method of claim 1, wherein the signal pre-processing comprises a zero-averaging process and a normalization process;
the zero equalization processing method comprises the following steps:
according to the number num of the data of the net fluorescence signal and each data value x i Obtaining a mean value u = (x) of the net fluorescent signal 1 +x 2 +…+x i )/num；
Calculating the standard deviation
Performing zero equalization processing
Obtaining a zero-equalization signal;
the normalization processing method comprises the following steps:
obtaining a maximum value min (x) of the zero-averaged signal i ) And minimum value min (x) i ) Passing each data value of the zero-averaged signal
And scaling the signal to be between 0 and 1 to obtain the preprocessed signal.
6. The method of claim 1, wherein the fluorescence signal compensation algorithm comprises: each data value x of the preprocessed signal ii Substitution into the formula for compensating the fluorescent signal
Obtaining a compensated signal x ii '; t is the ambient temperature during detection;
the K value solving algorithm is as follows
Solving a K value; wherein, I F Representing the original fluorescence signal, I O Represents the fluorescence reference signal, A T Represents the fluorescence wavelength at the current temperature, tau represents the fluorescence absorbance, x represents the multiplication,
represents the fluorescence quenching rate;
the characteristic value characterization algorithm is based on the compensated signal x ii ' and K value calculating characteristic value z of the fluorescent explosive i ', the calculation formula is as follows: | z i ′*E[K*x ii ′]-A |, where E represents the mean value and A represents the compensated signal x ii ' an inverse of the formed signal matrix;
the eigenvalue comparison algorithm compares the eigenvalue z with the corresponding eigenvalue i Comparing the characteristic values with characteristic values Z in a pre-established material characteristic library one by one, if a comparison result is obtained, obtaining the type of the fluorescent explosive, and if the comparison result is not obtained, indicating that the fluorescent explosive does not exist.
7. The method of claim 6, wherein the substance feature library is created by obtaining room temperature signal feature value Z r Obtaining a high temperature signal characteristic value Z h Obtaining a low temperature signal characteristic value Z l A fitting step;
the characteristic value Z of the room temperature signal is obtained r Comprises the following steps:
respectively collecting multiple fluorescent explosives at room temperature T r Converting the fluorescence intensity signals of the plurality of fluorescence channels into digital fluorescence signals;
eliminating the fluorescence background of the digital fluorescence signal to obtain a net fluorescence signal;
performing signal pretreatment on the net fluorescent signal to obtain a pretreated signal;
calculating the room temperature signal characteristic value Z corresponding to the multiple fluorescent explosives according to the following formula r ：
Wherein Z is r Is a room temperature signal characteristic value, i is a primary data set of the preprocessed signal, j is another secondary data set of the preprocessed signal remeasured after i, s is the time for obtaining the primary data set, the unit of s is min, T r Is the temperature at room temperature, T r The unit of (A) is;
obtaining the characteristic value Z of the high-temperature signal h Comprises the following steps:
respectively collecting multiple fluorescent explosives at high temperature T h Converting the fluorescence intensity signals of the plurality of fluorescence channels into digital fluorescence signals;
eliminating the fluorescence background of the digital fluorescence signal to obtain a net fluorescence signal;
performing signal preprocessing on the net fluorescence signal to obtain a preprocessed signal;
calculating the high-temperature signal characteristic value Z corresponding to the multiple fluorescent explosives according to the following formula h ：
Wherein Z is h Is a high temperature signal characteristic value, i is a primary data set of the preprocessed signal, j is another secondary data set of the preprocessed signal re-measured after i, s is the time to obtain the primary data set, s is in units of min, T h Is the temperature at high temperature, T h The unit of (a) is;
obtaining a low temperature signal characteristic value Z l Comprises the following steps:
respectively collecting multiple fluorescent explosives at low temperature T l Converting the fluorescence intensity signals of the plurality of fluorescence channels into digital fluorescence signals;
eliminating the fluorescence background of the digital fluorescence signal to obtain a net fluorescence signal;
performing signal pretreatment on the net fluorescent signal to obtain a pretreated signal;
calculating the low-temperature signal characteristic value Z corresponding to the multiple fluorescent explosives according to the following formula l ：
Wherein Z is l Is a low temperature signal characteristic value, i is a primary data set of the preprocessed signal, j is another secondary data set of the preprocessed signal remeasured after i, s is the time for obtaining the primary data set, the unit of s is min, T l At a low temperature, T l The unit of (A) is;
the fitting step is to use the room temperature signal characteristic value Z r The high temperature signal characteristic value Z h The characteristic value Z of the low-temperature signal l Fit to a library of material characteristics.
8. An explosives detection system, comprising:
the multi-channel signal acquisition unit is used for acquiring fluorescence intensity signals of a plurality of fluorescence channels of the fluorescent explosives, converting the fluorescence intensity signals into digital fluorescence signals and calculating fluorescence response values of the fluorescent explosives;
the signal preprocessing unit is used for eliminating the fluorescence background of the fluorescence response value to obtain a net fluorescence signal; performing signal preprocessing on the net fluorescence signal to obtain a preprocessed signal;
a signal characterization unit for performing fluorescence signal compensation algorithm, K value solving algorithm and characteristic value characterization algorithm on the preprocessed signal to obtain a characteristic value z of the fluorescent explosive i ′；
A substance detection algorithm unit for comparing the characteristic value z with a characteristic value i ' one for one with the characteristic values Z in the pre-established substance characteristic libraryAnd comparing, if a comparison result is obtained, obtaining the type of the fluorescent explosive, and if the comparison result is not obtained, indicating that the fluorescent explosive does not exist.
9. The explosives detection system of claim 8 further comprising a central processing unit in communication with the multi-channel signal acquisition unit, the signal pre-processing unit, the signal characterization unit, and the material detection algorithm unit, respectively; and/or
The explosives detection system further includes: and the sound and light alarm unit generates a sound and light alarm signal when the type of the fluorescent explosive is obtained.
10. The explosives detection system of claim 8, further comprising a build materials library unit comprising:
a room temperature characteristic value acquisition module for respectively acquiring multiple fluorescent explosives at room temperature T r Obtaining room temperature signal characteristic value Z from fluorescence intensity signals of multiple fluorescence channels r ；
A high temperature characteristic value acquisition module for respectively acquiring multiple fluorescent explosives at high temperature T h Obtaining the high temperature signal characteristic value Z from the fluorescence intensity signals of a plurality of fluorescence channels h ；
A low-temperature characteristic value acquisition module for acquiring multiple fluorescent explosives at low temperature T l Obtaining the characteristic value Z of the low-temperature signal from the fluorescence intensity signals of a plurality of fluorescence channels l ；
A fitting module for fitting the room temperature signal characteristic value Z r The high temperature signal characteristic value Z h The low temperature signal characteristic value Z l Fit to a library of material characteristics.
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