Signal Compensation Method, System, Device and Medium for Laser Detection of Nanoparticles

Abstract

The present invention discloses a signal compensation method, system, device and medium for laser detection of nanoparticles, which relates to the field of laser measurement. The method includes: obtaining first electrical signal data when no target particles enter at the current time point; predicting the first electrical signal data at the next time point according to the linear regression fitting formula of the first electrical signal data and the current time point to obtain the next predicted data; obtaining second electrical signal data when target particles enter at the next time point; subtracting the next predicted data from the second electrical signal data to obtain the peak data before calibration at the next time point; if calibration is required, adjusting the parameter values in the linear regression fitting formula and performing re-prediction to obtain the final peak data at the next time point; and correcting the measurement count of the target particles by using the omega function to obtain the actual count of the target particles. The present invention realizes the correction of the influence of high temperature and particle overlap on the laser electrical signal, and improves the measurement accuracy.

Description

Technical Field

[0001] The present invention relates to the field of laser measurement, and particularly to a signal compensation method, system, device and medium for laser detection of nanoparticles. Background Art

[0002] Laser measurement is widely used in life. For example: (1) Laser ranging, laser rangefinders generally use two methods to measure distance: pulse method and phase method. The process of pulse method ranging is as follows: The laser emitted by the rangefinder is reflected by the measured object and then received by the rangefinder. The rangefinder simultaneously records the round-trip time of the laser, which can be used to measure the vehicle distance; (2) Laser spectral analysis, through the interaction between light and matter, to identify the structure, composition, state and its changes of the matter and its system, and has been applied in fields such as minerals, metallurgy, materials, and even biology and medicine; (3) Laser scanning, collecting the reflected and scattered light generated when the laser shines on the object, and then analyzing its signal to determine information such as the distance and shape of the object. Nanoparticle measurement is one of the laser measurements.

[0003] Traditional laser measurement of nanoparticles is carried out at room temperature, and the same method is not applicable at high temperatures. The reasons are as follows: First, the signal noise of the laser will change at high temperatures, resulting in inaccurate signals; Second, the signal will mutate after the high-temperature particles contact the laser receiver; Third, there is no calibration for overlapping particles. Summary of the Invention

[0004] Based on this, embodiments of the present invention provide a signal compensation method, system, device and medium for laser detection of nanoparticles to correct the influence of high temperature and particle overlap on the laser electrical signal, and overcome the problem that the traditional method has noise drift of the electrical signal at high temperature and particle overlap, resulting in inaccurate measured electrical signals.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A signal compensation method for laser detection of nanoparticles, the method is used for a nanoparticle measurement device;

[0007] The nanoparticle measurement device includes: a particle generator, a laser, an experimental cavity and a laser receiver; the particle generator is used to generate target particles; the laser is used to emit laser; the experimental cavity is located on the emission paths of the particle generator and the laser; the laser receiver is located on the emission optical path of the experimental cavity; the laser receiver is used to convert the emitted light of the experimental cavity into electrical signal data; the temperature of the working environment of the nanoparticle measurement device is greater than the set temperature;

[0008] The method includes:

[0009] Obtain the first electrical signal data at the current time point; the first electrical signal data is the electrical signal data corresponding to the first outgoing light of the experimental cavity; the first outgoing light is the light emitted by the experimental cavity after receiving the laser.

[0010] Predict the first electrical signal data at the next time point according to the first electrical signal data at the current time point and the linear regression fitting formula at the current time point, and obtain the next predicted data.

[0011] Obtain the second electrical signal data at the next time point; the second electrical signal data is the electrical signal data corresponding to the second outgoing light of the experimental cavity; the second outgoing light is the light emitted by the experimental cavity after receiving the laser and the target particles, and the light emitted after the laser passes through the target particles.

[0012] Subtract the next predicted data from the second electrical signal data at the next time point to obtain the peak data before calibration at the next time point.

[0013] When the data volume difference is less than or equal to the set difference, use the peak data before calibration at the next time point as the peak data at the next time point; the data volume difference is the difference between the data volume of the first data less than zero and the data volume of the peak data at the next time point; the first data is the peak data in the time period from the previous time point to the next time point.

[0014] When the data volume difference is greater than the set difference, take the ratio of the peak data at the next time point to the peak data at the previous time point less than the set ratio as the target, and adjust the parameter values in the linear regression fitting formula at the current time point to obtain the adjusted linear regression fitting formula at the current time point.

[0015] Predict the first electrical signal data at the next time point according to the first electrical signal data at the current time point and the adjusted linear regression fitting formula at the current time point, and obtain the next adjusted predicted data.

[0016] Subtract the next adjusted predicted data from the second electrical signal data at the next time point to obtain the peak data after calibration at the next time point, and use the peak data after calibration at the next time point as the peak data at the next time point.

[0017] Statistically determine the number of target particles by the rising edges of the peak data at each time point within the set measurement period to obtain the measurement count.

[0018] Modify the measurement count using the omega function to obtain the actual count of the target particles.

[0019] Optionally, the linear regression fitting formula is determined according to the first electrical signal data at n time points before the current time point.

[0020] Optionally, the linear regression fitting formula is:

[0021]

[0022] where J(a, b) is the sum of squared errors; a is the constant term parameter; b is the first-order term parameter; ε i is the error value of x at the i-th time point i ; y i is the first electrical signal data of x at the i-th time point i .

[0023] Optionally, the method of using the omega function to correct the measurement count to obtain the actual count of the target particles specifically includes:

[0024] Determining the coincidence rate of the target particles according to the omega function;

[0025] Calculating the particle light shielding time according to the coincidence rate and the measurement count;

[0026] Calculating the actual count of the target particles according to the particle light shielding time, the measurement count, and the duration of the set measurement period.

[0027] Optionally, the formula for the actual count is:

[0028]

[0029] where N a is the actual count; n m is the measurement count; T r is the duration of the set measurement period; T d is the particle light shielding time.

[0030] Optionally, the set ratio is 2.

[0031] The present invention also provides a signal compensation system for laser detection of nanoparticles, and the system is used for a nanoparticle measuring device;

[0032] The nanoparticle measuring device includes: a particle generator, a laser, an experimental cavity, and a laser receiver; the particle generator is used to generate target particles; the laser is used to emit laser; the experimental cavity is located on the emission path of the particle generator and the laser; the laser receiver is located on the emission optical path of the experimental cavity; the laser receiver is used to convert the emitted light of the experimental cavity into electrical signal data; the temperature of the working environment of the nanoparticle measuring device is greater than the set temperature;

[0033] The system includes:

[0034] A first acquisition module, configured to acquire first electrical signal data at a current time point; the first electrical signal data is electrical signal data corresponding to a first outgoing light of the experimental cavity; the first outgoing light is the light emitted by the experimental cavity after receiving the laser;

[0035] A first prediction module, configured to predict first electrical signal data at a next time point according to the first electrical signal data at the current time point and a linear regression fitting formula at the current time point, so as to obtain a next prediction data;

[0036] A second acquisition module, configured to acquire second electrical signal data at the next time point; the second electrical signal data is electrical signal data corresponding to a second outgoing light of the experimental cavity; the second outgoing light is the light emitted by the experimental cavity after receiving the laser and the target particles, and the light emitted after the laser passes through the target particles;

[0037] A peak data calculation module, configured to subtract the next prediction data from the second electrical signal data at the next time point to obtain peak data before calibration at the next time point;

[0038] A first peak data determination module, configured to use the peak data before calibration at the next time point as the peak data at the next time point when a data volume difference is less than or equal to a set difference; the data volume difference is the difference between the data volume of the first data less than zero and the data volume of the peak data at the next time point; the first data is the peak data within a time period from the previous time point to the next time point;

[0039] A fitting formula adjustment module, configured to, when the data volume difference is greater than the set difference, take the ratio of the peak data at the next time point to the peak data at the previous time point being less than a set ratio as a target, and adjust parameter values in the linear regression fitting formula at the current time point to obtain an adjusted linear regression fitting formula at the current time point;

[0040] An adjusted prediction module, configured to predict first electrical signal data at a next time point according to the first electrical signal data at the current time point and the adjusted linear regression fitting formula at the current time point, so as to obtain a next adjusted prediction data;

[0041] A second peak data determination module, configured to subtract the next adjusted prediction data from the second electrical signal data at the next time point to obtain peak data after calibration at the next time point, and use the peak data after calibration at the next time point as the peak data at the next time point;

[0042] A measurement count determination module, configured to count the number of rising edges of the peak data at each time point within a set measurement period to determine the number of the target particles, so as to obtain a measurement count;

[0043] An actual count determination module is configured to correct the measured count by using an omega function to obtain the actual count of the target particles.

[0044] The present invention also provides an electronic device, including a memory and a processor. The memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the above-mentioned signal compensation method for laser detection of nanoparticles.

[0045] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned signal compensation method for laser detection of nanoparticles is implemented.

[0046] According to the specific embodiments provided by the present invention, the following technical effects are disclosed:

[0047] The embodiment of the present invention provides a signal compensation method, system, device and medium for laser detection of nanoparticles. First, obtain the first electrical signal data when no target particles enter, that is, the signal background noise. According to the first electrical signal data and the linear regression fitting formula at the current time point, predict the first electrical signal data at the next time point to obtain the next predicted data. At the next time point, obtain the second electrical signal data when target particles enter. Subtract the next predicted data from the second electrical signal data to obtain the peak data before calibration at the next time point. If calibration is required, adjust the parameter values in the linear regression fitting formula and perform re-prediction to obtain the final peak data at the next time point. Use the omega function to correct the measured count of the target particles to obtain the actual count of the target particles. After the present invention performs real-time fitting on the signal background noise, removes the background noise from the data, eliminates the influence of noise mutation caused by the entry of high-temperature particles, and then corrects the measured count of the target particles through the omega function, so as to obtain a more real count signal, solving the problem of overlapping particles. Therefore, the present invention realizes the correction of the influence of high temperature and particle overlap on the laser electrical signal, and overcomes the problem that the traditional method has inaccurate electrical signal measurement due to the noise drift of the electrical signal at high temperature and the overlap of particles. Description of the Drawings

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0049] Figure 1 It is a flowchart of the signal compensation method for laser detection of nanoparticles provided by the embodiment of the present invention;

[0050] Figure 2Structural diagram of the nanoparticle measurement device provided by the embodiment of the present invention;

[0051] Figure 3 Schematic diagram of the laser electrical signal at high temperature provided by the embodiment of the present invention;

[0052] Figure 4 Schematic diagram of the compensated laser electrical signal at high temperature provided by the embodiment of the present invention;

[0053] Figure 5 Schematic diagram of the laser electrical signal for counting provided by the embodiment of the present invention;

[0054] Figure 6 Schematic diagram of the mutated laser electrical signal provided by the embodiment of the present invention;

[0055] Figure 7 Schematic diagram of the overlapping laser electrical signal provided by the embodiment of the present invention;

[0056] Figure 8 Structural diagram of the signal compensation system for laser detection of nanoparticles provided by the embodiment of the present invention. Detailed implementation manners

[0057] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0058] Traditional laser measurement of nanoparticles is carried out at room temperature, and the same method is not applicable at high temperature. The reasons are as follows: First, the signal noise of the laser will change at high temperature, resulting in inaccurate signals; Second, the signal will mutate when high-temperature particles contact the laser receiver; Third, there is no calibration for overlapping particles.

[0059] The embodiment of the present invention proposes a signal compensation method for laser detection of nanoparticles at high temperature based on linear regression and omega function. The noise of the laser electrical signal that continuously changes at high temperature is fitted by linear regression to correct the signal, and through real-time judgment, the influence of noise mutation caused by the entry of high-temperature particles is eliminated. The fitting line for correction dynamically adjusts with the change of temperature. Therefore, it has a good calibration function for the signal change caused by high temperature. At the same time, through the calibration of the number of measured particles by the omega function, the problem of overlapping particles can be solved. The embodiment of the present invention can realize the correction of the influence of high temperature and particle overlap on the laser electrical signal, and overcome the problems of the traditional method such as the noise drift of the electrical signal at high temperature and the overlap of particles, which in turn lead to inaccurate measured electrical signals.

[0060] To make the above objects, features, and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0061] Embodiment 1

[0062] See Figure 1 , the signal compensation method for laser detecting nanoparticles in this embodiment, and the method is used for a nanoparticle measuring device.

[0063] The nanoparticle measuring device includes: a particle generator, a laser, an experimental cavity, and a laser receiver; the particle generator is used to generate target particles; the laser is used to emit laser light; the experimental cavity is located on the outgoing paths of the particle generator and the laser; the laser receiver is located on the outgoing light path of the experimental cavity; the laser receiver is used to convert the outgoing light of the experimental cavity into electrical signal data. The temperature of the working environment of the nanoparticle measuring device is greater than the set temperature. In practical applications, the set temperature is greater than 100 °C, that is, the nanoparticle measuring device works in a high-temperature environment.

[0064] The method includes:

[0065] Step 101: Obtain first electrical signal data at the current time point; the first electrical signal data is the electrical signal data corresponding to the first outgoing light of the experimental cavity; the first outgoing light is the light emitted by the experimental cavity after receiving the laser.

[0066] Step 102: Predict the first electrical signal data at the next time point according to the first electrical signal data at the current time point and the linear regression fitting formula at the current time point to obtain the next predicted data.

[0067] Step 103: Obtain second electrical signal data at the next time point; the second electrical signal data is the electrical signal data corresponding to the second outgoing light of the experimental cavity; the second outgoing light is the light emitted by the experimental cavity after receiving the laser and the target particles, and the laser passes through the target particles and then emits.

[0068] Step 104: Subtract the next predicted data from the second electrical signal data at the next time point to obtain the peak data before calibration at the next time point.

[0069] Step 105: When the data volume difference is less than or equal to the set difference, use the peak data before calibration at the next time point as the peak data at the next time point; the data volume difference is the difference between the data volume of the first data less than zero and the data volume of the peak data at the next time point; the first data is the peak data in the time period from the previous time point to the next time point.

[0070] Step 106: When the difference in data volume is greater than the set difference, then taking the ratio of the peak data at the next time point to the peak data at the previous time point being less than the set ratio as the target, adjust the parameter values in the linear regression fitting formula at the current time point to obtain the adjusted linear regression fitting formula at the current time point.

[0071] Among them, the set ratio can be 2.

[0072] Step 107: According to the first electrical signal data at the current time point and the adjusted linear regression fitting formula at the current time point, predict the first electrical signal data at the next time point to obtain the next adjusted prediction data.

[0073] Step 108: Subtract the next adjusted prediction data from the second electrical signal data at the next time point to obtain the calibrated peak data at the next time point, and use the calibrated peak data at the next time point as the peak data at the next time point.

[0074] Step 109: Count the number of rising edges of the peak data at each time point within the set measurement period to determine the number of particles of the target particle, and obtain the measurement count.

[0075] Step 110: Use the omega function to correct the measurement count to obtain the actual count of the target particle.

[0076] In one example, the linear regression fitting formula is determined according to the first electrical signal data at n time points before the current time point. Specifically, the linear regression fitting formula is:

[0077]

[0078] Among them, J(a, b) is the sum of squared errors; a is the constant term parameter; b is the first-order term parameter; ε i is the error value at the i-th time point x i ; y i is the first electrical signal data at the i-th time point x i .

[0079] In one example, Step 110 specifically includes:

[0080] 1) Determine the coincidence rate of the target particle according to the omega function. Specifically, the calculation formula for the coincidence rate is:

[0081] λ a Δt = -W(-λ m Δt);

[0082] λ a is the coincidence rate; λ mThe measurement rate can be obtained by the number of measurement counts / the number of time points in the set measurement period; W is the omega function; Δt is the known average pulse width (FWTM), and through this formula, the repetition rate λ can be calculated. a 。

[0083] 2) Calculate the particle light-shielding time according to the coincidence rate and the measurement count. Specifically, the calculation formula for the particle light-shielding time is:

[0084] T d =(λ a +1)n m Δt;

[0085] T d is the particle light-shielding time; n m is the measurement count.

[0086] 3) Calculate the actual count of the target particles according to the particle light-shielding time, the measurement count, and the duration of the set measurement period. Specifically, the formula for the actual count is:

[0087]

[0088] where F a is the actual count; T r is the duration of the set measurement period.

[0089] This embodiment is based on a signal real-time compensation method for detecting high-concentration nanoparticles at high temperatures by a laser system, and a compensation method for analyzing measurement signals in the particle coincidence state based on the omega function. This compensation method provides an effective means for signal calibration at high temperatures and high concentrations.

[0090] In practical applications, a more specific implementation process of the above signal compensation method for laser detection of nanoparticles is as follows:

[0091] First, a detailed introduction to the nanoparticle measurement device used in this method is given.

[0092] See Figure 2, a nanoparticle measuring device, comprising: an air compressor 101 for generating air at a certain pressure; a particle generator 201 for generating particles to be detected; temperature controllers 301, 302, 303 for stabilizing the environment in which the particles are transported at a fixed high temperature; a high-temperature resistant air delivery pipe 401 for transporting the nanoparticles generated by the particle generator into the experimental cavity; a high-precision high-temperature resistant flowmeter 501 for detecting the flow rate of the delivery pipe, converting the flow rate into an electrical signal and transmitting the signal to a computer; a laser 601 for emitting laser light; a first lens 701 for converging the laser light; a second lens 702 combined with the first lens 701 for narrowing the optical path; the first lens 701 is a convex lens and the second lens 702 is a concave lens; an experimental cavity 801, the laser optical path direction of which is a transparent section allowing the laser light to enter and exit; a laser receiver 901 for receiving the optical signal and converting it into a corresponding electrical signal; a signal acquisition card 1001 for converting the electrical signal into computer-readable data; an oscilloscope 1101 for detecting and observing the electrical signal; and a computer 1201 for analyzing the electrical signal.

[0093] The temperature controller 301 is embedded in the particle generator 201, the temperature controller 302 is externally attached to the high-temperature resistant air delivery pipe 501, and the temperature controller 303 is embedded in the experimental cavity 901. The temperature controllers 301, 302, 303 can be high-temperature heating rods and are connected to the computer by a circuit.

[0094] Under the action of the air compressor 101 and the particle generator 201, the particles are passed through the high-temperature resistant air delivery pipe 401 and detected by the high-precision high-temperature resistant flowmeter 501 while passing through, and then the particles enter the transparent experimental cavity 801.

[0095] The laser 601 generates a beam of laser light under the action of the first lens 701 and the second lens 702, enters the experimental cavity 801, passes through the nanoparticles, and after the optical signal is received by the laser receiver 901 and converted into an electrical signal, the signal is transmitted to the oscilloscope 1101 through a circuit and to the computer 1201 through the signal acquisition card 1001.

[0096] Based on the above nanoparticle measuring device, the signal compensation method of the present specific implementation will be introduced in detail below.

[0097] In a high-temperature environment, using a light-concentrating technique, a laser is passed through an experimental cavity. Inside the cavity, after the laser acts on the particles, the laser exits the cavity and is received by a laser receiver. The laser receiver generates an electrical signal corresponding to the light intensity, and the electrical signal is input into an oscilloscope and a computer through a circuit for signal processing. When the working temperature reaches 100 °C or higher, the signal floor noise of the laser receiver itself will drift. At the same time, under high particle concentration conditions, the overlapping particles on the optical path will cause a counting error in the signal of the laser receiver. Therefore, by recording the electrical signal data by the computer, fitting the signal floor noise in real time, removing the floor noise from the data, and then correcting the particle count through the omega function, a more accurate counting signal can be obtained. The detailed steps are as follows:

[0098] Step 1: Under the action of a temperature controller, the working environments of the experimental cavity, the high-temperature-resistant air delivery pipe, and the particle generator are stabilized at a high temperature. Among them, the temperature of the high-temperature-resistant air transport pipe is 30 °C lower than that of the experimental cavity and the particle generator, and the particle generator and the experimental cavity maintain the same high temperature.

[0099] Step 2: After the temperature is stabilized in Step 1, at this time, no particles enter, so all the received signals are noise. It can be seen from the oscilloscope that the noise signal is constantly rising. Here, the electrical signal of the laser receiver is input into the computer through a signal acquisition card. At this time, 200,000 discrete electrical signal data corresponding to the current electrical signal voltage per second are obtained, and the signal is as Figure 3 shown. Figure 3 In the figure, the ordinate represents the value of the voltage corresponding to the optical signal converted into a voltage signal, and the abscissa represents the actual physical meaning of each point. The abscissa represents "a period of time" determined by the acquisition efficiency of "signal acquisition card 1001". The acquisition efficiency of this experiment is 200K per second, so the length of "a period of time" is 1 / 200K (s). Each point on the abscissa in the figure is 1 / 200K (s), Figure 3 representing the signal within 1 second.

[0100] Step 3: Compare the signal data along the time line. The count at each time point (for example, the interval between two time points is 0.1 second) is recorded as k, the signal at this time point is recorded as e(k), and the next signal is recorded as e(k + 1), where k = 0, 1, 2..... Set e = e(k + 1) - e(k), and then make a judgment. When e ≠ 0 and its absolute value |e| / |e(k)| > 0.05, perform a linear regression fitting on the signal data within 0.1 second before and after the k time point, and determine the formula for calculating the sum of squared errors as the linear regression fitting formula:

[0101]

[0102] Among them, J(a, b) is the sum of squared errors; a is the constant term parameter; b is the linear term parameter; a and b are the parameters to be determined; ε i is the error value of x i at the i-th time point; y i is the first electrical signal data (voltage value) of x i at the i-th time point; x i is the time point corresponding to y i ; the first electrical signal data at the time point after n time points can be fitted by formula (1).

[0103] Step 4: Gradient compensation is performed on the original data obtained by the acquisition card through the computer and the predicted data of the function y i . At this time, the denoised data within 0.1 second before and after the k-th time point is obtained and denoted as data1, and the predicted data of the function y i is denoted as data2. At the same time, counting is performed according to the set counting baseline of y i , and the above two types of data are saved. The compensated signal diagram is as shown in Figure 4 . Figure 4 The meanings of the horizontal and vertical coordinates in Figure 3 are the same as those in Figure 4 and will not be elaborated here.

[0104] Step 5: Turn on the particle generator and the air compressor to form an oversaturated steam flow with a certain pressure and flow rate. Transport the particles generated by the particle generator to the experimental cavity through a high-temperature resistant air delivery pipe. After the particles pass through the laser optical path, the signal of the laser receiver changes. At the same time, input the signal of the laser receiver into the oscilloscope for observation and input it into the computer through the signal acquisition card.

[0105] Step 6: Subtract the predicted data obtained in Step 4 from the data obtained in Step 5 to obtain the peak data available for counting, denoted as data3. Record the number of particles by recording the rising edge, and calculate the particle concentration through the flow rate data transmitted to the computer by the flowmeter, as shown in Figure 5 . Figure 5 The meanings of the horizontal and vertical coordinates in Figure 3 are the same as those in Figure 5 and will not be elaborated here.

[0106] Step 7: Due to the high particle concentration, the following two situations will occur during the process of Step 6: ① After some high-temperature particles enter, the noise suddenly increases and mutates (as shown in Figure 6) Data data4, and then it enters a linear increase as before. The increase in noise will pull the statistical peak count to 10 or even 100 times, making it impossible to count. At this time, by comparing the processed peak data data3 obtained in step 6 with the denoised data data1 in the same time period, it can be obtained that the amount of data where data1 < 0 far exceeds that of data3. Based on this condition, it is determined that y i fails. Then, the peak count of the peak data at the previous time point is taken and compared with the mutation data data4 at the current time point. At this time, by continuously increasing the a value in y i until the ratio of the peak counts of data4 to data3 is less than 2 times (since the noise fluctuations are basically the same, eliminating the noise will directly reduce the signal to about 1 times the normal value). At this time, the calibrated peak data can be normally counted through step 4; ② At high concentrations, overlapping particles enter the optical path at the same time, resulting in the rising edge count counting multiple overlapping particles as one particle, as shown in Figure 7 (as shown by the arrow in Figure 7 ), there is a rising edge coincidence phenomenon), thus causing an error in the particle concentration. Among them, Vi is the set threshold, and the count higher than the threshold will be counted. Therefore, it is necessary to eliminate the error, and its conversion formula is as follows:

[0107]

[0108] where N a is the actual count, n m is the measured count, T r is the duration of the set measurement period (i.e., the total sampling time), and T d is the particle light shielding time. Among them, n m is obtained by computer counting, T r is obtained by computer cumulative duration, and T d needs to be measured and calculated.

[0109] Among them, Figure 6 and Figure 7 the meanings of the horizontal and vertical coordinates are the same as those in Figure 3 , which will not be elaborated here. Figure 6 represents the signal within 1 second, Figure 7 is the local signal.

[0110] Step 8: Measure the average pulse width (FWTM) Δt through an oscilloscope. Since the rate at which particles enter the experimental cavity occurs randomly and can be represented by a Poisson distribution, the omega function is introduced here to represent the relationship between the measurement rate and the coincidence rate, and the coincidence rate formula for particles is obtained as:

[0111] λ a Δt = -W(-λ m Δt) (3)

[0112] where λ a is the required coincidence rate, and λ m is the measurement rate, which can be obtained from the number of measurement counts / the number of time points after discretizing the sampling time. W is the omega function, and Δt is the known average pulse width (FWTM). Through this formula, the repetition rate λ a can be calculated, and T d can be calculated from the repetition rate. The formula is as follows:

[0113] T d = (λ a + 1)n m Δt (4)

[0114] Through the above formula, the missing counts can be compensated, so that the count data can be restored to the data recording the actual number of particles.

[0115] Step 9: Through Steps 7 and 8, the processed data data5 is obtained. The computer integrates the data data5 with the normal data in Steps 5 and 6, and outputs the compensated count result.

[0116] The signal compensation method for laser detection of nanoparticles at high temperature based on linear regression and omega function proposed in the embodiment of the present invention corrects the signal by fitting the continuously changing noise of the laser electrical signal at high temperature through linear regression, and eliminates the influence of noise mutation caused by the entry of high-temperature particles through real-time judgment. The fitting line for correction dynamically adjusts with the change of temperature, so it has a good calibration function for the signal change caused by high temperature. At the same time, as described in Step 7, the problem of overlapping particles can be solved by calibrating the measured number of particles through the omega function. The embodiment of the present invention realizes the correction of the influence of high temperature and particle overlap on the laser electrical signal, and overcomes the problems of noise drift of the electrical signal at high temperature and particle overlap in the traditional method, which leads to inaccurate measured electrical signals.

[0117] Embodiment 2

[0118] In order to execute the method corresponding to Embodiment 1 above to achieve the corresponding functions and technical effects, a signal compensation system for laser detection of nanoparticles is provided below.

[0119] See Figure 8 , the system is used for a nanoparticle measuring device.

[0120] The nanoparticle measuring device includes: a particle generator, a laser, an experimental cavity, and a laser receiver; the particle generator is used to generate target particles; the laser is used to emit laser light; the experimental cavity is located on the emission paths of the particle generator and the laser; the laser receiver is located on the emission optical path of the experimental cavity; the laser receiver is used to convert the emitted light of the experimental cavity into electrical signal data; the temperature of the working environment of the nanoparticle measuring device is greater than the set temperature;

[0121] The square system includes:

[0122] A first acquisition module 801, configured to acquire first electrical signal data at the current time point; the first electrical signal data is the electrical signal data corresponding to the first emitted light of the experimental cavity; the first emitted light is the light emitted by the experimental cavity after receiving the laser.

[0123] A first prediction module 802, configured to predict the first electrical signal data at the next time point according to the first electrical signal data at the current time point and the linear regression fitting formula at the current time point, and obtain the next predicted data.

[0124] A second acquisition module 803, configured to acquire second electrical signal data at the next time point; the second electrical signal data is the electrical signal data corresponding to the second emitted light of the experimental cavity; the second emitted light is the light emitted by the experimental cavity after receiving the laser and the target particles, and the laser passes through the target particles.

[0125] A peak data calculation module 804, configured to subtract the next predicted data from the second electrical signal data at the next time point to obtain the peak data before calibration at the next time point.

[0126] A first peak data determination module 805, configured to use the peak data before calibration at the next time point as the peak data at the next time point when the data volume difference is less than or equal to the set difference; the data volume difference is the difference between the data volume of the first data less than zero and the data volume of the peak data at the next time point; the first data is the peak data within the time period from the previous time point to the next time point.

[0127] A fitting formula adjustment module 806, configured to adjust the parameter values in the linear regression fitting formula at the current time point with the goal that the ratio of the peak data at the next time point to the peak data at the previous time point is less than the set ratio when the data volume difference is greater than the set difference, and obtain the adjusted linear regression fitting formula at the current time point.

[0128] The adjustment prediction module 807 is configured to predict the first electrical signal data at the next time point according to the first electrical signal data at the current time point and the adjusted linear regression fitting formula at the current time point, so as to obtain the next adjusted prediction data.

[0129] The second peak data determination module 808 is configured to subtract the next adjusted prediction data from the second electrical signal data at the next time point to obtain the calibrated peak data at the next time point, and use the calibrated peak data at the next time point as the peak data at the next time point.

[0130] The measurement count determination module 809 is configured to count the rising edges of the peak data at each time point within a set measurement period to determine the number of the target particles, so as to obtain the measurement count.

[0131] The actual count determination module 810 is configured to correct the measurement count by using the omega function to obtain the actual count of the target particles.

[0132] Embodiment III

[0133] This embodiment provides an electronic device, including a memory and a processor. The memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the signal compensation method for laser detecting nanoparticles in Embodiment I.

[0134] Optionally, the above-mentioned electronic device may be a server.

[0135] In addition, an embodiment of the present invention further provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, it implements the signal compensation method for laser detecting nanoparticles in Embodiment I.

[0136] In this specification, each embodiment is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other. For the system disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0137] Specific examples are used in this article to elaborate on the principles and implementation manners of the present invention. The descriptions of the above embodiments are only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A signal compensation method for laser detection of nanoparticles, characterized in that, The method is used for a nanoparticle measuring device; The nanoparticle measuring device includes: a particle generator, a laser, an experimental cavity, and a laser receiver; the particle generator is used to generate target particles; the laser is used to emit laser light; the experimental cavity is located on the outgoing paths of the particle generator and the laser; the laser receiver is located on the outgoing light path of the experimental cavity; the laser receiver is used to convert the outgoing light of the experimental cavity into electrical signal data; the temperature of the working environment of the nanoparticle measuring device is greater than a set temperature; The method includes: Obtain first electrical signal data at a current time point; the first electrical signal data is the electrical signal data corresponding to the first outgoing light of the experimental cavity; the first outgoing light is the light emitted by the experimental cavity after receiving the laser; Predict the first electrical signal data at the next time point according to the first electrical signal data at the current time point and the linear regression fitting formula at the current time point to obtain the next predicted data; Obtain second electrical signal data at the next time point; the second electrical signal data is the electrical signal data corresponding to the second outgoing light of the experimental cavity; the second outgoing light is the light emitted by the experimental cavity after receiving the laser and the target particles, and the laser passes through the target particles; Subtract the next predicted data from the second electrical signal data at the next time point to obtain the peak data before calibration at the next time point; When the data volume difference is less than or equal to the set difference, the peak data before calibration at the next time point is used as the peak data at the next time point; the data volume difference is the difference between the data volume of the first data less than zero and the data volume of the peak data at the next time point; the first data is the peak data in the time period from the previous time point to the next time point; When the data volume difference is greater than the set difference, taking the ratio of the peak data at the next time point to the peak data at the previous time point being less than the set ratio as the target, adjust the parameter values in the linear regression fitting formula at the current time point to obtain the adjusted linear regression fitting formula at the current time point; Predict the first electrical signal data at the next time point according to the first electrical signal data at the current time point and the adjusted linear regression fitting formula at the current time point to obtain the next adjusted predicted data; Subtract the next adjusted predicted data from the second electrical signal data at the next time point to obtain the calibrated peak data at the next time point, and use the calibrated peak data at the next time point as the peak data at the next time point; Count the rising edges of the peak data at each time point within a set measurement period to determine the number of the target particles, and obtain the measurement count; Use the omega function to correct the measurement count to obtain the actual count of the target particles.

2. The signal compensation method for laser detection of nanoparticles according to claim 1, wherein The linear regression fitting formula is determined according to the first electrical signal data at n time points before the current time point.

3. A signal compensation method for laser detection of nanoparticles according to claim 2, characterized in that, The linear regression fitting formula is: Among them, J(a, b) is the sum of squared errors; a is the constant term parameter; b is the linear term parameter; ε i is the error value of x at the i-th time point i ; y i is the first electrical signal data of x at the i-th time point i .

4. A signal compensation method for laser detection of nanoparticles according to claim 1, characterized in that The using the omega function to correct the measurement count to obtain the actual count of the target particles specifically includes: Determine the coincidence rate of the target particles according to the omega function; Calculate the particle light-shielding time based on the coincidence rate and the measurement count; Calculate the actual count of the target particles based on the particle light-shielding time, the measurement count, and the duration of the set measurement period.

5. A signal compensation method for laser detection of nanoparticles according to claim 4, characterized in that The formula for the actual count is: Among them, N a is the actual count; n m is the measured count; T r is the duration of the set measurement period; T d is the particle light-shielding time.

6. A signal compensation method for laser detection of nanoparticles according to claim 1, characterized in that, The set ratio is 2.

7. A signal compensation system for laser detection of nanoparticles, characterized in that, The system is used for a nanoparticle measurement device; The nanoparticle measurement device includes: a particle generator, a laser, an experimental cavity, and a laser receiver; the particle generator is used to generate target particles; the laser is used to emit laser light; the experimental cavity is located on the outgoing paths of the particle generator and the laser; the laser receiver is located on the outgoing light path of the experimental cavity; the laser receiver is used to convert the outgoing light of the experimental cavity into electrical signal data; the temperature of the working environment of the nanoparticle measurement device is greater than the set temperature. The system includes: A first acquisition module, configured to acquire first electrical signal data at a current time point; the first electrical signal data is the electrical signal data corresponding to the first outgoing light of the experimental cavity; the first outgoing light is the light emitted by the experimental cavity after receiving the laser. A first prediction module, configured to predict the first electrical signal data at the next time point based on the first electrical signal data at the current time point and the linear regression fitting formula at the current time point, to obtain the next predicted data. A second acquisition module, configured to acquire second electrical signal data at the next time point; the second electrical signal data is the electrical signal data corresponding to the second outgoing light of the experimental cavity; the second outgoing light is the light emitted by the experimental cavity after receiving the laser and the target particles, and the laser passes through the target particles. A peak data calculation module, configured to subtract the next predicted data from the second electrical signal data at the next time point to obtain the peak data before calibration at the next time point. A first peak data determination module, configured to use the peak data before calibration at the next time point as the peak data at the next time point when the data volume difference is less than or equal to the set difference; the data volume difference is the difference between the data volume of the first data less than zero and the data volume of the peak data at the next time point; the first data is the peak data in the time period from the previous time point to the next time point. A fitting formula adjustment module, configured to adjust the parameter values in the linear regression fitting formula at the current time point with the goal that the ratio of the peak data at the next time point to the peak data at the previous time point is less than the set ratio when the data volume difference is greater than the set difference, to obtain the adjusted linear regression fitting formula at the current time point. An adjusted prediction module, configured to predict the first electrical signal data at the next time point based on the first electrical signal data at the current time point and the adjusted linear regression fitting formula at the current time point, to obtain the next adjusted predicted data. A second peak data determination module, configured to subtract the next adjusted predicted data from the second electrical signal data at the next time point to obtain the peak data after calibration at the next time point, and use the peak data after calibration at the next time point as the peak data at the next time point. A measurement count determination module, configured to count the rising edges of the peak data at each time point within a set measurement period to determine the number of particles of the target particles, and obtain a measurement count; An actual count determination module, configured to correct the measurement count by using an omega function to obtain the actual count of the target particles.

8. An electronic device, characterized in that, It includes a memory and a processor. The memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the signal compensation method for laser detecting nanoparticles according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, It stores a computer program, and when the computer program is executed by a processor, it implements the signal compensation method for laser detecting nanoparticles according to any one of claims 1 to 6.

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