A light scattering module automatic cleaning method, device and light scattering dust meter

Abstract

The present invention discloses a light scattering module automatic cleaning method, device and light scattering dust monitor, which automatically adjust the detection parameters in the detection algorithm according to different working conditions and environments, ensure accurate detection results in various complex environments, accurately judge the pollution degree of the light scattering module, automatically identify when cleaning is needed, realize automatic cleaning of the light scattering module, reduce manual intervention, and improve the continuity and stability of equipment operation. In addition, through automatic judgment and perception, unnecessary cleaning and energy consumption are avoided, which helps to reduce the overall energy consumption of the equipment and achieve green environmental protection.

Description

Technical Field

[0001] The invention belongs to the technical field of dust concentration monitoring using a light scattering method, and in particular relates to the technical field of an automatic cleaning method for a light scattering module. Background Art

[0002] The main principle of the light scattering dust monitor is that dust enters the light scattering module, and scattering occurs when the laser passes through the area to be measured containing particles. The detector detects the light scattering signal and calculates the particle concentration through processing. Especially for dust monitors that have been running for a period of time at sites such as garbage incineration and medical waste treatment, various pollutants such as ammonium salt crystals and calcifications are usually deposited in the scattering chamber of the light scattering module. These pollutants can cause various measurement problems such as continuous high measurements and distorted results. Therefore, it is necessary to clean and maintain the sediment inside the scattering module chamber in a timely manner.

[0003] For example, a Chinese patent with patent number CN 116165110 A discloses a method for cleaning a light scattering module. By installing a vibration device inside the scattering chamber, the extremely fine particles attached to the inner wall of the scattering chamber can be caused to detach from the wall and enter the filtering device with the air flow inside the chamber, thereby achieving the cleaning effect of the light scattering module.

[0004] However, since the interior of the scattering chamber is not visible to the operator, the operator cannot accurately judge when the chamber needs to be cleaned and whether the pollutants have been removed. In the subsequent dust concentration measurement process, the residual particles will have an adverse effect on the measurement results of the light scattering dust monitor, thereby reducing the accuracy of the data measurement, causing the test results of the light scattering dust monitor to be distorted and affecting the accuracy of the data measurement. Summary of the invention

[0005] In view of the problems existing in the prior art, the present invention proposes a light scattering module automatic cleaning method, device and light scattering dust meter, which can ensure that pollutants on the inner wall of the chamber are cleaned, thereby improving the accuracy of measurement data.

[0006] In order to achieve the above-mentioned object of the invention, the present invention adopts the following technical solutions:

[0007] A method for automatically cleaning a light scattering module, comprising the following steps:

[0008] S1, threshold Sure

[0009] Based on the detection algorithm, determine the threshold , the detection algorithm is specifically:

[0010]

[0011] is the threshold value; M is the upper limit of the detector detection signal; C is the measurement range of the dust equipment; b is the particle concentration constant, which is defined by the user; K is the concentration calculation correction coefficient, which is defined by the user; D is the signal amplification factor, which is defined by the user; is the proportional constant, which is defined by the user; Q is the dark current signal value of the dust meter equipment, which is measured by the dust meter;

[0012] S2. Light scattering module cleaning

[0013] Scan the background signal B in the light scattering module, if the background signal B is greater than the threshold , the light scattering module is automatically cleaned.

[0014] Through the detection algorithm, the threshold is calculated , when the background signal B is greater than the threshold , it means that the pollution level in the light scattering module has affected the normal operation of the dust monitor, and the light scattering module needs to be automatically cleaned. The background signal B is the signal value obtained by measuring in the absence of particles to be measured (such as dust), to ensure that the obtained signal only comes from the background noise and environmental factors of the instrument. It is usually measured synchronously when the dust monitor is zero-calibrated.

[0015] The detection algorithm in this technical solution , taking into account factors such as the detector upper limit value, dark current signal value, and measurement range of different types of dust monitors, it can adapt to the needs of different products and working conditions and has a wide range of applicability. The specific description is as follows: M is the detector detection signal upper limit value, unit: mV. The detection signal upper limits of the detectors corresponding to different models of dust monitors are different, and are determined based on the actual working parameters of the product. Q is the dark current signal value of the dust monitor equipment, unit: mV, measured by the dust monitor. C is the measurement range of the dust equipment, unit: mg / m³, the measurement range of the dust monitor is usually 5mg / m³, 10mg / m³, etc. The thresholds corresponding to dust monitors with different ranges K is the concentration calculation correction factor, and D is the signal amplification factor. Users can customize the concentration calculation correction factor K and the signal amplification factor D according to actual conditions, and technicians can also make adjustments based on experience to make the algorithm more flexible and accurate.

[0016] This technical solution automatically adjusts the detection parameters in the detection algorithm according to different working conditions and environments, ensuring that accurate detection results can be obtained in various complex environments, accurately judging the degree of contamination of the light scattering module, automatically identifying when cleaning is required, and realizing automatic cleaning of the light scattering module, reducing manual intervention, and improving the continuity and stability of equipment operation. In addition, through automatic judgment and perception, unnecessary cleaning and energy consumption are avoided, which helps to reduce the overall energy consumption of the equipment and achieve green environmental protection.

[0017] As a preferred embodiment of the present invention, the S2, light scattering module cleaning, specifically comprises the following steps:

[0018] Establishing a neural network model, inputting environmental characteristic parameters and pollutant characteristic parameters into the neural network model, and outputting control parameters;

[0019] The environmental characteristic parameters include the humidity in the chamber, the temperature in the chamber and the background signal B, the pollutant characteristic parameters include the pollutant type and the pollutant mass, and the control parameters include the vibration control parameters and the purge control parameters.

[0020] The neural network model can use the feedforward neural network algorithm in the prior art, and through the composite mapping of simple nonlinear processing units, a complex nonlinear processing capability can be obtained. Using the feedforward neural network algorithm, based on the environmental characteristic parameters and the pollutant characteristic parameters under different working conditions on site, the host computer system outputs the control parameters. Among them, the transformation relationship from input to output of the feedforward neural network algorithm is:

[0021]

[0022]

[0023] In the formula, is the input feature vector, yes arrive The connection weight, output It is the classification result according to different characteristics.

[0024] A large number of vibration decontamination experimental data sets were collected in the early stage. The data sets include input data and output data. The input data are specific environmental characteristic parameters, such as the humidity in the chamber, the temperature in the chamber and the background signal B, and also include pollutant parameters, such as pollutant type and pollutant quality. The output data includes vibration control parameters and purge control parameters. In the early stage, the neural network model was deep-learned and trained based on a large amount of experimental data to output more accurate vibration control parameters and purge control parameters. Compared with the traditional fixed parameters given to the drive circuit for vibration decontamination, the control parameter output based on the feedforward neural network algorithm is more intelligent.

[0025] As a preferred embodiment of the present invention, the vibration control parameters include driving voltage, vibration frequency, and vibration time, and the control parameters further include purge time, purge frequency, and purge angle.

[0026] An automatic cleaning device for a light scattering module comprises a threshold determination module and a cleaning module, wherein the threshold determination module determines whether the background signal B is higher than the threshold If yes, a start signal is output to the cleaning module, and the cleaning module automatically cleans the light scattering module.

[0027] As a preferred embodiment of the present invention, the cleaning module comprises a control unit, a vibration unit and a purge unit;

[0028] The control unit is used to input environmental characteristic parameters and pollutant characteristic parameters into the control parameter output module, and output vibration control parameters and purge control parameters to the vibration unit and the purge unit; the environmental characteristic parameters include the humidity in the chamber, the temperature in the chamber and the background signal B, and the pollutant characteristic parameters include the pollutant type and the pollutant mass.

[0029] As a preferred embodiment of the present invention, the vibration unit includes a driving circuit and a piezoelectric vibration device, the driving circuit receives the vibration control parameters and controls the vibration of the piezoelectric vibration device, and the piezoelectric vibration device collects pollutant mass parameters and feeds them back to the control unit.

[0030] The piezoelectric vibration device achieves the vibration effect through the inverse piezoelectric effect of the piezoelectric crystal itself, that is, the material is deformed by the electric field. When pollutants adhere to the surface of the piezoelectric vibration device, they will also generate electrical signals to the control unit after being stressed due to the positive piezoelectric effect of the piezoelectric crystal itself.

[0031] As a preferred embodiment of the present invention, the purge unit includes an air intake valve and an angle adjuster, and the purge control parameters include purge time, purge frequency and purge angle; the air intake valve receives the purge time information and the purge frequency information to control the purge time and frequency; the angle adjuster receives the purge angle information to control the purge angle.

[0032] As a preferred embodiment of the present invention, when the vibration of the vibration unit ends, the control unit sends a vibration end signal to the intake valve, and the intake valve opens after receiving the vibration end signal.

[0033] Vibrating first and then blowing can make it easier for pollutants attached to the surface to be carried away by the airflow, thereby improving cleaning efficiency while saving energy and protecting the environment.

[0034] A light scattering dust monitor comprises a light scattering module, a signal detector and an automatic cleaning device for the light scattering module, and also comprises a temperature sensor and a humidity sensor located inside the light scattering module; the signal detector, the temperature sensor and the humidity sensor respectively output the background signal B, the humidity in the chamber and the temperature in the chamber to the automatic cleaning device for the light scattering module.

[0035] A computer-readable storage medium stores a computer program, wherein the computer-readable storage medium stores instructions, and when the instructions are executed on a computer or a processor, the computer or the processor executes the steps of the automatic cleaning method of the light scattering module.

[0036] In summary, the present invention has the following beneficial effects:

[0037] 1. Automatically adjust the detection parameters in the detection algorithm according to different working conditions and environments to ensure accurate detection results in various complex environments, accurately determine the degree of contamination of the light scattering module, automatically identify when cleaning is required, realize automatic cleaning of the light scattering module, reduce manual intervention, and improve the continuity and stability of equipment operation.

[0038] 2. In the early stage, the neural network model is deeply trained based on a large amount of experimental data to output more accurate vibration control parameters and purge control parameters. Compared with the traditional fixed parameters given to the drive circuit for vibration decontamination, the control parameter output based on the feedforward neural network algorithm is more intelligent.

[0039] 3. The piezoelectric vibration device utilizes the inverse piezoelectric effect and direct piezoelectric effect of piezoelectric crystals to achieve vibration control and pollutant quality information detection, realizing multifunctional integration.

[0040] 4. Vibrating first and then blowing can make it easier for pollutants attached to the surface to be carried away by the airflow, thereby improving cleaning efficiency while saving energy and protecting the environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 To determine whether to start the cleaning flow chart;

[0043] Figure 2 Cleaning flow chart for light scattering module;

[0044] Figure 3 This is a schematic diagram of the internal modules of the light scattering dust monitor;

[0045] Figure 4 This is a schematic diagram of the automatic cleaning device module for the light scattering module;

[0046] Figure 5 This is the bottom view of the actual light scattering dust monitor;

[0047] Figure 6 This is a cross-sectional view of the light scattering dust monitor;

[0048] Figure 7 This is a structural diagram of the piezoelectric vibration device.

[0049] In the figure, 1. piezoelectric vibration device, 11. vibration insulator, 12. diaphragm, 13. piezoelectric crystal sheet, 2. light scattering module, 3. driving circuit, 4. purge unit. DETAILED DESCRIPTION

[0050] The present invention is further described below by specific examples and drawings. The examples of the present invention are intended to enable those skilled in the art to better understand the present invention, and do not limit the present invention in any way.

[0051] like Figure 1 , 2 As shown, a method for automatically cleaning the light scattering module 2 is as follows:

[0052] S1, threshold Sure

[0053] Based on the detection algorithm, determine the threshold , the detection algorithm is as follows:

[0054]

[0055] is the threshold value; M is the upper limit of the detector detection signal; C is the measurement range of the dust equipment; b is the particle concentration constant, which is defined by the user; K is the concentration calculation correction coefficient, which is defined by the user; D is the signal amplification factor, which is defined by the user; is the proportional constant, which is defined by the user; Q is the dark current signal value of the dust meter equipment, which is measured by the dust meter;

[0056] Through the detection algorithm, the threshold is calculated , when the background signal B is greater than the threshold , it means that the pollution level in the light scattering module 2 has affected the normal operation of the dust monitor, and the light scattering module 2 needs to be automatically cleaned. The background signal B is the signal value obtained by measuring in the absence of particles to be measured (such as dust), to ensure that the obtained signal only comes from the background noise and environmental factors of the instrument. It is usually measured synchronously when the dust monitor is zero-calibrated. The detection algorithm in this technical solution , taking into account factors such as the detector upper limit value, dark current signal value, and measurement range of different types of dust monitors, it can adapt to the needs of different products and working conditions and has a wide range of applicability. The specific description is as follows: M is the detector detection signal upper limit value, unit: mV. The detection signal upper limits of the detectors corresponding to different models of dust monitors are different, and are determined based on the actual working parameters of the product. Q is the dark current signal value of the dust monitor equipment, unit: mV, measured by the dust monitor. C is the measurement range of the dust equipment, unit: mg / m³. The measurement range of the dust monitor is usually 5mg / m³, 10mg / m³, etc. The thresholds corresponding to dust monitors with different ranges Different. K is the concentration calculation correction factor, and D is the signal amplification factor. Users can customize the concentration calculation correction factor K and signal amplification factor D according to actual conditions, and technicians can also make adjustments based on experience to make the algorithm more flexible and accurate.

[0057] S2, light scattering module 2 cleaning

[0058] Scan the background signal B in the light scattering module 2. If the background signal B is greater than the threshold , then the light scattering module 2 is automatically cleaned. Specifically: establish a neural network model, input the environmental characteristic parameters and the pollutant characteristic parameters into the neural network model, and output the control parameters;

[0059] The environmental characteristic parameters include the humidity in the chamber, the temperature in the chamber and the background signal B, the pollutant characteristic parameters include the pollutant type and the pollutant mass, and the control parameters include the vibration control parameters and the purge control parameters.

[0060] The neural network model can use the feedforward neural network algorithm in the existing technology. Through the composite mapping of simple nonlinear processing units, complex nonlinear processing capabilities can be obtained. Using the feedforward neural network algorithm, based on the environmental characteristic parameters and pollutant characteristic parameters under different working conditions on site, the host computer system outputs control parameters. Among them, the transformation relationship from input to output of the feedforward neural network algorithm is:

[0061]

[0062] In the formula, is the input feature vector, yes arrive The connection weight, output It is the classification result according to different characteristics.

[0063] In the early stage, a large number of vibration decontamination experimental data sets were collected. The data sets include input data and output data. The input data are specific environmental characteristic parameters, such as humidity in the chamber, temperature in the chamber and background signal B, and also include pollutant parameters, such as pollutant type and pollutant quality. The output data includes vibration control parameters and purge control parameters. In the early stage, deep learning training of the neural network model was carried out based on a large amount of experimental data to output more accurate vibration control parameters and purge control parameters. The vibration control parameters include driving voltage, vibration frequency, and vibration time. The control parameters also include purge time, purge frequency and purge angle. Compared with the traditional fixed parameters given to the drive circuit for vibration decontamination, the control parameter output based on the feedforward neural network algorithm is more intelligent.

[0064] like Figure 3 A light scattering dust monitor includes a light scattering module 2, a signal detector and an automatic cleaning device for the light scattering module. It also includes a temperature sensor and a humidity sensor located inside the light scattering module 2. The signal detector, the temperature sensor and the humidity sensor output the background signal B, the humidity in the chamber and the temperature in the chamber to the automatic cleaning device for the light scattering module respectively. The automatic cleaning device includes a threshold determination module and a cleaning module. The threshold determination module determines whether the background signal B is higher than the threshold. If so, a start signal is output to the cleaning module, and the cleaning module automatically cleans the light scattering module 2.

[0065] like Figure 4 As shown, the cleaning module specifically includes a control unit, a vibration unit and a purge unit 4. The control unit includes a neural network model, which is used to input environmental characteristic parameters and pollutant characteristic parameters into the control parameter output module, and output vibration control parameters and purge control parameters to the vibration unit and the purge unit 4. The environmental characteristic parameters include the humidity in the chamber, the temperature in the chamber and the background signal B, which are measured by the temperature sensor, the humidity sensor and the signal detector respectively. The pollutant characteristic parameters include the pollutant type and the pollutant mass.

[0066] First, in the early stage of model training, the model is fed with pollutants of different types and qualities, and pollutant removal data under different working conditions (temperature, humidity, etc.). The types of pollutants are determined in the early stage, such as ammonium salt crystals (ammonium chloride (NH4Cl), ammonium sulfate ((NH4)2SO4), etc., calcification (calcium carbonate (CaCO3), calcium hydroxide (Ca(OH)2), etc.). In the early stage, the model is trained according to these different types of pollutants that are actually determined, so that it can output the optimal pollution removal solution. For on-site applications, such as cement, waste incineration and other industries, its desulfurization and denitrification purification processes are also determined, so it is believed that the types of pollutants generated are also determined. Before automatic cleaning, the staff can manually write the pollutant type into the host computer and output it to the control unit.

[0067] The purge unit 4 includes an air intake valve and an angle regulator. The purge control parameters include purge time, purge frequency and purge angle. The air intake valve receives the purge time information and the purge frequency information to control the purge time and frequency. The angle regulator receives the purge angle information to control the purge angle. Figure 5 As shown, a purge air path is provided below the light scattering module 2 and is connected to an air inlet valve in the purge unit 4 , so that compressed air from the outside enters the light scattering module 2 through the purge unit 4 for purge.

[0068] like Figure 6 As shown, the vibration unit includes a driving circuit 3 and a piezoelectric vibration device 1, and the piezoelectric vibration device 1 is located at the bottom of the light scattering module 2. The driving circuit 3 receives the vibration control parameters and controls the piezoelectric vibration device 1 to vibrate. The piezoelectric vibration device 1 collects the pollutant mass parameters and feeds them back to the control unit. Figure 7 As shown, the piezoelectric vibration device 1 is specifically composed of a vibration insulator 11, a diaphragm 12 and a piezoelectric crystal sheet 13. When the control unit sends the control parameters to the driving circuit 3, the piezoelectric crystal sheet 13 undergoes mechanical deformation under the action of the electric field due to its own inverse piezoelectric effect, and drives the vibration insulator 11 to jump up and down through the diaphragm 12, thereby generating a vibration effect. At the same time, due to the positive piezoelectric effect of the piezoelectric crystal itself, an electrical signal is generated after being subjected to stress and fed back to the control unit, so the pollutant quality parameters can be collected through the piezoelectric vibration device 1 and fed back to the control unit.

[0069] Furthermore, the vibration and purging sequence can be predetermined. When the vibration unit finishes vibrating, the control unit sends a vibration end signal to the air intake valve, and the air intake valve opens after receiving the vibration end signal. Vibrating first and then purging can make it easier for pollutants attached to the surface to be carried away by the airflow, thereby improving cleaning efficiency while saving energy and protecting the environment.

[0070] In addition, two different vibration modes, intermittent vibration and continuous vibration, can be set, and two different vibration modes can be cycled periodically. For example, the vibration unit is pre-set to vibrate intermittently first, start the vibration for 5 seconds, stop for 3 seconds, poll 10 times, and then vibrate continuously, wherein the duration of continuous vibration is output by the neural network model. By setting different vibration modes, the reattachment of stains during the cleaning process can be reduced. Intermittent vibration helps to break the structure of the stains, while continuous vibration can take away the stains. The combination of the two can more effectively prevent the stains from re-depositing on the surface of the module. By combining the neural network model to control the duration of continuous vibration, the cleaning intensity and time can be adjusted according to the actual situation of the stains, so as to achieve a more intelligent and efficient cleaning process. At the same time, a reasonable vibration mode can reduce the physical damage to the light scattering module 2, because excessive continuous vibration may cause damage to the module. Intermittent vibration can effectively remove stains without excessively damaging the module. By analogy, the purge mode can also set two different purge modes, intermittent purge and continuous purge. For example, the purge unit 4 is preset to purge intermittently first, start the purge for 3 seconds, stop for 2 seconds, poll 8 times, and then continue to purge. The continuous purge time comes from the purge time issued by the control system.

[0071] In summary, this technical solution automatically adjusts the detection parameters in the detection algorithm according to different working conditions and environments, ensuring that accurate detection results can be obtained in various complex environments, accurately judging the degree of contamination of the light scattering module, automatically identifying when cleaning is needed, and realizing automatic cleaning of the light scattering module 2, reducing manual intervention, and improving the continuity and stability of equipment operation. In addition, combined with the neural network model, more accurate vibration control parameters and purge control parameters are output. Compared with the traditional fixed parameters given to the drive circuit 3 for vibration decontamination, the control parameter output based on the feedforward neural network algorithm is more intelligent.

[0072] It should be noted that the above embodiments are only detailed descriptions of the preferred embodiments and principles of the present invention. For ordinary technicians in this field, there will be changes in the specific implementation methods based on the ideas provided by the present invention, and these changes are also considered to be within the scope of protection of the present invention.

Claims

1. A method for automatically cleaning a light scattering module, characterized in that: The following steps are involved: S1, threshold B i Sure Based on the detection algorithm, determine the threshold B i , the detection algorithm is specifically: B i is the threshold value; M is the upper limit of the signal detection signal of the signal detector; C is the measurement range of the dust monitor equipment; b is the particle concentration constant, which is defined by the user; K is the concentration calculation correction coefficient, which is defined by the user; D is the signal amplification factor, which is defined by the user; ε is the proportional constant, which is defined by the user; Q is the dark current signal value of the dust meter equipment, which is measured by the dust meter; S2. Light scattering module cleaning Scan the background signal B in the light scattering module. If the background signal B is greater than the threshold value B i , the light scattering module is automatically cleaned.

2. The automatic cleaning method of a light scattering module according to claim 1, characterized in that: The S2, light scattering module cleaning specifically comprises the following steps: Establishing a neural network model, inputting environmental characteristic parameters and pollutant characteristic parameters into the neural network model, and outputting control parameters; The environmental characteristic parameters include the humidity in the chamber, the temperature in the chamber and the background signal B, the pollutant characteristic parameters include the pollutant type and the pollutant mass, and the control parameters include the vibration control parameters and the purge control parameters.

3. The automatic cleaning method of a light scattering module according to claim 2, characterized in that: The vibration control parameters include driving voltage, vibration frequency and vibration time, and the control parameters also include purge time, purge frequency and purge angle.

4. An automatic cleaning device for a light scattering module, used to implement the method according to claim 1, characterized in that: It includes a threshold determination module and a cleaning module, wherein the threshold determination module determines whether the background signal B is higher than the threshold B i If yes, a start signal is output to the cleaning module, and the cleaning module automatically cleans the light scattering module.

5. The automatic cleaning device for a light scattering module according to claim 4, characterized in that: The cleaning module includes a control unit, a vibration unit and a purge unit; the control unit outputs vibration control parameters and purge control parameters to the vibration unit and the purge unit.

6. The automatic cleaning device for a light scattering module according to claim 5, characterized in that: The vibration unit includes a driving circuit and a piezoelectric vibration device. The driving circuit receives the vibration control parameters and controls the piezoelectric vibration device to vibrate. The piezoelectric vibration device collects pollutant mass parameters and feeds them back to the control unit.

7. The automatic cleaning device for a light scattering module according to claim 6, characterized in that: The purge unit includes an air intake valve and an angle adjuster, and the purge control parameters include a purge time, a purge frequency, and a purge angle; the air intake valve receives the purge time and the purge frequency; and the angle adjuster receives the purge angle.

8. The automatic cleaning device for a light scattering module according to claim 7, characterized in that: When the vibration unit finishes vibrating, the control unit sends a vibration end signal to the intake valve, and the intake valve opens after receiving the vibration end signal.

9. A light scattering dust monitor, comprising a light scattering module and a signal detector, characterized in that: It also includes an automatic cleaning device for a light scattering module as described in claim 4, and a temperature sensor and a humidity sensor located inside the light scattering module; the signal detector, the temperature sensor and the humidity sensor respectively output the background signal B, the temperature in the chamber and the humidity in the chamber to the automatic cleaning device for the light scattering module.

10. A computer-readable storage medium having a computer program stored thereon, wherein the computer-readable storage medium has instructions stored therein, and when the instructions are executed on a computer or a processor, the computer or the processor executes the steps of the method according to any one of claims 1 to 3.

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