CN111912755B - Mining dust concentration sensor, sensor system and method - Google Patents
Mining dust concentration sensor, sensor system and method Download PDFInfo
- Publication number
- CN111912755B CN111912755B CN202010791038.0A CN202010791038A CN111912755B CN 111912755 B CN111912755 B CN 111912755B CN 202010791038 A CN202010791038 A CN 202010791038A CN 111912755 B CN111912755 B CN 111912755B
- Authority
- CN
- China
- Prior art keywords
- sensor
- data
- unit
- light wave
- analysis
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 239000000428 dust Substances 0.000 title claims abstract description 67
- 238000000034 method Methods 0.000 title claims abstract description 44
- 238000005065 mining Methods 0.000 title claims abstract description 9
- 238000012544 monitoring process Methods 0.000 claims abstract description 30
- 238000012545 processing Methods 0.000 claims abstract description 23
- 238000004458 analytical method Methods 0.000 claims description 29
- 238000012549 training Methods 0.000 claims description 20
- 239000000758 substrate Substances 0.000 claims description 17
- 238000007405 data analysis Methods 0.000 claims description 15
- 238000001914 filtration Methods 0.000 claims description 14
- 238000006243 chemical reaction Methods 0.000 claims description 13
- 238000001228 spectrum Methods 0.000 claims description 13
- 238000013528 artificial neural network Methods 0.000 claims description 12
- 238000001514 detection method Methods 0.000 claims description 12
- 230000005284 excitation Effects 0.000 claims description 12
- 239000011888 foil Substances 0.000 claims description 11
- 239000002184 metal Substances 0.000 claims description 11
- 210000002569 neuron Anatomy 0.000 claims description 8
- 239000011159 matrix material Substances 0.000 claims description 6
- 230000000644 propagated effect Effects 0.000 claims description 4
- 230000001953 sensory effect Effects 0.000 claims description 4
- 230000002159 abnormal effect Effects 0.000 claims description 3
- 238000007781 pre-processing Methods 0.000 claims description 3
- 238000010276 construction Methods 0.000 claims 1
- 230000006698 induction Effects 0.000 abstract description 2
- 230000006870 function Effects 0.000 description 14
- 238000004364 calculation method Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 238000002474 experimental method Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 239000002245 particle Substances 0.000 description 3
- 239000003086 colorant Substances 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000010183 spectrum analysis Methods 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 230000003321 amplification Effects 0.000 description 1
- 238000012550 audit Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 210000004027 cell Anatomy 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000007274 generation of a signal involved in cell-cell signaling Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 230000001678 irradiating effect Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000003199 nucleic acid amplification method Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/06—Investigating concentration of particle suspensions
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/06—Investigating concentration of particle suspensions
- G01N15/075—Investigating concentration of particle suspensions by optical means
Landscapes
- Chemical & Material Sciences (AREA)
- Dispersion Chemistry (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention belongs to the technical field of sensors, and particularly relates to a mining dust concentration sensor, a sensor system and a method. The sensor includes: the dust concentration sensor comprises a sensor shell, a dust concentration sensor unit and an energy supply unit; the dust concentration sensor unit and the energy supply unit are both arranged in the sensor shell; the energy supply unit is used for supplying energy to the dust concentration sensor unit; characterized in that the dust concentration sensor unit includes: a transmitting unit, a receiving unit and an analyzing unit; the transmitting unit comprises a first transmitting unit and a second transmitting unit, the first transmitting unit and the second transmitting unit both transmit light waves, and the receiving unit receives the reflected light waves and the direct light waves. The problem of reduced induction accuracy caused by interference of the emitted light wave on the reflected light wave is solved by processing the emitted light wave and the reflected light wave of the sensor; meanwhile, a unique sensor system is constructed for the mine cavity environment, the mine cavity environment is better adapted, and the monitoring accuracy is improved.
Description
Technical Field
The invention belongs to the technical field of sensors, and particularly relates to a mining dust concentration sensor, a sensor system and a method.
Background
The dust sensor is a mode for counting or measuring mass concentration of suspended particles in air by using an MIE scattering theory. Generally, a dust sensor has an air duct, a light emitting tube, a lens, and a light sensing element. The air flow flows in the air duct, when the air flow flows above the photosensitive element, the air flow is irradiated by laser to generate scattered light, the photosensitive element receives the scattered light, and the condition of dust in the air flow is obtained through analysis of the scattered light.
In the prior art, dust sensors monitor dust in the air mainly through a photosensitive element. The photosensitive element is located below the detection point, and the detection point is usually located above the detection point through which the gas freely flows. Resulting in a reduction in the sensing accuracy of the dust.
Patent No. CN201720660428.8U: a high accuracy laser dust sensor discloses a high accuracy laser dust sensor, includes: a laser emission module; the optical signal receiving module is matched with the laser emitting module; the electrical signal generating module is electrically connected with the optical signal receiving module; the signal amplification module is electrically connected with the electric signal generation module; the distinguishing module is electrically connected with the signal amplifying module and is used for distinguishing electric signals fed back by dust with different particle sizes; the resolution module comprises a plurality of stages of resolution components, and adjacent two stages of resolution components are electrically connected; the filtering module is electrically connected with the distinguishing module and used for filtering interference signals in the electric signals; and the data storage and correction module is electrically connected with the filtering module and is used for storing calibration deviation data of the sensor and correcting the output electric signal.
The sensor can identify weak signals by amplifying signals and correcting data, has strong anti-interference capability, can highly accurately measure the dust content in the environment, but has no influence on the interference of the sensor, and cannot adapt to complex scenes in mine holes.
The patent numbers are: CN201410072979.3A patent: a dust sensor for on-line monitoring dust concentration comprises a box body which is internally provided with an air inlet channel and an air outlet channel which can form a sealed detection area and are positioned at two opposite sides of the detection area, an air inlet pipe and an air outlet pipe which are respectively positioned in the air inlet channel and the air outlet channel, a laser transmitter communicated with the detection area from one end part of the box body, wherein the air inlet pipe and the air outlet pipe are positioned on the same straight line, laser emitted by the laser transmitter is intersected with connecting lines of the air inlet pipe and the air outlet pipe, the dust sensor also comprises a light receiver component positioned above the detection area and a light reflecting component which can be detachably arranged at the bottom of the detection area, wherein the light receiver component is used for receiving information of refracted light generated by irradiating gas particles in the detection area with laser light emitted by the laser transmitter, and meanwhile, the information is received and converted into a signal which is transmitted to a signal processing system of the measuring instrument.
The dust monitoring precision is improved by arranging the air inlet pipe and the air outlet pipe, but the dust monitoring precision is difficult to implement in some complex environments. And still not to reject interference of the emitted light with the received light.
Disclosure of Invention
In view of the above, the main object of the present invention is to provide a dust concentration sensor, a sensor system and a method for mining, which avoid the problem of decreased sensing accuracy caused by interference of reflected light waves caused by emitted light waves by processing the emitted light waves and the reflected light waves of the sensor; meanwhile, a unique sensor system is constructed for the mine cavity environment, the mine cavity environment is better adapted, and the monitoring accuracy is improved.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
a mining dust concentration sensor, the sensor comprising: the dust concentration sensor comprises a sensor shell, a dust concentration sensor unit and an energy supply unit; the dust concentration sensor unit and the energy supply unit are both arranged in the sensor shell; the energy supply unit is used for supplying energy to the dust concentration sensor unit; characterized in that the dust concentration sensor unit includes: a transmitting unit, a receiving unit and an analyzing unit; the transmitting unit comprises a first transmitting unit and a second transmitting unit, and after the first transmitting unit and the second transmitting unit both transmit light waves, the receiving unit receives the reflected light waves and the direct light waves; and the analysis unit sequentially performs the steps of direct light wave filtering, reflected light wave conversion and frequency spectrum drawing according to the received reflected light wave and the received direct light wave.
Further, the direct light wave filtering performs the following steps: calculating the direct light wave of the first transmitting unit and the direct light wave of the second transmitting unit received by the receiving unit through Laplace transform by using the following formulaTime delay: wherein S is1(t) is the direct light wave of the first emission unit, PS1(t)]For its corresponding laplace transform; s2(t) is the direct light wave of the second emission unit, P*[S2(t)]A conjugate transform that is its corresponding laplace transform; f is P [ S ]1(t) and P*[S2(t)]Corresponding frequency, x is the unknown quantity of the laplace transform; and then, calculating the cross-correlation coefficient between the direct light wave of the first emission unit and the direct light wave of the second emission unit:wherein, denotes S2(t) inverse laplacian transform; will be provided withAmplifying by c times to obtain a reflected light wave after filtering the direct light wave:
further, the method of reflected light wave conversion performs the following steps: the reflected light wave is represented by the following formula:wherein α is an amplitude constant, fcIs a carrier frequency, MsIs a period, tau (t) is a time delay parameterB is the bandwidth, N1Is a rate constant, set to 1.5; the reflected light waves are converted to baseband signals by the following equation:
performing laplace transform on the baseband signal to obtain a frequency spectrum, wherein according to the frequency spectrum, the phase of the baseband signal is obtained as follows: phi (t) ═ 0.8 pi fcτ(t)。
Furthermore, the sensor shell consists of two layers of same electromagnetic shielding structures; the electromagnetic shielding structure includes: a substrate, and a metal foil wrapped around the substrate, wherein the substrate comprises a top surface, a bottom surface, and a plurality of sidewalls, wherein the metal foil covers only the top surface and the plurality of sidewalls of the substrate; the metal foil has opposing first and second surfaces, wherein the first surface is located over the substrate.
A sensor system, the system comprising: a plurality of sensor tuples and analysis processors which are uniformly distributed in the mine hole; the sensor tuple includes: 5 sensors uniformly distributed on the circumference with the same circle center; the circle center position is provided with a tuple processor; the tuple processor is respectively in signal connection with all the sensors; a shielding pool is arranged outside the tuple processor in a surrounding manner; the circumference is a shielding shell; the tuple processor is in signal connection with the analysis processor, and is configured to perform analog-to-digital conversion on data sensed by the sensor and send the data to the analysis processor.
Furthermore, the shielding shell has the same structure as the sensor shell; the shielding pool is formed by a plurality of same structures which are stacked and surrounded by the same circle center.
Further, the analysis processor performs the following processing on the received data in sequence: performing data preprocessing, including: removing the unique attribute, processing missing values and abnormal value detection and processing; and carrying out data specification processing, including: removing an average value, calculating a covariance matrix, calculating an eigenvalue and an eigenvector of the covariance matrix, sorting the eigenvalues from large to small, reserving the largest eigenvector, and converting data into a new space constructed by the eigenvector; finally, new processed data are obtained, and the data are irrelevant pairwise, but the original information is kept; carrying out data standardization processing, and scaling the data in proportion to enable the data to fall into a specific interval; and carrying out data analysis on the data subjected to the data standardization processing to obtain a dust monitoring result.
A method, the method performing the steps of:
step 1: the method comprises the following steps of (1) enabling 5 sensors to form a group together with a tuple processor to form sensor tuples, and uniformly distributing the sensor tuples inside a mine hole;
step 2: and the analysis processor receives the data sensed by the sensor, and performs data analysis on the received data to complete dust monitoring in the mine hole.
Furthermore, 5 sensors are in a group and are uniformly distributed on the circumference with the same circle center; the tuple processor is arranged at the position of the circle center; a shielding pool is arranged outside the tuple processor in a surrounding manner; the circumference is a shielding shell; and the tuple processor is in signal connection with the analysis processor, and sends the data sensed by the sensor to the analysis processor after performing analog-to-digital conversion on the data.
Further, the step 2: the analysis processor receives data sensed by the sensor, performs data analysis on the received data, and completes the dust monitoring method in the mine hole to execute the following steps: obtaining training sensory data for modeling as input variables using xiRepresenting, wherein i represents the ith variable in the data; setting a weight function of wiExpressing, performing convolution operation on each input variable and the corresponding weight function to obtain a first intermediate result; setting an excitation function, wherein the excitation function is as follows:setting the neuron threshold of the neural network as follows: theta; calculating the first intermediate result, the excitation function and the neuron threshold to obtain a forward directionThe result of the neural network is:calculating a training error of the forward neural network; the output variable E of the training is 'mine hole dust index', but a predicted value generated after model training is O, so that the obtained error function is as follows:wherein m represents the number of the input modeling samples at this time, and i represents the ith variable; the update weight w is propagated backwards; reversely transmitting data from the output layer to the input layer, readjusting the value of the weight w until the model error reaches the minimum, and stopping training to complete model creation; data analysis was performed using the generative model.
The mining dust concentration sensor, the sensor system and the method have the following beneficial effects: the problem of reduced induction accuracy caused by interference of the emitted light wave on the reflected light wave is solved by processing the emitted light wave and the reflected light wave of the sensor; meanwhile, a unique sensor system is constructed for the mine cavity environment, the mine cavity environment is better adapted, and the monitoring accuracy is improved. The method is mainly realized by the following steps: 1. direct light wave filtering and reflected light wave conversion are adopted, so that the inaccuracy of dust monitoring caused by interference of reflected light by direct light is avoided; 2. electromagnetic shielding shell: the electromagnetic shielding shell is used for the sensor shell, so that the problem of inaccurate dust monitoring caused by electromagnetic interference is effectively avoided; meanwhile, aiming at the sensor system, the shielding shells with the same structure are used for removing electromagnetic interference; 3. sensor system without dead angle: according to the invention, the sensors are arranged in a complicated manner, the dust monitoring is carried out by using sensor tuples, 5 sensors which work in cooperation with each other are arranged in the sensor tuples, and the sensors monitor the same monitoring area pairwise, so that the monitoring accuracy is ensured; 4. and (3) carrying out data analysis on the sensed data: the invention corrects the data error and analyzes the data aiming at the sensed data, so that the final sensing result is more accurate.
Drawings
Fig. 1 is a schematic structural diagram of a dust concentration sensor unit according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a sensing method according to an embodiment of the present invention.
FIG. 3 is a schematic structural diagram of a sensor cell provided in an embodiment of the present invention;
fig. 4 is a schematic diagram of an experiment curve of dust monitoring accuracy rate varying with experiment times of the mine dust concentration sensor, the sensor system and the method provided by the embodiment of the invention, and a schematic diagram of a comparison experiment effect in the prior art.
The method comprises the following steps of 1-sensor, 2-monitoring area, 3-shielding pool, 4-tuple processor, 5-sensor shell I, 6-sensor shell II, 7-first shielding shell electromagnetic shielding structure and 8-second shielding shell electromagnetic shielding structure.
Detailed Description
The method of the present invention will be described in further detail below with reference to the accompanying drawings and embodiments of the invention.
Example 1
As shown in fig. 1, a mining dust concentration sensor, the sensor comprises: the dust concentration sensor comprises a sensor shell, a dust concentration sensor unit and an energy supply unit; the dust concentration sensor unit and the energy supply unit are both arranged in the sensor shell; the energy supply unit is used for supplying energy to the dust concentration sensor unit; characterized in that the dust concentration sensor unit includes: a transmitting unit, a receiving unit and an analyzing unit; the transmitting unit comprises a first transmitting unit and a second transmitting unit, and after the first transmitting unit and the second transmitting unit both transmit light waves, the receiving unit receives the reflected light waves and the direct light waves; and the analysis unit sequentially performs the steps of direct light wave filtering, reflected light wave conversion and frequency spectrum drawing according to the received reflected light wave and the received direct light wave.
By adopting the technical scheme, the sensor processes the transmitting light wave and the reflected light wave, so that the problem of reduced sensing accuracy caused by interference of the transmitting light wave on the reflected light wave is solved; meanwhile, a unique sensor system is constructed for the mine cavity environment, the mine cavity environment is better adapted, and the monitoring accuracy is improved. The method is mainly realized by the following steps: 1. direct light wave filtering and reflected light wave conversion are adopted, so that the inaccuracy of dust monitoring caused by interference of reflected light by direct light is avoided; 2. electromagnetic shielding shell: the electromagnetic shielding shell is used for the sensor shell, so that the problem of inaccurate dust monitoring caused by electromagnetic interference is effectively avoided; meanwhile, aiming at the sensor system, the shielding shells with the same structure are used for removing electromagnetic interference; 3. sensor system without dead angle: according to the invention, the sensors are arranged in a complicated manner, the dust monitoring is carried out by using sensor tuples, 5 sensors which work in cooperation with each other are arranged in the sensor tuples, and the sensors monitor the same monitoring area pairwise, so that the monitoring accuracy is ensured; 4. and (3) carrying out data analysis on the sensed data: the invention corrects the data error and analyzes the data aiming at the sensed data, so that the final sensing result is more accurate.
Example 2
On the basis of the above embodiment, the direct optical wave filtering performs the following steps: calculating the time delay between the direct light wave of the first transmitting unit and the direct light wave of the second transmitting unit received by the receiving unit through Laplace transform by using the following formula: wherein S is1(t) is the direct light wave of the first emission unit, PS1(t)]For its corresponding laplace transform; s2(t) is the direct light wave of the second emission unit, P*[S2(t)]A conjugate transform that is its corresponding laplace transform; f is P [ S ]1(t) and P*[S2(t)]Corresponding frequency, x is the unknown quantity of the laplace transform; and then, calculating the cross-correlation coefficient between the direct light wave of the first emission unit and the direct light wave of the second emission unit:wherein, denotes S2(t) inverse laplacian transform; will be provided withAmplifying by c times to obtain a reflected light wave after filtering the direct light wave:
example 3
On the basis of the above embodiment, the method for converting reflected light waves performs the following steps: the reflected light wave is represented by the following formula: wherein α is an amplitude constant, fcIs a carrier frequency, MsIs a period, tau (t) is a time delay parameter, B is a bandwidth, N1Is a rate constant, set to 1.5; the reflected light waves are converted to baseband signals by the following equation:
performing laplace transform on the baseband signal to obtain a frequency spectrum, wherein according to the frequency spectrum, the phase of the baseband signal is obtained as follows: phi (t) ═ 0.8 pi fcτ(t)。
In particular, spectral analysis is a technique for decomposing a complex signal into simpler signals. Many physical signals can be represented as the sum of many simple signals of different frequencies. The spectral analysis is performed to find information (e.g., amplitude, power, strength, or phase) of a signal at different frequencies.
The light source is composed of different colors, the light of each color has different frequency, and the occupied proportion may also be different. The triple prism refracts light of different frequencies to different positions by means of refraction, so that light of different colors can be seen. Similarly, a common light source may be treated with a prism to project a continuous or discontinuous band of colored light. The color of a band of light indicates its frequency, while the light and dark can indicate how much of its proportion is, which is the spectrum of light, commonly referred to as the spectrum. If the color contents of all frequencies are a sample, the resultant color will be white, and the spectrum whose amplitude corresponds to the frequency will be a horizontal line. Signals with a horizontal line of the spectrum will therefore be generally referred to as "white".
Example 4
On the basis of the previous embodiment, the sensor shell consists of two layers of same electromagnetic shielding structures; the electromagnetic shielding structure includes: a substrate, and a metal foil wrapped around the substrate, wherein the substrate comprises a top surface, a bottom surface, and a plurality of sidewalls, wherein the metal foil covers only the top surface and the plurality of sidewalls of the substrate; the metal foil has opposing first and second surfaces, wherein the first surface is located over the substrate.
Specifically, in the sensor shell, the substrate and the metal foil jointly form an electromagnetic shielding structure; the two layers of electromagnetic shielding structures are respectively a first electromagnetic shielding structure 5 and a second electromagnetic shielding structure 6.
Example 5
A sensor system, the system comprising: a plurality of sensor tuples and analysis processors which are uniformly distributed in the mine hole; the sensor tuple includes: 5 sensors 1 uniformly distributed on the circumference with the same center; the circle center position is provided with a tuple processor 4; the tuple processor 4 is respectively in signal connection with all the sensors 1; a shielding pool 3 is arranged outside the tuple processor 4 in a surrounding manner; the circumference is a shielding shell; the tuple processor 4 is in signal connection with the analysis processor, and is configured to perform analog-to-digital conversion on the data sensed by the sensor and send the data to the analysis processor.
Specifically, every two adjacent sensors monitor one monitoring area 2 together, and the dust concentration condition in the monitoring area 2 is monitored by the two sensors together.
Example 6
On the basis of the previous embodiment, the shielding shell has the same structure as the sensor shell; the shielding pool 3 is formed by a plurality of same structures which are stacked and surrounded by the same circle center.
Specifically, the sensor housing, like the sensor housing, includes a sensor housing I5 and a sensor housing II6, and the shielding housing is also composed of two layers of electromagnetic shielding structures. The substrate and the metal foil jointly form an electromagnetic shielding structure; the two layers of electromagnetic shielding structures are a first shielding shell electromagnetic shielding structure 7 and a second shielding shell electromagnetic shielding structure 8 respectively.
Example 7
On the basis of the previous embodiment, the analysis processor performs the following processing on the received data in sequence: performing data preprocessing, including: removing the unique attribute, processing missing values and abnormal value detection and processing; and carrying out data specification processing, including: removing an average value, calculating a covariance matrix, calculating an eigenvalue and an eigenvector of the covariance matrix, sorting the eigenvalues from large to small, reserving the largest eigenvector, and converting data into a new space constructed by the eigenvector; finally, new processed data are obtained, and the data are irrelevant pairwise, but the original information is kept; carrying out data standardization processing, and scaling the data in proportion to enable the data to fall into a specific interval; and carrying out data analysis on the data subjected to the data standardization processing to obtain a dust monitoring result.
In particular, the original data should be audited mainly from the aspects of completeness and accuracy. The integrity check mainly checks whether units or individuals to be checked have omission or not and whether all the check items or indexes are complete or not. The accuracy audit mainly comprises two aspects: firstly, whether the data material truly reflects the objective actual condition and whether the content accords with the reality is checked; and secondly, checking whether the data has errors or not, calculating whether the data is correct or not, and the like. The method for checking the data accuracy mainly comprises logic check and calculation check. The logical check mainly checks whether the data is in accordance with the logic, whether the content is reasonable and whether the items or the numbers have the phenomenon of mutual contradiction. The calculation check is to check whether each item of data in the questionnaire has errors in calculation results and calculation methods, and is mainly used for checking quantitative (numerical type) data.
For the second-hand data obtained through other channels, besides checking the integrity and accuracy of the second-hand data, the applicability and timeliness of the data should be emphasized. Second-hand data can come from a variety of sources, some of which may have been obtained through special investigation for a particular purpose or have been processed as required for a particular purpose. For the user, it should be clear first of all the source of the data, the aperture of the data and the related background data, so as to determine whether these data meet the needs of self-analysis and research, whether rework and so on are needed, and the hard cover can not be handled blindly. In addition, the timeliness of the data is also checked, and for some problems with strong timeliness, if the acquired data is too late, the significance of research may be lost. In general, the most up-to-date statistics should be used as much as possible. After the data is audited, the data is confirmed to be suitable for actual needs, and further processing and arrangement are necessary.
Example 8
A method, the method performing the steps of:
step 1: the sensors are grouped into a group by 5, the sensors and a tuple processor 4 jointly form a sensor tuple, and the sensor tuple is uniformly distributed in the mine hole;
step 2: and the analysis processor receives the data sensed by the sensor, and performs data analysis on the received data to complete dust monitoring in the mine hole.
Example 9
On the basis of the previous embodiment, 5 sensors are in a group and are uniformly distributed on the circumference with the same circle center; the tuple processor 4 is arranged at the position of the circle center; the tuple processor 4 is surrounded by 3; the circumference is a shielding shell; and the tuple processor 4 is in signal connection with the analysis processor, and sends the data sensed by the sensor to the analysis processor after performing analog-to-digital conversion on the data.
Example 10
On the basis of the above embodiment, the step 2: the analysis processor receives data sensed by the sensor, performs data analysis on the received data, and completes the dust monitoring method in the mine hole to execute the following steps: obtaining training sensory data for modeling as input variables using xiRepresenting, wherein i represents the ith variable in the data; setting a weight function of wiExpressing, performing convolution operation on each input variable and the corresponding weight function to obtain a first intermediate result; setting an excitation function, wherein the excitation function is as follows:setting the neuron threshold of the neural network as follows: theta; and operating the first intermediate result, the excitation function and the neuron threshold value to obtain a result of the forward neural network, wherein the result is as follows:calculating a training error of the forward neural network; the output variable E of the training is 'mine hole dust index', but a predicted value generated after model training is O, so that the obtained error function is as follows:wherein m represents the number of the input modeling samples at this time, and i represents the ith variable; the update weight w is propagated backwards; reversely transmitting data from the output layer to the input layer, readjusting the value of the weight w until the model error reaches the minimum, and stopping training to complete model creation; data analysis was performed using the generative model.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process and related description of the system described above may refer to the corresponding process in the foregoing method embodiments, and will not be described herein again.
It should be noted that, the system provided in the foregoing embodiment is only illustrated by dividing the functional modules, and in practical applications, the functions may be distributed by different functional modules according to needs, that is, the modules or steps in the embodiment of the present invention are further decomposed or combined, for example, the modules in the foregoing embodiment may be combined into one module, or may be further split into multiple sub-modules, so as to complete all or unit functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the modules or steps, and are not to be construed as unduly limiting the present invention.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes and related descriptions of the storage device and the processing device described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Those of skill in the art would appreciate that the various illustrative modules, method steps, and modules described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that programs corresponding to the software modules, method steps may be located in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. To clearly illustrate this interchangeability of electronic hardware and software, various illustrative components and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as electronic hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The terms "first," "second," and the like are used for distinguishing between similar elements and not necessarily for describing or implying a particular order or sequence.
The terms "comprises," "comprising," or any other similar term are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention.
Claims (8)
1. A mining dust concentration sensor, characterized in that the sensor comprises: the dust concentration sensor comprises a sensor shell, a dust concentration sensor unit and an energy supply unit; the dust concentration sensor unit and the energy supply unit are both arranged in the sensor shell; the energy supply unit is used for supplying energy to the dust concentration sensor unit; characterized in that the dust concentration sensor unit includes: a transmitting unit, a receiving unit and an analyzing unit; the transmitting unit comprises a first transmitting unit and a second transmitting unit, and after the first transmitting unit and the second transmitting unit both transmit light waves, the receiving unit receives the reflected light waves and the direct light waves; the analysis unit sequentially performs steps of direct light wave filtering, reflected light wave conversion and frequency spectrum drawing according to the received reflected light wave and the received direct light wave;
the direct light wave filtering performs the following steps: calculating the time delay between the direct light wave of the first transmitting unit and the direct light wave of the second transmitting unit received by the receiving unit through Laplace transform by using the following formula:wherein S is1(t) is the direct light wave of the first emission unit, PS1(t)]For its corresponding laplace transform; s2(t) is the direct light wave of the second emission unit, P*[S2(t)]A conjugate transform that is its corresponding laplace transform; f is P [ S ]1(t) and P*[S2(t)]Corresponding frequency, x is the unknown quantity of the laplace transform; and then, calculating the cross-correlation coefficient between the direct light wave of the first emission unit and the direct light wave of the second emission unit: wherein, denotes S2(t) inverse laplacian transform; will be provided withAmplifying by c times to obtain a reflected light wave after filtering the direct light wave:
the method for reflected light wave conversion performs the following steps: the reflected light wave is represented by the following formula:wherein α is an amplitude constant, fcIs a carrier frequency, MsIs a period, tau (t) is a time delay parameter, B is a bandwidth, N1Is a rate constant, set to 1.5; by such asThe reflected light waves are converted to baseband signals according to the following formula:
performing laplace transform on the baseband signal to obtain a frequency spectrum, wherein according to the frequency spectrum, the phase of the baseband signal is obtained as follows: phi (t) ═ 0.8 pi fcτ(t)。
2. The sensor of claim 1, wherein the sensor housing is comprised of two layers of identical electromagnetic shielding structures; the electromagnetic shielding structure includes: a substrate, and a metal foil wrapped around the substrate, wherein the substrate comprises a top surface, a bottom surface, and a plurality of sidewalls, wherein the metal foil covers only the top surface and the plurality of sidewalls of the substrate; the metal foil has opposing first and second surfaces, wherein the first surface is located over the substrate.
3. A sensor system based on a sensor according to any one of claims 1 to 2, characterized in that the system comprises: a plurality of sensor tuples and analysis processors which are uniformly distributed in the mine hole; the sensor tuple includes: 5 sensors uniformly distributed on the circumference with the same circle center; the circle center position is provided with a tuple processor; the tuple processor is respectively in signal connection with all the sensors; a shielding pool is arranged outside the tuple processor in a surrounding manner; the circumference is a shielding shell; the tuple processor is in signal connection with the analysis processor, and is configured to perform analog-to-digital conversion on data sensed by the sensor and send the data to the analysis processor.
4. The system of claim 3, wherein the shield housing is of the same construction as the sensor housing; the shielding pool is formed by a plurality of same structures which are stacked and surrounded by the same circle center.
5. The system of claim 3, wherein the analysis processor performs the following on the received data in sequence: performing data preprocessing, including: removing the unique attribute, processing missing values and abnormal value detection and processing; and carrying out data specification processing, including: removing an average value, calculating a covariance matrix, calculating an eigenvalue and an eigenvector of the covariance matrix, sorting the eigenvalues from large to small, reserving the largest eigenvector, and converting data into a new space constructed by the eigenvector; finally, new processed data are obtained, and the data are irrelevant pairwise, but the original information is kept; carrying out data standardization processing, and scaling the data in proportion to enable the data to fall into a specific interval; and carrying out data analysis on the data subjected to the data standardization processing to obtain a dust monitoring result.
6. A sensor method based on the system of claim 3, wherein the method performs the steps of:
step 1: the method comprises the following steps of (1) enabling 5 sensors to form a group together with a tuple processor to form sensor tuples, and uniformly distributing the sensor tuples inside a mine hole;
step 2: the analysis processor receives data sensed by the sensor, performs data analysis on the received data, and completes the dust monitoring method in the mine hole to execute the following steps: obtaining training sensory data for modeling as input variables using xiRepresenting, wherein i represents the ith variable in the data; setting a weight function of wiExpressing, performing convolution operation on each input variable and the corresponding weight function to obtain a first intermediate result; setting an excitation function, wherein the excitation function is as follows:setting the neuron threshold of the neural network as follows: theta; and operating the first intermediate result, the excitation function and the neuron threshold value to obtain a result of the forward neural network, wherein the result is as follows: calculating a training error of the forward neural network; the output variable E of the training is 'mine hole dust index', but a predicted value generated after model training is O, so that the obtained error function is as follows:wherein m represents the number of the input modeling samples at this time, and i represents the ith variable; the update weight w is propagated backwards; reversely transmitting data from the output layer to the input layer, readjusting the value of the weight w until the model error reaches the minimum, and stopping training to complete model creation; data analysis was performed using the generative model.
7. The method of claim 6, wherein the sensors are grouped into 5 groups, evenly distributed on the circumference with the same center; the tuple processor is arranged at the position of the circle center; a shielding pool is arranged outside the tuple processor in a surrounding manner; the circumference is a shielding shell; and the tuple processor is in signal connection with the analysis processor, and sends the data sensed by the sensor to the analysis processor after performing analog-to-digital conversion on the data.
8. The method of claim 6, wherein the step 2: the analysis processor receives data sensed by the sensor, performs data analysis on the received data, and completes the dust monitoring method in the mine hole to execute the following steps: obtaining training sensory data for modeling as input variables using xiRepresenting, wherein i represents the ith variable in the data; setting a weight function of wiExpressing, performing convolution operation on each input variable and the corresponding weight function to obtain a first intermediate result; setting an excitation function, wherein the excitation function is as follows:setting the neuron threshold of the neural network as follows: theta; and operating the first intermediate result, the excitation function and the neuron threshold value to obtain a result of the forward neural network, wherein the result is as follows:calculating a training error of the forward neural network; the output variable E of the training is 'mine hole dust index', but a predicted value generated after model training is O, so that the obtained error function is as follows:wherein m represents the number of the input modeling samples at this time, and i represents the ith variable; the update weight w is propagated backwards; reversely transmitting data from the output layer to the input layer, readjusting the value of the weight w until the model error reaches the minimum, and stopping training to complete model creation; data analysis was performed using the generative model.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202010791038.0A CN111912755B (en) | 2020-08-07 | 2020-08-07 | Mining dust concentration sensor, sensor system and method |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202010791038.0A CN111912755B (en) | 2020-08-07 | 2020-08-07 | Mining dust concentration sensor, sensor system and method |
Publications (2)
Publication Number | Publication Date |
---|---|
CN111912755A CN111912755A (en) | 2020-11-10 |
CN111912755B true CN111912755B (en) | 2021-08-10 |
Family
ID=73284702
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202010791038.0A Active CN111912755B (en) | 2020-08-07 | 2020-08-07 | Mining dust concentration sensor, sensor system and method |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN111912755B (en) |
Citations (36)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPH08184566A (en) * | 1995-01-05 | 1996-07-16 | Yokogawa Electric Corp | Analytical apparatus for fine-particle component |
CN1270560A (en) * | 1997-09-16 | 2000-10-18 | 金特克斯公司 | Moisture sensor and windshield fog detector |
CN1410756A (en) * | 2002-11-14 | 2003-04-16 | 上海交通大学 | Particle radius, concentration photosensor |
CN1715865A (en) * | 2005-07-22 | 2006-01-04 | 公安部沈阳消防研究所 | Smoke concentration detector |
CN101402013A (en) * | 2008-10-31 | 2009-04-08 | 敬林 | Detecting and maintaining apparatus for dry type filter element of air filter for automobile |
CN102288523A (en) * | 2011-07-19 | 2011-12-21 | 中国科学技术大学 | Granular grain diameter distribution measuring device based on linear array CCD (charge-coupled device) |
CN102301224A (en) * | 2008-12-16 | 2011-12-28 | 西利奥斯技术公司 | Optical Absorption Probe Provided With Emission Source Monitoring |
EP2405287A1 (en) * | 2010-07-08 | 2012-01-11 | Centre National D'etudes Spatiales | Device for remote laser detection and interferometry method |
CN203114302U (en) * | 2013-03-15 | 2013-08-07 | 山东中煤工矿物资集团有限公司 | Contractive protective support frame of roadway |
CN103903383A (en) * | 2014-04-23 | 2014-07-02 | 中国科学技术大学 | Optical receiving module of high-sensitivity fire smoke alarm |
CN203681574U (en) * | 2014-02-24 | 2014-07-02 | 山东中煤工矿物资集团有限公司 | Monorail single-action conjoined car arrester |
CN204740186U (en) * | 2015-02-09 | 2015-11-04 | 中国科学院上海药物研究所 | Heterogeneous liquid subsides automatic monitoring equipment |
CN204964337U (en) * | 2015-09-21 | 2016-01-13 | 北京艾克艾瑞科技有限公司 | Sensor |
CN205067286U (en) * | 2015-10-26 | 2016-03-02 | 杭州泽天科技有限公司 | Particulate matter detection device |
CN106644861A (en) * | 2015-10-29 | 2017-05-10 | 上海基恩自动化设备有限公司 | Particulate matter concentration measuring instrument |
CN106855492A (en) * | 2016-12-02 | 2017-06-16 | 山东科技大学 | Mine Dust Concentration dynamic detection system and Dust Concentration dynamic monitoring method |
CN206300873U (en) * | 2016-11-22 | 2017-07-04 | 合肥硕佳电子科技有限公司 | A kind of optics dust concentration detection means |
CN206440578U (en) * | 2016-12-30 | 2017-08-25 | 聚光科技(杭州)股份有限公司 | The detection means of particulate matter in gas |
CN206804486U (en) * | 2017-06-08 | 2017-12-26 | 广州勒夫迈智能科技有限公司 | A kind of high-precision laser dust sensor |
CN107576601A (en) * | 2017-09-20 | 2018-01-12 | 张家港朗亿机电设备有限公司 | Suitable for the particulate matter on-line checking and analysis meter in urban track traffic place |
CN207096040U (en) * | 2017-08-16 | 2018-03-13 | 王�琦 | A kind of coal-mine fire smoke detection system |
CN207263577U (en) * | 2017-06-06 | 2018-04-20 | 宁波方太厨具有限公司 | A kind of range hood of the real-time detection part of oil smoke concentration and the application component |
CN108051348A (en) * | 2017-12-05 | 2018-05-18 | 西人马(厦门)科技有限公司 | A kind of detecting system and method for fluid non-metallic particle concentration |
CN109323968A (en) * | 2018-12-17 | 2019-02-12 | 北京理工大学 | A kind of calibration system and its method applied to dust cloud cluster distribution of concentration |
CN109490160A (en) * | 2017-09-12 | 2019-03-19 | 日立-Lg数据存储韩国公司 | Using the dust sensor of impactor |
CN109596491A (en) * | 2018-11-30 | 2019-04-09 | 荆门博谦信息科技有限公司 | Aerosol detection method and device |
CN109973331A (en) * | 2019-05-05 | 2019-07-05 | 内蒙古工业大学 | A kind of fan blade of wind generating set fault diagnosis algorithm based on bp neural network |
CN110095477A (en) * | 2018-01-31 | 2019-08-06 | 西克工程有限公司 | Measure the analyzer of micronic dust |
JP2019164133A (en) * | 2018-03-19 | 2019-09-26 | Jfeスチール株式会社 | Mist determination method of periphery of block object on conveyor, and property measurement method of block object on conveyor |
CN209624304U (en) * | 2019-03-18 | 2019-11-12 | 重庆工程职业技术学院 | Underground coal mine environment monitoring device based on laser detection |
CN110849781A (en) * | 2018-08-21 | 2020-02-28 | 唯亚威通讯技术有限公司 | Alarm condition detector based on multispectral sensor |
CN110987736A (en) * | 2019-12-18 | 2020-04-10 | 华中科技大学 | Aerosol particle spectrum and concentration measuring device and method |
CN111060429A (en) * | 2019-12-27 | 2020-04-24 | 民政部一零一研究所 | Ultralow dust measuring device |
CN210953731U (en) * | 2019-10-10 | 2020-07-07 | 上海蒙恩电子科技有限公司 | Smoke tester for connector |
WO2020150746A1 (en) * | 2019-01-18 | 2020-07-23 | Mission Bio | Method, apparatus and system to detect sub-particle flowrate in a closed systems |
CN111491508A (en) * | 2017-12-20 | 2020-08-04 | 英特维特国际股份有限公司 | System for fish ectoparasite monitoring in aquaculture |
Family Cites Families (12)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
IT1276468B1 (en) * | 1995-07-04 | 1997-10-31 | Hospal Dasco Spa | AUTOMATIC DIALYSIS METHOD AND EQUIPMENT |
KR100472205B1 (en) * | 2002-12-21 | 2005-03-10 | 한국전자통신연구원 | Position Controller And Controlling Method for Time Delay Compensation of the Image Tracker in Electro-Optical Tracking System |
CN102749386B (en) * | 2011-04-19 | 2015-01-28 | 香港科技大学 | System and method for in-situ hydration monitoring and damage detection of concrete structure and sensors used by system and method |
CN102353622B (en) * | 2011-07-01 | 2013-03-27 | 黑龙江科技学院 | Monitoring and measuring method for dust concentration in working faces in underground coal mine |
CN102435221B (en) * | 2011-09-16 | 2015-07-01 | 山西科致成科技有限公司 | Unattended intelligent calibrator for mine gas sensors |
US9790878B2 (en) * | 2014-01-17 | 2017-10-17 | Ford Global Technologies, Llc | One dimensional three way catalyst model for control and diagnostics |
CN107003246A (en) * | 2014-11-25 | 2017-08-01 | 因格瑞恩股份有限公司 | The fluid behaviour of porous material LIBS |
CN106528997B (en) * | 2016-10-28 | 2020-08-14 | 江苏天瑞仪器股份有限公司 | Method for drawing regional particle hourly concentration distribution map |
US10393660B2 (en) * | 2016-11-06 | 2019-08-27 | JianFeng Zhang | Apparatus and method for measuring concentration of materials in liquid or gas |
CN107044947B (en) * | 2017-05-02 | 2019-11-19 | 山西大学 | A kind of recognition methods of the PM2.5 pollution index based on characteristics of image |
CN109186975A (en) * | 2018-08-22 | 2019-01-11 | 四川日机密封件股份有限公司 | A kind of Hydrodynamic pressure type seal face unlatching Rotating speed measring method |
CN109434251B (en) * | 2018-10-22 | 2021-04-02 | 湖北文理学院 | Welding seam image tracking method based on particle filtering |
-
2020
- 2020-08-07 CN CN202010791038.0A patent/CN111912755B/en active Active
Patent Citations (36)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPH08184566A (en) * | 1995-01-05 | 1996-07-16 | Yokogawa Electric Corp | Analytical apparatus for fine-particle component |
CN1270560A (en) * | 1997-09-16 | 2000-10-18 | 金特克斯公司 | Moisture sensor and windshield fog detector |
CN1410756A (en) * | 2002-11-14 | 2003-04-16 | 上海交通大学 | Particle radius, concentration photosensor |
CN1715865A (en) * | 2005-07-22 | 2006-01-04 | 公安部沈阳消防研究所 | Smoke concentration detector |
CN101402013A (en) * | 2008-10-31 | 2009-04-08 | 敬林 | Detecting and maintaining apparatus for dry type filter element of air filter for automobile |
CN102301224A (en) * | 2008-12-16 | 2011-12-28 | 西利奥斯技术公司 | Optical Absorption Probe Provided With Emission Source Monitoring |
EP2405287A1 (en) * | 2010-07-08 | 2012-01-11 | Centre National D'etudes Spatiales | Device for remote laser detection and interferometry method |
CN102288523A (en) * | 2011-07-19 | 2011-12-21 | 中国科学技术大学 | Granular grain diameter distribution measuring device based on linear array CCD (charge-coupled device) |
CN203114302U (en) * | 2013-03-15 | 2013-08-07 | 山东中煤工矿物资集团有限公司 | Contractive protective support frame of roadway |
CN203681574U (en) * | 2014-02-24 | 2014-07-02 | 山东中煤工矿物资集团有限公司 | Monorail single-action conjoined car arrester |
CN103903383A (en) * | 2014-04-23 | 2014-07-02 | 中国科学技术大学 | Optical receiving module of high-sensitivity fire smoke alarm |
CN204740186U (en) * | 2015-02-09 | 2015-11-04 | 中国科学院上海药物研究所 | Heterogeneous liquid subsides automatic monitoring equipment |
CN204964337U (en) * | 2015-09-21 | 2016-01-13 | 北京艾克艾瑞科技有限公司 | Sensor |
CN205067286U (en) * | 2015-10-26 | 2016-03-02 | 杭州泽天科技有限公司 | Particulate matter detection device |
CN106644861A (en) * | 2015-10-29 | 2017-05-10 | 上海基恩自动化设备有限公司 | Particulate matter concentration measuring instrument |
CN206300873U (en) * | 2016-11-22 | 2017-07-04 | 合肥硕佳电子科技有限公司 | A kind of optics dust concentration detection means |
CN106855492A (en) * | 2016-12-02 | 2017-06-16 | 山东科技大学 | Mine Dust Concentration dynamic detection system and Dust Concentration dynamic monitoring method |
CN206440578U (en) * | 2016-12-30 | 2017-08-25 | 聚光科技(杭州)股份有限公司 | The detection means of particulate matter in gas |
CN207263577U (en) * | 2017-06-06 | 2018-04-20 | 宁波方太厨具有限公司 | A kind of range hood of the real-time detection part of oil smoke concentration and the application component |
CN206804486U (en) * | 2017-06-08 | 2017-12-26 | 广州勒夫迈智能科技有限公司 | A kind of high-precision laser dust sensor |
CN207096040U (en) * | 2017-08-16 | 2018-03-13 | 王�琦 | A kind of coal-mine fire smoke detection system |
CN109490160A (en) * | 2017-09-12 | 2019-03-19 | 日立-Lg数据存储韩国公司 | Using the dust sensor of impactor |
CN107576601A (en) * | 2017-09-20 | 2018-01-12 | 张家港朗亿机电设备有限公司 | Suitable for the particulate matter on-line checking and analysis meter in urban track traffic place |
CN108051348A (en) * | 2017-12-05 | 2018-05-18 | 西人马(厦门)科技有限公司 | A kind of detecting system and method for fluid non-metallic particle concentration |
CN111491508A (en) * | 2017-12-20 | 2020-08-04 | 英特维特国际股份有限公司 | System for fish ectoparasite monitoring in aquaculture |
CN110095477A (en) * | 2018-01-31 | 2019-08-06 | 西克工程有限公司 | Measure the analyzer of micronic dust |
JP2019164133A (en) * | 2018-03-19 | 2019-09-26 | Jfeスチール株式会社 | Mist determination method of periphery of block object on conveyor, and property measurement method of block object on conveyor |
CN110849781A (en) * | 2018-08-21 | 2020-02-28 | 唯亚威通讯技术有限公司 | Alarm condition detector based on multispectral sensor |
CN109596491A (en) * | 2018-11-30 | 2019-04-09 | 荆门博谦信息科技有限公司 | Aerosol detection method and device |
CN109323968A (en) * | 2018-12-17 | 2019-02-12 | 北京理工大学 | A kind of calibration system and its method applied to dust cloud cluster distribution of concentration |
WO2020150746A1 (en) * | 2019-01-18 | 2020-07-23 | Mission Bio | Method, apparatus and system to detect sub-particle flowrate in a closed systems |
CN209624304U (en) * | 2019-03-18 | 2019-11-12 | 重庆工程职业技术学院 | Underground coal mine environment monitoring device based on laser detection |
CN109973331A (en) * | 2019-05-05 | 2019-07-05 | 内蒙古工业大学 | A kind of fan blade of wind generating set fault diagnosis algorithm based on bp neural network |
CN210953731U (en) * | 2019-10-10 | 2020-07-07 | 上海蒙恩电子科技有限公司 | Smoke tester for connector |
CN110987736A (en) * | 2019-12-18 | 2020-04-10 | 华中科技大学 | Aerosol particle spectrum and concentration measuring device and method |
CN111060429A (en) * | 2019-12-27 | 2020-04-24 | 民政部一零一研究所 | Ultralow dust measuring device |
Non-Patent Citations (1)
Title |
---|
基于光散射法燃煤电厂烟尘浓度的在线测量系统研究;雷志伟;《分布式能源》;20181031;第3卷(第5期);第28-33页 * |
Also Published As
Publication number | Publication date |
---|---|
CN111912755A (en) | 2020-11-10 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN104897607B (en) | Portable near infrared spectrum food modeling and quick detection integral method and system | |
CN105092540B (en) | The fast high-precision detecting method of content of heavy metal lead in a kind of edible oil | |
CN104535528B (en) | The method of BP neural network extract real-time TDLAS gas absorption spectra absorbances | |
EP0939896B1 (en) | Infrared measuring gauges | |
Chen et al. | Quantitative analysis of soil nutrition based on FT-NIR spectroscopy integrated with BP neural deep learning | |
CN101975759A (en) | Transmission-type nondestructive measuring device and method of water content of plant leaves | |
CN111912755B (en) | Mining dust concentration sensor, sensor system and method | |
Shao et al. | A new approach to discriminate varieties of tobacco using vis/near infrared spectra | |
Zhang et al. | Application of swarm intelligence algorithms to the characteristic wavelength selection of soil moisture content | |
CN113758891B (en) | Method, device and equipment for calculating component concentration of mixed gas and storage medium | |
Schmetz et al. | Decision support by interpretable machine learning in acoustic emission based cutting tool wear prediction | |
CN112903543B (en) | Light scattering-based aerosol particle ellipticity measurement method and system | |
Ye et al. | Water chemical oxygen demand prediction model based on the CNN and ultraviolet-visible spectroscopy | |
CN111220571B (en) | Second harmonic signal fitting method and system based on amplitude dispersion | |
CN108645817B (en) | Multi-type mixed particle mass concentration online measurement method | |
CN109521002A (en) | A kind of fuel characteristic measurement method of solid fuel particle stream | |
CN113624745B (en) | Method for improving long-term stability of laser-induced breakdown spectroscopy based on light spots | |
CN113607674B (en) | Multi-gas detection method and system based on MEMS gas sensor array | |
Stashkiv et al. | nCode GlyphWorks Software Use for Test Data Processing. | |
Gulyanon et al. | A comparative study of noise augmentation and deep learning methods on Raman spectral classification of contamination in hard disk drive | |
Liu et al. | Digital techniques and trends for seed phenotyping using optical sensors | |
CN111581008A (en) | Abnormal value rapid and accurate detection method based on parallel cloud computing | |
Jin et al. | Study on the accuracy of photoacoustic spectroscopy system based on multiple linear regression correction algorithm | |
CN113176222B (en) | Gas concentration inversion method based on direct absorption spectrum | |
CN117216474A (en) | Optical cavity ring-down information extraction method, device and storage medium |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |