CN111613245B - Ore quality analysis method and equipment based on sound signal processing - Google Patents
Ore quality analysis method and equipment based on sound signal processing Download PDFInfo
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
- G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
- G06F17/141—Discrete Fourier transforms
- G06F17/142—Fast Fourier transforms, e.g. using a Cooley-Tukey type algorithm
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
- G10L25/18—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters the extracted parameters being spectral information of each sub-band
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
- G10L25/24—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters the extracted parameters being the cepstrum
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
- H04R1/00—Details of transducers, loudspeakers or microphones
- H04R1/08—Mouthpieces; Microphones; Attachments therefor
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The application discloses an ore quality analysis method and analysis equipment based on sound signal processing, wherein the method comprises the following steps: the sound collection device collects sample sound signals generated when the ore falls down to impact the metal plate in real time and transmits the sample sound signals to the analysis processing device; the analysis processing device converts the received sample sound signal into a sound waveform file; the analysis processing device performs fast Fourier transform on the sound waveform file and draws an amplitude spectrogram of the sound signal; and performing time domain and frequency domain analysis on the sample sound signal based on the sound waveform file and the amplitude spectrogram so as to predict the ore quality. The application can analyze the hardness and the volume of the fed ore in real time, so that the running state of the ore grinding system can be correspondingly adjusted to improve the running efficiency.
Description
Technical Field
The application relates to the technical field of acoustic signal processing analysis, in particular to an ore quality analysis method and equipment based on sound signal processing.
Background
Currently, as a modern information detection technology, the application range of acoustic technology is wider and wider. Acoustic technology is widely used in various fields such as aviation, construction, energy, geological exploration, automobiles and the like. Specifically, in the field of control optimization of the running process of the semi-autogenous mill, domestic experts and scholars adopt numerical calculation, simulation and other methods to do a great deal of research work: a method for recognizing the state of a unit based on sound signals as disclosed in publication number CN108106717a, predicting ore quality by establishing a standard characteristic parameter library; the method comprises the steps of constructing a fuzzy neural network by collecting historical data and parameters of a crusher to perform a great deal of concentrated training on the crusher, and obtaining the relation between each parameter of the crusher and the hardness of the ore. Although the technical scheme disclosed above has advanced to some extent, due to the complexity of the quality of ore fed into the autogenous mill and the semi-autogenous mill, the proposed operation process control optimization lacks universality and has poor expected effect, and the requirements of large-scale popularization and real-time operation regulation and control cannot be completely met.
Disclosure of Invention
The application mainly aims to provide an ore quality analysis method and equipment based on sound signal processing, which aim to ensure that the hardness and the volume of fed ore are analyzed in real time, so that the running state of an ore grinding system can be changed correspondingly to improve the running efficiency.
In order to achieve the above object, the present application provides an ore quality analysis method based on sound signal processing, which provides an ore quality analysis device, the device comprises an ore conveying device, a metal plate arranged below the ore conveying device, a sound collecting device arranged below the metal plate, and an analysis processing device; the method comprises the following steps:
the sound collection device collects sample sound signals generated when the ore falls down to impact the metal plate in real time and transmits the sample sound signals to the analysis processing device;
the analysis processing device converts the received sample sound signal into a sound waveform file;
the analysis processing device performs fast Fourier transform on the sound waveform file and draws an amplitude spectrogram of the sound signal;
and performing time domain and frequency domain analysis on the sample sound signal based on the sound waveform file and the amplitude spectrogram so as to predict the ore quality.
Preferably, the step of performing fast fourier transform on the sound waveform file by the analysis processing device and drawing an amplitude spectrogram of the sound signal further includes the steps of:
and analyzing the sound waveform file after the fast Fourier transform to determine the distribution range of the frequency of the sample sound signal on the frequency domain and the proportion of each frequency.
Preferably, after the step of analyzing the sound waveform file after the fast fourier transform to determine the distribution range of the frequencies of the sample sound signal in the frequency domain and the proportion of each frequency, the method further comprises the steps of:
the filter is designed.
Preferably, after the step of performing fast fourier transform on the sound waveform file and drawing the amplitude spectrogram of the sound signal, the method further includes the steps of:
performing boundary threshold processing on the amplitude spectrogram, and dividing an ore collision sound frequency concentration area by taking the boundary threshold as a boundary;
and filtering the interference noise which appears outside the concentrated area frequency band through the filter.
Preferably, the sound types referred to by the sample sound signal include: the ores with different volumes, the ores with different masses and the ores with different hardness respectively collide with the metal plate to generate sounds.
Preferably, the mineral resistance comprises hardness and volume.
The application also provides ore resistance analysis equipment based on sound signal processing, which comprises an ore conveying device, a metal plate arranged below the ore conveying device, a sound collecting device arranged at the lower side of the metal plate and an analysis processing device; wherein, the liquid crystal display device comprises a liquid crystal display device,
the sound collection device is used for collecting sample sound signals generated when the ore falls down to strike the metal plate in real time and transmitting the sample sound signals to the analysis processing device;
the analysis processing device is used for converting the received sample sound signal into a sound waveform file; performing fast Fourier transform on the sound waveform file, and drawing an amplitude spectrogram of the sound signal; and performing time-domain and frequency-domain analysis on the sample sound signal based on the sound waveform file and the amplitude spectrogram to predict the mineral resistance.
Preferably, the metal plate is a plain steel plate or a stainless steel plate.
Preferably, the distance from the impact point of the ore and the metal plate to the discharge hole of the conveying device is 30-50 cm; and the included angle between the metal plate and the ground is 30-60 degrees.
Compared with the prior art, the technical scheme of the application has the following advantages:
1. the ore property is predicted in advance through an ore sound signal and is submitted to a control theory of a follow-up autogenous mill or a semi-autogenous mill as known information, so that the aim of improving economy and efficiency by optimizing a control method is fulfilled;
2. the signal acquisition and analysis are a non-contact real-time monitoring method, and have the advantages of simple and safe installation operation and stable signal; the collected sound signals are transmitted through an air medium, and the sound collecting device is closer to the generating source, so that the influence of insufficient sound signal intensity is effectively avoided; meanwhile, the signal analysis processing equipment is far away from the ore falling conveying area, so that the safety risk of workers is reduced;
3. the signal acquisition equipment has low cost and good popularization and application prospect; the equipment for collecting the ore collision sound is mainly a microphone, a recording pen or small equipment with the function of collecting the sound in real time, the equipment is extremely low in maintenance cost, can be repeatedly used and uses engineering sites with different working environments;
4. the whole set of system equipment is simple and easy to use, does not need great investment of manpower and material resources, can not influence site construction at the same time, and is suitable for different engineering environments.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to the structures shown in these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of the structure of an ore resistance analysis apparatus of the present application;
FIG. 2 is a flow chart of the method of analyzing ore resistance according to the present application;
FIG. 3 is a waveform diagram of a hematite sample acoustic signal according to an embodiment of the present application;
FIG. 4 is a graph of spectral amplitude of a hematite sample acoustic signal according to one embodiment of the present application;
FIG. 5 is an enlarged partial view of the frequency 0-250Hz segment of FIG. 4;
FIG. 6 is an enlarged partial view of the frequency 250-700Hz segment of FIG. 4;
fig. 7 is an enlarged view of a portion of the frequency 1050-2000Hz segment of fig. 4.
The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that all directional indicators (such as up, down, left, right, front, and rear … …) in the embodiments of the present application are merely used to explain the relative positional relationship, movement, etc. between the components in a particular posture (as shown in the drawings), and if the particular posture is changed, the directional indicator is changed accordingly.
In addition, the technical solutions of the embodiments of the present application may be combined with each other, but it is necessary to be based on the fact that those skilled in the art can implement the technical solutions, and when the technical solutions are contradictory or cannot be implemented, the combination of the technical solutions should be considered as not existing, and not falling within the scope of protection claimed by the present application.
The application provides an ore quality analysis device.
Referring to fig. 1, in an embodiment of the application, an ore quality analysis apparatus based on acoustic signal processing includes an ore conveying device 2, a metal plate 4 disposed below the ore conveying device 2, an acoustic collection device 3 disposed below the metal plate 4, and an analysis processing device 5; the sound collection device 3 is used for collecting sample sound signals generated when the ore 1 falls down and impacts the metal plate 4 in real time and transmitting the sample sound signals to the analysis processing device 5; the analysis processing device 5 is used for converting the received sample sound signal into a sound waveform file; performing fast Fourier transform on the sound waveform file, and drawing an amplitude spectrogram of the sound signal; and performing time-domain and frequency-domain analysis on the sample sound signal based on the sound waveform file and the amplitude spectrogram to predict the mineral resistance.
In the present embodiment, the analyzing apparatus includes an ore conveying device 2 (e.g., a WEISI/weiss 002 type rubber conveyor belt), a metal plate 4 (a plain steel plate or 304 stainless steel plate, for example, 500×500×10mm in size), a sound collecting device 3 (e.g., a da DH-HSA200 pickup) fixed to the lower side of the metal plate 4, a sound transmitting device 6 (e.g., an audio line), and a sound analyzing and processing device 5, and the analyzing and processing device 5 generally includes a computer main body and a display. The ore quality analysis device in the embodiment mainly plays three roles of generating and collecting ore sound signals, extracting the ore sound signals and analyzing and processing the ore sound signals. In the generation and collection part of ore sound signals, a steel plate or stainless steel plate with the specification of 500 multiplied by 10mm is inclined 30-60 degrees (preferably 45 degrees) with the ground and is arranged at the position 30-50 cm below the falling part of the ore 1, a Dahua DH-HSA200 sound pickup is arranged below the steel plate for collecting the sound signals, and the collected sound signals are transmitted to a computer host for analysis through an audio line. The collection of ore sound signals comes from the sound signals generated by the falling impact of the ore 1 on the steel plate and the collision of the ore, the extraction of the ore sound signals is used for extracting characteristic frequencies, and the analysis processing of the ore sound signals is used for comparing the characteristic frequencies and predicting the hardness of the ore.
The application provides the basic working principle of analysis equipment as follows:
firstly, a sample sound signal generated by the ore freely falling from the ore conveying device 2 and impacting on a steel plate obliquely arranged below is collected in real time by utilizing the sound collecting device 3, and then transmitted to the analysis processing device 5, and the sound signal is recorded and analyzed in real time by the analysis processing device 5;
then, the sound signal analysis processing device 5 carries out filtering noise reduction means processing on the collected sound signals to remove the interference of environmental noise or other interference noise, and then carries out frequency spectrum analysis on the filtered ore sound signals to obtain an amplitude spectrogram of the signals;
and then carrying out qualitative analysis of specific characteristic parameters according to the time domain and frequency domain graphs of the sound signals, carrying out characteristic extraction by utilizing the characteristics of short-time zero-crossing rate, waveform index, pulse factor, specific frequency amplitude and the like of the frequency domain of the signals, and finally predicting the ore property by utilizing the correlation between the characteristic value and hardness of the sound signals.
Based on the device, as shown in fig. 2, the application provides an ore quality analysis method based on sound signal processing, which comprises the following steps:
s10, the sound collecting device collects sample sound signals generated when the ore falls down to strike the metal plate in real time and transmits the sample sound signals to the analysis processing device;
s20, the analysis processing device converts the received sample sound signal into a sound waveform file;
s30, the analysis processing device performs fast Fourier transform on the sound waveform file and draws an amplitude spectrogram of the sound signal;
s40, carrying out time domain and frequency domain analysis on the sample sound signal based on the sound waveform file and the amplitude spectrogram so as to predict the ore quality.
Preferably, in the step S30 of performing fast fourier transform on the sound waveform file by the analysis processing device and drawing the amplitude spectrogram of the sound signal, the method further includes the steps of:
s31, analyzing the sound waveform file after the fast Fourier transform, and determining the distribution range of the frequency of the sample sound signal on the frequency domain and the proportion of each frequency.
Preferably, after the step S31 of analyzing the sound waveform file after the fast fourier transform to determine the distribution range of the frequencies of the sample sound signal in the frequency domain and the proportion of the respective frequencies, the method further includes the steps of:
s32, designing a filter.
Preferably, after the step S30 of performing fast fourier transform on the sound waveform file and drawing the amplitude spectrogram of the sound signal, the method further includes the steps of:
s33, carrying out boundary threshold processing on the amplitude spectrogram, and dividing an ore collision sound frequency concentration area by taking the boundary threshold as a boundary;
s34, filtering the interference noise outside the concentrated area frequency band through the filter.
In short, the method provided in this embodiment may be roughly divided into several steps:
step 1: the sample sound signals generated by collision during the process that a batch of ores with random properties are placed on an ore conveying device and naturally drop to a steel plate are taken as and stored as sampling signals.
Step 2: the acquired sample sound signal is converted to a.WAV digital format in the audio signal.
Step 3: storing the WAV audio sampling signal into MATLAB subdirectory, reading the corresponding sound signal of each time period through calling command [ y, fs ] = audioread ('filename. WAV'), carrying out Fourier transform and analyzing the distribution range of the sample sound signal frequency on the frequency domain and the proportion of each frequency; before performing fourier transform on the audio sampling signal, several eigenvalues in the sampling signal are extracted and analyzed, including amplitude root mean square, waveform index, pulse factor, zero crossing rate, etc. Wherein the amplitude root mean square is the average of the sum of the signal sequence amplitude squares; the waveform index is the ratio of the amplitude root mean square of the signal to the absolute average value of the signal, and is a characteristic parameter which is typical of the time domain characteristics of the signal and reflects the waveform characteristics of the signal; the pulse factor of the signal is the ratio of the maximum value of the signal amplitude to the average value of the absolute value of the signal, and is the reflection of the maximum value of the signal time domain; the zero crossing rate is the most important time domain feature of the signal as the reflection of the signal frequency, is also the simplest feature in the time domain analysis of the signal, the amplitude of the signal is zero-valued from positive to negative, zero-valued is zero-valued from negative to positive, the number of zero crossings of the signal in one second is counted, and the zero crossing rate is called zero crossing rate.
Step 4: designing a frequency boundary threshold value reasonably according to the distribution range of frequencies in a template signal spectrogram, and designing a proper filter by taking the boundary threshold value as a limit to filter sound signals irrelevant to ore collision in sample signals;
step 5: and carrying out characteristic parameter mathematical analysis on the time domain signal graph and the signal spectrogram after short-time Fourier transformation and filtering, and predicting to obtain the ore property corresponding to the frequency band graph. After fourier transforming the audio sample signal, several eigenvalues are extracted, including amplitude spectrograms, dominant frequency values, discrete cosine transform related features, MFCCs (Mel Frequency Cepstrum Coefficient, mel frequency cepstrum parameters). The specific pattern of the amplitude spectrogram is that in the frequency domain description of the signals, the frequency is taken as an abscissa, the amplitude ratio of each frequency cost of the component signals is taken as an ordinate, the amplitude spectrogram reflects the distribution condition of the amplitude of the sound signals along with the frequency, and different frequency spectrum characteristics of different sound signals exist along with different sound signals in the process that ores with different properties fall into a conveyor belt. Therefore, the spectral characteristics of the sound signal can well reflect the properties of the ore corresponding to the frequency band; the main frequency value represents the frequency with the highest amplitude or the highest proportion after the signal is subjected to frequency spectrum conversion, and the frequency characteristic of the signal can be represented most; the MFCC is a cepstrum transformation of a nonlinear spectrum based on Mel frequency, and has better noise immunity and higher recognition performance. The time domain waveform characteristics and the frequency domain characteristics of the ores with different properties are different, and then the characteristics such as the short time zero crossing rate, the waveform index, the pulse factor, the specific frequency amplitude and the like of the frequency domain of the signals can be utilized for extracting the characteristics. Experiments show that the characteristic value of the signal has a certain correlation with the hardness. Therefore, the method has certain feasibility for carrying out the characteristic analysis and treatment on the ores with different characteristics by utilizing different characteristic values.
Specifically, the sound types to which the sample sound signal relates include: the ores with different volumes, the ores with different masses and the ores with different hardness respectively collide with the metal plate to generate sounds. Ore resistance includes hardness and volume characteristics of the ore.
Referring to fig. 3 to 5, the following will take hematite with a mohs hardness value of 5.5 to 6.5 as an example, and specifically describe how to determine the ore properties based on the present method:
in the interval of 0-500Hz, low frequency and multiple peaks occur, and the multiple peak amplitude is greater than 0.004, which shows that ores with higher energy exist in the frequency band interval.
In the 500-1150Hz interval, low frequency, multiple peaks and single peaks appear successively, the multiple peak amplitude is distributed in 0.002-0.004, the single peak amplitude is 0.00225, and the ore with higher energy exists in the frequency band interval.
In the range of 1000-2000Hz, broadband and multimodal conditions occur, and most of the amplitude in the frequency range is distributed between 0.001-0.0015, and the multimodal amplitudes are all greater than 0.002, indicating that a large number of ores of similar nature exist in the frequency range, and that a plurality of high-energy ores exist.
In the audio signal, the two times of larger 'clattering' sounds appear at about 0.5s and 2.5s, which are consistent with the situation that high amplitude appears in two frequency bands of 0-500Hz and 500-1150Hz in a spectrogram, and the two times of larger 'clattering' sounds are in a low frequency band and have energy, so that the corresponding hematite ore belongs to the ore with larger volume and smaller hardness between 5.5 and 5.8 of Mohs hardness; the sound that a large amount of ores fall in 4-6s is consistent with the conditions of wide frequency and constant amplitude of 1000-2000Hz, the corresponding hematite ores are between 5.8 and 6.2 in Mohs hardness, and the volume of the ore particles in the frequency range is mostly medium, so that individual large ores exist.
In the working condition process, the ore is fed into the autogenous mill and the semi-autogenous mill, and is accompanied by a low-frequency vibration signal (less than or equal to 100 Hz) and a sound signal (20-20000 Hz) which can be heard by human ears. In fact, the ore will generate different impact sounds during the process of conveying and different sound signals have different frequency spectrum characteristics, and the hardness of the ore will become one of the important factors affecting the impact of the ore to make sound. The application provides a method for analyzing ore quality based on sound signal processing, which comprises the following steps: the collision sound signals sent by the ore conveying and falling process are used for carrying out real-time acquisition and recording, noise reduction, filtering and spectrum analysis. The application has certain guiding significance for predicting the ore property in advance and regulating and controlling the running states of the autogenous mill and the semi-autogenous mill in real time so as to achieve the economic purposes of energy conservation and high efficiency.
The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the scope of the application, and all equivalent structural changes made by the description of the present application and the accompanying drawings or direct/indirect application in other related technical fields are included in the scope of the application.
Claims (9)
1. An ore quality analysis method based on sound signal processing is characterized in that an ore quality analysis device is provided, and the device comprises an ore conveying device, a metal plate arranged below the ore conveying device, a sound collecting device arranged below the metal plate and an analysis processing device; the method comprises the following steps:
the sound collection device collects sample sound signals generated when the ore falls down to impact the metal plate in real time and transmits the sample sound signals to the analysis processing device;
the analysis processing device converts the received sample sound signal into a sound waveform file;
the analysis processing device performs fast Fourier transform on the sound waveform file and draws an amplitude spectrogram of the sound signal;
and carrying out time domain and frequency domain analysis on the sample sound signal based on the sound waveform file and the amplitude spectrogram, carrying out qualitative analysis on specific characteristic parameters according to time domain and frequency domain graphs of the sound signal, carrying out characteristic extraction by utilizing the short-time zero-crossing rate, the waveform index, the pulse factor and the specific frequency amplitude sum of the frequency domain of the signal, and finally predicting the ore property by utilizing the correlation between the characteristic value and the hardness of the sound signal.
2. The ore resistance analysis method according to claim 1, wherein the step of plotting the amplitude spectrogram of the sound signal in the analysis processing means by performing a fast fourier transform on the sound waveform file further comprises the steps of:
and analyzing the sound waveform file after the fast Fourier transform to determine the distribution range of the frequency of the sample sound signal on the frequency domain and the proportion of each frequency.
3. The ore resistance analysis method according to claim 2, further comprising, after the step of analyzing the sound waveform file after the fast fourier transform to determine a distribution range of frequencies of the sample sound signal in a frequency domain and a proportion of each frequency, the steps of:
the filter is designed.
4. The ore resistance analysis method according to claim 3, further comprising, after the step of subjecting the sound waveform file to a fast fourier transform, the step of plotting a magnitude spectrum of the sound signal:
performing boundary threshold processing on the amplitude spectrogram, and dividing an ore collision sound frequency concentration area by taking the boundary threshold as a boundary;
and filtering the interference noise which appears outside the concentrated area frequency band through the filter.
5. The ore resistance analysis method according to any one of claims 1 to 4, wherein the sound type to which the sample sound signal relates includes: the ores with different volumes, the ores with different masses and the ores with different hardness respectively collide with the metal plate to generate sounds.
6. The method of mineral spirits analysis according to any one of claims 1 to 4, wherein the mineral spirits comprise hardness and volume.
7. The ore quality analysis equipment based on the sound signal processing is characterized by comprising an ore conveying device, a metal plate arranged below the ore conveying device, a sound collecting device arranged at the lower side of the metal plate and an analysis processing device; wherein, the liquid crystal display device comprises a liquid crystal display device,
the sound collection device is used for collecting sample sound signals generated when the ore falls down to strike the metal plate in real time and transmitting the sample sound signals to the analysis processing device;
the analysis processing device is used for converting the received sample sound signal into a sound waveform file; performing fast Fourier transform on the sound waveform file, and drawing an amplitude spectrogram of the sound signal; and carrying out time domain and frequency domain analysis on the sample sound signal based on the sound waveform file and the amplitude spectrogram, carrying out qualitative analysis on specific characteristic parameters according to the time domain and frequency domain graphs of the sound signal, carrying out characteristic extraction by utilizing the short-time zero-crossing rate, the waveform index, the pulse factor and the specific frequency amplitude sum of the frequency domain of the signal, and finally predicting the ore property by utilizing the correlation between the characteristic value and the hardness of the sound signal.
8. The ore resistance analysis apparatus according to claim 7, wherein the metal plate is a plain steel plate or a stainless steel plate.
9. The ore resistance analysis apparatus according to claim 8, wherein a distance from an impact point of the ore with the metal plate to a discharge port of the conveyor is 30 to 50 cm; and the included angle between the metal plate and the ground is 30-60 degrees.
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