Space particulate matter concentration detection method and device in industrial and mining environment

Abstract

The invention provides a space particulate matter concentration detection method and device in an industrial and mining environment, relates to the technical field of industrial and mining environment detection, and solves the technical problem of low detection accuracy in the prior art. The method comprises the following steps: sensing a charge signal of the suspended particulate matter through a charge acquisition probe; the charge acquisition probe is used for generating induction current according to electrostatic induction charge characteristics of the suspended particulate matters; carrying out amplification processing and noise processing on the charge signal, and adopting a differential signal processing mechanism to eliminate environmental noise in the noise processing; the processed charge signals are input into a particulate matter characteristic model, and the spatial particulate matter concentration is obtained through calculation; the particulate matter characteristic model is used for representing an association relationship between electrostatic induction charge characteristics and concentrations of particulate matters with different particle sizes. The method is used in the detection process of the space particulate matter concentration.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial and mining environment detection, and in particular to a method and device for detecting spatial particulate matter concentration in an industrial and mining environment. BACKGROUND

[0002] In an industrial and mining environment, accurate detection of spatial particulate matter concentration is crucial for production safety and worker health. Currently, traditional detection techniques mostly use optical sensors, which are based on light scattering principles to calculate particulate matter concentration. However, there are obvious defects: first, they are easily contaminated by dust, oil, and other contaminants in the industrial and mining environment, which can adhere to the optical components, affecting light transmission and scattering, leading to decreased precision and requiring frequent maintenance. For example, the high temperature and high dust environment in a metallurgical plant can cause the sensor to fail within a short period of time. Second, they are easily disturbed by environmental noise, such as electromagnetic noise and mechanical vibration noise in the industrial and mining environment, which can severely affect the detection signal, leading to deviations in the results. For example, strong electromagnetic interference in a coal mine can significantly reduce the detection accuracy. Third, the detection algorithms are insufficient, as existing algorithms do not fully consider the complex characteristics of particulate matter and the diversity of the environment, making it difficult to distinguish the influence of different particle sizes and not being optimized for interference factors, making it difficult to extract true concentration information.

[0003] Therefore, the detection scheme in the related art has the technical problem of low accuracy. SUMMARY

[0004] The present application provides a method and device for detecting spatial particulate matter concentration in an industrial and mining environment, solving the technical problem of low detection accuracy in the prior art.

[0005] To achieve the above-mentioned purpose, the present application adopts the following technical solutions: In a first aspect, a method for detecting spatial particulate matter concentration in an industrial and mining environment is provided, comprising: sensing the charge signal of suspended particulate matter through a charge collection probe; the charge collection probe is used to generate an induced current based on the electrostatic induction charge characteristics of suspended particulate matter; amplifying and processing the charge signal, wherein the noise processing adopts a differential signal processing mechanism to eliminate environmental noise; inputting the processed charge signal into a particulate matter characteristic model to calculate the spatial particulate matter concentration; the particulate matter characteristic model is used to represent the correlation between the electrostatic induction charge characteristics of different particle sizes of particulate matter and the concentration.

[0006] Based on the technical scheme, the charge collection probe based on the electrostatic induction principle is adopted, compared with the traditional optical sensor, the core detection component does not need to rely on light transmission and scattering, and the precision reduction problem caused by the attachment of dust, oil stains and other pollutants is avoided in principle, and the maintenance frequency and cost are reduced. Secondly, the weak signal is enhanced through amplification processing, and the environmental noise is targetedly eliminated by combining the differential signal processing mechanism, solving the problem of detection deviation caused by electromagnetic interference, mechanical vibration and other noises in the traditional technology. Finally, the particle characteristic model is introduced, the correlation between particle size and charge quantity is established, the defect that the traditional algorithm cannot distinguish the influence of different particle sizes is overcome, and the concentration calculation is more accurate. In summary, the technical scheme provided by the embodiment can further improve the accuracy and reliability of concentration detection in industrial and mining environments.

[0007] In combination with the first aspect, in a possible implementation manner, the amplification processing is implemented by: inputting the charge signal into a multi-stage charge amplification circuit for multi-stage amplification processing; and the multi-stage charge amplification circuit is composed of at least two stages of amplifiers in cascade.

[0008] In combination with the first aspect, in a possible implementation manner, the multi-stage charge amplification circuit includes three-stage cascaded amplifiers: the first-stage amplifier is an operational amplifier with a field effect tube input, used for preliminary amplification of the signal; the second-stage amplifier is an amplifier with an automatic gain control function, used for amplitude detection of the input signal through a peak detection circuit, and automatic adjustment of the amplification multiple according to the detected amplitude; the peak detection circuit in the second-stage amplifier is composed of a diode, a capacitor and a resistor; and the third-stage amplifier is an amplifier with a low-pass filter function, used for filtering high-frequency noise through a low-pass filter and further amplifying the signal; and the low-pass filter is composed of a feedback resistor and a capacitor in series.

[0009] In combination with the first aspect, in a possible implementation manner, the noise processing is implemented by: inputting the charge signal after the multi-stage amplification processing into a signal differential processing module for differential processing; the signal differential processing module is composed of a noise simulation circuit and a subtracter; the noise simulation circuit is used for simulating environmental noise; and the subtracter is used for differential processing of the signal.

[0010] In combination with the first aspect, in a possible implementation manner, the method includes: dividing the charge signal after the multi-stage amplification processing into a first original signal and a second original signal; performing signal simulation on the second original signal through a noise simulation circuit to obtain a first noise signal; the first noise signal is used for simulating environmental noise; and differential processing is performed on the first original signal and the first noise signal through a subtracter to eliminate the noise component in the first original signal.

[0011] In conjunction with the first aspect mentioned above, in one possible implementation, the method further includes: using a temperature compensation module to perform signal compensation on the charge signal according to the real-time monitored ambient temperature.

[0012] In conjunction with the first aspect mentioned above, in one possible implementation, the particulate matter characteristic model is established by collecting charge data of particulate matter of different sizes under various industrial and mining environments, as well as industrial and mining environment data, and constructing a particulate matter characteristic model based on the collected charge data and industrial and mining environment data.

[0013] In conjunction with the first aspect mentioned above, in one possible implementation, the particulate matter characteristic model consists of a particle size calculation model and a concentration calculation model, wherein the particle size calculation model satisfies the following formula: ; in, For charge quantity, For particulate matter particle size, These represent various indicators in the industrial and mining environmental data. For regression coefficients, This is the error term; The concentration calculation model satisfies the following formula: ; in, For space particulate matter concentration, This is the reunification correction factor. For the first The number of particles in each particle size range For the first Average volume of particles in each particle size range For particulate matter density, The sampling volume.

[0014] Secondly, a spatial particulate matter concentration detection device for industrial and mining environments is provided, comprising: a communication unit and a processing unit; the communication unit is used to sense the charge signal of suspended particulate matter through a charge acquisition probe; the charge acquisition probe is used to generate an induced current based on the electrostatic induction charge characteristics of suspended particulate matter; the processing unit is used to amplify and noise-process the charge signal, wherein the noise processing adopts a differential signal processing mechanism to eliminate environmental noise; the processing unit is used to input the processed charge signal into a particulate matter characteristic model to calculate the spatial particulate matter concentration; the particulate matter characteristic model is used to characterize the correlation between the electrostatic induction charge characteristics and concentration of particulate matter of different sizes.

[0015] In conjunction with the second aspect above, in one possible implementation, the processing unit is used to input the charge signal into a multi-stage charge amplifier circuit for multi-stage amplification processing; the multi-stage charge amplifier circuit is composed of at least two cascaded amplifier stages.

[0016] In a possible implementation manner of the second aspect, the multi-stage charge amplification circuit comprises three-stage cascaded amplifiers, wherein the first-stage amplifier is an operational amplifier with a field effect tube input, configured to preliminarily amplify the signal; the second-stage amplifier is an amplifier with an automatic gain control function, configured to detect the amplitude of the input signal through a peak detection circuit and automatically adjust the amplification multiple according to the detected amplitude; the peak detection circuit in the second-stage amplifier is composed of a diode, a capacitor and a resistor; and the third-stage amplifier is an amplifier with a low-pass filter function, configured to filter out high-frequency noise through a low-pass filter and further amplify the signal; the low-pass filter is composed of a feedback resistor and a capacitor in series.

[0017] In a possible implementation manner of the second aspect, the processing unit is configured to input the charge signal processed by the multi-stage amplification into a signal differential processing module for differential processing; the signal differential processing module is composed of a noise simulation circuit and a subtractor; the noise simulation circuit is configured to simulate environmental noise; and the subtractor is configured to perform differential processing on the signal.

[0018] In a possible implementation manner of the second aspect, the processing unit is configured to: divide the charge signal processed by the multi-stage amplification into a first original signal and a second original signal; perform signal simulation on the second original signal through a noise simulation circuit to obtain a first noise signal; the first noise signal is configured to simulate environmental noise; and perform differential processing on the first original signal and the first noise signal through a subtractor to eliminate the noise component in the first original signal.

[0019] In a possible implementation manner of the second aspect, the processing unit is configured to: perform signal compensation on the charge signal according to the real-time monitored environmental temperature through a temperature compensation module.

[0020] In a possible implementation manner of the second aspect, the particulate matter characteristic model is established by: collecting charge quantity data and industrial and mining environment data of particulate matters with different particle sizes in various industrial and mining environments, and constructing the particulate matter characteristic model based on the collected charge quantity data and industrial and mining environment data.

[0021] In a possible implementation manner of the second aspect, the particulate matter characteristic model is composed of a particle size calculation model and a concentration calculation model, and the particle size calculation model satisfies the following formula: ; wherein, Q represents the charge quantity, D represents the particle size of the particulate matter, respectively represent each index in the industrial and mining environment data, is a regression coefficient, error term; The concentration calculation model satisfies the following formula: ; wherein, is the spatial particulate matter concentration, is the agglomeration correction coefficient, is the number of particulate matters in the jth particle size interval, is the average volume of particulate matters in the jth particle size interval, is the density of particulate matters, is the sampling volume.

[0022] In a third aspect, the present application provides a spatial particulate matter concentration detection device in a mining environment, comprising: a processor and a storage medium; the storage medium comprises instructions, and the processor is configured to execute the instructions to implement the method described in any of the above embodiments. The spatial particulate matter concentration detection device in the mining environment can be an electronic device, or a chip in an electronic device.

[0023] In a fourth aspect, the present application provides a computer readable storage medium, which stores instructions, and when the instructions are executed on a spatial particulate matter concentration detection device in a mining environment, the spatial particulate matter concentration detection device in the mining environment executes the method described in any of the above embodiments.

[0024] In a fifth aspect, the present application provides a computer program product comprising instructions, and when the computer program product is executed on a spatial particulate matter concentration detection device in a mining environment, the spatial particulate matter concentration detection device in the mining environment executes the method described in any of the above embodiments.

[0025] It should be understood that the description of technical features, technical solutions, advantages or similar language in the present application does not imply that all features and advantages can be achieved in any single embodiment. On the contrary, it can be understood that the description of a feature or advantage means that the specific technical feature, technical solution or advantage is included in at least one embodiment. Therefore, the description of technical features, technical solutions or advantages in the specification does not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions and advantages described in the embodiments can be combined in any appropriate manner. Those skilled in the art will understand that the embodiments can be implemented without one or more specific technical features, technical solutions or advantages of a particular embodiment. In other embodiments, additional technical features and advantages can be identified in specific embodiments that do not embody all embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1 ​​A system architecture diagram of a spatial particulate matter concentration detection system in an industrial and mining environment provided in this application embodiment; Figure 2 A schematic flowchart illustrating a method for detecting spatial particulate matter concentration in an industrial or mining environment, provided in an embodiment of this application; Figure 3 A schematic flowchart of another method for detecting spatial particulate matter concentration in an industrial or mining environment provided in this application embodiment; Figure 4 A schematic flowchart of another method for detecting spatial particulate matter concentration in an industrial or mining environment provided in this application embodiment; Figure 5 This is a schematic diagram of the structure of a space particulate matter concentration detection device provided in an embodiment of this application; Figure 6 This is a schematic diagram of the hardware structure of a space particulate matter concentration detection device provided in an embodiment of this application. Detailed Implementation

[0027] In the description of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. The "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" means one or more, and "multiple" means two or more. The terms "first," "second," etc., do not limit the quantity or order of execution, and "first," "second," etc., do not necessarily imply differences.

[0028] It should be noted that, in this application, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0029] In industrial and mining environments, accurate detection of particulate matter concentration is crucial, as it relates not only to production safety but also to the health of workers. Currently, various technologies exist for detecting particulate matter concentration in industrial and mining environments. Among them, traditional optical sensors are widely used, primarily based on the principle of light scattering, calculating particulate matter concentration by measuring the degree to which particles scatter light. However, this detection technology has several insurmountable drawbacks.

[0030] First, optical sensors are highly susceptible to interference from environmental noise. Industrial and mining environments contain numerous complex noise sources, such as electromagnetic noise from equipment operation and mechanical vibration noise. This noise severely affects the detection signal of optical sensors, causing significant deviations in the detection results and making it impossible to accurately reflect the true particulate matter concentration. For example, in underground coal mines, the frequent operation of various large mechanical equipment generates strong electromagnetic interference, drastically reducing the detection accuracy of optical sensors and rendering them unable to provide reliable data support for safe production.

[0031] Secondly, existing detection algorithms have shortcomings. Many current algorithms fail to fully consider the complex characteristics of particulate matter and the diversity of industrial and mining environments, resulting in poor accuracy in concentration detection. For example, existing algorithms cannot accurately distinguish the impact of particulate matter of different sizes on the detection results, leading to significant errors in calculating particulate matter concentration. Furthermore, most existing algorithms are not optimized for complex interference factors in industrial and mining environments, making it difficult to effectively extract true particulate matter concentration information when faced with environmental noise interference.

[0032] Furthermore, traditional optical sensors face the problem of interference from environmental characteristics in practical applications. Industrial and mining environments contain large amounts of pollutants such as dust and oil, and may also experience extreme weather conditions such as high or low temperatures. These factors affect light transmission and scattering, leading to a continuous decline in detection accuracy. Once a sensor is interfered with, frequent maintenance or component replacement is required, increasing maintenance costs and workload, and also affecting the continuity of detection work. For example, in the high-temperature, high-dust environment of a metallurgical plant, optical sensors often become malfunctioning within a short period due to contamination.

[0033] In summary, existing technologies for detecting particulate matter concentration in industrial and mining environments have significant shortcomings in terms of environmental noise interference, detection algorithms, and environmental resistance. There is an urgent need for a new detection method to address these issues, improve the accuracy and reliability of detection, and meet the needs of industrial and mining enterprises for safe production and environmental protection.

[0034] In view of this, this application provides a method for detecting spatial particulate matter concentration in industrial and mining environments. It employs a charge acquisition probe based on the principle of electrostatic induction. Compared to traditional optical sensors, its core detection component does not rely on light transmission and scattering, thus avoiding the accuracy degradation caused by the adhesion of pollutants such as dust and oil, and reducing maintenance frequency and cost. Secondly, by amplifying and enhancing weak signals, and combining this with a differential signal processing mechanism to specifically eliminate environmental noise, the method solves the problem of detection deviation caused by electromagnetic interference, mechanical vibration, and other noises in traditional technologies. Finally, by introducing a particulate matter characteristic model and establishing a correlation between particle size and charge, the method overcomes the deficiency of traditional algorithms in distinguishing the influence of different particle sizes, resulting in more accurate concentration calculations. In summary, the technical solution provided in this embodiment can further improve the accuracy and reliability of concentration detection in industrial and mining environments.

[0035] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0036] Figure 1 This is an architectural diagram of a spatial particulate matter concentration detection system in an industrial or mining environment, provided as an embodiment of this application. Figure 1 As shown, the spatial particulate matter concentration detection system in this industrial and mining environment includes: a charge acquisition probe 101 and a spatial particulate matter concentration detection device 102.

[0037] The charge acquisition probe 101 is used to generate an induced current based on the electrostatic charge characteristics of suspended particulate matter. The space particulate matter concentration detection device 102 is used to amplify and process the charge signal and input the processed charge signal into the particulate matter characteristic model to calculate the space particulate matter concentration.

[0038] For example, the charge acquisition probe 101 includes a sensing electrode, a shielding insulation layer, a threaded snap fastener, and a conical airflow guide. The sensing electrode can be a ring-shaped metal electrode, whose symmetrical structure ensures stable sensing of particles moving in different directions. The electrode has an inner diameter of 10mm, an outer diameter of 15mm, and an axial length of 20mm. This size design balances the sensing area and airflow resistance (resistance coefficient ≤0.3 in industrial environments with wind speeds of 1-5m / s). The sensing electrode is wrapped with a shielding insulation layer to prevent leakage current from forming between the electrode and external metal components. A grounding shield is installed outside the insulation layer, which is directly connected to the system ground potential via a wire to isolate external electromagnetic noise. The threaded snap fastener connects the charge acquisition probe 101 to the spatial particulate matter concentration detection device 102 and is detachable, allowing for easy replacement of the probe when the sensing sensitivity is poor. The conical airflow guide integrated at the front end of the charge acquisition probe 101 guides the airflow vertically through the sensing electrode area, increasing the particulate matter throughput to over 90% and reducing signal fluctuations caused by airflow turbulence.

[0039] Among them, the noise processing adopts a differential signal processing mechanism to eliminate environmental noise, and the particulate matter characteristic model is used to characterize the correlation between the electrostatic induced charge characteristics and concentration of particles of different sizes.

[0040] In some embodiments, the spatial particulate matter concentration detection device 102 includes a multi-stage charge amplification circuit, a signal differential processing module, a temperature compensation module, and a particulate matter characteristic model unit.

[0041] The induced current signal output by the charge acquisition probe 101 can be connected to the input terminal of the multi-stage charge amplification circuit in the space particulate matter concentration detection device 102 through a dedicated circuit.

[0042] The system includes a multi-stage charge amplifier circuit for amplifying the charge signal. A signal differential processing module performs differential processing on the amplified signal to eliminate environmental noise. A temperature compensation module monitors the ambient temperature in real time and compensates for the charge signal based on temperature. A particulate matter characteristic model unit stores models characterizing the relationship between the electrostatic induced charge characteristics and concentration of particles of different sizes.

[0043] It should be understood that the spatial particulate matter concentration detection device in the industrial and mining environment embodiments of this application can be the spatial particulate matter concentration detection device 102 in the aforementioned spatial particulate matter concentration detection system for industrial and mining environments, or it can be a module of the spatial particulate matter concentration detection device 102. This module can be a software module, a hardware module, or a combination of software and hardware modules. For example, the aforementioned charge acquisition probe 101 and spatial particulate matter concentration detection device 102 can be modules within the same electronic device, and the spatial particulate matter concentration detection device in the industrial and mining environment can be this electronic device or a module within it, connected via an internal communication circuit.

[0044] It should be noted that the various embodiments of this application can be referenced or learned from each other. For example, the same or similar steps, method embodiments, system embodiments and device embodiments can be referenced from each other without limitation.

[0045] Figure 2 This is a flowchart illustrating a method for detecting spatial particulate matter concentration in an industrial or mining environment, as provided in an embodiment of this application. Figure 2 As shown, the method includes the following steps: Step 201: Sensing the charge signal of suspended particulate matter using a charge acquisition probe.

[0046] Among them, the charge acquisition probe is used to generate induced current based on the electrostatic induction charge characteristics of suspended particulate matter.

[0047] It should be noted that the charge acquisition probe works based on the principle of electrostatic induction. When suspended particulate matter in the air passes near the probe, due to the electrostatic induction charge characteristics of the suspended particulate matter itself, a weak charge will be induced on the probe surface, forming an induced current, thereby converting the presence of particulate matter into a detectable electrical signal.

[0048] In some implementations, the charge acquisition probe employs a detachable structure, facilitating replacement in case of malfunction or performance degradation, thus adapting to harsh industrial environments such as high-dust mines. For example, in the high-dust environment of underground coal mines, the charge acquisition probe can be replaced via a quick-disassembly structure, avoiding the impact on detection accuracy caused by decreased sensing sensitivity due to long-term use.

[0049] Step 202: Amplify and process the charge signal for noise.

[0050] Among them, the noise processing adopts a differential signal processing mechanism to eliminate environmental noise.

[0051] It should be noted that the charge signals acquired by the charge acquisition probe are usually quite weak and need to be amplified to meet subsequent processing requirements. At the same time, electromagnetic noise and other factors in the industrial environment can interfere with the signal; differential processing can effectively eliminate noise and retain a pure charge signal.

[0052] In some implementations, amplification can be achieved through multi-stage amplifier circuits, while noise processing can be accomplished through a signal differential processing module.

[0053] In one possible implementation, the space particulate matter concentration detection device can also use a temperature compensation module to compensate the charge signal according to the real-time monitored ambient temperature.

[0054] It should be noted that changes in ambient temperature can affect the performance of the charge acquisition probe, thus impacting the accuracy of the detection results. The temperature compensation module can monitor the ambient temperature in real time and compensate for the detected charge signal according to a pre-established temperature-charge correction model. For example, when the ambient temperature rises, the temperature compensation module will automatically adjust the signal amplitude to offset the impact of changes in the sensitivity of the charge acquisition probe caused by the temperature increase, ensuring the accuracy of the detection results in a wide temperature range.

[0055] It should be noted that the order of amplification and noise processing can be adjusted according to the actual circuit design. For example, amplification can be performed first, followed by differential processing, or preliminary noise processing can be performed first, followed by amplification. For instance, strong electromagnetic noise in coal mines can cause a large amount of interference to the charge signal. By amplifying the signal through multiple stages and then performing differential processing, the proportion of noise can be significantly reduced.

[0056] Step 203: Input the processed charge signal into the particulate matter characteristic model to calculate the spatial particulate matter concentration.

[0057] Among them, the particulate matter characteristic model is used to characterize the correlation between the electrostatic induced charge characteristics and concentration of particulate matter of different particle sizes.

[0058] It should be noted that this particulate matter characteristic model can be established based on a large amount of experimental data, reflecting the quantitative relationship between the charge and concentration of particles of different sizes. By inputting the processed charge signal, the corresponding particulate matter concentration can be deduced.

[0059] In some implementations, the particulate matter characteristic model can be modified by incorporating environmental parameters (such as temperature and humidity) to improve calculation accuracy.

[0060] For example, in the high-temperature environment of a metallurgical plant, the processed charge signal combined with temperature parameters can be used to call a model to more accurately calculate the actual concentration of agglomerated particles at high temperatures.

[0061] Based on the above technical solutions, this application employs a charge acquisition probe based on the principle of electrostatic induction. Compared to traditional optical sensors, its core detection component does not rely on light transmission and scattering, thus avoiding the accuracy degradation caused by the adhesion of contaminants such as dust and oil, and reducing maintenance frequency and costs. Secondly, by amplifying and enhancing weak signals, and combining this with a differential signal processing mechanism to specifically eliminate environmental noise, the problem of detection deviation caused by electromagnetic interference, mechanical vibration, and other noises in traditional technologies is solved. Finally, a particulate matter characteristic model is introduced, and by establishing the correlation between particle size and charge, the deficiency of traditional algorithms in distinguishing the influence of different particle sizes is overcome, making concentration calculation more accurate. In summary, the technical solution provided in this embodiment can further improve the accuracy and reliability of concentration detection in industrial and mining environments.

[0062] As one possible embodiment of this application, combined with Figure 2 ,like Figure 3 As shown, step 202 above can be achieved through the following steps.

[0063] Step 301: Input the charge signal into a multi-stage charge amplifier circuit for multi-stage amplification.

[0064] The multi-stage charge amplifier circuit is composed of at least two cascaded amplifier stages.

[0065] In some embodiments, the multi-stage charge amplifier circuit includes three cascaded amplifiers.

[0066] The first-stage amplifier is an operational amplifier with a field-effect transistor (FET) input, used for initial signal amplification.

[0067] For example, this operational amplifier can be a junction field-effect transistor (JFET) input operational amplifier with extremely low input bias current and high input impedance characteristics. This operational amplifier can initially amplify weak charge signals with minimal noise introduction. Because the charge signal is very weak, the amplifier's input bias current can significantly affect the signal, and the operational amplifier's low input bias current characteristic effectively reduces this effect. Furthermore, its high input impedance ensures that the charge signal is not significantly attenuated by the input load.

[0068] For example, the gain of this first-stage amplifier can be set to approximately 10 times, achieved by adjusting the values ​​of the feedback resistor and the input resistor. For instance, by selecting high-precision metal film resistors, with a feedback resistor Rf1 of 100kΩ and an input resistor Rin1 of 10kΩ, the gain can be calculated to be approximately 10 times according to the operational amplifier gain formula.

[0069] The second-stage amplifier is an amplifier with automatic gain control (AGC) function. It is used to detect the amplitude of the input signal through a peak detection circuit and automatically adjust the amplification factor according to the detected amplitude. The peak detection circuit in the second-stage amplifier consists of diodes, capacitors, and resistors.

[0070] For example, the second-stage amplifier can automatically adjust its amplification factor based on the amplitude of the input signal. First, this application can use a peak detection circuit to detect the amplitude of the signal amplified by the previous stage. This peak detection circuit consists of a diode, a capacitor, and a resistor. When the signal passes through the diode, the capacitor is charged to the peak voltage of the signal, and this voltage is fed back to the gain control pin of the second-stage amplifier through the resistor. When the input signal amplitude is large, the second-stage amplifier automatically reduces its gain; when the input signal amplitude is small, it automatically increases its gain. This ensures that the amplifier operates optimally under input signals of varying strengths, while further suppressing noise. The gain range of the second-stage amplifier can be automatically adjusted between 5 and 50 times, dynamically changing according to the actual input signal amplitude.

[0071] The third-stage amplifier is an amplifier with a low-pass filter, used to filter out high-frequency noise and further amplify the signal. The low-pass filter consists of a feedback resistor and a capacitor connected in series.

[0072] For example, the third-stage amplifier can filter out high-frequency noise while amplifying the signal. The third-stage amplifier has low noise characteristics and a good frequency response. In its feedback loop, in addition to the conventional feedback resistor, a capacitor is connected in series to form a low-pass filter together with the feedback resistor. For example, the feedback resistor Rf3 is 100kΩ, and the capacitor Cf3 is 10nF. According to the low-pass filter cutoff frequency formula fc=1 / (2π Rf3 Using Cf3, the cutoff frequency can be calculated to be approximately 160Hz. This effectively filters out high-frequency noise above 160Hz, while the useful particulate charge signal is further amplified, with an amplification factor of about 10. Through the cascading of these three amplifier stages, the charge signal is amplified by 500-5000 times (depending on the dynamic changes in the gain of the second-stage amplifier), while noise is effectively suppressed.

[0073] In this way, the embodiments of this application further optimize the signal amplification and noise suppression effects through the design of a three-stage cascaded amplifier. The first-stage amplifier uses an operational amplifier with a field-effect transistor input, utilizing its low bias current and high impedance characteristics to minimize interference with weak charge signals while initially amplifying the signal, laying a clean signal foundation for subsequent processing. The second-stage amplifier introduces an amplifier with automatic gain control, dynamically adjusting the gain through a peak detection circuit to ensure that the signal maintains an appropriate amplitude at different concentrations, avoiding the problems of signals that are too weak to detect or too strong to saturate. The third-stage amplifier uses an amplifier with a low-pass filter to filter out high-frequency noise while amplifying the signal, further purifying the signal. After the three stages work together, noise is effectively suppressed while signal gain is improved, enabling a clear charge signal to be obtained even in industrial and mining environments with extremely weak signals and complex noise. This provides high-quality input for subsequent differential processing and concentration calculation, significantly improving the adaptability and accuracy of the detection system.

[0074] Step 302: Input the charge signal after multi-stage amplification into the signal differential processing module for differential processing.

[0075] The signal differential processing module consists of a noise simulation circuit and a subtractor. The noise simulation circuit is used to simulate environmental noise, and the subtractor is used to perform differential processing on the signal.

[0076] Differential processing involves comparing the original signal with the analog noise signal to eliminate the noise components shared by both, thereby extracting a pure charge signal.

[0077] In some implementations, noise simulation circuits generate reference signals that are similar to actual noise by analyzing the spectral characteristics of ambient noise.

[0078] For example, in an industrial or mining environment where there is electromagnetic interference of unknown frequency, a noise simulation circuit can remove the interference in the current industrial or mining environment from the original signal by simulating reference noise and processing it with a subtractor.

[0079] Based on the above technical solution, this embodiment employs a multi-stage amplification circuit with at least two cascaded amplifiers. By amplifying the weak charge signal stage by stage, it solves the problem of the original signal being too weak to detect. Simultaneously, parameter optimization of each amplifier stage reduces noise introduction, ensuring that the signal-to-noise ratio is not excessively diluted during signal amplification. Furthermore, this application can use a differential signal processing module to first split the amplified signal, then generate reference noise through a noise simulation circuit, and finally eliminate common-mode noise using a subtractor. This process specifically addresses the interference problem of complex noises (such as electromagnetic noise) in industrial and mining environments. Therefore, this embodiment further improves signal quality through multi-stage signal amplification and differential processing to eliminate noise, providing a more reliable input for subsequent concentration calculations and enhancing detection accuracy and stability.

[0080] As one possible embodiment of this application, combined with Figure 3 ,like Figure 4 As shown, step 302 above can be achieved through the following steps.

[0081] Step 401: Divide the charge signal after multi-stage amplification into a first original signal and a second original signal.

[0082] For example, the signal differential processing module can divide the input charge signal into two original signals, namely the first original signal and the second original signal. The two original signals are consistent with the input charge signal. One of them directly enters one input terminal of the subtractor, and the other enters the noise analog circuit for subsequent processing.

[0083] Step 402: The second original signal is simulated by a noise simulation circuit to obtain the first noise signal.

[0084] The first noise signal is used to simulate environmental noise.

[0085] For example, the noise simulation circuit generates a first noise signal that is similar to the actual noise but does not contain particulate charge signals by analyzing and simulating environmental noise. That is, the second original signal can be divided into particulate charge signal components and noise components, and the noise simulation circuit is used to simulate and reconstruct the noise components from the input original signal.

[0086] In some embodiments, the noise simulation circuit consists of a frequency-selective filter, an inverse attenuation circuit, and a noise calibration unit.

[0087] The second original signal contains particulate charge signals (mainly low-frequency components, typically below 50Hz) and environmental noise (such as electromagnetic interference, which is mostly 50Hz power frequency and harmonics, and mechanical vibration noise, which is mostly high-frequency above 100Hz). By using a frequency-selective filter and setting a high-pass filter mode with a cutoff frequency of 50Hz, the low-frequency particulate charge signals are filtered out, while the high-frequency environmental noise components are retained.

[0088] The filtered noise signal is then subjected to amplitude adjustment via an inverse attenuation circuit to make the amplitude of the analog noise consistent with the noise component in the first original signal. The inverse attenuation circuit consists of a variable resistor network and an operational amplifier. By adjusting the resistor ratio (such as the ratio of the feedback resistor to the input resistor), the amplitude of the noise signal is attenuated to the target range (such as 95%-105% of the noise content in the original signal).

[0089] The noise calibration unit performs spectral compensation on the signal by comparing the spectral characteristics of simulated noise with those of ambient noise (e.g., using Fast Fourier Transform analysis). If the simulated noise has insufficient energy in a certain frequency band (e.g., 200Hz), the frequency band enhancement module (a resonant circuit composed of a tuned inductor and capacitor) in the calibration circuit is used to boost the signal energy in that frequency band, ensuring that the spectral consistency between the simulated noise and the actual ambient noise is above 90%.

[0090] Through the above processing, the particulate charge signal in the second original signal is filtered out, and the remaining noise is adjusted and calibrated to form a first noise signal that is highly consistent with the actual environmental noise, providing an accurate noise reference for the subtractor, thereby eliminating environmental noise to the maximum extent in differential processing.

[0091] Step 403: Perform differential processing on the first original signal and the first noise signal using a subtractor to eliminate the noise component in the first original signal.

[0092] In the subtractor, the first original signal is subtracted from the first noise signal, which can effectively eliminate most of the environmental noise and obtain a relatively pure particulate charge signal. For example, in industrial and mining environments with strong electromagnetic interference, the differential signal processing module can accurately extract the particulate charge signal from a signal with a large amount of electromagnetic noise, providing a reliable data basis for subsequent concentration calculations.

[0093] Based on the above technical solution, this application ensures the consistency of noise components in the two paths by splitting the amplified signal into two original signals. Then, the second original signal is processed by a noise simulation circuit to generate a reference signal containing only environmental noise, achieving accurate noise simulation. Finally, a subtractor is used to subtract the two signals, effectively removing the noise component from the first original signal. Therefore, this solution can extract a pure charge signal from a signal mixed with a large amount of electromagnetic noise, avoiding detection deviations caused by noise in traditional technologies. This allows the system to maintain stable detection accuracy even in complex noise environments, providing reliable data for subsequent concentration calculations, and is particularly suitable for industrial and mining scenarios with dense noise sources, such as coal mines and chemical plants.

[0094] As one possible embodiment of this application, the particulate matter characteristic model is established by: collecting charge data of particulate matter of different sizes in various industrial and mining environments and industrial and mining environment data, and constructing a particulate matter characteristic model based on the collected charge data and industrial and mining environment data.

[0095] Among them, industrial and mining environmental data include at least one of temperature, humidity, air pressure, and electromagnetic field strength.

[0096] It should be noted that, in order to construct an accurate particulate matter characteristic model, in addition to using a standard particulate matter generator to produce particulate matter of different sizes and conducting tests under various working conditions, specific measurement equipment can also be introduced to ensure high data accuracy. For example, a scanning electron microscope (SEM) can be used to measure the particle size and analyze the morphology of particulate matter in actual industrial and mining environments to obtain particulate matter sample data that better reflects the actual situation.

[0097] During model construction, various environmental factors, such as temperature, humidity, air pressure, and electromagnetic field strength, can be collected in different experimental environments. These factors can all affect the electrostatic induced charge of particulate matter. The accuracy of this environmental data can be ensured using environmental monitoring instruments such as temperature and humidity sensors, air pressure sensors, and electromagnetic radiation detectors.

[0098] Furthermore, to improve the model's generalization ability, this application can also employ cross-validation, dividing the experimental data into multiple subsets. Each time, a subset is used as the training set, and the remaining subset as the validation set. Through multiple cross-validations, the model's performance on different data subsets is evaluated, thereby selecting the optimal model parameters and ensuring that the model maintains high accuracy under various operating conditions.

[0099] For example, the particulate matter characteristic model consists of a particle size calculation model and a concentration calculation model. The particle size calculation model satisfies the following formula: ; ; in, For charge quantity, For particulate matter particle size, These represent various indicators in the industrial and mining environmental data. For regression coefficients, This is the error term; The concentration calculation model satisfies the following formula: ; in, For space particulate matter concentration, This is the reunification correction factor. For the first The number of particles in each particle size range For the first Average volume of particles in each particle size range For particulate matter density, The sampling volume is the sample volume. For example, the average volume of particulate matter can be calculated using the formula for the volume of a sphere, where the radius of the sphere can be the average radius of that particle size range. The sampling volume can be calculated from the sampling flow rate and the sampling time.

[0100] For example, this application can preprocess the charge quantity signal. Since outliers or noise interference may exist in actual detection, the charge quantity signal can be smoothed by a median filtering algorithm to avoid the influence of outliers. Median filtering replaces the value of each point in the signal with the median of that point and its neighborhood, removing isolated noise points and retaining the main features of the signal.

[0101] The preprocessed charge signal is converted into a digital signal via an analog-to-digital converter (ADC) and input to the microprocessor. Based on real-time environmental monitoring data, including temperature, humidity, air pressure, and electromagnetic field strength, the microprocessor uses fuzzy logic algorithms to evaluate the similarity between the environmental data and the corresponding environmental conditions of each model, and selects the best-matching particulate matter characteristic model from a pre-stored model library.

[0102] Subsequently, this application can calculate the corresponding particle size based on the selected particulate matter characteristic model. Since the particulate matter characteristic model may yield multiple solutions, constraints based on physical rules and practical experience can be introduced for screening. For example, most dust particles in a coal mine environment have a particle size between 0.1 μm and 100 μm. Solutions with particle sizes that are significantly outside this range are excluded, and the particle size calculation results are optimized by combining the particle motion trajectory and distribution law.

[0103] In actual calculations, multiple continuously acquired charge signals will yield multiple corresponding particle size values. Subsequently, these particle size values ​​are statistically analyzed to divide them into several particle size intervals (such as 0.1-1μm, 1-10μm, 10-100μm, etc.), and the proportion of particle size in each interval to the total number is calculated, thus obtaining the particle size distribution.

[0104] For example, in a coal mine environment, a spatial particulate matter concentration detection device continuously collects 100 charge signals within a preset time period, and calculates 100 particle size values ​​using the above formula. Among them, 30 are in the 0.1-1μm range, 50 are in the 1-10μm range, and 20 are in the 10-100μm range. The proportions of each range are 30%, 50%, and 20%, respectively.

[0105] After obtaining the particle size distribution, the spatial particulate matter concentration can be calculated using a concentration calculation model. In addition to considering the mass of individual particles and the number of particles per unit volume, corrections are made for particle agglomeration. In actual industrial and mining environments, particles may agglomerate, leading to changes in actual particle size and mass. By introducing an agglomeration correction coefficient, and based on factors such as particle concentration, particle size distribution, and environmental conditions, agglomeration can be quantitatively analyzed, resulting in a more accurate calculation of the spatial particulate matter concentration.

[0106] For example, in a coal mine environment, the particle size calculation model shows that the proportion of particles in the 0.1-1μm size range is 30%, 1-10μm accounts for 50%, and 10-100μm accounts for 20%, with a sampling volume of 0.01m³, an agglomeration correction coefficient K=1.2, and a coal dust density of 1300kg / m³. Based on the above data, the average volume of 0.1-1μm particles can be obtained. 1-10μm average volume 10-100μm average volume Substituting these values ​​into the concentration calculation model above, the concentration of particulate matter in the air can be calculated. .

[0107] To improve the stability and reliability of the detection results, this application can also employ a sliding window averaging algorithm to process the concentration data. This sliding window averaging algorithm is used to average the concentration data within a certain time window, smoothing out concentration abrupt changes caused by instantaneous interference or measurement fluctuations, so that the detection results better reflect the actual trend of particulate matter concentration changes.

[0108] The entire detection process is efficiently processed by a built-in microprocessor, ensuring that the detection results can be accurately output in a short time, meeting the needs for rapid and accurate detection of particulate matter concentration in industrial and mining environments.

[0109] The foregoing mainly describes the solutions of the embodiments of this application from the perspective of device implementation. It is understood that each device, such as a space particulate matter concentration detection device, includes at least one of the hardware structures and software modules corresponding to each function in order to achieve the above-mentioned functions. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is implemented in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0110] This application embodiment can divide the space particulate matter concentration detection device into functional units according to the above method example. For example, each function can be divided into separate functional units, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0111] When using integrated units, Figure 5 A possible structural schematic diagram of the space particulate matter concentration detection device (referred to as space particulate matter concentration detection device 50) involved in the above embodiments is shown. The space particulate matter concentration detection device 50 includes a processing unit 501 and a communication unit 502, and may also include a storage unit 503. Figure 5 The schematic diagram shown can be used to illustrate the structure of the space particulate matter concentration detection device involved in the above embodiments.

[0112] when Figure 5 The schematic diagram shown illustrates the structure of the space particulate matter concentration detection device involved in the above embodiments. The processing unit 501 is used to control and manage the operation of the space particulate matter concentration detection device, the communication unit 502 is used for the space particulate matter concentration detection device to communicate with other devices, and the storage unit 503 is used to store the program code and data of the space particulate matter concentration detection device.

[0113] For example, the communication unit 502 is used to sense the charge signal of suspended particulate matter through a charge acquisition probe; the charge acquisition probe is used to generate an induced current based on the electrostatic induction charge characteristics of suspended particulate matter.

[0114] The processing unit 501 is used to amplify the charge signal and process noise, wherein the noise processing adopts a differential signal processing mechanism to eliminate environmental noise.

[0115] The processing unit 501 is used to input the processed charge signal into the particulate matter characteristic model to calculate the spatial particulate matter concentration; the particulate matter characteristic model is used to characterize the correlation between the electrostatic induced charge characteristics and concentration of particles of different sizes.

[0116] In one possible implementation, the processing unit 501 is used to input the charge signal into a multi-stage charge amplifier circuit for multi-stage amplification processing; the multi-stage charge amplifier circuit is composed of at least two cascaded amplifier stages.

[0117] In one possible implementation, the multi-stage charge amplifier circuit includes three cascaded amplifiers: the first stage amplifier is an operational amplifier with a field-effect transistor input, used for initial signal amplification; the second stage amplifier is an amplifier with automatic gain control, used to detect the amplitude of the input signal through a peak detection circuit and automatically adjust the amplification factor according to the detected amplitude; the peak detection circuit in the second stage amplifier consists of diodes, capacitors, and resistors; the third stage amplifier is an amplifier with a low-pass filter, used to filter out high-frequency noise and further amplify the signal; the low-pass filter consists of a feedback resistor and a capacitor in series.

[0118] In one possible implementation, the processing unit 501 is used to input the charge signal after multi-stage amplification into the signal differential processing module for differential processing; the signal differential processing module consists of a noise simulation circuit and a subtractor; the noise simulation circuit is used to simulate environmental noise; the subtractor is used to perform differential processing on the signal.

[0119] In one possible implementation, the processing unit 501 is used to: divide the charge signal after multi-stage amplification into a first original signal and a second original signal; simulate the second original signal through a noise simulation circuit to obtain a first noise signal; use the first noise signal to simulate environmental noise; and perform differential processing on the first original signal and the first noise signal through a subtractor to eliminate the noise component in the first original signal.

[0120] In one possible implementation, the processing unit 501 is used to: compensate the charge signal according to the real-time monitored ambient temperature via a temperature compensation module.

[0121] In one possible implementation, the particulate matter characteristic model is established by collecting charge data of particulate matter of different sizes in various industrial and mining environments, as well as industrial and mining environment data, and constructing a particulate matter characteristic model based on the collected charge data and industrial and mining environment data.

[0122] In one possible implementation, the particulate matter characteristic model consists of a particle size calculation model and a concentration calculation model, with the particle size calculation model satisfying the following formula: ; in, For charge quantity, For particulate matter particle size, These represent various indicators in the industrial and mining environmental data. For regression coefficients, This is the error term; The concentration calculation model satisfies the following formula: ; in, For space particulate matter concentration, This is the reunification correction factor. For the first The number of particles in each particle size range For the first Average volume of particles in each particle size range For particulate matter density, The sampling volume.

[0123] The processing unit 501 can be a processor or a controller, and the communication unit 502 can be a communication interface, transceiver, transceiver circuit, transceiver device, etc. The term "communication interface" is a general term and may include one or more interfaces. The storage unit 503 can be a memory. When the spatial particulate matter concentration detection device 50 is a chip, the processing unit 501 can be a processor or a controller, and the communication unit 502 can be an input interface and / or an output interface, pins, or circuits, etc. The storage unit 503 can be a storage unit within the chip (e.g., a register, cache, etc.) or a storage unit located outside the chip (e.g., read-only memory (ROM), random access memory (RAM, etc.)).

[0124] The communication unit can also be called a transceiver unit. The antenna and control circuit with transceiver functions in the space particulate matter concentration detection device 50 can be considered as the communication unit 502 of the space particulate matter concentration detection device 50, and the processor with processing functions can be considered as the processing unit 501 of the space particulate matter concentration detection device 50. Optionally, the device in the communication unit 502 used to implement the receiving function can be considered as a communication unit, which is used to execute the receiving steps in the embodiments of this application. The communication unit can be a receiver, a receiver circuit, etc. The device in the communication unit 502 used to implement the transmitting function can be considered as a transmitting unit, which is used to execute the transmitting steps in the embodiments of this application. The transmitting unit can be a transmitter, a transmitter, a transmitting circuit, etc.

[0125] Figure 5 If the integrated units in the process are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, in essence, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. Storage media for storing computer software products include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0126] Figure 5 The units in the process can also be called modules; for example, a processing unit can be called a processing module.

[0127] This application embodiment also provides a hardware structure diagram of a space particulate matter concentration detection device (denoted as space particulate matter concentration detection device 60), see [link to diagram]. Figure 6 The spatial particulate matter concentration detection device 60 includes a processor 601, and optionally, a memory 602 connected to the processor 601.

[0128] In the first possible implementation, see Figure 6The space particulate matter concentration detection device 60 also includes a transceiver 603. The processor 601, memory 602, and transceiver 603 are connected via a bus. The transceiver 603 is used to communicate with other devices or communication networks. Optionally, the transceiver 603 may include a transmitter and a receiver. The device in the transceiver 603 that implements the receiving function can be considered as a receiver, which is used to perform the receiving steps in the embodiments of this application. The device in the transceiver 603 that implements the transmitting function can be considered as a transmitter, which is used to perform the transmitting steps in the embodiments of this application.

[0129] Based on the first possible implementation method Figure 6 The schematic diagram shown can be used to illustrate the structure of the space particulate matter concentration detection device involved in the above embodiments.

[0130] in, Figure 6 The diagram can also illustrate the system chip in the space particulate matter concentration detection device. In this case, the actions performed by the aforementioned space particulate matter concentration detection device can be implemented by this system chip; the specific actions performed can be found above and will not be repeated here.

[0131] In implementation, each step of the method provided in this embodiment can be completed by integrated logic circuits in the processor or by instructions in software form. The steps of the method disclosed in the embodiments of this application can be directly manifested as being executed by a hardware processor, or being executed by a combination of hardware and software modules in the processor.

[0132] The processor in this application may include, but is not limited to, at least one of the following: a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a microcontroller unit (MCU), or an artificial intelligence processor, etc., which are various computing devices that run software. Each computing device may include one or more cores for executing software instructions to perform calculations or processing. The processor may be a separate semiconductor chip or integrated with other circuits into a single semiconductor chip. For example, it may be integrated with other circuits (such as encoding / decoding circuits, hardware acceleration circuits, or various bus and interface circuits) to form a SoC (System-on-a-Chip), or it may be integrated as a built-in processor within an ASIC. The ASIC with the integrated processor may be packaged separately or together with other circuits. In addition to the cores for executing software instructions to perform calculations or processing, the processor may further include necessary hardware accelerators, such as field-programmable gate arrays (FPGAs), PLDs (programmable logic devices), or logic circuits that implement dedicated logic operations.

[0133] The memory in the embodiments of this application may include at least one of the following types: read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions; random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions; or electrically erasable programmable-only memory (EEPROM). In some scenarios, the memory may also be a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0134] This application also provides a computer-readable storage medium including instructions that, when run on a computer, cause the computer to perform any of the methods described above.

[0135] This application also provides a computer program product containing instructions that, when run on a computer, cause the computer to perform any of the methods described above.

[0136] This application also provides a chip including a processor and an interface circuit. The interface circuit is coupled to the processor. The processor is used to run computer programs or instructions to implement the above-described method. The interface circuit is used to communicate with other modules outside the chip.

[0137] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software programs, implementation can be, in whole or in part, in the form of a computer program product. This computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device containing one or more servers, data centers, etc., that can be integrated with the medium. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state disks (SSDs)).

[0138] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, disclosure, and appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0139] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of this application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from the spirit and scope of this application. Thus, if such modifications and modifications of this application fall within the scope of the claims of this application and their equivalents, this application is also intended to include such modifications and modifications.

Claims

1. A method for detecting spatial particulate matter concentration in industrial and mining environments, characterized in that, include: The charge signal of suspended particulate matter is sensed by a charge acquisition probe; The charge acquisition probe is used to generate an induced current based on the electrostatic charge characteristics of the suspended particulate matter. The charge signal is amplified and noise-processed, wherein the noise processing employs a differential signal processing mechanism to eliminate environmental noise; The processed charge signal is input into the particulate matter characteristic model to calculate the spatial particulate matter concentration; the particulate matter characteristic model is used to characterize the correlation between the electrostatic induced charge characteristics and concentration of particles of different sizes.

2. The method according to claim 1, characterized in that, The amplification process is achieved in the following way: The charge signal is input into a multi-stage charge amplifier circuit for multi-stage amplification; the multi-stage charge amplifier circuit is composed of at least two cascaded amplifier stages.

3. The method according to claim 2, characterized in that, The multi-stage charge amplifier circuit includes three cascaded amplifiers: The first-stage amplifier is an operational amplifier with field-effect transistor input, used for initial signal amplification; The second-stage amplifier is an amplifier with automatic gain control, used to detect the amplitude of the input signal through a peak detection circuit and automatically adjust the amplification factor according to the detected amplitude; the peak detection circuit in the second-stage amplifier consists of diodes, capacitors, and resistors; The third-stage amplifier is an amplifier with a low-pass filter function, used to filter out high-frequency noise and further amplify the signal; the low-pass filter consists of a feedback resistor and a capacitor connected in series.

4. The method according to claim 2, characterized in that, The noise processing is achieved through the following methods: The charge signal, after multi-stage amplification, is input into a signal differential processing module for differential processing. The signal differential processing module consists of a noise simulation circuit and a subtractor. The noise simulation circuit is used to simulate environmental noise, and the subtractor is used to perform differential processing on the signal.

5. The method according to claim 4, characterized in that, The step of inputting the multi-stage amplified charge signal into the differential signal processing module for differential processing includes: The charge signal after multi-stage amplification is divided into a first original signal and a second original signal. The second original signal is simulated by the noise simulation circuit to obtain a first noise signal; the first noise signal is used to simulate environmental noise. The subtractor performs differential processing on the first original signal and the first noise signal to eliminate the noise component in the first original signal.

6. The method according to claim 1, characterized in that, The method further includes: The charge signal is compensated by a temperature compensation module according to the real-time monitored ambient temperature.

7. The method according to claim 1, characterized in that, The particulate matter characteristic model is established by collecting charge data of particulate matter of different sizes under various industrial and mining environments, as well as industrial and mining environment data, and constructing the particulate matter characteristic model based on the collected charge data and industrial and mining environment data.

8. The method according to claim 1, characterized in that, The particulate matter characteristic model consists of a particle size calculation model and a concentration calculation model. The particle size calculation model satisfies the following formula: ； in, For charge quantity, For particulate matter particle size, These represent various indicators in the industrial and mining environmental data. For regression coefficients, This is the error term; The concentration calculation model satisfies the following formula: ； in, For space particulate matter concentration, This is the reunification correction factor. For the first The number of particles in each particle size range For the first Average volume of particles in each particle size range For particulate matter density, The sampling volume.

9. A spatial particulate matter concentration detection device for industrial and mining environments, characterized in that, The device includes: a communication unit and a processing unit; The communication unit is used to sense the charge signal of suspended particulate matter through a charge acquisition probe; the charge acquisition probe is used to generate an induced current based on the electrostatic induction charge characteristics of the suspended particulate matter. The processing unit is used to amplify and process the charge signal, wherein the noise processing employs a differential signal processing mechanism to eliminate environmental noise. The processing unit is used to input the processed charge signal into the particulate matter characteristic model to calculate the spatial particulate matter concentration; the particulate matter characteristic model is used to characterize the correlation between the electrostatic induced charge characteristics and concentration of particles of different sizes.

10. A spatial particulate matter concentration detection device for industrial and mining environments, characterized in that, include: A processor and a communication interface; the communication interface is coupled to the processor, the processor being used to run computer programs or instructions to implement the spatial particulate matter concentration detection method in an industrial and mining environment as described in any one of claims 1-8.

Images