1018results about "Particle size analysis" patented technology

The invention relates to the technical field of copper rod detection, in particular to a copper rod grain size detection method and system based on double-frequency eddy current analysis, and the method comprises the steps: synchronously obtaining a high-frequency eddy current response signal and a low-frequency eddy current response signal of the surface of a moving copper rod according to an orthogonally arranged double-frequency eddy current probe, and collecting the moving speed of the copper rod in real time; performing phase demodulation on the high-frequency eddy current response signal and the low-frequency eddy current response signal to respectively obtain a first phase angle and a second phase angle; calculating an instantaneous difference value between the first phase angle and the second phase angle, and generating an original phase difference characteristic quantity; performing dynamic compensation on the original phase difference characteristic quantity based on the movement speed of the copper rod to generate a speed compensation phase difference characteristic quantity; and inputting the speed compensation phase difference characteristic quantity into a pre-calibrated grain size mapping relation model to obtain a real-time grain size grade value. According to the invention, eddy current response interference caused by speed change in the continuous movement process of the copper rod is effectively eliminated, and high-precision detection of the grain size grade is realized.

The invention relates to the related technical field of dust detection, and provides a mining induced charge and light scattering fusion dust detection device and method.The dust detection device comprises a workbench, a sampling assembly and a detection assembly, and the sampling assembly comprises a sampling air pump, a conical airflow homogenizer and a closed sampling cavity; the detection assembly comprises an annular sliding rail, a light scattering detection module and an electrostatic induction detection module; the dust detection method comprises the following steps: S1, device initialization and zero calibration; s2, sampling preparation of dust-containing airflow; s3, dual-module synchronous signal acquisition; s4, data processing and temperature and humidity compensation; s5, carrying out anti-interference protection assembly; and S6, data fusion and result output. S7, device cleaning and standby; through cooperative work of the double detection modules, dust detection signals in different concentration intervals are accurately captured, wide-range coverage from low concentration to high concentration is achieved, and synchronous monitoring of dust concentration and particle size characteristics is achieved.

The invention discloses a wind power generation equipment gearbox oil state detection method and system, and the method comprises the steps: obtaining an oil sample, carrying out the quantitative analysis of solid, liquid and gas pollutants through an oil pollution degree analysis model, and processing the parameters such as kinematic viscosity through an oil physicochemical property analysis model; and after the tensors of the two results are fused, feature decoupling and deep feature mining are performed, and finally, the oil state is judged by a state evaluation decision model. The system is correspondingly provided with six units such as a sample acquisition unit and a pollution analysis unit to realize the functions. According to the scheme, the defects that traditional detection is single and correlation analysis is lacked are overcome, a certain index is not independently detected any more, pollution and physicochemical property information is comprehensively integrated, the relation between the pollution and the physicochemical property information is deeply analyzed through tensor fusion, feature decoupling and other technical means, accurate evaluation of the oil state of the gearbox is achieved, and the detection accuracy is improved. And misjudgment caused by one-sided detection or no consideration of mutual relations is effectively avoided.

Methods and systems for water quality assessment are disclosed. The method includes obtaining a first input data indicative of properties of a first liquid sample, the first input data including turbidity data and total suspended solids data for the first liquid sample, where the first liquid sample is acquired from a liquid source. The method further includes determining, using a computer processor and a machine learning model, a first predicted particle-size distribution of the first liquid sample based on the first input data, where particle-size distribution is controlled, at least in part, by a set of dosage parameters configurable by a water quality system. The method further includes determining, with an optimizer applied to the machine learning model, an optimal set of dosage parameters based on the first predicted particle-size distribution and adjusting the set of dosage parameters of the water quality system to the optimal set of dosage parameters.

The invention provides a sensor fusion-based pig feed crushing particle size monitoring method, which comprises the following steps of: synchronously acquiring vibration, acoustic emission and optical signals, carrying out band-pass filtering, normalization, feature extraction and wavelet packet decomposition, and dynamically adjusting the confidence coefficient of each channel in combination with working condition perception and a sensor performance knowledge base so as to monitor the crushing particle size of the pig feed. Weighted fusion and conflict evidence modulation of multi-source signals are achieved, the improved Dempster-Shafer evidence theory is introduced to improve the abnormal granularity recognition accuracy, key parameters of crushing equipment are controlled in a linkage mode through a closed-loop feedback mechanism, high-robustness detection and self-adaptive adjustment of the granularity state are achieved, and the method has the advantages of being high in robustness and high in robustness. And the stability and the automation level of the feed crushing process are improved.

A method and apparatus used to determine atmospheric extinction coefficients in conjunction with an unmanned aerial vehicle are provided. The unmanned aerial vehicle may include a central body, at least one motor operatively connected to the central body, a condensation particle detection payload, and a data acquisition board including memory operatively connected to the data acquisition board, and a processor configured to generate extinction information.

The invention discloses a clean workshop dynamic rating management system based on multi-modal sensing data. The system comprises a data acquisition module which is used for deploying a modal sensor to acquire environment data and personnel behavior original multi-modal data; the risk analysis module is used for preprocessing the original multi-modal data, extracting basic modal features, introducing a cross-modal early fusion algorithm based on the basic modal features to realize cross-modal feature association mining, and obtaining a risk event feature set; the cleanliness quantification module is used for constructing a particle reference model, predicting theoretical particle concentration by training the particle reference model based on the risk event feature set, calculating a ratio of actual to theoretical, and obtaining a dynamic interference coefficient for quantifying the influence of personnel equipment behaviors on cleanliness; and the rating management module is used for generating a pollution thermodynamic diagram in the digital twin body based on the dynamic interference coefficient, and triggering standard exceeding early warning to realize dynamic rating of the clean workshop.

The invention provides an acoustic emission ore pulp granularity online detection system and method based on a Hertz collision mode, and belongs to the technical field of ore dressing process monitoring and control. The system comprises an ore pulp bypass circulation module, a particle size calculation module and a particle size measurement control module. A built-in acoustic emission sensor is used for collecting acoustic emission signals generated by Hertz elastic collision between signal ore pulp particles and a probe; multi-band energy features and time domain features are extracted in combination with wavelet packet decomposition, and a fusion feature vector is constructed by fusing ore pulp flow velocity and density information; the fusion feature vector is input into a BP neural network model after dimension reduction processing, and the proportion of particles with the particle size smaller than a set threshold value in the ore pulp is predicted online. Full-process management of sampling, preparing, measuring and liquid discharging is achieved through automatic control, and the system has the advantages of being high in measuring precision and automation degree, low in cost, high in anti-interference capacity and the like, is suitable for continuous online monitoring of the ore pulp granularity on the ore dressing site and provides data support for flotation process optimization.

The invention discloses a method, equipment and medium for measuring the central particle size and the particle concentration of aerosol particles, which are applied to an aerosol device adopting a charge diffusion method, and the central particle size and the particle concentration of the particles in gas to be measured are shown in the specification. Wherein S1 is the integral of an induced current I detected during ion collection in a positive pulse high-voltage period during particle size separation, and S2 is the integral of the induced current I detected during ion collection in a positive pulse low-voltage period during particle size separation; m is a polynomial fitting order, a fitting polynomial coefficient, a particle concentration calculation proportionality coefficient and a central particle size influence coefficient. Equipment adopting the method is small in size, low in manufacturing cost and high in measurement accuracy, so that the method can be popularized and used for scene requirements of vehicle or air detection and the like.

The invention relates to the technical field of graphene processing, and particularly discloses a nano graphene powder preparation method which comprises the following steps: weighing a graphite raw material; opening a discharge valve of the feeding cabin, and feeding the graphite raw material into the pressure stabilizing cabin; a graphite raw material is sucked into a first-stage jet mill high-pressure cabin from a pressure stabilizing cabin, first-stage jet milling is conducted, and a first-stage abrasive material is obtained; the first-stage grinding material with the thickness not larger than 10 micrometers is conveyed to a second-stage jet mill high-pressure cabin; processing the first-stage abrasive material by a second-stage jet mill high-pressure cabin to obtain a second-stage abrasive material; the second-stage grinding material with the thickness not larger than 100 nanometers is conveyed to a flow energy depolymerization cabin; the flow energy depolymerization cabin is used for processing the secondary grinding material, and nano graphene powder is obtained through screening; the high-purity graphite raw material is processed through a three-stage gradient processing framework, a negative feedback regulation scheme is introduced in each processing link, the quality of products in different stages is controlled, a progressive processing technology is constructed, and the product rate is greatly increased.

The invention relates to the technical field of quartz sand purification, in particular to a quartz sand impurity removal method based on multi-source data fusion, which comprises the following steps: carrying out on-line detection on preprocessed quartz sand particle flow to obtain data representing granularity and internal bubble abundance; the particle flow is intelligently sorted based on the data; different groups of materials are guided into an independent temperature control area of a rotary kiln, and a differential calcining process is applied; online detection and effect evaluation are carried out after calcination, and a differentiated acid leaching process is carried out on qualified and unqualified material groups according to the online detection and effect evaluation; and finally, comprehensive quality detection is carried out, and a circular treatment process is intelligently started according to reasons for substandard standards. By means of multi-source data fusion, dynamic regulation and control over the raw materials and core procedures are achieved, the purification efficiency and the product purity are remarkably improved, the adaptability of the technology to fluctuation of the raw materials is enhanced, meanwhile, acid liquor consumption and energy consumption are effectively reduced, and refined and intelligent green production is achieved.

The invention relates to the technical field of abnormity early warning, in particular to a putty powder granularity abnormity early warning system fusing multi-source data, which comprises a particle size form extraction module, a form offset identification module, a data weight calibration module, a multi-dimensional risk scoring module and a trend driving grading module. According to the method, the particle size sequences of multiple stages of discharging, mixing, stirring and packaging are combined, the continuous fluctuation characteristic track is constructed based on the time sequence, and the particle size statistical index differences among the procedures are subjected to quantitative comparison, so that the stage identification of the particle size abnormal change trend is realized; meanwhile, performing difference measurement and calculation and correction factor adjustment on a particle size mean value offset section, performing risk quantification by matching particle boundary morphological parameters and particle size variation coefficients, enhancing the multi-dimensional stability of anomaly recognition, and performing driving classification on risk levels through the consistency of a particle size variation trend and a scoring result on the basis of the multi-dimensional stability. And the particle risk identification precision and the timeliness of the early warning result are improved.

The invention discloses a bridge underwater safety detection method based on opto-acoustic dual modes, and relates to the technical field of underwater engineering detection, and the method comprises the following steps: obtaining water area particle size distribution and concentration data by using a multi-frequency acoustic suspended sediment concentration profiler, establishing an environmental parameter database, configuring a shipborne detection device according to the initial environmental parameter database, and detecting the underwater safety of a bridge according to the shipborne detection device. The method comprises the following steps: setting a three-axis stabilized platform carrying a polarization imaging camera and a multispectral LED array, deploying a multi-beam detection module and ADCP equipment, and establishing a unified coordinate system; multi-angle original intensity images are collected through a polarized optical imaging module, and multi-exposure fusion is carried out after processing is carried out through a mutual information minimization algorithm. An optical image and an acoustic point cloud are deeply fused by adopting a graph neural network, the detection precision is improved through dynamic weight optimization of an attention mechanism and a space-time registration algorithm, a multi-index fusion model is constructed based on fuzzy logic and a D-S evidence theory for adaptive early warning decision, upgrading from single-parameter alarm to multi-dimensional decision is realized, and the false alarm rate is reduced.

The invention discloses a material granularity monitoring method, device and equipment and a storage medium, and relates to the technical field of granularity monitoring, and the method comprises the following steps: obtaining material flow data and an original material vibration signal, and filtering the original material vibration signal to obtain a pure material vibration signal; extracting multi-dimensional vibration characteristic signals of the pure material vibration signal in a time domain dimension, a frequency domain dimension and a nonlinear dimension; inputting the multi-dimensional vibration characteristic signal and the pure material vibration signal into a granularity inversion system for deduction to obtain an initial material granularity proportion; and calculating based on the initial material granularity proportion, the pure material vibration signal and the material flow data to obtain a compensation coefficient, and calibrating the initial material granularity proportion based on the compensation coefficient to determine the target material granularity proportion. The system can adapt to severe industrial environments with high dust and the like, hardware cost and maintenance difficulty are reduced, the whole process from signal processing to granularity calibration is automatic, the response speed is high, and the precision is high.

The utility model discloses automatic fine aggregate screening equipment which comprises a screening assembly, a driving assembly, a weighing assembly and a controller, the screening assembly comprises a screen containing groove, a screen cover is arranged above the screen containing groove, a collecting funnel is connected below the screen containing groove, a collecting disc is arranged at the position, below the collecting funnel, of the screen containing groove, and a test screen can be placed in the screen containing groove; the driving assembly comprises a motor, an eccentric wheel is installed on an output shaft of the motor, an eccentric roller is arranged at the bottom of the screen containing groove, the side face of the screen containing groove is hinged to one end of a push rod, a spring is arranged on a rod body of the push rod and connected to a fixing piece, and the other end of the push rod penetrates and extends to the outer side of the fixing piece and is provided with a driven roller in rolling contact with the eccentric wheel; the weighing assembly comprises an electronic balance, a supporting frame is arranged on the electronic balance, and a collecting disc is placed on the supporting frame; the controller is connected with the motor to control start-stop and rotation speed of the motor. The fine aggregate screening device can replace a manual hand screen, and meanwhile dust diffusion during fine aggregate screening is avoided.

The invention discloses a method for quantitatively testing the content of organic carbon and disseminated particle size in gold ore, and belongs to the technical field of gold ore detection. The method comprises the steps of sample preparation and quantitative testing, wherein palm wax serves as a cementing agent, barite serves as a standard sample, and a to-be-tested sample is prepared through melting, mixing, consolidation, polishing and carbon spraying; the quantitative test is based on an automatic process mineralogy test system, palm wax backgrounds are removed through backgrounds deduction, an energy spectrum library is established to recognize organic carbon and barite particles, the area ratio of the organic carbon and the barite particles is measured, the mass ratio is converted by combining the density ratio, the organic carbon content of raw ore is inversely calculated, and meanwhile, the particle size distribution of the organic carbon is counted. The method solves the problem that the content and granularity of organic carbon cannot be accurately quantified at the same time in the prior art, has the advantages of high test precision, high efficiency, comprehensive parameters and simplicity and convenience in operation, and can provide key data for optimization of a gold ore cyanidation leaching process.

The invention discloses a sulfur-containing magnetite beneficiation method which specifically comprises the following steps: S1, raw material preparation: crushing sulfur-containing magnetite to obtain small-particle sulfur-containing magnetite; s2, physicochemical property determination: detecting the particle size of the small-particle sulfur-containing magnetite, the content of magnetite in the small-particle sulfur-containing magnetite and the content of sulfur minerals in the small-particle sulfur-containing magnetite; s3, magnetic separation: according to the physicochemical properties of the small-particle sulfur-containing magnetite detected in the step S2, automatically controlling the magnetic field intensity of a magnetic separation process through a controller, and carrying out magnetic separation to obtain magnetite and sulfur-containing ore; the magnetic field intensity of the magnetic separation process is dynamically adjusted according to the iron ore particle size of the sulfur-containing magnetite, the magnetite content in the sulfur-containing magnetite and the sulfur mineral content, the trends of iron minerals and sulfur minerals are accurately controlled, optimal adjustment of the element trends is achieved, and the recovery rate of iron and sulfur is increased.

The utility model relates to the technical field of cement negative pressure screen analysis instruments, and discloses a full-automatic cement negative pressure screen analysis instrument which comprises a machine body, a fixing plate is fixedly connected to the top of the inner wall of the machine body, a first stepping motor is fixedly connected to the center of the bottom of the fixing plate, and the output end of the first stepping motor is fixedly connected with a lead screw. A lifting rod is in threaded connection with the outer ring of the lead screw, a lifting frame is fixedly connected to the top of the lifting rod, a screening tool is arranged on one side of the inner ring of the lifting frame, a screen is arranged at the bottom of the inner wall of the screening tool, and a linear bearing penetrates through and is fixedly connected to the center of the bottom of the fixing plate. The left side of the bottom of the fixing plate is fixedly connected with a second stepping motor. According to the vibrating screen, the bearing frame is driven by the first stepping motor, the lifting frame, the screen tool, the screen cover, the vibrator and the second stepping motor to rotate, materials are weighed, the first stepping motor is started to work, the lead screw is driven to rotate, and the screen tool is driven to move downwards.

The invention relates to the technical field of thermoelectric pollutant detection, and discloses a thermoelectric power generation emission pollutant detection system and method based on big data. According to the method, a plurality of emission parameters such as flue gas component concentration, particle size distribution, emission flow rate, temperature and pressure and the like of a chimney emission port are collected in real time through a distributed sensor network; the multi-dimensional parameters are transmitted to a big data processing platform, parameter relevance is analyzed through a pollution feature knowledge base, and a potential target pollutant combination mode is identified; calling a preset rule base according to the mode, and dividing emission parameters into a macroscopic emission characteristic region and a microscopic component characteristic region based on a dynamic region segmentation algorithm; respectively processing the parameters of the two regions through a two-stage pollutant detection model, and generating a pollutant standard exceeding early warning result and a component traceability analysis report. According to the method, comprehensive and accurate detection of pollutants discharged by thermoelectric power generation is realized, and the monitoring requirement of a complex emission environment is met.

The invention relates to the technical field of space environment detection, in particular to a photoelectric sensor for space dust particle size detection, which comprises an optical signal collection assembly and an electric signal conversion and acquisition assembly, and is characterized in that the optical signal collection assembly comprises a support, a front pressing plate, an optical filter and a sensor; the electric signal conversion and acquisition assembly comprises a circuit box body, a photoelectric conversion detector, a signal processing circuit board, a signal wire passing sleeve and a circuit box cover. The support of the particle size sensor is used as a main structure, and other structures are subjected to split design and integrated precision assembly, so that the processing difficulty of a complex structure is reduced, the machining precision of a single structure is effectively improved, and the form and position processing error of each structural part is effectively controlled; the non-imaging compound paraboloid condensation sensor is independently designed and processed, so that the processing precision of a product is improved; and vibration reduction design is independently carried out on internal structural components, so that the mechanical impact resistance of weak parts is improved.

The invention relates to the technical field of energy materials and fluid engineering, and provides a micro-nano particle morphology detection and slurrying performance prediction method, device and equipment, and the method comprises the steps: preprocessing a slurry fuel sample, collecting the micro-morphology image and particle size distribution data of particles in the slurry fuel sample, and forming an original data set; based on a regional convolutional neural network algorithm, carrying out segmentation and feature extraction on the micromorphological image in the original data set to identify particle morphological parameters in the micromorphological image; establishing a dynamic database containing particle morphology parameters and particle size distribution data; based on the data of the dynamic database, a grey correlation method is adopted to quantify the correlation degree between the particle morphology parameters and the corresponding pulping performance indexes, and linear fitting is carried out; and constructing a pulping performance prediction model to obtain a pulping performance prediction value. The method can be directly applied to quality control and process optimization in slurry fuel production, and assists in improving the production efficiency and the product quality consistency.

The invention discloses an oil abrasive particle detection device based on pseudo-random signal excitation and an inversion method, a pseudo-random excitation module outputs pseudo-random alternating current excitation formed by combining a plurality of waveforms, and the pseudo-random waveforms are not less than three frequency waveforms; the sensing module is a three-coil balance type, double-coil mutual inductance type or single-coil self-inductance type inductance sensing unit, coils in the sensing module construct a detection area with an alternating magnetic field under the excitation of alternating current, and the alternating magnetic field has pseudo-random characteristics; the signal acquisition and fitting module detects an induced voltage signal of the coil, and signal pulses generated by the metal abrasive particles under different excitation waveforms are obtained through fitting according to pseudo-random alternating current excitation characteristics; and the abrasive particle feature inversion module performs inversion according to the abrasive particle pulse signals generated by fitting under each excitation, and calculates the equivalent radius, the conductivity and the magnetic conductivity corresponding to the abrasive particles, so as to determine the material and the size of the abrasive particles. According to the invention, the material, size and concentration information of the abrasive particles can be accurately obtained.

The invention discloses a method for monitoring tail gas emission of an aircraft refueling vehicle, and solves the problem that PM quantitative data acquisition is inaccurate in a complex environment of an airport. The method comprises the following steps: stably sampling and removing aviation kerosene steam through a sampling probe integrating sensing and flow guide adjustment; synchronously detecting and deducting background gas interference; performing secondary dehumidification and constant-temperature pretreatment on the tail gas; the double detection units are linked to collect multi-dimensional data, and a nonlinear interference correction model is constructed through fusion; and adopting an exclusive calibration mode and marking invalid data. The method further comprises the design of wind speed adaptation, condensation backflow prevention, working condition and area label optimization, double-link encryption transmission, safe installation, optical signal compensation, graded maintenance and the like. The method can resist multiple environmental interferences, PM detection errors are controllable, data updating is efficient, and airport safety operation and compliance monitoring requirements are met.

The invention discloses a single vesicle particle electrochemical signal identification and classification method and identification system, and the method comprises the steps: S1, preparing a carbon fiber ultramicro electrode, applying a voltage in a patch clamp system constant potential mode, and collecting picoampere-level current response generated by oxidation of a single vesicle content on the surface of the electrode; s2, carrying out smooth filtering on the collected original current signal, and realizing characteristic peak identification and multi-dimensional physical / statistical characteristic extraction by using a multi-parameter peak identification method based on a threshold value; s3, carrying out preprocessing operations including missing value processing, standardization, principal component analysis dimension reduction and category equalization on the extracted peak value characteristic data; and S4, on the basis of the preprocessed feature data, constructing and training at least one machine learning model for automatically identifying the particle size category of the vesicle particles. The method combines the advantages of single-vesicle electrochemical high-sensitivity measurement and intelligent algorithm classification, and has the advantages of high recognition efficiency, good repeatability, wide applicability and the like.

The invention discloses a vein quartz type high-purity quartz mine identification and evaluation method and device, relates to the field of vein quartz type high-purity quartz mine quality evaluation, and can solve the problems of time consumption, high cost, strong destructiveness and low efficiency of traditional vein quartz type high-purity quartz mine identification at the present stage. Measuring physical property parameters of the plurality of samples to be identified; wherein the physical property parameters comprise light transmittance, water content, unit cell parameters and particle size related parameters; inputting the physical property parameters of the plurality of samples to be identified into the purity evaluation model, and determining the purity estimation value of the vein quartz sample; determining an evaluation result of the vein quartz sample according to the purity estimation value of the vein quartz sample; wherein the evaluation result is used for representing whether the vein quartz sample has the potential of vein quartz type high-purity quartz ore. The method is used for identifying the purity of the vein quartz type high-purity quartz ore and evaluating the potential of the vein quartz type high-purity quartz ore.

The invention relates to a powder sphericity detection method, relates to the technical field of powder property detection, and solves the problem of poor accuracy of an existing powder sphericity detection method. The detection method comprises the following steps: randomly extracting powder to be detected, and obtaining a contour descriptor of each powder particle; calculating a geometrical shape factor and a mesoscopic descriptor of each powder particle according to the contour descriptor of each powder particle; classifying the powder to be detected according to the section morphology of the powder particles, and counting the number of the powder particles in the powder with different section morphology types; according to the number of the powder particles in the powder of each section morphology type and the geometrical shape factor and the mesoscopic descriptor of each powder particle, calculating the average sphericity value of the powder of each section morphology type; and calculating the final sphericity degree of the powder to be measured based on the average sphericity degree value of the section morphology type powder. According to the invention, scientificity and accuracy of a powder sphericity detection result can be ensured.

The invention discloses a cement thickness degree detection device, and belongs to the technical field of cement thickness degree detection, the cement thickness degree detection device comprises a cabinet body, a fixed screen seat is arranged on the top side in the cabinet body, and the bottom side in the fixed screen seat is connected with a sealing gasket. A detection worker manually operates a circular plate to drive two arc-shaped sliding blocks, a circular sleeve and an elastic conical cover to move downwards, in the up-down reciprocating motion process of the elastic conical cover, vibration is generated on the surface of the bottom of the elastic conical cover, adhered cement fine particles are effectively shaken off, sample residues are remarkably reduced, and therefore the accuracy of a detection result is guaranteed; in the downward moving process of a circular sleeve, a connecting shaft, a mounting sleeve and a plurality of spoilers are driven to synchronously and slowly rotate under the acting force of a guide rod and an arc-shaped groove, and ordered mild airflow shearing force is formed in the top area of the test screen through the slow rotating motion of the spoilers so as to assist and promote the fine powder penetration phenomenon of the test screen; and the screen analysis efficiency and uniformity of the device are improved.

The invention relates to the technical field of fluid flow, particle concentration and size testing, and provides a power system flow state measuring method, and a measuring device comprises a metering pipe body, a cylindrical streaming piece, a protective shell and a processing terminal; the cylindrical flow-around piece is arranged in the metering pipe body; the protective shell is arranged on the outer wall surface of the metering pipe body, the pressure sensor and the frequency sensor are arranged in the protective shell, and the detection end of the frequency sensor is located at the downstream of the cylindrical streaming piece; the pressure sensor and the frequency sensor are electrically connected with the processing terminal; a skin part is arranged on the cylinder surface of the cylindrical streaming piece in the incoming flow direction, a plurality of sensing units used for sensing the impact force of the fixed particles are uniformly arranged on the skin part, and the skin part is electrically connected with the pressure sensor. The impact times and average impact force of the particles in the medium to be measured are measured through the skin part and the pressure sensing part, and the concentration and the size of the particles in the medium to be measured are effectively measured in cooperation with data measured by the frequency sensor.

Disclosed is a kind of wind powder fineness probe rod detection device, comprising: wind powder pipeline, opposite shot switch, probe rod;Probe rod is inserted into the wind powder pipeline, wind powder pipeline is opened in the vertical direction of the probe rod Two symmetrical through holes, the end of the probe rod is located on the connecting line of two symmetrical through holes, and the outside of two symmetrical through holes is provided with two corresponding opposite shot switches.Detection device can detect the wear degree of probe rod, and the insertion depth of probe rod can be adjusted according to the wear state.The structure is simple, the manufacturing cost is low, the installation and maintenance are convenient, and it is easy to operate.