1579results about "Particle size analysis" patented technology

The invention discloses a grinding machine internal part temperature anomaly detection method based on vibration signal analysis, which comprises the following steps that grinding data are collected through sensor deployment, and the sensors comprise a temperature sensor, a vibration sensor, an infrared thermal imaging sensor, a magnetic resistance current sensor and an inductance type particle sensor; carrying out preprocessing and feature extraction on the collected data; model construction and training are carried out based on data of preprocessing and feature extraction; according to the method, the abnormal condition of the temperature of the part is predicted by detecting the vibration signal of the internal part, the content of metal particles in lubricating oil is detected through the oil analysis sensor, and the abrasion degree of the bearing is judged in combination with the vibration signal. And motor current harmonic characteristics are monitored, and overload or rotor imbalance problems are identified.

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 provides a grain impurity rate and breakage rate monitoring system and method for a combine harvester. The grain impurity rate and breakage rate monitoring system comprises a grain sampling device, an image acquisition device and a control device, the grain sampling device is installed on the side wall of the vertical grain conveying barrel, a grain inlet of the grain sampling device is connected with a grain outlet hole in the side wall of the vertical grain conveying barrel through a hose, and a grain outlet of the grain sampling device is connected with a feeding port of the image acquisition device through a connecting pipe; the image acquisition device is connected with the control device, an image acquisition module of the control device controls the image acquisition device to acquire a grain image, and online calculation of the impurity rate and the breakage rate of the grain is realized on the grain image through a YOLOv8-Seg instance segmentation model of the image processing module in combination with an impurity rate and breakage rate mathematical model; finally, a detection result is displayed on a display screen in real time, visual numerical values can be provided for an operator of the combine harvester, and the operator is helped to adjust working parameters of the harvester. The method can be used for harvesting rice, wheat, oilseed rape and soybeans, and has wide applicability.

The invention discloses a method for measuring the particle size of metal powder by using a scanning electron microscope, which comprises the following steps: step 1, sample treatment: adhering a small conductive adhesive tape on a sample table, sprinkling metal powder on the adhered conductive adhesive tape, and purging to obtain the conductive adhesive tape adhered with the metal powder; step 2, parameter setting: setting acceleration voltage, working distance, diaphragm, image contrast, brightness, magnification times and scanning speed of a scanning electron microscope to observe the conductive adhesive tape adhered with the metal powder obtained in the step 1 to obtain an electronic image of metal powder particles; and processing and calculating the electronic image of the metal powder particles by using software to obtain the average particle size of the metal powder particles. According to the method disclosed by the invention, a solvent is not needed to disperse the sample, pollution of the solvent to the sample is effectively avoided, and the result is more accurate than that of directly measuring the particle size by using length software.

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 relates to a detection method and application of a bainite steel structure and / or grain boundary for nuclear power equipment, and relates to the technical field of new material detection. According to the detection method, the problem that the structure and the grain boundary of bainite steel for nuclear power equipment are difficult to display can be solved, a major breakthrough of a grain size detection technology is achieved, the clear and complete grain boundary can be accurately presented, and scientific evaluation of the grain size through a comparison method, a cut point method, an area method and the like according to standard requirements is further supported. When the detection method is used for evaluating the grain size, the results are highly consistent, the repeatability is good, and the problems of subjective experience dependence and measurement errors caused by blurred grain boundary display in a traditional nitric acid and alcohol corrosion method are effectively avoided.

The invention discloses a soil nitrogen leaching capacity monitoring method based on a frequency domain reflectometry, and relates to the technical field of soil water environment monitoring, in particular to a nitrogen leaching online monitoring method based on the frequency domain reflectometry (FDR), the Darcy law and a Van Genuchten (VG) model. And a monitoring method with high precision and sensitivity, multi-parameter synchronous detection and low cost is provided for the soil nitrogen leaching capacity. Comprising the following steps: 1, collecting and pretreating a soil sample; 2, placing the treated soil sample in a soil experiment cylinder; 3, inserting the multi-parameter sensors into the soil experiment cylinder in a layered manner; 4, 1000 ml of water is injected, and data acquisition is carried out; and 5, realizing soil nitrogen leaching capacity monitoring in combination with a Darcy law and a VG model.

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.

Analytical methods and systems for assessing analyte distribution uniformity in cosmetic compositions are provided. An analysis system (1) for evaluating analyte distribution uniformity of a cosmetic composition comprises: a binarization and calibration module (41) for binarizing and calibrating a chemical image of the cosmetic composition to obtain a binarized and calibrated image; the image segmentation module (42) is used for splitting the binarized and calibrated image into small grids; and an analyte distribution uniformity evaluation module (43) for calculating the number of pixels corresponding to the analyte in the small grid to obtain an analyte distribution uniformity.

The invention provides a method for measuring particle granularity and morphology, and belongs to the technical field of process mineralogy. According to the method, the particle size of the particles is analyzed on the basis of particle centroids in combination with geometric and statistical parameters, and meanwhile, morphological characteristics of the characterization particles are analyzed. A specific particle size measurement result is an equivalent radius result based on the centroid and can directly reflect the overall size of the particles, and further, a morphological characteristic result is based on centroid distance statistics and can quantify irregularities of the particles, such as roundness, length-diameter ratio and roughness; according to the test method, measurement is carried out on a two-dimensional plane, the measurement result is not influenced by a selected direction, accurate granularity and morphology characterization can be provided for particles with complex morphology (such as a dendritic or porous structure), and a reliable test method is provided for analysis of complex particle systems (such as geological sediments, industrial powder or biological particles).

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 a method for measuring and calculating the article position of continuous silver ore with the particle size of 0.037 mm or above, and belongs to the technical field of mineral grade detection. The silver grade in the sample is measured and calculated through screening; leaching and measuring the content of silver monomers and continuous silver minerals, and reselecting to obtain a heavy sand product; carrying out fluorescent dyeing on the heavy sand product, and selecting monomer silver minerals; analyzing the color of the silver mineral through point scanning data of a scanning electron microscope, melting the monomer silver mineral to obtain the volume of the monomer silver mineral, and calculating the content of the monomer silver mineral; and carrying out conversion to obtain the continuous silver ore article position with the size of more than 0.037 mm. According to the method, the target mineral is rapidly and accurately obtained by adopting density medium reselection and fluorescent dye tracing; introducing a scanning electron microscope to measure and calculate the actual density of the silver mineral to replace the theoretical density, smelting the silver mineral in a mold, and carrying out volume measurement and calculation to obtain the actual metal amount closer to the actual silver mineral; measuring and calculating errors caused by impurities and irregular shapes of mineral particles are reduced; and the calculated grade of the silver mineral is more accurate.

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 provides a pore geometric morphology fine characterization method based on confining pore forming of mineral particles, which comprises the following steps: S1, performing fine characterization of the mineral particles based on geometric morphology characteristics, and accurately identifying the mineral particles in combination with characterization results; s2, performing geometric shape quantitative characterization on different types of pores from the angles of pore diameters, roundness, flatness, shape indexes and corner angle indexes to obtain geometric shape parameters of the pores; s3, on the basis of the recognized mineral particles, the names of confinement pores are determined, clustering analysis is conducted on the confinement pores in combination with the geometric morphology quantitative characterization result obtained in the step S2, and a pore classification scheme is established; and S4, based on a classification scheme and a quantitative characterization result, determining development characteristics of shale reservoir pores, establishing a porosity contribution index model formula, and matching and verifying a reservoir physical property result. According to the method, through comprehensive analysis of the pore morphological characteristics, solid data support and theoretical basis can be provided for scientific evaluation of a high-quality pore development mode.

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 pet food particle forming quality detection method and system based on image recognition, and relates to the technical field of image recognition. The method comprises the steps that particles are conveyed through a conveying unit, and a unique ID is determined for each particle; acquiring three-dimensional geometric data, multispectral data and polarized light data of the particles on line through a three-dimensional geometric detection station, a multispectral detection station and a polarized light detection station in sequence; and the processing control module binds the multi-dimensional data with the unique identifier ID and inputs the data to a two-stage analysis model. According to the model, whether particles have defects or not is identified through a multi-branch deep neural network, and then correlation analysis is carried out on the identified defects and upstream production process parameters through a causal inference model. According to the invention, through multi-modal data fusion and deep analysis, not only can unqualified products be accurately removed, but also data support can be provided for real-time optimization of a production process, and the depth and efficiency of product quality control are improved.

The invention relates to the technical field of dust monitoring, in particular to an engineering construction dust monitoring system based on multi-dimensional data analysis, which is provided with a feature acquisition module, a region screening module, a joint monitoring analysis module, a dust migration identification module and a monitoring point regulation and control module, a dust concentration monitoring value, a dust particle size monitoring value and a wind direction sub-vector are obtained through a feature obtaining module, a dust generation source region is screened through a region screening module, and whether dust diffusion has diffusion superposition features or not is judged through a combined monitoring analysis module. A dust generation source area is divided into different dust migration areas through the dust migration identification module, and an extension path and a dust monitoring point are determined through the monitoring point regulation and control module, so that the dust generation source is rapidly identified, the monitoring point is adaptively adjusted according to the position distribution and the wind direction of the dust generation source, and the dust generation efficiency is improved. And the monitoring efficiency and the monitoring reliability of the engineering construction dust monitoring system are improved.

The invention relates to the technical field of recycling of recycled asphalt pavement materials, in particular to an intelligent spraying method and system for a regenerant. The method comprises the following steps: detecting the actual penetration depth of a regenerant in mixture particles, the radius of the mixture particles and the real-time temperature of the mixture in real time; calculating a penetration depth ratio according to the actual penetration depth and the mixture particle radius; determining the spraying sub-stage of the current spraying in the spraying process according to the penetration depth ratio; and adjusting the spraying amount and temperature of spraying equipment according to the real-time temperature of the mixture based on the spraying sub-stage. The problems that in the prior art, when a recycled asphalt pavement material is regenerated, the surface layer of the material can rapidly adsorb a regenerant to form a compact saturated layer, so that the regenerant is prevented from further permeating into particles, then core aged asphalt is not effectively softened, and performance recovery is insufficient are solved. And the regeneration performance of the recycled asphalt pavement material is improved.

The invention relates to the technical field of emulsified asphalt demulsification time determination, in particular to an emulsified asphalt demulsification time determination method, which comprises the following steps: monitoring the potential absolute value and conductivity of an emulsion in real time, and when the potential absolute value is reduced to a set potential absolute value threshold value and the short-term sudden acceleration rate of the conductivity exceeds a set rate threshold value, confirming that demulsification is started. Recording a demulsification starting time point; acquiring particle size parameters of asphalt particles and torque of a stirring shaft in real time; calculating a demulsification progress index based on the variance of the potential absolute value, the variance of the electric conductivity, the variance of the particle size parameter and the variance of the torque; comparing the demulsification progress index with a set progress index threshold value, and judging a demulsification ending time point; calculating a difference value between the demulsification ending time point and the demulsification starting time point to obtain demulsification time; various physical and chemical parameter changes in the demulsification process of the emulsified asphalt are comprehensively considered, limitation of a single index is avoided, and the demulsification starting time and the demulsification ending time are accurately determined, so that the accuracy and the reliability of a measurement result are improved.

The invention discloses an abrasive particle detection system and method. The abrasive particle detection system and method comprises the following steps: acquiring a particle waveform signal output by an abrasive particle sensor; performing analog-to-digital conversion and filtering processing on the particle waveform signal; and performing waveform interpretation and feature extraction on the processed particle waveform signal, and calculating the particle size according to a waveform interpretation result and a feature extraction result. According to the invention, particle waveform data is firstly collected in real time, and then analog-to-digital conversion and filtering processing are carried out on the particle waveform data to filter interference signals, so that the detection sensitivity is improved, and the risk of misjudgment is reduced; then, waveform judgment and feature extraction are carried out on the filtered particle waveform signals, and finally the size of the particles is calculated and the material of the particles is judged according to a waveform judgment result and a feature extraction result.

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 is suitable for the technical field of coating detection, and provides a scraper fineness testing method based on machine vision and an intelligent algorithm, and the method comprises the following steps: capturing a particle distribution image through a 4K line scanning camera and a multi-angle LED light source; through Gaussian-median hybrid filtering, adaptive threshold segmentation (calculating a dynamic threshold based on a neighborhood mean value or a Gaussian weighted mean value), sub-pixel edge refinement (bicubic interpolation) and form / color / texture feature extraction, particles, bubbles, scratches and other interference items are distinguished, A-method and B-method dual-mode analysis is supported, and a particle distribution diagram and a form parameter report are generated. According to the method, manual operation errors are eliminated, the recognition precision of particles with the particle size larger than or equal to 2.5 microns is improved, automation and data traceability of the detection process are achieved, and the method is suitable for paint fineness detection within the range of 0-150 microns.

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 invention discloses a production and preparation process of an energy-saving synergistic raw material catalyst, particularly relates to the field of carbon-based raw material catalysts, and aims to optimize the formation condition of a pore structure, accurately regulate and control the type and concentration distribution of surface functional groups and introduce a defect detection and quantitative evaluation method based on image recognition. The catalytic activity and stability of the catalyst are improved; according to the process, through the stage steps of drying, acid pickling, pyrolysis, surface modification and the like, and in combination with a surface characterization means and quantitative indexes, collaborative optimization of the structure and chemical performance of the catalyst is ensured. The catalyst is comprehensively improved from a microstructure to chemical characteristics, and is widely applied to the fields of energy conversion, pollution treatment and the like.