CN113933088B - Intelligent sampling and pretreatment system for coal samples - Google Patents
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Abstract
The invention relates to the technical field of intelligent sampling, in particular to an intelligent sampling and preprocessing system for coal samples, which comprises a sample taking unit, a sample identifying unit, a cloud storage unit, a server, a sample primary dividing unit, a sample judging unit and a sample displaying unit; the sampling unit is used for collecting coal data in real time, calibrating the coal data collected in real time into coal mining information, and transmitting the coal mining information to the sampling unit; according to the invention, through the correlation analysis of the related data and the whole calculation of the data, the influence value on the coal is calculated, and the data is reversely deduced according to the influence value and the acquired data, so that the aspect that the acquired data needs to be regulated under the condition of meeting the requirement is calculated, the accuracy of data analysis is improved, the time consumed by the data analysis is saved, and the working efficiency is improved.
Description
Technical Field
The invention relates to the technical field of intelligent sampling, in particular to an intelligent sampling and pretreatment system for coal samples.
Background
The coal quality control method is characterized in that the coal is used as an energy source to occupy important positions in national economy development, so that fairness and fairness are guaranteed, and human factor interference is avoided.
Then in the process of developing coal and producing coal, the quantity of the coal is too large, and the coal cannot be analyzed one by one, so that people collect samples according to the analysis of the coal mine, and collect regular or irregular samples from some coals.
The existing coal collection process is to collect the coal randomly through a simple machine, the collected samples are random in size, the collected samples are subjected to preliminary pretreatment, namely, the collected samples are simply crushed and divided, the collected sample sizes cannot be set and calculated according to the sample grades or evaluation scores required by people, and in the pretreatment process, the specification adjustment cannot be carried out according to the sorting analysis and calculation of the related numerical values.
Therefore, we propose an intelligent sampling and pretreatment system for coal samples.
Disclosure of Invention
The invention aims to provide an intelligent sampling and preprocessing system for coal samples, which is used for obtaining coal mine types corresponding to collected data by carrying out corresponding identification matching on the collected data and the previous data, and carrying out sorting and extraction of related data according to the coal mine types, so that the correlation degree of the previous data analysis and the collected data is ensured, the persuasion of the data is improved, the time required by data matching is saved, and the working efficiency is improved; through the association analysis of the related data in the past and the whole calculation of the data, the influence value on the coal is calculated, and the data is reversely deduced according to the influence value and the acquired data, so that the aspect that the acquired data needs to be regulated under the condition of meeting the requirement is calculated, the accuracy of the data analysis is improved, the time consumed by the data analysis is saved, and the working efficiency is improved.
The aim of the invention can be achieved by the following technical scheme: an intelligent sampling and preprocessing system for coal samples comprises a sample taking unit, a sample recognizing unit, a cloud storage unit, a server, a sample preliminary dividing unit, a sample judging unit and a sample displaying unit;
the sampling unit is used for collecting coal data in real time, calibrating the coal data collected in real time into coal mining information, and transmitting the coal mining information to the sampling unit;
the sample identification unit acquires cloud carbon information from the cloud storage unit, carries out sample identification operation on the cloud carbon information and coal mining information to obtain carbon ruler data, carbon seed data, carbon color data, carbon fragment data, carbon total data, carbon level data and carbon selection data corresponding to the carbon map data, and transmits the carbon ruler data, the carbon seed data, the carbon fragment data, the carbon total data, the carbon level data and the carbon selection data to the sample initial separation unit;
the sample primary separation unit is used for carrying out sample primary analysis operation on carbon ruler data, carbon seed data, carbon quality data, carbon color data, carbon broken data, carbon total data, carbon level data and carbon selection data corresponding to the carbon map data to obtain ruler average value, the most average value, quality average value, most average value, broken average ratio, most difference value, average value selection, most difference value, optimal ratio, conversion and other deviation adjustment factors, and transmitting the same to the sample judgment unit;
the sample judgment unit acquires coal mining information from the sample recognition unit, and performs sample judgment operation together with deviation adjustment factors such as a rule average value, a rule-most difference value, a quality average value, a prime difference value, a broken average value ratio, a broken difference value, a mean value selection, a most selected difference value, a good gloss ratio and a conversion to obtain a sampling value, and transmits the sampling value to the sample display unit;
and the sample display unit receives and displays the sampling value, and a worker carries out processing adjustment of the sample according to the received and displayed sampling value.
Further, the coal mining information comprises coal shadow data, coal ruler data, coal quality data, coal color data, coal breakage data, coal total data, coal grade data and coal separation data;
the cloud carbon information comprises carbon map data, carbon ruler data, carbon seed data, carbon quality data, carbon color data, carbon crushing data, carbon total data, carbon grade data and carbon selection data.
Further, the specific operation process of the sample operation is as follows:
acquiring charcoal image data and coal shadow data, matching the charcoal image data with the coal shadow data to obtain a matching error signal and a matching correct signal, transmitting the matching error signal to a sampling unit, and acquiring coal mining information again by the sampling unit according to the matching error signal;
and selecting carbon map data corresponding to the coal shadow data, and carbon ruler data, carbon seed data, carbon quality data, carbon color data, carbon crushing data, carbon total data, carbon level data and carbon selection data corresponding to the carbon map data according to the matching correct signals.
Further, the specific operation procedure of the primary analysis operation of the sample is as follows:
selecting carbon ruler data, carbon color data, carbon crushing data, carbon total data, carbon grade data and carbon selection data corresponding to carbon seed data;
selecting carbon ruler data corresponding to a plurality of groups of carbon seed data in the record, calculating the average size of the carbon ruler data by calculating the average value of the carbon ruler data, and calibrating the average size of the carbon ruler data as the ruler average value;
calculating the difference value between the rule average value and the plurality of carbon rule data to calculate a plurality of carbon rule difference values, sorting the plurality of carbon rule difference values from large to small to obtain a carbon rule difference value sorting, and calibrating the value of the first sorting value in the carbon rule difference value sorting as the most rule difference value;
according to the calculation method of the ruler average value and the most ruler difference value, processing the carbonaceous data into a mass average value and a most mass difference value;
according to the calculation method of the rule average value and the rule difference value, the carbon crushing data are processed into a crushing average value, a rule difference value and a crushing average occupation ratio;
according to the calculation method of the rule average value and the most rule difference value, processing the carbon selection data into a selection average value and a most selection difference value;
performing duty ratio processing and grading range processing on the carbon color data and the carbon data respectively to obtain a priority duty ratio and a grading range value;
and (3) taking the ruler average value, the most ruler difference value, the mass average value, the most mass difference value, the crushed average value ratio, the most crushed difference value, the selected average value, the most selected difference value, the excellent ratio and the grade average value corresponding to the carbon ruler data into a grade conversion calculation formula together, calculating a grade conversion deviation adjustment factor e, and calibrating the grade conversion deviation adjustment factor e as a conversion deviation adjustment factor.
Further, the specific treatment process of the average broken value, the maximum broken difference value and the average broken ratio is as follows:
selecting the carbon crushed data corresponding to a plurality of groups of carbon seed data in the record, carrying out summation calculation on the sizes of the plurality of carbon crushed data, dividing the numerical value obtained after summation calculation by the total number of the carbon crushed data, thus calculating the average crushing size of the carbon crushed data, calibrating the average crushing size of the carbon crushed data as a crushing average value, carrying out duty ratio calculation on the crushing average value and the carbon total data corresponding to the carbon seed data, and calculating the crushing average duty ratio;
and carrying out difference calculation on the crushed average value and the plurality of pieces of crushed data to calculate a plurality of crushed difference values, sorting the plurality of crushed difference values from large to small to obtain a crushed difference value sorting, and calibrating a value of a first rank in the crushed difference value sorting as the most crushed difference value.
Further, the specific process of performing the duty ratio processing on the charcoal data comprises the following steps:
selecting carbon color data corresponding to a plurality of carbon seed data, identifying and calibrating the carbon color data, when pure black is identified, calibrating the carbon color data into high-quality color, when an impurity color outside the pure black is identified, calibrating the carbon color data into poor color, counting the times of the high-quality color and the poor color, and performing duty ratio calculation, wherein the ratio of the good color to the poor color is/(the times of the poor color and the times of the high-quality color);
the specific process for grading and ranging the carbon data comprises the following steps:
selecting carbon level data corresponding to a plurality of groups of carbon seed data in a record, carrying out average value calculation on the plurality of carbon level data, calculating a level average value, carrying out difference value calculation on the plurality of carbon level data and the level average value, calculating a difference value between the carbon level data and the level average value, sorting the carbon level difference value from large to small, thereby obtaining carbon level sorting data, selecting a first numerical value in the carbon level sorting data, calibrating the first numerical value as a numerical value, carrying out addition and subtraction calculation on the numerical value and the level average value, and calculating a grading range value, wherein the specific process is as follows: the method comprises the steps of (1) combining a maximum value and a minimum value to form a grading range value, calibrating carbon data smaller than the minimum value as low-grade coal, calibrating carbon data in the grading range value as synthetic coal, and calibrating carbon data larger than the maximum value as high-grade coal.
Further, the level conversion calculation formula is specifically:
wherein JJ is expressed as a level average, C1 is expressed as a scale average, C2 is expressed as a mass average, C3 is expressed as a broken average, C4 is expressed as a broken average ratio, C5 is expressed as a selected average, C6 is expressed as a you-zesh ratio, u1 is expressed as a most-scale difference, u2 is expressed as a most-mass difference, u3 is expressed as a most-broken difference, u5 is expressed as a most-selected difference, u4 is expressed as a numerical conversion factor of a broken average ratio, u6 is expressed as a numerical conversion adjustment factor of a you-zesh ratio, r2 is expressed as a weight coefficient of a broken average ratio, r1 is expressed as a weight coefficient of a you-zesh ratio, wherein r3 is expressed as a weight coefficient of a comprehensive numerical conversion of the scale average, the mass average, the broken average and the selected average, e is expressed as a level conversion deviation adjustment factor, and is calibrated as a conversion deviation adjustment factor, so that the value of e is calculated according to a calculation formula, wherein other values except e are known values or preset values.
Further, the specific operation procedure of the sample judgment operation is as follows:
selecting coal scale data, coal quality data, coal color data, coal crushing data, coal total data and coal selection data according to the coal shadow data, respectively carrying out difference calculation on the coal scale data, the coal quality data, the coal crushing data and the coal selection data and the scale mean value, the quality mean value, the crushing mean value and the selection mean value, and calculating a coal scale difference value, a coal quality difference value, a coal crushing difference value and a coal selection difference value;
according to the processing mode of the optimal ratio, processing the coal color data to obtain a real ratio, and according to the processing mode of the average ratio, calculating the real ratio;
setting a preset grade value of an acquired sample, calibrating the preset grade value as a pre-grade value, and introducing the pre-grade value, coal scale data, coal quality data, coal crushing data, coal separation data, coal scale difference value, coal quality difference value, coal crushing difference value, coal separation difference value, real gloss ratio, real crushing ratio and conversion deviation adjustment factors into a grade conversion calculation formula;
and according to the required coal level data, reversely deducing a level conversion calculation formula, calculating the requirements for various values in the coal mining information, sampling, calibrating the values into sampling values, and transmitting the sampling values to a sample display unit.
The invention has the beneficial effects that:
(1) The collected data and the previous data are correspondingly identified and matched, so that the coal mine type corresponding to the collected data is obtained, and the related data are tidied and extracted according to the coal mine type, so that the association degree between the previous data analysis and the collected data is ensured, the persuasion of the data is improved, the time required by the data matching is saved, and the working efficiency is improved;
(2) Through the association analysis of the related data in the past and the whole calculation of the data, the influence value on the coal is calculated, and the data is reversely deduced according to the influence value and the acquired data, so that the aspect that the acquired data needs to be regulated under the condition of meeting the requirement is calculated, the accuracy of the data analysis is improved, the time consumed by the data analysis is saved, and the working efficiency is improved.
Drawings
The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a system block diagram of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention discloses an intelligent sampling and preprocessing system for coal samples, which comprises a sampling unit, a cloud storage unit, a server, a sample primary separation unit, a sample judgment unit and a sample display unit;
the sample acquisition unit is used for acquiring coal data in real time and calibrating the coal data acquired in real time into coal mining information, the coal mining information comprises coal shadow data, coal ruler data, coal quality data, coal color data, coal crushed data, coal total data, coal grade data and coal sorting data, the coal shadow data refers to images of coal in the real-time detection coal acquisition sample, the coal ruler data refers to the size of the coal in the real-time detection coal acquisition sample, the coal quality data refers to the weight of the coal in the real-time detection coal acquisition sample, the coal color data refers to the color of the coal in the real-time detection coal acquisition sample, the color is mainly divided into pure black, the size of chips falling from the coal in the real-time detection coal acquisition sample, the coal total data refers to the sum of the quantity selected by the coal sample in the real-time detection coal acquisition sample, the coal grade data refers to the time of sorting in the real-time detection coal acquisition sample, and the quality data, the coal grade data and the coal crushed data are transmitted to the coal sample acquisition unit;
the cloud storage unit internally stores cloud carbon information related to a traditional coal collection sample, the cloud carbon information comprises carbon image data, carbon ruler data, carbon seed data, carbon quality data, carbon color data, carbon broken data, carbon total data, carbon grade data and carbon selection data, the carbon image data refers to an appearance image of coal in the traditional coal collection sample, the carbon ruler data refers to the size of the coal in the traditional coal collection sample, the carbon seed data refers to the variety type of the coal in the traditional coal collection sample, the carbon quality data refers to the weight of the coal in the traditional coal collection sample, the carbon color data refers to the color of the coal in the traditional coal collection sample, the color is mainly divided into pure black, dirt, yellow and red black, the carbon broken data refers to the size of fragments dropped by the coal in the traditional coal collection sample, the carbon total data refers to the sum of the quantity originally selected by the coal sample in the traditional coal collection sample, the carbon grade data refers to the quality of the coal in the traditional coal collection sample through numerical value, and the carbon quality data refers to the size of the coal in the traditional coal collection sample;
the sample identification unit acquires carbon image data, carbon ruler data, carbon seed data, carbon color data, carbon crushed data, carbon total data, carbon level data and carbon selection data from the cloud storage unit, and carries out sample identification operation on the carbon image data, the carbon ruler data, the carbon seed data, the carbon color data, the carbon crushed data, the carbon total data, the carbon level data and the carbon selection data together with coal shadow data, the coal ruler data, the coal color data, the coal crushed data, the coal total data, the coal level data and the coal selection data, wherein the specific operation process of the sample identification operation comprises the following steps:
the method comprises the steps of obtaining carbon image data and coal shadow data, and matching the carbon image data and the coal shadow data, and specifically comprises the following steps:
when the matching results of the carbon image data and the coal shadow data are inconsistent, judging that coal of the same kind as the coal shadow data does not exist in the carbon image data, producing a matching error signal, transmitting the matching error signal to a sampling unit, and acquiring coal mining information again by the sampling unit according to the matching error signal;
when the matching result of the carbon image data and the coal shadow data is consistent, judging that coal of the same kind as the coal shadow data exists in the carbon image data, and generating a matching correct signal;
selecting carbon map data corresponding to the coal shadow data, and carbon ruler data, carbon seed data, carbon quality data, carbon color data, carbon crushing data, carbon total data, carbon level data and carbon selection data corresponding to the carbon map data according to the matching correct signals;
transmitting the carbon ruler data, the carbon seed data, the carbon color data, the carbon broken data, the carbon total data, the carbon grade data and the carbon selection data to a sample primary separation unit through a server together with corresponding coal shadow data, coal quality data, the coal broken data, the coal total data, the coal grade data and the coal selection data;
the sample primary separation unit is used for carrying out sample primary analysis operation on carbon ruler data, carbon seed data, carbon color data, carbon broken data, carbon total data, carbon grade data and carbon selection data together with corresponding coal shadow data, coal quality data, coal broken data, coal total data, coal grade data and coal selection data, wherein the specific operation process of the sample primary analysis operation is as follows:
selecting carbon ruler data, carbon color data, carbon crushing data, carbon total data, carbon grade data and carbon selection data corresponding to carbon seed data;
selecting carbon ruler data corresponding to a plurality of groups of carbon seed data in the record, and carrying out average calculation on the carbon ruler data so as to calculate the average size of the carbon ruler data, and calibrating the average size of the carbon ruler data as a ruler average value, wherein the specific process of carrying out average calculation on the carbon ruler data is as follows: firstly, summing the sizes of a plurality of carbon ruler data, dividing the numerical value obtained after summing calculation by the total number of the carbon ruler data, and calculating a ruler average value;
calculating the difference value between the rule average value and the plurality of carbon rule data to calculate a plurality of carbon rule difference values, sorting the plurality of carbon rule difference values from large to small to obtain a carbon rule difference value sorting, and calibrating the value of the first sorting value in the carbon rule difference value sorting as the most rule difference value;
selecting carbonaceous data corresponding to a plurality of groups of carbonaceous data in the record, and carrying out average calculation on the carbonaceous data so as to calculate the average weight of the carbonaceous data, and calibrating the average weight of the carbonaceous data as a mass average value, wherein the specific process of carrying out average calculation on the carbonaceous data is as follows: firstly, carrying out summation calculation on the weight of a plurality of carbonaceous data, and dividing the numerical value obtained after summation calculation by the total number of the carbonaceous data, thereby calculating a quality average value;
calculating the difference value between the quality average value and the plurality of carbon data to calculate a plurality of carbon difference values, sorting the plurality of carbon difference values from large to small to obtain a carbon difference value sorting, and calibrating the last numerical value in the carbon difference value sorting as the most quality difference value;
selecting the carbon crushed data corresponding to a plurality of groups of carbon seed data in the record, and calculating the average crushing size of the carbon crushed data by carrying out average calculation on the carbon crushed data, calibrating the average crushing size of the carbon crushed data as a crushed average value, carrying out duty ratio calculation on the crushed average value and the total carbon data corresponding to the carbon seed data, and calculating the crushed average duty ratio, wherein the specific process of carrying out average calculation on the carbon crushed data is as follows: firstly, carrying out summation calculation on the sizes of a plurality of pieces of carbon data, and dividing the numerical value obtained after summation calculation by the total number of the pieces of carbon data so as to calculate a broken average value;
calculating the difference value between the crushed average value and the plurality of pieces of crushed data to calculate a plurality of pieces of crushed difference values, sorting the plurality of pieces of crushed difference values from large to small to obtain a crushed difference value sorting, and calibrating the value of the first rank in the crushed difference value sorting as the most crushed difference value;
selecting carbon selection data corresponding to a plurality of groups of carbon seed data in a record, and carrying out average calculation on the carbon selection data so as to calculate the average time of the carbon selection data, and calibrating the average time of the carbon selection data as a selection average value, wherein the specific process of carrying out average calculation on the carbon selection data is as follows: firstly, carrying out summation calculation on the time of a plurality of carbon selection data, dividing the numerical value obtained after summation calculation by the total number of the carbon selection data, and thus calculating a selection average value;
calculating the difference value between the selected average value and the carbon selection data to calculate a plurality of carbon selection difference values, sorting the carbon selection difference values from large to small to obtain a carbon selection difference value sorting, and calibrating the value of the first sorting in the carbon selection difference value sorting as the most selected difference value;
selecting carbon color data corresponding to a plurality of carbon seed data, identifying and calibrating the carbon color data, when pure black is identified, calibrating the carbon color data into high-quality color, when an impurity color outside the pure black is identified, calibrating the carbon color data into poor color, counting the times of the high-quality color and the poor color, and performing duty ratio calculation, wherein the ratio of the good color to the poor color is/(the times of the poor color and the times of the high-quality color);
selecting carbon level data corresponding to a plurality of groups of carbon seed data in a record, carrying out average value calculation on the plurality of carbon level data, calculating a level average value, carrying out difference value calculation on the plurality of carbon level data and the level average value, calculating a difference value between the carbon level data and the level average value, sorting the carbon level difference value from large to small, thereby obtaining carbon level sorting data, selecting a first numerical value in the carbon level sorting data, calibrating the first numerical value as a numerical value, carrying out addition and subtraction calculation on the numerical value and the level average value, and calculating a grading range value, wherein the specific process is as follows: the method comprises the steps of (1) combining a maximum value and a minimum value to form a grading range value, calibrating carbon-based data smaller than the minimum value as low-grade coal, calibrating carbon-based data in the grading range value as synthetic coal, and calibrating carbon-based data larger than the maximum value as high-grade coal;
and (3) bringing the ruler average value, the longest ruler difference value, the mass average value, the worst difference value, the crushed average ratio, the latest difference value, the selected average value, the latest difference value, the excellent ratio and the grade average value corresponding to the carbon ruler data into a grade conversion calculation formula:
wherein JJ is represented as a level average, C1 is represented as a scale average, C2 is represented as a mass average, C3 is represented as a crushed average, C4 is represented as a crushed average ratio, C5 is represented as a selected average, C6 is represented as a euzeruman ratio, u1 is represented as a most scale difference, u2 is represented as a most mass difference, u3 is represented as a most crushed difference, u5 is represented as a most selected difference, u4 is represented as a numerical conversion factor of the crushed average ratio, u6 is represented as a numerical conversion adjustment factor of the euzeruman ratio, r2 is represented as a weight coefficient of the crushed average ratio, r1 is represented as a weight coefficient of the euzeruman ratio,wherein r3 is represented as a weight coefficient for comprehensive numerical conversion of a rule mean value, a quality mean value, a broken mean value and a selected mean value, e is represented as a grade conversion deviation adjustment factor, and the grade conversion deviation adjustment factor is calibrated to be a conversion deviation adjustment factor, so that the numerical value of e is calculated according to a calculation formula, wherein other numerical values except e in the calculation formula are known values or preset values;
transmitting the rule average value, the rule difference value, the quality average value, the quality difference value, the average broken value occupation ratio, the average broken value, the average selected value, the optimal difference value, the optimal occupation ratio, the transfer bias adjustment factors, the coal shadow data, the coal rule data, the coal quality data, the coal color data, the coal broken data, the coal total data, the coal grade data and the coal selection data to a sample judgment unit;
the sample judging unit is used for carrying out sample judging operation on the rule average value, the most rule difference value, the quality average value, the most quality difference value, the crushed average value ratio, the most crushed difference value, the average value selection, the most selected difference value, the excellent ratio, the transfer and other deviation adjustment factors, and the coal shadow data, the coal quality data, the coal crushed data, the coal total data, the coal grade data and the coal selected data together, wherein the specific operation process of the sample judging operation is as follows:
selecting coal scale data, coal quality data, coal color data, coal crushing data, coal total data and coal selection data according to the coal shadow data, respectively carrying out difference calculation on the coal scale data, the coal quality data, the coal crushing data and the coal selection data and the scale mean value, the quality mean value, the crushing mean value and the selection mean value, and calculating a coal scale difference value, a coal quality difference value, a coal crushing difference value and a coal selection difference value;
according to the processing mode of the optimal ratio, processing the coal color data to obtain a real ratio, and according to the processing mode of the average ratio, calculating the real ratio;
setting a preset grade value of an acquired sample, calibrating the preset grade value as a pre-grade value, and introducing the pre-grade value, coal scale data, coal quality data, coal crushing data, coal separation data, coal scale difference value, coal quality difference value, coal crushing difference value, coal separation difference value, real gloss ratio, real crushing ratio and conversion deviation adjustment factors into a grade conversion calculation formula;
according to the required coal level data, reversely deducing a level conversion calculation formula, calculating the requirements required by various values in coal mining information, sampling, calibrating the values to sample values, and transmitting the sample values to a sample display unit, wherein the sample values refer to sample values required to be regulated, namely, the values in the level conversion calculation formula in the record are subtracted from the values calculated on the basis of the original calculation formula, so that a regulating difference value is obtained;
the sample display unit receives and displays the sampling value, and a worker carries out processing adjustment of the sample according to the received and displayed sampling value, and the sample display unit is specifically an intelligent computer.
When the invention works, coal data is acquired in real time through the sampling unit, the coal data acquired in real time is calibrated into coal mining information, the coal mining information comprises coal shadow data, coal ruler data, coal quality data, coal color data, coal crushed data, coal total data, coal grade data and coal dressing data, and the coal mining information is transmitted to the sampling unit, cloud carbon information related to a previous coal acquisition sample is stored in the cloud storage unit, the cloud carbon information comprises carbon map data, carbon ruler data, carbon seed data, carbon quality data, carbon color data, carbon crushed data, carbon total data, carbon grade data and carbon dressing data, the sampling unit acquires the carbon map data, the carbon ruler data, the carbon seed data, the carbon quality data, the carbon color data, the carbon crushed data, the carbon total data, the carbon grade data and the carbon dressing data from the cloud storage unit, and performing sample recognition operation on the carbon map data, the carbon ruler data, the carbon seed data, the carbon color data, the carbon crushed data, the carbon total data, the carbon level data and the carbon selection data together with the coal shadow data, the coal ruler data, the coal quality data, the coal color data, the coal crushed data, the coal total data, the coal level data and the coal selection data to obtain carbon ruler data, carbon seed data, carbon color data, the carbon crushed data, the carbon total data, the carbon level data and the carbon selection data corresponding to the carbon map data, the sample primary analysis unit performs sample primary analysis operation on the carbon ruler data, the carbon seed data, the carbon color data, the carbon crushed data, the carbon total data, the carbon grade data and the carbon selection data together with the corresponding coal shadow data, the coal quality data, the coal crushed data, the coal total data, the coal grade data and the coal selection data to obtain a mean value, a most ruler difference value, a quality mean value, a most quality difference value, a crushed mean value, a crushed average ratio, a most crushed difference value, a selected mean value, a most selected difference value, the optimal-gloss ratio, the transformed-average deviation factor, the coal shadow data, the coal ruler data, the coal quality data, the coal color data, the coal broken data, the coal total data, the coal grade data and the coal selection data are transmitted to a sample judgment unit together, the sample judgment unit carries out sample judgment operation on the ruler average value, the most-ruler difference value, the quality average value, the most-quality difference value, the broken average value ratio, the most-broken difference value, the average value, the most-selected difference value, the optimal-gloss ratio, the transformed-average deviation factor, the coal shadow data, the coal quality data, the coal broken data, the coal total data, the coal grade data and the coal selection data together to obtain a sampling value, and the sampling value is transmitted to a sample display unit; and the sample display unit receives and displays the sampling value, and processes and adjusts the sample according to the received and displayed sampling value.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.
Claims (1)
1. The intelligent sampling and preprocessing system for the coal samples is characterized by comprising a sampling unit, a cloud storage unit, a server, a sample primary separation unit, a sample judgment unit and a sample display unit;
the sampling unit is used for collecting coal data in real time, calibrating the coal data collected in real time into coal mining information, and transmitting the coal mining information to the sampling unit;
the cloud storage unit is internally stored with cloud carbon information related to a previous coal collection sample, the sample identification unit acquires cloud carbon information from the cloud storage unit, carries out sample identification operation on the cloud carbon information and coal mining information to obtain carbon ruler data, carbon seed data, carbon color data, carbon fragment data, carbon total data, carbon level data and carbon selection data corresponding to carbon map data, and transmits the carbon ruler data, the carbon seed data, the carbon quality data, the carbon fragment data, the carbon total data, the carbon level data and the carbon selection data to the server;
the server is used for carrying out sample primary analysis operation on carbon ruler data, carbon seed data, carbon quality data, carbon color data, carbon broken data, carbon total data, carbon level data and carbon selection data corresponding to the carbon map data to obtain a ruler average value, a most ruler difference value, a quality average value, a most quality difference value, a broken average value ratio, a most broken difference value, a selected average value, a most selected difference value, a priority ratio, a conversion and other deviation adjustment factors, and transmitting the deviation factors to the sample judgment unit together;
the sample judgment unit acquires coal mining information from the sample recognition unit, and performs sample judgment operation together with deviation adjustment factors such as a rule average value, a rule-most difference value, a quality average value, a prime difference value, a broken average value ratio, a broken difference value, a mean value selection, a most selected difference value, a good gloss ratio and a conversion to obtain a sampling value, and transmits the sampling value to the sample display unit;
the sample display unit receives and displays the sampling value, and a worker carries out processing adjustment of the sample according to the received and displayed sampling value;
the coal mining information comprises coal shadow data, coal ruler data, coal quality data, coal color data, coal breakage data, coal total data, coal grade data and coal separation data; the coal shadow data refers to the image of coal in a coal acquisition sample, the coal ruler data refers to the size of the coal in the coal acquisition sample, the coal quality data refers to the weight of the coal in the coal acquisition sample, the coal color data refers to the color of the coal in the coal acquisition sample, the color is mainly divided into pure black, pollution, yellowing and redness, the coal breakage data refers to the size of fragments dropped by the coal in the coal acquisition sample, the total coal data refers to the sum of the number originally selected by the coal sample in the coal acquisition sample, the coal grade data refers to the quality grade of the coal in the coal acquisition sample, the coal sorting data refers to the sorting time of the coal in the coal acquisition sample in the sample acquisition process, and the coal shadow data, the coal quality data, the coal breakage data, the total coal grade data and the coal sorting data are transmitted to a sample identification unit;
the cloud carbon information comprises carbon map data, carbon ruler data, carbon seed data, carbon color data, carbon crushing data, carbon total data, carbon grade data and carbon selection data; the coal map data refer to the appearance images of coal in the traditional coal collection samples, the coal ruler data refer to the size of the coal in the traditional coal collection samples, the coal seed data refer to the variety type of the coal in the traditional coal collection samples, the coal color data refer to the weight of the coal in the traditional coal collection samples, the colors are mainly divided into pure black, dirty, yellow and red black, the crushed data refer to the size of fragments dropped by the coal in the traditional coal collection samples, the total coal data refer to the sum of the quantity of the coal samples originally selected in the traditional coal collection samples, the grade of the coal in the traditional coal collection samples is represented by a numerical value, and the coal selection data refer to the time of sorting the coal in the traditional coal collection samples in the sample collection process;
the specific operation process of the sample identification operation is as follows:
acquiring charcoal image data and coal shadow data, matching the charcoal image data with the coal shadow data to obtain a matching error signal and a matching correct signal, transmitting the matching error signal to a sampling unit, and acquiring coal mining information again by the sampling unit according to the matching error signal;
selecting carbon map data corresponding to the coal shadow data, and carbon ruler data, carbon seed data, carbon quality data, carbon color data, carbon crushing data, carbon total data, carbon level data and carbon selection data corresponding to the carbon map data according to the matching correct signals;
the specific operation process of the primary analysis operation of the sample is as follows:
selecting carbon ruler data, carbon color data, carbon crushing data, carbon total data, carbon grade data and carbon selection data corresponding to carbon seed data;
selecting carbon ruler data corresponding to a plurality of groups of carbon seed data in the record, calculating the average size of the carbon ruler data by calculating the average value of the carbon ruler data, and calibrating the average size of the carbon ruler data as the ruler average value;
calculating the difference value between the rule average value and the plurality of carbon rule data to calculate a plurality of carbon rule difference values, sorting the plurality of carbon rule difference values from large to small to obtain a carbon rule difference value sorting, and calibrating the value of the first sorting value in the carbon rule difference value sorting as the most rule difference value;
according to the calculation method of the ruler average value and the most ruler difference value, processing the carbonaceous data into a mass average value and a most mass difference value;
according to the calculation method of the rule average value and the rule difference value, the carbon crushing data are processed into a crushing average value, a rule difference value and a crushing average occupation ratio;
according to the calculation method of the rule average value and the most rule difference value, processing the carbon selection data into a selection average value and a most selection difference value;
performing duty ratio processing and grading range processing on the carbon color data and the carbon data respectively to obtain a priority duty ratio and a grading range value;
the ruler average value, the most ruler difference value, the quality average value, the most quality difference value, the crushed average value ratio, the most crushed difference value, the average value selection, the most selected difference value, the excellent ratio and the grade average value which correspond to the carbon ruler data are taken into a grade conversion calculation formula together, a grade conversion deviation adjustment factor e is calculated, and the grade conversion deviation adjustment factor e is calibrated to be a conversion deviation adjustment factor;
the specific treatment process of the average broken value, the most broken difference value and the average broken ratio is as follows:
selecting the carbon crushed data corresponding to a plurality of groups of carbon seed data in the record, carrying out summation calculation on the sizes of the plurality of carbon crushed data, dividing the numerical value obtained after summation calculation by the total number of the carbon crushed data, thus calculating the average crushing size of the carbon crushed data, calibrating the average crushing size of the carbon crushed data as a crushing average value, carrying out duty ratio calculation on the crushing average value and the carbon total data corresponding to the carbon seed data, and calculating the crushing average duty ratio;
calculating the difference value between the crushed average value and the plurality of pieces of crushed data to calculate a plurality of pieces of crushed difference values, sorting the plurality of pieces of crushed difference values from large to small to obtain a crushed difference value sorting, and calibrating the value of the first rank in the crushed difference value sorting as the most crushed difference value;
the specific process of carrying out the duty ratio treatment on the carbon color data comprises the following steps:
selecting carbon color data corresponding to a plurality of carbon seed data, identifying and calibrating the carbon color data, when pure black is identified, calibrating the carbon color data into high-quality color, when an impurity color outside the pure black is identified, calibrating the carbon color data into poor color, counting the times of the high-quality color and the poor color, and performing duty ratio calculation, wherein the ratio of the good color to the poor color is/(the times of the poor color and the times of the high-quality color);
the specific process for grading and ranging the carbon data comprises the following steps:
selecting carbon level data corresponding to a plurality of groups of carbon seed data in a record, carrying out average value calculation on the plurality of carbon level data, calculating a level average value, carrying out difference value calculation on the plurality of carbon level data and the level average value, calculating a difference value between the carbon level data and the level average value, sorting the carbon level difference value from large to small, thereby obtaining carbon level sorting data, selecting a first numerical value in the carbon level sorting data, calibrating the first numerical value as a numerical value, carrying out addition and subtraction calculation on the numerical value and the level average value, and calculating a grading range value, wherein the specific process is as follows: the method comprises the steps of (1) combining a maximum value and a minimum value to form a grading range value, calibrating carbon-based data smaller than the minimum value as low-grade coal, calibrating carbon-based data in the grading range value as synthetic coal, and calibrating carbon-based data larger than the maximum value as high-grade coal;
the grade conversion calculation method specifically comprises the following steps:
wherein JJ is represented as a level average, C1 is represented as a scale average, C2 is represented as a mass average, C3 is represented as a broken average, C4 is represented as a broken average ratio, C5 is represented as a selected average, C6 is represented as a euzest ratio, u1 is represented as a most-scale difference, u2 is represented as a most-mass difference, u3 is represented as a most-broken difference, u5 is represented as a most-selected difference, u4 is represented as a numerical conversion factor of the broken average ratio, u6 is represented as a numerical conversion adjustment factor of the euzest ratio, r2 is represented as a weight coefficient of the broken average ratio, r1 is represented as a weight coefficient of the euzest ratio, wherein r3 is represented as a weight coefficient of the integrated numerical conversion of the scale average, the mass average, the broken average and the selected average, e is represented as a level conversion deviation adjustment factor, and is marked as a conversion adjustment factor, so that the value of e is calculated according to a calculation formula, wherein other values except e are known values or preset values;
the specific operation process of the sample judgment operation is as follows:
selecting coal scale data, coal quality data, coal color data, coal crushing data, coal total data and coal selection data according to the coal shadow data, respectively carrying out difference calculation on the coal scale data, the coal quality data, the coal crushing data and the coal selection data and the scale mean value, the quality mean value, the crushing mean value and the selection mean value, and calculating a coal scale difference value, a coal quality difference value, a coal crushing difference value and a coal selection difference value;
according to the processing mode of the optimal ratio, processing the coal color data to obtain a real ratio, and according to the processing mode of the average ratio, calculating the real ratio;
setting a preset grade value of an acquired sample, calibrating the preset grade value as a pre-grade value, and introducing the pre-grade value, coal scale data, coal quality data, coal crushing data, coal separation data, coal scale difference value, coal quality difference value, coal crushing difference value, coal separation difference value, real gloss ratio, real crushing ratio and conversion deviation adjustment factors into a grade conversion calculation formula;
and according to the required coal level data, reversely deducing a level conversion calculation formula, calculating the requirements for various values in the coal mining information, sampling, calibrating the values into sampling values, and transmitting the sampling values to a sample display unit.
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