CN-121998656-A - Coal quality supervision method and system based on big data and infrared measurement

CN121998656ACN 121998656 ACN121998656 ACN 121998656ACN-121998656-A

Abstract

The invention provides a coal quality supervision method and system based on big data and infrared measurement, wherein the method comprises the steps of establishing and storing carriage size data and empty weight data of a coal transporting vehicle to a database, scanning a coal pile in a carriage through an infrared three-dimensional scanner after the vehicle loads coal to obtain coal pile surface profile data, calculating the loading volume of the coal pile according to the carriage size data and the coal pile surface profile data, obtaining the total weight of the vehicle through an automobile scale, combining the corresponding empty weight in the database to obtain the net weight of the coal, calculating the real-time density of the current coal according to the net weight and the loading volume of the coal, calling a weighted average value of the historical density in the same batch as the current coal from a big data platform, comparing the weighted average value of the real-time density with the historical density, and triggering abnormal coal early warning if the deviation exceeds a preset threshold value. The efficiency and the accuracy of coal quality supervision are effectively improved.

Inventors

NIU GUODONG
LIANG YONGJI
WANG ZENGGANG
HU JIAWEI
WANG KAIJIANG
TU DAN
ZHAO JIE
QIN YAN

Assignees

华能铜川照金煤电有限公司

Dates

Publication Date: 20260508
Application Date: 20251222

Claims (10)

1. The coal quality monitoring method based on big data and infrared measurement is characterized by comprising the following steps of: Establishing and storing carriage size data and empty weight data of the coal-carrying vehicle into a database; After the coal is loaded on the vehicle, scanning a coal pile in a carriage by an infrared three-dimensional scanner to obtain surface profile data of the coal pile; Calculating the loading volume of the coal pile according to the carriage size data and the coal pile surface profile data; acquiring the total weight of the vehicle through an automobile scale, and calculating to obtain the net weight of coal by combining the corresponding empty weight in a database; calculating the real-time density of the currently transported coal according to the net weight of the coal and the loading volume; the historical density weighted average value of the same batch as the current coal is called from the big data platform; And comparing the real-time density with the weighted average value of the historical density, and triggering abnormal early warning of the coal quality if the deviation exceeds a preset threshold value.
2. The coal quality supervision method based on big data and infrared measurement according to claim 1, further comprising, before the creating and storing the cabin size data and the empty weight data of the coal transportation vehicle in the database: Collecting the length, width and height dimensions of the inner wall of a carriage of the coal-carrying vehicle, and storing the dimensions in a database in a correlated manner; an infrared calibration system is arranged in the empty car channel, and external dimension scanning is carried out on the passing empty car to obtain calibration scanning data; And comparing the calibration scanning data with the recorded data in the database, and prohibiting the coal transportation vehicle from entering a heavy vehicle detection flow if the error exceeds a set range.
3. The coal quality monitoring method based on big data and infrared measurement according to claim 1, wherein the calculating the loading volume of the coal pile comprises: Acquiring three-dimensional point cloud data of the coal pile surface through the infrared three-dimensional scanner; reconstructing a compartment internal space model according to compartment size data recorded in the database; and calculating the actual occupied volume of the coal pile as a loading volume through registration of the three-dimensional point cloud data and the compartment internal space model.
4. The coal quality monitoring method based on big data and infrared measurement according to claim 1, wherein the retrieving the historical density weighted average of the same batch as the current coal from the big data platform comprises: extracting density records of all historical transportation of the same batch from a big data platform according to the current transportation mine point information and batch number; weighting the historical density data based on the time distance, and calculating to obtain a dynamically updated weighted average density as a historical density weighted average; and setting a density deviation threshold based on statistical analysis, comparing the real-time density with the weighted average density, and judging whether to trigger abnormal coal early warning according to the comparison result.
5. The coal quality monitoring method based on big data and infrared measurement according to claim 4, further comprising: recording all the information of the coal transportation vehicles triggering the abnormal early warning of the coal quality and the subsequent manual test results; dynamically adjusting the density deviation threshold according to the consistency of the manual test result and the system early warning judgment; And updating the calculated weight of the weighted average density according to the newly added data, so that the weighted average of the historical density is adaptively optimized.
6. The coal quality monitoring method based on big data and infrared measurement according to claim 2, wherein the comparing the calibration scan data with the recorded data in the database comprises: If the error between the calibration scanning data and the recorded data is within a first preset range, generating a calibration prompt and automatically suggesting to correct the recorded data; And if the error exceeds a second preset range, triggering structural abnormality alarm and forcing the coal transportation vehicle to exit the current detection flow until recordation is completed.
7. The coal quality monitoring method based on big data and infrared measurement according to claim 1, wherein the calculating of the net weight of coal by combining the corresponding empty weight in the database comprises: and automatically associating the coal-carrying vehicle with corresponding data by identifying the license plate number of the coal-carrying vehicle and/or reading RFID electronic tag information arranged on the coal-carrying vehicle.
8. The coal quality supervision method based on big data and infrared measurement according to claim 1, wherein after the triggering of the coal quality abnormality pre-warning, further comprising: outputting an audible and visual alarm signal, and marking the corresponding coal transportation vehicle information as an abnormal state on a monitoring interface; Guiding a supervisor to manually sample and test the coal carried by the coal transportation vehicle corresponding to the abnormal state; And (3) the final coal quality data obtained by manual assay is recorded into a system and is stored in association with the real-time density data calculated at this time to form a closed-loop feedback record.
9. The coal quality monitoring method based on big data and infrared measurement according to any of claims 1-8, wherein the big data platform contains a plurality of logically related databases for supporting the operation of the method, the databases comprising at least: the vehicle archive is used for storing the record size, the empty vehicle quality and the calibration history of the coal transportation vehicle; the transportation transaction library is used for storing mine points, batches, weight, volume and calculating density information according to train numbers; the density benchmark library is used for storing and dynamically updating density statistics benchmark values according to the dimensions of the mine points and the batch; and the alarm log library is used for recording all early warning events and subsequent processing results.
10. A coal quality monitoring system based on big data and infrared measurement, comprising: The building module is used for building and storing carriage size data and empty weight data of the coal-carrying vehicle into a database; the scanning module is used for scanning the coal pile in the carriage through the infrared three-dimensional scanner after the coal is loaded on the vehicle, so as to acquire the surface profile data of the coal pile; the calculation module is used for calculating the loading volume of the coal pile according to the carriage size data and the coal pile surface profile data, acquiring the total weight of the vehicle through an automobile scale, and combining the corresponding empty weight in a database to calculate the net weight of the coal; And the early warning module is used for calling a historical density weighted average value of the same batch as the current coal from the big data platform, comparing the real-time density with the historical density weighted average value, and triggering abnormal early warning of the coal quality if the deviation exceeds a preset threshold value.

Description

Coal quality supervision method and system based on big data and infrared measurement Technical Field The invention relates to the technical field of coal quality supervision, in particular to a coal quality supervision method and system based on big data and infrared measurement. Background Coal is an important energy source and industrial raw material, and the quality of the coal directly influences combustion efficiency, pollutant emission and equipment safety. In the links of coal purchasing, transporting and factory entering acceptance, effective supervision on coal quality is a key for ensuring fuel quality, controlling cost and realizing compliance operation. The traditional coal quality supervision method mainly relies on manual sampling and laboratory testing, and the typical flow is that a supervision staff randomly extracts part of coal samples and sends the samples to a laboratory for industrial analysis, elemental analysis and calorific value measurement. In order to improve efficiency, some automatic sampling and online detection devices have appeared in recent years, but are still limited to fixed-point and contact measurement, and cannot be linked with vehicle identity, transportation batch and historical data, so that a monitoring system for the whole transportation process, data driving and intelligent judgment cannot be formed. Particularly in the transportation link, how to quickly, non-contact and non-omission identify the abnormal coal quality is still lack of effective technical means. Therefore, a coal quality monitoring method capable of realizing automation, real-time and whole course and effectively preventing cheating is needed to improve the efficiency and accuracy of coal quality monitoring. Disclosure of Invention The invention provides a coal quality supervision method and system based on big data and infrared measurement, which are used for solving the defects of low automation level and poor supervision efficiency and accuracy of coal quality supervision. On one hand, the invention provides a coal quality monitoring method based on big data and infrared measurement, which comprises the following steps: Establishing and storing carriage size data and empty weight data of the coal-carrying vehicle into a database; After the coal is loaded on the vehicle, scanning a coal pile in a carriage by an infrared three-dimensional scanner to obtain surface profile data of the coal pile; Calculating the loading volume of the coal pile according to the carriage size data and the coal pile surface profile data; acquiring the total weight of the vehicle through an automobile scale, and calculating to obtain the net weight of coal by combining the corresponding empty weight in a database; calculating the real-time density of the currently transported coal according to the net weight of the coal and the loading volume; the historical density weighted average value of the same batch as the current coal is called from the big data platform; And comparing the real-time density with the weighted average value of the historical density, and triggering abnormal early warning of the coal quality if the deviation exceeds a preset threshold value. According to the coal quality supervision method based on big data and infrared measurement provided by the invention, before the carriage size data and the empty weight data of the coal transportation vehicle are established and stored in the database, the method further comprises the following steps: Collecting the length, width and height dimensions of the inner wall of a carriage of the coal-carrying vehicle, and storing the dimensions in a database in a correlated manner; an infrared calibration system is arranged in the empty car channel, and external dimension scanning is carried out on the passing empty car to obtain calibration scanning data; And comparing the calibration scanning data with the recorded data in the database, and prohibiting the coal transportation vehicle from entering a heavy vehicle detection flow if the error exceeds a set range. According to the coal quality monitoring method based on big data and infrared measurement, the method for calculating the loading volume of the coal pile comprises the following steps: Acquiring three-dimensional point cloud data of the coal pile surface through the infrared three-dimensional scanner; reconstructing a compartment internal space model according to compartment size data recorded in the database; and calculating the actual occupied volume of the coal pile as a loading volume through registration of the three-dimensional point cloud data and the compartment internal space model. According to the coal quality supervision method based on big data and infrared measurement, the historical density weighted average value of the same batch as the current coal is called from a big data platform, and the method comprises the following steps: extracting density records of all historical transportati