CN116662891A - Working state identification method of coal mining machine - Google Patents
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Abstract
The application discloses a working state identification method of a coal mining machine, relates to the technical field of coal mining, and can improve the efficiency of identifying the working state of the coal mining machine to a certain extent. The working state identification method of the coal mining machine comprises the steps of collecting time sequence data of the number of the hydraulic support in a preset period, and undersampling the obtained time sequence data to obtain preprocessing data; acquiring a first section and a second section based on time sequence data of the hydraulic support numbers in a preset period, wherein the first section is configured to identify a high-order triangular coal acquisition area, and the second section is configured to identify a low-order triangular coal acquisition area; establishing a sliding window, identifying each data point in the preprocessed data through the sliding window to obtain key points, classifying and judging each key point based on the first interval and the second interval, and calculating and outputting the number of cutting tool holders, the miswork time and the overhaul time.
Description
Technical Field
The application relates to the technical field of coal mining, in particular to a working state identification method of a coal mining machine.
Background
The fully-mechanized coal mining working face is a stoping working face of comprehensive mechanized equipment of underground coal mine equipment, and is the forefront and the most complex working link of coal mine production. A standard fully-mechanized mining face is generally composed of 200-300 pieces of equipment, and core equipment comprises a coal mining machine, a scraper conveyor, a hydraulic support, a reversed loader, a crusher, a belt conveyor and the like. The classification of the working conditions of a shearer generally includes the following: the coal is broken, the work is mishandled and overhauled, and the working state of the coal cutter is identified, so that the enterprise can be helped to analyze the coal mining progress, and the coal mining efficiency is improved.
The method for identifying the working state of the coal mining machine disclosed in the related art is to manually analyze and identify the position data of the hydraulic support recorded when the coal mining machine works under the mine, and then obtain the working state of the coal mining machine based on the result.
Because the coal mining machine can generate more recorded data in the running process, workers analyze and identify the working state of the coal mining machine by analyzing the recorded data, and the defect of low efficiency of identifying the working state of the coal mining machine exists in the process.
Disclosure of Invention
The working state identification method of the coal mining machine can improve the efficiency of identifying the working state of the coal mining machine to a certain extent.
The embodiment of the application provides a working state identification method of a coal mining machine, which comprises the following steps:
acquiring time sequence data of the serial numbers of the hydraulic supports in a preset period, and undersampling the acquired time sequence data to obtain preprocessing data;
acquiring a first section and a second section based on time sequence data of the hydraulic support numbers in a preset period, wherein the first section is configured to identify a high-order triangular coal acquisition area, and the second section is configured to identify a low-order triangular coal acquisition area;
establishing a sliding window, identifying each data point in the preprocessed data through the sliding window to obtain key points, classifying and judging each key point based on the first interval and the second interval, and calculating and outputting the number of cutting tool holders, the miswork time and the overhaul time.
In some embodiments, the undersampling the acquired time series data to obtain the preprocessed data includes:
adding a first interval, dividing the acquired time sequence data of the hydraulic support number into a plurality of pretreatment areas, acquiring a data point in each pretreatment area, and arranging the plurality of acquired data points in time sequence to acquire pretreatment data; the width of each pretreatment area is the length corresponding to the first interval.
In some embodiments, the acquiring the first interval and the second interval includes:
acquiring the initial position and the final position of the serial numbers of the hydraulic support acquired by the triangular coal;
carrying out probability density estimation on time sequence data of the hydraulic support numbers in a preset period;
acquiring a corresponding hydraulic support number data interval by triangular coal with the maximum output probability;
identifying and acquiring a first interval and a second interval; the first section is a section where hydraulic support number data close to the initial position is located, and the second section is a section where hydraulic support number data close to the final position is located.
In some embodiments, the establishing a sliding window includes:
adding a second interval, and establishing a sliding window, wherein the width of the sliding window is twice the length corresponding to the second interval;
the sliding window comprises a first area and a second area which are adjacently arranged, and the widths of the first area and the second area are the lengths corresponding to the second interval.
In some embodiments, the identifying the preprocessed data through the sliding window to obtain the keypoints comprises:
sequentially identifying each data point in the preprocessed data in time sequence through a sliding window;
acquiring the slope of a connecting line formed by adjacent data points in the first area and the second area of each data point and judging whether the current data point is a key point or not;
if the current data point is a key point, reading and storing time sequence data of the hydraulic support number corresponding to the data point; if the current data point is not a key point, identifying the next data point according to time sequence;
the key points are that the connecting lines formed by the data points in the corresponding first area are a line segment, and the connecting lines formed by the data points in the second area are also a line segment.
In some embodiments, the identifying each data point in the pre-processed data in chronological order through the sliding window comprises:
and if the length of the data points contained in the first area or the second area of the current data point is smaller than the second interval, reading the maximum data points contained in the first area or the second area to identify the current data point.
In some embodiments, the classifying each keypoint based on the first interval and the second interval includes:
traversing all key points based on a first interval, and identifying the key points in the first interval as starting and ending points of high-level triangular coal;
traversing all key points based on the second interval, and identifying the key points in the second interval as starting and ending points of low-level triangular coal;
counting the number of key points between the starting and ending points of all the high-level triangular coals and the starting and ending points of the low-level triangular coals, and outputting the number of the key points as the number of cutting tool holders.
In some embodiments, the classifying each keypoint based on the first interval and the second interval further comprises:
searching a key point with the slope of the connecting line of the adjacent data points in the next first area being 0 and the slope of the connecting line of the adjacent data points in the second area being positive or negative when the slope of the connecting line of the adjacent data points in the first area of the current key point is positive or negative and the slope of the connecting line of the adjacent data points in the second area is 0;
and acquiring the interval time of the two key points and judging the error time or the overhaul time.
In some embodiments, the acquiring the interval time of the two key points and making the mishour or service time judgment includes:
when the interval time of the two key points is smaller than a first threshold value, ignoring the corresponding time period;
when the interval time of the two key points is greater than or equal to a first threshold value, the corresponding time period is identified as a false work time period;
when the interval time of the two key points is larger than the second threshold value, the corresponding time period is identified as the overhaul time period.
In some embodiments, the calculating and outputting the number of cutting tools holders, the time to miswork, and the time to service further comprises:
after all maintenance time periods in a preset period are acquired, judging two adjacent maintenance time periods in sequence;
when the head-to-tail time length of two adjacent overhaul time periods is smaller than a third threshold value, merging the two overhaul time periods into one overhaul time period, and modifying the corresponding overhaul time length; otherwise, the next two adjacent overhaul periods are judged.
One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
according to the embodiment of the application, the acquired time sequence data of the serial numbers of the hydraulic support in the preset period is undersampled, so that the capacity occupied by the data in the sample is reduced; the pre-processed data after undersampling is further screened according to the sliding window, key points in the pre-processed data are obtained, the capacity occupied by sample data can be further reduced, and therefore the recognition efficiency of the working state of the coal mining machine is improved, and the working state of the coal mining machine is conveniently recognized with higher accuracy; meanwhile, the first section and the second section are obtained based on the obtained time sequence data of the hydraulic support numbers of the coal mining machine, so that the number of cutting tool holders in the working process of the coal mining machine is calculated, and the defect of low efficiency of manually calculating the number of cutting tool holders is overcome.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments of the present application will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a coal mining machine working state identification method provided by an embodiment of the application;
FIG. 2 is a schematic diagram of a coal mining machine working state recognition method according to an embodiment of the present application;
fig. 3 is a schematic diagram of data points identified as key points in the method for identifying the working state of the coal mining machine according to the embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In the description of the embodiments of the present application, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," "outer," and the like indicate or are based on the orientation or positional relationship shown in the drawings, merely to facilitate description of the embodiments of the present application and simplify description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present application. The terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Furthermore, the terms "mounted," "connected," "coupled," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the embodiments of the present application will be understood by those of ordinary skill in the art according to specific circumstances.
Referring to fig. 1 and 2, fig. 1 is a flowchart of a method for identifying a working state of a coal mining machine according to an embodiment of the present application; fig. 2 is a schematic structural diagram of a coal mining machine working state identification method according to an embodiment of the present application.
In some embodiments, the present application provides a method for identifying the working state of a coal mining machine, which includes steps 110, 120 and 130.
Step 110, collecting the time sequence data of the serial numbers of the hydraulic supports in a preset period, and undersampling the obtained time sequence data to obtain preprocessing data.
Step 120, acquiring a first section and a second section based on time sequence data of the hydraulic support numbers in a preset acquisition period, wherein the first section is configured to identify a high-order triangular coal acquisition area, and the second section is configured to identify a low-order triangular coal acquisition area;
and 130, establishing a sliding window, identifying each data point in the preprocessed data through the sliding window to obtain key points, classifying and judging each key point based on the first interval and the second interval, and calculating and outputting the number of cutting tool holders, the miswork time and the overhaul time.
Because the time sequence data of the hydraulic support numbers are obtained by recording the installed sensors at intervals of a few seconds, the data volume is large, the time sequence data of the hydraulic support numbers are sampled in an undersampling mode, the characteristic that the time sequence data of the hydraulic support numbers are continuously changed is utilized, therefore preprocessing data with small occupied capacity is obtained, and then data points in the preprocessing data are sequentially identified through a sliding window, so that the efficiency of acquiring the working state of the coal mining machine can be improved.
Undersampling the acquired time sequence data of the hydraulic support numbers in a preset period, so that the capacity occupied by the data in the sample is reduced; the pre-processed data after undersampling is further screened according to the sliding window, key points in the pre-processed data are obtained, the capacity occupied by sample data can be further reduced, and therefore the recognition efficiency of the working state of the coal mining machine is improved, and the working state of the coal mining machine is conveniently recognized with higher accuracy; meanwhile, the first section and the second section are obtained based on the obtained time sequence data of the hydraulic support numbers of the coal mining machine, so that the number of cutting tool holders in the working process of the coal mining machine is calculated, and the defect of low efficiency of manually calculating the number of cutting tool holders is overcome.
In some embodiments, the method provided by the application includes, when undersampling acquired time series data to obtain preprocessed data: adding a first interval, dividing the acquired time sequence data of the hydraulic support number into a plurality of pretreatment areas, acquiring a data point in each pretreatment area, wherein the data point is the maximum value in each pretreatment area, and arranging the plurality of acquired data points in time sequence to acquire pretreatment data; the width of each pretreatment area is the length corresponding to the first interval.
By adding the first interval, the capacity of the preprocessed data can be reduced to the capacity of the acquired time sequence data divided by the capacity of the first interval, the workload of subsequent sample identification and judgment can be greatly reduced, and the aim of greatly improving the efficiency of identifying the working state of the coal mining machine can be fulfilled.
In some embodiments, the method for identifying a working state of a coal mining machine provided by the application includes, when acquiring a first interval and a second interval: acquiring the initial position and the final position of the serial numbers of the hydraulic support acquired by the triangular coal; carrying out probability density estimation on time sequence data of the hydraulic support numbers in a preset period; acquiring a corresponding hydraulic support number data interval by triangular coal with the maximum output probability; identifying and acquiring a first interval and a second interval; the first section is a section where hydraulic support number data close to the initial position is located, and the second section is a section where hydraulic support number data close to the final position is located.
Based on the time sequence data of the hydraulic support of the coal mining machine acquired by acquisition, the limit critical value of the hydraulic support when triangular coal is acquired, and then the first section and the second section are acquired based on a probability distribution mode, so that the probability of errors generated in the calculation process of the first section and the second section can be reduced, and the reliability of identifying the working state of the coal mining machine can be improved.
In some embodiments, the method for identifying the working state of the coal mining machine provided by the application comprises the following steps of: adding a second interval, and establishing a sliding window, wherein the width of the sliding window is twice the length corresponding to the second interval; the sliding window comprises a first area and a second area which are adjacently arranged, the widths of the first area and the second area are the lengths corresponding to the second interval, and the length of the second interval is larger than that of the first interval.
For each data point of the preprocessing data, the first area and the second area both comprise a plurality of adjacent data points, the current data point is judged through the plurality of adjacent data points in the first area and the second area, the probability of error in the whole analysis process when the data point is an abnormal value can be reduced to a certain extent, and the reliability of identifying the working state of the coal mining machine can be greatly improved.
Referring to fig. 3, fig. 3 is a schematic diagram illustrating data points identified as key points in a method for identifying a working state of a coal mining machine according to an embodiment of the present application.
In some embodiments, the method provided by the application includes the steps of: sequentially identifying each data point in the preprocessed data in time sequence through a sliding window; acquiring the slope of a connecting line formed by adjacent data points in the first area and the second area of each data point and judging whether the current data point is a key point or not; if the current data point is a key point, reading and storing time sequence data of the hydraulic support number corresponding to the data point; if the current data point is not a key point, identifying the next data point according to time sequence; the connecting line formed by the adjacent data points in the first area corresponding to the key point is a line segment, and the connecting line formed by the adjacent data points in the second area is also a line segment.
When the connecting line in the first area corresponding to the current data point is a line segment and the connecting line in the second area is a line segment, the probability that the current data point is an abnormal point can be eliminated, and therefore the probability that accuracy is reduced due to abnormal data recording in the data analysis process is reduced.
In some embodiments, identifying each data point in the pre-processed data in chronological order through the sliding window comprises: and if the length of the data points contained in the first area or the second area of the current data point is smaller than the second interval, reading the maximum data points contained in the first area or the second area to identify the current data point.
In some embodiments, the method provided by the present application includes, when performing classification judgment on each key point based on the first interval and the second interval: traversing all key points based on a first interval, and identifying the key points in the first interval as starting and ending points of high-level triangular coal; traversing all key points based on the second interval, and identifying the key points in the second interval as starting and ending points of low-level triangular coal; counting the number of key points between the starting and ending points of all the high-level triangular coals and the starting and ending points of the low-level triangular coals, and outputting the number of the key points as the number of cutting tool holders.
Counting the number of cutting tool holders corresponding to the whole time sequence data, merging the data before the first key point and after the last key point, and adding one to the number of the cutting tool holders if the total number of the merged hydraulic support is greater than k% of the total number of the hydraulic support type, wherein k is an over-parameter which can be adjusted according to the working state of an actual coal mining machine, and the value of k is generally greater than 90; otherwise, the output number of the cutting tool holders is the calculated number of the cutting tool holders, and the corresponding time of each tool is output.
In some embodiments, the method provided by the present application further includes, when executing the classification judgment on each key point based on the first interval and the second interval: searching a key point with the slope of the connecting line of the adjacent data points in the next first area being 0 and the slope of the connecting line of the adjacent data points in the second area being positive or negative when the slope of the connecting line of the adjacent data points in the first area of the current key point is positive or negative and the slope of the connecting line of the adjacent data points in the second area is 0; and acquiring the interval time of the two key points and judging the error time or the overhaul time.
And when the slope of the connecting line of the first area or the second area of the key point is 0, indicating that the coal mining machine is in a shutdown state, and judging the time of the coal mining machine in the shutdown state, so as to obtain the miswork time and the overhaul time.
In some embodiments, the method provided by the application comprises the following steps of obtaining the interval time of two key points and judging the error time or the overhaul time: when the interval time of the two key points is smaller than a first threshold value, ignoring the corresponding time period; when the interval time of the two key points is greater than or equal to a first threshold value, the corresponding time period is identified as a false work time period; when the interval time of the two key points is larger than the second threshold value, the corresponding time is identified as a maintenance time period. The first threshold is a preset value set according to the working state of the actual coal mining machine, the first threshold is far smaller than the second threshold, the second threshold is set to be smaller than 25% of the empirical overhaul duration, and the second threshold is an adjustable super parameter set according to the working state of the actual coal mining machine.
The data quantity for counting the whole false work time in the later period is reduced by deleting the time period which is smaller than the first threshold value, so that the efficiency for counting the whole false work time is improved; by identifying a time period greater than the second threshold as a service time period, efficiency in acquiring the overall service time is improved.
In some embodiments, the method provided by the application further comprises the steps of calculating and outputting the number of the cutting tool holders, the working error time and the overhauling time: after all maintenance time periods in a preset period are acquired, judging two adjacent maintenance time periods in sequence; when the head-to-tail time length of two adjacent overhaul time periods is smaller than a third threshold value, merging the two overhaul time periods into one overhaul time period, and modifying the corresponding overhaul time length; otherwise, the next two adjacent overhaul periods are judged.
In order to facilitate the further identification of the overhaul time period, the third threshold value is used for judging two adjacent overhaul time periods, so that the method can be attached to actual working conditions more, the probability that subsequent calculation is affected due to large data volume can be reduced, and the accuracy of acquiring the working state of the coal mining machine is improved.
According to the embodiment of the application, the acquired time sequence data of the serial numbers of the hydraulic support in the preset period is undersampled, so that the capacity occupied by the data in the sample is reduced; the pre-processed data after undersampling is further screened according to the sliding window, key points in the pre-processed data are obtained, the capacity occupied by sample data can be further reduced, and therefore the recognition efficiency of the working state of the coal mining machine is improved, and the working state of the coal mining machine is conveniently recognized with higher accuracy; meanwhile, the first section and the second section are obtained based on the obtained time sequence data of the hydraulic support numbers of the coal mining machine, so that the number of cutting tool holders in the working process of the coal mining machine is calculated, and the defect of low efficiency of manually calculating the number of cutting tool holders is overcome.
In this specification, each embodiment is described in a progressive manner, and the same or similar parts of each embodiment are referred to each other, and each embodiment is mainly described as a difference from other embodiments.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the present application; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced with equivalents; such modifications and substitutions do not depart from the spirit of the application.
Claims (10)
1. A method for identifying working conditions of a coal mining machine, the method comprising:
acquiring time sequence data of the serial numbers of the hydraulic supports in a preset period, and undersampling the acquired time sequence data to obtain preprocessing data;
acquiring a first section and a second section based on time sequence data of the hydraulic support numbers in a preset period, wherein the first section is configured to identify a high-order triangular coal acquisition area, and the second section is configured to identify a low-order triangular coal acquisition area;
establishing a sliding window, identifying each data point in the preprocessed data through the sliding window to obtain key points, classifying and judging each key point based on the first interval and the second interval, and calculating and outputting the number of cutting tool holders, the miswork time and the overhaul time.
2. The method for recognizing a working condition of a coal mining machine according to claim 1, wherein the undersampling the acquired time series data to obtain preprocessed data comprises:
adding a first interval, dividing the acquired time sequence data of the hydraulic support number into a plurality of pretreatment areas, acquiring a data point in each pretreatment area, and arranging the plurality of acquired data points in time sequence to acquire pretreatment data; the width of each pretreatment area is the length corresponding to the first interval.
3. The method of claim 1, wherein the acquiring the first interval and the second interval comprises:
acquiring the initial position and the final position of the serial numbers of the hydraulic support acquired by the triangular coal;
carrying out probability density estimation on time sequence data of the hydraulic support numbers in a preset period;
acquiring a corresponding hydraulic support number data interval by triangular coal with the maximum output probability;
identifying and acquiring a first interval and a second interval; the first section is a section where hydraulic support number data close to the initial position is located, and the second section is a section where hydraulic support number data close to the final position is located.
4. The method of claim 1, wherein the establishing a sliding window comprises:
adding a second interval, and establishing a sliding window, wherein the width of the sliding window is twice the length corresponding to the second interval;
the sliding window comprises a first area and a second area which are adjacently arranged, and the widths of the first area and the second area are the lengths corresponding to the second interval.
5. The method for recognizing a working condition of a coal mining machine according to claim 4, wherein the step of recognizing the preprocessing data through the sliding window to obtain the key point comprises:
sequentially identifying each data point in the preprocessed data in time sequence through a sliding window;
acquiring the slope of a connecting line formed by adjacent data points in the first area and the second area of each data point and judging whether the current data point is a key point or not;
if the current data point is a key point, reading and storing time sequence data of the hydraulic support number corresponding to the data point; if the current data point is not a key point, identifying the next data point according to time sequence;
the key points are that the connecting lines formed by the data points in the corresponding first area are a line segment, and the connecting lines formed by the data points in the second area are also a line segment.
6. The method of claim 5, wherein the sequentially identifying each data point in the preprocessed data in time sequence through the sliding window comprises:
and if the length of the data points contained in the first area or the second area of the current data point is smaller than the second interval, reading the maximum data points contained in the first area or the second area to identify the current data point.
7. The method of claim 6, wherein the classifying each key point based on the first interval and the second interval comprises:
traversing all key points based on a first interval, and identifying the key points in the first interval as starting and ending points of high-level triangular coal;
traversing all key points based on the second interval, and identifying the key points in the second interval as starting and ending points of low-level triangular coal;
counting the number of key points between the starting and ending points of all the high-level triangular coals and the starting and ending points of the low-level triangular coals, and outputting the number of the key points as the number of cutting tool holders.
8. The method for recognizing a working condition of a coal mining machine according to claim 7, wherein the classifying judgment of each key point based on the first section and the second section further comprises:
searching a key point with the slope of the connecting line of the adjacent data points in the next first area being 0 and the slope of the connecting line of the adjacent data points in the second area being positive or negative when the slope of the connecting line of the adjacent data points in the first area of the current key point is positive or negative and the slope of the connecting line of the adjacent data points in the second area is 0;
and acquiring the interval time of the two key points and judging the error time or the overhaul time.
9. The method for identifying a working state of a coal mining machine according to claim 8, wherein the steps of acquiring an interval time between two key points and judging a malfunction time or a maintenance time include:
when the interval time of the two key points is smaller than a first threshold value, ignoring the corresponding time period;
when the interval time of the two key points is greater than or equal to a first threshold value, the corresponding time period is identified as a false work time period;
when the interval time of the two key points is larger than the second threshold value, the corresponding time period is identified as the overhaul time period.
10. The method of claim 9, wherein the calculating and outputting the number of cutting tools, the time of misoperations, and the time of maintenance further comprises:
after all maintenance time periods in a preset period are acquired, judging two adjacent maintenance time periods in sequence;
when the head-to-tail time length of two adjacent overhaul time periods is smaller than a third threshold value, merging the two overhaul time periods into one overhaul time period, and modifying the corresponding overhaul time length; otherwise, the next two adjacent overhaul periods are judged.
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| CN202310955004.4A CN116662891B (en) | 2023-08-01 | 2023-08-01 | Working state identification method of coal mining machine |
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| CN202310955004.4A CN116662891B (en) | 2023-08-01 | 2023-08-01 | Working state identification method of coal mining machine |
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Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104564117A (en) * | 2015-01-16 | 2015-04-29 | 北京煤科天玛自动化科技有限公司 | Key point and following stage based automatic control method of working face hydraulic bracket of triangular coal area at working face end head |
| CN106640168A (en) * | 2016-12-15 | 2017-05-10 | 广州日滨科技发展有限公司 | Control method and device of coal-mine fully-mechanized mining equipment and same |
| JP2019027170A (en) * | 2017-07-31 | 2019-02-21 | Ｃｍｉ株式会社 | Mining condition monitoring apparatus, mining condition monitoring method, and computer program |
| CN110318796A (en) * | 2019-07-18 | 2019-10-11 | 精英数智科技股份有限公司 | A kind of method, apparatus and system identifying hydraulic moving stand state |
| CN111255519A (en) * | 2020-02-20 | 2020-06-09 | 郑州普泽能源科技有限公司 | Coal cutter pushing progress statistical method |
| US20210134135A1 (en) * | 2019-10-30 | 2021-05-06 | University Of Science And Technology Beijing | Multi-system, multi-parameter, integrated, comprehensive early warning method and system for coal and rock dynamic disaster |
| WO2021174744A1 (en) * | 2020-03-05 | 2021-09-10 | 天地科技股份有限公司 | Unified position and posture monitoring system for fully mechanized coal mining face devices |
| CN113847027A (en) * | 2021-08-31 | 2021-12-28 | 国能榆林能源有限责任公司 | Automatic classification method for triangular coal cutter of fully mechanized mining equipment and coal cutter information extraction method |
| CN114111918A (en) * | 2020-12-30 | 2022-03-01 | 中国矿业大学 | Intelligent diagnosis method for working state and supporting quality of hydraulic support |
| CN115420233A (en) * | 2022-09-07 | 2022-12-02 | 国能榆林能源有限责任公司 | Method for identifying abnormality of position sensor of coal mining machine |
| CN115481530A (en) * | 2022-09-05 | 2022-12-16 | 山东科技大学 | Coal mine working face pressure prediction system and method based on region division |
| CN115497039A (en) * | 2022-09-13 | 2022-12-20 | 北京天玛智控科技股份有限公司 | Intelligent identification method and device for video images of fully mechanized coal mining face |
| CN116108696A (en) * | 2023-04-04 | 2023-05-12 | 山东拓新电气有限公司 | Height path planning method for cutting drum of coal cutter |
| CN116182836A (en) * | 2023-03-07 | 2023-05-30 | 北京天玛智控科技股份有限公司 | Coal cutter positioning method and device suitable for fully-mechanized coal mining |
-
2023
- 2023-08-01 CN CN202310955004.4A patent/CN116662891B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104564117A (en) * | 2015-01-16 | 2015-04-29 | 北京煤科天玛自动化科技有限公司 | Key point and following stage based automatic control method of working face hydraulic bracket of triangular coal area at working face end head |
| CN106640168A (en) * | 2016-12-15 | 2017-05-10 | 广州日滨科技发展有限公司 | Control method and device of coal-mine fully-mechanized mining equipment and same |
| JP2019027170A (en) * | 2017-07-31 | 2019-02-21 | Ｃｍｉ株式会社 | Mining condition monitoring apparatus, mining condition monitoring method, and computer program |
| CN110318796A (en) * | 2019-07-18 | 2019-10-11 | 精英数智科技股份有限公司 | A kind of method, apparatus and system identifying hydraulic moving stand state |
| US20210134135A1 (en) * | 2019-10-30 | 2021-05-06 | University Of Science And Technology Beijing | Multi-system, multi-parameter, integrated, comprehensive early warning method and system for coal and rock dynamic disaster |
| CN111255519A (en) * | 2020-02-20 | 2020-06-09 | 郑州普泽能源科技有限公司 | Coal cutter pushing progress statistical method |
| WO2021174744A1 (en) * | 2020-03-05 | 2021-09-10 | 天地科技股份有限公司 | Unified position and posture monitoring system for fully mechanized coal mining face devices |
| CN114111918A (en) * | 2020-12-30 | 2022-03-01 | 中国矿业大学 | Intelligent diagnosis method for working state and supporting quality of hydraulic support |
| CN113847027A (en) * | 2021-08-31 | 2021-12-28 | 国能榆林能源有限责任公司 | Automatic classification method for triangular coal cutter of fully mechanized mining equipment and coal cutter information extraction method |
| CN115481530A (en) * | 2022-09-05 | 2022-12-16 | 山东科技大学 | Coal mine working face pressure prediction system and method based on region division |
| CN115420233A (en) * | 2022-09-07 | 2022-12-02 | 国能榆林能源有限责任公司 | Method for identifying abnormality of position sensor of coal mining machine |
| CN115497039A (en) * | 2022-09-13 | 2022-12-20 | 北京天玛智控科技股份有限公司 | Intelligent identification method and device for video images of fully mechanized coal mining face |
| CN116182836A (en) * | 2023-03-07 | 2023-05-30 | 北京天玛智控科技股份有限公司 | Coal cutter positioning method and device suitable for fully-mechanized coal mining |
| CN116108696A (en) * | 2023-04-04 | 2023-05-12 | 山东拓新电气有限公司 | Height path planning method for cutting drum of coal cutter |
Non-Patent Citations (3)
| Title |
|---|
| DONG WEI 等: "Online shearer-onboard personnel detection method for the intelligent fully mechanized mining face", 《SAGEJOURNALS》, vol. 236, no. 6 * |
| 刘凯: "基于分数阶微积分的煤矸图像边界标记识别研究", 《CNKI学位》, vol. 2019, no. 1 * |
| 常波峰 等: "采煤机与液压支架相对位置精确检测技术研究", 《煤矿机械》, vol. 43, no. 10, pages 39 - 41 * |
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