Multi-working-face-based fully-mechanized coal mining data analysis method and apparatus, and engineering machine

Abstract

The present application relates to the technical field of fully-mechanized coal mining, and particularly relates to a multi-working-face-based fully-mechanized coal mining data analysis method and apparatus, and an engineering machine. The multi-working-face-based fully-mechanized coal mining data analysis method comprises: performing unified network deployment on devices on a plurality of working faces; performing data acquisition on the devices that have undergone the unified network deployment; performing selective reporting on the acquired data; and analyzing the reported data to obtain a data analysis result, the data analysis result comprising process model parameters associated with the working faces and / or automation rates associated with the working faces. The present application can at least improve the efficiency of device management, device maintenance and data analysis.

Description

Data analysis methods, devices, and engineering machinery for multi-face coal mining.

[0001] This application claims priority to Chinese patent application filed on July 23, 2024, with application number "202410987737.0" and entitled "Data Analysis Method, Device and Engineering Machinery for Coal Mine Fully Mechanized Mining Based on Multiple Working Faces", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of coal mine fully mechanized mining technology, and in particular to a data analysis method, device and engineering machinery for coal mine fully mechanized mining based on multiple working faces. Background Technology

[0003] With the development of fully mechanized coal mining technology, the level of automation in fully mechanized coal mining is constantly improving. Typically, the networks of different working faces are not interconnected, and each working face conducts its own independent data analysis; the data from different working faces are not correlated.

[0004] In related technologies, because the networks of different work surfaces are not interconnected, devices belonging to different work surfaces cannot communicate directly, which brings inconvenience to communication transmission; because the data of different work surfaces are not interconnected, it is impossible to obtain the relationship between the data of different work surfaces, which brings inconvenience to equipment management, equipment maintenance and data analysis, resulting in reduced efficiency of equipment management, equipment maintenance and data analysis.

[0005] Therefore, there is an urgent need to design a data analysis method, device, and engineering machinery for fully mechanized coal mining based on multiple working faces, in order to at least improve the efficiency of equipment management, equipment maintenance, and data analysis.

[0006] Application content

[0007] This application aims to address at least one of the technical problems existing in the prior art or related technologies.

[0008] Therefore, this application provides a data analysis method for fully mechanized coal mining based on multiple working faces, a data analysis device for fully mechanized coal mining based on multiple working faces, an electronic device, a computer-readable storage medium, and an engineering machinery. Through this application, the efficiency of equipment management, equipment maintenance, and data analysis is improved.

[0009] According to the first aspect of this application, a method for analyzing coal mine fully mechanized mining data based on multiple working faces is provided, comprising: uniformly deploying equipment in multiple working faces via a network; collecting data from the uniformly deployed equipment via the network; selectively reporting the collected data; and analyzing the reported data to obtain data analysis results, the data analysis results including process model parameters associated with the working face and / or automation rate associated with the working face.

[0010] According to a second aspect of this application, a coal mine fully mechanized mining data analysis device based on multiple working faces is provided, comprising: a deployment unit for unified network deployment of equipment in multiple working faces; a data acquisition unit for data acquisition from the equipment deployed through the unified network; a reporting unit for selectively reporting the acquired data; and an analysis unit for analyzing the reported data to obtain data analysis results, the data analysis results including process model parameters associated with the working face and / or automation rate associated with the working face.

[0011] According to a third aspect of this application, an electronic device is provided, comprising: a processor and a memory, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory to execute the coal mine fully mechanized mining data analysis method based on multiple working faces in the first aspect or its various implementations.

[0012] According to a fourth aspect of this application, a computer-readable storage medium is provided for storing a computer program that causes a computer to execute the coal mine fully mechanized mining data analysis method based on multiple working faces as described in the first aspect or its various implementations.

[0013] According to the fifth aspect of this application, an engineering machinery is provided, including a coal mine fully mechanized mining data analysis device based on multiple working faces as described in the second aspect or its various implementations, or an electronic device as described in the third aspect or its various implementations.

[0014] One of the above technical solutions has the following advantages or beneficial effects:

[0015] The data analysis method for fully mechanized coal mining based on multiple working faces in this application allows for unified network deployment of equipment across multiple working faces; data collection from these network-deployed devices; selective reporting of the collected data; and analysis of the reported data to obtain data analysis results. These results include process model parameters associated with the working face and / or automation rates associated with the working face. Because of the unified network deployment, network address conflicts between devices are reduced or even avoided, facilitating equipment deployment, operation, and maintenance, and consequently improving the efficiency of data collection, equipment management, maintenance, and analysis. The selective reporting of data filters out redundant data, improving data validity and further enhancing the efficiency of data collection and analysis. Since data analysis results associated with each working face can be obtained, comparative analysis between multiple working faces can be performed based on these results, facilitating data analysis.

[0016] The coal mine fully mechanized mining data analysis device based on multiple working faces provided in this application can realize the coal mine fully mechanized mining data analysis method based on multiple working faces described above. Since the coal mine fully mechanized mining data analysis method based on multiple working faces described above has the above-mentioned technical effects, the coal mine fully mechanized mining data analysis device based on multiple working faces should also have the corresponding technical effects.

[0017] The electronic device provided in this application can realize the coal mine fully mechanized mining data analysis method based on multiple working faces described above. Since the coal mine fully mechanized mining data analysis method based on multiple working faces described above has the above-mentioned technical effects, the electronic device should also have the corresponding technical effects.

[0018] The computer-readable storage medium provided in this application includes the coal mine fully mechanized mining data analysis device based on multiple working faces described above. Since the coal mine fully mechanized mining data analysis device based on multiple working faces described above has the above-mentioned technical effects, the computer-readable storage medium should also have the corresponding technical effects.

[0019] The engineering machinery provided in this application includes the coal mine fully mechanized mining data analysis device based on multiple working faces described above. Since the coal mine fully mechanized mining data analysis device based on multiple working faces described above has the above-mentioned technical effects, the engineering machinery should also have the corresponding technical effects. Attached Figure Description

[0020] Figure 1 shows a flowchart of a coal mine fully mechanized mining data analysis method based on multiple working faces according to an embodiment of this application;

[0021] Figure 2 shows a schematic diagram of the structure of a coal mine fully mechanized mining data analysis device based on multiple working faces according to an embodiment of this application.

[0022] Explanation of reference numerals in the attached figures: 210, Deployment unit; 220, Data acquisition unit; 230, Reporting unit; 240, Analysis unit. Detailed Implementation

[0023] In related technologies, equipment can be deployed on work surfaces and connected to a network. Each work surface has its own network. When multiple networks are independent of each other, equipment belonging to different work surfaces cannot communicate with each other, causing communication difficulties. Each work surface has its own supporting system, and these systems are independent of each other, requiring separate installation, management, and maintenance. This involves repetitive work, increasing costs and reducing efficiency. Furthermore, the data from different work surfaces is not interconnected, requiring separate data analysis for each work surface. The lack of correlation between multiple data analysis results makes comparison and other processing impossible, increasing the difficulty of data analysis and impacting its efficiency.

[0024] To address at least one of the technical problems existing in the prior art or related technologies, this application provides a method for analyzing fully mechanized coal mining data based on multiple working faces, a device for analyzing fully mechanized coal mining data based on multiple working faces, an electronic device, a computer-readable storage medium, and an engineering machine. The following detailed description is provided with reference to Figures 1 and 2.

[0025] Referring to Figure 1, one embodiment of this application provides a data analysis method for fully mechanized coal mining based on multiple working faces, including: step S110, uniformly deploying equipment in multiple working faces via a network; step S120, collecting data from the uniformly deployed equipment via the network; step S130, selectively reporting the collected data; and step S140, analyzing the reported data to obtain data analysis results, which include process model parameters associated with the working face and / or automation rate associated with the working face.

[0026] It should be noted that the equipment here can be any equipment operating in the working face, such as equipment performing coal mining operations, data acquisition, control operations, and transportation operations. All working faces are in the same network environment, with equipment connected to a unified network. The equipment can communicate with each other and can provide various services, such as data monitoring and data reporting services, which are accessible. Unified network deployment avoids network conflicts, such as network address conflicts, facilitating deployment, management, and maintenance. Due to the unified network deployment, unified data acquisition and management are possible, improving work efficiency. Selective reporting of collected data is possible to achieve data filtering, and data can be divided into various types according to function, data source, etc. Different selective reporting methods can be used for different types of data, effectively reducing data redundancy. Data analysis here can include statistical and comparative operations. Data analysis results can include working face identifiers, allowing users to obtain the working face corresponding to each data analysis result, facilitating comparison and statistical operations. Because the data analysis results are correlated with the working face, the operating status of the equipment in the working face can be obtained. Furthermore, adjustments can be made to the equipment by comparing it with the operating status of similar equipment, such as adjusting process model parameters, which provides convenience for fully mechanized coal mining operations. These process model parameters can include, for example, valve opening degree, motor speed, and hydraulic cylinder pressure.

[0027] It should be further explained that, for unified deployment, a unified hardware system and a unified software system can be set up to manage all equipment across all working faces. This centralized management approach reduces repetitive and tedious work, improving work efficiency. After data analysis, the results can be displayed, providing staff with effective reference data and further improving work efficiency. Accordingly, the coal mine fully mechanized mining data analysis method, through its exemplary embodiment, can improve data integration and comprehensive analysis capabilities, enhance the level of intelligence, reduce the inconvenience caused by independent deployment of working faces, and reduce or even eliminate the problem of low efficiency in manual operation.

[0028] Furthermore, the network deployment of devices in multiple work areas is unified, including: deploying devices in the same network; setting multiple network address ranges for the network, with no two network address ranges having the same network address; and assigning network addresses to devices, with the network addresses of devices in the same work area being within the same network address range.

[0029] In an exemplary embodiment, the network address range may include multiple consecutive network addresses. By allocating network addresses, network addresses can be reserved for new devices, facilitating device expansion. Allocating network addresses also avoids network address conflicts, simplifying maintenance.

[0030] Furthermore, data is collected from the devices deployed uniformly through the network, including: setting up a corresponding data collection terminal for each work area; the data collection terminal collects data from the devices in the corresponding work area.

[0031] It should be noted that data collection via a data acquisition terminal allows for unified management of multiple work areas, facilitating data management. Furthermore, the collected data can be labeled with work area identifiers to indicate the data source, thus facilitating data analysis.

[0032] Furthermore, after collecting data from equipment deployed uniformly through the network, the coal mine fully mechanized mining data analysis method also includes: storing the data collected by the data acquisition terminal in a basic data table; determining whether the amount of data in the basic data table is greater than or equal to a predetermined amount of data, and determining whether the data acquisition time of the data acquisition terminal has reached the predetermined acquisition time; when the amount of data is greater than or equal to the predetermined amount of data, or the data acquisition time has reached the predetermined acquisition time, storing the data in the basic data table in a business scenario data table, which is associated with the working face data table, and the working face data table is used to store working face-related data.

[0033] It should be noted that the data table in the exemplary embodiment may include multiple data entries, each with various attributes, and the data volume can be measured by the number of data entries. Through the above operations, data can be distributed, improving network stability. The working face data table can use the working face identifier as the primary key, and the data in the business scenario data table has the working face identifier attribute. Through the working face identifier, a relationship is established between the working face data table and the business scenario data table. During data analysis, data from the business scenario data table and the working face data table can be retrieved through a lookup operation. For example, working face data corresponding to process model parameters and / or automation rate can be obtained. The working face data can record working face information, such as working face depth and the number of roadways.

[0034] Furthermore, the collected data is real-time data, and selective reporting of the collected data includes: determining whether the collected data has changed; when the collected data has changed, or when the collected data has not changed within a predetermined data collection period, determining whether the collected data is greater than or equal to the lower limit and less than or equal to the upper limit; when the collected data is greater than or equal to the lower limit and less than or equal to the upper limit, determining whether the amount of change in the collected data is greater than the data change threshold; and when the amount of change in the collected data is greater than the data change threshold, or when the interval between the current time and the last reporting time reaches a predetermined reporting interval, the collected data is reported.

[0035] It should be noted that before selectively reporting data, the data can be categorized. Collected data can be classified into real-time data, coal mining machine movement trajectory data, abnormal alarm data, and control panel event data. Coal mining machine movement trajectory data can be referred to as coal cutting trajectory data. For control panel event data and / or abnormal alarm data, selective reporting is not required to prevent equipment instability caused by misoperation. Judgment based on lower and upper data limits can be understood as data range judgment, and judgment based on data change thresholds can be understood as jitter judgment. Through such judgments, some abnormal data can be excluded, reducing data redundancy and improving work efficiency.

[0036] Furthermore, the collected data is the movement trajectory data of the coal mining machine. The collected data is selectively reported, including: determining the position of the coal mining machine using infrared light; determining the coal mining machine's own position; calculating a first relative distance between the currently determined position and the previously determined position; comparing the first relative distance with a predetermined distance; reporting the currently determined position when the first relative distance equals the predetermined distance; determining a second relative distance between the previously determined position and the position determined by infrared light when the first relative distance is greater than the predetermined distance; comparing the second relative distance with a predetermined distance; and reporting the currently determined position when the second relative distance equals the predetermined distance.

[0037] It should be noted that the distance between the coal mining machine and the predetermined position can be calculated using the transmission speed of infrared light and the time it takes for the infrared light to reach the coal mining machine, thus determining the position of the coal mining machine. The coal mining machine itself can also determine its position. The number of hydraulic supports that the coal mining machine traverses can be determined by the distance; here, "traversing hydraulic supports" can be understood as the hydraulic supports that the coal mining machine passes over. Distance-based comparisons can filter out abnormal data, improve data accuracy, reduce interfering data, and improve the efficiency and accuracy of data analysis.

[0038] Furthermore, the data analysis results include process model parameters associated with the working face. The reported data is analyzed to obtain the following results: Business statistics are performed for a single working face, including: coal mining volume statistics, mine pressure statistics, coal mining machine drum trajectory statistics, working face coal cutting trajectory statistics, coal mining machine action statistics, and electro-hydraulic control action statistics. The statistical results for each business scenario are stored in the corresponding business scenario statistical result table, and each business scenario statistical result table is associated with the working face data table, which stores relevant working face data. A reference business scenario statistical result table is selected from multiple business scenario statistical result tables. The process model parameters corresponding to the reference business scenario statistical result table are determined and applied to each working face.

[0039] It should be noted that data analysis can include single-face analysis and multi-face analysis. After data analysis, the results can be displayed for comparison and analysis, and appropriate actions can be taken. Single-face analysis yields the process model parameters associated with the face; multi-face analysis yields the automation rate associated with each face. The analysis may include data summarization, comparison, sorting, and calculations. Based on one or more parameters such as work efficiency and coal production, a statistical result table for a reference business scenario can be selected. By applying the process model parameters to each face, the working capacity of the equipment within the face can be improved, thereby increasing work efficiency.

[0040] Furthermore, the reported data includes: the number of actions included in each of the multiple cuts performed, including the number of coal mining machine actions and the number of electro-hydraulic control actions; and the data analysis results include the automation rate associated with the working face. The reported data is analyzed to obtain the data analysis results, including: calculating the automation rate corresponding to each cut based on the number of actions; and determining the automation rate of the working face targeted by the multiple cuts based on the calculated automation rate.

[0041] It should be noted that the cutting here can be referred to as a complete cut. A complete cut is a term used in the field of fully mechanized coal mining. For example, the process of cutting the top coal from the top of the drum as it moves upward to cutting the bottom coal from the bottom of the drum as it moves downward is called a single cutting operation, also known as a complete cut.

[0042] It should be further explained that, to calculate the automation rate, the actions of the coal mining machine and the electro-hydraulic control actions can be statistically analyzed. Coal mining machine actions include: left rocker arm raising, left rocker arm lowering, right rocker arm raising, right rocker arm lowering, coal mining machine leftward movement, and coal mining machine rightward movement. Electro-hydraulic control actions include the following actions of the hydraulic support: pushing the conveyor, moving the support, raising the support column, lowering the support column, extending the side support, and retracting the side support. Each action can be recorded with either an automatic operation indicator or a manual operation indicator. The percentage of automatic actions out of the total number of actions in a single cutting operation is the automation rate of that cutting operation. Three cutting operations can be selected, with automation rates R1, R2, and R3, respectively. These three cutting operations target the same working face; for example, these three cutting operations are the first cut, the middle cut, and the last cut. The automation rate of this working face is R = (R1 + 4 × R2 + R3) / 6. The automation rates of different working faces can be compared to analyze the reasons for low automation rates and take corresponding measures to adjust the automation rate to improve efficiency.

[0043] It should be further noted that the following data can also be statistically analyzed: power on / off rate, equipment failure rate, equipment maintenance data, and data on the personnel associated with each equipment. This can be expanded as needed. After data analysis, the results can be displayed, such as single-workface analysis, multi-workface display, and comprehensive multi-workface analysis, which helps improve work efficiency.

[0044] In another embodiment, referring to Figure 2, a coal mine fully mechanized mining data analysis device based on multiple working faces is provided, including: a deployment unit 210 for unified network deployment of equipment in multiple working faces; a data acquisition unit 220 for data acquisition from the equipment deployed through the unified network; a reporting unit 230 for selectively reporting the acquired data; and an analysis unit 240 for analyzing the reported data to obtain data analysis results, including process model parameters associated with the working face and / or automation rate associated with the working face.

[0045] Furthermore, the deployment unit 210 is also used to: deploy devices in the same network; set multiple network address ranges for the network, where no two network address ranges have the same network address; and assign network addresses to devices, where the network addresses of devices in the same working plane are in the same network address range.

[0046] Furthermore, the acquisition unit 220 is also used to: set a corresponding data acquisition terminal for each working face; the data acquisition terminal performs data acquisition on the equipment in the corresponding working face.

[0047] Furthermore, the acquisition unit 220 is also used to: after acquiring data from devices deployed uniformly through the network, store the data acquired by the data acquisition terminal in a basic data table; determine whether the amount of data in the basic data table is greater than or equal to a predetermined amount of data, and determine whether the data acquisition time of the data acquisition terminal has reached the predetermined acquisition time; when the amount of data is greater than or equal to the predetermined amount of data, or the data acquisition time has reached the predetermined acquisition time, store the data in the basic data table in a business scenario data table, the business scenario data table being associated with the work surface data table, and the work surface data table being used to store work surface-related data.

[0048] Furthermore, the collected data is real-time data, and the reporting unit 230 is also used to: determine whether the collected data has changed; when the collected data changes, or when the collected data has not changed within a predetermined data collection period, determine whether the collected data is greater than or equal to the lower limit and less than or equal to the upper limit; when the collected data is greater than or equal to the lower limit and less than or equal to the upper limit, determine whether the amount of change in the collected data is greater than the data change threshold; when the amount of change in the collected data is greater than the data change threshold, or when the interval between the current time and the last reporting time reaches a predetermined reporting interval, report the collected data.

[0049] Furthermore, the collected data is the movement trajectory data of the coal mining machine. The reporting unit 230 is also used for: determining the position of the coal mining machine using infrared light; determining the position of the coal mining machine itself; calculating the first relative distance between the current determined position of the coal mining machine and the previously determined position of the coal mining machine; comparing the first relative distance with a predetermined distance; reporting the current determined position of the coal mining machine when the first relative distance is equal to the predetermined distance; determining the second relative distance between the previously determined position of the coal mining machine and the position of the coal mining machine determined using infrared light when the first relative distance is greater than the predetermined distance; comparing the second relative distance with the predetermined distance; and reporting the current determined position of the coal mining machine when the second relative distance is equal to the predetermined distance.

[0050] Furthermore, the data analysis results include process model parameters associated with the working face. The analysis unit 240 is also used for: performing business statistics for a single working face, including: coal mining volume statistics, mine pressure statistics, coal mining machine drum trajectory statistics, working face coal cutting trajectory statistics, coal mining machine action statistics, and electro-hydraulic control action statistics; storing the statistical results of each type of business statistics in the corresponding business scenario statistical result table, with each business scenario statistical result table associated with the working face data table, which is used to store working face-related data; selecting a reference business scenario statistical result table from multiple business scenario statistical result tables; and determining the process model parameters corresponding to the reference business scenario statistical result table, which are then applied to each working face.

[0051] Furthermore, the reported data includes: the number of actions included in each of the multiple cuts performed, including the number of coal mining machine actions and the number of electro-hydraulic control actions; the data analysis results include the automation rate associated with the working face; the analysis unit 240 is also used to: calculate the automation rate corresponding to each cut based on the number of actions; and determine the automation rate of the working face targeted by the multiple cuts based on the calculated automation rate.

[0052] Since the coal mine fully mechanized mining data analysis device based on multiple working faces provided in the exemplary embodiments can realize the coal mine fully mechanized mining data analysis method based on multiple working faces in any of the above exemplary embodiments, the coal mine fully mechanized mining data analysis device based on multiple working faces has all the beneficial effects of the coal mine fully mechanized mining data analysis method based on multiple working faces provided in any of the above embodiments. The implementation method of the coal mine fully mechanized mining data analysis device based on multiple working faces can be implemented with reference to the embodiments of the coal mine fully mechanized mining data analysis method based on multiple working faces, and will not be repeated here.

[0053] In another embodiment, an electronic device is provided, including a processor and a memory, the memory for storing a computer program, and the processor for calling and running the computer program stored in the memory to execute the multi-face coal mine fully mechanized mining data analysis method in the exemplary embodiment.

[0054] Since the electronic device provided in the exemplary embodiments can implement the coal mine fully mechanized mining data analysis method based on multiple working faces of any of the above exemplary embodiments, the electronic device has all the beneficial effects of the coal mine fully mechanized mining data analysis method based on multiple working faces provided in any of the above embodiments. The implementation of the electronic device can be carried out with reference to the embodiments of the coal mine fully mechanized mining data analysis method based on multiple working faces, and will not be repeated here.

[0055] In another embodiment, a computer-readable storage medium is provided for storing a computer program that causes a computer to execute the coal mine fully mechanized mining data analysis method based on multiple working faces as described in the exemplary embodiment.

[0056] Since the computer-readable storage medium provided in the exemplary embodiments can implement the coal mine fully mechanized mining data analysis method based on multiple working faces of any of the above exemplary embodiments, the computer-readable storage medium has all the beneficial effects of the coal mine fully mechanized mining data analysis method based on multiple working faces provided in any of the above embodiments. The implementation of the computer-readable storage medium can be implemented with reference to the embodiments of the coal mine fully mechanized mining data analysis method based on multiple working faces, and will not be repeated here.

[0057] In another embodiment, an engineering machine is provided, including a coal mine fully mechanized mining data analysis device based on multiple working faces as shown in the exemplary embodiment, or an electronic device as shown in the exemplary embodiment.

[0058] Since the engineering machinery provided in the exemplary embodiments may include the coal mine fully mechanized mining data analysis device based on multiple working faces of any of the above exemplary embodiments, the engineering machinery has all the beneficial effects of the coal mine fully mechanized mining data analysis device based on multiple working faces provided in any of the above embodiments. The implementation of the engineering machinery can be carried out with reference to the embodiments of the coal mine fully mechanized mining data analysis device based on multiple working faces, which will not be repeated here.

[0059] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0060] In this application, unless otherwise expressly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0061] In this application, unless otherwise expressly specified and limited, "above" or "below" the second feature can mean that the first and second features are in direct contact, or that they are in indirect contact through an intermediate medium. Furthermore, "above," "on top of," and "over" the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0062] In the description of this specification, the terms "one embodiment," "some embodiments," "embodiment," "exemplary model," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0063] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make modifications, alterations, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A multi-working face based coal mine fully mechanized mining data analysis method, wherein, The method comprises the following steps: network uniformly deploying devices in multiple working faces; collecting data from the network uniformly deployed devices; selectively reporting the collected data; analyzing the reported data to obtain data analysis results, which include process model parameters associated with the working faces and / or automation rates associated with the working faces; when the data analysis results include process model parameters associated with the working faces, the analyzing the reported data to obtain data analysis results comprises: conducting business statistics for a single working face, which includes coal mining amount statistics, mine pressure statistics, shearer drum track statistics, working face coal cutting track statistics, shearer action statistics, and electro-hydraulic control action statistics; storing the statistical results of each type of business statistics in a corresponding business scenario statistical result table, each of which is associated with a working face data table used for storing working face related data; selecting a reference business scenario statistical result table from the multiple business scenario statistical result tables; determining process model parameters corresponding to the reference business scenario statistical result table, which are used for being applied to each working face; when the data analysis results include automation rates associated with the working faces, the reported data includes the number of actions contained in each cutting of multiple performed cuttings, which includes the number of shearer actions and the number of electro-hydraulic control actions, and the analyzing the reported data to obtain data analysis results comprises: calculating the automation rate corresponding to each cutting based on the number of actions; determining the automation rate of the working face to which the multiple cuttings are directed based on the calculated automation rates.

2. The method according to claim 1, wherein, The network uniformly deploying devices in multiple working faces comprises: deploying the devices in the same network; setting multiple network address ranges for the network, and there is no same network address between any two network address ranges; allocating network addresses for the devices, and the network addresses of the devices in the same working face are in the same network address range. The collecting data from the network uniformly deployed devices comprises: setting a corresponding data collection terminal for each working face; the data collection terminal collects data from the devices in the corresponding working face.

3. The method according to claim 2, wherein, After collecting data from the network uniformly deployed devices, the method further comprises: storing the data collected by the data collection terminal in a basic data table; judging whether the data amount in the basic data table is greater than or equal to a predetermined data amount, and whether the data collection time length of the data collection terminal reaches a predetermined collection time length; when the data amount is greater than or equal to the predetermined data amount, or the data collection time length reaches the predetermined collection time length, storing the data in the basic data table into a business scenario data table, which is associated with a working face data table used for storing working face related data.

4. The method according to claim 1, wherein, The collected data is real-time data, and the selectively reporting the collected data comprises: judging whether the collected data has changed; When the collected data changes or does not change in a predetermined data collection period, it is determined whether the collected data is greater than or equal to a data lower limit and less than or equal to a data upper limit; When the collected data is greater than or equal to the data lower limit and less than or equal to the data upper limit, it is determined whether a change amount of the collected data is greater than a data change threshold; When the change amount of the collected data is greater than the data change threshold or when an interval between a current time and a last reporting time reaches a predetermined reporting interval, the collected data is reported.

5. The method for analyzing data of fully mechanized mining in a coal mine based on multiple working faces according to claim 1, wherein, The collected data is coal mining machine moving track data, and the selective reporting of the collected data comprises: Determining the position of the coal mining machine by using infrared rays; The coal mining machine determines its own position; Calculating a first relative distance between the current determined position of the coal mining machine and the last determined position of the coal mining machine; Comparing the first relative distance with a predetermined distance; When the first relative distance is equal to the predetermined distance, the current determined position of the coal mining machine is reported; When the first relative distance is greater than the predetermined distance, a second relative distance between the last determined position of the coal mining machine and the position of the coal mining machine determined by using infrared rays is determined; Comparing the second relative distance with the predetermined distance; When the second relative distance is equal to the predetermined distance, the current determined position of the coal mining machine is reported.

6. A multi-face based coal mine fully mechanized mining data analysis device, wherein, Comprise: A deployment unit configured to uniformly deploy devices in multiple working faces via a network; A collection unit configured to collect data from the devices uniformly deployed via the network; A reporting unit configured to selectively report the collected data; An analysis unit configured to analyze the reported data to obtain data analysis results, the data analysis results comprising process model parameters associated with the working faces and / or automation rates associated with the working faces; When the data analysis results comprise the process model parameters associated with the working faces, the analysis unit is further configured to: perform business statistics for a single working face, the business statistics comprising: coal mining amount statistics, mine pressure statistics, coal mining machine drum track statistics, working face coal cutting track statistics, coal mining machine action statistics, and electro-hydraulic control action statistics; store statistical results of each kind of business statistics in a corresponding business scenario statistical result table, each business scenario statistical result table being associated with a working face data table configured to store working face related data; select a reference business scenario statistical result table from the multiple business scenario statistical result tables; and determine process model parameters corresponding to the reference business scenario statistical result table, the process model parameters being applied to each working face; When the reported data comprises the number of actions contained in each cutting in multiple performed cuttings, the number of actions comprising the number of coal mining machine actions and the number of electro-hydraulic control actions, and the data analysis results comprise the automation rates associated with the working faces, the analysis unit is further configured to: calculate the automation rate corresponding to each cutting based on the number of actions; and determine the automation rate of the working face to which the multiple cuttings are directed based on the calculated automation rates.

7. An electronic device, comprising: Comprise: A processor and a memory for storing a computer program, the processor being configured to invoke and run the computer program stored in the memory to execute the method for analyzing fully-mechanized coal mining data based on multiple working faces according to any one of claims 1 to 5.

8. A computer readable storage medium, wherein, A computer program for causing a computer to execute the method for analyzing fully-mechanized coal mining data based on multiple working faces according to any one of claims 1 to 5.

9. A working machine, wherein An electronic device comprising the device for analyzing fully-mechanized coal mining data based on multiple working faces according to claim 6, or comprising the electronic device according to claim 7.

Images