Device for delineating coal resources for coking

Abstract

The invention relates to the technical field of coal resource exploration, in particular to a coking coal resource delineation device which comprises an assembling base, a supporting frame is fixedly connected to the top of the assembling base, and the coking coal resource delineation device further comprises an atmosphere monitoring convenient disassembling and assembling mechanism, a driving assembly is installed at the top of the assembling base, and an assembling assembly is installed at the driven end of the driving assembly; the guiding assembly is installed at the top of the supporting frame and connected with the assembling assembly, the installing assembly is installed on the assembling assembly, and the sampling assembly is installed on the installing assembly. According to the device for delineating the coal resources for coking, the sampling assembly is lowered to a lower safety height through the assembling assembly and the mounting assembly by the controller, the process needs to be stable, damage to equipment or sampling results is avoided, continuous monitoring is achieved, the system needs to continuously monitor wind speed changes, the height is repeatedly adjusted when necessary, and the sampling efficiency is improved. And the equipment safety and the data accuracy in the whole monitoring period are ensured.

Description

Technical Field

[0001] The invention relates to the technical field of coal resource exploration, in particular to a device for delineating coal resources for coking. Background Art

[0002] In the coking industry, the quality and characteristics of coal resources play a vital role in the output and quality of coking products. However, due to the complex distribution of coal resources and the significant differences in coal seam thickness, physical properties, and chemical composition, how to accurately and efficiently identify coal resources suitable for coking technology has always been a difficult problem to be solved in the industry. At present, the existing technologies have the following deficiencies:

[0003] 1. Low sampling efficiency and poor representativeness. In the existing technology, coal sampling mostly relies on manual or semi-automatic equipment. In the complex coal mine environment, manual sampling is inefficient and has large errors, and cannot cover the diversity of the entire coal seam. The selection of sampling points is often based on experience and lacks accurate geological exploration information, which leads to insufficient representativeness of the sampling results, thus affecting the comprehensive assessment of coal resources. In addition, the manual sampling process is time-consuming and costly, and there are safety hazards in deep wells and dangerous terrain conditions;

[0004] 2. Single detection means and low analysis accuracy. Traditional coal resource delineation methods mainly rely on physical property detection, such as coal seam thickness and density. However, a single physical detection method is difficult to fully reflect the coking suitability of coal, because the coking process not only depends on the physical structure of coal, but also is affected by the chemical composition of coal (such as sulfur content, volatile matter, ash, etc.). Therefore, due to the single detection means in the existing technology, it is impossible to accurately evaluate the coking suitability of coal, which leads to insufficient resource utilization and even wrong decisions;

[0005] 3. Lack of data integration and intelligent analysis. Existing technologies are relatively backward in data processing and analysis capabilities. Usually, physical detection and chemical analysis results cannot be integrated on a unified platform for comprehensive evaluation, resulting in the judgment of coal coking suitability relying on human experience and lack of systematic and intelligent analysis methods. At the same time, existing technologies rarely use modern algorithms such as machine learning to learn and optimize historical data, resulting in low data utilization efficiency and difficulty in coping with complex and dynamic changes in coal characteristics;

[0006] 4. Lack of a suitability scoring model. Most of the suitability evaluation methods in the existing technology are based on a single indicator or empirical formula, which is difficult to accurately reflect the actual coking performance of coal. Different coal seams may perform well in certain indicators, but are not suitable for coking when considered comprehensively. The lack of a comprehensive scoring model to quantify the suitability of coal makes the accuracy of delineating coal resources for coking low, resulting in a reduction in the effectiveness of coal mine resource development decisions;

[0007] 5. Lack of automated delineation and decision-making support tools. In the existing coal resource delineation process, the delineation of coal suitability basically relies on manual analysis and manually drawn resource distribution maps. There is a lack of automated delineation tools, which not only consumes a lot of manpower and material resources, but is also prone to omissions or errors. Decision makers lack scientific and reliable data support when making resource utilization plans, and it is difficult to effectively evaluate the development priorities of different coal seams, resulting in insufficient or inaccurate decisions. Summary of the invention

[0008] In order to solve the problem of the current equipment used for coking coal resource delineation in the market, a scoring model that integrates physical and chemical properties is used to quantify the coking suitability of coal, solving the problem that traditional technology cannot accurately reflect the coking performance of coal. The scoring model takes into account multiple key characteristics of coal and adapts to different coal seam characteristics by adjusting the weight coefficient, providing a more scientific applicability scoring method, thereby improving the accuracy of resource delineation.

[0009] To achieve the above-mentioned purpose, the first embodiment of the present application proposes a device for delineating coal resources for coking, comprising an assembly base, the top of which is fixedly connected to a support frame, and a coal sampling system for automatically collecting coal samples from a coal seam, wherein the sampling system comprises a robotic arm, drilling equipment, a sensor and a real-time monitoring module for ensuring the accuracy of the sampling position and the representativeness of the coal sample; a physical parameter detection module for detecting the physical properties of the sampled coal, including but not limited to the density, acoustic wave propagation velocity and resistivity of the coal, and the module can provide real-time feedback on the hardness, internal structure and hierarchical distribution information of the coal; a chemical analysis module, including equipment such as a gas chromatograph and an X-ray fluorescence spectrometer, for analyzing key chemical parameters of the coal such as volatile matter, sulfur content, ash content, calorific value, etc.; a data processing unit for receiving data from the physical parameter detection module and the chemical analysis module, and generating a coal coking suitability score based on the physical and chemical properties of the coal; a suitability delineation algorithm module, which uses the coal suitability score to delineate coal resource areas through an algorithm, and coal resources suitable for coking will be marked for further utilization.

[0010] The device for delineating coking coal resources in the embodiment of the present application solves the problems of low sampling efficiency, single detection means, inaccurate applicability assessment, and high manual dependence in the delineation process in the prior art through technical means such as automated sampling, multi-dimensional detection, intelligent scoring and delineation. Each embodiment demonstrates the innovativeness of the present invention and its beneficial effects in practical applications, making it have a wide range of application prospects in the development of coking coal resources.

[0011] In addition, the device for delineating coking coal resources proposed in the present application may also have the following additional technical features:

[0012] As a preferred solution of the present invention, the coal sampling system has an automatic path planning function, which can adaptively adjust the sampling path based on the geological map and coal seam structure information to ensure the uniformity and comprehensiveness of the sampling. The system can also adjust the sampling frequency according to different geological conditions to ensure the sampling efficiency in complex geological environments.

[0013] As a preferred solution of the present invention, the physical parameter detection module includes an acoustic wave attenuation detection unit and a resistivity measurement unit, which evaluates the internal structure of the coal by detecting the acoustic wave attenuation of the coal seam, and evaluates the conductivity and water content of the coal by the change in resistivity, and calculates the physical suitability index of the coal.

[0014] As a preferred solution of the present invention, the chemical analysis module has an online real-time monitoring function, which can automatically analyze the ash content, sulfur content, volatile matter and calorific value of the collected samples, and automatically generate the coking suitability evaluation of the coal through the algorithm. The module can also connect to the database through the cloud platform and automatically compare historical data, thereby improving the accuracy and reliability of the analysis results.

[0015] As a preferred solution of the present invention, the data processing unit analyzes the physical and chemical data based on a multivariate linear regression model or a machine learning algorithm, and automatically adjusts the weight coefficients in the model. The calculation formula of the coking suitability score (S) is as follows: S = α1·P h +α2·A s +α3·T v Among them, P h Indicates the physical properties of coal, A s Indicates the ash content of coal, T v It represents the calorific value of coal, and α1, α2 and α3 are weighting coefficients.

[0016] As a preferred solution of the present invention, the multivariate linear regression model is based on a large amount of actual sampling data, and the optimal weight parameters are obtained through iterative training to improve the accuracy of the applicability score. The model can also be adaptively adjusted according to the geology and resource distribution of different coal mines to adapt to the coal characteristics under different environments.

[0017] As a preferred solution of the present invention, the applicability delineation algorithm module can automatically generate a coal resource map based on the coking applicability score of coal, which identifies the distribution of coal resources suitable for coking in different regions and provides the utilization priority of coal in different regions. The delineation algorithm module can also predict future resource development based on historical data.

[0018] As a preferred solution of the present invention, the coal sampling system can automatically detect environmental parameters such as temperature, humidity, and pressure in the sampling area in combination with geological conditions, and correct the sampling results according to these environmental parameters. The sampling system can also operate flexibly in complex terrain, including automatically adjusting the mechanical arm angle and drilling depth to ensure sampling accuracy in rugged terrain or steep coal seams.

[0019] As a preferred solution of the present invention, the physical parameter detection module also includes a radar device for detecting the thickness of the coal seam. The device can accurately measure the thickness changes of the coal seam, and combine the resistivity and sound wave attenuation data to evaluate the uniformity of the coal seam, so as to better judge the physical suitability of the coal.

[0020] As a preferred solution of the present invention, the chemical analysis module also includes an analysis unit for detecting rare elements (such as uranium, thorium, etc.) in coal. The unit can identify trace elements present in coal and analyze the content of these elements to ensure the safety and environmental protection of coal, especially to avoid the production of harmful substances during the coking process.

[0021] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] 1. In the present invention, by introducing an automated coal sampling system, the present invention can adaptively adjust the sampling path and frequency in combination with information such as geological conditions, coal seam thickness, and structural characteristics, thereby ensuring the efficiency and comprehensiveness of the sampling process. At the same time, the automated equipment can perform accurate sampling under complex terrain conditions, greatly reducing manual errors and improving sampling representativeness;

[0024] 2. In the present invention, multi-dimensional detection means are used to comprehensively evaluate coal characteristics. The physical parameter detection module of the present invention is combined with the chemical analysis module to simultaneously detect the physical and chemical characteristics of coal, such as density, acoustic wave attenuation, resistivity, ash content, sulfur content, volatile matter, calorific value, etc., and comprehensively evaluate the coking suitability of coal. Compared with the traditional single physical detection method, the present invention realizes a more accurate evaluation of coal resources through multi-dimensional data collection;

[0025] 3. In the present invention, data integration and intelligent analysis, the system of the present invention is equipped with a data processing unit, which can integrate physical and chemical data on the same platform, and perform intelligent analysis through multivariate linear regression or machine learning algorithms. This systematic data processing method not only improves the efficiency of data utilization, but also can perform adaptive adjustments based on historical data to ensure the accuracy and reliability of the evaluation results of the suitability of coking coal;

[0026] 4. In the present invention, a suitability scoring model is created. The present invention quantifies the coking suitability of coal by using a scoring model that integrates physical and chemical properties, thereby solving the problem that the traditional technology cannot accurately reflect the coking performance of coal. The scoring model takes into account multiple key characteristics of coal and adapts to different coal seam characteristics by adjusting the weight coefficient, providing a more scientific suitability scoring method, thereby improving the accuracy of resource delineation;

[0027] 5. In the present invention, automatic delineation and resource distribution map generation, the suitability delineation algorithm module of the present invention can automatically generate a resource distribution map according to the suitability score, and identify the coal resource areas suitable for coking. The automatic delineation tool greatly reduces the workload of manual analysis and improves the accuracy of resource distribution through intelligent means, providing decision makers with scientific and systematic resource development suggestions, avoiding the limitations of manual analysis in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 The present invention is a flow chart of the device for delineating coal resources for coking. DETAILED DESCRIPTION

[0029] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limitations on the present application. On the contrary, the embodiments of the present application include all changes, modifications and equivalents that fall within the spirit and connotation of the appended claims.

[0030] The following describes the device for delineating coking coal resources according to an embodiment of the present application in conjunction with the accompanying drawings.

[0031] The present application provides a device for delineating coal resources for coking, which quantifies the coking suitability of coal through a scoring model that integrates physical and chemical properties, solving the problem that traditional technologies cannot accurately reflect the coking performance of coal. The scoring model takes into account multiple key characteristics of coal, and adapts to different coal seam characteristics by adjusting weight coefficients, providing a more scientific applicability scoring method, thereby improving the accuracy of resource delineation.

[0032] like Figure 1 As shown, the device for delineating coal resources for coking in the embodiment of the present application may include a coal sampling system for automatically collecting coal samples from coal seams, the sampling system including a robotic arm, drilling equipment, sensors and a real-time monitoring module for ensuring the accuracy of the sampling position and the representativeness of the coal sample, a physical parameter detection module for detecting the physical properties of the sampled coal, including but not limited to the density, acoustic wave propagation velocity and resistivity of the coal, the module being able to provide real-time feedback on the hardness, internal structure and hierarchical distribution information of the coal, a chemical analysis module including equipment such as a gas chromatograph and an X-ray fluorescence spectrometer for analyzing key chemical parameters of the coal such as volatile matter, sulfur content, ash content and calorific value, a data processing unit for receiving data from the physical parameter detection module and the chemical analysis module, generating a coal coking suitability score based on the physical and chemical properties of the coal, and a suitability delineation algorithm module for delineating coal resource areas through an algorithm using the coal suitability score, and coal resources suitable for coking will be marked for further use.

[0033] It should be noted that the device described in this embodiment can accurately sample coal seams through the cooperation of a mechanical arm, drilling equipment, sensors and real-time monitoring modules. Traditional technologies often rely on manual sampling, which is not only inefficient but also inaccurate, especially in complex terrain and when the structure of coal seams changes frequently, it is difficult to ensure the representativeness of the samples. The present device improves the collection accuracy of coal samples through intelligent sampling planning and automatic monitoring. In traditional coal sampling methods, representative samples in coal seams are difficult to obtain due to manual operation and equipment limitations. The present device realizes automatic sampling through a mechanical arm combined with drilling equipment, and the sensors and monitoring modules provide real-time feedback, which can adjust the sampling strategy at any time according to the geological conditions on site. This greatly improves the sampling efficiency and accuracy, especially for areas with complex coal seam distribution. In addition, the close integration of the physical detection module and the chemical analysis module enables the device to not only collect samples, but also perform detailed analysis of the samples.

[0034] Furthermore, if Figure 1 As shown, the coal sampling system has an automatic path planning function, which can adaptively adjust the sampling path based on the geological map and coal seam structure information to ensure the uniformity and comprehensiveness of the sampling. The system can also adjust the sampling frequency according to different geological conditions to ensure the sampling efficiency in complex geological environments.

[0035] It should be noted that the path planning function described in this embodiment. Traditional coal sampling often lacks systematic path planning, and it is easy to have problems such as repeated sampling or missing certain key areas during the sampling process. By introducing the automatic path planning function, this device can adaptively adjust the sampling path according to the actual geological conditions to ensure the uniformity and comprehensiveness of the sampling. Especially in the development of large-scale coal mines, this function is particularly important. Through path planning, the device can design the optimal sampling route according to the distribution characteristics and structural changes of the coal seam, avoiding the blindness in human operation. The system can also monitor the changes in geological conditions in real time and dynamically adjust the sampling frequency to ensure efficient work in complex geological environments. In addition, this function can also reduce the occurrence of repeated sampling, saving time and cost. This function of adaptively adjusting the sampling path not only improves the evaluation efficiency of coal resources, but also ensures the accuracy of the evaluation results.

[0036] The physical parameter detection module includes an acoustic wave attenuation detection unit and a resistivity measurement unit. The internal structure of the coal is evaluated by detecting the acoustic wave attenuation of the coal seam, and the conductivity and moisture content of the coal are evaluated by the change of resistivity, and the physical suitability index of the coal is calculated.

[0037] It should be noted that the comprehensive evaluation of the physical properties of coal by acoustic wave attenuation and resistivity measurement described in this embodiment. Traditional physical detection methods are usually limited to the measurement of a single parameter, while the present invention adopts a combination of multiple physical parameter detection methods to ensure the comprehensiveness of the data. Acoustic wave attenuation can effectively reflect the internal structure of coal, especially when detecting the thickness distribution and hardness of coal seams. Resistivity measurement can reveal the conductivity and water content information of coal, thereby providing a more reliable data basis for subsequent applicability analysis. Through the dual detection of acoustic wave attenuation and resistivity, this device can more accurately evaluate the applicability of coal from a physical perspective. Especially in the case of complex coal seam structure or changeable coal physical properties, a single detection method often cannot provide sufficient information support, and this multi-dimensional detection method can make up for this defect. Combined with real-time feedback and data processing units, the physical parameter detection module can significantly improve the screening and evaluation efficiency of coking coal.

[0038] The chemical analysis module has an online real-time monitoring function, which can automatically analyze the ash content, sulfur content, volatile matter and calorific value of the collected samples, and automatically generate the coking suitability evaluation of the coal through the algorithm. The module can also connect to the database through the cloud platform and automatically compare historical data, thereby improving the accuracy and reliability of the analysis results.

[0039] It should be noted that the traditional chemical analysis described in this embodiment often relies on laboratory analysis, which is not only time-consuming but also has delayed analysis results, and cannot guide the development of coal resources in a timely manner. The chemical analysis module in this device can generate a coking applicability evaluation in a timely manner by real-time monitoring of chemical indicators such as volatile matter, ash, sulfur content, calorific value, etc. of coal samples, providing support for the efficient use of coal. The chemical analysis module is also connected to the database through a cloud platform, and can be compared with historical data to further improve the accuracy of the analysis results. This method of real-time data processing and historical data comparison significantly improves the reliability of the analysis, especially when the coal composition changes greatly. The real-time monitoring function can effectively capture these changes and make corresponding adjustments, avoiding the misjudgment caused by data lag in traditional methods. Online monitoring and automatic generation of applicability reports not only speed up the speed of resource assessment, but also provide timely decision support for subsequent coal mine development.

[0040] The data processing unit analyzes the physical and chemical data based on a multivariate linear regression model or a machine learning algorithm and automatically adjusts the weight coefficients in the model. The calculation formula for the coking suitability score (S) is as follows: S = α1·P h +α2·A s +α3·T v Among them, P h Indicates the physical properties of coal, A s Indicates the ash content of coal, T v It represents the calorific value of coal, and α1, α2 and α3 are weighting coefficients.

[0041] It should be noted that the coking suitability scoring model of the data processing unit described in this embodiment is clearly proposed, and a specific calculation formula is given. The model is based on a multiple linear regression algorithm, combining physical and chemical data to derive a comprehensive suitability score S. The score can accurately reflect the performance of coal in the coking process and provide a quantitative basis for the efficient use of coal. The weighting coefficients α1, α2 and α3 in the formula can be adjusted according to different coal mine conditions, making the scoring model highly adaptable. This data-based automated analysis greatly improves the accuracy of coal resource assessment and avoids the deviation caused by subjective judgment in traditional assessment methods. In addition, the scoring model can also be iteratively optimized based on continuously collected data, so that its evaluation results are more in line with actual application needs.

[0042] The multivariate linear regression model is based on a large amount of actual sampling data, and the optimal weight parameters are obtained through iterative training to improve the accuracy of the suitability score. The model can also be adaptively adjusted according to the geology and resource distribution of different coal mines to adapt to the coal characteristics under different environments.

[0043] It should be noted that the machine learning algorithm described in this embodiment can train a large amount of historical data, thereby optimizing the weighting coefficient of the scoring model, and finally deriving the applicability scoring standard that best suits the current coal mine conditions. The scoring methods in the prior art are usually fixed and difficult to adapt to the conditions of different coal mines. This model can tailor the scoring standards for each specific coal mine through the adaptive adjustment function. Through the accumulation and analysis of a large amount of data, the prediction accuracy of the model will be improved, thereby providing more forward-looking guidance for future coal resource development. This adaptive model based on machine learning not only improves the scientific nature of the evaluation, but also can adjust the scoring algorithm according to different environmental conditions, making it widely applicable.

[0044] The suitability delineation algorithm module can automatically generate a coal resource map based on the coking suitability score of coal, which identifies the distribution of coal resources suitable for coking in different regions and provides the utilization priority of coal in different regions. The delineation algorithm module can also predict future resource development based on historical data.

[0045] It should be noted that the embodiment describes the identification of coal resources suitable for coking in different areas. This mapping visualization tool greatly facilitates the development and management of coal mines, allowing them to see at a glance which areas of coal resources are most suitable for coking, thereby giving priority to development. In addition, the delineation algorithm module also has a prediction function, which can make predictions about future resource development based on historical data and current evaluation results. Compared with traditional manual evaluation methods, this algorithm-based delineation and prediction is not only faster, but also more accurate, providing more scientific decision-making support for coal mine development.

[0046] The coal sampling system described above can automatically detect environmental parameters such as temperature, humidity, and pressure in the sampling area in combination with geological conditions, and can correct the sampling results based on these environmental parameters. The sampling system can also operate flexibly in complex terrain, including automatically adjusting the mechanical arm angle and drilling depth to ensure sampling accuracy in rugged terrain or steep coal seams.

[0047] It should be noted that the sampling system described in this embodiment can be dynamically adjusted according to actual environmental parameters such as temperature, humidity and pressure to ensure the accuracy of the sampling results. Traditional sampling systems usually ignore the influence of environmental conditions, resulting in large deviations in sampling results, while this device improves the accuracy of sampling by correcting environmental parameters. This sampling method is particularly suitable for coal mine development in complex terrain or extreme climatic conditions. By automatically adjusting the angle of the robotic arm and the drilling depth, the system can maintain efficient operation in rugged terrain, ensuring the accuracy and representativeness of the sampling results.

[0048] The physical parameter detection module also includes a radar device for detecting the thickness of the coal seam, which can accurately measure the thickness changes of the coal seam and evaluate the uniformity of the coal seam in combination with the resistivity and sound wave attenuation data, so as to better judge the physical suitability of the coal.

[0049] It should be noted that the traditional coal seam thickness detection method described in this embodiment usually relies on a single physical method, which is difficult to accurately reflect the actual thickness changes of complex coal seams. This device can better evaluate the uniformity and thickness distribution of coal seams through radar measurement combined with resistivity and acoustic wave data. This multi-dimensional detection method can significantly improve the evaluation accuracy of coal physical properties and provide reliable data support for subsequent coking suitability analysis. Especially in the case of complex coal seam structures and large thickness variations, the application of this multiple detection method is extremely advantageous.

[0050] The chemical analysis module also includes an analysis unit for detecting rare elements in coal (such as uranium, thorium, etc.). The unit can identify trace elements present in coal and analyze the content of these elements to ensure the safety and environmental protection of coal, especially to avoid the production of harmful substances during the coking process.

[0051] It should be noted that the function of the chemical analysis module described in this embodiment specifically adds the detection of rare elements (such as uranium, thorium, etc.) in coal. Traditional coal chemical analysis often only focuses on conventional components and ignores the presence of rare elements. This device ensures the safety and environmental protection of coal during the coking process by detecting these rare elements. This design is particularly suitable for areas with high requirements for coal environmental protection. It can effectively avoid the production of harmful substances during the coking process and ensure the clean use of coal. In addition, the analysis of rare elements can also provide new ideas for the further development and utilization of resources.

[0052] Specifically, the introduction of an automated coal sampling system and path planning function makes the sampling process more efficient and suitable for complex geological environments, reducing human errors. The physical and chemical parameters are combined for analysis. Through a comprehensive scoring system of physical and chemical properties, the coking suitability of coal can be more accurately evaluated. The suitability scoring model is optimized through a machine learning algorithm, making the scoring results more accurate and adaptively adjusted based on historical data, significantly improving the accuracy of suitability judgment. The suitability delineation algorithm can directly generate resource distribution maps and utilization priorities, providing decision-making support tools for coal mine development and greatly improving decision-making efficiency.

[0053] Example 1: Application of automated coal sampling system

[0054] Implementation steps:

[0055] 1. The system automatically receives geological data and generates the best sampling path based on information such as the thickness and layer distribution of the coal seam;

[0056] 2. The robotic arm cooperates with the drilling equipment to perform automated sampling and can operate flexibly in complex terrain and deep wells;

[0057] 3. The sampling system adjusts the sampling frequency according to the real-time monitoring of the physical characteristics of the coal seam to ensure the uniformity and representativeness of the samples.

[0058] Beneficial effects: Through the automated sampling system, this embodiment significantly improves the representativeness of coal samples, can adapt to complex geological environments, avoids errors and safety hazards in traditional manual sampling, and greatly improves sampling efficiency.

[0059] Example 2: Application of comprehensive physical and chemical detection methods

[0060] Implementation steps:

[0061] 1. For the collected coal samples, the physical parameter detection module evaluates the hardness and internal structure of the coal through acoustic wave attenuation and resistivity measurement;

[0062] 2. The chemical analysis module uses gas chromatography to detect the chemical properties of coal such as volatile matter, sulfur content, ash content and calorific value;

[0063] 3. Transfer the physical and chemical data to the data processing unit for integration.

[0064] Beneficial effects:

[0065] This embodiment uses multi-dimensional detection methods to comprehensively evaluate the physical and chemical properties of coal, providing more comprehensive and accurate data support for subsequent applicability analysis. Compared with traditional single detection methods, this embodiment significantly improves the accuracy of coking coal evaluation.

[0066] Example 3: Application of the suitability delineation and scoring model

[0067] Implementation steps:

[0068] 1. Data processing unit uses the formula S = α1·P h +α2·A s +α3·T v Physical properties of coal h , ash content A s , calorific value T v , perform weighted comprehensive analysis and generate a coking suitability score;

[0069] 2. The suitability delineation algorithm module generates a coal resource distribution map based on the scoring results and identifies priority development areas suitable for coking;

[0070] 3. The system automatically updates the scoring model and combines historical data for learning and optimization.

[0071] Beneficial effects:

[0072] This embodiment uses the applicability scoring model to achieve a quantitative assessment of the applicability of coal coking and provides a scientific development decision support tool. Through the intelligent delineation algorithm, it can automatically generate a resource distribution map and identify priority development areas, effectively improving the efficiency and accuracy of resource utilization.

[0073] In summary, the present invention provides an efficient and accurate coal resource delineation device, which solves the problems of low sampling efficiency, single detection means, inaccurate applicability assessment and high manual dependence in the delineation process in the prior art through technical means such as automated sampling, multi-dimensional detection, intelligent scoring and delineation. Each embodiment demonstrates the innovativeness of the present invention and its beneficial effects in practical applications, making it have broad application prospects in the development of coking coal resources.

[0074] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A device for delineating coal resources for coking, characterized in that: include: A coal sampling system for automatically collecting coal samples from coal seams. The sampling system includes a mechanical arm, drilling equipment, sensors, and a real-time monitoring module to ensure the accuracy of the sampling position and the representativeness of the coal samples; Physical parameter detection module, used to detect the physical properties of sampled coal, including but not limited to the density, acoustic wave propagation velocity and resistivity of coal. This module can provide real-time feedback on the hardness, internal structure and layer distribution information of coal; Chemical analysis module, including gas chromatography, X-ray fluorescence spectrometer and other equipment, used to analyze key chemical parameters of coal such as volatile matter, sulfur content, ash content, calorific value, etc.; A data processing unit, for receiving data from the physical parameter detection module and the chemical analysis module, and generating a coal coking suitability score based on the physical and chemical properties of the coal; The suitability delineation algorithm module uses the coal suitability score to algorithmically delineate coal resource areas, and coal resources suitable for coking will be marked for further utilization.

2. The device for delineating coking coal resources according to claim 1, characterized in that: The coal sampling system has an automatic path planning function, which can adaptively adjust the sampling path based on geological maps and coal seam structure information to ensure uniformity and comprehensiveness of sampling. The system can also adjust the sampling frequency according to different geological conditions to ensure sampling efficiency in complex geological environments.

3. The device for delineating coking coal resources according to claim 2, characterized in that: The physical parameter detection module includes an acoustic wave attenuation detection unit and a resistivity measurement unit. The internal structure of the coal is evaluated by detecting the acoustic wave attenuation of the coal seam, and the conductivity and water content of the coal are evaluated by the change of resistivity, and the physical suitability index of the coal is calculated.

4. The device for delineating coking coal resources according to claim 3, characterized in that: The chemical analysis module has an online real-time monitoring function, which can automatically analyze the ash content, sulfur content, volatile matter and calorific value and other parameters of the collected samples, and automatically generate a coking suitability evaluation of the coal through an algorithm. The module can also connect to the database through a cloud platform and automatically compare historical data, thereby improving the accuracy and reliability of the analysis results.

5. The device for delineating coking coal resources according to claim 3, characterized in that: The data processing unit analyzes the physical and chemical data based on a multivariate linear regression model or a machine learning algorithm, and automatically adjusts the weight coefficients in the model. The calculation formula of the coking suitability score (S) is as follows: S = α1·P h +α2·A s +α3·T v Among them, P h Indicates the physical properties of coal, A s Indicates the ash content of coal, T v It represents the calorific value of coal, and α1, α2 and α3 are weighting coefficients.

6. The device for delineating coking coal resources according to claim 5, characterized in that: The multivariate linear regression model is based on a large amount of actual sampling data and obtains the optimal weight parameters through iterative training to improve the accuracy of the suitability score. The model can also be adaptively adjusted according to the geology and resource distribution of different coal mines to adapt to the coal characteristics under different environments.

7. The device for delineating coking coal resources according to claim 6, characterized in that: The suitability delineation algorithm module can automatically generate a coal mine resource map based on the coking suitability score of the coal. The map identifies the distribution of coal resources suitable for coking in different regions and provides the utilization priority of coal in different regions. The delineation algorithm module can also predict future resource development based on historical data.

8. The device for delineating coking coal resources according to claim 2, characterized in that: The coal sampling system, combined with geological conditions, can automatically detect environmental parameters such as temperature, humidity, and pressure in the sampling area, and correct the sampling results based on these environmental parameters. The sampling system can also operate flexibly in complex terrain, including by automatically adjusting the robotic arm angle and drilling depth to ensure sampling accuracy in rugged terrain or steep coal seams.

9. The device for delineating coking coal resources according to claim 3, characterized in that: The physical parameter detection module also includes a radar device for detecting the thickness of the coal seam, which can accurately measure the thickness changes of the coal seam and evaluate the uniformity of the coal seam in combination with the resistivity and sound wave attenuation data, so as to better judge the physical suitability of the coal.

10. The device for delineating coking coal resources according to claim 3, characterized in that: The chemical analysis module also includes an analysis unit for detecting rare elements in coal (such as uranium, thorium, etc.). The unit can identify trace elements present in coal and analyze the content of these elements to ensure the safety and environmental protection of coal, especially to avoid the production of harmful substances during the coking process.

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