Comprehensive monitoring method and device for coal quality, electronic equipment and storage medium

Abstract

The invention relates to a comprehensive monitoring method and device for coal quality, electronic equipment and a storage medium, and the method comprises the steps: obtaining an image of the surface of a coal flow, and determining a roughness index of the coal flow according to the image; determining the subsample quantity of the coal flow according to the roughness index, and evaluating the uniform distribution condition of the coal flow according to the subsample quantity; detecting the calorific value, moisture, sulfur content and ash content of the coal according to the near-infrared and X-ray fusion fluorescence spectrum to generate coal detection data; comparing the coal detection data with a pre-constructed coal flow evaluation model, and evaluating whether the to-be-detected coal is within a preset normal value range; and managing the coal detection data and the analysis result to generate a coal quality detection report of the to-be-detected coal. Therefore, the problems that real-time rapid detection requirements cannot be met, behavior prediction of the coal under different processing and using conditions is difficult to realize, and accuracy and timeliness of coal quality control are difficult to ensure in the prior art are solved.

Description

Technical Field

[0001] This application relates to the field of coal quality testing technology, and in particular to a comprehensive monitoring method, device, electronic equipment and storage medium for coal quality. Background Technology

[0002] Currently, rapid coal quality testing can be achieved in the field of coal quality inspection, but existing technologies have many shortcomings.

[0003] In related technologies, a coal flow model can be established based on actual coal flow equipment data, and coal samples can be extracted from the coal flow transportation equipment for testing without the need for manual sampling and testing, thus enabling rapid testing of coal quality.

[0004] However, in related technologies, on the one hand, the detection of the physical properties of coal flow is not comprehensive enough. For example, the monitoring of parameters such as coal flow roughness, sample size, and uniformity is not precise enough and lacks real-time performance, making it difficult to accurately assess the floatability and processing wear of coal, as well as the representativeness of the samples. On the other hand, in terms of chemical property detection, existing methods are mostly destructive testing, which is slow and cannot meet the requirements of real-time rapid detection. Furthermore, it is difficult to effectively establish internal relationship models between physicochemical properties, making it difficult to predict the behavior of coal under different processing and usage conditions. In addition, in terms of data management, there is a lack of an effective centralized management system, which cannot generate detailed test reports for operators to refer to, making it difficult to ensure the accuracy and timeliness of coal quality control, and these issues urgently need to be improved. Summary of the Invention

[0005] This application provides a comprehensive monitoring method, device, electronic equipment, and storage medium for coal quality, in order to solve the problems of related technologies, such as difficulty in accurately assessing the floatability and processing wear of coal and the representativeness of samples, inability to meet the requirements of real-time and rapid detection, inability to effectively establish an internal relationship model between physicochemical properties, difficulty in predicting the behavior of coal under different processing and use conditions, and difficulty in ensuring the accuracy and timeliness of coal quality control.

[0006] The first aspect of this application provides a comprehensive monitoring method for coal quality, comprising the following steps: acquiring an image of the coal flow surface and determining a roughness index of the coal flow based on the image; determining a sample size of the coal flow based on the roughness index and assessing the uniformity of the coal flow based on the sample size to generate assessment data of the coal flow; based on the assessment data, detecting the calorific value, moisture, sulfur content, and ash content of the coal using near-infrared and X-ray fusion fluorescence spectroscopy to generate coal detection data; comparing the coal detection data with a pre-constructed coal flow assessment model to generate a comparison result, and assessing whether the coal to be tested is within a preset normal value range based on the comparison result; and, if the coal to be tested is within the preset normal value range, managing the coal detection data and analysis results to generate a coal quality detection report for the coal to be tested.

[0007] Optionally, in one embodiment of this application, the step of evaluating the uniform distribution of the coal flow based on the sample size to generate evaluation data of the coal flow includes: evaluating the distribution of the coal flow based on the roughness index; analyzing the sample size differences of each part of the coal flow based on the distribution, and determining whether the coal flow meets a preset uniform distribution condition based on the sample size differences; if the coal flow meets the preset uniform distribution condition, then generating evaluation data of the coal flow based on the uniform distribution of the coal flow.

[0008] Optionally, in one embodiment of this application, after assessing whether the coal to be tested is within a preset normal value range based on the comparison results, the method further includes: triggering an early warning mechanism for the coal to be tested if the coal to be tested is not within the preset normal value range; generating at least one early warning action according to the early warning mechanism, and executing the at least one early warning action according to the early warning level to alert the user to the abnormal information of the coal to be tested.

[0009] Optionally, in one embodiment of this application, the formula for calculating the received basis lower heating value of the coal is:

[0010] in, The net calorific value of the coal is the received basis. To analyze the higher heating value, To analyze the basic hydrogen content, To receive the base moisture.

[0011] A second aspect of this application provides a comprehensive coal quality monitoring device, comprising: an acquisition module for acquiring an image of the surface of a coal flow and determining a roughness index of the coal flow based on the image; an evaluation module for determining a sample size of the coal flow based on the roughness index and evaluating the uniformity distribution of the coal flow based on the sample size to generate evaluation data of the coal flow; a generation module for detecting the calorific value, moisture, sulfur content, and ash content of the coal based on the evaluation data using near-infrared and X-ray fusion fluorescence spectroscopy to generate coal detection data; a comparison module for comparing the coal detection data with a pre-constructed coal flow evaluation model, generating a comparison result, and evaluating whether the coal to be tested is within a preset normal value range based on the comparison result; and a monitoring module for managing the coal detection data and analysis results when the coal to be tested is within the preset normal value range to generate a coal quality detection report of the coal to be tested.

[0012] Optionally, in one embodiment of this application, the evaluation module includes: an evaluation unit, used to evaluate the distribution of the coal flow based on the roughness index; a judgment unit, used to analyze the sample quantity differences of each part of the coal flow according to the distribution, and to determine whether the coal flow meets the preset uniform distribution conditions based on the sample quantity differences; and a generation unit, used to generate evaluation data of the coal flow based on the uniform distribution of the coal flow when the coal flow meets the preset uniform distribution conditions.

[0013] Optionally, in one embodiment of this application, it further includes: a triggering module, configured to trigger an early warning mechanism for the coal to be tested if the coal to be tested is not within the preset normal value range after evaluating whether the coal to be tested is within the preset normal value range based on the comparison result; and a prompting module, configured to generate at least one early warning action according to the early warning mechanism, and execute the at least one early warning action according to the early warning level, so as to prompt the user of the abnormal information of the coal to be tested.

[0014] Optionally, in one embodiment of this application, the formula for calculating the received basis lower heating value of the coal is:

[0015] in, The net calorific value of the coal is the received basis. To analyze the higher heating value, To analyze the basic hydrogen content, To receive the base moisture.

[0016] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the comprehensive monitoring method for coal quality as described in the above embodiments.

[0017] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described comprehensive coal quality monitoring method.

[0018] This application provides high-precision real-time detection data, ensuring the timeliness and accuracy of coal quality control. It integrates multiple detection technologies, comprehensively covering the physical and chemical properties of coal, and utilizes artificial intelligence algorithms for intelligent data analysis and early warning, reducing manual intervention. It offers an intuitive user interface and detailed reports, facilitating operator understanding and use, thereby improving the efficiency and accuracy of coal quality control and providing scientific data support for coal processing, use, and trade. This solves the problems of related technologies, such as difficulty in accurately assessing coal floatability and processing wear, insufficient sampling representativeness, inability to meet real-time rapid detection requirements, inability to effectively establish internal relationship models between physicochemical properties, difficulty in predicting coal behavior under different processing and use conditions, and difficulty in ensuring the accuracy and timeliness of coal quality control.

[0019] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0020] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart illustrating a comprehensive monitoring method for coal quality according to an embodiment of this application; Figure 2 This is a schematic diagram of the overall architecture of an enhanced rapid coal quality detection and early warning system according to an embodiment of this application; Figure 3 A flowchart illustrating the establishment and early warning of an internal relationship model according to an embodiment of this application; Figure 4 This is a schematic diagram of the structure of a comprehensive coal quality monitoring device provided according to an embodiment of this application; Figure 5 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0021] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0022] The following description, with reference to the accompanying drawings, describes a comprehensive coal quality monitoring method, apparatus, electronic device, and storage medium according to embodiments of this application. Addressing the problems mentioned in the background art, such as the difficulty in accurately assessing coal floatability and processing wear, as well as sampling representativeness, the inability to meet real-time rapid detection requirements, the inability to effectively establish internal relationship models between physicochemical properties, the difficulty in predicting coal behavior under different processing and usage conditions, and the difficulty in ensuring the accuracy and timeliness of coal quality control, this application provides a comprehensive coal quality monitoring method. This method can provide high-precision real-time detection data, ensuring the timeliness and accuracy of coal quality control. It integrates multiple detection technologies, comprehensively covers the physical and chemical properties of coal, utilizes artificial intelligence algorithms to achieve intelligent data analysis and early warning, reduces manual intervention, provides an intuitive user interface and detailed reports, facilitating operator understanding and use, thereby improving the efficiency and accuracy of coal quality control and providing scientific data support for coal processing, use, and trade. This solves the problems of related technologies, such as the inability to accurately assess the floatability and processing wear of coal, the inability to sample representativeness, the inability to meet the requirements of real-time and rapid detection, the inability to effectively establish the internal relationship model between physicochemical properties, the difficulty in predicting the behavior of coal under different processing and use conditions, and the difficulty in ensuring the accuracy and timeliness of coal quality control.

[0023] Specifically, Figure 1 This is a flowchart illustrating a comprehensive monitoring method for coal quality provided in an embodiment of this application.

[0024] like Figure 1 As shown, the comprehensive monitoring method for coal quality includes the following steps: In step S101, an image of the coal flow surface is acquired, and the roughness index of the coal flow is determined based on the image.

[0025] It is understood that the coal flow in the embodiments of this application is usually in a dynamic state; the roughness index of the coal flow can be one or a set of values ​​used to quantify the degree of irregularity on the surface of the coal flow.

[0026] In actual implementation, the embodiments of this application can comprehensively monitor coal samples in real time by integrating multiple detection technologies and intelligent analysis systems. In terms of detecting the physical properties of coal flow, laser scanning technology is used to scan the surface of coal flow in real time and analyze the image data to calculate the roughness index of the coal flow surface.

[0027] This application embodiment can adjust the operating parameters of the production equipment in a timely manner by monitoring the roughness of the coal flow in real time, thereby reducing energy consumption, improving processing efficiency, facilitating precise control of the physical properties of the coal flow, and helping to maintain the consistency of the final product quality.

[0028] In the physical property detection, in addition to laser scanning technology, this application can also use machine vision and image processing technology to obtain the roughness information of the coal flow surface, observe the coal flow surface through a camera, and then evaluate the roughness of the coal flow.

[0029] In step S102, the sample size of the coal flow is determined based on the roughness index, and the uniformity of the coal flow is evaluated based on the sample size to generate evaluation data of the coal flow.

[0030] It is understood that, in the embodiments of this application, the subsample size of the coal stream can be a small sample selected from the entire coal stream, which can represent the overall quality characteristics.

[0031] In actual implementation, such as Figure 2 As shown, the embodiments of this application can formulate a reasonable sampling plan based on the specific conditions of the coal flow, including the number, weight, and collection location of subsamples, monitor the amount of subsamples in the coal flow, and evaluate the uniformity of coal flow distribution to generate evaluation data of the coal flow, so as to make improvement suggestions or directly adjust production parameters based on the evaluation data.

[0032] The embodiments of this application can monitor the sample size of coal flow and assess its distribution uniformity, providing strong support for improving efficiency, saving costs and protecting the environment. It can accurately and in real time detect the physical characteristics of coal flow, including roughness, sample size, uniformity, etc., providing more accurate data support for coal processing and quality control.

[0033] Optionally, in one embodiment of this application, the evaluation of the uniform distribution of the coal flow based on the sample size to generate evaluation data of the coal flow includes: evaluating the distribution of the coal flow based on the roughness index; analyzing the differences in the sample size of each part of the coal flow based on the distribution, and determining whether the coal flow meets the preset uniform distribution conditions based on the differences in the sample size; if the coal flow meets the preset uniform distribution conditions, then generating evaluation data of the coal flow based on the uniform distribution of the coal flow.

[0034] It is understood that the preset uniformity condition in the embodiments of this application can be whether the coal flow meets the condition of uniform distribution.

[0035] Specifically, the embodiments of this application can evaluate the distribution of coal flow based on roughness index, analyze the differences in the sample quantity of each part of the coal flow based on the distribution, and determine whether the coal flow meets certain uniform distribution conditions based on the differences in the sample quantity. If the coal flow meets certain uniform distribution conditions, evaluation data of the coal flow is generated based on the uniform distribution of the coal flow.

[0036] Furthermore, in this embodiment of the application, if the roughness and number of samples exceed a preset range, the data can be uploaded to the early warning system, triggering an alarm and stopping the subsequent scanning of the near-infrared and X-ray fluorescence fusion spectroscopy scanning module, and triggering an alarm to output the cause of the deviation.

[0037] It should be noted that the preset uniformity conditions can be set by those skilled in the art according to the actual situation, and no specific restrictions are imposed here.

[0038] In step S103, based on the evaluation data, the calorific value, moisture, sulfur content and ash content of the coal are detected by near-infrared and X-ray fusion fluorescence spectroscopy to generate coal detection data.

[0039] It is understood that the calorific value of coal in the embodiments of this application can be the heat released when a unit mass of coal is completely burned; the sulfur content can be the sulfur content in the coal; and the ash content can be the proportion of non-combustible substances remaining after the coal is completely burned.

[0040] In this application, the embodiments can achieve rapid and non-destructive testing of coal's calorific value, moisture, sulfur content, and ash content based on evaluation data and near-infrared and X-ray fusion fluorescence spectroscopy analysis. Artificial intelligence algorithms are used to analyze and process the test data in real time to generate coal test data. This application can achieve rapid and non-destructive testing of the chemical properties of coal, such as calorific value, moisture, sulfur content, and ash content, thereby improving testing efficiency and accuracy.

[0041] In terms of chemical property detection, in addition to near-infrared spectroscopy and X-ray fluorescence spectroscopy, this application also employs Raman spectroscopy. Raman spectroscopy can provide information on the molecular structure of coal. Through Raman spectroscopy analysis, the calorific value, moisture content, sulfur content, ash content, and other chemical properties of coal can be detected rapidly and non-destructively, providing another effective detection method for the system.

[0042] The embodiments of this application can provide high-precision real-time detection data to ensure the timeliness and accuracy of coal quality control.

[0043] In step S104, the coal detection data is compared with the pre-built coal flow assessment model to generate a comparison result, and the coal to be detected is assessed based on the comparison result to determine whether it is within the preset normal value range.

[0044] It is understood that the coal flow assessment model in the embodiments of this application can be an internal relationship model between the physical and chemical properties of coal.

[0045] In actual implementation, the embodiments of this application can establish an internal relationship model between the physical and chemical properties of coal based on a large amount of historical data and coal testing data, compare the coal testing data with the pre-constructed coal flow assessment model, generate comparison results, and evaluate whether the sample is within the normal range based on the comparison results.

[0046] The embodiments of this application can establish an internal relationship model between the physicochemical properties of coal to predict the accuracy of coal index prediction, assess whether the coal to be tested is within the preset normal value range, and provide support for triggering the early warning mechanism of the coal to be tested or generating a coal quality test report of the coal to be tested.

[0047] Optionally, in one embodiment of this application, after assessing whether the coal to be tested is within a preset normal value range based on the comparison results, the method further includes: triggering an early warning mechanism for the coal to be tested if it is not within the preset normal value range; generating at least one early warning action according to the early warning mechanism, and executing at least one early warning action according to the early warning level to alert the user to abnormal information about the coal to be tested.

[0048] It is understood that at least one warning action in the embodiments of this application can be an acoustic alarm, an optical alarm, or a text alarm.

[0049] As one possible implementation, embodiments of this application can trigger an early warning mechanism for the coal to be tested when it is outside a certain normal value range. Specifically, when this application detects data deviating from a preset model range, it automatically triggers an early warning, provides a detailed analysis of the reasons for the deviation, generates at least one early warning action based on the early warning mechanism, and executes at least one early warning action according to the early warning level to alert the user to the abnormal information of the coal to be tested. The internal relationship model establishment and early warning flowchart are shown below. Figure 3 As shown.

[0050] For example, in this application embodiment, when the warning mechanism for the coal to be detected is triggered, an audible alert can be emitted to notify the user of the abnormal information of the coal to be detected; in another example, in this application embodiment, when the warning mechanism for the coal to be detected is triggered, a flashing light can be emitted to notify the user of the abnormal information of the coal to be detected; and in yet another example, in this application embodiment, when the warning mechanism for the coal to be detected is triggered, a text message can be sent to the user's mobile phone to notify the user of the abnormal information of the coal to be detected.

[0051] This application enables effective management of testing data and provides timely warnings when data deviates from the normal range, facilitating operators to quickly locate problems and take measures to ensure the accuracy and timeliness of coal quality control.

[0052] In one embodiment of this application, the formula for calculating the lower heating value of coal on a received basis is as follows:

[0053] in, The net calorific value of coal is the received basis. To analyze the higher heating value, To analyze the basic hydrogen content, To receive the base moisture.

[0054] In step S105, when the coal to be tested is within the preset normal value range, the coal testing data and analysis results are managed to generate a coal quality test report for the coal to be tested.

[0055] It is understood that the coal quality test report in this application embodiment may include sample information, test items and their results.

[0056] In actual implementation, the system in this application embodiment can centrally manage all detection data and analysis results, and support the generation of detailed detection reports, including comprehensive assessment of coal quality, early warning event records, and suggested improvement measures.

[0057] This application's embodiments can integrate multiple detection technologies to comprehensively cover the physical and chemical properties of coal. It utilizes artificial intelligence algorithms to achieve intelligent data analysis and early warning, reducing manual intervention, providing an intuitive user interface and detailed reports, making it easy for operators to understand and use. This improves the efficiency and accuracy of coal quality control, increasing the ratio of the difference between rapid coal quality testing and manual comparison within 144 kcal / kg by about 8%, and providing scientific data support for coal processing, use, and trade.

[0058] In terms of data management and report generation, in addition to the existing centralized management model, a distributed database system can also be used. This involves distributing the detection data across multiple nodes, synchronizing and sharing the data via a network, improving data security and reliability, and also meeting the needs for large-scale data storage and rapid querying.

[0059] The comprehensive coal quality monitoring method proposed in this application provides high-precision real-time detection data, ensuring the timeliness and accuracy of coal quality control. It integrates multiple detection technologies, comprehensively covering the physical and chemical properties of coal, and utilizes artificial intelligence algorithms for intelligent data analysis and early warning, reducing manual intervention. It provides an intuitive user interface and detailed reports, facilitating operator understanding and use, thereby improving the efficiency and accuracy of coal quality control and providing scientific data support for coal processing, use, and trade. This solves the problems of related technologies, such as difficulty in accurately assessing coal floatability and processing wear, sampling representativeness, inability to meet real-time rapid detection requirements, inability to effectively establish internal relationship models between physicochemical properties, difficulty in predicting coal behavior under different processing and use conditions, and difficulty in ensuring the accuracy and timeliness of coal quality control.

[0060] Next, referring to the accompanying drawings, a comprehensive coal quality monitoring device proposed according to an embodiment of this application is described.

[0061] Figure 4 This is a schematic diagram of the structure of the comprehensive monitoring device for coal quality according to an embodiment of this application.

[0062] like Figure 4 As shown, the comprehensive coal quality monitoring device 10 includes: an acquisition module 100, an evaluation module 200, a generation module 300, a comparison module 400, and a monitoring module 500.

[0063] Specifically, the acquisition module 100 is used to acquire an image of the coal flow surface and determine the roughness index of the coal flow based on the image.

[0064] Evaluation module 200 is used to determine the sample size of the coal flow based on the roughness index and to evaluate the uniformity of the coal flow based on the sample size in order to generate evaluation data of the coal flow.

[0065] The generation module 300 is used to generate coal detection data by detecting the calorific value, moisture, sulfur content and ash content of coal based on the evaluation data and by using near-infrared and X-ray fusion fluorescence spectroscopy.

[0066] The comparison module 400 is used to compare coal detection data with a pre-built coal flow assessment model, generate comparison results, and assess whether the coal to be tested is within the preset normal value range based on the comparison results.

[0067] The monitoring module 500 is used to manage coal testing data and analysis results when the coal to be tested is within a preset normal value range, so as to generate a coal quality test report for the coal to be tested.

[0068] Optionally, in one embodiment of this application, the evaluation module 200 includes: an evaluation unit, a judgment unit, and a generation unit.

[0069] The evaluation unit is used to assess the distribution of coal flow based on the roughness index.

[0070] The judgment unit is used to analyze the differences in the sample quantity of each part of the coal flow based on the distribution, and to determine whether the coal flow meets the preset uniform distribution conditions based on the differences in the sample quantity.

[0071] The generation unit is used to generate evaluation data of the coal flow based on the uniform distribution of the coal flow, provided that the coal flow meets the preset uniform distribution conditions.

[0072] Optionally, in one embodiment of this application, the comprehensive coal quality monitoring device 10 further includes a trigger module and a prompt module.

[0073] The triggering module is used to trigger an early warning mechanism for the coal under test if it is outside the preset normal value range after assessing whether the coal under test is within the preset normal value range based on the comparison results.

[0074] The alert module is used to generate at least one alert action according to the early warning mechanism, and execute at least one alert action according to the alert level, so as to alert the user to abnormal information of the coal to be detected.

[0075] Optionally, in one embodiment of this application, the formula for calculating the lower heating value of coal on a received basis is:

[0076] in, The net calorific value of coal is the received basis. To analyze the higher heating value, To analyze the basic hydrogen content, To receive the base moisture.

[0077] It should be noted that the explanation of the aforementioned embodiment of the comprehensive monitoring method for coal quality also applies to the comprehensive monitoring device for coal quality in this embodiment, and will not be repeated here.

[0078] The comprehensive coal quality monitoring device proposed in this application can provide high-precision real-time detection data, ensuring the timeliness and accuracy of coal quality control. It integrates multiple detection technologies, comprehensively covering the physical and chemical properties of coal, and utilizes artificial intelligence algorithms for intelligent data analysis and early warning, reducing manual intervention. It provides an intuitive user interface and detailed reports, facilitating operator understanding and use, thereby improving the efficiency and accuracy of coal quality control and providing scientific data support for coal processing, use, and trade. This solves the problems of related technologies, such as difficulty in accurately assessing coal floatability and processing wear, sampling representativeness, inability to meet real-time rapid detection requirements, inability to effectively establish internal relationship models between physicochemical properties, difficulty in predicting coal behavior under different processing and use conditions, and difficulty in ensuring the accuracy and timeliness of coal quality control.

[0079] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 501, the processor 502, and the computer program stored on the memory 501 and capable of running on the processor 502.

[0080] When the processor 502 executes the program, it implements the comprehensive coal quality monitoring method provided in the above embodiments.

[0081] Furthermore, electronic devices also include: Communication interface 503 is used for communication between memory 501 and processor 502.

[0082] The memory 501 is used to store computer programs that can run on the processor 502.

[0083] Memory 501 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0084] If the memory 501, processor 502, and communication interface 503 are implemented independently, then the communication interface 503, memory 501, and processor 502 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 5The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0085] Optionally, in a specific implementation, if the memory 501, processor 502, and communication interface 503 are integrated on a single chip, then the memory 501, processor 502, and communication interface 503 can communicate with each other through an internal interface.

[0086] Processor 502 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0087] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described comprehensive coal quality monitoring method.

[0088] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is 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.

[0089] Furthermore, 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. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0090] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0091] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0092] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0093] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.

[0094] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0095] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. 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 changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A comprehensive monitoring method for coal quality, characterized in that, Includes the following steps: Acquire an image of the coal flow surface and determine the roughness index of the coal flow based on the image; The sample size of the coal flow is determined based on the roughness index, and the uniformity of the coal flow is evaluated based on the sample size to generate evaluation data of the coal flow. Based on the aforementioned evaluation data, the calorific value, moisture, sulfur content, and ash content of coal are detected using near-infrared and X-ray fusion fluorescence spectroscopy to generate coal detection data. The coal detection data is compared with the pre-built coal flow assessment model to generate a comparison result, and the coal to be tested is evaluated based on the comparison result to determine whether it is within the preset normal value range. When the coal to be tested is within the preset normal value range, the coal testing data and analysis results are managed to generate a coal quality testing report for the coal to be tested.

2. The method according to claim 1, characterized in that, The step of assessing the uniformity of the coal flow based on the subsample size to generate assessment data for the coal flow includes: The distribution of the coal flow is evaluated based on the roughness index. Analyze the differences in the sample quantity of each part of the coal flow based on the distribution, and determine whether the coal flow meets the preset uniform distribution condition based on the differences in the sample quantity. If the coal flow meets the preset uniform distribution condition, then the evaluation data of the coal flow is generated based on the uniform distribution of the coal flow.

3. The method according to claim 1, characterized in that, After assessing whether the coal to be tested is within a preset normal range based on the comparison results, the process also includes: If the coal to be tested is not within the preset normal value range, an early warning mechanism for the coal to be tested is triggered. At least one warning action is generated according to the warning mechanism, and the at least one warning action is executed according to the warning level to alert the user to the abnormal information of the coal to be detected.

4. The method according to claim 1, characterized in that, The formula for calculating the net calorific value of the coal on a received basis is as follows: in, The net calorific value of the coal is the received basis. To analyze the higher heating value, To analyze the basic hydrogen content, To receive the base moisture.

5. A comprehensive monitoring device for coal quality, characterized in that, include: An acquisition module is used to acquire an image of the coal flow surface and determine the roughness index of the coal flow based on the image; An evaluation module is used to determine the sample size of the coal flow based on the roughness index, and to evaluate the uniformity of the coal flow based on the sample size, so as to generate evaluation data of the coal flow. The generation module is used to generate coal detection data by detecting the calorific value, moisture, sulfur content and ash content of coal based on the evaluation data and by using near-infrared and X-ray fusion fluorescence spectroscopy. The comparison module is used to compare the coal detection data with the pre-built coal flow assessment model, generate comparison results, and assess whether the coal to be detected is within the preset normal value range based on the comparison results. The monitoring module is used to manage the coal testing data and analysis results when the coal to be tested is within the preset normal value range, so as to generate a coal quality testing report for the coal to be tested.

6. The apparatus according to claim 5, characterized in that, The evaluation module includes: An evaluation unit is used to evaluate the distribution of the coal flow based on the roughness index; The judgment unit is used to analyze the difference in the sample quantity of each part of the coal flow based on the distribution, and to determine whether the coal flow meets the preset uniform distribution condition based on the difference in the sample quantity. The generation unit is used to generate evaluation data of the coal flow based on the uniform distribution of the coal flow, provided that the coal flow meets the preset uniform distribution conditions.

7. The apparatus according to claim 5, characterized in that, Also includes: The triggering module is used to trigger an early warning mechanism for the coal to be tested if the coal to be tested is not within the preset normal value range after evaluating whether the coal to be tested is within the preset normal value range based on the comparison results. The prompting module is used to generate at least one early warning action according to the early warning mechanism, and execute the at least one early warning action according to the early warning level, so as to prompt the user with abnormal information about the coal to be detected.

8. The apparatus according to claim 5, characterized in that, The formula for calculating the net calorific value of the coal on a received basis is as follows: in, The net calorific value of the coal is the received basis. To analyze the higher heating value, To analyze the basic hydrogen content, To receive the base moisture.

9. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the comprehensive monitoring method for coal quality as described in any one of claims 1-4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the comprehensive monitoring method for coal quality as described in any one of claims 1-4.

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