A coal microcrystalline structure deterioration characterization method and system under multi-environment coupling

Abstract

The application relates to the technical field of intelligent sensing systems, in particular to a coal microcrystalline structure degradation characterization method and system under multi-environment coupling, which comprises the following steps: based on multi-environment factors, carrying out simulation experiments, measuring the maximum reflectivity of the coal microcrystalline structure, and combining the XRD method to calculate the characteristic parameters of the coal microcrystalline structure; according to the coupling relationship between the change degrees of the multi-environment factor data and the coupling relationship between the multi-environment factor data and the change degrees of the characteristic parameters of the coal microcrystalline structure, the degradation contribution degree of the multi-environment factor data to the coal microcrystalline structure is determined; and a prediction model of the degradation degree of the coal microcrystalline structure is constructed in combination with the degradation contribution degree, so as to predict the coal microcrystalline structure degradation characterization result under multi-environment coupling. The application solves the problem of low degradation characterization precision caused by the lack of coal quality degradation rule research under multi-environment coupling in the prior art, and improves the precision of the coal microcrystalline structure degradation degree prediction.

Description

Technical Field

[0001] This application relates to the field of intelligent sensing system technology, specifically to a method and system for characterizing the deterioration of coal microcrystalline structure under multi-environment coupling. Background Technology

[0002] Coal has long held a stable position as a core energy source, and its storage and logistics system has become a crucial hub for ensuring energy security and regulating supply and demand balance. However, under conditions of long storage periods, large transportation distances, and significant environmental variations (such as high temperature and humidity, diurnal temperature differences, and oxygen exposure), coal is highly susceptible to oxidation, spontaneous combustion, moisture migration, and pulverization, leading to coal quality deterioration. This not only results in decreased calorific value, increased ash content, and poorer caking properties, reducing coal utilization efficiency, but also increases the risk of spontaneous combustion and other safety hazards, causing economic losses and increasing the difficulty of subsequent washing and processing, severely restricting the efficient operation of the coal storage and logistics system. Therefore, it is urgent to conduct in-depth research on the laws of coal deterioration and accurately predict the degree of coal quality deterioration in order to formulate timely coal temperature control plans and effectively avoid the risks of coal quality deterioration.

[0003] However, existing research on coal deterioration is mostly limited to single storage environments (such as open-air storage) or single deterioration types (such as spontaneous combustion), lacking exploration of the coal quality deterioration patterns under the coupling of multiple environments across the entire "storage-logistics" chain. This results in a single environmental factor characterizing the deterioration of coal microcrystalline structure, making it difficult to reveal the deterioration mechanism under complex operating conditions. Furthermore, existing temperature control technologies lack specificity and cannot adapt to the actual engineering needs of different storage methods and different coal types (especially low-rank coal and thermal coal), making it difficult to accurately obtain the degree of coal quality deterioration. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a method and system for characterizing the deterioration of coal microcrystalline structure under multi-environment coupling. The specific technical solution adopted is as follows: In a first aspect, embodiments of this application provide a method for characterizing the deterioration of coal microcrystalline structure under multi-environment coupling, the method comprising the following steps: Obtain data on multiple environmental factors related to coal mining; Simulation experiments were conducted based on multiple environmental factors to determine the maximum reflectivity of coal microcrystalline structure, and the characteristic parameters of coal microcrystalline structure were calculated using the XRD method. Based on the coupling relationship between the variability of multiple environmental factors and the coupling relationship between the variability of multiple environmental factors and the characteristic parameters of coal microcrystal structure, the contribution of multiple environmental factors to the deterioration of coal microcrystal structure is determined. A predictive model for the degree of degradation of coal microcrystalline structure was constructed by combining the degradation contribution to predict the degradation characterization results of coal microcrystalline structure under multi-environment coupling.

[0005] Preferably, the multi-environmental factor data includes: temperature, humidity, oxygen content, and mechanical vibration intensity during coal storage and transportation.

[0006] Preferably, the characteristic parameters of the coal microcrystalline structure include the vertical distance between the aromatic lamellae, the stacking height of the aromatic lamellae, the elongation of the aromatic lamellae, and the number of aromatic lamellae.

[0007] Preferably, the process for determining the contribution of the multi-environmental factor data to the deterioration of coal microcrystalline structure characteristic parameters is as follows: During any given experiment, a sequence of variation for each environmental factor and a sequence of variation for each characteristic parameter of coal microcrystalline structure were constructed: Based on the correlation between the variation sequence of each environmental factor data and any coal microcrystalline structure characteristic parameter, and the correlation between the variation sequence of each environmental factor data and the data of all other environmental factors, the contribution of each environmental factor data to any coal microcrystalline structure characteristic parameter is determined, and then weighted fusion is used to obtain the contribution of each environmental factor data to the deterioration of the coal microcrystalline structure.

[0008] Preferably, the method for constructing the degree of change sequence is as follows: During any experiment, the absolute difference between each measurement point and its adjacent previous measurement point is calculated and divided by the absolute value of each data point at the adjacent previous measurement point. This is recorded as the degree of change of each data point at each time point. The sequence of the degree of change of each data point at all times during any experiment is recorded as the degree of change sequence.

[0009] Preferably, the contribution of each environmental factor data to any coal microcrystalline structure characteristic parameter includes: During any experiment, the mean of the absolute values ​​of the correlation coefficients between each environmental factor data and all other environmental factor data on the degree of change sequence is calculated and denoted as the environmental coupling coefficient. The absolute value of the correlation coefficient between the variation sequence of each environmental factor data and any characteristic parameter of coal microcrystalline structure is calculated and denoted as the direct coupling coefficient. The indirect coupling coefficient is calculated based on the environmental coupling coefficient and the direct coupling coefficient. The contribution of each environmental factor data to any coal microcrystalline structure characteristic parameter is positively correlated with the direct coupling coefficient and the indirect coupling coefficient, respectively.

[0010] Preferably, the degradation contribution is the weighted sum of the contributions of each environmental factor data to the microcrystalline structure characteristic parameters of all coal types.

[0011] Preferably, the predictive model for the degree of degradation of coal microcrystalline structure includes: The maximum reflectance of each measurement point adjacent to the previous measurement point and the coal type, along with the variability of multiple environmental factors and their corresponding deterioration contributions, are used as input features of the neural network. The maximum reflectance at each measurement point is used as the output target of the neural network. The neural network is trained to obtain a predictive model for the degree of deterioration of coal microcrystalline structure.

[0012] Preferably, the predicted characterization results of coal microcrystalline structure degradation under multi-environment coupling include: The system inputs multi-environmental factor data at the current moment into a trained predictive model for the degree of deterioration of coal microcrystalline structure, and outputs the maximum reflectivity of the coal microcrystalline structure at the current moment to characterize the degree of deterioration of the coal microcrystalline structure. Secondly, this application also provides a coal microcrystalline structure deterioration characterization system under multi-environment coupling, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described methods for characterizing coal microcrystalline structure deterioration under multi-environment coupling.

[0013] This application has at least the following beneficial effects: 1. This application effectively shortens the experimental research cycle and improves data acquisition efficiency by proportionally shortening the entire "storage-transportation" cycle. At the same time, by using maximum reflectance combined with XRD method to calculate multiple microscopic characteristic parameters, it can comprehensively and accurately quantify the degree of deterioration of coal microcrystalline structure from two dimensions: macroscopic optical properties and microscopic crystal structure. This provides a high-quality data foundation for subsequent analysis of deterioration laws under multi-environment coupling and construction of prediction models. 2. This application constructs a change degree sequence to explore the coupling characteristics between environmental factors and the direct correlation between environmental factors and coal microcrystalline structure characteristic parameters. By utilizing the environmental coupling coefficient, direct coupling coefficient, and indirect coupling coefficient, it successfully separates the direct and indirect effects of a single environmental factor in a complex coupled system, achieving accurate quantification of the degradation contribution of each environmental factor. This solves the problem that traditional methods have difficulty distinguishing the degree of independent influence of each factor under multiple environmental coupling effects, providing key characteristic parameter support for the subsequent construction of a high-precision degradation prediction model. 3. This application effectively captures the temporal dependence and nonlinear characteristics between environmental factors and changes in coal microcrystalline structure by inputting the degradation contribution that can reflect the multi-environment coupling effect into the LSTM neural network as a key feature. This overcomes the limitation of traditional methods that ignore the environmental coupling effect and improves the accuracy of predicting the degree of degradation of coal microcrystalline structure. Attached Figure Description

[0014] Figure 1A flowchart illustrating the steps of a method for characterizing the deterioration of coal microcrystalline structure under multi-environment coupling, provided in one embodiment of this application; Figure 2 This is a schematic diagram of the degradation contribution extraction process provided in one embodiment of this application. Detailed Implementation

[0015] The following description, in conjunction with the accompanying drawings, details the specific scheme of the coal microcrystalline structure deterioration characterization method and system provided in this application under multi-environment coupling.

[0016] Please see Figure 1 The diagram illustrates a flowchart of a method for characterizing the deterioration of coal microcrystalline structure under multi-environment coupling, according to an embodiment of this application. The method includes the following steps: Step S1: Obtain data on multiple environmental factors related to coal.

[0017] Based on actual projects of Zhongyun International (such as coal reserves in a mining area in Indonesia and domestic thermal coal reserve bases), multi-environmental factor data of different coal types (such as low-rank coal and thermal coal) are obtained during storage sites and transportation. This multi-environmental factor data includes temperature, humidity, oxygen content, and mechanical vibration intensity during transportation, primarily collected using intelligent sensing systems deployed on storage sites and transportation vehicles (such as trucks or ships). These intelligent sensing systems consist of temperature and humidity sensors, electrochemical oxygen sensors, and mechanical vibration sensors. In this embodiment, the data acquisition frequency is set to 0.5 hours / time.

[0018] Step S2: Conduct simulation experiments based on multiple environmental factors, determine the maximum reflectivity of the coal microcrystalline structure, and calculate the characteristic parameters of the coal microcrystalline structure using the XRD method.

[0019] Given the long cycle of the coal "storage-transportation" chain, the cycle of the entire chain and its various links is shortened proportionally to reduce the experimental research cycle. In this embodiment, the cycle is shortened by a ratio of 10:1. Taking temperature data as an example: after the cycle is shortened proportionally, one temperature data point is equal to the average of 10 adjacent and consecutive temperature data points in the original field temperature data, and the next data point is equal to the average of the next 10 consecutive temperature data points, and so on. Similarly, experimental humidity data, experimental oxygen content data, and experimental mechanical vibration data after the cycle is shortened proportionally are obtained as multi-environmental factor data for subsequent laboratory experimental stages.

[0020] During the experimental phase, coal samples of different coal types were obtained (in this embodiment, low-rank coal samples were used as an example). The experimental temperature, humidity, oxygen content, and mechanical vibration intensity data, after the period was shortened proportionally, were used as the experimental multi-environmental factor data. The multi-environmental factor data of the coal samples were automatically adjusted every 0.5 hours in chronological order. For example, 1 hour after the start of the experiment, the multi-environmental factor data was adjusted to the 3rd data element in the experimental data, 1.5 hours after the experiment was started, it was adjusted to the 4th data element, and so on.

[0021] Before each adjustment, the maximum reflectance of the coal sample was measured using a fully automated coal vitrinite reflectance measurement method to characterize the degree of deterioration of the coal microcrystalline structure under multi-environment coupling. At least 100 measurement points were required; in this embodiment, 500 points were measured. The initial maximum reflectance was the maximum reflectance measured before the start of the experimental phase (i.e., before the first adjustment of multi-environment factor data). Simultaneously, X-ray diffraction curves of the coal sample were obtained using the XRD method, with the incident wavelength λ set to 0.154178 nm. The X-ray diffraction curves were input into MDI Jade 6.5 software, and the Bragg angles corresponding to the 002 and 101 peaks were extracted. , And the full width at half maximum (FWHM) of peaks 002 and 101. , The 002 and 101 peaks are the two most representative peaks in the X-ray diffraction curve of coal microcrystalline structure, reflecting the orderliness of aromatic microcrystals in the vertical and horizontal directions, respectively. The higher the degree of coal deterioration, the higher the peak height, the lower the peak width, the shift in diffraction angle, and the greater the packing and elongation of the microcrystalline structure.

[0022] Next, based on the extracted parameters, four characteristic parameters of the coal microcrystalline structure were calculated: the vertical distance between aromatic lamellae. Stacking height of aromatic flakes Extensibility of aromatic flakes and the number of aromatic flakes The calculation formula is as follows: ; ; ; ; In the formula, Let π be 3.14159; , The characteristic coefficients are determined according to the Debye-Scherrer formula, and in this embodiment, they are set to 0.9 and 1.84 respectively. , , The dimensions of all are nanometers (nm). Dimensionless. In particular, the initial maximum reflectivity and initial characteristic parameters of the coal microcrystalline structure (i.e., before the start of the experiment at 0h) were obtained before the start of the experimental phase, and the corresponding multi-environmental factor data are consistent with the current experimental scenario.

[0023] in, , , , The characteristic parameters are all calculated according to standard calculation formulas, reflecting the characteristics of coal microcrystalline structure, which are within the scope of known technology, and the specific implementation principle will not be elaborated here.

[0024] Step S3: Based on the coupling relationship between the variability of multiple environmental factors and the coupling relationship between the variability of multiple environmental factors and the variability of coal microcrystalline structure characteristic parameters, determine the contribution of multiple environmental factors to the deterioration of coal microcrystalline structure characteristic parameters.

[0025] Generally, within the same time period, the greater the fluctuation of multiple environmental factors, the more significant the impact on the degradation rate of coal microcrystalline structure. If the degradation rate increases, the characteristic parameters of the coal microcrystalline structure change more significantly; conversely, if the degradation rate decreases or stabilizes, the characteristic parameters of the coal microcrystalline structure change less. However, these multiple environmental factors are not isolated but rather have complex coupling relationships (e.g., mechanical vibration generates heat, leading to temperature increases, which accelerate oxidation reactions). Changes in a single environmental factor are often accompanied by synergistic changes in other environmental factors, and this coupling effect collectively influences the degradation process of the coal microcrystalline structure. Therefore, to isolate and quantify the independent roles of each environmental factor in a complex coupled environment, it is necessary to deeply analyze the coupling relationships between the variability of multiple environmental factor data and the coupling relationship between the data of multiple environmental factors and the variability of the characteristic parameters of the coal microcrystalline structure, thereby determining the contribution of each environmental factor to the degradation of the characteristic parameters of the coal microcrystalline structure.

[0026] In any experiment, to analyze the degree of change of coal microcrystalline structure characteristic parameters under multi-environment coupling at adjacent measurement points, the absolute difference of each data point between each measurement point and its adjacent previous measurement point is calculated and divided by the absolute value of each data point at the adjacent previous measurement point. This is recorded as the degree of change of each data point at each time point. The sequence of the degree of change of each data point at all times in any experiment is recorded as the degree of change sequence. Before calculating the ratio, in order to prevent the denominator from being zero and thus rendering the calculation meaningless, this embodiment adds a preset zero-prevention factor to the absolute value of each data point at the adjacent previous measurement point in the denominator to prevent the denominator from being zero. In this embodiment, the preset zero-prevention factor is 0.01. Under the premise of ensuring that the denominator is not zero and does not excessively affect the calculation results, the implementer can also set the value of the preset zero-prevention factor according to the specific situation. This embodiment does not impose any special restrictions.

[0027] Generally, the greater the fluctuation in environmental factor data, the faster the degradation rate of coal microcrystalline structure and the greater the variability of characteristic parameters. To quantify the coupled influence of environmental factors on the degree of degradation of coal microcrystalline structure, this embodiment determines the contribution of multiple environmental factor data to the degradation of coal microcrystalline structure characteristic parameters based on the coupling relationship between the variability of multiple environmental factor data and the coupling relationship between the variability of multiple environmental factor data and the variability of coal microcrystalline structure characteristic parameters. The specific process is as follows: (1): Calculate the environmental coupling coefficient. In any experiment, the degree of change of each environmental factor data and the degree of change of each coal microcrystalline structure characteristic parameter are respectively formed into a degree of change sequence of each environmental factor data and a degree of change sequence of each coal microcrystalline structure characteristic parameter; calculate the mean of the absolute values ​​of the correlation coefficients between each environmental factor data and all other environmental factor data on the degree of change sequence, and record it as the environmental coupling coefficient of each environmental factor data.

[0028] The environmental coupling coefficient is used to characterize the degree of synergy between changes in a single environmental factor and changes in all other environmental factors, reflecting the interaction strength of an environmental factor in a multi-environment coupling system. The larger the environmental coupling coefficient, the easier it is for changes in the corresponding environmental factors to cause significant changes in other environmental factors (e.g., drastic temperature changes leading to a significant decrease in humidity or accelerated oxygen consumption), reflecting the greater potential of the corresponding environmental factors to have an indirect impact on the deterioration of coal microcrystalline structure. Conversely, the smaller the environmental coupling coefficient, the more independent the corresponding environmental factors are, and their changes are less likely to cause coordinated fluctuations in other environmental factors, resulting in a weaker indirect driving force on the deterioration of coal microcrystalline structure.

[0029] (2): Calculate the direct coupling coefficient. During any experiment, calculate the absolute value of the correlation coefficient between the data of each environmental factor and the characteristic parameter of coal microcrystalline structure, and record it as the direct coupling coefficient.

[0030] The direct coupling coefficient is used to characterize the degree of direct correlation between changes in a single environmental factor and changes in characteristic parameters of coal microcrystal structure, reflecting the strength of the corresponding environmental factor in directly affecting the coal microcrystal structure and causing changes in its orderliness; The larger the direct coupling coefficient, the more directly and significantly the fluctuations of the corresponding environmental factors can cause drastic changes in the characteristic parameters of coal microcrystalline structure (such as the spacing between aromatic lamellae and the stacking height), reflecting that the corresponding environmental factors are the direct dominant factors leading to the deterioration of coal microcrystalline structure. Conversely, the smaller the direct coupling coefficient, the weaker the direct impact of the changes in the corresponding environmental factors on the characteristic parameters of coal microcrystalline structure, and the less likely they are to be the main driving force for deterioration.

[0031] (3): Calculate the indirect coupling coefficient. In any experiment, calculate the product of the direct coupling coefficient between each environmental factor data and any coal microcrystalline structure characteristic parameter and the corresponding environmental coupling coefficient, and use it as the indirect coupling coefficient between each environmental factor data and any coal microcrystalline structure characteristic parameter.

[0032] The indirect coupling coefficient is used to characterize the degree of coupling influence of a single environmental factor on the characteristic parameters of coal microcrystalline structure by influencing other environmental factors. It reflects the contribution of the chain action path of "environmental factor A → environmental factor B / ... → coal microcrystalline structure". The larger the indirect coupling coefficient, the higher the degree to which the corresponding environmental factor indirectly exacerbates the deterioration of the coal microcrystalline structure by inducing changes in other environmental factors, reflecting the hidden and derivative effects of a single factor under the coupling of multiple environments; conversely, the smaller the indirect coupling coefficient, the weaker the ability of the corresponding environmental factor to induce other environmental factors to participate in the deterioration process, and the less significant its indirect damage path.

[0033] (4): Calculate the contribution. In this embodiment, the contribution of each environmental factor data to any coal microcrystalline structure characteristic parameter is positively correlated with the direct coupling coefficient and the indirect coupling coefficient, respectively.

[0034] It should be understood that a positive correlation means that the dependent variable increases as the independent variable increases, and the dependent variable decreases as the independent variable decreases. The specific relationship can be additive or multiplicative, etc., and is determined by the actual application. This application does not impose any special restrictions.

[0035] Preferably, as an implementation method, in this embodiment, the average of the direct coupling coefficient and the indirect coupling coefficient between each environmental factor data and any coal microcrystalline structure characteristic parameter is used as the contribution of each environmental factor data to any coal microcrystalline structure characteristic parameter. This is used to characterize the comprehensive contribution weight of a single environmental factor to the deterioration of a specific characteristic parameter of the coal microcrystalline structure after comprehensively considering the direct coupling effect and the indirect coupling effect.

[0036] The contribution degree is used to characterize the contribution weight of a single environmental factor to the deterioration of a specific characteristic parameter of coal microcrystalline structure after comprehensively considering the direct and indirect coupling effects. It reflects the comprehensive effect intensity of the corresponding environmental factor on specific microstructural indicators (such as interlamellar spacing). The greater the contribution, the more likely the corresponding environmental factor is to be the key cause of the deterioration of the coal microcrystalline structure characteristic parameters. When regulating this microstructure parameter, the corresponding environmental factor should be controlled first. Conversely, the smaller the contribution, the less likely the corresponding environmental factor has an impact on the coal microcrystalline structure characteristic parameters. It plays a secondary role in the deterioration process of the coal microcrystalline structure characteristic parameters.

[0037] (5): Obtain the deterioration contribution. The weighted sum of the contributions of each environmental factor data to all coal microcrystalline structure characteristic parameters is taken as the deterioration contribution of each environmental factor data to the coal microcrystalline structure. In this embodiment, the weight of any coal microcrystalline structure characteristic parameter is set to 0.25. In actual application, as other implementation methods, the implementer can also set the weight value according to the specific situation. This embodiment does not impose any special restrictions.

[0038] Preferably, the schematic diagram of the degradation contribution extraction process provided in this embodiment is as follows: Figure 2 As shown.

[0039] The degradation contribution is used to characterize the overall contribution of a single environmental factor to the overall degradation of the coal microcrystalline structure, reflecting the overall influence of the environmental factor on coal quality degradation in a multi-environment coupling system. The greater the contribution of degradation, the more likely the corresponding environmental factor is to be the key factor leading to the decline in coal quality and the destruction of its microstructure. In actual storage and logistics, it should be the focus of monitoring and control. Conversely, the smaller the contribution of degradation, the more likely the corresponding environmental factor has a limited effect on the overall degradation of the coal microcrystalline structure and is a secondary influencing factor.

[0040] Step S4: Construct a predictive model for the degree of coal microcrystalline structure degradation by combining the degradation contribution, so as to predict the characterization results of coal microcrystalline structure degradation under multi-environment coupling.

[0041] Since the degree of degradation of coal microcrystalline structure under multi-environment coupling is usually characterized by maximum reflectivity, a predictive model for the degree of degradation of coal microcrystalline structure needs to be constructed to achieve accurate prediction. The specific construction process is as follows: Historical experimental data is used as training samples input into the predictive neural network. This historical experimental data includes: the maximum reflectance of different coal types and adjacent measurement points during the historical experiments; the degree of change of each environmental factor (temperature, humidity, oxygen content, mechanical vibration intensity); and the degradation contribution of each environmental factor calculated in step S3. The maximum reflectance at each measurement point is used as the output target of the neural network for training. Specifically, a Long Short-Term Memory (LSTM) network is selected as the neural network, with a learning rate of 0.001, an Adam optimizer, a mean squared error loss function, and 300 training iterations. After training, a trained predictive model for the degree of coal microcrystalline structure degradation is obtained.

[0042] The current environmental factor data is input into the trained prediction model of the degree of deterioration of coal microcrystalline structure. The maximum reflectivity of the coal microcrystalline structure at the current moment is output to characterize the degree of deterioration of the coal microcrystalline structure. The higher the maximum reflectivity, the more serious the deterioration of the coal microcrystalline structure, and the more timely anti-deterioration measures need to be taken; otherwise, the deterioration is slight.

[0043] The process of training the Long Short-Term Memory (LSTM) network is a well-known technique and will not be elaborated further.

[0044] Thus, this embodiment effectively captures the temporal dependence and nonlinear characteristics between environmental factors and changes in coal microcrystalline structure by inputting the degradation contribution, which reflects the coupling effect of multiple environments, into the LSTM neural network as a key feature. It overcomes the limitation of traditional methods that ignore the coupling effect of the environment, and achieves accurate prediction of the maximum reflectivity of coal microcrystalline structure. It can quickly and in real time quantify and assess the degradation status of coal during storage and logistics, and provides reliable technical support for timely formulation of anti-degradation measures and reduction of coal loss.

[0045] Based on the same inventive concept as the above method, this application embodiment also provides a coal microcrystalline structure deterioration characterization system under multi-environment coupling, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described methods for characterizing coal microcrystalline structure deterioration under multi-environment coupling.

Claims

1. A method for characterizing the deterioration of coal microcrystalline structure under multi-environment coupling, characterized in that, The method includes the following steps: Obtain data on multiple environmental factors related to coal mining; Simulation experiments were conducted based on multiple environmental factors to determine the maximum reflectivity of coal microcrystalline structure, and the characteristic parameters of coal microcrystalline structure were calculated using the XRD method. Based on the coupling relationship between the variability of multiple environmental factors and the coupling relationship between the variability of multiple environmental factors and the characteristic parameters of coal microcrystal structure, the contribution of multiple environmental factors to the deterioration of coal microcrystal structure is determined. A predictive model for the degree of degradation of coal microcrystalline structure was constructed by combining the degradation contribution to predict the degradation characterization results of coal microcrystalline structure under multi-environment coupling.

2. The method for characterizing the deterioration of coal microcrystalline structure under multi-environment coupling as described in claim 1, characterized in that, The data on multiple environmental factors include: temperature, humidity, oxygen content at coal storage sites and during transportation, as well as the intensity of mechanical vibration during transportation.

3. The method for characterizing the deterioration of coal microcrystalline structure under multi-environment coupling as described in claim 1, characterized in that, The characteristic parameters of the coal microcrystalline structure include the vertical distance between the aromatic lamellae, the stacking height of the aromatic lamellae, the elongation of the aromatic lamellae, and the number of aromatic lamellae.

4. The method for characterizing the deterioration of coal microcrystalline structure under multi-environment coupling as described in claim 3, characterized in that, The process for determining the contribution of the multi-environmental factor data to the deterioration of coal microcrystalline structure characteristic parameters is as follows: In any given experiment, a sequence of the degree of change of data for each environmental factor and a sequence of the degree of change of characteristic parameters of each coal microcrystalline structure were constructed respectively: Based on the correlation between the variation sequence of each environmental factor data and any coal microcrystalline structure characteristic parameter, and the correlation between the variation sequence of each environmental factor data and the data of all other environmental factors, the contribution of each environmental factor data to any coal microcrystalline structure characteristic parameter is determined, and then weighted fusion is used to obtain the contribution of each environmental factor data to the deterioration of the coal microcrystalline structure.

5. The method for characterizing the deterioration of coal microcrystalline structure under multi-environment coupling as described in claim 4, characterized in that, The method for constructing the degree of change sequence is as follows: During any experiment, the absolute difference between each measurement point and its adjacent previous measurement point is calculated and divided by the absolute value of each data point at the adjacent previous measurement point. This is recorded as the degree of change of each data point at each time point. The sequence of the degree of change of each data point at all times during any experiment is recorded as the degree of change sequence.

6. The method for characterizing the deterioration of coal microcrystalline structure under multi-environment coupling as described in claim 5, characterized in that, The contribution of each environmental factor data to any coal microcrystalline structure characteristic parameter includes: During any experiment, the mean of the absolute values ​​of the correlation coefficients between each environmental factor data and all other environmental factor data on the degree of change sequence is calculated and denoted as the environmental coupling coefficient. The absolute value of the correlation coefficient between the variation sequence of each environmental factor data and any characteristic parameter of coal microcrystalline structure is calculated and denoted as the direct coupling coefficient. The indirect coupling coefficient is calculated based on the environmental coupling coefficient and the direct coupling coefficient. The contribution of each environmental factor data to any coal microcrystalline structure characteristic parameter is positively correlated with the direct coupling coefficient and the indirect coupling coefficient, respectively.

7. The method for characterizing the deterioration of coal microcrystalline structure under multi-environment coupling as described in claim 6, characterized in that, The degradation contribution is the weighted sum of the contributions of each environmental factor data to the microcrystalline structure characteristic parameters of all coal types.

8. The method for characterizing the deterioration of coal microcrystalline structure under multi-environment coupling as described in claim 5, characterized in that, The predictive model for the degree of degradation of coal microcrystalline structure includes: The maximum reflectance of each measurement point adjacent to the previous measurement point and the coal type, along with the variability of multiple environmental factors and their corresponding deterioration contributions, are used as input features of the neural network. The maximum reflectance at each measurement point is used as the output target of the neural network. The neural network is trained to obtain a predictive model for the degree of deterioration of coal microcrystalline structure.

9. The method for characterizing the deterioration of coal microcrystalline structure under multi-environment coupling as described in claim 1, characterized in that, The predicted characterization results of coal microcrystalline structure degradation under multi-environment coupling include: The current environmental factor data is input into the trained prediction model of the degree of deterioration of coal microcrystalline structure, and the maximum reflectivity of coal microcrystalline structure at the current moment is output to characterize the degree of deterioration of coal microcrystalline structure.

10. A coal microcrystalline structure deterioration characterization system under multi-environment coupling, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method for characterizing the deterioration of coal microcrystalline structure under multiple environmental coupling as described in any one of claims 1-9.

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