Coal mining face mine pressure prediction method, device, and electronic equipment

Abstract

The present disclosure provides a method, apparatus, and electronic device for predicting the mine pressure of a coal mining face, including: obtaining the working condition data of a target coal mining face, and establishing at least one data factor of the target coal mining face based on the working condition data; for any data factor, calculating the initial granulation parameter and the trend intensity parameter of the data factor; adjusting the initial granulation parameter based on the trend intensity parameter to generate a target granulation parameter; constructing a target prediction data interval based on the target granulation parameter and the data factor, and inputting the target prediction data interval into a mine pressure prediction model to output the predicted mine pressure data of the target coal mining face, where the mine pressure prediction model includes a spatial attention mechanism unit. By adaptively adjusting the granularity of each data factor, the changing characteristics of the data can be captured more precisely, and at the same time, by converting the data value of the feature factor into a form that can better present the uncertainty of the mine pressure, more reliable support is provided for mine pressure prediction.

Description

Technical Field

[0001] The present disclosure relates to the technical field of coal mining safety, and particularly to a method, a device, and an electronic device for predicting the mine pressure of a coal mining face. Background Art

[0002] In the field of coal mining, the prediction of the mine pressure of a coal mining face is crucial for safe production and efficient operation. As the mining extends deeper, the geological conditions are complex, and the law of mine pressure manifestation is difficult to measure. Traditional mine pressure prediction methods, such as the empirical analogy method and the mechanical analysis method, rely on expert experience and simple mechanical models, and have insufficient prediction accuracy and reliability in complex mining environments. Summary of the Invention

[0003] The present disclosure aims to solve at least one of the technical problems in the related art to some extent.

[0004] To this end, one object of the present disclosure is to propose a method for predicting the mine pressure of a coal mining face.

[0005] The second object of the present disclosure is to propose a device for predicting the mine pressure of a coal mining face.

[0006] The third object of the present disclosure is to propose an electronic device.

[0007] The fourth object of the present disclosure is to propose a non-transitory computer-readable storage medium.

[0008] The fifth object of the present disclosure is to propose a computer program product.

[0009] To achieve the above object, a first aspect embodiment of the present disclosure proposes a method for predicting the mine pressure of a coal mining face, including: obtaining the working condition data of a target coal mining face, and establishing at least one data factor of the target coal mining face based on the working condition data, where the data factor at least includes a spatial feature factor; for any data factor, calculating the initial granulation parameter and the trend intensity parameter of the data factor; adjusting the initial granulation parameter based on the trend intensity parameter to generate a target granulation parameter; constructing a target prediction data interval based on the target granulation parameter and the data factor, and inputting the target prediction data interval into a mine pressure prediction model to output the predicted mine pressure data of the target coal mining face, where the mine pressure prediction model includes a spatial attention mechanism unit.

[0010] According to an embodiment of the present disclosure, the constructing a target prediction data interval based on the target granulation parameter and the data factor includes: calculating a data lower limit value and a data upper limit value of the data factor based on the target granulation parameter and the data value of the data factor; constructing a data interval of the data factor based on the data lower limit value and the data upper limit value as the target prediction data interval.

[0011] According to an embodiment of the present disclosure, calculating the lower limit value and the upper limit value of the data factor based on the data value of the target granulation parameter and the data factor includes: taking the difference between the data value of the data factor and the target granulation parameter as the lower limit value of the data factor, and taking the sum of the data value of the data factor and the target granulation parameter as the upper limit value of the data factor.

[0012] According to an embodiment of the present disclosure, adjusting the initial granulation parameter based on the trend intensity parameter to generate a target granulation parameter includes: comparing the trend intensity parameter with a first trend intensity threshold and a second trend intensity threshold respectively; adjusting the initial granulation parameter based on the comparison result to generate the target granulation parameter.

[0013] According to an embodiment of the present disclosure, adjusting the initial granulation parameter based on the comparison result to generate the target granulation parameter includes: obtaining an adjustment factor; in response to the trend intensity parameter being less than the first trend intensity threshold, taking the sum obtained by adding 1 and the adjustment factor as the first target adjustment factor, and multiplying the first target adjustment factor by the initial granulation parameter to calculate and obtain the target granulation parameter; or, in response to the trend intensity parameter being greater than or equal to the first trend intensity threshold and less than or equal to the second trend intensity threshold, taking the initial granulation parameter as the target granulation parameter; or, in response to the trend intensity parameter being greater than the second trend intensity threshold, taking the difference obtained by subtracting the adjustment factor from 1 as the second target adjustment factor, and multiplying the second target adjustment factor by the initial granulation parameter to calculate and obtain the target granulation parameter.

[0014] According to an embodiment of the present disclosure, before adjusting the initial granulation parameter based on the comparison result to generate the target granulation parameter, it further includes: obtaining an adjustment coefficient and window data corresponding to the data factor, and obtaining a candidate granulation parameter after adjusting the initial granulation parameter based on the comparison result; calculating the moving standard deviation of the data factor based on the window data, and calculating the fluctuation factor of the data factor based on the moving standard deviation and the adjustment coefficient; calculating the target granulation parameter based on the fluctuation factor and the candidate granulation parameter.

[0015] According to an embodiment of the present disclosure, calculating the initial granulation parameter and the trend intensity parameter of the data factor includes: calculating the data factor based on a locally weighted regression algorithm to calculate and obtain the initial granulation parameter and the trend intensity parameter.

[0016] According to an embodiment of the present disclosure, establishing the mine pressure prediction model includes: obtaining a plurality of candidate information particles of the target coal mining face; calculating a coverage value, a specificity value, a stability value, and a variance value based on the candidate information particles; calculating respective weight values of the coverage value, the specificity value, and the stability value based on the variance value; constructing an objective function of the target coal mining face based on the weight values, the coverage value, the specificity value, and the stability value, and establishing the mine pressure prediction model based on the objective function.

[0017] To achieve the above object, an embodiment of the second aspect of the present disclosure provides a mine pressure prediction device for a coal mining face, including: an acquisition module, configured to acquire working condition data of a target coal mining face and establish at least one data factor of the target coal mining face based on the working condition data, where the data factor at least includes a spatial feature factor; a calculation module, configured to calculate an initial granulation parameter and a trend intensity parameter of any data factor; an adjustment module, configured to adjust the initial granulation parameter based on the trend intensity parameter to generate a target granulation parameter; and a prediction module, configured to construct a target prediction data interval based on the target granulation parameter and the data factor, and input the target prediction data interval into a mine pressure prediction model to output predicted mine pressure data of the target coal mining face, where the mine pressure prediction model includes a spatial attention mechanism unit.

[0018] To achieve the above object, an embodiment of the third aspect of the present disclosure provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to implement the mine pressure prediction method for a coal mining face as described in the embodiment of the first aspect of the present disclosure.

[0019] To achieve the above object, an embodiment of the fourth aspect of the present disclosure provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to implement the mine pressure prediction method for a coal mining face as described in the embodiment of the first aspect of the present disclosure.

[0020] To achieve the above object, an embodiment of the fifth aspect of the present disclosure provides a computer program product, including a computer program, where the computer program is used to implement the mine pressure prediction method for a coal mining face as described in the embodiment of the first aspect of the present disclosure when executed by a processor.

[0021] Therefore, by adaptively adjusting the granularity of each data factor, the change characteristics of the data can be captured more accurately. At the same time, by converting the data values of the feature factors into values that can better present the uncertainty of the mine pressure, more reliable support is provided for mine pressure prediction. Description of the Drawings

[0022] Figure 1 It is a schematic diagram of a method for predicting the mine pressure of a coal mining face in an embodiment of the present disclosure;

[0023] Figure 2 It is a schematic diagram of another method for predicting the mine pressure of a coal mining face in an embodiment of the present disclosure;

[0024] Figure 3 It is a schematic diagram of another method for predicting the mine pressure of a coal mining face in an embodiment of the present disclosure;

[0025] Figure 4 It is a schematic diagram of a process of optimization using IGA in an embodiment of the present disclosure;

[0026] Figure 5 It is a schematic diagram of a device for predicting the mine pressure of a coal mining face in an embodiment of the present disclosure;

[0027] Figure 6 It is a schematic diagram of an electronic device in an embodiment of the present disclosure. Detailed implementation manners

[0028] The following details the embodiments of the present disclosure. The examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present disclosure, and should not be construed as a limitation of the present disclosure.

[0029] In the technical solution of the present disclosure, the acquisition, storage, use, processing, etc. of data all comply with the relevant regulations of relevant laws and regulations.

[0030] It should be noted that in the embodiments of the present application, some industry-existing solutions such as certain software, components, models, etc. may be mentioned. They should be regarded as exemplary. The purpose is only to illustrate the feasibility in the implementation of the technical solution of the present application, but it does not mean that the applicant has already or necessarily used this solution.

[0031] Figure 1 It is a schematic diagram of a method for predicting the mine pressure of a coal mining face in an embodiment of the present disclosure. As Figure 1 shown, the method for predicting the mine pressure of a coal mining face includes the following steps:

[0032] S101, obtain the working condition data of the target coal mining face, and establish at least one data factor for the target coal mining face based on the working condition data. The data factor includes at least a spatial feature factor.

[0033] The method for predicting the mine pressure of a coal mining face of an embodiment of the present application can be applied to the scenario of predicting the mine pressure of a coal mining face. The executor of the mine pressure prediction of a coal mining face of an embodiment of the present application can be the device for predicting the mine pressure of a coal mining face of an embodiment of the present application, and the device for predicting the mine pressure of a coal mining face can be set on an electronic device.

[0034] In current technologies, models (such as Long Short-Term Memory (LSTM)) only focus on time series features and ignore the spatial correlation between sensors. In this disclosure, by adding spatial feature factors to the working condition data and adding local spatial attention mechanism units to the mine pressure prediction model, it is possible to predict the mine pressure of the target coal mining face in both time and space dimensions.

[0035] It should be noted that, in addition to the spatial characteristic factor, the data factor may also include multiple factors, which are not limited here, for example, time characteristic factors, pressure characteristic factors, temperature characteristic factors, etc.

[0036] In the disclosed embodiment, there are many methods for obtaining the working condition data of the target coal mining face, which are not limited here. For example, the data can be collected and obtained through a sensor detection system, and the working condition data can be collected and analyzed through an automatic control system, and the data can be simulated and analyzed through data analysis and simulation software.

[0037] S102: For any data factor, calculate the initial granulation parameter and trend strength parameter of the data factor.

[0038] In current technology, with the rise of intelligent prediction technology, prediction systems and methods based on granular neural networks have attracted attention due to their unique data processing advantages. They can divide data into different granularities and mine data features. However, existing information granularity allocation schemes have limitations. On the one hand, they are mostly based on fixed rules or simple statistical analysis to determine information granularity, which is difficult to dynamically adjust with the real-time changes in mine pressure data. Because the mine pressure of the coal mining face is affected by many dynamic factors such as changes in coal seam thickness, differences in roof lithology, and mining progress, information granularity needs to be adaptively adjusted to reflect data characteristics; on the other hand, existing schemes are not flexible enough in balancing data generalization and detail. If the granularity is too coarse, key details will be lost, resulting in a decrease in prediction accuracy. If the granularity is too fine, too much noise will be introduced, increasing model complexity and computational burden, and reducing prediction efficiency.

[0039] Information granularity refers to the degree to which data is divided into "grains" or units of different sizes. Finer information granularity means more detailed data representation, while coarser information granularity means a higher level of data abstraction.

[0040] Information granularity adaptive adjustment is an intelligent data processing technology aimed at dynamically adjusting the granularity of data (i.e., the refinement degree or aggregation level of data) according to the characteristics of the data to optimize the effect of data analysis. This method is particularly applicable to scenarios that require processing large amounts of complex data sets, such as mine pressure monitoring, downhole working condition analysis, etc. By adjusting the information granularity, the accuracy and efficiency of the model can be improved while reducing the consumption of computing resources.

[0041] The initial granulation parameter refers to the setting used to define the refinement degree or aggregation level at which data is initially divided into "granules" during the information granularity adaptive adjustment process.

[0042] The trend intensity parameter is an index used to measure the significance of the change trend in each local area of a data sequence. It is defined based on the locally weighted average and local slope calculated by local weighted regression and is used to guide the preliminary adjustment of the granulation parameter.

[0043] In the embodiments of the present disclosure, there can be various methods for calculating the initial granulation parameter and the trend intensity parameter of the data factor, and no specific limitation is made here.

[0044] In one possible implementation, the data values of the data factor can be calculated through a preset algorithm to obtain the initial granulation parameter and the trend intensity parameter of the data factor.

[0045] In another possible implementation, the data values of the data factor can also be processed through a processing model to output the initial granulation parameter and the trend intensity parameter of the data factor. The processing model is pre-trained and can be stored in the storage space of an electronic device for convenient retrieval and use when needed.

[0046] S103, Adjust the initial granulation parameter based on the trend intensity parameter to generate the target granulation parameter.

[0047] In the current technology, there can be various methods for adjusting the granulation parameter. For example, the adjustment methods in the current technology can be as follows C1 and C2.

[0048] Information granularity symmetric allocation scheme C1

[0049] Each time series granularity parameter interval is symmetric, that is, εi- = εi+ = εi.

[0050]

[0051] Where, i = 1, 2, …, n

[0052] Information granularity non-uniform allocation scheme C2

[0053] The granularity levels of different time series data are different.

[0054]

[0055] where \(i = 1, 2, \ldots, n\)

[0056] The solution in the present disclosure is an adaptive weighted granularity allocation solution for initial granulation parameters through trend intensity parameters.

[0057] S104. Construct a target prediction data interval based on the target granulation parameter and the data factor, and input the target prediction data interval into the mine pressure prediction model to output the predicted mine pressure data of the target coal mining face. The mine pressure prediction model includes a spatial attention mechanism unit.

[0058] In the embodiment of the present disclosure, first, the working condition data of the target coal mining face is obtained, and at least one data factor of the target coal mining face is established based on the working condition data. The data factor at least includes a spatial feature factor. Then, for any data factor, the initial granulation parameter and the trend intensity parameter of the data factor are calculated. Then, the initial granulation parameter is adjusted based on the trend intensity parameter to generate a target granulation parameter. Finally, a target prediction data interval is constructed based on the target granulation parameter and the data factor, and the target prediction data interval is input into the mine pressure prediction model to output the predicted mine pressure data of the target coal mining face. Thus, by adaptively adjusting the granularity of each data factor, the change characteristics of the data can be captured more accurately. At the same time, by converting the data value of the feature factor into a form that can better present the uncertainty of the mine pressure, more reliable support is provided for mine pressure prediction.

[0059] In the embodiment of the present disclosure, to calculate the initial granulation parameter and the trend intensity parameter of the data factor, the locally weighted regression algorithm can be used to calculate the data factor to obtain the initial granulation parameter and the trend intensity parameter.

[0060] In a possible implementation manner, for each data point in the sequence , the locally weighted average value and the local slope are calculated using locally weighted regression (LWR). In the LWR calculation, given the bandwidth \(h\), the weight of the data point to is determined by the Gaussian kernel function, and the formula is

[0061]

[0062] where the kernel function , which determines the influence degree of different data points on , and the distance The closer the point, the higher the weight. The local weighted average is obtained by weighted summation and normalization of surrounding data points, and the calculation formula is:

[0063]

[0064] Local slope reflects the change trend near the data point and is obtained by taking the derivative of the local weighted regression curve with respect to i. In actual calculations, since the expression of is relatively complex, numerical methods (such as the finite difference method) are usually used for derivation. For example, using the central difference formula:

[0065]

[0066] where is a small increment, and its value needs to be reasonably determined according to the sampling frequency and change characteristics of the data. Among them, is the trend intensity parameter of the i-th data factor.

[0067] In the above embodiments, based on the target granulation parameter and the data factor, a target prediction data interval is constructed, and it can also be further explained by Figure 2 The method includes:

[0068] S201, calculating the data lower limit value and the data upper limit value of the data factor based on the data values of the target granulation parameter and the data factor.

[0069] In the embodiments of the present disclosure, after obtaining the target granulation parameter, a preset algorithm can be used to calculate the data values of the standard granulation parameter and the data factor to calculate and obtain the data lower limit value and the data upper limit value.

[0070] For example, the preset algorithm can be as follows:

[0071]

[0072]

[0073] where a is the data lower limit value, b is the data upper limit value, is the data value of the i-th data factor, is the target granulation parameter of the i-th data factor.

[0074] S202, constructing a data interval of the data factor based on the data lower limit value and the data upper limit value as the target prediction data interval.

[0075] In the embodiments of the present disclosure, taking the example in S201 as an example, the data interval is .

[0076] In an embodiment of the present disclosure, first, a lower data limit value and an upper data limit value of a data factor are calculated based on a target granulation parameter and a data value of the data factor, and then a data interval of the data factor is constructed based on the lower data limit value and the upper data limit value as a target prediction data interval.

[0077] In the above embodiment, first, a lower data limit value and an upper data limit value of a data factor are calculated based on a target granulation parameter and a data value of the data factor, and then a data interval of the data factor is constructed based on the lower data limit value and the upper data limit value as a target prediction data interval. Thus, by establishing the target prediction data interval, not only can the accuracy and reliability of strata pressure prediction be significantly improved, but also the resource allocation can be effectively optimized and the decision-making support ability can be enhanced, and the robustness and calculation efficiency of the model are further improved by defining the data range and constructing the data interval.

[0078] In the above embodiment, the initial granulation parameter is adjusted based on the trend intensity parameter to generate a target granulation parameter, and it can also be through Figure 3 For further explanation, the method includes:

[0079] S301, comparing the trend intensity parameter with a first trend intensity threshold and a second trend intensity threshold respectively.

[0080] In an embodiment of the present disclosure, the first trend intensity threshold and the second trend intensity threshold are critical values for judging whether the initial granulation parameter needs to be changed.

[0081] It should be noted that the first trend intensity threshold and the second trend intensity threshold are designed in advance and can be changed according to actual design needs, and no limitation is made here.

[0082] S302, adjusting the initial granulation parameter based on the comparison result to generate a target granulation parameter.

[0083] In an embodiment of the present disclosure, an adjustment factor can be obtained first. In response to the trend intensity parameter being less than the first trend intensity threshold, the sum obtained by adding 1 and the adjustment factor is used as a first target adjustment factor, and the first target adjustment factor is multiplied by the initial granulation parameter to calculate and obtain the target granulation parameter. Or in response to the trend intensity parameter being greater than or equal to the first trend intensity threshold and less than or equal to the second trend intensity threshold, the initial granulation parameter is used as the target granulation parameter. Or in response to the trend intensity parameter being greater than the second trend intensity threshold, the difference obtained by subtracting the adjustment factor from 1 is used as a second target adjustment factor, and the second target adjustment factor is multiplied by the initial granulation parameter to calculate and obtain the target granulation parameter.

[0084] In a possible implementation manner, define the trend intensity parameter to measure the intensity of the local trend at the data point. Assume that the initial granulation parameter is , and the adjustment factor is . According to the magnitude of τ, the granulation parameter is preliminarily adjusted according to the following rules: when , it indicates that the data changes relatively gently. At this time, let , and appropriately increase the granulation parameter to include more data information; when , it means that the data changes violently. Let , and reduce the granulation parameter so that the information granule focuses more on local details; when , keep the granulation parameter unchanged, that is . Here, and are preset trend intensity thresholds, which need to be determined according to the historical fluctuation range and actual experience of the mine pressure data.

[0085] In another possible implementation, to further consider the fluctuation characteristics of the data, a data fluctuation factor is introduced. First, the initial granulation parameter can be adjusted based on the comparison result. Before generating the target granulation parameter, the window data corresponding to the adjustment coefficient and the data factor can also be obtained, and the candidate granulation parameter after adjusting the initial granulation parameter based on the comparison result can be obtained. Then, the moving standard deviation of the data factor is calculated based on the window data, and the fluctuation factor of the data factor is calculated based on the moving standard deviation and the adjustment coefficient. Finally, the target granulation parameter is calculated based on the fluctuation factor and the candidate granulation parameter.

[0086] In one possible implementation, the local fluctuation degree of the data can be measured by calculating the moving standard deviation of the mine pressure data. The window size is set to w, and the calculation formula of the moving standard deviation is:

[0087]

[0088] where is the average value of the data within the window

[0089] The mean value of the moving standard deviation is:

[0090]

[0091] Finally, the granulation parameter after preliminary adjustment is adjusted twice according to the moving standard deviation, and the formula is

[0092]

[0093] Where c is the adjustment coefficient. This formula enables the granulation parameter to be dynamically adjusted according to the real-time fluctuations of the data. In areas with large data fluctuations, the granulation parameter is further increased to better cover the data change range; in areas with small fluctuations, the granulation parameter is maintained or appropriately reduced to improve the accuracy of the information granules. Through the above steps, this solution can adaptively adjust the granularity of the information granules according to the local trend and fluctuation characteristics of the mine pressure data, thereby better balancing the generalization and details of the data during the information granulation process, and providing more representative input information for subsequent mine pressure prediction based on the granular neural network.

[0094] The inventors found through experiments that neural networks in the current technology (such as LSTM, Transformer) mostly adopt fixed granularity or static adjustment strategies, and it is difficult to adapt to the dynamic changes of mine pressure data. The self-adaptive weighted granularity allocation scheme of this method: calculate the trend strength ( ) and the fluctuation factor (moving standard deviation ) of the data points through local weighted regression, and dynamically adjust the granulation parameter . For example, when , is reduced to focus on local details; when the data fluctuates violently, is further increased to cover the change range. Experiments show that the objective function value Q of the proposed solution in this disclosure is improved by about 15% compared with the traditional fixed granularity solution, and the interval prediction coverage rate (cov) is stable above 74%, significantly better than the existing methods.

[0095] In the embodiments of this disclosure, to establish the mine pressure prediction model, multiple candidate information particles of the target coal mining face can be obtained first, then the coverage value, specificity value, stability value, and variance value are calculated based on the candidate information particles, and then the weight values of the coverage value, specificity value, and stability value are calculated based on the variance value. Finally, the objective function of the target coal mining face is constructed based on the weight values, coverage value, specificity value, and stability value, and the mine pressure prediction model is established based on the objective function.

[0096] It should be noted that the coverage rate measures the degree to which the information granules can cover the initial data. It is expected that as much initial data as possible is included in the information granules. The higher the coverage rate, the better the information granules can cover the original data set and reduce data loss.

[0097] Specificity requires that the information granules have a clear meaning and express the meaning of the original data as specifically as possible. The higher the specificity, the more specific the expression of the information granules and the more accurately they can reflect the characteristics of the original data.

[0098] Stability measures the consistency of the data within an information granule. A stable information granule means that the data within it changes less and has lower volatility. The higher the stability, the more consistent the data within the information granule and the smaller the data fluctuations.

[0099] In one possible implementation, the objective function can be as follows:

[0100]

[0101] where cov is the coverage (coverage, cov): it is expected that as many initial data as possible are included in the information granule. By calculating the granularity output , where

[0102]

[0103] is the candidate information particle, is the set of information granule outputs.

[0104] Sp is the specificity (specificity, sp): it is required that the constructed information granule has a clear meaning, that is, it expresses the meaning of the original data as specifically as possible. The calculation formula is:

[0105]

[0106] and are the maximum and minimum values of the k-th information granule respectively.

[0107] Sta is the stability (sta): for the information granule , the stability is measured by calculating the ratio of the range to the mean of the data within the information granule, that is , the overall stability

[0108]

[0109] where and are the maximum and minimum values of the i-th information granule respectively.

[0110] Var is the prediction variance (var): assuming that the prediction result set is obtained under different granulation parameters, the prediction variance is

[0111] ,

[0112] where is the average prediction result.

[0113] Through this objective function, the performance of information granules in different aspects can be comprehensively considered, and the weights can be adjusted according to actual needs , optimize the information granulation effect, and lay a foundation for subsequent analysis and prediction.

[0114] The inventor found through experiments that the methods in the current technology usually take the mean square error (MSE) as the only optimization goal, resulting in an inability to balance generality and detail during information granulation. This method proposes a multi-index fusion objective function:

[0115]

[0116] Through the weighted optimization of coverage (cov), specificity (sp), stability (sta), and prediction variance (var), it is ensured that the information granules contain sufficient data (high cov) and avoid redundancy (high sp). During the optimization process, an improved genetic algorithm (IGA) is adopted. As Figure 4 shown, it is based on the traditional genetic algorithm and adds an elite retention strategy and an adaptive crossover and mutation probability mechanism. Let the individual fitness be , the crossover probability and the mutation probability are adaptively adjusted according to the formula. When , , when , , where is the maximum fitness of the current population, is the average fitness, is the fitness of the current individual, is a preset parameter. With the optimized configuration of information granules, the improvement of the objective function, and this improved genetic algorithm, the information granulation of mine pressure data can be efficiently processed. The optimal granulation parameters are found through the optimized objective function, the performance of the mine pressure prediction model is improved, and it helps with coal mine safety production. In practical applications, the parameters need to be adjusted according to the characteristics of mine pressure data and prediction requirements, and this framework is extensible and can be optimized and expanded according to different situations.

[0117] Spatial attention mechanism processing:

[0118] Traditional models (such as the Long Short-Term Memory (LSTM) network) only focus on time series features and ignore the spatial correlation between sensors. This method achieves a double breakthrough through a spatio-temporal attention mechanism:

[0119] Local spatial attention mechanism unit:

[0120] Focus on the correlation of different time series collected by a single sensor, calculate the attention weights, determine the importance of local features in prediction, and obtain the local spatial attention output vector. For the time series data of each sensor, analyze the internal relationship between local time series according to the data features and spatial positions, and provide local spatial feature information for subsequent prediction.

[0121] Global spatial attention mechanism unit:

[0122] Consider the dynamic influence of other sensor target sequences on a specific sensor, calculate the attention weights between sensors, adaptively select relevant sensors to participate in prediction with reference to the geospatial similarity, and calculate the global spatial attention output vector after updating the attention weights. This process will consider the mutual influence between sensors at different support positions, comprehensively consider the spatial relationship of the entire coal mining face, and improve the prediction accuracy.

[0123] The inventors found through experiments that after introducing spatio-temporal attention, the root mean square error (RMSE) of the model loss function decreased from 0.931 to 0.904, and the prediction curve was more consistent with the true value, proving that the effective utilization of spatial information significantly improved the prediction accuracy.

[0124] Corresponding to the coal mining face strata pressure prediction methods provided in the above several embodiments, an embodiment of the present disclosure also provides a coal mining face strata pressure prediction device. Since the coal mining face strata pressure prediction device provided in the embodiment of the present disclosure corresponds to the coal mining face strata pressure prediction methods provided in the above several embodiments, the implementation manners of the above coal mining face strata pressure prediction methods are also applicable to the coal mining face strata pressure prediction device provided in the embodiment of the present disclosure and will not be described in detail in the following embodiments.

[0125] Figure 5 is a schematic diagram of a coal mining face strata pressure prediction device according to an embodiment of the present disclosure. As Figure 5 shown, the coal mining face strata pressure prediction device 500 includes: an acquisition module 510, a calculation module 520, an adjustment module 530, and a prediction module 540.

[0126] The acquisition module 510 is configured to acquire the working condition data of the target coal mining face and establish at least one data factor of the target coal mining face based on the working condition data. The data factor includes at least a spatial feature factor.

[0127] The calculation module 520 is configured to calculate the initial granulation parameter and the trend intensity parameter of any data factor.

[0128] The adjustment module 530 is configured to adjust the initial granulation parameter based on the trend intensity parameter to generate a target granulation parameter.

[0129] A prediction module 540, configured to construct a target prediction data interval based on a target granulation parameter and a data factor, and input the target prediction data interval into a mine pressure prediction model to output predicted mine pressure data of a target coal mining face, where the mine pressure prediction model includes a spatial attention mechanism unit.

[0130] According to an embodiment of the present disclosure, constructing a target prediction data interval based on a target granulation parameter and a data factor includes: calculating a lower data limit value and an upper data limit value of the data factor based on the data values of the target granulation parameter and the data factor; constructing a data interval of the data factor based on the lower data limit value and the upper data limit value as the target prediction data interval.

[0131] According to an embodiment of the present disclosure, calculating a lower data limit value and an upper data limit value of the data factor based on the data values of the target granulation parameter and the data factor includes: taking the difference between the data value of the data factor and the target granulation parameter as the lower data limit value, and taking the sum of the data value of the data factor and the target granulation parameter as the upper data limit value.

[0132] According to an embodiment of the present disclosure, adjusting an initial granulation parameter based on a trend intensity parameter to generate a target granulation parameter includes: comparing the trend intensity parameter with a first trend intensity threshold and a second trend intensity threshold respectively; adjusting the initial granulation parameter based on the comparison result to generate a target granulation parameter.

[0133] According to an embodiment of the present disclosure, adjusting the initial granulation parameter based on the comparison result to generate a target granulation parameter includes: obtaining an adjustment factor; in response to the trend intensity parameter being less than the first trend intensity threshold, taking the sum of 1 and the adjustment factor as a first target adjustment factor, and multiplying the first target adjustment factor by the initial granulation parameter to calculate and obtain the target granulation parameter; or, in response to the trend intensity parameter being greater than or equal to the first trend intensity threshold and less than or equal to the second trend intensity threshold, taking the initial granulation parameter as the target granulation parameter; or, in response to the trend intensity parameter being greater than the second trend intensity threshold, taking the difference between 1 and the adjustment factor as a second target adjustment factor, and multiplying the second target adjustment factor by the initial granulation parameter to calculate and obtain the target granulation parameter.

[0134] According to an embodiment of the present disclosure, before adjusting the initial granulation parameter based on the comparison result to generate a target granulation parameter, it further includes: obtaining an adjustment coefficient and window data corresponding to the data factor, and obtaining candidate granulation parameters after adjusting the initial granulation parameter based on the comparison result; calculating a moving standard deviation of the data factor based on the window data, and calculating a fluctuation factor of the data factor based on the moving standard deviation and the adjustment coefficient; calculating the target granulation parameter based on the fluctuation factor and the candidate granulation parameter.

[0135] According to an embodiment of the present disclosure, calculating an initial granulation parameter and a trend intensity parameter of a data factor includes: calculating the data factor based on a locally weighted regression algorithm to calculate and obtain the initial granulation parameter and the trend intensity parameter.

[0136] Thus, by adaptively adjusting the granularity of each data factor, the changing characteristics of the data can be captured more accurately. At the same time, by converting the data value of the feature factor into a form that can better present the uncertainty of the mine pressure, more reliable support is provided for mine pressure prediction.

[0137] To implement the above embodiments, an electronic device 600 is further proposed in the embodiments of the present disclosure. Figure 6 It is a schematic diagram of an electronic device according to an embodiment of the present disclosure. As Figure 6 shown, the electronic device 600 includes: a processor 601 and a memory 602 communicatively connected to the processor. The memory 602 stores instructions executable by at least one processor. The instructions are executed by at least one processor 601 to implement the coal mining face mine pressure prediction method as in the present disclosure Figures 1 - 4 embodiment.

[0138] To implement the above embodiments, a non-transitory computer-readable storage medium storing computer instructions is further proposed in the embodiments of the present disclosure, wherein the computer instructions are used to cause a computer to implement the coal mining face mine pressure prediction method as in the present disclosure Figures 1 - 4 embodiment.

[0139] To implement the above embodiments, a computer program product is further proposed in the embodiments of the present disclosure, including a computer program that, when executed by a processor, implements the coal mining face mine pressure prediction method as in the present disclosure Figures 1 - 4 embodiment.

[0140] It should be noted that personal information from users should be collected for legal and reasonable purposes and should not be shared or sold outside of these legitimate uses. In addition, such collection / sharing should be carried out after obtaining the informed consent of the users, including but not limited to notifying the users to read the user agreement / user notice and signing an agreement / authorization including authorizing the relevant user information before the users use the function. In addition, any necessary steps should be taken to safeguard and protect access to such personal information data and ensure that others with access to the personal information data comply with their privacy policies and procedures.

[0141] This application is expected to provide an implementation scheme for users to selectively prevent the use or access of personal information data. That is, the present disclosure is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk can be minimized by restricting data collection and deleting the data. In addition, when applicable, personal identifiers are removed from such personal information to protect the privacy of users.

[0142] In the description of the foregoing embodiments, the descriptions referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0143] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0144] Any process or method description shown in the flowchart or described in other ways herein can be understood as representing a module, segment, or part of code including one or more executable instructions for implementing a customized logic function or process. The scope of the preferred embodiments of the present application includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0145] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite ordered listing of executable instructions for implementing logical functions, which can be embodied specifically in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device. For the purposes of this specification, a "computer-readable medium" can be any device that contains, stores, communicates, propagates, or transports a program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection portion having one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable medium on which the program can be printed, as the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpretation, or otherwise processing as appropriate, and then storing it in a computer memory.

[0146] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0147] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the method of implementing the above embodiments can be completed by a program instructing relevant hardware, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0148] In addition, each functional unit in various embodiments of the present application may be integrated into one processing module, may exist physically alone for each unit, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0149] The above-mentioned storage medium may be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for predicting the mine pressure in a coal mining face, characterized in that, Including: Obtain the working condition data of the target coal mining face, and establish at least one data factor of the target coal mining face based on the working condition data. The data factor at least includes a spatial feature factor, where the spatial feature factor is used to describe the features or variables of the spatial correlation between sensors; For any data factor, calculate the initial granulation parameter and trend intensity parameter of the data factor. The initial granulation parameter refers to the information granulation into which the data is initially divided during the adaptive adjustment of information granulation; Adjust the initial granulation parameter based on the trend intensity parameter to generate a target granulation parameter. The trend intensity parameter is an index used to measure the significant degree of the change trend in each local area of the data sequence; Construct a target prediction data interval based on the target granulation parameter and the data factor, and input the target prediction data interval into the strata pressure prediction model to output the predicted strata pressure data of the target coal mining face. The strata pressure prediction model includes a spatial attention mechanism unit.

2. The method according to claim 1, wherein The constructing a target prediction data interval based on the target granulation parameter and the data factor includes: Calculate the data lower limit value and data upper limit value of the data factor based on the target granulation parameter and the data value of the data factor; Construct the data interval of the data factor based on the data lower limit value and the data upper limit value as the target prediction data interval.

3. The method according to claim 2, wherein The calculating the data lower limit value and data upper limit value of the data factor based on the target granulation parameter and the data value of the data factor includes: Take the difference between the data value of the data factor and the target granulation parameter as the data lower limit value, and take the sum of the data value of the data factor and the target granulation parameter as the data upper limit value.

4. The method according to claim 1, wherein The adjusting the initial granulation parameter based on the trend intensity parameter to generate a target granulation parameter includes: Compare the trend intensity parameter with a first trend intensity threshold and a second trend intensity threshold respectively; Adjust the initial granulation parameter based on the comparison result to generate the target granulation parameter.

5. The method according to claim 4, wherein The adjusting the initial granulation parameter based on the comparison result to generate the target granulation parameter includes: Obtain an adjustment factor; In response to the trend intensity parameter being less than the first trend intensity threshold, take the sum of 1 and the adjustment factor as the first target adjustment factor, and multiply the first target adjustment factor by the initial granulation parameter to calculate and obtain the target granulation parameter; or In response to the trend intensity parameter being greater than or equal to the first trend intensity threshold and less than or equal to the second trend intensity threshold, take the initial granulation parameter as the target granulation parameter; or In response to the trend intensity parameter being greater than the second trend intensity threshold, take the difference between 1 and the adjustment factor as the second target adjustment factor, and multiply the second target adjustment factor by the initial granulation parameter to calculate and obtain the target granulation parameter.

6. The method according to claim 4 or 5, characterized in that Before adjusting the initial granulation parameter based on the comparison result to generate the target granulation parameter, the following steps are further included: Obtain the window data corresponding to the adjustment coefficient and the data factor, and obtain the candidate granulation parameter after adjusting the initial granulation parameter based on the comparison result; Calculate the moving standard deviation of the data factor based on the window data, and calculate the fluctuation factor of the data factor based on the moving standard deviation and the adjustment coefficient; Calculate the target granulation parameter based on the fluctuation factor and the candidate granulation parameter.

7. The method according to claim 1, characterized in that, Calculating the initial granulation parameter and the trend intensity parameter of the data factor includes: Calculating the data factor based on the locally weighted regression algorithm to calculate and obtain the initial granulation parameter and the trend intensity parameter.

8. The method according to claim 1, wherein Establishing the mine pressure prediction model includes: Obtain multiple candidate information particles of the target coal mining face; Calculate the coverage value, the specificity value, the stability value and the variance value based on the candidate information particles; Calculate the weight values of the coverage value, the specificity value and the stability value respectively based on the variance value; Construct the objective function of the target coal mining face based on the weight values, the coverage value, the specificity value and the stability value, and establish the mine pressure prediction model based on the objective function.

9. A device for predicting the mine pressure of a coal mining face, characterized in that, It includes: An acquisition module, configured to acquire the working condition data of the target coal mining face, and establish at least one data factor of the target coal mining face based on the working condition data, where the data factor at least includes a spatial feature factor, and the spatial feature factor is used to describe the feature or variable of the spatial correlation between sensors; A calculation module, configured to calculate the initial granulation parameter and the trend intensity parameter of any data factor, where the initial granulation parameter refers to the setting of the refinement degree or aggregation level used to define the data initially divided into "granules" during the adaptive adjustment of the information granularity; An adjustment module, configured to adjust the initial granulation parameter based on the trend intensity parameter to generate a target granulation parameter, where the trend intensity parameter is an index used to measure the significant degree of the change trend of each local area in the data sequence; A prediction module, configured to construct a target prediction data interval based on the target granulation parameter and the data factor, and input the target prediction data interval into the mine pressure prediction model to output the predicted mine pressure data of the target coal mining face, where the mine pressure prediction model includes a spatial attention mechanism unit.

10. An electronic device, characterized in that, It includes a memory and a processor; Wherein, the processor runs the program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the method according to any one of claims 1-8.

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