Emulsion explosive thermal aging detonation velocity attenuation prediction method

Abstract

The present application relates to the technical field of detonation velocity prediction, in particular to an emulsion explosive thermal aging detonation velocity attenuation prediction method, comprising: obtaining the boundary conditions of emulsion explosive thermal aging, obtaining the corresponding detonation velocity characteristic parameters under the boundary conditions based on the value range of each boundary condition, and constructing a detonation velocity correlation model; determining the current processing correlation entity from the detonation velocity correlation model, and obtaining at least one set of correlation parameters from the detonation velocity correlation model according to the combination relationship of the correlation entity; fitting the correlation parameters into a detonation velocity attenuation curve according to the different heating time and detonation velocity with temperature and sensitizer type as conditions, determining the change trend of the detonation velocity attenuation curve based on the value range of the attenuation rate of the detonation velocity attenuation curve; selecting the temperature nodes in the detonation velocity attenuation curve based on the change trend of the detonation velocity attenuation curve, triggering data configuration warning according to the deviation proportion under each temperature node; and realizing the accuracy and efficiency of detonation velocity attenuation prediction.

Description

Technical Field

[0001] This invention relates to the field of detonation velocity prediction technology, specifically a method for predicting the detonation velocity decay of emulsion explosives due to thermal aging. Background Technology

[0002] Detonation velocity is an important quality indicator of emulsion explosives. When industrial emulsion explosives are used for loading and blasting in high-temperature tunnel blasting, ordinary detonating cord detonators soften and become unstable when the ambient temperature reaches 60°C. This reduces the impact performance of the blast on the rock and affects the stability of the blast. It is necessary to verify the attenuation of the detonation velocity of emulsion explosives under high-temperature blasting.

[0003] For example, Chinese Patent Publication No. CN113515891A discloses a method for predicting and optimizing the quality of emulsion explosives, including the following steps: acquiring historical data of emulsion explosives, preprocessing the historical data, performing correlation analysis on the process parameters of the emulsion explosive production process and the quality of the emulsion explosives, and selecting process parameters; establishing a random forest emulsion explosive quality prediction model, and designing a queue competition algorithm to optimize the parameters of the random forest model; based on the established random forest emulsion explosive quality prediction model, taking the maximum saturation and detonation velocity of the emulsion explosive quality indicators as the optimization objectives, and the operating range of process parameters as the constraints, establishing a dual-objective optimization model for emulsion explosive quality, and designing a multi-objective queue competition algorithm to solve it, and obtaining the optimal process operating parameters under the condition of maximizing saturation and detonation velocity.

[0004] For example, Chinese Patent Publication No. CN113626978A discloses an online prediction method and system for the detonation velocity of civil explosive emulsions. This method acquires the current operating parameters of the civil explosive emulsion, extracts key operating parameters from these parameters, and inputs these key operating parameters into a pre-established detonation velocity prediction model to obtain the predicted detonation velocity value. This invention achieves online monitoring of the detonation velocity of civil explosive emulsions by acquiring real-time operating parameters and, based on these parameters and the detonation velocity prediction model, performing online prediction of the detonation velocity corresponding to the real-time operating parameters. The entire process is time-saving and labor-saving, significantly improving detonation velocity detection efficiency.

[0005] In existing technologies, a model for predicting detonation velocity is obtained by randomly sampling historical parameters of emulsion explosives using a forest, processing detonation velocity-related parameters, and performing regression interpretation using negative gradients based on detonation velocity conditions. However, when using this data to analyze emulsion explosives, the tolerance of the emulsion explosive itself to ambient temperature and the impact of multiple ambient temperatures on the current performance of the emulsion explosive are ignored, resulting in a lack of correlation in the detonation velocity treatment of the emulsion explosive itself and a fixed form of treatment boundary. Summary of the Invention

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a method for predicting the detonation velocity decay of emulsion explosives under thermal aging, characterized in that it includes: S1, acquiring a data set of emulsion explosive thermal aging, taking the time point of reaching a preset temperature value as the starting point, and acquiring the boundary conditions of emulsion explosive thermal aging, including temperature boundary, heating time boundary and sensitizer type boundary.

[0007] S2 extends the boundary conditions to test scenarios with combinations of multiple boundary conditions. Based on the value range of each boundary condition, it obtains the corresponding detonation velocity feature parameters under the boundary conditions and constructs a detonation velocity correlation model.

[0008] S3. Determine the currently processed associated entity from the detonation velocity association model, and according to the combination relationship of the associated entities, search for the time point when the detonation velocity feature parameter error value appears, and obtain at least one set of associated parameters from the detonation velocity association model.

[0009] S4. Using temperature and sensitizer type as conditions, and according to different heating times and detonation velocities, the associated parameters are fitted into a detonation velocity decay curve. Based on the range of values ​​of the decay rate of the detonation velocity decay curve, the changing trend of the detonation velocity decay curve is determined.

[0010] S5, based on the changing trend of the detonation velocity decay curve, selects temperature nodes in the detonation velocity decay curve, performs curve cross-solving based on the characteristic values ​​under each temperature node, and triggers data configuration warning based on the deviation ratio of each temperature node after cross-solving.

[0011] The beneficial effects of this invention are as follows: First, this invention uses multivariate regression and dynamic boundary interval identification to take the effective intervals of temperature, heating time, and sensitizer type as boundary conditions, and extends them to multiple combined test scenarios to check the boundary conditions of the current detonation velocity decay, so that the currently defined boundary can cover multiple high-temperature blasting environments as much as possible, thereby improving the accuracy of subsequent processing measurements.

[0012] Second, this invention constructs a similarity matrix by using factor points, axial points, and center points in the boundary parameter set, and then combines it with hierarchical associated entity processing to achieve error tracing and parameter association. It analyzes and processes the multiple sets of parameters currently included in a hierarchical structure to locate the error situation existing in the current analysis, thereby improving the accuracy of error location and the interpretability of model analysis.

[0013] Third, this invention uses an exponential fitting decay rate, combined with the slope value output change trend, and triggers an early warning by the deviation ratio of multiple temperature nodes under the change trend, to determine the change trend of the current blasting decay and the law of the change trend under relative temperature, thereby improving the accuracy of dynamic analysis of emulsion explosives. Attached Figure Description

[0014] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0015] Figure 1 This is a flowchart illustrating a method for predicting the detonation velocity decay of emulsion explosives due to thermal aging.

[0016] Figure 2 This is a flowchart illustrating step S1 of a method for predicting the detonation velocity decay of emulsion explosives due to thermal aging.

[0017] Figure 3 This is a flowchart illustrating step S2 of a method for predicting the detonation velocity decay of emulsion explosives due to thermal aging.

[0018] Figure 4 This is a flowchart illustrating step S3 of a method for predicting the detonation velocity decay of emulsion explosives due to thermal aging.

[0019] Figure 5 This is a flowchart illustrating step S4 of a method for predicting the detonation velocity decay of emulsion explosives due to thermal aging.

[0020] Figure 6 This is a flowchart illustrating step S5 of a method for predicting the detonation velocity decay of emulsion explosives due to thermal aging. Detailed Implementation

[0021] The embodiments of the present invention are described in detail below. The embodiments described below are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. Where specific techniques or conditions are not specified in the embodiments, they shall be performed in accordance with the techniques or conditions described in the literature in the art or in accordance with the product manual.

[0022] See Figure 1 A method for predicting the detonation velocity decay of emulsion explosives under thermal aging includes: S1, acquiring a data set of emulsion explosive thermal aging, taking the time point when the preset temperature value is reached as the starting point, and acquiring the boundary conditions of emulsion explosive thermal aging, including temperature boundary, heating time boundary and sensitizer type boundary.

[0023] S2 extends the boundary conditions to test scenarios with combinations of multiple boundary conditions. Based on the value range of each boundary condition, it obtains the corresponding detonation velocity feature parameters under the boundary conditions and constructs a detonation velocity correlation model.

[0024] S3. Determine the currently processed associated entity from the detonation velocity association model, and according to the combination relationship of the associated entities, search for the time point when the detonation velocity feature parameter error value appears, and obtain at least one set of associated parameters from the detonation velocity association model.

[0025] S4. Using temperature and sensitizer type as conditions, and according to different heating times and detonation velocities, the associated parameters are fitted into a detonation velocity decay curve. Based on the range of values ​​of the decay rate of the detonation velocity decay curve, the changing trend of the detonation velocity decay curve is determined.

[0026] S5, based on the changing trend of the detonation velocity decay curve, selects temperature nodes in the detonation velocity decay curve, performs curve cross-solving based on the characteristic values ​​under each temperature node, and triggers data configuration warning based on the deviation ratio of each temperature node after cross-solving.

[0027] Preferably, when it is determined that the current emulsion explosive is aging, the temperature range of the thermal aging test is set to 50-90℃ according to the thermal stability characteristics of the emulsion explosive, and 50℃ is selected as the preset temperature value; when the temperature reaches 50℃, the starting time point is recorded, and then the detonation velocity value is monitored at fixed time intervals, such as 12h, and the corresponding data set is extracted.

[0028] Temperature, heating time, sensitizer type, and detonation velocity value from the dataset are input into a multivariate regression model to calculate the contribution weight of each parameter to the detonation velocity decay rate. Then, the thermal aging boundary conditions are determined through sensitivity analysis, forming boundaries with respect to temperature, heating time, and sensitizer type, respectively.

[0029] The temperature boundary was analyzed by taking 50-90℃ as the key range to examine the effect of different temperature gradients on detonation velocity.

[0030] The time boundary was determined by analyzing the nonlinear relationship between heating time and detonation velocity decay based on the results of accelerated aging experiments.

[0031] Sensitizer type boundaries are determined by comparing the detonation velocity differences of different sensitizers under the same temperature-time conditions.

[0032] The data under different boundaries are then normalized to form a correlation model corresponding to the detonation velocity decay rate. The data in the correlation model are then compared and the error of the correlation model is calculated to obtain the final output correlation data.

[0033] like Figure 2 As shown, the implementation of step S1 includes: S11, based on the acquired data set, using the temperature, heating time, sensitizer type and detonation velocity value contained in the data set as input parameters, performing multivariate regression to determine the contribution weights of temperature, heating time and sensitizer type in sequence.

[0034] S12, according to the contribution weights of temperature, heating time and sensitizer type, determine the effective boundary intervals under the temperature boundary, heating time boundary and sensitizer type boundary in sequence, and use the effective boundary intervals as the boundary conditions of the output.

[0035] When performing multivariate analysis, a multivariate linear regression model is used with detonation velocity as the dependent variable and temperature, heating time, and sensitizer type as independent variables. During the analysis, temperature, heating time, sensitizer type, and detonation velocity are standardized to eliminate dimensions. After representing multiple sensitizer types digitally, their contribution weights are calculated for each. The effective boundary interval is then determined based on multiple sets of data corresponding to the maximum contribution weight. For example, for temperature boundaries, multiple sets of data corresponding to the maximum contribution weight are obtained, and the difference between the maximum and minimum values ​​is compared with the size of the normal temperature range.

[0036] When it undergoes multivariate regression, its structure is represented as follows: ;in, This represents the detonation velocity characteristic value, used to explain the prediction method of the detonation velocity value under the current multiple linear regression. At this time, the detonation velocity decay rate, the peak value of the detonation velocity, and the value of the detonation velocity in each test can be selected to determine the correlation between temperature, heating time and sensitizer type and the current detonation velocity, so as to obtain the detonation velocity characteristic value output under the multiple linear regression. This refers to temperature and is used to describe the temperature value used in thermal aging analysis. This indicates the heating time, used to describe the time required for the thermal aging reaction. Represents the intercept term; The regression coefficient represents temperature, indicating the form of temperature change under multiple regression. Regression coefficients representing heating time; The regression coefficients represent the types of sensitizers. The m-th type of sensitizer is used to describe the sensitizer type number; M represents the number of sensitizer types, ranging from 1 to M-1; the above regression coefficients are the main content of the current multiple linear regression analysis to determine the form of its detonation velocity prediction. The numerical form representing the m-th type of sensitizer, i.e., when its type is When the time condition is met, the value is 1; otherwise, it is 0. This represents the deviation, typically 0.001, used to describe the error in the current calculation. After performing multiple linear regression, the calculation content in the current scenario is determined. At this point, it is necessary to use continuous regression to obtain the detonation velocity characteristic value. When the detonation velocity characteristic value satisfies the minimum mean absolute error and the maximum coefficient of determination, the corresponding regression coefficient is determined, and the detonation velocity characteristic value is obtained using the regression coefficient.

[0037] As for the calculation of contribution weights, it is based on the contribution weight of temperature.

[0038] ;in, Indicates the contribution weight of temperature; This represents the estimated value of the regression coefficient for temperature, expressed in absolute form, to illustrate the relative weight of temperature in the current input dataset. It should be noted that the above estimated value is calculated using maximum likelihood estimation. The standard deviation of temperature values ​​is used to standardize the scale differences of variables. This represents an estimate of the regression coefficient for heating time; The standard deviation of heating time; Estimates of the regression coefficients representing the sensitizer type; This represents the standard deviation of the sensitizer type in numerical form; at this point, a contribution weight is set to describe the weight of the temperature value relative to the detonation velocity value in the current dataset, in order to measure the impact of the temperature value on the detonation velocity.

[0039] The calculation method for the contribution weight of heating time and sensitizer type is the same as that for temperature calculation.

[0040] Compared to the overall steps, step S1 uses correlation analysis to determine the range of values ​​for the boundary conditions currently used in the data set. This identifies the range of values ​​for conditions such as temperature, time, and sensitizer type that can directly affect detonation velocity under thermal aging treatment. It also explains the adjustability of boundary conditions during analysis and the weight display under boundary condition normalization, reducing the problem of non-correlation in detonation velocity analysis caused by abnormal combinations of boundary conditions.

[0041] Preferably, the effective boundary interval in step S12 is implemented by: when identifying the effective boundary interval of the temperature boundary, fixing the heating time and sensitizer type, using the maximum and minimum values ​​of the current temperature as input, calculating the detonation velocity characteristic value, and dividing the detonation velocity characteristic value by the difference between the maximum and minimum values ​​of the current temperature to obtain the detonation velocity characteristic value relative to each temperature, which is regarded as the influence rate of temperature change on detonation velocity. Thus, after explaining the influence of temperature on detonation velocity, when the influence rate of temperature change on detonation velocity is greater than the standard influence rate, the maximum and minimum values ​​of the current temperature are regarded as the output temperature boundary.

[0042] It should be noted that the standard influence rate is the influence rate of temperature change on detonation velocity obtained from the data set that satisfies the maximum likelihood estimation.

[0043] Preferably, when identifying the effective boundary range of heating time, it is necessary to use the critical heating time for detonation velocity decay as a constraint, and take the data exceeding the critical heating time as the output heating time boundary. In this case, the critical heating time used when significant detonation velocity decay occurs can be obtained based on historical data, and the values ​​exceeding this critical heating time can be used as the main content of subsequent analysis.

[0044] Preferably, when identifying the effective boundary interval of sensitizer types, the portion of data where the average difference between all sensitizers exceeds that of the reference sensitizer is used as the output sensitizer type boundary. In this case, the reference sensitizer can produce the maximum value obtained by the sensitizer under multiple linear regression analysis. Using the average difference in detonation velocity characteristic values ​​as the standard, any other sensitizer type whose average difference in detonation velocity characteristic values ​​exceeds the standard value is used as the output sensitizer type boundary. The data obtained in this case represents the portion where the thermal aging reaction has a significant impact on detonation velocity, and can serve as the basis for subsequent analysis combinations.

[0045] At this point, the output boundary conditions will include the contribution weight of each boundary condition, which will facilitate the subsequent construction of the burst velocity correlation model under the corresponding boundary conditions.

[0046] It should be noted that during the thermal aging process, the detonation rate decay under different conditions is explained by controlling the heating time at a single constant temperature value.

[0047] In one embodiment of the present invention, the detonation velocity characteristic parameters include multiple sets of parameters such as the instantaneous value of the currently measured detonation velocity, the average and standard deviation of multiple measurements, the maximum detonation velocity, and the minimum detonation velocity, to illustrate the trend of detonation velocity change.

[0048] Preferably, the detonation velocity is based on the time difference of shock wave propagation and the distance between sensors. The detonation velocity of the explosive after thermal aging is obtained by combining the time difference of the starting points of the pressure curves of two adjacent pressure sensors with the distance between the pressure sensors.

[0049] Under the current processing steps, the purpose of constructing the detonation velocity correlation model is to further analyze multiple sets of detonation velocity values ​​under corresponding boundary conditions, reveal the multi-parameter coupling effect, form the correlation response of the multi-parameter feature set, and combine the comprehensive values ​​of detonation velocity under multiple tests to define the detonation velocity response surface corresponding to the current boundary conditions through multi-parameter correlation, so as to obtain the multi-parameter combination corresponding to each boundary condition.

[0050] like Figure 3 As shown, the implementation of step S2 includes: S21, starting from any combination of boundary conditions, setting at least one set of boundary parameters, and constructing a correlation between the detonation velocity characteristic value obtained by the boundary parameter set and the detonation velocity characteristic parameter; at this time, the characteristic value under multivariate analysis is correlated with the actual detonation velocity value. These values ​​can be normalized and added to an array or matrix to illustrate the corresponding correlation.

[0051] When constructing the association between detonation velocity characteristic values ​​and detonation velocity characteristic parameters, the implementation method includes: S211, obtaining the number of factor points, axial points and center points of the extreme value combination of the boundary parameter group, treating the data corresponding to the factor points, axial points and center points as sample points, and analyzing the detonation velocity characteristic values ​​and detonation velocity characteristic parameters.

[0052] The factor points mentioned above represent all extreme value combinations of the boundary parameter set, namely the upper and lower limits of the current temperature boundary, the upper and lower limits of the heating time, and the upper and lower limits of the sensitizer type and quantity. These are multiple data points combined, and the factor points are represented as 8, which is a three-dimensional parameter set that combines the three types of boundary conditions.

[0053] As for the axial points, which represent the points that extend outside the extreme values, since the current boundary conditions belong to the filtered interval, they are extended into the extreme values ​​of the boundary parameter group. Multiple points are arbitrarily selected from the value range of the current temperature boundary, heating time boundary, and the number of sensitization types for feature association.

[0054] As for the center point, it is a combination of multiple three-dimensional data sets, including the median value of the temperature boundary, the median value of the heating time, and the median value of the number of sensitization types. These data are used to evaluate the errors existing under the current correlation.

[0055] S212, with the number of repetitions of the center point as a limit, calculate the average error of the detonation velocity feature value and the detonation velocity feature parameter corresponding to the sample point, and use it as the correlation feature value constructed by the detonation velocity feature value and the detonation velocity feature parameter.

[0056] Preferably, when calculating the average variance of the detonation velocity characteristic value and the detonation velocity characteristic parameter, the correlation between the detonation velocity characteristic value and the actual measured detonation velocity is illustrated by inputting the detonation velocity characteristic values ​​corresponding to multiple sample points into the detonation velocity characteristic parameter, which includes the instantaneous value of the detonation velocity, the average and standard deviation of multiple measurements, the maximum value of the detonation velocity, and the minimum value of the detonation velocity. The values ​​are calculated with these five dimensions respectively, and the average error is calculated.

[0057] S22: Construct a similarity matrix using the detonation velocity eigenvalues ​​and detonation velocity eigenparameters, and combine multiple parameters using the contribution weights of multiple boundary conditions in the current boundary parameter group to set up the detonation velocity correlation model.

[0058] When constructing the similarity matrix, the number of sample points currently used is used as the basis. The correlation feature values ​​calculated each time are input into the similarity matrix, and after being weighted according to the contribution weight and the corresponding boundary conditions, their correlation feature values ​​are introduced to form a rapid correlation model.

[0059] For each set of boundary parameters at each sample point, the detonation velocity correlation model It can be represented as shown below.

[0060] ;in, These represent the correlation characteristic values, used to illustrate the correlation between the multivariate linear analysis and the current actual detonation velocity; Indicates the contribution weight of heating time; This indicates the contribution weight of the sensitizer type; at this point, the associated feature value is used to correct its value to illustrate the correlation between the corresponding boundary conditions.

[0061] In one embodiment of the present invention, after the detonation velocity correlation model is set up, the current detonation velocity correlation model is used for solving, such as using the value of the detonation velocity correlation model when the detonation velocity decays, and using the value of the detonation velocity correlation model for solving the detonation velocity correlation model to obtain the error under the detonation velocity solution, and using the time point of the error to look up the parameters to obtain the correlation parameters related to the current error.

[0062] Preferably, the aforementioned associated entities include multiple parameters such as temperature, heating time, and sensitizer type. In this case, associated entities will be obtained from the dataset, and the associated entities related to the error will be identified in the current detonation velocity association model. These entities will then be output as associated parameters in the form of data sets.

[0063] like Figure 4 As shown, the implementation of step S3 includes: S31, based on the number of operations in which errors occur for each data in the dataset, the corresponding boundary conditions are regarded as associated entities.

[0064] S32, determine the hierarchical relationship between each associated entity, and use the mapping relationship between the current associated entity and the next level associated entity as a constraint condition to traverse each associated entity.

[0065] S33, when the current associated entity is at the highest level, output the data mapped by the current associated entity as the associated parameter.

[0066] When setting the hierarchical relationship between temperature, heating time, and sensitizer type, since the hierarchical relationship is a nested structure, the selection of different parameters needs to meet certain constraints to be valid. In this case, temperature can be used as the highest level condition. For example, temperature will directly affect the heating time set under the current thermal aging reaction, while the sensitizer type will be affected by the temperature setting range. At this time, a hierarchical relationship composed of temperature-heating time-sensitizer type can be formed, or the hierarchical relationship can be set according to the frequency of occurrence of these three data under operational errors. The one with the most occurrences is set as the highest level, and the one with the fewest occurrences is set as the lowest level. Multiple data are mapped according to their frequency of occurrence, forming a relationship where a single data value maps to multiple data values. These data relationships are regarded as the output correlation parameters.

[0067] Preferably, when selecting associated entities, it is necessary to obtain values ​​such as temperature, heating time, and sensitizer type when the error occurs. These values ​​are combined using vectors, normalized, and other data in the current dataset are used to calculate cosine similarity with the vector composed of error values, with values ​​greater than 0.6 or 0.8 selected. These values ​​represent situations extremely similar to the scenario where the error occurred. The average and standard deviation of these values ​​are calculated according to the corresponding number of operations. The data related to the error are then selected from the dataset within the range of the average plus or minus three times the standard deviation and used as associated entities. This prevents uncertainty in data calculation due to scenarios with few errors or relatively dispersed error values, thereby improving the correlation of multiple sets of data selected for the error.

[0068] Preferably, the method for determining the hierarchical relationship between each associated entity further includes: summing the values ​​of each associated entity and calculating the average value, updating the current associated entity according to the rate of change of the average value after each associated entity extraction, and extracting the associated parameters from the updated associated entity.

[0069] At this point, calculating the average value of the associated entities is a further determination. Under the circumstances of multiple error extractions, the average value of the associated entities represents the central position of the data, indicating the overall average level under the current error. When the average value changes, it indicates that the current detonation velocity decay may be affected by the cumulative effect of multiple operation errors. It is necessary to verify the detonation velocity decay pattern during operation under a relatively similar overall situation.

[0070] At this point, the updated data will be stored in the database. The update method is to record the proportion of change in its mean and mark the corresponding data to verify the subsequently obtained burst velocity decay curve.

[0071] In one embodiment of the present invention, in step S4, the correlation parameters with errors are fitted into decay curves. Specifically, using temperature and sensitizer type as conditions, and according to different heating times, the correlation parameters are fitted to determine the decreasing trend of the decay curve under multiple curve descriptions. The differences in the maximum value and decay rate of the decay curve under different temperatures and sensitizer conditions are determined, and the rate of change is identified to illustrate the changing trend of the current detonation velocity decay curve. At this point, a detonation velocity decay curve is generated using heating time and detonation velocity value as coordinate axes.

[0072] When fitting correlation parameters, an exponential parameter is used to fit the detonation velocity decay curve, and the trend of detonation velocity decay is analyzed to determine the correlation trend of detonation velocity decay. The basic model of the detonation velocity decay curve. It can be represented as shown below.

[0073] For example, ;in, This indicates the initial detonation velocity, representing the detonation velocity without thermalization. This indicates the stable detonation velocity. This value is selected from the standard deviation of multiple detonation velocities to illustrate the relatively stable value that the detonation velocity can maintain after the thermal aging reaction. Represents an exponential constant; This represents the decay rate constant. At this point, multiple detonation velocity values ​​will be substituted into the current basic model. Under the condition of exponential decay, the decay rate will be recorded, and the consistency of the decay rate constant under the detonation velocity decay curves of multiple temperature and sensitizer types will be compared.

[0074] like Figure 5 As shown, the implementation of step S4 includes: S41, obtaining the decay rate corresponding to the detonation velocity decay curve according to the combination of each set of associated parameters, and obtaining the range of values ​​for the decay rate when the decay rate conforms to the exponential fitting.

[0075] S42 arranges and combines the associated parameters according to the range of decay rate values, fits the associated parameters under the same decay rate, and outputs the trend of the detonation velocity decay curve with the slope value corresponding to the fitted associated parameters.

[0076] Knowing the range of decay rate values ​​in the current processing, the decay rate values ​​are arranged and combined with the corresponding correlation parameters. After sorting the decay rate values ​​from largest to smallest, the correlation parameters with the same decay rate are counted. Then, the correlation parameters under the same decay rate are fitted using the least squares method. When the correlation parameters satisfy the condition of minimizing the sum of squared residuals, the slope of the detonation velocity decay curve formed by the corresponding correlation parameters is used as the trend of the current output to illustrate the obvious decay situation under detonation velocity decay.

[0077] In one embodiment of the present invention, the processing in step S5 is biased towards the fitted detonation velocity decay curve. By analyzing the characteristic values ​​of time nodes and temperature nodes, and based on historical decay trends such as decay rate, initial detonation velocity, stable detonation velocity and external conditions such as temperature changes, these values ​​are coupled to calculate the maximum difference in the current detonation velocity decay. These maximum differences are then mapped to the corresponding temperature nodes to indicate whether the currently selected temperature nodes need to be corrected.

[0078] It should be noted that the temperature node is used to describe the value of the detonation velocity decay rate and the slope at a specific temperature, to show whether the detonation velocity decay curve generated under the condition of the corresponding temperature conforms to the normal decreasing range.

[0079] like Figure 6As shown, the implementation of step S5 includes: S51, cross-solving the corresponding decay rate, initial detonation velocity and stable detonation velocity of each temperature node to determine the deviation ratio under each temperature node.

[0080] S52, if the deviation ratio at the current temperature node is greater than the preset ratio value, trigger a data configuration warning based on the curve change fluctuation rate of the explosion velocity decay curve corresponding to the current temperature node.

[0081] S53, if the deviation ratio at the current temperature node is less than the preset ratio value, trigger a data configuration warning based on the peak decay rate of the current temperature node.

[0082] When performing cross-solution as described above, the deviations between the decay rate, initial detonation velocity, and stable detonation velocity obtained from the current dataset at the same temperature node and the corresponding data in the historical data are calculated separately. These deviations are then converted into proportional values ​​and combined in a weighted manner to obtain the deviation ratio. The decay rate is a unique value under the detonation velocity decay curve marked at each temperature. In this case, the weight can be set based on the ratio of the current decay rate value to the sum of the decay rates at all temperature nodes. The initial detonation velocity and stable detonation velocity are set in the same way as the decay rate, thus obtaining the deviation ratio for each temperature node.

[0083] The preset ratio is set based on the average deviation ratio in historical data when errors occur, to filter out parts with obvious data deviations. When it exceeds the preset ratio, a real-time warning will be issued based on the fluctuation rate of the curve to determine whether the current detonation velocity decay is normal. At this time, the fluctuation rate of the curve represents the slope of the detonation velocity decay curve, that is, the warning is issued based on the relative value of the changing trend.

[0084] When the value is less than the preset ratio, it indicates that the current focus is on identifying whether there is a rapidly decaying part in the detonation velocity decay curve. The peak value of the decay rate is used to check the change pattern of the current detonation velocity decay curve, and the corresponding data is output to the external terminal to achieve real-time early warning of the current emulsion explosive processing.

[0085] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention, which are still covered within the protection scope of the present invention.

Claims

1. A method for predicting the detonation velocity decay of emulsion explosives due to thermal aging, characterized in that, include: S1, acquire the data set of emulsion explosive thermal aging, taking the time point of reaching the preset temperature value as the starting point, and acquire the boundary conditions of emulsion explosive thermal aging, including temperature boundary, heating time boundary and sensitizer type boundary; S2 extends the boundary conditions to test scenarios with combinations of multiple boundary conditions. Based on the value range of each boundary condition, it obtains the corresponding detonation velocity feature parameters under the boundary conditions and constructs a detonation velocity correlation model. S3, determine the currently processed associated entity from the detonation velocity association model, and according to the combination relationship of the associated entities, search at the time point when the detonation velocity feature parameter error value appears, and obtain at least one set of associated parameters from the detonation velocity association model; S4. Using temperature and sensitizer type as conditions, and according to different heating time and detonation velocity, the associated parameters are fitted into a detonation velocity decay curve. Based on the range of the decay rate of the detonation velocity decay curve, the changing trend of the detonation velocity decay curve is determined. S5, based on the changing trend of the detonation velocity decay curve, selects the temperature nodes in the detonation velocity decay curve, performs curve cross-solving based on the characteristic values ​​under each temperature node, and triggers data configuration warning based on the deviation ratio of each temperature node after cross-solving. The implementation of step S1 includes: S11, based on the acquired data set, using the temperature, heating time, sensitizer type, and detonation velocity value contained in the data set as input parameters, performing multivariate regression to determine the contribution weights of temperature, heating time, and sensitizer type in sequence; S12, according to the contribution weights of temperature, heating time, and sensitizer type, determining the effective boundary intervals under the temperature boundary, heating time boundary, and sensitizer type boundary in sequence, and using the effective boundary intervals as the output boundary conditions.

2. The method for predicting the detonation velocity decay of emulsion explosives due to thermal aging according to claim 1, characterized in that, The implementation methods for the effective boundary interval in step S12 include: When identifying the effective boundary interval of sensitizer type boundaries, the portion of the data where the average difference of all sensitizers is greater than that of the reference sensitizer is used as the output sensitizer type boundary. When identifying the effective boundary range of heating time, the critical heating time of detonation velocity decay is used as the limit, and data exceeding the critical heating time are used as the output heating time boundary. When identifying the effective boundary interval of the temperature boundary, if the influence rate of temperature change on detonation velocity is greater than the standard influence rate, the current maximum and minimum temperature values ​​are regarded as the output temperature boundary.

3. The method for predicting the detonation velocity decay of emulsion explosives due to thermal aging according to claim 1, characterized in that, The detonation velocity characteristic parameters include the instantaneous value of the currently measured detonation velocity, the average and standard deviation of multiple measurements, the maximum detonation velocity, and the minimum detonation velocity.

4. The method for predicting the detonation velocity decay of emulsion explosives due to thermal aging according to claim 1, characterized in that, Step S2 can be implemented in the following ways: S21, starting from any combination of boundary conditions, set at least one set of boundary parameters, and establish a correlation between the detonation velocity characteristic value obtained by the boundary parameter set and the detonation velocity characteristic parameter. S22: Construct a similarity matrix using the detonation velocity eigenvalues ​​and detonation velocity eigenparameters, and combine multiple parameters using the contribution weights of multiple boundary conditions in the current boundary parameter group to set up the detonation velocity correlation model.

5. The method for predicting the detonation velocity decay of emulsion explosives due to thermal aging according to claim 4, characterized in that, When establishing a correlation between detonation velocity characteristic values ​​and detonation velocity characteristic parameters, the implementation methods include: S211, obtain the number of factor points, axial points and center points of the extreme value combination of the boundary parameter group, regard the data corresponding to the factor points, axial points and center points as sample points, and analyze the detonation velocity characteristic value and detonation velocity characteristic parameter; S212, with the number of repetitions of the center point as a limit, calculate the average error of the detonation velocity feature value and the detonation velocity feature parameter corresponding to the sample point, and use it as the correlation feature value constructed by the detonation velocity feature value and the detonation velocity feature parameter.

6. The method for predicting the detonation velocity decay of emulsion explosives due to thermal aging according to claim 1, characterized in that, Step S3 can be implemented in the following ways: S31, based on the number of operations in which errors occur for each data in the dataset, the corresponding boundary conditions are regarded as associated entities; S32, determine the hierarchical relationship between each associated entity, and use the mapping relationship between the current associated entity and the next level associated entity as a constraint condition to traverse each associated entity; S33, when the current associated entity is at the highest level, output the data mapped by the current associated entity as the associated parameter.

7. The method for predicting the detonation velocity decay of emulsion explosives under thermal aging according to claim 6, characterized in that, The methods for determining the hierarchical relationships between related entities also include: The sum of each associated entity is calculated and the average is obtained. The current associated entity is updated according to the rate of change of the average after each associated entity extraction. The associated parameters are then extracted from the updated associated entity.

8. The method for predicting the detonation velocity decay of emulsion explosives due to thermal aging according to claim 1, characterized in that, Step S4 can be implemented in the following ways: S41. Based on the combination of each set of related parameters, obtain the decay rate corresponding to the detonation velocity decay curve. If the decay rate conforms to the exponential fitting, obtain the range of values ​​for the decay rate. S42 arranges and combines the associated parameters according to the range of decay rate values, fits the associated parameters under the same decay rate, and outputs the trend of the detonation velocity decay curve with the slope value corresponding to the fitted associated parameters.

9. The method for predicting the detonation velocity decay of emulsion explosives due to thermal aging according to claim 1, characterized in that, Step S5 can be implemented in the following ways: S51, cross-solve the corresponding decay rate, initial detonation velocity and stable detonation velocity of each temperature node to determine the deviation ratio under each temperature node; S52, if the deviation ratio at the current temperature node is greater than the preset ratio value, trigger a data configuration warning based on the curve change fluctuation rate of the detonation velocity decay curve corresponding to the current temperature node. S53, if the deviation ratio at the current temperature node is less than the preset ratio value, trigger a data configuration warning based on the peak decay rate of the current temperature node.

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