Electronic detonator detonation dynamic regulation and control method and system based on performance monitoring

Abstract

The invention discloses an electronic detonator detonation dynamic regulation and control method and system based on performance monitoring, and relates to the technical field of electronic detonators. The method comprises the following steps: acquiring environmental stress parameters and detonator performance parameters in real time, and performing time sequence analysis to realize dynamic feature extraction; identifying an abnormal fluctuation time period, analyzing and extracting fluctuation characteristics, and quantifying the interaction influence of the environment and performance parameters; a performance degradation model is constructed based on voltage / resistance time sequence data, the degradation rate is dynamically calculated in combination with batch characteristics and acceleration test data, and the detonation failure probability is simulated and predicted; a detonation parameter safety interval is generated through reliability level mapping, and optimization design of trigger parameters and energy is achieved through timing compensation, discreteness grouping, network topology optimization and energy dynamic adjustment strategies; a closed-loop dynamic regulation and control strategy is formed, and high-reliability detonation of the electronic detonator under complex working conditions is guaranteed. According to the method, multi-dimensional data analysis and an intelligent optimization algorithm are fused, and the safety of blasting engineering and the system stability are effectively improved.

Description

Technical Field

[0001] This invention belongs to the field of electronic detonator technology, specifically relating to a dynamic control method and system for electronic detonator initiation based on performance monitoring. Background Technology

[0002] With the widespread application of electronic detonators in blasting engineering, the requirements for their safety and reliability are becoming increasingly stringent. Traditional electronic detonator production and use primarily rely on manual inspection and sampling, which is insufficient to comprehensively cover product performance fluctuations and lacks systematic analysis of the dynamic correlation between complex environmental stresses (such as temperature, humidity, and electromagnetic interference) and detonator performance parameters (voltage, resistance). While existing technologies have incorporated electronic control modules to improve delay accuracy and networked detection, the following limitations still exist: Insufficient environmental adaptability: Traditional electronic detonators have a weak real-time response capability to environmental stress (such as vibration, temperature and humidity changes), and have failed to establish a quantitative correlation model between environmental parameters and performance degradation, which makes them prone to detonation anomalies under extreme working conditions.

[0003] Limited failure prediction capability: Existing technologies mostly rely on static parameter thresholds to determine the detonator status, lacking the extraction of time-series fluctuation characteristics and degradation trend modeling of key parameters such as voltage and resistance, making it difficult to accurately predict the probability of detonation failure.

[0004] Lack of dynamic control mechanism: Traditional detonation system parameters (voltage, resistance, triggering sequence) are mostly fixed designs, which cannot dynamically adjust the energy compensation strategy according to real-time performance degradation data. This can easily lead to synchronization errors or energy overload due to local parameter drift.

[0005] Insufficient system compatibility: The existing detonation network topology design does not fully consider the performance dispersion of detonators, resulting in timing deviations and reliability bottlenecks when multiple detonators work together, which can easily lead to a chain reaction of local failures, especially in large-scale network scenarios.

[0006] In recent years, although some patents have proposed optimizing delay control through technologies such as wireless communication and intelligent encoders, a closed-loop control system based on real-time performance monitoring has not yet been established. To address these issues, this invention proposes a dynamic control method and system for electronic detonator initiation based on performance monitoring. Summary of the Invention

[0007] In order to overcome the shortcomings and deficiencies of the existing technology, the first objective of this invention is to provide a dynamic control method for the initiation of electronic detonators based on performance monitoring; the second objective of this invention is to provide a dynamic control system for the initiation of electronic detonators based on performance monitoring.

[0008] The first objective of this invention is achieved through the following technical solution: The dynamic control method for electronic detonator initiation based on performance monitoring is as follows: Step 1: Collect real-time performance data of electronic detonators through sensors, extract dynamic time-series characteristics of detonation voltage and resistance value, and obtain voltage time-series fluctuation data and resistance value time-series fluctuation data. Step 2: Fit the detonator performance degradation model based on voltage time-series fluctuation data and resistance value time-series fluctuation data to obtain detonator performance degradation data, and estimate the detonation failure risk based on the detonator performance degradation data to obtain the detonation failure probability. Step 3: Map the safety threshold of the detonation parameters according to the probability of detonation failure to obtain the safety range of the detonation parameters; optimize the detonation system parameters based on the detonator performance degradation data based on the safety range of the detonation parameters to obtain the detonation parameter optimization design data; adjust the detonation energy based on the detonation parameter optimization design data to obtain the detonation energy compensation data. Step 4: Based on the optimized design data of detonation parameters and the detonation energy compensation data, formulate a dynamic control strategy for the electronic detonator detonation, and send the dynamic control strategy for the electronic detonator detonation to the terminal for execution.

[0009] Preferably, the real-time performance data of the electronic detonator includes environmental stress parameters, detonator performance parameters, and a time dimension; the environmental stress parameters include real-time monitoring values ​​of temperature, humidity, vibration acceleration, and electromagnetic interference intensity; the detonator performance parameters include real-time voltage values ​​of the initiation circuit and detonator resistance values; the time dimension is that all data are timestamped and form a continuous time series.

[0010] Preferably, dynamic temporal feature extraction includes: Time-domain analysis: The sliding window method is used to calculate statistical characteristics, quantify the short-term fluctuation trend of parameters, and identify periods of abnormal fluctuations; Frequency domain analysis: Converting time-series data into frequency domain signals using Fast Fourier Transform to extract the dominant frequency, amplitude, and spectral energy characteristics; Correlation analysis: Calculate the correlation between environmental stress parameters and detonator performance parameters, and classify the correlation strength based on the absolute value of the correlation coefficient and the p-value.

[0011] Preferably, the detonator performance degradation model fitting includes: Analyze the time-series fluctuation data of resistance values ​​to identify the periods of resistance value increase; Based on the timing fluctuation data of the detonation voltage, the voltage decay period is analyzed to obtain the voltage decay period; Calculate voltage attenuation and attenuation duration to quantify energy loss attenuation gradient data; Based on the energy loss attenuation gradient data, resistance thermal accumulation localization analysis is performed on the time-series fluctuation data of resistance value to obtain resistance thermal accumulation localization data. Based on the energy loss attenuation gradient data and the resistance heat accumulation location data, a detonator performance degradation model was fitted to obtain detonator performance degradation data.

[0012] Preferably, the assessment of detonation failure risk includes: The performance parameter drift data is obtained by performing cumulative calculation on the detonator performance degradation data; Based on the performance parameter drift data, a degradation trend regression analysis was performed on the detonator performance degradation data to obtain detonator performance degradation trend regression data; Logarithmic transformation of parameters was performed on the regression data of detonator performance degradation trend to obtain logarithmically transformed data of detonator performance degradation; Based on batch characteristic data and initiation reliability acceleration data of electronic detonators, the logarithmic transformation data of detonator performance degradation is used to perform dynamic calculations of electronic detonator performance degradation between different batches, so as to obtain dynamic data of performance degradation per unit time. Based on batch characteristic data of electronic detonators, the initiation failure probability is simulated using dynamic data of performance degradation, and initiation failure probability estimation data is generated. The risk of detonation failure is estimated based on the detonation failure probability estimation data, and the detonation failure probability is obtained.

[0013] Preferably, the optimization of the detonation system parameters includes: Based on the system compatibility matching data, the detonation timing loss compensation matching is performed to obtain the detonation timing loss compensation data. A parameter dispersion difference analysis was performed on the detonator performance degradation data to obtain the dispersion difference data of detonator performance parameters; Synchronization matching of the detonation system is performed based on the discrete difference data of detonator performance parameters to obtain synchronization matching data of the detonation system. The detonation network topology parameters are designed based on the discrete difference data of detonator performance parameters and the synchronization matching data of the detonation system. Based on the detonation timing loss compensation data and detonation network topology parameters, the detonation system parameters are optimized to obtain the detonation parameter optimization design data.

[0014] Preferably, the detonation energy adjustment process includes: Calculate the energy requirement for each detonator based on the optimized parameters; Perform system compatibility constraint analysis to ensure that the adjusted energy is within the physical limits of the system components; The energy compensation value is dynamically adjusted based on real-time monitoring data. The dynamic adjustment strategies include voltage compensation, time compensation, and group compensation. Energy is evenly distributed in a multi-detonator system to avoid local overload.

[0015] Preferably, the dynamic control strategy includes detonation timing adjustment, energy compensation triggering, and synchronization control. The dynamic control strategy is transmitted to the terminal wirelessly or via wired means. The terminal includes a detonator, a sensor, and an actuator.

[0016] The second objective of this invention is achieved through the following technical solution: A performance-monitoring-based dynamic control system for electronic detonators is used to implement a performance-monitoring-based dynamic control method for electronic detonators. The system includes: Data acquisition module: used to acquire real-time performance data of electronic detonators through sensors. The data includes environmental stress parameters, detonator performance parameters and time dimension information. Feature extraction module: used to extract dynamic time-series features of detonation voltage and resistance value from real-time performance data, and obtain voltage time-series fluctuation data and resistance value time-series fluctuation data; Performance degradation analysis module: used to fit the detonator performance degradation model based on voltage time-series fluctuation data and resistance value time-series fluctuation data, and obtain detonator performance degradation data; Failure Risk Estimation Module: Used to estimate the risk of detonation failure based on detonator performance degradation data, and obtain the probability of detonation failure; Parameter optimization module: It is used to map the safety threshold of the detonation parameters according to the probability of detonation failure, obtain the safety range of the detonation parameters, and optimize the detonation system parameters based on the detonator performance degradation data based on the safety range to obtain the detonation parameter optimization design data. Energy adjustment module: used to adjust the detonation energy based on the detonation parameter optimization design data to obtain detonation energy compensation data; Control strategy formulation module: used to formulate dynamic control strategies for electronic detonator detonation based on detonation parameter optimization design data and detonation energy compensation data; Execution terminal: Used to receive and execute dynamic control strategies.

[0017] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. This invention uses a dynamic time-series feature extraction strategy based on time domain, frequency domain, and correlation analysis to perform multi-dimensional time-series correlation analysis between environmental stress parameters and detonator performance parameters; it achieves the characterization of the correlation between environmental factors and detonator performance. This multi-dimensional time-series fusion analysis method can capture the dynamic fluctuation law of parameters and the causes of abnormalities in real time, thereby improving the early warning capability of detonator performance abnormalities.

[0018] 2. This invention combines cumulative performance parameter drift calculation, multi-model regression analysis, and multi-batch dynamic risk assessment. It calculates the cumulative resistance / voltage drift through time-weighted integration, combines batch characteristic data and accelerated test data, utilizes the Arrhenius model to convert acceleration factors, and then generates the failure time distribution through Monte Carlo simulation. This achieves a leap from static threshold judgment to dynamic degradation trajectory prediction. Compared to traditional reliability assessments based on fixed thresholds, this method considers the degradation trend of parameters over time, batch differences, and the influence of environmental stress. It can more accurately quantify the probability of detonation failure at different times, providing a scientific risk quantification basis for detonation decisions and reducing the risk of detonation failure due to misjudgment of performance degradation.

[0019] 3. This invention, based on a safety range mapped by failure probability, employs early triggering / dynamic delay compensation for timing losses, performs group optimization through resistance / voltage discreteness analysis, and combines genetic algorithms to optimize synchronization errors, energy losses, and reliability targets. Furthermore, it dynamically adjusts energy parameters through voltage / time / group compensation strategies. This multi-dimensional collaborative optimization mechanism can specifically address the performance differences of different detonators, environmental interference, and network topology effects, ensuring that the detonation energy accurately matches the real-time state of the detonator, thus improving the synchronization, stability, and safety of the detonation system in complex environments. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 The flowchart of the dynamic control method for electronic detonator initiation based on performance monitoring of the present invention is shown; Figure 2 The block diagram of the electronic detonator initiation dynamic control system based on performance monitoring of the present invention is shown. Figure 3 The flowchart of the fitting of the detonator performance degradation model of the present invention is shown; Figure 4 A flowchart illustrating the risk estimation of detonation failure in this invention is shown. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more exemplary embodiments. Numerous specific details are provided in the following description to give a full understanding of exemplary embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced with one or more of the specific details omitted, or other methods, components, steps, etc., can be employed. In other instances, well-known structures, methods, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0024] Example 1: See Figure 1 As shown in the figure, the dynamic control method for electronic detonator initiation based on performance monitoring in this embodiment has the following process: Step 1: Real-time performance data of the electronic detonator is collected using sensors to obtain real-time performance data of the electronic detonator. Dynamic time-series features of the initiation voltage and resistance value are extracted from the real-time performance data of the electronic detonator, yielding time-series fluctuation data of the initiation voltage and resistance value of the electronic detonator, respectively. The real-time performance data of the electronic detonator includes environmental stress parameters, detonator's own performance parameters, and the time dimension.

[0025] Environmental stress parameters: real-time monitoring values ​​of external environment such as temperature, humidity, vibration acceleration, and electromagnetic interference intensity.

[0026] Detonator performance parameters: Real-time voltage value of the initiation circuit, detonator resistance value, and other data that directly reflect the working status of the detonator.

[0027] Time dimension: All data are timestamped, forming a continuous time series.

[0028] Dynamic time-series feature extraction is the process of extracting the dynamic fluctuation features of parameters over time through time series analysis methods. Specifically: S11. Time Domain Analysis: Statistical characteristics, including mean, standard deviation, maximum / minimum value, and slope, are calculated using the sliding window method to quantify the short-term fluctuation trend of parameters and identify abnormal fluctuation periods. For example, when the sliding window size is set to 1 second and the step size is 0.1 seconds, the voltage is calculated to suddenly drop to 3.5V at t=5s, and recovers after 2s. This abnormal fluctuation period is marked as an abnormal fluctuation period.

[0029] S12. Frequency Domain Analysis: Time-series data is converted into frequency domain signals using Fast Fourier Transform (FFT) to extract features such as dominant frequency, amplitude, and spectral energy. For example, after FFT transformation, the voltage fluctuation shows a dominant frequency of 0.5Hz, an amplitude of 0.2V, and low spectral energy, indicating no significant periodic fluctuations.

[0030] S13. Correlation Analysis: Calculate the correlation between environmental stress parameters and detonator performance parameters. After dynamic time-series feature extraction, obtain a set of characteristic data on the changes in initiation voltage and resistance over time.

[0031] The correlation between multiple environmental stresses and the performance of multiple detonators was analyzed, including but not limited to temperature and resistance, electromagnetic interference and voltage, and humidity and resistance. The p-value represents the probability of observing a more extreme correlation in the current sample data, assuming that "environmental stress is not correlated with detonator performance" (null hypothesis is true). The p-value is used to determine whether the correlation is statistically significant (usually p < 0.05 is considered significant).

[0032] Based on the absolute value of the correlation coefficient Determine the strength of the association: when When, it is considered a strong correlation; when When, it is considered a moderate correlation; when When the correlation is weak or not significant, it is considered a weak association or no significant association.

[0033] The correlation coefficient R = +0.85 between temperature increase and resistance increase, with a significance level of p < 0.011000, is considered a strong positive correlation (significant). The abnormal increase in resistance needs to be interpreted in conjunction with temperature data.

[0034] The correlation coefficient between electromagnetic interference intensity and voltage fluctuation was R=-0.72, with a significance level of p<0.05850, which was considered a moderate negative correlation (significant). This indicates that voltage stability decreases when electromagnetic interference increases.

[0035] The correlation coefficient between humidity change and resistance value is R=+0.35, with a significance level of p=0.12900, which is considered a weak positive correlation (not significant). This indicates that humidity has no significant effect on resistance, and temperature should be given priority.

[0036] Step 2: Fit the detonator performance degradation model based on the timing fluctuation data of the detonation voltage and the timing fluctuation data of the resistance value to obtain the detonator performance degradation data; estimate the detonation failure risk based on the detonator performance degradation data to obtain the detonation failure probability.

[0037] S21. Perform environmental stress fluctuation analysis on the real-time performance data of the electronic detonator to obtain environmental stress fluctuation data.

[0038] Environmental stress fluctuation data are obtained by calculating the rate of change and fluctuation range of environmental stress parameters over a period of time. For example, the rate of change of temperature over a set time period and the fluctuation range of humidity throughout the monitoring process can be calculated.

[0039] S23. Fit the detonator performance degradation model based on the timing fluctuation data of the detonation voltage and the timing fluctuation data of the resistance value to obtain the detonator performance degradation data.

[0040] See Figure 3 As shown, specifically, the resistance value rise period is analyzed based on the time-series fluctuation data, and then the voltage decay period is analyzed based on the time-series fluctuation data of the detonation voltage to obtain the voltage decay period: A threshold for resistance rise is set according to the detonator design specifications, and a sliding window is used for detection; the mean and slope (rate of change) of the resistance value within the window are calculated. If the slope of multiple consecutive windows exceeds the threshold, it is marked as a resistance rise period, and adjacent rise periods are merged into a continuous period; a voltage decay threshold is set according to the detonation circuit design requirements, and the initial mean of the voltage is calculated. If the voltage remains below 90% of the reference voltage in subsequent periods, it is marked as a decay period; the voltage decay amount ( =Initial voltage - Final voltage), calculate the decay duration ( =End time -Start time), and perform energy loss attenuation gradient quantization on the voltage attenuation period to obtain energy loss attenuation gradient data: ;in, This refers to the voltage attenuation. The duration of decay; Used as a reference resistor; This data represents the energy loss attenuation gradient, describing the rate and extent of energy loss during voltage decay under a unit reference resistance. Analysis of resistance thermal accumulation location based on energy loss attenuation gradient data and time-series fluctuation data of resistance values: ;in, This indicates the temperature at the detonator resistor heat accumulation positioning point; The diameter of the detonator conductor; The thermal conductivity of the material is used to obtain the resistance heat accumulation location data. Based on the energy loss attenuation gradient data and the resistance heat accumulation location data, the detonator performance degradation model is fitted to obtain the detonator performance degradation data.

[0041] S24. Based on the accelerated detonation reliability data, estimate the detonator performance degradation data to obtain the detonation failure risk and the detonation failure probability.

[0042] See Figure 4 As shown, the performance parameter drift accumulation calculation is performed on the detonator performance degradation data to obtain the performance parameter drift data. Specifically, the parameter values ​​of the detonator under the initial normal state are obtained as the reference values, and the resistance value is... Voltage value As a monitoring parameter; for each time point t, calculate the difference between the parameter value and the baseline value: (Resistance drift); The resistance drift at time t represents the deviation between the actual resistance of the electronic detonator at time t and the initial normal state resistance reference value, reflecting the degree of resistance degradation and shift over time. The resistance reference value of the detonator under initial normal conditions (the standard resistance value of the detonator when its performance has not degraded, used as a comparison reference); (Voltage drift); The voltage drift at time t represents the deviation between the initial normal state voltage reference value of the detonator and the actual voltage at time t, reflecting the degree of voltage decay and shift over time. This is the voltage reference value of the detonator under its initial normal state (the standard voltage value when the detonator is working normally). The cumulative drift of the parameter over the time interval [0, T] is calculated using the time-weighted integral method. Cumulative resistance drift = ; Cumulative voltage drift = ; For discrete time series data (e.g., sampling interval Δt = 0.1s), the cumulative drift can be approximated as: Cumulative resistance drift = ; For the i-th sampling time The resistance drift (the resistance drift value at each sampling point after discretization). The number of sampling points for the discrete-time series (dividing the continuous time [0,T] into N sampling intervals); The sampling interval (the time difference between two adjacent sampling times) is the sampling interval.

[0043] Cumulative voltage drift = ; For the i-th sampling time Voltage drift (voltage drift value at each sampling point after discretization); The number of sampling points for the discrete-time series (dividing the continuous time [0,T] into N sampling intervals); The sampling interval (the time difference between two adjacent sampling times) is the sampling interval.

[0044] Based on the performance parameter drift data, a degradation trend regression analysis was performed on the detonator performance degradation data to obtain detonator performance degradation trend regression data; performance parameter drift data: cumulative drift (cumulative resistance drift, cumulative voltage drift); degradation trend regression analysis: by fitting the relationship between drift and time through regression models (linear, exponential, polynomial), the drift at future moments is predicted, reflecting the change in degradation rate.

[0045] Linear regression: Applicable to drift that changes at a constant rate over time (such as linear voltage decay).

[0046] Exponential regression: Applicable to accelerated changes in drift (such as nonlinear increases in resistance).

[0047] Multinomial regression: Applicable to complex nonlinear trends (such as degradation that is slow at first and then rapid).

[0048] Logarithmic transformation of the detonator performance degradation trend regression data was performed to obtain logarithmically transformed detonator performance degradation data; logarithmic transformation: the natural logarithm (ln) or the common logarithm (log) was taken from the detonator performance degradation trend regression data. 10 This transforms a non-linear relationship into a linear one.

[0049] Acquire batch characteristic data of electronic detonators; perform dynamic calculations on the performance degradation of electronic detonators across different batches based on accelerated detonation reliability data and batch characteristic data, obtaining dynamic performance degradation data per unit time; Batch characteristic data of electronic detonators: differences in production parameters and material properties (e.g., wire diameter, material thermal conductivity, initial resistance range) between different batches of detonators. Accelerated detonation reliability data: failure time distribution data obtained through accelerated testing (high temperature, high vibration). Dynamic performance degradation data: degradation rate per unit time (e.g., per second, per minute) obtained by dynamically calculating the performance degradation models of different batches of detonators.

[0050] Dynamic calculation combines batch characteristic data and initiation reliability acceleration data of electronic detonators to analyze the differences in degradation rate of different batches of detonators per unit time. Specifically, a correlation model between batch characteristics and degradation rate is established, with batch characteristics as input and degradation rate as output. Based on the Arrhenius model or inverse power law model, the initiation reliability acceleration data is converted into degradation rate correction coefficients, i.e., acceleration factors, under actual use conditions. The degradation rate per unit time is dynamically calculated as follows: Degradation rate per unit time = Basic degradation rate × Batch characteristic correction coefficient × Acceleration factor.

[0051] Based on batch characteristic data of electronic detonators, the probability of detonation failure is simulated using dynamic data on performance degradation, generating estimated detonation failure probability data. Specifically, based on batch characteristic data of electronic detonators, the cumulative risk assessment of detonation failure between different batches is performed on dynamic data on performance degradation, resulting in cumulative detonation failure risk data; cumulative failure risk: the cumulative probability of failure of a certain batch of detonators per unit time.

[0052] Failure Cumulative Risk Assessment: Statistically analyze failure cases from different batches within a specific time period and calculate the cumulative risk rate. ;in, Survival probability represents the probability that an electronic detonator will still function normally (not fail) at time t. For time The failure count represents the number of electronic detonators that failed at that point in time. For time The number of non-failed samples at that time, and the total number of electronic detonators that were still working normally at that time. Let i be the i-th time point, used to count the time scale of failure.

[0053] The initial initiation failure probability is calculated based on the cumulative risk data of initiation failure and the acceleration data of initiation reliability. The initial failure probability calculation involves converting the failure time under test conditions into the failure time under actual service conditions, using the acceleration data of initiation reliability. ;in, To activate energy; Boltzmann's constant; For temperature; The failure time is the time it takes for the electronic detonator to fail in a real blasting environment, under actual use conditions. To accelerate the failure time under test conditions, it indicates the failure time measured in accelerated aging tests such as high temperature and high vibration. The thermodynamic temperature under actual operating conditions is expressed in Kelvin (K). The thermodynamic temperature under accelerated experimental conditions is expressed in Kelvin (K).

[0054] Based on the cumulative risk data of detonation failure and the initial detonation failure probability, the detonation reliability parameter correction calculation is performed to obtain the detonation reliability correction data; the reliability parameter correction calculation is performed by adjusting the model parameters (such as degradation rate and failure threshold) according to the actual performance degradation data.

[0055] Based on the detonation reliability correction data, a detonation failure probability simulation is performed to generate detonation failure probability estimation data; based on the detonation failure probability estimation data, a detonation failure risk estimation is performed to obtain the detonation failure probability.

[0056] Failure probability simulation: Based on the corrected model parameters, a failure probability distribution is generated through statistical simulation, and a failure time distribution is generated using Monte Carlo simulation. Samples are drawn from the corrected parameter distribution (such as degradation rate and failure threshold) to simulate the degradation path. Calculate the failure time t, the number of times to repeat, and the distribution of failure times is statistically analyzed; The resistance value of the electronic detonator at time t reflects the resistance state after degradation over time (i.e., the real-time value of the resistance as it degrades). The resistance reference value is the initial normal state of the detonator, and the standard resistance value of the electronic detonator when no performance degradation has occurred (as a reference for degradation comparison). The rate of resistance degradation describes how quickly the resistance increases (performance degrades) per unit time. For time, the duration from the initial moment to the current moment (used to quantify the time span of degradation). This is the failure time of an electronic detonator, which is the time required for the resistance to degrade to the failure threshold (beyond this time, the detonator cannot be reliably detonated). This is the failure threshold for the resistor. When the detonator resistance reaches this value, it is determined to be a performance failure (unable to meet the detonation reliability requirements).

[0057] Step 3: Map the safety threshold of the detonation parameters according to the probability of detonation failure to obtain the safety range of the detonation parameters; optimize the detonation system parameters based on the detonator performance degradation data based on the safety range of the detonation parameters to obtain the detonation parameter optimization design data; adjust the detonation energy based on the detonation parameter optimization design data to obtain the detonation energy compensation data.

[0058] S31. Normalize the detonation failure probability to obtain normalized detonation failure probability data.

[0059] S32. Based on the normalized data of detonation failure probability, the detonation reliability acceleration data is mapped to the safety threshold of detonation parameters between different reliability levels to obtain the safety range of detonation parameters.

[0060] Reliability rating 99%; detonation voltage 3.5-4.5V, resistance 2.0-3.0Ω; Reliability rating 95%; detonation voltage 3.0-5.0V, resistance 1.8-3.2Ω; Reliability level 90%: detonation voltage 2.5-5.5V, resistance 1.5-3.5Ω.

[0061] S33. Perform initiation system compatibility matching based on the safe range of initiation parameters to obtain system compatibility matching data.

[0062] Detonation parameter safety range: The safe range of parameters such as voltage and resistance mapped according to the failure probability.

[0063] System compatibility: Whether the components of the detonation system (detonator, detonator, wire, network topology) can operate stably within the safe range.

[0064] System compatibility matching data: The compatibility results of each component of the detonation system (such as detonators, wires, and detonators) within the safe range (such as voltage adjustment range and resistance tolerance).

[0065] S34. Optimize the initiation system parameters based on the detonator performance degradation data according to the system compatibility matching data to obtain optimized design data for the initiation parameters. S34 includes the following steps: S341. Perform initiation timing loss compensation matching based on the system compatibility matching data to obtain initiation timing loss compensation data; calculate the expected response time offset for each detonator based on the detonator performance degradation data (such as voltage attenuation and resistance rise rate); compensation strategies include early triggering and dynamic delay; early triggering is to trigger high-resistance drift detonators in advance to compensate for their response delay; dynamic delay is to dynamically adjust the delay time of the trigger signal according to the voltage attenuation gradient. S342. Perform parameter dispersion difference analysis on the detonator performance degradation data to obtain detonator performance parameter dispersion difference data; identify the differences in performance degradation among different detonators to provide a basis for group optimization; calculate the dispersion index for resistance dispersion and voltage dispersion; resistance dispersion is the standard deviation of the resistance drift of all detonators; voltage dispersion is the variance of the voltage attenuation; group detonators with similar dispersion into the same group and adopt the same compensation strategy. Detonators with high discreteness are individually marked, and their triggering parameters are adjusted preferentially. S343. Based on the discreteness difference data of detonator performance parameters, the synchronization matching of the detonation system is performed to obtain the synchronization matching data of the detonation system; the deviation Δt between the expected arrival time and the actual arrival time of the triggering signal of each detonator is calculated. The synchronization strategy includes a time synchronization protocol and hardware compensation; the time synchronization protocol uses the IEEE 1588 Precision Time Protocol (PTP) to synchronize the triggering signal; the hardware compensation involves connecting a capacitor in parallel next to the detonator with high resistance discreteness to adjust the charging time. S344. Based on the discrete difference data of detonator performance parameters and the synchronization matching data of the initiation system, the initiation network topology parameters are designed to obtain the initiation network topology parameters; S344 includes the following steps: obtaining blasting engineering design data; extracting initiation network paths from the blasting engineering design data to obtain initiation network path data; initiation network path extraction involves extracting detonator layout, conductor length, and node positions from the blasting engineering design data; based on the discrete difference data of detonator performance parameters, performing detonator performance parameter difference analysis on the initiation network path data to obtain the performance parameter differences between initiation network paths; performance parameter difference analysis involves calculating the path... The resistance difference ΔR and voltage difference ΔV of the detonator are calculated. Based on the synchronization matching data of the detonation system, the performance parameter differences are matched using detonation timing flow rate to obtain detonation timing flow rate matching data. The performance parameter differences are then subjected to parameter fluctuation behavior learning to obtain performance parameter fluctuation behavior learning data. Based on the detonation system synchronization matching data and the detonation timing flow rate matching data, the performance parameter fluctuation behavior learning data is used to perform intelligent detonation reliability matching to obtain intelligent detonation reliability matching data. Finally, based on the detonation timing flow rate matching data, the detonation system synchronization matching data, and the intelligent detonation reliability matching data, the detonation network topology parameters are designed to obtain the detonation network topology parameters.Topology optimization strategies include resistance equalization and electromagnetic shielding. Resistance equalization involves increasing the cross-sectional area of ​​the conductor on paths with high resistance; electromagnetic shielding involves adding a shielding layer to paths with strong electromagnetic interference. S345. Based on the detonation timing loss compensation data and the detonation network topology parameters, optimize the detonation system parameters to obtain optimized detonation parameter design data. Use a genetic algorithm or linear programming to optimize the following objectives: minimize synchronization error (Δt≤0.1ms), minimize energy loss (cumulative resistance ≤10Ω), and maximize system reliability (failure probability ≤5%). The optimized detonation parameter design data includes the optimized triggering timing table, network topology diagram, and energy compensation values.

[0066] S35. Based on system compatibility matching data and detonation parameter optimization design data, the detonation energy is adjusted to obtain detonation energy compensation data. The energy compensation data consists of adjusted energy parameters such as voltage, current, and charging time, ensuring reliable detonator triggering.

[0067] The initiation energy adjustment process is as follows: Calculate the energy requirement for each detonator based on the optimized parameters (such as resistance and voltage). ;in, Detonation energy (the energy required to trigger the detonator); Trigger voltage; This refers to the detonator resistor (the resistor in the triggering circuit). This refers to the charging time (the duration of the trigger voltage). System compatibility constraint analysis ensures that the adjusted energy is within the physical limits of the system components; The energy compensation value is dynamically adjusted based on real-time monitoring data. The strategies include voltage compensation, time compensation, and group compensation. Voltage compensation increases the trigger voltage of detonators with rising resistance. Time compensation extends the charging time to compensate for energy loss. Group compensation adjusts the energy parameters of detonator groups with high dispersion individually. In a multi-detonator system, energy is evenly distributed to avoid local overload.

[0068] Step 4: Formulate a dynamic control strategy for electronic detonator detonation based on the optimized design data of detonation parameters and the detonation energy compensation data. The resulting dynamic control strategy for electronic detonator detonation is then sent to the terminal to execute the dynamic control method for electronic detonator detonation based on performance monitoring.

[0069] The beneficial effects of this embodiment are as follows: real-time monitoring and dynamic control improve the safety and reliability of electronic detonator initiation; accurately identify abnormal fluctuations in environment and performance, quantify the probability of failure and optimize initiation parameters to reduce blasting risk; data-driven intelligent compensation strategies (voltage / time / group compensation) adapt to complex working conditions and extend equipment life; dynamic topology optimization and energy balance distribution reduce local overload and ensure the collaborative efficiency of multiple detonators; and the overall intelligent and refined management of the blasting process is realized, taking into account both safety and efficiency.

[0070] Example 2: See Figure 2 As shown, the electronic detonator initiation dynamic control system based on performance monitoring in this embodiment includes: Data acquisition module: used to acquire real-time performance data of electronic detonators through sensors, including environmental stress parameters, detonator performance parameters and time dimension information; Feature extraction module: used to extract dynamic time-series features of the detonation voltage and resistance value from the real-time performance data, and obtain voltage time-series fluctuation data and resistance value time-series fluctuation data; Performance degradation analysis module: used to fit the detonator performance degradation model based on the voltage time-series fluctuation data and resistance value time-series fluctuation data to obtain detonator performance degradation data; Failure risk estimation module: used to estimate the initiation failure risk of the detonator performance degradation data and obtain the initiation failure probability; Parameter optimization module: used to map the safety threshold of the detonation parameters according to the detonation failure probability, obtain the safety range of the detonation parameters, and optimize the detonation system parameters based on the detonator performance degradation data based on the safety range, to obtain the detonation parameter optimization design data; Energy adjustment module: used to perform initiation energy adjustment processing based on the optimized design data of the initiation parameters to obtain initiation energy compensation data; Control strategy formulation module: used to formulate dynamic control strategies for electronic detonator detonation based on the detonation parameter optimization design data and the detonation energy compensation data; Execution terminal: used to receive and execute the dynamic control strategy.

[0071] The beneficial effects of this embodiment are as follows: This system improves the safety and reliability of electronic detonator detonation by combining real-time monitoring and dynamic control; multi-dimensional data acquisition and time-series feature extraction accurately identify environmental and performance anomalies; performance degradation models and failure probability prediction enable risk prediction; parameter optimization and energy compensation dynamically adapt to degradation states to ensure detonation stability; closed-loop control strategies effectively cope with complex working conditions, reduce failure risks, and significantly improve the safety and efficiency of blasting projects.

[0072] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

[0073] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to specific implementations. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A dynamic control method for electronic detonator initiation based on performance monitoring, characterized in that, The process is as follows: Step 1: Collect real-time performance data of electronic detonators through sensors, extract dynamic time-series characteristics of detonation voltage and resistance value, and obtain voltage time-series fluctuation data and resistance value time-series fluctuation data. Step 2: Fit the detonator performance degradation model based on voltage time-series fluctuation data and resistance value time-series fluctuation data to obtain detonator performance degradation data, and estimate the detonation failure risk based on the detonator performance degradation data to obtain the detonation failure probability. Step 3: Map the detonation parameters to safety thresholds based on the detonation failure probability to obtain the detonation parameter safety range; Based on the safe range of the initiation parameters, the initiation system parameters are optimized using the detonator performance degradation data to obtain initiation parameter optimization design data. Based on the optimized design data of the initiation parameters, the initiation energy is adjusted to obtain initiation energy compensation data. Step 4: Based on the optimized design data of the detonation parameters and the detonation energy compensation data, formulate a dynamic control strategy for the electronic detonator detonation, and send the dynamic control strategy for the electronic detonator detonation to the terminal for execution.

2. The method for dynamic control of electronic detonator initiation based on performance monitoring according to claim 1, characterized in that, The real-time performance data of the electronic detonator includes environmental stress parameters, detonator performance parameters, and a time dimension; the environmental stress parameters include real-time monitoring values ​​of temperature, humidity, vibration acceleration, and electromagnetic interference intensity; the detonator performance parameters include real-time voltage values ​​of the initiation circuit and detonator resistance values; the time dimension is that all data are timestamped and form a continuous time series.

3. The method for dynamic control of electronic detonator initiation based on performance monitoring according to claim 1, characterized in that, The dynamic temporal feature extraction includes: Time-domain analysis: The sliding window method is used to calculate statistical characteristics, quantify the short-term fluctuation trend of parameters, and identify periods of abnormal fluctuations; Frequency domain analysis: Converting time-series data into frequency domain signals using Fast Fourier Transform to extract the dominant frequency, amplitude, and spectral energy characteristics; Correlation analysis: Calculate the correlation between environmental stress parameters and detonator performance parameters, and classify the correlation strength based on the absolute value of the correlation coefficient and the p-value.

4. The method for dynamic control of electronic detonator initiation based on performance monitoring according to claim 1, characterized in that, The detonator performance degradation model fitting includes: Analyze the time-series fluctuation data of resistance values ​​to identify the periods of resistance value increase; Based on the timing fluctuation data of the detonation voltage, the voltage decay period is analyzed to obtain the voltage decay period; Calculate voltage attenuation and attenuation duration to quantify energy loss attenuation gradient data; Based on the energy loss attenuation gradient data, resistance thermal accumulation location analysis is performed on the resistance value time-series fluctuation data to obtain resistance thermal accumulation location data. Based on the energy loss attenuation gradient data and the resistance heat accumulation location data, a detonator performance degradation model is fitted to obtain detonator performance degradation data.

5. The method for dynamic control of electronic detonator initiation based on performance monitoring according to claim 1, characterized in that, The assessment of the risk of detonation failure includes: The detonator performance degradation data is calculated by accumulating the performance parameter drift to obtain the performance parameter drift data; Based on the drift data of the performance parameters, a regression analysis of the degradation trend of the detonator performance was performed to obtain the detonator performance degradation trend regression data. Logarithmic transformation of the detonator performance degradation trend regression data was performed to obtain logarithmically transformed detonator performance degradation data; Based on the batch characteristic data and detonation reliability acceleration data of electronic detonators, the logarithmic transformation data of the detonator performance degradation is used to perform dynamic calculations of electronic detonator performance degradation between different batches, so as to obtain dynamic performance degradation data per unit time. Based on the batch characteristic data of electronic detonators, the initiation failure probability is simulated using the dynamic data of performance degradation, and initiation failure probability estimation data is generated. Based on the estimated detonation failure probability data, the risk of detonation failure is estimated to obtain the detonation failure probability.

6. The method for dynamic control of electronic detonator initiation based on performance monitoring according to claim 1, characterized in that, The optimization of the detonation system parameters includes: Based on the system compatibility matching data, the detonation timing loss compensation matching is performed to obtain the detonation timing loss compensation data. A parameter dispersion difference analysis was performed on the detonator performance degradation data to obtain the dispersion difference data of detonator performance parameters; Based on the discrete difference data of the detonator performance parameters, the synchronization of the detonation system is matched to obtain the synchronization matching data of the detonation system. Based on the discrete difference data of the detonator performance parameters and the synchronization matching data of the initiation system, the initiation network topology parameters are designed to obtain the initiation network topology parameters. Based on the initiation timing loss compensation data and the initiation network topology parameters, the initiation system parameters are optimized to obtain initiation parameter optimization design data.

7. The method for dynamic control of electronic detonator initiation based on performance monitoring according to claim 1, characterized in that, The detonation energy adjustment process includes: Calculate the energy requirement for each detonator based on the optimized parameters; Perform system compatibility constraint analysis to ensure that the adjusted energy is within the physical limits of the system components; The energy compensation value is dynamically adjusted based on real-time monitoring data. The dynamic adjustment strategy includes voltage compensation, time compensation, and group compensation. Energy is evenly distributed in a multi-detonator system to avoid local overload.

8. The method for dynamic control of electronic detonator initiation based on performance monitoring according to claim 1, characterized in that, The dynamic control strategy includes detonation timing adjustment, energy compensation triggering, and synchronization control. The dynamic control strategy is transmitted to the terminal via wireless or wired means. The terminal includes a detonator, a sensor, and an actuator.

9. A dynamic control system for electronic detonator initiation based on performance monitoring, used to implement the dynamic control method for electronic detonator initiation based on performance monitoring as described in claim 1, characterized in that, The system includes: Data acquisition module: used to acquire real-time performance data of electronic detonators through sensors, including environmental stress parameters, detonator performance parameters and time dimension information; Feature extraction module: used to extract dynamic time-series features of the detonation voltage and resistance value from the real-time performance data, and obtain voltage time-series fluctuation data and resistance value time-series fluctuation data; Performance degradation analysis module: used to fit the detonator performance degradation model based on the voltage time-series fluctuation data and resistance value time-series fluctuation data to obtain detonator performance degradation data; Failure risk estimation module: used to estimate the initiation failure risk of the detonator performance degradation data and obtain the initiation failure probability; Parameter optimization module: used to map the safety threshold of the detonation parameters according to the detonation failure probability, obtain the safety range of the detonation parameters, and optimize the detonation system parameters based on the detonator performance degradation data based on the safety range, to obtain the detonation parameter optimization design data; Energy adjustment module: used to perform initiation energy adjustment processing based on the optimized design data of the initiation parameters to obtain initiation energy compensation data; Control strategy formulation module: used to formulate dynamic control strategies for electronic detonator detonation based on the detonation parameter optimization design data and the detonation energy compensation data; Execution terminal: used to receive and execute the dynamic control strategy.

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