Anti-electromagnetic interference dynamic threshold control system and method for electronic exploder

Abstract

The invention relates to the technical field of electronic exploder control, in particular to an anti-electromagnetic interference dynamic threshold control system and method for an electronic exploder, and aims to solve the problem that the electronic exploder is easily interfered in a complex electromagnetic environment to cause false triggering or missing detection. According to the system and the method, surrounding electromagnetic signals are acquired, time domain, frequency domain, waveform distortion and interference source features are extracted and fused to generate clustering feature vectors, and the signals are divided into pure interference, pure detonation control and superposed signals by using an improved K-means algorithm; and based on the clustering result and the interference intensity, adjusting an identification threshold in real time through a staged dynamic threshold adjustment formula, introducing a multi-dimensional reference factor of historical interference data after the interference is weakened, and stably recovering the threshold to a normal level by adopting a staged adaptive recovery mechanism. According to the system and the method, the anti-interference capability of the electronic detonator in a complex electromagnetic environment can be remarkably improved, and the safety and the accuracy of detonation operation are ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electronic initiator control, more particularly, it relates to an electronic initiator anti-electromagnetic interference dynamic threshold control system and method. BACKGROUND

[0002] Electronic initiators are widely used in industrial blasting, engineering construction and other scenarios, and their safe and reliable operation is directly related to construction safety.

[0003] However, there are often complex electromagnetic interferences around electronic initiators, such as periodic electromagnetic signals generated by industrial equipment (motors, high-frequency furnaces, etc.), high-frequency pulse interference of radio waves, and transient strong electromagnetic pulses of natural phenomena such as lightning. These interference signals may have similarities in frequency, amplitude, and other characteristics with the initiation control signal, which is easy to be misidentified as an effective initiation command, leading to false triggering; at the same time, if the intensity of the interference signal is too large, it may mask the real initiation control signal, leading to missed detection.

[0004] Traditional electronic initiators use fixed thresholds for signal recognition, and existing dynamic recognition methods do not consider the stage characteristics of blasting: when the threshold is set too high, weak intensity effective initiation signals may be missed; when the threshold is set too low, it is easy to be triggered by interference signals, which seriously affects the safety and reliability of initiation.

[0005] Therefore, there is an urgent need for a technical solution that can dynamically adjust the recognition threshold according to electromagnetic interference to solve the problems of poor threshold adaptability and weak anti-interference ability. SUMMARY

[0006] In view of the deficiencies in the prior art, the purpose of the present application is to provide an electronic initiator anti-electromagnetic interference dynamic threshold control system and method.

[0007] To achieve the above purpose, the present application provides the following technical solutions: The electronic initiator anti-electromagnetic interference dynamic threshold control method has the following process: Step 1, signal acquisition and preprocessing.

[0008] Acquire electromagnetic signals and preprocess them; Step 2, feature extraction and fusion.

[0009] Extract the time domain features, frequency domain features, waveform distortion features and interference source features of the preprocessed signals, and perform feature fusion processing to form a clustering feature vector; Step 3, signal clustering analysis.

[0010] Using the formed clustering feature vector, the signals are divided into three categories: pure interference signals, pure initiation control signals and superimposed signals, by improving the K-means algorithm; Step four, dynamic threshold adjustment.

[0011] The dynamic threshold adjustment formula is introduced to calculate the minimum signal intensity threshold value of the electronic initiator to identify the effective initiation control signal. Step five, dynamic threshold recovery.

[0012] When the electromagnetic interference is weakened or disappeared, the threshold value is adjusted from the anti-interference state to the level suitable for identifying the normal initiation control signal. The application also provides a dynamic threshold control system for electronic initiators against electromagnetic interference, which comprises a signal acquisition and preprocessing module, a feature extraction and fusion module, a signal clustering analysis module, a dynamic threshold adjustment module and a dynamic threshold recovery module. Signal acquisition and preprocessing module: acquires and preliminarily processes electromagnetic signals, and obtains standardized signal data through band-pass filtering, denoising and smoothing and normalization processing. Feature extraction and fusion module: used for extracting and integrating signal features, first extracting multi-dimensional features such as time domain, frequency domain, waveform distortion and interference source, then fusing them into "main frequency-amplitude" deviation, comprehensive waveform matching degree, interference stability index, etc. Signal clustering analysis module: classifies signals, directly classifies them by fusion feature values in simple environment, and classifies them into three categories of pure interference, pure initiation control and superimposed signals in complex environment through an improved K-means algorithm combined with clustering feature vectors, and dynamically updates the cluster center. Dynamic threshold adjustment module: dynamically adjusts the threshold value of the initiator to identify effective signals, according to the "clustering category-threshold adjustment amplitude-interference intensity level" rule, divides it into initialization, signal standby, initiation instruction and post-initiation stages, and calculates and limits the threshold value range by using the threshold value calculation formula and weight calculation formula. Dynamic threshold recovery module: recovers the threshold value after the interference is weakened, calculates the time decay similarity factor, recovery lag coefficient and authenticity verification factor by means of the historical interference database, divides it into fast adjustment and stable convergence stages, and makes the threshold value smoothly recover to the normal level.

[0013] Further, the specific process of feature fusion processing in step two is as follows: The time-domain-frequency-domain feature fusion calculates a deviation degree of a "dominant frequency-amplitude" feature pair from a "dominant frequency-amplitude" feature pair of a standard initiation control signal; the waveform feature fusion fuses the Euclidean distance and the waveform similarity in the waveform distortion feature to obtain a comprehensive waveform matching degree; and the interference source feature fusion fuses the frequency stability and the pulse repetition period in the interference source feature to obtain an interference stability index.

[0014] Further, the dynamic threshold adjustment formula in step four is as follows: ; wherein, is an adjusted threshold value, is a reference threshold value, is an interference intensity coefficient, is a clustering category coefficient, is an adjustment coefficient.

[0015] Further, the interference intensity coefficient in the dynamic threshold adjustment formula is calculated by a peak value of the amplitude and a spectrum entropy of the signal; the clustering category coefficient is determined according to a clustering result and is calculated by using an inverse sigmoid function.

[0016] Further, the adjustment coefficient in the dynamic threshold adjustment formula is calculated according to stage features of four stages, specifically including an initialization stage, a signal standby stage, an initiation instruction stage and a post-initiation stage.

[0017] Further, the multi-dimensional reference factor of the historical interference data in step five specifically includes: a time decay similarity factor , and a specific calculation formula is as follows: ; wherein, represents a time decay coefficient, is a time difference between an interference occurrence timestamp and a current time stamp, is a decay coefficient; represents a type similarity, and a cosine similarity of a spectrum feature vector is calculated; a recovery lag coefficient , and a calculation method is as follows: ; wherein, ; a value range ; is a lag weight of a single historical data, wherein , is a maximum value in a database; a reality verification factor : combine the fluctuation characteristics of historical interference, calculate the matching degree of the current interference weakening trend and the historical similar interference attenuation trend.

[0018] Further, the phased and adaptive threshold recovery mechanism is specifically as follows: According to the authenticity check factor, the threshold recovery phase is divided into a rapid adjustment phase and a stable convergence phase, in the rapid adjustment phase, the threshold is rapidly lowered but a certain safety margin is reserved, and the formula is used to calculate the current threshold , wherein is the influence degree of the quantized interference intensity on the threshold adjustment, is the adjustment value when the interference is the strongest, is the current interference intensity, is the peak intensity of the current interference; in the stable convergence phase, the threshold slowly converges to the reference threshold , and the formula is used to calculate the threshold , wherein is a convergence coefficient, is the time length of entering the stable phase; is a time attenuation similarity factor. Compared with the prior art, the present application has the following beneficial effects: 1. Precise threshold control is realized by combining the characteristics of the blasting phase: the present application designs a phased weight coefficient calculation mechanism by integrating the feature requirements of each phase (initialization, signal standby, detonation instruction, post-detonation) of blasting. This dynamic threshold adjustment method realizes the targeted optimization of anti-interference protection and effective signal recognition in each phase, avoiding pre-detonation false triggering, ensuring reliable reception of detonation instructions, and preventing secondary false operation after detonation.

[0019] 2. Adaptive threshold recovery: the present application introduces multi-dimensional reference factors (time attenuation similarity factor, recovery lag coefficient, authenticity check factor) of historical interference data, and designs a two-stage recovery mechanism of rapid adjustment and stable convergence; through historical data correlation analysis, the threshold is rapidly lowered with a safety margin when the interference is initially weakened, and slowly converges to the reference threshold when the attenuation trend is stable, avoiding false recovery caused by temporary interference pause, ensuring that the threshold adapts to normal detonation signal recognition in time after the interference disappears, and balancing safety protection and response efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 It is a flowchart of the dynamic threshold control method of the electronic initiator against electromagnetic interference; Figure 2 It is a schematic diagram of the present application for fusing the multi-dimensional features obtained by the feature extraction module; ​​Figure 3 Module block diagram of the dynamic threshold control system of the electromagnetic interference resistant electronic initiator of the application. DETAILED DESCRIPTION

[0021] Embodiment one, with reference to Figure 1 The dynamic threshold control method of the electromagnetic interference resistant electronic initiator of the embodiment is as follows: Step one, signal acquisition and preprocessing.

[0022] Electromagnetic signal acquisition: a wideband magnetic sensor (such as an Anisotropic Magnetoresistance sensor, sampling frequency ≥ 1 MHz) is used to cover the 0.1 Hz-1 GHz frequency band, which can capture low-frequency magnetic fields (such as power line 50 Hz interference) and high-frequency pulse interference (such as radio waves) propagating at a distance. To improve the comprehensiveness of the acquisition, three magnetic sensors with different orientations are set up to collect magnetic field signals in the X, Y and Z axes respectively, and through synthetic processing, the spatial three-dimensional magnetic field signal distribution is obtained, avoiding signal omission caused by single direction acquisition; Signal preprocessing: through band-pass filtering, direct current drift and ultra-high frequency noise are removed, and the filter circuit uses an active filter with adjustable cutoff frequency, which can automatically adjust the filter parameters according to the main interference frequency range found in the subsequent feature extraction. The signal is denoised and smoothed, and for sudden signals (such as lightning interference), it is quickly tracked, and for smooth signals (such as industrial equipment interference), it is deeply smoothed to retain the waveform details of the effective signal. In addition, signal normalization processing is added to map signals of different amplitude ranges to the 0-10V interval, which facilitates the standardization processing of subsequent feature extraction and clustering analysis; Step two, feature extraction and fusion.

[0023] S21, multi-dimensional feature extraction is performed on the preprocessed signal: Time domain features: signal amplitude peak value, duration (such as the initiation control signal is usually a 10ms±2ms rectangular wave, and the interference signal may last for several seconds or be a transient pulse); Frequency domain features: main frequency components are extracted through FFT transformation (such as the initiation control signal main frequency is fixed at 10kHz, and the interference may contain 200kHz, 500kHz, etc. stray frequency); Waveform distortion features: calculate the Euclidean distance of the signal and the standard initiation control waveform (the distance value increases significantly when interference is superimposed), and the waveform similarity of the signal (using the Dynamic Time Warping (DTW) algorithm to calculate); Interference source characteristics: supplement the extraction of the signal’s “frequency stability” (e.g., high frequency jump rate of radio signals, stable frequency of industrial equipment signals) and “pulse repetition period” (e.g., lightning interference is a single pulse, motor interference is a periodic pulse). S22, such as Figure 2 As shown, the multi-dimensional features obtained from the feature extraction module are fused: Time-frequency domain feature fusion: The peak amplitude in the time domain is fused with the dominant frequency component in the frequency domain to construct a "dominant frequency-amplitude" feature pair. Because the detonation control signal has a fixed dominant frequency and a relatively stable amplitude range, while interference signals often do not have this correspondence between their dominant frequency and amplitude. By calculating the deviation of this feature pair from the standard detonation control signal's "dominant frequency-amplitude" feature pair, the smaller the deviation, the more likely the signal is to be a valid detonation control signal. The deviation calculation formula is: ; in The current signal frequency. The standard detonation control signal frequency, This represents the current peak signal amplitude. This represents the peak value of the standard detonation control signal. Waveform Feature Fusion: This method fuses the Euclidean distance and waveform similarity features from waveform distortion characteristics to obtain a comprehensive waveform matching degree. The Euclidean distance reflects the overall difference between the signal waveform and the standard waveform, while waveform similarity focuses more on the morphological similarity of the waveforms. Combining the two provides a more comprehensive assessment of the waveform matching degree. The formula for calculating the comprehensive waveform matching degree is as follows: ; in For Euclidean distance, For waveform similarity, The closer the value is to 1, the higher the waveform matching degree. Interference source feature fusion: Frequency stability and pulse repetition period are fused from the interference source features to obtain an interference stability index. Signals with high frequency stability and a fixed pulse repetition period are more likely to originate from stable interference sources such as industrial equipment, while signals with high frequency hopping rates and no fixed pulse repetition period are more likely to originate from unstable interference sources such as radio waves. The formula for calculating the interference stability index is: ; in To ensure frequency stability (values ​​ranging from 0 to 1, with a higher stability value closer to 1), the signal's main frequency is sampled multiple times within a fixed-width time window (100ms in this example), and the standard deviation of these main frequency data is calculated. and mean Then through the formula The calculation yielded the result. For the pulse repetition period coefficient of variation (the smaller the coefficient of variation, the more stable the period), the pulse repetition period of the signal (10 in this embodiment) is sampled, and the standard deviation of these period values is calculated And the mean , and then calculated Multi-domain feature fusion: the deviation degree , the comprehensive waveform matching degree , and the interference source stability index obtained by the above fusion are taken as three-dimensional feature components to form the final clustering feature vector . Then, the fusion feature value F is calculated based on the vector, and the weights are determined according to the importance of each feature in distinguishing signal types. In this embodiment, the deviation degree weight is 0.4, the comprehensive waveform matching degree weight is 0.4, and the interference stability index weight is 0.2, and the calculation method is Step three, signal clustering analysis.

[0024] For a simple and stable magnetic field environment, the F value can be directly used for signal classification; For a complex and uncertain magnetic field environment, the clustering feature vector is used to divide the signals into three categories: pure interference signals, pure initiation control signals, and superimposed signals by improving the K-means algorithm; to improve the clustering effect, principal component analysis is performed on the fused features before clustering to further reduce the feature dimension and reduce the influence of redundant information on the clustering result; a clustering center updating mechanism is introduced, and the clustering center is recalculated every 100 groups of signal data, so that the clustering model can adapt to the slow changes of signal features in long-term use; Step four, dynamic threshold adjustment.

[0025] S41, the threshold value adjusted in the present application is the minimum signal strength threshold of the electronic initiator to recognize the effective initiation control signal (i.e. the signal amplitude needs to reach this threshold value to be judged as a possible initiation control instruction, and the signal below this threshold value is directly regarded as interference). Based on this, a three-dimensional mapping rule of "clustering category-threshold adjustment amplitude-interference intensity level" is established, wherein the interference intensity level is comprehensively evaluated as weak, medium and strong according to the signal amplitude peak value, spectral entropy and other features. The dynamic threshold adjustment formula is introduced: ; wherein, is the adjusted threshold value, is the reference threshold value, is the adjustment coefficient (calculated by refining the features in each stage)​​ is the interference intensity coefficient, is the clustering category coefficient; the specific calculation formula of each coefficient is as follows: Interference intensity coefficient: calculated by the amplitude peak value and the spectral entropy of the signal, the formula is: ; wherein, is the amplitude peak value of the signal, is the historical maximum amplitude peak value of the signal, and E is the spectral entropy of the signal, is the historical maximum spectral entropy of the signal. The value range of is When , it is weak interference, , it is medium interference, , it is strong interference; Clustering category coefficient: determined according to the clustering result, calculated by using the inverse sigmoid function: ; wherein is the adjustment coefficient (in this embodiment , the value is 5), is the fusion feature value; in this embodiment, when the signal is a pure interference signal, , the formula is substituted to obtain ; when the signal is a superimposed signal, , the formula is substituted to obtain ; when the signal is a pure initiation control signal, , the formula is substituted to obtain ; S42, fine calculation of each stage feature , when each stage is converted, smooth switching is realized by 10 ms linear transition (such as during the transition ), and the rules are as follows (the values in the formula can be adjusted according to the actual scene, and the adjustment result needs to meet the stage feature): S421, initialization stage (0~10s): strengthen anti-interference protection.

[0026] Stage feature: transient interference such as power-on impact and static electricity needs to be filtered, and the threshold needs to be kept at a high starting point; Weight calculation model: ( is the time in the stage, unit: s, range 0-10); (slightly improve clustering impact with time); Threshold range limit: the lower limit of the threshold is forcibly set , and there is no upper limit; S422, signal standby stage (10s~before detonation): balance sensitivity and safety.

[0027] Stage characteristics: need to consider anti-persistent interference and weak signal detection, threshold needs to adapt to the environment dynamically; Weight calculation model: For comprehensive waveform matching degree, range 0-1); Threshold range limit: the threshold is forced to be , out of range, take the boundary value; S423, detonation instruction stage (5s before detonation to instruction confirmation): priority to ensure effective signal identification.

[0028] Stage characteristics: need to reduce threshold, ensure effective instruction not missed, while preventing deception interference; Weight calculation model: ); The smaller, The greater, the sensitivity of effective signal clustering is enhanced); Threshold range limit: the upper limit of threshold is forced to be , avoid threshold too high to miss, the lower limit is not limited; S424, post-detonation stage (instruction confirmation to 3s after detonation): prevent secondary misfire.

[0029] Stage characteristics: need to deal with electromagnetic pulse generated by blasting, threshold is high first and then low gradually recover; Weight calculation model: For post-detonation time, unit: s, exponential decay); ); Threshold range limit: within 100ms after detonation (Hard protection), 100ms threshold lower limit ; Step five, dynamic threshold recovery.

[0030] The core goal of threshold recovery is to adjust the threshold from anti-interference state to the level of adapting to normal detonation control signal identification smoothly and safely when electromagnetic interference is weakened or disappeared, both to avoid misfire caused by too fast recovery of interference signal, and to prevent the recovery of too slow affecting the response efficiency of effective detonation control instruction. Combined with the characteristics of the suddenness, periodicity and type correlation of electromagnetic interference, the present application introduces multi-dimensional reference factors of historical interference data, designs a phased and adaptive threshold recovery mechanism, as follows: ​​​​​​S51, historical interference database construction: first, a dynamically updated historical interference database (data sources can be pre-blasting field survey data for a period of time, or to carry out small-scale targeted test blasting to obtain historical interference data) is established, and the key characteristic parameters of each interference event are stored, including: interference timestamp , interference intensity level (weak / medium / strong, corresponding to quantized values 1 / 2 / 3), interference duration (from the time when the interference appears to the time when it weakens to the weak level, unit: seconds), spectral feature vector (containing the main frequency and secondary frequency of the interference signal, representing the number of frequency components), interference type label (based on the type of clustering results of historical data, such as industrial equipment interference, radio interference, lightning interference, etc.); S52, historical reference factor calculation: based on the relevance of the current interference state and historical data, three core reference factors are calculated to reflect the influence of historical interference on the current recovery process; S521, time decay similarity factor : used to measure the time relevance and type similarity of the current interference and historical interference, the larger the value, the stronger the relevance of the historical interference and the current interference. Electromagnetic interference has certain spatio-temporal repeatability (such as the similar daily industrial equipment interference regularity in a certain area), and recent historical interference of the same type has more reference value for current recovery; the specific calculation formula is as follows: ; wherein represents the time decay coefficient, is the time difference (unit: hour), is the current timestamp, and the decay coefficient in the present embodiment ; represents the type similarity, which is calculated using the cosine similarity of the spectral feature vector, , is the spectral feature vector of the current interference; S522, recovery lag coefficient : reflects the lagging effect of historical interference on recovery speed. The greater the intensity and the longer the duration of historical interference, the more complex the interference environment in the region, and the stronger the threshold recovery lag; the calculation method is as follows: ; wherein to avoid a denominator of 0; the value range ; is the lag weight of a single historical data, wherein , To ensure that the historical maximum value in the database is met. ; S523, Authenticity Verification Factor This is used to determine the "authenticity" of the current interference reduction, avoiding false recovery caused by brief pauses in interference. It combines the fluctuation characteristics of historical interference (e.g., lightning interference is a single, sudden event, while industrial interference exhibits periodic fluctuations) to calculate the degree of matching between the current interference reduction trend and the attenuation trend of similar historical interferences. Extracting historical similar electromagnetic interference ( The decay curve characteristics are as follows: using the process of interference intensity decreasing from the peak to a weak level as a sample, the decay rate function is fitted. ; Calculate the attenuation rate function of the current interference. The similarity between the two is calculated using the dynamic time warping algorithm. The larger the value, the more the current decay trend conforms to historical patterns, and the higher the authenticity of the interference reduction. S53. Phased Threshold Recovery Process: Based on the above reference factors, the threshold recovery is divided into a rapid adjustment phase and a stable convergence phase. The switching between the two phases is determined by the authenticity verification factor. trigger( (When it enters a stable phase) S531, Rapid Adjustment Phase ( When interference initially weakens but the attenuation trend has not yet stabilized (such as in the initial stage after a sudden interference), the threshold is rapidly lowered while retaining a certain safety margin. The formula is: ; in, This embodiment is used to quantify the impact of interference intensity on threshold adjustment. , This is the current threshold (the adjustment value when the interference is strongest). Given the current interference intensity, This represents the peak intensity of the interference; this phase involves... The greater the historical interference, the more limited the downward adjustment range. The larger the adjustment, the more conservative the downward adjustment; S532, Stable Convergence Phase ( Once the decay trend stabilizes, the threshold moves towards the baseline threshold. Slow convergence, introducing a time decay similarity factor Optimize convergence rate: ; in, The convergence coefficient is 0.1 / h in this embodiment. The duration of the stable phase; The larger the historical relevance is, the slower the convergence is, and the frequent threshold fluctuation in the repeated interference environment is avoided; Embodiment two, refer to Figure 3 The dynamic threshold control system of the electronic initiator against electromagnetic interference comprises a signal acquisition and preprocessing module, a feature extraction and fusion module, a signal clustering analysis module, a dynamic threshold adjustment module and a dynamic threshold recovery module. The signal acquisition and preprocessing module acquires and preliminarily processes electromagnetic signals, and obtains standardized signal data through band-pass filtering, denoising and smoothing and normalization processing. The feature extraction and fusion module is used for extracting signal features and integrating them, first extracts multi-dimensional features such as time domain, frequency domain, waveform distortion and interference source, then fuses them into "main frequency-amplitude" deviation, comprehensive waveform matching degree, interference stability index and the like, forms a clustering feature vector and calculates a fusion feature value. The signal clustering analysis module classifies signals, directly classifies signals by the fusion feature value in a simple environment, and classifies signals into three categories of pure interference, pure initiation control and superimposed signals in a complex environment through an improved K-means algorithm combined with the clustering feature vector, and dynamically updates the clustering center. The dynamic threshold adjustment module dynamically adjusts the threshold value of the initiator to recognize effective signals, according to the "clustering category-threshold adjustment amplitude-interference intensity level" rule, divides the threshold value into initialization, signal standby, initiation instruction and post-initiation stages, and calculates and limits the threshold value range by using a threshold calculation formula and a weight calculation formula. The dynamic threshold recovery module recovers the threshold value after the interference is weakened, calculates a time decay similarity factor, a recovery lag coefficient and a reality verification factor by means of a historical interference database, divides the threshold value into a rapid adjustment stage and a stable convergence stage, and smoothly recovers the threshold value to a normal level. Through the above detailed description of the embodiments, the dynamic threshold control system and method of the electronic initiator against electromagnetic interference can collect electromagnetic signals, extract features and generate a clustering feature vector by fusion, divide signals into three categories of pure interference, pure initiation control and superimposed signals by using a K-means algorithm, adjust the recognition threshold value in real time by a phased dynamic threshold adjustment formula based on the clustering result and the interference intensity, introduce multi-dimensional reference factors of historical interference data after the interference is weakened, adopt a phased adaptive recovery mechanism to smoothly recover the threshold value to a normal level, significantly improve the anti-interference ability of the electronic initiator in a complex electromagnetic environment, and ensure the safety and accuracy of the initiation operation.

[0031] The above formulas are dimensionless values, and the preset parameters in the formulas are set by a person skilled in the art according to actual conditions.

[0032] The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, the above-described embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center through a wired or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0033] It should be understood that the size of the sequence number of each process described above in various embodiments of the present application does not mean the order of execution, and the execution order of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0034] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be realized in electronic hardware, or a combination of computer software and electronic hardware. Whether the functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0035] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0036] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the division of the above-described device embodiments is only a logical function division, and there can be another division manner for actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different units, or the among different units, can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0037] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, and the computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, and various other media that can store program codes.

[0038] The above describes only the specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A dynamic threshold control method for electromagnetic interference resistance in electronic detonators, characterized in that, The method flow is as follows: Step 1: Signal Acquisition and Preprocessing: Acquire electromagnetic signals around the electronic detonator and perform preprocessing; Step 2, Feature Extraction and Fusion: Extract time-domain features, frequency-domain features, waveform distortion features, and interference source features from the preprocessed signal, and perform feature fusion processing to generate clustering feature vectors; Step 3: Signal clustering analysis: Using the generated clustering feature vectors, the signals are divided into three categories—pure interference signals, pure detonation control signals, and superimposed signals—through an improved K-means algorithm. Step 4: Dynamic Threshold Adjustment: A dynamic threshold adjustment formula is introduced to calculate the minimum signal strength threshold for the electronic detonator to identify an effective detonation control signal; and the adjustment coefficient in the formula is dynamically calculated based on the characteristics of each stage. Step 5, Dynamic Threshold Recovery: When the electromagnetic interference weakens or disappears, a multi-dimensional reference factor of historical interference data is introduced, and a phased, adaptive threshold recovery mechanism is adopted to adjust the threshold from the anti-interference state to a level that is compatible with the identification of normal detonation control signals.

2. The dynamic threshold control method for electromagnetic interference resistance of an electronic detonator according to claim 1, characterized in that, The feature fusion process described in step two is as follows: Time-frequency domain feature fusion calculates the deviation between the "dominant frequency-amplitude" feature pair and the "dominant frequency-amplitude" feature pair of the standard detonation control signal; waveform feature fusion combines the Euclidean distance and waveform similarity in the waveform distortion features to obtain the comprehensive waveform matching degree; Interference source feature fusion combines the frequency stability and pulse repetition period of interference source features to obtain an interference stability index.

3. The dynamic threshold control method for electromagnetic interference resistance of an electronic detonator according to claim 1, characterized in that, The dynamic threshold adjustment formula mentioned in step four is as follows: ； in, The adjusted threshold As the baseline threshold, The interference intensity coefficient, Clustering category coefficients This is for adjusting the coefficient.

4. The dynamic threshold control method for electromagnetic interference resistance of an electronic detonator according to claim 3, characterized in that, The interference intensity coefficient in the dynamic threshold adjustment formula is calculated using the peak amplitude and spectral entropy of the signal; the clustering category coefficient is determined based on the clustering results and is calculated using the inverse sigmoid function.

5. The dynamic threshold control method for electromagnetic interference resistance of an electronic detonator according to claim 3, characterized in that, The adjustment coefficient mentioned in the dynamic threshold adjustment formula The calculation is based on the characteristics of the four stages, specifically including: initialization stage, signal waiting stage, detonation command stage, and post-detonation stage.

6. The dynamic threshold control method for electromagnetic interference resistance of an electronic detonator according to claim 1, characterized in that, The multi-dimensional reference factor for historical interference data mentioned in step five is calculated as follows: Using formula Calculate the time decay similarity factor ,in, Indicates the time decay coefficient. The time difference between the timestamp of the historical interference and the timestamp of the current moment. The attenuation coefficient; Indicates type similarity; Using formula Calculate the recovery lag coefficient value range ;in, ; It is the lag weight of a single historical data point, where , This represents the maximum historical interference intensity level and duration in the database. For the duration of historical interference, Historical interference intensity level; Authenticity verification factor Based on the fluctuation characteristics of historical interference, calculate the degree of matching between the current interference weakening trend and the historical attenuation trend of similar interference.

7. The dynamic threshold control method for electromagnetic interference resistance of an electronic detonator according to claim 1, characterized in that, The phased, adaptive threshold recovery mechanism is as follows: Based on the authenticity verification factor, the threshold recovery stage is divided into a rapid adjustment stage and a stable convergence stage. In the rapid adjustment stage, the threshold is rapidly lowered while retaining a certain safety margin, using the formula... Calculate the current threshold ,in, It quantifies the degree to which the intensity of the interference affects the threshold adjustment. This is the threshold adjustment value when the interference is strongest. Given the current interference intensity, This represents the peak intensity of the interference; the threshold during the stable convergence phase is adjusted towards the reference threshold. Slow convergence, using the formula Calculate the threshold ,in, The convergence coefficient is . The duration of the stable phase; This is the time decay similarity factor.

8. A dynamic threshold control system for electromagnetic interference suppression of an electronic detonator, used to implement the dynamic threshold control method for electromagnetic interference suppression of an electronic detonator as described in any one of claims 1-7, characterized in that, The system includes a signal acquisition and preprocessing module, a feature extraction and fusion module, a signal clustering analysis module, and a dynamic threshold adjustment and dynamic threshold recovery module. Signal acquisition and preprocessing module: Acquires and pre-processes electromagnetic signals, and obtains standardized electromagnetic signal data through bandpass filtering, noise reduction and smoothing and normalization. Feature extraction and fusion module: Extracts and integrates electromagnetic signal features. First, it extracts time domain, frequency domain, waveform distortion and interference source features, and then fuses them into "main frequency-amplitude" deviation, comprehensive waveform matching degree and interference stability index, forming cluster feature vectors and calculating fusion feature values; Signal clustering analysis module: Classifies signals. In simple environments, it directly classifies signals using fused feature values; in complex environments, it uses an improved K-means algorithm combined with clustering feature vectors to classify signals into three categories: pure interference, pure detonation control, and superimposed signals. Dynamic threshold adjustment module: Dynamically adjusts the signal strength threshold of the valid signal that the detonator recognizes. Based on the rule of "cluster category - threshold adjustment range - interference intensity level", it calculates and limits the threshold range using the dynamic threshold adjustment formula in the initialization, signal waiting, detonation command and post-detonation stages. Dynamic threshold recovery module: After the interference weakens, the threshold is restored. With the help of historical interference database, the time decay similarity factor, recovery lag coefficient and authenticity verification factor are calculated. The module is divided into rapid adjustment and stable convergence stages to make the threshold smoothly recover to the level that is compatible with the recognition of standard detonation control signals.

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