Programmable multi-stage energy regulation system based on load characteristic parameterization matching

Abstract

This invention relates to the core energy regulation technology field of electronic detonator electronic control modules, and discloses a programmable multi-level energy regulation system based on load characteristic parameterized matching. The system includes parameterized modeling, strategy generation, safety verification, real-time monitoring, and adjustment response modules. The modeling module receives baseline parameters of the physical characteristics of the controlled object and translates them into a feature dataset. The strategy generation module converts the feature dataset into a sequence of control excitation commands. The safety verification module generates a response permission signal based on identity attribute characteristics. The real-time monitoring module collects transient response signals and calculates the cumulative energy state quantity. The adjustment response module uses the cumulative energy state quantity to implement feedforward correction, causing the transient physical state of the controlled object to converge within the response tolerance band. This invention implements feedforward correction to offset nonlinear impedance drift, significantly improving the detonation accuracy and batch consistency of the control module. It can also be applied to production testing equipment, enhancing the adaptability to heterogeneous loads.

Description

Technical Field

[0001] This invention belongs to the core energy regulation technology field of electronic detonator electronic control module, and particularly relates to a programmable multi-level energy regulation system based on load characteristic parameter matching. Background Technology

[0002] With the high-quality development of civilian explosives, electronic detonators, as a core component of modern blasting engineering, directly determine the safety and efficiency of blasting projects through their detonation reliability. As the brain of electronic detonators, the energy regulation accuracy of the electronic control module is the key to achieving precise detonation. Currently, in precision energy excitation and industrial control regulation, ensuring that the energy supply strategy matches the physical characteristics of the load is the foundation for stable system operation. Especially in the field of electronic detonator detonation control, the impedance characteristics of the controlled load are greatly affected by production batches, material purity, and ambient temperature. Existing control systems typically set discrete energy output levels for the nominal impedance or sensitivity parameters of the load in the initial state, and achieve power adaptation for different specifications of actuators through preset fixed levels. This regulation method based on static parameter correlation is widely used in conventional industrial conditions.

[0003] During the high-speed energy injection process at the microsecond level, the electronic detonator head accumulates heat, causing a nonlinear shift in its impedance characteristics. The transient thermal resistance distortion caused by energy injection causes the initially set energy output level to deviate from the actual absorption range of the load in the middle and later stages of the injection process. This not only reduces energy utilization efficiency but also increases the risk of physical damage to the load. Although the industry has tried to improve robustness by refining the sampling range or adding end protection circuits, these methods are still static approximations or post-event interceptions. Although traditional barcode registration systems and management platforms have achieved identity recognition, they lack the ability to actively track and provide real-time feedback adjustment for the microscopic evolution trajectory inside the energy injection pulse. This makes it easy for the electronic control module of the electronic detonator to suffer from insufficient detonation energy or overload damage when facing complex heterogeneous loads, making it difficult to meet the high-precision requirements of integrated R&D, design, and production of electronic detonators.

[0004] Therefore, how to construct a regulation mechanism that can sense the evolution of the physical characteristics of the load and realize the dynamic trajectory correction of the energy strategy has become the technical problem to be solved by this invention. Summary of the Invention

[0005] This invention provides a programmable multi-level energy regulation system based on load characteristic parameter matching. This system is the core energy regulation unit of the electronic control module of an electronic detonator and can also be integrated into supporting instruments for electronic detonator production and testing. It includes: The parametric modeling module is used to receive baseline parameters of the physical properties of the controlled object and translate the baseline parameters into a standardized feature dataset; The strategy generation module is used to process the standardized feature dataset according to the parameterized matching model and map the standardized feature dataset into a sequence of control excitation instructions. The security verification module is used to obtain the identity attribute characteristics of the controlled object and generate a response permission signal based on the matching degree result between the identity attribute characteristics and the control stimulus command sequence. The real-time monitoring module is used to collect the transient response signal of the controlled object during the energy injection cycle and calculate the cumulative energy state of the controlled object based on the transient response signal. The adjustment response module has its input terminals connected to the output terminals of the strategy generation module, the security verification module, and the real-time monitoring module, respectively. When a response permission signal is received, it generates multi-level control excitations according to the control excitation command sequence and uses the accumulated energy state quantity to perform feedforward correction on the multi-level control excitations, so that the transient physical state of the controlled object converges to the preset response tolerance band.

[0006] Preferably, the internal rules for implementing feedforward correction in the adjustment response module include: step S21, using the accumulated energy state quantity as an addressing index to retrieve the corresponding nominal response reference value in the preset response model; step S22, calculating the characteristic offset between the real-time response value represented by the transient response signal and the nominal response reference value; step S23, calculating the feedforward compensation coefficient based on the characteristic offset, and using the feedforward compensation coefficient to modulate the duty cycle of the output signal to adjust the energy output amplitude of the multi-level control excitation, thereby offsetting the nonlinear impedance drift of the controlled object caused by energy accumulation.

[0007] Preferably, the standardized feature dataset includes: response impedance curves, sensitivity distribution coefficients, energy injection time constants, and physical geometric parameters.

[0008] Preferably, the security verification module is used to compare the batch features recorded in the identity attribute features with the preset adaptation features of the control incentive instruction sequence to achieve logical anchoring of heterogeneous controlled objects.

[0009] Preferably, the sampling frequency of the real-time monitoring module is not less than 1MHz, used to extract the high-frequency transient electrical parameter characteristics of the controlled object during the energy injection process from 10μs to 500μs.

[0010] Preferably, the adjustment response module is equipped with response circuit breaker logic, which is used to cut off the output of multi-level control excitation when the feature offset continues to exceed the preset safety tolerance limit.

[0011] Preferably, the control excitation command sequence has programmable properties, supporting the adjustment of the timing trajectory of energy injection according to the reference parameters of different batches.

[0012] Preferably, the adjustment response module includes a multi-channel digital-to-analog converter circuit, which is used to generate multi-level control excitation and to correct the output accuracy using a current sampling feedback loop.

[0013] Preferably, the adjustment response module includes a multi-stage power switch network, and the conduction combination of the multi-stage power switch network is controlled by a sequence of control excitation commands to achieve gradient adjustment of the output energy density.

[0014] Compared with existing technologies, the programmable multi-level energy regulation system based on load characteristic parameter matching of the present invention has the following advantages: 1. Significantly improves the detonation accuracy and reliability of the electronic control module of electronic detonators: In programmable multi-level energy regulation, a transient impedance evolution trajectory tracking mechanism is constructed to achieve dynamic response to nonlinear drift of load impedance within the energy injection cycle; by collecting voltage and current sampling sequences, the accumulated energy state quantity is calculated in real time, and this state quantity is used as an addressing index to obtain the nominal impedance reference threshold, thereby generating a feedforward compensation coefficient; this closed-loop regulation logic based on energy state migration enables the system to guide the load state to converge to the preset tolerance band by automatically modulating the duty cycle of the output signal when the physical characteristics of the load undergo high-frequency transient distortion; this regulation method overcomes the mismatch between static energy output and time-varying load state in traditional schemes, improving the precision of the energy transfer process and the robustness of system operation.

[0015] 2. Constructing a proactive protection system for the entire lifecycle of electronic detonators: By leveraging the synergistic coupling of parametric modeling, safety verification, and monitoring fine-tuning mechanisms, a proactive protection system is constructed throughout the entire process. The system extracts the physical digital characteristics of the load and generates matching instructions. Combined with hardware identification code verification, the system achieves alignment between the instructions and the load model. With real-time sampling at a frequency of 1MHz and abnormal offset fuse logic, a logical closed loop covering the entire process of parameter configuration, instruction verification, and excitation execution is formed. This multi-layered interlocking mechanism ensures that the system still has reliable operating boundaries when facing loads with significantly different physical characteristics, effectively avoiding the risk of load damage caused by inaccurate initial configuration or thermal feedback distortion, and achieving a technological leap from passive post-event interception to proactive process defense.

[0016] 3. Improve batch consistency and reduce production costs in electronic detonator mass production: By establishing a software-defined parameterized mapping architecture, the adaptive capability of the control system to loads with heterogeneous physical characteristics is enhanced. This control mode based on physical logic translation enables the electronic control module of the electronic detonator to automatically map and generate the optimal energy strategy according to the measured reference parameters of different batches or models of loads. This approach expands the adaptability of the control system while maintaining hardware architecture stability, reduces the reconstruction cost of the system when facing diverse load requirements, and ensures a high degree of resonance between energy output and load physical characteristics, significantly improving the pass rate and consistency of electronic detonator mass production.

[0017] 4. Can be directly integrated into electronic detonator production and testing equipment: The technical solution of this invention is not only applicable to the electronic control module of electronic detonators itself, but can also be integrated into electronic detonator production and testing instruments and functional software for precise testing and calibration of the energy response characteristics of electronic detonators, thereby further improving production and testing efficiency and accuracy. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the core module interaction and closed-loop control of the multi-level energy regulation system of this invention. Figure 2 This is a multi-dimensional logic diagram of the energy regulation system of the present invention, showing precise adaptation and stable response. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0020] It should be noted that all directional and positional terms used in this invention, such as: up, down, left, right, front, back, vertical, horizontal, inner, outer, top, low, lateral, longitudinal, center, etc., are only used to explain the relative positional relationship and connection between the components in a specific state (as shown in the accompanying drawings). They are only for the convenience of describing this invention and do not require that this invention be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention. In addition, the descriptions of "first," "second," etc., in this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated.

[0021] In the description of this invention, unless otherwise explicitly specified and limited, the terms installation, connection, and linking should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections; they can refer to direct connections or indirect connections through an intermediate medium; they can refer to the internal connection of two components. For those skilled in the art, the specific meaning of the above terms in this invention can be understood according to the specific circumstances.

[0022] In the description of this specification, references to the terms "an embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example, and the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0023] A programmable multi-level energy regulation system based on load characteristic parameter matching includes: The parametric modeling module is used to receive baseline parameters of the physical properties of the controlled object and translate the baseline parameters into a standardized feature dataset; The strategy generation module is used to process the standardized feature dataset according to the parameterized matching model and map the standardized feature dataset into a sequence of control excitation instructions. The security verification module is used to obtain the identity attribute characteristics of the controlled object and generate a response permission signal based on the matching degree result between the identity attribute characteristics and the control stimulus command sequence. The real-time monitoring module is used to collect the transient response signal of the controlled object during the energy injection cycle and calculate the cumulative energy state of the controlled object based on the transient response signal. The adjustment response module has its input terminals connected to the output terminals of the strategy generation module, the security verification module, and the real-time monitoring module, respectively. When a response permission signal is received, it generates multi-level control excitations according to the control excitation command sequence and uses the accumulated energy state quantity to perform feedforward correction on the multi-level control excitations, so that the transient physical state of the controlled object converges to the preset response tolerance band.

[0024] Preferably, the internal rules for implementing feedforward correction in the adjustment response module include: step S21, using the accumulated energy state quantity as an addressing index to retrieve the corresponding nominal response reference value in the preset response model; step S22, calculating the characteristic offset between the real-time response value represented by the transient response signal and the nominal response reference value; step S23, calculating the feedforward compensation coefficient based on the characteristic offset, and using the feedforward compensation coefficient to modulate the duty cycle of the output signal to adjust the energy output amplitude of the multi-level control excitation, thereby offsetting the nonlinear impedance drift of the controlled object caused by energy accumulation.

[0025] Preferably, the standardized feature dataset includes: response impedance curves, sensitivity distribution coefficients, energy injection time constants, and physical geometric parameters.

[0026] Preferably, the adjustment response module follows the formula when calculating the feature offset: Where D is the feature offset. The real-time response value is characterized by the transient response signal. This is the nominal response reference value.

[0027] Preferably, the security verification module is used to compare the batch features recorded in the identity attribute features with the preset adaptation features of the control incentive instruction sequence to achieve logical anchoring of heterogeneous controlled objects.

[0028] Preferably, the sampling frequency of the real-time monitoring module is not less than 1MHz, used to extract the high-frequency transient electrical parameter characteristics of the controlled object during the energy injection process from 10μs to 500μs.

[0029] Preferably, the adjustment response module is equipped with response circuit breaker logic, which is used to cut off the output of multi-level control excitation when the feature offset continues to exceed the preset safety tolerance limit.

[0030] Preferably, the control excitation command sequence has programmable properties, supporting the adjustment of the timing trajectory of energy injection according to the reference parameters of different batches.

[0031] Preferably, the adjustment response module includes a multi-channel digital-to-analog converter circuit, which is used to generate multi-level control excitation and to correct the output accuracy using a current sampling feedback loop.

[0032] Preferably, the adjustment response module includes a multi-stage power switch network, and the conduction combination of the multi-stage power switch network is controlled by a sequence of control excitation commands to achieve gradient adjustment of the output energy density.

[0033] Example 1: This example relates to the energy regulation process in a precision electronic detonator initiation control system. The controlled object is a finished electronic detonator that has been registered through an electronic detonator barcode registration mobile internet application system. Internally, it integrates a sensitive load consisting of a propellant head and a bridge wire. The physical characteristics of this load fluctuate with production batches and ambient temperature. Furthermore, within the microsecond-level energy injection cycle, the transient impedance of the bridge wire experiences nonlinear drift due to Joule heat accumulation. The parametric modeling module receives baseline parameters of the controlled object's physical characteristics. These parameters can be directly retrieved from the database of the integrated management platform for electronic detonator R&D, design, and production via barcode indexing. The baseline parameters include propellant head sensitivity. Bridge wire diameter The system obtains the response impedance curve and energy injection time constant, and converts the baseline parameters into a standardized feature dataset. The strategy generation module processes the standardized feature dataset according to the parameterized matching model, converting it into a control excitation command sequence. This sequence determines the target energy output level based on a preset delay time constraint Δt. The safety verification module acquires the identity attribute features of the controlled object and compares the batch features recorded in the identity attribute features with the preset adaptation features of the control excitation command sequence. When the two match, a response permission signal is generated. The real-time monitoring module monitors the energy injection cycle within the specified time. The sampling frequency is used to collect the transient response signal of the controlled object during the energy injection process from 10μs to 500μs. The transient response signal includes voltage sampling data sequence and current sampling data sequence.

[0034] The discrete-time integral of the product of the voltage and current sampled data sequences determines the cumulative energy state at the current moment, while the transient response value at the current moment is obtained through division. The adjustment response module uses the accumulated energy state quantity as an addressing index to retrieve the corresponding nominal response reference value from the preset response model. And calculate the transient response value. Compared with the nominal response reference value The feature offset D; the formula for calculating feature offset D is as follows: Where D is the feature offset. The real-time response value is characterized by the transient response signal. The nominal response reference value is used; after calculating the characteristic offset D, the system introduces a discrete proportional-integral-derivative (PID) control law to calculate the feedforward compensation coefficient. Specifically, the formula for calculating the feedforward compensation coefficient is as follows: ,in, This is the proportional gain, used for rapid response to the current impedance deviation; This is the integral gain, used to eliminate the system's steady-state error; The differential gain is used to suppress the overshoot tendency of impedance nonlinear drift, and is calculated as follows. The numerical value is directly output as a scaling factor for the duty cycle adjustment. The adjustment response module calculates the feedforward compensation coefficient based on the characteristic offset D, and uses the feedforward compensation coefficient to modulate the duty cycle of the output signal, adjusting the energy output amplitude of the multi-level control excitation. This offsets the nonlinear impedance drift of the controlled object caused by energy accumulation, drives the transient physical state of the controlled object to converge to the preset response tolerance band, and maintains the accuracy of the energy transfer process and the stability of the system operation.

[0035] Example 2: To verify the operational stability of a high-energy pulse power regulation system under electromagnetic interference, the experiment employed a closed-loop control platform based on a 32-bit embedded processor architecture. This platform integrates an analog-to-digital converter sampling circuit with an effective resolution of 12 bits and a maximum sampling frequency of 20MHz. The controlled object was a drug head assembly load composed of bridge wires from different production batches. Gaussian white noise interference with a signal-to-noise ratio of 20dB was superimposed on the original drive signal, while simultaneously simulating 50Hz power frequency harmonics. The sampling period was... The determination logic is based on the balance between the load thermal response constant τ and the controller instruction processing cycle; when the shortest characteristic change duration exhibited by the controlled object during energy injection is 10μs, in order to ensure the capture of the nonlinear inflection point of the load impedance and to avoid sampling aliasing, the sampling period is... It must be less than one-fifth of the thermal response constant τ, where, The sampling period is τ, and the load thermal response constant is τ. This load thermal response constant τ is obtained through offline calibration using a step power test before the system leaves the factory or before a new batch is connected. The specific measurement method is as follows: a square wave test current of constant amplitude is injected into the controlled object, and the absolute time taken for the voltage across the load to rise from the initial moment to 63.2% of the system's steady-state bias value is recorded using a high-frequency data acquisition card. This physical measurement time is directly used as the load thermal response constant of the current heterogeneous controlled object. This is embedded within the parametric modeling module for use by the sampling frequency calculator, thereby setting the sampling period. Set as This setting ensures the integrity of transient feature extraction while keeping the processor's real-time computational load below 65%; based on the sensitivity of the drug tip... The initial distribution range is defined, with a preset energy injection time constant covering the interval from 50 μs to 800 μs. For the sensitivity of the drug head; the system sets the multi-level control excitation switching threshold at a characteristic point where the impedance change rate is greater than 15% / ms through a load temperature and impedance drift mapping model. The comparative experiment sets up a control group consisting of the existing fixed-level control method and an experimental group using the technical solution of this invention.

[0036] In an environment with an initial temperature of 60℃, the control group experienced a sharp increase in impedance due to load temperature rise. Lacking dynamic feedback compensation, the injected current dropped below the effective threshold after 150.5μs, ultimately accumulating an energy of 0.92J, failing to reach the 1.2J target required for detonation. The experimental group, after receiving voltage and current sampling data sequences from the real-time monitoring module, used a digital filtering algorithm to remove the superimposed 20dB Gaussian white noise and calculated the characteristic offset D to perform transient correction on the output level. The formula for calculating the characteristic offset D is: Where D is the feature offset. This is a real-time response value. The values ​​are nominal response reference values. Actual test data shows that the energy injection level was dynamically lowered from 5V to 4.2V at 60℃ and dynamically increased from 5V to 5.8V at -20℃. The energy output of the test group remained stable within the range of 1.25±0.02J at each temperature point. The controlled object exhibited a nonlinear saturation effect as the energy injection intensity increased. When the accumulated energy approached the physical limit of the load, the response sensitivity of the test group automatically converged according to the preset attenuation function, preventing impedance abrupt changes due to overcompensation. The final transient physical state of the load converged to within the preset response tolerance band, avoiding incorrect level switching caused by environmental disturbances. The closed-loop logic of the energy regulation system throughout the entire operating cycle provides physical support for the reliable response of the controlled object.

[0037] Example 3: In the application scenario of deep-hole blasting initiation control in underground mines, the core energy scheduling unit of the electronic detonator blasting hardware and software system faces interference from loop leakage current caused by high humidity environment and nonlinear increase in contact resistance of multi-level power switch network due to frequent operation. Interference factors cause static deviation between the initially configured standardized feature dataset and the physical state of the controlled object. The parameterized modeling module determines the in-situ calibration data before the energy injection cycle starts, that is, it uses the real-time monitoring module to collect the loop current data sequence driven by 1V detection voltage, and calculates the statistical average value of the loop current data sequence in the first 100 sampling points to determine the initial static impedance of the current loop. The system compares the initial static impedance with the preset healthy reference impedance to generate a hardware loss compensation operator σ. The calculation formula of the hardware loss compensation operator σ is as follows: Where σ is the hardware loss compensation operator, The initial static impedance of the circuit is measured in real time. The system records a health reference impedance. The adjustment response module uses the hardware loss compensation operator σ to perform global scaling correction on the voltage amplitude mapping relationship in the control excitation command sequence output by the strategy generation module. This offsets the energy loss caused by the aging of the power switch tube and maintains the physical fidelity of the energy transfer path. In the above physical process, although the static impedance test obtains the aging increment of the low-frequency contact resistance, the high-frequency dynamic switching loss of the multi-stage power switch network at the turn-on and turn-off edges is approximately linearly modulated by the aging of the equivalent series resistance within the microsecond-level high-frequency energy injection cycle. Through the previous factory test calibration, this system has established a physical mapping relationship that is directly proportional to the aging increment of the contact resistance and the high-frequency dynamic switching energy loss. Therefore, the statically extracted operator is used to pre-raise the amplitude of the reference driving voltage. At the overall envelope level of the macroscopic injected energy, the voltage gain integral can effectively fill the microscopic transient energy missing due to device aging in the high-frequency switching action.

[0038] After the energy injection procedure is initiated, the strategy generation module uses the impedance change rate characteristic point of 15% / ms as the logic trigger point for control switching. This threshold is based on the bridge wire diameter. The thermal resistance coefficient of the corresponding solid-liquid phase transition range of the metallic material was fitted and generated, representing the physical critical point at which the controlled object transitions from an electrically heated state to a chemically excited state. This threshold was based on data verification from fracture-initiation experiments conducted on 500 groups of controlled objects of the same specifications. The experiment involved real-time acquisition of high-frequency electrical parameter waveforms of the bridge wire as it rose from room temperature to the solid-liquid phase transition point (approximately 1400℃). Statistical results show that within the microsecond critical window where the bridge wire absorbs a large amount of latent heat and begins the phase transition, the slope of the measured transient resistivity change is significantly affected by the intensified electron scattering caused by the melting of the metal lattice structure. Abrupt increases occurred throughout the interval, generally concentrated between 14.2% / ms and 15.8% / ms. To ensure robust capture of this physical phase transition point, the system established 15% / ms as the safety benchmark threshold for the underlying switching logic. The real-time monitoring module extracted the first-order partial derivatives of the voltage sampling data sequence. When the first-order partial derivatives reached the 15% / ms threshold, the system determined that the controlled object had entered the quasi-steady-state phase transition region. At this point, the strategy generation module switched the control mode from constant power level to duty cycle adjustment mode based on the characteristic offset D. During this process, the adjustment response module followed the formula... The system calculates the deviation between the real-time response trajectory and the nominal response model, and adjusts the conduction frequency of the power switch according to a preset adaptive weighting coefficient, so that the accumulated energy state of the controlled object returns to the center of the response tolerance band within 10μs. The initial nominal value of the adaptive weighting coefficient is set to 1.0. During continuous energy injection, the system calculates the first-order difference of the characteristic offset D between two adjacent sampling periods to evaluate the tolerance convergence trend. If the first-order difference is positive three times in a row, it indicates that the load impedance has an accelerated divergence trend. The system then adjusts the weighting coefficient step by step according to a preset step size of 0.05 to weaken the drive output. Conversely, if the difference is negative, the existing weight is maintained or the weight is adjusted back to the nominal value according to the phase phase length. In this way, the error evolution rate is dynamically embedded into the control logic of the conduction frequency. Through the closed-loop logic based on physical phase state recognition and dynamic hardware compensation, the system solves the adaptability problem caused by environmental coupling interference and component performance degradation, so that the accuracy of the detonation energy delivery is kept near the target value of 1.25J.

[0039] Example 4: In a controlled laboratory discretization measurement scenario, the parametric modeling module utilizes a test bench with a 10MHz sampling bandwidth and 0.05% impedance measurement accuracy to acquire the physical evolution characteristics of the controlled object. Within a controlled temperature field ranging from 20℃ to 60℃, the system injects energy pulses into the load through a multi-stage power switching network in 5℃ increments. The real-time monitoring module synchronously records the impedance evolution path of the load at each energy level and obtains the nominal response reference value for the corresponding sampling point. The strategy generation module will specify the nominal response reference value. The data is stored in internal memory to construct a response model with the accumulated energy state as the independent variable, thereby establishing a quantitative mapping benchmark between the standardized feature dataset and the actual physical response.

[0040] When the system encounters a new batch of controlled objects deployed on-site, the safety verification module initiates an on-site calibration procedure before energy injection. The real-time monitoring module collects the static current value of the new batch of controlled objects under 0.5V excitation, calculates the average initial resistance of the load for this batch, and compares it with the corresponding impedance value obtained from offline calibration. The response adjustment module uses the resistance deviation rate as a compensation factor to adjust the nominal response reference value in the preset response model. The correction is implemented, and the correction calculation formula is as follows: ,in, This is the corrected nominal response reference value. The original nominal response reference value is ϵ, which is the resistance deviation rate determined by the static current value. The correction process ends within 5ms. The adjustment response module generates multi-level control excitation based on the correction reference, so that the adjustment error of the drive energy regulation system under different batch loads is within 2%.

[0041] Example 5: In an industrial test scenario verifying the stability of the detonation system, the controlled object is connected to a signal acquisition loop subject to external electromagnetic interference. The real-time monitoring module acquires the transient response signal of the controlled object at a sampling frequency of 1MHz during the energy injection cycle. The system uses a sliding window containing 50 sampling points to smooth the transient response signal and calculates the variance of the transient response signal within the sliding window to assess the environmental interference level. The response adjustment module determines the correction weight γ based on the environmental interference level. When the variance exceeds a preset deviation threshold, the response adjustment module reduces the correction weight γ from the nominal value of 1.0 to 0.85 and calculates the smoothed feature offset according to the formula. The formula is as follows: ,in, This is the smoothed feature offset. This is a real-time response value. This is the nominal response reference value. The feature offset of the previous sampling time is used; the smoothing process suppresses energy output fluctuations caused by random environmental noise, driving the physical state evolution trajectory of the controlled object to converge stably to the response tolerance band.

[0042] When the energy regulation system faces a situation where the power switching transistors experience increased conduction losses due to thermal fatigue, the safety verification module initiates a hardware health self-test during operational breaks. It measures the actual transmission efficiency of the power output circuit using a calibrated load with a known resistance value. The system compares the deviation between the actual transmission efficiency and the factory reference efficiency to determine the system-level gain compensation factor. The regulation response module uses the system-level gain compensation factor to implement 0.12V step adjustments to the voltage amplitude in the control excitation command sequence, maintaining the accumulated energy state acquired by the controlled object within the preset detonation threshold range. This regulation process, combining real-time noise suppression and hardware efficiency calibration, maintains the consistency of the energy regulation system's regulation accuracy under different environmental humidity levels and component aging conditions, without altering the physical conditioning circuit topology, by using a logic feedback loop to offset the nonlinear decay of hardware components.

[0043] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit of this application and the scope of protection of this invention, and all of these forms are within the protection scope of this application.

Claims

1. A programmable multi-level energy regulation system based on load characteristic parameter matching, characterized in that, include: The parametric modeling module is used to receive baseline parameters of the physical properties of the controlled object and translate the baseline parameters into a standardized feature dataset; The strategy generation module is used to process the standardized feature dataset according to the parameterized matching model and map the standardized feature dataset into a sequence of control excitation instructions. The security verification module is used to obtain the identity attribute characteristics of the controlled object and generate a response permission signal based on the matching degree result between the identity attribute characteristics and the control stimulus command sequence. The real-time monitoring module is used to collect the transient response signal of the controlled object during the energy injection cycle and calculate the cumulative energy state of the controlled object based on the transient response signal. The adjustment response module has its input terminals connected to the output terminals of the strategy generation module, the security verification module, and the real-time monitoring module, respectively. When a response permission signal is received, it generates multi-level control excitations according to the control excitation command sequence and uses the accumulated energy state quantity to perform feedforward correction on the multi-level control excitations, so that the transient physical state of the controlled object converges to the preset response tolerance band.

2. The programmable multi-level energy regulation system based on load characteristic parameter matching according to claim 1, characterized in that, The internal rules for implementing feedforward correction in the adjustment response module include: Step S21, using the accumulated energy state quantity as the addressing index to retrieve the corresponding nominal response reference value in the preset response model; Step S22, calculating the characteristic offset between the real-time response value represented by the transient response signal and the nominal response reference value; Step S23, calculating the feedforward compensation coefficient based on the characteristic offset, and using the feedforward compensation coefficient to modulate the duty cycle of the output signal to adjust the energy output amplitude of the multi-level control excitation.

3. The programmable multi-level energy regulation system based on load characteristic parameter matching according to claim 1, characterized in that, The standardized feature dataset includes: response impedance curves, sensitivity distribution coefficients, energy injection time constants, and physical geometric parameters.

4. The programmable multi-level energy regulation system based on load characteristic parameterized matching according to claim 1, characterized in that, The security verification module is used to compare the batch features recorded in the identity attribute features with the preset adaptation features of the control incentive instruction sequence in order to achieve logical anchoring of heterogeneous controlled objects.

5. A programmable multi-level energy regulation system based on load characteristic parameterized matching according to claim 1, characterized in that, The sampling frequency of the real-time monitoring module is no less than 1MHz, which is used to extract the high-frequency transient electrical parameter characteristics of the controlled object during the energy injection process from 10μs to 500μs.

6. A programmable multi-level energy regulation system based on load characteristic parameterized matching according to claim 2, characterized in that, The adjustment response module is equipped with response circuit breaker logic, which is used to cut off the output of multi-level control excitation when the feature offset continues to exceed the preset safety tolerance limit.

7. A programmable multi-level energy regulation system based on load characteristic parameterized matching according to claim 1, characterized in that, The control excitation command sequence is programmable, supporting the adjustment of the timing trajectory of energy injection based on the baseline parameters of different batches.

8. A programmable multi-level energy regulation system based on load characteristic parameter matching according to claim 1, characterized in that, The adjustment response module includes a multi-channel digital-to-analog converter circuit, which is used to generate multi-level control excitation and uses a current sampling feedback loop to correct the output accuracy.

9. A programmable multi-level energy regulation system based on load characteristic parameterized matching according to claim 1, characterized in that, The regulation response module includes a multi-stage power switch network. By controlling the sequence of excitation commands, the conduction combination of the multi-stage power switch network is adjusted to achieve gradient regulation of the output energy density.

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