Transient electromagnetic signal acquisition method and device in strong electromagnetic interference environment

Abstract

The invention provides a transient electromagnetic signal acquisition method and device in a strong electromagnetic interference environment, and the method comprises the steps: firstly obtaining an interference factor generated by an interference object to a transient electromagnetic instrument in a target state, and determining an upper limit value of a target magnetic moment; and then determining an upper limit value of the transmittable magnetic moment of the transient electromagnetic instrument by adopting a genetic algorithm according to the interference factor and a preset fitness function. And determining the final maximum emission magnetic moment by evaluating the influence of the transient electromagnetic instrument on the interference object. A specially-designed coil assembly is adopted for signal emission and comprises a first emission coil and a second emission coil with internal reverse current. And the signal is received by using a first receiving coil positioned between the large second transmitting coils and a second receiving coil forming a differential signal with the first receiving coil. The structural design and the self-adaptive magnetic moment control effectively improve the signal acquisition quality in a strong interference environment.

Description

Technical Field

[0001] The present invention belongs to the technical field of electromagnetic signal acquisition, and in particular relates to a method and device for acquiring transient electromagnetic signals in a strong electromagnetic interference environment. Background Art

[0002] Electromagnetic detection technology is widely used in geological exploration, mineral resource surveys, archaeological excavations, and engineering surveys. Transient electromagnetic detection, in particular, as a key geophysical exploration method, plays a vital role in resource exploration and environmental surveys due to its advantages, such as its deep detection depth and high resolution. Traditional electromagnetic detection technology is primarily used in relatively ideal field environments, such as those far from urban and industrial areas, where electromagnetic interference is minimal and the equipment can operate in a relatively pure electromagnetic environment.

[0003] With the acceleration of urbanization and the development of industrial technology, the application of electromagnetic detection has gradually expanded to complex environments such as urban areas, industrial parks, mining areas, airports, and ports. These areas are home to numerous power facilities, communications equipment, and industrial machinery, generating complex and variable electromagnetic interference. Traditional electromagnetic detection technologies, based on simple electromagnetic induction principles, work well in ideal environments but perform poorly in complex electromagnetic environments. With the advancement of modern industry and communications technology, the sources of electromagnetic interference in the environment are becoming increasingly numerous and complex. These include interference signals of varying frequencies and intensities generated by power lines, communications equipment, and industrial machinery. These interference signals are mixed with the target signals, making it difficult for traditional technologies to effectively distinguish them. Summary of the Invention

[0004] The present invention provides a method and device for collecting transient electromagnetic signals in a strong electromagnetic interference environment to solve the above technical problems.

[0005] In a first aspect, the present invention provides a method for collecting transient electromagnetic signals in a strong electromagnetic interference environment, which is applied to a transient electromagnetic instrument including a transmitting coil assembly and a receiving coil assembly. The method comprises the following steps:

[0006] Obtaining the interference factor generated by the interference object on the transient electromagnetic instrument in the adjusted target state, and determining the upper limit value of the target magnetic moment of the interference object in the target state;

[0007] The genetic algorithm is used to determine the upper limit of the magnetic moment that can be transmitted by the transient electromagnetic instrument when measuring at the measured position based on the interference factor and the preset fitness function.

[0008] If the transient electromagnetic instrument does not affect the state of the interference object when operating at the upper limit value of the transmittable magnetic moment, the upper limit value of the transmittable magnetic moment is determined as the maximum transmittable magnetic moment;

[0009] If the transient electromagnetic instrument affects the state of the interference object when operating at the upper limit value of the transmittable magnetic moment, the minimum value between the upper limit value of the transmittable magnetic moment and the upper limit value of the target magnetic moment is determined as the maximum transmittable magnetic moment;

[0010] An electromagnetic signal is transmitted based on the maximum transmitting magnetic moment using a transmitting coil assembly, wherein the transmitting coil assembly includes a first transmitting coil and a second transmitting coil, wherein the second transmitting coil is disposed inside the first transmitting coil, and a current path of the second transmitting coil is opposite to a current path of the first transmitting coil;

[0011] After the electromagnetic signal is transmitted, the transient electromagnetic signal is received by the receiving coil assembly. The receiving coil assembly includes a first receiving coil and a second receiving coil. The first receiving coil is arranged between the first transmitting coil and the second transmitting coil, and the second receiving coil and the first receiving coil form a differential signal.

[0012] Optionally, obtaining the interference factor generated by the interfering object on the transient electromagnetic instrument in the adjusted target state and determining the target magnetic moment upper limit value of the interfering object in the target state includes the following steps:

[0013] Collect the spectrum characteristics, field strength distribution and time variation of interference objects in the target detection area to build a dynamic electromagnetic interference environment model;

[0014] Conduct laboratory electromagnetic radiation effect tests on the interference object to determine the sensitivity threshold of the interference object at different frequencies and field strengths;

[0015] Combining the sensitivity threshold and the geometric relative position between the transient electromagnetic instrument and the interference object, and performing electromagnetic simulation based on the dynamic electromagnetic interference environment model, the electromagnetic induction parameters of the interference object in the target state are calculated as the interference factor;

[0016] A functional relationship model between the interference factor, the geometric relative position and the emission magnetic moment of the transient electromagnetic instrument is established, and the upper limit of the target magnetic moment of the interference object in the target state is determined by solving the functional relationship model.

[0017] Optionally, the method of using a genetic algorithm and, based on an interference factor and a preset fitness function, determining an upper limit value of a magnetic moment that can be transmitted by the transient electromagnetic instrument when measuring at a measured position comprises the following steps:

[0018] The emission magnetic moment to be optimized is encoded as a chromosome population in a genetic algorithm;

[0019] Construct the fitness function of the genetic algorithm. The fitness function is specifically expressed as:

[0020]

[0021] Where M is the emission magnetic moment, To predict the signal-to-noise ratio, is the kth interference factor, is the penalty function when the interference factor exceeds the safety threshold, Hardware constraint penalty for emission moment, is the weight coefficient;

[0022] Perform population optimization operations on the chromosome population based on the fitness function until the preset number of iterations or fitness value convergence conditions are met;

[0023] The emission magnetic moment corresponding to the chromosome individual with the highest fitness in the chromosome population after optimization is output as the upper limit of the emission magnetic moment.

[0024] Optionally, before transmitting the electromagnetic signal using the transmitting coil assembly, the following steps are included:

[0025] The geometric parameters, number of turns, relative position and current ratio of the two transmitting coils in the transmitting coil assembly are modeled using 3D electromagnetic simulation software to obtain a transmitting coil model.

[0026] Perform parameter simulation optimization on the model parameters in the transmitting coil model. The parameter simulation goal is to minimize the rate of change of the primary magnetic flux generated by the transmitting current of the transmitting coil assembly at the initial position of the receiving coil assembly.

[0027] Optimize and adjust the transmitting coil assembly according to the optimized model parameters;

[0028] An alternating current is fed into the transmitting coil assembly, and the positions of the two transmitting coils are adjusted so that the amplitude of the induced voltage generated by the transmitting current measured at the initial position of the receiving coil assembly is minimized.

[0029] Optionally, after determining the maximum emission magnetic moment, the method further includes the following steps:

[0030] The sensor array inside the transient electromagnetic instrument collects the noise characteristics of the ambient electromagnetic noise and the interference characteristics of the interference object in real time. The interference characteristics include the state parameters of the interference object and the near-field electromagnetic leakage characteristics;

[0031] Combining noise and interference characteristics and using machine learning models to dynamically predict the state change trend of the interference object in one or more future detection cycles, and evaluate the dynamic interference factor spectrum generated by the interference object on the transient electromagnetic instrument;

[0032] Based on the interference characteristics, a dynamic electromagnetic susceptibility model of the interference object is constructed, and the predicted interference characteristics of the interference object are obtained by using the dynamic electromagnetic susceptibility model;

[0033] The disturbance threshold of the external electromagnetic field is calculated based on the predicted interference characteristics, and the optimal emission magnetic moment interval is generated by combining the disturbance threshold and the currently determined maximum emission magnetic moment;

[0034] Based on the optimal launch magnetic moment interval, a multi-objective optimization method is used to generate the optimal launch strategy of the transient electromagnetic instrument.

[0035] Optionally, the step of generating an optimal launch strategy for a transient electromagnetic instrument using a multi-objective optimization method based on the optimal launch magnetic moment interval includes the following steps:

[0036] The optimization objectives are to maximize the signal-to-noise ratio of the expected emission signal of the transient electromagnetic instrument and minimize the influence of noise characteristics and predicted interference characteristics. A multi-objective optimization algorithm is used to generate a Pareto optimal solution set containing multiple emission strategies. Each emission strategy corresponds to a set of emission magnetic moments within the optimal emission magnetic moment range, as well as preset waveform characteristics and emission timing.

[0037] A risk quantification assessment is conducted for each launch strategy in the Pareto optimal solution set. The assessment includes the probability that the interference object state parameters will exceed the safety margin defined by the dynamic electromagnetic susceptibility model after the launch strategy is implemented, and the probability that the expected transmission signal quality will fail to meet the preset mission requirements.

[0038] The optimal launch strategy is selected from the Pareto optimal solution set based on the evaluation results of risk quantification.

[0039] Optionally, transmitting the electromagnetic signal using the transmitting coil assembly includes the following steps:

[0040] Control the transmitting coil assembly to transmit micro-disturbance pulses using a preset special code, and monitor the immediate response characteristics of the interference object to the micro-disturbance pulses;

[0041] Adjusting the launch parameters in the optimal launch strategy based on immediate response feature feedback;

[0042] The transmitting coil assembly is controlled to transmit electromagnetic signals according to the adjusted optimal transmitting strategy.

[0043] In a second aspect, the present invention also provides a transient electromagnetic signal acquisition device in a strong electromagnetic interference environment, comprising a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, it implements the transient electromagnetic signal acquisition method in a strong electromagnetic interference environment as described in the first aspect.

[0044] In a third aspect, the present invention further provides a computer-readable storage medium having instructions stored thereon, which, when executed by a processor, configures the processor to execute the method for collecting transient electromagnetic signals in a strong electromagnetic interference environment described in the first aspect.

[0045] The beneficial effects of the present invention are:

[0046] The present invention introduces an adaptive magnetic moment optimization mechanism based on a genetic algorithm, which can intelligently adjust the transmission power according to the real-time interference characteristics, while ensuring signal strength and avoiding adverse effects on surrounding sensitive equipment, thus achieving the best balance between detection effect and environmental friendliness. In particular, in terms of coil structure design, the present invention adopts an innovative combination of a large second transmitting coil reverse current configuration and a middle auxiliary receiving coil differential signal processing to form an efficient interference suppression mechanism, which can effectively eliminate environmental noise and self-excitation interference and significantly improve the signal-to-noise ratio of the signal. This structure not only enhances the system's resistance to external interference, but also optimizes the spatial distribution of transmitted energy and improves energy utilization efficiency. In practical applications, the present invention can operate stably in strong interference environments such as industrial areas, around power facilities, and near communication base stations, obtain high-quality detection data, and expand the application scope of transient electromagnetic detection technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a flow chart of a method for collecting transient electromagnetic signals in a strong electromagnetic interference environment in one embodiment of the present application. DETAILED DESCRIPTION

[0048] The following will be combined with the accompanying drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the embodiments described are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of this application.

[0049] The terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of this application can be implemented in an order other than that illustrated or described herein, and that the objects distinguished by "first," "second," and the like are generally of the same type, and do not limit the number of objects; for example, the first object can be one or more. In addition, the term "and / or" in the specification and claims refers to at least one of the connected objects, and the character " / " generally indicates that the objects connected are in an "or" relationship.

[0050] Figure 1 FIG1 is a flow chart of a method for collecting transient electromagnetic signals in a strong electromagnetic interference environment in one embodiment. The method for collecting transient electromagnetic signals in a strong electromagnetic interference environment is applied to a transient electromagnetic instrument including a transmitting coil assembly and a receiving coil assembly. It should be understood that although Figure 1The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 1 At least part of the steps in the above process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but may be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but may be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps. Figure 1 As shown, the method for collecting transient electromagnetic signals in a strong electromagnetic interference environment disclosed in the present invention specifically includes the following steps:

[0051] S101. Obtain an interference factor generated by an interference object on a transient electromagnetic instrument in an adjusted target state, and determine an upper limit value of a target magnetic moment of the interference object in the target state.

[0052] Among them, in a strong electromagnetic interference environment, it is first necessary to comprehensively analyze the electromagnetic characteristics of the interference object. Use a spectrum analyzer to scan the target detection area and record the radiation intensity of the interference object at different frequencies. For example, the radiation power of a communication device in the 2.4GHz frequency band is -30dBm. At the same time, use a field strength meter to measure the spatial electromagnetic field distribution and establish a three-dimensional field strength distribution map. Then, in a laboratory environment, conduct an electromagnetic radiation test on the interference object, gradually increase the radiation intensity until the equipment becomes abnormal, and record the critical threshold. For example, if an electronic device begins to have bit errors at a field strength of 10V / m, its sensitivity threshold is 10V / m. Combined with the geometric position relationship, use electromagnetic simulation software to calculate the induced current, voltage and other parameters of the interference object in the target state as interference factors. Finally, establish a functional relationship between the interference factor and the emission magnetic moment. , where M is the magnetic moment, d is the distance, and k and n are coefficients. By solving the inequality Obtain the upper limit of the target magnetic moment to ensure that the interference object can work normally under this magnetic moment.

[0053] S102. Using a genetic algorithm and based on the interference factor and a preset fitness function, determine the upper limit of the magnetic moment that can be transmitted by the transient electromagnetic instrument when measuring at the measured position.

[0054] Among them, the genetic algorithm searches for the optimal solution by simulating the biological evolution process. First, the emission magnetic moment is encoded as a binary chromosome. For example, the magnetic moment range of 0-100A·m² is encoded as a 10-bit binary string. A population of 50 individuals is initialized, each representing a possible emission magnetic moment value. A fitness function is constructed to evaluate the quality of each solution. The function comprehensively considers the two goals of maximizing the signal-to-noise ratio and minimizing interference. In the specific calculation, the predicted signal-to-noise ratio , where S is the signal strength and N is the noise strength. When the interference factor exceeds the safety threshold, the penalty function This will significantly reduce the fitness value. Population evolution is achieved through selection, crossover, and mutation. Selection retains individuals with high fitness, crossover swaps gene fragments between two individuals, and mutation randomly alters certain gene positions. After 100 generations of evolution, the magnetic moment corresponding to the individual with the highest fitness becomes the upper limit of the transmittable magnetic moment, which generally ensures measurement accuracy while minimizing interference with surrounding equipment.

[0055] S103. If the transient electromagnetic instrument does not affect the state of the interference object when operating at the upper limit value of the transmittable magnetic moment, the upper limit value of the transmittable magnetic moment is determined as the maximum transmittable magnetic moment.

[0056] After obtaining the upper limit of the transmittable magnetic moment, it is necessary to assess whether this magnetic moment will adversely affect the interfering object. Electromagnetic field simulations calculate the electromagnetic field strength generated at the interfering object's location when the transient electromagnetic instrument operates at the upper limit of the transmittable magnetic moment. For example, if the calculated electric field strength at a distance of 50 meters is 5 V / m and the interfering object's sensitivity threshold is 10 V / m, then 5 V / m < 10 V / m, indicating no impact. In this case, the upper limit of the transmittable magnetic moment is directly set as the maximum transmittable magnetic moment. Conversely, if the calculated electric field strength is 15 V / m > 10 V / m, an impact will occur on the interfering object, and the smaller of the upper limit of the transmittable magnetic moment and the upper limit of the target magnetic moment is selected as the maximum transmittable magnetic moment. This dual constraint mechanism ensures that measurement requirements are met while protecting surrounding sensitive equipment, achieving a balance between measurement performance and electromagnetic compatibility. Through this adaptive adjustment, the maximum transmittable magnetic moment typically increases the usable range by 20-30% compared to traditional fixed settings.

[0057] S104. If the transient electromagnetic instrument will affect the state of the interference object when operating at the upper limit of the transmittable magnetic moment, the minimum value between the upper limit of the transmittable magnetic moment and the upper limit of the target magnetic moment is determined as the maximum transmittable magnetic moment.

[0058] S105. Transmit an electromagnetic signal based on the maximum transmitting magnetic moment and using the transmitting coil assembly.

[0059] The transmitting coil assembly utilizes an innovative dual-coil countercurrent design to optimize electromagnetic field distribution. The transmitting coil assembly consists of a first transmitting coil and a second transmitting coil, located within the first. The second transmitting coil's current path is opposite to that of the first. The first transmitting coil typically has a diameter of 1-2 meters and 10-20 turns, carrying the primary transmitting current. The second transmitting coil, with a diameter approximately 0.6-0.8 times that of the first, has fewer turns and is concentrically located within the first. The key technology lies in the fact that the currents in the two coils flow in opposite directions. When a clockwise current flows through the first transmitting coil, a counterclockwise current flows through the second. This design, based on Ampere's law, generates magnetic fields that superimpose in the near field but cancel in the far field, resulting in an optimized field pattern. The current ratio is typically set between 3:1 and 5:1 for the first transmitting coil and 5:1 for the second transmitting coil. During transmission, a square wave or trapezoidal wave pulse current is fed into the coil assembly. The pulse width is usually 1-10 milliseconds, and the current amplitude is determined according to the maximum transmission magnetic moment.

[0060] S106. After the electromagnetic signal is transmitted, the transient electromagnetic signal is received by the receiving coil assembly.

[0061] Among them, the receiving coil assembly adopts a differential configuration to suppress common-mode interference and enhance the target signal. The receiving coil assembly includes a first receiving coil and a second receiving coil, the first receiving coil is arranged between the first transmitting coil and the second transmitting coil, and the second receiving coil and the first receiving coil form a differential signal. The first receiving coil is located in the annular area between the first transmitting coil and the second transmitting coil, and the diameter is between the two, usually 0.7-0.9 times the diameter of the first transmitting coil. The second receiving coil is set at a certain distance from the first receiving coil, and the two form a spatially separated differential pair. The working principle is based on the different attenuation characteristics of near-field and far-field signals: the secondary transient electromagnetic field from the underground target is a near-field signal, and the signal strength difference at the two receiving coils is obvious; while the far-field electromagnetic interference signal has basically the same intensity at the two coils. The differential amplifier circuit calculates the difference between the two coil signals , where k is the correction coefficient. Through this differential processing, common-mode interference is effectively cancelled out, while the target signal is retained and amplified.

[0062] In one embodiment, obtaining the interference factor generated by the interfering object on the transient electromagnetic instrument in the adjusted target state and determining the target magnetic moment upper limit value of the interfering object in the target state includes the following steps:

[0063] Collect the spectrum characteristics, field strength distribution and time variation of interference objects in the target detection area to build a dynamic electromagnetic interference environment model;

[0064] Conduct laboratory electromagnetic radiation effect tests on the interference object to determine the sensitivity threshold of the interference object at different frequencies and field strengths;

[0065] Combining the sensitivity threshold and the geometric relative position between the transient electromagnetic instrument and the interference object, and performing electromagnetic simulation based on the dynamic electromagnetic interference environment model, the electromagnetic induction parameters of the interference object in the target state are calculated as the interference factor;

[0066] A functional relationship model between the interference factor, the geometric relative position and the emission magnetic moment of the transient electromagnetic instrument is established, and the upper limit of the target magnetic moment of the interference object in the target state is determined by solving the functional relationship model.

[0067] In this embodiment, a broadband spectrum analyzer is used to perform a full-band scan in the target detection area. The frequency range generally covers 10kHz to 18GHz, and the power spectrum density of each frequency point is recorded. For example, a -40dBm WiFi signal is detected in the 2.4GHz band, and a -35dBm mobile communication signal is detected in the 900MHz band. At the same time, multiple field strength probes are deployed to form a monitoring network to measure the electric field strength and magnetic field strength at different locations and establish a three-dimensional field strength distribution map. The time variation pattern is obtained by continuous 24-hour monitoring, and the periodic changes in signal strength are recorded, such as the communication signal strength increases by 10-15dB during working hours. Based on the collected data, a dynamic model is established using electromagnetic field theory:

[0068]

[0069] in is the time-varying amplitude, is the spatial distribution function, is the angular frequency.

[0070] In a standard electromagnetic compatibility laboratory, the interference object is placed on the test bench in the anechoic chamber, and a signal generator and a power amplifier are used to generate controllable electromagnetic radiation. The test process uses a step-by-step enhancement method: starting from the lowest power, the radiation intensity is gradually increased in steps of 1dB, while monitoring the working status of the interference object. For example, when testing an electronic device, the device works normally at a frequency of 1MHz and a field strength of 5V / m. When the field strength increases to 8V / m, slight interference occurs, and a serious fault occurs at 12V / m. The sensitivity threshold at this frequency is determined to be 8V / m. Repeat this process for each key frequency point to draw a sensitivity threshold curve. The test also includes two modes: pulse interference and continuous wave interference, with pulse width varying from 1μs to 1ms. The sensitivity function is obtained by fitting a large amount of test data: , where a, b, and c are fitting parameters.

[0071] Finite element electromagnetic simulation software was used to perform precise calculations based on the obtained sensitivity threshold and geometric position relationship. First, a three-dimensional geometric model was established, including the detailed structure of the transient electromagnetic instrument transmitting coil, the interference object, and the surrounding environment. Material parameters were set: the relative dielectric constant of air was 1, and the conductivity of the conductor was 5.8×10 7 S / m. In the simulation, the excitation source is set to a time-domain pulse current, and the waveform is an exponential decay function. ,in is the peak current, is the time constant. The electromagnetic field distribution is solved by Maxwell's equations to calculate the induced electric field at the location of the interfering object. and induced current The interference factor is defined as the ratio of the actual sensing parameter to the sensitivity threshold: .

[0072] Based on the simulation results, a mathematical relationship model between the interference factor, geometric position, and emission magnetic moment was established. Through multiple regression analysis, the functional relationship was obtained: , where M is the emission magnetic moment, r is the distance, θ is the angle, α, β, γ are the fitting coefficients, is the angle correction function.

[0073] For example, suppose the fitting result is , the correlation coefficient R² reaches above 0.95. To determine the upper limit of the target magnetic moment, set the safety constraint condition ,generally To leave a safety margin. By solving the inequality ,get When the distance r = 50m and the angle θ = 30°, the upper limit of the target magnetic moment is calculated to be 85A·m².

[0074] In one embodiment, using a genetic algorithm and based on an interference factor and a preset fitness function, determining the upper limit of the magnetic moment that can be transmitted by the transient electromagnetic instrument when measuring at a measured position includes the following steps:

[0075] The emission magnetic moment to be optimized is encoded as a chromosome population in a genetic algorithm;

[0076] Construct the fitness function of the genetic algorithm. The fitness function is specifically expressed as:

[0077]

[0078] Where M is the emission magnetic moment, To predict the signal-to-noise ratio, is the kth interference factor, is the penalty function when the interference factor exceeds the safety threshold, Hardware constraint penalty for emission moment, is the weight coefficient;

[0079] Perform population optimization operations on the chromosome population based on the fitness function until the preset number of iterations or fitness value convergence conditions are met;

[0080] The emission magnetic moment corresponding to the chromosome individual with the highest fitness in the chromosome population after optimization is output as the upper limit of the emission magnetic moment.

[0081] In this embodiment, chromosome encoding in the genetic algorithm is the process of converting a practical problem into a digital string that can be processed by a computer. The range of the emission magnetic moment is assumed to be 0-200 A·m 2 , using binary coding, a 16-bit binary number represents a magnetic moment value. The coding accuracy is 200 / 2 16 =0.003A·m 2 , meeting the engineering precision requirements. For example, the magnetic moment value is 100A·m 2 The corresponding binary encoding is: First calculate 100 / 200×65535=32767, and convert it to binary to get 10000000000000000. The initial population contains 100 individuals, each representing a possible emission magnetic moment solution. The population is initialized using a random generation method, using a pseudo-random number generator to generate 100 integers in the range [0,65535], and then converting them into the corresponding magnetic moment value. For example, the random number 32768 corresponds to a magnetic moment value of 100.0015A·m 2 , the random number 16384 corresponds to a magnetic moment value of 50.0008A·m 2 This encoding method ensures complete coverage of the search space, and each chromosome can uniquely correspond to a specific magnetic moment value.

[0082] The fitness function is a mathematical expression that evaluates the quality of each solution, taking into account two key factors: signal quality and interference constraints. The fitness function is specifically expressed as:

[0083]

[0084] Where M is the emission magnetic moment, To predict the signal-to-noise ratio, is the kth interference factor, is the penalty function when the interference factor exceeds the safety threshold, Hardware constraint penalty for emission moment, is the weight coefficient. The interference factor penalty function adopts a piecewise function to ensure that the fitness drops sharply when the interference factor exceeds the safety threshold. Hardware constraint penalty Limit the magnetic moment to not exceed the maximum capacity of the device, 200A·m².

[0085] Population optimization achieves evolution through three basic operations: selection, crossover, and mutation. In the selection operation, the probability of each individual being selected is proportional to its fitness, with individuals with higher fitness having a greater chance of inheriting genes to the next generation. The crossover operation randomly selects two parents and exchanges genes at random positions on the chromosomes, with a crossover probability of 0.8. For example, if parent 1 is 10000000000000000 and parent 2 is 01000000000000000, after the fifth crossover, offspring 1 will be 10000000000000000 and offspring 2 will be 010000000000000000. The mutation operation randomly flips a position on the chromosome with a probability of 0.01 to increase population diversity and prevent premature convergence. After each generation of evolution, the average and best fitness of the population are calculated. Convergence is considered achieved when the best fitness changes by less than 0.1% for 20 consecutive generations, or when the preset maximum number of iterations of 500 generations is reached.

[0086] After evolution is complete, the individual with the highest fitness is selected from the final population as the optimal solution. First, the fitness of all individuals is sorted, and the chromosome with the highest fitness value is selected. For example, the binary code of the optimal individual is 11000000000000000, which corresponds to a decimal value of 49152. This translates to an actual magnetic moment of 49152 / 65535×200=150.0A·m². To verify the reliability of the solution, it is also necessary to check whether the magnetic moment satisfies all constraints: whether the signal-to-noise ratio meets the expected requirements, whether all interference factors are within a safe range, and whether the hardware capability is exceeded. If the optimal solution does not meet some constraints, suboptimal solutions are selected for testing until a feasible solution that satisfies all constraints is found.

[0087] In one embodiment, the following steps are included before transmitting the electromagnetic signal using the transmitting coil assembly:

[0088] The geometric parameters, number of turns, relative position and current ratio of the two transmitting coils in the transmitting coil assembly are modeled using 3D electromagnetic simulation software to obtain a transmitting coil model.

[0089] Perform parameter simulation optimization on the model parameters in the transmitting coil model. The parameter simulation goal is to minimize the rate of change of the primary magnetic flux generated by the transmitting current of the transmitting coil assembly at the initial position of the receiving coil assembly.

[0090] Optimize and adjust the transmitting coil assembly according to the optimized model parameters;

[0091] An alternating current is fed into the transmitting coil assembly, and the positions of the two transmitting coils are adjusted so that the amplitude of the induced voltage generated by the transmitting current measured at the initial position of the receiving coil assembly is minimized.

[0092] In this embodiment, professional electromagnetic simulation software such as ANSYS Maxwell or CST Studio Suite is used to establish an accurate three-dimensional model. The first transmitting coil is designed as a circular structure with a diameter set to 2.0 meters. It uses a multi-strand twisted copper wire with a wire diameter of 2.5 mm², 15 turns, and a coil thickness of 0.05 meters. The second transmitting coil is concentrically arranged inside the first transmitting coil, with a diameter of 1.2 meters, 8 turns, and the same specification wire. The vertical spacing between the two coils is set to 0.1 meters, and the second transmitting coil is located above the first transmitting coil. In the material parameter setting, the conductivity of the copper wire is 5.8×10 7 S / m, a relative magnetic permeability of 1, and a relative permittivity of 1 for the air dielectric. Current excitation was reversed: the first transmitting coil had a current amplitude of 100A and a phase of 0°; the second transmitting coil had a current amplitude of 60A and a phase of 180°, achieving a current ratio of 5:3. Boundary conditions were set as radiation boundaries, and the computational domain was expanded to at least five times the coil size to ensure far-field accuracy. Adaptive tetrahedral meshing was used for mesh generation, with the mesh density increased to 0.01 meters near the coil conductors and the mesh size relaxed to 0.2 meters in the far-field region. The core objective of parameter optimization was to reduce direct coupling between the transmitting coil and the receiving coil. The receiving coil was located in the annular region between the large and second transmitting coils, 1.5 meters from the center.

[0093] The calculation formula for the primary magnetic flux change rate is: ,in is the mutual inductance coefficient. Four key parameters are optimized by parametric sweep: the diameter of the first transmitting coil D1 varies in the range of 1.8-2.2 meters, the diameter of the second transmitting coil D2 varies in the range of 1.0-1.4 meters, the current ratio k varies in the range of 0.4-0.8, and the vertical spacing h varies in the range of 0.05-0.15 meters. The magnetic flux change rate of the receiving coil position is calculated for each set of parameter combinations, and the objective function is set as A genetic algorithm was used for global optimization, and after 200 generations of evolution, the optimal parameter combination was obtained: D1 = 2.05 m, D2 = 1.25 m, k = 0.62, and h = 0.08 m. The optimized magnetic flux change rate was reduced from the initial design's -15 mWb / s to -0.8 mWb / s, a 94% reduction. This significant improvement significantly reduces direct interference to the receiving coil, improving the measurement system's signal-to-noise ratio and detection accuracy.

[0094] Based on the simulation optimization results, the actual hardware was precisely adjusted. The diameter of the first transmitting coil was adjusted from the original design of 2.0 meters to 2.05 meters by rewinding or adjusting the support frame. The diameter of the second transmitting coil was adjusted from 1.2 meters to 1.25 meters, and the vertical spacing was precisely adjusted from 0.1 meters to 0.08 meters. The current ratio was adjusted by modifying the current distribution of the driver circuit. The current of the first transmitting coil remained unchanged at 100A, while the current of the second transmitting coil was adjusted from 60A to 62A, achieving the target ratio k=0.62. The coil position was adjusted using a precise mechanical adjustment device, with positioning accuracy within ±2mm. Low-impedance welding was used for wire connections to ensure uniform current distribution. The support structure was constructed of non-magnetic materials such as aluminum alloy or fiberglass to avoid affecting the magnetic field distribution. Laser rangefinders and levels were used to ensure geometric accuracy during the adjustment process, and all critical dimensions were measured three times and the average value was obtained.

[0095] A 1kHz sinusoidal alternating current was fed into the adjusted transmitting coil assembly for practical verification. A 100A RMS current was fed to the first transmitting coil, and a 62A RMS reverse current was fed to the second transmitting coil. The current amplitude and phase were precisely controlled using a power amplifier and current sensor. A high-precision induction coil with a 0.5-meter diameter and 50 turns was placed at the initial position of the receiving coil assembly as a monitoring probe. It was connected to a high-input-impedance differential amplifier and a digital oscilloscope. The induced voltage generated by the changing transmitting current was measured, with an initial value of 85mV. Fine adjustments were made to the two transmitting coils using a fine-tuning mechanism in 1mm increments, with the induced voltage changes recorded after each adjustment. The position of the first transmitting coil remained fixed, while adjustments were primarily made to the radial and axial positions of the second transmitting coil. This significant improvement validates the theoretical design and simulation optimization, minimizing direct coupling between the transmitting coil and the receiving coil, providing an excellent hardware foundation for high-precision transient electromagnetic measurements.

[0096] In one embodiment, the following steps are included before receiving the transient electromagnetic signal by the receiving coil assembly in the transient electromagnetic instrument:

[0097] Electromagnetic simulation optimization is performed based on the geometric parameters, number of turns, and initial position of the two receiving coils in the receiving coil assembly. The optimization goal of the electromagnetic simulation optimization is to ensure that the two receiving coils have consistent response characteristics to far-field and spatially uniformly distributed common-mode interference signals, while having different response characteristics to near-field and underground target secondary transient electromagnetic field signals.

[0098] The output signal of the receiving coil assembly is connected to the differential amplifier front-end circuit and the analog-to-digital conversion processing circuit.

[0099] In this embodiment, the optimized design of the receiving coil assembly is based on the different propagation characteristics of near-field and far-field electromagnetic signals. The first receiving coil is set in the annular area of ​​the transmitting coil, with a diameter of 1.6 meters and 20 turns; the second receiving coil is located 3 meters from the center, with a diameter of 0.8 meters and 10 turns. Two typical signal sources are established in the simulation: the far-field interference source is simulated as a plane wave at a distance of 1000 meters, with a frequency range of 1-100kHz and a field strength of 10V / m; the near-field target signal is simulated as a magnetic dipole source at a depth of 50 meters underground with a magnetic moment of 1A·m². The optimization objective function is designed as:

[0100]

[0101] in are the responses of the two coils to the far-field signal, is the response to the near-field signal, The desired near-field response ratio is usually set to 2-3. The coil diameter, number of turns, relative position and other parameters are optimized through parametric scanning. After 500 iterative calculations, the optimal configuration is obtained: the first receiving coil has a diameter of 1.65 meters and 18 turns, the second receiving coil has a diameter of 0.75 meters and 12 turns, and the distance between the two coils is 2.8 meters. The output signal of the receiving coil assembly is processed by a specially designed differential amplifier circuit. The front end uses the high input impedance instrumentation amplifier AD8429 with an input impedance greater than 10¹²Ω. The signal of the first receiving coil is connected to the positive input terminal, and the signal of the second receiving coil is connected to the negative input terminal to realize hardware differential operation: , where G is the adjustable gain (1-1000 times) and k is the balance coefficient (adjustable from 0.8 to 1.2). The circuit design includes low-noise power supply filtering, shield grounding, and temperature compensation. After differential amplification, the signal enters a 24-bit high-precision ADC (such as the ADS1274), with a sampling rate set to 1MHz and a dynamic range of 120dB. Digital signal processing uses an FPGA for real-time filtering and data preprocessing, including 50Hz power frequency notching, high-pass filtering (cutoff frequency 100Hz), and low-pass filtering (cutoff frequency 50kHz). The processed digital signal is transmitted to a host computer via a USB 3.0 or Ethernet interface for further analysis.

[0102] In one embodiment, after determining the maximum emission magnetic moment, the method further includes the following steps:

[0103] The sensor array inside the transient electromagnetic instrument collects the noise characteristics of the ambient electromagnetic noise and the interference characteristics of the interference object in real time. The interference characteristics include the state parameters of the interference object and the near-field electromagnetic leakage characteristics;

[0104] Combining noise and interference characteristics and using machine learning models to dynamically predict the state change trend of the interference object in one or more future detection cycles, and evaluate the dynamic interference factor spectrum generated by the interference object on the transient electromagnetic instrument;

[0105] Based on the interference characteristics, a dynamic electromagnetic susceptibility model of the interference object is constructed, and the predicted interference characteristics of the interference object are obtained by using the dynamic electromagnetic susceptibility model;

[0106] The disturbance threshold of the external electromagnetic field is calculated based on the predicted interference characteristics, and the optimal emission magnetic moment interval is generated by combining the disturbance threshold and the currently determined maximum emission magnetic moment;

[0107] Based on the optimal launch magnetic moment interval, a multi-objective optimization method is used to generate the optimal launch strategy of the transient electromagnetic instrument.

[0108] In this embodiment, the transient electromagnetic instrument integrates a multi-type sensor array for environmental monitoring. Electromagnetic noise monitoring utilizes broadband electric field probes (10kHz-1GHz) and magnetic field probes (1Hz-100kHz), deployed at four locations on the device, to collect real-time data on the background electromagnetic environment. Noise characteristic parameters include power spectral density, crest factor, and time-domain statistical characteristics. For example, the 50Hz power frequency interference intensity measured in an industrial area was -20dBμV / m, and the broadband noise floor was -45dBμV / m. Interference object monitoring is achieved using a near-field probe array consisting of eight small induction coils (10cm diameter) positioned around the device to monitor electromagnetic leakage from electronic equipment within a range of 5-50 meters. Status parameter monitoring utilizes optical sensors, vibration sensors, and temperature sensors to obtain real-time information on the operating status of the interfering object. For example, the transmit power of a communications base station was monitored to fluctuate periodically between 30-45dBm, with a 10-minute period. Near-field electromagnetic leakage characteristics are extracted through spectrum analysis, recording the frequency, amplitude, and modulation characteristics of the leakage signal. The data acquisition frequency is 100Hz, generating a data vector containing 128 characteristic parameters per second. All sensor data is collected via the CAN bus and sent to the central processing unit to create a real-time environmental electromagnetic situation map.

[0109] A hybrid prediction model combining a long short-term memory (LSTM) network and support vector regression (SVR) is used. The LSTM network is designed to capture long-term dependencies in time series. The network structure consists of three hidden layers, each with 128 neurons, and an input window length of 60 time steps (corresponding to 10 minutes of historical data). The SVR model handles nonlinear mapping relationships and uses a radial basis function kernel with a penalty parameter C=100 and a kernel parameter γ=0.01. The training dataset consists of seven consecutive days of monitoring data, totaling 1 million samples. During the prediction process, the feature vectors of the 60 sampling points before the current moment are input into the LSTM network, which outputs a rough prediction for the next six time steps. The SVR model then refines the prediction based on current environmental parameters. For example, the transmit power change of a mobile communication base station within the next hour is predicted: the current power is 35dBm, the LSTM predicts it will be 38dBm one hour later, and the SVR correction results in 37.2dBm. The dynamic interference factor spectrum is calculated using the predicted state parameters: ,in is the predicted power, d is the distance, and G(θ) is the directivity function.

[0110] The dynamic electromagnetic susceptibility model is described by the state space equation: , where x(t) is the state vector, including parameters such as the device's internal temperature, operating current, and signal processing load; u(t) is the external electromagnetic field input; and w(t) is the process noise. The system matrix A and input matrix B are obtained through system identification methods, using the least squares method to fit historical data. The sensitivity function is defined as: ,in is the device error rate, E is the external field strength, and f is the frequency. The sensitivity curve is obtained by fitting extensive experimental data. The model also considers the dynamic changes in device state. When the device is under high load, the sensitivity threshold decreases by 20-30%. The predicted interference signature is derived through forward calculation of the model: given a predicted external electromagnetic environment, the device's response characteristics and possible anomaly probability at future moments are calculated. The safe disturbance threshold for the external electromagnetic field is then calculated based on the predicted interference signature.

[0111] The disturbance threshold is defined as the maximum allowable electromagnetic field strength that allows the disturbed object to maintain normal working conditions. The calculation formula is: ,in The sensitivity threshold is the basic sensitivity threshold, SF is the safety factor (usually 0.5-0.8), and CF is the state correction factor. The state correction factor is dynamically adjusted according to the current working state of the device: CF = 1.0 in normal state, CF = 0.7 in high load state, and CF = 1.3 in standby state. For example, if the sensitivity threshold of an industrial controller in normal working state is 5V / m and the safety factor is 0.6, the disturbance threshold is 3V / m. Combined with the electromagnetic field propagation model Calculate the relationship between the emitted magnetic moment and the field strength, where M is the magnetic moment, ω is the angular frequency, and r is the distance. By solving the inequality The upper limit of the magnetic moment is obtained, and the minimum value is taken when considering multiple interfering objects. The optimal transmission magnetic moment interval is set to [0.3×M_max,M_max], where the lower limit ensures sufficient signal strength and the upper limit ensures electromagnetic compatibility.

[0112] Finally, a non-dominated sorting genetic algorithm (NSGA-II) was used for multi-objective optimization, simultaneously considering both maximizing signal quality and minimizing interference risk. This optimization yielded a Pareto solution set consisting of multiple non-dominated solutions, each representing a transmission strategy. Decision-makers can then select the most appropriate strategy from the Pareto solution set based on specific mission requirements, achieving the optimal balance between signal quality and electromagnetic compatibility.

[0113] In one embodiment, based on the optimal launch magnetic moment interval, a multi-objective optimization method is used to generate an optimal launch strategy for a transient electromagnetic instrument, including the following steps:

[0114] The optimization objectives are to maximize the signal-to-noise ratio of the expected emission signal of the transient electromagnetic instrument and minimize the influence of noise characteristics and predicted interference characteristics. A multi-objective optimization algorithm is used to generate a Pareto optimal solution set containing multiple emission strategies. Each emission strategy corresponds to a set of emission magnetic moments within the optimal emission magnetic moment range, as well as preset waveform characteristics and emission timing.

[0115] A risk quantification assessment is conducted for each launch strategy in the Pareto optimal solution set. The assessment includes the probability that the interference object state parameters will exceed the safety margin defined by the dynamic electromagnetic susceptibility model after the launch strategy is implemented, and the probability that the expected transmission signal quality will fail to meet the preset mission requirements.

[0116] The optimal launch strategy is selected from the Pareto optimal solution set based on the evaluation results of risk quantification.

[0117] In this embodiment, an improved non-dominated sorting genetic algorithm NSGA-III is used for multi-objective optimization to simultaneously handle three conflicting objective functions. The first objective is to maximize the signal-to-noise ratio:

[0118]

[0119] Where M is the emission magnetic moment, G(τ,α) is the waveform gain function, is the total noise power.

[0120] The second goal is to minimize the impact of noise: ,in is the influence degree of the kth type of noise, Weight coefficient.

[0121] The third goal is to minimize the impact of interference: ,in is the interference factor for the j-th interference object, is the importance weight of the object. The optimization variables include the emission magnetic moment M∈[80,150]A·m², pulse width τ∈[2,8]ms, waveform parameter α∈[0.5,2.0], and emission frequency f∈[0.5,5]Hz. The algorithm sets a population size of 200, the number of reference points to 91, and runs for 300 generations. After optimization, a Pareto frontier containing 85 non-dominated solutions is obtained, each representing a unique combination of emission strategies. Next, a detailed risk quantification analysis is required for each emission strategy in the Pareto solution set. The probability of exceeding the safety margin is calculated using Monte Carlo simulation, accounting for random fluctuations in environmental parameters and measurement uncertainty. The simulation generates 10,000 random scenarios, each of which contains a random perturbation of the state parameters of the interfering object, with the perturbation amplitude determined based on historical statistical data.

[0122] For each scenario, the response parameters of the interference object after the launch strategy is implemented are calculated to determine whether the safety threshold is exceeded. Signal quality risk assessment is based on the distribution statistics of the signal-to-noise ratio, taking into account the impact of factors such as geological conditions and equipment aging on signal quality. The probability of quality failure is defined as: , calculated by integrating the probability density function. Then, based on the risk quantification assessment results, a multi-criteria decision-making method was used to screen the optimal strategy from the Pareto solution set. First, safety risk thresholds and quality risk thresholds were set, and then strategies exceeding the thresholds were eliminated, resulting in 62 feasible solutions from 85 solutions. A comprehensive evaluation index was then constructed:

[0123]

[0124] in The "robustness" metric is a normalized signal-to-noise ratio (SNR). The weighting coefficient is set based on task priority. Robustness is calculated through parameter sensitivity analysis and measures the strategy's adaptability to environmental changes. Comprehensively evaluated, the strategy maintains high signal quality while keeping various risks within acceptable limits and demonstrating good environmental adaptability.

[0125] In one embodiment, transmitting an electromagnetic signal using a transmitting coil assembly includes the following steps:

[0126] Control the transmitting coil assembly to transmit micro-disturbance pulses using a preset special code, and monitor the immediate response characteristics of the interference object to the micro-disturbance pulses;

[0127] Adjusting the launch parameters in the optimal launch strategy based on immediate response feature feedback;

[0128] The transmitting coil assembly is controlled to transmit electromagnetic signals according to the adjusted optimal transmitting strategy.

[0129] In this embodiment, the perturbation pulses are encoded using a special pseudo-random binary sequence (PRBS) with a sequence length of 127 bits, a symbol width of 50 μs, and a total duration of 6.35 ms. The pulse amplitude is set to 10% of the predetermined transmitted magnetic moment, ensuring a detectable but non-destructive effect on the interfering object. The transmitting coil generates a precisely encoded pulse sequence using a high-speed switching circuit, with a switching frequency of 20 kHz and a current rise time of less than 10 μs. Simultaneously, a multi-channel monitoring system is activated, comprising eight near-field electromagnetic probes that collect real-time electromagnetic field changes around the interfering object. The sampling frequency is set to 1 MHz to ensure capture of transient responses. An optical sensor monitors changes in the interfering object's status indicator light, with an image acquisition frequency of 1000 fps. A vibration sensor monitors the device's mechanical response, with an accelerometer range of ±50 g and a frequency response range of DC to 10 kHz. A temperature sensor monitors the device's thermal response, with an accuracy of ±0.1°C and a response time of 1 second. All sensor data is synchronously recorded using a high-speed data acquisition card with a time synchronization accuracy of 1 μs. The response characteristic parameters, including response delay time, peak amplitude, spectrum characteristics and decay time constant, are extracted through correlation analysis to establish the real-time response fingerprint of the interference object.

[0130] Response characteristic analysis uses a combination of wavelet transform and Hilbert-Huang transform to extract time-frequency domain feature vectors. Key characteristic parameters include:

[0131] Response Strength Index , response delay , spectrum center of gravity and quality factor The parameter adjustment rules are based on the fuzzy logic controller design: when RI>0.8, it means that the interference object is highly sensitive, and the emission magnetic moment adjustment coefficient ; When RI < 0.3, the sensitivity is low, ; When 0.3≤RI≤0.8, . Pulse width modulation is based on the response delay: ,in is the reference delay time. The transmission frequency is adjusted according to the center of gravity of the spectrum: when When it deviates from the expected value, adjust the transmission frequency appropriately to avoid sensitive frequency bands.

[0132] Based on the optimal transmission strategy adjusted by feedback, the transmitting coil assembly is controlled to execute the actual electromagnetic signal transmission. The transmission control system utilizes a high-precision digital signal processor (DSP) and field-programmable gate array (FPGA) working together to achieve microsecond-level timing control accuracy. The transmission sequence is generated according to the adjusted parameters: a magnetic moment of 115A·m² corresponds to a current of 153A for the first transmitting coil and 95A for the second transmitting coil (a current ratio of 1.6:1), a pulse width of 4.2ms, and a repetition period of 454ms (corresponding to a frequency of 2.2Hz). The waveform uses an optimized trapezoidal pulse with a rise time of 0.5ms, a plateau time of 3.2ms, and a fall time of 0.5ms to ensure smooth establishment and dissipation of the electromagnetic field.

[0133] During transmission, coil current, voltage, and temperature are monitored in real time, and closed-loop feedback control ensures accurate transmission parameters. Current control accuracy reaches ±0.5%, and timing control accuracy reaches ±1μs. Immediately after transmission, the system switches to receive mode, and the receive coil assembly begins collecting transient electromagnetic response signals. The dead time of the entire transmit-receive cycle is controlled within 50μs, ensuring effective signal acquisition during early time periods.

[0134] In one embodiment, the method may further include the following steps:

[0135] After each transmission and data collection is completed with the final maximum transmission parameter set, the complete parameters of this transmission, the actual collected signal quality indicators, and the actual status change data of the monitored interference object are automatically recorded;

[0136] The recorded transmission parameters, signal quality indicators and interference object state change data are used as training samples;

[0137] Using training samples, through online or offline learning, iteratively update the machine learning model, dynamic interference factor spectrum model, dynamic electromagnetic susceptibility model, and agent models or parameters in the multi-objective optimization algorithm;

[0138] Through reinforcement learning or experience replay mechanisms, the intelligent decision-making engine's strategy selection logic and the efficiency of generating Pareto optimal solutions are continuously optimized;

[0139] This enables the entire transmission control and receiving system to be able to self-learn, adapt and continuously evolve in the face of a changing strong electromagnetic interference environment.

[0140] In this embodiment, after each transmission, a data recording system automatically collects and stores comprehensive, multi-dimensional information. Transmission parameter records include: actual transmission magnetic moment, pulse width, transmission frequency, waveform parameters, current ratio of the second transmitting coil, and transmission time stamp. Signal quality indicators, such as signal-to-noise ratio, effective signal bandwidth, early time period signal strength, late time period signal strength, signal distortion, and phase consistency index, are obtained through real-time analysis. Interference object status monitoring data includes: changes in electromagnetic field intensity at each monitoring point; device status parameters such as CPU utilization, memory usage, communication error rate, and operating temperature; and abnormal indicators detected by optical and vibration sensors. All data is stored in a time series database in a standardized format, with data acquisition accuracy reaching 16 bits and a storage frequency of 1000 records per second. A data quality verification mechanism is established to ensure data reliability through outlier detection and consistency verification.

[0141] Recorded multi-source heterogeneous data is converted into standardized machine learning training samples. Feature engineering techniques are used to construct sample feature vectors: transmission parameter features are normalized, signal quality indicators are logarithmically transformed and standardized, and interference object status data is reduced in dimensionality using principal component analysis, retaining 95% of the variance. Sample labeling includes multiple target variables: signal quality level (excellent / good / fair / poor, coded 0-3), interference risk level (low / medium / high, coded 0-2), and system stability index (0-1 continuous value). Data preprocessing includes missing value filling, outlier handling, and time series alignment. Data augmentation techniques, including noise injection and parameter perturbation, are used to expand the sample set to 3-5 times the original data, improving the model's generalization and robustness.

[0142] A hybrid update strategy combining online and offline learning is employed. The machine learning prediction model employs an incremental learning approach, gradually updating the LSTM network weights based on new samples. The dynamic interference factor spectrum model adjusts parameters using a Bayesian update mechanism: the prior distribution is combined with new observations to produce the posterior distribution. The dynamic electromagnetic susceptibility model uses Kalman filtering for state estimation updates, and the surrogate model in the multi-objective optimization algorithm is updated using Gaussian process regression. The covariance matrix and mean function are updated after each new evaluation point. The model update frequency is set to: online updates are performed every 10 samples, and offline batch updates are performed every 1000 samples. An early stopping mechanism is employed during the update process to prevent overfitting; training is terminated when the validation set loss does not decrease for five consecutive times.

[0143] A reinforcement learning framework based on a deep Q-network (DQN) was constructed to optimize policy selection. The state space is defined as the electromagnetic feature vector S_t of the current environment, which contains 128-dimensional features such as noise level, interfering object status, and historical performance indicators. The action space A comprises five types of decisions: launch strategy selection (from the Pareto solution set), parameter fine-tuning amplitude, launch timing selection, risk threshold adjustment, and model update frequency control. The experience replay buffer stores the most recent 10,000 experience tuples, and 32 batches are randomly sampled for network updates during each training session. The ε-greedy strategy balances exploration and exploitation, with the ε value decaying linearly from 0.9 to 0.1. The DQN network architecture consists of three hidden layers, each with 256 neurons, using the Reluctant Unit (ReLU) activation function. The target network is updated every 100 steps, with a learning rate of 0.0001. Finally, a complete self-evolving closed-loop system is established, achieving continuous optimization in complex electromagnetic environments. The self-learning mechanism is implemented through a meta-learning framework, which allows the network to quickly adapt to new environmental conditions. The meta-learner uses the Model-Agnostic Meta-Learning (MAML) algorithm to rapidly adjust model parameters on a small number of new samples. The adaptive mechanism includes an environmental change detection module that monitors changes in the distribution of key indicators using statistical process control methods and automatically triggers model retraining when significant changes are detected.

[0144] The present invention also discloses a transient electromagnetic signal acquisition device in a strong electromagnetic interference environment, comprising a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, it implements the transient electromagnetic signal acquisition method in a strong electromagnetic interference environment as described in any one of the above embodiments.

[0145] Among them, the processor can adopt a central processing unit (CPU). Of course, according to actual usage, other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. can also be adopted. The general-purpose processor can adopt a microprocessor or any conventional processor, etc., and this application does not impose any restrictions on this.

[0146] Among them, the memory can be an internal storage unit of a computer device, such as a hard disk or memory of a computer device, or an external storage device of a computer device, such as a plug-in hard disk, smart memory card (SMC), secure digital card (SD) or flash memory card (FC) equipped on the computer device. In addition, the memory can also be a combination of an internal storage unit and an external storage device of a computer device. The memory is used to store computer programs and other programs and data required by the computer device. The memory can also be used to temporarily store data that has been output or is to be output. This application does not impose any restrictions on this.

[0147] The present invention also discloses a computer-readable storage medium having instructions stored thereon. When the instructions are executed by a processor, the processor is configured to execute the transient electromagnetic signal acquisition method in a strong electromagnetic interference environment described in any one of the above embodiments.

[0148] Among them, the computer program can be stored in a machine-readable medium, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or certain middleware, etc. The machine-readable medium includes any entity or device that can carry computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the machine-readable medium includes but is not limited to the above-mentioned components.

[0149] Among them, through this computer-readable storage medium, the transient electromagnetic signal acquisition method under a strong electromagnetic interference environment in the above embodiment is stored in the computer-readable storage medium, and is loaded and executed on the processor to facilitate the storage and application of the above method.

[0150] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of protection of the present application is limited to these examples. In line with the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of different aspects of one or more embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0151] The one or more embodiments of this application are intended to encompass all such substitutions, modifications, and variations that fall within the broad scope of this application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of this application should be included in the scope of protection of this application.

Claims

1. A method for collecting transient electromagnetic signals in a strong electromagnetic interference environment, characterized in that: Applied to a transient electromagnetic instrument comprising a transmitting coil assembly and a receiving coil assembly, the method comprises the following steps: Obtaining the interference factor generated by the interference object on the transient electromagnetic instrument in the adjusted target state, and determining the upper limit value of the target magnetic moment of the interference object in the target state; The genetic algorithm is used to determine the upper limit of the magnetic moment that can be transmitted by the transient electromagnetic instrument when measuring at the measured position based on the interference factor and the preset fitness function. If the transient electromagnetic instrument does not affect the state of the interference object when operating at the upper limit value of the transmittable magnetic moment, the upper limit value of the transmittable magnetic moment is determined as the maximum transmittable magnetic moment; If the transient electromagnetic instrument affects the state of the interference object when operating at the upper limit value of the transmittable magnetic moment, the minimum value between the upper limit value of the transmittable magnetic moment and the upper limit value of the target magnetic moment is determined as the maximum transmittable magnetic moment; An electromagnetic signal is transmitted based on the maximum transmitting magnetic moment using a transmitting coil assembly, wherein the transmitting coil assembly includes a first transmitting coil and a second transmitting coil, wherein the second transmitting coil is disposed inside the first transmitting coil, and a current path of the second transmitting coil is opposite to a current path of the first transmitting coil; After the electromagnetic signal is transmitted, the transient electromagnetic signal is received by the receiving coil assembly. The receiving coil assembly includes a first receiving coil and a second receiving coil. The first receiving coil is arranged between the first transmitting coil and the second transmitting coil, and the second receiving coil and the first receiving coil form a differential signal.

2. The method for collecting transient electromagnetic signals in a strong electromagnetic interference environment according to claim 1, characterized in that: The obtaining of the interference factor generated by the interference object on the transient electromagnetic instrument in the adjusted target state and determining the target magnetic moment upper limit value of the interference object in the target state comprises the following steps: Collect the spectrum characteristics, field strength distribution and time variation of interference objects in the target detection area to build a dynamic electromagnetic interference environment model; Conduct laboratory electromagnetic radiation effect tests on the interference object to determine the sensitivity threshold of the interference object at different frequencies and field strengths; Combining the sensitivity threshold and the geometric relative position between the transient electromagnetic instrument and the interference object, and performing electromagnetic simulation based on the dynamic electromagnetic interference environment model, the electromagnetic induction parameters of the interference object in the target state are calculated as the interference factor; A functional relationship model between the interference factor, the geometric relative position and the emission magnetic moment of the transient electromagnetic instrument is established, and the upper limit of the target magnetic moment of the interference object in the target state is determined by solving the functional relationship model.

3. The method for collecting transient electromagnetic signals in a strong electromagnetic interference environment according to claim 1, characterized in that: The method of using a genetic algorithm and determining the upper limit of the magnetic moment that can be transmitted by the transient electromagnetic instrument when measuring the measured position based on the interference factor and the preset fitness function includes the following steps: The emission magnetic moment to be optimized is encoded as a chromosome population in a genetic algorithm; Construct the fitness function of the genetic algorithm. The fitness function is specifically expressed as: ； Where M is the emission magnetic moment, To predict the signal-to-noise ratio, is the kth interference factor, is the penalty function when the interference factor exceeds the safety threshold, Hardware constraint penalty for emission moment, is the weight coefficient; Perform population optimization operations on the chromosome population based on the fitness function until the preset number of iterations or fitness value convergence conditions are met; The emission magnetic moment corresponding to the chromosome individual with the highest fitness in the chromosome population after optimization is output as the upper limit of the emission magnetic moment.

4. The method for collecting transient electromagnetic signals in a strong electromagnetic interference environment according to claim 1, characterized in that: Before the electromagnetic signal is transmitted by the transmitting coil assembly, the following steps are included: The geometric parameters, number of turns, relative position and current ratio of the two transmitting coils in the transmitting coil assembly are modeled using 3D electromagnetic simulation software to obtain a transmitting coil model. Perform parameter simulation optimization on the model parameters in the transmitting coil model. The parameter simulation goal is to minimize the rate of change of the primary magnetic flux generated by the transmitting current of the transmitting coil assembly at the initial position of the receiving coil assembly. Optimize and adjust the transmitting coil assembly according to the optimized model parameters; An alternating current is fed into the transmitting coil assembly, and the positions of the two transmitting coils are adjusted so that the amplitude of the induced voltage generated by the transmitting current measured at the initial position of the receiving coil assembly is minimized.

5. The method for collecting transient electromagnetic signals in a strong electromagnetic interference environment according to claim 4, characterized in that: Before receiving the transient electromagnetic signal through the receiving coil assembly in the transient electromagnetic instrument, the following steps are included: Electromagnetic simulation optimization is performed based on the geometric parameters, number of turns, and initial position of the two receiving coils in the receiving coil assembly. The optimization goal of the electromagnetic simulation optimization is to ensure that the two receiving coils have consistent response characteristics to far-field and spatially uniformly distributed common-mode interference signals, while having different response characteristics to near-field and underground target secondary transient electromagnetic field signals. The output signal of the receiving coil assembly is connected to the differential amplifier front-end circuit and the analog-to-digital conversion processing circuit.

6. The method for collecting transient electromagnetic signals in a strong electromagnetic interference environment according to claim 1, characterized in that: After determining the maximum emission magnetic moment, the following steps are also included: The sensor array inside the transient electromagnetic instrument collects the noise characteristics of the ambient electromagnetic noise and the interference characteristics of the interference object in real time. The interference characteristics include the state parameters of the interference object and the near-field electromagnetic leakage characteristics; Combining noise and interference characteristics and using machine learning models to dynamically predict the state change trend of the interference object in one or more future detection cycles, and evaluate the dynamic interference factor spectrum generated by the interference object on the transient electromagnetic instrument; Based on the interference characteristics, a dynamic electromagnetic susceptibility model of the interference object is constructed, and the predicted interference characteristics of the interference object are obtained by using the dynamic electromagnetic susceptibility model; The disturbance threshold of the external electromagnetic field is calculated based on the predicted interference characteristics, and the optimal emission magnetic moment interval is generated by combining the disturbance threshold and the currently determined maximum emission magnetic moment; Based on the optimal launch magnetic moment interval, a multi-objective optimization method is used to generate the optimal launch strategy of the transient electromagnetic instrument.

7. The method for collecting transient electromagnetic signals in a strong electromagnetic interference environment according to claim 6, characterized in that: The method of generating the optimal launch strategy of the transient electromagnetic instrument based on the optimal launch magnetic moment interval by using a multi-objective optimization method includes the following steps: The optimization objectives are to maximize the signal-to-noise ratio of the expected emission signal of the transient electromagnetic instrument and minimize the influence of noise characteristics and predicted interference characteristics. A multi-objective optimization algorithm is used to generate a Pareto optimal solution set containing multiple emission strategies. Each emission strategy corresponds to a set of emission magnetic moments within the optimal emission magnetic moment range, as well as preset waveform characteristics and emission timing. A risk quantification assessment is conducted for each launch strategy in the Pareto optimal solution set. The assessment includes the probability that the interference object state parameters will exceed the safety margin defined by the dynamic electromagnetic susceptibility model after the launch strategy is implemented, and the probability that the expected transmission signal quality will fail to meet the preset mission requirements. The optimal launch strategy is selected from the Pareto optimal solution set based on the evaluation results of risk quantification.

8. The method for collecting transient electromagnetic signals in a strong electromagnetic interference environment according to claim 7, characterized in that: The method of transmitting an electromagnetic signal by using a transmitting coil assembly comprises the following steps: Control the transmitting coil assembly to transmit micro-disturbance pulses using a preset special code, and monitor the immediate response characteristics of the interference object to the micro-disturbance pulses; Adjusting the launch parameters in the optimal launch strategy based on immediate response feature feedback; The transmitting coil assembly is controlled to transmit electromagnetic signals according to the adjusted optimal transmitting strategy.

9. A transient electromagnetic signal acquisition device in a strong electromagnetic interference environment, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for collecting transient electromagnetic signals in a strong electromagnetic interference environment as described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instruction is executed by a processor, the processor is configured to execute the method for collecting transient electromagnetic signals in a strong electromagnetic interference environment according to any one of claims 1 to 8.

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