Deicing method, system and device based on gas-liquid discharge mode conversion and intelligent regulation and control

Abstract

The invention relates to a deicing method, system and device based on gas-liquid discharge mode conversion and intelligent regulation, and the method comprises the steps: monitoring and predicting an icing state, and adjusting the output of a multi-gear adjustable power supply according to a prediction result, so as to generate weak spark discharge; when the icing condition is met, the needed energy is calculated according to the monitoring data to adjust the output of the multi-gear adjustable power supply so as to generate dielectric barrier glow discharge, and the discharge parameter is dynamically adjusted to maximize the strong heat input effect; the forming time and position of a gas-liquid mixture cavity under the condition of dielectric barrier glow discharge are predicted in real time by introducing a thermodynamic principle and an ice layer melting model, and after the cavity is detected to be formed, the optimal opportunity and energy of strong arc discharge are determined according to cavity parameters; therefore, the output of the multi-gear adjustable power supply is adjusted to generate strong arc discharge, and the cavity of the gas-liquid mixture is induced to expand and break, so that the ice layer falls off. The invention provides a more efficient, environment-friendly and reliable deicing solution.

Description

Technical Field

[0001] The present invention relates to the field of deicing control technology, and in particular to a deicing method, system and device based on gas-liquid discharge mode conversion and intelligent regulation. Background Art

[0002] In cold climates, icing poses a serious threat to various equipment and structures, such as aircraft wings, wind turbine blades, and power transmission lines. Traditional deicing methods mainly include mechanical deicing, thermal deicing, and chemical deicing. However, each of these methods has obvious disadvantages.

[0003] Although mechanical deicing is direct and effective, it is easy to damage the surface of the equipment, especially for equipment with complex geometric shapes. It is more difficult to operate and has high risks. In addition, manual operation is difficult and dangerous in bad weather. Thermal deicing melts the ice layer by heating, which has high energy consumption and overheating may damage the equipment. There are also technical difficulties in uniformly heating large equipment and completely melting the ice layer. Chemical deicing relies on chemical reagents, which may pollute the environment and corrode equipment. Long-term use may also affect equipment performance.

[0004] In recent years, deicing methods based on discharge phenomena have gradually emerged, among which gas-liquid discharge mode conversion technology has attracted widespread attention due to its high efficiency and environmental protection. However, despite the many advantages of gas-liquid discharge deicing technology, there are still some problems that need to be solved in the existing technology.

[0005] On the one hand, the conversion and control of the discharge mode lack intelligence. In the existing gas-liquid discharge deicing system, the adjustment of the discharge parameters usually relies on preset programs or manual operations, and cannot be adjusted in real time according to the actual icing state. This results in insufficient or excessive discharge energy during the deicing process, affecting the deicing efficiency and energy utilization. On the other hand, the prediction of the formation time and position of the gas-liquid mixture cavity is not accurate enough. In the gas-liquid discharge deicing process, the formation of the gas-liquid mixture cavity is the key to the strong arc discharge inducing the shedding of the ice layer. However, the existing technology lacks accuracy and reliability in predicting the time and position of cavity formation, which makes it difficult to determine the optimal time and energy of the strong arc discharge. This not only affects the deicing effect, but may also cause unnecessary damage to the equipment. Summary of the invention

[0006] 1. Technical issues to be resolved

[0007] In view of the above-mentioned shortcomings and deficiencies of the prior art, the present invention provides a deicing method, system and device based on gas-liquid discharge mode conversion and intelligent regulation, which solves the technical problems that the discharge mode conversion and control of the existing gas-liquid discharge deicing technology lack intelligence, and the formation time and position prediction of the gas-liquid mixture cavity is not accurate enough, which affects the deicing efficiency and equipment safety.

[0008] (2) Technical solution

[0009] To achieve the above object, the main technical solutions adopted by the present invention include:

[0010] In a first aspect, an ice removal method based on gas-liquid discharge mode conversion and intelligent regulation provided by an embodiment of the present invention includes: monitoring and predicting the icing state by implementing weak spark discharge, verifying the prediction result according to the dynamic characteristic change of the weak spark discharge, and adjusting the output of a multi-stage adjustable power supply according to the verified prediction result to generate weak spark discharge with icing state feedback; when the preset icing condition is reached, calculating the required energy according to the acquired monitoring data to adjust the output of the multi-stage adjustable power supply to generate dielectric barrier glow discharge, and dynamically adjusting the discharge parameters to maximize the strong heat input effect; by introducing the thermodynamic principle and the ice layer melting model, predicting in real time the formation time and position of the gas-liquid mixture cavity under the condition of dielectric barrier glow discharge, after detecting the formation of the cavity, determining the optimal timing and energy of the strong arc discharge according to the cavity parameters; based on the optimal timing and energy of the strong arc discharge, adjusting the output of the multi-stage adjustable power supply to generate strong arc discharge, inducing the expansion and rupture of the gas-liquid mixture cavity and causing the ice layer to fall off.

[0011] Optionally, monitoring and predicting the icing state by implementing weak spark discharge, verifying the prediction result according to the dynamic characteristic change of the weak spark discharge, and adjusting the output of the multi-stage adjustable power supply according to the verified prediction result to generate weak spark discharge with icing state feedback includes:

[0012] Before icing occurs, controlling the multi-stage adjustable power supply to output a weak spark start pulse signal with reference parameters, generating intermittent weak spark discharge between the tungsten electrodes, and establishing a surface dielectric characteristic detection reference;

[0013] Real-time collecting a multi-dimensional environmental parameter set including the mass of supercooled water droplets, the impact velocity of supercooled water droplets, the radius of water droplets, the temperature of supercooled water droplets, the dry bulb temperature, the humidity, and the wall temperatures at multiple positions, and an arc time-frequency feature set including the peak-to-valley ratio of the discharge current, the fluctuation frequency, the energy ratio of the main frequency band of the light intensity, and the frequency band offset;

[0014] Performing feature engineering calculation on the multi-dimensional environmental parameter set to obtain an environmental parameter feature vector including the collision energy density, the temperature difference feature, the wet and cold index, the average impact velocity within a set period, and the change rate of temperature over time, and performing vectorization processing on the arc time-frequency feature set to obtain an arc time-frequency feature vector;

[0015] Performing standardization processing on the obtained environmental parameter feature vector and arc time-frequency feature vector to form a basic data set;

[0016] Calculate the data separation index and self-similarity dimension parameters based on the basic dataset, add them to the basic dataset to form an extended dataset;

[0017] Use a hybrid attention mechanism model including an input layer, a self-attention layer, a sequence attention layer, and a hybrid layer to establish an icing prediction model. Set a fully connected layer in the output layer of the icing prediction model. Train the icing prediction model based on the extended dataset, and map the output of the icing prediction model to the range of icing probability to obtain the icing probability;

[0018] When the icing probability is greater than or equal to the set icing probability threshold, control the output of the multi-stage adjustable power supply according to the obtained dynamic characteristic changes of the weak spark discharge to generate a weak spark pulse signal with an adaptive duty cycle, and apply it to the tungsten electrodes to generate a weak spark discharge with icing state feedback between the tungsten electrodes;

[0019] In the state of weak spark discharge with icing state feedback, at least two features among the arc time-frequency features are obtained in real time for joint verification. When the verification error exceeds the set first error threshold, trigger the online parameter update mechanism of the icing prediction model. When the verification error exceeds the set second error threshold continuously for N times, re-initialize the surface dielectric property detection benchmark, clear the current extended dataset and re-collect data.

[0020] Optionally, perform feature engineering calculations on the multi-dimensional environmental parameter set to obtain an environmental parameter feature vector including collision energy density, temperature difference feature, wet and cold index, average impact velocity within a set time period, and the rate of change of temperature over time, including:

[0021] Within a set time window, according to the mass of supercooled water droplets, the impact velocity of supercooled water droplets, and the radius of the water droplets, combined with the density of water, calculate the collision energy density per unit area within a set time period;

[0022] Calculate the difference between the wall temperature and the supercooled water droplet temperature to obtain an initial temperature difference value;

[0023] By obtaining the spatial derivative of the temperature in each direction of the wall surface, determine the magnitude and direction of the temperature gradient. According to the magnitude and direction of the temperature gradient, determine the temperature gradient coefficient, apply the determined temperature gradient coefficient to the initial temperature difference value for correction calculation, and take the absolute value of the corrected temperature difference value to obtain the temperature difference feature;

[0024] Calculate the dew point temperature according to the humidity and the dry bulb temperature, use an empirical formula or a chart to convert the dew point temperature and the estimated environmental relative humidity into the wet bulb temperature, and calculate the difference between the wet bulb temperature and the dry bulb temperature to obtain the wet and cold index;

[0025] Calculate the average impact velocity within a set time period and the rate of change of temperature over time.

[0026] Optionally, calculate the data separation index and the self-similarity dimension parameter based on the basic dataset, add them to the basic dataset, and form an extended dataset including:

[0027] Select the embedding dimension and time delay adapted to the characteristics of the basic dataset, and use the selected embedding dimension and time delay to perform phase space reconstruction on the time series data in the basic dataset;

[0028] In the reconstructed phase space, find the nearest neighbor points of each point. As time evolves, calculate the rate of change of the distance between each point and its nearest neighbor point, and introduce the distribution density or directionality of the points within the neighborhood to weight the rate of change of the distance to obtain the data separation index;

[0029] Divide the time series data in the basic dataset into multiple boxes of different sizes, where each box represents a segment of the time series data within a specific size range;

[0030] Count the number of data points contained in each box. By changing the box size and repeating the counting of the number of data points in each box, obtain the relationship between the number of boxes and the number of data points at different sizes. By fitting the relationship curve, obtain the self-similarity dimension parameter;

[0031] Use the data separation index and the self-similarity dimension parameter as new feature dimensions, and splice or merge them with the original features in the basic dataset to form a preliminary fusion feature set containing the original features and the newly added data separation index and self-similarity dimension parameter features;

[0032] Use a selected one-base learning algorithm to perform training and learning on the preliminary fusion feature set, sort according to the weights or coefficients of the features obtained from the training and learning, and recursively remove the features with the smallest weights or coefficients until the predetermined number of features or performance indicators are reached to form an extended dataset after iterative screening.

[0033] Optionally, when the preset icing condition is reached, calculate the required energy based on the acquired monitoring data to adjust the output of the multi-stage adjustable power supply to generate dielectric barrier glow discharge, and dynamically adjust the discharge parameters to maximize the strong heat input effect, including:

[0034] Real-time monitor and quantitatively process the ice layer thickness and icing speed to obtain the current icing degree;

[0035] When the icing degree is greater than or equal to the preset icing degree threshold, trigger the set mode conversion condition, and calculate the energy required for the dielectric barrier glow discharge through the dielectric barrier glow discharge energy calculation formula;

[0036] According to the calculated energy required for the dielectric barrier glow discharge, send a glow discharge driving signal to the multi-stage adjustable power supply to switch the tungsten electrode to the dielectric barrier glow discharge mode;

[0037] Adjust the combination according to the energy required for dielectric barrier glow discharge and the preset discharge parameter adjustment to maximize the strong heat input effect of dielectric barrier glow discharge, and adjust the discharge parameters;

[0038] Adjust the dielectric barrier glow discharge parameters of the tungsten electrode through a multi-stage adjustable power supply according to the adjusted discharge parameters, and simultaneously monitor the process parameters of the ice layer change in real time;

[0039] Among them, the calculation formula for the energy of dielectric barrier glow discharge is:

[0040]

[0041] In the formula, α·h β is the basic energy term, α is the thickness influence coefficient, β is the thickness exponent, is the ice formation speed v b is the dynamic influence term on the required energy, γ is the speed influence coefficient, v0 is the reference speed, δ is the speed influence exponent, represents the combined influence term of the thermal conductivity and specific heat capacity of ice on the required energy, ε is the thermal conductivity - specific heat influence coefficient, k is the thermal conductivity, c is the specific heat capacity, h is the ice layer thickness, v c is the critical speed;

[0042] The discharge parameter adjustment combination includes:

[0043] The adjustment term for the discharge frequency is:

[0044]

[0045] In the formula, Δf is the adjustment term for the discharge frequency, k f is the adjustment coefficient, sgn is the sign function, and returns +1 or -1 according to the sign of the partial derivative of, is the objective function G obtained from expert experience and historical data f the rate of change with the frequency f, is the adjustment exponent, used to control the sensitivity of the step size to the magnitude of the partial derivative, random(-χ,χ) represents a random value within the range (-χ,χ);

[0046] The adjustment term for the voltage is:

[0047]

[0048] In the formula, ΔV is the segmented voltage adjustment step size, V min is the lower limit value of the target voltage, V max is the upper limit value of the target voltage, V t is the target voltage value, V cis the current voltage, segmented according to the relative positions of V c , V min and V max . k v1 , k v2 and k v3 are adjustment coefficients within different segments. is the objective function G obtained from expert experience and historical data. V is the rate of change of the objective function G v with respect to the voltage V, d I is the voltage adjustment direction, the current change rate is ΔI / Δt, k total is the feedback adjustment coefficient, and ΔV

[0049] The adjustment term for power is:

[0050] Δp = k p ·(E c - E o );

[0051] P n = min(max(P c + Δp, P min ), P max );

[0052] In the formula, ΔP is the adjustment term for power, E o is the energy output at the current power, E c is the energy required for dielectric barrier glow discharge, k P is the power adjustment coefficient, P n is the new adjusted power, P c is the current power, P max is the maximum power that the system can withstand, P min is the minimum power that the system can withstand.

[0053] Optionally, by introducing the principles of thermodynamics and the ice melting model, the formation time and position of the gas-liquid mixture cavity under dielectric barrier glow discharge conditions are predicted in real time. After detecting the formation of the cavity, the optimal timing and energy of the strong arc discharge are determined based on the cavity parameters, including:

[0054] Collect real-time discharge data during the dielectric barrier glow discharge process, including discharge parameters, the intensity of the thermal input effect under different discharge parameters, and the ice layer temperature distribution;

[0055] Based on the real-time discharge data, establish an ice melting simulation model by introducing the influencing factors of heat conduction, latent heat of phase change, and external thermal input effect of the ice layer;

[0056] Discretize the continuous spatial and temporal domains into a finite number of grid points or nodes. At each grid point, convert the ice melting simulation model into a difference equation or a finite element equation;

[0057] Input the collected real-time discharge data as the external heat input boundary condition into the ice melting simulation model. Use time-stepping iteration to solve the discretized ice melting simulation model. During the iterative solution process, record the temperature distribution, melting phase field, and cavity formation at each time step;

[0058] When the preset cavity formation condition is met, record the time step at which the cavity first forms, and convert it to the actual time to predict the cavity formation time. At the same time, determine the position and boundary of the cavity in the ice melting region;

[0059] Obtain the cavity formation speed by dividing the change in cavity volume or area between adjacent time points by the time interval;

[0060] Determine the optimal timing of strong arc discharge based on the cavity formation time and the cavity formation speed; among them, the optimal timing satisfies that the cavity formation speed reaches the set formation stability value or starts to decelerate, and the cavity formation time is within the safe time when the change in volume or area after cavity formation is less than the set value;

[0061] Calculate the optimal energy required for strong arc discharge according to the size and position of the cavity through the optimal energy formula;

[0062] Among them, the ice melting simulation model is:

[0063]

[0064] In the formula, q(f, V, P) is the heat input effect coefficient, which is a function determined by expert experience and historical data corresponding to the discharge frequency including frequency f, voltage V, and power P. Q(x, y, z, t; f, V, P) is the external heat input distribution, representing the heat input intensity distribution under different discharge parameters. (x, y, z) is the spatial position, t is the time, Q(x, y, z, t; f, V, P) = q(x, y, z) · q(f, V, P) · g(t), q(x, y, z) is the basic heat input distribution in space, g(t) is the time modulation function, β0 is the heat conduction coefficient, is the spatial Laplacian operator of the ice layer temperature, γ0 is the latent heat coefficient of phase change, is the partial derivative of the melting phase field with respect to time, δ0 is the environmental temperature influence coefficient, T b is the ice layer temperature, T amb is the environmental temperature;

[0065] The optimal energy formula is:

[0066]

[0067] In the formula, E f is the optimal energy, η is the energy conversion efficiency of the discharge, and ∫∫∫V k represents the triple integral over the cavity volume V k ε0 is the vacuum permittivity, is the value of the electric field strength at the position r, F o is the position factor, F s is the shape factor.

[0068] In a second aspect, an ice removal system based on gas-liquid discharge mode conversion and intelligent regulation according to an embodiment of the present invention includes: a weak spark discharge module, configured to monitor and predict the icing state by implementing weak spark discharge, verify the prediction result according to the dynamic characteristic change of the weak spark discharge, and adjust the output of a multi-stage adjustable power supply according to the verified prediction result to generate a weak spark discharge with icing state feedback; a glow discharge module, configured to calculate the required energy according to the acquired monitoring data to adjust the output of the multi-stage adjustable power supply to generate a dielectric barrier glow discharge and dynamically adjust the discharge parameters to maximize the strong heat input effect when reaching the preset icing condition; a timing and energy determination module, configured to introduce the thermodynamic principle and the ice layer melting model to predict in real time the formation time and position of the gas-liquid mixture cavity under the condition of dielectric barrier glow discharge, and after detecting the formation of the cavity, determine the optimal timing and energy of the strong arc discharge according to the cavity parameters; and a strong arc discharge module, configured to adjust the output of the multi-stage adjustable power supply based on the optimal timing and energy of the strong arc discharge to generate a strong arc discharge, induce the expansion and rupture of the gas-liquid mixture cavity and cause the ice layer to fall off.

[0069] In a third aspect, an ice removal device based on gas-liquid discharge mode conversion and intelligent regulation according to an embodiment of the present invention includes: an insulating layer; at least a pair of tungsten electrodes, with the insulating layer covering and sealingly connecting to the outside of the tungsten electrodes; a high-voltage wire, connected between the tungsten electrodes and the multi-stage adjustable power supply for transmitting electric energy; a multi-stage adjustable power supply, capable of outputting electrical signals with different voltages, currents, and pulse widths to the tungsten electrodes to control the tungsten electrodes to perform discharge modes including weak spark discharge, dielectric barrier glow discharge, and strong arc discharge; and a main controller, connected to the multi-stage adjustable power supply for executing the ice removal method based on gas-liquid discharge mode conversion and intelligent regulation as described above.

[0070] Optionally, the thickness of the insulating layer is greater than 2.5 mm, and the withstand voltage exceeds Umax = 25 kV, where Umax is the maximum output voltage of the multi-stage adjustable power supply; each pair of tungsten electrodes includes a positive tungsten electrode and a negative tungsten electrode, and the shapes of the positive tungsten electrode and the negative tungsten electrode are both cylindrical, with a diameter of 2 - 3 mm, the distance between each pair of tungsten electrodes is 2 - 3 mm, and an electrically heated copper patch is arranged between each pair of tungsten electrodes.

[0071] Optionally, the first gear of the multi - gear adjustable power supply outputs a weak spark start pulse signal, causing a weak spark discharge to occur between a pair of tungsten electrodes, with an output voltage Umax. The power of the weak spark discharge is less than 10 W. The icing state is monitored and predicted in real time. When the weak spark discharge cannot continue due to icing, the first gear ends; the second gear of the multi - gear adjustable power supply outputs a glow discharge drive signal, causing a dielectric barrier glow discharge to occur between a pair of tungsten electrodes. The sine - wave frequency is 3 - 5 kHz, the output voltage is 4 - 6 kV, and the glow discharge power is 1800 - 2200 W. When a cavity is detected to be formed, the second gear ends; the third gear of the multi - gear adjustable power supply outputs a strong arc trigger pulse signal, causing a strong arc discharge to occur between the two tungsten electrodes within the gas - liquid mixture cavity. The output voltage is about 10 - 15 kV, and the single - discharge energy is 25 - 30 joules. The third gear is turned on 80 - 120 ms after the second gear ends working.

[0072] (III) Beneficial effects

[0073] The beneficial effects of the present invention are as follows: First, by monitoring and predicting the icing state and timely adjusting the output of the multi - gear adjustable power supply according to the prediction results to generate a weak spark discharge, this step makes the de - icing process more intelligent, can respond to icing situations in advance, effectively prevent ice accumulation, and avoid the lag and passivity in traditional de - icing methods.

[0074] Furthermore, when the preset icing conditions are reached, the present invention can accurately calculate the required energy based on the obtained monitoring data, adjust the power supply output to generate a dielectric barrier glow discharge, and dynamically adjust the discharge parameters to maximize the strong heat input effect. By accurately calculating the required energy and using the method of dynamically adjusting the discharge parameters, the de - icing process is made more precise, efficient, and stable, further improving the de - icing efficiency.

[0075] Moreover, by introducing the thermodynamic principle and the ice - melting model of the ice layer, the present invention can predict in real time the formation time and position of the gas - liquid mixture cavity under the condition of dielectric barrier glow discharge. This prediction ability enables the accurate determination of the best timing and energy of the strong arc discharge, thus avoiding potential damage to the equipment caused by improper discharge timing or energy.

[0076] Finally, based on the best timing and energy of the strong arc discharge, the output of the multi - gear adjustable power supply is adjusted to generate a strong arc discharge, inducing the expansion and rupture of the gas - liquid mixture cavity and causing the ice layer to fall off, achieving an efficient and safe de - icing effect.

[0077] Thus, through intelligent monitoring and prediction, precise energy calculation and adjustment, and real-time cavity formation prediction, the present invention is more environmentally friendly than traditional chemical de-icing methods and will not cause pollution to the environment. Through an efficient de-icing process, the present invention reduces the equipment downtime and maintenance costs caused by icing. This not only significantly improves the efficiency, accuracy, safety of the de-icing method and the operation efficiency of the equipment, but also saves a large amount of human and material resources through precise energy control and intelligent discharge mode conversion. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 is a schematic flowchart of the method provided by an embodiment of the present invention;

[0079] Figure 2 is a specific flowchart of step S1 of the method provided by an embodiment of the present invention;

[0080] Figure 3 is a specific flowchart of step S12 of the method provided by an embodiment of the present invention;

[0081] Figure 4 is a specific flowchart of step S15 of the method provided by an embodiment of the present invention;

[0082] Figure 5 is a specific flowchart of step S2 of the method provided by an embodiment of the present invention;

[0083] Figure 6 is a specific flowchart of step S3 of the method provided by an embodiment of the present invention;

[0084] Figure 7 is a schematic diagram of the de-icing process of the device provided by an embodiment of the present invention.

[0085]

DESCRIPTION OF THE REFERENCE NUMERALS

[0086] 1: Weak spark discharge phenomenon; 2: Insulating layer; 3: Tungsten electrode assembly; 4: Ice layer; 5: Dielectric barrier glow discharge phenomenon; 6: Cavity; 7: Strong arc discharge phenomenon. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0087] To better explain the present invention for easy understanding, the present invention will be described in detail below with reference to the drawings through specific embodiments.

[0088] Before that, to facilitate understanding of the technical solution provided by the present invention, some concepts will be introduced below.

[0089] Weak spark start pulse signal: This is a special electrical pulse signal with the characteristics of high voltage, small current and low energy (specifically, voltage ≥ 10 kV and current in the milliampere range), and its pulse width is in the nanosecond level. This signal is used to generate weak spark discharge between two tungsten electrodes.

[0090] Weak spark discharge: Weak spark discharge refers to the discharge phenomenon that occurs between two tungsten electrodes due to the action of the weak spark start pulse signal. This discharge is relatively weak, specifically a spark discharge with a current in the milliampere range, but it is sufficient to play a role in the initial stage when supercooled water droplets impact the wall surface and gradually freeze. When the supercooled water droplets continuously impact the wall surface to form relatively compact clear ice or mixed ice, the weak spark discharge may not be able to continue, and at this time, it is necessary to switch to a stronger discharge mode.

[0091] Glow discharge driving signal: This is a sine wave signal with low voltage, large current and high frequency (specifically, voltage < 4 kV and current in the ampere range). In the de-icing device, this signal is used to generate dielectric barrier glow discharge between two tungsten electrodes.

[0092] Dielectric barrier glow discharge: Combining the dielectric barrier structure and the characteristics of glow discharge, it realizes the efficient, large-area and uniform plasma generation under atmospheric pressure or near atmospheric pressure, so as to be used for the debonding and melting of ice from the wall surface. Dielectric barrier glow discharge has relatively high energy and can produce a strong heat input effect to heat the interior of the ice layer. Under the action of the strong heat input effect, the local ice layer near the two tungsten electrodes will melt to generate a gas-liquid mixture cavity.

[0093] Strong arc trigger pulse: This is a pulse signal with medium voltage, large current and a pulse width in the microsecond level, specifically 4 ≤ voltage < 10 kV and current in the hundreds of amperes or more range. This signal is used to generate strong arc discharge in the gas-liquid mixture cavity between two tungsten electrodes.

[0094] Strong arc discharge: It refers to the strong discharge phenomenon that occurs in the gas-liquid mixture cavity between two tungsten electrodes due to the action of the strong arc trigger pulse (specifically, an arc discharge with an instantaneous voltage reaching hundreds of amperes or more). This discharge can induce the generation of rapidly expanding bubbles, and when the bubbles impact and break against the ice layer, a strong impact force will be generated.

[0095] Such as Figure 1As shown in the figure, a de-icing method based on gas-liquid discharge mode conversion and intelligent regulation proposed by an embodiment of the present invention includes: monitoring and predicting the icing state through weak spark discharge, verifying the prediction result according to the dynamic characteristic change of weak spark discharge, and adjusting the output of a multi-stage adjustable power supply according to the verified prediction result to generate weak spark discharge with icing state feedback; when the preset icing condition is reached, calculating the required energy according to the acquired monitoring data and adjusting the output of the multi-stage adjustable power supply to generate dielectric barrier glow discharge, and dynamically adjusting the discharge parameters to maximize the strong heat input effect; by introducing the thermodynamic principle and the ice layer melting model, predicting in real time the formation time and position of the gas-liquid mixture cavity under the condition of dielectric barrier glow discharge, and after detecting the formation of the cavity, determining the optimal timing and energy of the strong arc discharge according to the cavity parameters; based on the optimal timing and energy of the strong arc discharge, adjusting the output of the multi-stage adjustable power supply to generate strong arc discharge, inducing the expansion and rupture of the gas-liquid mixture cavity and causing the ice layer to fall off.

[0096] First of all, by monitoring and predicting the icing state and adjusting the output of the multi-stage adjustable power supply in a timely manner according to the prediction result to generate weak spark discharge, this step makes the de-icing process more intelligent, can respond to the icing situation in advance, effectively prevent the accumulation of ice layers, and avoid the lag and passivity in traditional de-icing methods.

[0097] Furthermore, when the preset icing condition is reached, the present invention can accurately calculate the required energy according to the acquired monitoring data, adjust the power supply output to generate dielectric barrier glow discharge, and dynamically adjust the discharge parameters to maximize the strong heat input effect. By accurately calculating the required energy and using the method of dynamically adjusting the discharge parameters, the de-icing process is made more accurate, efficient and stable, and the de-icing efficiency is further improved.

[0098] Moreover, by introducing the thermodynamic principle and the ice layer melting model, the present invention can predict in real time the formation time and position of the gas-liquid mixture cavity under the condition of dielectric barrier glow discharge. This prediction ability enables the accurate determination of the optimal timing and energy of the strong arc discharge, thus avoiding potential damage to the equipment caused by improper discharge timing or energy.

[0099] Finally, based on the optimal timing and energy of the strong arc discharge, adjusting the output of the multi-stage adjustable power supply to generate strong arc discharge, inducing the expansion and rupture of the gas-liquid mixture cavity and causing the ice layer to fall off, achieving an efficient and safe de-icing effect.

[0100] Thus, through intelligent monitoring and prediction, precise energy calculation and adjustment, and real-time prediction of cavity formation, the present invention is more environmentally friendly than traditional chemical de-icing methods and will not cause pollution to the environment. At the same time, through precise energy control and intelligent discharge mode conversion, energy consumption is also reduced. Through an efficient de-icing process, the present invention reduces equipment downtime and maintenance costs caused by icing. This not only significantly improves the efficiency, accuracy, safety of the de-icing method and the operating efficiency of the equipment, but also saves a large amount of human and material resources.

[0101] To better understand the above technical solution, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings.

[0102] Specifically, an embodiment of the present invention provides a de-icing method based on gas-liquid discharge mode conversion and intelligent regulation, including:

[0103] S1. Monitor and predict the icing state by implementing weak spark discharge, verify the prediction result according to the dynamic characteristic change of the weak spark discharge, and adjust the output of a multi-stage adjustable power supply according to the verified prediction result to generate weak spark discharge with icing state feedback.

[0104] Further, as Figure 2 shown, step S1 includes:

[0105] S11. Before icing occurs, control the multi-stage adjustable power supply to output a weak spark start pulse signal with reference parameters, generate intermittent weak spark discharge between tungsten electrodes, and establish a surface dielectric property detection reference. Among them, the surface dielectric property detection reference includes a voltage amplitude of 0.5 - 2 kV, a pulse width of 10 - 50 μs, and a repetition frequency of 10 - 50 Hz.

[0106] S12. Real-time collect a multi-dimensional environmental parameter set including the mass of supercooled water droplets, the impact velocity of supercooled water droplets, the radius of water droplets, the temperature of supercooled water droplets, the dry bulb temperature, the humidity, and the wall temperatures at multiple positions, and an arc time-frequency feature set including the peak-valley ratio of the discharge current, the fluctuation frequency, the energy proportion of the main optical frequency band, and the frequency band offset.

[0107] Even further, as Figure 3 shown, step S12 includes:

[0108] S121. Within a set time window, according to the mass of supercooled water droplets, the impact velocity of supercooled water droplets, and the radius of water droplets, and in combination with the density of water, obtain the collision energy density per unit area within a set period:

[0109]

[0110] where, σ is the impact energy density per unit area, ρ is the density of water, r is the radius of the water droplet, and v s is the impact velocity of the water droplet.

[0111] S122. Calculate the difference between the wall temperature and the subcooled water droplet temperature to obtain the initial temperature difference value; determine the magnitude and direction of the temperature gradient by obtaining the spatial derivative of the temperature in each direction of the wall, and determine the temperature gradient coefficient according to the magnitude and direction of the temperature gradient. Apply the determined temperature gradient coefficient to the initial temperature difference value for correction calculation, and take the absolute value of the corrected temperature difference value to obtain the temperature difference characteristic.

[0112] In a specific embodiment, first, subtract the wall temperature values of multiple points obtained in real time from the water droplet temperature values to obtain the initial temperature difference value. Then, use a numerical differentiation method (such as the finite difference method) to calculate the spatial derivative of the temperature near the wall, that is, the temperature gradient. The temperature gradient is a vector, and its direction and magnitude respectively represent the fastest direction and the rate of change of temperature change. Furthermore, determine the temperature gradient coefficient according to the magnitude and direction of the temperature gradient, and apply the determined temperature gradient coefficient to the initial temperature difference value for correction calculation (such as multiplication calculation), and take the absolute value of the corrected temperature difference value to obtain the temperature difference characteristic. Verify the corrected temperature difference value to ensure that it conforms to physical laws and actual situations. If abnormal or unreasonable results are found, it is necessary to re-analyze and evaluate the temperature gradient.

[0113] Specifically, the magnitude of the temperature gradient is:

[0114]

[0115] where, is the spatial derivative of the temperature in the horizontal direction, T i+1,j and T i-1,j are the temperature values of two adjacent positions of the current point (i, j) in the x direction respectively, Δx is the distance between the corresponding two positions, is the spatial derivative of the temperature in the y direction, T i,j+1 and T i,j-1 are the temperature values of two adjacent positions of the current point (i, j) in the vertical direction respectively, and Δy is the distance between the corresponding two positions.

[0116] The direction of the temperature gradient is:

[0117]

[0118] The temperature gradient coefficient is:

[0119]

[0120] Where \(a_0\) and \(b_0\) are constants, \(\theta_0\) is the reference direction angle. The value range of \(a_0\) obtained by fitting using experimental data is \([0, 1, 10]\), the value range of \(b_0\) is \([0, 1, 5]\), and the value range of \(\theta_0\) is \([0, 2\pi]\).

[0121] S123. Calculate the dew point temperature based on the humidity and the dry bulb temperature. Use an empirical formula or a chart to convert the dew point temperature and the estimated ambient relative humidity into the wet bulb temperature. By calculating the difference between the wet bulb temperature and the dry bulb temperature, the wet cooling index is obtained.

[0122] In step S123, measure the current humidity value of the environment through a measuring device. This humidity value can be the absolute humidity, relative humidity, or other forms of humidity representation; at the same time, measure the dry bulb temperature of the environment, that is, the actual temperature of the air.

[0123] Then, using the known humidity and dry bulb temperature values, combined with relevant physical principles or empirical formulas, calculate the dew point temperature of the environment; the dew point temperature is the temperature at which water vapor in the air reaches saturation and is a function of humidity and temperature.

[0124] According to the calculated dew point temperature and the relative humidity of the environment (if not directly measured, it can be estimated by other means or assume a typical value), use an empirical formula or consult a relevant chart to convert the dew point temperature and the relative humidity into the wet bulb temperature; the wet bulb temperature is the temperature value considering the influence of air humidity on temperature perception and is usually lower than the dry bulb temperature.

[0125] Subsequently, compare the converted wet bulb temperature with the measured dry bulb temperature and calculate the difference between them; this difference is the wet cooling index, which reflects the heat carried away by the evaporation of moisture in the air and is an index for evaluating the influence of air humidity and temperature on icing. It is used to evaluate the influence of air conditions on the icing potential and provides strong support for icing prediction and anti-icing measures.

[0126] S124. Calculate the average impact speed and the rate of change of temperature over time within a set time period.

[0127] S13. Perform feature engineering calculations on the multi-dimensional environmental parameter set to obtain an environmental parameter feature vector containing the collision energy density, temperature difference feature, wet cooling index, average impact speed within a set time period, and the rate of change of temperature over time, and vectorize the arc time-frequency feature set to obtain an arc time-frequency feature vector.

[0128] S14. Standardize the obtained environmental parameter feature vector and arc time-frequency feature vector to form a basic data set.

[0129] S15. Calculate the data separation index and the self-similarity dimension parameter based on the basic data set and add them to the basic data set to form an extended data set.

[0130] Further, as Figure 4 shown, step S15 includes:

[0131] S151. Select an embedding dimension and a time delay adapted to the characteristics of the basic data set, and use the selected embedding dimension and time delay to perform phase space reconstruction on the time series data in the basic data set.

[0132] S152. In the reconstructed phase space, find the nearest neighbor points of each point. As time evolves, calculate the rate of change of the distance between each point and its nearest neighbor point, and introduce the distribution density or directionality of the points within the neighborhood to weight the rate of change of the distance, so as to obtain a data separation index.

[0133] In a specific embodiment, an embedding dimension and a time delay adapted to the characteristics of the basic data set are selected; the selected embedding dimension and time delay are used to perform phase space reconstruction on the time series data in the basic data set to reveal its potential dynamic characteristics; in the reconstructed phase space, a search algorithm such as a KD tree or a ball tree algorithm is adopted to quickly and accurately find the nearest neighbor points of each point to improve the calculation efficiency; as time evolves, not only calculate the rate of change of the distance between each point and its nearest neighbor point, but also combine the local dynamic characteristics of the time series data, such as by considering the distribution density or directionality of the points within the neighborhood, to weight the rate of change of the distance, so as to obtain a data separation index as:

[0134]

[0135] In the formula, t0 is the time step, ω i is the weighting coefficient of the i-th data point determined by empirical data, and T(i) is the distance between the i-th time point and the nearest neighbor point.

[0136] S153. Divide the time series data in the basic data set into multiple boxes of different sizes, and each box represents a segment of the time series data within a specific size range.

[0137] S154. Count the number of data points contained in each box. By changing the box size, repeat counting the number of data points contained in each box, obtain the relationship between the number of boxes and the number of data points under different sizes, and obtain a self-similarity dimension parameter by fitting the relationship curve.

[0138] In another specific embodiment, the time series data in the basic dataset is divided into multiple boxes of different sizes. Each box represents a segment of the time series data within a specific size range. For each delimited box, count the number of data points it contains. This step involves counting the time series data within the box range to determine the data density within each box. Change the size of the box and repeat the above steps of data division and data point counting. By changing the box size, the distribution of the time series data at different sizes can be obtained. Then, based on the number of boxes at different sizes and the number of data points contained in each box, establish the relationship between the box size and the number of data points. By fitting the relationship curve between the box size and the number of data points obtained, use linear regression or logarithmic fitting to obtain the self-similarity dimension parameter, which is a key indicator describing the complexity and self-similarity of the time series data.

[0139] S155. Use the data separation index and the self-similarity dimension parameter as new feature dimensions, and splice or merge them with the original features in the basic dataset to form a preliminary fusion feature set containing the original features and the newly added data separation index and self-similarity dimension parameter features.

[0140] S156. Use a selected base learner to perform training and learning on the preliminary fusion feature set, sort according to the weights or coefficients of the features obtained from the training and learning, and recursively remove the features with the smallest weights or coefficients until the predetermined number of features or performance indicators are reached to form an extended dataset after iterative screening.

[0141] S16. Use a hybrid attention mechanism model including an input layer, a self-attention layer, a sequence attention layer, and a hybrid layer to establish an icing prediction model. Set a fully connected layer in the output layer of the icing prediction model, train the icing prediction model based on the extended dataset, and map the output of the icing prediction model to the range of icing probabilities to obtain the icing probability.

[0142] Specifically, the components of the constructed icing prediction model are detailed as follows:

[0143] Input layer: Receive the preprocessed extended dataset as input.

[0144] Self-attention layer: Adopt the self-attention mechanism in Transformer to calculate the global dependencies in the input data. The self-attention mechanism generates attention weights through the operations of query (Q), key (K), and value (V) matrices, and performs weighted summation on the input data to capture the global features in the data. Specifically, use the calculation formula: Attention(Q, K, V) = softmax(QK T / sqrt(d k ))V, where Q, K, and V are the query, key, and value matrices respectively, and dk is the dimension of the key.

[0145] Sequence attention layer: Combining the traditional sequence attention mechanism in LSTM to capture local temporal features in the input data. The sequence attention mechanism calculates attention weights through the hidden state of LSTM and performs weighted summation on the input data, thereby emphasizing the temporal dependence relationship in the data.

[0146] Hybrid layer: Fusing spatio-temporal features and arc time-frequency features through the outputs of the self-attention layer and the sequence attention layer to form the output of the hybrid attention mechanism layer, and mapping it to the icing probability through a fully connected layer.

[0147] S17. When the icing probability is greater than or equal to the set icing probability threshold, control the output of the multi-stage adjustable power supply according to the dynamic feature changes of the weak spark discharge including the frequency band offset and the proportion of the main frequency band energy of the light intensity to generate a weak spark pulse signal with an adaptive duty cycle, and apply it to the tungsten electrodes to generate a weak spark discharge with icing state feedback between the tungsten electrodes.

[0148] Specifically, the weak spark pulse signal with an adaptive duty cycle follows the following duty cycle dynamic adjustment rule:

[0149]

[0150] In the formula, D n is the new duty cycle, D b is the reference duty cycle, f ref is the reference frequency point (the center frequency of the main frequency band of the discharge light intensity under a clean surface (ice-free state)), △f is the frequency band offset, P s is the proportion of the main frequency band energy of the light intensity. And the peak-to-valley ratio of the discharge current is K c , the fluctuation frequency is f c .

[0151] S18. In the state of weak spark discharge with icing state feedback, at least two features among the arc time-frequency features are obtained in real time for joint verification. When the verification error exceeds the set first error threshold, trigger the online update mechanism of the parameters of the icing prediction model. When the verification error exceeds the set second error threshold continuously for N times, re-initialize the surface dielectric property detection benchmark, clear the current extended data set and re-collect data.

[0152] In a specific embodiment, according to the arc time-frequency features [K' c , f′ c , P′ S , Δf'] updated by real-time acquisition, calculate the verification error:

[0153]

[0154] Wherein, K' c , P s ' are respectively the measured peak-valley ratio of the discharge current, and are respectively the predicted peak-valley ratio of the discharge current and the proportion of the main frequency band energy of the light intensity;

[0155] When E val is greater than E th1 (the first threshold value of 0.15 - 0.25), trigger the online update mechanism of the parameters of the icing probability model; when E val is greater than E th2 (the second threshold value of 0.3 - 0.4) for N = 3 consecutive times, re-initialize the surface dielectric property detection reference, clear the current extended data set and re-collect data.

[0156] S2. When the preset icing condition is reached, calculate the required energy according to the acquired monitoring data to adjust the output of the multi-stage adjustable power supply to generate dielectric barrier glow discharge, and dynamically adjust the discharge parameters to maximize the strong heat input effect.

[0157] Further, as Figure 5 shown, step S2 includes:

[0158] S21. Monitor and quantitatively process the ice layer thickness and icing speed in real time to obtain the current icing degree.

[0159] In one embodiment, a non-linear function is constructed to describe the relationship between the icing degree, the ice layer thickness and the icing speed; collect the ice layer thickness and icing speed data under different conditions, and the corresponding icing degree data (use an array of high-frequency pulsed laser rangefinders to measure the ice layer thickness in a non-contact manner, and eliminate environmental vibration noise through the dynamic differential algorithm; measure the icing speed by the collaborative measurement of a Doppler radar and an infrared thermal imager, and jointly invert the icing speed based on the phase shift of the reflected signal on the ice layer surface and the temperature distribution gradient).

[0160] Use statistical software or programming tools (such as the SciPy or NumPy libraries in Python) for data fitting to obtain the optimal coefficient values in the non-linear function. Furthermore, verify the effectiveness of the formula through theoretical calculations or numerical simulations, and adjust the coefficients to match the actual observed data. Finally, the following icing degree formula is obtained:

[0161] I = 0.5·h 2 + 1.5·v b + 0.1·h·v b ;

[0162] Wherein, h is the ice layer thickness, and v b is the icing speed.

[0163] S22. When the degree of icing is greater than or equal to the preset icing degree threshold, resulting in the inability to continue weak spark discharge, trigger the set mode conversion condition, and calculate the required energy of dielectric barrier glow discharge through the dielectric barrier glow discharge energy calculation formula. Among them, the dielectric barrier glow discharge energy calculation formula is:

[0164]

[0165] In the formula, α·h β is the basic energy term, α is the thickness influence coefficient, β is the thickness exponent, which determines the non-linear degree of the influence of thickness on energy, and the value range is 1.2 - 1.8, is the dynamic influence term of the icing speed v b on the required energy, γ is the speed influence coefficient, v0 is the reference speed used to normalize the icing speed, δ is the speed influence exponent, and the value range is 0.5 - 1.2, which determines the non-linear degree of the influence of speed change on energy; represents the combined influence term of the thermal conductivity and specific heat capacity of ice on the required energy, ε is the thermal conductivity - specific heat influence coefficient, and the value range is 0.05 - 0.15, k is the thermal conductivity of ice, c is the specific heat capacity of ice, h is the ice layer thickness, v c is the critical speed, which is used to adjust the change rate of the logarithmic function.

[0166] S23. According to the calculated required energy of dielectric barrier glow discharge, send a glow discharge drive signal to the multi-stage adjustable power supply to switch the tungsten electrode to the dielectric barrier glow discharge mode.

[0167] S24. Adjust the discharge parameters according to the required energy of dielectric barrier glow discharge and the preset combination of discharge parameters to maximize the strong heat input effect of dielectric barrier glow discharge.

[0168] In order to dynamically adjust the discharge parameters to maximize the strong heat input effect of dielectric barrier glow discharge, the following combination of discharge parameter adjustments is preset:

[0169] The adjustment term of the discharge frequency is:

[0170]

[0171] In the formula, Δf is the adjustment term of the discharge frequency, k f is the adjustment coefficient, and the value range is [0.01, 1.0], sgn is the sign function, and it returns +1 or -1 according to the sign of the partial derivative is the objective function G f (generally take a quadratic function) obtained from expert experience and historical data, which is the rate of change with the frequency f, ​It is the adjustment exponent, a non - negative real number, used to control the sensitivity of the step size to the magnitude of the partial derivative. random(-χ,χ) represents a random value within the range (-χ,χ). Through the random(-χ,χ) term, in the stable discharge state, the frequency can be adjusted randomly or according to a certain rule (such as a sine wave, square wave, etc.) within the jitter range.

[0172] The adjustment term of the voltage is:

[0173]

[0174] In the formula, ΔV is the segmented voltage adjustment step size, V min is the lower limit value of the target voltage, V max is the upper limit value of the target voltage, V t is the target voltage value, V c is the current voltage. According to the relative positions of V c , V min and V max , it is segmented. k v1 , k v2 and k v3 are the adjustment coefficients within different segments. is the objective function G V (generally taken as a quadratic function) obtained from expert experience and historical data, which is the rate of change of G with respect to the voltage V. d v is the voltage adjustment direction. The current change rate is ΔI / Δt, and k I is the feedback adjustment coefficient, with a value range of [0.001, 0.1]. ΔV total is the final voltage adjustment amount.

[0175] The adjustment term of the power is:

[0176] Δp = k p ·(E c - E o );

[0177] P n = min(max(P c + Δp, P min ), P max );

[0178] In the formula, ΔP is the adjustment term of the power, E o is the energy output at the current power, E c is the energy required for dielectric barrier glow discharge, k P is the power adjustment coefficient, with a value range of [0.01, 1.0], P n is the new adjusted power, P c is the current power, P max is the maximum power that the system can withstand, Pmin is the minimum power that the system can withstand.

[0179] S25. Adjust the dielectric barrier glow discharge parameters of the tungsten electrode through a multi-stage adjustable power supply according to the adjusted discharge parameters, and simultaneously monitor the process parameters of the ice layer change in real time.

[0180] S3. By introducing the principles of thermodynamics and the ice layer melting model, predict the formation time and location of the gas-liquid mixture cavity under dielectric barrier glow discharge conditions in real time. After detecting the formation of the cavity, determine the optimal timing and energy of the strong arc discharge according to the cavity parameters.

[0181] Further, as Figure 6 shown, step S3 includes:

[0182] S31. Collect real-time discharge data during the dielectric barrier glow discharge process, including discharge parameters, the intensity of the heat input effect under different discharge parameters, and the ice layer temperature distribution.

[0183] S32. According to the real-time discharge data, establish an ice layer melting simulation model by introducing the influencing factors of heat conduction, latent heat of phase change, and external heat input effect of the ice layer.

[0184] Specifically, the ice layer melting simulation model is:

[0185]

[0186] In the formula, q(f, V, P) is the heat input effect coefficient, which is a function determined by expert experience and historical data corresponding to the discharge frequency including frequency f, voltage V, and power P (q(f, V, P) can be taken as 0.05·f 0.8 ·V 1.2 ·P 0.5 , representing the influence of these discharge parameters on the heating efficiency of the external heat source. Q(x, y, z, t; f, V, P) is the external heat input distribution, representing the heat input intensity distribution under different discharge parameters. (x, y, z) is the spatial position, t is the time, Q(x, y, z, t; f, V, P) = q(x, y, z)·q(f, V, P)·g(t), q(x, y, z) is the basic heat input distribution in space, g(t) is the time modulation function (such as linear function, piecewise function, pulse function, etc.). β0 is the heat conduction coefficient, with a value of 1.05×10 -6 ~1.15×10 -6 m 2 / s, is the spatial Laplacian operator of the ice layer temperature, γ0 is the latent heat of phase change coefficient, such as γ0 = L / ρ1·Cp (where L is the latent heat of fusion of ice, ρ1 is the density of ice, C p is the specific heat capacity of ice). is the partial derivative of the melting phase field with respect to time, δ0 is the environmental temperature influence coefficient, δ = h0 / ρ1·Cp (where h0 is the convective heat transfer coefficient, ρ1 is the density of ice, and Cp is the specific heat capacity of ice), T b is the ice layer temperature, T amb is the environmental temperature.

[0187] It should be emphasized that the ice layer melting simulation model takes into account the spatial and temporal variations of temperature, heat input, and melting degree, making the model closer to the actual physical process. By introducing the melting phase field and the latent heat coefficient of phase change, the heat absorption during the ice layer melting process can be more accurately described. The coefficients and functions in the ice layer melting simulation model can be adjusted according to specific experimental data or theoretical derivations, enabling the model to adapt to different ice layer conditions and external heat input situations.

[0188] S33. Discretize the continuous spatial and temporal domains into a finite number of grid points or nodes. At each grid point, convert the ice layer melting simulation model into a difference equation or a finite element equation. For complex ice layer shapes or boundary conditions, the finite element method can be used for solution.

[0189] S34. Input the collected real-time discharge data as the external heat input boundary condition into the ice layer melting simulation model, and use time stepping iteration to solve the discretized ice layer melting simulation model. During the iterative solution process, record the temperature distribution, melting phase field, and the formation of cavities at each time step. Specifically, time stepping algorithms (such as the explicit Euler method, implicit Euler method, etc.) can be used to gradually solve the ice layer melting model formula and update the temperature distribution and melting phase field. Record the temperature distribution, melting phase field, and the formation of cavities at each time step.

[0190] S35. When the preset cavity formation condition is met, record the time step at which the cavity first forms and convert it to the actual time to predict the cavity formation time. At the same time, determine the position and boundary of the cavity in the ice layer melting area. Extract the time point when the cavity first appears and convert it to the actual time to obtain the specific time point when the cavity first forms. Through image processing, identify and extract the specific position of the cavity in the ice layer melting area and determine the boundary of the cavity, including its shape, size, and spatial distribution.

[0191] S36. Divide the change in the volume or area of the cavity between adjacent time points by the time interval to obtain the cavity formation speed.

[0192] S37. Determine the optimal timing of the strong arc discharge based on the formation time and formation speed of the cavity; the optimal discharge timing should be selected after the cavity is formed and stabilized, and when the cavity formation speed is moderate or starts to decelerate, to ensure the best discharge effect and the least interference to the cavity formation process. Therefore, the optimal timing meets the condition that the cavity formation speed reaches the set formation stability value or starts to decelerate, and the cavity formation time is within the safe time when the change in volume or area after the cavity is formed is less than the set value. Considering the specific stage or empirical optimal discharge time window during the cavity formation process makes the discharge timing more in line with the actual application requirements.

[0193] S38. According to the size and position of the cavity, calculate the optimal energy required for the strong arc discharge through the optimal energy formula.

[0194] The present invention comprehensively considers factors such as the cavity volume, position, dielectric breakdown characteristics, discharge device efficiency, and energy loss. It calculates the breakdown energy through the spatial integration of the electric field strength, and introduces a position factor and a shape factor for adjustment. Finally, the optimal energy required for the strong arc discharge is obtained:

[0195]

[0196] In the formula, E f is the optimal energy, η is the energy conversion efficiency of the discharge, ∫∫∫V k represents the triple spatial dimension integration of the cavity volume V k , ε0 is the vacuum permittivity, is the value of the electric field strength at the position r, F p is the position factor, which is a coefficient used to correct the influence of the cavity position on the electric field distribution, reflecting the adjustment of the spatial position of the cavity in the device on the discharge energy requirement. F o = actual position electric field strength / electric field strength when the cavity is in an ideal uniform electric field,

[0197] F s is the shape factor, which is a coefficient used to correct the influence of the cavity geometry on the breakdown path, reflecting the electric field distortion effect caused by the cavity shape. k1 represents the amplification effect of the cavity sharpness on the electric field distortion, with a value range of 0.3 - 0.5, and k2 represents the influence of the cavity extension direction on the breakdown path, with a value range of 0.05 - 0.25.

[0198] S4. Based on the optimal timing and energy of the strong arc discharge, adjust the output of the multi - stage adjustable power supply to generate a strong arc discharge, induce the expansion and rupture of the cavity of the gas - liquid mixture, and cause the ice layer to fall off. Based on the optimal timing t c and energy E arc of the strong arc discharge, control the multi - stage adjustable power supply to output in three stages: Pre - breakdown stage: Apply E at time t c ​arc 40 - 60% of it is used as the pre-breakdown energy; Avalanche stage: When the vibration frequency of the cavity interface is detected to be > 10 kHz, an additional 20% of E is supplemented. arc Maintenance stage: When the cavity diameter-to-length ratio L / W > 2, the remaining energy is applied and a mechanical resonance pulse is triggered.

[0199] After that, ice layer shedding verification is carried out: The integral value E of the acoustic emission signal energy captured within the characteristic frequency band (80 - 120 kHz) is monitored by an acoustic emission sensor in the time domain. ae When the following conditions are met: E ae (t + Δt) / E ae (t) > 3 and dE ae / dt > 1 kJ / ms, it is determined that the ice layer has completely shed.

[0200] On the other hand, an ice removal system based on gas-liquid discharge mode conversion and intelligent regulation is provided in an embodiment of the present invention, including: A weak spark discharge module, which is used to monitor and predict the icing state by implementing weak spark discharge, verify the prediction result according to the dynamic characteristic changes of weak spark discharge, and adjust the output of a multi-stage adjustable power supply according to the verified prediction result to generate weak spark discharge with icing state feedback. A glow discharge module, which is used to calculate the required energy according to the acquired monitoring data to adjust the output of the multi-stage adjustable power supply to generate dielectric barrier glow discharge when the preset icing condition is reached, and dynamically adjust the discharge parameters to maximize the strong heat input effect. A timing and energy determination module, which is used to introduce the thermodynamic principle and the ice layer melting model to predict the formation time and position of the gas-liquid mixture cavity under the condition of dielectric barrier glow discharge in real time. After the cavity is detected to be formed, the optimal timing and energy of the strong arc discharge are determined according to the cavity parameters. A strong arc discharge module, which is used to adjust the output of the multi-stage adjustable power supply based on the optimal timing and energy of the strong arc discharge to generate strong arc discharge, induce the expansion and rupture of the gas-liquid mixture cavity and cause the ice layer to shed.

[0201] In addition, an ice removal device based on gas-liquid discharge mode conversion and intelligent regulation is also provided in an embodiment of the present invention, including: An insulating layer; At least a pair of tungsten electrodes, and the insulating layer covers and is hermetically connected to the outside of the tungsten electrodes; High-voltage wires, which are connected between the tungsten electrodes and the multi-stage adjustable power supply for transmitting electric energy; A multi-stage adjustable power supply, which can output electrical signals with different voltages, currents and pulse widths to the tungsten electrodes to control the tungsten electrodes to perform discharge modes including weak spark discharge, dielectric barrier glow discharge and strong arc discharge; And a main controller, which is connected to the multi-stage adjustable power supply and is used to execute the ice removal method based on gas-liquid discharge mode conversion and intelligent regulation as described above. The control time accuracy of the main controller can be controlled at the order of 10 milliseconds.

[0202] The thickness of the insulating layer is greater than 2.5 mm, and the withstand voltage exceeds Umax = 25 kV, where Umax is the maximum output voltage of a multi - stage adjustable power supply. The insulating layer and the tungsten electrode are hermetically connected through silicone rubber. A pair of tungsten electrodes includes a positive tungsten electrode and a negative tungsten electrode. The tungsten electrodes are made of materials such as ablation - resistant pure tungsten, molybdenum - tungsten alloy, niobium - tungsten alloy, etc. Their shapes are all cylindrical, with a diameter of 2 - 3 mm. The distance between the pair of tungsten electrodes is 2 - 3 mm, which can form a cavity of sufficient size between the two electrodes and can achieve breakdown discharge under atmospheric pressure. The tungsten electrodes and the multi - stage adjustable power supply are connected through high - voltage wires.

[0203] It is worth mentioning that a circular electric - heating copper patch can be selectively added between the positive tungsten electrode and the negative tungsten electrode to accelerate the formation of the gas - liquid mixture cavity.

[0204] It should be clear that the first gear of the multi - stage adjustable power supply outputs a weak - spark start pulse signal, causing a weak - spark discharge between a pair of tungsten electrodes, with an output voltage of Umax. The weak - spark discharge power is less than 10 W. The icing state is monitored and predicted in real - time. When the icing causes the weak - spark discharge to be unable to continue, the first gear ends; the second gear of the multi - stage adjustable power supply outputs a glow - discharge drive signal, causing a dielectric - barrier glow discharge between a pair of tungsten electrodes. The sine - wave frequency is 3 - 5 kHz, the output voltage is 4 - 6 kV, and the glow - discharge power is 1800 - 2200 W. When the cavity formation is detected, the second gear ends; the third gear of the multi - stage adjustable power supply outputs a strong - arc trigger pulse signal, causing a strong - arc discharge in the gas - liquid mixture cavity between the two tungsten electrodes. The output voltage is about 10 - 15 kV, and the single - discharge energy is 25 - 30 joules. The third gear is turned on 80 - 120 ms after the second gear ends its operation. The working duration of the third gear is fixed, which is 45 - 50 microseconds.

[0205] Reference Figure 7As shown in the figure, the specific de-icing process of the de-icing device is as follows: First, supercooled water droplets hit the wall surface and gradually freeze. The multi-stage adjustable power supply is adjusted to the first gear, and a high-voltage, small-current, low-energy nanosecond pulse-width pulse signal is output. A weak spark discharge phenomenon 1 occurs between the two tungsten electrodes in the tungsten electrode assembly 3. A current detection module is set inside the multi-stage adjustable power supply, and the signal is transmitted to the main controller. The main controller judges whether icing occurs by detecting the discharge current pulse width and amplitude. The supercooled water droplets continue to hit the wall surface, forming a relatively compact clear ice or mixed ice on the wall surface to form an ice layer 4, and the weak spark discharge cannot continue. Secondly, the multi-stage adjustable power supply is adjusted to the second gear, and a low-voltage, large-current, high-frequency sine wave signal is output. A dielectric barrier glow discharge phenomenon 5 occurs between the two tungsten electrodes. The dielectric barrier glow discharge has a higher discharge energy and produces a strong heat input effect to heat the tiny air bubbles inside the ice layer. Under the action of the strong heat input effect, the local ice layer near the two tungsten electrodes melts to produce a cavity 6, that is, a gas-liquid mixture cavity. The shape of the gas-liquid mixture cavity is approximately spherical cap-shaped, the height of the spherical cap is about 4 mm, and the bottom diameter of the spherical cap is the distance between the two tungsten electrodes. Then, the multi-stage adjustable power supply is adjusted to the third gear, and a medium-voltage, large-current, microsecond pulse-width pulse signal is output. A strong arc discharge phenomenon 7 occurs between the two tungsten electrodes in the gas-liquid mixture cavity, inducing the generation of rapidly expanding bubbles. The bubbles hit and break on the ice layer, generating a strong impact force, causing radial and circumferential cracks in the ice layer and falling off from the wall surface. A large amount of energy is injected into the gas-liquid mixture in a short time, inducing the generation of explosive bubbles to achieve ice cracking.

[0206] In summary, the present invention provides a de-icing method, system and device based on gas-liquid discharge mode conversion and intelligent control. First, the present invention monitors and predicts the icing state, and flexibly adjusts the output of the multi-stage adjustable power supply according to the prediction result to generate weak spark discharge for preliminary intervention. When the monitoring data indicates that the preset icing condition is reached, the required energy is calculated based on the obtained data, and the power supply output is adjusted accordingly to convert to the dielectric barrier glow discharge mode, and the discharge parameters are dynamically adjusted to ensure the maximization of the strong heat input effect and effectively accelerate the ice layer melting process. Further, the present invention introduces the thermodynamics principle and the ice layer melting model, which can predict the formation time and position of the gas-liquid mixture cavity under the dielectric barrier glow discharge condition in real time. Once the formation of the cavity is detected, according to the specific parameters of the cavity, the optimal timing and required energy of the strong arc discharge are accurately determined. On this basis, by adjusting the output of the multi-stage adjustable power supply, the strong arc discharge is triggered, and this process will induce the rapid expansion and rupture of the gas-liquid mixture cavity, so that the ice layer falls off under the physical action to achieve efficient de-icing.

[0207] To implement the above method, the present invention also designs a special de-icing device. The device includes an insulating layer 2 for protecting and isolating internal components; at least a pair of tungsten electrodes covered and hermetically connected by the insulating layer, serving as the core components for discharging; high-voltage wires responsible for transmitting electrical energy from a multi-stage adjustable power supply to the tungsten electrodes; the multi-stage adjustable power supply itself, capable of outputting electrical signals with different voltages, currents, and pulse widths to precisely control the tungsten electrodes to perform various discharge modes such as weak spark discharge, dielectric barrier glow discharge, and strong arc discharge; and a main controller closely connected to the multi-stage adjustable power supply, responsible for executing the entire de-icing method based on the conversion of gas-liquid discharge modes and intelligent regulation.

[0208] It should be emphasized that the solution provided by the present invention is used for easily icing parts such as the wings of unmanned aerial vehicles, the lips of aero-engines, and the blades of wind turbines, and can effectively solve the icing problems of these parts in cold environments, improving the safety and reliability of the equipment.

[0209] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can be implemented in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can be implemented in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0210] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications after learning the basic creative concepts. Therefore, the claims should be interpreted to include the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0211] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention should also include these modifications and variations.

Claims

1. An ice removal method based on gas-liquid discharge mode conversion and intelligent regulation, characterized in that Including: By implementing weak spark discharge monitoring and predicting the icing state, verifying the prediction results according to the dynamic characteristic changes of weak spark discharge, and adjusting the output of the multi-stage adjustable power supply according to the verified prediction results to generate weak spark discharge with icing state feedback; When the preset icing condition is reached, calculate the required energy according to the acquired monitoring data to adjust the output of the multi-stage adjustable power supply to generate dielectric barrier glow discharge, and dynamically adjust the discharge parameters to maximize the strong heat input effect; By introducing the thermodynamic principle and the ice layer melting model, predict the formation time and position of the gas-liquid mixture cavity under the condition of dielectric barrier glow discharge in real time. After detecting the formation of the cavity, determine the optimal timing and energy of strong arc discharge according to the cavity parameters; Based on the optimal timing and energy of strong arc discharge, adjust the output of the multi-stage adjustable power supply to generate strong arc discharge, induce the expansion and rupture of the gas-liquid mixture cavity and make the ice layer fall off.

2. The de-icing method based on gas-liquid discharge mode conversion and intelligent regulation according to claim 1, characterized in that, By implementing weak spark discharge monitoring and predicting the icing state, verifying the prediction results according to the dynamic characteristic changes of weak spark discharge, and adjusting the output of the multi-stage adjustable power supply according to the verified prediction results to generate weak spark discharge with icing state feedback includes: Before icing occurs, control the multi-stage adjustable power supply to output a weak spark start pulse signal with reference parameters, generate intermittent weak spark discharge between the tungsten electrodes, and establish a surface dielectric characteristic detection reference; Collect in real time a multi-dimensional environmental parameter set including the mass of supercooled water droplets, the impact velocity of supercooled water droplets, the radius of water droplets, the temperature of supercooled water droplets, the dry bulb temperature, the humidity, and the wall temperatures at multiple positions, and an arc time-frequency feature set including the peak-to-valley ratio of discharge current, the fluctuation frequency, the proportion of the main frequency band energy of light intensity, and the frequency band offset; Perform feature engineering calculations on the multi-dimensional environmental parameter set to obtain an environmental parameter feature vector including the collision energy density, the temperature difference feature, the wet and cold index, the average impact velocity within a set period, and the change rate of temperature over time, and perform vectorization processing on the arc time-frequency feature set to obtain an arc time-frequency feature vector; Perform standardization processing on the obtained environmental parameter feature vector and arc time-frequency feature vector to form a basic data set; Calculate the data separation index and the self-similarity dimension parameter based on the basic data set and add them to the basic data set to form an extended data set; Use a hybrid attention mechanism model including an input layer, a self-attention layer, a sequence attention layer, and a hybrid layer to establish an icing prediction model. Set a fully connected layer at the output layer of the icing prediction model, train the icing prediction model based on the extended data set, and map the output of the icing prediction model to the range of icing probability to obtain the icing probability; When the icing probability is greater than or equal to the set icing probability threshold, control the output of the multi-stage adjustable power supply according to the dynamic characteristic changes of the acquired weak spark discharge to generate a weak spark pulse signal with an adaptive duty cycle, and apply it to the tungsten electrodes to generate weak spark discharge with icing state feedback between the tungsten electrodes; In the weak spark discharge state with icing state feedback, at least two features among the time-frequency characteristics of the arc are obtained in real time for joint verification. When the verification error exceeds the set first error threshold, the online update mechanism of the parameters of the icing prediction model is triggered. When the verification error exceeds the set second error threshold continuously for N times, the detection benchmark of the surface dielectric property is reinitialized, the current extended data set is cleared, and data is collected again.

3. The de-icing method based on gas-liquid discharge mode conversion and intelligent regulation according to claim 2, wherein, Perform feature engineering calculations on the multi-dimensional environmental parameter set to obtain an environmental parameter feature vector including the collision energy density, temperature difference feature, wet and cold index, average impact velocity within a set time period, and the change rate of temperature over time, including: Within the set time window, based on the mass of supercooled water droplets, the impact velocity of supercooled water droplets, and the radius of the water droplets, and combined with the density of water, calculate the collision energy density per unit area within the set time period; Calculate the difference between the wall temperature and the supercooled water droplet temperature to obtain the initial temperature difference value; By obtaining the spatial derivative of the temperature in each direction of the wall surface, determine the magnitude and direction of the temperature gradient. According to the magnitude and direction of the temperature gradient, determine the temperature gradient coefficient, and apply the determined temperature gradient coefficient to the initial temperature difference value for correction calculation. Take the absolute value of the corrected temperature difference value to obtain the temperature difference feature; Calculate the dew point temperature based on the humidity and dry bulb temperature. Use an empirical formula or chart to convert the dew point temperature and the estimated environmental relative humidity into the wet bulb temperature. By calculating the difference between the wet bulb temperature and the dry bulb temperature, obtain the wet and cold index; Calculate the average impact velocity within the set time period and the change rate of temperature over time.

4. The de-icing method based on gas-liquid discharge mode conversion and intelligent regulation according to claim 2, characterized in that Calculate the data separation index and the self-similarity dimension parameter based on the basic data set and add them to the basic data set to form an extended data set, including: Select the embedding dimension and time delay suitable for the characteristics of the basic data set. Using the selected embedding dimension and time delay, perform phase space reconstruction on the time series data in the basic data set; In the reconstructed phase space, find the nearest neighbor points of each point. As time evolves, calculate the change rate of the distance between each point and its nearest neighbor point, and introduce the distribution density or directionality of the points within the neighborhood to weight the change rate of the distance to obtain the data separation index; Divide the time series data in the basic data set into multiple boxes of different sizes, and each box represents a segment of the time series data within a specific size range; Count the number of data points contained in each box. By changing the box size, repeat counting the number of data points contained in each box to obtain the relationship between the number of boxes and the number of data points at different sizes. By fitting the relationship curve, obtain the self-similarity dimension parameter; Take the data separation index and the self-similarity dimension parameter as new feature dimensions, and splice or merge them with the original features in the basic data set to form a preliminary fusion feature set including the original features and the newly added data separation index and self-similarity dimension parameter features; Use the selected one-base learning algorithm to perform training and learning on the preliminary fusion feature set. Sort according to the weights or coefficients of the features obtained from the training and learning, and recursively remove the features with the smallest weights or coefficients until the predetermined number of features or performance indicators are reached to form an extended data set after iterative screening.

5. The de-icing method based on gas-liquid discharge mode conversion and intelligent regulation according to claim 2, wherein, When the preset icing condition is reached, calculate the required energy based on the acquired monitoring data to adjust the output of the multi-stage adjustable power supply to generate dielectric barrier glow discharge, and dynamically adjust the discharge parameters to maximize the strong heat input effect, including: Real-time monitor and quantitatively process the ice layer thickness and icing speed to obtain the current icing degree; When the icing degree is greater than or equal to the preset icing degree threshold, trigger the set mode conversion condition, and calculate the required energy of the dielectric barrier glow discharge through the dielectric barrier glow discharge energy calculation formula; According to the calculated required energy of the dielectric barrier glow discharge, send a glow discharge drive signal to the multi-stage adjustable power supply to switch the tungsten electrode to the dielectric barrier glow discharge mode; Adjust the combination of the required energy of the dielectric barrier glow discharge and the preset discharge parameter to maximize the strong heat input effect of the dielectric barrier glow discharge and adjust the discharge parameters; Adjust the dielectric barrier glow discharge parameters of the tungsten electrode through the multi-stage adjustable power supply according to the adjusted discharge parameters, and simultaneously monitor the process parameters of the ice layer change in real time; Among them, The dielectric barrier glow discharge energy calculation formula is: where α·h β is the basic energy term, α is the thickness influence coefficient, β is the thickness exponent, is the dynamic influence term of the icing speed v b on the required energy, γ is the speed influence coefficient, v0 is the reference speed, δ is the speed influence exponent, represents the combined influence term of the thermal conductivity and specific heat capacity of ice on the required energy, ε is the thermal conductivity-specific heat influence coefficient, k is the thermal conductivity, c is the specific heat capacity, h is the ice layer thickness, v c is the critical speed; The discharge parameter adjustment combination includes: The adjustment item of the discharge frequency is: where Δf is the adjustment term of the discharge frequency, k f is the adjustment coefficient, sgn is the sign function, and according to the partial derivative returns +1 or -1 according to the sign, is the rate of change of the objective function G f with respect to the frequency f, is the adjustment exponent used to control the sensitivity of the step size to the magnitude of the partial derivative, and random(-χ,χ) represents a random value within the range (-χ,χ); The adjustment item of the voltage is: where ΔV is the step size of segmented voltage adjustment, V min is the lower limit value of the target voltage, V max is the upper limit value of the target voltage, V t is the target voltage value, V c is the current voltage, segmented according to the relative positions of V c , V min and V max , k v1 , k v2 and k v3 are the adjustment coefficients in different segments, is the objective function G obtained from expert experience and historical data V is the rate of change of the objective function G with respect to the voltage V, d v is the voltage adjustment direction, the current change rate is ΔI / Δt, k I is the feedback adjustment coefficient, ΔV total is the final voltage adjustment amount; The adjustment item of the power is: Δp = k p ·(E c - E o ); P n = min(max(P c + Δp, P min ), P max ); Where, ΔP is the adjustment term of power, E o is the energy output at the current power, E c is the energy required for the dielectric barrier glow discharge, k P is the power adjustment coefficient, P n is the new adjusted power, P c is the current power, P max is the maximum power that the system can withstand, P min is the minimum power that the system can withstand.

6. The de-icing method based on gas-liquid discharge mode conversion and intelligent regulation according to any one of claims 1-5, characterized in that, By introducing the thermodynamics principle and the ice layer melting model, predict the formation time and position of the gas-liquid mixture cavity under the condition of dielectric barrier glow discharge in real time. After detecting the formation of the cavity, determine the optimal timing and energy of the strong arc discharge according to the cavity parameters, including: Collect the real-time discharge data during the dielectric barrier glow discharge process, including discharge parameters, the intensity of the heat input effect under different discharge parameters, and the ice layer temperature distribution; According to the real-time discharge data, establish an ice layer melting simulation model by introducing the influencing factors of heat conduction, latent heat of phase change, and external heat input effect of the ice layer; Discretize the continuous space and time domains into a finite number of grid points or nodes. At each grid point, convert the ice layer melting simulation model into a difference equation or a finite element equation; Input the collected real-time discharge data as the external heat input boundary condition into the ice layer melting simulation model, and use time stepping iteration to solve the discretized ice layer melting simulation model. During the iterative solution process, record the temperature distribution, melting phase field, and cavity formation situation at each time step; When the preset cavity formation condition is met, record the time step when the cavity is first formed, and convert it into the actual time to predict the cavity formation time. At the same time, determine the position and boundary of the cavity in the ice layer melting area; Obtain the cavity formation speed by dividing the change in the cavity volume or area between adjacent time points by the time interval; Determine the optimal timing of the strong arc discharge according to the cavity formation time and the cavity formation speed; among them, the optimal timing satisfies that the cavity formation speed reaches the set formation stable value or starts to decelerate, and the cavity formation time is within the safe time when the change in the cavity volume or area after cavity formation is less than the set value; Calculate the optimal energy required for the strong arc discharge according to the size and position of the cavity through the optimal energy formula; Among them, The ice layer melting simulation model is: In the formula, q(f, V, P) is the heat input effect coefficient, which is a function determined by expert experience and historical data corresponding to the discharge frequency including frequency f, voltage V, and power P. Q(x, y, z, t; f, V, P) is the external heat input distribution, representing the heat input intensity distribution under different discharge parameters. (x, y, z) is the spatial position, t is the time, Q(x, y, z, t; f, V, P) = q(x, y, z)·q(f, V, P)·g(t), q(x, y, z) is the basic heat input distribution in space, g(t) is the time modulation function, β0 is the heat conduction coefficient, is the spatial Laplacian operator of the ice layer temperature, γ0 is the latent heat of phase change coefficient, is the partial derivative of the melting phase field with respect to time, δ0 is the environmental temperature influence coefficient, T b is the ice layer temperature, T amb is the environmental temperature; The optimal energy formula is: where E f is the optimal energy, η is the energy conversion efficiency of the discharge, and ∫∫∫V k represents the triple spatial integration over the cavity volume V k , ε0 is the vacuum permittivity, is the value of the electric field strength at position r, F o is the position factor, and F s is the shape factor.

7. An ice removal system based on gas-liquid discharge mode conversion and intelligent regulation, characterized in that, Including: A weak spark discharge module, which is used to monitor and predict the icing state by implementing weak spark discharge, verify the prediction result according to the change of the dynamic characteristics of weak spark discharge, and adjust the output of a multi-stage adjustable power supply according to the verified prediction result to generate weak spark discharge with icing state feedback; A glow discharge module, which is used to calculate the required energy according to the acquired monitoring data to adjust the output of a multi-stage adjustable power supply to generate dielectric barrier glow discharge when the preset icing condition is reached, and dynamically adjust the discharge parameters to maximize the strong heat input effect; A timing and energy determination module, which is used to introduce the thermodynamic principle and the ice layer melting model to predict the formation time and position of the gas-liquid mixture cavity under the condition of dielectric barrier glow discharge in real time. After detecting the formation of the cavity, determine the best timing and energy of the strong arc discharge according to the cavity parameters; A strong arc discharge module, which is used to adjust the output of a multi-stage adjustable power supply based on the best timing and energy of the strong arc discharge to generate strong arc discharge, induce the expansion and rupture of the gas-liquid mixture cavity and make the ice layer fall off.

8. An ice removal device based on gas-liquid discharge mode conversion and intelligent regulation, characterized in that, Comprising: An insulating layer; At least a pair of tungsten electrodes, and the insulating layer covers and is hermetically connected to the outside of the tungsten electrodes; High-voltage wires, which are connected between the tungsten electrodes and the multi-stage adjustable power supply for transmitting electric energy; A multi-stage adjustable power supply, which can output electrical signals with different voltages, currents and pulse widths to the tungsten electrodes to control the tungsten electrodes to perform discharge modes including weak spark discharge, dielectric barrier glow discharge and strong arc discharge; And, A main controller, which is connected to the multi-stage adjustable power supply and is used to execute the de-icing method based on gas-liquid discharge mode conversion and intelligent regulation as described in any one of claims 1-6.

9. The de-icing device based on gas-liquid discharge mode conversion and intelligent regulation as described in claim 8, characterized in that The thickness of the insulating layer is greater than 2.5 mm, and the withstand voltage exceeds Umax = 25 kV, where Umax is the maximum output voltage of the multi-stage adjustable power supply; Each pair of tungsten electrodes includes a positive tungsten electrode and a negative tungsten electrode. The shapes of the positive tungsten electrode and the negative tungsten electrode are both cylindrical, with a diameter of 2-3 mm. The distance between each pair of tungsten electrodes is 2-3 mm, and an electrically heated copper patch is arranged between each pair of tungsten electrodes.

10. The de-icing device based on gas-liquid discharge mode conversion and intelligent regulation as described in claim 8, characterized in that The first gear of the multi-stage adjustable power supply outputs a weak spark start pulse signal to generate weak spark discharge between a pair of tungsten electrodes, with an output voltage of Umax and a weak spark discharge power lower than 10 W. The icing state is monitored and predicted in real time. When the weak spark discharge cannot continue due to icing, the first gear ends; The second gear of the multi-stage adjustable power supply outputs a glow discharge drive signal to generate dielectric barrier glow discharge between a pair of tungsten electrodes, with a sine wave frequency of 3-5 kHz, an output voltage of 4-6 kV, and a glow discharge power of 1800-2200 W. When the formation of the cavity is detected, the second gear ends; The third gear output of the multi-gear adjustable power supply triggers a strong arc pulse signal, causing a strong arc discharge between the two tungsten electrodes in the gas-liquid mixture cavity. The output voltage is about 10 - 15 kV, and the single discharge energy is 25 - 30 joules. The third gear is turned on 80 - 120 ms after the second gear finishes working.

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