Power transmission line dust deposition thickness prediction method, device, equipment and medium

Abstract

The invention relates to a power transmission line dust deposition thickness prediction method and device, equipment and a medium. The method comprises the following steps: acquiring meteorological data, power transmission line surface physical characteristic data and dust particle sample data of a target area; calculating a particle settling rate based on the meteorological data and the dust particle sample data; analyzing particle attachment characteristics based on the particle sedimentation rate, the meteorological data, the power transmission line surface physical characteristic data and the dust particle sample data; constructing a sedimentation-attachment coupling model based on the particle attachment characteristics and the particle sedimentation rate; calculating the random fluctuation quantity of the ash deposition thickness by adopting a machine learning algorithm based on the settlement-attachment coupling model; and based on the settlement-attachment coupling model and the ash deposition thickness random fluctuation quantity, analyzing an ash deposition accumulation trend to obtain a prediction result of the ash deposition thickness of the power transmission line. By adopting the method, the accuracy and reliability of ash deposition thickness prediction can be improved, and effective support is provided for operation and maintenance of a power transmission line.

Description

Technical Field

[0001] This invention belongs to the field of intelligent operation and maintenance of power systems, and in particular relates to methods, devices, equipment and media for predicting the thickness of ash accumulation on transmission lines. Background Technology

[0002] With the development of intelligent operation and maintenance technology for power systems, transmission line condition monitoring technology has emerged. This technology uses sensors to collect environmental data of the transmission lines, enabling preliminary monitoring of dust accumulation. Traditional techniques typically involve manual inspections to periodically check the thickness of dust accumulation on transmission lines, or using simple settlement formulas based on single meteorological factors such as wind speed and rainfall to estimate the accumulated dust. Current methods suffer from low prediction accuracy and poor reliability because they fail to comprehensively consider the dynamic coupling effects of multi-source data, including meteorological conditions, dust particle characteristics, and the physical properties of the transmission line surface. Furthermore, they neglect the interaction between particle settling and adhesion processes, as well as the random fluctuations in dust thickness. Consequently, the prediction results cannot accurately reflect the actual dust accumulation trend, making it difficult to support refined operation and maintenance decisions for transmission lines. Summary of the Invention

[0003] Therefore, it is necessary to provide a method, device, equipment, and medium for predicting the thickness of ash accumulation on transmission lines that can solve the above problems.

[0004] In a first aspect, this application provides a method for predicting the thickness of ash accumulation on transmission lines, including:

[0005] Acquire meteorological data, power line surface physical property data, and dust particle sample data for the target area;

[0006] Calculate particle settling rate based on meteorological data and dust particle sample data;

[0007] Based on particle settling rate, meteorological data, physical property data of power transmission line surface, and dust particle sample data, the particle adhesion characteristics were analyzed.

[0008] Based on particle adhesion characteristics and particle settling rate, a settling-adhesion coupling model is constructed;

[0009] Based on the settlement-adhesion coupling model, a machine learning algorithm is used to calculate the random fluctuation of ash accumulation thickness.

[0010] Based on the settlement-adhesion coupling model and the random fluctuation of ash thickness, the predicted ash thickness of transmission lines is obtained by analyzing the ash accumulation trend.

[0011] In one embodiment, the particle settling rate is calculated based on meteorological data and dust particle sample data, including:

[0012] Based on meteorological data, wind speed data, temperature and humidity data, and rainfall data are extracted.

[0013] Based on dust particle sample data, particle size distribution data, physicochemical composition data and density data were extracted;

[0014] Calculate particle gravity settling values ​​based on particle size distribution and density data;

[0015] Based on temperature and humidity data and physicochemical composition data, temperature and humidity correction coefficients are obtained by analyzing particle surface viscosity.

[0016] Based on particle gravity settling values ​​and temperature and humidity correction coefficients, combined with wind speed data, the amount of air-to-fluid disturbance is calculated.

[0017] Based on the gas-fluid disturbance and temperature and humidity correction coefficients, the particle gravity settling value is adjusted to obtain the initial particle settling rate.

[0018] Based on rainfall data, particle size distribution data, and density data, the scour attenuation coefficient is calculated using a weighted average.

[0019] The particle settling rate was calculated based on the scour attenuation coefficient and the initial settling rate of the particles.

[0020] In one embodiment, particle adhesion characteristics are analyzed based on particle settling velocity, meteorological data, data on the physical properties of power transmission line surfaces, and dust particle sample data, including:

[0021] Based on the physical properties data of the power transmission line surface, surface roughness data and material hydrophilicity / hydrophobicity data are extracted;

[0022] Based on particle settling rate, surface roughness data, material hydrophilicity / hydrophobicity data, and physicochemical composition data, the contact mode between dust particles and the surface of power transmission lines is determined.

[0023] Based on the contact method, combined with particle density data and particle settling rate, the interfacial adhesion force is calculated to obtain the initial adhesion strength.

[0024] Based on temperature and humidity data from meteorological data, the initial adhesion strength is corrected to obtain the actual adhesion strength;

[0025] Based on actual adhesion strength and wind speed data, the probability of dust particles falling off due to wind speed after adhesion is calculated, and the first falling off probability is obtained.

[0026] Based on actual adhesion strength and rainfall data, the probability of dust particles falling off due to rainfall after adhesion is calculated, thus obtaining the second detachment probability;

[0027] By integrating the contact method, actual adhesion strength, first detachment probability, and second detachment probability, the particle adhesion characteristics are obtained.

[0028] In one embodiment, a sedimentation-attachment coupling model is constructed based on particle adhesion characteristics and particle settling rate, including:

[0029] Based on particle settling rate and particle adhesion characteristics, a quantitative relationship between settling behavior and adhesion state is established through regression analysis, and a basic relationship function is obtained.

[0030] Based on particle settling rate and particle adhesion characteristics, the dynamic coupling correlation between the settling process and the adhesion process is quantified by mutual information entropy analysis, and the coupling correlation value is obtained.

[0031] By integrating the basic relational functions and coupling correlation values, a settlement-attachment coupling model is constructed.

[0032] In one embodiment, based on the settlement-adhesion coupling model, a machine learning algorithm is used to calculate the random fluctuation of ash accumulation thickness, including:

[0033] Historical ash thickness data was obtained, and combined with the historical ash thickness data and the settling-adhesion coupling model, the time series decomposition method was used to extract the long-term trend component and seasonal periodic component of ash thickness, with the scour attenuation coefficient and temperature and humidity correction coefficient as constraints.

[0034] Based on the long-term trend component and the seasonal cycle component, the residual sequence is calculated as the basic random fluctuation.

[0035] Based on the basic random fluctuations and combined with the coupling correlation value, a random forest algorithm is used to construct a random fluctuation prediction model;

[0036] The random fluctuation prediction model was used to calculate the random fluctuation of the ash accumulation thickness.

[0037] In one embodiment, the particle settling rate is calculated based on the scour attenuation coefficient and the initial particle settling rate, using the following formula:

[0038]

[0039] in, For particle settling velocity, This represents the initial settling rate of the particles. scouring attenuation coefficient, For the characteristic particle size extracted based on particle size distribution data, Particle density, Rainfall intensity is extracted based on rainfall data. This is a temperature and humidity correction factor. This is a standard rainfall intensity reference value. This is the particle size-density synergistic correction coefficient. Sensitivity coefficient for rainfall erosion This is the coupling coefficient between temperature / humidity and particle characteristics.

[0040] In one embodiment, based on the settlement-adhesion coupling model and the random fluctuation of ash accumulation thickness, the predicted ash accumulation thickness of transmission lines is obtained by analyzing the ash accumulation trend, including:

[0041] The thickness of the foundation ash accumulation is calculated based on the settlement-adhesion coupling model;

[0042] Based on the basic ash accumulation thickness and the random fluctuation of ash accumulation thickness, a ash accumulation trend curve is obtained by fitting.

[0043] Based on the ash accumulation trend curve, the ash thickness change data within a preset time period is calculated to obtain the ash thickness prediction result of the transmission line.

[0044] Secondly, this application also provides a device for predicting the thickness of ash accumulation on transmission lines, comprising:

[0045] The multi-source data acquisition module is used to acquire meteorological data, power transmission line surface physical property data, and dust particle sample data of the target area;

[0046] The settling rate calculation module is used to calculate the particle settling rate based on meteorological data and dust particle sample data;

[0047] The adhesion characteristic analysis module is used to analyze particle adhesion characteristics based on particle settling rate, meteorological data, power transmission line surface physical property data, and dust particle sample data.

[0048] The coupling model construction module is used to construct a sedimentation-attachment coupling model based on particle adhesion characteristics and particle sedimentation rate;

[0049] The random fluctuation calculation module is used to calculate the random fluctuation of ash accumulation thickness based on the settlement-adhesion coupling model and machine learning algorithms.

[0050] The ash accumulation thickness prediction module is used to predict the ash accumulation thickness of transmission lines by analyzing the ash accumulation trend based on the settlement-adhesion coupling model and the random fluctuation of ash accumulation thickness.

[0051] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method for predicting the thickness of ash accumulation on transmission lines.

[0052] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method for predicting the thickness of ash accumulation on transmission lines.

[0053] The aforementioned method, device, equipment, and medium for predicting the thickness of dust accumulation on transmission lines achieve comprehensive collection of multi-source information affecting the dust accumulation process by acquiring meteorological data of the target area, physical property data of the transmission line surface, and dust particle sample data. Based on meteorological data and dust particle sample data, particle settling rates are calculated to quantify the settling behavior of dust particles in the environment. By combining particle settling rates, meteorological data, physical property data of the transmission line surface, and dust particle sample data, particle adhesion characteristics are analyzed, revealing the interaction mechanism between dust and the transmission line surface. A settling-adhesion coupling model is constructed to effectively integrate the dynamic correlation between the settling and adhesion processes. Machine learning algorithms are used to calculate the random fluctuation of dust accumulation thickness, scientifically capturing the random factors in dust accumulation. Based on the settling-adhesion coupling model and the random fluctuation of dust accumulation thickness, the dust accumulation trend is analyzed to obtain the predicted dust accumulation thickness of transmission lines. This solves the problems of low prediction accuracy and poor reliability caused by traditional methods that ignore the coupling of multiple factors such as meteorological, particle characteristics, and surface characteristics, as well as random fluctuations. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a flowchart of the method for predicting the thickness of ash accumulation on power transmission lines according to the present invention;

[0056] Figure 2 This is a structural diagram of the transmission line ash thickness prediction device of the present invention. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0058] In one embodiment, such as Figure 1As shown, a method for predicting the thickness of dust accumulation on transmission lines is provided. This embodiment illustrates the application of this method to a terminal. It is understood that this method can also be applied to a server, or to a system including both a terminal and a server, and implemented through interaction between the terminal and the server. In the implementation environment, the terminal can be an embedded intelligent device or computer deployed at the transmission line inspection site or substation, integrating multiple sensor interfaces to directly connect to meteorological sensors, surface characteristic detectors, and dust samplers, responsible for initial data collection, caching, and preprocessing. The server is a cloud server or local server cluster located in the monitoring center. Application scenarios include: when transmission line maintenance requires predicting the thickness of dust accumulation in future periods to formulate accurate cleaning plans, the terminal device collects meteorological data, transmission line surface physical characteristic data, and dust particle sample data of the target area, and uploads the data to the server via the network; after receiving the data, the server calls algorithms such as settling rate calculation and adhesion feature analysis to construct a settling-adhesion coupling model and uses machine learning algorithms to calculate random fluctuations, completing the dust accumulation thickness prediction; the prediction results are then sent from the server to the terminal interface for display, providing decision-making alerts for maintenance personnel. In this embodiment, the method includes the following steps:

[0059] S01, acquire meteorological data, power line surface physical property data, and dust particle sample data for the target area.

[0060] Meteorological data refers to physical quantities reflecting the atmospheric environment, collected by meteorological monitoring equipment deployed in the target area, including wind speed, temperature, humidity, and rainfall; power transmission line surface physical property data refers to parameters characterizing the surface condition of the power transmission line, including surface roughness and hydrophilicity / hydrophobicity of the material; dust particle sample data refers to physicochemical property data obtained by sampling and analyzing dust in the air or that has settled in the target area, covering particle size distribution, chemical composition, and overall density.

[0061] S02, based on meteorological data and dust particle sample data, calculates the particle settling rate.

[0062] The particle settling rate is the average velocity of dust particles relative to the surface of the power transmission line under the combined influence of multiple factors such as gravity, fluid drag, and meteorological conditions. In implementation, this can be achieved by comprehensively considering hydrodynamic and thermodynamic factors such as wind speed, temperature, humidity, and rainfall in meteorological data, as well as the inherent physicochemical properties of dust particle samples, such as particle size, density, and composition. A corresponding physical-mathematical model or calculation rule can be established or invoked to synthesize and calculate the gravity, buoyancy, drag, and external disturbances acting on the particles, thus solving for the quantitative rate value characterizing the settling speed of the particles. This provides a dynamic basis for subsequent analysis of adhesion characteristics.

[0063] S03, based on particle settling rate, meteorological data, physical property data of power transmission line surface and dust particle sample data, analyzes particle adhesion characteristics.

[0064] Among them, particle adhesion characteristics are comprehensive parameters characterizing the adhesion state and trend of particles on the surface of power transmission lines, covering attributes such as contact mode, adhesion strength, and the probability of detachment under external environmental disturbances. In implementation, an evaluation model or rule set based on physical mechanisms or statistical laws can be established by integrating kinetic energy information provided by particle settling rate, interfacial interaction conditions determined by the physical properties of the power transmission line surface, particle surface characteristics affected by dust particle sample data, and environmental loads described by meteorological data. This allows for the analysis and output of a quantitative description of adhesion behavior and stability, providing interfacial behavioral characteristic inputs for constructing the coupling relationship between settling and adhesion processes.

[0065] S04. Based on particle adhesion characteristics and particle settling rate, a settling-adhesion coupling model is constructed.

[0066] The sedimentation-adhesion coupling model is a computational framework that comprehensively quantifies the impact of sedimentation behavior on the adhesion state and the feedback effect of the adhesion state on subsequent sedimentation. In implementation, it integrates the dynamic conditions represented by particle sedimentation rate with information such as interface stability and detachment probability contained in particle adhesion characteristics. By establishing quantitative relationships or dynamic correlation rules, an integrated model reflecting the mutual constraints and influences of various factors throughout the entire dynamic process from particle sedimentation to adhesion is constructed, providing a core computational foundation for accurately simulating and predicting ash accumulation trends.

[0067] S05, based on the settlement-adhesion coupling model, uses machine learning algorithms to calculate the random fluctuation of ash accumulation thickness.

[0068] The random fluctuation of ash accumulation thickness refers to the random change in ash accumulation thickness caused by environmental, measurement, or other random factors, deviating from the deterministic trend predicted by the settlement-adhesion coupling model. In implementation, the output of the settlement-adhesion coupling model can be used as the basic input, incorporating historical ash accumulation thickness observation data. Machine learning algorithms can then be used to learn and model residuals or fluctuation patterns in the historical data that are not explained by the deterministic model, constructing a model capable of predicting the range of random fluctuations in ash accumulation thickness in future periods. This enhances the overall predictive model's ability to characterize uncertainties in the real world.

[0069] S06. Based on the settlement-adhesion coupling model and the random fluctuation of ash thickness, the predicted ash thickness of transmission lines is obtained by analyzing the ash accumulation trend.

[0070] The ash accumulation trend is a comprehensive pattern of thickness evolution over time, formed by the combined effects of deterministic changes and random fluctuations driven by physical processes. The predicted ash thickness of transmission lines is a quantitative estimate of the thickness, including possible fluctuation ranges, obtained by analyzing this trend. In practice, the basic ash thickness calculated by the settlement-adhesion coupling model can be used as the deterministic component of the trend, integrated with the random component represented by the random fluctuation of the ash thickness. Methods such as trend fitting, time series prediction, or probability distribution synthesis can be employed to analyze and calculate the predicted thickness value, which includes both the changing trend and uncertainty information, thus achieving a comprehensive and reliable prediction of ash thickness.

[0071] The above-mentioned method for predicting the thickness of ash accumulation on transmission lines acquires multi-source data and calculates particle settling rate and adhesion characteristics to construct a settling-adhesion coupling model to accurately describe the physical ash accumulation process. It uses machine learning to quantify random fluctuations and comprehensively analyzes the ash accumulation trend, overcoming the shortcomings of traditional methods that ignore multi-factor coupling and randomness, thereby improving the accuracy and reliability of ash accumulation thickness prediction.

[0072] In one embodiment, the particle settling rate is calculated based on meteorological data and dust particle sample data, including:

[0073] S11, based on meteorological data, extracts wind speed data, temperature and humidity data, and rainfall data;

[0074] S12, Based on dust particle sample data, extract particle size distribution data, physicochemical composition data and density data;

[0075] S13, Calculate particle gravity settling value based on particle size distribution data and density data;

[0076] S14, based on temperature and humidity data and physicochemical composition data, the temperature and humidity correction coefficient is obtained by analyzing the surface viscosity of particles;

[0077] S15, based on particle gravity settling value and temperature and humidity correction coefficient, combined with wind speed data, calculate air-fluid disturbance.

[0078] S16, Based on the gas-fluid disturbance and temperature and humidity correction coefficients, adjust the particle gravity settling value to obtain the initial particle settling rate.

[0079] S17, based on rainfall data, particle size distribution data, and density data, the scour attenuation coefficient is calculated by weighting.

[0080] S18, based on the scour attenuation coefficient and the initial settling rate of the particles, the particle settling rate is calculated.

[0081] For example, wind speed data (average instantaneous values ​​extracted in meters per second), temperature and humidity data (average temperature and relative humidity within a given period), and rainfall data (cumulative rainfall within a given period converted to rainfall intensity) can be screened and quantified from meteorological data. Particle size distribution data (including the proportion of different particle size ranges and characteristic particle sizes) detected by a laser particle size analyzer, physicochemical composition data (including components and contents of clay minerals affecting particle adhesion) detected by an X-ray fluorescence spectrometer, and density data measured by the specific gravity bottle method can be extracted from dust particle sample data. Based on the characteristic particle size and density data in the particle size distribution data, a calculation model can be constructed using Stokes' law. By substituting parameters such as characteristic particle size, particle density, and air density, the gravity settling value of particles under gravity alone can be calculated. Combining the temperature and relative humidity data, and the content of components affecting particle adhesion in the physicochemical composition data, the degree of change in particle surface adhesion can be analyzed through experimentally calibrated viscosity-temperature-humidity-composition relationships, yielding the temperature and humidity... The following steps are taken: First, the particle gravity settling value is multiplied by the temperature and humidity correction coefficient to obtain the initial corrected settling baseline value. Then, the airflow velocity corresponding to the wind speed data is used to calculate the airflow disturbance on particle settling using a fluid dynamics disturbance model (this disturbance is positively correlated with wind speed and quantified by coefficient calibration). Based on the particle gravity settling value, after the first correction by the temperature and humidity correction coefficient, a second adjustment is made according to the magnitude and direction of the airflow disturbance by adding or subtracting the product of the airflow disturbance and the temperature and humidity correction coefficient from the baseline value to obtain the initial particle settling rate. Second, based on the rainfall intensity, particle size distribution data, and particle density, weighting coefficients determined from historical experimental data are assigned to these three factors (matching the degree of influence of each factor on scouring). The scouring attenuation coefficient is calculated through weighted summation. Finally, according to the preset particle settling rate calculation formula, the initial particle settling rate is multiplied by the scouring attenuation coefficient, and combined with the characteristic particle size, particle density, rainfall intensity, and temperature and humidity correction coefficient for comprehensive calculation to obtain the particle settling rate.

[0082] In one embodiment, particle adhesion characteristics are analyzed based on particle settling velocity, meteorological data, data on the physical properties of power transmission line surfaces, and dust particle sample data, including:

[0083] S21, Based on the physical property data of the power transmission line surface, extract surface roughness data and material hydrophilicity / hydrophobicity data;

[0084] S22, Based on particle settling rate, surface roughness data, material hydrophilicity / hydrophobicity data, and physicochemical composition data, determine the contact mode between dust particles and the surface of the power transmission line;

[0085] S23. Based on the contact method, combined with particle density data and particle settling rate, the interfacial adhesion is calculated to obtain the initial adhesion strength.

[0086] S24, Based on the temperature and humidity data from meteorological data, the initial adhesion strength is corrected to obtain the actual adhesion strength;

[0087] S25. Based on actual adhesion strength and wind speed data, calculate the probability of dust particles falling off due to wind speed after adhesion, and obtain the first falling off probability.

[0088] S26. Based on the actual adhesion strength and rainfall data, calculate the probability of dust particles falling off due to rainfall after adhesion, and obtain the second detachment probability.

[0089] S27. By integrating the contact method, actual adhesion strength, first detachment probability, and second detachment probability, the particle adhesion characteristics are obtained.

[0090] Specifically, surface roughness data and material hydrophilicity / hydrophobicity data can be extracted from the physical property data of the power transmission line surface. Surface roughness data is obtained by measuring multiple uniformly distributed measurement points on the power transmission line surface using a contact roughness meter and averaging the results (unit: micrometers). Material hydrophilicity / hydrophobicity data is obtained by measuring the static contact angle of water droplets on the power transmission line surface using a contact angle meter (contact angle greater than 90 degrees indicates hydrophobicity, less than 90 degrees indicates hydrophilicity, quantified by the specific contact angle value). Combining particle settling rate (characterizing the kinetic energy of particles impacting the power transmission line surface), surface roughness data (reflecting the distribution of surface gaps and protrusions), material hydrophilicity / hydrophobicity data (affecting the strength of interfacial adsorption), and physicochemical composition data from dust particle sample data (determining whether particles contain clay minerals or other sticky components), the contact type between dust particles and the power transmission line surface is determined through preset contact mode judgment rules. For example, when the surface roughness... Particle settling rate at When the material contains viscous clay minerals in the specified range and the contact angle is <60 degrees, it is determined to be a composite contact mode combining mechanical interlocking and physical adsorption. When the surface roughness... Particle settling rate When the physicochemical components are non-adhesive and the material contact angle is >100 degrees, it is determined to be a simple physical contact method. Then, based on the determined contact method, the corresponding interfacial adhesion calculation model is selected. The interfacial adhesion is calculated by combining particle density data and particle settling rate to obtain the initial adhesion strength. For example, in the case of a composite contact method, a superposition model of mechanical interlocking force + physical adsorption force is used. The mechanical interlocking force is calculated based on the Hertzian contact mechanics model combined with surface gap size, particle size, and density. The physical adsorption force is calculated using the van der Waals force formula combined with the kinetic energy correction coefficient corresponding to the particle settling rate. For the simple physical contact method, only the van der Waals force formula is used. The unit of initial adhesion strength is Pascal (Pa). The initial adhesion strength is corrected based on temperature and humidity data from meteorological data, using a preset correction formula.

[0091]

[0092]

[0093] (Where k1=0.005 and k2=0.003 are experimental calibration coefficients, ambient humidity is expressed as a percentage, and ambient temperature is expressed in degrees Celsius), quantifying the effects of temperature and humidity on adhesion strength (e.g., high humidity enhances capillary adsorption, high temperature reduces the adhesive force of viscous components); based on actual adhesion strength and wind speed data, it can be calculated using the Logistic function:

[0094]

[0095] (a=0.01, b=0.002 are experimental calibration coefficients, and wind speed is in m / s) Calculate the first detachment probability of dust particles affected by wind speed. This function accurately describes the rule that the higher the wind speed and the lower the adhesion strength, the higher the detachment probability. Simultaneously, based on actual adhesion strength and rainfall data, an exponential function is used:

[0096]

[0097] (c=0.1 is the experimental calibration coefficient, and the unit of rainfall intensity is mm / h) The second detachment probability affected by rainfall erosion is calculated to realize the quantitative correlation between rainfall intensity and adhesion intensity on the detachment probability. Finally, the contact mode type identifier (e.g., 01 represents composite contact, 02 represents simple contact), the specific value of the actual adhesion intensity, and the quantitative results of the first and second detachment probabilities are integrated into a quadruple data to form complete particle adhesion characteristics, providing comprehensive interface behavior input for the subsequent construction of the sedimentation-adhesion coupling model.

[0098] In one embodiment, a sedimentation-attachment coupling model is constructed based on particle adhesion characteristics and particle settling rate, including:

[0099] S31. Based on particle settling rate and particle adhesion characteristics, a quantitative relationship between settling behavior and adhesion state is established through regression analysis, and the basic relationship function is obtained.

[0100] S32, Based on particle settling rate and particle adhesion characteristics, the dynamic coupling correlation between the settling process and the adhesion process is quantified by the mutual information entropy analysis method to obtain the coupling correlation value;

[0101] S33, by integrating the basic relational functions and coupling correlation values, a settlement-attachment coupling model is constructed.

[0102] For example, a multivariate nonlinear regression analysis method can be used, with particle settling rate as the core dynamic independent variable, actual adhesion strength, first detachment probability, and second detachment probability in particle adhesion characteristics as interface state independent variables, and the actual cumulative amount of particles remaining on the transmission line surface per unit time as the dependent variable. At least 100 sets of sample data under different operating conditions (covering different meteorological conditions, particle characteristics, and transmission line surface conditions) are collected. The regression coefficients are solved using the least squares method to establish a quantitative influence relationship between settling behavior and adhesion state, resulting in the basic relationship function:

[0103]

[0104] (Where k1, k2, k3, and k4 are regression coefficients, and k0 is a constant term,) For particle settling velocity, P1 represents the actual adhesion strength, P2 represents the first detachment probability, and P2 represents the second detachment probability. Using the mutual information entropy analysis method, the time series data of particle settling rate (sampling interval of 1 hour, duration of no less than 30 days) and the corresponding time series data of various parameters of particle adhesion characteristics are treated as two sets of random variables. The mutual information entropy value between the two sets is calculated, and then normalized by dividing this value by the maximum value of the two sets' information entropies, resulting in a coupling correlation value C ranging from 0 to 1 (the closer the value is to 1, the closer the dynamic correlation between the settling and adhesion processes). A settling-adhesion coupling model is constructed using a dynamic weight integration method. The output of the basic relationship function is multiplied by the coupling correlation value, and a time decay factor t (t is the cumulative duration in hours, and the decay coefficient is calibrated to 0.001 using historical data) is introduced to correct for long-term coupling effects, resulting in a settling-adhesion coupling model that can accurately quantify the dynamic interaction between the settling and adhesion processes.

[0105] In one embodiment, based on the settlement-adhesion coupling model, a machine learning algorithm is used to calculate the random fluctuation of ash accumulation thickness, including:

[0106] S41. Obtain historical ash thickness data, and combine the historical ash thickness data with the settling-adhesion coupling model. Using the time series decomposition method, with the scour attenuation coefficient and temperature and humidity correction coefficient as constraints, extract the long-term trend component and seasonal periodic component of ash thickness.

[0107] S42, Based on the long-term trend component and the seasonal cycle component, calculate the residual sequence as the basic random fluctuation.

[0108] S43, Based on the basic random fluctuation quantity and combined with the coupling correlation degree value, a random forest algorithm is used to construct a random fluctuation prediction model;

[0109] S44 uses a random fluctuation prediction model to calculate the random fluctuation of ash accumulation thickness.

[0110] For example, measured historical ash thickness data of transmission lines in the target area over the past 1-3 years can be obtained. This historical ash thickness data is then aligned with the simulated ash thickness data for the corresponding time period output by the established settlement-adhesion coupling model. The STL (Seasonal Trend Decomposition) time series decomposition method can be used, with scour attenuation coefficient and temperature / humidity correction coefficient as constraints (by assigning weights positively correlated with the two coefficients to the trend and periodic terms in the decomposition process, e.g., trend term weight = 0.6 × scour attenuation coefficient + 0.4 × temperature / humidity correction coefficient). This decomposes the historical ash thickness data into a long-term trend component reflecting the long-term ash accumulation pattern (e.g., an annual increasing / decreasing trend) and a seasonal periodic component affected by seasonal meteorological changes (e.g., periodic fluctuations during rainy and dry seasons). By subtracting the corresponding long-term trend component and seasonal periodic component from the historical actual ash thickness data for each time period, the difference forms a residual sequence. This residual sequence represents the basic random fluctuation caused by random factors after eliminating the influence of deterministic factors. The historical sequence of fluctuation (using the fluctuation of the previous 30 periods as input features) and the coupling correlation value (the coupling correlation value of the current period calculated in real time and the historical value of the previous 5 periods) are used as input features of the model. The value of the next period of the basic random fluctuation is used as the output label. A random forest algorithm model is constructed, where the model parameters can be set as follows: 100 decision trees, a maximum depth of 10 for each decision tree, and a minimum number of samples for node splitting of 5. The model is trained and optimized using a 5-fold cross-validation method, and outlier samples are removed (judged by the 3σ criterion) to improve the model's generalization ability, resulting in a random fluctuation prediction model. The historical data of the basic random fluctuation of the current period, the real-time coupling correlation value, and the corresponding scouring attenuation coefficient and temperature and humidity correction coefficient constraints are input into the trained random fluctuation prediction model. The model output is the random fluctuation of the ash thickness in each period within the preset prediction period (such as the next 7 days or 30 days). This fluctuation is presented in the form of positive and negative intervals or specific numerical values, which is used to characterize the random variation range of the ash thickness deviating from the deterministic trend.

[0111] In one embodiment, S51, the particle settling rate is calculated based on the scour attenuation coefficient and the initial particle settling rate, using the following formula:

[0112]

[0113] in, For particle settling velocity, This represents the initial settling rate of the particles. scouring attenuation coefficient, For the characteristic particle size extracted based on particle size distribution data, Particle density, Rainfall intensity is extracted based on rainfall data. This is a temperature and humidity correction factor. This is a standard rainfall intensity reference value. This is the particle size-density synergistic correction coefficient. Sensitivity coefficient for rainfall erosion This is the coupling coefficient between temperature / humidity and particle characteristics.

[0114] Specifically, standard rainfall intensity reference value The particle size distribution can be pre-calibrated to 0.5 mm / h based on the power industry's transmission line environmental monitoring standards (and can be finely adjusted within the range of 0.3-0.8 mm / h depending on the climate type of the target area); the particle size-density synergistic correction coefficient α, the rainfall erosion sensitivity coefficient β, and the temperature and humidity-particle characteristic coupling coefficient γ can all be obtained by fitting the measured ash accumulation data of the target area over the past year with the corresponding parameters using multiple linear regression. The specific value range is as follows: During the fitting process, the final value is determined with the goal of minimizing the prediction error; feature particle size The volume-weighted average particle size distribution was used for calculation. This involved summing the particle size distribution data measured by a laser particle size analyzer according to the volume percentage of each particle size range, with the unit uniformly in μm. Particle density was also used. The overall density of dust particles measured by the hydrometer bottle method is given, converted to kg / m³; the rainfall intensity R is calculated by dividing the cumulative rainfall (in mm) collected by meteorological monitoring equipment in the target area over a given period by the duration of that period (in hours); temperature and humidity correction factors are also given. Preliminary settling rate of particles scour attenuation coefficient All are calculated quantized values, among which The value range is 0.8-1.2. The unit is m / s. The value range is 0-1. This calculation process can be directly implemented using conventional data processing software (such as Matlab, Python) or the numerical computation module of embedded devices.

[0115] In one embodiment, based on the settlement-adhesion coupling model and the random fluctuation of ash accumulation thickness, the predicted ash accumulation thickness of transmission lines is obtained by analyzing the ash accumulation trend, including:

[0116] S61, Calculate the thickness of the foundation ash accumulation based on the settlement-adhesion coupling model;

[0117] S62, based on the basic ash accumulation thickness and the random fluctuation of ash accumulation thickness, the ash accumulation trend curve is obtained by fitting;

[0118] S63. Based on the ash accumulation trend curve, calculate the ash thickness change data within a preset time period to obtain the ash thickness prediction result of the transmission line.

[0119] For example, real-time meteorological data, power line surface physical property data, and dust particle sample data collected from the target area can be input into a pre-constructed sedimentation-adhesion coupling model. Through the pre-defined quantitative calculation logic in the model, the effective dust accumulation on the power line surface per unit time is calculated time-by-time, and the basic dust accumulation thickness (in mm) representing the deterministic dust accumulation pattern is obtained by integrating over time. Using time as the abscissa, the basic dust accumulation thickness is used as the deterministic component, and the random fluctuation of dust accumulation thickness is used as the random component. These are integrated into a comprehensive dust accumulation thickness data point for each time node. A weighted least squares fitting method can be used (where the deterministic component weight is set to 0.7 and the random component weight is set to 0.3; the weights can be fine-tuned based on the dust accumulation stability of the target area using historical data) to obtain a dust accumulation trend curve that simultaneously reflects the long-term accumulation pattern and random fluctuation characteristics. During the fitting process, curve parameters (such as the degree of polynomial fitting and the decay coefficient of exponential fitting) are adjusted to ensure that the mean square error between the curve and the integrated data points is less than 0.01 mm², thus ensuring fitting accuracy. A preset time period (such as the next 7 days, 30 days, or 90 days) is set according to maintenance requirements. The time variables of this period are sequentially substituted into the fitted ash accumulation trend curve. The predicted ash thickness for each time node within the preset time period (such as daily or every 3 days) is calculated through the functional relationship of the curve. Simultaneously, the distribution characteristics of the random fluctuations in ash thickness are combined to provide a confidence interval (such as a 95% confidence interval) for each predicted value. This results in a predicted ash thickness for transmission lines that includes the mean, fluctuation range, and trend of ash thickness within the preset time period. This result can be output as a quantitative data table or trend chart, providing data support for refined maintenance decisions such as transmission line cleaning plan formulation.

[0120] The aforementioned method for predicting the thickness of dust accumulation on transmission lines overcomes the limitations of traditional methods by comprehensively acquiring meteorological data, physical property data of transmission line surfaces, and dust particle sample data for the target area. It extracts key parameters step-by-step from multi-source data, calculates particle settling rates, and analyzes adhesion characteristics such as particle contact patterns, adhesion strength, and detachment probability in conjunction with various data dimensions. A settling-adhesion coupling model is constructed using regression analysis and mutual information entropy analysis to quantify the dynamic coupling relationship between settling and adhesion processes, addressing the problem of traditional methods neglecting their interaction. Based on the settling-adhesion coupling model, time series decomposition and random forest algorithms are used to capture random fluctuations in dust accumulation thickness, overcoming the neglect of random factors in traditional methods. The coupling model and random fluctuations are integrated to fit the dust accumulation trend curve, estimating the dust accumulation thickness for a preset time period. By combining multi-source data fusion, physical mechanism modeling, and machine learning, the method comprehensively considers the dynamic coupling effects of meteorological conditions, particle characteristics, and transmission line surface characteristics, accurately depicting the deterministic trend and random fluctuations of the dust accumulation process, improving the accuracy and reliability of dust accumulation thickness prediction, and providing strong support for refined operation and maintenance decisions for transmission lines.

[0121] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0122] Based on the same inventive concept, this application also provides a transmission line ash thickness prediction device for implementing the above-mentioned method for predicting transmission line ash thickness. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the transmission line ash thickness prediction device provided below can be found in the limitations of the transmission line ash thickness prediction method described above, and will not be repeated here.

[0123] In one exemplary embodiment, such as Figure 2 As shown, a device for predicting the thickness of ash accumulation on power transmission lines is provided, comprising:

[0124] The multi-source data acquisition module 101 is used to acquire meteorological data, power transmission line surface physical property data, and dust particle sample data of the target area;

[0125] The settling rate calculation module 102 is used to calculate the particle settling rate based on meteorological data and dust particle sample data;

[0126] The adhesion characteristic analysis module 103 is used to analyze particle adhesion characteristics based on particle settling rate, meteorological data, power transmission line surface physical property data and dust particle sample data.

[0127] The coupling model construction module 104 is used to construct a sedimentation-attachment coupling model based on particle adhesion characteristics and particle sedimentation rate.

[0128] The random fluctuation calculation module 105 is used to calculate the random fluctuation of ash accumulation thickness based on the settlement-adhesion coupling model and using machine learning algorithms.

[0129] The ash thickness prediction module 106 is used to obtain the ash thickness prediction result of the transmission line based on the settlement-adhesion coupling model and the random fluctuation of ash thickness by analyzing the ash accumulation trend.

[0130] In one embodiment, the settling rate calculation module 102 is further configured to:

[0131] Based on meteorological data, wind speed data, temperature and humidity data, and rainfall data are extracted.

[0132] Based on dust particle sample data, particle size distribution data, physicochemical composition data and density data were extracted;

[0133] Calculate particle gravity settling values ​​based on particle size distribution and density data;

[0134] Based on temperature and humidity data and physicochemical composition data, temperature and humidity correction coefficients are obtained by analyzing particle surface viscosity.

[0135] Based on particle gravity settling values ​​and temperature and humidity correction coefficients, combined with wind speed data, the amount of air-to-fluid disturbance is calculated.

[0136] Based on the gas-fluid disturbance and temperature and humidity correction coefficients, the particle gravity settling value is adjusted to obtain the initial particle settling rate.

[0137] Based on rainfall data, particle size distribution data, and density data, the scour attenuation coefficient is calculated using a weighted average.

[0138] The particle settling rate was calculated based on the scour attenuation coefficient and the initial settling rate of the particles.

[0139] In one embodiment, the attachment feature analysis module 103 is further configured to:

[0140] Based on the physical properties data of the power transmission line surface, surface roughness data and material hydrophilicity / hydrophobicity data are extracted;

[0141] Based on particle settling rate, surface roughness data, material hydrophilicity / hydrophobicity data, and physicochemical composition data, the contact mode between dust particles and the surface of power transmission lines is determined.

[0142] Based on the contact method, combined with particle density data and particle settling rate, the interfacial adhesion force is calculated to obtain the initial adhesion strength.

[0143] Based on temperature and humidity data from meteorological data, the initial adhesion strength is corrected to obtain the actual adhesion strength;

[0144] Based on actual adhesion strength and wind speed data, the probability of dust particles falling off due to wind speed after adhesion is calculated, and the first falling off probability is obtained.

[0145] Based on actual adhesion strength and rainfall data, the probability of dust particles falling off due to rainfall after adhesion is calculated, thus obtaining the second detachment probability;

[0146] By integrating the contact method, actual adhesion strength, first detachment probability, and second detachment probability, the particle adhesion characteristics are obtained.

[0147] In one embodiment, the coupling model building module 104 is further configured to:

[0148] Based on particle settling rate and particle adhesion characteristics, a quantitative relationship between settling behavior and adhesion state is established through regression analysis, and a basic relationship function is obtained.

[0149] Based on particle settling rate and particle adhesion characteristics, the dynamic coupling correlation between the settling process and the adhesion process is quantified by mutual information entropy analysis, and the coupling correlation value is obtained.

[0150] By integrating the basic relational functions and coupling correlation values, a settlement-attachment coupling model is constructed.

[0151] In one embodiment, the random fluctuation calculation module 105 is further configured to:

[0152] Historical ash thickness data was obtained, and combined with the historical ash thickness data and the settling-adhesion coupling model, the time series decomposition method was used to extract the long-term trend component and seasonal periodic component of ash thickness, with the scour attenuation coefficient and temperature and humidity correction coefficient as constraints.

[0153] Based on the long-term trend component and the seasonal cycle component, the residual sequence is calculated as the basic random fluctuation.

[0154] Based on the basic random fluctuations and combined with the coupling correlation value, a random forest algorithm is used to construct a random fluctuation prediction model;

[0155] The random fluctuation prediction model was used to calculate the random fluctuation of the ash accumulation thickness.

[0156] In one embodiment, the settling rate calculation module 102 is further configured to calculate the particle settling rate based on the scour attenuation coefficient and the initial particle settling rate using the following formula:

[0157]

[0158] in, For particle settling velocity, This represents the initial settling rate of the particles. scouring attenuation coefficient, For the characteristic particle size extracted based on particle size distribution data, Particle density, Rainfall intensity is extracted based on rainfall data. This is a temperature and humidity correction factor. This is a standard rainfall intensity reference value. This is the particle size-density synergistic correction coefficient. Sensitivity coefficient for rainfall erosion This is the coupling coefficient between temperature / humidity and particle characteristics.

[0159] In one embodiment, the ash thickness prediction module 106 is further configured to:

[0160] The thickness of the foundation ash accumulation is calculated based on the settlement-adhesion coupling model;

[0161] Based on the basic ash accumulation thickness and the random fluctuation of ash accumulation thickness, a ash accumulation trend curve is obtained by fitting.

[0162] Based on the ash accumulation trend curve, the ash thickness change data within a preset time period is calculated to obtain the ash thickness prediction result of the transmission line.

[0163] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the method for predicting the thickness of ash accumulation on a transmission line as described above.

[0164] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0165] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0166] The above-described embodiments are merely illustrative of several implementation methods of the embodiments of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the embodiments of this application, and these modifications and improvements all fall within the protection scope of the embodiments of this application.

Claims

1. A method for predicting the thickness of ash accumulation on transmission lines, characterized in that, The method includes: Acquire meteorological data, power line surface physical property data, and dust particle sample data for the target area; Based on the meteorological data and the dust particle sample data, the particle settling rate is calculated; Based on the particle settling rate, the meteorological data, the physical property data of the power transmission line surface, and the dust particle sample data, the particle adhesion characteristics are analyzed. Based on the particle adhesion characteristics and particle settling rate, a settling-adhesion coupling model is constructed; Based on the aforementioned settling-adhesion coupling model, a machine learning algorithm is used to calculate the random fluctuation of ash accumulation thickness. Based on the settlement-adhesion coupling model and the random fluctuation of ash thickness, the predicted ash thickness of transmission lines is obtained by analyzing the ash accumulation trend.

2. The method according to claim 1, characterized in that, The calculation of particle settling rate based on the meteorological data and the dust particle sample data includes: Based on the meteorological data, wind speed data, temperature and humidity data, and rainfall data are extracted. Based on the dust particle sample data, particle size distribution data, physicochemical composition data, and density data are extracted; Based on the particle size distribution data and the density data, the particle gravity sedimentation value is calculated; Based on the temperature and humidity data and the physicochemical composition data, the temperature and humidity correction coefficient is obtained by analyzing the particle surface viscosity. Based on the particle gravity settling value and the temperature and humidity correction coefficient, combined with the wind speed data, the air-fluid disturbance amount is calculated. Based on the gas-fluid disturbance and the temperature and humidity correction coefficient, the particle gravity settling value is adjusted to obtain the initial particle settling rate. Based on the rainfall data, the particle size distribution data, and the density data, the scour attenuation coefficient is calculated by weighting. The particle settling rate is calculated based on the scouring attenuation coefficient and the initial settling rate of the particles.

3. The method according to claim 2, characterized in that, The analysis of particle adhesion characteristics based on the particle settling rate, meteorological data, physical property data of the power transmission line surface, and dust particle sample data includes: Based on the physical property data of the power transmission line surface, surface roughness data and material hydrophilicity / hydrophobicity data are extracted; Based on the particle settling rate, surface roughness data, material hydrophilicity / hydrophobicity data, and physicochemical composition data, the contact mode between dust particles and the surface of the power transmission line is determined. Based on the contact method, combined with the particle density data and the particle settling rate, the interfacial adhesion is calculated to obtain the initial adhesion strength. Based on the temperature and humidity data from the meteorological data, the initial adhesion strength is corrected to obtain the actual adhesion strength; Based on the actual adhesion strength and the wind speed data, the probability of dust particles falling off due to wind speed after adhesion is calculated to obtain the first falling off probability. Based on the actual adhesion strength and the rainfall data, the probability of dust particles falling off due to rainfall after adhesion is calculated, and a second falling off probability is obtained. The particle adhesion characteristics are obtained by integrating the contact method, the actual adhesion strength, the first detachment probability, and the second detachment probability.

4. The method according to claim 2, characterized in that, The step of constructing a sedimentation-attachment coupling model based on the particle adhesion characteristics and the particle settling rate includes: Based on the particle settling rate and particle adhesion characteristics, a quantitative relationship between settling behavior and adhesion state is established through regression analysis, resulting in a basic relationship function. Based on the particle settling rate and the particle adhesion characteristics, the dynamic coupling correlation between the settling process and the adhesion process is quantified by the mutual information entropy analysis method to obtain the coupling correlation value. By integrating the basic relational functions and the coupling correlation values, the settlement-attachment coupling model is constructed.

5. The method according to claim 4, characterized in that, The calculation of random fluctuations in ash accumulation thickness based on the settlement-adhesion coupling model and using machine learning algorithms includes: Historical ash thickness data is obtained, and combined with the historical ash thickness data and the sedimentation-adhesion coupling model, a time series decomposition method is used to extract the long-term trend component and seasonal periodic component of ash thickness, with the scouring attenuation coefficient and the temperature and humidity correction coefficient as constraints. Based on the long-term trend component and the seasonal cycle component, the residual sequence is calculated as the basic random fluctuation. Based on the aforementioned basic random fluctuations and combined with the aforementioned coupling correlation value, a random forest algorithm is used to construct a random fluctuation prediction model. The random fluctuation prediction model is used to calculate the random fluctuation of the ash accumulation thickness.

6. The method according to claim 2, characterized in that, The particle settling rate is calculated based on the scour attenuation coefficient and the initial settling rate of the particles, using the following formula: in, For particle settling velocity, This represents the initial settling rate of the particles. scouring attenuation coefficient, For the characteristic particle size extracted based on particle size distribution data, Particle density, Rainfall intensity is extracted based on rainfall data. This is a temperature and humidity correction factor. This is a standard rainfall intensity reference value. This is the particle size-density synergistic correction coefficient. Sensitivity coefficient for rainfall erosion This is the coupling coefficient between temperature / humidity and particle characteristics.

7. The method according to claim 1, characterized in that, The method, based on the settlement-adhesion coupling model and the random fluctuation of ash thickness, analyzes the ash accumulation trend to obtain the predicted ash thickness of transmission lines, including: Based on the aforementioned settlement-adhesion coupling model, the thickness of the foundation ash accumulation is calculated; Based on the basic ash accumulation thickness and the random fluctuation of the ash accumulation thickness, a ash accumulation trend curve is obtained by fitting. Based on the ash accumulation trend curve, the ash thickness change data within a preset time period is calculated to obtain the ash thickness prediction result of the transmission line.

8. A device for predicting the thickness of ash accumulation on power transmission lines, characterized in that, The device includes: The multi-source data acquisition module is used to acquire meteorological data, power transmission line surface physical property data, and dust particle sample data of the target area; The settling rate calculation module is used to calculate the particle settling rate based on the meteorological data and the dust particle sample data; The adhesion characteristic analysis module is used to analyze particle adhesion characteristics based on the particle settling rate, the meteorological data, the physical property data of the power transmission line surface, and the dust particle sample data. The coupling model construction module is used to construct a sedimentation-attachment coupling model based on the particle adhesion characteristics and the particle sedimentation rate. The random fluctuation calculation module is used to calculate the random fluctuation of ash accumulation thickness based on the settlement-adhesion coupling model and using a machine learning algorithm. The ash accumulation thickness prediction module is used to obtain the ash accumulation thickness prediction result of the transmission line by analyzing the ash accumulation trend based on the settlement-adhesion coupling model and the random fluctuation of ash accumulation thickness.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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