De-icing Method and System Based on Inductive Magnetothermal Acoustics

Abstract

This invention relates to the field of inductive magnetothermal acoustic technology, and discloses a method and system for ice melting and de-icing based on inductive magnetothermal acoustic technology. The method involves real-time acquisition of ice surface state signal data, constructing a multi-dimensional signal processing system to output standardized ice melting signals; establishing a parameter database based on magnetothermal acoustic conversion technology, using a feature matching algorithm to locate target parameters and generate control labels; constructing a historical ice melting case information database, combining a multi-factor weight allocation model, and using a decision tree algorithm to assign weights and calculate ice melting matching scores to generate a priority sequence; employing an improved genetic algorithm to solve for the optimal ice melting scheme; and outputting results and tracking feedback through an interactive verification platform. This method can precisely control the ice melting process, comprehensively consider multiple factors, effectively improve ice melting efficiency, reduce energy consumption, and achieve intelligent and precise ice melting, making it suitable for ice melting and de-icing work in various scenarios.

Description

Technical Field

[0001] This invention relates to the field of inductive magnetothermal acoustic technology, specifically to a method and system for melting and de-icing based on inductive magnetothermal acoustic. Background Technology

[0002] In the power and transportation sectors, ice cover poses a serious threat to equipment operation and traffic safety, making ice melting and de-icing technology a key focus of the industry. Traditional ice melting and de-icing methods have several shortcomings. Firstly, at the signal acquisition and processing level, most technologies rely on a single type of sensor to obtain ice surface signals. The acquired data has a limited dimension and cannot fully reflect the physical characteristics of the ice surface. Furthermore, the signal preprocessing is simple, involving only basic filtering and noise reduction, which is insufficient to effectively remove interference and clutter in complex environments, resulting in low accuracy and reliability of subsequent ice melting signals.

[0003] In terms of parameter matching and control, existing technologies lack a systematic parameter database, making it impossible to comprehensively collect and manage standard parameters for common application scenarios, equipment types, and ice layer materials based on magnetothermal-acoustic conversion technology. The correlation between parameters has also not been fully explored, making it difficult to quickly and accurately locate target parameters and generate reasonable control labels when ice melting needs are detected, thus affecting the timeliness and accuracy of ice melting operations.

[0004] In terms of utilizing historical data, traditional methods have not built a database of historical ice-melting cases, making it impossible to dynamically record ice-melting records for different scenarios, materials, and ice thicknesses. They lack the ability to summarize and utilize historical experience, and cannot optimize based on historical data when facing new ice-melting tasks. This results in a lack of reference for the formulation of ice-melting solutions, making it difficult to adapt to complex and ever-changing actual situations.

[0005] In the priority allocation stage, existing technologies have not established a scientific multi-factor weight allocation model, and cannot comprehensively consider factors such as scene type, material characteristics, ice thickness and environmental temperature and humidity to reasonably sort the priority of ice melting tasks. This can easily lead to unreasonable resource allocation, resulting in high-importance and complex ice melting tasks not being processed in a timely manner, thus affecting the overall ice melting efficiency.

[0006] In terms of scheme optimization, traditional ice melting methods have insufficient algorithm optimization capabilities when solving ice melting schemes. They cannot efficiently optimize with the goal of maximizing ice melting efficiency, minimizing energy consumption threshold, and shortening action time. This results in ice melting schemes with high energy consumption, long action time, and low efficiency, making it difficult to meet the requirements for energy saving and efficiency in practical applications.

[0007] Existing ice-melting systems lack an effective feedback tracking mechanism, making it impossible to continuously track and optimize the actual ice-melting effect. This results in poor system adaptability and reliability, making it difficult to maintain good ice-melting performance under different environments and operating conditions. Summary of the Invention

[0008] The purpose of this invention is to provide a method and system for melting and de-icing based on inductive magnetothermal acoustics, so as to solve the problems mentioned in the background art.

[0009] To achieve the above objectives, the present invention provides an ice-melting and de-icing method based on inductive magnetothermoacoustic methods, the method comprising:

[0010] Real-time acquisition of ice surface status signal data, construction of a multi-dimensional signal processing system, and output of standardized ice melting signals through a signal preprocessing module;

[0011] A parameter database is established based on magnetothermal-acoustic conversion technology. When the need for ice melting is detected, the target parameters are located and control tags are generated through feature matching algorithms.

[0012] Build a database of historical ice melting cases to dynamically record ice melting records for different scenarios, materials, and ice thicknesses;

[0013] A multi-factor weight allocation model is established. By combining scene type, material characteristics, ice thickness and environmental temperature and humidity factors, the decision tree algorithm is used to assign weights to each indicator. A weighted summation method is used to construct a matching model to calculate the ice melting matching score, thereby generating an ice melting priority sequence.

[0014] An improved genetic algorithm is used to solve the optimal ice melting scheme, with optimization objectives set in the direction of maximizing ice melting efficiency, minimizing energy consumption threshold, and shortening action time;

[0015] The final ice-melting result is output through the interactive verification platform, and feedback on the actual ice-melting effect is continuously tracked.

[0016] Preferably, the real-time acquisition of ice surface state signal data, the construction of a multi-dimensional signal processing system, and the output of standardized ice melting signals through a signal preprocessing module are specifically as follows:

[0017] Based on the acquisition of signal data of the ice surface area by inductive sensors, the signal acquisition terminal receives the raw signal from the sensor and performs time-domain filtering, frequency-domain denoising and edge enhancement preprocessing to remove environmental interference noise.

[0018] Based on the physical properties and magnetothermal-acoustic response of the ice surface, parameters closely related to the ice melting characteristics were selected as candidate features using a feature selection algorithm.

[0019] Candidate features are divided into magnetic permeability features, thermal diffusion features, and acoustic impedance features, and a multi-level feature system is constructed. The overall ice melting matching degree is used as the top-level feature, and the top-level feature is decomposed into several first-level features. Each first-level feature is further subdivided into several second-level features.

[0020] The signal preprocessing module is trained using a neural network model based on a multi-level feature system.

[0021] The location information of the ice surface is associated with the melting characteristics of the preprocessed signal to establish a location-feature mapping relationship;

[0022] Signal analysis software was selected as the magnetothermal-acoustic analysis platform. The area was marked on the interface according to the actual position of the ice surface. Different brightness levels were used for visualization based on the different feature differences. Areas with large feature differences were marked with high brightness, and areas with small feature differences were marked with low brightness.

[0023] Preferably, the establishment of the parameter database based on magnetothermal-acoustic conversion technology specifically includes:

[0024] Collect standard parameter data for common application scenarios, equipment types, and ice materials, including basic parameters such as magnetic saturation value, thermal conductivity value, and sound velocity value;

[0025] Obtain the physical property information of the parameters, including medium density, interfacial bonding strength, and surface roughness parameters;

[0026] The parameters are abstracted into data nodes, and the magnetothermal-acoustic correlations between parameters are abstracted into data correlations, thus constructing a node-correlation data structure.

[0027] Establish the database architecture, including defining data tables, setting fields, and establishing data relationships, storing parameter information, attribute information, and associations;

[0028] The preprocessed data is imported into the database of the magnetothermal-acoustic analysis software to establish a parameter database.

[0029] Preferably, the step of locating target parameters and generating control labels through feature matching algorithms specifically involves:

[0030] Based on the parameter database, each parameter node is regarded as a sample point in the dataset, and the magnetothermal-acoustic correlation between parameters is regarded as the similarity between samples;

[0031] Using the signal characteristics of the area to be melted as the query point, the feature distance to each sample point is calculated using a dynamic programming algorithm. When the sample point with the highest matching degree is traversed, the feature information of that sample point is recorded.

[0032] By analyzing and calculating the feature distances, and combining them with information on ice thickness and material properties, the matching range of the target parameters is obtained.

[0033] Extract the magnetic saturation value, thermal conductivity value, and sound velocity value of the target parameters from the parameter database to generate control parameters;

[0034] The control parameters of the target parameters, scene type, and ice material are combined into a label and visualized on the interface of the magnetothermal-acoustic analysis software.

[0035] Preferably, the construction of a historical ice-melting case information database, dynamically recording ice-melting records for different scenarios, materials, and ice thicknesses, specifically includes:

[0036] The main dimensions for constructing the information database include melting records for different scene types, ice material, and ice thickness.

[0037] At the same time, specific information fields are planned for each dimension, including application scenarios, equipment types, ice material, standard parameters, actual control parameters, and user operation feedback.

[0038] By connecting the ice melting equipment management system with the user feedback platform, we continuously update the matching results, energy consumption feedback, and operation effect information of historical ice melting cases.

[0039] A database of historical ice-melting cases was constructed based on key dimensions and data fields.

[0040] Preferably, the establishment of the multi-factor weight allocation model involves combining scene type, material characteristics, ice thickness, and environmental temperature and humidity factors, using a decision tree algorithm to assign weights to each indicator, and employing a weighted summation method to construct a matching model to calculate the ice melting matching score, thereby generating an ice melting priority sequence. Specifically:

[0041] Based on the scenario type, standard parameters, and ice thickness, the importance level of ice melting is classified, with high-frequency usage scenarios or needs with clear standard parameters set as high importance level, and low-frequency usage scenarios or needs without clear parameters set as low importance level.

[0042] Ice materials are classified into metal surface ice, concrete surface ice, and glass surface ice. Metal surface ice has a high degree of complexity in melting, while glass surface ice has a low degree of complexity.

[0043] The ambient temperature and humidity are divided into standard temperature and humidity, high temperature and high humidity environment, and low temperature and low humidity environment.

[0044] Based on the decision tree algorithm, weights are assigned to the importance level, complexity level, and environmental temperature and humidity factors of ice melting;

[0045] A weighted summation method is used to construct a matching model. Each index value is multiplied by its corresponding weight and then summed to obtain the matching score for each ice melting task.

[0046] Based on the matching score calculated by the matching model, all ice melting tasks are sorted, with tasks with higher scores having higher matching priority, thus generating an ice melting priority sequence.

[0047] Preferably, the improved genetic algorithm is used to solve for the optimal ice-melting scheme, with the optimization objectives set in the direction of maximizing ice-melting efficiency, minimizing energy consumption threshold, and shortening processing time. Specifically:

[0048] A set of candidate solutions is randomly generated as the initial population;

[0049] Calculate the fitness value for each candidate solution based on the optimization objective;

[0050] The optimal solution is selected as the parent individual based on the fitness value, and the crossover and mutation probabilities of other individuals are updated.

[0051] By simulating the genetic evolution and information transmission behavior of the population, the candidate solutions are continuously updated iteratively until the termination condition is met;

[0052] Select one optimal solution from the final population as the optimal ice melting scheme.

[0053] Preferably, the ice melting matching score is specifically:

[0054] The matching score is determined by the weights of the importance level of ice melting, the complexity level, the environmental temperature and humidity, and the time factor. The weight of the importance level of ice melting corresponds to the influence of the scene type and standard parameters, the weight of the complexity level corresponds to the influence of the ice material, the weight of the environmental temperature and humidity corresponds to the influence of temperature and humidity conditions, and the weight of the time factor corresponds to the urgency of the ice melting requirement. The final matching score is obtained by multiplying each weight by the corresponding level score and then summing them up.

[0055] Preferably, the optimization objective is specifically:

[0056] The optimization objectives include maximizing ice melting efficiency, minimizing energy consumption threshold, and shortening the action time. The ice melting efficiency objective is achieved by calculating the magnetothermal-acoustic matching degree between candidate solutions and target parameters. The energy consumption threshold objective is achieved by limiting the energy consumption difference range between candidate solutions and standard parameters. The action time objective is achieved by reducing the number of algorithm iterations and computation time. The final optimization objective is the comprehensive optimization of the three sub-objectives.

[0057] Preferably, the present invention also includes an inductive magnetothermal-acoustic de-icing system for implementing the above-mentioned inductive magnetothermal-acoustic de-icing method, the system comprising the following modules:

[0058] The signal acquisition and preprocessing module is used to acquire ice surface status signal data in real time, construct a multi-dimensional signal processing system, and output standardized ice melting signals.

[0059] The control tag generation module establishes a parameter database based on magnetothermal-acoustic conversion technology. When a melting demand is detected, the target parameters are located and control tags are generated through a feature matching algorithm.

[0060] The historical case information database module is used to dynamically record melting records for different scenarios, materials, and ice thicknesses;

[0061] The priority generation module establishes a multi-factor weight allocation model, which combines scene type, material characteristics, ice thickness and environmental temperature and humidity factors, uses decision tree algorithm to assign weights to each indicator, and uses weighted summation method to build a matching model to calculate the ice melting matching degree score, thereby generating an ice melting priority sequence.

[0062] The optimal solution module uses an improved genetic algorithm to find the optimal ice melting solution, with the optimization objectives of maximizing ice melting efficiency, minimizing energy consumption threshold, and shortening processing time.

[0063] The feedback tracking module outputs the final ice-melting result through the interactive verification platform and continuously tracks the feedback on the actual ice-melting effect.

[0064] Compared with the prior art, the beneficial effects of the present invention are:

[0065] By acquiring real-time ice surface status signals using inductive sensors and constructing a multi-dimensional signal processing system, the system performs preprocessing on the signals, including time-domain filtering, frequency-domain denoising, and edge enhancement. Simultaneously, it filters relevant parameters based on feature selection algorithms, constructs a multi-level feature system, and completes training using a neural network model. This enables the comprehensive and accurate acquisition of information such as the magnetic permeability, thermal diffusion, and acoustic impedance characteristics of the ice surface. The system correlates and visualizes the ice surface location information with melting characteristics, making the analysis of the ice surface status more accurate and intuitive, and providing a reliable signal foundation for subsequent melting operations.

[0066] A parameter database is established based on magnetothermal-acoustic conversion technology. Standard parameters and physical property information of common scenarios, equipment types, and ice materials are collected. A node-association data structure is constructed to systematically manage parameter information and relationships. When an ice melting requirement is detected, a feature matching algorithm is used to calculate the feature distance using a dynamic programming algorithm. Combined with ice thickness and material characteristics, the target parameter matching range is determined, control parameters are extracted, and labels are generated. This enables rapid and accurate positioning of target parameters, improving the targeting and timeliness of ice melting operations.

[0067] By building a historical ice-melting case database, we can dynamically record ice-melting records for different scenarios, materials, and ice thicknesses. Through data integration with the ice-melting equipment management system and user feedback platform, we can continuously update information such as matching results, energy consumption feedback, and operational effects. This allows us to make full use of historical experience to provide a reference for new ice-melting tasks, making the formulation of ice-melting solutions more scientific and reasonable, and continuously optimizing the ice-melting effect.

[0068] A multi-factor weight allocation model was established, which combines scene type, material characteristics, ice thickness and environmental temperature and humidity factors. The decision tree algorithm was used to assign weights to each indicator. The ice melting matching degree score was calculated by weighted summation and a priority sequence was generated. This model can comprehensively consider multiple factors, rationally allocate resources, and ensure that ice melting tasks with high importance and complexity are processed first, thereby improving the overall efficiency and resource utilization of ice melting operations.

[0069] An improved genetic algorithm is used to find the optimal ice-melting scheme. The optimization objectives are to maximize ice-melting efficiency, minimize energy consumption threshold, and shorten the action time. By randomly generating the initial population, calculating fitness values, selecting parent individuals and updating crossover and mutation probabilities, and continuously updating candidate solutions, the comprehensive optimal ice-melting scheme can be found in a short time, which significantly improves ice-melting efficiency, reduces energy consumption, and shortens action time.

[0070] The interactive verification platform outputs the final ice-melting results and continuously tracks the feedback on the actual ice-melting effect. It can adjust and optimize the ice-melting scheme according to the actual situation, making the system more adaptable and reliable, and maintaining good ice-melting performance under different environments and working conditions. Attached Figure Description

[0071] Figure 1 This is a schematic diagram illustrating the working principle of the inductive magnetothermal acoustic de-icing method described in this invention.

[0072] Figure 2 A flowchart for constructing the signal preprocessing module;

[0073] Figure 3 A flowchart for creating a parameter database;

[0074] Figure 4 A flowchart for feature matching and control label generation. Detailed Implementation

[0075] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0076] Please see Figures 1-4 This invention provides a method for melting and de-icing based on inductive magnetothermal acoustics, the specific implementation steps of which are as follows:

[0077] Real-time acquisition of ice surface status signal data, construction of a multi-dimensional signal processing system, and output of standardized ice melting signals through a signal preprocessing module.

[0078] A parameter database is established based on magnetothermal-acoustic conversion technology. When a melting demand is detected, the target parameters are located and control tags are generated through feature matching algorithms.

[0079] Build a database of historical ice melting cases to dynamically record ice melting records for different scenarios, materials, and ice thicknesses.

[0080] A multi-factor weight allocation model is established. By combining scene type, material characteristics, ice thickness and environmental temperature and humidity factors, a decision tree algorithm is used to assign weights to each indicator. A weighted summation method is used to construct a matching model to calculate the ice melting matching score, thereby generating an ice melting priority sequence.

[0081] An improved genetic algorithm is used to solve the optimal ice melting scheme, with optimization objectives set in the direction of maximizing ice melting efficiency, minimizing energy consumption threshold, and shortening action time.

[0082] The final ice-melting result is output through the interactive verification platform, and feedback on the actual ice-melting effect is continuously tracked.

[0083] Example 1: In the process of real-time acquisition of ice surface status signal data, construction of a multi-dimensional signal processing system, and output of standardized ice melting signals through a signal preprocessing module, the signal data of the ice surface area is first acquired using inductive sensors. The inductive sensors can be selected according to the actual application scenario. For example, in the scenario of power transmission line ice melting, induction coil sensors arranged around the line can be used; in the scenario of ice melting of large structures such as bridges, embedded magneto-thermal-acoustic composite sensors can be used. After receiving the raw signal from the sensor, the signal acquisition terminal performs time-domain filtering processing. By setting a specific time window and filtering coefficients, time-domain noise caused by environmental vibration, electromagnetic interference, etc., is removed. Next, frequency-domain denoising is performed, using Fourier transform to convert the time-domain signal to the frequency domain, and using a bandpass filter to filter out high-frequency interference signals and low-frequency environmental noise. Then, edge enhancement preprocessing is performed, using differential operators and other methods to highlight edge features in the signal, thereby effectively removing environmental interference clutter.

[0084] Based on the physical properties of the ice surface, such as its density and crystal structure, and the magnetothermal-acoustic response law—that is, the characteristics of the thermal and acoustic effects produced by ice under the action of a magnetic field—a feature selection algorithm is used to screen parameters closely related to the ice-melting characteristics from the preprocessed signal as candidate features. Feature selection algorithms such as the Relief-F algorithm are used to determine candidate features by calculating the correlation between each parameter and the ice-melting effect. Candidate features are categorized into three types: magnetic permeability features, thermal diffusion features, and acoustic impedance features. Magnetic permeability features reflect the magnetization ability of the ice surface in a magnetic field and can be obtained by measuring the relative magnetic permeability of the ice surface; thermal diffusion features reflect the thermal conductivity of the ice surface and can be obtained through thermal conductivity experiments to obtain the thermal diffusion coefficient; acoustic impedance features represent the propagation and reflection characteristics of sound waves on the ice surface and can be measured using acoustic wave detection equipment.

[0085] When constructing a multi-level feature system, the overall ice-melting matching degree is used as the top-level feature. This top-level feature is decomposed into several first-level features, such as magnetothermal-acoustic response intensity and feature stability. Each first-level feature is further subdivided into several second-level features. For example, magnetothermal-acoustic response intensity can be subdivided into magnetic field strength response features and temperature change features, while feature stability can be subdivided into feature consistency at different time points and feature repeatability under different environmental conditions. Based on the multi-level feature system, a neural network model is used to train the signal preprocessing module. The neural network model can be a convolutional neural network (CNN) or a recurrent neural network (RNN). By inputting a large amount of ice surface signal data and corresponding ice-melting effect labels, the model is trained, and the model's weights and biases are adjusted to enable it to accurately preprocess the input signal.

[0086] The location information of the ice surface is correlated with the melting characteristics of the preprocessed signals to establish a location-feature mapping relationship. Specifically, a unique location identifier is assigned to each sensor that collects signals, such as the tower number and line segment on a transmission line, or the specific location coordinates on a bridge. The signal characteristics collected by the sensor are mapped to this location identifier to form a location-feature mapping table. Signal analysis software is selected as the magnetothermal-acoustic analysis platform. Common professional signal analysis software such as MATLAB and LabVIEW can be used. Regions are marked on the software interface according to the actual location of the ice surface. For example, the location of each line segment is marked on the 3D model of the transmission line, and the location of different areas is marked on the plan view of the bridge. Different brightness levels are used for visualization based on the differences in features; areas with large feature differences are highlighted, and areas with small feature differences are highlighted. For example, when the magnetic permeability characteristics of a certain area differ significantly from other areas, that area is highlighted on the interface so that operators can intuitively understand the distribution of ice surface features.

[0087] Throughout the implementation process, the signal acquisition terminal needs to possess high-speed data acquisition and real-time processing capabilities to ensure timely acquisition and preprocessing of ice surface state signals. The placement of inductive sensors must consider the ice surface coverage and signal acquisition uniformity to avoid signal blind spots. Training the neural network model requires a large amount of sample data covering ice surface signals from different scenarios, materials, and ice thicknesses to ensure the model's generalization ability and accuracy. Establishing the position-feature mapping relationship requires accurate recording of sensor position information and corresponding signal characteristics to avoid mapping errors. The selection of signal analysis software must consider whether its functionality meets the requirements of magnetothermal-acoustic analysis, as well as ease of operation and visualization effects. Through these steps, real-time acquisition of ice surface state signal data is achieved, a multi-dimensional signal processing system is constructed, and standardized melting signals are output through the signal preprocessing module, providing reliable signal support for subsequent ice melting and de-icing operations.

[0088] Example 2: When establishing a parameter database based on magnetothermal-acoustic conversion technology, standard parameter data for common application scenarios, equipment types, and ice layer materials are first collected. Common application scenarios include power transmission lines, bridges, and communication base stations. Equipment types cover different models of power transmission line towers, bridge steel structures, and base station antennas. Ice layer materials include pure ice, mixed ice crystals, and snow-covered ice layers. The collected standard parameter data includes basic parameters such as magnetic saturation value, thermal conductivity value, and sound velocity value. For example, the magnetic saturation value of pure ice is approximately within a certain range, the thermal conductivity value is the value at a specific temperature, and the sound velocity value is the performance under different ice layer densities. The physical property information of the parameters is obtained. The medium density is calculated by measuring the mass and volume of the ice layer sample, the interfacial bonding strength is determined by tensile or shear tests, and the surface roughness parameter is measured using a surface profilometer.

[0089] Parameters are abstracted as data nodes, each containing information such as the parameter's name, value, and unit. The magnetic, thermo-acoustic relationships between parameters are abstracted as data associations; for example, changes in magnetic saturation value affect changes in thermal conductivity value, and this influence relationship is a data association. A node-association data structure is constructed, with data nodes as vertices and data associations as edges, forming a network structure that clearly represents the relationships between parameters. A database architecture is established, including the definition of data tables, such as parameter tables, attribute tables, and association tables. Field settings are determined according to actual needs; for example, the parameter table contains fields such as parameter ID, parameter name, parameter value, and unit. Data relationships are established through foreign key associations to store parameter information, attribute information, and association relationships.

[0090] The preprocessed data is imported into the database of the magnetothermal-acoustic analysis software. During the import process, the data needs to be validated to ensure its accuracy and completeness, avoiding missing or incorrect data, thereby establishing a parameter database. When locating target parameters and generating control labels using feature matching algorithms, each parameter node is considered a sample point in the dataset based on the established parameter database. Each sample point contains multiple feature attributes, and the magnetothermal-acoustic correlation between parameters is considered the similarity between samples; that is, the higher the correlation between two sample points, the greater their similarity.

[0091] Using the signal characteristics of the area to be melted as query points, a dynamic programming algorithm is used to calculate the feature distance to each sample point. Dynamic programming decomposes a complex problem into subproblems and progressively solves for the optimal solution. When calculating the feature distance, distance metrics such as Euclidean distance and Manhattan distance can be used. When the sample point with the highest matching degree is encountered, its feature information is recorded, including the parameter values, application scenario, and ice material. The calculated feature distances are analyzed, and combined with information on ice thickness and material properties, to determine the matching range of the target parameters. For example, if the feature distance is less than a certain threshold, the sample point is considered to fall within the matching range of the target parameters.

[0092] The magnetic saturation, thermal conductivity, and sound velocity values ​​of the target parameters are extracted from the parameter database to generate control parameters. These control parameters need to be adjusted according to actual ice melting requirements; for example, they may require appropriate modifications under different ambient temperatures. The target parameters, control parameters, scene type, and ice material are combined into a label. The label format can be designed according to the requirements of the magnetic, thermal, and acoustic analysis software, such as using JSON or XML, and displayed visually on the software interface. The labels can be displayed in list, chart, or other formats on the software interface for easy viewing and understanding by operators.

[0093] In establishing the parameter database, it is crucial to ensure the representativeness and reliability of the collected data. For data on special scenarios or rare ice materials, experimental or actual measurement methods are necessary to avoid using outdated or inaccurate data. When constructing the node-association data structure, the various relationships between parameters must be fully considered to ensure the data structure accurately reflects the physical laws of magnetothermal-acoustic conversion technology. When using dynamic programming algorithms to calculate feature distances, appropriate algorithm parameters, such as distance measurement methods and thresholds, need to be set to improve the accuracy and efficiency of feature matching. When generating control tags, it is essential to ensure that the tag information is complete, accurate, and can be correctly identified and processed by the magnetothermal-acoustic analysis software. Through these steps, a parameter database based on magnetothermal-acoustic conversion technology is established, and target parameters are located and control tags are generated using feature matching algorithms, providing accurate parameter support and control basis for subsequent ice melting and de-icing operations.

[0094] Example 3: Constructing a historical ice-melting case database to dynamically record ice-melting records for different scenarios, materials, and ice thicknesses. First, the main dimensions for constructing the database must be obtained. These dimensions include ice-melting records for different scenario types, ice materials, and ice thicknesses. Specific scenario types can be categorized as power transmission line scenarios, bridge scenarios, building roof scenarios, and communication equipment scenarios. Each scenario type can be further subdivided; for example, power transmission line scenarios can be categorized by voltage level as high voltage, ultra-high voltage, etc. Ice materials can be categorized as pure ice, mixed ice (such as a mixture of ice and snow), salt-coated ice, and ice containing impurities, etc. Ice thickness is divided into different ranges based on the actual measurement range, such as 0-10mm, 10-20mm, 20-30mm, etc.

[0095] Simultaneously, specific information fields are planned for each dimension, including application scenarios, equipment types, ice material, standard parameters, actual control parameters, and user operation feedback. Application scenarios need to record the specific usage environment in detail, such as a specific section of a transmission line or the name and location of a bridge; equipment types should be specified down to the specific equipment model, such as a certain model of transmission tower or a certain type of bridge support; ice material should record not only the general category but also the specific characteristics of the material, such as impurity content and density; standard parameters include the corresponding magnetic saturation value, thermal conductivity value, and sound velocity value for that scenario and material; actual control parameters are the magnetothermal-acoustic control parameters used in the actual ice melting process, such as the applied magnetic field strength, heating power, and sound wave frequency; user operation feedback includes the operator's subjective evaluation of the ice melting effect and problems encountered during the operation.

[0096] By integrating the ice-melting equipment management system with the user feedback platform, the system continuously updates the matching results, energy consumption feedback, and operational effectiveness information of historical ice-melting cases. The ice-melting equipment management system stores equipment operating data, including the time, parameters used, and operating status of each ice-melting operation; the user feedback platform collects feedback from operators and maintenance personnel. During integration, data from the two systems is synchronized and integrated via a data interface to ensure timely updates to the historical ice-melting case database. For example, after an ice-melting operation is completed, the ice-melting equipment management system automatically uploads the operation data to the database, while feedback submitted by operators on the user feedback platform is also synchronized to the database.

[0097] A historical ice-melting case information database is constructed based on key dimensions and data fields. This database can be stored using a relational database such as MySQL or Oracle. Multiple tables are created within the database, each corresponding to a different dimension and information field. Efficient data storage and retrieval are achieved by establishing relationships between these tables. For example, tables for scenarios, materials, thickness, and cases can be created. The case table is linked to the scenario, material, and thickness tables via foreign keys to store specific information for each case.

[0098] A multi-factor weighting model was established, combining scenario type, material characteristics, ice thickness, and environmental temperature and humidity factors. A decision tree algorithm was used to assign weights to each indicator, and a weighted summation method was employed to construct a matching model to calculate the ice-melting matching score, thereby generating an ice-melting priority sequence. Based on scenario type, standard parameters, and ice thickness, ice-melting importance levels were classified. High-frequency usage scenarios or needs with clearly defined standard parameters were assigned a high importance level, such as the ice-melting needs of high-voltage power transmission lines, which have a significant impact on power supply and relatively clear standard parameters. Low-frequency usage scenarios or needs without clearly defined parameters were assigned a low importance level, such as the ice-melting needs of building roofs in some remote areas, which have a low usage frequency and unclear standard parameters.

[0099] Ice materials are categorized into metal surface ice, concrete surface ice, and glass surface ice. Metal surface ice presents the highest complexity for melting due to the high thermal conductivity of metal and the strong bond between the ice and the metal surface. More factors need to be considered during melting, such as avoiding damage to the metal surface. Glass surface ice presents the lowest complexity; the relatively smooth glass surface results in a weaker bond between the ice and the glass, making melting easier. Ambient temperature and humidity are also categorized into standard temperature and humidity, high-temperature and high-humidity environments, and low-temperature and low-humidity environments. Standard temperature and humidity environments refer to conditions within a certain normal range. High-temperature and high-humidity environments may affect the performance of ice-melting equipment, while low-temperature and low-humidity environments may cause the ice to harden further, increasing the difficulty of melting.

[0100] Based on the decision tree algorithm, weights are assigned to the importance level, complexity level, and environmental temperature and humidity factors of ice melting. The decision tree algorithm constructs a tree structure by learning from historical data for classifying and predicting new data. When constructing the decision tree, the ice melting effect is used as the objective variable, and the importance level, complexity level, and environmental temperature and humidity factors are used as feature variables. The importance of each feature variable in the decision tree is determined by calculating its information gain, thus obtaining the weight of each indicator. Let the weight of the ice melting importance level be... The weight of the complexity level is The weight of environmental temperature and humidity factors is The time factor has a weight of 1. Each weight satisfies ,in This reflects the degree to which scenario type and standard parameters affect the ice melting task. This reflects the influence of the ice layer's material. This indicates the role of environmental temperature and humidity conditions. The degree of urgency of the ice melting needs.

[0101] A weighted summation method is used to construct a matching model, where each index value is multiplied by its corresponding weight and then summed to obtain a matching score for each ice-melting task. Let the ice-melting importance level score be... The complexity level score is The environmental temperature and humidity factors scored as follows: The time factor score is Then the matching score The calculation formula is:

[0102]

[0103] in, Values ​​are assigned based on the importance level of the ice melting process. Higher importance levels are assigned higher scores, such as 80-100 points, while lower importance levels are assigned lower scores, such as 0-40 points. The ice layer is assigned a value based on the complexity level of its material. Ice on metal surfaces has a high complexity level and can be assigned a value of 60-100 points, while ice on glass surfaces has a low complexity level and can be assigned a value of 0-40 points. Based on the classification and assignment of environmental temperature and humidity, the standard temperature and humidity environment is assigned a score of 40-60, while the high temperature and high humidity or low temperature and low humidity environment is assigned a score of 0-40 or 60-100 depending on the specific circumstances. The score is assigned based on the urgency of the ice-melting need, with urgent needs assigned 80-100 points and non-urgent needs assigned 0-40 points.

[0104] Based on the matching score calculated by the matching model, all ice-melting tasks are sorted, with tasks scoring higher having higher matching priority, thus generating an ice-melting priority sequence. During the sorting process, common sorting algorithms such as bubble sort and quick sort can be used to ensure the accuracy and rationality of the priority sequence.

[0105] When constructing a historical ice-melting case database, it is crucial to ensure the completeness and accuracy of the data. Missing data must be promptly supplemented and corrected to prevent data quality issues from impacting subsequent analysis and decision-making. When classifying the importance and complexity levels of ice-melting operations, the needs and characteristics of actual application scenarios must be fully considered to ensure the rationality of the classification. When assigning weights using the decision tree algorithm, sufficient historical data is needed for training to improve the accuracy and reliability of the weights. When calculating the matching score, it is essential to ensure that the scoring standards for each indicator are consistent to avoid biases caused by human factors. Through these steps, the historical ice-melting case database and the multi-factor weight allocation model are constructed, providing a scientific basis and method for prioritizing ice-melting tasks.

[0106] Example 4: In implementing the multi-factor weight allocation model, it is necessary to classify the ice material and determine its corresponding complexity level. For example, when dealing with ice on a metal surface, due to the good thermal conductivity of metal, the bond between the ice and the metal surface is often quite tight. During the melting process, it is necessary not only to consider the melting efficiency of the ice but also to avoid damage to the metal substrate due to magnetothermal and acoustic effects, such as ice formation on the steel components of transmission line towers. In this case, the melting complexity level of ice on metal surfaces is set as high. The situation is different for ice on concrete surfaces. Concrete has many surface pores, and the bonding force between the ice and the concrete surface is relatively weak. However, the concrete structure may have defects such as cracks. During melting, it is necessary to prevent water from seeping in and causing structural damage. Its complexity level is between that of metal and glass. For ice on glass surfaces, taking the glass panels of bridge railings or the glass windows of buildings as examples, the glass surface is smooth, and the adhesion strength between the ice and the glass is low. During the melting process, it is only necessary to focus on the melting of the ice without much consideration for substrate damage. Therefore, the complexity level of ice on glass surfaces is classified as low.

[0107] The classification of ambient temperature and humidity also needs to be considered in conjunction with specific scenarios. A standard temperature and humidity environment can be set as a temperature between 0℃ and 10℃ and a relative humidity between 40% and 60%. Under these conditions, the physical properties of the ice layer are relatively stable, and the ice-melting operation is less affected by environmental factors. In high-temperature and high-humidity environments, such as rainy summer regions where temperatures may reach above 25℃ and relative humidity exceeds 80%, the ice layer may be in a semi-melted state. During the ice-melting process, the issues of water evaporation and equipment heat dissipation need to be considered. Conversely, in low-temperature and low-humidity environments, such as frigid northern regions where temperatures are below -10℃ and relative humidity is below 30%, the ice layer becomes harder, and the efficiency of magnetothermal and acoustic energy transfer may be affected. Both of these environments are considered non-standard temperature and humidity environments and need to be reflected in the weighting allocation.

[0108] When assigning weights to various factors using the decision tree algorithm, data from a historical ice-melting case database is used as the foundation. For example, when handling ice-melting tasks on a high-voltage section of a transmission line, this scenario is a high-frequency use case with clearly defined standard parameters, so the importance level of ice-melting is determined to be high; the ice material is metallic surface ice, resulting in a high complexity level; and the environmental temperature and humidity are within the standard range, so the corresponding weight allocation will favor the importance and complexity levels. The decision tree algorithm determines the weight ratio of each factor by learning from a large number of similar cases. Suppose that in a certain training iteration, the weight for the importance level of ice-melting is 0.4, the weight for the complexity level is 0.3, the weight for environmental temperature and humidity is 0.2, and the weight for the time factor is 0.1. These weight values ​​will be continuously updated and adjusted based on new case data.

[0109] When constructing the matching model to calculate the ice-melting matching score, a specific ice-melting task is used as an example. Assume an ice-melting task occurs on a bridge on a main urban road. This bridge is a critical part of a transportation hub, the scenario type is bridge scenario, and due to frequent vehicle traffic, it is a high-frequency usage scenario. Furthermore, the bridge management department has clear standard parameters for ice-melting; therefore, the importance level score for ice-melting is 90 points. The ice material is concrete surface ice. Considering the safety of the bridge structure, the complexity level score for ice-melting is 60 points. The ambient temperature and humidity are low and low, with a temperature of -15℃ and relative humidity of 20%. Under these conditions, the ice layer is hard, increasing the difficulty of ice-melting; the environmental temperature and humidity factor score is 30 points. In addition, the ice-melting task is urgent due to important traffic activities the following day; the time factor score is 90 points.

[0110] According to the matching model, the matching score is calculated by multiplying each indicator value by its corresponding weight and then summing the results. If the weights of each factor are 0.4 for the importance level of ice melting, 0.3 for the complexity level, 0.2 for the environmental temperature and humidity, and 0.1 for the time factor, then the matching score for this task is: 90×0.4+60×0.3+30×0.2+90×0.1=36+18+6+9=69 points. Another ice melting task occurs at a communication base station in a mountainous area. The scenario type is a communication equipment scenario, which belongs to a low-frequency usage scenario and the standard parameters are unclear. The ice melting importance level score is 40 points; the ice material is glass surface ice, and the complexity level score is 20 points; the environmental temperature and humidity are within the standard range, and the score is 50 points; the ice melting requirement is routine maintenance, and the time factor score is 30 points. Assuming the weighting is the same as in the previous example, the matching score is: 40×0.4+20×0.3+50×0.2+30×0.1=16+6+10+3=35 points.

[0111] The ice-melting tasks were ranked based on the calculated matching scores. The bridge ice-melting task, with a score of 69, scored higher than the communication base station task (35 points), therefore the bridge ice-melting task has a higher priority and should be scheduled first. In practical applications, multiple ice-melting tasks may exist simultaneously. It is necessary to calculate the matching score for each task and rank them to generate an ice-melting priority sequence. For example, if three tasks exist simultaneously: Task A (matching score 75), Task B (matching score 55), and Task C (matching score 80), the priority sequence would be Task C, Task A, and Task B, and the ice-melting operations would be carried out in this order.

[0112] In calculating the ice-melting matching score, the correlation between each weight and the corresponding level score needs to be dynamically adjusted according to the actual situation. For example, in frigid regions during winter, low temperature and low humidity environments are common. In this case, the weight of environmental temperature and humidity factors may be appropriately increased to more accurately reflect the impact of the environment on the ice-melting task. The weight of the time factor corresponds to the urgency of the ice-melting demand. For example, if a sudden severe weather event causes rapid ice accumulation, the weight of the time factor will be significantly increased, causing such tasks to be prioritized earlier in the priority sequence.

[0113] Throughout the implementation process, it is necessary to continuously collect new ice-melting case data, constantly enriching the historical ice-melting case information database, providing more training samples for the decision tree algorithm, and making weight allocation more accurate. Simultaneously, the matching model needs to be regularly evaluated and optimized to ensure that it can reasonably calculate the ice-melting matching score in different scenarios and environments, generating accurate priority sequences. For example, historical cases should be reviewed quarterly, and the weights of the decision tree model updated to adapt to environmental differences brought about by seasonal changes and the needs of newly emerging ice-melting scenarios. In this way, the dynamic optimization of the multi-factor weight allocation model and the accurate calculation of matching scores can be achieved, providing a scientific and reasonable basis for prioritizing ice-melting tasks and ensuring the efficient implementation of ice-melting and de-icing work.

[0114] Example 5: When using an improved genetic algorithm to solve for the optimal ice-melting scheme, a set of candidate solutions needs to be randomly generated as the initial population. The structure of the candidate solutions must match the parameter system of the ice-melting scheme. For example, each candidate solution can be represented as a vector containing magnetothermal-acoustic control parameters, including parameters such as magnetic field strength, heating power, acoustic frequency, and action time. The size of the initial population is determined according to the complexity of the problem, usually set to 50 to 200 candidate solutions to ensure population diversity. When generating the initial population, the value range of each parameter is determined based on the standard parameters in the parameter database and actual ice-melting experience. For example, the value range of the magnetic field strength can be set to 100mT to 500mT, and the value range of the heating power can be set to 500W to 2000W to ensure that the initial candidate solutions are within a reasonable range.

[0115] The fitness value of each candidate solution is calculated based on the optimization objectives, which include maximizing ice-melting efficiency, minimizing the energy consumption threshold, and shortening the application time. The ice-melting efficiency objective is achieved by calculating the magnetothermal-acoustic matching degree between the candidate solution and the target parameters; a higher magnetothermal-acoustic matching degree indicates a greater potential for ice-melting efficiency. The energy consumption threshold objective is achieved by limiting the energy consumption difference between the candidate solution and the standard parameters; a smaller energy consumption difference better meets energy-saving requirements. The application time objective is achieved by reducing the number of algorithm iterations and computation time, and is also related to the application time parameter set in the candidate solution. The design of the fitness function needs to comprehensively consider these three sub-objectives. For example, the matching degree of ice-melting efficiency can be used as a positive correlation factor, while energy consumption difference and application time can be used as negative correlation factors. The fitness value of each candidate solution is obtained by weighted summation.

[0116] The optimal solution is selected as the parent individual based on its fitness value, and the crossover and mutation probabilities of other individuals are updated. Candidate solutions with higher fitness values ​​have a greater probability of being selected as parents. Common selection methods include roulette wheel selection and tournament selection. For example, in roulette wheel selection, the probability of each candidate solution being selected is directly proportional to its fitness value; the higher the fitness value, the larger its proportion in the roulette wheel, and the higher its probability of being selected. After selecting the parent individual, the crossover and mutation probabilities of other individuals are dynamically adjusted based on the parent's fitness value. Generally, individuals with high fitness values ​​have lower crossover and mutation probabilities to retain superior genes, while individuals with low fitness values ​​have higher crossover and mutation probabilities to increase population diversity and explore new solution spaces.

[0117] By simulating the genetic evolution and information transmission behavior of a population, candidate solutions are continuously updated iteratively until a termination condition is met. The genetic evolution process includes crossover and mutation operations. Crossover involves exchanging genes between two parent individuals to generate new offspring. The position and method of crossover are determined based on the characteristics of the parameters; for example, arithmetic crossover can be used for continuous parameters, while single-point or multi-point crossover can be used for discrete parameters. Mutation involves randomly altering the genes of individuals to prevent the population from getting trapped in local optima. The probability and magnitude of mutation are adjusted according to the parameter range and the optimization process. During the iteration process, the optimal solution and average fitness value of each generation are recorded. The iteration process terminates when the number of iterations reaches a preset maximum value, or when the optimal solution no longer changes significantly over several consecutive generations.

[0118] A set of optimal solutions is selected from the final population as the optimal ice-melting scheme. The optimal solution in the final population is the candidate solution with the highest fitness value after multiple generations of evolution. This solution comprehensively considers factors such as ice-melting efficiency, energy consumption, and processing time, and can achieve good results in actual ice-melting operations. For example, the final optimal ice-melting scheme may be a magnetic field strength of 300mT, a heating power of 1200W, a sound wave frequency of 20kHz, and a processing time of 30 minutes. This scheme achieves a good balance between ice-melting efficiency, energy consumption, and processing time.

[0119] To achieve the ice-melting efficiency target in the optimization objective, it is necessary to compare the magnetothermal-acoustic parameters in the candidate solutions with the target parameters and calculate the degree of matching between them. The target parameters are derived from a parameter database and are determined based on factors such as the ice material, thickness, and scene type of the area to be melted. For example, for ice on a metal surface, the target magnetic saturation value is within a specific range, and the thermal conductivity and sound velocity values ​​also have corresponding standards. By calculating the degree of deviation between the candidate solution parameters and the target parameters, the degree of matching for ice-melting efficiency is determined. The smaller the deviation, the higher the degree of matching, and the better the achievement of the ice-melting efficiency target.

[0120] Achieving the energy consumption threshold target requires calculating the theoretical energy consumption of candidate solutions during the ice-melting process and comparing it with the energy consumption under standard parameters, limiting the difference between the two to a certain range. The energy consumption under standard parameters is obtained through statistical analysis of data from a historical ice-melting case database. For example, for a certain type of ice layer and material, the standard energy consumption is 1.5 kW·h of energy consumed per square meter of ice layer. The theoretical energy consumption of candidate solutions is calculated based on their parameters and the characteristics of the ice layer. For example, the energy consumption is obtained by multiplying the heating power by the action time, and then adjusted according to the area and thickness of the ice layer to ensure that the energy consumption of candidate solutions does not exceed a certain percentage of the standard energy consumption, such as 120%.

[0121] Achieving the target action time can be achieved in two ways: firstly, by optimizing the algorithm's iterative process to reduce computation time, such as by employing more efficient crossover and mutation operations to lower the algorithm's time complexity; and secondly, by controlling the action time parameter in the candidate solutions, minimizing the action time while ensuring ice melting efficiency. For example, while ensuring complete ice melting, the action time can be reduced from 40 minutes to 30 minutes by adjusting the magnetothermal-acoustic parameters, thus achieving the target action time.

[0122] In implementing improved genetic algorithms, it is crucial to appropriately set the initial parameters, such as initial population size, crossover probability, mutation probability, and number of iterations. These parameters affect the algorithm's convergence speed and solution quality. For example, an initial population size that is too small may lead to insufficient population diversity, making it prone to getting trapped in local optima; an excessively high crossover probability may disrupt excellent gene combinations, while an excessively low probability will hinder the population's evolutionary speed. Therefore, in practical applications, it is necessary to debug and optimize the algorithm parameters based on the specific ice-melting problem and historical data to obtain better solution results.

[0123] The interactive verification platform outputs the final ice-melting results and continuously tracks feedback on the actual ice-melting effect. The platform provides a user-friendly interface where operators can view the specific parameters and expected effects of the optimal ice-melting solution. Simultaneously, the platform outputs the solution to the ice-melting equipment to guide its operation. After the ice-melting operation is completed, the platform collects equipment operating data and operator feedback, including actual ice-melting time, energy consumption, and ice melting status. This feedback is compared and analyzed with data in a historical ice-melting case database to evaluate the actual effectiveness of the optimal solution. If shortcomings are found, such as excessive energy consumption or excessively long ice-melting time, this feedback is added as new case data to the historical case database, providing a reference for future optimization of the ice-melting solution.

[0124] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0125] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for melting and de-icing ice based on inductive magnetothermal acoustics, characterized in that, Includes the following steps: Real-time acquisition of ice surface status signal data, construction of a multi-dimensional signal processing system, and output of standardized ice melting signals through a signal preprocessing module; A parameter database is established based on magnetothermal-acoustic conversion technology. When the need for ice melting is detected, the target parameters are located and control tags are generated through feature matching algorithms. Build a database of historical ice melting cases to dynamically record ice melting records for different scenarios, materials, and ice thicknesses; A multi-factor weight allocation model is established. By combining scene type, material characteristics, ice thickness and environmental temperature and humidity factors, the decision tree algorithm is used to assign weights to each indicator. A weighted summation method is used to construct a matching model to calculate the ice melting matching score, thereby generating an ice melting priority sequence. An improved genetic algorithm is used to solve the optimal ice melting scheme, with optimization objectives set in the direction of maximizing ice melting efficiency, minimizing energy consumption threshold, and shortening action time; The final ice-melting result is output through the interactive verification platform, and feedback on the actual ice-melting effect is continuously tracked.

2. The ice-melting and de-icing method based on inductive magnetothermal acoustics according to claim 1, characterized in that, The real-time acquisition of ice surface state signal data is used to construct a multi-dimensional signal processing system. A standardized ice-melting signal is output through a signal preprocessing module. Specifically: Based on the acquisition of signal data of the ice surface area by inductive sensors, the signal acquisition terminal receives the raw signal from the sensor and performs time-domain filtering, frequency-domain denoising and edge enhancement preprocessing to remove environmental interference noise. Based on the physical properties and magnetothermal-acoustic response of the ice surface, parameters closely related to the ice melting characteristics were selected as candidate features using a feature selection algorithm. Candidate features are divided into magnetic permeability features, thermal diffusion features, and acoustic impedance features, and a multi-level feature system is constructed. The overall ice melting matching degree is used as the top-level feature, and the top-level feature is decomposed into several first-level features. Each first-level feature is further subdivided into several second-level features. The signal preprocessing module is trained using a neural network model based on a multi-level feature system. The location information of the ice surface is associated with the melting characteristics of the preprocessed signal to establish a location-feature mapping relationship; Signal analysis software was selected as the magnetothermal-acoustic analysis platform. The area was marked on the interface according to the actual position of the ice surface. Different brightness levels were used for visualization based on the different feature differences. Areas with large feature differences were marked with high brightness, and areas with small feature differences were marked with low brightness.

3. The ice-melting and de-icing method based on inductive magnetothermoacoustic as described in claim 2, characterized in that, The parameter database established based on magnetothermal-acoustic conversion technology is specifically as follows: Collect standard parameter data for common application scenarios, equipment types, and ice materials, including basic parameters such as magnetic saturation value, thermal conductivity value, and sound velocity value; Obtain the physical property information of the parameters, including medium density, interfacial bonding strength, and surface roughness parameters; The parameters are abstracted into data nodes, and the magnetothermal-acoustic correlations between parameters are abstracted into data correlations, thus constructing a node-correlation data structure. Establish the database architecture, including defining data tables, setting fields, and establishing data relationships, storing parameter information, attribute information, and associations; The preprocessed data is imported into the database of the magnetothermal-acoustic analysis software to establish a parameter database.

4. The ice-melting and de-icing method based on inductive magnetothermoacoustic as described in claim 3, characterized in that, The step of locating target parameters and generating control labels through feature matching algorithms specifically involves: Based on the parameter database, each parameter node is regarded as a sample point in the dataset, and the magnetothermal-acoustic correlation between parameters is regarded as the similarity between samples; Using the signal characteristics of the area to be melted as the query point, the feature distance to each sample point is calculated using a dynamic programming algorithm. When the sample point with the highest matching degree is traversed, the feature information of that sample point is recorded. By analyzing and calculating the feature distances, and combining them with information on ice thickness and material properties, the matching range of the target parameters is obtained. Extract the magnetic saturation value, thermal conductivity value, and sound velocity value of the target parameters from the parameter database to generate control parameters; The control parameters of the target parameters, scene type, and ice material are combined into a label and visualized on the interface of the magnetothermal-acoustic analysis software.

5. The ice-melting and de-icing method based on inductive magnetothermal acoustics according to claim 4, characterized in that, The aforementioned construction of a historical ice-melting case database dynamically records ice-melting records for different scenarios, materials, and ice thicknesses, specifically: The main dimensions for constructing the information database include melting records for different scene types, ice material, and ice thickness. At the same time, specific information fields are planned for each dimension, including application scenarios, equipment types, ice material, standard parameters, actual control parameters, and user operation feedback. By connecting the ice melting equipment management system with the user feedback platform, we continuously update the matching results, energy consumption feedback, and operation effect information of historical ice melting cases. A database of historical ice-melting cases was constructed based on key dimensions and data fields.

6. The ice-melting and de-icing method based on inductive magnetothermoacoustic as described in claim 5, characterized in that, The establishment of a multi-factor weight allocation model involves combining scene type, material characteristics, ice thickness, and environmental temperature and humidity factors. A decision tree algorithm is used to assign weights to each indicator, and a weighted summation method is employed to construct a matching model to calculate the ice-melting matching score, thereby generating an ice-melting priority sequence. Specifically: Based on the scenario type, standard parameters, and ice thickness, the importance level of ice melting is classified, with high-frequency usage scenarios or needs with clear standard parameters set as high importance level, and low-frequency usage scenarios or needs without clear parameters set as low importance level. Ice materials are classified into metal surface ice, concrete surface ice, and glass surface ice. Metal surface ice has a high degree of complexity in melting, while glass surface ice has a low degree of complexity. The ambient temperature and humidity are divided into standard temperature and humidity, high temperature and high humidity environment, and low temperature and low humidity environment. Based on the decision tree algorithm, weights are assigned to the importance level, complexity level, and environmental temperature and humidity factors of ice melting; A weighted summation method is used to construct a matching model. Each index value is multiplied by its corresponding weight and then summed to obtain the matching score for each ice melting task. Based on the matching score calculated by the matching model, all ice melting tasks are sorted, with tasks with higher scores having higher matching priority, thus generating an ice melting priority sequence.

7. The ice-melting and de-icing method based on inductive magnetothermoacoustic as described in claim 6, characterized in that, The improved genetic algorithm is used to solve for the optimal ice-melting scheme, with the optimization objectives set in the direction of maximizing ice-melting efficiency, minimizing energy consumption threshold, and shortening processing time. Specifically: A set of candidate solutions is randomly generated as the initial population; Calculate the fitness value for each candidate solution based on the optimization objective; The optimal solution is selected as the parent individual based on the fitness value, and the crossover and mutation probabilities of other individuals are updated. By simulating the genetic evolution and information transmission behavior of the population, the candidate solutions are continuously updated iteratively until the termination condition is met; Select one optimal solution from the final population as the optimal ice melting scheme.

8. The ice-melting and de-icing method based on inductive magnetothermoacoustic as described in claim 7, characterized in that, The ice melting matching score is specifically as follows: The matching score is determined by the weights of the importance level of ice melting, the complexity level, the environmental temperature and humidity, and the time factor. The weight of the importance level of ice melting corresponds to the influence of the scene type and standard parameters, the weight of the complexity level corresponds to the influence of the ice material, the weight of the environmental temperature and humidity corresponds to the influence of temperature and humidity conditions, and the weight of the time factor corresponds to the urgency of the ice melting requirement. The final matching score is obtained by multiplying each weight by the corresponding level score and then summing them up.

9. The ice-melting and de-icing method based on inductive magnetothermoacoustic as described in claim 8, characterized in that, The optimization objective is specifically as follows: The optimization objectives include maximizing ice melting efficiency, minimizing energy consumption threshold, and shortening the action time. The ice melting efficiency objective is achieved by calculating the magnetothermal-acoustic matching degree between candidate solutions and target parameters. The energy consumption threshold objective is achieved by limiting the energy consumption difference range between candidate solutions and standard parameters. The action time objective is achieved by reducing the number of algorithm iterations and computation time. The final optimization objective is the comprehensive optimization of the three sub-objectives.

10. An ice-melting and de-icing system based on inductive magnetothermal acoustics, used to implement the ice-melting and de-icing method based on inductive magnetothermal acoustics as described in any one of claims 1 to 9, characterized in that, Includes the following modules: The signal acquisition and preprocessing module is used to acquire ice surface status signal data in real time, construct a multi-dimensional signal processing system, and output standardized ice melting signals. The control tag generation module establishes a parameter database based on magnetothermal-acoustic conversion technology. When a melting demand is detected, the target parameters are located and control tags are generated through a feature matching algorithm. The historical case information database module is used to dynamically record melting records for different scenarios, materials, and ice thicknesses; The priority generation module establishes a multi-factor weight allocation model, which combines scene type, material characteristics, ice thickness and environmental temperature and humidity factors, uses decision tree algorithm to assign weights to each indicator, and uses weighted summation method to build a matching model to calculate the ice melting matching degree score, thereby generating an ice melting priority sequence. The optimal solution module uses an improved genetic algorithm to find the optimal ice melting solution, with the optimization objectives of maximizing ice melting efficiency, minimizing energy consumption threshold, and shortening processing time. The feedback tracking module outputs the final ice-melting result through the interactive verification platform and continuously tracks the feedback on the actual ice-melting effect.

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