Induction heating path optimization method suitable for heterogeneous silencing structure

Abstract

The invention discloses an induction heating path optimization method suitable for a heterogeneous noise elimination structure, and particularly relates to the field of induction heating path optimization of the heterogeneous noise elimination structure, and the method comprises the steps: building a grid coordinate in a target region, collecting the heat conductivity coefficient, the specific heat capacity, the thermal resistance value and the induction coil gap value of each grid node in the grid coordinate, generating a thermal response model; and performing heat conduction calculation on the thermal response model and a preset heating power curve, calculating a temperature rise value of each grid node in a fixed preset heating time, marking the grid nodes of which the temperature rise values reach or exceed a safe temperature threshold and the adjacent grid nodes as heat concentration areas, and generating risk area data after gathering the heat concentration areas. A risk assessment and dynamic reordering mechanism based on local thermal characteristic difference is introduced in a heating path planning process, so that advanced identification and real-time avoidance of a heat concentration area are realized.

Description

Technical Field

[0001] This invention relates to the field of induction heating path optimization technology for heterogeneous noise reduction structures, and more specifically, to a method for optimizing the induction heating path of heterogeneous noise reduction structures. Background Technology

[0002] When removing heterogeneous noise-absorbing structures by induction heating, existing technologies usually plan the heating path based on the geometry of the outer surface and the overall average thermal characteristics, assuming that the material heat transfer characteristics of each local area are relatively consistent.

[0003] However, in the actual sound-absorbing covering layer of underwater vehicles, there are significant differences in the thermal conductivity, heat capacity and interfacial bonding state of adhesives, fillers and matrix. These differences are not only reflected in the overall area, but also unevenly distributed in the range of millimeters or even smaller scales.

[0004] Meanwhile, changes in the curvature of the shell surface and the presence of microcracks, bubbles, or localized debonding within can cause sudden changes in localized thermal resistance. This results in heat not spreading uniformly outward during heating, but rather concentrating along paths with lower resistance. Consequently, even areas not directly heated may experience a rapid temperature rise due to heat accumulation, exceeding safety thresholds and potentially damaging the substrate or causing uncontrolled debonding. Existing path planning methods cannot accurately identify these potential heat concentration risks during the planning phase, nor can they adjust the path in a timely manner during execution to mitigate these risks, leading to an imbalance in the overall thermal field distribution. Therefore, the core issue is that existing induction heating path planning lacks a mechanism for early identification and dynamic avoidance of localized heat concentration phenomena, making it difficult to simultaneously ensure heating efficiency and structural safety in the complex heat transfer environment of heterogeneous sound-absorbing structures. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide an induction heating path optimization method applicable to heterogeneous noise reduction structures. By introducing a risk assessment and dynamic reordering mechanism based on local thermal characteristic differences during the heating path planning process, the method enables early identification and real-time avoidance of areas with concentrated heat, thereby solving the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for optimizing the induction heating path of heterogeneous noise reduction structures, comprising:

[0007] S1. Establish grid coordinates in the target area, collect the thermal conductivity, specific heat capacity, thermal resistance and induction coil gap value of each grid node in the grid coordinates, and generate a thermal response model.

[0008] S2. Perform heat conduction calculations on the thermal response model and the preset heating power curve, calculate the temperature rise value of each grid node within a fixed preset heating time, mark the grid nodes whose temperature rise value reaches or exceeds the safe temperature threshold and their adjacent grid nodes as heat concentration areas, and generate risk area data by summarizing the heat concentration areas.

[0009] S3. Based on the geometric contour of the target area and the risk area data, generate a path set covering all grid nodes, and divide the path set into high-risk detour path segments, medium-risk power reduction path segments, and low-risk conventional path segments, and generate scanning speed parameters and heating power parameters for each path segment.

[0010] S4. Perform induction heating operations in the order of the path set. During the induction heating operation of each path segment, collect the temperature value and gap value of the corresponding grid node of the path segment in real time. Map the real-time collected data to the thermal response model and compare the mapped data with the risk area data. Generate the risk assessment result of the path segment based on the comparison result.

[0011] S5. When the risk assessment result shows that the temperature rise rate of the current path segment has reached the upper limit of the temperature rise rate, the path reordering is performed based on the path set to generate a new path execution order. The heating power parameters and scanning speed parameters of the high-risk detour path segment, the medium-risk power reduction path segment, and the low-risk conventional path segment in the new path execution order are adjusted respectively. The heating operation continues to be performed according to the adjusted path execution order until the heating operation of all grid nodes in the target area is completed.

[0012] In a preferred embodiment, S1 includes:

[0013] S1-1. Establish a regular grid coordinate system in the target area. Collect thermal conductivity, specific heat capacity, thermal resistance and induction coil gap value at each grid node. Write the collected results into the original data table in the order of the nodes.

[0014] S1-2. Perform data correction operation on the original data table. When any collected value exceeds the upper or lower limit of the sensor range, the value is discarded. Calculate the median of multiple collection results for the same node and write the correction result into the correction data table.

[0015] S1-3. Input the correction data table into the interpolation calculation process, perform bilinear interpolation operation in each grid cell to generate interpolation data points covering the inside of the grid cell, and write the interpolation data points into the interpolation expansion table. At the same time, perform gradient smoothing constraints during the interpolation process to ensure that the numerical difference between adjacent nodes does not exceed the preset gradient upper limit.

[0016] S1-4. Subtract the interpolation expansion table from the correction data table point by point to generate a residual data table. When the residual of any node in the residual data table reaches or exceeds the upper limit of the residual, divide the corresponding grid cell into a refinement cell. Add a node in the refinement cell and repeat steps S1-1 and S1-2. Update the correction data table and regenerate the interpolation expansion table and the residual data table until all residuals are lower than the upper limit of the residual.

[0017] S1-5. When the residual data table meets the upper limit condition of the residual, the thermal conductivity, specific heat capacity, thermal resistance and induction coil gap value are combined into a quaternary parameter vector at each grid node, and all quaternary parameter vectors are arranged into a three-dimensional array according to the grid coordinate order. The three-dimensional array is the thermal response model.

[0018] In a preferred embodiment, S2 includes:

[0019] S2-1. Align each grid node of the thermal response model with the preset heating power curve in time. Divide the fixed preset heating time into equally spaced time steps, with the time step size denoted as δt. Obtain the corresponding power sample value P at each time step. (n) And establish a one-to-one correspondence with the grid node parameters;

[0020] Mesh node parameters include thermal conductivity k i Specific heat capacity c i Thermal resistance r i Induction coil gap value d i ;

[0021] S2-2. Perform iterative calculations of heat dissipation conduction at each time step, and initialize the temperature rise value of each grid node. And update the temperature rise value of each grid node according to the following formula:

[0022]

[0023] in, Let N(i) represent the temperature rise value of the i-th grid node at the n-th time step; N(i) is the set of adjacent grid nodes that share the grid surface with grid node i. The symmetric thermal flux coefficient is based on thermal conductivity; i represents the index of the currently calculated grid node; j represents the index of the grid node adjacent to i; k i k represents the thermal conductivity of grid node i; j This represents the thermal conductivity of grid node j; The boundary input term assigned to grid node i is calculated using the following formula:

[0024]

[0025] Where, ∑p (·) represents the normalization factor for summing over all grid nodes; p represents the index of any grid node when performing power allocation normalization summation, and grid node p is used to traverse all nodes; r p This represents the thermal resistance of grid node p during power distribution; d p This represents the gap value between the induction coils of grid node p during the power distribution process.

[0026] In a preferred embodiment, S2 further includes:

[0027] S2-3. After the cumulative time covers the fixed preset heating time, extract the temperature rise value of each grid node. The temperature rise value ΔT of each grid node i With the safe temperature threshold T safe Compare and generate a set of nodes that exceed the temperature rise limit {i|ΔT}. i ≥T safe}; where T safe This indicates the safe temperature threshold, used to determine whether the temperature rise exceeds the limit; This indicates that grid node i is at the last time step n of the iteration. end The calculated temperature rise value;

[0028] S2-4. Starting from the set of nodes with excessive temperature rise, perform a domain expansion marking within their respective sets of adjacent grid nodes N(i), marking the nodes with excessive temperature rise and their adjacent grid nodes together as heat concentration areas; perform coordinate merging and deduplication processing on all heat concentration areas, and output risk area data consisting of area number, area boundary coordinates and grid node index.

[0029] In a preferred embodiment, S3 includes:

[0030] S3-1. Generate a risk proximity sequence: On the grid coordinates of the target area, calculate the shortest geometric distance D from each grid node i to the data boundary of the risk area. i Set the maximum distance D 上限 And define risk proximity:

[0031]

[0032] Among them, S i This represents the risk proximity of grid node i, with a value range of [0,1].

[0033] S3-2, Generate the Coverage Path Set: Construct an undirected adjacency graph G = (V, E) on the grid coordinates, where V is the set of all grid nodes and E is the set of adjacent node pairs sharing a grid edge; starting from the entry node, progressively select the next visited node, defining the ternary cost of the candidate edge (i, j): C ij =(max(S) i ,S j ),|κ ij |,L ij And select the candidate edge with the minimum cost in lexicographical order, which includes comparing the first component, then the second component, and finally the third component; C ij L represents the combined cost of candidate path edges from grid node i to adjacent grid node j; ij Let |κ be the geometric length of the edge (i,j). ij | is the direction vector u from the previously selected edge. prev With the current candidate edge direction vector u ij The change in the included angle is defined as Traverse all unvisited nodes according to the selection rules and output the set of paths that cover all grid nodes;

[0034] S3-3, Path Set Segmentation: The path set obtained in S3-2 is sequentially scanned according to the node risk proximity threshold rule:

[0035]

[0036] Group consecutive path sequences belonging to the same category into a single path segment, and count the set of nodes for each path segment s. Average risk proximity

[0037] S3-4. Generate heating power parameters and scanning speed parameters for each path segment: Set the upper limit of heating power P. 上限 , Circumduction scanning speed v 绕行 Energy target e per unit path length 目标 And generate parameters for each path segment s according to the following formula:

[0038]

[0039] Among them, P s Let v be the heating power parameter for path segment s. s The scanning speed parameter for path segment s;

[0040] S3-5, Output parameterized path set: Write all path segments obtained in S3-3 and the heating power parameters and scanning speed parameters calculated in S3-4 into the parameterized path set.

[0041] In a preferred embodiment, S4 includes:

[0042] Induction heating operations are performed sequentially according to the order of the path set. During the execution of each path segment, real-time temperature values ​​and induction coil gap values ​​are collected for each grid node covered by the path segment, and the collected results are stored as a real-time data sequence according to the grid node number.

[0043] The real-time acquired data sequence is input into the thermal response model. In the thermal response model, the temperature change of each grid node is calculated, and a real-time temperature distribution covering all grid nodes of the path segment is formed.

[0044] In a preferred embodiment, S4 further includes:

[0045] The real-time temperature distribution status is matched one by one with the risk area data according to the grid node number. The real-time temperature difference and gap difference are calculated for each grid node, and the calculation results are stored as a comparison result matrix.

[0046] In the comparison result matrix, a set of grid nodes whose real-time temperature difference and gap difference both exceed a preset threshold are identified, and this set of grid nodes is marked as a risk trigger node set.

[0047] Based on the spatial distribution of the risk triggering node set, a risk assessment result for the path segment is generated, and the risk assessment result is used as the input condition for subsequent path segment adjustments.

[0048] The technical effects and advantages of this invention are as follows:

[0049] 1. By introducing a path division and dynamic reordering mechanism based on risk regions, the solution can identify and avoid the risk of local heat concentration in real time, thereby avoiding matrix overheating or uncontrolled debonding, and solving the problem of lack of dynamic risk control in existing methods.

[0050] 2. In the path planning stage, three types of path segments are introduced: high-risk detour, medium-risk power reduction, and low-risk conventional path segments. This enables different regions to allocate differentiated energy based on heat transfer differences, thereby achieving a balanced distribution of the thermal field in complex heterogeneous materials.

[0051] 3. During the execution phase, temperature and gap data of grid nodes are collected and compared with thermal response model. This enables dynamic correction of risk assessment results and ensures that heating parameters match the actual material state during path execution.

[0052] 4. By monitoring the temperature rise rate in real time and setting an upper limit threshold, the path execution sequence and heating power are automatically adjusted when a local area reaches the critical value, so as to achieve continuous operation under safety constraints and reduce the risk of local thermal damage.

[0053] 5. The overall approach balances global coverage with local flexibility, maintaining the efficiency of complete removal of the anechoic coating while improving the controllability and stability of the process, making it suitable for application scenarios with complex curved surfaces and heterogeneous anechoic structures. Attached Figure Description

[0054] Figure 1 This is a flowchart of the method steps of the present invention. Detailed Implementation

[0055] 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.

[0056] Refer to the instruction manual appendix Figure 1 An embodiment of the present invention provides an induction heating path optimization method applicable to heterogeneous noise reduction structures, comprising:

[0057] S1. Establish grid coordinates in the target area, collect the thermal conductivity, specific heat capacity, thermal resistance and induction coil gap value of each grid node in the grid coordinates, and generate a thermal response model.

[0058] S2. Perform heat conduction calculations on the thermal response model and the preset heating power curve, calculate the temperature rise value of each grid node within a fixed preset heating time, mark the grid nodes whose temperature rise value reaches or exceeds the safe temperature threshold and their adjacent grid nodes as heat concentration areas, and generate risk area data by summarizing the heat concentration areas.

[0059] S3. Based on the geometric contour of the target area and the risk area data, generate a path set covering all grid nodes, and divide the path set into high-risk detour path segments, medium-risk power reduction path segments, and low-risk conventional path segments, and generate scanning speed parameters and heating power parameters for each path segment.

[0060] S4. Perform induction heating operations in the order of the path set. During the induction heating operation of each path segment, collect the temperature value and gap value of the corresponding grid node of the path segment in real time. Map the real-time collected data to the thermal response model and compare the mapped data with the risk area data. Generate the risk assessment result of the path segment based on the comparison result.

[0061] S5. When the risk assessment result shows that the temperature rise rate of the current path segment has reached the upper limit of the temperature rise rate, the path reordering is performed based on the path set to generate a new path execution order. The heating power parameters and scanning speed parameters of the high-risk detour path segment, the medium-risk power reduction path segment, and the low-risk conventional path segment in the new path execution order are adjusted respectively. The heating operation continues to be performed according to the adjusted path execution order until the heating operation of all grid nodes in the target area is completed.

[0062] For S5, it should be noted that when the risk assessment result shows that the temperature rise rate of the current path segment has reached the upper limit of the temperature rise rate, the system immediately suspends the heating operation of the path segment and records the unprocessed grid nodes as "nodes to be detoured"; then, the system starts path reordering: first, it extracts all unexecuted path segments and the nodes to be detoured generated due to the suspension, and reclassifies them into three categories according to the classification rules of S3: low-risk regular path segments, medium-risk power reduction path segments, and high-risk detour path segments;

[0063] When generating a new path execution order, the system uses the end grid node of the current path segment as a reference point and prioritizes selecting the low-risk conventional path segment with the closest geometric distance to that node as the next execution segment. If there are multiple candidate path segments, the system further compares the direction turning angle and the path segment length, and selects the segment with the smallest direction change and the shortest length. After all low-risk path segments are completed, the system selects medium-risk power reduction path segments and high-risk detour path segments in sequence according to the same rules until all grid nodes are covered.

[0064] During parameter adjustment, high-risk detour segments in the new path sequence are set to "zero-power heating," meaning the scanning speed is maintained without applying heat; medium-risk segments continue to use the original reduced power and corresponding speed parameters; low-risk segments have their heating power increased to the upper limit, and the appropriate scanning speed is recalculated based on the energy target per unit path length; the adjusted path sequence and parameters are uniformly written into the execution instruction sequence and used as the basis for scheduling subsequent operations.

[0065] While continuing operations in the new order, the system will execute the S4 real-time data acquisition and risk comparison steps again after each path segment is completed. Once a new path segment triggers the upper limit of the temperature rise rate again, the system will immediately repeat the above path reordering and parameter adjustment process to ensure that the risk is controllable. The "nodes to be detoured" generated due to the previous pause will be regarded as independent path segments and included in the next round of reordering until the heating operation of all grid nodes in the target area is completed.

[0066] S1 includes:

[0067] S1-1. Establish a regular grid coordinate system in the target area. Collect thermal conductivity, specific heat capacity, thermal resistance and induction coil gap value at each grid node. Write the collected results into the original data table in the order of the nodes.

[0068] S1-2. Perform data correction operation on the original data table. When any collected value exceeds the upper or lower limit of the sensor range, the value is discarded. Calculate the median of multiple collection results for the same node and write the correction result into the correction data table.

[0069] S1-3. Input the correction data table into the interpolation calculation process, perform bilinear interpolation operation in each grid cell to generate interpolation data points covering the inside of the grid cell, and write the interpolation data points into the interpolation expansion table. At the same time, perform gradient smoothing constraints during the interpolation process to ensure that the numerical difference between adjacent nodes does not exceed the preset gradient upper limit.

[0070] S1-4. Subtract the interpolation expansion table from the correction data table point by point to generate a residual data table. When the residual of any node in the residual data table reaches or exceeds the upper limit of the residual, divide the corresponding grid cell into a refinement cell. Add a node in the refinement cell and repeat steps S1-1 and S1-2. Update the correction data table and regenerate the interpolation expansion table and the residual data table until all residuals are lower than the upper limit of the residual.

[0071] S1-5. When the residual data table meets the upper limit condition of the residual, the thermal conductivity, specific heat capacity, thermal resistance and induction coil gap value are combined into a quaternary parameter vector at each grid node, and all quaternary parameter vectors are arranged into a three-dimensional array according to the grid coordinate order. The three-dimensional array is the thermal response model.

[0072] It should be noted that in the formula structure involved in this scheme, dimensionless terms can be used as proportional or structural adjustment factors. When combined with quantities with units, they only play a role in numerical scaling and do not introduce new physical dimensions. Therefore, they will not change or confuse the overall unit system. This combination of "dimensionless terms and terms with units" can be understood as a composite structural expression commonly used in mathematical physics modeling. It conforms to the principle of dimensional consistency and has a clear physical interpretation basis.

[0073] Secondly, in the formula structure of this scheme, if multiple variables with different physical units are involved, including but not limited to time, mass or energy variables, their joint appearance is to express the collaborative modeling relationship of multiple physical mechanisms. Each variable can form a unified structure through function mapping, ratio combination or normalization adjustment, with clear units and clear meaning. The overall expression conforms to the principle of dimensional consistency and the conventional formula of engineering modeling.

[0074] In this solution, constants, weights, adjustment factors, threshold parameters, proportional coefficients, etc., are all adjustable control parameters for different application environments. Their values ​​depend on the target equipment configuration, data input characteristics, and performance optimization goals. During the implementation phase, they are set to converge within a reasonable range through model verification, performance constraints, or engineering calibration. Although these parameters do not have a unique preset value, they have clear adjustment logic and calculation paths. They belong to the deterministic setting process in engineering implementation. The purpose of this setting is to ensure that the solution is both universally adaptable and reproducible and operable, without affecting its technical clarity and feasibility.

[0075] S2 includes:

[0076] S2-1. Align each grid node of the thermal response model with the preset heating power curve in time. Divide the fixed preset heating time into equally spaced time steps, with the time step size denoted as δt. Obtain the corresponding power sample value P at each time step. (n) And establish a one-to-one correspondence with the grid node parameters;

[0077] Mesh node parameters include thermal conductivity k i Specific heat capacity c i Thermal resistance r i Induction coil gap value d i ;

[0078] S2-2. Perform iterative calculations of heat dissipation conduction at each time step, and initialize the temperature rise value of each grid node. And update the temperature rise value of each grid node according to the following formula:

[0079]

[0080] in, Let N(i) represent the temperature rise value of the i-th grid node at the n-th time step; N(i) is the set of adjacent grid nodes that share the grid surface with grid node i. The symmetric thermal flux coefficient is based on thermal conductivity; i represents the index of the currently calculated grid node; j represents the index of the grid node adjacent to i; k i k represents the thermal conductivity of grid node i; j This represents the thermal conductivity of grid node j; The boundary input term assigned to grid node i is calculated using the following formula:

[0081]

[0082] Where, ∑ p (·) represents the normalization factor for summing over all grid nodes; p represents the index of any grid node when performing power allocation normalization summation, and grid node p is used to traverse all nodes; rp This represents the thermal resistance of grid node p during power distribution; d p This represents the gap value between the induction coils of grid node p during the power distribution process.

[0083] S2 also includes:

[0084] S2-3. After the cumulative time covers the fixed preset heating time, extract the temperature rise value of each grid node. The temperature rise value ΔT of each grid node i With the safe temperature threshold T safe Compare and generate a set of nodes that exceed the temperature rise limit {i|ΔT}. i ≥T safe}; where T safe This indicates the safe temperature threshold, used to determine whether the temperature rise exceeds the limit; This indicates that grid node i is at the last time step n of the iteration. end The calculated temperature rise value;

[0085] S2-4. Starting from the set of nodes with excessive temperature rise, perform a domain expansion marking within their respective sets of adjacent grid nodes N(i), marking the nodes with excessive temperature rise and their adjacent grid nodes together as heat concentration areas; perform coordinate merging and deduplication processing on all heat concentration areas, and output risk area data consisting of area number, area boundary coordinates and grid node index.

[0086] S3 includes:

[0087] S3-1. Generate a risk proximity sequence: On the grid coordinates of the target area, calculate the shortest geometric distance D from each grid node i to the data boundary of the risk area. i Set the maximum distance D 上限 And define risk proximity:

[0088]

[0089] Among them, S i This represents the risk proximity of grid node i, with a value range of [0,1].

[0090] S3-2, Generate the Coverage Path Set: Construct an undirected adjacency graph G = (V, E) on the grid coordinates, where V is the set of all grid nodes and E is the set of adjacent node pairs sharing a grid edge; starting from the entry node, progressively select the next visited node, defining the ternary cost of the candidate edge (i, j): C ij =(max(S) i ,S j ),|κ ij |,Lij And select the candidate edge with the minimum cost in lexicographical order, which includes comparing the first component, then the second component, and finally the third component; C ij L represents the combined cost of candidate path edges from grid node i to adjacent grid node j; ij Let |κ be the geometric length of the edge (i,j). ij | is the direction vector u from the previously selected edge. prev With the current candidate edge direction vector u ij The change in the included angle is defined as Traverse all unvisited nodes according to the selection rules and output the set of paths that cover all grid nodes;

[0091] S3-3, Path Set Segmentation: The path set obtained in S3-2 is sequentially scanned according to the node risk proximity threshold rule:

[0092]

[0093] Group consecutive path sequences belonging to the same category into a single path segment, and count the set of nodes for each path segment s. Average risk proximity

[0094] S3-4. Generate heating power parameters and scanning speed parameters for each path segment: Set the upper limit of heating power P. 上限 , Circumduction scanning speed v 绕行 Energy target e per unit path length 目标 And generate parameters for each path segment s according to the following formula:

[0095]

[0096] Among them, P s Let v be the heating power parameter for path segment s. s The scanning speed parameter for path segment s;

[0097] S3-5, Output parameterized path set: Write all path segments obtained in S3-3 and the heating power parameters and scanning speed parameters calculated in S3-4 into the parameterized path set. The parameterized path set is used for path execution and subsequent monitoring and comparison processes.

[0098] S4 includes:

[0099] Induction heating operations are performed sequentially according to the order of the path set. During the execution of each path segment, real-time temperature values ​​and induction coil gap values ​​are collected for each grid node covered by the path segment, and the collected results are stored as a real-time data sequence according to the grid node number.

[0100] The real-time acquired data sequence is input into the thermal response model. In the thermal response model, the temperature change of each grid node is calculated, and a real-time temperature distribution covering all grid nodes of the path segment is formed.

[0101] S4 also includes:

[0102] The real-time temperature distribution status is matched one by one with the risk area data according to the grid node number. The real-time temperature difference and gap difference are calculated for each grid node, and the calculation results are stored as a comparison result matrix.

[0103] In the comparison result matrix, a set of grid nodes whose real-time temperature difference and gap difference both exceed a preset threshold are identified, and this set of grid nodes is marked as a risk trigger node set.

[0104] Based on the spatial distribution of the risk triggering node set, a risk assessment result for the path segment is generated, and the risk assessment result is used as the input condition for subsequent path segment adjustments.

[0105] It should be noted that the solution was developed based on the need to optimize the induction heating path in the heterogeneous sound-absorbing structure.

[0106] During the construction phase, the solution acquires the geometric contour of the target area and, in conjunction with risk area data, comprehensively divides the target area into a set of paths covering all grid nodes. This set of paths is subdivided based on the distribution characteristics of risk areas. High-risk detour paths are used to avoid areas where temperature accumulates rapidly, medium-risk power-reducing paths are used to control energy input to avoid overheating, and low-risk conventional paths maintain a stable heating process. This division not only ensures the integrity of the overall path coverage but also enables the formation of differentiated energy control mechanisms in different areas, thereby achieving a balanced and safe heating process.

[0107] During the execution phase, induction heating operations are carried out segment by segment according to the order of the path set, and the temperature and gap values ​​of the corresponding grid nodes are collected in real time during the execution of each path segment. The real-time collected data is mapped to the pre-established thermal response model and compared with the risk area data to obtain the risk assessment result of the path segment. Through this node-by-node comparison mechanism, the dynamic controllability of the heating process can be guaranteed, and misjudgment or abnormal accumulation caused by sudden changes in local conditions can be avoided.

[0108] When the risk assessment results indicate that the temperature rise rate of a certain path segment has reached the upper limit, the scheme further activates the path reordering mechanism. This mechanism rearranges the execution order of the paths based on the current path set. The new execution order is not a simple replacement, but rather, based on the latest risk distribution, the heating power parameters and scanning speed parameters are adjusted for high-risk detour path segments, medium-risk power reduction path segments, and low-risk conventional path segments, respectively. The adjusted path order re-covers the grid nodes of the target area, enabling the induction heating process to continue under the premise of risk control until all grid nodes in the target area have completed the heating operation.

[0109] The entire implementation process takes into account both the global coverage of path planning and the flexibility of local execution; through the dynamic interaction of risk data and thermal response model, the path and parameters can be adjusted in real time during the execution process, thereby avoiding the risk of misjudgment or overheating caused by a single fixed strategy; ultimately ensuring the safety and stability of the heterogeneous sound-absorbing structure in the induction heating process, and also improving the efficiency and accuracy of heating operations.

[0110] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing the induction heating path of heterogeneous noise reduction structures, characterized in that, include: S1. Establish grid coordinates in the target area, collect the thermal conductivity, specific heat capacity, thermal resistance and induction coil gap value of each grid node in the grid coordinates, and generate a thermal response model. S2. Perform heat conduction calculations on the thermal response model and the preset heating power curve, calculate the temperature rise value of each grid node within a fixed preset heating time, mark the grid nodes whose temperature rise value reaches or exceeds the safe temperature threshold and their adjacent grid nodes as heat concentration areas, and generate risk area data by summarizing the heat concentration areas. S3. Based on the geometric contour of the target area and the risk area data, generate a path set covering all grid nodes, and divide the path set into high-risk detour path segments, medium-risk power reduction path segments, and low-risk conventional path segments, and generate scanning speed parameters and heating power parameters for each path segment. S4. Perform induction heating operations in the order of the path set. During the induction heating operation of each path segment, collect the temperature value and gap value of the corresponding grid node of the path segment in real time. Map the real-time collected data to the thermal response model and compare the mapped data with the risk area data. Generate the risk assessment result of the path segment based on the comparison result. S5. When the risk assessment result shows that the temperature rise rate of the current path segment has reached the upper limit of the temperature rise rate, the path reordering is performed based on the path set to generate a new path execution order. The heating power parameters and scanning speed parameters of the high-risk detour path segment, the medium-risk power reduction path segment, and the low-risk conventional path segment in the new path execution order are adjusted respectively. The heating operation continues to be performed according to the adjusted path execution order until the heating operation of all grid nodes in the target area is completed.

2. The induction heating path optimization method for heterogeneous noise reduction structures according to claim 1, characterized in that: S1 includes: S1-1. Establish a regular grid coordinate system in the target area. Collect thermal conductivity, specific heat capacity, thermal resistance and induction coil gap value at each grid node. Write the collected results into the original data table in the order of the nodes. S1-2. Perform data correction operation on the original data table. When any collected value exceeds the upper or lower limit of the sensor range, the value is discarded. Calculate the median of multiple collection results for the same node and write the correction result into the correction data table. S1-3. Input the correction data table into the interpolation calculation process, perform bilinear interpolation operation in each grid cell to generate interpolation data points covering the inside of the grid cell, and write the interpolation data points into the interpolation expansion table. At the same time, perform gradient smoothing constraints during the interpolation process to ensure that the numerical difference between adjacent nodes does not exceed the preset gradient upper limit. S1-4. Subtract the interpolation expansion table from the correction data table point by point to generate a residual data table. When the residual of any node in the residual data table reaches or exceeds the upper limit of the residual, divide the corresponding grid cell into a refinement cell. Add a node in the refinement cell and repeat steps S1-1 and S1-2. Update the correction data table and regenerate the interpolation expansion table and the residual data table until all residuals are lower than the upper limit of the residual. S1-5. When the residual data table meets the upper limit condition of the residual, the thermal conductivity, specific heat capacity, thermal resistance and induction coil gap value are combined into a quaternary parameter vector at each grid node, and all quaternary parameter vectors are arranged into a three-dimensional array according to the grid coordinate order. The three-dimensional array is the thermal response model.

3. The induction heating path optimization method for heterogeneous noise reduction structures according to claim 2, characterized in that: S2 includes: S2-1. Align each grid node of the thermal response model with the preset heating power curve in time. Divide the fixed preset heating time into equally spaced time steps, with the time step size denoted as δt. Obtain the corresponding power sample value P at each time step. (n) And establish a one-to-one correspondence with the grid node parameters; Mesh node parameters include thermal conductivity k i Specific heat capacity c i Thermal resistance r i Induction coil gap value d i ; S2-2. Perform iterative calculations of heat dissipation conduction at each time step, and initialize the temperature rise value ΔT for each grid node. i (0) =0, and update the temperature rise value of each grid node according to the following formula: in, Let N(i) represent the temperature rise value of the i-th grid node at the n-th time step; N(i) is the set of adjacent grid nodes that share the grid surface with grid node i. The symmetric thermal flux coefficient is based on thermal conductivity; i represents the index of the currently calculated grid node; j represents the index of the grid node adjacent to i; k i k represents the thermal conductivity of grid node i; j This represents the thermal conductivity of grid node j; The boundary input term assigned to grid node i is calculated using the following formula: Where, ∑ p (·) represents the normalization factor for summing over all grid nodes; p represents the index of any grid node when performing power allocation normalization summation, and grid node p is used to traverse all nodes; r p This represents the thermal resistance of grid node p during power distribution; d p This represents the gap value between the induction coils of grid node p during the power distribution process.

4. The induction heating path optimization method for heterogeneous noise reduction structures according to claim 3, characterized in that: S2 also includes: S2-3. After the cumulative time covers the fixed preset heating time, extract the temperature rise value of each grid node. The temperature rise value ΔT of each grid node i With the safe temperature threshold T safe Compare and generate a set of nodes that exceed the temperature rise limit {i|ΔT}. i ≥T safe }; where T safe This indicates the safe temperature threshold, used to determine whether the temperature rise exceeds the limit; This indicates that grid node i is at the last time step n of the iteration. end The calculated temperature rise value; S2-4. Starting from the set of nodes with excessive temperature rise, perform a domain expansion marking within their respective sets of adjacent grid nodes N(i), marking the nodes with excessive temperature rise and their adjacent grid nodes together as heat concentration areas; perform coordinate merging and deduplication processing on all heat concentration areas, and output risk area data consisting of area number, area boundary coordinates and grid node index.

5. The induction heating path optimization method for heterogeneous noise reduction structures according to claim 4, characterized in that: S3 includes: S3-1. Generate a risk proximity sequence: On the grid coordinates of the target area, calculate the shortest geometric distance D from each grid node i to the data boundary of the risk area. i Set the maximum distance D 上限 And define risk proximity: Among them, S i This represents the risk proximity of grid node i, with a value range of [0,1]. S3-2, Generate the Coverage Path Set: Construct an undirected adjacency graph G = (V, E) on the grid coordinates, where V is the set of all grid nodes and E is the set of adjacent node pairs sharing a grid edge; starting from the entry node, progressively select the next visited node, defining the ternary cost of the candidate edge (i, j): C ij =(max(S) i ,S j ),|κ ij |,L ij And select the candidate edge with the minimum cost in lexicographical order, which includes comparing the first component, then the second component, and finally the third component; C ij L represents the combined cost of candidate path edges from grid node i to adjacent grid node j; ij Let |κ be the geometric length of the edge (i,j). ij | is the direction vector u from the previously selected edge. prev With the current candidate edge direction vector u ij The change in the included angle is defined as Traverse all unvisited nodes according to the selection rules and output the set of paths that cover all grid nodes; S3-3, Path Set Segmentation: The path set obtained in S3-2 is sequentially scanned according to the node risk proximity threshold rule: Group consecutive path sequences belonging to the same category into a single path segment, and count the set of nodes for each path segment s. Average risk proximity S3-4. Generate heating power parameters and scanning speed parameters for each path segment: Set the upper limit of heating power P. 上限 , Circumduction scanning speed v 绕行 Energy target e per unit path length 目标 And generate parameters for each path segment s according to the following formula: Among them, P s Let v be the heating power parameter for path segment s. s The scanning speed parameter for path segment s; S3-5, Output parameterized path set: Write all path segments obtained in S3-3 and the heating power parameters and scanning speed parameters calculated in S3-4 into the parameterized path set.

6. The induction heating path optimization method for heterogeneous noise reduction structures according to claim 5, characterized in that: S4 includes: Induction heating operations are performed sequentially according to the order of the path set. During the execution of each path segment, real-time temperature values ​​and induction coil gap values ​​are collected for each grid node covered by the path segment, and the collected results are stored as a real-time data sequence according to the grid node number. The real-time acquired data sequence is input into the thermal response model. In the thermal response model, the temperature change of each grid node is calculated, and a real-time temperature distribution covering all grid nodes of the path segment is formed.

7. The induction heating path optimization method for heterogeneous noise reduction structures according to claim 6, characterized in that: S4 also includes: The real-time temperature distribution status is matched one by one with the risk area data according to the grid node number. The real-time temperature difference and gap difference are calculated for each grid node, and the calculation results are stored as a comparison result matrix. In the comparison result matrix, a set of grid nodes whose real-time temperature difference and gap difference both exceed a preset threshold are identified, and this set of grid nodes is marked as a risk trigger node set. Based on the spatial distribution of the risk triggering node set, a risk assessment result for the path segment is generated, and the risk assessment result is used as the input condition for subsequent path segment adjustments.

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