A machine learning-based effecter parameter self-adaptive adjustment method

Abstract

The application relates to a machine learning-based effecter parameter self-adaptive adjustment method, a parameter correlation network model is constructed to obtain relationship data among parameters in an acoustic equipment effecter, and a topological structure description of the parameter correlation network is generated; a displacement chain reaction is simulated and analyzed to determine the propagation direction and intensity distribution of an influence path; then, the action weight of a key node is analyzed, abnormal fluctuations are positioned according to the action weight of the key node, and the specific position and influence range of the abnormal fluctuations are determined; a subtle change mode of a slight displacement is extracted, whether a chain reaction is triggered is judged, and a potential risk level is evaluated, and the priority of the influence range is sorted; a parameter adjustment scheme is generated according to the priority sorting, and effecter configuration parameters of an optimal compensation scheme are obtained.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of sound equipment, in particular to an effecter parameter adaptive adjustment method based on machine learning. BACKGROUND

[0002] The core of the sound effecter parameter is to ensure the stability and efficiency of the system performance by accurately regulating multiple parameters. Traditional schemes often rely on manual parameter adjustment or static models based on rules, which are difficult to adapt to parameter changes in dynamic environments. Moreover, when facing multi-parameter coupling, there is a lack of analysis capability for the deep mutual influence between parameters, which leads to difficulties in quickly and accurately identifying the problem source and formulating effective coping strategies when the parameters change abnormally.

[0003] The correlation between parameters is a complex network structure. The change of each parameter may trigger a chain reaction of other parameters. For example, when a parameter is displaced, it is difficult to accurately predict the influence path and intensity of other parameters through traditional methods. In addition, abnormal pattern recognition of parameter displacement is also a major problem. Due to the lack of in-depth analysis of the network topology structure, existing methods are prone to misjudgment or omission when detecting small or atypical displacements.

[0004] Therefore, it is a key problem to construct an audio equipment effecter adjustment method that can effectively capture the correlation network between parameters, predict the chain effect of parameter displacement, and achieve abnormal pattern detection and optimal compensation through network structure analysis. SUMMARY

[0005] In order to solve the problems existing in the prior art, the present application aims to provide an effecter parameter adaptive adjustment method based on machine learning.

[0006] The effecter parameter adaptive adjustment method based on machine learning described in the present application comprises the following steps:

[0007] A parameter correlation network model is constructed to obtain the dependency relationship data between parameters in the effecter, and a topological structure description of the parameter correlation network is generated;

[0008] According to the topological structure description, the displacement chain reaction is simulated and analyzed to determine the propagation direction and intensity distribution of the influence path;

[0009] According to the propagation direction and intensity distribution of the influence path, the action weight of the key node is analyzed;

[0010] According to the action weight of the key node, the abnormal fluctuation is located to determine the specific position and influence range of the abnormal fluctuation;

[0011] According to the specific position and influence range of the abnormal fluctuation, the fine change pattern of the micro displacement is extracted, it is judged whether a chain reaction is triggered and a potential risk level is evaluated;

[0012] According to the potential risk level, influence range data is integrated, and a priority ranking of the influence range is determined;

[0013] According to the priority ranking, a parameter adjustment scheme is generated, and configuration parameters of an optimal compensation scheme are obtained.

[0014] Preferably, the construction parameter correlation network model is used to obtain the dependency relationship data between parameters in the effect device, including:

[0015] An initial parameter set is constructed by collecting parameter data of the effect device, and a correlation analysis method is used to generate a preliminary matrix of the dependency relationship between parameters;

[0016] Significant dependency relationships are extracted from the preliminary matrix, and a weighted directed graph model is used to generate an initial structure of the parameter correlation network;

[0017] By calculating the weight of each parameter node, the initial structure is simplified, and a simplified parameter correlation network structure is generated;

[0018] According to the simplified structure, the directional relationship between parameters is analyzed, and a description matrix is obtained;

[0019] The parameter groups are divided through the description matrix, and the hierarchical distribution of the topological structure is determined;

[0020] The key path and node are extracted from the hierarchical distribution, and the final parameter correlation network topological structure description is generated.

[0021] Preferably, the simulation analysis of the displacement chain reaction according to the topological structure description includes:

[0022] A network model based on graph theory is constructed, and an initial correlation matrix between parameters is generated;

[0023] According to the initial correlation matrix, a parameter displacement triggering scenario is simulated, and a set of directly affected nodes is predicted;

[0024] Through the node set, the chain effect of displacement reaction is tracked, the propagation direction of the influence path is identified, and the node sequence and edge weight change on the path are obtained;

[0025] The change trend of the intensity distribution is calculated for the node sequence, and the affected degree of each node is quantified;

[0026] If the change trend exceeds a preset threshold, the influence path is hierarchically divided, and the distribution characteristics of high-intensity and low-intensity propagation regions are obtained;

[0027] According to the distribution characteristics, a dynamic propagation diagram of displacement response is constructed, and the topological structure description is updated.

[0028] Preferably, the propagation direction and intensity distribution of the influence path are analyzed to analyze the action weight of the key node, including:

[0029] The propagation direction and intensity distribution are obtained from the propagation data of the influence path, and the action weight of each node is determined.

[0030] If the action weight exceeds a preset threshold, the key node is calculated by a weighted network model to generate a key node set.

[0031] According to the key node set, the parameter dynamic change of the corresponding node is obtained, and the change trend is determined.

[0032] By comparing the change trend with historical data, the time point and node position of abnormal fluctuation are located.

[0033] The affected path and node are obtained from the node position, and the influence range is determined.

[0034] According to the influence range, the affected nodes are grouped, and the classification characteristics of abnormal fluctuation are obtained.

[0035] Preferably, the specific position and influence range of abnormal fluctuation are extracted to extract the subtle change pattern of micro displacement, including:

[0036] Abnormal fluctuation information is collected through a sensor, the specific position and influence range of abnormal fluctuation are divided, and basic distribution characteristics are obtained.

[0037] According to the basic distribution characteristics, the related data of micro displacement are extracted, the subtle change pattern is separated, and the significance feature is determined.

[0038] The significance feature is compared with a preset threshold, and if the threshold is exceeded, it is determined that a chain reaction may be triggered, and a preliminary evaluation result is obtained.

[0039] According to the preliminary evaluation result, a non-typical pattern is obtained, a feature comparison is performed through a pre-established pattern library, and a classification category is determined.

[0040] The classification category and influence range are combined to analyze the potential risk level, and a quantitative result is generated.

[0041] According to the quantitative result, a comprehensive evaluation is performed to determine whether a warning standard is reached.

[0042] Preferably, the influence range data is integrated according to the potential risk level, including:

[0043] By integrating atypical patterns and classification results, the distribution characteristics of potential risks are obtained, and preliminary judgment results are generated.

[0044] Based on the preliminary judgment results, if the risk level exceeds the preset threshold, the data on the scope of impact will be deeply mined to determine the preliminary boundary.

[0045] Relevant indicators of displacement influence are extracted from the preliminary boundary to quantify the degree of displacement influence and determine key distribution areas.

[0046] The impact of the key distribution areas on system performance is analyzed to obtain the overall performance fluctuation trend;

[0047] If the fluctuation trend shows a continuous deviation, the degree of influence is weighted and calculated to determine the core parameters;

[0048] The range sorting is adjusted based on the core parameters to generate the final priority sorting result.

[0049] Preferably, the step of generating parameter adjustment schemes based on priority ranking includes:

[0050] By analyzing the compensation mechanism, the data distribution characteristics of the scope of influence are obtained, and the weight of its effect on the overall system is determined.

[0051] The priority sorting is performed in layers based on the data distribution characteristics to obtain the sorting criteria for each layer;

[0052] By adjusting the direction based on the sorting criteria and matching parameters, key nodes with high correlation are extracted from the parameter network to determine the degree of impact on the overall configuration.

[0053] If the correlation is higher than a preset threshold, then a multi-scenario test is conducted using a simulation strategy to obtain parameter response data;

[0054] Based on the parameter response data, dynamic change trends are extracted to determine the preliminary framework of the optimal solution;

[0055] By fine-tuning the initial framework, the final optimized compensation mechanism is generated.

[0056] Preferably, the configuration parameters for obtaining the optimal compensation scheme include:

[0057] By verifying the final optimization results, the adaptability of the optimal solution under different influence ranges is determined, and its stability performance is identified.

[0058] Based on the stability performance, key configuration data is extracted from the parameter association network to generate a preliminary set of configuration parameters;

[0059] For the preliminary set, parameter matching is performed in combination with the priority ranking result to determine the adjustment range of each parameter;

[0060] The parameter associated network is dynamically updated through the adjustment range to obtain updated network response data;

[0061] The compensation effect is analyzed according to the network response data to determine whether the preset performance standard is met;

[0062] If the performance standard is met, the preliminary set is determined as the final configuration parameter set.

[0063] The effecter parameter adaptive adjustment method based on machine learning has the advantages that by constructing a parameter associated network model, the topological structure and influence path between parameters are analyzed, key nodes are identified and abnormal fluctuations are evaluated, so that an optimal compensation scheme is generated. The method first establishes a parameter relationship network, simulates and analyzes displacement chain reactions, determines the propagation direction and intensity distribution, then locates abnormal fluctuations according to the key node weight, extracts the micro displacement change mode, evaluates the potential risk, finally integrates the influence range data to generate a parameter adjustment scheme, and obtains the optimal effecter configuration.

[0064] The application can accurately identify and compensate for parameter abnormalities in the effecter of the sound equipment, effectively improve the sound quality performance, and provide a new technical means for parameter optimization of the sound equipment. BRIEF DESCRIPTION OF DRAWINGS

[0065] Figure 1 is the flow of the effecter parameter adaptive adjustment method based on machine learning Figure 1 .

[0066] Figure 2 is the flow of the effecter parameter adaptive adjustment method based on machine learning Figure 2 . DETAILED DESCRIPTION

[0067] As shown in Figures 1-2 , the effecter parameter adaptive adjustment method based on machine learning includes the following steps:

[0068] As shown in Figures 1-2 , in step S101, a parameter associated network model is constructed to obtain the relationship data between parameters in the effecter of the sound equipment, and a topological structure description of the parameter associated network is generated.

[0069] Further, in step S101, by collecting the original data of the effect parameters from the sound equipment, the preliminary interaction information between parameters is obtained to obtain a basic data set;

[0070] According to the basic data set, the correlation characteristics between the effect parameters are extracted by using a data analysis method, and a preliminary mode of parameter relationship is determined;

[0071] If the correlation characteristics in the preliminary mode meet the preset threshold, a correlation network model is constructed by using a graph theory method, and a network topology structure between the parameters is generated;

[0072] Through optimization processing of the network topology structure, the detailed characteristics of the topology structure are obtained, and the completeness of the structure description is judged;

[0073] If the completeness of the structure description does not reach the preset standard, data supplement is performed for the missing part, and a perfect topology structure description is obtained;

[0074] According to the perfect topology structure description, an intuitive relationship data view is generated by using a visualization tool, and a final parameter correlation network representation is determined.

[0075] Specifically, in step S101, in the process of constructing the sound device effecter parameter correlation network model to obtain the relationship data between the parameters and generate the topology structure description, first, the original parameter data is obtained through data acquisition and preprocessing, and three key parameters are extracted from a digital reverb effecter, including reverb time (Reverb Time, unit: second), decay rate (Decay Rate, unit: decibel / second) and pre-delay (Pre-Delay, unit: millisecond). 100 groups of data samples are collected, in which the reverb time ranges from 0.5 to 5.0 seconds, the decay rate ranges from -6.0 to -1.0 decibel / second, and the pre-delay ranges from 10 to 100 milliseconds;

[0076] Then, the correlation between the parameters is calculated by using the Pearson correlation coefficient algorithm, for example, the correlation coefficient between the reverb time and the decay rate is -0.75, indicating that they are negatively correlated, and the longer the reverb time, the smaller the decay rate;

[0077] The correlation coefficient between the reverb time and the pre-delay is 0.32, indicating a weak positive correlation; the correlation coefficient between the decay rate and the pre-delay is -0.15, which is close to no correlation;

[0078] Subsequently, based on the correlation coefficient matrix, a parameter correlation network model is constructed, the parameters are regarded as nodes, the correlation coefficient absolute value greater than 0.5 is regarded as an edge, and the weight is the correlation coefficient value, forming a weighted undirected graph, in which there is a strong correlation edge between the reverb time and the decay rate, and the weight is 0.75, while other edges are ignored due to low correlation coefficient;

[0079] Further, the network topology is optimized by a graph theory algorithm such as the minimum spanning tree (Kruskal algorithm), and a tree structure centered on the reverb time is calculated, the edge weight is sorted and the connection between the reverb time and the decay rate is preferentially retained, ensuring that the network is simple and the key relationship is prominent;

[0080] Finally, the topology description is generated, output in the form of an adjacency matrix, represented as a 3x3 matrix, with the reverberation time and decay rate corresponding to the position values of 0.75, and other positions being 0, clearly reflecting the strong correlation between parameters.

[0081] As shown in Figures 1-2 Step S102, according to the topology description, the displacement chain reaction is simulated and analyzed to determine the propagation direction and intensity distribution of the influence path.

[0082] Further, in step S102, by collecting data of the topology structure, an initial model of the displacement reaction is constructed, the preliminary characteristics of the reaction mechanism are obtained, and the mapping relationship of the structure correlation is obtained.

[0083] According to the mapping relationship of the structure correlation, the change trajectory of the propagation path is tracked by using the simulation analysis method, and the key nodes of the path influence are determined.

[0084] For the key nodes of the path influence, the triggering conditions of the chain effect are analyzed, and if the triggering conditions meet the preset threshold, the dynamic changes of the effect transmission are recorded, and the priority of the direction analysis is determined.

[0085] Through the priority of the direction analysis, combined with the characteristic data of the intensity distribution, the specific range of the effect transmission is obtained, and the quantization result of the distribution characteristics is obtained.

[0086] According to the quantization result of the distribution characteristics, the potential influence of the displacement reaction is analyzed, and if the potential influence exceeds the preset range, the parameters of the simulation analysis are adjusted, and the optimization scheme of the reaction mechanism is determined.

[0087] According to the optimization scheme of the reaction mechanism, the associated data of the topology structure is updated, the corrected trajectory of the propagation path is obtained, and the final distribution of the path influence is obtained.

[0088] Through the final distribution of the path influence, combined with the updated data of the intensity distribution, the long-term trend of the chain effect is analyzed, and the stable state of the effect transmission is determined.

[0089] Specifically, in step S102, when simulating the topology structure of the displacement chain reaction, first, a network model based on graph theory is constructed to describe the topological relationship of displacement propagation, assuming a network containing 10 nodes (representing force elements), the connection strength between each node is represented by a weight value, ranging from 0.1 to 1.0, the weight value is calculated by historical data, for example, the weight of node 1 to node 2 is 0.8, indicating a strong propagation influence.

[0090] Next, the propagation direction is determined using the Breadth-First Search algorithm (BFS). Starting from the initial force node (e.g., node 1), the force transmission probability of each adjacent node is calculated. The probability formula is P = weight value × initial displacement value. The preset initial displacement value is 5.0 units, so the transmission probability of node 2 is 0.8 × 5.0 = 4.0 units.

[0091] Subsequently, the propagation intensity distribution is analyzed, and the cumulative displacement influence of each node is calculated using the weighted average method. For example, node 3 receives influence through two paths, node 2 and node 4, with path weights of 0.6 and 0.3, respectively. The cumulative displacement is (4.0 × 0.6 + 5.0 × 0.3) = 3.9 units. By iteratively calculating all nodes, a strength distribution map is generated, with node displacement values ranging from 1.2 to 4.5 units, showing a gradual weakening trend from the center to the periphery.

[0092] To further optimize the analysis, a dynamic damping coefficient (set to 0.05) is introduced, reducing the displacement value by 5% after each transmission. For example, when node 2 transmits to node 3, the displacement value is adjusted to 4.0 × (1-0.05) = 3.8 units, simulating energy dissipation.

[0093] Finally, the results are compared with actual engineering data. If the error exceeds 10% (e.g., predicted displacement 4.0 units, actual 3.5 units), the model is calibrated by adjusting the weight value (e.g., from 0.8 to 0.7) to ensure prediction accuracy.

[0094] The above processes are implemented through programming. The NetworkX library in Python is used to build the network and calculate the propagation path, combined with NumPy for matrix operations, to automatically generate visual distribution maps, ensuring the scientificity and efficiency of the analysis.

[0095] As shown in Figures 1-2 Step S103, for the propagation direction and intensity distribution of the influence path, analyze the action weight of the key node, locate the abnormal fluctuation according to the action weight of the key node, and determine the specific position and influence range of the abnormal fluctuation.

[0096] Further, in step S103, the relevant data of the influence path is obtained, and the propagation direction and intensity distribution are preliminarily sorted. The structured representation of path propagation is obtained by constructing a data matrix.

[0097] According to the structured representation of path propagation, the key nodes are extracted using the node recognition method, and the action weight of each node is quantified using the preset weight calculation rule to determine the importance ranking of the nodes.

[0098] The action weight of the key node is matched with the data characteristics of the abnormal fluctuation. If it is detected that the weight of a node exceeds a preset threshold, the node is determined as a potential source of the abnormal fluctuation.

[0099] According to the node position of the potential source and the data of the propagation direction, the influence path of the abnormal fluctuation is analyzed, the set of adjacent nodes affected is obtained, and the preliminary influence range of the abnormal fluctuation is obtained.

[0100] The data of the intensity distribution is used for secondary verification for the nodes in the preliminary influence range. If the intensity distribution value of a node deviates from the normal interval, the node is determined as the actual influence area of the abnormal fluctuation.

[0101] Based on the set of nodes in the actual influence area, the specific position of the abnormal fluctuation is traced, the detailed fluctuation propagation chain is obtained, and the core position of the abnormal fluctuation is determined by combining the path analysis technology.

[0102] According to the data of the core position and the propagation chain, the boundary information of the influence range is integrated, the distribution view of the abnormal fluctuation is generated by using a visualization tool, and the final influence range and position details are determined.

[0103] Specifically, in step S103, the action weight of the key node is analyzed according to the propagation direction and the intensity distribution of the influence path, and the abnormal fluctuation is positioned according to the weight, so that the specific position and the influence range of the abnormal fluctuation are determined. The following method can be used to achieve this.

[0104] A social network propagation model is preset, which includes 100 nodes. The edges between the nodes represent the information propagation path, and the weight of the edge represents the propagation strength (range 0 to 1).

[0105] First, the propagation direction and the intensity distribution are calculated. The PageRank algorithm is used, the damping coefficient is set to 0.85, and the PageRank value of each node is iteratively calculated to reflect its importance in the propagation path.

[0106] For example, the PageRank value of node A is 0.12, and the PageRank value of node B is 0.08, indicating that A has a greater influence in the propagation.

[0107] Next, the action weight of the key node is analyzed, the weighted index is constructed by combining the node degree and the PageRank value: weight = 0.6 × PageRank value + 0.4 × normalized degree (degree range normalized to 0 to 1).

[0108] The degree of node A is 10, and after normalization, it is 0.5. The weight of node A is 0.6 × 0.12 + 0.4 × 0.5 = 0.272.

[0109] Node B degree is 5, normalized to 0.25, weight is 0.6*0.08+0.4*0.25=0.148;

[0110] Nodes with higher weights (such as A) are considered critical nodes;

[0111] To locate abnormal fluctuations, set the propagation intensity anomaly threshold to the mean plus 2 standard deviations. Assuming the propagation intensity of a certain path is 0.9, exceeding the threshold of 0.85, it is marked as abnormal;

[0112] By tracing the abnormal path, locate the critical node A and its neighbor node C (weight 0.25);

[0113] Impact range analysis uses depth-first search, starting from node A, to calculate the affected node set. Assuming it involves 20 nodes, the impact range accounts for 20%;

[0114] If the data is insufficient, you can generate propagation intensity (normal distribution, mean 0.5, standard deviation 0.1) and combine business scenarios (such as social media public opinion monitoring) to infer the source of the anomaly, such as a node suddenly publishing high-heat content;

[0115] Finally, output the abnormal fluctuation location as node A, with an impact range of 20 nodes, and weight analysis and path tracing forming a closed logical chain.

[0116] As shown in Figures 1-2 Step S104, for the specific location and impact range of the abnormal fluctuation, extract the subtle change pattern of the micro displacement, judge whether it triggers a chain reaction and evaluate the potential risk level, integrate the impact range data according to the potential risk level, and determine the priority ranking of the impact range.

[0117] Further, in step S104, for the abnormal fluctuation data, obtain the specific fluctuation signal data from the monitoring system, and use signal processing technology to denoise the original fluctuation signal to obtain filtered fluctuation feature data;

[0118] According to the filtered fluctuation feature data, analyze the specific location information of the fluctuation signal, determine the geographic coordinate point where the abnormal fluctuation occurs through spatial positioning technology, and judge the core area distribution of the abnormal fluctuation;

[0119] For the data of the core area distribution, obtain the micro displacement records in the area, and use time series analysis method to extract the subtle change pattern of the displacement data to determine the trend characteristics of the displacement change;

[0120] According to the trend characteristics of the displacement change, analyze whether there is a trigger condition of chain reaction, if the trend characteristics show that the displacement change exceeds the preset threshold, it is determined that there is a possibility of chain reaction, and the preliminary evaluation result of the chain reaction is obtained;

[0121] Based on the preliminary evaluation results of the chain reaction, combined with environmental data within the influence range, the potential risk is quantitatively evaluated by using a support vector machine model to determine the grade division of the potential risk;

[0122] According to the grade division of the potential risk, multi-source data within the influence range is integrated, and the priority of each region within the range is sorted by data fusion technology to obtain the priority sequence of the influence range;

[0123] For the priority sequence of the influence range, the corresponding regional monitoring strategy adjustment scheme is generated, and the resource allocation optimization of the high-priority region is performed through the automatic scheduling system to determine the final monitoring deployment scheme.

[0124] Specifically, in step S104, for the analysis of the specific position and influence range of abnormal fluctuations, first, the micro-displacement data is collected through a high-precision sensor network, and the sensors are distributed in a 100x100 meter grid area, collecting 1000 displacement data per second, with an accuracy of 0.01 millimeters;

[0125] The collected data is analyzed by using the Fourier transform algorithm to extract abnormal fluctuation signals with a frequency of 0.1 to 10 Hz, calculate the amplitude mean value, for example, 0.05 millimeters, and judge whether it exceeds the normal threshold value of 0.03 millimeters, and locate the abnormal point such as (50, 50) coordinates;

[0126] Next, based on the finite element analysis model, the propagation of displacement fluctuations in the region is simulated, the material rigidity coefficient is preset to 2000 pascal, the fluctuation propagation distance is calculated, and it is found that the influence range is a circular area with the abnormal point as the center and a radius of 20 meters;

[0127] When judging the possibility of triggering a chain reaction, a Markov chain model is used, the displacement amplitude, frequency and material parameters are input, the state transition probability is calculated, and the chain reaction probability is 0.3, which is lower than the threshold value of 0.5, and the chain reaction has not been triggered;

[0128] The potential risk level evaluation is based on a fuzzy logic algorithm, the chain reaction probability 0.3 and the influence range area 1256 square meters are input, and the risk level output is "medium", with a quantitative value of 0.6 (full score 1.0);

[0129] When integrating the influence range data, combined with the geographic information system (GIS), the influence range is divided into high, medium and low priority areas, based on the population density (0.1 people per square meter) and the infrastructure value (1000 yuan per square meter), the comprehensive priority score is calculated, the high-priority area is the core 10-meter radius area with a population density greater than 0.15 people, with a score of 0.8, and the medium-priority area is the surrounding 10 to 20-meter area, with a score of 0.5;

[0130] Through the above analysis, a complete logical chain from fluctuation positioning to risk assessment is formed, ensuring that the technical process is rigorous and data-driven.

[0131] As shown in Figures 1-2 Step S105, generate a parameter adjustment scheme according to the priority ranking to obtain the effecter configuration parameters of the optimal compensation scheme.

[0132] Further, in step S105, by analyzing the relationship between priority and ranking method, the initial parameter adjustment data set is obtained;

[0133] For each index in the data set, a preset threshold is used for preliminary screening to obtain a parameter adjustment basic set that meets the conditions;

[0134] According to the data in the basic set, in combination with the correlation between the compensation scheme and the optimal scheme, a logical deduction method is applied to determine the priority order of parameter adjustment;

[0135] For the priority order, if the weight of a certain parameter exceeds the preset range, it is subjected to secondary calibration to obtain the adjusted parameter sequence;

[0136] Get the adjusted parameter sequence, combine the mapping relationship between the effecter and the configuration parameters, and deduce the corresponding configuration parameter combination;

[0137] For each group of data in the combination, through information comparison, it is judged whether it meets the requirements of the business target to obtain the verified configuration parameter set;

[0138] According to the verified configuration parameter set, in combination with the logic of adjustment strategy and scheme generation, a preliminary compensation scheme framework is generated;

[0139] For each configuration in the framework, if there is a deviation, it is corrected through the information processing link to determine the final scheme framework;

[0140] Through the final scheme framework, in combination with the correlation between the optimal scheme and the optimization result, a support vector machine algorithm is applied to optimize and adjust the scheme;

[0141] For abnormal data generated in the optimization process, if its influence range exceeds the preset standard, data cleaning is performed to obtain the optimized compensation scheme;

[0142] Get the optimized compensation scheme, combine the business target with the logical deduction, and perform multi-dimensional verification on the scheme;

[0143] For the verification result, through information integration, it is judged whether the scheme meets the expected standard to obtain the final effecter configuration parameters.

[0144] Specifically, in step S105, in the process of generating a parameter adjustment scheme to obtain the effect of the optimal compensation scheme of the effecter configuration parameter, first, the device running state data is automatically obtained through the data acquisition system, for example, the input signal strength of a certain audio effecter is-6.5dB, the output signal strength is-3.2dB, the delay time is 5ms, and the environmental noise level is recorded as 40dB;

[0145] Next, based on the priority sorting algorithm, the priority of parameter adjustment is divided into three levels of signal strength optimization, delay correction and noise suppression, and the priority weights are 0.6, 0.3 and 0.1 respectively. The comprehensive optimization target value is obtained by weighted calculation, for example, the signal strength optimization target value is-4.0dB, the delay target value is 3ms, and the noise suppression target value is 35dB;

[0146] The system then uses a genetic algorithm to search for parameters, sets the initial population size to 100 and the iteration number to 50 times, and selects the optimal parameter combination by calculating the fitness function (for example, fitness=0.6*signal strength proximity+0.3*delay proximity+0.1*noise proximity), including signal gain adjustment of 1.2 times, delay correction of-2ms, and noise threshold setting of 38dB;

[0147] Next, the system verifies the parameter effect through simulation test, simulates the input signal strength variation range between-7.0dB and-5.0dB, calculates the output signal stability error as 0.3dB, which is lower than the preset threshold 0.5dB, and confirms the parameter feasibility;

[0148] Finally, the system automatically pushes the optimal parameter configuration to the effecter hardware, monitors the adjusted running data in real time, including signal strength stability at-4.1dB, delay at 3.1ms, and noise level reduced to 36dB, and generates an optimization report, which analyzes that signal strength optimization contributes 60% to the overall effect improvement, delay correction contributes 30%, and noise suppression contributes 10%, thereby forming a closed-loop optimization logic to ensure that the parameter adjustment is highly matched with the actual demand.

[0149] For those skilled in the art, various corresponding changes and deformations can be made according to the above described technical solutions and concepts, and all of these changes and deformations should belong to the protection scope of the claims of the present application.

Claims

1. A method for adaptive adjustment of effector parameters based on machine learning, characterized in that, include: A parameter association network model is constructed to obtain the relationship data between various parameters in the effects unit of an audio device, and a topological description of the parameter association network is generated. The construction of the parameter association network model includes: collecting effects unit parameter data to construct an initial parameter set; using the Pearson correlation coefficient algorithm to calculate the correlation between parameters and obtain a correlation coefficient matrix; constructing a weighted undirected graph based on dependencies with absolute correlation coefficient values ​​greater than 0.5; and obtaining a topological description in the form of an adjacency matrix. Based on the topological description, a simulation analysis of the displacement chain reaction is performed to determine the propagation direction and intensity distribution of the influence path. The simulation analysis includes: constructing a graph-based network model, where the connection strength between nodes is represented by weight values; using a breadth-first search algorithm to calculate the transmission probability starting from the initial stressed node; calculating the cumulative displacement influence of nodes using a weighted average method and iteratively obtaining the intensity distribution map; and introducing a dynamic damping coefficient to simulate energy dissipation. Based on the propagation direction and intensity distribution of the aforementioned impact path, the influence weights of key nodes are analyzed, and the abnormal fluctuations are located according to the influence weights of key nodes to determine the specific location and impact range of the abnormal fluctuations; wherein, the analysis of the influence weights of key nodes includes: calculating the importance of nodes using the PageRank algorithm; For the specific location and scope of the abnormal fluctuations, extract the subtle change patterns of the small displacements, determine whether a chain reaction is triggered and assess the potential risk level, integrate the scope of impact data based on the potential risk level, and determine the priority ranking of the scope of impact. Generate parameter adjustment schemes based on priority, and obtain the effector configuration parameters for the optimal compensation scheme.

2. The method for adaptive adjustment of effector parameters based on machine learning according to claim 1, characterized in that, The simulation analysis of displacement chain reaction based on topological description includes: Construct a 10-node network model based on graph theory, where the connection strength between nodes is represented by weight values ​​ranging from 0.1 to 1.0; The breadth-first search algorithm is used to calculate the transmission probability starting from the initial force-bearing node: P = weight value × initial displacement value; The cumulative displacement of nodes is calculated using the weighted average method, and an intensity distribution map is generated iteratively. A dynamic damping coefficient of 0.05 is introduced to simulate energy dissipation, and the displacement value decreases by 5% after each propagation. When the error between the predicted displacement and the actual displacement exceeds 10%, the weight values ​​are adjusted to calibrate the model.

3. The method for adaptive adjustment of effector parameters based on machine learning according to claim 1, characterized in that, The analysis of the influence path's propagation direction and intensity distribution, and the weighting of key nodes' roles, includes: In the propagation model, the PageRank algorithm is used to calculate node importance, and the damping coefficient is set to 0.85; Construct a weighted index: Weight = 0.6 × PageRank value + 0.4 × standardized degree; The threshold for abnormal propagation intensity is set as the mean plus twice the standard deviation. When the path propagation intensity exceeds the threshold, the set of affected nodes is calculated by depth-first search, and the percentage of the affected area is expressed as a percentage.

4. The method for adaptive adjustment of effector parameters based on machine learning according to claim 1, characterized in that, The extraction of subtle change patterns in minute displacements, based on the specific location and impact range of abnormal fluctuations, includes: By collecting abnormal fluctuation information through sensor data, the specific location and impact range of abnormal fluctuations can be determined, and basic distribution characteristics can be obtained. Based on the basic distribution characteristics, relevant data of minute displacements are extracted, subtle change patterns are separated, and significant features are determined; The significant features are compared with a preset threshold. If the threshold is exceeded, it is determined that a chain reaction may be triggered, and a preliminary evaluation result is obtained. Based on the preliminary assessment results, atypical patterns are obtained, and feature comparison is performed using a pre-established pattern library to determine the classification category. Based on the aforementioned classification categories and scope of impact, the potential risk level is analyzed, and quantitative results are generated. A comprehensive evaluation is conducted based on the quantitative results to determine whether the warning criteria have been met.

5. The method for adaptive adjustment of effector parameters based on machine learning according to claim 1, characterized in that, The integration of impact range data based on potential risk levels includes: By integrating atypical patterns and classification results, the distribution characteristics of potential risks are obtained, and preliminary judgment results are generated. Based on the preliminary judgment results, if the risk level exceeds the preset threshold, the data on the scope of impact will be deeply mined to determine the preliminary boundary. Relevant indicators of displacement influence are extracted from the preliminary boundary to quantify the degree of displacement influence and determine key distribution areas. The impact of the key distribution areas on system performance is analyzed to obtain the overall performance fluctuation trend; If the fluctuation trend shows a continuous deviation, the degree of influence is weighted and calculated to determine the core parameters; The range sorting is adjusted based on the core parameters to generate the final priority sorting result.

6. The method for adaptive adjustment of effector parameters based on machine learning according to claim 1, characterized in that, The parameter adjustment scheme generated according to priority sorting includes: By analyzing the compensation mechanism, the data distribution characteristics of the scope of influence are obtained, and the weight of its effect on the overall system is determined. The priority sorting is performed in layers based on the data distribution characteristics to obtain the sorting criteria for each layer; By adjusting the direction based on the sorting criteria and matching parameters, key nodes with high correlation are extracted from the parameter network to determine the degree of impact on the overall configuration. If the correlation is higher than a preset threshold, then a multi-scenario test is conducted using a simulation strategy to obtain parameter response data; Based on the parameter response data, dynamic change trends are extracted to determine the preliminary framework of the optimal solution; By fine-tuning the initial framework, the final optimized compensation mechanism is generated.

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