Machine learning-based effector parameter adaptive adjustment method

Abstract

The invention relates to an effector parameter adaptive adjustment method based on machine learning, and the method comprises the steps: constructing a parameter association network model to obtain relation data among parameters in a sound equipment effector, and generating a topological structure description of a parameter association network; carrying out simulation analysis on the displacement chain reaction, and determining a propagation direction and intensity distribution of an influence path; analyzing the action weight of the key node, positioning the abnormal fluctuation according to the action weight of the key node, and determining the specific position and the influence range of the abnormal fluctuation; extracting a subtle change mode of the infinitesimal displacement, judging whether a chain reaction is triggered or not, evaluating a potential risk level, and determining a priority sequence of an influence range; and generating a parameter adjustment scheme according to the priority ranking, and obtaining effector configuration parameters of the optimal compensation scheme.

Description

Technical Field

[0001] The present application relates to the technical field of audio equipment, and in particular to a method for adaptively adjusting effector parameters based on machine learning. Background Art

[0002] The core of sound effects parameters lies in ensuring the stability and efficiency of system performance through precise control of multiple parameters. Traditional solutions often rely on manual parameter adjustment or static rule-based models, which are difficult to adapt to parameter changes in dynamic environments. When faced with multi-parameter coupling, they lack the ability to analyze the deep interactions between parameters. This makes it difficult to quickly and accurately identify the root cause of the problem and develop effective response strategies when the system parameters change abnormally.

[0003] The correlation between parameters manifests as a complex network structure. Changes in each parameter can trigger chain reactions in other parameters. For example, when a parameter shifts, the path and intensity of its impact on other parameters are difficult to accurately predict using traditional methods. Furthermore, identifying abnormal patterns of parameter shifts is a major challenge. Due to a lack of in-depth analysis of the network topology, existing methods are prone to misjudgment or omission when detecting small or atypical shifts.

[0004] Therefore, constructing an audio equipment effects adjustment method that can effectively capture the correlation network between parameters, predict the chain effect of parameter displacement, and realize abnormal mode detection and optimization compensation through network structure analysis has become a key issue that needs to be solved urgently. Summary of the Invention

[0005] In order to solve the problems existing in the above-mentioned prior art, the purpose of this application is to provide a method for adaptive adjustment of effector parameters based on machine learning.

[0006] The method for adaptively adjusting effector parameters based on machine learning described in this application includes the following steps:

[0007] Construct a parameter association network model to obtain the dependency data between the parameters in the effector and generate a topological structure description of the parameter association network;

[0008] Simulating and analyzing the displacement chain reaction based on the topological structure description to determine the propagation direction and intensity distribution of the impact path;

[0009] Analyze the impact weights of key nodes based on the propagation direction and intensity distribution of the impact path;

[0010] Positioning abnormal fluctuations according to the weights of the key nodes to determine the specific location and impact range of the abnormal fluctuations;

[0011] Based on the specific location and impact range of the abnormal fluctuation, the subtle change pattern of small displacement is extracted to determine whether a chain reaction is triggered and assess the potential risk level;

[0012] Integrate the impact scope data according to the potential risk level and determine the priority of the impact scope;

[0013] A parameter adjustment plan is generated according to the priority ranking to obtain configuration parameters of the optimal compensation plan.

[0014] Preferably, the step of constructing a parameter association network model to obtain dependency data between parameters in the effector includes:

[0015] By collecting the effector parameter data, an initial parameter set is constructed, and a preliminary matrix of the dependency relationship between parameters is generated using the correlation analysis method;

[0016] Extracting significant dependencies from the preliminary matrix and applying a weighted directed graph model to generate an initial structure of a parameter association network;

[0017] By calculating the weight of each parameter node, the initial structure is simplified to generate a simplified parameter association network structure;

[0018] Analyzing the directional relationship between parameters according to the simplified structure to obtain a description matrix;

[0019] Dividing the parameter groups by the description matrix to determine the hierarchical distribution of the topological structure;

[0020] Critical paths and nodes are extracted from the hierarchical distribution to generate a final parameter-associated network topology description.

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

[0022] Construct a network model based on graph theory and generate the initial correlation matrix between parameters;

[0023] Simulating parameter displacement triggering scenarios based on the initial correlation matrix and predicting a set of directly affected nodes;

[0024] Tracking the chain effect of the displacement reaction through the node set, identifying the propagation direction of the impact path, and obtaining the node sequence and edge weight changes on the path;

[0025] Calculate the change trend of the intensity distribution for the node sequence and quantify the degree of influence of each node;

[0026] If the change trend exceeds a preset threshold, the impact path is divided into layers to obtain the distribution characteristics of high-intensity and low-intensity propagation areas;

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

[0028] Preferably, the step of analyzing the weights of key nodes based on the propagation direction and intensity distribution of the influencing path includes:

[0029] Obtaining propagation direction and intensity distribution from the propagation data of the influencing path, and determining the influence weight of each node;

[0030] If the action weight exceeds a preset threshold, the key nodes are calculated through a weighted network model to generate a key node set;

[0031] Obtaining dynamic changes in parameters of corresponding nodes according to the key node set and determining a change trend;

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

[0033] Obtaining the affected paths and nodes from the node position to determine the impact range;

[0034] The affected nodes are grouped according to the impact range to obtain classification features of abnormal fluctuations.

[0035] Preferably, the extraction of subtle change patterns of small displacements based on the specific location and impact range of abnormal fluctuations includes:

[0036] Collect abnormal fluctuation information through sensor data, divide the specific location and impact range of the abnormal fluctuation, and obtain basic distribution characteristics;

[0037] Extracting relevant data of small displacements based on the basic distribution characteristics, separating subtle change patterns, and determining significant features;

[0038] Compare the significant features with a preset threshold. If the threshold is exceeded, it is determined that a chain reaction may be triggered, and a preliminary assessment result is obtained;

[0039] Obtaining atypical patterns based on the preliminary assessment results, performing feature comparisons through a pre-established pattern library, and determining classification categories;

[0040] Analyze potential risk levels based on the classification categories and impact scope, and generate quantitative results;

[0041] A comprehensive assessment is conducted based on the quantitative results to determine whether the warning standards have been met.

[0042] Preferably, said integrating impact scope data according to potential risk levels includes:

[0043] By integrating data on atypical patterns and classification results, we can obtain the distribution characteristics of potential risks and generate preliminary judgment results;

[0044] Based on the preliminary determination result, if the risk level exceeds the preset threshold, the impact range data is deeply mined to determine the preliminary boundary;

[0045] Extracting relevant indicators of displacement impact from the preliminary boundary, quantifying the degree of displacement impact, and determining key distribution areas;

[0046] Analyze the impact of the key distribution areas on system performance and obtain the fluctuation trend of the overall performance;

[0047] If the fluctuation trend shows a continuous deviation, the impact degree is weighted and the core parameters are determined;

[0048] The core parameters are adjusted in range order to generate a final priority ordering result.

[0049] Preferably, generating a parameter adjustment scheme according to priority ranking includes:

[0050] By analyzing the compensation mechanism, we can obtain the data distribution characteristics of the impact range and determine the weight of the impact on the overall system;

[0051] Performing hierarchical processing on the priority ranking according to the data distribution characteristics to obtain the ranking basis of each level;

[0052] By adjusting the direction of the matching parameters according to the sorting, key nodes with high correlation are extracted from the parameter network to determine the degree of influence on the overall configuration;

[0053] If the correlation is higher than a preset threshold, multiple scenario tests are performed through simulation strategies to obtain parameter response data;

[0054] Extracting dynamic change trends based on the parameter response data and determining a preliminary framework for an optimal solution;

[0055] By fine-tuning the preliminary framework, the final compensation mechanism optimization results are generated.

[0056] Preferably, obtaining configuration parameters of the optimal compensation solution includes:

[0057] By verifying the final optimization results, we can judge the adaptability of the optimal solution under different impact ranges and determine its stability performance;

[0058] extracting key configuration data from the parameter association network based on the stability performance and generating a preliminary set of configuration parameters;

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

[0060] Dynamically updating the parameter association network by adjusting the amplitude to obtain updated network response data;

[0061] Analyzing the compensation effect based on the network response data to determine whether the preset performance standard is met;

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

[0063] The method for adaptively adjusting effector parameters based on machine learning described in this application has the advantage of generating an optimal compensation plan by constructing a parameter association network model, analyzing the topological structure and influence path between parameters, identifying key nodes and evaluating abnormal fluctuations. The method first establishes a parameter relationship network, simulates and analyzes the displacement chain reaction, determines the propagation direction and intensity distribution, then locates abnormal fluctuations according to the key node weights, extracts small displacement change patterns, evaluates potential risks, and finally integrates the impact range data to generate a parameter adjustment plan and obtain the optimal effector configuration.

[0064] The present invention can accurately identify and compensate for parameter anomalies in the effects of audio equipment, effectively improve sound quality performance, and provide a new technical means for parameter optimization of audio equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 This is the process of the method for adaptively adjusting the parameters of an effector based on machine learning described in this application Figure 1 ;

[0066] Figure 2 This is the process of the method for adaptively adjusting the parameters of an effector based on machine learning described in this application Figure 2 . DETAILED DESCRIPTION

[0067] like Figure 1-Figure 2 As shown, the method for adaptively adjusting effector parameters based on machine learning described in this application includes the following steps:

[0068] like Figure 1-Figure 2 As shown, in step S101, a parameter association network model is constructed to obtain relationship data between various parameters in the audio equipment effector, and generate a topological structure description of the parameter association network.

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

[0070] Based on the basic data set, data analysis methods are used to extract the correlation characteristics between effect parameters and determine the preliminary pattern of parameter relationships;

[0071] If the correlation features in the preliminary pattern meet the preset threshold, the graph theory method is used to construct the correlation network model and generate the network topology structure between the parameters;

[0072] By optimizing the network topology, we can obtain the detailed features of the topology and judge the completeness of the structure description.

[0073] If the completeness of the structural description does not meet the preset standard, the data of the missing parts will be supplemented to obtain a complete topological structure description;

[0074] Based on the complete topological structure description, visualization tools are used to generate intuitive relational data views and determine the final parameter association network representation.

[0075] Specifically, in step S101, in the process of constructing an audio equipment effector parameter association network model to obtain relationship data between parameters and generate a topological structure description, the original parameter data is first obtained through data acquisition and preprocessing. Three key parameters are extracted from a digital reverberation effector: reverberation time (Reverb Time, unit: seconds), decay rate (Decibels per second), and pre-delay (Pre-Delay, unit: milliseconds). 100 sets of data samples are collected, where the reverberation time ranges from 0.5 to 5.0 seconds, the decay rate ranges from -6.0 to -1.0 decibels per second, and the pre-delay ranges from 10 to 100 milliseconds.

[0076] Next, the Pearson correlation coefficient algorithm was used to calculate the correlation between the parameters. For example, the correlation coefficient between reverberation time and decay rate was -0.75, indicating that the two were negatively correlated. The longer the reverberation time, the smaller the decay rate.

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

[0078] Subsequently, a parameter correlation network model was constructed based on the correlation coefficient matrix. Parameters were regarded as nodes, and those with correlation coefficients greater than 0.5 were regarded as edges with the same weight as the correlation coefficient value. This formed a weighted undirected graph, in which a strong correlation edge existed between reverberation time and decay rate with a weight of 0.75, while other edges were ignored due to their low correlation coefficients.

[0079] Furthermore, the network topology is optimized through graph theory algorithms such as the minimum spanning tree (Kruskal algorithm), and a tree structure centered on the reverberation time is calculated. After sorting the edge weights, the connection between the reverberation time and the decay rate is prioritized to ensure that the network is concise and the key relationships are highlighted.

[0080] Finally, a topological structure description is generated and output in the form of an adjacency matrix, represented as a 3x3 matrix, where the values ​​of the reverberation time and decay rate corresponding positions are 0.75 and the values ​​of other positions are 0, which clearly reflects the strong correlation between the parameters.

[0081] like Figure 1-Figure 2 As shown, in step S102, a simulation analysis is performed on the displacement chain reaction according to the topological structure description to determine the propagation direction and intensity distribution of the impact path.

[0082] Furthermore, in step S102, by collecting data of the topological structure, an initial model of the displacement response is constructed, preliminary characteristics of the response mechanism are obtained, and a mapping relationship of the structural association is obtained;

[0083] Based on the mapping relationship of structural association, simulation analysis methods are used to track the changing trajectory of the propagation path and determine the key nodes affected by the path;

[0084] Analyze the triggering conditions of chain effects at the key nodes of path impact. If the triggering conditions meet the preset threshold, record the dynamic changes of effect transmission and determine the priority of directional analysis.

[0085] By combining the priority of directional analysis with the characteristic data of intensity distribution, the specific range of effect transmission is obtained, and the quantitative results of distribution characteristics are obtained;

[0086] Based on the quantitative results of the distribution characteristics, the potential impact of the displacement response is analyzed. If the potential impact exceeds the preset range, the simulation analysis parameters are adjusted to determine the optimization scheme of the reaction mechanism;

[0087] Based on the optimization scheme of the reaction mechanism, the associated data of the topological structure is updated, the corrected trajectory of the propagation path is obtained, and the final distribution of the path impact is obtained;

[0088] By combining the final distribution of path impacts with the updated data of intensity distribution, the long-term trend of chain effects is analyzed and the stable state of effect transmission is determined.

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

[0090] Next, the breadth-first search algorithm (BFS) is used to determine the propagation direction. Starting from the initial force-bearing node (such as node 1), the force transfer 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 transfer probability of node 2 is 0.8 × 5.0 = 4.0 units.

[0091] Subsequently, the propagation intensity distribution was analyzed, and the weighted average method was used to calculate the cumulative displacement impact of each node. For example, node 3 received the impact through two paths, node 2 and node 4, with path weights of 0.6 and 0.3 respectively. The cumulative displacement was (4.0 × 0.6 + 5.0 × 0.3) = 3.9 units. By iteratively calculating all nodes, an intensity distribution map was generated. The node displacement values ​​ranged from 1.2 to 4.5 units, showing a trend of gradually weakening from the center to the periphery.

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

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

[0094] The above process is all implemented through programming, using the NetworkX library in Python to build the network and calculate the propagation path, combined with NumPy for matrix operations, and automatically generating visual distribution maps to ensure the scientific and efficient analysis.

[0095] like Figure 1-Figure 2 As shown, in step S103, the action weights of key nodes are analyzed according to the propagation direction and intensity distribution of the impact path, and the abnormal fluctuation is located according to the action weights of the key nodes to determine the specific location and impact range of the abnormal fluctuation.

[0096] Furthermore, in step S103, relevant data affecting the path is obtained, and preliminary sorting is performed on the propagation direction and intensity distribution, and a structured representation of the path propagation is obtained by constructing a data matrix;

[0097] Based on the structured representation of path propagation, the node identification method is used to extract key nodes, and the weight of each node is quantified using the preset weight calculation rules to determine the importance ranking of the nodes;

[0098] The data features of abnormal fluctuations are matched by the weights of key nodes. If a node weight is detected to exceed the preset threshold, the node is judged as a potential source of abnormal fluctuations.

[0099] Based on the node location of the potential source and the data on the propagation direction, the impact path of the abnormal fluctuation is analyzed, the set of affected adjacent nodes is obtained, and the preliminary impact range of the abnormal fluctuation is obtained;

[0100] For the nodes within the initial impact range, a secondary verification is performed using the intensity distribution data. If the intensity distribution value of a node deviates from the normal range, the node is determined to be the actual impact area of ​​the abnormal fluctuation;

[0101] Based on the node set of the actual impact area, combined with path analysis technology, the specific location of the abnormal fluctuation is traced, the detailed fluctuation propagation chain is obtained, and the core location of the abnormal fluctuation is determined;

[0102] Based on the data of the core location and transmission chain, the boundary information of the impact range is integrated, and visualization tools are used to generate a distribution view of abnormal fluctuations to determine the final impact range and location details.

[0103] Specifically, in step S103, the weights of key nodes are analyzed based on the propagation direction and intensity distribution of the impact path, and the abnormal fluctuation is located according to the weights to determine the specific location and impact range of the abnormal fluctuation. This can be achieved by the following method:

[0104] There is a preset social network communication model with 100 nodes. The edges between nodes represent the information propagation path, and the edge weight represents the propagation intensity (ranging from 0 to 1);

[0105] First, the propagation direction and intensity distribution are calculated. The PageRank algorithm is used with a damping coefficient of 0.85. 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 that of node B is 0.08, indicating that A has a greater influence in the spread;

[0107] Next, we analyze the role weights of key nodes and construct a weighted index by combining node degrees and PageRank values: weight = 0.6 × PageRank value + 0.4 × normalized degree (degree range is normalized to 0 to 1);

[0108] The default degree of node A is 10, which is normalized to 0.5, and its weight is 0.6×0.12+0.4×0.5=0.272;

[0109] The degree of node B is 5, which is 0.25 after normalization, and the weight is 0.6×0.08+0.4×0.25=0.148;

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

[0111] To locate abnormal fluctuations, the propagation intensity anomaly threshold is set to the mean plus 2 times the standard deviation. For example, if the propagation intensity of a path is 0.9 and exceeds the threshold of 0.85, it is marked as abnormal.

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

[0113] The impact range analysis uses a depth-first search, starting from node A, to calculate the affected node set. Assuming that 20 nodes are involved, the impact range accounts for 20%;

[0114] If data is insufficient, we can simulate the spread intensity (normal distribution, mean 0.5, standard deviation 0.1) and combine it with business scenarios (such as social media public opinion monitoring) to infer the source of the anomaly, such as a node suddenly publishing highly popular content.

[0115] Finally, the abnormal output fluctuation was located at node A, affecting 20 nodes. Weight analysis and path tracing formed a closed logic chain.

[0116] like Figure 1-Figure 2 As shown, in step S104, for the specific location and impact range of the abnormal fluctuation, the subtle change pattern of the small displacement is extracted to determine whether a chain reaction is triggered and the potential risk level is evaluated. The impact range data is integrated according to the potential risk level to determine the priority ranking of the impact range.

[0117] Furthermore, in step S104, for the abnormal fluctuation data, specific fluctuation signal data is obtained from the monitoring system, and the original fluctuation signal is denoised using signal processing technology to obtain filtered fluctuation characteristic data;

[0118] Based on the filtered fluctuation characteristic data, the specific location information of the fluctuation signal is analyzed, and the geographical coordinates of the abnormal fluctuation are determined through spatial positioning technology to determine the core area distribution of the abnormal fluctuation;

[0119] Based on the data distributed in the core area, we obtain records of small displacements within the area, use time series analysis methods to extract subtle change patterns in the displacement data, and determine the trend characteristics of displacement changes;

[0120] Based on the trend characteristics of displacement changes, analyze whether there are triggering conditions for a chain reaction. If the trend characteristics show that the displacement change exceeds the preset threshold, it is determined that there is a possibility of a chain reaction and a preliminary assessment result of the chain reaction is obtained;

[0121] Based on the preliminary assessment results of the chain reaction and combined with the environmental data within the affected area, a support vector machine model is used to quantitatively assess the potential risks and determine the level of potential risks;

[0122] Based on the level of potential risks, integrate multi-source data within the impact range, prioritize each area within the range through data fusion technology, and obtain a priority sequence for the impact range;

[0123] Based on the priority sequence of the impact range, the corresponding regional monitoring strategy adjustment plan is generated, and resource allocation optimization is performed on high-priority areas through the automated scheduling system to determine the final monitoring deployment plan.

[0124] Specifically, in step S104, to analyze the specific location and impact range of the abnormal fluctuation, a high-precision sensor network is first used to collect tiny displacement data. The preset sensors are distributed in a 100×100 meter grid area, collecting displacement data 1000 times per second with an accuracy of 0.01 mm.

[0125] Use the Fourier transform algorithm to perform spectrum analysis on the collected data, extract abnormal fluctuation signals with a frequency between 0.1 and 10 Hz, calculate their amplitude mean, for example, 0.05 mm, determine whether it exceeds the normal threshold of 0.03 mm, and locate the abnormal point, such as the (50,50) coordinate;

[0126] Next, based on the finite element analysis model, the propagation of displacement fluctuations within the area was simulated. The material stiffness coefficient was preset to 2000 Pascals, and the wave propagation distance was calculated. It was found that the affected area was a circular area with a radius of 20 meters centered on the anomaly point.

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

[0128] The potential risk level assessment is based on a fuzzy logic algorithm. With an input of a chain reaction probability of 0.3 and an affected area of ​​1,256 square meters, the output risk level is "medium" with a quantitative value of 0.6 (out of 1.0);

[0129] When integrating the impact range data, the impact range was divided into high, medium and low priority areas in combination with the Geographic Information System (GIS). Based on the population density (0.1 person per square meter) and the infrastructure value (1000 yuan per square meter), the comprehensive priority score was 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. 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 Figure 1-Figure 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 the parameter adjustment scheme to obtain the effector configuration parameters of the optimal compensation scheme, the device operation status data is first automatically obtained through the data acquisition system, for example, the input signal strength of a certain audio effector is collected as -6.5dB, the output signal strength is -3.2dB, the delay time is 5ms, and the ambient noise level is recorded as 40dB;

[0145] Next, based on a priority sorting algorithm, the parameter adjustment priorities are divided into three levels: signal strength optimization, delay correction, and noise suppression, with priority weights of 0.6, 0.3, and 0.1, respectively. A weighted calculation is used to determine the comprehensive optimization target values, such as -4.0dB for signal strength optimization, 3ms for delay, and 35dB for noise suppression.

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

[0147] Next, the system verified the parameter effects through simulation tests. The simulated input signal strength ranged from -7.0dB to -5.0dB, and the calculated output signal stability error was 0.3dB, which was lower than the preset threshold of 0.5dB, confirming the feasibility of the parameters.

[0148] Finally, the system automatically pushes the optimal parameter configuration to the effects hardware and monitors the adjusted operating data in real time, including signal strength stabilization at -4.1dB, latency of 3.1ms, and noise level reduced to 36dB. It also generates an optimization report, which analyzes that signal strength optimization contributes 60% to the overall effect improvement, latency correction contributes 30%, and noise suppression contributes 10%, thus forming a closed-loop optimization logic to ensure that parameter adjustments are highly matched with actual needs.

[0149] Those skilled in the art can make various other corresponding changes and deformations based on the technical solutions and concepts described above, and all of these changes and deformations should fall within the scope of protection of the claims of this application.

Claims

1. A method for adaptively adjusting effector parameters based on machine learning, characterized in that: include: Construct a parameter association network model to obtain the relationship data between various parameters in the audio equipment effector and generate a topological structure description of the parameter association network; Simulate and analyze the displacement chain reaction based on the topological structure description to determine the propagation direction and intensity distribution of the impact path; Analyze the weights of key nodes based on the propagation direction and intensity distribution of the impact path, locate the abnormal fluctuations based on the weights of key nodes, and determine the specific location and impact range of the abnormal fluctuations; Based on the specific location and impact range of the abnormal fluctuation, the subtle change pattern of small displacement is extracted to determine whether a chain reaction is triggered and the potential risk level is assessed. The impact range data is integrated according to the potential risk level to determine the priority of the impact range; Generate a parameter adjustment plan based on priority sorting and obtain the effector configuration parameters of the optimal compensation plan.

2. The method for adaptively adjusting effector parameters based on machine learning according to claim 1, characterized in that: The constructing of the parameter association network model includes: Construct an initial parameter set by collecting the reverberation time, decay rate and pre-delay parameters of the digital reverberation effector; The Pearson correlation coefficient algorithm is used to calculate the correlation between parameters and generate a correlation coefficient matrix, in which the correlation coefficient between reverberation time and decay rate is negative and the absolute value is greater than 0.5; Construct a weighted undirected graph based on the dependency relationships with absolute values ​​of correlation coefficients greater than 0.5; The network topology is optimized by using the Kruskal minimum spanning tree algorithm to retain the strong correlation between reverberation time and decay rate; Generate a topological structure description in the form of an adjacency matrix, in which the corresponding position values ​​of the reverberation time and the decay rate are correlation coefficient values.

3. The method for adaptively adjusting effector parameters based on machine learning according to claim 1, characterized in that: The simulation analysis of the displacement chain reaction based on the topological structure description includes: A 10-node network model based on graph theory was constructed, with the connection strength between nodes represented by a weight value of 0.1-1.0; The breadth-first search algorithm is used to calculate the transfer probability starting from the initial force node: P = weight value × initial displacement value; The influence of the node cumulative displacement is calculated by weighted average method, and the strength distribution diagram 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 value is adjusted to perform model calibration.

4. The method for adaptively adjusting effector parameters based on machine learning according to claim 1, characterized in that: The analysis of the weights of key nodes based on the propagation direction and intensity distribution of the impact path includes: In the propagation model, the PageRank algorithm is used to calculate the node importance, and the damping coefficient is set to 0.85; Construct a weighted index: weight = 0.6 × PageRank value + 0.4 × normalized degree; The abnormal threshold of transmission intensity is set as the mean plus 2 times the standard deviation; When the path propagation intensity exceeds the threshold, the affected node set is calculated through depth-first search, and the affected range is expressed as a percentage.

5. The method for adaptively adjusting effector parameters based on machine learning according to claim 1, characterized in that: The method of extracting subtle change patterns of small displacements based on the specific location and impact range of abnormal fluctuations includes: Abnormal fluctuation information is collected through sensor data, the specific location and impact range of the abnormal fluctuation are divided, and basic distribution characteristics are obtained; Extracting relevant data of small displacements based on the basic distribution characteristics, separating subtle change patterns, and determining significant features; Compare the significant features with a preset threshold. If the threshold is exceeded, it is determined that a chain reaction may be triggered, and a preliminary assessment result is obtained; Obtaining atypical patterns based on the preliminary assessment results, performing feature comparisons using a pre-established pattern library, and determining classification categories; Analyze potential risk levels based on the classification categories and impact scope, and generate quantitative results; A comprehensive assessment is conducted based on the quantitative results to determine whether the warning standards have been met.

6. The method for adaptively adjusting effector parameters based on machine learning according to claim 1, characterized in that: The integration of impact data based on potential risk levels includes: By integrating data on atypical patterns and classification results, we can obtain the distribution characteristics of potential risks and generate preliminary judgment results; Based on the preliminary determination result, if the risk level exceeds the preset threshold, the impact range data is deeply mined to determine the preliminary boundary; Extracting relevant indicators of displacement impact from the preliminary boundary, quantifying the degree of displacement impact, and determining key distribution areas; Analyze the impact of the key distribution areas on system performance and obtain the fluctuation trend of the overall performance; If the fluctuation trend shows a continuous deviation, the impact degree is weighted and the core parameters are determined; The core parameters are adjusted in range order to generate a final priority ordering result.

7. The method for adaptively adjusting effector parameters based on machine learning according to claim 1, characterized in that: Generating a parameter adjustment plan according to priority ranking includes: By analyzing the compensation mechanism, we can obtain the data distribution characteristics of the impact range and determine the weight of the impact on the overall system; Performing hierarchical processing on the priority ranking according to the data distribution characteristics to obtain the ranking basis of each level; By adjusting the direction of the matching parameters according to the sorting, key nodes with high correlation are extracted from the parameter network to determine the degree of influence on the overall configuration; If the correlation is higher than a preset threshold, multiple scenario tests are performed through simulation strategies to obtain parameter response data; Extracting dynamic change trends based on the parameter response data and determining a preliminary framework for an optimal solution; By fine-tuning the preliminary framework, the final compensation mechanism optimization results are generated.

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