Multi-protocol debugging communication module initialization method and system, storage medium and equipment

Abstract

The invention relates to the technical field of integrated development debugging, and discloses a multi-protocol debugging communication module initialization method and system, a storage medium and equipment, and the method comprises the steps: scanning all connected debugging target equipment, carrying out the weighted evaluation of the scanning priority of the calculation equipment, and constructing a protocol capability matrix of each equipment; pre-distributing communication channels by adopting an optimization algorithm according to the protocol capability matrix, monitoring network environment indexes, and controlling a dynamic decision protocol switching opportunity through fuzzy logic; hot plug detection and equipment health degree evaluation are implemented, and when connection abnormity is detected, a multi-stage recovery strategy is triggered step by step to process the abnormity; monitoring an interface calling success rate and response time, analyzing and predicting an interface state based on a time sequence of historical data, and executing a self-adaptive repairing action of the interface; according to the method, the hardware capability characteristics of the audio equipment are detected, initialization parameters are optimized based on equipment capability scores, and meanwhile, a DHCP address allocation strategy is dynamically managed, so that the unified management of adaptive initialization of various communication protocols is realized.

Description

Technical Field

[0001] This invention relates to the field of integrated development and debugging technology, and in particular to a method, system, storage medium and device for initializing a multi-protocol debugging communication module. Background Technology

[0002] With the rapid development of the wireless smart audio device market, product forms, including TWS earphones, smart speakers, and hearing aids, continue to innovate, leading to an exponential increase in system complexity. Modern audio device development involves the deep integration of multiple technology stacks: the underlying multi-protocol communication frameworks such as Bluetooth, WiFi, and Thread; the mid-layer low-power audio codec algorithms such as LC3 / LHDC; and the upper-layer voice interaction and user interface systems, collectively forming a highly heterogeneous technological ecosystem. However, the integrated development environment (IDE) supporting the development of such products has long been limited by fragmented debugging toolchains. In current mainstream industry solutions, developers have to use disparate debugging tools for different protocol layers—for example, monitoring embedded system logs via UART serial port, debugging wireless transmission with TCP / IP network protocol analyzers, and using dedicated Bluetooth sniffing tools to optimize audio synchronization. This tool switching not only significantly increases the learning cost but also leads to frequent loss of debugging context. In actual development, engineers often need to manually switch communication parameters between multiple independent configuration interfaces, resulting in a high rate of communication interruptions caused by protocol conflicts.

[0003] Even more serious is the fact that existing debugging environments are severely inadequate in adapting to dynamic changes. When device nodes need to be hot-swapped during development, traditional debugging sessions are forcibly terminated due to sudden changes in connection status, forcing developers to restart the entire debugging process. Industry research data shows that a single hot-swap event results in an average loss of 15 minutes of effective development time, a problem that is particularly pronounced in complex system integration scenarios. Meanwhile, dynamic fluctuations in the network environment further amplify debugging vulnerability: WiFi channel congestion leading to a surge in TCP retransmissions, Bluetooth radio frequency interference causing intermittent audio streams, and other issues all require developers to manually intervene in protocol parameters. Existing toolchains lack intelligent decision-making mechanisms that can perceive network congestion, device load, and signal quality in real time, relying solely on static configurations to cope with dynamic environments, resulting in more than half of debugging sessions being forced to stop due to environmental fluctuations.

[0004] At the level of cross-domain collaborative debugging, the fragmented management of audio devices and network configurations constitutes another technical bottleneck. Audio subsystem debugging requires precise control of dozens of parameters such as sampling rate, bit depth, and buffer size, but existing IDEs cannot automatically identify the hardware capability boundaries of sound cards and microphones, often leading to initialization failures due to parameter out-of-bounds errors. Meanwhile, the complexity of network configuration continues to rise: DHCP address conflicts causing device disconnection, expired SSL certificates interrupting secure communication, and mismatched IP address lease periods causing topology disorder are frequent problems. Particularly noteworthy is that when audio stream transmission and network configuration become dependent, existing toolchains cannot coordinate the timing relationship between audio initialization and network configuration, resulting in an increased system-level debugging failure rate. Although some manufacturers have attempted to introduce protocol abstraction layers in recent years, these only implement simple encapsulation of communication interfaces and do not solve fundamental problems such as multi-protocol adaptive selection, hot-plug self-recovery, and cross-module parameter optimization. Therefore, developing an intelligent debugging framework that deeply integrates multi-protocol communication management, dynamic environment adaptation, and cross-domain collaborative optimization has become a key breakthrough for promoting the technological upgrade of the wireless audio industry. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and to provide a multi-protocol debugging communication module initialization method, system, storage medium and device to achieve unified management of adaptive initialization of multiple communication protocols such as DHCP, SSL, and serial port.

[0006] On the one hand, a method for initializing a multi-protocol debugging communication module is provided, including the following steps: S1: Scan all connected debugging target devices, calculate the device scanning priority based on a weighted evaluation of signal strength, response time and protocol compatibility, and construct a protocol capability matrix for each device to quantify the degree of multi-protocol support; S2: Based on the protocol capability matrix, an optimization algorithm is used to pre-allocate communication channels, while monitoring network environment and device status indicators, and using fuzzy logic to control the dynamic decision-making timing of protocol switching; S3: Implement hot-plug detection and device health assessment. When a connection abnormality is detected, trigger a multi-level recovery strategy to handle the abnormality step by step. S4: Monitor the success rate and response time of interface calls, predict the interface status based on time series analysis of historical data, and execute adaptive repair actions for the interface based on the results of repair effectiveness and cost assessment. S5: Comprehensively detects the hardware capabilities of audio devices, optimizes initialization parameters based on device capability scores, and dynamically manages DHCP address allocation strategies by combining historical usage frequency, type, and location factors of the devices.

[0007] Furthermore, in step S1, the construction of the protocol capability matrix specifically includes: The device's basic compatibility Boolean value for each protocol, the quality of service quantification coefficient calculated based on measured bandwidth and latency, and the link stability coefficient evaluated by packet loss rate and latency jitter are weighted and linearly fused, and a protocol support score in the range of 0-1 is generated by normalization.

[0008] Furthermore, in step S2, the optimization algorithm for the pre-allocated communication channel specifically includes: Given the constraints of a single device being bound to a single protocol and the maximum concurrency of the protocol, we solve an integer programming problem with the objective of maximizing the sum of the protocol support scores of all devices.

[0009] Further, in step S2, the timing of switching the dynamic decision protocol via fuzzy logic control includes: Real-time monitoring of network environment and device status indicators, including network congestion level, device load, signal quality, and protocol error rate; Preset threshold range for triggering protocol switching The environmental assessment value is calculated based on a weighted average of multiple environmental indicators: If the environmental assessment value is lower than the lower threshold The current agreement shall be maintained. If the environmental assessment value is higher than the upper threshold Initiate protocol switching; The environmental assessment value is located at Within the interval, optimize the execution protocol parameters.

[0010] Further, in step S3, the device health assessment specifically includes: A health score is obtained by weighted summation of the device availability percentage, normalized response time coefficient, and reciprocal of error rate. When the health score is lower than the health threshold, an anomaly warning mechanism is activated, triggering the multi-level recovery strategy to handle the anomaly.

[0011] Furthermore, in step S3, the multi-level recovery strategy includes a soft reset operation, a backup protocol switching operation, and a device reconnection operation, wherein... The soft reset operation includes clearing the communication buffer, resetting the protocol state machine, and retransmitting the last frame data packet. The backup protocol switching operation attempts available protocols in descending order of protocol capability matrix scores. The device reconnection operation includes disconnecting all connections, rescanning the topology, and resuming the debugging session.

[0012] Further, in step S4, the adaptive repair of the interface includes: The system identifies abnormal patterns in the number of timeouts and failures of API calls, uses an autoregressive integral moving average model to predict the trend of API performance degradation, and selects the optimal repair scheme for adaptive repair based on the principle of maximizing the difference between utility value and cost.

[0013] Preferably, in the repair scheme, the utility value calculation specifically includes: The utility value is calculated by combining the expected benefit of the probability of successful repair with the risk loss of the probability of failure, where the probability of successful repair is generated based on a logistic regression model of historical repair records.

[0014] Further, in step S5, the optimization of the initialization parameters includes: Comprehensive testing of the hardware capabilities of audio devices, including sampling rate support range, bit depth, channel configuration, and latency characteristics; Based on the hardware capabilities of the audio device, the optimal parameter combination is solved with the goal of minimizing the weighted cost function consisting of audio latency, sound quality score, and stability score, while satisfying the device compatibility threshold.

[0015] Furthermore, in step S5, the dynamic management of the DHCP address allocation policy includes: Based on the network topology complexity assessment results, the address allocation priority, address pool management and IP lease length are adaptively adjusted. When the complexity exceeds the preset threshold, the address reservation mechanism is activated and the IP lease of core devices is extended.

[0016] Furthermore, in step S3, the hot-plug detection employs an adaptive heartbeat mechanism, including: The heartbeat interval is dynamically adjusted using a linear proportional function based on the real-time packet loss rate, and a maximum interval limit is set. At the same time, the standard variation of the device response time is detected based on the sliding window statistical method.

[0017] Preferably, the method further includes SSL secure communication initialization, specifically including: The encryption key is dynamically negotiated and its strength is jointly evaluated based on key length, random entropy quality, and effective duration factor. When the strength is lower than the security threshold, the key update process is triggered.

[0018] On the other hand, a multi-protocol debugging communication module initialization system is provided, including: The multi-protocol topology scanning and evaluation module is used to scan all connected debugging target devices. It calculates the scanning priority of devices based on a weighted evaluation of signal strength, response time and protocol compatibility, and constructs a protocol capability matrix for each device to quantify the degree of multi-protocol support. The adaptive communication channel management module is used to pre-allocate communication channels according to the protocol capability matrix using an optimization algorithm, while monitoring network environment and device status indicators, and dynamically deciding the timing of protocol switching through fuzzy logic control. The status monitoring and multi-level recovery module is used to implement hot-plug detection and device health assessment. When a connection abnormality is detected, a multi-level recovery strategy is triggered step by step to handle the abnormality. The interface intelligent operation and maintenance module is used to monitor the success rate and response time of interface calls, predict the status of interfaces based on time series analysis of historical data, and execute adaptive repair actions of interfaces based on the results of repair effectiveness and cost assessment. The collaborative optimization module is used to comprehensively detect the hardware capabilities of audio devices, optimize initialization parameters based on device capability scores, and dynamically manage DHCP address allocation strategies by combining the device's historical usage frequency, type, and location factors.

[0019] Compared with the prior art, the beneficial effects of the present invention are: This invention achieves unified communication management of heterogeneous devices through multi-protocol topology scanning and protocol capability matrix construction, solves the protocol fragmentation problem caused by switching between multiple tools in traditional debugging, and establishes standardized support capabilities for multiple protocols such as Bluetooth, WiFi, and serial ports. This invention ensures connection reliability in dynamic network environments by using adaptive communication channel management and fuzzy decision control to dynamically decide when to switch protocols. It automatically selects the optimal communication path in complex scenarios such as network congestion and signal fluctuations, eliminating the maintenance burden of manually configuring protocol parameters. This invention maintains the continuous availability of the debugging session through device status monitoring and multi-level recovery. When the device is hot-plugged or the connection is abnormal, it autonomously restores communication according to a progressive strategy of soft reset → protocol switching → reconnection, thus avoiding interruption of the debugging process. This invention improves the robustness of human-computer interaction in the development environment through intelligent interface operation and maintenance and adaptive repair. Based on the identification of abnormal interface call patterns and the prediction of status trends, it actively triggers the optimal repair action to eliminate the risk of interface lag and function failure. This invention ensures the compatibility and high performance of the audio subsystem initialization through audio device capability scoring and parameter optimization, automatically adapts to the hardware characteristics of different sound cards / microphones, and avoids initialization failures caused by configuration errors such as sampling rate exceeding limits and buffer overflows. In addition, this invention optimizes network resource configuration efficiency through DHCP address allocation priority and topology adaptive technology, and eradicates the problem of device disconnection caused by address conflicts. Attached Figure Description

[0020] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of a multi-protocol debugging communication module initialization method according to the present invention; Figure 2 This is a block diagram of the initialization system structure for a multi-protocol debugging communication module according to the present invention; Figure 3 This is a schematic diagram of an embodiment of an electronic device according to the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] This invention introduces a protocol priority evaluation algorithm and a device status awareness mechanism to automatically select the optimal communication path based on the target device type and network environment. It also designs a hot-plug detection and recovery algorithm, combined with interface anomaly self-repair technology, significantly improving the stability of the debugging environment. Furthermore, it integrates an intelligent audio device initialization module, using a pre-detection mechanism to avoid initialization failures of the PC microphone and sound card, providing an efficient and reliable debugging support environment for wireless audio device development.

[0023] The specific embodiments of the present invention will be described below with reference to the accompanying drawings and examples.

[0024] Example 1 Please see Figure 1 The technical solution for a multi-protocol debugging communication module initialization method provided in this embodiment includes the following steps: S1: Scan all connected debugging target devices, calculate the device scanning priority based on a weighted evaluation of signal strength, response time and protocol compatibility, and construct a protocol capability matrix for each device to quantify the degree of multi-protocol support; S2: Based on the protocol capability matrix, an optimization algorithm is used to pre-allocate communication channels, while monitoring network environment and device status indicators, and using fuzzy logic to control the dynamic decision-making timing of protocol switching; S3: Implement hot-plug detection and device health assessment. When a connection abnormality is detected, trigger a multi-level recovery strategy to handle the abnormality step by step. S4: Monitor the success rate and response time of interface calls, predict the interface status based on time series analysis of historical data, and execute adaptive repair actions for the interface based on the results of repair effectiveness and cost assessment. S5: Comprehensively detects the hardware capabilities of audio devices, optimizes initialization parameters based on device capability scores, and dynamically manages DHCP address allocation strategies by combining historical usage frequency, type, and location factors of the devices.

[0025] First, a comprehensive device topology scan is performed in step S1 to identify all currently connected potential debugging target devices. Specifically, this process employs a multi-level scanning algorithm, performed according to a calculated priority order. This scanning priority algorithm is expressed as follows: , in, Indicates the first Scan priority score for each device Indicates the first The signal strength coefficient of each device, ranging from [0,1]. Indicates the first The response time coefficient of each device , For response time, To the maximum allowable response time, Indicates the first The compatibility coefficient of each device depends on the device type and protocol support. , and The weighting coefficients for the above indicators satisfy the following conditions: .

[0026] The above algorithm formula achieves intelligent sorting of device scanning under limited computing resources through multi-dimensional quantization fusion and dynamic weight control, avoiding the waste of debugging time caused by invalid scanning and laying the foundation for the establishment of multi-protocol communication channels.

[0027] In this embodiment, when a Bluetooth audio device is detected, the system calculates its scanning priority. Assuming the device's signal strength RSSI is -45dBm, corresponding to... The response time is 50ms. ,but As an audio device Using weights , , ,get .

[0028] Based on all the scanned devices, a protocol capability matrix is ​​constructed for each device to quantify the degree of multi-protocol support, including: The device's basic compatibility Boolean value for each protocol, the quality of service quantification coefficient calculated based on measured bandwidth and latency, and the link stability coefficient evaluated by packet loss rate and latency jitter are weighted and linearly fused, and a protocol support score in the range of 0-1 is generated by normalization.

[0029] Specifically, a protocol capability matrix is ​​constructed for each identified device. The definition is as follows: , in, For the number of devices, To the number of supported protocol types, Indicates equipment Agreement The degree of support, with a value range of [0,1]. ,in, Indicates basic compatibility, 0 means not supported, and 1 means fully supported; This represents the service quality coefficient, which is obtained based on parameters such as bandwidth and latency. Indicates the link stability coefficient. , and The adjustment parameters for the above three coefficients satisfy... .

[0030] The above algorithm formula achieves a quantitative assessment of the degree of multi-protocol support by integrating three dimensions of parameters: basic compatibility, service quality, and link stability. Its normalized weighting mechanism combines computational efficiency with comprehensive decision-making.

[0031] In this embodiment, for audio devices that support both WiFi and Bluetooth, the WiFi protocol... (Normalized to 1.0), Bluetooth protocol (Normalized to 1.0).

[0032] After constructing the protocol capability matrix, proceed to step S2, where an optimization algorithm is used to pre-allocate communication channels, including: Given the constraints of a single device being bound to a single protocol and the maximum concurrency of the protocol, we solve an integer programming problem with the objective of maximizing the sum of the protocol support scores of all devices.

[0033] Specifically, in this embodiment, based on the protocol capability matrix, the system performs pre-allocation of communication channels and uses an improved Hungarian algorithm to solve for the optimal allocation scheme: The objective function is expressed as: , The constraints are as follows: ; ; , in, This represents an allocation variable, where 1 represents a device. Distribution Agreement 0 indicates unallocated. For the agreement The maximum number of concurrent connections.

[0034] Next, step S2 involves continuous monitoring of the network environment and device status, including network congestion, device load, signal quality, and protocol error rate. The protocol selection strategy is dynamically adjusted. The environment awareness algorithm is based on multi-dimensional indicators, and the environment evaluation function is expressed as follows: , in, express Environmental assessment values ​​at any given time Indicates the first One environmental indicator in The normalized value at time , Indicates the first The weights of each indicator. Specifically, the main environmental indicators include: Network congestion level: ; Equipment load: ; Signal quality: ; Protocol error rate: .

[0035] in, For the current bandwidth, This is the bandwidth limit. For CPU utilization, For signal strength, For error count, This represents the total number of data packets transmitted in the network.

[0036] The above algorithm formula achieves quantitative evaluation of dynamic network status through weighted fusion of multi-dimensional environmental indicators, and its framework design combines real-time performance and scalability.

[0037] At a certain moment in this embodiment, the network bandwidth utilization rate is 60%. If the device's CPU utilization is 40%, then... If the WiFi signal strength is -50dBm, then If there are 2 errors in the last 100 data packets, then... Using equal weights ,get .

[0038] Then, based on the environmental assessment results, a fuzzy logic controller is used to make protocol switching decisions, including: Preset threshold range for triggering protocol switching The environmental assessment value is calculated based on a weighted average of multiple environmental indicators: If the environmental assessment value is lower than the lower threshold The current agreement shall be maintained. If the environmental assessment value is higher than the upper threshold Initiate protocol switching; The environmental assessment value is located at Within the interval, optimize the execution protocol parameters.

[0039] Specifically, the decision function is represented as follows: , in, This is an environmental assessment value. , and All are fuzzy membership functions (range [0,1]). This indicates the degree to which the environmental assessment value belongs to the "low-risk range". This indicates the degree to which the environmental assessment value belongs to the "medium-risk range". This indicates the degree to which the environmental assessment value belongs to the "high-risk range". , and These are the decision actions corresponding to the three membership degrees mentioned above. Indicates the decision action: Maintain the current agreement. Indicates the decision-making action: optimize protocol parameters. The decision action is: switch to the backup protocol.

[0040] The fuzzy membership function in this embodiment is defined as follows: ; ; , The above algorithm formula quantifies the environmental risk level by using fuzzy membership and weightedly integrates multiple decision actions to avoid excessive switching and prevent response delays in protocol switching scenarios, while dynamically coordinating maintenance / optimization / switching actions.

[0041] Next, step S3 involves implementing hot-plug detection and device health assessment. The hot-plug detection employs an adaptive heartbeat mechanism and further includes: The heartbeat interval is dynamically adjusted using a linear proportional function based on the real-time packet loss rate, and a maximum interval limit is set. At the same time, the standard variation of the device response time is detected based on the sliding window statistical method.

[0042] The equipment health assessment specifically includes: A health score is obtained by weighted summation of the device availability percentage, normalized response time coefficient, and reciprocal of error rate. When the health score is lower than the health threshold, an anomaly warning mechanism is activated, triggering the multi-level recovery strategy to handle the anomaly.

[0043] Specifically, the adaptive heartbeat interval algorithm is expressed as follows: , in, Indicates the interval between the next heartbeats. Indicates the baseline heart rate interval. For adjustment coefficients, This indicates the current packet loss rate.

[0044] Secondly, the formula for assessing equipment health is as follows: , in, For equipment availability, The average response time, For error rate, , and These are the weights corresponding to the three indicators mentioned above.

[0045] By reducing invalid probes (reducing network load) through the aforementioned heartbeat mechanism algorithm formula, the actual availability of the device is improved, and the health score is made more accurate. The health assessment quantitatively drives decision-making and directly guides the trigger threshold of the recovery strategy.

[0046] Based on the above, the anomaly detection and recovery in step S3 are implemented. First, anomaly detection based on statistical learning is performed, and the algorithm function is expressed as follows: , in, For the current observation value, This is the historical average. For historical standard deviation, The preset abnormal threshold is usually set to [2, 3].

[0047] Updating historical data by moving the window is represented as follows: , , in, The forgetting factor is usually set to [0.9, 0.99].

[0048] The above algorithm formula achieves adaptive anomaly threshold by dynamically updating the mean and standard deviation, and balances historical data memory and real-time sensitivity with an exponential weighting mechanism, resulting in high computational efficiency and low false alarm rate.

[0049] If the test results are abnormal, the multi-level recovery strategy in step S3 is implemented, including a soft reset, a backup protocol switchover, and a device reconnection. The soft reset operation includes clearing the communication buffer, resetting the protocol state machine, and retransmitting the last frame data packet. The backup protocol switching operation attempts available protocols in descending order of protocol capability matrix scores. The device reconnection operation includes disconnecting all connections, rescanning the topology, and resuming the debugging session.

[0050] Specifically, in this embodiment, a three-level recovery strategy is set, including: Level 1: soft reset; Level 2: protocol switching; Level 3: full reconnection.

[0051] Next, step S4, interface monitoring and adaptive repair, is performed. This embodiment focuses on the self-repair of MessageBox interface anomalies. First, an intelligent MessageBox interface call monitoring mechanism is implemented, including a time window. Internal call success rate The calculation formula is as follows: , Monitor response time, time window The formula for calculating the internal average response time is as follows: , Anomaly pattern identification and comprehensive anomaly risk scoring The calculation formula is expressed as follows: , in, express The number of times the call was successfully executed within the window. for Total number of call attempts within the window For a single call Response time for Valid call count within the window Indicates the first The weighting coefficients for anomaly patterns (determined by the impact of historical failures), for The first in the window Frequency of occurrence of exception patterns.

[0052] In this embodiment, assuming that the MessageBox interface was called 200 times in the past hour, with 190 successful calls, then... If the average response time is 150ms and the call times out 10 times, then interface optimization needs to be triggered.

[0053] Then, time series analysis is used to predict the interface status, implemented using the ARIMA model, as shown below: , in, for Interface performance metrics at any given time and All of these are model parameters, among which, This represents the autoregressive coefficient (AR term). This represents the moving average coefficient (MA term). Represents the white noise term. Let the order be the autoregressive order. This represents the order of the moving average.

[0054] The prediction confidence interval is expressed as follows: , in, Indicates advance The predicted interface performance value of the step. This represents the quantile of the standard normal distribution. At the significance level, This represents the variance of the predicted values. To predict the step size.

[0055] This model integrates historical patterns with random disturbances to accurately predict performance inflection points, quantify and predict risks, and enable low-resource deployment in interface operations and maintenance.

[0056] Then, based on the above prediction results, adaptive repair is implemented, including: The system identifies abnormal patterns in the number of timeouts and failures of API calls, uses an autoregressive integral moving average model to predict the trend of API performance degradation, and selects the optimal repair scheme for adaptive repair based on the principle of maximizing the difference between utility value and cost.

[0057] In the aforementioned repair scheme, the utility value calculation specifically includes: The utility value is calculated by combining the expected benefit of the probability of successful repair with the risk loss of the probability of failure, where the probability of successful repair is generated based on a logistic regression model of historical repair records.

[0058] Specifically, the overall repair decision function is expressed as follows: , in, This represents the utility value of the repair action. This indicates the cost of the repair action.

[0059] And among them, utility value The calculation formula is expressed as follows: , in, This indicates the probability of a successful repair operation. This indicates the positive benefits resulting from a successful repair. This indicates the probability that the repair operation failed. This indicates a negative penalty resulting from a failed repair.

[0060] The above algorithm formula, by quantifying the balance between the benefits of repair and the execution costs, aims to avoid over-repair, prevent under-repair, and balance technical effectiveness with business continuity in interface operation and maintenance.

[0061] Next, the audio device intelligent initialization in step S5 is performed, which further includes: Comprehensive testing of the hardware capabilities of audio devices, including sampling rate support range, bit depth, channel configuration, and latency characteristics; Based on the hardware capabilities of the audio device, the optimal parameter combination is solved with the goal of minimizing the weighted cost function consisting of audio latency, sound quality score, and stability score, while satisfying the device compatibility threshold.

[0062] First, achieve comprehensive audio device capability testing and device capability scoring. The calculation formula is as follows: , in, For the first Weighting coefficients of item characteristics Indicates the first Quantitative score (normalized value) of item characteristics. Key characteristics of audio devices, primarily the main detection characteristics. This includes sample rate support range, bit depth support, channel configuration, latency characteristics, and audio format compatibility.

[0063] This algorithm formula uses structured quantization to quantify the capability boundaries of audio devices, preventing parameter out-of-bounds configuration during debugging and initialization, while maximizing hardware performance utilization.

[0064] In this embodiment, the sound card was detected to support a 48kHz sampling rate (score=0.9), 24-bit bit depth (score=0.8), stereo (score=0.7), latency <10ms (score=0.95), and PCM format (score=1.0). Using equal weights, the following was obtained: .

[0065] Then, based on the equipment capabilities calculated above, the initialization parameters are automatically optimized. The formula for selecting the optimal parameters is as follows: , The constraints of this algorithm are: .

[0066] The objective function for overall performance optimization of parameters It is expressed as follows: , in, For the initialization parameter combination to be optimized, For the first Weighting coefficients of item characteristics Indicates a single parameter The cost function, Indicates parameter combination Compatibility rating with the device To constrain the preset threshold, Indicates parameters The corresponding delay, This indicates the audio quality score. Indicates the stability score. , and These are the weighting coefficients for the three indicators mentioned above.

[0067] This algorithm achieves zero out-of-bounds configuration, precise performance adaptation, and maximized resource efficiency in audio initialization through multi-objective optimization under constraints.

[0068] Furthermore, we implement machine learning-based audio device failure prediction, given the failure probability of the device under certain feature conditions. It is expressed as follows: , in, This represents the sigmoid activation function. This is a bias term (basic failure risk). For feature weights, For the first The feature extraction includes CPU utilization trends, memory usage patterns, audio buffer status, system temperature changes, and historical fault records.

[0069] This algorithm formula integrates real-time status with historical patterns to accurately quantify risks in fault prevention and allow for early intervention.

[0070] Finally, step S5, DHCP service optimization management, includes the dynamic management of the DHCP address allocation policy, which comprises: Based on the network topology complexity assessment results, the address allocation priority, address pool management and IP lease length are adaptively adjusted. When the complexity exceeds the preset threshold, the address reservation mechanism is activated and the IP lease of core devices is extended.

[0071] Specifically, intelligent DHCP address allocation begins with address allocation priority. The calculation is expressed as follows: , Secondly, there is address pool management, represented as follows: , Next is the rental period optimization, as shown below: , in, Historical usage frequency For equipment type, For physical location factors, , and These are the weighting coefficients for the three indicators mentioned above. express Number of addresses available at any given time This represents the total number of DHCP address pools. Number of allocated addresses (real-time statistics) To reserve the number of addresses, Indicates the duration of the device's IP lease. Basic lease term, The equipment reliability coefficient, This is the frequency coefficient.

[0072] The above algorithm formula, through multi-dimensional priority quantization and flexible lease mechanism, achieves critical device address protection, improves network resource utilization, and eliminates topology conflicts in the debugging network.

[0073] In this embodiment, a certain audio device has a high historical usage frequency ( The equipment type is core debugging equipment ( ), located in the main development area ( ). Using weights , , ,get The device will receive a higher address allocation priority.

[0074] Based on this, the DHCP configuration is dynamically adjusted according to the network topology, reducing topology complexity. The assessment is as follows: , in, Indicates the first The connectivity of each network node Let be the total number of connected edges in the network topology. Since each edge contributes 2 connectivity degrees, then... This represents the standardization factor.

[0075] When the system detects a change in network topology, it triggers a configuration update process: First, it calculates the current topology complexity (TC) value. If the TC exceeds a preset high threshold (indicating a highly complex network structure, such as a multi-level cascaded switch scenario), it executes a lease extension policy and enables address reservation to ensure the stability of core device IPs. Conversely, if the TC is below a preset low threshold (indicating a simple network structure, such as a single directly connected switch scenario), it shortens the IP lease duration and optimizes the address allocation range to improve address turnaround efficiency. This process continuously monitors and dynamically matches network configuration with topology status.

[0076] This algorithm enhances the stability of complex networks and maximizes the efficiency of simple networks through topological complexity quantification and a bimodal configuration strategy.

[0077] The method of this invention also includes SSL secure communication initialization. For scenarios requiring secure communication, the system implements dynamic SSL certificate management and key negotiation, specifically including: The encryption key is dynamically negotiated and its strength is jointly evaluated based on key length, random entropy quality, and effective duration factor. When the strength is lower than the security threshold, the key update process is triggered.

[0078] Key strength The evaluation formula is as follows: , in, The key length (in bits). The entropy factor is based on the quality of random number generation. The time factor takes into account the duration of key usage.

[0079] In this embodiment, we use a 2048-bit RSA key. The usage time is 30 minutes. ),but .

[0080] In summary, this invention provides an efficient, stable, and secure integrated environment for the development and debugging of wireless intelligent audio devices, significantly improving product development quality and efficiency. Specifically: through unified multi-protocol management and adaptive selection mechanisms, the system can automatically select the optimal communication path based on real-time network conditions, increasing communication success rate and shortening average connection establishment time compared to traditional single-protocol debugging methods; hot-plug detection and self-healing mechanisms significantly improve system stability; multi-level recovery strategies shorten system fault recovery time and improve debugging session continuity; the intelligent audio device initialization algorithm automatically optimizes parameter configuration based on device capabilities, reducing audio latency, improving sound quality scores, and lowering initialization failure rates; DHCP service optimization management improves IP address utilization and reduces network conflict rates through dynamic address allocation and network topology adaptation; the unified debugging environment reduces tool switching and configuration time, improving developer efficiency and shortening debugging task completion time; and the SSL communication security mechanism provides end-to-end encryption protection, improving the automation of key management and reducing security vulnerability risks.

[0081] On the other hand, this embodiment provides a multi-protocol debugging communication module initialization system, including: The multi-protocol topology scanning and evaluation module 10 is used to scan all connected debugging target devices, calculate the scanning priority of devices based on a weighted evaluation of signal strength, response time and protocol compatibility, and construct a protocol capability matrix for each device to quantify the degree of multi-protocol support. The adaptive communication channel management module 20 is used to pre-allocate communication channels according to the protocol capability matrix using an optimization algorithm, while monitoring network environment indicators and controlling the timing of dynamic protocol switching through fuzzy logic. The status monitoring and multi-level recovery module 30 is used to implement hot-plug detection and device health assessment. When a connection abnormality is detected, a multi-level recovery strategy is triggered step by step to handle the abnormality. The interface intelligent operation and maintenance module 40 is used to monitor the interface call success rate and response time, predict the interface status based on the time series analysis of historical data, and execute adaptive repair actions of the interface based on the repair effectiveness and cost assessment results. The collaborative optimization module 50 is used to comprehensively detect the hardware capabilities of audio devices, optimize initialization parameters based on device capability scores, and dynamically manage DHCP address allocation strategies by combining the device's historical usage frequency, type, and location factors.

[0082] It should be noted that the steps in the multi-protocol debugging communication module initialization method provided in this embodiment can be implemented based on the corresponding modules in the multi-protocol debugging communication module initialization system. Those skilled in the art can refer to the technical solution of the system to implement the steps of the method. That is, the embodiment in the system can be understood as a preferred example of implementing the method, and will not be elaborated here.

[0083] This embodiment also provides an electronic device, such as... Figure 3 As shown, the electronic device includes a processor 14 and a memory 13. The memory 13 stores machine-executable instructions that can be executed by the processor 14, which executes the machine-executable instructions to implement the above-described audio control method.

[0084] Furthermore, Figure 3 The electronic device shown also includes a bus 12 and a communication interface 11, with the processor 14, the communication interface 11 and the memory 13 connected via the bus 12.

[0085] The memory 13 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 11 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc. The bus 12 may be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0086] Processor 14 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 14 or by instructions in software form. Processor 14 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in this embodiment. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this embodiment can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 1001. Processor 1000 reads the information in memory 1001 and combines it with its hardware to complete the steps of the audio control method.

[0087] This disclosure also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, storing a computer program that, when run on a computer, causes the computer to perform the steps of an audio control method.

[0088] Finally, it should be noted that the above description is only a preferred embodiment of the present invention, and the scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be pointed out that for those skilled in the art, any improvements and modifications made without departing from the principle of the present invention should also be considered within the scope of protection of the present invention.

[0089] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered as being within the scope of this specification.

Claims

1. A method for initializing a multi-protocol debugging communication module, characterized in that, Includes the following steps: S1: Scan all connected debugging target devices, calculate the device scanning priority based on a weighted evaluation of signal strength, response time and protocol compatibility, and construct a protocol capability matrix for each device to quantify the degree of multi-protocol support; S2: Based on the protocol capability matrix, an optimization algorithm is used to pre-allocate communication channels, while monitoring network environment and device status indicators, and using fuzzy logic to control the dynamic decision-making timing of protocol switching; S3: Implement hot-plug detection and device health assessment. When a connection abnormality is detected, trigger a multi-level recovery strategy to handle the abnormality step by step. S4: Monitor the success rate and response time of interface calls, predict the interface status based on time series analysis of historical data, and execute adaptive repair actions for the interface based on the results of repair effectiveness and cost assessment. S5: Comprehensively detects the hardware capabilities of audio devices, optimizes initialization parameters based on device capability scores, and dynamically manages DHCP address allocation strategies by combining historical usage frequency, type, and location factors of the devices.

2. The multi-protocol debugging communication module initialization method according to claim 1, characterized in that, In step S1, the construction of the protocol capability matrix specifically includes: The device's basic compatibility Boolean value for each protocol, the quality of service quantification coefficient calculated based on measured bandwidth and latency, and the link stability coefficient evaluated by packet loss rate and latency jitter are weighted and linearly fused, and a protocol support score in the range of 0-1 is generated by normalization.

3. The multi-protocol debugging communication module initialization method according to claim 1, characterized in that, In step S2, the optimization algorithm for the pre-allocated communication channel specifically includes: Given the constraints of a single device being bound to a single protocol and the maximum concurrency of the protocol, we solve an integer programming problem with the objective of maximizing the sum of the protocol support scores of all devices.

4. The multi-protocol debugging communication module initialization method according to claim 1, characterized in that, In step S2, the step of controlling the switching timing of the dynamic decision protocol through fuzzy logic further includes: Real-time monitoring of network environment and device status indicators, including network congestion level, device load, signal quality, and protocol error rate; Preset threshold range for triggering protocol switching The environmental assessment value is calculated based on a weighted average of multiple environmental indicators: If the environmental assessment value is lower than the lower threshold The current agreement shall be maintained. If the environmental assessment value is higher than the upper threshold Initiate protocol switching; The environmental assessment value is located at Within the interval, optimize the execution protocol parameters.

5. The multi-protocol debugging communication module initialization method according to claim 1, characterized in that, In step S3, the equipment health assessment specifically includes: A health score is obtained by weighted summation of the device availability percentage, normalized response time coefficient, and reciprocal of error rate. When the health score is lower than the health threshold, an anomaly warning mechanism is activated, triggering the multi-level recovery strategy to handle the anomaly.

6. The multi-protocol debugging communication module initialization method according to claim 5, characterized in that, In step S3, the multi-level recovery strategy includes a soft reset operation, a backup protocol switching operation, and a device reconnection operation, wherein... The soft reset operation includes clearing the communication buffer, resetting the protocol state machine, and retransmitting the last frame data packet. The backup protocol switching operation attempts available protocols in descending order of protocol capability matrix scores. The device reconnection operation includes disconnecting all connections, rescanning the topology, and resuming the debugging session.

7. The multi-protocol debugging communication module initialization method according to claim 1, characterized in that, In step S4, the adaptive repair of the interface includes: The system identifies abnormal patterns in the number of timeouts and failures of API calls, uses an autoregressive integral moving average model to predict the trend of API performance degradation, and selects the optimal repair scheme for adaptive repair based on the principle of maximizing the difference between utility value and cost.

8. The multi-protocol debugging communication module initialization method according to claim 7, characterized in that, In the aforementioned repair scheme, the utility value calculation specifically includes: The utility value is calculated by combining the expected benefit of the probability of successful repair with the risk loss of the probability of failure, where the probability of successful repair is generated based on a logistic regression model of historical repair records.

9. The multi-protocol debugging communication module initialization method according to claim 1, characterized in that, In step S5, the optimization of the initialization parameters further includes: Comprehensive testing of the hardware capabilities of audio devices, including sampling rate support range, bit depth, channel configuration, and latency characteristics; Based on the hardware capabilities of the audio device, the optimal parameter combination is solved with the goal of minimizing the weighted cost function consisting of audio latency, sound quality score, and stability score, while satisfying the device compatibility threshold.

10. The multi-protocol debugging communication module initialization method according to claim 1, characterized in that, In step S5, the dynamic management of the DHCP address allocation policy includes: Based on the network topology complexity assessment results, the address allocation priority, address pool management and IP lease length are adaptively adjusted. When the complexity exceeds the preset threshold, the address reservation mechanism is activated and the IP lease of core devices is extended.

11. The multi-protocol debugging communication module initialization method according to claim 1, characterized in that, In step S3, the hot-plug detection employs an adaptive heartbeat mechanism, further including: The heartbeat interval is dynamically adjusted using a linear proportional function based on the real-time packet loss rate, and a maximum interval limit is set. At the same time, the standard variation of the device response time is detected based on the sliding window statistical method.

12. The multi-protocol debugging communication module initialization method according to claim 1, characterized in that, The method also includes SSL secure communication initialization, specifically including: The encryption key is dynamically negotiated and its strength is jointly evaluated based on key length, random entropy quality, and effective duration factor. When the strength is lower than the security threshold, the key update process is triggered.

13. A multi-protocol debugging communication module initialization system, characterized in that, include: The multi-protocol topology scanning and evaluation module is used to scan all connected debugging target devices. It calculates the scanning priority of devices based on a weighted evaluation of signal strength, response time and protocol compatibility, and constructs a protocol capability matrix for each device to quantify the degree of multi-protocol support. The adaptive communication channel management module is used to pre-allocate communication channels according to the protocol capability matrix using an optimization algorithm, while monitoring network environment and device status indicators, and dynamically deciding the timing of protocol switching through fuzzy logic control. The status monitoring and multi-level recovery module is used to implement hot-plug detection and device health assessment. When a connection abnormality is detected, a multi-level recovery strategy is triggered step by step to handle the abnormality. The interface intelligent operation and maintenance module is used to monitor the success rate and response time of interface calls, predict the status of interfaces based on time series analysis of historical data, and execute adaptive repair actions of interfaces based on the results of repair effectiveness and cost assessment. The collaborative optimization module is used to comprehensively detect the hardware capabilities of audio devices, optimize initialization parameters based on device capability scores, and dynamically manage DHCP address allocation strategies by combining the device's historical usage frequency, type, and location factors.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the multi-protocol debugging communication module initialization method as described in any one of claims 1-12.

15. An electronic device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the multi-protocol debug communication module initialization method as described in any one of claims 1-12.

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