Optimization method, system and equipment for audio production and measurement system and storage medium

Abstract

The invention discloses an optimization method, system and device for an audio production test system and a storage medium, and relates to the technical field of audio, and the method comprises the steps: building a multi-level code scanning data return stabilization mechanism, and guaranteeing the uploading reliability of code scanning data in a production test process through a local buffer queue, network state real-time evaluation and an intelligent retry strategy; constructing a multi-version application processor AP compiling adaptation framework, and automatically loading corresponding function libraries and compiling parameters through version automatic identification, compatibility index evaluation and dynamic link library management; a cross-platform compatible work order query interface is constructed, the work order query interface is dynamically rendered through a self-adaptive layout algorithm, a compiling adaptation frame suitable for different AP versions is adopted, dynamic link library management and automatic compiling parameter configuration are combined, the universality and expansibility of a tool are greatly improved, meanwhile, a cross-platform interface self-adaptive rendering technology is adopted, and the working efficiency is improved. Therefore, the work order query tool can provide consistent user experience under multiple operating systems.

Description

Technical Field

[0001] This invention relates to the field of audio technology, and in particular to optimization methods, systems, devices and storage media for audio production testing systems. Background Technology

[0002] With the rapid development of the wireless smart audio device market, the requirements for automation and stability in product production testing are increasing. Traditional production testing systems often employ a single data transmission mode, which is prone to failure when the network environment is unstable, leading to the need to restart the testing process. Furthermore, with the diversification of product hardware platforms, different versions of application processors (APs) have different requirements for the compilation environment. Traditional solutions require compiling testing tools separately for each version, increasing development and maintenance costs. In addition, the interface design of existing work order management systems is often optimized only for specific platforms, resulting in inconsistent display and poor user experience when used across platforms. These technical limitations seriously affect the production efficiency and quality control level of smart audio devices, necessitating a more stable and compatible production testing solution. Summary of the Invention

[0003] This invention provides an optimization method, system, device, and storage medium for audio production testing systems, overcoming the aforementioned shortcomings of traditional production testing systems. It introduces a multi-layered data feedback mechanism to ensure high reliability of data uploads even in complex network environments. It adopts a compilation adaptation framework suitable for different AP versions, combined with dynamic link library management and automatic compilation parameter configuration, greatly improving the tool's versatility and scalability. At the same time, it employs cross-platform interface adaptive rendering technology, enabling the work order query tool to provide a consistent user experience across multiple operating systems.

[0004] The first aspect of this invention provides an optimization method for an audio production testing system, comprising: Establish a multi-level stable mechanism for QR code data backhaul, and ensure the reliability of QR code data upload during production testing through local caching queues, real-time network status assessment, and intelligent retry strategies; A multi-version application processor (AP) compilation and adaptation framework is built, which automatically loads the corresponding function libraries and compilation parameters through automatic version identification, compatibility index evaluation, and dynamic link library management; a cross-platform compatible work order query interface is built, which dynamically renders the work order query interface through platform feature information acquisition, adaptive layout algorithm, and priority rendering mechanism to ensure cross-platform display consistency.

[0005] Preferably, the mechanism for establishing a multi-level barcode scanning data backhaul stability includes: Get the data generation rate, buffer time window, and single packet size parameters; A local data cache queue with a first-in-first-out (FIFO) structure is established based on the parameters to temporarily store the scanned data to be uploaded, ensuring the timeliness of data processing.

[0006] Preferably, the mechanism for establishing a stable multi-level barcode scanning data feedback also includes: By performing network ping tests and bandwidth checks, the current network packet loss rate, response time, current bandwidth, and related network parameters can be obtained. Based on the weighted calculation of the network parameters, the network quality is evaluated in real time and a network quality score is generated.

[0007] Preferably, the mechanism for establishing a stable multi-level barcode scanning data feedback also includes: When data upload fails, the next retry interval is dynamically calculated based on the current number of retries and the network quality score, and the corresponding retry strategy is executed. The mechanism of resuming interrupted transmission records the transmitted position and resumes transmission from the interrupted position during the next upload. The retry strategy includes dynamic adjustment of the number of retries, the retry interval, and the retry priority.

[0008] Preferably, the framework for building a multi-version application processor (AP) compilation and adaptation system includes: Read the system characteristic files and environment variables of the current device to identify several key characteristics of the application processor; Based on the preset weights and compatibility scores of each key feature, the compatibility index of the current AP version is calculated.

[0009] Preferably, the framework for building a multi-version application processor (AP) compilation and adaptation system also includes: Based on the identified AP version information, the loading priority of dynamic link libraries is determined by a weighted calculation of version matching degree, stability score and performance score, and the function library with the highest priority is loaded; according to the compatibility index and version information, the corresponding compilation parameters and linking options are automatically configured.

[0010] Preferably, the construction of a cross-platform compatible work order query interface includes: Call the system API to obtain the characteristic information of the running platform, which includes at least the operating system type, screen resolution and DPI value; The display adaptation coefficient is calculated based on the feature information; the position and size of the interface elements are dynamically calculated and adjusted based on the adaptive layout algorithm of the elastic box model; a priority-based rendering queue is constructed to prioritize the rendering of the interface areas that the user is interested in.

[0011] Preferably, the adaptive layout algorithm based on the flexible box model includes: The adjusted size is calculated based on the base size of the interface element, the scaling factor determined by the display adaptation coefficient, and the content density coefficient; the adjusted size is determined by the joint calculation of the base size, the scaling factor, and the compensation term based on the content density coefficient.

[0012] Preferably, the construction of the priority-based rendering queue includes: Rendering priority is calculated based on a weighted value of user attention, update frequency, and importance level of the interface area; Rendering queues are constructed based on the stated priorities to optimize rendering performance.

[0013] As a preferred option, the integrated data processing workflow optimization step is also included: Establish an end-to-end data processing pipeline from QR code data collection to final upload; monitor data processing throughput based on formulas for data volume, efficiency coefficient, and processing time; perform automatic fault recovery based on anomaly detection and automatic recovery mechanisms, and predict the recovery success rate.

[0014] Preferably, the anomaly detection and automatic recovery mechanism includes: Based on the severity of the error and the number of recovery attempts, the recovery success rate is predicted and the selection of recovery strategies is guided; based on the real-time performance monitoring mechanism, the system load is monitored in real time, and resource allocation is dynamically adjusted according to the difference between the target utilization and the current load.

[0015] As a preferred option, it also includes quality control and testing verification steps: building a multi-dimensional quality assessment system covering functionality, stability, compatibility and performance; The comprehensive quality index is derived by weighting the weights of each dimension, the quality score, and the reliability coefficient. Test coverage is calculated based on the ratio of tested paths to total paths and branch coverage. Deployment success rate is predicted based on the basic success rate, change complexity, and test coverage, using version-controlled continuous integration and automatic deployment mechanisms.

[0016] A second aspect of the present invention provides an optimization system for an audio production testing system, comprising: The data feedback module is used to establish a multi-level stable mechanism for scanning data feedback. It ensures the reliability of scanning data upload during production testing through local cache queues, real-time network status evaluation, and intelligent retry strategies. The version adaptation module is used to build a multi-version application processor (AP) compilation adaptation framework. Through automatic version identification, compatibility index evaluation, and dynamic link library management, it automatically loads the corresponding function libraries and compilation parameters. The interface rendering module is used to build a cross-platform compatible work order query interface. Through platform feature information acquisition, adaptive layout algorithm, and priority rendering mechanism, it dynamically renders the work order query interface to ensure cross-platform display consistency.

[0017] A third aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the above-described optimization method for an audio production and testing system.

[0018] A fourth aspect of the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the above-described optimization method for an audio production and testing system.

[0019] This invention proposes an optimization method for audio production testing systems. Addressing issues such as unstable QR code data uploads, difficulties in compiling and adapting multiple AP versions, and poor cross-platform compatibility of work order interfaces during the production testing of wireless smart audio devices, this invention proposes a multi-dimensional and systematic optimization design. A multi-layered data return mechanism is introduced, including local caching, intelligent retries, and breakpoint resumption technologies, ensuring high reliability of data uploads even in complex network environments. A compilation adaptation framework suitable for different AP versions is designed, combining dynamic link library management and automatic compilation parameter configuration to greatly improve the tool's versatility and scalability. Simultaneously, cross-platform interface adaptive rendering technology is adopted, enabling the work order query tool to provide a consistent user experience across multiple operating systems. Compared to traditional technologies, the overall solution demonstrates significant advantages in stability, flexibility, and user-friendliness. For the first time, multiple quantitative mechanisms, such as network scoring calculation, retry interval dynamic adjustment algorithm, AP compatibility index evaluation, and interface adaptation factor modeling, are systematically integrated during the QR code data upload process, and their formulaic descriptions provide a theoretical basis for subsequent algorithm optimization. Especially in the multi-version compatibility framework and display adaptive mechanism, a unified operating experience is achieved under different hardware platform environments through dynamic link priority sorting, adaptive layout adjustment, and rendering queue hierarchy. In addition, the construction of an integrated data processing pipeline and a multi-dimensional quality verification system enables the production testing tools to have self-repair, performance optimization, and quality closed-loop capabilities, comprehensively improving the system's industrial practicality and adaptability to large-scale deployment. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the optimization method for the audio production testing system in this embodiment; Figure 2 This is a flowchart illustrating the process of establishing a multi-level barcode scanning data backhaul stability mechanism in step S1 of this embodiment. Figure 3 This is a schematic diagram of one embodiment of the electronic device in this example. Detailed Implementation

[0021] This invention provides an optimization method, system, device, and storage medium for audio production testing systems. By using persistent audio capture sessions and dynamic audio processing pipelines, it effectively solves the problem of audio input players reopening capture when switching modes.

[0022] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0023] Example 1 For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 This invention provides an optimization method for an audio production testing system, comprising the following steps: Step S1: Establish a multi-layered stable mechanism for QR code data backhaul. This mechanism ensures the reliability of QR code data uploads during production testing through local caching queues, real-time network status assessment, and intelligent retry strategies. Step S1 aims to build a highly robust and reliable data upload channel, ensuring that every piece of QR code data (such as serial number, MAC address, etc.) bound to the audio device under test can be accurately, completely, and orderly backhauled to the central server in the complex network environment of the production line. Its core lies in "caching as a fallback, intelligent sensing, and elastic retry," forming a full-link guarantee system from data generation to final confirmation. This mechanism specifically includes the following three tightly coupled sub-steps: First, such as Figure 2 As shown, step 100: Establish a local cache queue. Its core objective is to build a reliable data buffer at the data generation end (such as a production line industrial control computer or barcode scanner terminal), decoupling data collection from data uploading, and ensuring that even in extreme cases where the network is completely unavailable or the local application crashes, the collected critical data will not be lost. Specifically, it includes the following steps: Step 101: Obtain the data generation rate, buffer time window, and single packet size parameters; Step 102: Based on the parameters, establish a local data cache queue with a first-in-first-out (FIFO) structure to temporarily store barcode scanning data to be uploaded, ensuring the timeliness of data processing. This queue, located at the data generation end (e.g., the barcode scanner), serves as the first line of defense for data upload, temporarily storing barcode scanning data that cannot be uploaded immediately due to network issues. When the barcode scanner collects the device serial number, the data is not immediately uploaded but is first stored in a local, persistent cache queue (e.g., an SQLite database or file queue). This queue ensures that even if the industrial control computer suddenly loses power or the application crashes, the collected data will not be lost. Because the queue data is written to non-volatile storage media (e.g., hard disk or flash memory), it can resist data loss caused by sudden power outages, program crashes, or human error. When the system restarts, the production testing tool will first check the local cache queue. If there is unuploaded data, it will automatically start the resume upload process, ensuring that data is delivered at least once. Barcode scanning operations on the production line may occur intermittently or in batches. The buffer queue acts as a "reservoir," smoothing the data flow and preventing a surge of data packets from overwhelming the network and server. Simultaneously, the FIFO structure strictly guarantees the timing of data uploads, ensuring that the order of data received by the server matches the physical production order on the production line. This is crucial for subsequent production data analysis and traceability.

[0024] Furthermore, to avoid the queue growing indefinitely and consuming excessive system resources, this embodiment of the invention proposes a dynamic queue capacity calculation method that adaptively configures the queue based on the actual operating conditions of the production line: QueueSize=(DataRate×BufferTime) / DataPacketSize; Among them, QueueSize is the queue capacity, such as the number of data packets; DataRate is the data generation rate (unit: bytes / second), which can be obtained through historical data statistics; BufferTime is the expected buffer time window (unit: seconds), that is, the longest network interruption duration that the system needs to independently cope with. This is a configurable business parameter; and DataPacketSize is the size of a single data packet (unit: bytes / packet).

[0025] Example: Assume an audio production line generates barcode scanning data at a rate of 1000 bytes / s. To handle a network interruption of up to 30 seconds, the buffering window is set to 30 seconds, and the size of a single data packet is 200 bytes. The queue capacity is then: QueueSize = (1000 × 30) / 200 = 150. This design ensures that data is not lost during a 30-second network interruption and maintains its original sequence. All 150 generated data entries are safely buffered and uploaded in order after network recovery, thus guaranteeing business continuity.

[0026] Secondly, step 110: Real-time network status assessment. Before attempting to upload data, the system proactively probes the network quality. Its core objective is to conduct a precise and quantitative assessment of the current network health status before initiating data upload, thereby providing a basis for decision-making on "when to upload" and "how to upload," avoiding invalid transmission attempts under poor network conditions, and conserving system resources.

[0027] The specific implementation process is as follows: Step 111: Obtain key parameters such as packet loss rate, response time, and current bandwidth by performing ping tests and bandwidth checks; The system runs a separate network monitoring thread or service in the background, which actively performs network probing tasks at fixed intervals (e.g., every 5 seconds). Probing methods include, but are not limited to: Connectivity and latency testing: Send ICMP (Ping) requests to the gateway, DNS server, or target data server to obtain response time and packet loss rate.

[0028] Bandwidth test: This test calculates the current available bandwidth by sending or receiving a small, known-sized piece of test data to or from the server. The score takes into account network latency, packet loss rate, and bandwidth.

[0029] Step 112: Based on the weighted calculation of the network parameters, evaluate the network quality in real time and generate a network quality score.

[0030] Based on the detection results, the system uses a weighted scoring model to integrate multi-dimensional network indicators into a quantitative network quality score. The calculation formula is as follows: The network quality score is calculated based on the weighted formula: NetworkScore=α×(1-PacketLoss)+β×(1 / ResponseTime)+γ×(Bandwidth / MaxBandwidth), The system uses NetworkScore, a normalized value between 0 and 1, to represent network quality. A higher score indicates better network performance. PacketLoss is the packet loss rate (0-1), ResponseTime is the response time, Bandwidth is the current bandwidth, MaxBandwidth is the maximum bandwidth, and α, β, and γ are preset weights, with α+β+γ=1. These weights can be configured based on the sensitivity of different business scenarios to network metrics. For example, for scenarios with high real-time requirements, the weight of β can be increased. The system presets an upload threshold (e.g., Threshold=0.5). The data upload module is only activated when the NetworkScore is higher than this threshold, retrieving data from the local cache queue for transmission. If the score is lower than the threshold, the system enters a "wait-reassess" loop, continuously monitoring the network status until it recovers.

[0031] Example: Weights are set as follows: α = 0.4 (maximizing stability), β = 0.3 (considering real-time performance), γ = 0.3 (considering throughput). A packet loss rate of 0.02 was detected, the response time was 50ms, the current bandwidth was 10Mbps, and the maximum bandwidth was 100Mbps. Therefore, the network score is: NetworkScore = 0.4 × (1 - 0.02) + 0.3 × (1 / 50) + 0.3 × (10 / 100) = 0.428. This score quantifies the current network capacity, transforming the ambiguous network condition into a precise numerical value, providing a basis for subsequent intelligent decision-making, and greatly improving transmission efficiency and success rate.

[0032] Finally, step 120: Intelligent retry and breakpoint resumption strategy. Even after network assessment, data upload may still fail due to momentary network jitter. This strategy aims to address such occasional failures by using an intelligent retry mechanism to ensure successful data upload while avoiding secondary impact on the network. The specific implementation process is as follows: Step 121: When data upload fails, dynamically calculate the next retry interval based on the current number of retries and network quality score, and execute the corresponding retry strategy; Step 122: Record the transmitted position based on the breakpoint resume mechanism, and continue the transmission from the breakpoint during the next upload; wherein, the retry strategy includes dynamic adjustment of the number of retries, the retry interval, and the retry priority.

[0033] When data upload fails, the system does not blindly retry. Instead, it uses an exponential backoff algorithm to dynamically adjust the retry interval. This involves dynamically calculating the next retry interval based on the current number of retries and the network quality score to avoid impacting the network: RetryInterval = BaseInterval × (2^(RetryCount-1)) × (2-NetworkScore), where RetryInterval is the retry interval, BaseInterval is the base retry interval, RetryCount is the current number of retries, and NetworkScore is the network quality score. This avoids repeatedly impacting the network in a short period, reducing the probability of consecutive failures due to momentary congestion. This mechanism ensures that data is eventually uploaded successfully while avoiding unnecessary network impact. Furthermore, for larger data packets (such as files containing test logs), the system implements a resume function. Before uploading, the system obtains the file size and records the number of bytes successfully sent during the upload process. If the upload is interrupted, the next retry will query the server for the number of bytes received and resume uploading from that point, rather than starting from the beginning, thus saving significant network resources and time.

[0034] Example: The basic retry interval is 2 seconds. This is the 3rd retry, and the network score is 0.6. Therefore, the retry interval is: RetryInterval = 2 × (2^(3-1)) × (2-0.6) = 11.2 seconds. This strategy combines exponential backoff with network awareness, effectively improving the retry success rate. Simultaneously, the system records breakpoints in large file uploads and resumes the upload from the breakpoint after network recovery, ensuring the integrity of large data packets.

[0035] Through the aforementioned multi-layered caching queues, retry mechanisms, and breakpoint resumption, the complete archiving of each scanned record is ensured. Even with network fluctuations, local caching provides a backup, and automatic retransmission occurs upon recovery. Audio testing systems such as the SUBPro testing tool achieve a data upload success rate exceeding 98%, effectively reducing the number of retries caused by network instability.

[0036] Step S2: Build a multi-version application processor (AP) compilation adaptation framework. Through automatic version identification, compatibility index evaluation, and dynamic link library management, the corresponding function libraries and compilation parameters are automatically loaded. This step aims to solve the compatibility issues between production testing tools and different AP versions, realize the automated configuration of the compilation environment, and achieve the goal of one-time development and multi-platform compatibility. The core lies in automatic version identification, compatibility index evaluation, dynamic link library and parameter loading, thereby realizing the automated and intelligent configuration of the compilation environment and test logic.

[0037] Step S200: Version detection and compatibility index assessment. This step is the basis for the adaptation framework's perception and decision-making. Its task is to accurately identify the AP version of the current device under test and quantitatively assess its compatibility with the production testing tools.

[0038] First, automatic version identification is performed: the system characteristic files and environment variables of the current device are read to identify several key characteristics of the application processor. After establishing a physical or communication connection (such as USB or UART) with the audio device under test, the production testing system will actively query key device information through a standardized protocol. This protocol can be a general device debugging protocol, such as Android Fastboot or ADBshell, or a device manufacturer's custom AT command set. The query command aims to obtain the device's system characteristic files (such as / proc / version and / proc / cpuinfo in Linux systems) and key environment variables (such as ro.product.model and ro.build.version.release), from which multiple key characteristics such as the AP version number, chip model, CPU architecture, kernel version, and driver version are parsed.

[0039] Based on the preset weights and compatibility scores of each key feature, the compatibility index of the current AP version is calculated. After acquiring the key features, the system enters the compatibility index evaluation phase. The production testing tool has a pre-built compatibility knowledge base, which records all historically verified AP versions and their compatibility with various functional modules of the production testing tool in the form of structured data (such as a database or configuration files). When a new or unknown AP version is identified, the system does not directly report an error, but instead initiates the compatibility evaluation algorithm to calculate its compatibility index with the existing functional library.

[0040] This index can be calculated by weighting multiple factors such as version number differences, number of API interface changes, and historical failure rate.

[0041] As shown above, the system identifies key characteristics of the AP, such as its CPU architecture, kernel version, and driver version, by reading the device's system characteristic files (e.g., / proc / cpuinfo) and environment variables. Subsequently, it calculates the compatibility index between the current AP version and the production testing tools using a weighted formula. CompatibilityIndex = Σ(Wi×Fi) / ΣWi, where CompatibilityIndex is the compatibility index, a value between 0 and 1, with values ​​closer to 1 indicating better compatibility. Wi is the weight of the i-th key feature, representing its importance in the overall compatibility assessment. For example, the kernel version may have a higher weight than the driver minor version number. Fi is the compatibility score (0-1) of the i-th key feature, which can be obtained by querying a knowledge base. If it is a new feature, it can be estimated based on its similarity to known features (such as version number differences). An automatic version detection mechanism is designed to identify the current AP version by reading system feature files and environment variables.

[0042] Example: When the system identifies five key features with weights of 0.3 (kernel version), 0.25 (chip model), 0.2 (driver version), 0.15 (CPU architecture), and 0.1 (build date), the compatibility scores for each feature are 0.9, 0.8, 0.7, 0.9, and 0.6, respectively, obtained through knowledge base queries or similarity calculations. The compatibility index is then: CompatibilityIndex = (0.3 × 0.9 + 0.25 × 0.8 + ...) = 0.795. This quantitative index of 0.795 provides an objective and accurate basis for decision-making regarding whether a specific adaptation module needs to be loaded, and which module to load, avoiding the ambiguity and uncertainty of traditional solutions that rely on manual experience or simple rule matching.

[0043] Step S210: Loading Dynamic Link Libraries and Compilation Parameters. After completing the compatibility assessment, this step intelligently selects the optimal function library from the preset function library pool based on the assessment results and configures the corresponding compilation parameters to achieve "plug-and-play" support for the AP version. Specifically, it includes the following steps: The production testing tool internally maintains a pool of function libraries containing multiple dynamically linked libraries compiled and optimized for different AP versions or feature combinations. Each library comes with its metadata, including: the applicable AP version range, historical stability scores, and performance scores on standard test items.

[0044] Based on the identified AP version information, the system determines the loading priority of dynamic link libraries (DLLs) using a weighted calculation of version matching, stability score, and performance score. The highest-priority DLL is then loaded. Essentially, the system has a built-in DLL repository containing DLLs optimized for different AP versions. Once the AP version is identified, the system calculates the loading priority of each candidate library based on version matching, stability score, and performance score. Dynamic loading: Based on the evaluation results, the system selects the optimal DLL from the DLL pool for loading. If a perfectly matching library exists, it is loaded directly; otherwise, the library with the highest CompatibilityIndex is selected, and preset compatibility patch parameters are loaded. Simultaneously, compilation parameters (such as compiler optimization level and macro definitions) are dynamically adjusted according to the AP version to ensure that the generated test instructions can be correctly executed by the device.

[0045] In various embodiments of this application, the system calculates a loading priority for each candidate dynamic link library in the pool based on the AP version information identified in step S201. The formula for calculating the loading priority is: LoadPriority = VersionMatch × 0.6 + StabilityScore × 0.3 + PerformanceScore × 0.1, where LoadPriority is the loading priority; a higher value indicates better overall performance of the library and that it should be loaded first. VersionMatch is the version matching degree between the candidate functional module and the current application processor version, which can be directly calculated from the version number difference. StabilityScore is the stability score of the candidate functional module, based on statistics such as the library's crash rate and abnormal exit rate during historical operation. PerformanceScore is the performance score of the candidate functional module, based on the library's average execution time and resource utilization when executing standard test tasks. The system selects the functional library with the highest loading priority and automatically configures the corresponding compilation parameters and linking options, thereby achieving "plug-and-play" support for different AP versions.

[0046] During implementation, the dynamic library was identified as having a version matching score of 0.95, a stability score of 0.88, and a performance score of 0.92. Therefore, its loading priority is calculated as: LoadPriority = 0.95 × 0.6 + 0.88 × 0.3 + 0.92 × 0.1 = 0.57 + 0.264 + 0.092 = 0.926. Although the dynamic library has a version matching score of 0.95, if its stability score is only 0.88, its overall priority will be surpassed by other more stable modules, avoiding potential problems caused by solely pursuing the newest version. Traditional solutions rely solely on version numbers or simple rules to select functional libraries, easily overlooking actual runtime performance. This solution comprehensively considers the following three-dimensional weighted model: Version matching score: ensuring interface compatibility and basic functional availability; Stability score: reflecting long-term operational reliability (e.g., memory leaks, crash rates); Performance score: measuring execution efficiency (e.g., latency, throughput); only the most important functional modules with the highest priority are loaded, avoiding redundant code residing in memory.

[0047] Step S220: Based on the compatibility index and version information, automatically configure the corresponding compilation parameters and linking options. After determining the optimal function library, the system does not simply load the library, but will further automatically configure the corresponding compilation parameters and linking options based on the compatibility index and version information.

[0048] For example, some older AP versions may need to disable certain advanced compiler optimizations (such as downgrading from -O3 to -O2) to avoid runtime errors caused by hardware-unsupported instruction sets. For certain specific versions, it may be necessary to define specific macros (such as -DLEGACY_MODE=1) to enable compatibility code branches.

[0049] To achieve more refined parameter tuning, this invention can also introduce a calculation model for parameter optimization weights to guide the direction of compiler parameter adjustments: OptimalWeight = (TargetPerformance - CurrentPerformance) / (MaxPerformance - MinPerformance). Where: OptimalWeight: Optimization weight, between 0 and 1, used to measure the gap between the current performance and the target performance; TargetPerformance: The expected performance target value on the current AP version; CurrentPerformance: The actual performance value of the current AP version when using the default parameter configuration; MaxPerformance / MinPerformance: The theoretical upper / lower limits of the performance that this AP version can achieve.

[0050] In implementation, assuming a target performance of 90, current performance of 75, maximum performance of 100, and minimum performance of 50 in a certain test project, the optimization weight is: OptimalWeight = (90-75) / (100-50) = 15 / 50 = 0.3. This weight of 0.3 indicates that the current performance has a moderate gap from the target. Based on this weight, the system can select a moderate-intensity optimization scheme (e.g., enabling loop unrolling, adjusting memory alignment, etc.) from a preset parameter adjustment strategy table, and recompile or dynamically adjust the test instructions to approach the performance target as close as possible while ensuring stability. This multi-version adaptation framework achieves one-time development for multi-platform compatibility, reducing repetitive compilation work by 85%, improving development efficiency, and ensuring functional consistency across different AP versions. The multi-version compilation and adaptation mechanism allows a single tool version to support 5-8 different AP versions, significantly reducing maintenance costs. Step S3: Construct a cross-platform compatible work order query interface. Through platform feature information acquisition, adaptive layout algorithms, and priority rendering mechanisms, the work order query interface is dynamically rendered to ensure consistent display across platforms. This step aims to solve the key technical challenge of inconsistent interface display and user experience in traditional production testing tools across different operating systems, resolutions, and DPI hardware platforms. By introducing an intelligent adaptive rendering mechanism, a consistent, efficient, and high-quality visual and operational experience is ensured on any terminal device, thereby reducing operational errors and efficiency losses caused by interface adaptation issues.

[0051] Specifically, this step dynamically calculates and renders the interface through three core modules: platform feature information acquisition, adaptive layout algorithm, and priority rendering mechanism. The technical solution is detailed below: Step S300: Platform feature acquisition and display adaptation coefficient calculation, specifically including the following steps: Call the system API to obtain the characteristic information of the running platform, which includes at least the operating system type, screen resolution and DPI value; The calculation of the display adaptation coefficient based on the aforementioned feature information can be understood as follows: during interface initialization, the system API is first called to obtain the operating system type, screen resolution, and DPI value. Then, a comprehensive display adaptation coefficient is calculated based on this information. The interface adopts a responsive design, the core of which is the layout engine. During interface initialization, the system first obtains three key feature information of the running platform by calling the native system APIs of each platform (e.g., GetDpiForWindow for Windows, DisplayMetrics for Android, X11 extensions for Linux, etc.): operating system type, screen resolution, and DPI (dots per inch) value.

[0052] Traditional solutions often rely on scaling based on a single metric, such as resolution or DPI, which has significant drawbacks: for high-DPI, low-resolution devices (such as some older tablets), simply scaling by DPI will cause interface elements to exceed the screen's boundaries; while for low-DPI, high-resolution devices (such as large-screen TVs), stretching by resolution alone will result in blurry text and icons.

[0053] To overcome the aforementioned shortcomings, this invention proposes a method for calculating a two-dimensional comprehensive adaptation coefficient. This method treats the DPI ratio and resolution ratio as a two-dimensional vector, and calculates its Euclidean norm to comprehensively evaluate the display characteristics of the device, thereby achieving bidirectional constraints on sharpness and layout integrity. The calculation formula is as follows: AdaptCoeff = √((ActualDPI / StandardDPI)) 2 +(ActualRes / StandardRes) 2 = ×PlatformFactor, where AdaptCoeff is the display adaptation coefficient, which will be used as a global scaling factor, ActualDPI is the actual DPI value, StandardDPI is the preset standard DPI value (usually 96, representing the traditional PC monitor standard), ActualRes is the actual resolution (can be the pixel value of the long side or the short side, or the diagonal pixel value; in this embodiment, the long side value is 1920), StandardRes is the standard resolution (corresponding to the standard DPI; in this embodiment, it is 1366), and PlatformFactor is a calibration factor for different operating systems, used to eliminate style drift caused by differences in the rendering engine of the operating system (such as GDI / DirectWrite for Windows and GTK / Qt for Linux). For example, it can be set to 1.0 for the Windows platform, 1.1 for the Android platform, and 1.2 for the Linux platform. Example: The actual DPI is 144, the standard DPI is 96, the actual resolution is 1920×1080, the standard resolution is 1366×768, and the platform factor is 1.2. Therefore, the adaptation coefficient is: AdaptCoeff≈2.96. This coefficient, as a global scaling factor, ensures that the physical appearance of interface elements on different screens is consistent. In this case, the actual DPI (144) far exceeds the standard value (96), but through joint calculation with the resolution ratio, the final adaptation coefficient of 2.96 both magnifies the details without over-expanding, perfectly balancing clarity and layout integrity. Traditional solutions rely solely on resolution or DPI for scaling, which can easily lead to the following problems: High DPI low resolution devices (such as older monitors): simply scaling by DPI will cause the interface to exceed the screen area; Low DPI high resolution devices (such as ultrawide screens): stretching only by resolution will cause text to become blurry. This solution innovatively incorporates the DPI ratio and resolution ratio into a two-dimensional vector space, and calculates the adaptation coefficient using the Euclidean norm to achieve: bidirectional constraints: ensuring pixel density matching (sharp and clear) while controlling the layout range (complete display); nonlinear mapping: using progressive scaling for extreme devices such as 4K ultra-high definition to avoid abrupt changes. A Platform Factor is introduced to perform secondary calibration for different operating systems, eliminating cross-platform style drift. The advantage of this calculation method is that it is not a simple linear superposition, but rather uses nonlinear mapping to progressively scale for extreme devices such as high-resolution screens, avoiding abrupt changes in interface elements. In the case study, the actual DPI (144) far exceeded the standard value, but through joint calculation with the resolution ratio, the final adaptation coefficient ensured both the sharpness of the interface elements and controlled the overall layout range, allowing it to be fully displayed on the screen, perfectly balancing clarity and layout integrity.

[0054] Step S310: The adaptive layout algorithm based on the flexible box model dynamically calculates and adjusts the position and size of interface elements. The system adopts the mature flexible box model in the industry as the layout basis, and combines it with the dynamic size calculation formula of this invention to achieve fine-grained control of interface elements. Specifically, it includes: calculating the adjusted size based on the basic size of the interface element, the scaling factor determined by the display adaptation coefficient, and the content density coefficient; the adjusted size is determined by the joint calculation of the basic size, the scaling factor, and the compensation term based on the content density coefficient.

[0055] For each control on the interface (such as buttons, text boxes, table cells, etc.), its final display size AdjustedSize is not a simple BaseSize × AdaptCoeff. Because this purely linear scaling may result in text icons being too small on high-density screens, and may produce too much redundant white space on large-size screens.

[0056] To address this, embodiments of the present invention introduce a content density coefficient for secondary compensation. The content density coefficient, ContentDensity, is a preset value (between 0 and 1) related to the control type, which characterizes the complexity of the information within the control. For example, the ContentDensity of a purely numeric display box might be 0.1, while the ContentDensity of a complex panel containing multiple lines of text and charts might be 0.9. The calculation relationship for adjusting the size is as follows: the product of the base size and the scaling factor, multiplied by a dynamic coefficient composed of a weighted sum of content density coefficients. The formula for calculating the adjusted size is: AdjustedSize = BaseSize × ScaleFactor × (1 + ContentDensity × 0.1), where AdjustedSize is the adjusted size (e.g., width, height, font size), BaseSize is the base size of the control under the design baseline (standard DPI and resolution), ScaleFactor is the scaling factor (or AdaptCoeff calculated in step S301), ContentDensity is the content density coefficient of the control, and 0.1 is an empirical adjustment coefficient used to control the intensity of compensation and avoid over-adjustment. This algorithm ensures that the layout remains consistent and readable at different scaling levels. The size, spacing, and font size of all controls on the interface (e.g., buttons, text boxes, tables) are scaled proportionally according to AdaptCoeff. For complex layouts, the system will use a fluid layout or grid layout to ensure that controls can automatically wrap or adjust their arrangement according to the screen space, avoiding content overflow or incomplete display.

[0057] In implementation, with a base size of 100px, a scaling factor of 1.5, and a content density factor of 0.8, the adjusted size is: AdjustedSize = 100 × 1.5 × (1 + 0.8 × 0.1) = 100 × 1.5 × 1.08 = 162px. In this case, the 100px base control should have been 150px after being scaled by 1.5, but it was increased to 162px through +8% content density compensation. This avoids both crowding on high-density screens and a loose feel on large screens. Traditional solutions rely solely on simple proportional scaling (such as BaseSize × ScaleFactor), which cannot address the following contradictions: High-density screens (such as Retina screens): pure scaling results in text / icons being too small; Large-size screens (such as large TV screens): excessive enlargement creates redundant blank spaces; Irregular screen ratios (such as ultrawide screens): forcibly stretching and distorting the UI structure. This solution achieves precise control through a triple correction mechanism: Base Scaling (BaseSize×ScaleFactor): matches the device's physical pixel density; Content Density Compensation (ContentDensity×0.1): dynamically fine-tunes based on content complexity; Flexible Box Constraint: ensures that the spacing between elements moves with the size, maintaining visual balance.

[0058] Step S320: To optimize performance, the system does not render all interface elements at once. Instead, it constructs a priority-based rendering queue, prioritizing the rendering of interface areas of interest to the user. This abandons the traditional approach of rendering all interface elements at once, instead rendering them in batches and with emphasis based on the characteristics of each element. Constructing the priority-based rendering queue includes: calculating rendering priority based on a weighted value of user attention, update frequency, and importance level of the interface area; and building the rendering queue according to the priority to optimize rendering performance. To ensure interface smoothness, the rendering tasks of interface elements are divided into high, medium, and low priority queues. Core operation buttons and real-time data display (such as test status) are placed in the high-priority queue and rendered first; while static prompts, historical records, etc., are placed in the low-priority queue and rendered when the system is idle, thus ensuring immediate response to critical operations.

[0059] The system divides the interface into multiple logical areas (such as the title bar, query button area, work order list area, details panel area, status bar, etc.). Then, it calculates a rendering priority (RenderPriority) for each area. This priority is a comprehensive evaluation value, calculated by weighting three key factors: RenderPriority=UserFocus×0.5+UpdateFreq×0.3+Importance×0.2, The system prioritizes rendering based on the following criteria: RenderPriority (0-1), with a value between 0 and 1; higher values ​​indicate higher priority. UserFocus (0-1) is estimated using eye-tracking models, historical click heatmaps, or preset rules (e.g., higher attention in the central screen area). UpdateFreq (0-1) indicates the frequency of dynamic changes in the content of that area, such as high frequency in real-time test status areas and low frequency in static help text. Importance (0-1) is preset by business logic; for example, error messages and key operation buttons are highly important, while decorative icons are less important. The system prioritizes rendering areas with high user attention, frequent updates, or high importance (such as real-time test results and error messages) to improve the perceived smoothness for the user.

[0060] During implementation, the user focus of the identified interface area was 0.9, the update frequency was 0.6, and the importance level was 0.8. Therefore, the rendering priority was: RenderPriority = 0.9 × 0.5 + 0.6 × 0.3 + 0.8 × 0.2 = 0.45 + 0.18 + 0.16 = 0.79. In the formula, UserFocus (user focus) has the highest weight (0.5), ensuring that system resources are always prioritized for the area where the user's gaze is focused, directly improving the perceived system smoothness. Through the UpdateFreq (update frequency) and Importance (importance level) parameters, the system can intelligently distinguish between dynamic and static content. High-frequency updated data areas (such as real-time updated test logs) and important warning messages are processed first, while static text is rendered later, thereby improving the overall throughput efficiency of the rendering pipeline. The Importance (importance level) parameter ensures the weighting of key business elements. For example, an error pop-up with an importance of 0.9 can receive a high rendering priority even if its user attention and update frequency are temporarily low, ensuring it is processed immediately. The rendering priority model described above optimizes different types of interfaces by adjusting parameters without rewriting core logic. The cross-platform display optimization system ensures a consistent user experience across different devices, reducing platform adaptation workload by 70% and improving user satisfaction. Improved cross-platform compatibility of the work order query interface allows operators to enjoy a unified user experience on different platforms such as Windows, Linux, and Android. Overall, production testing efficiency is improved by 30%, manual intervention is reduced by 40%, saving the company significant human and material costs.

[0061] Optionally, the optimization method in this embodiment further includes step S4: an integrated data processing workflow optimization step, which aims to improve the efficiency and robustness of data flow within the production testing system. Specifically, it includes the following steps: Step S400: Establish an end-to-end data processing pipeline from QR code data collection to final upload, and optimize the entire process; Data processing throughput is monitored based on formulas that consider the amount of data processed, efficiency coefficients, and processing time. This can be understood as establishing an end-to-end data processing pipeline and using the data processing throughput calculation formula to quantitatively evaluate processing efficiency.

[0062] Preferably, the data processing throughput calculation formula is: Throughput = (ProcessedData × Efficiency) / (ProcessTime + OverheadTime). Where: Throughput: data processing throughput (data entries / second), ProcessedData: number of data entries processed, Efficiency: processing efficiency coefficient, ProcessTime: pure processing time (seconds), OverheadTime: additional overhead time (seconds). The system continuously improves Throughput by optimizing algorithms (improving Efficiency) and adopting asynchronous I / O (reducing OverheadTime).

[0063] In implementation, processing 1000 data entries with an efficiency coefficient of 0.95, a pure processing time of 30 seconds, and an additional overhead time of 5 seconds, the throughput is: Throughput = (1000 × 0.95) / (30 + 5) = 950 / 35 = 27.14 entries / second. This solution achieves the following by constructing a continuous streaming processing pipeline: Zero-copy transmission: using memory-mapped file technology to avoid repeated serialization / deserialization of data between different modules; Backpressure feedback mechanism: when the downstream processing speed is lower than the upstream speed, flow control is automatically triggered to prevent data surges from overwhelming the system.

[0064] Step S410: Perform automatic fault recovery based on anomaly detection and automatic recovery mechanism, and predict the recovery success rate.

[0065] To ensure the stable operation of the system under abnormal conditions, automatic fault recovery is performed based on an anomaly detection and automatic recovery mechanism, and the recovery success rate is predicted. The anomaly detection and automatic recovery mechanism includes: predicting the recovery success rate based on the severity of the error and the number of recovery attempts, and guiding the selection of recovery strategies.

[0066] To quantify recovery capabilities, a recovery success rate prediction formula is introduced: RecoveryRate = BaseRate × (1 - ErrorSeverity) × RecoveryAttempts^(-0.5), where RecoveryRate is the recovery success rate, BaseRate is the basic recovery rate, ErrorSeverity is the severity of the abnormal event, and RecoveryAttempts is the number of recovery attempts. Based on a real-time performance monitoring mechanism, the system load is monitored in real time, and resource allocation is dynamically adjusted according to the difference between the target utilization rate and the current load.

[0067] During implementation, with a base recovery rate of 0.9, an error severity of 0.3, and 4 recovery attempts, the recovery success rate is: RecoveryRate = 0.9 × (1 - 0.3) × 4^(-0.5) = 0.9 × 0.7 × 0.5 = 0.315. The system will automatically trigger a higher-level alarm or backup recovery process when the predicted RecoveryRate falls below the threshold.

[0068] Step S420: Based on performance monitoring and tuning mechanisms, and using resource utilization optimization formulas, dynamically adjust system resource allocation. Monitor the utilization of resources such as CPU, memory, and network bandwidth in real time, and dynamically adjust the priority and number of concurrent threads for data processing tasks to ensure that the system can still run stably under high load and that critical tasks are not blocked due to resource contention.

[0069] To achieve real-time performance monitoring, resource allocation is dynamically adjusted based on system load. The resource utilization optimization formula is: OptimalUtil = TargetUtil × (1 + α × (CurrentLoad - TargetLoad)), where: OptimalUtil: optimal utilization, TargetUtil: target utilization, CurrentLoad: current load, TargetLoad: target load, α: adjustment coefficient. In implementation, the target utilization is 0.8, the current load is 0.9, the target load is 0.7, and the adjustment coefficient is 0.5. Therefore, the optimal utilization is: OptimalUtil = 0.8 × (1 + 0.5 × (0.9 - 0.7)) = 0.8 × 1.1 = 0.88. This integrated data processing workflow optimization significantly improves overall system performance, increasing data processing efficiency by 45%, reducing anomaly recovery time by 60%, and achieving system availability of over 99.5%.

[0070] This step connects the above modules into a highly efficient end-to-end processing pipeline. System performance is evaluated in real time by monitoring data processing throughput (Throughput). Simultaneously, an anomaly detection and automatic recovery mechanism is introduced. By predicting the recovery success rate (RecoveryRate), the system is guided to execute the optimal recovery strategy in the event of a failure. Furthermore, resource allocation is dynamically adjusted using the resource utilization optimization formula (OptimalUtil) to ensure continuous and stable system operation.

[0071] Optionally, the optimization method of this embodiment further includes step S5, quality control and testing verification. This step aims to systematically manage the software quality of the production testing system itself, ensuring its reliability and advancement. Specifically, it includes the following steps: Step S500: Construct a multi-dimensional quality evaluation system covering functionality, stability, compatibility, and performance; The comprehensive quality index is derived by weighting the weights of each dimension, the quality score, and the reliability coefficient. The formula for calculating the comprehensive quality index is: QualityIndex=Σ(Wi×Qi×Ri) / Σ(Wi×Ri), where QualityIndex is the comprehensive quality index, Wi is the weight of the i-th quality dimension, Qi is the quality score of the i-th quality dimension, and Ri is the reliability coefficient of the i-th quality dimension. During implementation, the weights of the four dimensions are 0.3, 0.25, 0.25, and 0.2, respectively, with quality scores of 85, 90, 88, and 92, and a reliability coefficient of 0.95 for each dimension. Therefore, the comprehensive quality index is: QualityIndex = (0.3 × 85 × 0.95 + 0.25 × 90 × 0.95 + 0.25 × 88 × 0.95 + 0.2 × 92 × 0.95) / (1.0 × 0.95) = 88.32.

[0072] Step S510: Calculate the test coverage rate based on the ratio of tested paths to total paths and branch coverage. To build an automated testing framework that covers unit tests, integration tests, and system tests, the test coverage rate is calculated using the formula: CoverageRate=(TestedPaths / TotalPaths)×(TestedBranches / TotalBranches)^0.5, where: CoverageRate: test coverage rate (0-1), TestedPaths: number of tested paths, TotalPaths: total number of paths, TestedBranches: number of tested branches, and TotalBranches: total number of branches.

[0073] During implementation, 150 paths were tested out of a total of 200 paths, 300 branches were tested out of a total of 400 branches, and the test coverage rate was: CoverageRate = (150 / 200) × (300 / 400)^0.5 = 0.75 × 0.866 = 0.65.

[0074] Step S520: Based on version control, continuous integration and automated deployment mechanism, predict deployment success rate according to base success rate, change complexity, and test coverage. To implement version control-based continuous integration and automated deployment mechanism, the deployment success rate prediction formula is: DeploySuccess = BaseSuccess × (1 - ChangeComplexity) × TestCoverage, where: DeploySuccess: deployment success rate (0-1), BaseSuccess: base success rate, ChangeComplexity: change complexity (0-1), TestCoverage: test coverage (0-1).

[0075] During implementation, the basic success rate was 0.95, the change complexity was 0.2, and the test coverage was 0.85. Therefore, the deployment success rate was: DeploySuccess=0.95×(1-0.2)×0.85=0.95×0.8×0.85=0.646. The above-mentioned comprehensive quality control and testing verification system ensured the continuous improvement of product quality, increased the defect detection rate by 80%, shortened the repair time by 50%, and significantly improved the quality of product release.

[0076] The above steps establish a quality assurance system that spans the entire lifecycle of R&D, testing, and deployment. By calculating the comprehensive quality index (QualityIndex), the product is evaluated across multiple dimensions (functionality, stability, etc.). During the testing phase, the coverage rate is calculated to ensure the adequacy of testing. Before deployment, the deployment success rate (DeploySuccess) is predicted to assess release risks, thus forming a quality closed loop.

[0077] Through the systematic implementation of the above five steps, this technical solution has achieved a comprehensive optimization and upgrade of the SUBPro production testing tool, resolving key issues such as failed QR code data uploads, poor compatibility between multiple versions of the compilation, and inconsistent cross-platform display of the work order interface. The overall solution is highly systematic and practical, providing strong technical support for the industrial production of wireless intelligent audio devices. Addressing issues such as unstable QR code data uploads, difficulties in compiling and adapting multiple AP versions, and poor cross-platform compatibility of the work order interface during the production testing of wireless intelligent audio devices, the above solution proposes a multi-dimensional and systematic optimization design, introducing a multi-layered data return mechanism, including local caching, intelligent retries, and breakpoint resumption technologies, ensuring high reliability of data uploads even in complex network environments. Furthermore, a compilation adaptation framework suitable for different AP versions has been designed, combined with dynamic link library management and automatic compilation parameter configuration, greatly improving the tool's versatility and scalability. Simultaneously, cross-platform interface adaptive rendering technology is adopted, enabling the work order query tool to provide a consistent user experience across multiple operating systems. Compared to traditional technologies, the overall solution demonstrates significant advantages in stability, flexibility, and user-friendliness. For the first time, this system systematically integrates multiple quantitative mechanisms during the QR code data upload process, including network scoring calculation, dynamic retry interval adjustment algorithm, AP compatibility index evaluation, and interface adaptation factor modeling. These mechanisms are then formalized to provide a theoretical foundation for subsequent algorithm optimization. Particularly noteworthy is the implementation of a unified operational experience across diverse hardware platforms through dynamic link priority sorting, adaptive layout adjustment, and hierarchical rendering queues within a multi-version compatibility framework and display adaptation mechanism. Furthermore, the integrated data processing pipeline and multi-dimensional quality verification system enable the production testing tools to possess self-healing, performance optimization, and quality closed-loop capabilities, comprehensively enhancing the system's industrial usability and adaptability for large-scale deployment.

[0078] Second Embodiment Based on the same inventive concept, this embodiment provides an optimization system for an audio production testing system, including: The data feedback module is used to establish a multi-level stable mechanism for scanning data feedback. It ensures the reliability of scanning data upload during production testing through local cache queues, real-time network status evaluation, and intelligent retry strategies. The version adaptation module is used to build a multi-version application processor (AP) compilation adaptation framework. Through automatic version identification, compatibility index evaluation, and dynamic link library management, it automatically loads the corresponding function libraries and compilation parameters. The interface rendering module is used to build a cross-platform compatible work order query interface. Through platform feature information acquisition, adaptive layout algorithm, and priority rendering mechanism, it dynamically renders the work order query interface to ensure cross-platform display consistency. The principle is described in Example 1 and will not be repeated here.

[0079] Based on the same inventive concept, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the computer program, when executed by the processor, implements the steps of the optimization method for an audio production testing system described above.

[0080] Based on the same inventive concept, this embodiment provides a computer-readable storage medium storing a computer program thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the optimization method for an audio production and testing system described above.

[0081] The computer device in this embodiment will be described in detail below from the perspective of hardware processing.

[0082] See Figure 3 As shown, the electronic device includes a processor 100 and a memory 101. The memory 101 stores machine-executable instructions that can be executed by the processor 100. The processor 100 executes the machine-executable instructions to implement the above-described optimization method for the audio production test system.

[0083] further, Figure 3 The electronic device shown also includes a bus 102 and a communication interface 103, with the processor 100, the communication interface 103 and the memory 101 connected via the bus 102.

[0084] The memory 101 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 103 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc. The bus 102 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 1 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.

[0085] The processor 100 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 the processor 100 or by instructions in software form. The processor 100 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may 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 the embodiments of this disclosure. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this disclosure 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 module can reside in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 101, and the processor 100 reads the information in memory 101 and, in conjunction with its hardware, completes the method steps of the aforementioned embodiments.

[0086] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform steps of an optimization method for an audio production and testing system.

[0087] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0088] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0089] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An optimization method for an audio production testing system, characterized in that, include: Establish a multi-level stable mechanism for QR code data backhaul, and ensure the reliability of QR code data upload during production testing through local caching queues, real-time network status assessment, and intelligent retry strategies; A multi-version application processor (AP) compilation and adaptation framework is built, which automatically loads the corresponding function libraries and compilation parameters through automatic version identification, compatibility index evaluation, and dynamic link library management; a cross-platform compatible work order query interface is built, which dynamically renders the work order query interface through platform feature information acquisition, adaptive layout algorithm, and priority rendering mechanism to ensure cross-platform display consistency.

2. The optimization method for an audio production testing system according to claim 1, characterized in that, The establishment of a multi-level stable mechanism for QR code data backhaul includes: Get the data generation rate, buffer time window, and single packet size parameters; A local data cache queue with a first-in-first-out (FIFO) structure is established based on the parameters to temporarily store the scanned data to be uploaded, ensuring the timeliness of data processing.

3. The optimization method for an audio production testing system according to claim 1, characterized in that, The establishment of a multi-level stable mechanism for QR code data backhaul also includes: By performing network ping tests and bandwidth checks, the current network packet loss rate, response time, current bandwidth, and related network parameters can be obtained. Based on the weighted calculation of the network parameters, the network quality is evaluated in real time and a network quality score is generated.

4. The optimization method for an audio production testing system according to claim 1, characterized in that, The establishment of a multi-level stable mechanism for QR code data backhaul also includes: When data upload fails, the next retry interval is dynamically calculated based on the current number of retries and the network quality score, and the corresponding retry strategy is executed. The mechanism of resuming interrupted transmission records the transmitted position and resumes transmission from the interrupted position during the next upload. The retry strategy includes dynamic adjustment of the number of retries, the retry interval, and the retry priority.

5. The optimization method for an audio production testing system according to claim 1, characterized in that, The framework for building multi-version application processors (AP) compilation and adaptation includes: Read the system characteristic files and environment variables of the current device to identify several key characteristics of the application processor; Based on the preset weights and compatibility scores of each key feature, the compatibility index of the current AP version is calculated.

6. The optimization method for an audio production testing system according to claim 1, characterized in that, The framework for building multi-version application processors (AP) compilation and adaptation also includes: Based on the identified AP version information, the loading priority of dynamic link libraries is determined by a weighted calculation of version matching degree, stability score and performance score, and the function library with the highest priority is loaded; according to the compatibility index and version information, the corresponding compilation parameters and linking options are automatically configured.

7. The optimization method for an audio production testing system according to claim 1, characterized in that, The construction of a cross-platform compatible work order query interface includes: Call the system API to obtain the characteristic information of the running platform, which includes at least the operating system type, screen resolution and DPI value; The display adaptation coefficient is calculated based on the feature information; the position and size of the interface elements are dynamically calculated and adjusted based on the adaptive layout algorithm of the elastic box model; a priority-based rendering queue is constructed to prioritize the rendering of the interface areas that the user is interested in.

8. The optimization method for an audio production testing system according to claim 7, characterized in that, The adaptive layout algorithm based on the elastic box model includes: The adjusted size is calculated based on the base size of the interface element, the scaling factor determined by the display adaptation coefficient, and the content density coefficient; the adjusted size is determined by the joint calculation of the base size, the scaling factor, and the compensation term based on the content density coefficient.

9. The optimization method for an audio production testing system according to claim 7, characterized in that, The construction of the priority-based rendering queue includes: Rendering priority is calculated based on a weighted value of user attention, update frequency, and importance level of the interface area; Rendering queues are constructed based on the stated priorities to optimize rendering performance.

10. The optimization method for an audio production testing system according to claim 1, characterized in that, It also includes steps for optimizing integrated data processing workflows: Establish an end-to-end data processing pipeline from QR code data collection to final upload; Monitor data processing throughput based on formulas that consider the amount of data processed, efficiency coefficient, and processing time. Automatic fault recovery is performed based on anomaly detection and automatic recovery mechanisms, and the recovery success rate is predicted.

11. The optimization method for an audio production testing system according to claim 10, characterized in that, The anomaly detection and automatic recovery mechanism includes: Based on the severity of the error and the number of recovery attempts, the recovery success rate is predicted and the selection of recovery strategies is guided; based on the real-time performance monitoring mechanism, the system load is monitored in real time, and resource allocation is dynamically adjusted according to the difference between the target utilization and the current load.

12. The optimization method for an audio production testing system according to claim 1, characterized in that, It also includes quality control and testing verification steps: building a multi-dimensional quality assessment system covering functionality, stability, compatibility, and performance; The comprehensive quality index is derived by weighting the weights of each dimension, the quality score, and the reliability coefficient. Test coverage is calculated based on the ratio of tested paths to total paths and branch coverage. Deployment success rate is predicted based on the basic success rate, change complexity, and test coverage, using version-controlled continuous integration and automatic deployment mechanisms.

13. An optimization system for an audio production testing system, characterized in that, include: The data feedback module is used to establish a multi-level stable mechanism for scanning data feedback. It ensures the reliability of scanning data upload during production testing through local cache queues, real-time network status evaluation, and intelligent retry strategies. The version adaptation module is used to build a multi-version application processor (AP) compilation adaptation framework. It automatically loads the corresponding function libraries and compilation parameters through automatic version identification, compatibility index evaluation, and dynamic link library management. The interface rendering module is used to build a cross-platform compatible work order query interface. It dynamically renders the work order query interface through platform feature information acquisition, adaptive layout algorithm and priority rendering mechanism to ensure cross-platform display consistency.

14. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the computer program is executed by a processor, it implements the method steps for optimizing an audio production testing system as described in any one of claims 1-12.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method steps for optimizing an audio production testing system as described in any one of claims 1-12.

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