Automatic production testing method and system for intelligent audio equipment, storage medium and equipment

Abstract

The invention relates to the technical field of audio equipment production and measurement, and discloses an automatic production and measurement method and system for intelligent audio equipment, a storage medium and equipment, and the method comprises the steps: building a real-time monitoring firmware burning output directory, and monitoring a file change event through a file system event monitoring technology; extracting equipment identification information from the monitored firmware file and carrying out standardization processing on the equipment identification information, wherein the identification information comprises an MAC (Media Access Control) address and a UUID (User Unified Identifier); an equipment communication verification mechanism is established, the connection state is monitored in real time, and the signal quality is evaluated in multiple dimensions; multi-device concurrent testing is achieved through dynamic thread pool management and an intelligent testing task scheduling strategy; according to the method, a structured database is constructed, test data is stored, and data tracing under multi-dimensional conditions is performed, so that automatic acquisition and verification of equipment parameters are realized, simultaneous testing of multiple equipment is supported, an event-driven state monitoring mechanism is matched, the production and test efficiency is remarkably improved, and the production and test efficiency is improved. And a log recording and data tracing mechanism provides reliable guarantee for product quality management.

Description

Technical Field

[0001] The present invention relates to the technical field of audio device production testing, and in particular, to an automated production testing method, system, storage medium, and device for intelligent audio devices. Background Art

[0002] In the production process of intelligent audio devices, product testing is a key link to ensure quality. Traditional production testing solutions mainly rely on manual operations. Testers need to manually burn firmware, record MAC addresses, configure device parameters, etc. This method has problems such as low efficiency and easy errors. Especially in the device communication verification link, due to the lack of automated testing means, it is often necessary for manual judgment and recording of test results, which cannot meet the requirements of large-scale production. At the same time, existing testing tools generally lack a data traceability function. Once a quality problem occurs, it is very difficult to quickly locate and solve. In a batch production scenario, these problems will lead to low production efficiency and increased costs, seriously affecting product competitiveness.

[0003] During the current production testing process of audio devices, the burning of device firmware and parameter configuration generally adopt manual operation methods, and key parameters such as MAC addresses and UUIDs need to be manually recorded and input, which are prone to errors and omissions. At the same time, the verification of the device communication status lacks automated means, and the testing efficiency is low, unable to meet the requirements of batch production. This manual intervention method not only increases production costs but also is difficult to ensure the consistency of product quality. Therefore, a new solution is urgently needed. Summary of the Invention

[0004] The purpose of the present invention is to solve the above-mentioned disadvantages existing in the prior art, and provide an automated production testing method, system, storage medium, and device for intelligent audio devices. The production testing process realizes full automation, and the full-process testing from firmware burning to parameter configuration can be completed without manual intervention. At the same time, a complete log record and data traceability mechanism provide a reliable guarantee for product quality management.

[0005] On the one hand, an automated production testing method for intelligent audio devices is provided, including the following steps: S1: Monitor the firmware burning output directory in real time, and monitor file change events through file system event listening technology; S2: Extract device identification information from the monitored firmware file and standardize it. The identification information includes MAC address and UUID; S3: Establish a device communication verification mechanism, monitor the connection status in real time, and evaluate the signal quality in multiple dimensions; S4: Realize multi-device concurrent testing through dynamic thread pool management and intelligent test task scheduling strategies; S5: Construct a structured database to store the test data and trace the data under multi-dimensional conditions.

[0006] Further, in step S1, the real-time monitoring firmware flashing output directory includes: S11: Construct a hierarchical monitoring directory structure, including a root directory firmware flashing output directory and multiple functional sub-directories; S12: Set an adjustable monitoring period to implement hierarchical responses to different types of file change event types; S13: Adopt a hash check lock mechanism to prevent file read and write conflicts and ensure the consistency of file change time response and processing.

[0007] Further, in step S2, extracting the device identification information and performing standardization processing on it includes: S21: Define the format specifications of the device identification information, including the standard format, length, and valid character set; S22: Use regular expression matching to extract the device identification information from the original data; S23: Perform validity verification on the extracted device identification information, check whether the extracted device identification information conforms to the defined format specifications, record error logs, and mark it as invalid data; S24: Save the device identification information that has passed the validity verification to the structured database and associate it with the device information.

[0008] Further, in step S3, the establishment of the device communication verification mechanism includes: S301: Define a standardized communication protocol and command format; S302: Implement a connection mechanism based on a three-way handshake to ensure the sending and receiving capabilities of both the client and the server; S303: Adopt an exponential backoff algorithm for timeout retry management and dynamically adjust the waiting time according to the initial timeout time and the number of timeout retries.

[0009] Preferably, in step S3, the real-time monitoring of the connection state is performed through a heartbeat packet, supporting timeout retry and error recovery for connection anomalies, and further including: S311: Set the heartbeat packet sending period and the upper limit of the number of reconnections, send heartbeat packets regularly according to the sending period, if no response is received within the continuous upper limit of the number of reconnections, it is determined as a disconnection, and dynamically adjust the heartbeat interval of the heartbeat packet according to the network delay; S312: Obtain the real-time received received signal strength indication (RSSI) through the device API to detect the signal strength, use moving average filtering to eliminate instantaneous noise, set a threshold strength, if the filtered RSSI is less than the threshold strength, trigger an alarm and record the log.

[0010] Preferably, in step S3, the multi-dimensional evaluation of signal quality further includes: S313: Monitoring and recording the packet loss rate, signal strength, and response time through the heartbeat packet as evaluation metrics; S314: Performing weighted calculations on each evaluation metric according to the preset allocated weights to generate a comprehensive connection quality score; S315: Implementing hierarchical communication strategy adjustments based on the comprehensive connection quality score, including data retransmission and channel switching.

[0011] Furthermore, in step S4, the dynamic thread pool management includes: S401: Adopting an elastic thread pool model to dynamically adjust the number of threads based on the real-time load; S402: Performing task priority management based on the device type and task urgency; S403: Establishing a resource control strategy, introducing a resource weight factor to prevent overload, and setting an overload threshold for triggering flow control, rejecting new tasks, and recording overload events.

[0012] Preferably, in step S4, the intelligent test task scheduling strategy further includes: S411: Performing hash sharding on a batch of test tasks to achieve data localization processing and reduce cross-node communication; S412: Using an improved minimum connection method for load balancing distribution, defining node loads, and the scheduler selects the node with the minimum node load to allocate tasks; S413: Through a multi-level retry strategy, dynamically adjusting exception handling parameters by combining exponential backoff and a fuse mechanism.

[0013] Furthermore, in step S5, the construction of the structured database includes: Defining a graph relationship model associated with the device information table, test record table, and log record table, and achieving fast associated queries through an adjacency matrix; The database is constructed using a spatio-temporal hybrid index structure, combining a B+ tree and time series sharding to optimize storage efficiency and query performance; Performing differential encoding and lossy compression storage on its time series data.

[0014] Preferably, in step S5, the multi-dimensional conditional data traceability further includes: S51: Using a Merkle Tree hierarchical verification to ensure data integrity; S52: Performing an efficient composite query on the fields of the data, including device type and test results, based on inverted index and bitmap compression. S53: Analyze the data based on a statistical model, including anomaly detection and trend prediction.

[0015] Further, the method further includes an anomaly handling mechanism: Classify and process file parsing failures, communication interruptions, and thread anomalies; Record anomaly logs and generate a test report including error codes and solutions; Initiate an isolation detection process for continuously anomalous devices.

[0016] Further, the method further includes: Implement hash verification and digital signature verification during the firmware flashing stage; Automatically generate an electronic label for the tested device, including the MAC address, UUID, and test results; Establish an associated mapping between the test data and the production batch number.

[0017] On the other hand, an intelligent audio device automated production test system is provided, including: A directory monitoring module for real-time monitoring of the firmware flashing output directory and monitoring file change events through file system event listening technology; An identification processing module for extracting device identification information from the monitored firmware file and standardizing it, where the identification information includes the MAC address and UUID; A communication control module for establishing a device communication verification mechanism, real-time monitoring of the connection status, and multi-dimensional evaluation of signal quality; A parallel test module for achieving concurrent testing of multiple devices through dynamic thread pool management and intelligent test task scheduling strategies; A data management module for constructing a structured database, storing test data, and performing data traceability under multi-dimensional conditions.

[0018] In addition, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, it implements the intelligent audio device automated production test method described in any one of the above.

[0019] Meanwhile, an electronic device is provided, including: one or more processors; a storage device for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the intelligent audio device automated production test method described in any one of the above.

[0020] Compared with the prior art, the beneficial effects of the present invention are: The present invention realizes the automatic acquisition and verification of device parameters by monitoring the firmware burning directory in real time, automatically extracting and recording the device MAC address, and introducing a UUID automatic parsing mechanism, completing the full-process test from firmware burning to parameter configuration without manual intervention. Through automatic data acquisition and verification, the human error rate is reduced, and the product quality stability is improved; The present invention establishes a device communication verification mechanism to monitor the connection status based on heartbeat packets and evaluate the signal quality in multiple dimensions; The present invention designs a parallel test framework based on multi-threading to support simultaneous testing of multiple devices, and cooperates with an event-driven status monitoring mechanism to significantly improve the production test efficiency; The present invention designs a complete log recording and data traceability mechanism to provide reliable guarantee for product quality management. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention, but do not constitute a limitation to the present invention. In the drawings: Figure 1 is a schematic flow chart of an automatic production test method for an intelligent audio device of the present invention; Figure 2 is a schematic diagram of a detection directory structure of the present invention; Figure 3 is a schematic flow chart of the extraction and standardization process of device identification information of the present invention; Figure 4 is a schematic flow chart of establishing a device communication verification mechanism of the present invention; Figure 5 is a schematic flow chart of monitoring the connection status based on heartbeat packets of the present invention; Figure 6 is a schematic flow chart of evaluating the signal quality of the present invention; Figure 7 is a schematic flow chart of dynamic thread pool management of the present invention; Figure 8 is a schematic flow chart of a task scheduling strategy of the present invention; Figure 9 is a block diagram of the database structure for storing test data of the present invention; Figure 10 is a block diagram of the data traceability function structure with multi-dimensional conditions of the present invention; Figure 11 is a block diagram of the automatic production test system for an intelligent audio device of the present invention; Figure 12 is a schematic diagram of an embodiment of an electronic device of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0022] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the following will clearly and completely describe the technical solutions in the embodiments of this application with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, rather than all of them. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this application.

[0023] The present invention realizes automatic acquisition and verification of device parameters by monitoring the firmware burning directory in real time, automatically extracting and recording the device MAC address, and introducing a UUID automatic parsing mechanism. In addition, a parallel test framework based on multi-threading is designed to support simultaneous testing of multiple devices, and combined with an event-driven status monitoring mechanism, the production test efficiency is significantly improved.

[0024] The following describes the specific implementation manners of the present invention in conjunction with the accompanying drawings and embodiments.

[0025] Embodiment 1 Please refer to Figure 1 , which is the technical solution of an automatic production test method for an intelligent audio device provided in this embodiment, including the following steps: S1: Monitor the firmware burning output directory in real time, and monitor file change events through file system event listening technology; S2: Extract device identification information from the monitored firmware files and standardize it. The identification information includes the MAC address and UUID; S3: Establish a device communication verification mechanism to monitor the connection status in real time and evaluate the signal quality in multiple dimensions; S4: Implement concurrent testing of multiple devices through dynamic thread pool management and intelligent test task scheduling strategies; S5: Build a structured database to store test data and perform data traceability under multi-dimensional conditions.

[0026] Among them, in step S1, we first construct a hierarchical monitoring directory structure, including the root directory firmware burning output directory and multiple functional subdirectories. As Figure 2 shown, the root directory is the firmware burning output directory / firmware_output / (priority 1), which includes a log file directory / logs / , a configuration file directory / config / , and a test report directory / reports / below it. The monitoring directory structure with priority sorting is configured in the order of root directory → subdirectory. In this embodiment, the log file directory is used to store error logs and operation logs, the configuration file directory is used to store system configuration files, and the test report directory is used to output the test results of the audio device production test.

[0027] In this embodiment, the monitoring system selects the inotify file system event listening technology. By creating a file descriptor, attaching one or more monitors (a monitor is a path and a set of events), and then using the read method to obtain events from the descriptor. read does not consume the entire cycle and is blocked until an event occurs. Better still, since inotify works through traditional file descriptors, you can use the traditional select system call to passively monitor monitors and many other input sources. Both methods - blocking file descriptors and using select - avoid busy polling.

[0028] Then, an adjustable monitoring period is set to implement hierarchical responses to different types of file change event types. In this embodiment, we set a monitoring period of 100 ms to implement hierarchical responses to different file change event types, and the file change event types include file creation CREATE, file modification MODIFY, and file deletion DELETE.

[0029] On this basis, we adopt a hash check lock mechanism to prevent file read-write conflicts, ensure the consistency of file change time response and processing, trigger real-time notifications when file change events are detected, and subsequent processing flows.

[0030] Furthermore, in step S2, device identification information is extracted and standardized as Figure 3 shown, specifically including: S21: Define the format specifications of device identification information, including the standard format, length, and valid character set; S22: Use regular expressions to match and extract the device identification information from the original data; S23: Perform validity verification on the extracted device identification information, check that the extracted device identification information conforms to the defined format specifications, record error logs, and mark it as invalid data; S24: Save the device identification information that has passed the validity verification to a structured database and associate it with the device information.

[0031] In this embodiment, the automatic acquisition and verification of device parameters are realized through MAC address automatic processing and UUID automatic parsing respectively.

[0032] Among them, to extract the device MAC address and perform address standardization processing using regular expression matching and multiple verification mechanisms specifically includes: Define the MAC address format structure, including the standard format, length, and supported characters. In this embodiment, the specified standard format is: XX:XX:XX:XX:XX:XX, the length is 17 characters, and the supported character set is: 0-9, A-F. An example of a valid MAC address is: 12:34:56:78:9A:BC. Use regular expressions to match MAC addresses in the original data. The regular pattern in this embodiment is as follows: ([0-9A-Fa-f]{2}:){5}[0-9A-Fa-f]{2}, which means matching 6 groups of two-digit hexadecimal numbers, each group separated by a colon; Filter out invalid characters such as spaces and tabs in the input, convert the characters to the standard format, and correct other delimiters (such as "-") to colons (the regular expression needs to be extended according to actual requirements); Verify the validity of the extracted MAC address, and check whether the format, length, and character set are consistent with the defined MAC address format structure, including: checking whether the length is 17 characters, verifying whether each group is a legal hexadecimal number, and ensuring that the delimiter and its position are correct. In addition, if the verification fails, record the error log and mark it as invalid data; Save the MAC address that has been standardized and passed the validity verification to the structured database, and set a uniqueness constraint for the field.

[0033] In addition, the automatic parsing of device UUID data specifically includes: S211: Define the UUID format specification, including the standard format, total length, and supported characters. The standard format specified in this implementation is: XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX; the total length is 36 characters (grouped as 8-4-4-4-12 and separated by hyphens); the supported characters are: 0-9, a-f, A-F. A legal UUID example is: 550e8400-e29b-41d4-a716-446655440000. Use regular expressions to match UUID strings in the original data. The regular pattern in this embodiment is as follows: ([0-9A-Fa-f]{8}-[0-9A-Fa-f]{4}-[0-9A-Fa-f]{4}-[0-9A-Fa-f]{4}-[0-9A-Fa-f]{12}), which means matching the standard UUID format and forcing the correct position of the hyphens; For other delimiters (such as "_") or the case of missing delimiters, set extended matching: ([0-9A-Fa-f]{8}[-_]?[0-9A-Fa-f]{4}[-_]?[0-9A-Fa-f]{4}[-_]?[0-9A-Fa-f]{4}[-_]?[0-9A-Fa-f]{12}); Remove invalid characters such as spaces, tabs, or illegal symbols from the output, convert the characters to the standard format, and correct the delimiter to a hyphen. If the delimiter is missing, insert a hyphen as the delimiter according to the standard format rules; Perform validity verification on the extracted UUID, checking whether the format, length, and character set are consistent with the defined UUID format specification, including: whether the total length is 36 characters, whether the number of characters in each segment meets 8-4-4-4-12, and whether all characters belong to the hexadecimal character set. Then record the error log and mark the invalid UUID; Store the UUID that has been standardized and passed the validity verification into the database and associate it with the device information.

[0034] Furthermore, establish a device communication verification mechanism, as Figure 4 shown, specifically including: S301: Define a standardized command format, protocol structure: <cmd> 、 <len> 、 <data>、 <crc>, CMD (1 byte): Instruction type (e.g., 0x01 represents a handshake request), LEN (2 bytes): Data segment length (big-endian order), DATA (variable length): Specific parameters (e.g., device ID, configuration value), CRC (2 bytes): Checksum (CRC-16 algorithm); S302: Implement a connection mechanism based on a three-way handshake to ensure the sending and receiving capabilities of both the client and the server. Specifically, the client sends a SYN (e.g., sending CMD = 0x01, handshake request), the server responds with a SYN-ACK, returns the command code corresponding to the confirmation request (CMD = 0x02, handshake confirmation) and a random challenge code (e.g., a 4-byte random number), and then the client sends an ACK, calculates the hash value of the challenge code (e.g., MD5), and sends the command code corresponding to the final confirmation (CMD = 0x03, final confirmation); S303: Adopt an exponential backoff algorithm to support timeout retries, and dynamically adjust the waiting time according to the initial timeout time and the number of timeout retries. The specific formula is as follows: , where, is the timeout time, is the initial timeout time, is the number of retries. In this embodiment, the initial timeout time is set to 1 second, doubled for each retry, and retried at most 3 times; Finally, verify the data integrity through the CRC-16 algorithm.

[0035] Then, perform real-time monitoring of the connection status through a heartbeat packet, support timeout retries and error recovery for connection anomalies, as Figure 5 shown, specifically including: S311: Set the heartbeat packet sending period and the upper limit of reconnection attempts. In this embodiment, the heartbeat packet interval is configured as 1000 ms, and the upper limit of reconnection attempts is set to 3 times. Send heartbeat packets regularly according to the sending period. If no response is received for 3 consecutive times, it is determined as a disconnection, and the heartbeat interval of the heartbeat packet is dynamically adjusted according to the network latency (e.g., when the latency > 200 ms, the interval is increased to 1500 ms); S312: Obtain the received signal strength indication RSSI in real time through the device API to detect the signal strength, use moving average filtering to eliminate instantaneous noise, set the threshold strength to -70 dBm, and if the filtered RSSI is less than the threshold strength, trigger an alarm and record a log.

[0036] Then, based on this, perform an assessment of the signal quality. The connection quality scoring formula aims to comprehensively evaluate the reliability, signal strength, and response speed of communication, as Figure 6 shown, specifically including: S313: Monitor and record the packet loss rate, signal strength, and response time through the heartbeat packet as evaluation metrics; S314: Perform weighted calculation on each evaluation metric according to the preset allocated weights to obtain the comprehensive connection quality score , and the calculation formula is as follows: , Finally, ensure that the score is in the range of [0, 1] through normalization processing: Among them, represents the packet loss rate, represents the signal strength, represents the average time from packet sending to receiving confirmation, , , are the weights of the heartbeat packet loss rate, signal strength, and response time in the comprehensive evaluation respectively, and are dynamically adjusted according to the actual scenario. In this embodiment, we design the weight allocation as follows: Packet loss rate ( = 50%): Reflects the reliability of communication and is the core index of connection quality; Signal strength ( = 30%): Directly affects communication stability, and weak signals are prone to packet loss and delay; Response time ( = 20%): Affects the user experience, and high latency may cause real-time task failures; S315: Implement hierarchical communication strategy adjustment based on the comprehensive connection quality score, including data retransmission and channel switching. In this embodiment, it includes: If the connection quality score , start redundant data retransmission; If the connection quality score < 3, forcefully switch to the backup communication channel.

[0037] Build a multi-threaded parallel test framework, including the dynamic thread pool management and the intelligent test task scheduling strategy, and implement multi-device concurrent testing through the dynamic thread pool management and the intelligent task scheduling strategy respectively. Among them, the thread pool management is as Figure 7 shown, and specifically includes: S401: Adopt an elastic thread pool model, and dynamically adjust the number of threads based on the real-time load. In this embodiment, the core thread number is set to 4, the maximum thread number is 8, the task queue capacity is 16, and the idle thread timeout is 60s. The thread number adjustment formula is as follows: Among them, represents the current active thread number, Indicates the backlog of the task queue, Indicates the CPU usage rate (0 - 1 scale), Indicates the memory usage rate (0 - 1 scale), K = 2 is the queue load factor, and the maximum number of threads , the number of core threads ; S402: Based on the device type and the task urgency and their weight assignments to perform task prioritization management: , Among them, (1: flagship device, 2: standard device, 3: low - end device), (t is the task waiting time in minutes, with exponential decay to enhance timeliness); S403: Establish a resource control strategy, introduce a resource weight factor to prevent overload, and calculate the resource weight factor The formula is as follows: Meanwhile, set the overload threshold to 0.85. If the resource weight factor is greater than the overload threshold, that is when, trigger flow control, reject new tasks and record overload events.

[0038] In addition, perform intelligent task scheduling for test tasks, as Figure 8 shown, specifically including: S411: Perform hash sharding on a batch of test tasks to achieve data localization processing and reduce cross - node communication; S412: Use the improved minimum - connection method for load - balancing distribution, define the node load , the formula is as follows: Among them, is the CPU occupancy rate, is the memory occupancy rate, is the number of tasks to be processed by the node, and the scheduler selects the node with the minimum node load to assign tasks, , , are the weights assigned to the CPU occupancy rate, memory occupancy rate, and the number of tasks to be processed by the node respectively; S413: Through a multi - level retry strategy, combine exponential back - off and circuit - breaker mechanisms to dynamically adjust exception - handling parameters.

[0039] Furthermore, establish a structured database to store test data, and the database structure is as Figure 9 As shown, where A database is established using a spatio-temporal hybrid index structure. The database includes a device information table, a test record table, and a log record table. A graph relationship model associated with the device information table, the test record table, and the log record table is defined, and fast associated queries are realized through an adjacency matrix; Combined with B+ tree and time series sharding to optimize storage efficiency and query performance, where For the device information table, the structure includes: - device_id (primary key) - mac_address - uuid - firmware_version - test_status, Its primary key uses the consistent hashing algorithm to allocate storage nodes to achieve uniform data distribution; For the test record table, a combination of Delta-of-Delta and ZSTD compression is used for its time series data.

[0040] On this basis, based on the data storage of the structured database, a data traceability function based on multi-dimensional conditions is realized, such as Figure 10 As shown, including: Data integrity verification, using Merkle Tree hierarchical verification to ensure that the data cannot be tampered with; Multi-dimensional query optimization, including inverted index and bitmap compression. Among them, fields including device type and test results in the data are inverted indexed to establish a mapping from the segmentation term to the document ID, and the bitmap compression technology is used to store the document set to achieve efficient composite queries; Statistical analysis model, including anomaly detection and trend prediction. Among them, the threshold is dynamically set based on the 3σ criterion to detect anomaly points, and the formula is as follows: Where is the measured value, , is the measured value of the standard deviation; Then the Holt-Winters triple exponential smoothing method is used to predict the test pass rate, and the formula is as follows: Level component: , Trend component: , Seasonal component: , Predicted value: , Among them, is the horizontal smoothing coefficient, is the trend smoothing coefficient, is the seasonal smoothing coefficient, , , The values of all are between [0, 1], and the best effect can be achieved through multiple tests and adjustments. is the actual test value at the th time point, is the predicted leading step number, represents the linear extrapolation based on the current level component and trend component, indicating the prediction baseline without considering seasonality. represents the seasonal factor for adjusting the predicted value, aligning the historical seasonal components according to the period m, and the period length , with the unit of hour.

[0041] The method further includes an exception handling mechanism: Classify and handle file parsing failures, communication interruptions, and thread exceptions; Record exception logs and generate a test report including error codes and solutions; Start the isolation detection process for continuously abnormal devices.

[0042] In addition, the method further includes: Implement hash checksum and digital signature verification during the firmware flashing stage; Automatically generate an electronic label including the MAC address, UUID, and test results for the tested devices; Establish an associated mapping between the test data and the production batch number.

[0043] In summary, the method of this embodiment provides a complete set of intelligent audio device automated production test solutions through systematic design and implementation. This solution not only solves the efficiency and accuracy problems in traditional production testing but also provides a perfect data traceability ability, providing strong support for product quality management. The solution has good scalability and adaptability and can be flexibly configured and optimized according to actual production requirements. Practical applications show that this solution can significantly improve the production test efficiency, reduce the human error rate, and provide a reliable technical guarantee for the large-scale production of intelligent audio devices. Through the multi-thread parallel test framework, simultaneous testing of multiple devices is achieved, greatly improving the production test efficiency; through the heartbeat packet mechanism and signal strength monitoring, the reliability of device communication is ensured; through the database design and multi-dimensional traceability mechanism, a complete quality management system is realized. The solution also adopts dynamic thread pool management and intelligent task scheduling strategies to solve the resource scheduling problem in traditional production testing.

[0044] In addition, this embodiment also provides an intelligent audio device automated production test system, as Figure 11 As shown in the figure, it includes: A directory monitoring module, which is used to monitor the firmware burning output directory in real time and monitor file change events through file system event listening technology; An identification processing module, which is used to extract device identification information from the monitored firmware files and perform standardization processing on it. The identification information includes MAC address and UUID; A communication control module, which is used to establish a device communication verification mechanism, monitor the connection status in real time and evaluate the signal quality in multiple dimensions; A parallel test module, which is used to achieve concurrent testing of multiple devices through dynamic thread pool management and intelligent test task scheduling strategies; A data management module, which is used to build a structured database and store and trace test data according to multi-dimensional conditions.

[0045] Among them, Figure 11 In the intelligent audio device automated production testing system, the functions of each module and unit correspond to the steps in the above-mentioned embodiments of the intelligent audio device automated production testing method, and their functions and implementation processes will not be elaborated here one by one.

[0046] In this embodiment, an electronic device is also provided, as Figure 12 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, and the processor 14 executes the machine-executable instructions to implement the above audio control method.

[0047] Furthermore, Figure 12 The electronic device shown in the figure further includes a bus 12 and a communication interface 11, and the processor 14, the communication interface 11 and the memory 13 are connected through the bus 12.

[0048] Among them, the memory 13 may include a high-speed random access memory (RAM, Random Access Memory), and may also include a non-volatile memory, such as at least one disk memory. Through at least one communication interface 11 (which can be wired or wireless), a communication connection is realized between the system network element and at least one other network element, and the Internet, wide area network, local area network, metropolitan area network, etc. can be used. The bus 12 can be an ISA bus, a PCI bus or an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of simplicity of representation, Figure 12 only a two-way arrow is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0049] The processor 14 may be an integrated circuit chip with the ability to process signals. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in the processor 14 or the instructions in the form of software. The above-mentioned processor 14 may be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it may also be a digital signal processor (DSP for short), an application specific integrated circuit (ASIC for short), a field-programmable gate array (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in this embodiment. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with this embodiment can be directly embodied as being executed and completed by the hardware decoding processor, or executed and completed by a combination of the hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory 1001, and the processor 1000 reads the information in the memory 1001 and combines its hardware to complete the steps of the audio control method.

[0050] The present disclosure also provides a computer-readable storage medium. The computer-readable storage medium may be a non-volatile computer-readable storage medium, and the computer-readable storage medium may also be a volatile computer-readable storage medium. A computer program is stored in the computer-readable storage medium. When the computer program runs on a computer, the computer is enabled to execute the steps of the audio control method.

[0051] Finally, it should be noted that the above description is only a preferred implementation manner of the present invention. The protection scope of the present invention is not limited to the above embodiments. Any technical solutions falling within the idea of the present invention belong to the protection scope of the present invention. It should be pointed out that for those of ordinary skill in the art, several improvements and refinements made without departing from the principle of the present invention should also be regarded as within the protection scope of the present invention.

[0052] The various technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the various technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.< / crc> < / data> < / len> < / cmd>

Claims

1. An automated production and testing method for intelligent audio equipment, characterized in that: The steps include: S1: Real-time monitoring of the firmware burning output directory, and monitoring of file change events through file system event monitoring technology; S2: extracting device identification information from the monitored firmware file and performing standardization processing on the device identification information, wherein the identification information includes a MAC address and a UUID; S3: Establish a device communication verification mechanism to monitor the connection status in real time and evaluate the signal quality in multiple dimensions; S4: Implement multi-device concurrent testing through dynamic thread pool management and intelligent test task scheduling strategy; S5: Build a structured database to store test data and trace data under multi-dimensional conditions.

2. The method for automated production and testing of intelligent audio equipment according to claim 1, characterized in that: In step S1, the real-time monitoring firmware burning output directory further includes: S11: construct a hierarchical monitoring directory structure, including a root directory firmware burning output directory and multiple functional sub-directories; S12: Set an adjustable monitoring period and implement graded responses to different types of file change events; S13: A hash check lock mechanism is used to prevent file read and write conflicts and ensure consistency in file change time response and processing.

3. The method for automated production and testing of intelligent audio equipment according to claim 1, characterized in that: In step S2, extracting device identification information and performing standardization processing on it further includes: S21: Define the format specification of device identification information, including standard format, length and valid character set; S22: extracting the device identification information from the original data by using regular expression matching; S23: Verify the validity of the extracted device identification information, check whether the extracted device identification information complies with the defined format specification, record an error log and mark it as invalid data; S24: The device identification information that has passed the validity verification is saved in a structured database and associated with the device information.

4. The method for automated production and testing of intelligent audio equipment according to claim 1, characterized in that: In step S3, the establishing of the device communication verification mechanism further includes: S301: define standardized communication protocols and command formats; S302: Implement a connection mechanism based on a three-way handshake to ensure the sending and receiving capabilities of both the client and the server; S303: Use an exponential backoff algorithm to perform timeout retry management, and dynamically adjust the waiting time according to the initial timeout time and the number of timeout retries.

5. The method for automated production and testing of intelligent audio equipment according to claim 1, characterized in that: In step S3, real-time monitoring of the connection status is performed through a heartbeat packet, and timeout retry and error recovery of connection abnormalities are supported, further comprising: S311: setting a heartbeat packet sending cycle and an upper limit of reconnection times, sending heartbeat packets regularly according to the sending cycle, and if no response is received within the upper limit of continuous reconnection times, it is determined to be disconnected, and the heartbeat interval of the heartbeat packet is dynamically adjusted according to the network delay; S312: The real-time received signal strength indication RSSI is obtained through the device API to detect the signal strength, a moving average filter is used to eliminate instantaneous noise, and a threshold strength is set. If the RSSI after filtering is less than the threshold strength, an alarm is triggered and a log is recorded.

6. The automated production and testing method for intelligent audio equipment according to claim 5, characterized in that: In step S3, the multi-dimensional signal quality assessment further includes: S313: Monitor and record the packet loss rate, signal strength and response time through the heartbeat packet as evaluation indicators; S314: performing weighted calculation on each evaluation indicator according to preset allocated weights to generate a comprehensive connection quality score; S315: Implement hierarchical communication strategy adjustment based on the comprehensive connection quality score, including data retransmission and channel switching.

7. The method for automated production and testing of intelligent audio equipment according to claim 1, characterized in that: In step S4, the dynamic thread pool management further includes: S401: Adopt elastic thread pool model to dynamically adjust the number of threads based on real-time load; S402: performing task priority management based on device type and task urgency; S403: Establish a resource control strategy, introduce a resource weight factor to prevent overload, and set an overload threshold to trigger flow control, reject new tasks and record overload events.

8. The method for automated production and testing of intelligent audio equipment according to claim 7, characterized in that: In step S4, the intelligent test task scheduling strategy further includes: S411: Perform hash sharding on batches of test tasks to achieve data localization and reduce cross-node communication; S412: Using the improved minimum connection method to perform load balancing distribution, defining the node load, and the scheduler selects the node with the minimum node load to allocate tasks; S413: Dynamically adjust exception handling parameters through a multi-level retry strategy combined with exponential backoff and fuse mechanisms.

9. The method for automated production and testing of intelligent audio equipment according to claim 1, characterized in that: In step S5, the constructing of the structured database further comprises: Define the graph relationship model of the device information table, test record table and log record table, and implement fast association query through the adjacency matrix; The database is constructed using a spatiotemporal hybrid index structure, combining B+ trees with time series sharding to optimize storage efficiency and query performance; Differential encoding and lossy storage are implemented for its time series data.

10. The method for automated production and testing of intelligent audio equipment according to claim 9, characterized in that: In step S5, the data tracing of the multi-dimensional conditions further includes: S51: Merkle Tree hierarchical verification is used to ensure data integrity; S52: Perform efficient compound query based on inverted index and bitmap compression on data fields including device type and test results; S53: Perform statistical model-based analysis on data, including anomaly detection and trend prediction.

11. The method for automated production and testing of intelligent audio equipment according to claim 1, characterized in that: The method also includes an exception handling mechanism: Categorize and handle file parsing failures, communication interruptions, and thread exceptions; Record exception logs and generate test reports including error codes and solutions; Initiate isolation detection process for devices with continuous abnormalities.

12. The method for automated production and testing of intelligent audio equipment according to claim 1, characterized in that: The method further comprises: Implement hash checksum and digital signature verification during firmware burning stage; Automatically generate electronic labels including MAC address, UUID and test results for the tested devices; Establish the association mapping between test data and production batch number.

13. An automated production and testing system for intelligent audio equipment, characterized in that: include: Directory monitoring module, used to monitor the firmware burning output directory in real time and monitor file change events through file system event monitoring technology; An identification processing module, used to extract device identification information from the monitored firmware file and perform standardization processing on it, wherein the identification information includes a MAC address and a UUID; Communication control module, used to establish device communication verification mechanism, monitor connection status in real time and evaluate signal quality in multiple dimensions; Parallel testing module, which is used to implement multi-device concurrent testing through dynamic thread pool management and intelligent test task scheduling strategy; The data management module is used to build a structured database to store test data and trace data under multi-dimensional conditions.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by the processor, the automated production and testing method for an intelligent audio device as described in any one of claims 1 to 12 is implemented.

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 the one or more processors, enables the one or more processors to implement the automated production and testing method for intelligent audio devices as described in any one of claims 1 to 12.

Images