USB module multi-station synchronous test and data association method and system

Abstract

The invention relates to the technical field of electronic testing, and discloses a USB module multi-station synchronous testing and data association method and system, and the method comprises the steps: constructing a global schedulable resource map of a testing station, so as to create a resource load balancing table and a global testing database of the testing station; configuring a synchronous test controller of the test station by using the test task flow generated by the test station and the global schedulable resource atlas; reading the unique identity label of the USB module so as to establish an association mapping network of the USB module in the global test database; and executing multi-station synchronous testing and data association processing of the USB module based on the global schedulable resource atlas, the resource load balancing table, the synchronous testing controller, the collaborative optimization strategy set and the association mapping network. According to the invention, accurate synchronous control of multi-station test and automatic correlation analysis of test data can be realized.

Description

Technical Field

[0001] This invention relates to a method and system for multi-station synchronous testing and data association of USB modules, belonging to the field of electronic testing technology. Background Technology

[0002] USB module multi-station synchronous testing and data association is an advanced production testing method that integrates scheduling, resource pooling, and parallel control technologies. It is used to perform parallel testing of large-scale USB modules and build a full-process data traceability system, aiming to improve the efficiency, result consistency, and problem localization speed of large-scale USB module testing.

[0003] However, traditional USB module testing mainly relies on manual data aggregation after independent testing at multiple workstations. While this method can complete the detection and data collection of basic USB module indicators, it cannot achieve precise synchronous control of multi-workstation testing and automatic correlation analysis of test data, resulting in low efficiency of USB module testing and failing to meet the needs of large-scale USB module production testing. Summary of the Invention

[0004] This invention provides a method and system for multi-station synchronous testing and data association of USB modules. Its main purpose is to achieve precise synchronous control of multi-station testing and automatic correlation analysis of test data.

[0005] To achieve the above objectives, the present invention provides a method for multi-station synchronous testing and data association of a USB module, comprising: Receive the test task stream from the USB module and collect the status data stream of each test station in real time. Based on the status data stream, construct a global schedulable resource map of the test station. Based on the global schedulable resource map, a resource load balancing table for the test station is generated, and a global test database for the test station is created. Identify the synchronous test instructions and data acquisition requirements corresponding to the test station from the test task flow, and configure the synchronous test controller of the test station in conjunction with the global schedulable resource map; The task execution status, hardware resource utilization, and data flow throughput of the test station are monitored in real time to generate the resource load balancing table and the collaborative optimization strategy set of the synchronous test controller. Read the unique identifier of the USB module, and based on the unique identifier, establish an association mapping network of the USB module in the global test database; Based on the global schedulable resource map, the resource load balancing table, the synchronous test controller, the collaborative optimization strategy set, and the association mapping network, the multi-station synchronous test and data association processing of the USB module are performed to obtain the test data association results.

[0006] Optionally, the step of identifying the synchronous test instructions and data acquisition requirements corresponding to the test station from the test task flow, and configuring the synchronous test controller of the test station in conjunction with the global schedulable resource map, includes: Identify the time synchronization constraint boundaries in the synchronous test command and the synchronization timing margin corresponding to the data acquisition requirements; Based on the time synchronization constraint boundary and the synchronization timing margin, a timing coordination relationship is established between the synchronization test command and the data acquisition requirements; Extract the timing characteristics of the hardware interface and the bus communication delay parameters corresponding to the test station; Based on the timing coordination relationship, the priority of the trigger signal corresponding to the timing characteristics of the hardware interface is determined; The multi-channel synchronization triggering conditions corresponding to the data acquisition requirements are parsed out, and combined with the real-time load status of the test station, a dynamic configuration set of synchronization parameters for the test station is generated. Based on the trigger signal priority and the dynamic configuration set of synchronization parameters, a distributed clock synchronization mechanism for the test station is constructed. Configure the synchronous test controller of the test station according to the bus communication delay parameters and the distributed clock synchronization mechanism.

[0007] Optionally, the step of constructing the distributed clock synchronization mechanism for the test station based on the trigger signal priority and the dynamic configuration set of synchronization parameters includes: Match the clock synchronization master node corresponding to the priority of the trigger signal; Based on the trigger signal priority and the clock synchronization master node, establish the synchronization hierarchy topology of the test station; Based on the synchronization hierarchy topology, identify the master-slave synchronization relationship between the clock synchronization master node and the slave node; Based on the master-slave synchronization relationship and the dynamic configuration set of synchronization parameters, a clock synchronization strategy is established for all nodes in the synchronization hierarchy topology. Based on the synchronization hierarchy topology and the clock synchronization strategy, a distributed clock synchronization mechanism for the test station is constructed.

[0008] Optionally, establishing an association mapping network for the USB module in the global test database based on the unique identifier includes: Identify the unique identifier's encoding rules and extract the resource status table, task context table, and process data table from the global test database; According to the identification encoding rules, create a data association field for the USB module in the resource status table, the task context table, and the process data table; Based on the data association field, the frequency of access to the USB module in the global test database is counted. Based on the access frequency, set the database index optimization strategy for the USB module; Perform predefined association query processing on the global test database to associate the multimodal test data of the USB module in real time; Based on the multimodal test data, the relationships between the data tables of the USB module are analyzed; By establishing the relationships between the data tables, a personalized data association view for the USB module can be set. By combining the personalized data association view and the database index optimization strategy, an association mapping network for the USB module is established in the global test database.

[0009] Optionally, setting the database index optimization strategy for the USB module based on the access frequency includes: Based on the access frequency, determine the data query characteristics and system response timeliness indicators corresponding to the USB module; Based on the data query characteristics, the index structure type and data sharding parameters corresponding to the USB module are set; Based on the system response timeliness index, determine the index update triggering condition and cache preloading rule corresponding to the USB module; By integrating the index structure type, the data sharding parameters, the index update triggering conditions, and the cache preloading rules, a database index optimization strategy for the USB module is generated.

[0010] Optionally, constructing a global schedulable resource map of the test station based on the state data stream includes: Identify the core resource entity corresponding to the state data stream, and extract the static resource attributes of the core resource entity; Based on the static resource attributes, the core resource entity is instantiated as the resource node corresponding to the test station; Based on the connection configuration data and system layout information of the core resource entities, virtual connection edges are established between the resource nodes; The real-time data in the state data stream is dynamically associated with the resource node and the virtual connection edge to obtain the real-time state label corresponding to the resource node and the virtual connection edge; Based on the resource nodes, the virtual connection edges, and the real-time status labels, a global schedulable resource graph for the test workstation is constructed.

[0011] Optionally, generating the resource load balancing table for the test workstation based on the global schedulable resource map includes: Obtain the baseline load balancing threshold of the global schedulable resource graph in its initial state; Based on the baseline load balancing threshold, the ideal load balancing range for the test station is defined. Real-time monitoring of the real-time resource status sequence corresponding to the global schedulable resource map; The average load, standard deviation of load, and load change trend of each resource node in the global schedulable resource map are extracted from the real-time resource status sequence to form the load dynamic characteristics of the test station. Based on the ideal load balancing range and the load dynamic characteristics, a resource load balancing table for the test station is generated.

[0012] Optionally, the step of creating a global test database for the test station based on the global schedulable resource graph includes: The resource status elements, task flow elements, and data flow elements in the global schedulable resource graph are parsed out. Based on a preset test data model protocol, the resource status elements, the task flow elements, and the data flow elements are respectively encapsulated into resource status units, task context units, and process data units. Based on the resource status unit, the task context unit, and the process data unit, a resource status table, a task context table, and a process data table corresponding to the test station are established respectively to form the database logical structure of the test station. Extract the resource semantic tags from the resource status unit, the test item semantic tags from the task context unit, and the data feature semantic tags from the process data unit; Based on the resource semantic tags, the test item semantic tags, and the data feature semantic tags, a data association index is constructed between the resource status table, the task context table, and the process data table; Based on the data association index, a data association network corresponding to the database logical structure is generated; Based on the data association network and the database logical structure, a global test database for the test station is created.

[0013] Optionally, the real-time monitoring of the task execution status, hardware resource utilization, and data flow throughput of the test station to generate the resource load balancing table and the collaborative optimization strategy set of the synchronous test controller includes: Based on the task execution status, the hardware resource utilization rate, and the data flow throughput, calculate the system coordination efficiency index and load imbalance risk coefficient of the test station. Based on the system coordination efficiency index and the load imbalance risk coefficient, determine the load balancing optimization intensity level and synchronization control guarantee level corresponding to the test station; Based on the load balancing optimization intensity level, the load balancing strategy items in the resource load balancing table are selected; Based on the aforementioned synchronization control protection level, the synchronization control parameter items in the synchronization test controller are parsed out; Establish a policy coupling relationship mapping table between the load balancing policy items and the synchronization control parameter items; Based on the strategy coupling relationship mapping table, a resource reallocation instruction set corresponding to the load balancing strategy item and a parameter reconfiguration instruction set corresponding to the synchronization control parameter item are generated. Based on the resource reallocation instruction set and the parameter reconfiguration instruction set, a collaborative optimization strategy set for the resource load balancing table and the synchronous test controller is generated.

[0014] To address the aforementioned problems, the present invention also provides a multi-station synchronous testing and data association system for USB modules, the system comprising: The resource scheduling module is used to receive the test task stream from the USB module and collect the status data stream of each test station in real time. Based on the status data stream, it constructs a global schedulable resource map of the test station. The data storage module is used to generate a resource load balancing table for the test station based on the global schedulable resource map, and to create a global test database for the test station. The synchronization control module is used to identify the synchronization test instructions and data acquisition requirements corresponding to the test station from the test task flow, and to configure the synchronization test controller of the test station in conjunction with the global schedulable resource map. The test optimization module is used to monitor the task execution status, hardware resource utilization and data flow throughput of the test station in real time, so as to generate the resource load balancing table and the set of collaborative optimization strategies of the synchronous test controller. The data association module is used to read the unique identifier of the USB module and, based on the unique identifier, establish an association mapping network of the USB module in the global test database. The test execution module is used to perform multi-station synchronous testing and data association processing of the USB module based on the global schedulable resource graph, the resource load balancing table, the synchronous test controller, the collaborative optimization strategy set, and the association mapping network, and to obtain test data association results.

[0015] Compared to the problems described in the background art, this embodiment of the invention, by constructing a globally schedulable resource map of the test station based on the state data stream, can solve the resource coordination problem in multi-station synchronous testing, avoiding resource idleness or conflicts. Furthermore, by generating a resource load balancing table for the test station based on the globally schedulable resource map, this embodiment of the invention can clarify the correlation between the dynamic changes in test station resource load and the amount of USB module testing tasks, as well as the impact mechanism of different test items on the station resource occupancy rate, thereby optimizing the task allocation strategy for multi-station synchronous testing of USB modules. Finally, by creating a global test database for the test station based on the globally schedulable resource map, this embodiment of the invention can... The distributed data storage mode of SB module multi-station testing has been upgraded to a global data association and integration mode, accurately associating the dynamic matching relationship between USB module test data and test station resource status and environmental factors. Furthermore, this embodiment of the invention identifies the synchronous test instructions and data acquisition requirements corresponding to each test station from the test task flow, and configures the synchronous test controller of each test station in conjunction with the globally schedulable resource map. This accurately solves the timing misalignment problem of traditional independent multi-station USB module testing, breaks through the efficiency bottleneck of manual data aggregation, optimizes the resource collaboration logic and data association mechanism of multi-station testing, and significantly improves the efficiency and accuracy of USB module testing. This embodiment of the invention also monitors the test station in real time... The task execution status, hardware resource utilization, and data flow throughput are used to generate the resource load balancing table and the collaborative optimization strategy set of the synchronous test controller. This transforms the dynamic resource status and synchronization control requirements of multi-station testing into quantifiable scheduling parameters, providing clear numerical basis for the accurate adaptation of multi-station resource allocation and synchronization control, and improving the efficiency of judging the overall operating status of the test system and the degree of adaptation to large-scale test requirements. Furthermore, this embodiment of the invention establishes an association mapping network of the USB module in the global test database based on the unique identifier. This allows fragmented synchronous test data generated by multi-station parallelism to be aggregated into a traceable data chain with complete contextual relationships through a unified identifier. To address the data source confusion caused by test concurrency, this invention achieves precise binding of test results to physical modules. Finally, based on the global schedulable resource graph, the resource load balancing table, the synchronous test controller, the collaborative optimization strategy set, and the association mapping network, this embodiment of the invention executes multi-station synchronous testing and data association processing of the USB module, obtaining test data association results. This achieves a deep fit between the testing process and the dynamic scheduling requirements of multi-station resources, as well as the timing matching requirements of high-precision synchronous control. Simultaneously, it avoids the rigidity of the testing process caused by reliance on manual aggregation or the test data association obstacles caused by data silos, significantly improving the collaborative efficiency of USB module testing and the full lifecycle data traceability capability.Therefore, this invention can achieve precise synchronous control of multi-station testing and automatic correlation analysis of test data. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a method for multi-station synchronous testing and data association of a USB module according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the global schedulable resource map of a USB module multi-station synchronous testing and data association method provided in an embodiment of the present invention. Figure 3 This is a schematic diagram of a module for implementing a multi-station synchronous testing and data association system for a USB module, as provided in an embodiment of the present invention.

[0017] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] This application provides a method for multi-station synchronous testing and data association of USB modules. The execution subject of this method includes, but is not limited to, at least one electronic device that can be configured to execute the method provided in this application, such as a server or a terminal. In other words, the method can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0020] Reference Figure 1 The diagram shown is a flowchart illustrating a method for multi-station synchronous testing and data association of a USB module according to an embodiment of the present invention. In this embodiment, the method includes: S1. Receive the test task stream from the USB module and collect the status data stream of each test station in real time. Based on the status data stream, construct a global schedulable resource map of the test station.

[0021] This invention provides a global resource scheduling basis and data association foundation for multi-station synchronous testing of USB modules by receiving the test task stream from the USB module and collecting the status data stream of each test station in real time. The USB module refers to an electronic module with Universal Serial Bus (USB) interface protocol functionality, including core components such as a USB controller, interface circuit, and data transmission unit. The test task stream is a set of ordered test instructions and task parameters formulated for the testing requirements of the USB module, including the test task number, the model and specifications of the USB module to be tested, test items, test process steps, and task priority. The system includes information such as preset test duration and test pass criteria; the test station refers to a physical workstation used to perform USB module testing operations. The physical workstation is typically equipped with test instruments (such as oscilloscopes, signal generators, USB protocol analyzers), tooling fixtures, data acquisition equipment, and control terminals to independently complete one or more test items for the USB module; the status data stream refers to the set of data generated and output in real time by each test station during the execution of test tasks, reflecting the station's operating status and test process, including but not limited to the operating parameters of the station equipment, real-time test data of the USB module, station resource status, environmental parameters, etc.

[0022] Furthermore, by constructing a global schedulable resource map of the test station based on the state data stream, this embodiment of the invention can solve the resource coordination problem in multi-station synchronous testing and avoid resource idleness or conflict. The global schedulable resource map refers to a digital model used to characterize all schedulable resource entities and their dynamic relationships in the test system. The resource entities include test station nodes, instrument resource nodes, and network topology nodes.

[0023] As an embodiment of the present invention, the step of constructing a global schedulable resource map of the test station based on the state data stream includes: Identify the core resource entity corresponding to the state data stream, and extract the static resource attributes of the core resource entity; Based on the static resource attributes, the core resource entity is instantiated as the resource node corresponding to the test station; Based on the connection configuration data and system layout information of the core resource entities, virtual connection edges are established between the resource nodes; The real-time data in the state data stream is dynamically associated with the resource node and the virtual connection edge to obtain the real-time state label corresponding to the resource node and the virtual connection edge; Based on the resource nodes, the virtual connection edges, and the real-time status labels, a global schedulable resource graph for the test workstation is constructed.

[0024] The core resource entities refer to the basic physical or logical units in the testing system that can be independently identified and scheduled, including the main control equipment of the test station, test instruments, USB modules under test, and data transmission links between test stations; the static resource attributes refer to a set of parameters that describe the inherent capabilities of the core resource entities and do not change with the test task, including information such as device model, unique identifier, supported standards and protocols, performance parameter range, and physical interface type; the resource nodes refer to the abstract model formed by digitally mapping the core resource entities and their static resource attributes in the global schedulable resource graph, with each resource node serving as a unique, addressable, and operable data object in the graph; and the connection configuration data refers to the structured information describing the physical connection relationships between the core resource entities in the testing system. For example, a set of connection configuration data records that switch matrix channel A01 is connected to the power input port of test station 1; the system layout information refers to data describing the physical deployment location of test stations and instruments and their logical subordinate relationships, including station number, cabinet number, and production line number; the virtual connection edge refers to the relationship model established between different resource nodes in the global schedulable resource map based on the connection configuration data and system layout information, used to characterize their logical connection paths; for example, if there are two resource nodes, signal generator A and test station 1, a virtual connection edge can be established between signal generator A and test station 1, indicating that the former can be routed to the latter; the real-time data refers to the data stream continuously collected from the bottom layer of the test system, reflecting the instantaneous changes in the state of core resource entities and virtual connection edges, including equipment self-test status, task occupancy identifier, real-time performance readings, data throughput, error codes, etc.; the dynamic association processing refers to the process of mapping and updating real-time data to its corresponding resource nodes and virtual connection edges; the real-time status label refers to the labeled data attached to resource nodes or virtual connection edges after dynamic association processing, used to intuitively reflect their current state.

[0025] Optionally, the core resource entity corresponding to the status data stream can be determined by parsing a system configuration description file, such as an XML file; the static resource attributes of the core resource entity can be extracted using database queries, such as SQL query statements; the core resource entity can be instantiated into a resource node corresponding to the test station using the class instantiation mechanism in object-oriented programming (OOP); the dynamic association processing between the real-time data in the status data stream and the resource node and the virtual connection edge can be implemented using a complex event processing (CEP) engine.

[0026] To visually represent the various physical resources and their dynamic relationships in a multi-station USB module testing system, please refer to... Figure 2The diagram shown illustrates a globally schedulable resource map of a USB module multi-station synchronous testing and data association method according to an embodiment of the present invention. This map abstracts core resource entities such as test stations and instruments into resource nodes and constructs a logical topology through virtual connection edges, intuitively reflecting the static structure and dynamic operating status of the entire test system's resource pool. Specifically, test stations A and B in the diagram show load states of 65% and 30% respectively. The oscilloscope is marked "busy," while the signal generator is "idle." Simultaneously, USB module SN:001 is "under testing," while SN:002 is "queuing." This panoramic view, by associating and visualizing various resource entities and their dynamic states, allows the scheduling system to gain real-time insight into the operating status of the entire test environment. Based on this, the system can intelligently schedule the module under test SN:002 to the lower-load station B and assign it a currently "idle" signal generator, thereby effectively avoiding resource idleness and conflicts, and achieving efficient collaboration and optimized resource utilization for multi-station testing tasks.

[0027] S2. Based on the global schedulable resource map, generate the resource load balancing table for the test station and create the global test database for the test station.

[0028] This invention generates a resource load balancing table for the test workstations based on the globally schedulable resource map. This clarifies the correlation between the dynamic changes in the resource load of the test workstations and the amount of USB module testing tasks, as well as the impact mechanism of different test items on the workstation resource occupancy rate. This allows for targeted optimization of the task allocation strategy for multi-workstation synchronous testing of USB modules. The resource load balancing table refers to a decision support data set dynamically generated based on the globally schedulable resource map for quantitative evaluation and guidance of task allocation.

[0029] As an embodiment of the present invention, generating the resource load balancing table for the test station based on the globally schedulable resource map includes: Obtain the baseline load balancing threshold of the global schedulable resource graph in its initial state; Based on the baseline load balancing threshold, the ideal load balancing range for the test station is defined. Real-time monitoring of the real-time resource status sequence corresponding to the global schedulable resource map; The average load, standard deviation of load, and load change trend of each resource node in the global schedulable resource map are extracted from the real-time resource status sequence to form the load dynamic characteristics of the test station. Based on the ideal load balancing range and the load dynamic characteristics, a resource load balancing table for the test station is generated.

[0030] The initial state refers to the basic resource state presented by the global schedulable resource graph when the test system has no external test task load and only runs necessary system services. For example, the system state when all test stations and instruments are idle and the connection channels are unobstructed after the system has started up and passed the self-test; the benchmark load balancing threshold refers to the benchmark value determined by statistical analysis in the initial state to measure whether the system load is balanced, wherein the benchmark value includes the maximum allowable load rate of a single resource node and the global average load rate of the system; the ideal load balancing interval refers to the load range for efficient and stable system operation defined based on the benchmark load balancing threshold; the real-time resource status sequence refers to the set of status data of each resource node and virtual connection edge in the global schedulable resource graph, which are recorded continuously in chronological order, and its data items include at least resource identifier, timestamp, real-time load rate, and task queue length; the load average refers to the value obtained by arithmetically averaging the real-time load rate of a certain resource node within a specified time window, used to characterize the average busyness of the node. For example, the average load rate of spectrum analyzer A over the past 5 minutes is 65%; the load standard deviation refers to the dispersion of the real-time load rate of a resource node relative to its load mean within a specified time window, used to characterize the volatility and stability of the node's load; the load change trend refers to an indicator obtained by predicting the load rate trend of a resource node in the short term based on a time series analysis algorithm; the load dynamic characteristics refer to a feature vector composed of the load mean, the load standard deviation, and the load change trend, used to comprehensively describe the historical, current, and future load status of a resource node.

[0031] Optionally, the baseline load balancing threshold of the global schedulable resource map in its initial state can be obtained through statistical analysis methods based on historical data, such as calculating the mean and standard deviation; the real-time resource status sequence corresponding to the global schedulable resource map can be monitored in real time using a time series database, such as the continuous data acquisition and storage function of InfluxDB; and the resource load balancing table of the test station can be generated through a load balancing algorithm, such as a weighted round-robin algorithm.

[0032] Furthermore, this embodiment of the invention creates a global test database for the test station based on the global schedulable resource map, which can upgrade the distributed data storage mode of multi-station USB module testing to a global data association and integration mode, accurately associating the dynamic matching relationship between USB module test data and test station resource status and environmental factors. The global test database refers to a centralized data warehouse built on a unified data model for storing and managing all data related to the entire USB module testing process.

[0033] As an embodiment of the present invention, the step of creating a global test database for the test station based on the globally schedulable resource graph includes: The resource status elements, task flow elements, and data flow elements in the global schedulable resource graph are parsed out. Based on a preset test data model protocol, the resource status elements, the task flow elements, and the data flow elements are respectively encapsulated into resource status units, task context units, and process data units. Based on the resource status unit, the task context unit, and the process data unit, a resource status table, a task context table, and a process data table corresponding to the test station are established respectively to form the database logical structure of the test station. Extract the resource semantic tags from the resource status unit, the test item semantic tags from the task context unit, and the data feature semantic tags from the process data unit; Based on the resource semantic tags, the test item semantic tags, and the data feature semantic tags, a data association index is constructed between the resource status table, the task context table, and the process data table; Based on the data association index, a data association network corresponding to the database logical structure is generated; Based on the data association network and the database logical structure, a global test database for the test station is created.

[0034] The resource status elements refer to a set of data describing the inherent attributes and real-time status of test station and instrument resources, including: equipment identifier, equipment model, performance parameters, real-time load rate, health status, and connection status; the task flow elements refer to a set of data describing the execution process of test tasks, including task identifier, test program path, test step sequence, inter-step dependencies, pass / fail criteria, and task priority; the data flow elements refer to a set of data describing the transmission and storage paths of test data generated in the system, including data source identifier, data format, transmission protocol, target storage location, and data throughput; the test data model protocol refers to a predefined protocol used to standardize resource status. This document outlines a standardized protocol for encapsulating state elements, task flow elements, and data flow elements. It includes data field definitions, data type constraints, format specifications, and validation rules to ensure that different types of elements can be uniformly accessed by the database after encapsulation, avoiding data format chaos. The resource state unit refers to a data object formed by standardizing and encapsulating resource state elements according to the test data model protocol. The task context unit refers to an independent data unit formed by standardizing and encapsulating task flow elements based on the test data model protocol, used to record the complete context information of the test task. The process data unit refers to an independent data unit formed by standardizing and encapsulating data flow elements based on the test data model protocol. The resource status table refers to a structured data table established in the database logical structure based on the structure of the resource status unit, used to store the resource status information of all test workstations; the task context table refers to a structured data table established in the database logical structure based on the structure of the task context unit, used to store the context information of all test tasks, which can be associated with the process data table through the task ID to realize the binding of tasks and test results; the process data table refers to a structured data table established in the database logical structure based on the structure of the process data unit, used to store the core test results and environmental data of the USB module; the database logical structure refers to the structured data table consisting of the resource status table, task context table, and process data table. The database schema consists of tables and process data tables, and the preliminary foreign key relationships between them; the resource semantic tags are machine-readable identifiers extracted from resource status units that characterize resource functions and types; for example, labeling an oscilloscope as 'Measuring Instrument:Oscilloscope:High Frequency'; the test item semantic tags are machine-readable identifiers extracted from task context units that characterize test items and purposes; for example, labeling a test item as 'Performance Test:Power Consumption:Static'; the data feature semantic tags are machine-readable identifiers extracted from process data units that characterize data content and features; for example, labeling a set of waveform data as 'Waveform:Eye Diagram:USB2'.The tag '0'; the data association index refers to an index structure established between records in different data tables based on the similarity of semantic tags or predefined association rules, used to accelerate association queries; for example, an index is established on all records in the resource status table tagged 'Measuring Instrument: Oscilloscope'; the data association network refers to a global data relationship graph composed of all data association indexes and the association relationships between the records they point to.

[0035] Optionally, the resource status elements, task flow elements, and data flow elements in the global schedulable resource graph can be parsed using graph traversal algorithms, such as the depth-first search (DFS) algorithm; the resource status table, task context table, and process data table corresponding to the test station can be established using a data definition language (DDL) of a non-relational database, such as MongoDB; and the data association index between the resource status table, the task context table, and the process data table can be constructed using a knowledge graph embedding model, such as the TransE model.

[0036] S3. Identify the synchronous test instructions and data acquisition requirements corresponding to the test station from the test task flow, and configure the synchronous test controller of the test station in conjunction with the global schedulable resource map.

[0037] This invention identifies the synchronous test instructions and data acquisition requirements corresponding to the test station from the test task flow, and configures the synchronous test controller of the test station in conjunction with the global schedulable resource map. This can accurately solve the timing misalignment problem of traditional independent multi-station testing of USB modules, break through the efficiency bottleneck of manual data aggregation, optimize the resource coordination logic and data association mechanism of multi-station testing, and significantly improve the efficiency and accuracy of USB module testing.

[0038] The synchronous test instruction refers to a set of control commands used to coordinate multiple test stations to maintain precise synchronization in time in order to execute specific collaborative test operations. This includes global trigger signals, synchronization start / stop commands, phase alignment requirements, and delay compensation parameters. For example, a synchronous test instruction can require stations 1, 2, and 3 to simultaneously begin transmitting signals in a specified frequency band within 1 microsecond after receiving the global trigger signal. The data acquisition requirements refer to the standardized requirements for the type, format, accuracy, and timing of test data to complete the test task. These include requirements for the type of data acquired, sampling rate, storage depth, trigger conditions, and data timestamp accuracy. The synchronous test controller is the core logic unit responsible for generating and distributing synchronization control signals and coordinating the data acquisition behavior of each test station, based on the global schedulable resource map and the synchronous test instructions.

[0039] As an embodiment of the present invention, the step of identifying the synchronous test instructions and data acquisition requirements corresponding to the test station from the test task flow, and configuring the synchronous test controller of the test station in conjunction with the global schedulable resource map, includes: Identify the time synchronization constraint boundaries in the synchronous test command and the synchronization timing margin corresponding to the data acquisition requirements; Based on the time synchronization constraint boundary and the synchronization timing margin, a timing coordination relationship is established between the synchronization test command and the data acquisition requirements; Extract the timing characteristics of the hardware interface and the bus communication delay parameters corresponding to the test station; Based on the timing coordination relationship, the priority of the trigger signal corresponding to the timing characteristics of the hardware interface is determined; The multi-channel synchronization triggering conditions corresponding to the data acquisition requirements are parsed out, and combined with the real-time load status of the test station, a dynamic configuration set of synchronization parameters for the test station is generated. Based on the trigger signal priority and the dynamic configuration set of synchronization parameters, a distributed clock synchronization mechanism for the test station is constructed. Configure the synchronous test controller of the test station according to the bus communication delay parameters and the distributed clock synchronization mechanism.

[0040] Wherein, the time synchronization constraint boundary refers to the absolute time tolerance range that must be met for the coordinated action between multiple test stations as specified in the synchronization test instruction; the synchronization timing margin refers to the maximum time margin allowed by the data acquisition requirements for sampling clock phase offset and data effective window offset under the premise of satisfying the correctness of the test function. For example, the synchronization timing margin can be defined as follows: the sampling clock phase difference of each station's data acquisition device must be less than 10 nanoseconds; the timing coordination relationship refers to a mathematical model established based on the time synchronization constraint boundary and the synchronization timing margin, used to uniformly describe the coupling relationship between the synchronization of test actions and the synchronization of data acquisition; the hardware interface timing characteristics refer to the inherent time characteristics of the hardware devices equipped at the test station when responding to control commands, including timing characteristics such as trigger signal transmission delay, instrument programmable command response time, and sampling clock output jitter; the bus communication delay parameter refers to the time delay introduced by the communication bus used by the test system when transmitting control commands and data due to protocol overhead and physical transmission, including the average transmission delay, maximum delay fluctuation, and delay stability of the bus, which is the key basis for configuring a distributed clock synchronization mechanism; the trigger signal priority refers to the execution priority set for various trigger signals issued by the synchronous test controller according to the timing coordination relationship and the importance of the test task; the multi-channel synchronous trigger condition refers to the condition derived from the data acquisition requirements. The system includes several key features: 1) Triggering multi-channel data acquisition, such as simultaneously acquiring the combined conditions of USB module transmission rate, power supply voltage, and interface temperature; 2) Real-time load status, obtained through a globally schedulable resource map, refers to the real-time resource occupancy of the test station when configuring the synchronization controller, including the CPU utilization rate of the station's main control device, memory usage rate, current workload of the test instrument, and bus bandwidth utilization rate, which is the core basis for generating the dynamic configuration set of synchronization parameters; 3) The dynamic configuration set of synchronization parameters, generated based on multi-channel synchronization triggering conditions and the station's real-time load status, is a set of dynamic parameters used to configure the synchronization test controller, including trigger signal delay time, data acquisition interval, and clock calibration cycle, which can be adaptively adjusted according to load changes to ensure stable synchronization performance; 4) The distributed clock synchronization mechanism, based on trigger signal priority and the dynamic configuration set of synchronization parameters, is a calibration mechanism for unifying the clocks of multiple stations, such as the synchronization controller clock, test instrument clock, and main control device clock, including parameters such as clock source selection, clock calibration algorithm, and calibration frequency.

[0041] Optionally, the time synchronization constraint boundary in the synchronous test instruction and the synchronization timing margin corresponding to the data acquisition requirement can be identified using a regular expression rule engine; the timing coordination relationship between the synchronous test instruction and the data acquisition requirement can be established through a directed acyclic graph; and the dynamic configuration set of the synchronization parameters of the test station can be generated using a case-based reasoning (CBR) system.

[0042] As another embodiment of the present invention, the step of constructing a distributed clock synchronization mechanism for the test station based on the trigger signal priority and the dynamic configuration set of synchronization parameters includes: Match the clock synchronization master node corresponding to the priority of the trigger signal; Based on the trigger signal priority and the clock synchronization master node, establish the synchronization hierarchy topology of the test station; Based on the synchronization hierarchy topology, identify the master-slave synchronization relationship between the clock synchronization master node and the slave node; Based on the master-slave synchronization relationship and the dynamic configuration set of synchronization parameters, a clock synchronization strategy is established for all nodes in the synchronization hierarchy topology. Based on the synchronization hierarchy topology and the clock synchronization strategy, a distributed clock synchronization mechanism for the test station is constructed.

[0043] The clock synchronization master node refers to a specific test station or dedicated clock device in a distributed test system that is designated as a time reference source and is responsible for generating and distributing high-precision synchronization clock signals. For example, in a system consisting of multiple workstations, test workstation 1, equipped with a rubidium atomic clock, is designated as the master clock node for the entire system. The synchronization hierarchy topology refers to a topological model constructed based on trigger signal priority and the master clock node, used to clarify the clock synchronization hierarchy relationships among various test-related nodes. It includes the hierarchical affiliation of master and slave nodes and data interaction paths. The slave node, in the distributed clock synchronization mechanism, is a node that lacks the ability to generate a reference clock. It needs to receive the reference clock signal and calibration instructions from the master clock node and achieve clock synchronization with the master node through clock calibration. It is typically a hardware unit in the test workstation that performs auxiliary testing functions or has low clock accuracy, and serves as the execution end for clock synchronization. The master-slave synchronization relationship refers to the logical binding and interaction rules established between the master and slave nodes to achieve clock synchronization. The clock synchronization strategy refers to the specific operational rules and exception handling schemes formulated based on the synchronization hierarchy topology, master-slave synchronization relationship, and dynamic configuration set of synchronization parameters, used to guide all nodes in achieving clock synchronization. The clock synchronization strategy includes clock calibration methods, calibration timing, deviation threshold determination, and fault redundancy mechanisms.

[0044] Optionally, the clock synchronization master node corresponding to the priority of the trigger signal can be matched using a resource scheduling algorithm, such as a priority scheduling algorithm; the synchronization hierarchy topology of the test station can be constructed using a network topology generation algorithm, such as the minimum spanning tree algorithm (MST); and the clock synchronization strategy of all nodes in the synchronization hierarchy topology can be established using a clock synchronization protocol, such as the White Rabbit protocol.

[0045] S4. Monitor the task execution status, hardware resource utilization, and data flow throughput of the test station in real time to generate the resource load balancing table and the collaborative optimization strategy set of the synchronous test controller.

[0046] This invention, through real-time monitoring of the task execution status, hardware resource utilization, and data flow throughput of the test station, generates a set of collaborative optimization strategies for the resource load balancing table and the synchronous test controller. This transforms the dynamic resource status and synchronous control requirements of multi-station testing into quantifiable scheduling parameters, providing clear numerical basis for the accurate adaptation of multi-station resource allocation and synchronous control, and improving the efficiency of judging the overall operating status of the test system and the degree of adaptation to large-scale test requirements.

[0047] The task execution status refers to the dynamic progress of the test station during the execution of the USB module test task, including task completion progress, pending task queue, and task execution exception information. The hardware resource utilization rate refers to the resource usage of the hardware devices in the test station used to support USB module testing, including quantitative indicators such as CPU utilization, memory utilization, and test instrument load rate. The data flow throughput rate refers to the amount of data passing through the data acquisition, transmission, and storage system of the test station per unit time, including instrument sampling data generation rate, bus data transmission rate, and database write rate. The collaborative optimization strategy set refers to a set of decision instructions generated based on a comprehensive analysis of the task execution status, hardware resource utilization rate, and data flow throughput rate to coordinate resource load balancing and synchronous test control, including task dynamic migration strategy, synchronous test sequence rescheduling strategy, data flow priority adjustment strategy, and instrument resource pre-allocation strategy.

[0048] As an embodiment of the present invention, the real-time monitoring of the task execution status, hardware resource utilization, and data flow throughput of the test station to generate the resource load balancing table and the collaborative optimization strategy set of the synchronous test controller includes: Based on the task execution status, the hardware resource utilization rate, and the data flow throughput, calculate the system coordination efficiency index and load imbalance risk coefficient of the test station. Based on the system coordination efficiency index and the load imbalance risk coefficient, determine the load balancing optimization intensity level and synchronization control guarantee level corresponding to the test station; Based on the load balancing optimization intensity level, the load balancing strategy items in the resource load balancing table are selected; Based on the aforementioned synchronization control protection level, the synchronization control parameter items in the synchronization test controller are parsed out; Establish a policy coupling relationship mapping table between the load balancing policy items and the synchronization control parameter items; Based on the strategy coupling relationship mapping table, a resource reallocation instruction set corresponding to the load balancing strategy item and a parameter reconfiguration instruction set corresponding to the synchronization control parameter item are generated. Based on the resource reallocation instruction set and the parameter reconfiguration instruction set, a collaborative optimization strategy set for the resource load balancing table and the synchronous test controller is generated.

[0049] The system coordination efficiency index refers to a comprehensive score calculated by weighted fusion of multiple key performance indicators, including task execution status, hardware resource utilization, and data flow throughput, to quantitatively evaluate the overall operating efficiency of the test system. These key performance indicators include the average task progress rate, average resource utilization, standard deviation of resource utilization, task timeout ratio, average data throughput, and data flow interruption frequency of the test station. The load imbalance risk coefficient is a comprehensive risk value used to warn of impending performance bottlenecks or failures due to uneven load distribution. The load balancing optimization intensity level refers to the intervention level for load balancing operations based on the system coordination efficiency index and the load imbalance risk coefficient. The synchronization control guarantee level refers to the accuracy guarantee level for synchronization test control based on the system coordination efficiency index and the load imbalance risk coefficient, including three levels: high-precision guarantee, balance guarantee, and rapid response guarantee. The load balancing strategy item refers to resource... The load balancing table contains dynamically adjustable load allocation rules, including task allocation algorithms, resource reservation strategies, and load migration trigger conditions. The synchronization control parameter items refer to dynamically configurable operating parameters in the synchronization test controller, including trigger delay compensation values, clock synchronization cycles, and synchronization timeout thresholds. The strategy coupling relationship mapping table defines how adjustments to load balancing strategy items and synchronization control parameter items affect each other. The resource reallocation instruction set is a set of specific instructions generated based on the load balancing strategy items in the strategy coupling relationship mapping table to adjust the allocation of test station resources and tasks, including task splitting instructions, resource allocation instructions, and load threshold adjustment instructions. The parameter reconfiguration instruction set is a set of specific instructions generated based on the synchronization control parameter items in the strategy coupling relationship mapping table to adjust the parameters of the synchronization test controller, including calibration cycle adjustment instructions, instruction delay setting instructions, and deviation threshold modification instructions.

[0050] Optionally, the policy coupling relationship mapping table between the load balancing policy item and the synchronization control parameter item can be established by an association rule mining algorithm, such as the Apriori algorithm; the collaborative optimization policy set of the resource load balancing table and the synchronization test controller can be generated using a reinforcement learning algorithm, such as the Q-learning algorithm.

[0051] As another embodiment of the present invention, the calculation of the load imbalance risk coefficient of the test station based on the task execution status, the hardware resource utilization rate, and the data flow throughput rate includes: Identify the peak resource utilization rate and its duration in the hardware resource utilization rate; Extract the task queue stack length and high-priority task waiting time of the test station under the task execution state; Identify the peak instantaneous throughput and transmission delay fluctuation amplitude corresponding to the test station from the data stream throughput; The load imbalance risk coefficient of the test station is calculated by integrating the peak resource utilization rate and its duration, the task queue backlog length, the waiting time of the high-priority tasks, the peak instantaneous throughput, and the transmission delay fluctuation amplitude.

[0052] The peak resource utilization rate refers to the highest percentage of utilization of any key hardware resource in the test station within a specified monitoring time window. Key hardware resources include, but are not limited to, CPU, memory, test instrument ports, and bus bandwidth. The duration refers to the continuous or cumulative time during which the peak resource utilization rate exceeds a preset system safety threshold within the monitoring time window. The task queue backlog length refers to the number of test tasks ready but not yet allocated execution resources in the task buffer of the test station. The high-priority task waiting time refers to the time delay experienced by a high-priority test task from entering the ready state to actually being executed; for example, a high-priority emergency calibration task has been waiting in the queue for more than 30 seconds. The peak instantaneous throughput rate refers to the highest instantaneous value of the amount of data processed per unit time by the data acquisition or transmission system of the test station within a specified monitoring time window. The transmission delay fluctuation amplitude refers to the fluctuation range of the delay time of test instructions or data packets transmitted in the system's internal network or bus within a specified monitoring time window, usually characterized by standard deviation or maximum-minimum delay difference.

[0053] Optionally, a sliding window peak detection algorithm, such as a time-series-based local maximum detection algorithm, can be used to identify the peak resource utilization rate and its duration in the hardware resource utilization rate; a real-time task scheduling monitor, such as a priority queue-based monitoring module, can be used to extract the task queue accumulation length and high-priority task waiting time of the test station in the task execution state; and a network traffic analysis tool, such as Wireshark, can be used to identify the instantaneous throughput peak and transmission delay fluctuation amplitude corresponding to the test station from the data flow throughput.

[0054] For example, the load imbalance risk coefficient of the test station is calculated using the following formula. It should be noted that this calculation method is only one possible method and does not affect the implementation of the basic scheme above: in, Indicates the load imbalance risk coefficient. Indicates peak resource utilization. This indicates the safe upper limit for peak resource utilization. Indicates duration, This indicates the upper limit of tolerance for duration. Indicates the length of the task queue. Indicates the maximum allowed length of the task queue. Indicates the waiting time for high-priority tasks. This indicates the maximum tolerable waiting time for high-priority tasks. Indicates the peak instantaneous throughput. This represents the average data throughput. This indicates the maximum data throughput supported by the system. Indicates the amplitude of transmission delay fluctuation. This indicates the upper limit of tolerance for transmission delay fluctuations.

[0055] It should be noted that the above formula can achieve a multi-dimensional comprehensive assessment of the risk of load imbalance at test stations, improving the accuracy of resource scheduling and synchronization control. Among these, the resource overload item... This is used to reflect the synergistic destructive effect of instantaneous high load and its duration. For example, a brief peak in CPU usage has a limited impact on system stability, but if the peak continues to exceed a safe threshold... If this value increases significantly, it indicates that hardware resources are at risk of overload; task backlog item Used to characterize the saturation level of the system's processing capacity, when the number of queued tasks approaches the system's design limit. At that time, this item's contribution to the total risk increases quadratically, and the early warning system's throughput is about to reach its bottleneck; scheduling delay item Used to assess the degradation of real-time performance assurance capabilities, including high-priority task latency. The extension directly reflects the congestion status of the system's core response link; data surge items Used to quantify the impact of instantaneous data traffic on the average load; a higher value indicates that the system is experiencing sudden data pressure, which can easily lead to data acquisition loss or storage delay; communication instability item This is used to monitor the reliability of the internal collaborative infrastructure of the system, including transmission delay jitter. The increase of this factor will directly weaken the timing accuracy of multi-station synchronous testing. This factor can be squared to amplify its negative impact on overall stability.

[0056] S5. Read the unique identifier of the USB module, and based on the unique identifier, establish an association mapping network of the USB module in the global test database.

[0057] This invention, by reading the unique identifier of the USB module, can dynamically match the identity binding relationship between multi-source test data and the target module, accurately trace the correlation between performance parameters and process waveforms generated at each test station and the full life cycle quality of a specific module. The unique identifier refers to a globally unique identity credential assigned to each USB module through module programming, barcode marking, or radio frequency identification. The identity credential includes a unique combination of features such as serial number, hardware version number, and firmware identifier. Optionally, the unique identifier of the USB module can be read by a device descriptor parser, such as a USB protocol analyzer.

[0058] Furthermore, this embodiment of the invention establishes an association mapping network for the USB module in the global test database based on the unique identifier. This allows fragmented synchronous test data generated in parallel by multiple workstations to be aggregated into a traceable data chain with complete contextual relationships through a unified identifier. This solves the problem of data source confusion caused by test concurrency and achieves precise binding of test results to physical modules. The association mapping network refers to a structured association system established in the global test database with the unique identifier of the USB module as the root node, which associates with all relevant resource status data, task context data, and process data.

[0059] As an embodiment of the present invention, establishing an association mapping network for the USB module in the global test database based on the unique identifier includes: Identify the unique identifier's encoding rules and extract the resource status table, task context table, and process data table from the global test database; According to the identification encoding rules, create a data association field for the USB module in the resource status table, the task context table, and the process data table; Based on the data association field, the frequency of access to the USB module in the global test database is counted. Based on the access frequency, set the database index optimization strategy for the USB module; Perform predefined association query processing on the global test database to associate the multimodal test data of the USB module in real time; Based on the multimodal test data, the relationships between the data tables of the USB module are analyzed; By establishing the relationships between the data tables, a personalized data association view for the USB module can be set. By combining the personalized data association view and the database index optimization strategy, an association mapping network for the USB module is established in the global test database.

[0060] The identification encoding rule refers to the preset encoding specification used to generate and parse unique identifiers, including encoding structure, field meanings, and verification rules, to ensure the uniqueness and resolvability of the unique identifier in the global test database, achieving accurate identification and association of USB modules; the resource status table refers to a data table used to store the static attributes and dynamic status of test workstations and instrument resources; the task context table refers to a data table used to store test task description information; the process data table refers to a data table used to store the raw results and performance readings generated during test execution; the data association field refers to a field created in the resource status table, task context table, and process data table to associate with the unique identifier, for example, creating a field named USB_SN in the process data table to store the serial number of the USB module that generated the test data; the access frequency refers to the frequency of access to relevant data of a specific USB module through the data association field within a certain time window. The number of query, update, or insert operations; the database index optimization strategy refers to the strategy of adjusting the index type, storage location, etc. of USB module-related data in the global test database based on the access frequency; the predefined association query processing refers to the operation of executing pre-written and stored query statements in the database to obtain complete test information of a specific USB module through cross-table association; the multimodal test data refers to the set of test results of different types and structures generated by different test stations and different test instruments, including numerical data (such as voltage and current), waveform data (such as eye diagrams and spectrum diagrams), and protocol data (such as USB transaction decoding logs); the data table association relationship refers to the field matching logic relationship between the resource status table, task context table, and process data table used for connecting queries, determined by analyzing the source of multimodal test data; the personalized data association view refers to a customized virtual data table created for a specific type or batch of USB modules based on the data table association relationship.

[0061] Optionally, the data association fields of the USB module can be created in the resource status table, the task context table, and the process data table using a data definition language provided by the database management system, such as the ALTER TABLE language of SQL; a database query engine, such as the JOIN operation based on SQL, can be used to perform predefined association query processing of the global test database; the personalized data association view of the USB module can be constructed using a database view creation statement, such as the CREATE VIEW statement of SQL.

[0062] As another embodiment of the present invention, the step of setting the database index optimization strategy of the USB module according to the access frequency includes: Based on the access frequency, determine the data query characteristics and system response timeliness indicators corresponding to the USB module; Based on the data query characteristics, the index structure type and data sharding parameters corresponding to the USB module are set; Based on the system response timeliness index, determine the index update triggering condition and cache preloading rule corresponding to the USB module; By integrating the index structure type, the data sharding parameters, the index update triggering conditions, and the cache preloading rules, a database index optimization strategy for the USB module is generated.

[0063] The data query characteristics refer to a set of key attributes reflecting data query behavior patterns, including query fields, query methods, query frequency distribution, and access patterns of related tables. The system response timeliness indicators refer to performance metrics preset by the database for querying data requests from the USB module, such as the maximum allowed response time and data loading latency threshold, based on the business importance and access frequency of the USB module data. These metrics constrain the effectiveness boundaries of index optimization. The index structure type refers to the specific index implementation method selected to optimize query performance, including B+ tree indexes, hash indexes, and bitmap indexes. Optionally, the index structure type can be set using automated index management tools such as pt-index-usage. The data sharding parameters refer to key partitioning parameters set to achieve distributed data storage and parallel processing, including sharding dimensions, number of shards, and shard storage node mapping rules. Optionally, the data sharding parameters can be set using data routing middleware. The index update triggering conditions refer to the start conditions controlling index reconstruction or optimization, including time-based triggering conditions, data volume triggering conditions, and performance decay triggering conditions. The cache preloading rules refer to preset rules based on access frequency and system response timeliness indicators for preloading USB data. The rules for preloading frequently accessed data from modules into the database cache include the range of preloaded data, the loading timing, and the cache validity period. Optionally, the cache preloading rules can be implemented using machine learning prediction models, such as ARIMA time series prediction.

[0064] Optionally, the data query characteristics corresponding to the USB module can be determined by a database query plan analyzer, such as the EXPLAIN command; the system response timeliness indicators can be obtained using a database performance benchmarking tool, such as the sysbench tool; and the index update triggering conditions corresponding to the USB module can be determined by a database trigger, such as SQL Trigger.

[0065] S6. Based on the global schedulable resource map, the resource load balancing table, the synchronous test controller, the collaborative optimization strategy set, and the association mapping network, perform multi-station synchronous testing and data association processing of the USB module to obtain test data association results.

[0066] This invention, through its embodiments, utilizes the global schedulable resource map, the resource load balancing table, the synchronous test controller, the collaborative optimization strategy set, and the associated mapping network to perform multi-station synchronous testing and data association processing of the USB module, obtaining test data association results. This achieves a deep fit between the testing process and the dynamic scheduling requirements of multi-station resources, as well as the timing matching requirements of high-precision synchronous control. Simultaneously, it avoids the rigidity of the testing process caused by reliance on manual aggregation or the test data association obstacles caused by data silos, significantly improving the collaborative efficiency of USB module testing and the full lifecycle data traceability capability.

[0067] The test data association result refers to the structured output formed after intelligent aggregation and relationship mining of multi-station synchronous test data through the association mapping network. Specifically, the core logic for generating the test data association result includes: first, extracting the workstation resource nodes and instrument topology relationships that match the test tasks from the global schedulable resource map; then, using the resource load balancing table to dynamically distribute test tasks and formulate resource allocation strategies; next, generating inter-workstation synchronization trigger instructions based on the synchronous test controller; and triggering the collaborative optimization strategy set to perform load balancing and dynamic adjustment of synchronization parameters by real-time monitoring of the task execution status and data flow throughput of each workstation; simultaneously, the association mapping network continuously binds the generated multimodal test data with the unique identifier of the USB module, establishing a full-link association relationship between test data, resource status, and task context; finally, outputting a structured test report with full-process traceability, multi-dimensional association, and anomaly diagnosability.

[0068] Compared to the problems described in the background art, this embodiment of the invention, by constructing a globally schedulable resource map of the test station based on the state data stream, can solve the resource coordination problem in multi-station synchronous testing, avoiding resource idleness or conflicts. Furthermore, by generating a resource load balancing table for the test station based on the globally schedulable resource map, this embodiment of the invention can clarify the correlation between the dynamic changes in test station resource load and the amount of USB module testing tasks, as well as the impact mechanism of different test items on the station resource occupancy rate, thereby optimizing the task allocation strategy for multi-station synchronous testing of USB modules. Finally, by creating a global test database for the test station based on the globally schedulable resource map, this embodiment of the invention can... The distributed data storage mode of SB module multi-station testing has been upgraded to a global data association and integration mode, accurately associating the dynamic matching relationship between USB module test data and test station resource status and environmental factors. Furthermore, this embodiment of the invention identifies the synchronous test instructions and data acquisition requirements corresponding to each test station from the test task flow, and configures the synchronous test controller of each test station in conjunction with the globally schedulable resource map. This accurately solves the timing misalignment problem of traditional independent multi-station USB module testing, breaks through the efficiency bottleneck of manual data aggregation, optimizes the resource collaboration logic and data association mechanism of multi-station testing, and significantly improves the efficiency and accuracy of USB module testing. This embodiment of the invention also monitors the test station in real time... The task execution status, hardware resource utilization, and data flow throughput are used to generate the resource load balancing table and the collaborative optimization strategy set of the synchronous test controller. This transforms the dynamic resource status and synchronization control requirements of multi-station testing into quantifiable scheduling parameters, providing clear numerical basis for the accurate adaptation of multi-station resource allocation and synchronization control, and improving the efficiency of judging the overall operating status of the test system and the degree of adaptation to large-scale test requirements. Furthermore, this embodiment of the invention establishes an association mapping network of the USB module in the global test database based on the unique identifier. This allows fragmented synchronous test data generated by multi-station parallelism to be aggregated into a traceable data chain with complete contextual relationships through a unified identifier. To address the data source confusion caused by test concurrency, this invention achieves precise binding of test results to physical modules. Finally, based on the global schedulable resource graph, the resource load balancing table, the synchronous test controller, the collaborative optimization strategy set, and the association mapping network, this embodiment of the invention executes multi-station synchronous testing and data association processing of the USB module, obtaining test data association results. This achieves a deep fit between the testing process and the dynamic scheduling requirements of multi-station resources, as well as the timing matching requirements of high-precision synchronous control. Simultaneously, it avoids the rigidity of the testing process caused by reliance on manual aggregation or the test data association obstacles caused by data silos, significantly improving the collaborative efficiency of USB module testing and the full lifecycle data traceability capability.Therefore, this invention can achieve precise synchronous control of multi-station testing and automatic correlation analysis of test data.

[0069] like Figure 3 The diagram shown is a functional block diagram of a USB module multi-station synchronous testing and data association system according to the present invention.

[0070] The USB module multi-station synchronous testing and data association system 200 described in this invention can be installed in an electronic device. Depending on the functions implemented, the USB module multi-station synchronous testing and data association system may include a resource scheduling module 201, a data storage module 202, a synchronization control module 203, a test optimization module 204, a data association module 205, and a test execution module 206. The module described in this invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and which are stored in the memory of the electronic device.

[0071] In this embodiment of the invention, the functions of each module / unit are as follows: The resource scheduling module 201 is used to receive the test task stream of the USB module and collect the status data stream of each test station in real time, and construct a global schedulable resource map of the test station based on the status data stream. The data storage module 202 is used to generate a resource load balancing table for the test station based on the global schedulable resource map, and to create a global test database for the test station. The synchronization control module 203 is used to identify the synchronization test instructions and data acquisition requirements corresponding to the test station from the test task flow, so as to configure the synchronization test controller of the test station in combination with the global schedulable resource map. The test optimization module 204 is used to monitor the task execution status, hardware resource utilization and data flow throughput of the test station in real time, so as to generate the resource load balancing table and the collaborative optimization strategy set of the synchronous test controller. The data association module 205 is used to read the unique identifier of the USB module and, based on the unique identifier, establish an association mapping network of the USB module in the global test database; The test execution module 206 is used to perform multi-station synchronous testing and data association processing of the USB module based on the global schedulable resource map, the resource load balancing table, the synchronous test controller, the collaborative optimization strategy set, and the association mapping network, to obtain test data association results.

[0072] In detail, the modules in the USB module multi-station synchronous testing and data association system 200 described in this embodiment of the invention adopt the same methods as described above when in use. Figure 1 The USB module multi-station synchronous testing and data association method described herein uses the same technical means and can produce the same technical effect, so it will not be elaborated here.

[0073] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0074] Finally, it should be noted that in the above embodiments, each embodiment can be combined with each other or independent. Deleting any one of them will not affect the technical implementation of other embodiments. The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for multi-station synchronous testing and data association of a USB module, characterized in that, The method includes: Receive the test task stream from the USB module and collect the status data stream of each test station in real time. Based on the status data stream, construct a global schedulable resource map of the test station. Based on the global schedulable resource map, a resource load balancing table for the test station is generated, and a global test database for the test station is created. Identify the synchronous test instructions and data acquisition requirements corresponding to the test station from the test task flow, and configure the synchronous test controller of the test station in conjunction with the global schedulable resource map; The task execution status, hardware resource utilization, and data flow throughput of the test station are monitored in real time to generate the resource load balancing table and the collaborative optimization strategy set of the synchronous test controller. Read the unique identifier of the USB module, and based on the unique identifier, establish an association mapping network of the USB module in the global test database; Based on the global schedulable resource map, the resource load balancing table, the synchronous test controller, the collaborative optimization strategy set, and the association mapping network, the multi-station synchronous test and data association processing of the USB module are performed to obtain the test data association results.

2. The method for multi-station synchronous testing and data association of USB modules as described in claim 1, characterized in that, The step of identifying the synchronous test instructions and data acquisition requirements corresponding to the test station from the test task flow, and configuring the synchronous test controller of the test station in conjunction with the global schedulable resource map, includes: Identify the time synchronization constraint boundaries in the synchronous test command and the synchronization timing margin corresponding to the data acquisition requirements; Based on the time synchronization constraint boundary and the synchronization timing margin, a timing coordination relationship is established between the synchronization test command and the data acquisition requirements; Extract the timing characteristics of the hardware interface and the bus communication delay parameters corresponding to the test station; Based on the timing coordination relationship, the priority of the trigger signal corresponding to the timing characteristics of the hardware interface is determined; The multi-channel synchronization triggering conditions corresponding to the data acquisition requirements are parsed out, and combined with the real-time load status of the test station, a dynamic configuration set of synchronization parameters for the test station is generated. Based on the trigger signal priority and the dynamic configuration set of synchronization parameters, a distributed clock synchronization mechanism for the test station is constructed. Configure the synchronous test controller of the test station according to the bus communication delay parameters and the distributed clock synchronization mechanism.

3. The method for multi-station synchronous testing and data association of USB modules as described in claim 2, characterized in that, The distributed clock synchronization mechanism for the test station, based on the trigger signal priority and the dynamic configuration set of synchronization parameters, includes: Match the clock synchronization master node corresponding to the priority of the trigger signal; Based on the trigger signal priority and the clock synchronization master node, establish the synchronization hierarchy topology of the test station; Based on the synchronization hierarchy topology, identify the master-slave synchronization relationship between the clock synchronization master node and the slave node; Based on the master-slave synchronization relationship and the dynamic configuration set of synchronization parameters, a clock synchronization strategy is established for all nodes in the synchronization hierarchy topology. Based on the synchronization hierarchy topology and the clock synchronization strategy, a distributed clock synchronization mechanism for the test station is constructed.

4. The method for multi-station synchronous testing and data association of USB modules as described in claim 1, characterized in that, The step of establishing an association mapping network for the USB module in the global test database based on the unique identifier includes: Identify the unique identifier's encoding rules and extract the resource status table, task context table, and process data table from the global test database; According to the identification encoding rules, create a data association field for the USB module in the resource status table, the task context table, and the process data table; Based on the data association field, the frequency of access to the USB module in the global test database is counted. Based on the access frequency, set the database index optimization strategy for the USB module; Perform predefined association query processing on the global test database to associate the multimodal test data of the USB module in real time; Based on the multimodal test data, the relationships between the data tables of the USB module are analyzed; By establishing the relationships between the data tables, a personalized data association view for the USB module can be set. By combining the personalized data association view and the database index optimization strategy, an association mapping network for the USB module is established in the global test database.

5. The method for multi-station synchronous testing and data association of USB modules as described in claim 4, characterized in that, The step of setting the database index optimization strategy for the USB module based on the access frequency includes: Based on the access frequency, determine the data query characteristics and system response timeliness indicators corresponding to the USB module; Based on the data query characteristics, the index structure type and data sharding parameters corresponding to the USB module are set; Based on the system response timeliness index, determine the index update triggering condition and cache preloading rule corresponding to the USB module; By integrating the index structure type, the data sharding parameters, the index update triggering conditions, and the cache preloading rules, a database index optimization strategy for the USB module is generated.

6. The method for multi-station synchronous testing and data association of USB modules as described in claim 1, characterized in that, The step of constructing a global schedulable resource map for the test station based on the state data stream includes: Identify the core resource entity corresponding to the state data stream, and extract the static resource attributes of the core resource entity; Based on the static resource attributes, the core resource entity is instantiated as the resource node corresponding to the test station; Based on the connection configuration data and system layout information of the core resource entities, virtual connection edges are established between the resource nodes; The real-time data in the state data stream is dynamically associated with the resource node and the virtual connection edge to obtain the real-time state label corresponding to the resource node and the virtual connection edge; Based on the resource nodes, the virtual connection edges, and the real-time status labels, a global schedulable resource graph for the test workstation is constructed.

7. The method for multi-station synchronous testing and data association of USB modules as described in claim 1, characterized in that, The step of generating a resource load balancing table for the test workstation based on the globally schedulable resource graph includes: Obtain the baseline load balancing threshold of the global schedulable resource graph in its initial state; Based on the baseline load balancing threshold, the ideal load balancing range for the test station is defined. Real-time monitoring of the real-time resource status sequence corresponding to the global schedulable resource map; The average load, standard deviation of load, and load change trend of each resource node in the global schedulable resource map are extracted from the real-time resource status sequence to form the load dynamic characteristics of the test station. Based on the ideal load balancing range and the load dynamic characteristics, a resource load balancing table for the test station is generated.

8. The method for multi-station synchronous testing and data association of USB modules as described in claim 1, characterized in that, The step of creating a global test database for the test station based on the globally schedulable resource graph includes: The resource status elements, task flow elements, and data flow elements in the global schedulable resource graph are parsed out. Based on a preset test data model protocol, the resource status elements, the task flow elements, and the data flow elements are respectively encapsulated into resource status units, task context units, and process data units. Based on the resource status unit, the task context unit, and the process data unit, a resource status table, a task context table, and a process data table corresponding to the test station are established respectively to form the database logical structure of the test station. Extract the resource semantic tags from the resource status unit, the test item semantic tags from the task context unit, and the data feature semantic tags from the process data unit; Based on the resource semantic tags, the test item semantic tags, and the data feature semantic tags, a data association index is constructed between the resource status table, the task context table, and the process data table; Based on the data association index, a data association network corresponding to the database logical structure is generated; Based on the data association network and the database logical structure, a global test database for the test station is created.

9. The method for multi-station synchronous testing and data association of USB modules as described in claim 1, characterized in that, The real-time monitoring of the task execution status, hardware resource utilization, and data throughput of the test station to generate the resource load balancing table and the collaborative optimization strategy set of the synchronous test controller includes: Based on the task execution status, the hardware resource utilization rate, and the data flow throughput, calculate the system coordination efficiency index and load imbalance risk coefficient of the test station. Based on the system coordination efficiency index and the load imbalance risk coefficient, determine the load balancing optimization intensity level and synchronization control guarantee level corresponding to the test station; Based on the load balancing optimization intensity level, the load balancing strategy items in the resource load balancing table are selected; Based on the aforementioned synchronization control protection level, the synchronization control parameter items in the synchronization test controller are parsed out; Establish a policy coupling relationship mapping table between the load balancing policy items and the synchronization control parameter items; Based on the strategy coupling relationship mapping table, a resource reallocation instruction set corresponding to the load balancing strategy item and a parameter reconfiguration instruction set corresponding to the synchronization control parameter item are generated. Based on the resource reallocation instruction set and the parameter reconfiguration instruction set, a collaborative optimization strategy set for the resource load balancing table and the synchronous test controller is generated.

10. A multi-station synchronous testing and data association system for USB modules, characterized in that, The system includes: The resource scheduling module is used to receive the test task stream from the USB module and collect the status data stream of each test station in real time. Based on the status data stream, it constructs a global schedulable resource map of the test station. The data storage module is used to generate a resource load balancing table for the test station based on the global schedulable resource map, and to create a global test database for the test station. The synchronization control module is used to identify the synchronization test instructions and data acquisition requirements corresponding to the test station from the test task flow, and to configure the synchronization test controller of the test station in conjunction with the global schedulable resource map. The test optimization module is used to monitor the task execution status, hardware resource utilization and data flow throughput of the test station in real time, so as to generate the resource load balancing table and the set of collaborative optimization strategies of the synchronous test controller. The data association module is used to read the unique identifier of the USB module and, based on the unique identifier, establish an association mapping network of the USB module in the global test database. The test execution module is used to perform multi-station synchronous testing and data association processing of the USB module based on the global schedulable resource graph, the resource load balancing table, the synchronous test controller, the collaborative optimization strategy set, and the association mapping network, and to obtain test data association results.

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