POS-based NFCTAG test method and system

Abstract

The invention discloses a POS-based NFCTAG test method and system, and the method comprises the steps: generating a test task sequence based on a preset test matrix, and controlling a rotating disc to rotate and position a target test mobile phone; resolving a target space pose of an end effector of the mechanical arm through a space coordinate transformation algorithm and planning a collision-free motion path so as to establish an NFC communication link; in the NFC communication link establishment process, multi-modal feedback data are synchronously collected and input to a predefined success criterion decision model for fusion analysis, and a single test result is generated; and iteratively executing all test tasks and recording results, and finally generating a comprehensive evaluation report. According to the invention, full automation of the test process and systematic coverage of multi-dimensional parameters are realized, and the problems of low test efficiency, poor result consistency and limited test scene coverage in the existing test method are effectively solved.

Description

Technical Field

[0001] This invention relates to the field of automated testing technology for wireless communication devices, and in particular to a POS-based NFCTAG testing method and system. Background Technology

[0002] Near Field Communication (NFC) technology, a short-range wireless communication technology based on the principle of electromagnetic induction, has been widely used in financial payments, access control systems, and smart homes. Especially in financial POS devices, NFC performance (including swiping distance, recognition rate, and reading time) directly impacts user experience and product reputation. With the rapid development of mobile payment technology, the testing requirements for NFC tags on POS devices are becoming increasingly stringent, requiring testing of performance under various combinations of positions, angles, and distances. Currently, testing methods for the NFC function of POS devices mainly include manual testing and semi-automatic testing. Manual testing typically requires operators to place the test card or mobile phone above or below the device under test, manually move the test card to adjust the distance, and use tools such as a ruler to measure the distance value; the core operation relies on human intervention. Semi-automatic testing methods use some simple mechanical structures to assist in testing, but they still essentially cannot eliminate human operation.

[0003] While existing testing methods can basically verify NFC functionality, they have significant limitations. First, the entirely manual operation requires manual adjustment of positions, phone replacement, and data recording for each test, making the process time-consuming and labor-intensive, unsuitable for large-scale production testing. Furthermore, manual operation makes it difficult to ensure consistent testing conditions each time, especially for key parameters such as the angle and distance between the test card or phone and the device, leading to significant differences and instability in test results, affecting their reliability and comparability. Due to the limitations of manual operation, existing testing methods cannot systematically and comprehensively cover all possible testing scenarios, such as combinations of tests with different angles, distances, and phone models, potentially failing to detect certain underlying problems. Moreover, relying on simple tools like rulers for distance measurement introduces significant human error, and manually observing the device's response to determine test success is subjective and uncertain. In addition, manually recording test results is not only inefficient and prone to errors, but also lacks effective data management and analysis methods, making it difficult to deeply mine and analyze test data to form quantifiable test indicators and a systematic testing methodology.

[0004] These shortcomings make existing NFC testing methods unable to meet the ever-increasing testing demands, especially in scenarios requiring large-scale, high-efficiency, and high-precision automated testing, where the limitations of existing methods become even more apparent. Therefore, how to construct a complete testing system capable of automatically executing multi-factor, multi-level combined tests, achieving precise control of test parameters, full automation of the testing process, and systematic analysis of test data has become a key issue that needs to be addressed to improve the NFCTAG testing level of POS devices. Summary of the Invention

[0005] In view of this, the purpose of this invention is to propose a POS-based NFCTAG testing method and system, which generates task sequences by pre-setting a test matrix and coordinates the control of a robotic arm and a turntable to achieve automated testing of multi-dimensional parameters, solving the problems of low efficiency, poor consistency and difficulty in systematically covering multi-dimensional combination scenarios in manual testing.

[0006] To achieve the aforementioned technical objectives, in a first aspect, the present invention provides a POS-based NFCTAG testing method, comprising: A test task sequence is generated based on a preset test matrix. The test task sequence is used to execute test tasks. The preset test matrix defines a combination of multi-dimensional test parameters, which include at least test point location, spatial angle, and communication distance. According to the test task sequence, control the turntable to rotate to position the target test mobile phone to the predetermined initial position within the working range of the robotic arm; Based on the spatial coordinate transformation algorithm, the target spatial pose of the end effector of the robotic arm is calculated according to the multi-dimensional test parameters in the current test task, and a collision-free motion path is planned. The robotic arm is controlled to move along a collision-free motion path, so that the POS device installed on the end effector and the target test mobile phone reach the target spatial pose to establish an NFC communication link; During the establishment of the NFC communication link, multimodal feedback data from the communication interface between the vibration sensor and the POS device are collected simultaneously. Multimodal feedback data is input into a predefined success criterion decision model for fusion analysis to generate communication results and performance metrics for a single test. Iteratively execute the remaining test tasks in the test task sequence and record the communication results and performance metrics generated during all iterations; Based on all recorded communication results and performance metrics, a comprehensive evaluation report on the NFC TAG performance of POS devices is generated.

[0007] In some embodiments, a test task sequence is generated based on a preset test matrix, including: Obtain the preset test matrix, which contains the full combination of multi-dimensional test parameters; Establish a first user coordinate system fixed to the turntable, and based on the first user coordinate system, calculate the pose that the end effector of the robot arm needs to reach through forward kinematics based on the test point position and spatial angle in the multidimensional test parameters, and generate an initial coordinate set. Starting from the current spatial coordinates of the robotic arm, a weighted undirected graph is constructed based on the Traveling Salesman Problem optimization algorithm. The vertices of the weighted undirected graph are the spatial coordinates of each target in the initial coordinate set. The weight of the edge is defined by the Euclidean distance between the corresponding two vertices. The Hamiltonian circuit in the weighted undirected graph is solved by a heuristic search algorithm to find the shortest path. Based on the vertex visit order defined by the shortest path, the test tasks in the preset test matrix are reordered to generate an optimized test task sequence.

[0008] In some embodiments, based on a spatial coordinate transformation algorithm, the target spatial pose of the robotic arm's end effector is calculated according to the multidimensional test parameters in the current test task, including: Establish a second user coordinate system fixed to the turntable, and establish a tool coordinate system fixed to the end effector; Based on the test points in the multidimensional test parameters, determine the coordinates of the target contact point on the target test mobile phone in the second user coordinate system. Based on the spatial angles in the multidimensional test parameters, calculate the rotation transformation matrix of the tool coordinate system relative to the user coordinate system; Based on the target contact point coordinates and the rotation transformation matrix, the target position in the user coordinate system is calculated using the coordinate system chain multiplication tool, with the origin of the coordinate system in the user coordinate system as the target position. By combining the target position and the rotation transformation matrix, the complete target space pose of the end effector is generated.

[0009] In some embodiments, planning a collision-free motion path includes: Based on the target spatial pose and the current spatial pose of the robotic arm, an initial motion trajectory is generated through a path interpolation algorithm; Based on the relative positional relationship between the tool coordinate system and the second user coordinate system, calculate the minimum safe distance threshold during the movement of the robotic arm; Multiple path checkpoints are set on the initial motion trajectory, and the minimum safe distance threshold is verified at each path checkpoint. When any path checkpoint does not meet the minimum safe distance threshold, an alternative motion path to bypass the obstacle area is generated through a trajectory replanning algorithm. Based on the verification results of all path checkpoints, the final confirmed collision-free motion path is output.

[0010] In some embodiments, during the establishment of the NFC communication link, multimodal feedback data from the communication interface between the vibration sensor and the POS device is collected synchronously, including: The mechanical vibration waveform data generated by the target test mobile phone during NFC communication is collected in real time using a vibration sensor. The status command code stream corresponding to the NFC transaction process inside the device is obtained in real time through the communication interface of the POS device. The mechanical vibration waveform data and the status command code stream are time-stamped and synchronized to generate time-aligned multimodal feedback data.

[0011] In some embodiments, multimodal feedback data is input into a predefined success criterion decision model for fusion analysis to generate communication results and performance metrics for a single test, including: Feature extraction is performed on the mechanical vibration waveform data in the multimodal feedback data to obtain vibration feature vectors; The status command code stream in the multimodal feedback data is parsed according to the protocol to obtain the transaction status identifier; The vibration feature vector and the transaction status identifier are input into a pre-trained support vector machine classification model for joint decision-making to generate the probability of successful communication. The communication result of a single test is determined based on the comparison between the probability of successful communication and the preset decision threshold. The response time index of NFC communication is calculated based on the start time of the mechanical vibration waveform data and the effective time of the transaction status identifier.

[0012] In some embodiments, the vibration feature vector and the transaction status identifier are input into a pre-trained support vector machine classification model for joint decision-making to generate the communication success probability, including: The vibration feature vector is combined with the transaction status identifier to form the input feature space; The kernel function of the support vector machine classification model maps the input feature space to a high-dimensional feature space. Calculate the relative distance between the input features and the optimal classification hyperplane in a high-dimensional feature space; The probability of successful communication is generated based on the relative distance and a preset distance-probability conversion function.

[0013] In some embodiments, the remaining test tasks in the test task sequence are executed iteratively, and the communication results and performance metrics generated during all iterations are recorded, including: Establish a task execution queue based on the test task sequence; Retrieve test tasks from the task execution queue in sequence and execute the corresponding NFC communication test process; The communication results and performance metrics generated in each test process are stored in the structured test database in real time. Mark completed test tasks in the task execution queue and update the set of remaining test tasks; When the task execution queue is detected to be empty, a test completion signal is triggered and all test data stored in the structured test database is output.

[0014] In some embodiments, a comprehensive evaluation report on the NFCTAG performance of the POS device is generated based on all recorded communication results and performance metrics, including: Statistical analysis of the communication results was performed to calculate the communication success rate distribution of each test point under different spatial angles and communication distances. The response time data in the performance metrics are aggregated to generate statistical characteristics of how response time changes with communication distance; Based on the communication success rate distribution and statistical characteristics, an NFC communication stability score and effective communication range parameters are generated through a performance evaluation model. Based on the NFC communication stability score and effective communication range parameters, combined with the preset performance level standards, a comprehensive evaluation report is output, which includes performance level determination and identification of weak points.

[0015] In a second aspect, the present invention also provides a POS-based NFCTAG testing system, applicable to the method described in the first aspect. The system includes a turntable, a robotic arm, and a control unit. A target test mobile phone is mounted on the turntable; a POS device is mounted on the end effector of the robotic arm; the control unit is communicatively connected to the turntable, the robotic arm, the target test mobile phone, and the POS device. The control unit includes a test planning module, a motion control module, a data acquisition module, a decision analysis module, and an evaluation report module. The test planning module is used to generate a test task sequence based on a preset test matrix. The motion control module is used to control the rotation of the turntable and the movement of the robotic arm. The data acquisition module is used to synchronously acquire multimodal feedback data from the communication interface between the vibration sensor and the POS device. The decision analysis module is used to input the multimodal feedback data into a predefined success criterion decision model for fusion analysis. The evaluation report module is used to generate a comprehensive evaluation report based on all recorded communication results and performance indicators. The motion control module includes a coordinate calculation unit and a path planning unit. The coordinate calculation unit is used to execute a spatial coordinate transformation algorithm, and the path planning unit is used to plan a collision-free motion path. The data acquisition module includes a multi-source data synchronization unit for timestamping and synchronizing mechanical vibration waveform data and state command code streams.

[0016] By adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art: The present invention provides a POS-based NFCTAG testing method and system. This method generates a test task sequence based on a preset test matrix and controls a turntable to rotate and position the target test mobile phone. It calculates the target spatial pose of the robotic arm's end effector and plans a collision-free motion path through a spatial coordinate transformation algorithm to establish an NFC communication link. During the establishment of the NFC communication link, multimodal feedback data is simultaneously collected and input into a predefined success criterion decision model for fusion analysis to generate a single test result. All test tasks are iteratively executed and the results are recorded, ultimately generating a comprehensive evaluation report. The present invention achieves full automation of the testing process and systematic coverage of multi-dimensional parameters, effectively solving the problems of low testing efficiency, poor result consistency, and limited test scenario coverage in existing testing methods. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of steps S101 to S108 of the test method described in the specific implementation embodiment; Figure 2 This is a schematic diagram of the control unit described in the specific implementation method; Figure 3 This is a flowchart illustrating the testing method described in the specific implementation.

[0019] The reference numerals for the above figures are as follows: 1. Test planning module; 2. Motion control module; 3. Data acquisition module; 4. Decision Analysis Module. Detailed Implementation

[0020] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be particularly noted that the following embodiments are for illustrative purposes only and do not limit the scope of the invention. Similarly, the following embodiments are only some, not all, embodiments of the present invention, and all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Please see Figure 1In a first aspect, this embodiment provides a POS-based NFCTAG testing method, including: S101. Generate a test task sequence based on a preset test matrix. The test task sequence is used to execute test tasks. The preset test matrix defines a combination of multi-dimensional test parameters. The multi-dimensional test parameters include at least test point location, spatial angle, and communication distance. S102. According to the test task sequence, control the turntable to rotate so as to position the target test mobile phone to the predetermined initial position within the working range of the robotic arm; S103. Based on the spatial coordinate transformation algorithm, calculate the target spatial pose of the end effector of the robotic arm according to the multi-dimensional test parameters in the current test task, and plan a collision-free motion path. S104. Control the robotic arm to move along a collision-free motion path so that the POS device installed on the end effector and the target test mobile phone reach the target spatial pose to establish an NFC communication link. S105. During the establishment of the NFC communication link, multimodal feedback data from the communication interface between the vibration sensor and the POS device are collected simultaneously. S106. Input the multimodal feedback data into the predefined success criterion decision model for fusion analysis to generate the communication results and performance indicators of a single test; S107. Iteratively execute the remaining test tasks in the test task sequence and record the communication results and performance metrics generated during all iterations; S108. Based on all recorded communication results and performance indicators, generate a comprehensive evaluation report on the NFC TAG performance of the POS device.

[0022] In step S101, the preset test matrix is ​​a predefined data structure that systematically enumerates combinations of multiple dimensions of parameters required to test the NFC TAG performance of a POS device. Specifically, the test point represents a specific contact area on the surface of the target test phone used to establish NFC communication, such as the center point of the phone or multiple discrete positions defined according to the phone model; the spatial angle defines the relative orientation between the POS device and the target test phone, typically including the horizontal rotation angle and the vertical tilt angle; and the communication distance refers to the normal distance between the POS device and the NFC antenna plane of the target test phone. Preferably, the preset test matrix is ​​generated through exhaustive enumeration or orthogonal experimental design, ensuring the systematicity and completeness of the test scenario. The test task sequence is a list of instructions generated based on this matrix, executing these parameter combination tests sequentially. This step structures the complex multi-factor test scenario through the preset matrix, providing a clear execution plan for automated testing and solving the problem that manual testing struggles to systematically cover multi-dimensional combination scenarios.

[0023] In step S102, the turntable acts as a programmable rotating platform on which multiple target test phones can be fixedly mounted. The predetermined initial position refers to a fixed spatial coordinate within the reachable workspace of the robotic arm, after pre-calibration. According to the instructions of the test task sequence, the control unit drives the turntable to rotate a specific angle, precisely transporting the target test phone to be tested to this predetermined initial position. This position serves as the unified reference starting point for subsequent precise positioning operations by the robotic arm. This step achieves automated switching and coarse positioning of the test phones, automating the equipment replacement process in multi-phone testing scenarios and significantly improving testing efficiency.

[0024] In step S103, the spatial coordinate transformation algorithm is used to perform position and attitude transformations between different coordinate systems, such as chain multiplication of homogeneous transformation matrices. The target spatial pose is a six-dimensional vector that fully describes the target position and orientation of the robotic arm's end effector (and the POS device fixed to it) in space. The solution process first calculates the desired contact point and relative orientation in the user coordinate system fixed to the turntable or test environment based on the test point and spatial angle specified by the current test task. Then, through known coordinate system transformation relationships, the joint angles or end effector Cartesian coordinates in the robotic arm's base coordinate system are finally solved. Planning a collision-free motion path involves generating a smooth motion trajectory that avoids obstacles such as the robotic arm itself, the turntable, and mobile phones, based on the known initial and target poses, using path search algorithms (such as A* algorithm, RRT algorithm) or trajectory interpolation methods. This step is the core control link of automated testing. It transforms abstract multi-dimensional test parameters into specific motion commands that the robotic arm can execute, and ensures the safety and reliability of the motion process, achieving high precision and repeatability of test conditions.

[0025] In step S104, controlling the robotic arm to move along a planned collision-free motion path is a closed-loop control process. The motion control module sends a path point sequence to the robotic arm controller, and the robotic arm drives the servo motors of each joint, causing the end effector to move the POS device along a predetermined trajectory. When the robotic arm moves to the target spatial pose, the NFC antenna area of ​​the POS device and the designated test point of the target test phone form a relative pose at a preset spatial angle and communication distance, thus creating the physical conditions for establishing an NFC communication link. Once within the effective communication range, both devices automatically attempt to establish a connection based on the near-field communication protocol. This step, through high-precision motion control, transforms the theoretical pose into an actual physical contact state, and is a key execution action for establishing a controllable and measurable NFC communication environment.

[0026] In step S105, a vibration sensor is typically attached to the casing of the target test mobile phone to detect the weak mechanical vibrations generated by the operation of the internal coil or motor when the NFC card reader successfully reads the card. The communication interface of the POS device (such as USB or UART) is used to listen to the status command code stream output by its internal processor during the transaction process. These code streams contain status information of the protocol layer, such as card finding, anti-collision, and transaction processing. Synchronous acquisition refers to timestamping these two signals with different physical properties using a unified time reference (such as a system clock), thereby forming multimodal feedback data aligned on the time axis. This step, by introducing vibration sensing as a physical layer feedback and combining it with POS protocol layer feedback, constructs a more comprehensive and reliable test process observation system, providing a rich data foundation for accurately judging communication results.

[0027] In step S106, the predefined success criterion decision model is a data fusion and classification model. It takes the time-aligned multimodal feedback data collected in step S105 as input. Fusion analysis refers to extracting the characteristics of vibration data (such as amplitude and frequency) and parsing the key states in the communication command code stream (such as the "transaction successful" command), and then associating and comprehensively judging these heterogeneous information. For example, the model may set a rule: if a valid vibration feature and a successful transaction status command are detected simultaneously within a specific time window, the communication is judged as successful; otherwise, it is judged as a failure. Performance indicators can be calculated from the data; for example, the time difference from the start point of the vibration signal to the point where the successful transaction command appears can be calculated as the response time. This step replaces the subjective judgment of traditional manual observation with model-based automatic decision-making, realizing the objectification, standardization, and quantification of test result judgment.

[0028] In step S107, iterative execution is a loop control flow. After completing one test task, the next task is automatically retrieved from the test task sequence, and the process from steps S102 to S106 is repeated until all tasks in the sequence have been executed. All communication results (such as success / failure) and performance metrics generated during the iteration process are recorded in real time to the database or log file, ensuring data integrity and traceability. This step achieves automated traversal testing of the entire test matrix, extending the testing capability for a single parameter combination to a systematic evaluation of the entire test space, greatly improving testing efficiency and coverage.

[0029] In step S108, based on all recorded communication results and performance indicators, the system performs post-processing and comprehensive analysis. For example, it can statistically analyze success rate curves at different communication distances, examine the impact of different spatial angles on signal stability, or identify test points that lead to communication failures. The comprehensive evaluation report integrates these analytical results, providing an overall assessment of the POS device's NFC tag communication reliability, effective range, robustness, and other performance aspects in the form of charts and text, and may identify performance weaknesses. This step is the value output stage of the testing process; it transforms massive amounts of raw test data into intuitive performance conclusions with decision-support value, forming a complete closed loop from test execution to result analysis.

[0030] This embodiment provides a complete and systematic automated testing method for POS devices' NFCTAG (NFC Technology and Application) capabilities. It automates and serializes complex, multi-dimensional test scenarios through a pre-set test matrix and task sequence. Utilizing spatial coordinate transformation algorithms and collision-free motion path planning, it achieves high-precision automated reproduction of test conditions. Furthermore, it employs multimodal feedback data and a success criterion decision model to objectively quantify test results. Finally, it completes a systematic analysis of device performance through iterative execution and comprehensive evaluation. This method fundamentally changes the traditional testing mode that relies on manual operation. Through full-process automation and intelligence, it effectively solves a series of problems associated with existing testing methods, such as low testing efficiency, poor result consistency, limited coverage, and strong measurement subjectivity. It provides reliable technical support for the standardized and high-precision evaluation of POS device NFC performance.

[0031] In some embodiments, a test task sequence is generated based on a preset test matrix, including: Obtain the preset test matrix, which contains the full combination of multi-dimensional test parameters; Establish a first user coordinate system fixed to the turntable, and based on the first user coordinate system, calculate the pose that the end effector of the robot arm needs to reach through forward kinematics based on the test point position and spatial angle in the multidimensional test parameters, and generate an initial coordinate set. Starting from the current spatial coordinates of the robotic arm, a weighted undirected graph is constructed based on the Traveling Salesman Problem optimization algorithm. The vertices of the weighted undirected graph are the spatial coordinates of each target in the initial coordinate set. The weight of the edge is defined by the Euclidean distance between the corresponding two vertices. The Hamiltonian circuit in the weighted undirected graph is solved by a heuristic search algorithm to find the shortest path. Based on the vertex visit order defined by the shortest path, the test tasks in the preset test matrix are reordered to generate an optimized test task sequence.

[0032] In this embodiment, a predefined parameter combination table, i.e. a preset test matrix, is read from the system's configuration file or database. This matrix contains a full combination of dimensions such as test points, spatial angles, and communication distances, ensuring the completeness of the test scenario.

[0033] A unified reference framework for spatial calculations is provided by establishing a first user coordinate system fixed to the turntable. The first user coordinate system has its origin at the turntable's rotation center, and its coordinate axes are pre-calibrated based on the robot arm's installation position. Using this first user coordinate system as a reference, and based on the specific test points (such as the offset of the phone's surface relative to this coordinate system) and spatial angles (such as horizontal and vertical angles) in the multi-dimensional test parameters, each target position and orientation that the robot arm's end effector needs to reach can be determined one by one through forward kinematics calculations. Performing this calculation on all matrix entries generates an initial coordinate set containing all target spatial poses.

[0034] To optimize test execution efficiency, this embodiment employs an optimization algorithm based on the Traveling Salesman Problem to plan the test sequence. This method starts with the actual spatial coordinates of the robotic arm before it performs the task, treating each target spatial coordinate in the initial coordinate set as a vertex in a weighted undirected graph. The weight of the edge connecting any two vertices is defined by the Euclidean distance between the spatial coordinates represented by these two points, reflecting the approximate time cost of the robotic arm's movement between the two points. A heuristic search algorithm (such as a genetic algorithm or simulated annealing algorithm) is used to solve this weighted undirected graph to find a Hamiltonian cycle that visits all vertices exactly once and minimizes the total path length. This cycle represents the shortest path for the robotic arm's movement.

[0035] Based on the vertex visit order defined by this shortest path, the system reorders the original test tasks in the preset test matrix. This process generates an optimized test task sequence that allows the robotic arm to execute each test sequentially in the order of the shortest spatial movement trajectory, thereby minimizing idle travel time during point-to-point switching.

[0036] This embodiment focuses on the generation and optimization of test task sequences. By introducing path planning based on the Traveling Salesman Problem, the discrete test task sequencing problem is transformed into a continuous spatial motion optimization problem. This method intelligently plans the execution path of the robotic arm, significantly improving the execution efficiency of the test system from the motion control level. It is a key link in dealing with large-scale, multi-dimensional combined test scenarios and achieving efficient automated testing.

[0037] In some embodiments, based on a spatial coordinate transformation algorithm, the target spatial pose of the robotic arm's end effector is calculated according to the multidimensional test parameters in the current test task, including: Establish a second user coordinate system fixed to the turntable, and establish a tool coordinate system fixed to the end effector; Based on the test points in the multidimensional test parameters, determine the coordinates of the target contact point on the target test mobile phone in the second user coordinate system. Based on the spatial angles in the multidimensional test parameters, calculate the rotation transformation matrix of the tool coordinate system relative to the user coordinate system; Based on the target contact point coordinates and the rotation transformation matrix, the target position in the user coordinate system is calculated using the coordinate system chain multiplication tool, with the origin of the coordinate system in the user coordinate system as the target position. By combining the target position and the rotation transformation matrix, the complete target space pose of the end effector is generated.

[0038] In this embodiment, the process of calculating the target spatial pose of the robotic arm's end effector involves the establishment and transformation of multiple coordinate systems. First, a second user coordinate system is established, fixed to the turntable. This second user coordinate system uses a easily measurable physical feature point on the turntable (such as the center of the turntable or a reference point of a specific fixture) as its origin. Its coordinate axis directions are predefined and calibrated according to the test environment to ensure that it rotates with the turntable. Simultaneously, a tool coordinate system is established, fixed to the end effector. This coordinate system uses the center point of the NFC antenna on the POS device as its origin. Its coordinate axis directions are typically aligned with the outer contour of the POS device, and its precise transformation relationship relative to the end flange is determined through the robotic arm's calibration program.

[0039] Based on the test points in the multidimensional test parameters, the coordinates of the target contact point on the target test phone are determined in the second user coordinate system. Specifically, the test point parameters define the offset of the target contact point on the phone surface relative to the phone's own coordinate system (e.g., with the phone's center as the origin). Combined with the phone's fixed position on the turntable (which is known in the second user coordinate system), the three-dimensional coordinates of the target contact point in the second user coordinate system can be calculated through a simple coordinate translation transformation.

[0040] Based on the spatial angles in the multidimensional test parameters, the rotation transformation matrix of the tool coordinate system relative to the user coordinate system is calculated. The spatial angles typically include the horizontal rotation angle and the vertical tilt angle. The rotation transformation matrix is ​​a 4x4 homogeneous transformation matrix, and its upper-left 3x3 rotation submatrix is ​​obtained by multiplying the rotation matrices of each axis of the second user coordinate system by a specified angle (i.e., spatial angle). This matrix describes the attitude relationship of the tool coordinate system relative to the second user coordinate system.

[0041] When the POS device and the mobile phone make contact at the target contact point at a preset spatial angle, the position of the origin of the tool coordinate system (i.e., the center of the NFC antenna) in the second user coordinate system is equal to the coordinates of the target contact point minus a displacement vector determined by the rotation transformation matrix and the position of the origin of the tool coordinate system in the tool coordinate system (usually 0). The calculation of the target position of the origin of the tool coordinate system in the user coordinate system is achieved through chain multiplication of homogeneous transformation matrices, ultimately obtaining the three-dimensional coordinates of the origin of the tool coordinate system in the second user coordinate system, i.e., the target position.

[0042] By combining the calculated target position (a three-dimensional vector) with the aforementioned rotation transformation matrix (describing the attitude), the complete target space pose of the end effector can be generated. This pose is a six-degree-of-freedom parameter that can be directly used for motion control commands of the robotic arm.

[0043] This embodiment calculates the specific robotic arm pose from abstract test parameters. By establishing a clear second user coordinate system and tool coordinate system, and utilizing the homogeneous transformation matrix—a standard mathematical tool in robotics—the test point positions and spatial angle parameters are accurately converted into executable spatial coordinates and postures of the robotic arm. This process forms the mathematical foundation for achieving high-precision, repeatable automated testing. It ensures that the POS device and the mobile phone make contact with a preset, strictly consistent relative pose in each test, thus fundamentally solving the problem of inconsistent test conditions that are difficult to guarantee during manual operation, and providing a guarantee for obtaining reliable and comparable test data.

[0044] In some embodiments, planning a collision-free motion path includes: Based on the target spatial pose and the current spatial pose of the robotic arm, an initial motion trajectory is generated through a path interpolation algorithm; Based on the relative positional relationship between the tool coordinate system and the second user coordinate system, calculate the minimum safe distance threshold during the movement of the robotic arm; Multiple path checkpoints are set on the initial motion trajectory, and the minimum safe distance threshold is verified at each path checkpoint. When any path checkpoint does not meet the minimum safe distance threshold, an alternative motion path to bypass the obstacle area is generated through a trajectory replanning algorithm. Based on the verification results of all path checkpoints, the final confirmed collision-free motion path is output.

[0045] In this embodiment, the process of planning a collision-free motion path begins with generating an initial motion trajectory based on the target spatial pose and the current spatial pose of the robotic arm. This is typically achieved through a path interpolation algorithm, such as linear or circular interpolation in Cartesian space or joint space. The path interpolation algorithm inserts a series of intermediate path points between the starting point and the target point, forming a preliminary, smooth motion trajectory that does not consider obstacles.

[0046] The minimum safe distance threshold during robotic arm movement defines the minimum spatial separation that must be maintained between the robotic arm (specifically, the POS device carried at its end effector) and the target test mobile phone (whose position is described by a second user coordinate system) to prevent collisions. The minimum safe distance threshold can be calculated based on the physical dimensions of the POS device and mobile phone casings, the geometry of the gripper, and a preset safety margin. For example, it can be set as the sum of the maximum protruding dimensions of the device casings plus a fixed safety margin.

[0047] Multiple path checkpoints are set on the initial motion trajectory, obtained by sampling the initial trajectory at equal times or equal distances. At each path checkpoint, the system needs to verify whether the minimum safe distance threshold is met. During verification, the distance between the origin of the tool coordinate system (i.e., the POS device reference point) and the nearest point representing the mobile phone in the second user coordinate system is calculated when the robotic arm moves to the checkpoint, and this calculated distance is compared with the minimum safe distance threshold.

[0048] When any path checkpoint fails to meet the minimum safe distance threshold, it indicates a collision risk at that point on the initial trajectory. In this case, an alternative path is generated to bypass the obstacle area using a trajectory replanning algorithm. A common replanning algorithm is a random sampling algorithm (such as the Rapid Expanding Random Tree (RRT) or its variants), which randomly samples in the configuration space to find a new path from the current point (or the previous safe point) to the target point that avoids the "no-go zone" centered on the phone's location. After the alternative path is generated, the pathpoint safety check described above needs to be performed again.

[0049] If the initial trajectory meets the safe distance requirements at all checkpoints, it is directly used as the collision-free path. If replanning has been performed, the alternative path that successfully avoids all obstacles is output as the final collision-free path. The collision-free path consists of a series of safe pathpoint sequences and can be directly sent to the robotic arm controller for execution.

[0050] This embodiment ensures the robotic arm moves safely and without collision to a known target pose. Specifically, it introduces path checking and a collision detection mechanism based on distance thresholds, transforming abstract safety requirements into quantifiable numerical judgments. Furthermore, by integrating a trajectory replanning algorithm, it provides a dynamic solution for detected potential collision risks. This embodiment upgrades from passive checking to active planning, greatly enhancing the reliability and safety of the automated testing system operating in complex and compact spaces. It avoids test interruptions or hardware damage caused by equipment collisions, ensuring stable operation of long-term, unattended automated testing.

[0051] In some embodiments, during the establishment of the NFC communication link, multimodal feedback data from the communication interface between the vibration sensor and the POS device is collected synchronously, including: The mechanical vibration waveform data generated by the target test mobile phone during NFC communication is collected in real time using a vibration sensor. The status command code stream corresponding to the NFC transaction process inside the device is obtained in real time through the communication interface of the POS device. The mechanical vibration waveform data and the status command code stream are time-stamped and synchronized to generate time-aligned multimodal feedback data.

[0052] In this embodiment, a vibration sensor attached to the casing of the target test mobile phone is used to collect mechanical vibration waveform data in real time. This vibration sensor typically employs a piezoelectric or MEMS accelerometer, capable of sensitively capturing the weak mechanical vibration signals generated by the electromagnetic force acting on the NFC coil inside the phone during operation or by the vibration of the motor indicating a successful transaction. The collected raw analog signal is converted into a digital waveform data stream by an analog-to-digital converter.

[0053] Simultaneously, the status command code stream corresponding to the NFC transaction process is acquired in real time through the POS device's communication interface. This communication interface can be USB, UART, or SPI, etc., and is used to monitor the internal logs or protocol data packets output by the POS device's main control chip when processing NFC transactions. The status command code stream contains identification information for key process nodes such as "card search started," "card number read," "transaction authentication," and "transaction successful," and is transmitted in a specific data frame format.

[0054] To achieve precise time alignment between the two heterogeneous data sets, the mechanical vibration waveform data and the status command code stream need to be timestamped and synchronized. This process is typically driven by a unified clock source from the data acquisition module. Specifically, each acquired vibration data block and each parsed status command data packet is timestamped with a time stamp generated by the same high-precision system clock; the timestamp accuracy needs to be at the millisecond level or higher to ensure accurate correlation between vibration events and transaction status events. Finally, the vibration waveform data with synchronized timestamps and the status command code stream are merged to generate a time-aligned multimodal feedback data set for use by subsequent analysis models.

[0055] This embodiment collects test process feedback data from two independent dimensions: physical sensing and protocol monitoring, and solves the time synchronization problem of multi-source heterogeneous data. By introducing a high-precision unified clock to timestamp data from different sources, it ensures strict alignment of vibration signals and transaction instructions on the timeline, laying a reliable data foundation for subsequent accurate decision-making based on multimodal data fusion. This enables the system to objectively and accurately correlate physical layer contact feedback with protocol layer transaction status, thereby overcoming the potential for misjudgment and omissions that may arise from relying solely on manual observation or unilateral feedback from the POS.

[0056] In some embodiments, multimodal feedback data is input into a predefined success criterion decision model for fusion analysis to generate communication results and performance metrics for a single test, including: Feature extraction is performed on the mechanical vibration waveform data in the multimodal feedback data to obtain vibration feature vectors; The status command code stream in the multimodal feedback data is parsed according to the protocol to obtain the transaction status identifier; The vibration feature vector and the transaction status identifier are input into a pre-trained support vector machine classification model for joint decision-making to generate the probability of successful communication. The communication result of a single test is determined based on the comparison between the probability of successful communication and the preset decision threshold. The response time index of NFC communication is calculated based on the start time of the mechanical vibration waveform data and the effective time of the transaction status identifier.

[0057] In this embodiment, the feature extraction process typically includes analyzing waveform data from the time and frequency domains, such as calculating the peak amplitude, effective value, and zero-crossing rate of the vibration signal, or extracting the energy proportion of its main frequency component through fast Fourier transform. Arranging these calculated values ​​in order constitutes a multidimensional vibration feature vector.

[0058] Protocol parsing refers to decoding the code stream data according to the known internal communication protocol format of the POS device. The parsing process identifies the key instructions or status codes in the code stream that represent specific transaction stages (such as "transaction start", "authentication passed", "transaction completed") and maps them to preset, discrete transaction status identifiers, such as using the number "1" to represent success and "0" to represent failure or other intermediate states.

[0059] The pre-trained support vector machine (SVM) classification model is a machine learning model pre-trained using a large amount of historical test sample data labeled "success" and "failure". During training, the model learns the complex mapping relationship between the combined features of vibration feature vectors and transaction status identifiers and the final communication result (success or failure). In the application phase, the model receives new vibration feature vectors and transaction status identifiers as joint inputs, calculates them using its internal kernel and decision functions, and outputs a value between 0 and 1, representing the probability of successful communication. This probability value characterizes the model's confidence that the test result is "successful".

[0060] The preset decision threshold is a pre-defined probability threshold. When the calculated probability of successful communication is greater than or equal to this threshold, the NFC communication result is determined to be "successful"; otherwise, it is determined to be "failed". This threshold can be adjusted according to different tolerances for false positives to balance the sensitivity and specificity of the decision.

[0061] The starting time of the vibration waveform data can be determined by detecting the time when the vibration signal first exceeds the noise threshold; the effective time of the transaction status identifier is the timestamp corresponding to the parsed "transaction successful" status identifier. The response time metric is the difference between these two time points, which objectively quantifies the time elapsed from the establishment of physical contact to the completion of the transaction agreement.

[0062] This embodiment transforms raw multimodal data into quantifiable test results and indicators. Effective information is extracted from vibration data through feature engineering and combined with state identifiers obtained from protocol parsing to form the input of the decision-making model. A pre-trained support vector machine model is used for fusion decision-making, achieving intelligent result judgment that goes beyond simple rule-based judgment, improving the accuracy and robustness of the judgment. Simultaneously, response time is calculated based on precise timestamps, providing key performance quantification indicators. This embodiment constitutes the core link of intelligent analysis and quantitative evaluation in the automated testing system, freeing test results from subjective experience dependence and achieving objective, accurate, and repeatable automated evaluation.

[0063] In some embodiments, the vibration feature vector and the transaction status identifier are input into a pre-trained support vector machine classification model for joint decision-making to generate the communication success probability, including: The vibration feature vector is combined with the transaction status identifier to form the input feature space; The kernel function of the support vector machine classification model maps the input feature space to a high-dimensional feature space. Calculate the relative distance between the input features and the optimal classification hyperplane in a high-dimensional feature space; The probability of successful communication is generated based on the relative distance and a preset distance-probability conversion function.

[0064] In this embodiment, combining the vibration feature vector with the transaction status identifier to form the input feature space can be understood as concatenating the multidimensional numerical vector representing the physical vibration characteristics with the discrete identifier representing the protocol status (usually after numerical encoding, such as one-hot encoding) to form a feature array of a unified dimension for input to the machine learning model.

[0065] Mapping the input feature space to a higher-dimensional feature space using the kernel function of the Support Vector Machine (SVM) classification model is a key step in handling nonlinear classification problems. The kernel function (e.g., the Radial Basis Function (RBF) kernel) can directly calculate the inner product of sample points in the original feature space in the higher-dimensional space without explicitly calculating the higher-dimensional coordinates. This effectively maps potentially linearly inseparable feature combinations to a higher-dimensional space, making them linearly separable within that space.

[0066] The optimal hyperplane is a separating plane that maximizes the margin between two classes of samples (success and failure), determined by solving a convex optimization problem during model training. For a new input feature (i.e., a test sample), the model calculates the perpendicular distance between its mapping point in the high-dimensional feature space and this optimal hyperplane. The sign (positive or negative) of this distance indicates the predicted class of the sample, while its absolute value reflects the distance of the sample point from the decision boundary, i.e., the confidence level of the classification.

[0067] The distance-probability transformation function is a monotonic function that maps the calculated (signed) distance value to a probability interval between 0 and 1. Preferably, the sigmoid function is used as the distance-probability transformation function, which compresses the distance value and converts it into a probability value. For example, the larger and positive the distance value, the closer the converted success probability is to 1; the larger the negative and absolute value of the distance value, the closer the success probability is to 0; a distance value near 0 indicates a probability value near 0.5, suggesting a high degree of uncertainty in the model's judgment.

[0068] This embodiment generates probabilistic decisions using a support vector machine model, addresses the nonlinear separability of features through kernel function mapping, obtains a quantitative expression of classification confidence by calculating the distance to the optimal hyperplane, and then converts this confidence into an intuitive probability value using mathematical functions. This process ensures that the final output "communication success probability" is not only a binary classification result but also a continuous value with confidence information, enabling the setting of flexible decision thresholds and more refined result analysis, thus enhancing the interpretability and adaptability of the entire decision system.

[0069] In some embodiments, the remaining test tasks in the test task sequence are executed iteratively, and the communication results and performance metrics generated during all iterations are recorded, including: Establish a task execution queue based on the test task sequence; Retrieve test tasks from the task execution queue in sequence and execute the corresponding NFC communication test process; The communication results and performance metrics generated in each test process are stored in the structured test database in real time. Mark completed test tasks in the task execution queue and update the set of remaining test tasks; When the task execution queue is detected to be empty, a test completion signal is triggered and all test data stored in the structured test database is output.

[0070] In this embodiment, establishing a task execution queue based on the test task sequence can be understood as loading the optimized test task sequence into a first-in-first-out data structure in memory. Each element in the queue corresponds to a task to be executed that contains specific multi-dimensional test parameters.

[0071] The system sequentially retrieves test tasks from the task execution queue and executes the corresponding NFC communication test procedures, forming a cyclical control process. The system takes a task from the head of the queue, then drives the robotic arm, turntable, and other hardware, and calls the data acquisition and decision analysis module to completely execute the entire process from positioning and establishing a communication link to generating results and metrics.

[0072] After each test process is completed, the system immediately writes the communication results, response time, test parameters used, and corresponding timestamps into a relational database or time-series database as a record. The structured test database predefines the table structure for storing this data, ensuring data consistency and queryability.

[0073] Completed test tasks are marked in the task execution queue, and the set of remaining test tasks is updated. This can be achieved by removing retrieved task items from the queue. Preferably, a counter or list of remaining tasks can be maintained to reflect the test progress in real time.

[0074] When the task execution queue is detected to be empty, it indicates that all tasks in the test task sequence have been completed. At this point, the system triggers a test completion signal and outputs all test data stored in the structured test database. The output can be a data file containing all original data records, or it can directly provide a complete data interface for the subsequent evaluation report generation module.

[0075] This embodiment achieves reliable management and data persistence for large-scale, multi-task automated testing processes through a task queue mechanism and structured data storage. It organizes discrete test execution actions into an orderly and controllable pipeline operation, ensuring that test tasks are executed completely and without omission. Simultaneously, the real-time, structured data storage method provides a solid data foundation for the integrity and traceability of test data, as well as subsequent in-depth analysis and report generation, which is a crucial guarantee for the stable and efficient operation of the entire automated testing system.

[0076] In some embodiments, a comprehensive evaluation report on the NFCTAG performance of the POS device is generated based on all recorded communication results and performance metrics, including: Statistical analysis of the communication results was performed to calculate the communication success rate distribution of each test point under different spatial angles and communication distances. The response time data in the performance metrics are aggregated to generate statistical characteristics of how response time changes with communication distance; Based on the communication success rate distribution and statistical characteristics, an NFC communication stability score and effective communication range parameters are generated through a performance evaluation model. Based on the NFC communication stability score and effective communication range parameters, combined with the preset performance level standards, a comprehensive evaluation report is output, which includes performance level determination and identification of weak points.

[0077] In this embodiment, statistical analysis of communication results can be understood as querying all test records from the structured test database and then grouping them according to three dimensions: test location, spatial angle, and communication distance. For multiple repeated tests under each parameter combination, the proportion of successful communication out of the total number of tests is calculated, thus obtaining the communication success rate under that specific condition. Calculating and summarizing the success rates under all conditions results in a communication success rate distribution chart or data table reflecting the differences in the performance of the POS device under different spatial poses and distances.

[0078] The response time data in the performance metrics are aggregated, and the response time metrics are extracted from all successful tests at the same communication distance. Then, statistical calculations are performed on these response time data, such as calculating the mean, median, maximum, minimum, and standard deviation. By analyzing the trends of these statistical characteristics as a function of communication distance, the relationship between communication latency and distance can be quantitatively assessed.

[0079] The performance evaluation model can be a rule-based scoring algorithm or a simple regression / classification model. For example, the NFC communication stability score can be calculated by weighting factors such as the uniformity of the communication success rate distribution and the success rate at standard angles and distances. The effective communication range parameter can be determined by analyzing the curve of communication success rate decreasing with distance. For example, the distance corresponding to the first time the success rate falls below a preset threshold (such as 95%) can be defined as the effective communication distance under that test condition.

[0080] The preset performance level standards are predefined rules or lookup tables that map stability scores and effective communication range parameters to specific performance levels (such as "Excellent", "Good", "Average", "Poor"). Weakness identification is automatically performed by analyzing specific test points or angle combinations that are significantly below average in the communication success rate distribution. The final comprehensive evaluation report will present the performance level determination, key performance parameters, weakness analysis, and improvement suggestions in the form of a structured document or visual charts.

[0081] This embodiment transforms massive amounts of raw test data into performance conclusions with direct guiding significance. Through multi-dimensional statistical analysis and model-based comprehensive evaluation, it achieves a combined quantitative and qualitative assessment of the NFC tag performance of POS devices. It not only provides the overall performance level but also accurately pinpoints the specific test conditions where performance shortcomings exist. This provides data-driven, objective, and accurate decision-making support for product design optimization, quality control, and problem diagnosis, completing a value loop from automated testing to intelligent evaluation.

[0082] Please see Figure 2In a second aspect, this embodiment also provides a POS-based NFCTAG testing system, applicable to the method described in the first aspect. The system includes a turntable, a robotic arm, and a control unit. A target test mobile phone is mounted on the turntable; a POS device is mounted on the end effector of the robotic arm; the control unit is communicatively connected to the turntable, the robotic arm, the target test mobile phone, and the POS device. The control unit includes a test planning module 1, a motion control module 2, a data acquisition module 3, a decision analysis module 4, and an evaluation report module. The test planning module 1 is used to generate a test task sequence based on a preset test matrix, and the motion control module 2 is used to control the rotation of the turntable and the movement of the robotic arm. The data acquisition module 3 is used to synchronously acquire multimodal feedback data from the communication interface between the vibration sensor and the POS device. The decision analysis module 4 is used to input the multimodal feedback data into a predefined success criterion decision model for fusion analysis. The evaluation report module is used to generate a comprehensive evaluation report based on all recorded communication results and performance indicators. The motion control module 2 includes a coordinate calculation unit and a path planning unit. The coordinate calculation unit is used to execute spatial coordinate transformation algorithms, and the path planning unit is used to plan collision-free motion paths. The data acquisition module 3 includes a multi-source data synchronization unit, which is used to timestamp and synchronize mechanical vibration waveform data and status command code streams.

[0083] In this embodiment, the turntable is a programmable rotating platform used to carry and switch between multiple target test mobile phones. The robotic arm is a multi-degree-of-freedom industrial robot whose end effector is specifically designed for reliably gripping POS devices. The control unit is the core processing and scheduling center of the system, typically implemented by an industrial computer or a high-performance embedded controller, which connects to all hardware devices and sends control commands via wired or wireless communication protocols.

[0084] Test planning module 1 is responsible for generating and optimizing test task sequences based on preset test parameter combinations. Motion control module 2 receives task sequence instructions; its internal coordinate calculation unit calculates the target pose of the robotic arm based on test points and spatial angles, while the path planning unit generates a safe motion trajectory and coordinates the rotation of the turntable and the movement of the robotic arm. Data acquisition module 3 operates in real-time; its multi-source data synchronization unit ensures that waveform data acquired from the vibration sensor and POS device communication interface has a unified time reference with the instruction code stream. Decision analysis module 4 calls a pre-trained model to perform fusion analysis on the synchronized multimodal data, automatically determining the communication results of each test and calculating performance indicators. After completing all test tasks, the evaluation report module performs statistical analysis on all recorded results and indicators, automatically generating a structured comprehensive evaluation report.

[0085] The system provided in this embodiment, through hardware collaboration and software modular design, materializes each step of the aforementioned method into specific functional modules and execution units. The turntable and robotic arm coordinate their movements under the precise commands of the motion control module 2, reproducing complex test poses; the data acquisition and decision analysis module 4 replaces manual labor, achieving automation and intelligence in test process observation and result judgment. The entire system constitutes a complete automated testing platform, capable of efficiently, accurately, and reliably performing comprehensive NFC TAG performance testing and outputting objective, quantitative evaluation conclusions, fundamentally solving the problems of low efficiency, poor consistency, and incomplete coverage inherent in manual testing.

[0086] Furthermore, based on the above technical solutions, the following examples can be developed: In some implementations, the system control unit may be as follows: Figure 2 The system is modularly deployed, with the test management module (i.e., test planning module 1) serving as the core scheduling hub. This module is responsible for generating test task sequences based on a preset test matrix and coordinating the operation of each module. This module is connected to multiple execution and acquisition modules: one end connects to the motion control module, which is further subdivided into a robotic arm control module for controlling the robotic arm and a rotary tooling control module for controlling the turntable; the other end connects to a mobile service communication module for interacting with the phone under test, a sensor acquisition module for acquiring physical signals (i.e., part of data acquisition module 3), and a POS communication module for interacting with the POS device (i.e., part of data acquisition module 3). Additionally, the system may include a monitoring module for process monitoring. Downstream of the test management module (i.e., test planning module 1) is a data analysis and visualization module. In this embodiment, the data analysis and visualization module integrates the functions of the decision analysis module 4 and the evaluation report module. It is used to perform fusion analysis on the acquired multimodal feedback data to determine the communication results and performance indicators of a single test, and to generate a comprehensive evaluation report on the NFC TAG performance of the POS device based on the records generated during all iterative tests.

[0087] Please see Figure 3Based on the above system architecture, a complete testing methodology can include the following steps: First, environmental preparation is performed, i.e., the target test phone is mounted on the turntable, the POS device is mounted on the end effector of the robotic arm, and the configuration is confirmed on the back-end computer (i.e., the control unit); the back-end computer (i.e., the control unit) runs the test case; then, the preset test configurations are iterated through, and these configuration parameters include at least the phone model, test point, communication distance, horizontal rotation angle, angle between the phone and the POS device, and the number of tests; next, the turntable is controlled to rotate, moving the target test phone to the predetermined initial position; then, the phone is controlled to unlock and the NFC function is enabled; then... Then, based on the target spatial pose calculated by the spatial coordinate transformation algorithm, the robotic arm moves along the planned collision-free motion path at a set speed to a designated point (which includes angle information), and stays at that point for a set duration to attempt to establish an NFC communication link. After the stay ends, the robotic arm leaves the phone. During this process, the system synchronously collects multimodal feedback data through the vibration sensor and the communication interface of the POS device. Subsequently, the system records the performance data and communication success status of this test. Finally, it determines whether the test configuration has been traversed. If not, it returns to the step of traversing the configuration and continues to execute the next test task; if completed, it exports all test results.

[0088] To optimize testing efficiency, the system can also intelligently plan test sequences. Specifically, the system calculates the set of robotic arm coordinates corresponding to all test tasks, and with the optimization goal of shortening the movement distance of the main joint axes of the robotic arms and reducing the overall movement time, it reorders the test tasks using optimization algorithms (such as path planning algorithms based on nearest neighbor search), thereby generating an optimized test task sequence with a shorter total time. For extended systems with multiple robotic arms, a central scheduling algorithm can also coordinate the collaborative work of multiple robotic arms and the turntable. For example, it can dynamically allocate mobile phone resources on the turntable based on the remaining test tasks of each robotic arm, and control the robotic arms to move to the ready position in advance when the turntable is occupied, thereby further reducing the system's idle waiting time and improving the overall test throughput.

[0089] By adopting the above technical solutions, this invention differs from existing technologies and possesses the following beneficial effects: By generating a test task sequence based on a preset test matrix, a multi-dimensional test parameter combination including test points, spatial angles, and communication distance is systematically defined, thereby structuring complex multi-factor test scenarios and solving the problem that manual testing struggles to systematically cover multi-dimensional combination scenarios. Controlling the turntable rotation and robotic arm movement according to the test task sequence enables automated switching and precise positioning of the test phone. Through spatial coordinate transformation algorithms and collision-free motion path planning, abstract multi-dimensional parameters are transformed into high-precision, repeatable motion commands executable by the robotic arm, ensuring the consistency of test conditions and overcoming fluctuations in test results caused by manual operation. During the NFC communication link establishment process, multi-modal feedback data from vibration sensors and the POS device communication interface are simultaneously collected and fused using a predefined success criterion decision model, achieving objectivity and quantification of test result judgment, replacing traditional subjective judgment relying on manual observation, and improving the accuracy and reliability of the judgment. By iteratively executing all test tasks and recording the results, a comprehensive evaluation report is finally generated, completing a systematic and automated evaluation of the POS device's NFC TAG performance. The above technical solution achieves full automation of the testing process, significantly improves testing efficiency and result consistency, and provides reliable technical support for the standardized and high-precision evaluation of NFC performance of POS devices through systematic coverage and intelligent analysis of multi-dimensional parameters.

[0090] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0091] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0092] The above description is only a part of the embodiments of the present invention and does not limit the scope of protection of the present invention. Any equivalent device or equivalent process transformation made based on the content of the present invention specification and drawings, or direct or indirect application in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A POS-based NFCTAG testing method, characterized in that, include: A test task sequence is generated based on a preset test matrix. The test task sequence is used to execute test tasks. The preset test matrix defines a combination of multi-dimensional test parameters, which include at least test point location, spatial angle, and communication distance. According to the test task sequence, the turntable is controlled to rotate to position the target test mobile phone to a predetermined initial position within the working range of the robotic arm; Based on the spatial coordinate transformation algorithm, according to the multi-dimensional test parameters in the current test task, the target spatial pose of the end effector of the robotic arm is calculated, and a collision-free motion path is planned. Control the robotic arm to move along the collision-free motion path, so that the POS device installed on the end effector and the target test mobile phone reach the target spatial pose, so as to establish an NFC communication link; During the establishment of the NFC communication link, multimodal feedback data from the communication interface between the vibration sensor and the POS device are collected simultaneously. The multimodal feedback data is input into a predefined success criterion decision model for fusion analysis to generate communication results and performance indicators for a single test. Iteratively execute the remaining test tasks in the test task sequence and record the communication results and performance metrics generated during all iterations; Based on all recorded communication results and performance metrics, a comprehensive evaluation report on the NFC TAG performance of the POS device is generated.

2. The POS-based NFCTAG testing method according to claim 1, characterized in that, A sequence of test tasks is generated based on a preset test matrix, including: Obtain a preset test matrix, which contains all combinations of the multidimensional test parameters; A first user coordinate system is established and fixed to the turntable. Based on the first user coordinate system, the position and spatial angle of the end effector of the robotic arm are calculated by forward kinematics according to the test point position and spatial angle in the multidimensional test parameters, and an initial coordinate set is generated. Starting from the current spatial coordinates of the robotic arm, a weighted undirected graph is constructed based on the Traveling Salesman Problem optimization algorithm. The vertices of the weighted undirected graph are the spatial coordinates of each target in the initial coordinate set. The weight of the edge is defined by the Euclidean distance between the corresponding two vertices. A Hamiltonian circuit is solved in the weighted undirected graph using a heuristic search algorithm to obtain the shortest path. Based on the vertex visit order defined by the shortest path, the test tasks in the preset test matrix are reordered to generate the optimized test task sequence.

3. The POS-based NFCTAG testing method according to claim 1, characterized in that, Based on the spatial coordinate transformation algorithm, and according to the multidimensional test parameters in the current test task, the target spatial pose of the end effector of the robotic arm is calculated, including: Establish a second user coordinate system fixed to the turntable, and establish a tool coordinate system fixed to the end effector; Based on the test points in the multidimensional test parameters, determine the coordinates of the target contact point on the target test mobile phone in the second user coordinate system; Based on the spatial angles in the multidimensional test parameters, calculate the rotation transformation matrix of the tool coordinate system relative to the user coordinate system; Based on the target contact point coordinates and the rotation transformation matrix, the target position of the tool coordinate system origin in the user coordinate system is calculated by coordinate system chain multiplication. By combining the target position with the rotation transformation matrix, the complete target space pose of the end effector is generated.

4. The POS-based NFCTAG testing method according to claim 3, characterized in that, And plan collision-free motion paths, including: Based on the target spatial pose and the current spatial pose of the robotic arm, an initial motion trajectory is generated using a path interpolation algorithm; Based on the relative positional relationship between the tool coordinate system and the second user coordinate system, calculate the minimum safe distance threshold during the movement of the robotic arm; Multiple path checkpoints are set on the initial motion trajectory, and the minimum safe distance threshold is verified at each path checkpoint; When any path checkpoint does not meet the minimum safe distance threshold, an alternative motion path to bypass the obstacle area is generated by the trajectory replanning algorithm. Based on the verification results of all path checkpoints, the final confirmed collision-free motion path is output.

5. The POS-based NFCTAG testing method according to claim 1, characterized in that, During the establishment of the NFC communication link, multimodal feedback data from the communication interface between the vibration sensor and the POS device are collected synchronously, including: The vibration sensor collects the mechanical vibration waveform data generated by the target test mobile phone during NFC communication in real time. The status instruction code stream corresponding to the NFC transaction process inside the device is obtained in real time through the communication interface of the POS device. The mechanical vibration waveform data and the state command code stream are time-stamped and synchronized to generate time-aligned multimodal feedback data.

6. The POS-based NFCTAG testing method according to claim 1, characterized in that, The multimodal feedback data is input into a predefined success criterion decision model for fusion analysis to generate communication results and performance metrics for a single test, including: Feature extraction is performed on the mechanical vibration waveform data in the multimodal feedback data to obtain vibration feature vectors; The status instruction code stream in the multimodal feedback data is parsed to obtain the transaction status identifier; The vibration feature vector and the transaction status identifier are input into a pre-trained support vector machine classification model for joint decision-making to generate the communication success probability. Based on the comparison between the communication success probability and the preset decision threshold, the communication result of a single test is determined. The response time index of NFC communication is calculated based on the start time of the mechanical vibration waveform data and the effective time of the transaction status identifier.

7. The POS-based NFCTAG testing method according to claim 6, characterized in that, The vibration feature vector and the transaction status identifier are input into a pre-trained support vector machine classification model for joint decision-making to generate the communication success probability, including: The vibration feature vector is combined with the transaction status identifier to form an input feature space; The input feature space is mapped to a high-dimensional feature space through the kernel function of the support vector machine classification model; Calculate the relative distance between the input feature and the optimal classification hyperplane in the high-dimensional feature space; The communication success probability is generated based on the relative distance and a preset distance-probability conversion function.

8. The POS-based NFCTAG testing method according to claim 1, characterized in that, Iteratively execute the remaining test tasks in the test task sequence, and record the communication results and performance metrics generated during all iterations, including: Establish a task execution queue based on the test task sequence; The test tasks to be executed are retrieved sequentially from the task execution queue, and the corresponding NFC communication test procedures are executed. The communication results and performance metrics generated in each test process are stored in the structured test database in real time. Completed test tasks are marked in the task execution queue, and the set of remaining test tasks is updated. When the task execution queue is detected to be empty, a test completion signal is triggered and all test data stored in the structured test database is output.

9. The POS-based NFCTAG testing method according to claim 1, characterized in that, Based on all recorded communication results and performance metrics, a comprehensive evaluation report on the NFC TAG performance of the POS device is generated, including: Statistical analysis was performed on the communication results to calculate the communication success rate distribution of each test point under different spatial angles and communication distances; The response time data in the performance metrics are aggregated to generate statistical characteristics of how response time changes with communication distance; Based on the communication success rate distribution and the statistical characteristics, an NFC communication stability score and effective communication range parameters are generated through a performance evaluation model. Based on the NFC communication stability score and the effective communication range parameters, combined with the preset performance level standards, a comprehensive evaluation report is output, which includes performance level determination and identification of weak points.

10. A POS-based NFCTAG testing system, characterized in that, The system applicable to the method of any one of claims 1 to 9 comprises: A turntable, on which a target test mobile phone is mounted; A robotic arm, wherein the POS device is mounted on the end effector of the robotic arm; The control unit is communicatively connected to the turntable, robotic arm, target test mobile phone, and POS device. The control unit includes a test planning module, a motion control module, a data acquisition module, a decision analysis module, and an evaluation report module. The test planning module is used to generate a test task sequence based on a preset test matrix; the motion control module is used to control the rotation of the turntable and the movement of the robotic arm; the data acquisition module is used to synchronously acquire multimodal feedback data from the communication interface between the vibration sensor and the POS device; the decision analysis module is used to input the multimodal feedback data into a predefined success criterion decision model for fusion analysis; and the evaluation report module is used to generate a comprehensive evaluation report based on all recorded communication results and performance indicators. The motion control module includes a coordinate calculation unit and a path planning unit. The coordinate calculation unit is used to execute a spatial coordinate transformation algorithm, and the path planning unit is used to plan a collision-free motion path. The data acquisition module includes a multi-source data synchronization unit, which is used to timestamp and synchronize mechanical vibration waveform data with status command code stream.

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