Driver data analysis control method and system and electronic equipment

Abstract

The invention provides a driver data analysis control method and system and electronic equipment, and relates to the field of driver tests.The method comprises the steps that an oscillogram of a driver serves as a use case to be analyzed, and independent analysis is conducted on different corresponding stages in the use case, so that automatic analysis of waveform data is achieved, and the test efficiency is improved. The analysis capability and the identification precision of the driver data are improved; in addition, the method does not need a large amount of marked data in the analysis process, and the problem that in the prior art, when AI means such as deep learning are adopted for data analysis, resource consumption is large is solved.

Description

Driver data analysis and control methods, systems and electronic devices Technical Field

[0001] This invention relates to the field of driver testing, and in particular to a driver data analysis and control method, system, and electronic device. Background Technology

[0002] During driver performance testing, waveform data under different conditions needs to be collected and analyzed. For example, in servo testing of a driver, the response relationship between the speed setting and speed feedback needs to be tested in speed mode. To evaluate the differences at different speeds, different speeds need to be tried, resulting in a large number of waveforms. Calculating the dB (decibels) value requires FFT (fast Fourier transform) analysis, frequency response, etc. If each file is analyzed using the above methods, it would require a large amount of computation. Therefore, specific analysis techniques are needed to reduce the consumption of computing resources.

[0003] Fault analysis based on feature data typically involves identifying the presence of characteristic signals in the graph, similar to pattern recognition. However, this approach lacks versatility and cannot handle complex business logic. For example, if a scenario requires determining whether acceleration was successful, it would involve judging characteristic signals such as acceleration DI signals (digital input signals), speed commands, speed response, response time, overshoot, and peak speed. This is clearly difficult to achieve with pattern recognition-like analytical methods.

[0004] Existing technologies are beginning to employ AI methods such as deep learning to analyze driver data in order to detect product malfunctions. However, AI models require a large number of samples and their corresponding labeled data to identify waveform screenshot features. But when the waveform is long, the data often clusters in a small, specific area, resulting in poor recognition accuracy. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a driver data analysis and control method, system and electronic device. The method analyzes the waveform of the driver as a use case and performs independent analysis on the different stages corresponding to the use case, thereby realizing automated analysis of waveform data and improving the analysis capability and recognition accuracy of driver data. In addition, the method does not require a large amount of labeled data during the analysis process, which solves the problem of high resource consumption in the prior art when using AI methods such as deep learning for data analysis.

[0006] In a first aspect, embodiments of the present invention provide a driver data analysis and control method, the method comprising: determining a use case library corresponding to the driver based on the driver type parameters, and determining the use cases corresponding to the driver using the use case library; acquiring waveform parameters of the use cases and their corresponding segmentation parameters, and determining a waveform triggering strategy, a waveform acquisition strategy, and a waveform analysis strategy corresponding to the driver using the waveform parameters and segmentation parameters; controlling the driver to acquire waveform data in segments using the waveform triggering strategy, and acquiring the process waveform corresponding to the waveform data using the waveform acquisition strategy; and obtaining the analysis result corresponding to the use cases by analyzing the process waveform using the waveform analysis strategy.

[0007] Optionally, the steps of determining the use case library corresponding to the driver based on the driver's type parameters and using the use case library to determine the use cases corresponding to the driver include: obtaining historical use case data corresponding to the driver based on the waveform data corresponding to the driver; constructing the use case library corresponding to the driver using the historical use case data and based on the type data corresponding to the driver; obtaining the type parameters corresponding to the current driver, and using the type parameters to obtain the use cases corresponding to the driver from the use case library.

[0008] Optionally, the steps of obtaining the waveform parameters of the test case and their corresponding segmentation parameters, and using the waveform parameters and segmentation parameters to determine the waveform triggering strategy, waveform acquisition strategy, and waveform analysis strategy corresponding to the driver, include: determining the waveform parameters corresponding to the test case through the environmental parameters corresponding to the driver, and determining the test case executor, waveform analyzer, and waveform acquisition device corresponding to the driver; determining the segmentation parameters corresponding to the test case under the waveform parameters based on the test case executor, determining the start command corresponding to the driver using the segmentation parameters, and determining the waveform triggering strategy corresponding to the driver based on the start command; determining the acquisition parameters corresponding to the test case under the waveform parameters based on the waveform analyzer, determining the acquisition command corresponding to the driver using the acquisition parameters, and determining the waveform acquisition strategy corresponding to the driver based on the acquisition command; determining the configuration parameters corresponding to the test case in the test case library based on the waveform acquisition device, determining the analysis command corresponding to the driver using the configuration parameters, and determining the waveform analysis strategy corresponding to the driver using the analysis command.

[0009] Optionally, the waveform triggering strategy is used to control the driver to collect waveform data in segments, including: using the waveform triggering strategy to determine the segmented acquisition instruction corresponding to the use case executor; using the waveform acquisition device to control the driver to execute the data acquisition process corresponding to the waveform data according to the segmented acquisition instruction; using the waveform analyzer to initialize the data receiver corresponding to the driver, and using the data receiver to collect the waveform data after the driver executes the data acquisition process in real time.

[0010] Optionally, the waveform acquisition strategy is used to obtain the process waveform corresponding to the waveform data, including: determining the start entry judgment condition corresponding to the use case based on the waveform parameters; using the use case executor, judging the start features contained in the waveform data using the start entry judgment condition; using the waveform analyzer, setting the execution parameters corresponding to the waveform acquisition strategy according to the judgment result of the start features and the waveform parameters; using the execution parameters to obtain the waveform data from the waveform acquisition device, and using the waveform data to determine the process waveform.

[0011] Optionally, the process waveform corresponding to the waveform data is obtained using a waveform acquisition strategy, including: obtaining the injection data corresponding to the driver in real time using a preset waveform storage file; determining the virtual channel corresponding to the injection data through the waveform acquisition strategy; and obtaining the waveform data and the process waveform corresponding to the injection data based on the virtual channel.

[0012] Optionally, the step of obtaining the analysis result corresponding to the use case after analyzing the process waveform through the waveform analysis strategy includes: obtaining the buffer corresponding to the waveform data based on the waveform analyzer; analyzing the process waveform in the buffer using the waveform analysis strategy, and determining the judgment result corresponding to the process waveform based on the completion mark and timeout mark corresponding to the process waveform; clearing the data in the buffer, and determining the analysis result corresponding to the use case based on the judgment result.

[0013] Optionally, the method further includes: determining the use cases corresponding to the analysis results and updating the use cases to the use case library.

[0014] Secondly, the present invention provides a driver data analysis and control system, the system comprising: a test case determination module, used to determine the test case library corresponding to the driver based on the driver type parameters, and to determine the test cases corresponding to the driver using the test case library; a strategy acquisition module, used to acquire the waveform parameters of the test cases and their corresponding segmentation parameters, and to determine the waveform triggering strategy, waveform acquisition strategy and waveform analysis strategy corresponding to the driver using the waveform parameters and segmentation parameters; a segmentation processing control module, used to control the driver to acquire waveform data in segments using the waveform triggering strategy, and to acquire the process waveform corresponding to the waveform data using the waveform acquisition strategy; and a waveform analysis control module, used to analyze the process waveform through the waveform analysis strategy to obtain the analysis results corresponding to the test cases.

[0015] Thirdly, embodiments of the present invention also provide an electronic device, which includes a processor and a memory, the memory storing computer-executable instructions that can be executed by the processor, and the processor executing the computer-executable instructions to implement the steps of the driver data analysis and control method provided in the first aspect.

[0016] Fourthly, embodiments of the present invention also provide a storage medium storing computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement the steps of the driver data analysis and control method provided in the first aspect.

[0017] This invention provides a driver data analysis and control method, system, and electronic device. During driver performance testing, the method first determines the corresponding use case library based on the driver's type parameters and then uses this library to identify the corresponding use cases. Next, it acquires the waveform parameters and corresponding segmentation parameters of the use cases, and uses these parameters to determine the waveform triggering strategy, waveform acquisition strategy, and waveform analysis strategy for the driver. Subsequently, it uses the waveform triggering strategy to control the driver to acquire waveform data in segments, and uses the waveform acquisition strategy to obtain the corresponding process waveforms. Finally, it analyzes the process waveforms using the waveform analysis strategy to obtain the analysis results corresponding to the use cases. This method analyzes the driver's waveform as a single use case and performs independent analysis on different stages within the use case, thereby achieving automated waveform data analysis and improving the driver data analysis capabilities and recognition accuracy. Furthermore, this method does not require a large amount of labeled data during analysis, solving the problem of high resource consumption in existing technologies using deep learning and other AI methods for data analysis.

[0018] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0020] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 is a flowchart of a driver data analysis and control method provided in an embodiment of the present invention; Figure 2 is a flowchart of step S101 in a driver data analysis and control method provided in an embodiment of the present invention; Figure 3 is a flowchart of step S102 in a driver data analysis and control method provided in an embodiment of the present invention; Figure 4 is a flowchart of step S103 in a driver data analysis and control method provided in an embodiment of the present invention, in which a waveform triggering strategy is used to control the driver to collect waveform data in segments; Figure 5 is a flowchart of step S103 in a driver data analysis and control method provided in an embodiment of the present invention, in which a waveform acquisition strategy is used to obtain the process waveform corresponding to the waveform data; Figure 6 is a flowchart of step S103 in another driver data analysis and control method provided in an embodiment of the present invention, in which a waveform acquisition strategy is used to obtain the process waveform corresponding to the waveform data; Figure 7 is a flowchart of step S104 in a driver data analysis and control method provided in an embodiment of the present invention; Figure 8 is a flowchart of another driver data analysis and control method provided in an embodiment of the present invention; Figure 9 is an acceleration waveform diagram involved in a driver data analysis and control method provided in an embodiment of the present invention; Figure 10 is a structural schematic diagram of a driver data analysis and control system provided in an embodiment of the present invention; Figure 11 is a structural schematic diagram of an electronic device provided in an embodiment of the present invention.

[0022] Icons: 1010 - Test Case Determination Module; 1020 - Strategy Acquisition Module; 1030 - Segmentation Processing Control Module; 1040 - Waveform Analysis Control Module; 101 - Processor; 102 - Memory; 103 - Bus; 104 - Communication Interface. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] To facilitate understanding of this embodiment, a driver data analysis and control method disclosed in this embodiment of the invention will be described in detail first. As shown in Figure 1, the method includes: Step S101: Determine the use case library corresponding to the driver based on the type parameter of the driver, and use the use case library to determine the use case corresponding to the driver.

[0025] First, match the corresponding test case library based on the type parameters of the driver (such as servo, stepper, and other hardware types and application scenarios). The test case library stores test templates categorized by driver type. Then, select specific test cases that match the current test objectives from the matched test case library to ensure that the test focuses on the necessary content.

[0026] Step S102: Obtain the waveform parameters of the use case and their corresponding segmentation parameters, and use the waveform parameters and segmentation parameters to determine the waveform triggering strategy, waveform acquisition strategy and waveform analysis strategy corresponding to the driver.

[0027] Extract the core waveform parameters (such as speed commands, feedback waveforms, DI signals, and other key signals) and segmentation parameters (including stage division rules, segment trigger thresholds, and data length requirements for each stage) corresponding to the target use case. Using these two types of parameters as core inputs, formulate waveform triggering strategies (triggering conditions, triggering timing control), waveform acquisition strategies (acquisition timing, data storage format, redundant data filtering rules), and waveform analysis strategies (analysis dimensions, feature extraction methods, and judgment threshold standards) adapted to the use case through parameter association modeling and strategy adaptation algorithms.

[0028] Step S103: Use the waveform triggering strategy to control the driver to collect waveform data in segments, and use the waveform acquisition strategy to obtain the process waveform corresponding to the waveform data.

[0029] Based on the waveform triggering strategy defined in step S102, the driver is controlled by hardware trigger signals or software instructions to perform segmented waveform data acquisition, ensuring the timing accuracy and completeness of data acquisition at each stage; at the same time, following the parameter configuration of the waveform acquisition strategy, the continuous process waveform data generated during the acquisition process is captured in real time, and the preliminary preprocessing of the data (such as format conversion and outlier marking) is completed synchronously, providing high-quality data input for subsequent analysis.

[0030] Step S104: After analyzing the process waveform using a waveform analysis strategy, obtain the analysis results corresponding to the use cases.

[0031] Using the waveform analysis strategy determined in step S102, targeted analysis is performed on the preprocessed process waveform data, including but not limited to characteristic parameter calculation (such as response time, overshoot, and frequency response characteristics), data validity verification, and fault feature identification. Through the fusion and verification of multi-dimensional analysis results, a standardized analysis report corresponding to the target use case is finally output, clarifying the performance of the driver in the test scenario, whether there are any anomalies, and the anomaly location results.

[0032] Optionally, step S101, which determines the use case library corresponding to the driver based on the driver type parameter and uses the use case library to determine the use case corresponding to the driver, as shown in Figure 2, includes: step S201: obtaining historical use case data corresponding to the driver based on the waveform data corresponding to the driver.

[0033] The core of this step is to extract effective testing experience from historical test records, providing a practical basis for the subsequent construction of the test case library. Specifically, the driver will generate a large amount of waveform data in past tests (such as speed command waveforms, feedback waveforms, DI signal waveforms, etc. under different speed modes). Behind these waveform data are specific test scenarios, objectives, and execution processes, i.e., historical test case data. For example, a servo driver previously collected response waveforms during the acceleration phase in a 1000rpm speed mode test. Its corresponding historical test case data includes the test objective (verifying the acceleration response at 1000rpm), the types of waveforms collected (speed command, feedback, DI signal), and analysis metrics (response time, overshoot), etc.

[0034] By extracting these test elements associated with historical waveforms, structured historical test case data can be formed, providing the test case library with basic materials verified by actual testing, thus preventing the test case library from becoming an abstract template detached from reality.

[0035] Step S202: Utilize historical use case data and build a use case library corresponding to the driver based on the type data corresponding to the driver.

[0036] The core of this step is to categorize and organize test cases by type to ensure the professionalism and matching efficiency of the test case library. The specific operation is as follows: The type data of the driver is the key to distinguishing test requirements (such as hardware type: servo / stepper / frequency converter; application scenario: robot / machine tool / conveyor belt, etc.). The test objectives of different types of drivers are significantly different (for example, servo drivers need to focus on testing dynamic response, while stepper drivers need to focus on testing step loss rate).

[0037] When building the test case library, the historical test case data extracted in step S201 should be archived by type, using type data as the classification dimension. Historical test cases corresponding to the same type of driver (such as all historical test cases for the servo driver-robot scenario) are integrated into the same sub-library, and each test case must clearly indicate the applicable type range, test objective, core parameters, etc. This test case library built by type can not only ensure the matching degree between test cases and driver characteristics, but also lay the foundation for rapid test case selection in the future.

[0038] Step S203: Obtain the type parameter corresponding to the current driver, and use the type parameter to obtain the use case corresponding to the driver from the use case library.

[0039] The core of this step is to accurately locate applicable use cases, ensuring that the test objective matches the characteristics of the driver. The specific process is as follows: First, obtain the type parameters of the driver under test (e.g., driver model, factory configuration, or user input information can be read via Modbus); then, using these type parameters as search criteria, filter the use case library built in step S202, prioritizing sub-libraries that perfectly match the current type parameters (e.g., servo driver - CNC machine tool sub-library), and then select the use case from this sub-library that best fits the current test requirements (e.g., verifying uniform speed stability at 500 rpm). Through this layer-by-layer matching logic of type parameters - sub-library - specific use case, applicable use cases can be quickly identified, avoiding invalid tests or data redundancy caused by mismatch between use cases and driver types.

[0040] Optionally, step S102, which involves obtaining the waveform parameters of the use case and their corresponding segmentation parameters, and using the waveform parameters and segmentation parameters to determine the waveform triggering strategy, waveform acquisition strategy and waveform analysis strategy corresponding to the driver, as shown in Figure 3, includes: Step S301: Determine the waveform parameters corresponding to the use case through the environmental parameters corresponding to the driver, and determine the use case executor, waveform analyzer and waveform acquisition device corresponding to the driver.

[0041] The core of this step is to identify the key modules involved in the test analysis process, providing the operational framework for subsequent parameter configuration and strategy formulation. Specifically, the driver's test system includes three core functional components: a test case executor, responsible for executing test cases according to a preset process and controlling the driver to enter different operating stages (such as acceleration and constant speed), which is the executor of the test process; a waveform acquisition unit, responsible for acquiring various signal waveforms (such as speed commands, feedback signals, DI digital signals, etc.) during the driver's operation, which is the data collector; and a waveform analyzer, responsible for calculating and analyzing the acquired waveform data (such as FFT transformation, response time calculation, etc.), which is the data analyzer.

[0042] Acquiring these three components means clarifying the main participants in the entire testing process, from execution to data collection and analysis, laying the foundation for subsequent parameter matching and strategy formulation.

[0043] Step S302: Based on the test case executor, determine the segment parameters corresponding to the test case under the waveform parameters, use the segment parameters to determine the start command corresponding to the driver, and determine the waveform triggering strategy corresponding to the driver according to the start command.

[0044] Using the test case executor as the core scheduling unit, based on its preset test case execution flow and phase division rules, and combined with the waveform parameters determined in step S301, the segment parameters of the test case under the current waveform parameter configuration are derived (such as the time window of each test phase, phase switching conditions, and data acquisition priority of each segment). Based on the segment parameters, the start conditions of each test phase of the driver are clarified, and the corresponding start instructions are generated. Then, with the start instructions as the trigger core, the waveform trigger strategy is formulated, clarifying the triggering time, trigger signal type (hardware / software trigger), and trigger exception handling mechanism for each segment data acquisition.

[0045] Step S303: Based on the waveform analyzer, determine the acquisition parameters corresponding to the waveform parameters for the use case, use the acquisition parameters to determine the acquisition command corresponding to the driver, and determine the waveform acquisition strategy corresponding to the driver based on the acquisition command.

[0046] Based on the analytical capabilities of the waveform analyzer and the data requirements, and combined with the waveform parameters in step S301, suitable acquisition parameters (such as sampling frequency, data buffer size, data transmission protocol, redundant data filtering rules, etc.) are determined. Acquisition commands for precise control of the waveform acquisition device are generated according to the acquisition parameters, clarifying the start / stop timing of the acquisition device, data storage format, and real-time transmission requirements. Based on the acquisition commands and acquisition parameters, a complete waveform acquisition strategy is constructed to ensure the integrity, timeliness, and high quality of the acquired data.

[0047] Step S304: Based on the waveform acquisition device, determine the configuration parameters corresponding to the use case in the use case library, use the configuration parameters to determine the analysis instructions corresponding to the driver, and use the analysis instructions to determine the waveform analysis strategy corresponding to the driver.

[0048] Based on the acquisition capabilities and data output format of the waveform acquisition device, the preset configuration parameters (such as analysis algorithm selection, feature extraction dimensions, data validity verification rules, fault judgment criteria, etc.) corresponding to the test case are retrieved from the test case library. Analysis instructions to drive the waveform analyzer are generated according to the configuration parameters, clarifying the data analysis process and output requirements of the analyzer. Finally, with the analysis instructions as the core, a targeted waveform analysis strategy is constructed in combination with the configuration parameters to ensure that the analysis process accurately matches the test case requirements.

[0049] Optionally, a waveform triggering strategy is used to control the driver to collect waveform data in segments, as shown in Figure 4, including: Step S401: Use the waveform triggering strategy to determine the segmented acquisition instruction corresponding to the use case executor.

[0050] Based on a preset waveform triggering strategy and combined with the scheduling logic of the test case executor, segmented acquisition instructions corresponding to the test case executor are derived and generated. These instructions must clearly define the start / stop timing, acquisition duration, trigger signal type, and priority order of each test segment, providing a clear instruction basis for subsequent precise control of the driver's segmented acquisition.

[0051] Step S402: Using the waveform acquisition device, control the driver to execute the data acquisition process corresponding to the waveform data according to the segmented acquisition instructions.

[0052] After receiving the segmented acquisition command generated in step S401, the waveform acquisition unit, as the executor, precisely schedules and controls the driver to start the corresponding waveform data acquisition process according to the command requirements. During this process, the segmented timing specified in the command must be strictly followed to ensure the orderly connection of each segmented acquisition process and to ensure that the acquisition process fully matches the test case requirements and waveform triggering strategy.

[0053] Step S403: Initialize the data receiver corresponding to the driver using the waveform analyzer, and use the data receiver to acquire waveform data after the driver executes the data acquisition process in real time.

[0054] The waveform analyzer initiates the initialization operation to complete the parameter configuration and status readiness of the data receiver corresponding to the driver (such as setting the data reception format, buffer size, communication link verification, etc.). After initialization, the data receiver is used to capture the waveform data generated during the driver's acquisition process in real time, realizing the real-time reception and temporary storage of the acquired data, and providing raw data support for subsequent data analysis.

[0055] Optionally, the waveform acquisition strategy is used to obtain the process waveform corresponding to the waveform data, as shown in Figure 5, including: Step S501: Determine the start-up entry judgment condition corresponding to the use case based on the waveform parameters.

[0056] Based on the waveform parameters determined in step S301 (such as the threshold of the start-up characteristic signal, the sampling time window, and the benchmark value of key indicators), and combined with the test scenario and core objectives of the current use case (such as acceleration process testing and stable operation testing), the start-up entry judgment conditions corresponding to this use case are constructed. These conditions need to clearly define the identification criteria for start-up characteristics (such as the trigger level of a specific DI signal, the initial threshold of the speed command, and the rising edge slope of the signal), providing a quantitative basis for subsequent start-up characteristic judgment.

[0057] Step S502: The use case executor uses the startup entry judgment condition to determine the startup features contained in the waveform data.

[0058] The test case executor assumes the primary responsibility for decision-making, invoking the startup entry decision conditions constructed in step S501 to perform frame-by-frame verification and decision-making on the startup features contained in the collected raw waveform data. The decision-making process requires precise matching of the quantification standards of the startup features, clearly distinguishing between valid startup features (meeting the decision conditions) and invalid interference signals, and finally outputting the decision result of "valid" or "invalid" startup feature, while simultaneously marking the timing position corresponding to the startup feature.

[0059] Step S503: Using the waveform analyzer, set the execution parameters corresponding to the waveform acquisition strategy based on the determination result of the startup characteristics and the waveform parameters.

[0060] Using a waveform analyzer as the core processing unit, the system synchronously inputs the judgment results of the startup characteristics (including valid startup timing, characteristic signal strength, etc.) and the waveform parameters from step S301. Through parameter correlation analysis, it configures the core execution parameters of the waveform acquisition strategy accordingly. Specific execution parameters include sampling frequency fine-tuning values, effective data acquisition duration, data buffer partitioning rules, redundant data filtering thresholds, and process waveform segmentation identification rules, ensuring that the acquisition strategy adapts to the actual startup state of the current use case.

[0061] Step S504: Obtain waveform data from the waveform acquisition unit using the execution parameters, and determine the process waveform using the waveform data.

[0062] Based on the execution parameters configured in step S503, a precise data extraction command is sent to the waveform acquisition unit. Valid waveform data that meets the requirements of the execution parameters is filtered and extracted from the raw waveform data stored in the acquisition unit. The extracted valid data is then subjected to timing splicing and integrity verification to finally generate a process waveform that can continuously and completely reflect the entire process of the driver from startup to the target operating state, providing a standardized core data carrier for subsequent waveform analysis.

[0063] Optionally, the process waveform corresponding to the waveform data is obtained using a waveform acquisition strategy, as shown in Figure 6, including: Step S601: Obtain the injection data corresponding to the driver in real time using a preset waveform storage file.

[0064] Based on a pre-set standardized waveform storage file (which includes data storage path, format parsing rules, and real-time read / write permission configuration), the system receives and acquires the injected data generated by the driver during the test in real time via the data bus. This data may include test excitation signals (such as speed command signals and load simulation signals), calibration parameter data, and operating condition switching control signals, ensuring the real-time and complete capture of the injected data.

[0065] Step S602: Determine the virtual channel corresponding to the injected data through waveform acquisition strategy.

[0066] Based on the established waveform acquisition strategy, and considering the type of injected data (e.g., digital / analog), data transmission protocol, signal priority, and its association logic with waveform data, the strategy's built-in channel mapping algorithm and data adaptation rules accurately match and determine the dedicated virtual channel corresponding to the injected data. This virtual channel, serving as the collaborative transmission carrier for the injected data and waveform data, must meet the technical requirements of synchronous data transmission and interference-free interaction.

[0067] Step S603: Obtain the process waveforms corresponding to the waveform data and the injected data based on the virtual channel.

[0068] Based on the virtual channel determined in step S602, a synchronous transmission link between waveform data and injected data is established to realize the timing alignment and collaborative flow of the two types of data in the virtual channel. Through the data flow integration function of the virtual channel, the synchronously transmitted waveform data (raw acquisition data) and injected data are associated and spliced ​​in real time, and the integrity is verified and the format is standardized. Finally, a process waveform that can completely reflect the correspondence between injected excitation and waveform response is generated.

[0069] Optionally, step S104, which involves analyzing the process waveform using a waveform analysis strategy to obtain the analysis results corresponding to the use case, as shown in Figure 7, includes: step S701: obtaining the buffer corresponding to the waveform data based on the waveform analyzer.

[0070] Using the waveform analyzer as the core scheduling unit, and based on its preset hardware configuration parameters and software logic, a dedicated data buffer adapted to waveform data processing is located and acquired. This buffer is the associated storage unit of the waveform analyzer, with preset data storage addresses, buffer capacity thresholds, data read / write permissions, and timing synchronization rules. It is used to temporarily store the raw waveform data to be analyzed, ensuring efficient data retrieval during the analysis process.

[0071] Step S702: Analyze the process waveforms in the buffer using a waveform analysis strategy, and determine the judgment result corresponding to the process waveforms based on the completion flags and timeout flags corresponding to the process waveforms.

[0072] The established waveform analysis strategy is invoked to conduct a systematic analysis of the process waveform data temporarily stored in the data buffer. This analysis may include key feature parameter extraction (such as response time, overshoot, frequency response characteristics, etc.), signal integrity verification, and data timing continuity verification. The status markers inherent in the process waveforms are identified simultaneously, namely, completion markers indicating normal completion of the test process and timeout markers indicating abnormal interruption of the test process. By combining the waveform analysis results with the collaborative verification of the status markers, a preliminary judgment result corresponding to the process waveform is finally generated (such as normal test completion and performance meeting the standard, test timeout anomaly, incomplete waveform data requiring review, etc.).

[0073] Step S703: After clearing the data in the cache, determine the analysis results corresponding to the use cases based on the judgment results.

[0074] First, perform a data cache clearing operation to release storage resources and prevent residual data from interfering with the analysis process of other test cases. Then, based on the preliminary judgment result of step S702, and combined with the test objectives of the current test case and the preset qualification judgment criteria, perform secondary verification and information integration on the preliminary result, and finally output the complete analysis result corresponding to the test case, which includes core information such as a summary of the test process status, quantitative data of core performance indicators, location of abnormal problems (if they exist), and targeted optimization suggestions (if necessary).

[0075] Optionally, the method further includes: determining the use cases corresponding to the analysis results and updating the use cases to the use case library. A flowchart of another driver data analysis and control method is shown in Figure 8; the corresponding waveform is shown in Figure 9. Figure 9 shows a typical acceleration waveform. The thick line at the bottom is the DI trigger signal, and the waveform at the top is the speed command and feedback. The red curve corresponds to the speed command, and the green curve corresponds to the speed feedback; the thick purple line corresponds to the positive overtravel switch, the thick green line corresponds to the origin switch, the thick blue line corresponds to the servo enable, and the thick yellow line corresponds to the origin homing completion.

[0076] A waveform diagram is a test case with a specific test objective, which is to test whether DI can be started correctly. The test case has multiple steps (phases), such as startup enable and forward overrun. Each step is a small environment, and this environment is allowed to have its own judgment.

[0077] The specific steps are described below:

[0078] Each step must be indivisible, with simple and direct logic, ultimately implemented as a Python function. This model facilitates verification through thorough unit testing, allowing for complete data analysis throughout the testing process. The step-by-step completion process aligns with human usage habits, making it easier for testers to write test cases. Furthermore, it offers high scalability and functional reusability. All functionalities are composed of a series of steps.

[0079] Scenarios requiring real-time processing are generally those involving long actions, where offline data acquisition is sufficient, or where a comprehensive analysis of the acquired files is performed after data acquisition is triggered. In these cases, the issue of data segmentation arises. Therefore, the driver data analysis and control method in this embodiment designs a virtual channel approach, with the following logic: When storing waveforms, a virtual variable value is always stored. This value can be modified by the automated testing process. For example, when acquiring process 1, it can be modified to 1, and then to 2. In this way, during offline waveform analysis, the virtual channel can be used for rapid segmentation, while not placing too much emphasis on the driver's acquisition requirements.

[0080] In layman's terms, taking a test acceleration case as an example, the manual test execution process of the test case includes the following steps: 1. Reset the device; 2. Enable the device; 3. Set the speed parameters; 4. Acquire waveforms; 5. Accelerate; 6. Determine if the acceleration is correct and the error situation; 7. Wait for the running speed to stabilize and continue running; 8. Stop; 9. Determine if the acceleration is correct and the error situation; 10. Wait for the device to stop running; 11. Turn off waveform acquisition; 12. Cancel the enable.

[0081] Different types of equipment may have different operational requirements, different settings, and different required actions. However, some actions are fixed, such as resetting factory settings first; some approaches are fixed, such as acceleration and deceleration, one involving torque and the other speed; and some require waiting for a certain condition, in this case, waiting for the equipment to reach its destination. The design approach of the test case library (generally referring to the test case data section) is to extract the variable points into test case data and write the fixed patterns in the execution code. After combining the code and data, automated testing can be performed.

[0082] In simple terms, the purpose of this method is to design actions 3-10 into a combination of general patterns. This pattern can be understood as: setting conditions - setting flags - setting parameters - obtaining results.

[0083] During execution, various complex operations are broken down into multiple stages. Each stage can be evaluated for its status during execution, allowing for segmented evaluation. Once an anomaly is detected, execution can be stopped, thus avoiding wasting testing time.

[0084] Compared to conventional automated testing, which relies on discrete points for judgment when waveform analysis is impossible, automated waveform analysis significantly enhances the analytical capabilities of automated testing. This is because it struggles to identify acceleration processes and short-cycle features.

[0085] Compared to pattern recognition, the driver data analysis and control method in this embodiment can make full use of the techniques used in pattern recognition. Since the scene is segmented and the data source is relatively simple, the development difficulty can be greatly reduced.

[0086] Compared to machine learning, the driver data analysis and control method in this embodiment does not require a large amount of labeled data, and can even create its own data.

[0087] As can be seen from the above driver data analysis and control method, this method analyzes the driver waveform as a use case and performs independent analysis on the different stages corresponding to the use case, thereby realizing automated analysis of waveform data and improving the analysis capability and recognition accuracy of driver data. In addition, this method does not require a large amount of labeled data during the analysis process, which solves the problem of high resource consumption in the existing technology when using AI methods such as deep learning for data analysis.

[0088] Corresponding to the above-described driver data analysis and control method embodiment, this embodiment of the invention also provides a driver data analysis and control system, as shown in Figure 10. The system includes: a test case determination module 1010, used to determine the test case library corresponding to the driver based on the driver's type parameters, and to determine the test cases corresponding to the driver using the test case library; a strategy acquisition module 1020, used to acquire the waveform parameters of the test cases and their corresponding segmentation parameters, and to determine the waveform triggering strategy, waveform acquisition strategy, and waveform analysis strategy corresponding to the driver using the waveform parameters and segmentation parameters; a segmentation processing control module 1030, used to control the driver to acquire waveform data in segments using the waveform triggering strategy, and to acquire the process waveform corresponding to the waveform data using the waveform acquisition strategy; and a waveform analysis control module 1040, used to analyze the process waveform through the waveform analysis strategy to obtain the analysis results corresponding to the test cases.

[0089] As can be seen from the above-mentioned driver data analysis and control system, the system analyzes the driver waveform as a use case and performs independent analysis on the different stages corresponding to the use case, thereby realizing automated analysis of waveform data and improving the analysis capability and recognition accuracy of driver data. In addition, the system does not require a large amount of labeled data during the analysis process, which solves the problem of high resource consumption in the existing technology when using AI methods such as deep learning for data analysis.

[0090] The driver data analysis and control system provided in this embodiment of the invention has the same implementation principle and technical effects as the aforementioned driver data analysis and control method embodiment. For the sake of brevity, any parts not mentioned in the system embodiment can be referred to the corresponding content in the aforementioned driver data analysis and control method embodiment.

[0091] This embodiment also provides an electronic device, the structural schematic of which is shown in FIG11. The device includes a processor 101 and a memory 102. The memory 102 is used to store one or more computer instructions, which are executed by the processor to implement the steps of the above-mentioned driver data analysis and control method.

[0092] The electronic device shown in Figure 11 also includes a bus 103 and a communication interface 104. The processor 101, the communication interface 104, and the memory 102 are connected via the bus 103.

[0093] The memory 102 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. The bus 103 may be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only a single bidirectional arrow is used in Figure 11, but this does not indicate that there is only one bus or one type of bus.

[0094] The communication interface 104 is used to connect to at least one user terminal and other network units through a network interface, and to send encapsulated IPv4 packets or IPv4 packets to the user terminal through the network interface.

[0095] Processor 101 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 101 or by instructions in software form. The processor 101 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this disclosure. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this disclosure can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 102. The processor 101 reads the information in memory 102 and, in conjunction with its hardware, completes the steps of the method described in the foregoing embodiments.

[0096] This invention also provides a storage medium storing a computer program, which, when executed by a processor, performs the steps of the driver data analysis and control method described in the foregoing embodiments.

[0097] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, devices, and methods can be implemented in other ways. The system embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0098] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0099] In addition, 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.

[0100] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion 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, electronic device, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0101] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A driver data analysis and control method, characterized in that, The method includes: determining the use case library corresponding to the driver based on the driver's type parameters, and determining the use case corresponding to the driver using the use case library; obtaining the waveform parameters of the use case and its corresponding segmentation parameters, and determining the waveform triggering strategy, waveform acquisition strategy, and waveform analysis strategy corresponding to the driver using the waveform parameters and the segmentation parameters; controlling the driver to acquire waveform data in segments using the waveform triggering strategy, and acquiring the process waveform corresponding to the waveform data using the waveform acquisition strategy; and obtaining the analysis result corresponding to the use case by analyzing the process waveform using the waveform analysis strategy.

2. The driver data analysis and control method according to claim 1, characterized in that, The steps of determining the use case library corresponding to the driver based on the driver's type parameters and determining the use cases corresponding to the driver using the use case library include: obtaining historical use case data corresponding to the driver based on waveform data corresponding to the driver; constructing the use case library corresponding to the driver using the historical use case data and based on the type data corresponding to the driver; obtaining the type parameters corresponding to the current driver, and obtaining the use cases corresponding to the driver from the use case library using the type parameters.

3. The driver data analysis and control method according to claim 1, characterized in that, The steps of obtaining the waveform parameters of the use case and their corresponding segmentation parameters, and determining the waveform triggering strategy, waveform acquisition strategy, and waveform analysis strategy corresponding to the driver using the waveform parameters and the segmentation parameters, include: determining the waveform parameters corresponding to the use case through the environment parameters corresponding to the driver, and determining the use case executor, waveform analyzer, and waveform acquisition device corresponding to the driver; determining the segmentation parameters corresponding to the use case under the waveform parameters based on the use case executor, determining the start command corresponding to the driver using the segmentation parameters, and determining the waveform triggering strategy corresponding to the driver according to the start command; determining the acquisition parameters corresponding to the use case under the waveform parameters based on the waveform analyzer, determining the acquisition command corresponding to the driver using the acquisition parameters, and determining the waveform acquisition strategy corresponding to the driver according to the acquisition command; determining the configuration parameters corresponding to the use case in the use case library based on the waveform acquisition device, determining the analysis command corresponding to the driver using the configuration parameters, and determining the waveform analysis strategy corresponding to the driver using the analysis command.

4. The driver data analysis and control method according to claim 3, characterized in that, Controlling the driver to acquire waveform data in segments using the waveform triggering strategy includes: determining the segment acquisition instruction corresponding to the use case executor using the waveform triggering strategy; controlling the driver to execute the data acquisition process corresponding to the waveform data according to the segment acquisition instruction through the waveform acquisition device; initializing the data receiver corresponding to the driver using the waveform analyzer, and acquiring the waveform data after the driver executes the data acquisition process in real time using the data receiver.

5. The driver data analysis and control method according to claim 3, characterized in that, Acquiring the process waveform corresponding to the waveform data using the waveform acquisition strategy includes: determining the start-up entry judgment condition corresponding to the use case based on the waveform parameters; using the use case executor to judge the start-up features contained in the waveform data using the start-up entry judgment condition; using the waveform analyzer to set the execution parameters corresponding to the waveform acquisition strategy according to the judgment result of the start-up features and the waveform parameters; acquiring the waveform data from the waveform collector using the execution parameters, and determining the process waveform using the waveform data.

6. The driver data analysis and control method according to claim 3, characterized in that, The process waveform corresponding to the waveform data is obtained using the waveform acquisition strategy, including: acquiring the injection data corresponding to the driver in real time using a preset waveform storage file; determining the virtual channel corresponding to the injection data through the waveform acquisition strategy; and acquiring the process waveform corresponding to the waveform data and the injection data based on the virtual channel.

7. The driver data analysis and control method according to claim 3, characterized in that, The steps of obtaining the analysis result corresponding to the use case after analyzing the process waveform using the waveform analysis strategy include: obtaining the buffer corresponding to the waveform data based on the waveform analyzer; analyzing the process waveform in the buffer using the waveform analysis strategy, and determining the judgment result corresponding to the process waveform based on the completion flag and timeout flag corresponding to the process waveform; clearing the data in the buffer, and determining the analysis result corresponding to the use case based on the judgment result.

8. The driver data analysis and control method according to claim 3, characterized in that, The method further includes: determining the use case corresponding to the analysis result, and updating the use case to the use case library.

9. A driver data analysis and control system, characterized in that, The system includes: a test case determination module, used to determine the test case library corresponding to the driver based on the driver type parameters, and to determine the test cases corresponding to the driver using the test case library; a strategy acquisition module, used to acquire the waveform parameters of the test cases and their corresponding segmentation parameters, and to determine the waveform triggering strategy, waveform acquisition strategy, and waveform analysis strategy corresponding to the driver using the waveform parameters and the segmentation parameters; a segmentation processing control module, used to control the driver to acquire waveform data in segments using the waveform triggering strategy, and to acquire the process waveform corresponding to the waveform data using the waveform acquisition strategy; and a waveform analysis control module, used to analyze the process waveform through the waveform analysis strategy to obtain the analysis result corresponding to the test case.

10. An electronic device, characterized in that, The electronic device includes a processor and a memory, the memory storing computer-executable instructions that can be executed by the processor, the processor executing the computer-executable instructions to implement the steps of the driver data analysis and control method according to any one of claims 1 to 8.

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