Multi-scale synchronous data acquisition method and system based on event triggering

Abstract

The embodiment of the invention provides a multi-scale synchronous data collection method and system based on event triggering. The method comprises the following steps: synchronously acquiring original data of a plurality of physical quantities based on a unified time reference; according to a preset event triggering condition, dynamically switching the sampling frequency of the original data, and when the event triggering condition is met, adjusting the sampling frequency of the original data from a low-frequency sampling frequency to a high-frequency sampling frequency; when original data is collected at a high-frequency sampling frequency, virtual channel data with physical significance is generated based on the original data, and the virtual channel data and the original data are synchronously sealed and stored. The method is used for solving the problems of much redundant data and key information loss.

Description

Technical Field

[0001] This application relates to the field of electrical equipment testing technology, and in particular to a method and system for multi-scale synchronous data acquisition based on event triggering. Background Technology

[0002] Electrical equipment is widely used in key areas such as power systems, industrial automation, rail transportation, and new energy. Its safe and stable operation highly depends on the performance status of its internal core components (such as relays, trip units, and auxiliary switches). Therefore, conducting full life-cycle performance testing and health assessments of these components is fundamental to ensuring the reliability of equipment and systems.

[0003] Existing technologies generally use a fixed sampling frequency to continuously collect key signals of components during the test process (such as action time, contact resistance, coil current, mechanical vibration, etc.); the test process is executed in stages according to preset working conditions (such as room temperature performance test, aging accelerated test, life cycle test), and the obtained data is used for manual or semi-automatic analysis to determine whether it meets the standard threshold, and to evaluate the qualification or remaining life of the components accordingly.

[0004] However, existing technologies using trigger-based data acquisition suffer from problems such as excessive redundant data and loss of critical information, which affects the testing of components. Summary of the Invention

[0005] The event-triggered multi-scale synchronous data acquisition method and system provided in this application are used to solve the problems of redundant data and loss of key information in the prior art when using triggered data acquisition.

[0006] In a first aspect, embodiments of this application provide an event-triggered multi-scale synchronous data acquisition method, including:

[0007] Raw data of multiple physical quantities are collected synchronously based on a unified time reference;

[0008] Based on preset event triggering conditions, the sampling frequency of the raw data is dynamically switched. When the event triggering conditions are met, the sampling frequency of the raw data is adjusted from a low frequency sampling frequency to a high frequency sampling frequency.

[0009] When the original data is collected at a high frequency of sampling, virtual channel data with physical meaning is generated based on the original data, and the virtual channel data is synchronously sealed and stored with the original data.

[0010] In one possible implementation, the sampling frequency of the raw data is dynamically switched according to a preset event triggering condition, including:

[0011] Based on at least one of the preset static threshold trigger conditions and rate of change trigger conditions, determine whether to adjust the sampling frequency of the original data from a low-frequency sampling frequency to a high-frequency sampling frequency.

[0012] In one possible implementation, the sampling frequency of the raw data is dynamically switched according to a preset event triggering condition, including:

[0013] Based on preset event triggering conditions, the collected raw data is judged in real time to determine the triggering level that should be activated at the current time;

[0014] The sampling frequency of the raw data is dynamically switched based on the current trigger level to be activated and the trigger level that has been activated; the sampling frequency of the raw data is different for different multi-level event triggering mechanisms.

[0015] In one possible implementation, the collected raw data is analyzed in real time based on preset event triggering conditions to determine the trigger level to be activated, including:

[0016] Based on the type of raw data, the target raw data belonging to the multi-source joint data are fused through a multi-sensor fusion algorithm to obtain multi-source joint data;

[0017] Based on preset event triggering conditions, the system performs real-time judgment on other raw data in the multi-source combined data and / or raw data to determine the triggering level that should be activated at the moment; where other raw data refers to the raw data other than the target raw data.

[0018] In one possible implementation, the high-frequency sampling frequency of the raw data is adjusted based on historical raw data.

[0019] In one possible implementation, virtual channel data with physical meaning is generated based on the original data, and the virtual channel data and the original data are synchronously encapsulated and stored, including:

[0020] Based on the data type of the original data, the corresponding data processing model is invoked. Data processing includes at least one of mathematical or physical models.

[0021] Based on the data processing model, the raw data is processed in real time to obtain virtual channel data with physical meaning;

[0022] The virtual channel data is synchronously archived and stored with the original data.

[0023] In one possible implementation, the virtual channel data and the original data are encapsulated and stored as data frames with a unified timestamp.

[0024] In one possible implementation, before synchronously acquiring raw data of multiple physical quantities based on a unified time reference, the method further includes:

[0025] After the component under test (DUT) is mounted on a general-purpose test fixture and the excitation acquisition channel of the test module is connected to the corresponding pin of the DUT, in response to the user's operation, the system retrieves and loads the data acquisition strategy corresponding to the DUT from the strategy library, so as to synchronously acquire the raw data of multiple physical quantities based on a unified time reference according to the data acquisition strategy.

[0026] Secondly, embodiments of this application provide an event-triggered multi-scale synchronous data acquisition system, including a test module and a test host, wherein the test module and the test host are communicatively connected.

[0027] The test module is used to synchronously collect raw data of multiple physical quantities based on a unified time base and send the collected raw data to the test host.

[0028] The test host is used to dynamically switch the sampling frequency of the test module for the raw data according to preset event triggering conditions. When the event triggering conditions are met, the test module adjusts the sampling frequency of the raw data from a low frequency to a high frequency. When the test module collects the raw data at a high frequency, it generates virtual channel data with physical meaning based on the raw data and synchronously encapsulates and stores the virtual channel data and the raw data.

[0029] In one possible implementation, a universal test fixture is also included, which is used to fix the component under test and to connect the excitation acquisition channel of the test module to the corresponding pin of the component under test.

[0030] Thirdly, embodiments of this application provide an event-triggered multi-scale synchronous data acquisition device, including:

[0031] The synchronous acquisition module is used to synchronously acquire raw data of multiple physical quantities based on a unified time reference.

[0032] The dynamic switching module is used to dynamically switch the sampling frequency of the raw data according to preset event triggering conditions. Specifically, when the event triggering conditions are met, the sampling frequency of the raw data is adjusted from a low frequency sampling frequency to a high frequency sampling frequency.

[0033] The generation module is used to generate virtual channel data with physical meaning based on the original data when the original data is collected at a high frequency sampling frequency, and to synchronously encapsulate and store the virtual channel data with the original data.

[0034] Fourthly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0035] The memory stores instructions that the computer executes;

[0036] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0037] Fifthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0038] In a sixth aspect, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0039] The event-triggered multi-scale synchronous data acquisition method and system provided in this application synchronously acquire raw data of multiple physical quantities based on a unified time reference, and dynamically switch the sampling frequency of the raw data according to preset event triggering conditions. This allows for high-frequency sampling of the raw data when the event triggering conditions are met, generating physically meaningful virtual channel data based on the raw data, and synchronously sealing and storing the virtual channel data along with the raw data. This approach uses a low-frequency sampling frequency when the component is in a stable state, switching to a high-frequency sampling frequency only when critical events occur, thereby reducing storage and computing resource consumption. Furthermore, the dynamic adjustment of the sampling frequency through the event triggering mechanism avoids the "missed" problem of traditional time-triggered modes, ensuring that key performance characteristics are captured. This solves the problems of redundant data and loss of key information in existing technologies using trigger-based data acquisition. Attached Figure Description

[0040] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0041] Figure 1 A schematic diagram illustrating a scenario for the event-triggered multi-scale synchronous data acquisition method provided in this application;

[0042] Figure 2 A flowchart illustrating the event-triggered multi-scale synchronous data acquisition method provided in this application;

[0043] Figure 3 A schematic diagram of the modules of the event-triggered multi-scale synchronous data acquisition system provided in this application;

[0044] Figure 4 A schematic diagram of the structure of the event-triggered multi-scale synchronous data acquisition device provided in this application;

[0045] Figure 5 A schematic diagram of the structure of the electronic device provided in this application.

[0046] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0047] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0048] Currently, the industry generally adopts the traditional testing mode of discrete, single-item, and sequential execution for the performance testing of key components of electrical equipment. The typical process includes: using multiple independent instruments (such as megohmmeters, micro-ohmmeters, withstand voltage testers, millisecond meters, etc.) to complete single tests such as insulation, continuity, withstand voltage, and operating time; each test is strictly carried out in sequence according to the regulations and isolated from each other; data acquisition mostly relies on fixed-period time triggering methods and does not respond to changes in the actual state of the components; test results are mainly recorded manually or only a single final value is stored, lacking dynamic process data.

[0049] While this model can complete basic verification, it suffers from systemic defects in terms of efficiency, data quality, and analytical capabilities: frequent equipment and wiring changes lead to long testing cycles and high manpower and equipment costs; data from different projects are fragmented in time and space, forming data silos that are difficult to correlate and analyze; the fixed sampling strategy generates a large amount of redundant data during stable phases and is also prone to missing transient critical events such as contact bounce and temperature rise; ultimately, it can only provide static conclusions of pass or fail, and due to the lack of dynamic process information involving multiple physical quantities (such as instantaneous power consumption and dynamic impedance), it cannot support in-depth applications such as performance degradation modeling and lifetime prediction.

[0050] The event-triggered multi-scale synchronous data acquisition method provided in this application can synchronously acquire raw data of multiple physical quantities based on a unified time reference, and dynamically switch the sampling frequency of the raw data according to preset event triggering conditions. This allows for high-frequency sampling of the raw data when the event triggering conditions are met, generating physically meaningful virtual channel data based on the raw data, and synchronously sealing and storing the virtual channel data along with the raw data. This approach uses a low-frequency sampling frequency when the component is in a stable state, switching to a high-frequency sampling frequency only when critical events occur, thereby reducing storage and computing resource consumption. Furthermore, the dynamic adjustment of the sampling frequency through the event triggering mechanism avoids the "missed" problem of traditional time-triggered modes, ensuring that key performance characteristics are captured. This solves the problems of redundant data and loss of key information in existing technologies using trigger-based data acquisition.

[0051] Figure 1 A schematic diagram illustrating the scenario of the event-triggered multi-scale synchronous data acquisition method provided in this application, as shown below. Figure 1 As shown, the specific application scenario of this application can be a multi-scale synchronous data acquisition system based on event triggering. This system can include a test module and a test host. This application does not restrict the execution subject, as long as it can synchronously acquire raw data of multiple physical quantities based on a unified time reference; according to preset event triggering conditions, the sampling frequency of the raw data is dynamically switched. Specifically, when the event triggering conditions are met, the sampling frequency of the raw data is adjusted from a low-frequency sampling frequency to a high-frequency sampling frequency; when the raw data is acquired at a high-frequency sampling frequency, virtual channel data with physical meaning is generated based on the raw data, and the virtual channel data is synchronously sealed and stored with the raw data.

[0052] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0053] Figure 2 The flowchart illustrating the event-triggered multi-scale synchronous data acquisition method provided in this application is as follows: Figure 2 As shown, the method includes:

[0054] S201. Simultaneously collect raw data of multiple physical quantities based on a unified time reference.

[0055] In this context, a unified time reference can refer to the fact that sensors or acquisition channels are timestamped based on the same clock source to ensure that multiple data collected are strictly aligned in the time dimension.

[0056] Synchronous acquisition refers to the simultaneous activation of multiple sensors or data channels under the same trigger signal or clock control, sampling at the same pace to ensure that the acquired data of multiple physical quantities correspond precisely in time.

[0057] The raw data of multiple physical quantities can refer to the unprocessed analog / digital signals directly output by the sensor or only conditioned at the front end (such as filtering and amplification), reflecting the real physical state of the measured component at a specific moment (such as voltage, current, temperature, vibration, etc.).

[0058] In this embodiment of the application, before synchronously collecting the raw data of multiple physical quantities based on a unified time reference, the method further includes:

[0059] After the component under test (DUT) is mounted on a general-purpose test fixture and the excitation acquisition channel of the test module is connected to the corresponding pin of the DUT, in response to the user's operation, the system retrieves and loads the data acquisition strategy corresponding to the DUT from the strategy library, so as to synchronously acquire the raw data of multiple physical quantities based on a unified time reference according to the data acquisition strategy.

[0060] Among them, a general-purpose test fixture can refer to a hardware device that can be used to quickly and reliably achieve mechanical positioning and electrical connection of the component under test during the test process; it can be adapted to the shape and pin layout of various types of components through an adjustable or modular structure to ensure stable access of test signals.

[0061] The excitation acquisition channel of the test module can refer to a hardware interface unit that integrates signal excitation output and response signal acquisition functions. Each channel can independently output excitation signals such as voltage, current or pulse, and synchronously acquire the electrical response (such as voltage, current, on / off state, etc.) on the corresponding pin of the component under test. In the embodiments of this application, the channel is connected to the designated pin of the component under test through a cable or probe.

[0062] In some embodiments, the device under test (DUT) can be installed in a general-purpose test fixture by the user, and the excitation acquisition channel of the test module can be manually connected to the corresponding pin of the DUT.

[0063] A strategy library refers to a database used to store data acquisition strategies. This strategy library can be stored on the test host or retrieved from other servers. The data acquisition strategies in the strategy library can be pre-configured according to the components being tested. When in use, users can select the corresponding data acquisition strategy through the interactive system on the test host, or retrieve the corresponding data acquisition strategy based on the component type entered by the user, to execute the data acquisition for the component.

[0064] Data acquisition strategy refers to a systematic plan developed during testing or monitoring to efficiently and accurately acquire target information. It includes sampling frequency (such as low-frequency inspection or high-frequency triggering), sampling channel selection, trigger condition setting (such as threshold, rate of change or multi-sensor joint judgment), data storage method and resource scheduling mechanism, etc., to ensure the integrity and real-time performance of component testing.

[0065] After the component under test (DUT) is mounted on a general-purpose test fixture and the excitation acquisition channel of the test module is connected to the corresponding pin of the DUT, the user can select the corresponding test item from the display interface on the operation screen of the test system. The test system will retrieve the corresponding data acquisition strategy from the strategy library and load it according to the corresponding test item, so as to synchronously acquire the raw data of multiple physical quantities based on a unified time reference according to the data acquisition strategy.

[0066] S202. According to the preset event triggering conditions, dynamically switch the sampling frequency of the raw data. When the event triggering conditions are met, adjust the sampling frequency of the raw data from the low frequency sampling frequency to the high frequency sampling frequency.

[0067] Among them, the event triggering condition can refer to the pre-set criteria rules used to determine whether to initiate a specific response action during the data acquisition or monitoring process. These criteria rules can be based on the threshold of a single physical quantity (such as temperature exceeding 80°C), the rate of change of the signal (such as a sudden increase in the current slope), the characteristics of multi-sensor data fusion (such as abnormal vibration and rising temperature), or deviation from historical trends, etc., to accurately identify changes in equipment status or potential fault events. Alternatively, they can be based on a comprehensive judgment based on multiple physical quantities.

[0068] In this embodiment of the application, the event triggering condition can be part of the data acquisition strategy and can be pre-stored in the strategy library.

[0069] Sampling frequency refers to the number of times a physical quantity is measured and recorded per unit of time, and it is measured in Hertz (Hz). For example, a sampling frequency of 10 Hz means that 10 data points are collected per second.

[0070] In the component testing described in this application embodiment, the sampling frequency can include low-frequency sampling frequency and high-frequency sampling frequency. The low-frequency sampling frequency can refer to a strategy of acquiring signals at a low rate (e.g., 0.1Hz to 10Hz) per unit time. It has the advantages of reducing data volume, saving storage and power consumption, and is suitable for routine inspections during the performance stabilization stage. The high-frequency sampling frequency can refer to a strategy of densely acquiring signals at a higher rate (e.g., 1kHz to 1MHz or higher) per unit time. It has the advantages of accurately restoring signal details and providing high-resolution raw data for fault diagnosis and performance evaluation.

[0071] S203. When collecting raw data at a high-frequency sampling rate, generate virtual channel data with physical meaning based on the raw data, and synchronously encapsulate and store the virtual channel data with the raw data.

[0072] Among these, generating physically meaningful virtual channel data based on raw data can refer to constructing a new data channel that reflects the true physical state or performance characteristics of a device, even though it is not directly measured, by performing mathematical operations, model derivation, or multi-source fusion on the raw data of collected physical quantities (such as voltage, current, temperature, vibration, etc.). For example, power factor can be calculated using coil voltage and current, or contact health index can be generated by combining action time and contact resistance.

[0073] Synchronously storing virtual channel data and raw data involves packaging, marking, and persistently storing virtual channel data (such as calculated power, health index, and characteristic frequency) and corresponding raw physical quantity data (such as voltage, current, and temperature) in the same data file or database record according to a unified time base. This allows the raw data to serve as the source of analysis, ensuring traceability of the calculation process and avoiding "black box" conclusions. Furthermore, the raw data supports multi-granularity reuse and reanalysis during algorithm upgrades, virtual channel redefinition, or misjudgment troubleshooting. Simultaneously, in fault diagnosis or technical arbitration scenarios, combining raw waveforms with virtual features (such as contact bounce count and current waveform) enables cross-validation, significantly improving the reliability of judgments. This facilitates subsequent user operations.

[0074] In this embodiment, generating physically meaningful virtual channel data based on the original data and synchronously sealing and storing the virtual channel data and the original data includes:

[0075] Based on the data type of the original data, the corresponding data processing model is invoked. Data processing includes at least one of mathematical or physical models.

[0076] Based on the data processing model, the raw data is processed in real time to obtain virtual channel data with physical meaning;

[0077] The virtual channel data is synchronously archived and stored with the original data.

[0078] The system automatically identifies and calls upon matching data processing models based on the data type of the collected raw data (such as voltage waveforms, current transients, temperature sequences, vibration spectra, etc.). These models can include data processing models based on physical mechanisms (such as thermo-electric coupling models of electromagnetic coils and dynamic models of relay contact wear), data processing models based on mathematical methods (such as FFT spectrum analysis, wavelet denoising, sliding window statistics, normalized exponent calculation, etc.), and also both of the above-mentioned data processing models.

[0079] After selecting a matching data processing model, the raw data can be processed in real time to generate virtual channel data that reflects the intrinsic performance status of components. For example, power factor can be calculated using voltage and current, degradation trends can be fitted using action time series and contact resistance, or health scores can be generated by fusing multi-channel signals.

[0080] Finally, to ensure the integrity and traceability of the data chain, the generated virtual channel data and its corresponding original data are synchronously archived and stored according to a unified time benchmark. This not only preserves the original basis for the analysis results but also supports future algorithm iterations, fault reviews, or compliance audits, forming a closed-loop data management system encompassing original, derived, and verified data.

[0081] In this embodiment, the virtual channel data and the original data are encapsulated and stored as data frames with a unified timestamp.

[0082] The virtual channel data is aligned with the original data according to a unified timestamp and encapsulated into the same data frame for storage. This ensures that multi-source heterogeneous data is strictly synchronized, traceable, and can be analyzed in a correlated manner in the time dimension.

[0083] In this embodiment of the application, dynamically switching the sampling frequency of the raw data according to preset event triggering conditions includes:

[0084] Based on at least one of the preset static threshold trigger conditions and rate of change trigger conditions, determine whether to adjust the sampling frequency of the original data from a low-frequency sampling frequency to a high-frequency sampling frequency.

[0085] Among them, the static threshold trigger condition can refer to a fixed numerical limit that is set in advance. When the monitored physical quantity (such as temperature, current, voltage, etc.) exceeds or falls below the limit, it is judged as an abnormal event and a corresponding response is triggered.

[0086] For example, static threshold triggering conditions can include static threshold triggering. This trigger is activated when the instantaneous value of a certain KPI exceeds a fixed preset threshold. Such triggers are used to capture instantaneous, large-scale performance limit exceedance events.

[0087] The static threshold triggering condition can satisfy: ;

[0088] in, Represents the moment The collected number Instantaneous values ​​of key performance indicators; This represents a pre-set static constant threshold that remains unchanged throughout the test.

[0089] The rate of change trigger condition can refer to the trigger criterion set based on the rate of change of the measured physical quantity per unit time, which is used to detect slow evolution or trend anomalies.

[0090] For example, rate of change triggering conditions can include rate of change triggers. These triggers do not care about the absolute value of the metric, but rather the magnitude of its change. They are extremely sensitive to capturing dynamic processes that indicate a potential sharp deterioration in performance.

[0091] The rate of change trigger condition can satisfy:

[0092] ;

[0093] If the collected data is discrete, then difference is used to approximate it:

[0094] ;

[0095] in, and They represent the current moment. and the previous sampling time The collected KPI values; The sampling time interval; This represents the preset threshold for the maximum rate of change allowed for this KPI.

[0096] In this embodiment of the application, dynamically switching the sampling frequency of the raw data according to preset event triggering conditions includes:

[0097] Based on preset event triggering conditions, the collected raw data is judged in real time to determine the triggering level that should be activated at the current time;

[0098] The sampling frequency of the raw data is dynamically switched based on the current trigger level to be activated and the trigger level that has been activated; the sampling frequency of the raw data is different for different multi-level event triggering mechanisms.

[0099] Multiple event trigger conditions can be set, and each event trigger condition can correspond to a different trigger level. For example, event trigger conditions can be divided into three levels according to severity: Level 1 (low risk), Level 2 (medium risk), and Level 3 (high risk), with each level corresponding to a set of preset thresholds. When the raw data first exceeds the Level 1 threshold, the first trigger level is activated, and data is sampled according to the pre-configured first high-frequency sampling frequency. If the raw data further deteriorates and exceeds the Level 2 threshold, the trigger level is escalated to Level 2, and the sampling frequency is dynamically switched to a higher second high-frequency sampling frequency. If the data continues to deteriorate and reaches the Level 3 threshold, it is further escalated to the Level 3 trigger level, and the highest frequency sampling strategy is activated.

[0100] If the original data suddenly jumps from the normal state to above the second level, but does not go through the slow rise process of the first level, the sampling frequency of the original data can be changed to the second high-frequency sampling frequency.

[0101] In this embodiment of the application, the collected raw data is judged in real time according to preset event triggering conditions to determine the triggering level that should be activated, including:

[0102] Based on the type of raw data, the target raw data belonging to the multi-source joint data are fused through a multi-sensor fusion algorithm to obtain multi-source joint data;

[0103] Based on preset event triggering conditions, the system performs real-time judgment on other raw data in the multi-source combined data and / or raw data to determine the triggering level that should be activated at the moment; where other raw data refers to the raw data other than the target raw data.

[0104] Among them, multi-sensor fusion algorithms refer to the integration and processing of observation data from multiple different or similar types of sensors at the data level, feature level, or decision level through mathematical models or intelligent methods (such as weighted averaging, particle filtering, DS evidence theory, neural networks, etc.) to eliminate redundancy and make up for the limitations of a single sensor, thereby obtaining more accurate, reliable, and comprehensive state estimation or environmental perception results.

[0105] In this embodiment, the target raw data requiring joint analysis can be identified first based on the type of raw data (such as temperature, vibration, current, images, etc.). This raw data can come from multiple heterogeneous sensors and has complementarity or redundancy for event judgment. Subsequently, a multi-sensor fusion algorithm (such as weighted averaging, DS evidence theory, or deep learning fusion model) is used to perform spatiotemporal alignment and information fusion on these target raw data to generate more robust and accurate multi-source joint data, which serves as the core basis for subsequent event judgment.

[0106] After acquiring multi-source combined data, the system combines preset event triggering conditions (such as thresholds, trends, and combinational logic) to perform real-time comprehensive evaluation of the multi-source combined data itself and / or other original data that were not involved in the fusion (such as environmental noise, GPS location, and operation logs used only for auxiliary judgment). This enables a more comprehensive identification of abnormal states and determines the trigger level to be activated, thus providing a decision-making basis for subsequent dynamic adjustment of the sampling frequency.

[0107] In this embodiment, the high-frequency sampling frequency of the original data is adjusted based on historical original data.

[0108] The high-frequency sampling frequency of the raw data is not fixed, but can be adaptively adjusted based on the statistical characteristics, trends, or event patterns of historical raw data. For example, by analyzing the symptom patterns, data fluctuation amplitudes, or frequencies preceding abnormal events in historical data, the system can dynamically optimize sampling strategies for similar conditions in the future. It can automatically increase the sampling frequency during high-risk periods or areas, and appropriately decrease it during stable phases, thereby improving resource utilization efficiency while ensuring monitoring sensitivity.

[0109] Among them, the high-frequency sampling frequency is adaptively adjusted based on the statistical characteristics, trends, and event patterns of historical raw data. It can satisfy:

[0110] ;

[0111] in, This can be the sampling interval; the sampling interval can satisfy:

[0112] ;

[0113] in, It can refer to the minimum sampling interval of the original data. It can refer to the maximum sampling interval of the original data; These are the weighting coefficients, and In the embodiments of this application, It can be set based on prior knowledge, such as .

[0114] As a volatility factor, The volatility factor satisfies:

[0115] ;

[0116] in, It can be the standard deviation of the original data within the current sliding window; It can be the mean of the standard deviation under historical normal conditions; This can be used to control sensitivity, where, .

[0117] As a trend factor, The trend factor satisfies:

[0118] ;

[0119] in, It can be the slope of the linear fit for the forward sliding window; It can be used as a threshold for significant trends. This can be used to control sensitivity, where, .

[0120] As an event risk factor, The event risk factors satisfy:

[0121] ;

[0122] in, This can refer to the historical failure rate of this type of component under similar operating conditions; This can be used to control sensitivity, where, .

[0123] The event-triggered multi-scale synchronous data acquisition method provided in this application constructs an integrated intelligent testing method that combines multi-sensor spatiotemporal synchronous acquisition, adaptive high-frequency sampling driven by historical data, and full-dimensional dynamic feature extraction. This method achieves a dual improvement in testing efficiency and resource utilization, captures key transient events without omission, performs deep correlation analysis of multi-source data, and systematically enhances the ability to model component performance degradation and predict lifespan. It comprehensively breaks through the bottlenecks of traditional testing in terms of cost, efficiency, data integrity, and analytical depth.

[0124] Figure 3 This is a schematic diagram of the modules of the event-triggered multi-scale synchronous data acquisition system provided in this application, as shown below. Figure 3 As shown, the system includes a test module and a test host. The test module and the test host are communicatively connected.

[0125] The test module is used to synchronously collect raw data of multiple physical quantities based on a unified time base and send the collected raw data to the test host.

[0126] The test host is used to dynamically switch the sampling frequency of the test module for the raw data according to preset event triggering conditions. When the event triggering conditions are met, the test module adjusts the sampling frequency of the raw data from a low frequency to a high frequency. When the test module collects the raw data at a high frequency, it generates virtual channel data with physical meaning based on the raw data and synchronously encapsulates and stores the virtual channel data and the raw data.

[0127] The test host serves as the control core of the system. It is equipped with a processor, memory, and application software, and is responsible for all logical functions such as parsing and loading test strategies, real-time calculation of event triggering conditions, online synthesis of virtual channels, and storage of final data frames.

[0128] The multi-functional test module can serve as the system's execution unit, communicating with the test host. It integrates multiple programmable power supplies, a high-speed synchronous data acquisition card, and standardized sensor interfaces.

[0129] Among them, the testing strategy can refer to ;

[0130] in, It can refer to a collection of data collected by a sensor.

[0131] It can refer to a set of key performance indicators (KPIs) to be monitored, which are functions of time.

[0132] It can refer to the set of sampling rate parameters. It is a low-frequency sampling period. It is the sampling period for high-frequency acquisition after the event is triggered.

[0133] This can refer to a set of event triggering conditions. Each condition is a logical expression used to determine whether to trigger high-frequency data acquisition.

[0134] It can refer to a set of virtual channel synthesis models, each of which defines one or more required input raw channels, the mathematical formulas used for calculation, and the name and unit of the output virtual channel.

[0135] In this embodiment, the system may also include external sensors, which can be configured according to testing requirements.

[0136] In some embodiments, in traditional test data acquisition, the system records direct measurements from sensors, such as voltage, current, and temperature. These are fundamental physical quantities. However, in engineering practice, engineers and researchers are often more concerned with comprehensive indicators derived from these fundamental physical quantities that have more explicit physical meanings, such as instantaneous power consumption, dynamic impedance, and cumulative thermal stress.

[0137] Virtual channel synthesis is a method that, in the data acquisition process, applies a preset mathematical or physical model to two or more synchronous raw data channels in real time and automatically to calculate and generate one or more virtual data channels with physical meaning.

[0138] The calculation process and data acquisition process occur in parallel. The data from the virtual channel is generated and available simultaneously with the raw data. The virtual channel is not obtained through direct measurement, but rather through mathematical mining of the inherent correlations between multiple raw data streams.

[0139] The process for synthesizing this virtual channel can be as follows:

[0140] 1. Load Model: Load the "Test Strategy" before the test begins. This test strategy defines the virtual channel synthesis model. .

[0141] 2. Synchronous data input: At each moment of data acquisition, the system obtains a set of raw data points with perfectly aligned timestamps from the sensor.

[0142] 3. Real-time computation and synthesis: The processor of the test host immediately uses this set of synchronized raw data points as input and feeds them into the model set. The values ​​for all virtual channels are calculated using predefined formulas.

[0143] 4. Data storage: The virtual data channel and the original data channel are encapsulated together in a data frame with a unified timestamp for storage.

[0144] In this embodiment, a universal test fixture is also included. The universal test fixture is used to fix the component under test and to connect the excitation acquisition channel of the test module to the corresponding pin of the component under test.

[0145] In this embodiment of the application, when the component under test is a relay, if the temperature of the relay is to be collected, the method of using an event-triggered multi-scale synchronous data acquisition system may include:

[0146] Step 1: Loading the data acquisition strategy;

[0147] The relay to be tested is placed in the general-purpose test fixture. The operator selects "Temperature Rise Test" as the test item through the test host's software interface. The system then retrieves and loads the data acquisition strategy matching this test item from its internal strategy library. The data acquisition strategy includes the following information:

[0148] Sensor data acquisition: coil voltage coil current Contact voltage Contact current Casing temperature .

[0149] Key performance indicators being monitored: Coil housing temperature Contact on-state resistance .

[0150] Event triggering condition set: Static threshold trigger and rate of change trigger .

[0151] Sampling rate parameter set: Low frequency sampling rate and high frequency sampling rate .

[0152] Virtual channel synthesis model set: contact on-state resistance DC resistance of the coil Instantaneous power consumption of the coil Contact heating power .

[0153] Step 2: Event Triggering;

[0154] During the temperature rise test, the system continuously judges the preset event trigger conditions.

[0155] Static threshold trigger Monitoring: The system monitors the real-time temperature of the coil casing in parallel. The comparison is performed. The formula for judgment is:

[0156] ;

[0157] in, Indicates at time The measured temperature of the coil casing. The preset static safety temperature threshold represents the highest operating temperature allowed by the relay's insulation class. If this condition is met, the system immediately switches to high-frequency acquisition.

[0158] Rate of change trigger Monitoring: During low-frequency data acquisition, the host processor calculates the rate of change of resistance in real time. The formula for judgment is:

[0159] ;

[0160] in, Indicates at time Calculated rate of change of resistance; and They represent the current moment respectively. and the previous sampling time The contact resistance value in the on-state state; Indicates the sampling time interval; This indicates the preset maximum allowable rate of change threshold for the resistance.

[0161] Once any trigger condition is met, the system immediately switches from low-frequency acquisition mode to high-frequency acquisition mode to synchronously acquire high-frequency data from all channels.

[0162] Step 3: Multi-channel online virtual channel synthesis;

[0163] During the system's burst acquisition time window, the processor synthesizes data based on the virtual channel model set. Virtual channel synthesis is performed in parallel and online.

[0164] Virtual channel V1: Contact on-state resistance ;

[0165] Virtual channel V2: Coil DC resistance ;

[0166] Virtual Channel V3: Instantaneous Power Consumption of the Coil ;

[0167] Virtual Channel V4: Contact Heating Power .

[0168] Step 4: Data encapsulation and storage;

[0169] After the high-frequency acquisition and virtual channel synthesis process is completed, the system encapsulates all relevant information of this event in a structured manner, forming a complete event diagnostic record and storing it in the test host's memory. This record contains event metadata, high-resolution raw channel data sequences, and high-resolution virtual channel data sequences, all of which are aligned with a unified timestamp.

[0170] In this embodiment of the application, when the component under test is a relay, if the method of using an event-triggered multi-scale synchronous data acquisition system is to acquire data for measuring the relay's operating time, it may include:

[0171] Step 1: Loading the data acquisition strategy;

[0172] The relay to be tested is placed in the general-purpose test fixture. The operator selects "Action Time Measurement Test" as the test item through the software interface of the test host. The system then retrieves and loads the data acquisition strategy matching this test item from its internal strategy library. The data acquisition strategy includes the following information:

[0173] Sensor data acquisition: coil drive voltage Coil response current Contact state voltage ;

[0174] Key performance indicators being monitored: Total action time ;

[0175] Event triggering condition set: Rate of change trigger ;

[0176] Sampling rate parameter set: Low frequency sampling rate and high frequency sampling rate ;

[0177] Virtual Channel Synthesis Model Set: Action Time .

[0178] Step 2: Event Triggering;

[0179] During the data acquisition process, the processor of the test host monitors the acquired contact state voltage in real time. The analysis identifies contact bounce.

[0180] When the relay contacts are closed, the contact state voltage is ideally... It will drop from a high level to a low level instantaneously. However, if a bounce occurs, the voltage will oscillate violently and repeatedly between low and high levels. The processor performs real-time calculations. The system detects the rate of change of a threshold, and when this rate exceeds a threshold, it begins high-frequency sampling. The determination formula is as follows:

[0181] ;

[0182] Represents the moment The absolute value of the calculated rate of change of contact voltage; This represents a preset voltage change rate threshold, and the trigger will only be activated during abnormal, repetitive bouncing events.

[0183] Step 3: Virtual channel synthesis;

[0184] Total motion time: ; Indicates the start time of the action; Indicates the moment when the action ends.

[0185] The event-triggered multi-scale synchronous data acquisition method provided in this application solves the four major bottlenecks of traditional component testing through an integrated architecture and intelligent acquisition mechanism:

[0186] By adopting an integrated test host, multi-functional modules and universal fixtures, multiple test functions are integrated into a single platform, realizing process automation, significantly shortening the test cycle and reducing labor and equipment costs;

[0187] By using a built-in high-speed synchronous acquisition card, all sensor data are collected under a unified time reference, generating multi-physical quantity data with strictly aligned timestamps, completely breaking down data silos and supporting cross-dimensional correlation analysis;

[0188] An innovative event-triggered sampling mechanism is introduced. When the component is in a stable state, low-frequency sampling is used to suppress redundancy. Once a critical event is detected (such as contact bounce or sudden temperature rise), it automatically switches to high-frequency sampling, which balances data integrity and resource efficiency and effectively avoids information loss or data bloat caused by traditional fixed sampling.

[0189] By further integrating multi-channel online virtual channel synthesis technology, high-value derivative indicators such as dynamic resistance and instantaneous power consumption are generated in real time. This upgrades the test from static pass / fail judgment to performance evolution analysis, fault mechanism mining, and remaining lifetime prediction based on high-density, highly correlated dynamic data, laying a solid foundation for intelligent reliability assessment.

[0190] Figure 4 This is a schematic diagram of the structure of the event-triggered multi-scale synchronous data acquisition device provided in this application, as shown below. Figure 4 As shown, the event-triggered multi-scale synchronous data acquisition device 40 provided in this embodiment includes:

[0191] The synchronous acquisition module 401 is used to synchronously acquire raw data of multiple physical quantities based on a unified time reference.

[0192] The dynamic switching module 402 is used to dynamically switch the sampling frequency of the raw data according to the preset event triggering conditions. When the event triggering conditions are met, the sampling frequency of the raw data is adjusted from the low frequency sampling frequency to the high frequency sampling frequency.

[0193] The generation module 403 is used to generate virtual channel data with physical meaning based on the original data when the original data is collected at a high frequency sampling frequency, and to synchronously encapsulate and store the virtual channel data with the original data.

[0194] In one possible implementation, the dynamic switching module 402 can also be specifically used for:

[0195] Based on at least one of the preset static threshold trigger conditions and rate of change trigger conditions, determine whether to adjust the sampling frequency of the original data from a low-frequency sampling frequency to a high-frequency sampling frequency.

[0196] In one possible implementation, the dynamic switching module 402 can also be specifically used for:

[0197] Based on preset event triggering conditions, the collected raw data is judged in real time to determine the triggering level that should be activated at the current time;

[0198] The sampling frequency of the raw data is dynamically switched based on the current trigger level to be activated and the trigger level that has been activated; the sampling frequency of the raw data is different for different multi-level event triggering mechanisms.

[0199] In one possible implementation, the dynamic switching module 402 can also be specifically used for:

[0200] Based on the type of raw data, the target raw data belonging to the multi-source joint data are fused through a multi-sensor fusion algorithm to obtain multi-source joint data;

[0201] Based on preset event triggering conditions, the system performs real-time judgment on other raw data in the multi-source combined data and / or raw data to determine the triggering level that should be activated at the moment; where other raw data refers to the raw data other than the target raw data.

[0202] In one possible implementation, the dynamic switching module 402 can also be specifically used for:

[0203] The high-frequency sampling frequency of the raw data is adjusted based on historical raw data.

[0204] In one possible implementation, the generation module 403 can also be specifically used for:

[0205] Based on the data type of the original data, the corresponding data processing model is invoked. Data processing includes at least one of mathematical or physical models.

[0206] Based on the data processing model, the raw data is processed in real time to obtain virtual channel data with physical meaning;

[0207] The virtual channel data is synchronously archived and stored with the original data.

[0208] In one possible implementation, the generation module 403 can also be specifically used for:

[0209] Virtual channel data and raw data are encapsulated and stored as data frames with a unified timestamp.

[0210] In one possible implementation, the synchronous acquisition module 401 can also be specifically used for:

[0211] After the component under test (DUT) is mounted on a general-purpose test fixture and the excitation acquisition channel of the test module is connected to the corresponding pin of the DUT, in response to the user's operation, the system retrieves and loads the data acquisition strategy corresponding to the DUT from the strategy library, so as to synchronously acquire the raw data of multiple physical quantities based on a unified time reference according to the data acquisition strategy.

[0212] The event-triggered multi-scale synchronous data acquisition device 40 provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0213] Figure 5 A schematic diagram of the structure of the electronic device provided in this application. Figure 5 As shown, the electronic device 50 provided in this embodiment includes at least one processor 501 and a memory 502. Optionally, the device 50 further includes a communication component 503. The processor 501, memory 502, and communication component 503 are connected via a bus 504.

[0214] In a specific implementation, at least one processor 501 executes computer execution instructions stored in memory 502, causing at least one processor 501 to perform the above-described method.

[0215] The specific implementation process of processor 501 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0216] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0217] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0218] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0219] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0220] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0221] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0222] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0223] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0224] 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.

[0225] 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.

[0226] If a function 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, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0227] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0228] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A multi-scale synchronous data acquisition method based on event triggering, characterized in that, include: Raw data of multiple physical quantities are collected synchronously based on a unified time reference; According to preset event triggering conditions, the sampling frequency of the raw data is dynamically switched. When the event triggering conditions are met, the sampling frequency of the raw data is adjusted from a low frequency sampling frequency to a high frequency sampling frequency. When the original data is collected at a high-frequency sampling rate, virtual channel data with physical meaning is generated based on the original data, and the virtual channel data is synchronously sealed and stored with the original data.

2. The method according to claim 1, characterized in that, The step of dynamically switching the sampling frequency of the raw data according to preset event triggering conditions includes: Based on at least one of the preset static threshold triggering conditions and rate of change triggering conditions, determine whether to adjust the sampling frequency of the original data from a low-frequency sampling frequency to a high-frequency sampling frequency.

3. The method according to claim 1, characterized in that, The step of dynamically switching the sampling frequency of the raw data according to preset event triggering conditions includes: Based on preset event triggering conditions, the collected raw data is judged in real time to determine the triggering level that should be activated at the moment; The sampling frequency of the raw data is dynamically switched based on the current trigger level to be activated and the already activated trigger level; wherein, the sampling frequency of the raw data is different for different multi-level event triggering mechanisms.

4. The method according to claim 3, characterized in that, The step of performing real-time judgment on the collected raw data based on preset event triggering conditions to determine the trigger level that should be activated includes: Based on the type of the original data, the target original data belonging to the multi-source joint data is fused using a multi-sensor fusion algorithm to obtain the multi-source joint data; Based on preset event triggering conditions, the multi-source combined data and / or other raw data in the original data are judged in real time to determine the triggering level that should be activated at present; wherein, the other raw data are the raw data in the original data other than the target original data.

5. The method according to any one of claims 1 to 4, characterized in that, The high-frequency sampling frequency of the raw data is adjusted based on historical raw data.

6. The method according to claim 1, characterized in that, The step of generating physically meaningful virtual channel data based on the original data, and synchronously sealing and storing the virtual channel data and the original data, includes: Based on the data type of the original data, the corresponding data processing model is invoked, and the data processing includes at least one of a mathematical model or a physical model. Based on the data processing model, the raw data is processed in real time to obtain virtual channel data with physical meaning; The virtual channel data is synchronously sealed and stored with the original data.

7. The method according to any one of claims 1 or 6, characterized in that, The virtual channel data and the original data are encapsulated and stored as data frames with the same timestamp.

8. The method according to claim 1, characterized in that, Before synchronously acquiring raw data of multiple physical quantities based on a unified time reference, the method further includes: After the component under test is mounted on a general test fixture and the excitation acquisition channel of the test module is connected to the corresponding pin of the component under test, in response to the user's operation, the data acquisition strategy corresponding to the component under test is retrieved and loaded from the strategy library, so as to synchronously acquire the raw data of multiple physical quantities according to the data acquisition strategy and based on a unified time reference.

9. A multi-scale synchronous data acquisition system based on event triggering, characterized in that, It includes a test module and a test host, wherein the test module is communicatively connected to the test host, wherein, The test module is used to synchronously collect raw data of multiple physical quantities based on a unified time reference, and send the collected raw data to the test host. The test host is used to dynamically switch the sampling frequency of the test module on the raw data according to preset event triggering conditions. Specifically, when the event triggering conditions are met, the test module is controlled to adjust the sampling frequency of the raw data from a low frequency to a high frequency. When the test module collects the raw data at a high frequency, virtual channel data with physical meaning is generated based on the raw data, and the virtual channel data is synchronously sealed and stored with the raw data.

10. The system according to claim 9, characterized in that, It also includes a universal test fixture, which is used to fix the component under test and to connect the excitation acquisition channel of the test module to the corresponding pin of the component under test.

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