Simulation electronic technology course-oriented virtuality and reality combined digital intelligence method

Abstract

The invention relates to the technical field of education technology and electronic technology, in particular to an analog electronic technology course-oriented virtuality and reality combined digital intelligence method, which comprises the following steps of acquiring teaching voltage signals, dividing simulation and actual measurement fragments, identifying a slope abrupt change range, matching starting and ending positions, tracking node paths, analyzing a time relation and dividing abnormal sections. And associating the scoring segments with an index sequence, comparing waveform features, mapping task nodes, and generating a virtual-real synchronous scoring segment set. According to the method, a waveform slope abrupt change region is extracted to construct fragments corresponding to virtual and real data, a signal path is mapped in combination with a voltage change characteristic and a device connection relation, operation stages are divided according to a response time sequence and node distribution, abnormal concentrated sections in continuous fragments are identified, and sorting and index association of scoring fragments are recombined. And a traceable structural relationship between virtual and real data is established, and the recognition capability of the signal behavior change in the scoring task and the complete association of a result structure are enhanced.

Description

Technical Field

[0001] This invention relates to the fields of educational technology and electronic technology, and in particular to a digital and intelligent method that combines virtual and real elements for analog electronic technology courses. Background Technology

[0002] The fields of educational technology and electronic technology involve the integration of information processing in the teaching process with electronic system technology. Specifically, this includes key aspects such as digital modeling and virtual reconstruction of teaching content, real-time monitoring and intelligent intervention of the teaching process, automatic collection and in-depth analysis of learning data, and quantitative implementation of teaching evaluation. Within this field, educational technology focuses on the technological transformation of teaching concepts and methods, while electronic technology provides the engineering foundation for data acquisition, signal analysis, and control execution. The combination of the two gradually forms an application system supporting course experimental teaching, including intelligent simulation systems, remote experimental platforms, and intelligent evaluation mechanisms for learning outcomes. Among these, the traditional virtual-real integrated digitalization method for analog electronics courses refers to an organizational approach in electrical engineering education that utilizes simulation software and real experimental instruments to construct teaching scenarios to enhance students' understanding of the structure and operation of analog electronic circuits. Traditional methods typically acquire virtual data such as circuit waveforms, parameter settings, and signal characteristics through a pre-set simulation platform, and manually collect physical circuit operation data using instruments such as oscilloscopes, meters, and signal generators. The two types of data are compared only through graphical comparison or manual observation. The evaluation relies on the teacher's experience and judgment, lacks a unified data modeling and processing framework, and cannot achieve automatic correlation between virtual and real data, quantitative extraction and comparison of feature indicators, nor form a structured input basis that can be used for intelligent diagnosis.

[0003] In existing technologies, the comparison of simulated and measured waveforms is done through manual observation. However, due to the lack of a unified data analysis framework, it is difficult to accurately correspond the structural morphology and characteristic distribution of voltage response in actual teaching. This results in scoring results that are limited by experience-based judgment, and there is a risk of overlooking local abnormal responses due to blind spots in observation. Subjective errors are prone to occur in segment connection and task node evaluation, especially when multi-path signal convergence or asynchronous waveform changes are involved. The lack of data correlation makes it difficult to effectively judge the continuity between segments, which further affects the accuracy of the scoring task's feedback on real experimental operations and the controllability of teaching effectiveness. Summary of the Invention

[0004] To address the technical problems existing in the prior art, this invention provides a virtual-real integrated digital intelligence method for analog electronics technology courses; To achieve the above objectives, the present invention adopts the following technical solution: a virtual-real integrated digital intelligence method for analog electronics technology courses, comprising the following steps: S1: Obtain the teaching voltage signal in the analog electronics course, divide the simulation and actual measurement frame segments, identify the sudden change range of the slope change direction of the sampling point, extract the continuous structure segment, compare the start and end positions according to the shape, and obtain the corresponding segment group of the virtual and real waveforms. S2: Based on the corresponding segment group of the virtual and real waveforms, extract the change direction, level jump and peak position of the response segment, trace the path according to the device connection sequence, map the relationship of voltage change nodes, and obtain the device node path information set; S3: Based on the device node path information set, extract path response segments, analyze time overlap and sequential triggering order, divide continuous segment segments, and obtain a list of abnormal concentrated segments; S4: Based on the list of abnormal concentrated segments, find the position of the corresponding segment in the scoring task, identify the correspondence and time order of the indicator items, replace the original task segment relationship, reorganize the connection order of the evaluation segments, and obtain the scoring segment mapping set; S5: Based on the scoring segment mapping set, compare the change direction and amplitude characteristics of the scoring segments in the simulation and actual measurement, identify the linkage response segments at the same time point, and map them to the task node settings to obtain a set of virtual and real synchronized scoring segments.

[0005] As a further aspect of the present invention, the virtual and real waveform corresponding segment group includes simulated signal segments, measured signal segments, start-end point correspondence, and waveform structure fitting degree; the device node path information set includes response segment characteristics, device port arrangement order, signal transmission direction, and node distribution trajectory; the abnormal concentrated segment list includes time crossover relationship, start-end connection relationship, device path continuity, and course task stage distinction; the scoring segment mapping set includes waveform segment position, evaluation index items, time order, and scoring segment connection method; and the virtual and real synchronous scoring segment set includes voltage change trend, waveform direction relationship, amplitude response characteristics, peak segment time point structure, and course task original node structure.

[0006] As a further aspect of the present invention, the range of abrupt changes in the slope direction of the sampling point refers to the continuous time region in which the trend of the identified voltage waveform reverses. The continuous structural segment refers to the voltage waveform portion that has a coherent trend and complete morphological characteristics.

[0007] As a further aspect of the present invention, the voltage change node relationship refers to the relationship between waveform changes and device nodes in the circuit in terms of time and path. The task node settings refer to the configuration basis for corresponding the scoring segments with the operation nodes in the course task structure.

[0008] As a further aspect of the present invention, the specific steps of S1 are as follows: S101: Acquire the teaching voltage signal in the analog electronics course, divide the simulation signal and the measured signal into continuous frame segments under a unified time axis, compare the voltage change direction of adjacent sampling points in each frame segment, identify the range of continuous points where the change direction is reversed, define the coverage interval of the points on the time axis, and obtain the set of slope direction reversal intervals. S102: Based on the slope direction reversal interval set, a sliding window is defined in the simulation signal with the corresponding interval as the center. Signal segments in the window that show continuous transition characteristics of voltage change with time are extracted. The continuity and morphological integrity of the voltage trend inside the segment are judged to obtain the simulation signal response structure segment set. S103: Based on the set of simulated signal response structure segments, a sliding window with the same time span is called in the measured signal. The voltage change trend within the window is compared with the trend of the simulated segment to evaluate the consistency of the signal in terms of change direction and peak position, and the corresponding segment group of virtual and real waveforms is obtained.

[0009] As a further aspect of the present invention, the specific steps of S2 are as follows: S201: Based on the corresponding segment group of the virtual and real waveforms, extract the response change set in each waveform data segment, organize the position points where the voltage trend direction changes continuously in time order, and extract the start and end coordinates to obtain the response change point sequence set; S202: Based on the response change point sequence set, extract the voltage trend direction, level jump trend and peak region index position of each point segment, unify the similar structure sequences in the differentiated segments, and obtain the waveform response feature index group; S203: Based on the waveform response feature index group, obtain the order of device ports in the circuit connection relationship, map the action path segment by segment according to the signal propagation direction to the segment change position, and obtain the device node path information set.

[0010] As a further aspect of the present invention, the specific steps of S3 are as follows: S301: Based on the device node path information set, extract the corresponding waveform response segments along the path direction, identify the experimental operation timing range of each segment, compare whether the start and end points of adjacent segments on the time axis have intersections and continuous connections, and obtain a group of segments with connected operation timings. S302: Based on the operation timing connected segment group, analyze the relationship between the device path index of the segment and the signal transmission direction, filter out waveform segments with different sources but continuous response, embed them into a unified timing sequence according to the teaching task instruction number, and obtain a path continuous segment sequence. S303: Based on the continuous segment sequence of the path, extract the time segment with dense signal response distribution, divide the signal segment in the continuous time period including the intersecting path, and map the segment to the corresponding teaching task instruction structure to obtain the abnormal concentrated segment sequence.

[0011] As a further aspect of the present invention, the specific steps of S4 are as follows: S401: Based on the waveform segments in the abnormal concentrated segment sequence, compare them with the signal time nodes set in the scoring task configuration, extract the task number and time tag associated with each waveform segment, align the task setting area according to the waveform frame sequence number distribution relationship, and obtain the task time association group; S402: Based on the task number and corresponding signal frame sequence in the task time association group, locate the indicator item attached to the number in the scoring structure, extract the spacing relationship between the indicator item setting time and the waveform segment frame number, identify the waveform task index with discontinuous sequence number, and obtain the indicator spacing corresponding set. S403: Based on the index spacing correspondence set, replace the position segment of the corresponding segment in the original scoring task structure, assign waveform frame number range and task number order in sequence according to the teaching process, connect the segment and task node scoring relationship, and obtain the scoring segment mapping set.

[0012] As a further aspect of the present invention, in the process of extracting the task number and time tag associated with each waveform segment: according to the signal time node set in the scoring task configuration, the start time of the waveform segment in the abnormal concentration segment sequence is matched with the signal time node, the time interval relationship is determined, and it is analyzed whether the corresponding signal time node is the task triggering basis corresponding to the waveform segment. In the process of identifying waveform task indexes with discontinuous sequence numbers: in the task time association group, the signal frame sequence number interval associated with the task number is analyzed according to the frame sequence number order to identify frame segments where there are gaps in the signal frame sequence number interval.

[0013] As a further aspect of the present invention, the specific steps of S5 are as follows: S501: Based on the scoring segment mapping set, extract the voltage change trajectory of the scoring segments in the simulated signal and the measured signal, compare the waveform direction, level response amplitude and the location of the peak segment by frame time sequence, analyze whether the trends between corresponding segments are consistent, and obtain the signal trend comparison sequence; S502: Based on the signal trend comparison sequence, compare the time points of directional change, voltage fluctuation and peak change at the same frame point in the simulated and measured waveforms, identify the segments that change synchronously in time sequence, and obtain the synchronous waveform segment index group. S503: Based on the synchronous waveform segment index group, according to the order of nodes in the course task structure during the operation process, locate the correspondence between segments and nodes to obtain a set of virtual and real synchronous scoring segments.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, virtual and real data segments are constructed by extracting regions where waveform slope changes abruptly. The signal path is mapped by combining voltage change characteristics and device connection relationships. Operation stages are divided according to response timing and node distribution. Abnormal concentrated sections in continuous segments are identified. The order of scoring segments and index association are reorganized. A traceable structural relationship between virtual and real data is established, enhancing the ability to identify signal behavior changes in scoring tasks and the complete association of result structure. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the steps of the present invention; Figure 2 This is a detailed schematic diagram of S1 of the present invention; Figure 3 This is a detailed schematic diagram of S2 of the present invention; Figure 4 This is a detailed schematic diagram of S3 of the present invention; Figure 5 This is a detailed schematic diagram of S4 of the present invention; Figure 6 This is a detailed schematic diagram of S5 of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0019] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0020] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0021] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0022] Please see Figure 1 This invention provides a virtual-real integrated digital intelligence method for analog electronics technology courses, comprising the following steps: S1: Obtain the teaching voltage signal in the analog electronics course, divide the continuous frame segments of the simulation and measured signals under a unified time axis, identify the location range of the abrupt region according to the direction of the waveform slope change of adjacent sampling points, slide to select the simulation signal segment with a complete response structure, match the fit of the structure morphology of the measured signal, analyze the correspondence between the start and end points, and obtain the corresponding segment group of the virtual and real waveforms. S2: Based on the corresponding segment group of virtual and real waveforms, divide the response segments within the corresponding waveform segments according to the time sequence, extract the voltage change direction, level transition characteristics and peak occurrence position in the segments, compare with the device port arrangement order in the circuit connection relationship, trace the distribution of device nodes associated with segment changes along the signal transmission direction, and obtain the device node path information set. S3: Based on the device node path information set, extract the corresponding waveform response segments along the device node path, compare the time coverage of the differentiated segments in the experimental operation sequence, identify response segments with time overlap and beginning-end connection, merge device paths with different origins but appearing continuously in the operation process, and divide the processing intervals according to the order of the course task stages to obtain a list of abnormal concentrated segments. S4: Based on the list of abnormal concentrated segments, locate the waveform segment position corresponding to the abnormal segment in the scoring task settings, analyze the evaluation index items and time order associated with the segment in the scoring structure, replace the existing segment correspondence in the scoring task, and re-divide the sequential connection method between scoring segments according to the teaching experiment process to obtain the scoring segment mapping set. S5: Based on the scoring segment mapping set, compare the voltage change trends of the scoring segments in the simulated signal and the measured signal, analyze the time relationship between the waveform direction, amplitude response and peak segments in the signal source according to the point time sequence, identify the scoring segments that synchronously change behavior in the signal, and map them to the original node structure of the course task to obtain the set of virtual and real synchronous scoring segments.

[0023] The virtual and real waveform corresponding segment group includes simulated signal segments, measured signal segments, start and end point correspondence, and waveform structure fitting degree. The device node path information set includes response segment characteristics, device port arrangement order, signal transmission direction, and node distribution trajectory. The list of abnormal concentrated segments includes time intersection relationship, start and end connection relationship, device path continuity, and course task stage distinction. The scoring segment mapping set includes waveform segment position, evaluation index items, time order, and scoring segment connection method. The virtual and real synchronous scoring segment set includes voltage change trend, waveform direction relationship, amplitude response characteristics, peak segment time point structure, and course task original node structure.

[0024] Please see Figure 2 The specific steps of S1 are as follows: S101: Acquire the teaching voltage signal in the analog electronics course, divide the simulation signal and the measured signal into continuous frame segments under a unified time axis, compare the voltage change direction of adjacent sampling points in each frame segment, identify the range of continuous points where the change direction is reversed, define the coverage interval of the points on the time axis, and obtain the set of slope direction reversal intervals. Firstly, in the single-transistor common-emitter amplifier circuit experiment of the analog electronics course, the voltage signal sequence output by the virtual simulation software and the measured voltage signal sequence acquired by the oscilloscope were retrieved. Under a unified 10ms time base, the simulated signal with a sampling rate of 100kHz and the measured signal were cut into continuous frame segments of 1ms in length. Each frame was ensured to contain 100 discrete sampling points, and the sampling interval was 10 microseconds. The voltage amplitude of adjacent sampling points in each frame was extracted one by one, and the voltage difference was obtained by subtraction. The voltage difference was set as the reference term, and a 0.005V insensitivity zone base was introduced to address the circuit environment noise and the vertical resolution of the oscilloscope. The standard values ​​are compared. If the voltage difference is greater than 0.005V, the change direction of the point is marked as positive. If the voltage difference is less than -0.005V, it is marked as negative. If the absolute value of the voltage difference is less than or equal to 0.005V, it is recorded as flat. This is to eliminate the small voltage jitter caused by non-ideal background noise. Then, the points in the change direction sequence that show polarity reversal are retrieved. The region where at least 3 consecutive sampling points have the same direction and are opposite to the direction of the previous sequence is identified. The precise start index and end index of these turning points on the time axis are defined. By statistically analyzing all coordinate intervals that satisfy the slope reversal feature, the slope direction reversal interval set is obtained.

[0025] S102: Based on the slope direction reversal interval set, a sliding window is defined in the simulation signal with the corresponding interval as the center. The signal segments in the window that show continuous transition characteristics of voltage change with time are extracted. The continuity and morphological integrity of the voltage trend inside the segment are judged to obtain the set of simulation signal response structure segments. First, in the simulation signal processing flow, a dynamic sliding window with a span of 0.6 ms is constructed, extending 30 sampling points forward and backward along the time axis with the index coordinates of each inverted vertex as the geometric center. The window contains 61 consecutive sampling points. A subset of voltage values ​​changing with time is extracted from the window, and the change in the first derivative of the slope between adjacent points within the subset is calculated, i.e., the second-order difference value is calculated. This change is compared with a preset morphological integrity benchmark value, set to 0.12, which is based on the maximum ideal value of a standard sine wave with a frequency of 1 kHz at a sampling rate of 100 kHz. Regarding the second-order absolute difference, experimental data shows that the waveform exhibits excellent linear transition when the value is in the range of 0 to 0.15. If the average second-order absolute difference of a certain simulated signal within the window is 0.10, which is less than the reference value of 0.12, then the voltage trend within that segment is determined to have continuity and morphological integrity. If a voltage jump amplitude is detected to exceed 0.5V within a single step and the duration is less than 2 sampling points, then it is determined to be a pulse interference non-coherent segment and is removed. Through this logic, the simulated signal portion with a smooth trend is selected, resulting in a set of simulated signal response structure segments.

[0026] S103: Based on the set of simulated signal response structure segments, a sliding window with the same time span is called in the measured signal. The voltage change trend within the window is compared with the trend of the simulated segment to evaluate the consistency of the signal in the direction of change and the peak position, and the corresponding segment group of the virtual and real waveforms is obtained. First, a sliding window with the exact same time span as the simulated segment is retrieved from the measured signal sequence, and the point-by-point values ​​of the measured voltage waveform and the simulated voltage waveform are compared. The slope direction consistency between the measured segment and the simulated segment at the same frame point is calculated, and the peak voltage positions of both are extracted. Their offset on the time axis is calculated, and the maximum allowable offset is set to 5 sampling points, i.e., 0.05ms. The Pearson correlation coefficient is introduced to quantify the consistency of the trends of the two, and the consistency judgment threshold is set to 0.85. The setting of this threshold refers to the experiment on the influence of the distributed capacitance of components on the signal phase in analog circuits. Through statistical analysis of multiple sets of virtual and real comparison experimental data, if the calculated correlation coefficient is 0.94, which is much higher than the threshold of 0.85, and the time axis offset is 0.02ms, which is within the allowable limit, then the measured waveform is determined to accurately reproduce the dynamic characteristics of the simulation. By screening virtual and real segment pairs with correlation coefficients greater than the threshold and peak offsets within a reasonable range, the corresponding segment groups of virtual and real waveforms are obtained.

[0027] Please see Figure 3 The specific steps of S2 are as follows: S201: Based on the corresponding segments of the virtual and real waveforms, extract the response change set in each waveform data segment, organize the positions where the voltage trend direction changes continuously in chronological order, and extract the start and end coordinates to obtain the response change point sequence set; First, voltage change trajectory data is extracted from the corresponding segments of the virtual and real waveforms. Second-order difference operations are used to identify all extreme points where the voltage trend direction changes. The critical coordinates of the voltage changing from monotonically increasing to monotonically decreasing or to a plateau are identified and recorded. For example, in the output waveform of a common-emitter amplifier circuit, when the input signal amplitude is too large and the collector voltage drops to about 0.3V, the first inflection point at the voltage trough is captured, and its absolute time coordinate on the global time axis is recorded as 15.50ms. These points where the voltage trend direction changes continuously are organized in chronological order, and the start and end coordinates of each independent monotonic region are accurately extracted. These points with waveform topological characteristics are structured and encapsulated to obtain a sequence set of response change points.

[0028] S202: Based on the response change point sequence set, extract the voltage trend direction, level jump trend and peak region index position of each point segment, unify the similar structure sequences in the differentiated segments, and obtain the waveform response feature index group; First, for each point segment, the voltage trend direction, level jump trend, and peak region index location are extracted. To eliminate the influence of differences in measurement ranges at different acquisition terminals (e.g., 5V for simulation and 20V for actual measurement), the voltage amplitude is mapped to the 0-1 interval for normalization. The ratio of high-frequency noise power to fundamental power within the segment is calculated. If the ratio is greater than 10%, it is marked as a peak feature index. For differentiated segments caused by hardware sampling nonlinearity, by comparing the average voltage change rate between adjacent points, structures with similar slope change trends (i.e., relative slope deviation less than 8%) are defined as the same type of sequence, realizing the structured indexing of waveform micromorphology and obtaining waveform response feature index group.

[0029] S203: Based on the waveform response feature index group, obtain the order of device ports in the circuit connection relationship, map the action path segment by segment according to the signal propagation direction to the segment change position, and obtain the device node path information set; First, the physical order of the device ports in the analog electronic circuit connection relationship is obtained, such as the signal flow following the path of input terminal, coupling capacitor, base, collector, and output capacitor. According to the direction of signal propagation in the circuit, the waveform response characteristics are mapped to specific circuit nodes in chronological order. For example, the 180-degree phase reversal characteristic of the signal after being amplified by the transistor is identified and mapped to the corresponding collector output node. By comparing the evolution logic of signal characteristics under the action of different components segment by segment, a mapping chain between waveform segments, time coordinates and device physical node numbers is established to obtain the device node path information set.

[0030] Please see Figure 4 The specific steps of S3 are as follows: S301: Based on the device node path information set, extract the corresponding waveform response segments along the path direction, identify the experimental operation timing range of each segment, compare whether the start and end points of adjacent segments on the time axis have intersections and continuous connections, and obtain the operation timing connected segment group. First, extract the corresponding waveform response segments along the device node path direction, and identify the operation timing range of each segment from the experimental task book, such as the steady-state setup stage after power-on or the load resistance adjustment stage; extract the end point and start point of adjacent segments on the time axis, calculate the time interval between them, and compare it with the preset timing continuity deviation threshold of 1.5ms; if the time interval is less than 1.5ms, it is determined to be a logically continuous connection; if the start point is earlier than the end point of the previous segment, it is recorded as a cross-path response. By performing timing logic verification on all segments, isolated waveform fragments that do not conform to the causal relationship of the circuit are eliminated to obtain the operation timing connected segment group.

[0031] S302: Based on the operation timing connected segment group, analyze the relationship between the device path index of the segment and the signal transmission direction, filter waveform segments with different sources but continuous response, embed them into a unified timing sequence according to the teaching task instruction number, and obtain the path continuous segment sequence. First, the coupling relationship between the device path index and signal transmission direction of each segment in the sequentially connected segment group is analyzed. Waveform segments with different sources but continuous response relationships are selected. For example, the input voltage segment acquired by channel 1 and the amplified output segment acquired by channel 2 are associated and embedded into a unified timing sequence according to the teaching task instruction number. By calculating the cross-correlation coefficient between segments, if the coefficient is greater than 0.7, it is considered that there is a strong logical correlation between the two path segments. The segments are then prioritized according to the topological depth of signal propagation. The scattered test data are reconstructed into an evolution chain that conforms to the experimental logic to obtain a sequence of continuous path segments.

[0032] S303: Based on the continuous path segment sequence, extract the time segment with dense signal response distribution, divide the continuous time segment into signal segments including intersecting paths, map the segments to the corresponding teaching task instruction structure, and obtain the abnormal concentrated segment sequence. First, a time segment with dense signal response distribution is extracted by scanning a sliding window. The sum of squares of voltage fluctuations within a 10ms unit time window is calculated to characterize the energy distribution, and the energy concentration benchmark value is set to 0.65 joules. This value is obtained by measuring the power spectral density of 50Hz power frequency interference noise under experimental conditions. If the fluctuation energy in a certain segment exceeds 1.5 times the benchmark value (i.e., 0.975 joules), or if two logic level changes with phase conflict are detected at the same time, it is determined to be an abnormally concentrated segment. After segmenting, it is mapped to the specific teaching task instruction structure to obtain the abnormally concentrated segment sequence.

[0033] Please see Figure 5 The specific steps of S4 are as follows: S401: Based on the waveform segments in the abnormal concentrated segment sequence, compare them with the signal time nodes set in the scoring task configuration, extract the task number and time label associated with each waveform segment, align the task setting area according to the waveform frame sequence number distribution relationship, and obtain the task time association group; First, retrieve waveform segments from the anomaly concentration zone sequence, compare them with the preset signal time nodes in the scoring task configuration, and extract the task number and time tag associated with each waveform segment; calculate the absolute time corresponding to the waveform frame number, and calculate the synchronization interval between it and the task-set time point, setting the synchronization deviation threshold to 0.5 seconds; if the time interval is less than 0.5 seconds, it is determined that the waveform frame belongs to the operation observation range of the task, and the real-time signal sequence is precisely aligned with the scoring task area to obtain the task time association group.

[0034] S402: Based on the task number and corresponding signal frame sequence in the task time association group, locate the indicator item attached to the number in the scoring structure, extract the spacing relationship between the indicator item setting time and the waveform segment frame sequence number, identify the waveform task index with discontinuous sequence number, and obtain the indicator spacing corresponding set. First, locate the quantitative indicators attached to the scoring structure, such as voltage amplification factor or cutoff distortion percentage; extract the spacing relationship between the theoretical trigger time set for the indicator and the actual frame number of the waveform segment, and perform sequence number continuity detection, setting the spacing baseline coefficient to 1.2; if the difference in frame number corresponding to adjacent tasks is greater than 1.2 times the preset sampling frame number of the task, then identify the segment as a waveform task index with discontinuous sequence number, thereby capturing the skipping or long pause behavior that students may have during the experiment, and obtaining the corresponding set of indicator spacing.

[0035] S403: Based on the index interval correspondence set, replace the position segment of the corresponding segment in the original scoring task structure, assign waveform frame number range and task number order in sequence according to the teaching process, connect the segment and task node scoring relationship, and obtain the scoring segment mapping set; First, the preset static position intervals in the original scoring task structure are replaced to achieve a dynamic migration of the scoring timeline from a fixed template to the actual measurement sequence of students. According to the sequence of the teaching process, weight coefficients and scoring logic are reassigned to the waveform frame number ranges of each segment, and specific physical quantity calculation functions (such as effective value calculation functions) are attached to the corresponding task nodes. By establishing a direct connection between waveform feature segments and task node evaluation indicators, it is ensured that each scoring item is supported by corresponding measured data, thus obtaining a scoring segment mapping set.

[0036] Please see Figure 6 The specific steps of S5 are as follows: S501: Based on the scoring segment mapping set, extract the voltage change trajectory of the scoring segments in the simulated signal and the measured signal, compare the waveform direction, level response amplitude and the location of the peak segment by frame time sequence, analyze whether the trends between corresponding segments are consistent, and obtain the signal trend comparison sequence; First, the voltage change trajectories of the scoring segments in the simulated signal and the measured signal are extracted, and a multi-dimensional trend comparison is performed according to the frame time sequence. The consistency of the waveform slope turning, the absolute difference of the level response amplitude, and the position index of the peak segment are compared between the two, and the trend consistency score is calculated. The judgment benchmark coefficient is set to 0.92, which is obtained through autocorrelation analysis experiments on a large number of standard circuit waveforms. If the calculated correlation reaches 0.95 or above and the amplitude error is within 10%, the segment is judged to have a high degree of trend consistency. The comparison parameters of each dimension are summarized to obtain the signal trend comparison sequence.

[0037] S502: Based on the signal trend comparison sequence, compare the time points of directional change, voltage fluctuation and peak change at the same frame point in the simulated and measured waveforms, identify the segments that change synchronously in time sequence, and obtain the synchronous waveform segment index group. First, based on the signal trend comparison sequence, the synchronization time points of the direction change, voltage fluctuation and spike change of the simulated and measured waveforms at the same frame point are retrieved; the absolute value of the synchronization error at each frame point is calculated, and a synchronization time deviation threshold is introduced for judgment. This threshold is set to 8 microseconds, which is based on the group delay measurement experiment of the signal after passing through a multi-stage amplifier in the analog circuit, in order to eliminate inherent line delay; if the synchronization error between the two is less than 8 microseconds, the segment is identified as a segment that changes completely synchronously in time, and its corresponding frame index range is extracted to obtain the synchronization waveform segment index group.

[0038] S503: Based on the synchronous waveform segment index group, according to the order of nodes in the course task structure during the operation process, locate the correspondence between segments and nodes to obtain a set of virtual and real synchronous scoring segments; First, based on the logical topological order of each node in the course task structure during actual operation, the correspondence between the execution segment and the physical node is located. The synchronized quantitative indicators are then input into the scoring weight model for calculation. The specific task score is obtained by multiplying the weight of each task, the trend consistency score, and the synchronization judgment coefficient. If the voltage gain task weight is 0.3, the trend consistency score is 0.92, and the synchronization judgment is qualified, then the score for this item is 0.276. The scores of all nodes are summarized and deeply coupled with the operation logic to obtain the set of virtual and real synchronization scoring segments.

[0039] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included 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 virtual-real integrated digital intelligence method for analog electronics technology courses, characterized in that, Includes the following steps: S1: Obtain the teaching voltage signal in the analog electronics course, divide the simulation and actual measurement frame segments, identify the sudden change range of the slope change direction of the sampling point, extract the continuous structure segment, compare the start and end positions according to the shape, and obtain the corresponding segment group of the virtual and real waveforms. S2: Based on the corresponding segment group of the virtual and real waveforms, extract the change direction, level jump and peak position of the response segment, trace the path according to the device connection sequence, map the relationship of voltage change nodes, and obtain the device node path information set; S3: Based on the device node path information set, extract path response segments, analyze time overlap and sequential triggering order, divide continuous segment segments, and obtain a list of abnormal concentrated segments; S4: Based on the list of abnormal concentrated segments, find the position of the corresponding segment in the scoring task, identify the correspondence and time order of the indicator items, replace the original task segment relationship, reorganize the connection order of the evaluation segments, and obtain the scoring segment mapping set; S5: Based on the scoring segment mapping set, compare the change direction and amplitude characteristics of the scoring segments in the simulation and actual measurement, identify the linkage response segments at the same time point, and map them to the task node settings to obtain a set of virtual and real synchronized scoring segments.

2. The virtual-real integrated digital intelligence method for analog electronics technology courses according to claim 1, characterized in that, The virtual and real waveform corresponding segment group includes simulated signal segments, measured signal segments, start and end point correspondence, and waveform structure fitting degree. The device node path information set includes response segment characteristics, device port arrangement order, signal transmission direction, and node distribution trajectory. The abnormal concentrated segment list includes time intersection relationship, start and end connection relationship, device path continuity, and course task stage distinction. The scoring segment mapping set includes waveform segment position, evaluation index items, time order, and scoring segment connection method. The virtual and real synchronous scoring segment set includes voltage change trend, waveform direction relationship, amplitude response characteristics, peak segment time point structure, and course task original node structure.

3. The virtual-real integrated digital intelligence method for analog electronics technology courses according to claim 1, characterized in that, The range of abrupt changes in the slope direction of the sampling points refers to the continuous time region in which the trend of the voltage waveform reverses. The continuous structural segment refers to the voltage waveform portion that has a coherent trend and complete morphological characteristics.

4. The virtual-real integrated digital intelligence method for analog electronics technology courses according to claim 1, characterized in that, The voltage change node relationship refers to the relationship between waveform changes and device nodes in the circuit in terms of time and path. The task node settings refer to the configuration basis for corresponding scoring segments with operation nodes in the course task structure.

5. The virtual-real integrated digital intelligence method for analog electronics technology courses according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Acquire the teaching voltage signal in the analog electronics course, divide the simulation signal and the measured signal into continuous frame segments under a unified time axis, compare the voltage change direction of adjacent sampling points in each frame segment, identify the range of continuous points where the change direction is reversed, define the coverage interval of the points on the time axis, and obtain the set of slope direction reversal intervals. S102: Based on the slope direction reversal interval set, a sliding window is defined in the simulation signal with the corresponding interval as the center. Signal segments in the window that show continuous transition characteristics of voltage change with time are extracted. The continuity and morphological integrity of the voltage trend inside the segment are judged to obtain the simulation signal response structure segment set. S103: Based on the set of simulated signal response structure segments, a sliding window with the same time span is called in the measured signal. The voltage change trend within the window is compared with the trend of the simulated segment to evaluate the consistency of the signal in terms of change direction and peak position, and the corresponding segment group of virtual and real waveforms is obtained.

6. The virtual-real integrated digital intelligence method for analog electronics technology courses according to claim 1, characterized in that, The specific steps of S2 are as follows: S201: Based on the corresponding segment group of the virtual and real waveforms, extract the response change set in each waveform data segment, organize the position points where the voltage trend direction changes continuously in time order, and extract the start and end coordinates to obtain the response change point sequence set; S202: Based on the response change point sequence set, extract the voltage trend direction, level jump trend and peak region index position of each point segment, unify the similar structure sequences in the differentiated segments, and obtain the waveform response feature index group; S203: Based on the waveform response feature index group, obtain the order of device ports in the circuit connection relationship, map the action path segment by segment according to the signal propagation direction to the segment change position, and obtain the device node path information set.

7. The virtual-real integrated digital intelligence method for analog electronics technology courses according to claim 1, characterized in that, The specific steps for S3 are as follows: S301: Based on the device node path information set, extract the corresponding waveform response segments along the path direction, identify the experimental operation timing range of each segment, compare whether the start and end points of adjacent segments on the time axis have intersections and continuous connections, and obtain a group of segments with connected operation timings. S302: Based on the operation timing connected segment group, analyze the relationship between the device path index of the segment and the signal transmission direction, filter out waveform segments with different sources but continuous response, embed them into a unified timing sequence according to the teaching task instruction number, and obtain a path continuous segment sequence. S303: Based on the continuous segment sequence of the path, extract the time segment with dense signal response distribution, divide the signal segment in the continuous time period including the intersecting path, and map the segment to the corresponding teaching task instruction structure to obtain the abnormal concentrated segment sequence.

8. The virtual-real integrated digital intelligence method for analog electronics technology courses according to claim 1, characterized in that, The specific steps of S4 are as follows: S401: Based on the waveform segments in the abnormal concentrated segment sequence, compare them with the signal time nodes set in the scoring task configuration, extract the task number and time tag associated with each waveform segment, align the task setting area according to the waveform frame sequence number distribution relationship, and obtain the task time association group; S402: Based on the task number and corresponding signal frame sequence in the task time association group, locate the indicator item attached to the number in the scoring structure, extract the spacing relationship between the indicator item setting time and the waveform segment frame number, identify the waveform task index with discontinuous sequence number, and obtain the indicator spacing corresponding set. S403: Based on the index spacing correspondence set, replace the position segment of the corresponding segment in the original scoring task structure, assign waveform frame number range and task number order in sequence according to the teaching process, connect the segment and task node scoring relationship, and obtain the scoring segment mapping set.

9. The virtual-real integrated digital intelligence method for analog electronics technology courses according to claim 8, characterized in that, In the process of extracting the task number and time tag associated with each waveform segment: according to the signal time node set in the scoring task configuration, the start time of the waveform segment in the abnormal concentration segment sequence is matched with the signal time node, the time interval relationship is judged, and it is analyzed whether the corresponding signal time node is the task triggering basis corresponding to the waveform segment. In the process of identifying waveform task indexes with discontinuous sequence numbers: in the task time association group, the signal frame sequence number interval associated with the task number is analyzed according to the frame sequence number order to identify frame segments where there are gaps in the signal frame sequence number interval.

10. The virtual-real integrated digital intelligence method for analog electronics technology courses according to claim 1, characterized in that, The specific steps of S5 are as follows: S501: Based on the scoring segment mapping set, extract the voltage change trajectory of the scoring segments in the simulated signal and the measured signal, compare the waveform direction, level response amplitude and the location of the peak segment by frame time sequence, analyze whether the trends between corresponding segments are consistent, and obtain the signal trend comparison sequence; S502: Based on the signal trend comparison sequence, compare the time points of directional change, voltage fluctuation and peak change at the same frame point in the simulated and measured waveforms, identify the segments that change synchronously in time sequence, and obtain the synchronous waveform segment index group. S503: Based on the synchronous waveform segment index group, according to the order of nodes in the course task structure during the operation process, locate the correspondence between segments and nodes to obtain a set of virtual and real synchronous scoring segments.

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