Primary and secondary fusion centralized dtu device intelligent simulation test method and system

Abstract

The application discloses a primary and secondary fusion centralized DTU device intelligent simulation test method and system, and particularly relates to the field of power distribution network automation testing, and is used for solving the problems of loop cross interference signal separation and tracing, realizing accurate separation and tracing of cross-loop interference signals through frequency domain and time domain characteristic analysis and path tracking, and guaranteeing the accuracy of test data and the reliability of equipment evaluation; the method firstly extracts the frequency and phase characteristics of loop signals through multi-scale analysis to form the basis for interference signal separation; then, a dynamic characteristic trajectory is constructed to realize accurate separation of target signals and interference signals in the frequency domain and the time domain, and path tracking is performed in combination with propagation delay and phase offset to accurately locate the conduction path and source of the interference signal; finally, the separated signal characteristics are recorded in a characteristic library, and the interference identification standard is optimized with the aid of a real-time feedback mechanism to ensure that the system has self-adaptive identification capability in a variable test environment.

Description

Technical Field

[0001] The present invention relates to the field of automated testing of power distribution networks, and more particularly to an intelligent simulation test method and system for primary and secondary integrated centralized DTU equipment. Background Art

[0002] During the testing of centralized DTU equipment that integrates primary and secondary systems, to ensure the stability and safety of the power distribution network, the test equipment must be capable of real-time acquisition and monitoring of the signal status of different circuits. Due to the high degree of integration of distribution automation systems, the signals between primary and secondary equipment are interconnected during testing, enabling centralized DTU equipment to collect and analyze data from multiple dimensions. Interference between different circuits becomes particularly complex, especially in scenarios with dense wiring and highly complex power circuits. In such scenarios, various signals may be affected by electromagnetic interference, frequency disturbances, or phase shifts during transmission, resulting in cross-interference between signals in different circuits, further increasing the difficulty of determining the signal source.

[0003] Existing systems lack the ability to effectively identify and separate cross-interference signals. When processing multi-frequency tests, problems such as signal superposition or crosstalk often occur. Interference signals and target signals are difficult to accurately distinguish, resulting in limited authenticity and validity of test data. Especially in frequently changing test environments, signal conduction paths and interference sources are difficult to effectively track and locate. Equipment performance evaluation is prone to misjudgment or omission due to signal mixing, affecting the reliability of test results. In addition, the processing methods of existing test equipment are often limited to simple filtering methods in the frequency or time domain, and are unable to conduct in-depth analysis of signal propagation characteristics in complex loops, thereby limiting the equipment's interference management capabilities and data traceability accuracy in high-density connection environments. Summary of the Invention

[0004] The purpose of the present invention is to provide a primary-secondary fusion centralized DTU equipment intelligent simulation test method and system. Through multi-layer feature analysis and dynamic path tracing in the frequency domain and time domain, the precise separation and traceability of cross-loop interference signals are achieved. Under the synergistic effect of various modules, the accuracy of test data and the reliability of equipment evaluation are ensured.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for intelligent simulation testing of a primary and secondary integrated centralized DTU device, comprising:

[0007] Step 1: Perform multi-scale analysis to extract the features of different loop signals and form a preliminary feature division of the target signal and the cross-interference signal;

[0008] Step 2: Based on the preliminary feature segmentation results, a dynamic feature trajectory based on frequency domain feature parameters and time domain feature parameters is constructed to eliminate sudden interference components and retain the frequency and phase integrity of the target signal, thereby achieving separation of the target signal and the interference signal;

[0009] Step 3: Feature matching is performed on the separated target signal and interference signal with the feature templates in the feature library. If the signal feature match is successful, the matched feature template is directly called to determine the specific conduction path and source loop of the interference signal. If the match is unsuccessful, the specific conduction path and source loop of the interference signal are determined by analyzing the difference in propagation characteristics of the separated target signal and interference signal.

[0010] In step 4, the separated signal features are entered into a feature library, and real-time feedback is provided to optimize the processing standard for interference signal identification and form a feature template, and the updated feature library is fed back to step 3.

[0011] Preferably, the step 1 specifically includes:

[0012] Step 1-1: divide the original signal into multiple scales according to the time window, and divide it into long-scale signal and short-scale signal;

[0013] Step 1-2: The long and short scale signals are transformed by wavelet to obtain the time-frequency feature matrix M(f, t), and the target signal feature matrix M is generated by the time-frequency feature matrix g (f, t) and interference signal characteristic matrix M i (f, t), preliminarily determine the interference frequency segment of each loop; each element of the time-frequency feature matrix represents the feature intensity at frequency f and time point t, which is used to analyze the signal change pattern at each scale to distinguish the target signal of the loop from potential interference components;

[0014] Steps 1-3: Perform a double-layer frequency domain decomposition on the time-frequency feature matrix to calculate the signal distribution characteristics in the frequency domain. The double-layer frequency domain includes the signal's main frequency offset and phase shift coefficient. The main frequency offset is used to measure the distribution characteristics of the target signal at the main frequency. The phase shift coefficient is compared between different loops to find the corresponding relationship with the interference frequency.

[0015] In steps 1-4, by comparing the main frequency offset and phase shift coefficient, the different frequency band features in the time-frequency feature matrix are matched and screened, the frequency components belonging to cross-interference are identified, and marked in the time-frequency feature matrix. After the screening is completed, the preliminary feature division of the target signal and the cross-interference signal is obtained.

[0016] Preferably, the time window in step 1-1 is determined by the frequency f and the time resolution T. relationship.

[0017] Preferably, the main frequency offset F in steps 1-3 is o for:

[0018] F o =∑ f (f·M(f,t)) / ∑ f M(f,t).

[0019] Preferably, the formula for matching and screening different frequency band features in the time-frequency feature matrix in steps 1-4 is:

[0020]

[0021] Where ∈ is the frequency tolerance of the target signal, and δ is the phase deviation tolerance.

[0022] Preferably, the frequency domain characteristic parameter in step 2 includes a frequency domain local variation consistency coefficient, and the calculation method of the frequency domain local variation consistency coefficient is:

[0023] Based on the distribution of each frequency in the time-frequency feature matrix M(f,t), the local fluctuation characteristics of the frequency component in time are extracted;

[0024] Calculate the local standard deviation of the frequency distribution on the time axis, that is, the local energy fluctuation E of each frequency component 波动 (f);

[0025] The local coefficient of variation C of each frequency component is calculated using the ratio of the local standard deviation to the average intensity of the frequency component. 变异 (f);

[0026] The frequency domain local variation consistency coefficient is obtained by weighted summing of the local variation coefficients of all frequencies.

[0027] Preferably, the local standard deviation is:

[0028]

[0029] Where N is the total number of time samples, M(f,t) is the signal strength value of the corresponding frequency f at time t in the matrix, Represents the average value of the corresponding frequency component at all time points.

[0030] Preferably, the local coefficient of variation is:

[0031]

[0032] Among them, C 变异 (f) is the local coefficient of variation corresponding to frequency f.

[0033] Preferably, the frequency domain local variation consistency coefficient is:

[0034] FDLFCI=∑ f C 变异 (f)·exp(-α·|f-f0|);

[0035] Among them, f0 is the main frequency of the target signal, and α is the attenuation coefficient.

[0036] Preferably, the time domain characteristic parameter in step 2 includes a time-phase variation rate coefficient, and a method for calculating the time-phase variation rate coefficient includes:

[0037] Extract the phase information of each time point from the time-frequency feature matrix to calculate the phase change rate on the time axis;

[0038] Calculate the sum of squares of the difference between the phase change rate and the mean of the phase change rate to form a phase change rate fluctuation intensity matrix;

[0039] Combined with the offset weight on the time axis, the fluctuation intensity matrix is ​​weighted summed to obtain the time-phase variation rate coefficient.

[0040] Preferably, the phase change rate is:

[0041]

[0042] in, It is the time derivative of the phase, which represents the rate of change of the phase Φ(t) with respect to time t; Φ(t) is the phase of the frequency component in the time-frequency characteristic matrix.

[0043] Preferably, the time-phase variation rate coefficient is:

[0044]

[0045] Where t0 is the time center of the target signal, and β is the offset weight coefficient on the time axis.

[0046] Preferably, the step 2 includes:

[0047] The frequency domain local variation consistency coefficient and the time-phase variation rate coefficient are comprehensively calculated to form the target interference identification value TIIV, TIIV = FDLFCI × ln(TPVRC+1), where FDLFCI is the frequency domain local variation consistency coefficient and TPVRC is the time-phase variation rate coefficient;

[0048] If the signal identification value is higher than the preset threshold, the signal is considered to be an interference signal and is eliminated; otherwise, it is retained as the target signal.

[0049] Preferably, in step 3, determining the specific conduction path and source loop of the interference signal by analyzing the difference in propagation characteristics between the separated target signal and the interference signal specifically includes:

[0050] Step 3-1, calculating the propagation delay differences of the interference signal at different times to establish a time delay difference matrix;

[0051] Step 3-2: For the phase values ​​of the signal at different times and frequencies, calculate the phase change hour by hour, and accumulate the change to form the phase offset;

[0052] Step 3-3, combining the time delay difference matrix and the phase offset to generate a path weight coefficient;

[0053] Step 3-4: Based on the path weight coefficient, identify the main propagation path of the signal through point-by-point tracking. Select the point with the highest path weight coefficient as the initial propagation source of the interference signal, and determine the path nodes in sequence based on the adjacent weights to gradually restore the conduction path of the interference signal.

[0054] In steps 3-5, the propagation trajectory of the interference signal between different loops is identified according to the corresponding path, and the interference source loop is located.

[0055] Preferably, the step 4 specifically includes:

[0056] Step 4-1, normalizing the characteristic values ​​of the separated target signal and interference signal;

[0057] Step 4-2: Based on the normalized feature data, establish the pattern feature vector of the interference signal, and combine the average value and standard deviation of different interference signal features into a feature template to describe the typical distribution characteristics of the corresponding type of signal;

[0058] Step 4-3 compares the similarity of the newly entered interference signal features with the existing templates in the feature library to determine whether they conform to the existing feature pattern; specifically, when the new feature highly matches a template, the data of the new feature is included in the statistical range of the corresponding template; if the similarity is low, a new pattern template is generated for the corresponding signal type; the new signal features that conform to the existing pattern characteristics are dynamically included in the feature library, and the feature template is updated in real time.

[0059] A primary and secondary integrated centralized DTU equipment intelligent simulation test system, including:

[0060] The feature analysis module performs multi-scale analysis to extract the features of different loop signals, forms a preliminary feature division between the target signal and the cross-interference signal, and sends the division results to the signal separation module;

[0061] The signal separation module constructs dynamic feature trajectories based on the frequency and time domains, removes sudden interference components, and retains the frequency and phase integrity of the target signal, thereby separating the target signal from the interference signal. The separated target signal and interference signal data are then passed to the path tracking module and the feature feedback module.

[0062] The path tracking module matches the separated target and interference signals with the feature templates in the feature library. If the signal feature match is successful, the matched feature template is directly called to determine the specific conduction path and source loop of the interference signal. If the match is unsuccessful, the specific conduction path and source loop of the interference signal are determined by analyzing the differences in the propagation characteristics of the target and interference signals.

[0063] The feature feedback module records the separated signal features into the feature library, provides real-time feedback to optimize the processing standards for interference signal identification and form a feature template, and feeds back the updated template to the path tracking module.

[0064] Compared with the prior art, the significant advantages of the primary and secondary fusion centralized DTU equipment intelligent simulation test system of the present invention are:

[0065] The present invention realizes the precise separation and tracing of cross-loop interference signals through multi-layer feature analysis and dynamic path tracking in the frequency domain and time domain, ensuring the accuracy of test data and the reliability of equipment evaluation. The frequency and phase characteristics of the loop signal are extracted through multi-scale analysis, laying the foundation for the subsequent separation of interference signals; the construction of dynamic feature trajectories in the frequency domain and time domain further realizes the precise separation of target signals and interference signals. Based on the separated signal characteristics, path tracing is performed in combination with propagation delay and phase offset to accurately locate the conduction path and source of the interference signal. The separated signal characteristics are entered into the feature library, and the interference identification standard is optimized through a real-time feedback mechanism, so that the system can maintain the ability to identify and adapt to interference signals in a changing test environment. Each module works together to improve the accuracy of signal processing and traceability. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 This is a structural diagram of a primary and secondary fusion centralized DTU equipment intelligent simulation test system of the present invention.

[0067] Figure 2 The figure is a flow chart of a feature feedback module of a primary-secondary fusion centralized DTU equipment intelligent simulation test system of the present invention. DETAILED DESCRIPTION

[0068] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0069] The present invention provides a primary-secondary fusion centralized DTU equipment intelligent simulation test system, which is used to solve the problem of loop cross-interference signal separation and tracing. The system realizes the precise separation and tracing of cross-loop interference signals through frequency domain and time domain feature analysis and path tracing, thereby ensuring the accuracy of test data and the reliability of equipment evaluation. The system first extracts the frequency and phase characteristics of the loop signal through multi-scale analysis to form the basis for interference signal separation. Then, a dynamic feature trajectory is constructed to achieve precise separation of target signals and interference signals in the frequency domain and time domain, and path tracing is performed in combination with propagation delay and phase offset to accurately locate the conduction path and source of the interference signal. Finally, the separated signal features are entered into a feature library, and the interference identification criteria are optimized with the help of a real-time feedback mechanism to ensure that the system has adaptive identification capabilities in a changing test environment.

[0070] Figure 1 The present invention provides a primary-secondary fusion centralized DTU equipment intelligent simulation test system, which includes: a feature analysis module, a signal separation module, a path tracking module and a feature feedback module.

[0071] Feature analysis module: Performs multi-scale analysis to extract the features of different loop signals, forms a preliminary feature division of the target signal and the cross-interference signal, and sends the division results to the signal separation module.

[0072] Signal separation module: Based on the dynamic feature trajectories in the frequency domain and time domain, it separates the target signal from the interference signal, eliminates the sudden interference components and retains the frequency and phase integrity of the target signal. The separated target signal data is transmitted to the path tracking module and the feature feedback module.

[0073] Path tracing module: The separated target signal and interference signal are matched with the feature templates in the feature library. If the signal feature match is successful, the matched feature template is directly called to determine the specific conduction path and source loop of the interference signal; if the match is unsuccessful, the specific conduction path and source loop of the interference signal are determined by analyzing the differences in the propagation characteristics of the target signal and the interference signal.

[0074] Feature feedback module: The separated signal features are recorded in the feature library, and real-time feedback is provided to optimize the processing standards for interference signal identification and form a feature template, and the updated template is fed back to the path tracking module.

[0075] In a preferred embodiment, the feature analysis module includes the following contents:

[0076] In the testing environment of centralized DTU devices with integrated primary and secondary systems, high-density, closed wiring with dense signals often leads to cross-interference between different circuits. These interference signals, stemming from complex electromagnetic characteristics, easily interfere with each other during testing, thus affecting accurate evaluation of device performance. To ensure the accuracy of test data, multi-level analysis of the signals on each circuit is required. By decomposing the characteristics of the interference signals layer by layer, the target signal and cross-interference signals can be precisely separated.

[0077] In order to effectively separate the target signal from the cross-interference signal, a layer-by-layer analysis approach is adopted to extract the characteristic differences of each loop signal without interfering with the overall test signal. The specific processing method is as follows:

[0078] The original signal is divided into multiple scales according to the time window, which is divided into long-scale signals with larger time windows and short-scale signals with smaller time windows. Long-scale analysis is used to capture the macroscopic characteristics of the signal and reflect the overall frequency and phase distribution; short-scale analysis is used to observe instantaneous signal changes and focus on interference characteristics such as frequency mutations and waveform mutations. The selected time window is determined by the frequency f and the time resolution T, which is sufficient. to ensure that the scale division has sufficient sensitivity in capturing the details of rapidly changing signals.

[0079] The long and short scale signals are subjected to wavelet transform to obtain the time-frequency feature matrix M(f,t). Each element of the time-frequency feature matrix represents the feature intensity at frequency f and time point t. This feature matrix is ​​used to analyze the signal change pattern at each scale to distinguish the target signal of the loop from potential interference components. The time-frequency feature matrix can also generate the target signal feature matrix M g (f, t) and interference signal characteristic matrix M i (f, t), preliminarily determine the interference frequency segment of each loop.

[0080] The time-frequency feature matrix is ​​decomposed in two layers in the frequency domain to calculate the signal distribution characteristics in the frequency domain. The two-layer frequency domain characteristics include the main frequency offset and phase shift coefficient θ of the signal to reflect the unique influence of frequency on the signal characteristics. The main frequency offset is calculated by formula F o =∑ f (f·M(f,t)) / ∑ f M(f, t) is calculated. The main frequency offset is used to measure the distribution characteristics of the target signal at the main frequency. The phase shift coefficient is compared between different loops to find the corresponding relationship with the interference frequency. The two-level decomposition of frequency domain features can distinguish the steady-state characteristics of the target signal from the fluctuating characteristics of the interference signal.

[0081] Finally, the different frequency band features in the time-frequency feature matrix are matched and screened. By comparing the main frequency offset and phase shift coefficient, the frequency components belonging to cross-interference are identified and marked in the time-frequency feature matrix. The screening formula for forming the time-frequency feature matrix is ​​as follows: Where ∈ is the frequency tolerance of the target signal, and δ is the phase deviation tolerance. After the screening is completed, the preliminary feature division of the target signal and the cross-interference signal is obtained, providing an accurate basis for subsequent separation.

[0082] The feature analysis module uses multi-scale analysis to achieve refined extraction and differentiation of signal features from different loops, effectively distinguishing target signals from cross-interference signals in key features such as frequency and phase, forming a feature matrix with multi-level information. By constructing a time-frequency feature matrix and decomposing the two-layer frequency domain features, it accurately captures the dynamic changes of signals at different time scales, ensuring the feature integrity of long-scale and short-scale signals, thereby improving the sensitivity and accuracy of interference signal identification. Matching and screening of the main frequency offset and phase shift coefficient further effectively isolates the target signal, ensuring the reliable identification of cross-interference signals in high-density wiring environments, and laying a solid feature foundation for the subsequent separation and tracing of interference signals.

[0083] When separating the target signal from the cross-interference signal, the signal separation module analyzes the differences between the frequency domain and the time domain based on the dynamic feature trajectory to enhance the accuracy of interference identification. This step designs two parameters: the frequency domain local variation consistency coefficient and the time-phase variation rate coefficient. Starting from the local consistency of the frequency domain and the phase change rate of the time axis, respectively, by analyzing the different characteristic trajectories of the signal, the interference signal is eliminated to ensure the integrity of the target signal. The frequency domain local variation consistency coefficient is used to quantify the stationarity of the signal at a specific frequency and identify the high-frequency fluctuation characteristics of the interference signal; the time-phase variation rate coefficient measures the phase variation of the signal on the time axis, captures phase jumps, and then eliminates interference components. Through the complementary analysis of the two, a dynamic feature trajectory model is constructed to achieve accurate signal separation.

[0084] In a preferred embodiment, the signal separation module includes the following:

[0085] In signal separation, the dynamic characteristic trajectory of the target signal is significantly different from that of the cross-interference signal. This difference is reflected in the local fluctuation characteristics of the frequency domain and the phase change rate in the time domain. Usually, the target signal changes relatively smoothly in the frequency domain, while the interference signal exhibits suddenness or instability. At the same time, the phase change rate on the time scale is significantly different, and the interference signal often has a significant phase jump. Therefore, by calculating the characteristic parameters of the frequency domain and time domain and constructing a dynamic characteristic trajectory, the target signal and the interference signal can be effectively distinguished. The characteristic parameters of the frequency domain include the local change consistency coefficient of the frequency domain; the characteristic parameters of the time domain include the time-phase variation rate coefficient;

[0086] Frequency domain local variation consistency coefficient: Evaluates the local consistency of the signal in the frequency domain to identify stable target signals and wildly fluctuating interference signals.

[0087] Time-phase variation rate coefficient: measures the rate of phase change on the time axis and is used to identify interference signals with sudden phase changes.

[0088] The frequency domain local variation consistency coefficient is based on the time-frequency feature matrix obtained by the feature analysis module, which quantifies the temporal consistency of each frequency component to identify the stationary characteristics of the signal. The following is a detailed calculation process:

[0089] First, based on the distribution of each frequency in the feature matrix, the local fluctuation characteristics of the frequency components over time are extracted. Let the feature matrix be a two-dimensional matrix with time on the horizontal axis and frequency on the vertical axis. Each element in the matrix represents the intensity of a frequency component at a specific point in time.

[0090] Calculate the local standard deviation of the frequency distribution on the time axis to obtain the local energy fluctuation of each frequency component to identify the stability of the frequency component. The local standard deviation formula is as follows: Where N is the total number of time samples, M(f,t) is the signal strength value of the corresponding frequency f at time t in the matrix, The local coefficient of variation of each frequency component is calculated by using the ratio of the local standard deviation to the average intensity of the frequency component to quantify the consistency of the frequency change: This coefficient of variation characterizes the temporal fluctuation of the frequency component. A larger value indicates a more severe fluctuation, which usually corresponds to the characteristics of an interference signal; a smaller value indicates a smaller fluctuation, which usually corresponds to the stable characteristics of the target signal. Finally, by weighted summing the local coefficients of variation of all frequencies, the frequency domain local variation consistency coefficient FDLFCI is obtained, which further highlights the interference characteristics that are far away from the main frequency of the target signal. The formula is as follows: FDLFCI = ∑ f C 变异(f)·exp(-α·|f-f0|); where f0 is the main frequency of the target signal and α is the attenuation coefficient, which is used to reduce the impact of interference far from the main frequency. This coefficient integrates the fluctuation consistency of each frequency component to more accurately identify the presence of interfering signals in the frequency domain.

[0091] The frequency domain local variation consistency coefficient indicates the stability and consistency of a signal within a specific frequency range, reflecting the temporal stability of the signal's energy in the frequency domain. A larger value indicates greater temporal fluctuations in the frequency component, typically characteristic of an interference signal. A smaller value indicates a stable signal within the frequency band, with even energy distribution, consistent with the stability characteristics of the target signal. By quantifying the intensity of local frequency variations, this coefficient allows for more accurate determination of signal stability, helping to identify and distinguish abnormal fluctuating signals in the frequency domain.

[0092] The time-phase variation rate coefficient is based on the time-frequency feature matrix obtained by the feature analysis module, which quantifies the phase change rate of the signal on the time axis to identify the stability and mutation characteristics of the phase in the time domain. The specific calculation process is as follows:

[0093] Extract the phase information at each time point from the feature matrix to calculate the phase change rate on the time axis. Assuming that each element in the matrix contains phase information, the instantaneous phase change rate P(t) at the time point is defined and calculated as follows: in, It is the time derivative of the phase, which represents the rate of change of the phase Φ(t) with respect to time t; Φ(t) is the phase of the frequency component in the time-frequency feature matrix. The rate of change of the phase is calculated at each time point to capture the phase mutation characteristics that may be generated by the interference signal.

[0094] In order to identify the interference characteristics in the frequency domain, the square sum of the phase change rate is further calculated to form the phase change rate fluctuation intensity matrix Q(t) to characterize the concentration degree of phase variation: in, is the mean of the phase change rate at all time points, indicating the overall stable trend of the signal. A larger value of the fluctuation intensity matrix indicates a more drastic phase change, which usually indicates a sudden change in the interference signal.

[0095] Combined with the offset weights on the time axis, the fluctuation intensity matrix is ​​weighted and summed to obtain the time-phase variation rate coefficient TPVRC, which highlights the interference components far away from the time center: Where t0 is the time center of the target signal, and β is the time offset weighting factor, which is used to reduce the weight of interference that deviates from the target signal characteristics. The time-phase variation rate coefficient can capture the characteristics of sudden phase changes in the signal and help distinguish phase mutations in interfering signals.

[0096] The time-phase rate of variation coefficient (TPV) represents the rate of phase change of a signal along the time axis, reflecting the signal's phase stability and mutation trends at different moments. Larger values ​​indicate a faster phase change rate, with significant jumps or irregular fluctuations, often indicative of interference signals. Smaller values ​​indicate a more stable phase change, consistent with the continuous nature of the target signal. By quantifying the phase rate of variation, this coefficient provides sensitivity to temporal signal fluctuations, helping to isolate short-duration interference components and accurately identify time-domain characteristics.

[0097] Finally, the frequency domain local variation consistency coefficient and the time-phase variation rate coefficient parameters are combined to form the target interference identification value (TIIV), which serves as the basis for interference signal rejection. By leveraging the complementary characteristics of the frequency and time domains, the target interference identification value integrates the dynamic variation information of the signal: TIIV = FDLFCI × ln(TPVRC + 1). A logarithmic operation is used to smooth the differences between parameters to avoid the influence of outliers. If the target interference identification value exceeds a preset threshold, the signal is considered an interference signal and is rejected; otherwise, it is retained as the target signal.

[0098] After removing the interference signal, the remaining target signal undergoes an integrity check, examining its frequency and phase continuity to confirm that the removal process has not damaged the target signal structure. A dynamic compensation algorithm restores minor faults caused by interference removal, ensuring the integrity of the target signal. The resulting target signal dataset retains all key features of the loop signal, providing an accurate signal foundation for subsequent traceability analysis.

[0099] The signal separation module effectively separates target signals from interference signals through dynamic feature trajectories, establishing a complementary feature discrimination system in the frequency and time domains. The frequency domain local variation consistency coefficient captures the stationarity of frequency components, providing sensitive identification of sudden interfering signals. The time-phase variation rate coefficient quantifies the rate of phase change, highlighting sudden phase changes occurring on the time axis. The combination of these two allows for highly accurate signal separation in both the frequency and time domains, reducing the probability of misjudgment of target signals, ensuring the integrity and reliability of target signals in complex environments, and providing a stable foundation for subsequent signal analysis.

[0100] After separation processing by the signal separation module, the characteristic matrices of the target and interference signals are obtained. Based on the differences in the time-frequency propagation characteristics of the interference signal, the path tracking module constructs a delay and phase offset model of the interference signal, and uses path analysis methods to accurately locate the interference source and its propagation path.

[0101] In a preferred embodiment, the path tracking module includes the following:

[0102] Before processing, the target signal and interference signal obtained from the signal separation module are first feature matched, and the separated signal features are compared with the feature templates stored in the feature library for similarity to determine whether the signal conforms to the existing feature pattern. If the signal feature match is successful, the path information or processing rules in the corresponding feature template are directly called to quickly locate the interference source and conduction path; if the match is unsuccessful, the propagation delay difference of the interference signal at different times is calculated to establish a time delay difference matrix. The delay difference matrix quantifies the delay distribution of the signal by measuring the energy or eigenvalue difference of the signal at adjacent times, and captures the delay difference caused by the propagation distance or path characteristics of the signal in different paths. The area with a higher delay difference value in the delay difference matrix represents a more significant delay change in signal propagation, which usually corresponds to the main path of the interference signal.

[0103] Based on delay differential analysis, the path offset characteristics of the interference signal are further captured by analyzing the phase changes of the interference signal. The phase change of the signal at different times and frequencies is calculated hour by hour and accumulated to form a phase offset metric. This phase offset describes the phase variation of the signal in the time dimension and can reveal the different characteristics of the propagation path caused by phase offset. Interference signals often exhibit sudden phase changes, while the target signal is more stable in phase. Therefore, phase offset can better characterize the propagation characteristics of the interference signal.

[0104] The calculated results of delay differential and phase offset are combined to generate a path weight coefficient. This path weight coefficient normalizes the delay differential and phase offset to achieve a combined weighting effect of time delay and phase offset, highlighting the primary propagation path of the interfering signal. The path weight coefficient uses relatively high weights to identify key nodes in the interfering propagation path, making high-weight points on the path more prominent and helping to determine the signal's propagation direction.

[0105] Based on the path weight coefficients, the primary signal propagation path is identified through point-by-point tracking. The point with the highest path weight coefficient is selected as the initial source of the interference signal. Path nodes are then identified sequentially based on adjacent weights, gradually restoring the interference signal's transmission path. Ultimately, the corresponding paths identify the propagation trajectory of the interference signal between different loops, locating the interference source loop and providing a clear path basis for signal separation and subsequent processing.

[0106] The path tracking module uses refined path tracking techniques to accurately capture the delay and phase offset characteristics of interference signals during transmission, enabling clear location of the interference signal's propagation path. The combined analysis of the delay difference matrix and phase offset not only improves path tracking accuracy but also effectively reduces misidentification of target signals. The path tracking weight coefficient further enhances the significance of path nodes, enabling high-precision and reliable source location of interference signals, providing a scientific basis for tracing interference issues in complex loop environments.

[0107] like Figure 2 As shown, in a preferred embodiment, the feature feedback module includes the following contents:

[0108] First, the eigenvalues ​​of the separated target and interference signals are normalized to ensure the stability of the signal characteristics under different sampling environments. Normalization calculates the difference between the eigenvalue and its average value and standardizes the result, thus unifying the values ​​of different signal characteristics into a comparable range. Normalized eigenvalues ​​reduce the impact of environmental differences and establish a unified feature foundation for subsequent model building.

[0109] Based on the normalized feature data, a pattern feature vector for the interference signal is constructed. Information such as the mean and standard deviation of different interference signal characteristics is combined into a feature template, which describes the typical distribution characteristics of the corresponding signal class. By extracting the mean and variation range of different dimensions such as time, frequency, and phase, the feature vector generates a representative pattern template, helping to identify the degree of matching of new signals. Feature pattern modeling provides a clear standard for the structured description of interference signals, enabling the identification and differentiation of different types of interference signal characteristics.

[0110] Through a feedback mechanism, newly entered interference signal features are compared with existing templates in the signature library to determine if they match existing patterns. If a new feature closely matches a template, the new feature data is included in the template's statistical scope. If the similarity is low, a new pattern template is generated for the corresponding signal type. This feedback mechanism continuously adjusts and expands the signature library's recognition criteria by assessing signal similarity in real time, ensuring template adaptability in diverse environments.

[0111] As feedback optimization progresses, new signal features that match existing pattern characteristics are dynamically incorporated into the feature library, feature templates are updated in real time, and the mean and standard deviation of the pattern vectors are adjusted to expand the template's applicability. With each feedback and data entry, the feature library dynamically adapts to changes in new signal characteristics, continuously enhancing the accuracy and robustness of interference signal recognition. This dynamic update process ensures that the feature library maintains its ability to identify new interference signals in a constantly changing environment, providing continuously optimized feature support for subsequent processing and signal management.

[0112] The feature feedback module gradually improves the accuracy and adaptability of interference signal identification by entering the separated signal features into a feature library and dynamically updating and optimizing them. Normalization ensures the uniformity of feature values ​​and reduces the impact of environmental differences; feature pattern modeling provides a clear, structured description of different types of interference signals; the feedback mechanism continuously adjusts the recognition criteria through real-time similarity judgment, giving the feature library adaptive capabilities; dynamic updates further expand the scope of application of templates, allowing the feature library to remain highly sensitive to changes in the signal environment and quickly adapt to new interference features. In this process, the feature library continuously strengthens its own recognition capabilities, achieves continuous optimization of interference signals, and provides robust and accurate feature support for interference management in complex environments.

[0113] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0114] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a primary-secondary fusion centralized DTU equipment intelligent simulation test method. Through multi-layer feature analysis and dynamic path tracing in the frequency domain and time domain, it realizes the precise separation and tracing of cross-loop cross-interference signals, and through synergistic effect, ensures the accuracy of test data and the reliability of equipment evaluation.

[0115] First, the frequency and phase characteristics of each loop signal are extracted through multi-scale analysis to form a preliminary feature division between the target signal and the cross-interference signal, providing a clear feature basis for the subsequent separation of the interference signal;

[0116] Next, by constructing dynamic feature trajectories, the target signal and interference signal are accurately separated in the frequency and time domains, preserving the integrity of the target signal while effectively eliminating sudden interference components. This separation process lays the foundation for high-precision signal input for subsequent interference signal path tracking.

[0117] In path tracing, the interference signal is traced based on the separated signal characteristics, combined with propagation delay and phase offset, to accurately locate its conduction path and source location, providing support for comprehensive signal tracking.

[0118] Finally, the separated signal features are entered into the feature library, and the interference identification standards are continuously optimized through real-time feedback to form an adaptive feature template, ensuring that the system maintains the ability to keenly identify new interference signals in a changing test environment.

[0119] The close coordination of the modules of the present invention enables the present invention to have a high degree of recognition accuracy, dynamic adaptability and source positioning accuracy, thereby effectively ensuring the authenticity of data and the scientific nature of equipment evaluation in a complex power circuit environment, so as to solve the problems raised in the above background technology.

[0120] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

[0121] It should be noted that, in this document, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises", "includes" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article or device. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device that includes the element.

[0122] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for intelligent simulation test of primary and secondary fusion centralized DTU equipment, characterized in that: include: Step 1: Perform multi-scale analysis to extract the features of different loop signals and form a preliminary feature division of the target signal and the cross-interference signal; Step 2: Based on the preliminary feature segmentation results, a dynamic feature trajectory based on frequency domain feature parameters and time domain feature parameters is constructed to eliminate sudden interference components and retain the frequency and phase integrity of the target signal, thereby achieving separation of the target signal and the interference signal; The frequency domain characteristic parameters include the frequency domain local variation consistency coefficient, and the calculation method of the frequency domain local variation consistency coefficient is: Based on the distribution of each frequency in the time-frequency feature matrix M(f,t), the local fluctuation characteristics of the frequency component in time are extracted; Calculate the local standard deviation of the frequency distribution on the time axis, that is, the local energy fluctuation E of each frequency component 波动 (f); The local coefficient of variation C of each frequency component is calculated using the ratio of the local standard deviation to the average intensity of the frequency component. 变异 (f); The frequency domain local variation consistency coefficient is obtained by weighted summing of the local variation coefficients of all frequencies; The frequency domain local variation consistency coefficient is: FDLFCI=∑ f C 变异 (f)·exp(-α·|f-f0|); Where f0 is the main frequency of the target signal, and α is the attenuation coefficient; Step 3: Feature matching is performed on the separated target signal and interference signal with the feature templates in the feature library. If the signal feature match is successful, the matched feature template is directly called to determine the specific conduction path and source loop of the interference signal. If the match is unsuccessful, the specific conduction path and source loop of the interference signal are determined by analyzing the difference in propagation characteristics of the separated target signal and interference signal. In step 4, the separated signal features are entered into a feature library, and real-time feedback is provided to optimize the processing standard for interference signal identification and form a feature template, and the updated feature library is fed back to step 3.

2. The intelligent simulation test method for a centralized DTU device with primary and secondary integration according to claim 1 is characterized in that: The step 1 specifically includes: Step 1-1: divide the original signal into multiple scales according to the time window, and divide it into long-scale signal and short-scale signal; Step 1-2: The long and short scale signals are transformed by wavelet to obtain the time-frequency feature matrix M(f, t), and the target signal feature matrix M is generated by the time-frequency feature matrix g (f, t) and interference signal characteristic matrix M i (f, t), preliminarily determine the interference frequency segment of each loop; each element of the time-frequency feature matrix represents the feature intensity at frequency f and time point t, which is used to analyze the signal change pattern at each scale to distinguish the target signal of the loop from potential interference components; Steps 1-3: Perform a double-layer frequency domain decomposition on the time-frequency feature matrix to calculate the signal distribution characteristics in the frequency domain. The double-layer frequency domain includes the signal's main frequency offset and phase shift coefficient. The main frequency offset is used to measure the distribution characteristics of the target signal at the main frequency. The phase shift coefficient is compared between different loops to find the corresponding relationship with the interference frequency. In steps 1-4, by comparing the main frequency offset and phase shift coefficient, the different frequency band features in the time-frequency feature matrix are matched and screened, the frequency components belonging to cross-interference are identified, and marked in the time-frequency feature matrix. After the screening is completed, the preliminary feature division of the target signal and the cross-interference signal is obtained.

3. The intelligent simulation test method for a centralized DTU device with primary and secondary integration according to claim 2 is characterized in that: The time window in step 1-1 is determined by the frequency f and the time resolution T. relationship.

4. The intelligent simulation test method for a centralized DTU device with primary and secondary integration according to claim 2 is characterized in that: The main frequency offset F in steps 1-3 o for: F o =∑ f (f·M(f,t)) / ∑ f M(f,t)。 5. The intelligent simulation test method for a centralized DTU device with primary and secondary integration according to claim 2 is characterized in that: The formula for matching and screening different frequency band features in the time-frequency feature matrix in steps 1-4 is: Where ∈ is the frequency tolerance of the target signal, and δ is the phase deviation tolerance.

6. The intelligent simulation test method for a primary and secondary fusion centralized DTU device according to claim 1 is characterized in that: The local standard deviation is: Where N is the total number of time samples, M(f,t) is the signal strength value of the corresponding frequency f at time t in the matrix, Represents the average value of the corresponding frequency component at all time points.

7. The intelligent simulation test method for a primary and secondary fusion centralized DTU device according to claim 1 is characterized in that: The local coefficient of variation is: Among them, C 变异 (f) is the local coefficient of variation corresponding to frequency f.

8. The intelligent simulation test method for a centralized DTU device with primary and secondary integration according to claim 1 is characterized in that: The time domain characteristic parameter in step 2 includes a time-phase variation rate coefficient, and a calculation method of the time-phase variation rate coefficient includes: Extract the phase information of each time point from the time-frequency feature matrix to calculate the phase change rate on the time axis; Calculate the sum of squares of the difference between the phase change rate and the mean of the phase change rate to form a phase change rate fluctuation intensity matrix; Combined with the offset weight coefficient on the time axis, the fluctuation intensity matrix is ​​weighted summed to obtain the time-phase variation rate coefficient.

9. The intelligent simulation test method for a centralized DTU device with primary and secondary integration according to claim 8 is characterized in that: The phase change rate is: in, It is the time derivative of the phase, which represents the rate of change of the phase Φ(t) with respect to time t; Φ(t) is the phase of the frequency component in the time-frequency characteristic matrix.

10. The intelligent simulation test method for a centralized DTU device with primary and secondary integration according to claim 9 is characterized in that: The time-phase variation rate coefficient is: Among them, t0 is the time center of the target signal, β is the offset weight coefficient on the time axis, and the phase change rate fluctuation intensity matrix in, is the mean of the phase change rate at all time points, indicating the overall smooth trend of the signal.

11. The intelligent simulation test method for a primary and secondary fusion centralized DTU device according to claim 10 is characterized in that: The step 2 includes: The frequency domain local variation consistency coefficient and the time-phase variation rate coefficient are comprehensively calculated to form the target interference identification value TIIV, TIIV = FDLFCI × ln(TPVRC+1), where FDLFCI is the frequency domain local variation consistency coefficient and TPVRC is the time-phase variation rate coefficient; If the signal identification value is higher than the preset threshold, the signal is considered to be an interference signal and is eliminated; otherwise, it is retained as the target signal.

12. The intelligent simulation test method for a centralized DTU device with primary and secondary integration according to claim 1 is characterized in that: In step 3, the specific conduction path and source loop of the interference signal are determined by analyzing the difference in propagation characteristics between the separated target signal and the interference signal, which specifically includes: Step 3-1, calculating the propagation delay differences of the interference signal at different times to establish a time delay difference matrix; Step 3-2: For the phase values ​​of the signal at different times and frequencies, calculate the phase change hour by hour, and accumulate the change to form the phase offset; Step 3-3, combining the time delay difference matrix and the phase offset to generate a path weight coefficient; Step 3-4: Based on the path weight coefficient, identify the main propagation path of the signal through point-by-point tracking. Select the point with the highest path weight coefficient as the initial propagation source of the interference signal, and determine the path nodes in sequence based on the adjacent weights to gradually restore the conduction path of the interference signal. In steps 3-5, the propagation trajectory of the interference signal between different loops is identified according to the corresponding path, and the interference source loop is located.

13. The intelligent simulation test method for a centralized DTU device with primary and secondary integration according to claim 1 is characterized in that: The step 4 specifically includes: Step 4-1, normalizing the characteristic values ​​of the separated target signal and interference signal; Step 4-2: Based on the normalized feature data, establish the pattern feature vector of the interference signal, and combine the average value and standard deviation of different interference signal features into a feature template to describe the typical distribution characteristics of the corresponding type of signal; Step 4-3 compares the similarity of the newly entered interference signal features with the existing templates in the feature library to determine whether they conform to the existing feature pattern; specifically, when the new feature highly matches a template, the data of the new feature is included in the statistical range of the corresponding template; if the similarity is low, a new pattern template is generated for the corresponding signal type; the new signal features that conform to the existing pattern characteristics are dynamically included in the feature library, and the feature template is updated in real time.

14. A centralized DTU equipment intelligent simulation test system for primary and secondary integration that implements the method described in any one of claims 1 to 13, characterized in that: include: The feature analysis module performs multi-scale analysis to extract the features of different loop signals, forms a preliminary feature division between the target signal and the cross-interference signal, and sends the division results to the signal separation module; The signal separation module constructs dynamic feature trajectories based on the frequency and time domains, removes sudden interference components, and retains the frequency and phase integrity of the target signal, thereby separating the target signal from the interference signal. The separated target signal and interference signal data are then passed to the path tracking module and the feature feedback module. The path tracking module matches the separated target and interference signals with the feature templates in the feature library. If the signal feature match is successful, the matched feature template is directly called to determine the specific conduction path and source loop of the interference signal. If the match is unsuccessful, the specific conduction path and source loop of the interference signal are determined by analyzing the differences in the propagation characteristics of the target and interference signals. The feature feedback module records the separated signal features into the feature library, provides real-time feedback to optimize the processing standards for interference signal identification and form a feature template, and feeds back the updated template to the path tracking module.

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