Electromagnetic interference prediction method based on statistical analysis of electromagnetic environment monitoring data

Abstract

The invention discloses an electromagnetic interference prediction method based on statistical analysis of electromagnetic environment monitoring data, and belongs to the technical field of equipment electronics. The method comprises the following steps: acquiring frequency spectrum data, frequency spectrum, time and working condition information of each monitoring point in an electromagnetic environment to form a frequency spectrum sample; determining a monitoring point X with the lowest stability through spectrum stability calculation; and based on the spectrum sample, calculating statistics reflecting electromagnetic environment fluctuation conditions through data at different times and the same frequency point, generating a fluctuation characteristic spectrum of the statistics along with frequency change, and determining potential interference source equipment according to the correlation between the fluctuation characteristic spectrum of the monitoring point X and the fluctuation characteristic spectrum of other monitoring points in combination with the type of the equipment of the monitoring point X. The electromagnetic interference can be counted, analyzed and predicted based on the spectrum data.

Description

Technical Field

[0001] The present invention belongs to the field of equipment electronic technology, and in particular relates to an electromagnetic interference prediction method based on statistical analysis of electromagnetic environment monitoring data. Background Art

[0002] With the development of science and technology, electronic and electrical equipment or systems are gaining more and more widespread application. A large number of new technologies and products are being applied to various weapon platforms. High-power, high-voltage, and high-current equipment is frequently used. While the technical level of equipment has significantly improved, electromagnetic environment interference issues have also increased. How to solve electromagnetic interference and how to improve the efficiency of electromagnetic interference troubleshooting have always been issues that require special attention. In the past, the process of electromagnetic interference problem troubleshooting often encountered the problem of lack of electromagnetic environment level data at the time of interference problems, which affected the location and diagnosis of electromagnetic interference incidents. Therefore, there is a need for an electromagnetic environment monitoring system that allows operators to grasp the electromagnetic environment situation in real time. In the event of sudden electromagnetic interference, it can systematically analyze and process a large amount of electromagnetic environment monitoring data and backtrack the electromagnetic environment monitoring data, thereby greatly improving the efficiency of electromagnetic interference troubleshooting and better ensuring overall electromagnetic compatibility.

[0003] Currently, electromagnetic environment monitoring systems monitor a wide variety of items, covering a wide range of areas and over long periods of time, generating a large amount of overall electromagnetic environment monitoring data. This makes it difficult to intuitively grasp the overall electromagnetic environment status, such as trends in power grid quality and fluctuations in large amounts of spectrum data over time. The challenge of systematically analyzing and processing this electromagnetic environment monitoring data to visually present the overall electromagnetic environment situation, extract overall electromagnetic environment characteristics, and achieve early prediction of electromagnetic interference has become a crucial issue in electromagnetic environment monitoring data analysis. Summary of the Invention

[0004] In view of this, the present invention provides an electromagnetic interference prediction method based on statistical analysis of electromagnetic environment monitoring data, which can predict electromagnetic interference based on statistical analysis of spectrum data.

[0005] In order to solve the above technical problems, the present invention is implemented as follows.

[0006] An electromagnetic interference prediction method based on statistical analysis of electromagnetic environment monitoring data, comprising:

[0007] Step 1: Set up multiple monitoring points in the electromagnetic environment and obtain electromagnetic environment spectrum data at each monitoring point over a period of time; the electromagnetic environment spectrum data, time, and operating condition information constitute a spectrum sample;

[0008] Step 2: Calculate the spectrum stability of each monitoring point based on the spectrum samples; determine the monitoring point X with the lowest spectrum stability, and the corresponding device is x;

[0009] Step 3: Based on the spectrum samples, calculate the statistics reflecting the electromagnetic environment fluctuations by using data at different times and the same frequency points. Generate a characteristic spectrum of fluctuations in which the statistics change with frequency. Based on the correlation between the characteristic spectrum of fluctuations at monitoring point X and other monitoring points and the type of device x, identify the potential interference source device.

[0010] Specifically, step 3 includes the following steps: if device x is a high-power device, it indicates that device x is unstable and may be a potential interference source. If device x is a sensitive electronic device, based on the spectrum samples of monitoring point X, statistics reflecting electromagnetic environment fluctuations are calculated for the same frequency point data at different times to generate a fluctuation characteristic spectrum in which the statistics vary with frequency. It is determined whether the fluctuation characteristic spectrum of other monitoring points is positively correlated with that of monitoring point X. If the positively correlated monitoring point corresponds to a high-power device, the positively correlated monitoring point device is determined to be a potential interference source. If the positively correlated monitoring point corresponds to a sensitive electronic device, or if there is no positively correlated monitoring point, the process proceeds to step 4.

[0011] Step 4: Based on the spectrum samples at monitoring point X, a three-dimensional spectrum graph is constructed to show the spectrum changing over time. The mutation spectrum is searched for on the graph. If the frequency range of the mutation spectrum matches the frequency range of the above-threshold fluctuations in the characteristic fluctuation spectrum of monitoring point X, the mutation spectrum is used as the analysis sample. The operating condition information for a period of time before and after the mutation is traced back to further locate the potential interference source device based on the operating condition information. If there is no mutation or the frequency range does not match, proceed to step 5.

[0012] Step 5: Perform typical characteristic spectrum analysis based on the spectrum samples of monitoring point X.

[0013] Preferably, the mode and standard deviation are selected as statistics reflecting the fluctuation of the electromagnetic environment.

[0014] Preferably, the step 2 is: calculating the mode spectrum stability and standard deviation spectrum stability of each monitoring point based on the spectrum samples;

[0015] The mode spectrum stability is calculated by: based on spectrum samples over a period of time, calculating the amplitude mode for the same frequency point data at different times to generate a mode spectrum; calculating the proportion of frequency points in the mode spectrum whose mode values ​​are within a set mode interval as the mode spectrum stability;

[0016] The standard deviation spectrum stability is calculated as follows: based on spectrum samples over a period of time, the amplitude standard deviation is calculated for data of the same frequency point at different times to generate a standard deviation spectrum; the proportion of frequency points whose standard deviation values ​​in the standard deviation spectrum are within the set standard deviation range is calculated as the standard deviation spectrum stability;

[0017] The mode spectrum stability and standard deviation spectrum stability of each monitoring point are comprehensively compared to determine the monitoring point with the lowest spectrum stability.

[0018] Preferably, in step 3, the method of judging whether the fluctuation characteristic spectra of other monitoring points are positively correlated with the fluctuation characteristic spectrum of monitoring point X is:

[0019] Acquire a frequency band in the fluctuation characteristic spectrum whose amplitude exceeds a fluctuation threshold as a fluctuation frequency band;

[0020] Determine whether the fluctuation characteristic spectrum of other monitoring points also exceeds the fluctuation threshold in the fluctuation frequency band; if so, determine that the fluctuation trend of the corresponding monitoring point is positively correlated with the monitoring point X.

[0021] Preferably, the fluctuation characteristic spectrum includes fluctuation characteristic spectrums of multiple statistical quantities;

[0022] The method of judging whether the fluctuation characteristic spectrum of other monitoring points also exceeds the fluctuation threshold in the fluctuation frequency band is as follows: when the fluctuation characteristic spectrum of any statistical quantity exceeds the fluctuation threshold in any fluctuation frequency band, the corresponding monitoring point is judged to exceed the fluctuation threshold.

[0023] Preferably, in step 4, the method for determining whether the frequency range of the mutation spectrum matches the frequency range of the super-threshold fluctuation in the fluctuation characteristic spectrum of the monitoring point X is:

[0024] It is determined whether the frequency range of the mutation spectrum is within the fluctuation frequency band, or whether the overlap with the fluctuation frequency band exceeds a certain ratio; if so, it is determined that the frequency range matches.

[0025] Preferably, in step 5, the typical characteristic spectrum analysis is:

[0026] Typical characteristic spectra include maximum typical value spectrum and limit margin minimum value spectrum;

[0027] Determine the maximum typical value spectrum as follows: divide the bandwidth according to GJB151 to obtain the important frequency band; for each spectrum sample, determine the maximum amplitude of the spectrum sample in the important frequency band ; Find all spectrum samples The largest sample is used as the maximum typical value spectrum;

[0028] Determine the minimum spectrum of the limit margin as follows: For each spectrum sample, determine the nearest amplitude of the spectrum sample that is closest to the given spectrum limit in the important frequency band ; Find all spectrum samples The smallest sample is used as the limit margin minimum spectrum;

[0029] The maximum typical value spectrum and the limit margin minimum value spectrum are used as analysis samples, the maximum and minimum Typical characteristics

[0030] If the frequency range of the typical features in the analysis sample matches the frequency range of the above-threshold fluctuation, the operating condition information of the period before and after the typical feature appears at time t2 is traced back; the potential interference source equipment is further located based on the operating condition information; if the frequency range does not match, the typical interference source correlation spectrum analysis is performed on the analysis sample to retrieve the potential interference source equipment.

[0031] Preferably, the further locating of potential interference source equipment according to the working condition information is:

[0032] Based on the operating condition information traced over a period of time, determine whether the operating condition has changed; if so, determine that the device that caused the operating condition change is a possible potential interference source device; if the operating condition is stable, perform typical interference source correlation spectrum analysis on the analysis sample to retrieve potential interference source devices.

[0033] Preferably, the typical interference source correlation spectrum analysis is:

[0034] The analysis samples are placed in the typical interference source spectrum feature library for retrieval and matching, and frequency correlation analysis is carried out: peaks exceeding the set amplitude threshold are screened out from the analysis samples and typical interference sources respectively; the frequency points corresponding to the screened peaks in the analysis samples are the frequencies to be matched, and the frequency points corresponding to the screened peaks in the typical interference sources are the interference frequencies; determine whether the frequencies to be matched match the interference frequencies and their correlation; if they match, the device corresponding to the analysis sample is a potential interference source device, and the most relevant typical interference source is the interference type.

[0035] Preferably, the correlation between the frequency point to be matched and the interference frequency point is determined as follows:

[0036] If all the to-be-matched frequency points of the analysis sample have matching interference frequency points within the same typical interference source, then the analysis sample matches the typical interference source and is strongly correlated;

[0037] If some of the to-be-matched frequency points of the analysis sample have matching interference frequency points within the same typical interference source, then the analysis sample matches the typical interference source, but the correlation is weak;

[0038] If strong correlation and weak correlation exist at the same time, the typical interference source of strong correlation is the interference type of the potential interference source device.

[0039] Beneficial effects:

[0040] (1) The present invention collects spectrum samples over a period of time, calculates statistics for amplitude data at the same frequency point at different times, and constructs an array of statistics that changes with frequency, which is called a fluctuation characteristic spectrum. The fluctuation characteristic spectrum reflects the change of spectrum fluctuation over time. Based on the fluctuation characteristic spectrum, the spectrum stability can be determined, and the lowest stability monitoring point is used as the basis for analysis and comparison. At the same time, when collecting spectrum data, the operating condition information is also recorded to facilitate electromagnetic interference analysis and prediction based on the operating condition information. Based on these designs, the present invention realizes electromagnetic interference prediction through improvements in the calculation of fluctuation characteristic spectrum, the calculation of spectrum stability, and the addition of operating condition information, predicts the operating conditions and frequency range where electromagnetic interference may occur, and releases electromagnetic interference risks in advance. It can also quickly find and extract the spectrum characteristics of the interference signal when electromagnetic interference occurs, improve the efficiency of electromagnetic interference investigation, and provide data support for overall electromagnetic compatibility assessment.

[0041] (2) The present invention designs a set of electromagnetic interference prediction processes, which start from the certainty of interference source judgment from strong to weak, and combines spectrum stability calculation, spectrum correlation analysis, three-dimensional spectrum analysis, typical characteristic spectrum analysis, and typical interference source correlation spectrum analysis to achieve electromagnetic interference prediction.

[0042] (3) When determining potential interference sources based on the maximum typical value spectrum, the limit margin minimum value spectrum, or the mutation spectrum, the changes in operating conditions are further considered, and the equipment that causes the change in operating conditions is a possible potential interference source. Although the samples or data analyzed are the maximum typical value spectrum, the limit margin minimum value spectrum, or the mutation spectrum, the reason for these spectrum characteristics is that the operating conditions of certain equipment have changed. Therefore, the present invention adopts a reverse derivation process to extract time through the characteristic spectrum, and obtain the operating condition changes through time, thereby finding potential suspected interference sources.

[0043] (4) The data analysis accuracy of the present invention is high. Experimental verification shows that the data analysis accuracy is higher than 90%.

[0044] (5) The present invention has strong applicability. It can meet the needs of electromagnetic environment monitoring data analysis in different scenarios such as shielded rooms / darkrooms, land-based joint debugging systems, and cabin environments. It can also analyze electromagnetic compatibility test data and is independent of the hardware platform, thus having strong applicability.

[0045] (6) High degree of modularity. This data analysis method divides data statistical analysis into several parts: spectrum stability calculation, spectrum correlation analysis, three-dimensional spectrum analysis, typical characteristic spectrum analysis, and typical interference source correlation spectrum analysis. It can adopt modular design, has strong independence, flexible transplantation, and is easy to expand and maintain the algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1This is a composition diagram of the statistical analysis of electromagnetic environment monitoring data involved in an embodiment of the present invention.

[0047] Figure 2 Flowchart of an electromagnetic interference prediction method based on statistical analysis of electromagnetic environment monitoring data in an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0049] The electromagnetic interference prediction method based on statistical analysis of electromagnetic environment monitoring data provided by the present invention mainly targets spectrum data, such as power grid conducted interference (low frequency, radio frequency spectrum) and electromagnetic field radiation emission spectrum, both of which belong to spectrum data.

[0050] The electromagnetic interference prediction method provided by the embodiment of the present invention mainly includes three aspects: historical retrieval analysis, statistical analysis and spectrum stability calculation. Figure 1 As shown. Historical retrieval analysis refers to retrieving historical electromagnetic environment monitoring data, replaying and displaying the change patterns of data over time, and analyzing to obtain typical characteristics and three-dimensional spectra. Statistical calculation mainly includes mode and standard deviation calculation. Based on the concepts of standard deviation and mode in statistics, mode and standard deviation statistics are performed on electromagnetic environment monitoring data samples to generate electromagnetic environment monitoring mode spectrum and standard deviation spectrum, which can reflect the distribution of spectrum over time and thus reflect the spectrum fluctuation status. Spectrum stability calculation is based on the statistics of mode and standard deviation. It analyzes the fluctuation of the interference emission spectrum within the specified standard deviation range and the mode spectrum interval, and quantitatively reflects the stability of the interference emission spectrum within the time period.

[0051] The method of the present invention can carry out statistical analysis on the electromagnetic environment monitoring data of multiple monitoring points in the same environment. By utilizing the above-mentioned historical retrieval analysis, statistical analysis and spectrum stability calculation, the comprehensive change trend of the electromagnetic environment monitoring data can be intuitively obtained, and the electromagnetic environment fluctuation and spectrum stability state can be grasped in real time. Thus, the working conditions and frequency ranges where electromagnetic interference may occur can be predicted, and the electromagnetic interference risk can be released in advance. When electromagnetic interference occurs, the spectrum characteristics of the interference signal can be quickly found and extracted, thereby improving the efficiency of electromagnetic interference troubleshooting and providing data support for the overall electromagnetic compatibility assessment.

[0052] First, the three main analytical techniques adopted in the present invention are described in detail.

[0053] (1) Historical search analysis

[0054] Multiple monitoring points are set up in the electromagnetic environment to continuously collect or replay electromagnetic environment spectrum data over a period of time, and record the working condition information at each time point; the electromagnetic environment spectrum data and working condition information at a time point constitute a spectrum sample.

[0055] Here, the operating condition information may be any condition of the measured object that affects the electromagnetic data, such as the operating parameters of the measured object, the current position of the movable object, the power on / off condition, or the like.

[0056] Based on the spectrum samples over a period of time, three-dimensional spectrum data of time-frequency-amplitude can be constructed; three-dimensional spectrum data uses the spectrum of each moment obtained by the electromagnetic environment monitoring system to represent the relationship between the electromagnetic interference emission spectrum and time. Continuous signal of spectrum The mathematical expression is:

[0057]

[0058] Where A is the amplitude, is the angular frequency, is the initial phase, and t is the time variable.

[0059] Assume there are K spectrum samples in total, where the frequency axis is the same discrete frequency point, and the spectrum discrete signal of the kth spectrum sample at time t is The expression is:

[0060]

[0061] Where, is the spectrum amplitude, is the spectral phase.

[0062] Based on the three-dimensional spectrum data, three-dimensional spectrum analysis, typical characteristic spectrum analysis, and typical interference source correlation spectrum analysis can be performed. The specific contents are as follows:

[0063] (1) Three-dimensional spectrum analysis

[0064] Three-dimensional spectrum data can intuitively reflect how spectrum data changes over system operation time, as well as the level of interference spectrum amplitude. By adding the time dimension, the 3D spectrum data graph can visually display sudden changes in spectrum data, locate and extract 2D spectrum samples where sudden changes occurred, and extract operating condition information before and after the sudden changes. This allows us to identify suspicious operating condition changes from an operating condition perspective, thereby predicting the conditions under which electromagnetic interference may occur, mitigating electromagnetic interference risks in advance, and quickly finding and extracting interference signal spectral characteristics when electromagnetic interference occurs.

[0065] (2) Typical characteristic spectrum analysis

[0066] Typical characteristic spectrum types mainly include: maximum typical value spectrum and limit margin minimum value spectrum.

[0067] ① Maximum typical value spectrum

[0068] According to the standard bandwidth specified in GJB151, important frequency bands are designated.

[0069] For each spectrum sample, determine the maximum amplitude of the spectrum sample within the important frequency band ; Find all spectrum samples The largest sample is used as the maximum typical value of the spectrum. It is a typical feature.

[0070] Potential interference can be determined based on the time of appearance of typical features and the changes in operating conditions over a period of time. This allows the system to quickly find and extract the spectrum characteristics of the interference signal when electromagnetic interference occurs. It also allows the system to predict the operating conditions under which electromagnetic interference may occur, thereby mitigating electromagnetic interference risks in advance.

[0071] The formula is: For each spectrum sample , assuming that the important frequency band is bandwidth B, calculate its maximum amplitude within bandwidth B:

[0072]

[0073] Find all spectrum samples The largest spectrum sample, as the maximum typical value spectrum

[0074]

[0075] ② Spectrum of the minimum limit margin

[0076] According to the standard bandwidth specified in GJB151, important frequency bands are designated.

[0077] For each spectral sample, determine the nearest amplitude of the spectral sample within the frequency band of interest that is closest to a given spectral limit. ; Find all spectrum samples The smallest sample is used as the limit margin minimum spectrum. It is a typical feature.

[0078] Potential interference can be determined based on the time of appearance of typical features and the changes in operating conditions over a period of time. This allows the system to quickly find and extract the spectrum characteristics of the interference signal when electromagnetic interference occurs. It also allows the system to predict the operating conditions under which electromagnetic interference may occur, thereby mitigating electromagnetic interference risks in advance.

[0079] The formula is: For each spectrum sample , assuming that the important frequency band is bandwidth B, calculate the typical amplitude with the minimum standard limit margin within bandwidth B:

[0080]

[0081] Where, is the spectrum limit function;

[0082] Find all spectrum samples The smallest spectrum sample, as the limit margin minimum spectrum

[0083]

[0084] (3) Correlation spectrum analysis of typical interference sources

[0085] The electromagnetic environment monitoring spectrum data is placed in the typical interference source spectrum feature library (equipment electromagnetic compatibility detection spectrum data, including typical harmonic components, characteristic frequency points and phases, etc.) for retrieval and matching, and correlation analysis is carried out to extract the typical interference source spectrum that is most relevant to the electromagnetic environment monitoring spectrum data and compare it with the system operating conditions. This can quickly locate the interference source or narrow the interference source location range when electromagnetic interference occurs, and can also predict electromagnetic interference risks in advance.

[0086] (2) Statistical analysis

[0087] The statistics used in this embodiment are mode and standard deviation.

[0088] Mode and standard deviation statistics are performed on spectrum samples (random in time) within any time period, generating a mode spectrum and a standard deviation spectrum. This visually displays the degree of dispersion of the spectrum data, reflecting its distribution over time and the fluctuations of the spectrum data within that time period. Comparing the mode and standard deviation spectra at different monitoring points reveals the correlation between spectrum fluctuations at different monitoring points, i.e., their mutual influence. If a device at a monitoring point is sensitive electronic equipment, and a device with very similar spectrum fluctuations is a high-power device, it can be predicted that the high-power device is an interference source. If the device with very similar spectrum fluctuations is also sensitive electronic equipment, both devices may be interfered with by the other.

[0089] (1) The standard deviation spectrum is generated by calculating the amplitude standard deviation of the same frequency point data at different times based on the spectrum samples within a period of time to generate the standard deviation spectrum. Specifically, suppose there are K spectrum samples in total, each spectrum sample is a complex spectrum, and the kth spectrum data is expressed as (k=1,2,3…K), frequency axis . N represents N frequency points.

[0090] Standard Deviation Spectrum , where each point represents all frequency samples at frequency The standard deviation of the amplitude at :

[0091]

[0092] Where, is the frequency The mean of all sample amplitudes at different times:

[0093]

[0094] (2) The mode spectrum is generated by calculating the amplitude mode of the same frequency point data at different times based on the spectrum samples within a period of time to generate the mode spectrum. Specifically, the mode spectrum is recorded as , where each point represents all spectrum samples at frequency The mode of the amplitude at :

[0095]

[0096] Where Mode means taking the mode.

[0097] (3) Spectrum stability calculation

[0098] Based on the aforementioned mode and standard deviation statistics, the proportion of the spectrum within the specified standard deviation range and the mode spectrum interval (the probability of the spectrum within the range occurring) is calculated, so that the standard deviation spectrum stability and mode spectrum stability of each monitoring point can be obtained.

[0099] The standard deviation range and mode spectrum interval vary depending on the environment type. For example, the standard deviation range in a workshop is less than 6 and the mode spectrum interval is less than 10. Other environments may choose a standard deviation range of less than 3 and a mode spectrum interval of less than 8.

[0100] Based on the above main analysis technologies, the process of the electromagnetic interference prediction method based on statistical analysis of electromagnetic environment monitoring data provided in this embodiment is as follows: Figure 2 As shown, the following steps are included:

[0101] Step 1: Set up multiple monitoring points in the electromagnetic environment, obtain the electromagnetic environment spectrum data of each monitoring point over a period of time, and record the operating condition information at each time point; the electromagnetic environment spectrum data and operating condition information at a time point constitute a spectrum sample.

[0102] Assume that four typical electromagnetic environment monitoring points (A, B, C, and D) are set up and installed at a large-scale system test site. The equipment at these typical electromagnetic environment monitoring points can be categorized as high-power power generation and transformation equipment (e.g., inverters, generators, electric pumps, and refrigerators) and sensitive electronic equipment (e.g., monitoring equipment, display screens, and communications equipment).

[0103] Before the test, the electromagnetic environment monitoring system was connected to four monitoring points, A, B, C, and D. At the start of the test, the system was activated to collect real-time electromagnetic environment data from each monitoring point. For conducted interference (low-frequency and radio-frequency), grid voltage was collected; for radiated electromagnetic emissions, ambient electromagnetic field signals were collected. During this real-time collection process, operating status information for relevant equipment in the system was recorded at all times, including startup, shutdown, relocation, and changes in operating parameters. After the system had been running for a period of time, the electromagnetic environment monitoring data from each monitoring point was collated and analyzed, and spectrum data was calculated. The spectrum data, corresponding time, and operating condition information constituted a spectrum sample. Either the conducted grid spectrum or the radiated electromagnetic emission spectrum was selected for analysis.

[0104] Step 2: Calculate the spectrum stability of each monitoring point based on the spectrum samples, and determine the monitoring point with the lowest spectrum stability and its corresponding equipment.

[0105] In this step, the spectrum stability is calculated based on statistics reflecting the fluctuation of the electromagnetic environment. In this embodiment, the mode and standard deviation are used as statistics reflecting the fluctuation of the electromagnetic environment.

[0106] Spectrum stability calculations are performed on the spectrum data at four monitoring points, A, B, C, and D. Spectrum stability includes mode spectrum stability and standard deviation spectrum stability.

[0107] The mode spectrum stability is obtained by calculating the amplitude mode of the same frequency point data at different times based on spectrum samples over a period of time to generate a mode spectrum. The proportion of frequency points whose mode values ​​in the mode spectrum are within the set mode range is calculated as the mode spectrum stability.

[0108] The standard deviation spectrum stability is obtained by calculating the amplitude standard deviation of the same frequency point data at different times based on spectrum samples over a period of time to generate a standard deviation spectrum. The standard deviation spectrum stability is then calculated as the proportion of frequency points whose standard deviation values ​​fall within the set standard deviation range.

[0109] A comprehensive comparison of the mode spectrum stability and standard deviation spectrum stability of each monitoring point is performed to determine the monitoring point with the lowest spectrum stability, which is assumed to be monitoring point D. The mode spectrum stability and standard deviation spectrum stability often show consistency; monitoring points with low mode spectrum stability also have low standard deviation spectrum stability.

[0110] Step 3: If the equipment at monitoring point D is a high-power power generation and transformation equipment, it may indicate that the operating status of the equipment at monitoring point D is discontinuous stable operation or is in a continuous switching state, indicating that the equipment is unstable and may be a potential interference source, or the risk of being an interference source is relatively high.

[0111] If the device at monitoring point D is sensitive electronic equipment and cannot be an interference source, then the correlation between the statistical spectrum of the other monitoring points is determined to find a monitoring point with the same fluctuation as monitoring point D. If the device at this monitoring point is high-power power generation and transformation equipment, it indicates that the operating state is non-continuous stable operation or is in a state of continuous switching, which may be a potential interference source or pose a high risk of interference source. Therefore, it is predicted as a possible potential interference source device. If the monitoring point devices with the same fluctuation are also sensitive electronic equipment, or if no monitoring point with the same fluctuation is found, then the uncorrelation analysis in step 4 is performed.

[0112] This step is specifically implemented as follows: obtaining the standard deviation spectrum and the mode spectrum. Here, you can select either the standard deviation spectrum or the mode spectrum, or both.

[0113] Taking the standard deviation spectrum as an example, determine whether the standard deviation spectra of other monitoring points are positively correlated with that of monitoring point D. This determination is based on the frequency band in which the standard deviation spectrum exceeds the set fluctuation threshold th1 as the fluctuation frequency band. A fluctuation frequency band may consist of multiple segments. Determine whether the standard deviation spectra of other monitoring points also exceed the fluctuation threshold th1 within the fluctuation frequency band. If so, the standard deviation spectrum of the corresponding monitoring point is determined to be positively correlated with monitoring point D, meaning that the spectrum fluctuation trends of the two devices are the same.

[0114] In practice, if the fluctuation characteristic spectra of multiple statistical quantities are selected, for example, the standard deviation spectrum and the mode spectrum are selected to jointly perform positive correlation judgment, then if the spectrum amplitude in one of the fluctuation frequency bands of any spectrum in the standard deviation spectrum and the mode spectrum exceeds the fluctuation threshold th1, it is judged to exceed the fluctuation threshold.

[0115] Assume that the standard deviation spectrum of A among the three monitoring points A, B, and C is positively correlated with the standard deviation spectrum of D:

[0116] If the positively correlated monitoring point A device is a high-power power generation and transformation device, then the monitoring point A device can be determined as the potential interference source device that causes the lowest spectrum stability of the monitoring point D device within the standard deviation fluctuation frequency range;

[0117] If the positively correlated monitoring point A device is a sensitive electronic device, or no positively correlated monitoring point is found, all other monitoring points are considered to be interfered monitoring points, and the process goes to step 4 to perform irrelevant analysis.

[0118] Step 4: Uncorrelation analysis.

[0119] If it is determined that the standard deviation spectra of monitoring points A, B, and C are not correlated with the standard deviation spectrum of monitoring point D, a three-dimensional spectrum analysis is performed on monitoring point D. At this point, a three-dimensional spectrum graph of monitoring point D is generated. The mutation spectrum is searched on the three-dimensional spectrum graph; and the mutation frequency range is matched with the fluctuation frequency band of the standard deviation spectrum of monitoring point D. If the "frequency range match is consistent", the mutation spectrum can be extracted from the three-dimensional spectrum graph as an analysis sample. Based on the analysis sample, the time when the mutation spectrum occurred is queried, and the operating condition information for a period of time before and after the mutation moment is traced back to further locate the potential interfering equipment based on the operating condition information. If there is no mutation in the three-dimensional spectrum graph or the frequency range match is inconsistent, proceed to step 5.

[0120] The specific determination method of "frequency range matching consistency" may be: determining whether the frequency range of the mutation spectrum is within the fluctuation frequency band, or if the overlap with the fluctuation frequency band exceeds a set value, then determining that the frequency range matches consistently.

[0121] The specific implementation of "further locating potential interfering devices based on operating condition information" can be achieved by determining whether operating conditions have changed based on operating condition information traced back over a period of time. If operating conditions change frequently or significantly, the monitoring point device corresponding to the analysis sample (in this step, the mutation spectrum sample) is highly likely to have experienced electromagnetic interference due to the change in operating conditions. This indicates that the device causing the operating condition change is inevitably linked to the mutation spectrum, making it a potential interference source. If operating conditions are stable with minimal changes, it is still difficult to identify the potential interference source device. Further analysis of typical interference source correlation spectrums can be performed: the mutation spectrum samples are used as analysis samples and then searched and matched against a typical interference source spectrum signature database to identify potential interference source devices. See step 5 for implementation of "typical interference source correlation spectrum analysis."

[0122] Step 5: If it is determined in step 4 that there is no mutation spectrum in the three-dimensional spectrum graph, or the mutation frequency range does not match the standard deviation fluctuation frequency range, then enter this step. This step performs typical characteristic spectrum analysis on the spectrum samples of monitoring point D.

[0123] The specific process of typical characteristic spectrum analysis in this step is:

[0124] Determine the maximum typical value spectrum: specify the important frequency band according to the standard bandwidth specified in GJB151; for each spectrum sample of the D monitoring point, determine the maximum amplitude of the spectrum sample in the important frequency band ; Find all spectrum samples The largest sample is used as the maximum typical value of the spectrum.

[0125] Determine the minimum spectrum of the limit margin as follows: For each spectrum sample of the D monitoring point, determine the nearest amplitude of the spectrum sample that is closest to the given spectrum limit in the important frequency band ; Find all spectrum samples The smallest sample is used as the limit margin minimum spectrum.

[0126] If the typical characteristics [maximum typical value (maximum ) or the minimum limit margin (the smallest If the frequency range of the typical feature matches the aforementioned fluctuation frequency band, the operating condition information for the period before and after t2 can be traced based on the time t2 at which the typical feature appears. This operating condition information can then be used to further locate the potential interference source device. The specific method for "further locating the potential interference source device based on operating condition information" is the same as in step 4.

[0127] If the operating condition changes greatly, it means that the equipment that causes the operating condition change is inevitably related to the maximum typical value spectrum and the limit margin minimum value spectrum, and it is determined that the equipment that causes the operating condition change is a possible potential interference source device; if the operating condition is stable, it is necessary to further carry out typical interference source correlation spectrum analysis: the maximum typical value spectrum and the limit margin minimum value spectrum are used as analysis samples, and put into the typical interference source spectrum feature library for matching to retrieve potential interference source devices.

[0128] If the frequency range where the typical characteristics appear does not match the frequency range of the fluctuation frequency band, a typical interference source correlation spectrum analysis is directly performed on the maximum typical value spectrum and the limit margin minimum value spectrum to retrieve potential interference source devices.

[0129] The present invention involves correlation spectrum analysis of typical interference sources many times, and the specific implementation can be:

[0130] The analysis samples are placed in the typical interference source spectrum feature library for retrieval and matching, and frequency point correlation analysis is carried out to find the most relevant typical interference source, so as to locate the interference source or narrow the interference source location range and predict the electromagnetic interference risk in advance.

[0131] One implementation of frequency correlation analysis involves filtering out peaks exceeding a set amplitude threshold from both the analysis sample and typical interference sources. The frequencies corresponding to the filtered peaks in the analysis sample are called the target frequency points, while the frequencies corresponding to the filtered peaks in the typical interference sources are called the interference frequencies. A match and correlation between the target frequency points and the interference frequencies is determined. If they match, the device corresponding to the analysis sample is designated as a potential interference source, and the most relevant typical interference source is designated as the interference type.

[0132] In a preferred solution, the method for determining the correlation between the frequency point to be matched and the interference frequency point is:

[0133] If all the to-be-matched frequency points of the analysis sample have matching interference frequency points within the same typical interference source, then the analysis sample matches the typical interference source and is strongly correlated, and the credibility is high;

[0134] If some of the to-be-matched frequency points of the analysis sample have matching interference frequency points within the same typical interference source, then the analysis sample matches the typical interference source, but the correlation is weak and the credibility is low;

[0135] If strong correlation and weak correlation exist at the same time, the typical interference source of strong correlation is the interference type of the potential interference source device.

[0136] If either a strong correlation or a weak correlation exists, the matched typical interference source is the interference type of the potential interference source device.

[0137] When locating potential interference source devices using the above solution, the time corresponding to the relevant samples can also be extracted as the predicted interference occurrence time.

[0138] At this point, the process of this embodiment ends.

[0139] In summary, this embodiment performs mode and standard deviation statistics on spectrum samples over a period of time, calculates the proportion of the spectrum within the specified standard deviation range and the mode spectrum deviation interval, thereby obtaining the standard deviation spectrum stability and mode spectrum stability of each monitoring point, and can intuitively present the stability difference. Based on the stability difference, the standard deviation spectrum is retrieved, the standard deviation spectrum amplitude disturbance is identified, the standard deviation spectrum correlation of each monitoring point in the system is compared and analyzed, and the interference frequency range is predicted and located. This allows for rapid search and extraction of the spectrum characteristics of the interference signal when electromagnetic interference occurs, as well as for predicting the working conditions under which electromagnetic interference may occur, thereby eliminating the electromagnetic interference risk in advance.

[0140] By applying the present invention, it is possible to analyze and process electromagnetic environment monitoring data in different scenarios, intuitively obtain the comprehensive change trend of electromagnetic environment monitoring data, grasp the electromagnetic environment fluctuation and spectrum stability in real time, improve the efficiency of electromagnetic interference investigation, and provide data support for overall electromagnetic compatibility assessment.

[0141] Those skilled in the art can use other similar methods to implement specific electromagnetic environment monitoring data analysis based on the content disclosed in the present invention, and are not limited to Figure 1 and 2 The method shown.

[0142] The above specific embodiments merely illustrate the design principles of the present invention. The shapes and names of the components described herein may vary and are not limiting. Therefore, those skilled in the art may modify or substitute equivalents for the technical solutions described in the above embodiments. Such modifications and substitutions, without departing from the inventive spirit and technical solutions of the present invention, shall fall within the scope of protection of the present invention.

Claims

1. A method for predicting electromagnetic interference based on statistical analysis of electromagnetic environment monitoring data, characterized in that: include: Step 1: Set up multiple monitoring points in the electromagnetic environment and obtain electromagnetic environment spectrum data at each monitoring point over a period of time; the electromagnetic environment spectrum data, time, and operating condition information constitute a spectrum sample; Step 2: Calculate the spectrum stability of each monitoring point based on the spectrum samples; determine the monitoring point X with the lowest spectrum stability, and the corresponding device is x; Step 3: Based on the spectrum samples, calculate the statistics reflecting the electromagnetic environment fluctuations by using data at different times and the same frequency points. Generate a characteristic spectrum of fluctuations in which the statistics change with frequency. Based on the correlation between the characteristic spectrum of fluctuations at monitoring point X and other monitoring points and the type of device x, identify the potential interference source device.

2. The electromagnetic interference prediction method based on statistical analysis of electromagnetic environment monitoring data according to claim 1, characterized in that: The step 3 is specifically as follows: If device x is a high-power device, it indicates that device x is unstable and may be a potential interference source. If device x is a sensitive electronic device, based on the spectrum samples of monitoring point X, statistics reflecting the electromagnetic environment fluctuations are calculated by using data at different times and the same frequency point to generate a fluctuation characteristic spectrum in which the statistics change with frequency. It is determined whether the fluctuation characteristic spectrum of other monitoring points is positively correlated with that of monitoring point X. If the monitoring point with a positive correlation corresponds to a high-power device, the device at the positively correlated monitoring point is determined to be a potential interference source. If the monitoring point with a positive correlation corresponds to a sensitive electronic device, or there is no positive correlation monitoring point, the process proceeds to step 4. Step 4: Based on the spectrum samples at monitoring point X, a three-dimensional spectrum graph is constructed to show the spectrum changing over time. The mutation spectrum is searched for on the graph. If the frequency range of the mutation spectrum matches the frequency range of the above-threshold fluctuations in the characteristic fluctuation spectrum of monitoring point X, the mutation spectrum is used as the analysis sample. The operating condition information for a period of time before and after the mutation is traced back to further locate the potential interference source device based on the operating condition information. If there is no mutation or the frequency range does not match, proceed to step 5. Step 5: Perform typical characteristic spectrum analysis based on the spectrum samples of monitoring point X.

3. The electromagnetic interference prediction method based on statistical analysis of electromagnetic environment monitoring data according to claim 1, characterized in that: The mode and standard deviation are selected as the statistics reflecting the fluctuation of the electromagnetic environment.

4. The electromagnetic interference prediction method based on statistical analysis of electromagnetic environment monitoring data according to claim 3, characterized in that: The step 2 is: Calculate the mode spectrum stability and standard deviation spectrum stability of each monitoring point based on spectrum samples; The mode spectrum stability is calculated by: based on spectrum samples over a period of time, calculating the amplitude mode for the same frequency point data at different times to generate a mode spectrum; calculating the proportion of frequency points in the mode spectrum whose mode values ​​are within a set mode interval as the mode spectrum stability; The standard deviation spectrum stability is calculated as follows: based on spectrum samples over a period of time, the amplitude standard deviation is calculated for data of the same frequency point at different times to generate a standard deviation spectrum; the proportion of frequency points whose standard deviation values ​​in the standard deviation spectrum are within the set standard deviation range is calculated as the standard deviation spectrum stability; The mode spectrum stability and standard deviation spectrum stability of each monitoring point are comprehensively compared to determine the monitoring point with the lowest spectrum stability.

5. The electromagnetic interference prediction method based on statistical analysis of electromagnetic environment monitoring data according to claim 2, characterized in that: In step 3, the method for determining whether the fluctuation characteristic spectra of other monitoring points are positively correlated with the monitoring point X is: Acquire a frequency band in the fluctuation characteristic spectrum whose amplitude exceeds a fluctuation threshold as a fluctuation frequency band; Determine whether the fluctuation characteristic spectrum of other monitoring points also exceeds the fluctuation threshold in the fluctuation frequency band; if so, determine that the fluctuation trend of the corresponding monitoring point is positively correlated with the monitoring point X.

6. The electromagnetic interference prediction method based on statistical analysis of electromagnetic environment monitoring data according to claim 5, characterized in that: The fluctuation characteristic spectrum includes fluctuation characteristic spectrums of multiple statistical quantities; The method of judging whether the fluctuation characteristic spectrum of other monitoring points also exceeds the fluctuation threshold in the fluctuation frequency band is as follows: when the fluctuation characteristic spectrum of any statistical quantity exceeds the fluctuation threshold in any fluctuation frequency band, the corresponding monitoring point is judged to exceed the fluctuation threshold.

7. The electromagnetic interference prediction method based on statistical analysis of electromagnetic environment monitoring data according to claim 5, characterized in that: In step 4, the method for determining whether the frequency range of the mutation spectrum matches the frequency range of the super-threshold fluctuation in the fluctuation characteristic spectrum of the monitoring point X is: It is determined whether the frequency range of the mutation spectrum is within the fluctuation frequency band, or whether the overlap with the fluctuation frequency band exceeds a set ratio; if so, it is determined that the frequency range matches.

8. The electromagnetic interference prediction method based on statistical analysis of electromagnetic environment monitoring data according to claim 2, characterized in that: In step 5, the typical characteristic spectrum analysis is: Typical characteristic spectra include maximum typical value spectrum and limit margin minimum value spectrum; Determine the maximum typical value spectrum as follows: divide the bandwidth according to GJB151 to obtain the important frequency band; for each spectrum sample, determine the maximum amplitude of the spectrum sample in the important frequency band ; Find all spectrum samples The largest sample is used as the maximum typical value spectrum; Determine the minimum spectrum of the limit margin as follows: For each spectrum sample, determine the nearest amplitude of the spectrum sample that is closest to the given spectrum limit in the important frequency band ; Find all spectrum samples The smallest sample is used as the limit margin minimum spectrum; The maximum typical value spectrum and the limit margin minimum value spectrum are used as analysis samples, the maximum and minimum It is a typical feature; If the frequency range of the typical features in the analysis sample matches the frequency range of the above-threshold fluctuation, the operating condition information of the period before and after the typical feature appears at time t2 is traced back; the potential interference source equipment is further located based on the operating condition information; if the frequency range does not match, the typical interference source correlation spectrum analysis is performed on the analysis sample to retrieve the potential interference source equipment.

9. The electromagnetic interference prediction method based on statistical analysis of electromagnetic environment monitoring data according to claim 2 or 8, characterized in that: The device that further locates the potential interference source according to the working condition information is: Determine whether the operating condition has changed based on the operating condition information traced over a period of time; if so, determine that the device causing the operating condition change is a possible potential interference source device; if the operating condition is stable, perform typical interference source correlation spectrum analysis to retrieve potential interference source devices.

10. The electromagnetic interference prediction method based on statistical analysis of electromagnetic environment monitoring data according to claim 8, characterized in that: The typical interference source correlation spectrum analysis is as follows: The analysis samples are placed in the typical interference source spectrum feature library for retrieval and matching, and frequency correlation analysis is performed: peaks exceeding the set amplitude threshold are screened out from the analysis samples and typical interference sources respectively; the frequency points corresponding to the screened peaks in the analysis samples are the to-be-matched frequency points, and the frequency points corresponding to the screened peaks in the typical interference sources are the interference frequency points; determine whether the to-be-matched frequency points match the interference frequency points and their correlation; If there is a match, the device corresponding to the analyzed sample is a potential interference source device, and the most relevant typical interference source is the interference type.

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