Electromagnetic interference influence analysis method and device, and electronic equipment

Abstract

The application provides an electromagnetic interference influence analysis method and device and electronic equipment, and belongs to the technical field of electromagnetic signal processing. The method comprises the following steps: acquiring frequency spectrum monitoring data of a radio and frequency use information of a frequency use station; performing link construction on the frequency use information to obtain potential interference link information; performing bottom noise evaluation processing on the frequency spectrum monitoring data to obtain electromagnetic environment bottom noise information; screening the potential interference link information and the electromagnetic environment bottom noise information based on disturbed device parameters to determine a target interference link set and a target bottom noise value, wherein the disturbed device parameters are radio receiving devices to be subjected to interference influence analysis; performing signal-to-interference ratio calculation based on the target interference link set and the target bottom noise value to obtain a signal-to-interference ratio; and comparing the signal-to-interference ratio with a preset signal-to-interference ratio limit value to obtain an electromagnetic interference result. The above scheme can effectively improve the calculation accuracy of the electromagnetic interference evaluation result.

Description

Technical Field

[0001] This invention relates to the field of electromagnetic signal processing technology, specifically to methods, apparatus, and electronic equipment for analyzing the effects of electromagnetic interference. Background Technology

[0002] Existing electromagnetic interference (EMI) analysis methods primarily construct interference links based on frequency-using station information and assess noise floor using a fixed threshold, then calculate the signal-to-dryness ratio (SDR) to predict interference results. In practical applications, this method, relying on a fixed threshold to assess noise floor, cannot adapt to complex electromagnetic environment variations, leading to large noise floor estimation errors. Furthermore, it fails to specifically screen interference links and noise floor based on the parameters of the affected equipment, resulting in low interference calculation accuracy and large prediction bias.

[0003] Therefore, there is an urgent need for an electromagnetic interference impact analysis method that can dynamically assess the noise floor and improve the accuracy of electromagnetic interference calculations. Summary of the Invention

[0004] To address the aforementioned problems, this application provides an electromagnetic interference (EMI) impact analysis method to improve the calculation accuracy of EMI results. The technical solution is as follows: In a first aspect, the present invention provides a method for analyzing the effects of electromagnetic interference, including: Acquire radio spectrum monitoring data and frequency usage information from frequency-using stations; Link construction is performed on frequency usage information to obtain potential interference link information; noise floor assessment processing is performed on spectrum monitoring data to obtain electromagnetic environment noise floor information. The noise floor assessment processing is based on the feedback difference between the power spectrum of spectrum monitoring data and historical noise floor assessment results, which dynamically adjusts the threshold, and the feedback difference and the threshold are nonlinearly compared and summed to obtain electromagnetic environment noise floor information. Based on the parameters of the disturbed device, potential interference link information and electromagnetic environment noise floor information are screened to determine the target interference link set and target noise floor value. The parameters of the disturbed device are the radio receiving devices to be analyzed for interference impact. The signal-to-dryness ratio is calculated based on the target interference link set and the target noise floor value. The electromagnetic interference results are obtained by comparing the signal-to-dryness ratio with the preset signal-to-dryness ratio limit.

[0005] Combining the first aspect and the above implementation methods, in some possible implementation methods, the spectrum monitoring data is subjected to noise floor assessment processing to obtain electromagnetic environment noise floor information, including: The first number of consecutive times the feedback difference is less than zero is obtained. If the first number reaches the first preset number, the historical dynamic threshold is multiplied by the first number of times less than one raised to the power of the first parameter to determine the first current threshold. The second number of consecutive positive feedback differences is obtained. If the second number reaches the second preset number, the historical dynamic threshold is multiplied by the second number of times greater than one to determine the second current threshold. The feedback difference is nonlinearly compared and summed based on the first or second current threshold to obtain the electromagnetic environment noise floor information.

[0006] Combining the first aspect and the above implementation methods, in some possible implementation methods, the feedback difference is nonlinearly compared and summed based on the current threshold to obtain the electromagnetic environment noise floor information, including: If the absolute value of the feedback difference is less than the first current threshold or the second current threshold, then the feedback difference is determined as the first target adjustment value. If the absolute value of the feedback difference is greater than or equal to the first current threshold or the second current threshold, then the product of the current threshold and the sign function of the feedback difference is determined as the second target adjustment value. The historical noise floor assessment results are summed with either the first or second target adjustment value to obtain the electromagnetic environment noise floor information.

[0007] Combining the first aspect and the above implementation methods, in some possible implementation methods, link construction is performed on the frequency usage information to obtain potential interference link information, including: Extract station transmission power from frequency usage information; If the transmission power of a station exceeds a preset power threshold, the corresponding station is identified as a potential source of interference. Data structures are constructed based on the station parameters and antenna parameters of potential interference sources to obtain information on potential interference links.

[0008] Combining the first aspect and the above implementation methods, in some possible implementation methods, potential interference link information and electromagnetic environment noise floor information are filtered based on the parameters of the disturbed device to determine the target interference link set and target noise floor value, including: Using the location of the disturbed device as the center, spatial filtering of potential interference link information is performed based on the filtering radius to obtain a set of spatial interference links. Based on the frequency range of the disturbed equipment, the set of spatial interference links is filtered by frequency to determine the set of target interference links; The electromagnetic environment noise floor information is extracted based on the screening radius and the frequency range of the disturbed equipment to determine the target noise floor value.

[0009] Combining the first aspect and the above implementation methods, in some possible implementation methods, the electromagnetic environment noise floor information is extracted based on the screening radius and the frequency range of the disturbed device to determine the target noise floor value, including: Use the spectrum monitoring data within the selected radius as the target noise floor set; The target noise floor set is truncated based on the frequency range of the disturbed device to obtain the frequency-truncated noise floor set; The target noise floor value is determined by sampling the frequency truncation noise set.

[0010] Combining the first aspect and the above implementation methods, in some possible implementation methods, the signal-to-dryness ratio (SDR) is calculated based on the target interference link set and the target noise floor value, including: Obtain the jamming device links and then combine the jamming device links with the target jamming link set to obtain the lumped jamming link set; The path loss of the lumped interference link set is calculated to obtain the lumped interference power. Based on the frequency parameters of the disturbed equipment, the power is calculated to determine the receiver signal power. The signal-to-dryness ratio is obtained by calculating the ratio and logarithm of the receiver signal power, lumped interference power, and target noise floor.

[0011] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, acquiring radio spectrum monitoring data and frequency usage information of frequency-using stations includes: Acquire initial radio spectrum monitoring data and initial frequency usage information from frequency-using stations; The initial radio spectrum monitoring data and initial frequency usage information of the frequency-using stations are processed for format adaptation and cleaning to obtain the radio spectrum monitoring data and frequency usage information of the frequency-using stations.

[0012] Secondly, the present invention also provides an electromagnetic interference influence analysis device, comprising: The data acquisition unit is used to acquire radio spectrum monitoring data and frequency usage information from frequency-using stations; The data processing unit is used to construct links from frequency usage information to obtain potential interference link information; and to perform noise floor assessment processing on spectrum monitoring data to obtain electromagnetic environment noise floor information. The noise floor assessment processing is to dynamically adjust the threshold based on the feedback difference between the power spectrum of the spectrum monitoring data and the historical noise floor assessment results, and to perform nonlinear comparison and summation processing on the feedback difference and the threshold to obtain electromagnetic environment noise floor information. The data filtering unit is used to filter potential interference link information and electromagnetic environment noise floor information based on the parameters of the disturbed device, and to determine the target interference link set and target noise floor value. The parameters of the disturbed device are the radio receiving devices to be analyzed for interference impact. The data calculation unit is used to calculate the signal-to-dryness ratio based on the target interference link set and the target noise floor value, and obtain the signal-to-dryness ratio. The data comparison unit is used to compare the signal-to-dryness ratio with a preset signal-to-dryness ratio limit to obtain the electromagnetic interference results.

[0013] Thirdly, the present invention also provides an electronic device, including a radio receiver, a data acquisition unit, a memory, and a processor, wherein... Wireless receivers and data acquisition devices are used to acquire radio spectrum monitoring data and frequency usage information from frequency-using stations; Memory, used to store executable program code; The processor is used to construct links from frequency usage information to obtain potential interference link information; to perform noise floor assessment processing on spectrum monitoring data to obtain electromagnetic environment noise floor information; to filter potential interference link information and electromagnetic environment noise floor information based on the parameters of the disturbed equipment to determine the target interference link set and target noise floor value; to calculate the signal-to-dryness ratio based on the target interference link set and target noise floor value; and to compare the signal-to-dryness ratio with a preset signal-to-dryness ratio limit to obtain the electromagnetic interference result.

[0014] Fourthly, the present invention also provides a computer-readable storage medium for storing a computer-readable program or instruction, which, when executed by a processor, can implement the steps in the electromagnetic interference influence analysis method in any of the above implementations.

[0015] The beneficial effects of this invention are as follows: It acquires radio spectrum monitoring data and frequency usage information from frequency-using stations; it completes link construction based on the frequency usage information to obtain potential interference link information; it performs noise floor assessment processing on the spectrum monitoring data to obtain electromagnetic environment noise floor information: this noise floor assessment processing method dynamically adjusts the threshold based on the feedback difference between the power spectrum of the spectrum monitoring data and historical noise floor assessment results, and performs nonlinear comparison and summation processing on the feedback difference and the threshold, thereby obtaining electromagnetic environment noise floor information. This can improve the problem of large noise floor estimation errors caused by using fixed thresholds for noise floor assessment in existing technologies; it combines the parameters of the disturbed equipment to screen potential interference link information and electromagnetic environment noise floor information, determining the target interference link set and target noise floor value, solving the technical defect of not relying on the parameters of the disturbed equipment for screening; it calculates the signal-to-interference-plus-noise ratio (SIR) based on the target interference link set and the target noise floor value; it compares the SIR with a preset SIR threshold and outputs the electromagnetic interference assessment result, thereby effectively improving the calculation accuracy of the electromagnetic interference assessment result. Attached Figure Description To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating the electromagnetic interference impact analysis method provided by this invention; Figure 2 For the present invention Figure 1 A schematic diagram of an embodiment of S102; Figure 3 For the present invention Figure 1 A schematic diagram of an embodiment of S102; Figure 4 For the present invention Figure 1 A schematic diagram of an embodiment of S103; Figure 5 For the present invention Figure 1 A schematic diagram of an embodiment of S104; Figure 6 A schematic diagram of the electromagnetic interference influence analysis device provided by the present invention; Figure 7 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0018] In the description of the embodiments of the present invention, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0019] The terms "first," "second," etc., used in the embodiments of this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a technical feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature.

[0020] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0021] For example, in emergency command and communication support scenarios in complex urban areas, the surrounding electromagnetic environment changes drastically with traffic peaks. Existing methods use a fixed threshold to assess the noise floor. When the noise floor rises significantly during the morning rush hour, the fixed threshold cannot adapt to environmental changes, resulting in an estimated noise floor value that is far lower than the actual noise floor, leading to a large noise floor estimation error. Furthermore, existing methods calculate all detected potential interference links in the surrounding area without considering the actual operating frequency band and sensitivity (parameters of the interfered equipment) of the emergency communication vehicle receiver for targeted screening. This results in an artificially high calculated signal-to-dryness ratio (SDR), with predictions showing no interference risk. However, in practical applications, emergency communication vehicles experience extremely low SDRs due to severely underestimated noise floor and the failure to eliminate strong out-of-band interference, leading to communication disruptions and severely impacting emergency command and dispatch.

[0022] To address the aforementioned technical problems, this application provides an electromagnetic interference (EMI) impact analysis method. The method is executed by an EMI impact analysis device or an electronic device equipped with such a device. The following provides a detailed description; however, it should be noted that the order of description in the following embodiments is not intended to limit the preferred order of the embodiments.

[0023] Please see Figure 1 , Figure 1 This is a flowchart illustrating the electromagnetic interference impact analysis method provided in an embodiment of this application. Figure 1 As shown, the method in this application embodiment may include the following steps S101-S105: S101 acquires radio spectrum monitoring data and frequency usage information from frequency-using stations.

[0024] In this embodiment, radio spectrum monitoring data refers to structured binary data of the electromagnetic environment spectrum in a specific time and region, collected by a multi-source acquisition architecture consisting of fixed monitoring stations, mobile monitoring vehicles, UAV-borne monitoring equipment, and a spectrum sensor network. A frequency-using station refers to a station that uses radio frequencies for transmission or reception. Frequency-using information refers to the basic information of the frequency-using station, the information of the frequency-using equipment deployed in the station, and the frequency-using parameter information.

[0025] In one feasible implementation, fixed monitoring stations are deployed in the target urban area for long-term continuous spectrum monitoring. Preferably, the sampling frequency is set to 100MHz, the resolution bandwidth is 1kHz, and the monitoring frequency band is 30MHz~3GHz, in order to obtain structured spectrum monitoring binary data.

[0026] Deploying distributed spectrum sensor networks in industrial parks and border areas enables high-density, grid-based monitoring; mobile monitoring vehicles are used to cover blind spots in regional mobile monitoring; and unmanned aerial vehicles (UAVs) are used for high-altitude monitoring of complex terrain to compensate for ground obstruction. Simultaneously, structured, semi-structured, and unstructured frequency usage information from frequency-using stations and ITU-registered stations is acquired, including basic station information, frequency-using equipment information, and frequency usage parameter information.

[0027] The initial radio spectrum monitoring data and initial frequency usage information of the stations were format adapted and cleaned. Data with different formats and granularities were adapted into standard data with a unified format and granularity through a data adapter. The adapted dataset was deduplicated and despurious. Outliers were removed using a criterion of 3 times the standard deviation, and missing values ​​were filled using a linear interpolation algorithm. Spatiotemporal alignment was completed using an inverse distance weighted algorithm to achieve spatiotemporal registration of multi-source data. Finally, the preprocessed spectrum monitoring data was stored in a file system, and the frequency usage information of the stations was stored in a relational database, thus obtaining the radio spectrum monitoring data and the frequency usage information of the stations.

[0028] For example, suppose a fixed monitoring station collects raw spectrum binary data at a frequency of 2.4 GHz at a certain moment, and a frequency-using station reports semi-structured XML format data containing a transmit power of 20W. After conversion by a data adapter, both are converted into a unified standard format data. If there is an outlier in the data during this period, such as a sudden increase in signal strength to 100W due to equipment failure, it is removed after being judged by a criterion of 3 times the standard deviation. Then, a linear interpolation algorithm is used to fill in the missing value with normal data from the time before and after the time, and finally standardized spectrum monitoring data and frequency-using station frequency information are obtained.

[0029] S102, construct links from frequency usage information to obtain potential interference link information; perform background noise assessment processing on spectrum monitoring data to obtain electromagnetic environment background noise information.

[0030] Link construction refers to the process of homogenizing and structuring frequency usage information from different sources to form an interference link data structure. Potential interference link information refers to broad-spectrum data including station latitude and longitude, altitude, station frequency equipment frequency points, number of frequency points, frequency spacing, average power, peak power, required bandwidth, station antenna type, antenna efficiency, antenna equation, polarization, main lobe gain, first sidelobe level, average sidelobe level, beamwidth, main lobe-sidelobe angle, horizontal azimuth angle, vertical azimuth angle, and antenna height. Noise floor assessment processing refers to the process of accurately estimating noise in complex signal backgrounds based on recursive statistical filtering techniques. Electromagnetic environment noise floor information refers to relevant data on the spatial distribution of the lower limit of basic electromagnetic noise in a specific area.

[0031] In one feasible implementation, the frequency usage information is cleaned and organized, and data from different sources is homogenized and structured to construct a potential interference link data structure containing station parameters and antenna parameters. This data structure is then formed into a wide table and the frequency usage information of the stations that are connected and aggregated is persistently stored to obtain the potential interference link information.

[0032] For spectrum monitoring data, noise recursive statistical filtering technology is adopted. Combining the continuous update characteristics of recursive filters and the adaptability of nonlinear filters, the noise floor distribution of the input power spectrum is recursively estimated through memoryless nonlinear systems and self-feedback characteristics. The threshold is dynamically adjusted through continuous feedback and adaptive adjustment, and the feedback difference is nonlinearly compared and summed with the threshold to obtain the electromagnetic environment noise floor information.

[0033] For example, suppose that from the frequency usage information gathered in an industrial park, the coordinates of a certain station are extracted as 116.4 degrees east longitude and 39.9 degrees north latitude, with an altitude of 50 meters, a frequency of 2.4 GHz for the frequency-using equipment, an average power of 15 dBm, and an antenna main lobe gain of 12 dBi. These parameters are integrated to construct a wide table record, thus obtaining the potential interference link information. At the same time, the 2.4 GHz frequency band spectrum monitoring data collected in this area is input into a noise recursive statistical filter. The threshold is dynamically adjusted according to the difference between the power spectrum and the historical noise floor, and the amplitude is limited and summed to output a smooth noise floor estimation curve, thus obtaining the electromagnetic environment noise floor information.

[0034] S103, based on the parameters of the disturbed device, filter the potential interference link information and electromagnetic environment noise floor information to determine the target interference link set and target noise floor value.

[0035] In this embodiment, the parameters of the disturbed device refer to the frequency parameters and geographic information parameters of the radio receiving device to be analyzed for interference effects. The target interference link set refers to the set of potential interference link information retained after filtering by the location and frequency range of the disturbed device. The target noise floor value refers to the noise floor power value obtained by intercepting and calculating the noise floor power value based on the location and frequency range of the disturbed device.

[0036] In one feasible implementation, the location of the disturbed device is used as the center, and the potential interference link information of frequency-using stations within a screening radius of d is calculated to form a spatial interference link set; the link information in the spatial interference link set is screened based on the frequency range f1~f2 of the receiver frequency of the disturbed device to determine the target interference link set; the spectrum monitoring data within a screening radius of d is calculated based on the location of the disturbed device, and the electromagnetic environment noise floor near each monitoring station is obtained to form a target noise floor set.

[0037] The target noise floor set is truncated within the frequency range f1~f2 of the receiver of the disturbed device to obtain the frequency truncated noise floor set; each noise value in the frequency truncated noise floor set is sampled according to the sampling rate R, and the root mean square of the sampled data is calculated. Finally, the average of the root mean square data of each noise floor is determined as the target noise floor value.

[0038] For example, assuming the location of the disturbed device is 116.5 degrees east longitude and 39.95 degrees north latitude, the screening radius d is 10 kilometers, and the receiver frequency range f1~f2 is 2.4 GHz~2.42 GHz; firstly, three potential interference links are searched within a 10-kilometer radius to form a spatial interference link set. After screening at the 2.4 GHz~2.42 GHz frequency, one link with a frequency of 2.45 GHz is removed, and two links are retained to determine the target interference link set; at the same time, the noise floor data of two monitoring stations are obtained within a 10-kilometer radius, the noise value in the 2.4 GHz~2.42 GHz range is extracted, and sampling is performed at a sampling rate R of 1 Mbps. The root mean square of the noise floor data of each monitoring station is calculated, and then the average of the root mean squares of the two monitoring stations is calculated. Finally, the target noise floor value is -95 dBm.

[0039] S104, calculate the signal-to-dryness ratio based on the target interference link set and the target noise floor value to obtain the signal-to-dryness ratio.

[0040] In the embodiments of this application, the signal-to-dryness ratio refers to the ratio of the useful signal power received by the receiver to the sum of the lumped interference power and the target noise floor value.

[0041] In one feasible implementation, the interference device links are obtained, and the union of the interference device links and the target interference link set is used to obtain the lumped interference link set; the appropriate propagation model is selected according to the altitude and frequency band information of the lumped interference link set to calculate the path loss and obtain the lumped interference power.

[0042] The power value falling into the receiver is calculated based on the frequency parameters of the disturbed equipment, and the receiver signal power is determined. The signal-to-dryness ratio is obtained by calculating the ratio and logarithm of the receiver signal power, the lumped interference power and the target noise floor value.

[0043] For example, assuming the jamming device link contains one main jamming link, the union of this link with two links in the target jamming link set yields a lumped jamming link set containing three links. Based on the ITU-R P.1546 propagation model of the International Telecommunication Union Radiocommunication Sector, path loss is calculated separately for line-of-sight and non-line-of-sight scenarios, resulting in interference powers of -80dBm, -85dBm, and -90dBm for the three links falling into the receiver, respectively. The lumped jamming power is calculated to be -79.5dBm using the root mean square superposition method. The receiver signal power is calculated to be -60dBm based on the frequency parameters of the jammed device, and the target noise floor P_N is -95dBm. Therefore, the signal-to-dryness ratio is 10*log10(P_S / (P_I+P_N)) = 18.7dB.

[0044] S105, compare the signal-to-dryness ratio with the preset signal-to-dryness ratio limit to obtain the electromagnetic interference results.

[0045] In this embodiment, the preset signal-to-dryness ratio limit refers to the lowest signal-to-dryness ratio threshold allowed for the normal operation of the disturbed equipment. The electromagnetic interference result refers to the conclusion of whether the disturbed equipment is affected by interference.

[0046] In one feasible implementation, the signal-to-dryness ratio is compared with a preset signal-to-dryness ratio limit; if the signal-to-dryness ratio is greater than the preset signal-to-dryness ratio limit, it is determined that there is no interference effect, and an electromagnetic interference-free result is obtained; if the signal-to-dryness ratio is less than or equal to the preset signal-to-dryness ratio limit, it is determined that there is interference effect, and an electromagnetic interference-affected result is obtained.

[0047] For example, assuming the preset signal-to-dryness ratio limit of the disturbed device is 10dB, and the signal-to-dryness ratio calculated in the previous step is 18.7dB, since 18.7dB is greater than 10dB, it is determined that the disturbed device has no interference effect, and the result of no interference electromagnetic interference is obtained.

[0048] In summary, this application obtains radio spectrum monitoring data and frequency usage information of frequency-using stations; constructs links based on the frequency usage information to obtain potential interference link information; performs noise floor assessment processing on the spectrum monitoring data to obtain electromagnetic environment noise floor information: this noise floor assessment processing method dynamically adjusts the threshold based on the feedback difference between the power spectrum of the spectrum monitoring data and the historical noise floor assessment results, and performs nonlinear comparison and summation processing on the feedback difference and the threshold to obtain electromagnetic environment noise floor information, which can improve the problem of large noise floor estimation error caused by the use of fixed thresholds for noise floor assessment in the prior art; combines the parameters of the disturbed equipment to screen the potential interference link information and electromagnetic environment noise floor information to determine the target interference link set and the target noise floor value, solving the technical defect of not relying on the parameters of the disturbed equipment for screening; calculates the signal-to-interference-plus-noise ratio (SIR) based on the target interference link set and the target noise floor value; compares the SIR with the preset SIR threshold and outputs the electromagnetic interference assessment result, thereby effectively improving the calculation accuracy of the electromagnetic interference assessment result.

[0049] Please see Figure 2 , Figure 2 This is a flowchart illustrating the electromagnetic interference impact analysis method provided in an embodiment of this application. Figure 2 As shown, the method in this application embodiment may include the following steps S201-S203: S201, obtain the first number of times the feedback difference is continuously less than zero. If the first number reaches the first preset number, multiply the historical dynamic threshold by the first power of the first number of times less than one to determine the first current threshold.

[0050] In this embodiment, the feedback difference refers to the difference between the power spectrum of the received wireless broadband communication signal and the noise floor estimation result of the previous time step. The first preset number of times refers to a threshold n for the number of times the feedback difference is continuously less than zero. The historical threshold refers to the dynamic threshold calculated in the previous time step. The first parameter is a positive number 'a' less than 1, used to decrease the threshold when the feedback difference is continuously less than zero. The first current threshold is determined based on the first parameter 'a' and the historical threshold. The calculated dynamic threshold for the current time step .

[0051] In one feasible implementation, the feedback difference d(k) = S(k) - Y(k-1) between the power spectrum S(k) of the received wireless broadband communication signal and the previous time step estimation result Y(k-1) is calculated; the number of times n in which the feedback difference d(k) is continuously less than zero is counted; when the first number n reaches a first preset number, a historical threshold is obtained. The first parameter a, which is less than one, will be the historical threshold. The first current threshold is calculated by multiplying the first parameter a by the first power of n. = * a^n.

[0052] For example, suppose a historical threshold The first parameter a is 0.8. When the feedback difference d(k) is less than zero for three consecutive times, that is, when the first preset number of times n=3 is reached, the first current threshold is calculated. =5 * 0.8^3 = 2.56, thus gradually reducing the threshold size to prevent excessive decrease in the noise floor from causing a large deviation in the estimation results.

[0053] S202, obtain the second number of times the feedback difference is continuously greater than zero. If the second number reaches the second preset number, multiply the historical dynamic threshold by the second number of times greater than one to determine the second current threshold.

[0054] In this embodiment, the second preset number of times refers to a threshold n representing the number of times the feedback difference is continuously greater than zero. The second parameter is a positive number b greater than 1, used to increase the threshold when the feedback difference is continuously greater than zero. The second current threshold is determined based on the second parameter b and the historical threshold. The calculated dynamic threshold for the current time step .

[0055] In one feasible implementation, the statistical feedback difference d(k) is continuously greater than zero for a second number n. When the second number n reaches a second preset number, a historical threshold is obtained. The second parameter b, which is greater than one, will be used to determine the historical threshold. The second current threshold is calculated by multiplying the second parameter b by the second power of n. = * b^n.

[0056] For example, suppose a historical threshold The first parameter is 5, and the second parameter b is 1.5. When the feedback difference d(k) is greater than zero twice consecutively, that is, when the second number n=2 reaches the second preset number twice, the second current threshold is calculated. =5 * 1.5^2 = 11.25, thus gradually increasing the threshold size to make the estimation result closer to the true noise floor.

[0057] S203, based on the first current threshold or the second current threshold, perform nonlinear comparison and summation on the feedback difference to obtain the electromagnetic environment noise floor information.

[0058] In the embodiments of this application, nonlinear comparison and summation processing refers to the process of comparing the feedback difference with the dynamically adjusted current threshold, and then adding the feedback difference to the historical noise floor estimation result after limiting the adjustment based on the comparison result.

[0059] In one feasible implementation, when the feedback difference is continuously less than zero for a first preset number of times, the feedback difference is limited and adjusted based on a first current threshold; when the feedback difference is continuously greater than zero for a second preset number of times, the feedback difference is limited and adjusted based on a second current threshold; the adjusted feedback difference is added to the previous time step estimation result to obtain the electromagnetic environment noise floor information for the current time step.

[0060] For example, when the feedback difference is nonlinearly compared and summed using a first current threshold of 2.56, the feedback difference is more likely to exceed the threshold and be limited due to the reduced threshold, thereby suppressing the excessive drop in noise floor caused by the rise of the carrier signal; when the second current threshold of 11.25 is used, the feedback difference is more directly retained due to the increased threshold, making the noise floor quickly approach the true level, and finally outputting accurate electromagnetic environment noise floor information.

[0061] In summary, this application dynamically adjusts the threshold value. When the power spectrum frequency component rises or is in the carrier signal, the threshold is reduced to prevent the noise floor from falling excessively. When it is close to the noise floor, the threshold is increased to make the estimation result closer to the true noise floor. This avoids the accumulation of estimation errors caused by multi-carrier signals, improves the accuracy of noise floor estimation and adaptability to rapidly changing electromagnetic environments.

[0062] In one feasible implementation, when performing a nonlinear comparison and summation process on the feedback difference based on the current threshold to obtain the electromagnetic environment noise floor information, the following is specifically executed: If the absolute value of the feedback difference is less than the first current threshold or the second current threshold, then the feedback difference is determined as the first target adjustment value.

[0063] For example, the absolute value of the feedback difference is calculated and compared with a first current threshold or a second current threshold; if the absolute value of the feedback difference is less than the first current threshold or the second current threshold, the feedback difference is directly used as the first target adjustment value. Assuming the current threshold... If the value is 5, the feedback difference d(k) is 2, and its absolute value |d(k)|=2 is less than the first current threshold 5, then the feedback difference 2 is determined as the first target adjustment value e(k).

[0064] If the absolute value of the feedback difference is greater than or equal to the first current threshold or the second current threshold, then the product of the current threshold and the sign function of the feedback difference is determined as the second target adjustment value.

[0065] For example, the current threshold If the value is 5, the feedback difference d(k) is -8, and its absolute value |d(k)|=8 is greater than the second current threshold 5, then the sign function sgn(-8)=-1 is obtained for the feedback difference, and the product of the current threshold 5 and the sign function value -1, -5, is determined as the second target adjustment value e(k).

[0066] The historical noise floor assessment results are summed with either the first or second target adjustment value to obtain the electromagnetic environment noise floor information.

[0067] In this embodiment, the current threshold refers to the threshold dynamically adjusted based on the continuous positive and negative values ​​of the feedback difference. The first target adjustment value refers to the feedback difference itself retained when the absolute value of the feedback difference is less than the first current threshold or the second current threshold. The sign function is a function that extracts the sign of the input value; it returns 1 for positive numbers, -1 for negative numbers, and 0 for zero. The second target adjustment value is the product of the current threshold and the sign of the feedback difference when the absolute value of the feedback difference is greater than or equal to the first current threshold or the second current threshold. The historical noise floor assessment result refers to the noise floor estimate Y(k-1) of the previous time step.

[0068] In one feasible implementation, a first current threshold or a second current threshold is obtained. The feedback difference d(k) and the historical noise floor assessment result Y(k-1) are calculated; the absolute value of the feedback difference |d(k)| is calculated, and |d(k)| is compared with the first current threshold or the second current threshold. Compare; if |d(k)| is less than the first current threshold or the second current threshold... If the feedback difference d(k) is greater than or equal to the first target adjustment value e(k), then the first target adjustment value e(k) is determined. Then calculate the sign function sgn(d(k)) of the feedback difference d(k), and set the first current threshold or the second current threshold. The product of the symbol function sgn(d(k)) is determined as the second target adjustment value e(k); the historical noise floor evaluation result Y(k-1) is summed with the first target adjustment value e(k) or the second target adjustment value e(k) to obtain the electromagnetic environment noise floor information Y(k)=Y(k-1)+e(k) at the current time step.

[0069] For example, the specific form of the relevant formula for the nonlinear comparison and summation process in the above steps is as follows:

[0070]

[0071] Among them, formula parameters Represents the target adjustment value. Represents the feedback difference. Represents either the first current threshold or the second current threshold. The sign function representing the feedback difference. Electromagnetic ambient noise floor information representing the current time step. This represents the historical noise floor assessment results.

[0072] For example, the specific form of the formula related to the dynamic adjustment process of the first current threshold or the second current threshold mentioned above can be:

[0073]

[0074]

[0075]

[0076] Among them, formula parameters Represents either the first current threshold or the second current threshold. This represents the variance calculation. This represents the power spectrum of the received wireless broadband communication signal. Represents the length of the power spectrum. This represents the initial value of the threshold. This represents the number of samples used to calculate the initial threshold value. Represents the feedback difference. Represents the time step and takes the value , Represents either the first current threshold or the second current threshold. This represents a historical threshold. Represents the first parameter and = , This represents the second parameter and is 1.5. This represents the number of times the feedback difference is consecutively less than or greater than zero.

[0077] In summary, this application utilizes a memoryless nonlinear system and self-feedback characteristics to perform nonlinear comparison and summation of the feedback difference. Combined with a dynamically adjusted threshold, it effectively addresses changes in signal background and interference, prevents excessive noise floor degradation, and makes the estimation results approximate the true noise floor. This enables accurate noise estimation for non-stationary signals and rapidly changing radio environments.

[0078] Please see Figure 3 , Figure 3 This is a flowchart illustrating the electromagnetic interference impact analysis method provided in an embodiment of this application. Figure 3 As shown, the method in this application embodiment may include the following steps S301-S303: S301, extract the station's transmit power from the frequency information.

[0079] In this embodiment of the application, the station transmit power refers to the average or peak power of the radio signals transmitted by the frequency-using equipment deployed in the frequency-using station.

[0080] In one feasible implementation, the average power and peak power of the frequency-using equipment information are extracted from the frequency-using information of the frequency-using station, and the average power or peak power is used as the station's transmit power. For example, the frequency-using information reported by a certain frequency-using station is parsed, and the average power of the frequency-using equipment of the station is extracted to be 30dBm. Then, 30dBm is used as the station's transmit power.

[0081] S302. If the station's transmission power is greater than the preset power threshold, then the corresponding station is identified as a potential interference source.

[0082] In this embodiment, the preset power threshold refers to the minimum transmit power used to filter stations that may cause interference. Potential interference sources refer to frequency-using stations whose transmit power exceeds the preset power threshold.

[0083] In one feasible implementation, the extracted station transmit power is compared with a preset power threshold, where the preset power threshold is set to -10dBm. If the station transmit power is greater than -10dBm, the station is determined to have strong transmit power and is considered a potential interference source. For example, assuming the preset power threshold is -10dBm, if the extracted station transmit power is 30dBm, since 30dBm is greater than -10dBm, the station is determined to be a potential interference source. If another station has a transmit power of -15dBm, it is not considered a potential interference source.

[0084] S303 constructs a data structure based on the station parameters and antenna parameters of potential interference sources to obtain potential interference link information.

[0085] In this embodiment, station parameters refer to parameters including station latitude and longitude, altitude, frequency of station frequency-using equipment, number of frequency points, frequency spacing, average power, peak power, and required bandwidth. Antenna parameters refer to parameters including antenna type, antenna efficiency, antenna equation, polarization, main lobe gain, first sidelobe level, average sidelobe level, beamwidth, main lobe-sidelobe angle, horizontal azimuth, vertical azimuth, and antenna height. Data structure construction refers to the process of integrating station parameters and antenna parameters into a homogeneous and structured wide table data structure.

[0086] In one feasible implementation, based on the potential interference source, the corresponding station parameters and antenna parameters are extracted. The station parameters, such as station latitude and longitude, altitude, frequency of station frequency-using equipment, number of frequency points, frequency spacing, average power, peak power, and required bandwidth, as well as the antenna parameters, such as station antenna type, antenna efficiency, antenna equation, polarization, main lobe gain, first sidelobe level, average sidelobe level, beamwidth, main lobe-sidelobe angle, horizontal azimuth angle, vertical azimuth angle, and antenna height, are homogenized and structured to construct a wide table data structure and persist it to obtain the potential interference link information.

[0087] For example, for a identified potential interference source, its station coordinates are extracted as follows: 117 degrees east longitude, 40 degrees north latitude, altitude 60 meters, frequency of the frequency-using equipment is 3.5 GHz, average power is 30 dBm, required bandwidth is 20 MHz, antenna type is directional antenna, main lobe gain is 18 dBi, and horizontal azimuth is 0 degrees. These heterogeneous parameters are integrated to construct a wide table record containing complete parameters, thus obtaining the potential interference link information of the potential interference source.

[0088] In summary, this application filters out potential interference sources by using power thresholds and constructs data structures for the station parameters and antenna parameters of potential interference sources, thereby reducing the computational load of interference analysis and ensuring the integrity and structure of interference link data, providing an accurate data foundation for subsequent interference impact analysis.

[0089] Please see Figure 4 , Figure 4 This is a flowchart illustrating the electromagnetic interference impact analysis method provided in an embodiment of this application. Figure 4 As shown, the method in this application embodiment may include the following steps S401-S403: S401, using the location of the disturbed device as the center, performs spatial filtering on the potential interference link information based on the filtering radius to obtain a set of spatial interference links.

[0090] In this embodiment, the location of the disturbed device refers to the geographical coordinates of the radio receiving device to be analyzed for interference effects. The filtering radius refers to the spatial search distance d centered on the location of the disturbed device. Spatial filtering processing refers to the operation of extracting potential interference link information within the filtering radius. The spatial interference link set refers to the set of potential interference link information located within the filtering radius of the disturbed device.

[0091] In one feasible implementation, by obtaining the location of the disturbed device from the parameters of the disturbed device, and using the location of the disturbed device as the center, the potential interference link information within the search range according to the screening radius d is extracted to form a spatial interference link set.

[0092] For example, assuming the location of the disturbed device is 116.5 degrees east longitude and 39.9 degrees north latitude, and the screening radius d is 15 kilometers, the distance between each station and the disturbed device in the potential interference link information is calculated, and the information of 5 potential interference links with a distance of less than or equal to 15 kilometers is extracted to form a spatial interference link set containing 5 links.

[0093] S402, Based on the frequency range of the disturbed equipment, the set of spatial interference links is filtered by frequency to determine the set of target interference links.

[0094] In this embodiment, the frequency range of the disturbed device refers to the operating frequency range f1~f2 of the radio receiver to be analyzed for interference effects. Frequency filtering refers to the operation of retaining interfering links whose operating frequencies fall within the frequency range of the disturbed device.

[0095] In one feasible implementation, by obtaining the frequency range f1~f2 of the disturbed device in the parameters of the disturbed device, traversing the information of each link in the spatial interference link set, it is determined whether the frequency point or frequency band in the link information overlaps with the frequency range f1~f2 of the disturbed device; if there is an overlap, the link information is retained, and all the retained link information is determined as the target interference link set.

[0096] For example, assuming the frequency range of the disturbed device is 3.5GHz~3.52GHz, the spatial interference link set containing the above 5 links is traversed. Among them, the frequency points of 3 links fall within the range of 3.5GHz~3.52GHz or overlap, and the frequency points of the other 2 links are 2.4GHz and do not overlap. Then, the 2.4GHz link is removed, and the 3 links are retained to determine the target interference link set.

[0097] S403 extracts electromagnetic environment noise floor information based on the screening radius and the frequency range of the disturbed equipment to determine the target noise floor value.

[0098] In this embodiment of the application, "truncation" refers to the operation of extracting background noise data within the corresponding spatial and frequency range from the background noise information of the electromagnetic environment based on the location and frequency range of the disturbed device.

[0099] In one feasible implementation, the location of the disturbed device is used as the center, and spectrum monitoring data within a range of radius d is calculated and filtered. The electromagnetic environment noise floor near each monitoring station is obtained to form a target noise floor set. The target noise floor set is truncated based on the frequency range f1~f2 of the disturbed device to obtain a frequency truncated noise floor set. Each noise value in the frequency truncated noise floor set is sampled according to the sampling rate R, and the root mean square of the sampled data is calculated. Finally, the average of each noise root mean square data is determined as the target noise floor value.

[0100] For example, assuming there are two spectrum monitoring stations within a 15-kilometer radius of the disturbed device, electromagnetic environment noise data near these two monitoring stations are acquired to form a target noise floor set; the noise floor data is truncated according to the frequency range of the disturbed device (3.5 GHz to 3.52 GHz) to obtain a frequency-truncated noise floor set; each noise value in the frequency-truncated noise floor set is sampled according to the sampling rate R and the root mean square (RMS) is calculated. The RMS of the noise floor at monitoring station A is -97 dBm, and the RMS of the noise floor at monitoring station B is -95 dBm. The average is calculated to obtain a target noise floor value of -96 dBm.

[0101] In summary, this application uses the location and frequency range of the disturbed device to perform dual screening of potential interference link information and electromagnetic environment noise background information, eliminating spatially and frequency-irrelevant interference and noise background data, thereby reducing computational complexity and improving the pertinence and accuracy of electromagnetic interference impact analysis.

[0102] In one feasible implementation, when performing the step of extracting electromagnetic environment noise floor information based on the screening radius and the frequency range of the disturbed device to determine the target noise floor value, the specific execution is as follows: Use the spectrum monitoring data within the selected radius as the target noise floor set; The target noise floor set is truncated based on the frequency range of the disturbed device to obtain the frequency-truncated noise floor set.

[0103] In one feasible implementation, the location of the disturbed device is used as the center, and spectrum monitoring data within a screening radius of d is obtained. Electromagnetic environment noise data near each monitoring station is also obtained. The electromagnetic environment noise information corresponding to the spectrum monitoring data within the screening radius of d is used as the target noise set. Based on the frequency range f1~f2 of the disturbed device, the noise data in the target noise set is truncated, and only the noise data with frequencies in the range of f1~f2 is retained to obtain the frequency-truncated noise set.

[0104] For example, assuming the screening radius d of the disturbed device is 15 kilometers, and there are monitoring stations 1 and 2 within this range, the full-band noise floor data of these two monitoring stations are used as the target noise floor set; the frequency range of the disturbed device is 3.5 GHz to 3.52 GHz, then only the noise floor data corresponding to the 3.5 GHz to 3.52 GHz frequency band is extracted from the target noise floor set to form the frequency-extracted noise floor set.

[0105] The target noise floor value is determined by sampling the frequency truncation noise set.

[0106] In this embodiment, the target noise floor set refers to the set of electromagnetic environment noise floor data from all monitoring stations within the screening radius of the disturbed device. The frequency-truncation noise floor set refers to the subset of noise floor data in the target noise floor set whose frequencies fall within the frequency range of the disturbed device. Sampling refers to the process of extracting and statistically calculating the noise floor data according to a preset sampling rate.

[0107] In one feasible implementation, each noise value in the frequency truncation noise set is sampled according to a preset sampling rate R, the root mean square value of the sampled data is calculated, the average of each noise root mean square data is calculated, and the calculated average is determined as the target noise floor value.

[0108] For example, assuming the sampling rate R is 2 Mbps, the noise values ​​in the frequency truncation noise set are sampled. The root mean square of the sampled data of monitoring station 1 is -98 dBm and the root mean square of the sampled data of monitoring station 2 is -96 dBm. The average of these two root mean square data is calculated to obtain the target noise floor value of -97 dBm.

[0109] In summary, this application accurately extracts the true noise level of the location and frequency band of the disturbed device by sampling and calculating the root mean square average of the spatial and frequency truncation noise data, providing reliable noise data support for the accurate calculation of the signal-to-dryness ratio.

[0110] Please see Figure 5 , Figure 5 This is a flowchart illustrating the electromagnetic interference impact analysis method provided in an embodiment of this application. Figure 5 As shown, the method in this application embodiment may include the following steps S501-S503: In this embodiment, the jamming device link refers to the link between the primary jamming device and the jammed device, which is the focus of this analysis. Union refers to the operation of merging the set of jamming device links with the set of target jamming links and removing duplicate links. The lumped set of jamming links refers to a comprehensive set containing all jamming links that need to be considered.

[0111] In one feasible implementation, the interference device links are obtained, and the interference device links are merged with the target interference link set to remove duplicates, resulting in a lumped interference link set.

[0112] For example, suppose the jamming device link that is of particular interest contains 1 link, the target jamming link set contains 3 links, and these 3 links are different from the jamming device link. After merging them, we get a lumped jamming link set containing 4 links.

[0113] S502 calculates the path loss of the lumped interference link set to obtain the lumped interference power, and calculates the power based on the frequency parameters of the disturbed equipment to determine the receiver signal power.

[0114] In this embodiment, path loss calculation refers to calculating the power attenuation of the interfering signal during propagation based on the propagation model. Lumped interference power refers to the sum of the power reaching the receiver of the affected device from all lumped interference links. Frequency parameters refer to parameters such as the transmit power and gain of the affected device. Receiver signal power refers to the power of the useful signal received by the affected device.

[0115] In one feasible implementation, for each link in the lumped interference link set, a suitable propagation model is selected based on its altitude and frequency band information to calculate path loss. A distinction is made between line-of-sight (LAS) and non-LAS scenarios. For LAS scenarios, a free-space propagation model is used, while for non-LAS scenarios, terrain diffraction loss correction is introduced. The path loss of each link is calculated using the ITU-R P.1546 propagation model, thus obtaining the interference power falling into the receiver from each link. All interference powers are summed using the root mean square (RMS) superposition method to obtain the lumped interference power. Simultaneously, based on the frequency parameters of the interfered equipment, the power value of the signal falling into the receiver from the interfered equipment is calculated to determine the receiver signal power.

[0116] For example, assuming the lumped interference link set contains 4 links, 2 of which are in line-of-sight scenarios using the free-space propagation model, and 2 are in non-line-of-sight scenarios using the ITU-R P.1546 propagation model with terrain diffraction loss correction, the calculated interference power of the 4 links falling into the receiver is -82dBm, -85dBm, -90dBm, and -88dBm, respectively. The lumped interference power is calculated to be -80.1dBm using the root mean square superposition method. The receiver signal power is calculated to be -65dBm based on the frequency parameters such as the transmit power, antenna gain, and feeder loss of the interfered device.

[0117] S503 calculates the signal-to-dryness ratio by performing ratio and logarithmic calculations based on the receiver signal power, lumped interference power, and target noise floor value.

[0118] In the embodiments of this application, the ratio and logarithmic calculation refers to the process of calculating the ratio of the receiver signal power to the sum of the lumped interference power and the target noise floor value, and then taking the logarithm to convert it to decibels.

[0119] In one feasible implementation, the signal-to-dryness ratio is obtained by calculating the ratio of the receiver signal power to the sum of the lumped interference power and the target noise floor based on the receiver signal power, the lumped interference power, and the target noise floor value, and then taking the logarithm.

[0120] For example, the specific form of the formula for calculating the signal-to-interference-plus-noise ratio in the above steps can be:

[0121] Among them, formula parameters Represents the signal-to-interference-plus-noise ratio. Represents the receiver signal power. Represents the lumped interference power. This represents the target noise floor value.

[0122] For example, assume the receiver signal power -65dBm, lumped interference power The target noise floor value is -80.1dBm. The value is -97 dBm. Convert the decibel value to watts for calculation. Approximately , Approximately , Approximately ,but Approximately Calculations yielded

[0123] In summary, this application combines the interference device link and the potential interference link, distinguishes between line-of-sight and non-line-of-sight scenarios for path loss calculation, and combines the noise floor to calculate the signal-to-interference-plus-noise ratio, thereby comprehensively considering the superposition effect and propagation loss of multi-source interference, and improving the accuracy and practicality of interference impact analysis.

[0124] In one feasible implementation, when performing the steps of acquiring radio spectrum monitoring data and frequency usage information of frequency-using stations, the following is also specifically performed: Acquire initial radio spectrum monitoring data and initial frequency usage information from frequency-using stations.

[0125] The initial radio spectrum monitoring data and initial frequency usage information of the frequency-using stations are processed for format adaptation and cleaning to obtain the radio spectrum monitoring data and frequency usage information of the frequency-using stations.

[0126] In this embodiment, initial radio spectrum monitoring data refers to unprocessed raw spectrum binary data directly acquired by a multi-source acquisition architecture. Initial frequency usage information of frequency-using stations refers to directly acquired, unprocessed raw data from frequency-using stations. Format adaptation and cleaning refers to the process of converting data from different sources, formats, and granularities into unified standard data and performing anomaly removal and missing data filling.

[0127] In one feasible implementation, structured initial radio spectrum monitoring data, as well as structured, semi-structured, and unstructured initial frequency usage information from stations, are extracted; and the initial radio spectrum monitoring data and initial frequency usage information from stations with different formats and granularities are adapted into standard data with a unified format and granularity through a data adapter.

[0128] The adapted standard dataset is deduplicated and despurious. Outliers are identified using a 3-standard-deviation criterion, and samples exceeding 3 standard deviations are discarded. Missing values ​​are filled using a linear interpolation algorithm. Spatiotemporal alignment is achieved using an inverse distance weighted algorithm to complete the spatiotemporal registration of multi-source data. The preprocessed spectrum monitoring data is stored in a file system, and the frequency usage information of frequency stations is stored in a relational database, thus obtaining radio spectrum monitoring data and frequency usage information of frequency stations.

[0129] For example, suppose binary format spectrum monitoring data is extracted from fixed monitoring stations, and XML format frequency usage information is extracted from frequency-using stations. The data adapter adapts both to a comma-separated CSV format. When cleaning historical power data for a certain frequency point, suppose the average power is -90dBm and the standard deviation is 6dBm. If a data point with a power of -73dBm appears, it is judged as an outlier and removed because |-73-(-90)|=17>3*6. For the removed or missing time point data, the data before and after the time point are filled by a linear interpolation algorithm. For spatially misaligned monitoring station data, an inverse distance weighting algorithm is used to interpolate and register according to the distance weight. Finally, the cleaned spectrum data is stored in the file system, and the station data is stored in the relational database, resulting in standardized radio spectrum monitoring data and frequency usage information of frequency-using stations.

[0130] In summary, this application eliminates format differences and quality issues in the data sources by performing cleaning processes such as format adaptation, deduplication and despurious removal, outlier removal, missing value imputation, and spatiotemporal alignment on the initial data from multiple heterogeneous sources. This provides a high-quality and standardized data foundation for subsequent noise floor assessment and interference link construction, ensuring the reliability of the analysis results.

[0131] The following will combine Figure 6 This application provides a detailed description of the electromagnetic interference impact analysis device provided in its embodiments. It should be noted that... Figure 6 The electromagnetic interference analysis device in the present application is used to perform the analysis. Figures 1-5 The methods shown in the embodiments are for illustrative purposes only, illustrating the parts relevant to the embodiments of this application. For specific technical details not disclosed, please refer to this application. Figures 1-5 In the embodiment shown, the electromagnetic interference impact analysis device 600 may include a data acquisition unit 601, a data processing unit 602, a data filtering unit 603, a data calculation unit 604, and a data comparison unit 605, as detailed below: The data acquisition unit 601 is used to acquire radio spectrum monitoring data and frequency usage information of frequency-using stations; The data processing unit 602 is used to construct links from frequency usage information to obtain potential interference link information; and to perform noise floor assessment processing on spectrum monitoring data to obtain electromagnetic environment noise floor information. The noise floor assessment processing is to dynamically adjust the threshold based on the feedback difference between the power spectrum of the spectrum monitoring data and the historical noise floor assessment results, and to perform nonlinear comparison and summation processing on the feedback difference and the threshold to obtain electromagnetic environment noise floor information. The data filtering unit 603 is used to filter potential interference link information and electromagnetic environment noise floor information based on the parameters of the disturbed device, and to determine the target interference link set and target noise floor value. The parameters of the disturbed device are the radio receiving device to be subjected to interference impact analysis. The data calculation unit 604 is used to calculate the signal-to-dryness ratio based on the target interference link set and the target noise floor value, and obtain the signal-to-dryness ratio. The data comparison unit 605 is used to compare the signal-to-dryness ratio with a preset signal-to-dryness ratio limit to obtain the electromagnetic interference result.

[0132] The electromagnetic interference impact analysis device 600 provided in the above embodiments can realize the technical solutions described in the above electromagnetic interference impact analysis method embodiments. The specific implementation principles of each module or unit can be found in the corresponding content in the above electromagnetic interference impact analysis method embodiments, and will not be repeated here.

[0133] like Figure 7 As shown, the present invention also provides an electronic device 700. The electronic device 700 includes a processor 701, a memory 702, a display 703, and a data acquisition device 704. Figure 7 Only some components of the electronic device 700 are shown, but it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented instead.

[0134] In some embodiments, processor 701 may be a central processing unit (CPU), microprocessor, or other data processing chip, used to run program code stored in memory 702 or process data, such as the electromagnetic interference influence analysis method of the present invention.

[0135] In some embodiments, processor 701 may be a single server or a group of servers. The server group may be centralized or distributed. In some embodiments, processor 701 may be local or remote. In some embodiments, processor 701 may be implemented on a cloud platform. In one embodiment, the cloud platform may include a private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, intranet, multi-cloud, etc., or any combination thereof.

[0136] In some embodiments, memory 702 may be an internal storage unit of electronic device 700, such as a hard disk or memory of electronic device 700. In other embodiments, memory 702 may also be an external storage device of electronic device 700, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on electronic device 700.

[0137] Furthermore, the memory 702 may include both internal storage units of the electronic device 700 and external storage devices. The memory 702 is used to store application software and various types of data installed on the electronic device 700.

[0138] In some embodiments, display 703 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 703 is used to display information from electronic device 700 and to display a visual user interface. Radio receiver and data acquisition unit 704 is used to acquire radio spectrum monitoring data and frequency usage information from frequency-using stations. Components 701-704 of electronic device 700 communicate with each other via a system bus.

[0139] In one embodiment, when the processor 701 executes the electromagnetic interference effect analysis program in the memory 702, the following steps can be implemented: Acquire radio spectrum monitoring data and frequency usage information from frequency-using stations; Link construction is performed on frequency usage information to obtain potential interference link information; noise floor assessment processing is performed on spectrum monitoring data to obtain electromagnetic environment noise floor information. The noise floor assessment processing is based on the feedback difference between the power spectrum of spectrum monitoring data and historical noise floor assessment results, which dynamically adjusts the threshold, and the feedback difference and the threshold are nonlinearly compared and summed to obtain electromagnetic environment noise floor information. Based on the parameters of the disturbed device, potential interference link information and electromagnetic environment noise floor information are screened to determine the target interference link set and target noise floor value. The parameters of the disturbed device are the radio receiving devices to be analyzed for interference impact. The signal-to-dryness ratio is calculated based on the target interference link set and the target noise floor value. The electromagnetic interference results are obtained by comparing the signal-to-dryness ratio with the preset signal-to-dryness ratio limit.

[0140] It should be understood that when the processor 701 executes the electromagnetic interference effect analysis program in the memory 702, in addition to the functions mentioned above, it can also perform other functions, as can be found in the description of the corresponding method embodiments above.

[0141] Furthermore, the embodiments of the present invention do not specifically limit the type of the electronic device 700 mentioned. The electronic device 700 can be a portable electronic device such as a tablet computer, a personal digital assistant (PDA), a wearable device, or a laptop computer. Exemplary embodiments of the portable electronic device include, but are not limited to, portable electronic devices running iOS, Android, Microsoft, or other operating systems. The aforementioned portable electronic device can also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 700 may not be a portable electronic device, but rather a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0142] Accordingly, this application also provides a computer-readable storage medium for storing computer-readable programs or instructions. When the programs or instructions are executed by a processor, they can implement the steps or functions of the electromagnetic interference influence analysis methods provided in the above-described method embodiments.

[0143] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.), and the computer program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0144] The electromagnetic interference influence analysis method, apparatus, and electronic equipment provided by the present invention have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for analyzing the effects of electromagnetic interference, characterized in that, include: Acquire radio spectrum monitoring data and frequency usage information from frequency-using stations; Link construction is performed on the frequency usage information to obtain potential interference link information; The spectrum monitoring data is subjected to background noise assessment processing to obtain electromagnetic environment background noise information. The background noise assessment processing is to dynamically adjust the threshold based on the feedback difference between the power spectrum of the spectrum monitoring data and the historical background noise assessment results, and to perform nonlinear comparison and summation processing on the feedback difference and the threshold. Based on the parameters of the disturbed device, the potential interference link information and the electromagnetic environment noise floor information are filtered to determine the target interference link set and the target noise floor value. The parameters of the disturbed device are the radio receiving devices to be analyzed for interference effects. The signal-to-dryness ratio is calculated based on the target interference link set and the target noise floor value. The electromagnetic interference results are obtained by comparing the signal-to-dryness ratio with a preset signal-to-dryness ratio limit.

2. The method according to claim 1, characterized in that, The step of performing background noise assessment processing on the spectrum monitoring data to obtain electromagnetic environment background noise information includes: The first number of consecutive times the feedback difference is less than zero is obtained. If the first number reaches the first preset number, the historical dynamic threshold is multiplied by the first number raised to the power of the first parameter which is less than one, and the first current threshold is determined. The second number of times the feedback difference is continuously greater than zero is obtained. If the second number reaches the second preset number, the historical dynamic threshold is multiplied by the second number raised to the power of the second parameter which is greater than one to determine the second current threshold. The feedback difference is nonlinearly compared and summed based on the first current threshold or the second current threshold to obtain electromagnetic environment noise floor information.

3. The method according to claim 2, characterized in that, The step of performing nonlinear comparison and summation processing on the feedback difference based on the first current threshold or the second current threshold to obtain electromagnetic environment noise floor information includes: If the absolute value of the feedback difference is less than the first current threshold or the second current threshold, then the feedback difference is determined as the first target adjustment value; If the absolute value of the feedback difference is greater than or equal to the first current threshold or the second current threshold, then the product of the current threshold and the sign function of the feedback difference is determined as the second target adjustment value. The historical background noise assessment results are summed with either the first target adjustment value or the second target adjustment value to obtain the electromagnetic environment background noise information.

4. The method according to claim 1, characterized in that, The step of constructing links from the frequency usage information to obtain potential interference link information includes: Extract the station transmission power from the frequency usage information; If the transmission power of the station is greater than a preset power threshold, the corresponding station is determined to be a potential source of interference. Data structures are constructed based on the station parameters and antenna parameters of the potential interference sources to obtain potential interference link information.

5. The method according to claim 1, characterized in that, The step of filtering the potential interference link information and the electromagnetic environment noise floor information based on the parameters of the disturbed device to determine the target interference link set and the target noise floor value includes: Using the location of the disturbed device as the center, the potential interference link information is spatially filtered based on the filtering radius to obtain a set of spatial interference links. Based on the frequency range of the disturbed device, the set of spatial interference links is filtered by frequency to determine the set of target interference links; The electromagnetic environment noise floor information is extracted based on the filtering radius and the frequency range of the disturbed device to determine the target noise floor value.

6. The method according to claim 5, characterized in that, The step of extracting the electromagnetic environment noise floor information based on the screening radius and the frequency range of the disturbed device to determine the target noise floor value includes: The spectrum monitoring data within the range of the filtering radius is used as the target noise floor set; Based on the frequency range of the disturbed device, the target noise floor set is truncated to obtain the frequency-truncated noise floor set; The target noise floor value is determined by sampling the frequency truncation noise set.

7. The method according to claim 1, characterized in that, The step of calculating the signal-to-dryness ratio (SDR) based on the target interference link set and the target noise floor value includes: Obtain the jamming device links and then combine the jamming device links with the target jamming link set to obtain the lumped jamming link set; Path loss is calculated on the lumped interference link set to obtain the lumped interference power. Power is then calculated based on the frequency parameters of the interfered equipment to determine the receiver signal power. The signal-to-dryness ratio is obtained by calculating the ratio and logarithm of the receiver signal power, the lumped interference power, and the target noise floor value.

8. The method according to claim 1, characterized in that, The acquisition of radio spectrum monitoring data and frequency usage information of frequency-using stations includes: Acquire initial radio spectrum monitoring data and initial frequency usage information from frequency-using stations; The initial radio spectrum monitoring data and the initial frequency usage information of the frequency-using stations are processed for format adaptation and cleaning to obtain the radio spectrum monitoring data and the frequency usage information of the frequency-using stations.

9. An electromagnetic interference influence analysis device, characterized in that, The device includes: The data acquisition unit is used to acquire radio spectrum monitoring data and frequency usage information from frequency-using stations; The data processing unit is used to construct links from the frequency usage information to obtain potential interference link information; and to perform noise floor assessment processing on the spectrum monitoring data to obtain electromagnetic environment noise floor information. The noise floor assessment processing is to dynamically adjust the threshold based on the feedback difference between the power spectrum of the spectrum monitoring data and the historical noise floor assessment results, and to perform nonlinear comparison and summation processing on the feedback difference and the threshold. The data filtering unit is used to filter the potential interference link information and the electromagnetic environment noise floor information based on the parameters of the disturbed device, and determine the target interference link set and the target noise floor value. The parameters of the disturbed device are the radio receiving devices to be subjected to interference impact analysis. The data calculation unit is used to calculate the signal-to-dryness ratio based on the target interference link set and the target noise floor value, and obtain the signal-to-dryness ratio. The data comparison unit is used to compare the signal-to-dryness ratio with a preset signal-to-dryness ratio limit to obtain the electromagnetic interference result.

10. An electronic device, characterized in that, The electronic device includes a radio receiver, a data acquisition unit, a memory, and a processor, wherein, The radio receiver and the data acquisition unit are used to acquire radio spectrum monitoring data and frequency usage information of frequency-using stations; The memory is used to store executable program code; The processor is configured to construct links from the frequency usage information to obtain potential interference link information; perform noise floor assessment processing on the spectrum monitoring data to obtain electromagnetic environment noise floor information; filter the potential interference link information and the electromagnetic environment noise floor information based on the parameters of the disturbed device to determine the target interference link set and the target noise floor value; calculate the signal-to-dryness ratio based on the target interference link set and the target noise floor value to obtain the signal-to-dryness ratio; and compare the signal-to-dryness ratio with a preset signal-to-dryness ratio limit to obtain the electromagnetic interference result.

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