A mobile terminal carrier leakage signal detection and evaluation method and device

Abstract

This application provides a method and apparatus for detecting and evaluating carrier leakage signals in mobile terminals, belonging to the field of leakage detection technology. The method includes: performing carrier detection on mobile medical devices and surrounding mobile terminals in a hospital's mobile medical area to obtain carrier data; performing quantum spectrum analysis on the carrier data to determine the carrier leakage frequency band; calculating the leakage power density of the carrier leakage frequency band and the spectral overlap between the carrier leakage frequency band and the operating frequency band of the mobile medical device; determining an electromagnetic interference risk value based on the leakage power density and spectral overlap; generating a risk report when the electromagnetic interference risk value exceeds a preset first threshold; verifying the risk report using a deviation comparison method to obtain verification results, including inaccurate and accurate reports; and generating a risk warning based on the risk report corresponding to the accurate verification result. This application provides efficient technical support for leakage detection of medical devices in complex electromagnetic environments within hospitals.

Description

Technical Field

[0001] This application relates to the field of leakage detection technology, and in particular to a method and apparatus for detecting and evaluating carrier leakage signals in mobile terminals. Background Technology

[0002] With the widespread adoption of smart healthcare, mobile medical devices and various mobile terminals are densely deployed in hospitals. Consequently, wireless communication modules are prone to carrier leakage due to modulation imbalance and local oscillator leakage. Their radiated signals easily overlap with the operating frequency bands of medical equipment, causing electromagnetic interference. This leads to data distortion, false alarms, and even malfunctions in devices such as monitors and ventilators, threatening safe medical treatment. Traditional detection methods rely on conventional spectrum analysis, which suffers from narrow bandwidth, low accuracy, and difficulty in identifying weak leakage signals. Furthermore, they lack quantitative assessment and risk verification mechanisms for leakage power and frequency band overlap, making it impossible to accurately determine the interference level or output reliable warnings, thus failing to meet the real-time monitoring and safety management needs of hospitals in complex electromagnetic environments. Summary of the Invention

[0003] To address the aforementioned technical problems, this application provides a method and apparatus for detecting and evaluating carrier leakage signals in mobile terminals.

[0004] A first aspect of this application provides a method for detecting and evaluating carrier leakage signals in a mobile terminal, comprising: Carrier detection is performed on mobile medical devices and surrounding mobile terminals in the hospital's mobile medical area to obtain carrier data; Quantum spectrum analysis of the carrier data revealed the presence of a carrier leakage frequency band. Calculate the leakage power density of the carrier leakage frequency band and the spectral overlap between the carrier leakage frequency band and the operating frequency band of the mobile medical device; Based on the leakage power density and the spectral overlap, the electromagnetic interference risk value of the carrier leakage frequency band to mobile medical devices is determined; When the electromagnetic interference risk value is greater than a preset first threshold, a risk report is generated; The risk report is verified using the deviation comparison method to obtain verification results, which include inaccurate reports and accurate reports. Based on the verification results, a risk report corresponding to the accurate report is generated, and a risk warning is generated.

[0005] A second aspect of this application provides a mobile terminal carrier leakage signal detection and evaluation device, comprising: The carrier data acquisition module is used to perform carrier detection on mobile medical devices and surrounding mobile terminals in the hospital's mobile medical area to obtain carrier data. The quantum spectrum analysis module is used to determine the presence of a carrier leakage frequency band in the carrier data by performing quantum spectrum analysis on the carrier data; The leakage parameter calculation module is used to calculate the leakage power density of the carrier leakage frequency band and the spectral overlap between the carrier leakage frequency band and the operating frequency band of the mobile medical device. An electromagnetic risk assessment module is used to determine the electromagnetic interference risk value of the carrier leakage frequency band to mobile medical devices based on the leakage power density and the spectral overlap. The risk report generation module is used to generate a risk report when the electromagnetic interference risk value is greater than a preset first threshold. The report accuracy verification module is used to verify the risk report based on the deviation comparison method and obtain the verification results, which include inaccurate reports and accurate reports. The risk warning generation module is used to generate a risk warning based on the verification result corresponding to the accurate report.

[0006] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the above-described mobile terminal carrier leakage signal detection and evaluation method.

[0007] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described mobile terminal carrier leakage signal detection and evaluation method.

[0008] The beneficial effects of the mobile terminal carrier leakage signal detection and evaluation method and apparatus provided in this application are as follows: By performing carrier detection on medical equipment and surrounding mobile terminals within the hospital's mobile medical area and employing quantum spectrum analysis technology, this application can improve the accuracy of identifying weak carrier leakage frequency bands that are difficult to capture using traditional methods. Furthermore, by quantitatively calculating the leakage power density and spectral overlap, errors caused by subjective judgment are avoided. The risk report is verified using a deviation comparison method, effectively eliminating false alarms and improving the reliability of risk warnings while reducing interference from invalid alarms to medical work. When a high-risk leakage signal is detected, an accurate warning is automatically generated, which can promptly remind staff to take intervention measures such as shielding and frequency band adjustment. This reduces the electromagnetic interference of mobile terminal carrier leakage on precision medical equipment from the source, further ensuring the stable operation of medical equipment and the security of diagnostic data, and providing efficient technical support for leakage detection of medical equipment in complex electromagnetic environments within hospitals. Attached Figure Description

[0009] Figure 1 A flowchart illustrating a mobile terminal carrier leakage signal detection and evaluation method provided in an embodiment of this application; Figure 2 This is a structural block diagram of a mobile terminal carrier leakage signal detection and evaluation device provided in an embodiment of this application; Figure 3 This is a schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0010] In the following description, specific details such as particular device structures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known devices, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0011] To make the purpose, technical solution, and advantages of this application clearer, the following will be described in conjunction with the appendix. Figure 1-3 The following is an explanation using specific examples.

[0012] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a mobile terminal carrier leakage signal detection and evaluation method according to an embodiment of this application. The method includes: S101: Perform carrier detection on mobile medical devices and surrounding mobile terminals in the hospital's mobile medical area to obtain carrier data.

[0013] In this embodiment, prior deployment and parameter configuration must be completed before carrier detection. Specifically, based on the three-dimensional spatial layout of the hospital's mobile medical area (ward distribution, treatment area division, corridor orientation), multiple spectrum sensors with wideband reception capabilities are deployed. The sensor deployment density must ensure no detection blind spots and cover the working range of all mobile medical devices (monitors, portable ultrasound devices, infusion pumps, etc.) and the activity area of ​​mobile terminals within a 10-15 meter radius. Secondly, basic parameters such as the rated operating frequency band and communication protocol type of all mobile medical devices in the area are pre-entered, and the detection frequency band range of the sensors is set. Finally, the sensors are time-synchronized and calibrated to ensure the consistency of timestamps in the data collected by multiple sensors.

[0014] The spectrum sensor continuously acquires carrier signals from the target area according to a preset sampling frequency (set to a sampling interval greater than or equal to 1MHz based on the communication rate of medical devices and the signal characteristics of mobile terminals). During the acquisition process, it records the time-domain waveform, frequency information, and acquisition timestamp of the signal. For core treatment areas (ICU, operating room) with a high density of mobile medical devices, a high-frequency sampling mode is adopted to improve signal capture accuracy. At the same time, the sensor filters out fixed-frequency interference signals in the environment (power frequency interference generated by lighting equipment and air conditioning systems), and initially screens out carrier signals suspected to be generated by mobile terminals or medical devices themselves, thus obtaining carrier data.

[0015] In this embodiment, after obtaining the carrier data, the carrier data is subjected to noise reduction processing. Electromagnetic noise and irrelevant signal interference are removed by digital filtering technology (adaptive filtering, wavelet denoising) while retaining the effective carrier signal characteristics. Data collected by multiple sensors in the same time period are integrated and aligned. Finally, the processed carrier data is classified and stored according to the acquisition time period and detection location.

[0016] Among them, carrier detection refers to receiving carrier signals emitted or leaked by various electronic devices (mobile medical devices, mobile terminals) within a target area through professional spectrum detection equipment (spectrum sensors); carrier data includes key information such as the frequency range, time-domain waveform, power intensity, acquisition timestamp, and detection location of the carrier signal; mobile medical devices: medical instruments or devices with wireless communication functions used in hospital mobile medical scenarios; mobile terminals: portable electronic devices that generate carrier leakage within the hospital mobile medical area; hospital mobile medical area: specific areas within a hospital where mobile diagnosis and treatment and nursing services are carried out, such as wards, ICUs, operating rooms, emergency channels, and mobile medical vehicle operation areas.

[0017] S102: By performing quantum spectrum analysis on the carrier data, it is determined that there is a carrier leakage frequency band in the carrier data.

[0018] In this embodiment, quantum spectrum analysis is used to process the carrier data. This process relies on the characteristics of quantum superposition and quantum coherence to map the original carrier data to the quantum spectrum space. Through mechanisms such as quantum filtering and quantum coherence enhancement, environmental noise and irrelevant interference signals are suppressed, making the originally submerged weak leakage components stand out.

[0019] After completing quantum domain spectrum reconstruction and feature extraction, the analyzed spectral features are compared with a preset normal operating spectrum template and compliant frequency bands for medical equipment. Abnormal frequency points and bands, such as power anomalies, frequency band offsets, and unauthorized transmissions, are identified. Finally, the carrier leakage frequency bands present in the carrier data are determined, and key parameters such as their center frequency, bandwidth, and distribution range are recorded. The preset normal operating spectrum template is constructed based on the factory technical parameters of hospital mobile medical equipment, industry electromagnetic compatibility standards, and historical interference-free spectrum data for the region. The compliant frequency bands for medical equipment are determined according to national / international electromagnetic compatibility specifications for medical electrical equipment, the equipment's rated operating frequency band, and the hospital's dedicated wireless communication frequency band planning. The carrier leakage frequency band is the frequency range where carrier leakage signals are concentrated. This carrier leakage frequency band overlaps with or is adjacent to the operating frequency band of the mobile medical equipment and is a critical frequency band that causes electromagnetic interference and affects the normal operation of the equipment.

[0020] S103: Calculate the leakage power density of the carrier leakage band and the spectral overlap between the carrier leakage band and the operating frequency band of the mobile medical device.

[0021] In this embodiment, multiple spectrum sensors capture the arrival time difference or arrival angle of the same carrier leakage source signal, and the three-dimensional coordinates of the leakage source are calculated based on the multi-source localization principle. Next, the spatial distance between the three-dimensional coordinates and the location of the mobile medical device is input into an electromagnetic wave spatial propagation loss model pre-trained with hospital scene data. The leakage power density of the leakage signal at the location of the medical device is calculated through the model. Finally, the frequency ranges of the carrier leakage frequency band and the operating frequency band of the mobile medical device are extracted, and the corresponding power weight functions are convolved with the device frequency sensitivity function to obtain the spectral overlap degree representing the degree of overlap between the two frequency bands.

[0022] The arrival time difference refers to the time difference between the arrival of the same leaking source signal at different spectrum sensors. The angle of arrival refers to the incident angle of the leaking source signal when it propagates to each spectrum sensor. The leakage power density refers to the power of the leaked signal per unit area, representing the intensity of interference caused by the leaked signal to medical equipment. The spectral overlap is a quantitative indicator used to characterize the degree of overlap and mutual influence between the carrier leakage frequency band and the operating frequency band of the mobile medical device. The higher the spectral overlap value, the more severe the overlap between the two frequency bands, and the greater the risk of electromagnetic interference.

[0023] S104: Determine the electromagnetic interference risk value of the carrier leakage frequency band to mobile medical devices based on leakage power density and spectral overlap.

[0024] In this embodiment, firstly, the communication quality time series of the mobile medical device in a leak-free environment is collected, and its phase space is reconstructed using the coordinate delay method to obtain the chaotic attractor trajectory. Secondly, in the presence of carrier leakage, the communication quality time series of the mobile medical device is simultaneously collected and mapped to the same phase space. Finally, the maximum Lyapunov exponent difference or correlation dimension change between the attractor trajector trajectories in the two phase spaces is calculated as the electromagnetic interference risk value. The electromagnetic interference risk value is used to uniformly characterize the degree of danger of carrier leakage interfering with the normal operation of the mobile medical device, and serves as the basis for determining whether a risk report needs to be generated and an early warning issued.

[0025] S105: When the electromagnetic interference risk value is greater than the preset first threshold, a risk report is generated.

[0026] In this embodiment, after calculating the electromagnetic interference risk value of the carrier leakage frequency band to the mobile medical device using leakage power density and spectral overlap, the electromagnetic interference risk value is compared and judged in real time with a pre-set first threshold. This first threshold is a safety critical value determined comprehensively based on a large amount of measured data and standard specifications, taking into account the electromagnetic compatibility performance of the mobile medical device, clinical safety level, and hospital electromagnetic environment control requirements. When the calculated electromagnetic interference risk value exceeds the first threshold, it indicates that the strength and frequency band overlap of the current carrier leakage signal have exceeded the safe allowable range, posing a potential or even direct electromagnetic interference threat to the normal operation of the mobile medical device. At this time, a risk report generation process is initiated, integrating information such as the location of the leakage source, leakage frequency band, power density, spectral overlap, risk level, and affected medical devices to form a standardized and traceable risk report.

[0027] This embodiment also includes: adjusting the first threshold according to the functional zoning of the hospital's mobile medical area. Specifically, this includes: acquiring map information of the hospital's mobile medical area, identifying the electromagnetic susceptibility level of each functional zone, where operating rooms and ICUs are high-sensitivity zones, general wards are medium-sensitivity zones, and public areas are low-sensitivity zones; multiplying the electromagnetic interference risk value by the sensitivity level of the corresponding functional zone to obtain a weighted risk value; and triggering a risk warning when the weighted risk value is greater than the threshold of the corresponding zone. The threshold for the corresponding zone is a zone-specific safety critical value obtained by weighting and correcting the first threshold based on the electromagnetic safety sensitivity levels of different functional zones in the hospital. This threshold is used to achieve differentiated and refined interference risk assessment in different scenarios such as operating rooms, ICUs, general wards, and public areas.

[0028] S106: Validate risk reports based on the deviation comparison method to obtain validation results, which include inaccurate reports and accurate reports.

[0029] In this embodiment, based on the mobile medical device corresponding to the risk report, the communication quality time series of the corresponding mobile medical device is mapped to the phase space where the chaotic attractor trajectory of the mixed communication quality time series in a leak-free environment is located. The instantaneous deviation distance between the trajectory point of the current communication quality time series in the phase space and the chaotic attractor trajectory in the leak-free environment is calculated. Specifically, if the instantaneous deviation distance is less than a preset deviation threshold, the risk report is determined to be an inaccurate report; if the instantaneous deviation distance is greater than the deviation threshold, the risk report is determined to be a report to be verified. The report to be verified is then processed through a multi-dimensional fusion verification stage to obtain an accurate report; otherwise, it is an inaccurate report.

[0030] S107: Generate a risk warning based on the risk report corresponding to the verification result being an accurate report.

[0031] In this embodiment, after verifying the risk report using the deviation comparison method, the verification results are classified and screened, and the content that is determined to be an inaccurate report is removed to avoid unnecessary interference with medical work due to false alarms or false reports.

[0032] Specifically, for risk reports that are verified and deemed accurate, a corresponding risk warning is generated based on a preset alarm strategy. The risk warning clearly indicates the carrier leakage frequency band, leakage power density, affected mobile medical device number, location, and current electromagnetic interference risk level, enabling medical and equipment maintenance personnel to quickly grasp the source, scope, and severity of the risk. The risk warning generation mechanism is linked to the verification results; only verified accurate reports can trigger the warning process. Accurate reports are those that have undergone multi-dimensional verification, confirming the actual existence of carrier leakage, reliable interference risk assessment, and no significant data deviation.

[0033] The generated risk warnings are simultaneously transmitted to the medical equipment monitoring center and relevant responsible persons through various means such as visual interfaces, audio and visual prompts, and mobile message pushes.

[0034] As can be seen from the above, this application, by performing carrier detection on medical devices and surrounding mobile terminals within the hospital's mobile medical area and employing quantum spectrum analysis technology, can improve the accuracy of identifying weak carrier leakage frequency bands that are difficult to capture using traditional methods. Furthermore, by quantitatively calculating leakage power density and spectral overlap, errors caused by subjective judgment are avoided. Verification of risk reports using a deviation comparison method effectively eliminates false alarms, improving the reliability of risk warnings while reducing interference from invalid alarms to medical work. When a high-risk leakage signal is detected, an accurate warning is automatically generated, promptly reminding staff to take intervention measures such as shielding and frequency band adjustment. This reduces electromagnetic interference from mobile terminal carrier leakage to precision medical equipment at the source, further ensuring the stable operation of medical equipment and the security of diagnostic data, and providing efficient technical support for leakage detection of medical equipment in complex electromagnetic environments within hospitals.

[0035] In one embodiment of this application, calculating the leakage power density of the carrier leakage band and the spectral overlap between the carrier leakage band and the operating frequency band of the mobile medical device includes: Based on the time difference or angle of arrival of the same leakage source by multiple spectrum sensors in the hospital's mobile medical area, the three-dimensional coordinates of the leakage source in the carrier leakage frequency band are located. The leakage power density is obtained by inputting the three-dimensional coordinates and the distance between them and the location of the mobile medical equipment in the hospital into a pre-trained electromagnetic wave spatial propagation loss model. The frequency range of the carrier leakage band is convolved with the frequency range of the mobile medical device's operating band to obtain the spectral overlap.

[0036] In this embodiment, multiple spectrum sensors deployed at multiple points within the mobile medical area of ​​the hospital are used to synchronously receive radio frequency signals from the same leakage source. By measuring the time difference or angle of arrival of the signals to each sensor, and calculating based on the spatial geometric relationship of the area and the positioning algorithm, the coordinate position of the leakage source in three-dimensional space is determined.

[0037] After obtaining the three-dimensional coordinates of the leakage source, the actual spatial distance between these coordinates and the location of the affected mobile medical device is calculated. The distance value, environmental obstruction information, and other parameters are input into an electromagnetic wave spatial propagation loss model pre-trained with field data. The attenuation of the leaked signal at the medical device is calculated using the electromagnetic wave spatial propagation loss model, ultimately yielding the true and effective leakage power density at the receiving location of the medical device. Simultaneously, the frequency range of the carrier leakage band is convolved with the frequency range of the mobile medical device's operating frequency band to obtain the spectral overlap. Convolution is a frequency domain signal processing method used to comprehensively calculate the leakage signal power distribution and the device's frequency band sensitivity, thereby characterizing the degree of spectral overlap and interference coupling.

[0038] In this embodiment, the electromagnetic wave spatial propagation loss model is constructed based on the actual indoor propagation environment of a hospital's mobile medical area. First, a basic model framework is established based on the free-space propagation loss model. Then, corrections are made based on the impact of obstruction factors such as walls, mobile medical equipment, and obstacles on signal attenuation. When constructing the electromagnetic wave spatial propagation loss model, multi-point field measurements are conducted under different obstruction conditions and transmission distances within the hospital. Sample data such as the location of the leakage source, the location of the mobile medical equipment, the type of obstruction, and the received power density are collected. The measured data are then fitted and the calculated values ​​of the basic model are calibrated to form an electromagnetic wave spatial propagation loss model adapted to the medical scenario. The parameter settings of this electromagnetic wave spatial propagation loss model combine on-site calibration with adaptive adjustment. The core path loss index is adjusted according to the environmental obstruction level. First, the optimal path loss index under different obstruction levels is obtained by inverting historical measured data. Then, a lookup table method or machine learning model is used to achieve adaptive parameter matching. Simultaneously, propagation distance, obstacle material, and equipment height are used as auxiliary input parameters.

[0039] From the above, it can be concluded that this embodiment uses a multi-spectral sensor to achieve three-dimensional coordinate positioning of the leakage source by employing time difference of arrival or angle of arrival. This can determine the source location of the carrier leakage signal in the complex indoor environment of a hospital, effectively improving the accuracy and reliability of leakage source tracking. Furthermore, the pre-trained electromagnetic wave spatial propagation loss model calculates the leakage power density, effectively improving the accuracy of interference intensity calculation. By performing convolution operations on the leakage frequency band and the operating frequency band of the medical equipment to obtain the spectral overlap, the degree of overlap and coupling strength between the two can be quantified from the frequency domain dimension, improving the objectivity of interference impact.

[0040] In one embodiment of this application, the frequency range of the carrier leakage band is convolved with the frequency range of the mobile medical device's operating frequency band to obtain the spectral overlap, including: Based on the distribution of leakage power density within the carrier leakage frequency band, a power weighting function is constructed; The frequency sensitivity function and the power weighting function within the operating frequency band of the mobile medical device are convolved to obtain the spectral overlap function. Extract the peak value or integral value of the spectral overlap function as the spectral overlap degree.

[0041] In this embodiment, a power weighting function is constructed with frequency as the independent variable and power density as the dependent variable, based on the distribution of leakage power density across the entire frequency band. Simultaneously, a frequency sensitivity function is constructed based on the receiving sensitivity and anti-interference capabilities of the mobile medical device at different frequency points within its operating frequency band, representing the sensitivity of the mobile medical device to interference at different frequencies. This frequency sensitivity function is then convolved with the power weighting function to obtain a spectral overlap function that simultaneously represents the leakage power distribution and the device's frequency sensitivity characteristics.

[0042] Specifically, by extracting the peak value or integral value of the spectral overlap function within the frequency band, the overlap characteristics in the continuous frequency domain are transformed into a single quantitative index, which serves as the spectral overlap degree. The power weighting function describes the intensity distribution of the leaked signal at different frequencies; the frequency sensitivity function represents the sensitivity of the mobile medical device to interference signals at different frequency points within the operating frequency band; the spectral overlap function, a continuous function obtained by convolution, includes the leakage power distribution and device frequency sensitivity information, and is an intermediate result for calculating the spectral overlap degree; the peak value or integral value extracted from the spectral overlap function serves as a quantitative index to represent the actual interference coupling degree between carrier leakage and the operating frequency band of the medical device.

[0043] As can be seen from the above, this embodiment obtains the spectral overlap function by constructing a power weighting function based on the leakage power density distribution and performing convolution operations based on the frequency sensitivity function of the mobile medical device. The peak value or integral value of the spectral overlap function is used as the spectral overlap degree. This can reflect the energy distribution differences of carrier leakage at different frequencies and the sensitivity characteristics of mobile medical devices in the corresponding frequency bands. It realizes the coupling of interference signals and device vulnerability, effectively improves the refinement and objectivity of spectral overlap assessment, further avoids assessment errors caused by uneven power distribution and differences in the frequency response of mobile medical devices, and improves the accuracy and practicality of the entire carrier leakage detection and assessment.

[0044] In one embodiment of this application, determining the electromagnetic interference risk value of the carrier leakage frequency band to a mobile medical device based on leakage power density and spectral overlap includes: The communication quality time series of mobile medical devices in a leak-free environment was collected, and their phase space was reconstructed using the coordinate delay method to obtain the chaotic attractor trajectory. In the presence of carrier leakage, the communication quality time series of mobile medical devices are collected synchronously and mapped to the same phase space; Calculate the maximum Lyapunov exponential difference or correlation dimension change between attractor trajectories in two phase spaces as the electromagnetic interference risk value.

[0045] In this embodiment, the communication quality time series of the mobile medical device is first collected under the reference state of no carrier leakage and a clean electromagnetic environment. The phase space of the communication quality time series is reconstructed using the coordinate delay method, and the one-dimensional time series data is extended to a high-dimensional phase space, thereby restoring the inherent dynamic characteristics of the mobile medical device under normal communication state and forming a stable chaotic attractor trajectory.

[0046] In this embodiment, in a real-world detection environment where carrier leakage exists, the communication quality time series of the same mobile medical device is simultaneously acquired and mapped to the same phase space as the baseline state. This allows for a comparison of the dynamic characteristics of the normal and interfered states within a unified space. Simultaneously, the difference in the maximum Lyapunov exponent, or the change in the correlation dimension, between the baseline chaotic attractor and the interfered chaotic attractor is calculated, and this difference or change is used as the electromagnetic interference risk value. In this process, phase space reconstruction is performed on two sets of communication quality time series. Using either a small data volume method or a definition method, the maximum Lyapunov exponent under the baseline state and the maximum Lyapunov exponent under the disturbed state are fitted by tracking the average exponential divergence rate of adjacent trajectories in the phase space. The absolute value of the difference between the two is the difference in the index. For the change in correlation dimension, the Glasberg-Procascha algorithm is used. The correlation integrals of the baseline and the disturbed attractor are calculated separately. The correlation integral represents the degree of spatial correlation between different points on the chaotic attractor. Scale-free intervals are determined in a double logarithmic coordinate system, and their slopes are fitted to obtain the baseline correlation dimension and the disturbed correlation dimension. The absolute value of the difference between the two is the change in correlation dimension. The core principle of the Glasberg-Procascha algorithm (GP algorithm) is based on Takens' embedding theorem to reconstruct a one-dimensional time series into a high-dimensional phase space. By calculating the correlation integral of the point set in the phase space and fitting the slope using a power-law relationship in a double logarithmic coordinate system, the correlation dimension, which characterizes the geometric complexity of the chaotic attractor, is obtained. This algorithm is used to quantify the dynamic characteristics of communication quality time series of mobile medical devices. Specifically, by calculating the correlation dimension of the chaotic attractor formed after phase space reconstruction of the communication quality time series of mobile medical devices in a carrier-free reference environment and an environment with leakage interference, the absolute value of the difference between the two is used as the change in correlation dimension, and then the electromagnetic interference risk value is jointly constructed based on the difference in the maximum Lyapunov exponent.

[0047] The communication quality time series is a sequence of communication indicators from mobile medical devices collected continuously over time. The coordinate delay method is a classic approach for reconstructing the phase space of time series data. By delaying and reconstructing one-dimensional time-series data, it recovers the original high-dimensional dynamic characteristics of the system. Phase space represents the trajectory and evolution of a dynamic system. The maximum Lyapunov exponent represents the stability change of the system after interference. The correlation dimension represents the degree of damage to the system structure caused by interference. The electromagnetic interference risk value represents the severity of carrier leakage interference to medical devices.

[0048] From the above, it can be concluded that this embodiment obtains the reference chaotic attractor trajectory by collecting the communication quality time series of mobile medical devices in a leak-free environment and reconstructing the phase space using the coordinate delay method. Then, the communication quality series in the leaky environment is mapped to the same phase space. By calculating the maximum Lyapunov exponent difference or the change in correlation dimension, the electromagnetic interference risk value is determined. This can capture the disturbance and characteristic distortion caused by carrier leakage to the medical device communication system from the perspective of nonlinear dynamics, improve the detection sensitivity and evaluation accuracy under weak interference and intermittent interference, and further improve the accuracy and reliability of the entire carrier leakage detection and evaluation system.

[0049] In one embodiment of this application, a method for detecting and evaluating carrier leakage signals in a mobile terminal further includes: The time-domain waveform of the carrier leakage frequency band is collected, the burst period and duty cycle characteristics of the time-domain waveform are extracted, and it is determined whether the leakage signal is an intermittent pulse sequence. If it is determined to be an intermittent pulse sequence, the phase coupling degree between the intermittent leakage pulse and the medical device communication quality time series is calculated based on the burst period, duty cycle and chaotic attractor trajectory. When the phase coupling degree is greater than the preset coupling threshold, a risk enhancement coefficient is generated based on the phase coupling degree, and the product of the risk enhancement coefficient and the electromagnetic interference risk value is calculated to obtain the corrected electromagnetic interference risk value.

[0050] In this embodiment, the time-domain waveform of the carrier leakage frequency band is acquired and refined for analysis. Specifically, key time-domain features such as the burst period and duty cycle of the signal are extracted from the time-domain waveform, and the leakage signal is determined to be an intermittent pulse sequence based on the feature patterns. By identifying the dynamic features of the time-domain waveform, continuous leakage and burst leakage can be distinguished, solving the problem that traditional evaluation methods cannot reflect the instantaneous impact characteristics of pulse-type interference.

[0051] When carrier leakage is determined to be an intermittent pulse sequence, the phase coupling degree between the intermittent leakage pulse and the communication quality time series of the mobile medical device is calculated based on the acquired burst period, duty cycle parameters, and chaotic attractor trajectory. The phase coupling degree represents the degree of synchronization and mutual influence between the interfering pulse and the device communication timing. Specifically, when the calculated phase coupling degree is greater than a preset coupling threshold, it indicates strong coupling between the intermittent leakage signal and the medical device communication process, amplifying the interference hazard. In this case, a corresponding risk enhancement coefficient is generated based on the phase coupling degree, and multiplied by the original electromagnetic interference risk value to obtain a corrected electromagnetic interference risk value. Here, the time-domain waveform is the voltage or power waveform of the carrier leakage signal changing over time; the burst period refers to the time interval between two adjacent occurrences of the intermittent leakage pulse; the duty cycle refers to the proportion of effective transmission time of the intermittent pulse within one period; and the intermittent pulse sequence is a leakage signal composed of discontinuous, periodically occurring pulses. The phase coupling degree is a quantitative indicator characterizing the temporal synchronization and mutual influence strength between the intermittent leakage pulse and the communication quality time series of the medical device. The risk enhancement factor, based on the amplification factor generated by high phase coupling, is used to correct the original risk value, representing the increased harm caused by intermittent pulses. The preset coupling threshold is determined comprehensively based on the communication timing characteristics of the mobile medical device, historical measured data of carrier leakage pulse interference, the device's electromagnetic compatibility safety indicators, and the clinically permissible interference limit.

[0052] As can be seen from the above, this embodiment collects the time-domain waveform of the carrier leakage frequency band, extracts the burst period and duty cycle characteristics to determine whether it is an intermittent pulse sequence, and calculates the phase coupling degree between the interference and the communication quality of the medical equipment based on the burst period, duty cycle and chaotic attractor trajectory. When the phase coupling degree is greater than the preset coupling threshold, the electromagnetic interference risk value is corrected based on the risk enhancement coefficient. This can improve the accuracy of identifying transient, impulsive and strongly coupled with the timing of the equipment, high-hazard intermittent leakage interference. It makes up for the shortcomings of traditional assessment methods that cannot represent the time-domain impact and timing coupling effects, and makes the final electromagnetic interference risk value more consistent with the actual interference hazard level.

[0053] In one embodiment of this application, after obtaining the corrected electromagnetic interference risk value, the method further includes: Calculate the pulse width of an intermittent pulse sequence based on the burst period and duty cycle; Based on the pulse width and burst period, determine the interference period and interference silence period of the intermittent pulse sequence; Mobile medical devices adjust their data transmission and reception time slots to fall within the time window of the interference silence period to obtain the adjusted data transmission and reception time slots. Based on the adjusted data transmission and reception time slots, the communication quality time series of mobile medical devices is collected and remapped to the phase space where the chaotic attractor trajectory is located; If the recalculated maximum Lyapunov index returns to the baseline range in a leak-free environment, a valid avoidance confirmation report is generated.

[0054] In this embodiment, the pulse width is calculated by multiplying the burst period and the duty cycle. Then, based on the pulse width and the burst period, the interference period and the interference silence period are divided.

[0055] Specifically, mobile medical devices, based on identified periods of quiet interference, shift and lock their data transmission and reception time slots within these quiet periods, creating adjusted data transmission and reception time slots that avoid interference pulses. This proactive avoidance of intermittent leakage signals is achieved through improved communication timing. This time slot scheduling method based on quiet windows reduces the probability of interference coupling without affecting normal communication, without changing the operating frequency or power of the mobile medical devices. It is suitable for real-time anti-interference management of various mobile medical devices within hospitals.

[0056] After time slot adjustment, the communication quality time series of the mobile medical device is re-acquired and mapped to the chaotic attractor trajectory phase space in a leak-free environment. The maximum Lyapunov exponent is then recalculated. If the exponent recovers to the baseline range, a valid avoidance confirmation report is generated. The interference-existing period refers to the time period during which the intermittent carrier leakage signal generated by the mobile terminal is in transmission mode and interferes with the mobile medical device. The interference-free period refers to the safe time period during which the intermittent carrier leakage signal generated by the mobile terminal stops transmission and there is no spatial leakage interference. Data transmission and reception time slots are fixed time segments used by the mobile medical device for data transmission and reception.

[0057] As can be seen from the above, this embodiment calculates the pulse width and divides the interference period into the interference silence period, enabling the mobile medical device to adaptively adjust its data transmission and reception time slots to the silence period. The avoidance effect is verified by using phase space reconstruction and the maximum Lyapunov exponent. This achieves accurate and proactive avoidance of intermittent carrier leakage interference, effectively reducing the impact of interference without changing the device's operating frequency and transmission power. At the same time, the chaotic dynamics index objectively verifies whether the communication quality has recovered to the normal level, forming a complete closed loop from interference analysis, time slot optimization to effect confirmation. This effectively improves the anti-interference capability and operational stability of the mobile medical device in complex electromagnetic environments.

[0058] In one embodiment of this application, the risk report is verified based on the deviation comparison method to obtain the verification result, including: Based on the mobile medical devices corresponding to the risk report, the communication quality time series of the corresponding mobile medical devices is mapped to the phase space of the chaotic attractor trajectory of the mixed communication quality time series in a leak-free environment; Calculate the instantaneous deviation distance between the trajectory point of the current communication quality time series in phase space and the trajectory of the chaotic attractor in a leak-free environment; If the instantaneous deviation distance is less than the preset deviation threshold, the risk report is determined to be an inaccurate report; If the instantaneous deviation distance is greater than the deviation threshold, the risk report is determined to be a report to be verified, and an accurate report is obtained through a multi-dimensional fusion verification stage; otherwise, it is an inaccurate report.

[0059] In this embodiment, after generating a risk report, to avoid false alarms or misreports affecting the normal operation of medical equipment, the authenticity of the report needs to be verified. Specifically, taking the mobile medical device corresponding to the risk report as the object, its currently collected communication quality time series is mapped to a pre-established phase space containing the chaotic attractor trajectory in a leak-free environment, so that the real-time state and the baseline state can be intuitively compared in the same dynamic space.

[0060] In phase space, the instantaneous deviation distance between the trajectory points formed by the current communication quality time series and the trajectory of the chaotic attractor without leakage is calculated. This instantaneous deviation distance represents the degree of difference between the current operating state of the mobile medical device and its normal, interference-free state. The instantaneous deviation distance is compared with a preset deviation threshold. If the deviation distance is less than the threshold, it indicates that the mobile medical device's state has not experienced any significant abnormalities, and the risk report can be determined as an inaccurate report, thus eliminating invalid alarms. The method for setting the deviation threshold includes: First, under a standard clean electromagnetic environment with no carrier leakage and no external interference, the communication quality time series of the device is continuously collected, and the phase space is reconstructed using the coordinate delay method to obtain a stable chaotic attractor trajectory. Under this reference environment, the instantaneous deviation distance between multiple sets of trajectory points in normal communication states and the chaotic attractor is continuously calculated to form a normal deviation sample set. Statistical analysis is performed based on this normal deviation sample set to obtain the mean and standard deviation of all instantaneous deviation distances. Then, 1 to 3 times the standard deviation is added to the mean as an initial deviation threshold, so that the deviation threshold can cover the inherent fluctuation range of the mobile medical device during normal operation. Meanwhile, the initial threshold is corrected based on the type of mobile medical device, its sensitivity level, and the electromagnetic background noise level of the area. This allows deviations from the threshold to distinguish between normal jitter of the mobile medical device and trajectory distortion caused by real interference, thus ensuring that the verification process will not be misjudged due to noise or miss real carrier leakage interference.

[0061] If the instantaneous deviation distance exceeds the deviation threshold, a preliminary assessment indicates the presence of genuine interference, and the risk report is marked as a report awaiting verification. Further verification is then performed based on multi-dimensional data, including leakage power density, spectral overlap, temporal waveform characteristics, and location information, ultimately determining it to be an accurate report. If the multi-dimensional verification phase fails, it is still considered an inaccurate report. The multi-dimensional verification phase includes: responding to the generation of the risk report by performing secondary carrier detection on the surrounding environment to obtain verification carrier data; comparing the temporal correlation between the verification carrier data and the carrier data; determining the spatial direction of arrival of the carrier leakage frequency band using a multi-channel vector sensor and comparing it with the three-dimensional coordinates of the leakage source; simultaneously reading the internal bit error rate monitoring data of the mobile medical device to determine if its fluctuation trend is positively correlated; if at least two of the above verification methods are satisfied, the risk report is considered accurate; otherwise, it is considered a false alarm. Specifically, temporal correlation is used to determine whether the two data acquisitions are from the same stable leakage source, rather than random noise or transient clutter. The internal bit error rate monitoring data consists of real-time statistics from the mobile medical device's own communication module, including bit error rate, signal-to-noise ratio, packet loss rate, and other communication quality indicators.

[0062] As can be seen from the above, this embodiment maps the current communication quality time series of the mobile medical device to the chaotic attractor phase space in a leak-free environment, calculates the instantaneous deviation distance of the trajectory and compares it with a preset deviation threshold, and verifies and judges the risk report based on multi-dimensional fusion verification. This effectively filters out false alarms and misreports caused by environmental noise, normal equipment fluctuations and other factors, further improving the authenticity and reliability of the risk report, thereby avoiding invalid alarms from interfering with the operation of medical equipment and medical care.

[0063] In one embodiment of this application, a method for detecting and evaluating carrier leakage signals in a mobile terminal further includes: Obtain the three-dimensional spatial layout information of the mobile medical area in the hospital, including the location of walls, distribution of medical equipment, and materials of obstacles; Based on the three-dimensional coordinates of the leakage source, the location of the mobile medical device, and the three-dimensional spatial layout information, determine whether there is an obstruction between the leakage source and the mobile medical device and the type of obstruction. Determine the environmental shading level based on the type and quantity of obstructions; The path loss index in the electromagnetic wave spatial propagation loss model is adjusted based on the environmental obstruction level.

[0064] In this embodiment, the three-dimensional spatial layout information of the mobile medical area of ​​the hospital is first obtained. This three-dimensional spatial layout information includes scene data such as wall positions, distribution of medical equipment, and obstacle materials.

[0065] After determining the three-dimensional coordinates of the leak source and the location of the affected mobile medical device, the two sets of coordinate positions are mapped in the three-dimensional spatial layout model of the hospital's mobile medical area to construct a three-dimensional straight-line propagation path from the leak source to the mobile medical device. By performing spatial intersection detection on the three-dimensional straight-line propagation path and the solid models of walls, mobile medical devices, obstacles, etc. in the three-dimensional spatial layout information, it is determined whether there are any obstructions on the propagation path. Furthermore, the material, thickness, quantity, and distribution location of the obstructions are extracted, and the corresponding environmental obstruction level is determined based on this feature information. For example, the attenuation of electromagnetic waves is classified according to the material of the obstruction. Concrete walls and metal equipment are classified as high-attenuation obstructions, brick walls and plasterboard partitions as medium-attenuation obstructions, and glass, wood panels, and ordinary air as low-attenuation obstructions. Secondly, the number and stacking thickness of each type of obstruction along the propagation path are counted, and a weighted score is calculated based on the attenuation level, number, and total thickness of the obstructions. Finally, based on the weighted score results, the environmental obstruction level is divided into four levels: no obstruction, low obstruction, medium obstruction, and high obstruction. The lower the score, the lower the environmental obstruction level, indicating less signal propagation loss; the higher the score, the higher the environmental obstruction level, indicating greater signal propagation loss.

[0066] In this embodiment, the path loss index in the electromagnetic wave spatial propagation loss model is adjusted according to the obtained environmental obstruction level, so that the electromagnetic wave spatial propagation loss model can adaptively correct the signal attenuation amplitude according to the obstruction strength.

[0067] This embodiment also includes: constructing a digital twin model of the hospital's mobile medical area, the digital twin model including a three-dimensional spatial layout, equipment electromagnetic parameters, and dynamic personnel distribution; inputting the three-dimensional coordinates of the leakage source and the location of the mobile medical equipment into the digital twin model to simulate the propagation path and power attenuation of the leakage signal in the dynamic environment; if the simulation results show that the leakage signal will exceed a preset interference threshold in the future preset time period due to personnel movement or equipment status changes, then a predictive risk warning is generated.

[0068] The interference threshold is determined by collecting communication quality indicators of mobile medical devices in a leak-free, clean electromagnetic environment, and then correcting for these indicators by statistically calculating the mean and standard deviation. It is also determined comprehensively based on the sensitivity of the mobile medical devices, obstruction attenuation, and clinical safety level. The digital twin model is constructed by integrating various real-world information, including the three-dimensional spatial layout of the hospital's mobile medical area, wall and obstacle materials, medical device locations and electromagnetic parameters, and dynamic personnel distribution. This digital twin model represents the structural characteristics and dynamic changes of the physical space and supports real-time input of data such as the three-dimensional coordinates of the leakage source and the location of the mobile medical devices. It simulates the propagation path and power attenuation process of carrier leakage signals in dynamic environments such as complex obstructions, personnel movement, and changes in equipment status, thereby enabling the prediction and early warning of interference risks in future periods.

[0069] As can be seen from the above, this embodiment obtains the three-dimensional spatial layout information of the hospital's mobile medical area, integrates the three-dimensional coordinates of the leakage source and the mobile medical device to determine the obstruction and its type, determines the environmental obstruction level, and adjusts the path loss index of the electromagnetic wave spatial propagation loss model. This makes the propagation loss calculation more consistent with the complex indoor electromagnetic environment of the hospital, effectively improving the estimation accuracy of leakage power density, and thus improving the accuracy, scenario adaptability, and engineering practicality of the entire carrier leakage detection and electromagnetic interference assessment system.

[0070] In one embodiment of this application, adjusting the path loss index in the electromagnetic wave spatial propagation loss model based on the environmental obstruction level includes: Acquire historical measured data of the hospital's mobile medical area under different obstruction conditions; If the historical measured data is less than the preset second threshold, the path loss index in the electromagnetic wave space propagation loss model is adjusted using a lookup table method, including: Multiple path loss indices were inverted based on historical measured data, and a mapping table of occlusion level and path loss index was constructed. Input the current environmental occlusion level into the occlusion level-path loss index mapping table to obtain the optimal path loss index adapted to the current environment. If the historical measured data is greater than or equal to the second threshold, then machine learning is used to adjust the path loss exponent in the electromagnetic wave space propagation loss model, including: Historical measured data includes the location of the leakage source, the location of the mobile medical device, the actual measured power density, and the corresponding environmental shielding level under different shielding conditions; Using historical measured data as training samples and the path loss index as the parameter to be optimized, a path loss index prediction model is trained. By inputting the currently detected environmental occlusion level, the three-dimensional coordinates of the leakage source, and the location of the mobile medical device into the path loss index prediction model, the optimal path loss index adapted to the current environment can be obtained.

[0071] In this embodiment, the method first acquires historical measured data of the hospital's mobile medical area under different occlusion conditions, using this data as the basis for adaptive adjustment of the path loss index. A differentiated optimization strategy is then selected based on the amount of historical measured data. Specifically, when the amount of historical measured data is less than a preset second threshold, it indicates that the sample size is insufficient to support complex model training; therefore, a lookup table method is used. For example, the path loss index corresponding to multiple environmental occlusion levels is first derived based on the historical measured data, and an occlusion level-path loss index mapping table is established. During actual detection, the optimal path loss index suitable for the current environment can be obtained by looking up the table. The second threshold is determined by conducting multiple sets of comparative experiments in the same scenario, training the path loss index prediction model with both small and sufficient samples, and comparing the prediction accuracy. The number of samples corresponding to the stable improvement in accuracy is the second threshold. Simultaneously, adjustments are made based on the area of ​​the hospital's mobile medical area, the number of occlusion types, and the device deployment density, enabling the second threshold to distinguish between the applicable scenarios of the lookup table method and the machine learning method.

[0072] When the amount of historical measured data is greater than or equal to the second threshold, the system switches to machine learning to adjust the path loss index. For example, using the location of the leakage source, the location of the mobile medical device, the actual measured power density, and the environmental occlusion level under different occlusion conditions as training samples, and the path loss index as the parameter to be optimized, a dedicated path loss index prediction model is trained. This allows the path loss index prediction model to learn the inherent relationship between occlusion, distance, location, and signal attenuation in complex hospital scenarios. The path loss index prediction model is based on a machine learning regression model. It takes the environmental occlusion level, the three-dimensional coordinates of the leakage source, and the location of the mobile medical device as input features, and outputs the optimal path loss index. The path loss index prediction model uses the leakage source location, mobile medical device location, actual measured power density, and corresponding environmental occlusion level under different occlusion conditions in historical measured data to form a training sample set. The path loss index is used as the target parameter to be optimized, and the mean squared error is used as the loss function to iteratively train the path loss index prediction model. By continuously adjusting the internal weights of the path loss index prediction model, the error between the predicted output and the path loss index obtained by inversion from the measured data is minimized. Finally, a prediction model that can output the optimal path loss index based on environmental occlusion level and spatial location information is formed.

[0073] In this embodiment, during actual operation, the current environmental occlusion level, the three-dimensional coordinates of the leakage source, and the location of the mobile medical device are input into the trained prediction model, and the optimal path loss index adapted to the current environment is output.

[0074] As can be seen from the above, this embodiment compares the historical measured data volume under different occlusion conditions in the mobile medical area of ​​the hospital with the preset second threshold to obtain small and large amounts of data. Based on the data volume, a lookup table method or machine learning method is selected to adjust the path loss index in the electromagnetic wave spatial propagation loss model. This ensures deployment efficiency under small data volume and achieves high prediction accuracy under large data volume, effectively improving the accuracy, scene adaptability, and engineering practicality of electromagnetic wave spatial propagation loss calculation.

[0075] Corresponding to the mobile terminal carrier leakage signal detection and evaluation method in the above embodiments, Figure 2 This is a structural block diagram of a mobile terminal carrier leakage signal detection and evaluation device according to an embodiment of this application. For ease of explanation, only the parts relevant to the embodiment of this application are shown. References Figure 2 The mobile terminal carrier leakage signal detection and evaluation device 20 includes: a carrier data acquisition module 21, a quantum spectrum analysis module 22, a leakage parameter calculation module 23, an electromagnetic risk assessment module 24, a risk report generation module 25, a report accuracy verification module 26, and a risk alarm generation module 27.

[0076] Among them, the carrier data acquisition module 21 is used to perform carrier detection on mobile medical devices and surrounding mobile terminals in the hospital's mobile medical area to obtain carrier data; The quantum spectrum analysis module 22 is used to determine the presence of carrier leakage frequency bands in the carrier data by performing quantum spectrum analysis on the carrier data; The leakage parameter calculation module 23 is used to calculate the leakage power density of the carrier leakage frequency band and the spectral overlap between the carrier leakage frequency band and the operating frequency band of the mobile medical device. Electromagnetic risk assessment module 24 is used to determine the electromagnetic interference risk value of the carrier leakage frequency band to mobile medical devices based on leakage power density and spectral overlap. The risk report generation module 25 is used to generate a risk report when the electromagnetic interference risk value is greater than a preset first threshold. The report accuracy verification module 26 is used to verify risk reports based on the deviation comparison method and obtain verification results, which include inaccurate reports and accurate reports. The risk alert generation module 27 is used to generate risk warnings based on the risk report corresponding to the verification result being an accurate report.

[0077] See Figure 3 , Figure 3 This is a schematic block diagram of an electronic device provided according to an embodiment of this application. Figure 3The electronic device 300 in this embodiment may include one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memories 304 store computer programs, including program instructions. The processors 301 execute the program instructions stored in the memories 304. Specifically, the processors 301 are configured to invoke the program instructions to perform the functions of the modules in the aforementioned device embodiments, for example... Figure 2 The functions of the carrier data acquisition module 21, quantum spectrum analysis module 22, leakage parameter calculation module 23, electromagnetic risk assessment module 24, risk report generation module 25, report accuracy verification module 26, and risk alarm generation module 27 are shown.

[0078] It should be understood that, in the embodiments of this application, the processor 301 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0079] Input device 302 may include a touchpad, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint orientation information), a microphone, etc., and output device 303 may include a display (LCD, etc.), a speaker, etc.

[0080] The memory 304 may include read-only memory and random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include non-volatile random access memory. For example, the memory 304 may also store device type information.

[0081] In specific implementations, the processor 301, input device 302, and output device 303 described in the embodiments of this application can execute the implementation methods described in any embodiment of the mobile terminal carrier leakage signal detection and evaluation method provided in the embodiments of this application, or they can execute the implementation methods of the electronic devices described in the embodiments of this application, which will not be repeated here.

[0082] In another embodiment of this application, a computer-readable storage medium is provided. This computer-readable storage medium stores a computer program, which includes program instructions. When executed by a processor, the program instructions implement all or part of the processes in the methods described above. Alternatively, the computer program can instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0083] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the foregoing embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device. Furthermore, the computer-readable storage medium can include both internal and external storage units of the electronic device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0084] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0085] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the electronic devices and units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0086] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces or units, or it may be an electrical, mechanical, or other form of connection.

[0087] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0088] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0089] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method of mobile terminal carrier leakage signal detection and assessment, characterized by, include: Carrier detection is performed on mobile medical devices and surrounding mobile terminals in the hospital's mobile medical area to obtain carrier data; Quantum spectrum analysis of the carrier data revealed the presence of a carrier leakage frequency band. Calculate the leakage power density of the carrier leakage frequency band and the spectral overlap between the carrier leakage frequency band and the operating frequency band of the mobile medical device; Based on the leakage power density and the spectral overlap, the electromagnetic interference risk value of the carrier leakage frequency band to mobile medical devices is determined; When the electromagnetic interference risk value is greater than a preset first threshold, a risk report is generated; The risk report is verified using the deviation comparison method to obtain verification results, which include inaccurate reports and accurate reports. Based on the verification results, a risk report corresponding to the accurate report is generated, and a risk warning is generated.

2. The method of claim 1, wherein, The calculation of the leakage power density of the carrier leakage frequency band and the spectral overlap between the carrier leakage frequency band and the operating frequency band of the mobile medical device includes: Based on the time difference or angle of arrival of the same leakage source by multiple spectrum sensors in the hospital's mobile medical area, the three-dimensional coordinates of the leakage source in the carrier leakage frequency band are located. The distance between the three-dimensional coordinates and the location of the mobile medical equipment in the hospital is input into a pre-trained electromagnetic wave spatial propagation loss model to obtain the leakage power density. The frequency range of the carrier leakage band is convolved with the frequency range of the mobile medical device's operating frequency band to obtain the spectral overlap.

3. A method of mobile terminal carrier leakage signal detection and assessment according to claim 2, wherein, The step of convolving the frequency range of the carrier leakage band with the frequency range of the mobile medical device's operating frequency band to obtain the spectral overlap includes: Based on the distribution of the leakage power density within the carrier leakage frequency band, a power weighting function is constructed; The frequency sensitivity function within the operating frequency band of the mobile medical device is convolved with the power weighting function to obtain the spectral overlap function. The peak value or integral value of the spectral overlap function is extracted as the spectral overlap degree.

4. The method for detecting and evaluating carrier leakage signals in a mobile terminal according to claim 1, characterized in that, The determination of the electromagnetic interference risk value of the carrier leakage frequency band to mobile medical devices based on the leakage power density and the spectral overlap includes: The communication quality time series of mobile medical devices in a leak-free environment was collected, and their phase space was reconstructed using the coordinate delay method to obtain the chaotic attractor trajectory. In the presence of carrier leakage, the communication quality time series of mobile medical devices are collected synchronously and mapped to the same phase space; The maximum Lyapunov exponential difference or correlation dimension change between attractor trajectories in two phase spaces is calculated as the electromagnetic interference risk value.

5. A method of mobile terminal carrier leakage signal detection and assessment according to claim 4, wherein, Also includes: The time-domain waveform of the carrier leakage frequency band is acquired, the burst period and duty cycle characteristics of the time-domain waveform are extracted, and it is determined whether the leakage signal is an intermittent pulse sequence. If the intermittent pulse sequence is determined to be the intermittent pulse sequence, then the phase coupling degree between the intermittent leakage pulse and the medical device communication quality time series is calculated based on the burst period, the duty cycle and the chaotic attractor trajectory. When the phase coupling degree is greater than a preset coupling threshold, a risk enhancement coefficient is generated based on the phase coupling degree, and the product of the risk enhancement coefficient and the electromagnetic interference risk value is calculated to obtain the corrected electromagnetic interference risk value.

6. A method of mobile terminal carrier leakage signal detection and assessment according to claim 5, wherein, Following the obtained corrected electromagnetic interference risk value, the following is also included: The pulse width of the intermittent pulse sequence is calculated based on the burst period and the duty cycle. Based on the pulse width and the burst period, the interference presence period and interference silence period of the intermittent pulse sequence are determined; Based on the interference silence period, the mobile medical device adjusts its data transmission and reception time slots to fall within the time window of the interference silence period, thus obtaining the adjusted data transmission and reception time slots; Based on the adjusted data transmission and reception time slots, the communication quality time series of the mobile medical device is collected and remapped to the phase space where the chaotic attractor trajectory is located; If the recalculated maximum Lyapunov index returns to the baseline range in a leak-free environment, a valid avoidance confirmation report is generated.

7. The method for detecting and evaluating carrier leakage signals in a mobile terminal according to claim 5, characterized in that, The risk report is verified using the deviation comparison method to obtain verification results, including: Based on the mobile medical device corresponding to the risk report, the communication quality time series of the corresponding mobile medical device is mapped to the phase space where the chaotic attractor trajectory of the mixed communication quality time series in the leak-free environment is located; Calculate the instantaneous deviation distance between the trajectory point of the current communication quality time series in phase space and the trajectory of the chaotic attractor in a leak-free environment; If the instantaneous deviation distance is less than a preset deviation threshold, the risk report is determined to be an inaccurate report. If the instantaneous deviation distance is greater than the deviation threshold, the risk report is determined to be a report to be verified, and an accurate report is obtained through a multi-dimensional fusion verification stage; otherwise, it is an inaccurate report.

8. The method for detecting and evaluating carrier leakage signals in a mobile terminal according to claim 2, characterized in that, Also includes: Obtain three-dimensional spatial layout information of the mobile medical area in the hospital, including wall locations, distribution of medical equipment, and obstacle materials; Based on the three-dimensional coordinates of the leakage source, the location of the mobile medical device, and the three-dimensional spatial layout information, determine whether there is an obstruction between the leakage source and the mobile medical device and the type of obstruction. Determine the environmental shading level based on the type and quantity of obstructions; Based on the environmental obstruction level, the path loss index in the electromagnetic wave spatial propagation loss model is adjusted.

9. The method of claim 8, wherein the step of detecting and evaluating the carrier leakage signal of the mobile terminal comprises the steps of: determining the frequency of the carrier leakage signal; and determining the power of the carrier leakage signal. The adjustment of the path loss index in the electromagnetic wave spatial propagation loss model based on the environmental obstruction level includes: Acquire historical measured data of the hospital's mobile medical area under different obstruction conditions; If the amount of historical measured data is less than a preset second threshold, the path loss index in the electromagnetic wave space propagation loss model is adjusted using a lookup table method, including: Based on the historical measured data, multiple path loss indices are inverted, and a mapping table of occlusion level and path loss index is constructed. Input the current environmental occlusion level into the occlusion level-path loss index mapping table to obtain the optimal path loss index adapted to the current environment. If the amount of historical measured data is greater than or equal to the second threshold, then a machine learning method is used to adjust the path loss index in the electromagnetic wave space propagation loss model, including: The historical measured data includes the location of the leakage source, the location of the mobile medical device, the actual measured power density, and the corresponding environmental shielding level under different shielding conditions; The historical measured data is used as training samples, and the path loss index is used as the parameter to be optimized to train the path loss index prediction model. The path loss index prediction model is input into the currently detected environmental occlusion level, the three-dimensional coordinates of the leakage source, and the location of the mobile medical device to obtain the optimal path loss index adapted to the current environment.

10. A mobile terminal carrier leakage signal detection and assessment apparatus, characterized by, include: The carrier data acquisition module is used to perform carrier detection on mobile medical devices and surrounding mobile terminals in the hospital's mobile medical area to obtain carrier data. The quantum spectrum analysis module is used to determine the presence of a carrier leakage frequency band in the carrier data by performing quantum spectrum analysis on the carrier data; The leakage parameter calculation module is used to calculate the leakage power density of the carrier leakage frequency band and the spectral overlap between the carrier leakage frequency band and the operating frequency band of the mobile medical device. An electromagnetic risk assessment module is used to determine the electromagnetic interference risk value of the carrier leakage frequency band to mobile medical devices based on the leakage power density and the spectral overlap. The risk report generation module is used to generate a risk report when the electromagnetic interference risk value is greater than a preset first threshold. The report accuracy verification module is used to verify the risk report based on the deviation comparison method and obtain the verification results, which include inaccurate reports and accurate reports. The risk warning generation module is used to generate a risk warning based on the verification result corresponding to the accurate report.

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