A method, device, and storage medium for transmitting signals in a charging pile that is resistant to electromagnetic interference.

Abstract

This application discloses a method, apparatus, and storage medium for signal transmission of charging piles to resist electromagnetic interference, which improves the signal quality of charging piles during signal transmission. The method involves: acquiring plateau environmental data and plateau space meteorological data collected by a plateau meteorological station; identifying at least one interfering node of the charging pile based on the plateau space meteorological data; simultaneously acquiring time-domain waveform data and frequency-domain spectrum data of the interfering node on the charging pile; generating time-domain / frequency-domain interference characteristics for the interfering node based on the plateau space meteorological data, time-domain waveform data, and frequency-domain spectrum data; generating electromagnetic storm interference characteristics based on the frequency-domain spectrum data, electromagnetic storm data, and frequency-domain interference characteristics; generating plateau environmental interference characteristics based on the frequency-domain spectrum data, plateau environmental data, and frequency-domain interference characteristics; determining electromagnetic interference analysis data based on the time-domain interference characteristics, frequency-domain interference characteristics, electromagnetic storm interference characteristics, and plateau environmental interference characteristics; and performing interference cancellation processing based on the electromagnetic interference analysis data.

Description

Technical Field

[0001] This application relates to the field of charging piles, and in particular to a method, apparatus and storage medium for transmitting charging pile signals that are resistant to electromagnetic interference. Background Technology

[0002] With continuous innovation in charging piles, existing charging piles can not only charge vehicles but also connect to intelligent networks to charge some intelligent unmanned devices, thus expanding their application areas. This is especially true in unconventional fields, such as high-altitude regions. Meteorological observation stations in these areas often require drones, inspection robots, and other unmanned observation equipment to collect meteorological data at specific locations or even in specific airspaces under special weather conditions. Given the large number of unmanned observation devices and relatively few staff in high-altitude regions, adding more intelligent charging piles is often chosen to improve the operational efficiency of meteorological observation stations. These intelligent charging piles require stable power support from drones, inspection robots, and other unmanned observation equipment.

[0003] However, this also places higher demands on the quality, functionality, and robustness of charging piles. Due to their operation at high altitudes, charging piles must withstand extreme weather conditions, such as temperature differences of -40℃ to 50℃. To reduce the impact of the high-altitude environment, the internal chips, communication modules, and other functional modules of the charging piles have been reinforced, enabling them to operate stably under high-altitude temperatures. However, the high-altitude environment is complex and variable, and many indicators can still cause electromagnetic interference to the charging piles. In the extreme environment of high altitudes, the electromagnetic interference faced by charging piles differs from that in conventional environments. Due to the unique geographical, climatic, and industrial conditions of the plateau, a unique environment exists. In addition to the aforementioned electromagnetic interference caused by the high-altitude environment, the thin air (low air pressure) in high-altitude areas weakens the absorption and scattering of solar radiation, leading to solar storms, a special meteorological phenomenon, which can cause severe electromagnetic interference to charging piles. Current technologies typically only allow data collection from the operating circuits through internal monitoring equipment during charging pile operation, followed by interference analysis and the implementation of corresponding electromagnetic interference cancellation measures based on the analysis. This approach presents certain problems for charging stations in high-altitude areas. First, the existing data collection for charging stations in high-altitude areas is based on overall electromagnetic interference analysis, without considering the individual impacts of the high-altitude environment and solar storms on different functional modules. This makes the adjustment scheme inaccurate and inefficient. Second, it fails to combine meteorological data and solar storm data from the high-altitude area to assess the degree of interference, resulting in inaccurate interference analysis. Consequently, when weather changes occur in the high-altitude area, the signal quality of the charging station signal transmission process deteriorates. Summary of the Invention

[0004] This application discloses a method, device, and storage medium for transmitting signals from a charging pile that is resistant to electromagnetic interference, which improves the signal quality of the charging pile during signal transmission.

[0005] In a first aspect, embodiments of this application provide a method for transmitting signals from a charging pile that is resistant to electromagnetic interference, comprising: Acquire plateau environmental data and plateau space meteorological data collected by plateau meteorological stations, including electromagnetic storm data; When the charging pile is charging the unmanned observation equipment and the plateau space meteorological data shows that an electromagnetic interference storm occurs, at least one interference node of the charging pile is determined based on the plateau space meteorological data. After the protection against emergency electromagnetic shock, the time-domain waveform data and frequency-domain spectrum data of each interference node on the charging pile are acquired simultaneously. Based on plateau space meteorological data, time-domain waveform data, and frequency-domain spectrum data, time-domain interference characteristics and frequency-domain interference characteristics are generated for each interference node; Electromagnetic storm interference characteristics are generated for the interference nodes based on frequency domain spectrum data, electromagnetic storm data, and frequency domain interference characteristics. Based on the frequency domain spectrum data, plateau environment data, and frequency domain interference characteristics, plateau environment interference characteristics are generated for each interference node; Electromagnetic interference analysis data are determined for each interference node based on time-domain interference characteristics, frequency-domain interference characteristics, electromagnetic storm interference characteristics, and plateau environment interference characteristics; Interference cancellation processing is performed on each interference node of the charging pile based on electromagnetic interference analysis data.

[0006] Optional, the plateau space meteorological data includes the geomagnetic activity index, energy channel flux during non-storm periods, and the triaxial magnetic field change rate matrix. The frequency domain interference characteristics are storm-corrected spectral entropy characteristics, and the time domain interference characteristics are pulse storm coefficients. The specific steps for generating temporal and frequency domain interference characteristics for each interference node based on plateau space meteorological data, temporal waveform data, and frequency domain spectrum data include: Based on the geomagnetic activity index and the energy channel flux during non-storm periods, generate the energy channel flux during storm periods for each interfering node; Based on the energy channel flux, background correction weights, and frequency domain spectral data during the storm period, normalized power of each energy channel after background correction is generated for each interference node. Storm-corrected spectral entropy features are generated based on the normalized power of each energy channel after background correction. Real-time geomagnetic disturbance distance is generated based on the three-axis magnetic field change rate matrix under the geomagnetic activity index in plateau space meteorological data. A pulse storm coefficient is generated for each interference node based on real-time geomagnetic disturbance distance, signal frame length, signal standard deviation, and time-domain waveform data.

[0007] Optionally, electromagnetic storm interference characteristics include storm resonance enhancement factor and storm harmonic distortion characteristics, and electromagnetic storm data include storm resonance frequency set and geomagnetic induced current frequency. The specific steps for generating electromagnetic storm interference characteristics for interference nodes based on frequency domain spectrum data, electromagnetic storm data, and frequency domain interference characteristics include: A storm resonance enhancement factor is generated based on the storm resonance frequency set and frequency domain spectrum data; Storm harmonic distortion characteristics are generated based on geomagnetic induced current frequency and frequency domain spectrum data; Frequency domain noise correction is performed on the storm resonance enhancement factor and storm harmonic distortion characteristics based on the storm correction spectral entropy characteristics.

[0008] Optional features of plateau environmental disturbances include corona discharge index and ultraviolet induction factor, and plateau environmental data include measured atmospheric pressure and ultraviolet intensity. The specific steps for generating plateau environment interference characteristics for each interference node based on frequency domain spectral data, plateau environment data, and frequency domain interference characteristics include: A pressure correction factor is generated based on the measured atmospheric pressure. The corona discharge index is generated based on frequency domain spectral data and air pressure correction factor; An ultraviolet (UV) induction factor is generated based on the UV intensity and the average interference power of the interfering nodes.

[0009] Optionally, plateau space meteorological data may also include X-ray flux and geomagnetic field variations, and electromagnetic interference analysis data may include interference sources and target interference levels. The specific steps for determining electromagnetic interference analysis data for each interference node based on time-domain interference characteristics, frequency-domain interference characteristics, electromagnetic storm interference characteristics, and plateau environment interference characteristics include: Storm status analysis was conducted based on geomagnetic activity index, X-ray flux, geomagnetic field variation, and particle radiation flux from plateau space meteorological data. Obtain the reference spectral entropy range based on the storm state; When the storm-corrected spectral entropy feature is not within the range of the reference spectral entropy, the time-domain waveform data and frequency-domain spectrum data of each interference node on the charging pile are reacquired for feature calculation. When the storm-corrected spectral entropy characteristics are within the range of the reference spectral entropy, the source of interference and the initial degree of interference are determined based on the pulse storm coefficient. The initial interference level is corrected based on the interference source, electromagnetic storm interference characteristics, and plateau environment interference characteristics to generate target interference levels for different interference sources; The level of interference from different sources is determined as electromagnetic interference analysis data.

[0010] Optionally, when the charging pile is charging unmanned observation equipment and the plateau space meteorological data shows an electromagnetic interference storm, the specific steps for determining at least one interference node of the charging pile based on the plateau space meteorological data include: When charging piles charge unmanned observation equipment and electromagnetic interference storms are observed in plateau space meteorological data, determine the particle radiation flux in the plateau space meteorological data. Obtain the functional module information in the charging pile, and divide the functional modules in the functional module information into particle locking module, particle flipping module and single particle burn-out module; The influence factors of the particle locking module, particle flipping module, and single-particle burn-off module are generated based on the particle radiation flux. Functional modules whose influencing factors exceed the preset value are identified as interference nodes of the charging pile.

[0011] Optionally, the steps for performing interference cancellation processing on each interference node of the charging pile based on electromagnetic interference analysis data specifically include: Determine the interference suppression method based on the source of interference at the interfering node; When the interference suppression method is filter suppression, the target filter module is determined according to the source of the interference. Adjust the parameters of the target filtering module according to the level of target interference.

[0012] Secondly, embodiments of this application provide a charging pile signal transmission device resistant to electromagnetic interference, comprising: The acquisition unit is used to acquire plateau environmental data and plateau space meteorological data collected by plateau meteorological stations, including electromagnetic storm data. The first determining unit is used to determine at least one interference node of the charging pile based on the plateau space meteorological data when the charging pile is charging the unmanned observation equipment and the plateau space meteorological data shows that an electromagnetic interference storm has occurred. The acquisition unit is used to simultaneously acquire the time-domain waveform data and frequency-domain spectrum data of each interference node on the charging pile after the protection against emergency electromagnetic shock. The first generation unit is used to generate time-domain interference characteristics and frequency-domain interference characteristics for each interference node based on plateau space meteorological data, time-domain waveform data and frequency-domain spectrum data. The second generation unit is used to generate electromagnetic storm interference characteristics for the interference nodes based on frequency domain spectrum data, electromagnetic storm data and frequency domain interference characteristics. The third generation unit is used to generate plateau environment interference characteristics for each interference node based on frequency domain spectrum data, plateau environment data and frequency domain interference characteristics. The second determining unit is used to determine electromagnetic interference analysis data for each interference node based on time-domain interference characteristics, frequency-domain interference characteristics, electromagnetic storm interference characteristics, and plateau environment interference characteristics. The interference elimination unit is used to perform interference elimination processing for each interference node of the charging pile based on electromagnetic interference analysis data.

[0013] Optionally, the first determining unit specifically includes: When charging piles charge unmanned observation equipment and electromagnetic interference storms are observed in plateau space meteorological data, determine the particle radiation flux in the plateau space meteorological data. Obtain the functional module information in the charging pile, and divide the functional modules in the functional module information into particle locking module, particle flipping module and single particle burn-out module; The influence factors of the particle locking module, particle flipping module, and single-particle burn-off module are generated based on the particle radiation flux. Functional modules whose influencing factors exceed the preset value are identified as interference nodes of the charging pile.

[0014] Optionally, plateau space meteorological data may also include X-ray flux and geomagnetic field variations, and electromagnetic interference analysis data may include interference sources and target interference levels. The second determining unit specifically includes: Storm status analysis was conducted based on geomagnetic activity index, X-ray flux, geomagnetic field variation, and particle radiation flux from plateau space meteorological data. Obtain the reference spectral entropy range based on the storm state; When the storm-corrected spectral entropy feature is not within the range of the reference spectral entropy, the time-domain waveform data and frequency-domain spectrum data of each interference node on the charging pile are reacquired for feature calculation. When the storm-corrected spectral entropy characteristics are within the range of the reference spectral entropy, the source of interference and the initial degree of interference are determined based on the pulse storm coefficient. The initial interference level is corrected based on the interference source, electromagnetic storm interference characteristics, and plateau environment interference characteristics to generate target interference levels for different interference sources; The level of interference from different sources is determined as electromagnetic interference analysis data.

[0015] Optionally, the elimination unit specifically includes: The interference suppression method is determined based on the interference source and interference path of the interference node; When the interference suppression method is filtering suppression, the target filtering module is determined based on the interference source and interference path. Adjust the parameters of the target filtering module according to the level of target interference.

[0016] Optional, the plateau space meteorological data includes the geomagnetic activity index, energy channel flux during non-storm periods, and the triaxial magnetic field change rate matrix. The frequency domain interference characteristics are storm-corrected spectral entropy characteristics, and the time domain interference characteristics are pulse storm coefficients. The first generation unit specifically includes: Based on the geomagnetic activity index and the energy channel flux during non-storm periods, generate the energy channel flux during storm periods for each interfering node; Based on the energy channel flux, background correction weights, and frequency domain spectral data during the storm period, normalized power of each energy channel after background correction is generated for each interference node. Storm-corrected spectral entropy features are generated based on the normalized power of each energy channel after background correction. Real-time geomagnetic disturbance distance is generated based on the three-axis magnetic field change rate matrix under the geomagnetic activity index in plateau space meteorological data. A pulse storm coefficient is generated for each interference node based on real-time geomagnetic disturbance distance, signal frame length, signal standard deviation, and time-domain waveform data.

[0017] Optionally, electromagnetic storm interference characteristics include storm resonance enhancement factor and storm harmonic distortion characteristics, and electromagnetic storm data include storm resonance frequency set and geomagnetic induced current frequency. The second generation unit specifically includes: A storm resonance enhancement factor is generated based on the storm resonance frequency set and frequency domain spectrum data; Storm harmonic distortion characteristics are generated based on geomagnetic induced current frequency and frequency domain spectrum data; Frequency domain noise correction is performed on the storm resonance enhancement factor and storm harmonic distortion characteristics based on the storm correction spectral entropy characteristics.

[0018] Optional features of plateau environmental disturbances include corona discharge index and ultraviolet induction factor, and plateau environmental data include measured atmospheric pressure and ultraviolet intensity. The third generation unit specifically includes: A pressure correction factor is generated based on the measured atmospheric pressure. The corona discharge index is generated based on frequency domain spectral data and air pressure correction factor; An ultraviolet (UV) induction factor is generated based on the UV intensity and the average interference power of the interfering nodes.

[0019] Thirdly, embodiments of this application provide an electronic device, including: Processor, memory, input / output units, and bus; The processor is connected to memory, input / output units, and a bus; The memory stores a program, which the processor calls to execute, as in the first aspect and any optional charging pile signal transmission method of the first aspect.

[0020] Fourthly, embodiments of this application provide a computer-readable storage medium storing a program that, when executed on a computer, performs the first aspect and any optional charging pile signal transmission method of the first aspect.

[0021] As can be seen from the above technical solutions, the embodiments of this application have the following advantages: This application first acquires plateau environmental data and plateau space meteorological data collected by plateau meteorological stations, including electromagnetic storm data. When a charging pile charges unmanned observation equipment and plateau space meteorological data shows an electromagnetic interference storm, at least one interfering node of the charging pile is identified based on the plateau space meteorological data. After emergency electromagnetic shock protection, time-domain waveform data and frequency-domain spectrum data of each interfering node on the charging pile are acquired simultaneously. Time-domain interference characteristics and frequency-domain interference characteristics are generated for each interfering node based on the plateau space meteorological data, time-domain waveform data, and frequency-domain spectrum data. Electromagnetic storm interference characteristics are generated for the interfering node based on the frequency-domain spectrum data, electromagnetic storm data, and frequency-domain interference characteristics. Plateau environmental interference characteristics are generated for each interfering node based on the frequency-domain spectrum data, plateau environmental data, and frequency-domain interference characteristics. Electromagnetic interference analysis data is determined for each interfering node based on the time-domain interference characteristics, frequency-domain interference characteristics, electromagnetic storm interference characteristics, and plateau environmental interference characteristics. Interference cancellation processing is performed on each interfering node of the charging pile based on the electromagnetic interference analysis data.

[0022] By collecting high-altitude space meteorological data from the data acquisition station, electromagnetic interference from storms that the charging piles would be affected by was determined. After implementing emergency electromagnetic shock protection, the impact of the storm on each functional module within the charging pile was assessed based on the high-altitude space meteorological data, thus identifying potentially affected functional modules as interference nodes. Following emergency electromagnetic shock protection, time-domain waveform data and frequency-domain spectrum data were collected specifically for the interference nodes. Then, based on the high-altitude space meteorological data, time-domain waveform data, and frequency-domain spectrum data, time-domain interference characteristics and frequency-domain interference characteristics were generated for each interference node. Next, electromagnetic storm interference characteristics and high-altitude environment interference characteristics were generated for each interference node. The electromagnetic storm interference characteristics expressed the influence between the charging pile's operating state and the electromagnetic storm, while the high-altitude environment interference characteristics represented the state changes of the interference node caused by the high-altitude environment. Furthermore, these characteristics were used to determine the electromagnetic interference analysis data for the interference nodes, including the interference source, interference path, and interference degree. Finally, based on the electromagnetic interference analysis data, the appropriate adjustment method was selected, and the degree of adjustment was determined.

[0023] By analyzing the overall electromagnetic interference and combining it with the individual effects of factors such as the plateau environment and solar storms on different functional modules, and by combining meteorological data and solar storm data of the plateau region to assess the degree of interference, the accuracy of interference analysis is improved. This allows interference to be eliminated through interference analysis when meteorological changes occur in the plateau region, thereby improving the signal quality during the signal transmission process of the charging pile. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of the electromagnetic interference-resistant charging pile signal transmission method of this application; Figure 2 A schematic diagram of the method for generating time-frequency domain interference features for this application; Figure 3 A schematic diagram illustrating the method for generating electromagnetic storm interference characteristics in this application; Figure 4 This is a schematic diagram of the method for generating plateau environmental disturbance characteristics according to this application; Figure 5 A schematic diagram illustrating the method for generating electromagnetic interference analysis data for this application; Figure 6 This is a schematic diagram of the interference cancellation method of this application; Figure 7 A schematic diagram illustrating the method for identifying interfering nodes in this application; Figure 8 This is a schematic diagram of the electromagnetic interference-resistant charging pile signal transmission device of this application; Figure 9 This is a schematic diagram of the electronic device used in this application. Detailed Implementation

[0026] In the following description, specific details such as particular system architectures 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 systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0027] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0028] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0029] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0030] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0031] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0032] In existing technologies, the electromagnetic interference (EMI) problems faced by charging piles in the extreme environment of high-altitude areas differ from those in conventional environments. Due to the unique geographical, climatic, and industrial conditions of high-altitude regions, a distinct environment exists. In addition to the EMI generated by the aforementioned high-altitude environment, the thin air (low air pressure) in high-altitude areas weakens the absorption and scattering of solar radiation, leading to solar storms, a special meteorological phenomenon, that can cause severe EMI to charging piles. Current technologies typically only collect data from the operating circuitry through internal monitoring equipment while the charging pile is running, analyze the collected data for interference, and then implement corresponding EMI cancellation measures based on the analysis. This approach has certain problems for charging piles in high-altitude areas. First, existing data collection for high-altitude charging piles involves overall EMI analysis, failing to consider the individual impacts of the high-altitude environment and solar storms on different functional modules, making the adjustment scheme inaccurate and inefficient. Second, the failure to combine meteorological data and solar storm data from the high-altitude region for interference severity assessment results in inaccurate interference analysis, leading to a decline in signal quality during charging pile signal transmission when weather changes occur in high-altitude areas.

[0033] Based on this, this application discloses a charging pile signal transmission method, device and storage medium that resists electromagnetic interference, which improves the signal quality of the charging pile during signal transmission.

[0034] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0035] The method described in this application can be applied to servers, devices, terminals, or other devices with logical processing capabilities; therefore, this application does not limit its application. For ease of description, the following description uses a terminal as the executing entity.

[0036] Please see Figure 1 This application provides an embodiment of a charging pile signal transmission method resistant to electromagnetic interference, comprising: 101. Acquire plateau environmental data and plateau space meteorological data collected by plateau meteorological stations, including electromagnetic storm data; To meet the charging requirements of pollution monitoring equipment at plateau meteorological data collection stations, charging piles need to be installed at or near these stations. These stations can collect satellite signals, obtaining earlier observational data on solar storms (electromagnetic storm data). This predicted solar storm data is then transmitted to the plateau stations. The receiving stations integrate this solar storm data with their own plateau meteorological data to generate plateau space meteorological data. Plateau environmental data, such as air humidity, temperature, air pressure, and UV intensity, is collected in real-time or intermittently from the surrounding plateau areas. This plateau environmental data can affect the signal transmission of the charging piles. Some of this influence can be mitigated through physical means, such as adjusting humidity and temperature internally. However, some influences are difficult to eliminate.

[0037] 102. When the charging pile is charging the unmanned observation equipment and the plateau space meteorological data shows that an electromagnetic interference storm occurs, at least one interference node of the charging pile shall be determined based on the plateau space meteorological data. When the charging pile is charging the observation equipment, and the plateau meteorological data acquisition station determines that an electromagnetic interference storm is imminent, an emergency response plan will be implemented first. This involves taking strong interference protection measures (emergency electromagnetic shock protection) to the charging pile and other equipment to prevent equipment malfunctions caused by the sudden impact of the solar storm. Then, the terminal will identify at least one interfering node of the charging pile based on plateau space meteorological data. This will be analyzed separately for each functional module within the charging pile. A message description will be provided subsequently regarding the identification of at least one interfering node of the charging pile using plateau space meteorological data.

[0038] Functional modules that can be identified as interference nodes include electronic components that have been subjected to electromagnetic interference, such as power modules, control boards, and communication modules.

[0039] 103. After the protection against emergency electromagnetic shock, simultaneously acquire the time-domain waveform data and frequency-domain spectrum data of each interference node on the charging pile; After emergency electromagnetic shock protection, the terminal uses the EMI probe array inside the charging pile to collect data from selected interference nodes (functional modules susceptible to electromagnetic interference from solar storms), generating corresponding time-domain waveform data. Then, a Fast Fourier Transform is performed on the time-domain waveform data to generate frequency domain spectrum data. Next, the terminal preprocesses the collected raw data (time-domain waveform data) by applying DC component filtering, windowing (Hanning window), and band-limited filtering, dividing the continuous time-domain waveform data into fixed-duration analysis frames. The time-domain waveform data of each analysis frame is denoted as... Its corresponding frequency domain spectral data is denoted as , where i is the frame index, n is the number of time-domain sample points, and k is the number of frequency-domain spectral lines.

[0040] 104. Generate time-domain interference characteristics and frequency-domain interference characteristics for each interference node based on plateau space meteorological data, time-domain waveform data, and frequency-domain spectrum data; In this embodiment, the terminal generates temporal and frequency interference features for each interference node based on plateau space meteorological data, temporal waveform data, and frequency spectrum data. The temporal interference features are used to effectively identify some types of interference, such as determining whether the source of pulse interference is a solar storm or the device's own pulse. The frequency interference features are used to determine the background noise impact caused by the solar storm, and to perform noise cancellation correction for subsequent electromagnetic storm interference features and plateau environment interference features. The specific methods for generating temporal and frequency interference features will be described in detail in subsequent embodiments.

[0041] 105. Generate electromagnetic storm interference characteristics for the interfering nodes based on frequency domain spectrum data, electromagnetic storm data, and frequency domain interference characteristics; In this embodiment, the terminal generates electromagnetic storm interference features for the interfering node based on frequency domain spectrum data, electromagnetic storm data, and frequency domain interference features. Specifically, electromagnetic storm data is obtained in real time through a plateau data acquisition station, combined with frequency domain spectrum data for electromagnetic storm interference analysis, and noise correction is performed using frequency domain interference features to generate quantitative data on the electromagnetic storm interference experienced by the interfering node. The specific method for generating electromagnetic storm interference features will be described in detail in subsequent embodiments.

[0042] 106. Generate plateau environment interference characteristics for each interference node based on frequency domain spectral data, plateau environment data, and frequency domain interference characteristics; In this embodiment, the terminal generates plateau environment interference features for each interference node based on frequency domain spectrum data, plateau environment data, and frequency domain interference features. That is, it performs plateau environment interference analysis based on the plateau environment data acquired in real time by the plateau acquisition station and the frequency domain interference features, and uses the frequency domain interference features to correct noise, thereby generating quantitative data of plateau environment interference experienced by the interference node. The specific method of generating plateau environment interference features will be described in detail in subsequent embodiments.

[0043] 107. Determine electromagnetic interference analysis data for each interference node based on time-domain interference characteristics, frequency-domain interference characteristics, electromagnetic storm interference characteristics, and plateau environment interference characteristics; In this embodiment, the terminal determines electromagnetic interference analysis data for each interference node based on time-domain interference characteristics, frequency-domain interference characteristics, electromagnetic storm interference characteristics, and plateau environment interference characteristics. Specifically, the state of the solar storm is first analyzed, and then an appropriate interference identification model is selected based on the solar storm state. The time-domain interference characteristics, frequency-domain interference characteristics, electromagnetic storm interference characteristics, and plateau environment interference characteristics are input into the interference identification model to determine the electromagnetic interference analysis data. The specific determination method will be described in detail in subsequent embodiments.

[0044] 108. Perform interference elimination processing on each interference node of the charging pile based on electromagnetic interference analysis data.

[0045] After the terminal obtains the electromagnetic interference analysis data, it performs interference cancellation processing on each interference node of the charging pile based on the electromagnetic interference analysis data. The specific interference cancellation method will be described in detail in subsequent embodiments.

[0046] In this embodiment, firstly, plateau environmental data and plateau space meteorological data collected by plateau meteorological stations are acquired. The plateau space meteorological data includes electromagnetic storm data. When the charging pile charges unmanned observation equipment, and the plateau space meteorological data shows an electromagnetic interference storm, at least one interfering node of the charging pile is identified based on the plateau space meteorological data. After emergency electromagnetic shock protection, the time-domain waveform data and frequency-domain spectrum data of each interfering node on the charging pile are acquired simultaneously. Time-domain interference characteristics and frequency-domain interference characteristics are generated for each interfering node based on the plateau space meteorological data, time-domain waveform data, and frequency-domain spectrum data. Electromagnetic storm interference characteristics are generated for the interfering node based on the frequency-domain spectrum data, electromagnetic storm data, and frequency-domain interference characteristics. Plateau environmental interference characteristics are generated for each interfering node based on the frequency-domain spectrum data, plateau environmental data, and frequency-domain interference characteristics. Electromagnetic interference analysis data is determined for each interfering node based on the time-domain interference characteristics, frequency-domain interference characteristics, electromagnetic storm interference characteristics, and plateau environmental interference characteristics. Interference cancellation processing is performed on each interfering node of the charging pile based on the electromagnetic interference analysis data.

[0047] By collecting high-altitude space meteorological data from the data acquisition station, electromagnetic interference from storms that the charging piles would be affected by was determined. After implementing emergency electromagnetic shock protection, the impact of the storm on each functional module within the charging pile was assessed based on the high-altitude space meteorological data, thus identifying potentially affected functional modules as interference nodes. Following emergency electromagnetic shock protection, time-domain waveform data and frequency-domain spectrum data were collected specifically for the interference nodes. Then, based on the high-altitude space meteorological data, time-domain waveform data, and frequency-domain spectrum data, time-domain interference characteristics and frequency-domain interference characteristics were generated for each interference node. Next, electromagnetic storm interference characteristics and high-altitude environment interference characteristics were generated for each interference node. The electromagnetic storm interference characteristics expressed the influence between the charging pile's operating state and the electromagnetic storm, while the high-altitude environment interference characteristics represented the state changes of the interference node caused by the high-altitude environment. Furthermore, these characteristics were used to determine the electromagnetic interference analysis data for the interference nodes, including the interference source, interference path, and interference degree. Finally, based on the electromagnetic interference analysis data, the appropriate adjustment method was selected, and the degree of adjustment was determined.

[0048] By analyzing the overall electromagnetic interference and combining it with the individual effects of factors such as the plateau environment and solar storms on different functional modules, and by combining meteorological data and solar storm data of the plateau region to assess the degree of interference, the accuracy of interference analysis is improved. This allows interference to be eliminated through interference analysis when meteorological changes occur in the plateau region, thereby improving the signal quality during the signal transmission process of the charging pile.

[0049] Please see Figure 2 This application provides an embodiment of a method for generating time-frequency domain interference features. The plateau space meteorological data includes geomagnetic activity index, energy channel flux during non-storm periods, and a triaxial magnetic field change rate matrix. The frequency domain interference feature is the storm-corrected spectral entropy feature, and the time domain interference feature is the pulsar coefficient, including: 201. Generate the storm-period energy channel flux for each interfering node based on the geomagnetic activity index and the energy channel flux during non-storm periods; In this embodiment, the terminal first generates the storm-period energy channel flux for each interfering node based on the geomagnetic activity index and the energy channel flux during non-storm periods, using the following formula:

[0050] in, During storms, the geomagnetic activity index The flux of the k-th energy channel. The flux of the energy channel during the non-storm period is the flux in the kth channel, which is used as a reference benchmark for the flux during the storm period. The geomagnetic activity index represents the intensity of global geomagnetic disturbances. This indicates the positive portion, where Q is the threshold value for the geomagnetic activity index in the current light source region (the area of ​​the light source acquisition station). If Q is greater than... If the value is 0, then the value is 0; otherwise, the value is 0. , The storm enhancement factor represents the flux as a function of the geomagnetic activity index. Increased sensitivity, with It increases as it grows. The correction function for the frequency (or energy) of the k-th channel is typically obtained experimentally at a high-altitude acquisition station.

[0051] 202. Based on the energy channel flux, background correction weights, and frequency domain spectrum data during the storm period, generate the normalized power of each energy channel after background correction for each interference node; Next, the terminal generates the normalized power of each energy channel after background correction for each interference node based on the energy channel flux during the storm, background correction weights, and frequency domain spectrum data. The specific formula is as follows:

[0052] in, This represents the normalized power of each energy channel after background correction. During storms, the geomagnetic activity index Below, the flux of the k-th energy channel, Let k be the frequency domain spectral data of the i-th frame of the signal, and k be the frequency domain spectral line index. The background weights are adjusted, with values ​​ranging from 0 to 1. When it is 0, the subsequently calculated H is the standard spectral entropy. When the value is 1, it indicates that the predicted background is completely subtracted. This parameter can be obtained experimentally or set empirically. N is the time-domain waveform data, denoted as... The number of sample points for performing the Fast Fourier Transform.

[0053] 203. Generate storm-corrected spectral entropy features based on the normalized power of each energy channel after background correction; Next, the terminal generates storm-corrected spectral entropy features based on the normalized power of each energy channel after background correction, using the following formula:

[0054] in, This refers to the storm's corrected spectral entropy characteristics. The meaning of calculating the storm's corrected spectral entropy characteristics is: During a solar storm, background noise across the entire frequency band is uniformly increased, causing interference energy to be concentrated at specific frequencies. By calculating the storm-corrected spectral entropy characteristics, the noise level of the current solar storm can be quantified, enabling effective elimination of solar storm noise for subsequent features (features calculated using frequency domain spectral data).

[0055] 204. Generate real-time geomagnetic disturbance distance based on the three-axis magnetic field change rate matrix under the geomagnetic activity index in plateau space meteorological data; 205. Generate a pulse storm coefficient for each interference node based on the real-time geomagnetic disturbance distance, signal frame length, signal standard deviation, and time-domain waveform data.

[0056] Next, the terminal generates the real-time geomagnetic disturbance distance based on the triaxial magnetic field change rate matrix and geomagnetic activity index of the plateau space meteorological data, using the following formula:

[0057] in, This represents the real-time geomagnetic disturbance distance, specifically the Mahalanobis distance of the current geomagnetic disturbance, used to subsequently measure the distance between the current geomagnetic field state and periods of calm. The three-axis magnetic field change rate matrix is ​​expressed as follows: That is, the magnetic field along the x, y, and z axes, and the rate of change of the magnetic field along the x, y, and z axes. This represents the mean vector of the magnetic field during non-storm periods. is the inverse of the covariance matrix.

[0058] Next, the terminal generates a pulse storm coefficient for each interfering node based on the real-time geomagnetic disturbance distance, signal frame length, signal standard deviation, and time-domain waveform data, using the following formula:

[0059] in, This is the pulse storm coefficient. Let L be the signal standard deviation of the time-domain waveform data, and L be the signal frame length of the time-domain waveform data. This represents the real-time distance of geomagnetic disturbance. For time-domain waveform data, This is the threshold coefficient for the standard deviation of the signal.

[0060] The pulse storm coefficient can be decomposed into a pulse density factor and a geomagnetic correlation factor. The geomagnetic correlation factor reflects the frequency of pulse occurrence. Reflecting the spatiotemporal correlation between pulses and geomagnetic activity, the pulse density factor and geomagnetic correlation factor are analyzed together to identify the source of pulse interference, serving as a characteristic data point for subsequent analysis of electromagnetic interference data. The specific pulse storm coefficient can classify the degree of interference from solar storm pulses and local equipment pulses affecting charging pile equipment. This classification can be achieved through prior testing and stored in the charging pile's database.

[0061] Please see Figure 3 This application provides an embodiment of a method for generating electromagnetic storm interference features, wherein the electromagnetic storm interference features include storm resonance enhancement factor and storm harmonic distortion features, and the electromagnetic storm data includes storm resonance frequency set and geomagnetic induced current frequency; The specific steps for generating electromagnetic storm interference characteristics for interference nodes based on frequency domain spectrum data, electromagnetic storm data, and frequency domain interference characteristics include: 301. Generate storm resonance enhancement factors based on storm resonance frequency sets and frequency domain spectrum data; 302. Generate storm harmonic distortion characteristics based on geomagnetic induced current frequency and frequency domain spectrum data; 303. Based on the storm correction spectral entropy characteristics, frequency domain noise correction is performed on the storm resonance enhancement factor and storm harmonic distortion characteristics.

[0062] In this embodiment, the terminal generates a storm resonance enhancement factor based on the storm resonance frequency set and frequency domain spectrum data, as shown in the following formula:

[0063] in, As a storm resonance enhancement factor, it is a key feature characterizing the intensity of ionospheric disturbances, and therefore can be used as an observational feature of the source (solar storm) and path of disturbances. This is a set of resonant frequencies of the Earth-ionospheric cavity, including the fundamental frequency of 7.8 Hz, the second harmonic of 14.3 Hz, the third harmonic of 20.8 Hz, and the fourth harmonic of 27.3 Hz. Solar storms cause a significant increase in the amplitude of these frequency points. Let be the complex spectrum of the i-th signal, and a and b be the upper and lower limits of the reference background frequency band. This reference background frequency band covers the adjacent frequency band of the main harmonic frequency of the Earth-ionospheric cavity resonance frequency. The selection of this frequency band can ensure that the reference background and the signal have the same propagation and coupling characteristics, so that the storm resonance enhancement factor F can accurately reflect the resonance enhancement, rather than the overall rise of broadband noise. This reference background frequency band is obtained through experiments or by looking up a table.

[0064] Next, the storm correction spectral entropy feature is used to correct the frequency domain noise of the storm resonance enhancement factor, as shown in the following formula:

[0065] in, This is the storm resonance enhancement factor after frequency domain noise correction. To correct the spectral entropy features of the storm. The standard spectral entropy calculated under the current state, The influence factor of storm correction spectral entropy characteristics on storm resonance enhancement factor F is determined based on experiments or by referring to tables.

[0066] Next, the terminal generates storm harmonic distortion characteristics based on the geomagnetic induced current frequency and frequency domain spectrum data, as shown in the following formula:

[0067] in, As a feature of storm harmonic distortion, it characterizes the severity of nonlinear distortion (especially even harmonics) caused by geomagnetic induced current, and thus can serve as an observational feature of disturbance source (solar storm) and disturbance path. This refers to the frequency of the geomagnetic induced current, also known as the dominant frequency of the geomagnetic induced current. The frequency of the geomagnetic induced current is determined by the period of change in the geomagnetic field, and can be determined by finding the spectral line with the largest amplitude in the 0.1-1Hz frequency band. This is existing technology and will not be elaborated further. For harmonic order, This is the h-th harmonic frequency at the frequency of the geomagnetic induced current. Let be the complex spectrum of the i-th pin signal.

[0068] Next, the storm-corrected spectral entropy feature is used to correct the frequency domain noise of the storm harmonic distortion feature, as shown in the following formula:

[0069] in, This represents the storm harmonic distortion characteristics after frequency domain noise correction. To correct the spectral entropy features of the storm. The standard spectral entropy calculated under the current state, The influence factor of storm modified spectral entropy characteristics on storm harmonic distortion characteristics Z can be determined by experiments or by referring to tables, and will not be elaborated here.

[0070] Please see Figure 4 This application provides an embodiment of a method for generating plateau environmental disturbance characteristics, which include corona discharge index and ultraviolet radiation inducing factor. The plateau environmental data includes measured atmospheric pressure and ultraviolet radiation intensity, including: 401. Generate a pressure correction factor based on the measured atmospheric pressure; 402. Generate the corona discharge index based on frequency domain spectral data and air pressure correction factor; 403. Generate ultraviolet induction factors based on ultraviolet intensity and average interference power of interfering nodes.

[0071] In this embodiment, the terminal generates a pressure correction factor based on the measured atmospheric pressure, and then generates a corona discharge index based on the frequency domain spectrum data and the pressure correction factor, as shown in the following formula:

[0072] in, The corona discharge index, This is the pressure correction factor. Let be the complex spectrum of the i-th pin signal. This refers to the standard atmospheric pressure at sea level. To measure atmospheric pressure, it is acquired in real time using a pressure sensor built into or near the charging station. According to Paschen's Law, the initiation voltage and intensity of corona discharge are inversely proportional to atmospheric pressure. When atmospheric pressure P decreases, the corona discharge index calculates the proportion of high-frequency energy, thus explicitly and quantitatively reflecting the enhancing effect of low atmospheric pressure on corona discharge in the characteristic value. Therefore, the corona discharge index can serve as an observational characteristic of interference sources (high-altitude atmospheric pressure) and environmental disturbance paths.

[0073] The terminal generates an ultraviolet (UV) induction factor based on the UV intensity and the average interference power of the interfering nodes, using the following formula:

[0074] in, As an ultraviolet-induced factor, the more sensitive the system is to ultraviolet aging, the higher the potential reliability risk. Therefore, the ultraviolet-induced factor is used as an observational characteristic of the source of interference (ultraviolet-induced factor) and the disturbance path of environmental factors. This represents the average interference power, or average interference energy. The time-varying rate of change of the average interference power. This is the UV intensity index detected in real time.

[0075] It should be noted that the ultraviolet (UV) induced factor feature typically requires a large number of UV induced factors within the corresponding time series for feature evaluation. Therefore, after the UV induced factor is calculated, it is also necessary to obtain UV induced factors calculated for different time periods from the database for subsequent electromagnetic interference cancellation. Neither the corona discharge index nor the UV induced factor are pure signal features, but rather a fusion of signal features (spectrum, power) and environmental sensor data (air pressure, UV radiation), enabling direct modeling of the "environment-equipment" interaction.

[0076] Please see Figure 5This application provides an embodiment of a method for generating electromagnetic interference analysis data. The plateau space meteorological data also includes X-ray flux and geomagnetic field variations. The electromagnetic interference analysis data includes interference sources and target interference levels, including: 501. Analyze storm state based on geomagnetic activity index, X-ray flux, geomagnetic field variation, and particle radiation flux in plateau space meteorological data; In this embodiment, the terminal first determines the degree of the solar storm based on traditional solar storm parameters. Specifically, the terminal analyzes the input evaluation model based on the geomagnetic activity index, X-ray flux, geomagnetic field variation and particle radiation flux, and outputs storm state analysis such as calm period, small geomagnetic storm, moderate geomagnetic storm, strong geomagnetic storm or extremely strong geomagnetic storm.

[0077] 502. Obtain the reference spectral entropy range based on the storm state; The range of the storm state's influence on the charging pile's spectral entropy, i.e., the reference spectral entropy range, is obtained from the database based on the current storm state analysis results.

[0078] 503. When the storm correction spectral entropy feature is not within the range of the reference spectral entropy, reacquire the time-domain waveform data and frequency-domain spectrum data of each interference node on the charging pile for feature calculation; The storm-corrected spectral entropy feature is a calculated parameter of storm noise impact. The terminal compares the storm-corrected spectral entropy feature with the obtained reference spectral entropy range. If the storm-corrected spectral entropy feature is not within the reference spectral entropy range, it is necessary to reacquire the time-domain waveform data and frequency-domain spectrum data of each interference node on the charging pile, and then re-collect the time-domain waveform data and frequency-domain spectrum data. The re-acquired time-domain waveform data and frequency-domain spectrum data are then used to calculate the electromagnetic storm interference characteristics and the high-altitude environment interference characteristics.

[0079] 504. When the storm correction spectral entropy characteristics are within the range of the reference spectral entropy, the source of interference and the initial degree of interference are determined based on the pulse storm coefficient; When the storm-corrected spectral entropy feature is within the range of the reference spectral entropy, it indicates that the electromagnetic storm interference feature and the plateau environment interference feature are relatively accurate, eliminating the interference of solar storm noise. The terminal can then determine the source of interference and the initial level of interference based on the pulse storm coefficient.

[0080] Since the pulse storm coefficient can be decomposed into a pulse density factor and a geomagnetic correlation factor, the pulse density factor reflects the frequency of pulse occurrence, while the geomagnetic correlation factor reflects the spatiotemporal correlation between the pulse and geomagnetic activity. A comprehensive analysis of both factors allows for the analysis of the source of pulse interference. The specific magnitude of the pulse storm coefficient can classify the degree of interference from solar storm pulses and local equipment pulses affecting the charging pile equipment. This classification can be achieved through prior testing and stored in the charging pile's database. The terminal determines the interference sources based on the pulse storm coefficient, including solar storm pulse interference (caused by solar storm factors) and local equipment pulse interference (caused by environmental factors). It then determines the respective interference paths of solar storm pulse interference and local equipment pulse interference (whether through electromagnetic space conduction or from the charging pile itself or the power grid connected to the charging pile). Finally, based on the pulse storm coefficient, the total interference parameters affecting the charging pile are classified according to the sources of solar storm pulse interference and local equipment pulse interference, generating the degree of solar storm pulse interference and the degree of local equipment pulse interference.

[0081] 505. Based on the interference source, electromagnetic storm interference characteristics, and plateau environment interference characteristics, the initial interference level is corrected to generate the target interference level for different interference sources; Next, the terminal corrects the initial interference level based on the interference source, electromagnetic storm interference characteristics, and plateau environment interference characteristics to generate target interference levels for different sources. Specifically, the electromagnetic storm interference characteristics and plateau environment interference characteristics are first normalized to ensure they fall within the same interference dimension. Then, the difference between the ratio of the normalized electromagnetic storm interference characteristics and plateau environment interference characteristics and the ratio of the solar storm pulse interference level and the local device pulse interference level is analyzed to see if it is within a preset threshold. If not, the magnitudes of the solar storm pulse interference level and the local device pulse interference level need to be adjusted to improve the detection accuracy of the solar storm pulse interference level and the local device pulse interference level. Finally, the solar storm pulse interference level and the local device pulse interference level are determined as the target interference level.

[0082] 506. The target interference levels from different interference sources are determined as electromagnetic interference analysis data.

[0083] Finally, the terminal determines the target interference level from different interference sources as electromagnetic interference analysis data. This target interference level is more accurate and provides a basis for subsequent parameter adjustments.

[0084] Please see Figure 6 This application provides an embodiment of an interference cancellation processing method, comprising: 601. Determine the interference suppression method based on the source of interference at the interfering node; 602. When the interference suppression method is filter suppression, determine the target filter module based on the source of interference; 603. Adjust the parameters of the target filtering module according to the degree of target interference.

[0085] The terminal first determines the interference suppression method based on the source of the interference at the interference node. If the electromagnetic interference is conducted through an electromagnetic space path, the corresponding interference suppression method is used. If the electromagnetic interference is conducted from the charging pile body or the power grid connected to the charging pile, the corresponding interference suppression method is used.

[0086] When the interference suppression method is filtering suppression, the terminal determines the target filtering module based on the source of the interference. This means configuring the target filtering module at the interfering node, or using other filters capable of filtering the interfering node as the target filtering module (details omitted here). Once the target filtering module is selected, its parameters can be adjusted according to the level of interference. This method accurately analyzes the level and source of interference, arranging appropriate suppression methods along the interference path, thus improving the signal quality of the charging pile during signal transmission.

[0087] Please see Figure 7 This application provides an embodiment of a method for determining interfering nodes, comprising: 701. When the charging pile is charging the unmanned observation equipment and the plateau space meteorological data shows an electromagnetic interference storm, determine the particle radiation flux in the plateau space meteorological data. The unmanned observation equipment is an auxiliary observation device for plateau meteorological data collection stations, used for long-distance meteorological data acquisition. Specifically, when the charging pile terminal detects that the charging pile is charging the unmanned observation equipment, if it receives plateau space meteorological data transmitted from the plateau meteorological data collection station indicating possible abnormal weather such as solar storms, it first implements emergency electromagnetic shock protection for the charging pile equipment. The plateau meteorological data collection station not only collects meteorological data from surrounding stations but also receives data from meteorological satellites, thus enabling it to receive information about solar storms. However, electromagnetic interference caused by solar storms is difficult to detect in advance using satellite equipment. Therefore, in this embodiment, the particle radiation flux of solar storms, which can be measured, is selected for predicting the degree of interference. Specific particle radiation flux acquisition requires specialized equipment capable of directly or indirectly measuring the flux of high-energy particles (such as solar protons, cosmic rays, etc.). The specific equipment selection depends on the particle type, energy range, and deployment environment (such as plateau, space, or ground). For example, a scintillator detector.

[0088] 702. Obtain the functional module information in the charging pile, and divide the functional modules in the functional module information into particle locking module, particle flipping module and single particle burn-out module; Next, we need to iterate through the functional module information in the charging pile, that is, the classification of functional modules in the charging pile, and determine what type of damage different functional modules may be caused by solar storms.

[0089] Specifically, there are three types of single-event effects: single-event flip (SEU), single-event latch-up (SEL), and single-event burn-out (SEB).

[0090] 1. Single-event upset (SEE) refers to a high-energy particle impacting a storage module, causing a logical state reversal and resulting in data errors. Storage modules include SRAM, registers, etc.

[0091] 2. Single-event latch-up refers to a situation where a particle impact triggers a parasitic transistor to conduct, forming a high-current loop that may burn out the device. Functional modules with transistors will emerge.

[0092] 3. Single-event burnout mainly refers to the localized overheating and melting of power devices, leading to permanent failure. For example, MOSFETs.

[0093] The terminal initially divides the functional modules that may experience single-event flip, single-event latch-up, and single-event burn-out into particle locking module, particle flip module, and single-event burn-out module.

[0094] 703. Generate the influence factors of the particle locking module, particle flipping module, and single-particle burn-up module based on the particle radiation flux; Based on the particle radiation flux parameter of this solar storm, the terminal calculates the single-event latch-up level (single-event latch-up influence factor) that the particle locking module needs to withstand, the single-event flip-up level (single-event flip-up influence factor) that the particle flip-up module needs to withstand, and the single-event burn-up level (single-event burn-up influence factor) that the single-event burn-up module needs to withstand.

[0095] 704. Functional modules whose influencing factors are greater than the preset value are identified as interference nodes of the charging pile.

[0096] The terminal identifies functional modules whose influencing factors exceed preset values, assesses the impact of solar storm electromagnetic interference on these modules, and then identifies these functional modules as interference nodes of the charging pile.

[0097] Please see Figure 8 This application provides an embodiment of a charging pile signal transmission device resistant to electromagnetic interference, comprising: The acquisition unit 801 is used to acquire plateau environmental data and plateau space meteorological data collected by plateau meteorological acquisition stations. The plateau space meteorological data includes electromagnetic storm data. The first determining unit 802 is used to determine at least one interference node of the charging pile based on the plateau space meteorological data when the charging pile is charging the unmanned observation equipment and the plateau space meteorological data shows that an electromagnetic interference storm has occurred. Optionally, the first determining unit 802 specifically includes: When charging piles charge unmanned observation equipment and electromagnetic interference storms are observed in plateau space meteorological data, determine the particle radiation flux in the plateau space meteorological data. Obtain the functional module information in the charging pile, and divide the functional modules in the functional module information into particle locking module, particle flipping module and single particle burn-out module; The influence factors of the particle locking module, particle flipping module, and single-particle burn-off module are generated based on the particle radiation flux. Functional modules whose influencing factors exceed the preset value are identified as interference nodes of the charging pile.

[0098] The acquisition unit 803 is used to simultaneously acquire the time-domain waveform data and frequency-domain spectrum data of each interference node on the charging pile after the protection against emergency electromagnetic shock. The first generation unit 804 is used to generate time-domain interference characteristics and frequency-domain interference characteristics for each interference node based on plateau space meteorological data, time-domain waveform data and frequency-domain spectrum data. Optional, the plateau space meteorological data includes the geomagnetic activity index, energy channel flux during non-storm periods, and the triaxial magnetic field change rate matrix. The frequency domain interference characteristics are storm-corrected spectral entropy characteristics, and the time domain interference characteristics are pulse storm coefficients. The first generation unit 804 specifically includes: Based on the geomagnetic activity index and the energy channel flux during non-storm periods, generate the energy channel flux during storm periods for each interfering node; Based on the energy channel flux, background correction weights, and frequency domain spectral data during the storm period, normalized power of each energy channel after background correction is generated for each interference node. Storm-corrected spectral entropy features are generated based on the normalized power of each energy channel after background correction. Real-time geomagnetic disturbance distance is generated based on the three-axis magnetic field change rate matrix under the geomagnetic activity index in plateau space meteorological data. A pulse storm coefficient is generated for each interference node based on real-time geomagnetic disturbance distance, signal frame length, signal standard deviation, and time-domain waveform data.

[0099] The second generation unit 805 is used to generate electromagnetic storm interference characteristics for the interference node based on frequency domain spectrum data, electromagnetic storm data and frequency domain interference characteristics. Optionally, electromagnetic storm interference characteristics include storm resonance enhancement factor and storm harmonic distortion characteristics, and electromagnetic storm data include storm resonance frequency set and geomagnetic induced current frequency. The second generation unit 805 specifically includes: A storm resonance enhancement factor is generated based on the storm resonance frequency set and frequency domain spectrum data; Storm harmonic distortion characteristics are generated based on geomagnetic induced current frequency and frequency domain spectrum data; Frequency domain noise correction is performed on the storm resonance enhancement factor and storm harmonic distortion characteristics based on the storm correction spectral entropy characteristics.

[0100] The third generation unit 806 is used to generate plateau environment interference characteristics for each interference node based on frequency domain spectrum data, plateau environment data and frequency domain interference characteristics. Optional features of plateau environmental disturbances include corona discharge index and ultraviolet induction factor, and plateau environmental data include measured atmospheric pressure and ultraviolet intensity. The third generation unit 806 specifically includes: A pressure correction factor is generated based on the measured atmospheric pressure. The corona discharge index is generated based on frequency domain spectral data and air pressure correction factor; An ultraviolet (UV) induction factor is generated based on the UV intensity and the average interference power of the interfering nodes.

[0101] The second determining unit 807 is used to determine electromagnetic interference analysis data for each interference node based on time-domain interference characteristics, frequency-domain interference characteristics, electromagnetic storm interference characteristics, and plateau environment interference characteristics. Optionally, plateau space meteorological data may also include X-ray flux and geomagnetic field variations, and electromagnetic interference analysis data may include interference sources and target interference levels. The second determining unit 807 specifically includes: Storm state analysis was conducted based on the geomagnetic activity index, X-ray flux, geomagnetic field variation, and particle radiation flux in the aforementioned plateau space meteorological data. The reference spectral entropy range is obtained based on the storm state; When the storm-corrected spectral entropy feature is not within the range of the reference spectral entropy, the time-domain waveform data and frequency-domain spectrum data of each interference node on the charging pile are reacquired for feature calculation. When the storm-corrected spectral entropy feature is within the range of the reference spectral entropy, the source of interference and the initial degree of interference are determined based on the pulse storm coefficient. The initial interference level is corrected based on the interference source, the electromagnetic storm interference characteristics, and the plateau environment interference characteristics to generate target interference levels for different interference sources; The level of interference from different sources is determined as electromagnetic interference analysis data.

[0102] The interference elimination unit 808 is used to perform interference elimination processing for each interference node of the charging pile based on electromagnetic interference analysis data.

[0103] Optionally, the elimination unit 808 specifically includes: Determine the interference suppression method based on the source of interference at the interfering node; When the interference suppression method is filter suppression, the target filter module is determined according to the source of the interference. Adjust the parameters of the target filtering module according to the level of target interference.

[0104] Please see Figure 9 This application provides an electronic device, including: Processor 901, memory 902, input / output unit 903, and bus 904.

[0105] The processor 901 is connected to the memory 902, the input / output unit 903, and the bus 904.

[0106] The memory 902 stores a program, and the processor 901 calls the program to execute it, such as... Figure 1 , Figure 2 and Figure 3 , Figure 4 , Figure 5 , Figure 6 and Figure 7 The signal transmission method of charging piles in China.

[0107] This application provides a computer-readable storage medium on which a program is stored, and when the program is executed on a computer, it performs the following... Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 and Figure 7 The signal transmission method of charging piles in China.

[0108] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0109] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus 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. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0110] 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 this embodiment according to actual needs.

[0111] 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.

[0112] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for signal transmission of an electromagnetic interference resistant charging pile, characterized in that, The method comprises the following steps: acquiring plateau environment data and plateau space weather data collected by a plateau space weather collection station, wherein the plateau space weather data comprises electromagnetic storm data; when a charging pile is charging an unattended observation equipment and the plateau space weather data shows that an electromagnetic interference storm occurs, determining at least one interference node of the charging pile according to the plateau space weather data; after protection against emergency electromagnetic impact, synchronously acquiring time-domain waveform data and frequency-domain spectrum data of each interference node on the charging pile; generating time-domain interference features and frequency-domain interference features for each interference node according to the plateau space weather data, the time-domain waveform data and the frequency-domain spectrum data; generating electromagnetic storm interference features for the interference node according to the frequency-domain spectrum data, the electromagnetic storm data and the frequency-domain interference features; generating plateau environment interference features for each interference node according to the frequency-domain spectrum data, the plateau environment data and the frequency-domain interference features; determining electromagnetic interference analysis data for each interference node according to the time-domain interference features, the frequency-domain interference features, the electromagnetic storm interference features and the plateau environment interference features; performing interference elimination processing for each interference node of the charging pile according to the electromagnetic interference analysis data.

2. The charging pile signal transmission method according to claim 1, characterized in that, The plateau space weather data comprises geomagnetic activity index, energy channel flux in non-storm period and three-axis magnetic field change rate matrix, the frequency-domain interference features are storm correction spectrum entropy features, and the time-domain interference features are pulse storm coefficients; The step of generating time-domain interference features and frequency-domain interference features for each interference node according to the plateau space weather data, the time-domain waveform data and the frequency-domain spectrum data specifically comprises: generating energy channel flux in storm period for each interference node according to geomagnetic activity index and energy channel flux in non-storm period; generating normalized power of each energy channel after background correction for each interference node according to energy channel flux in storm period, background correction weight and the frequency-domain spectrum data; generating storm correction spectrum entropy features according to the normalized power of each energy channel after background correction; generating real-time geomagnetic disturbance distance according to the three-axis magnetic field change rate matrix under the geomagnetic activity index in the plateau space weather data; generating pulse storm coefficients for each interference node according to the real-time geomagnetic disturbance distance, signal frame length, signal standard deviation and the time-domain waveform data.

3. The charging pile signal transmission method according to claim 2, characterized in that, The electromagnetic storm interference features comprise storm resonance enhancement factor and storm harmonic distortion features, and the electromagnetic storm data comprises storm resonance frequency set and geomagnetic induced current frequency; The step of generating electromagnetic storm interference features for the interference node according to the frequency-domain spectrum data, the electromagnetic storm data and the frequency-domain interference features specifically comprises: generating storm resonance enhancement factor according to the storm resonance frequency set and the frequency-domain spectrum data; generating storm harmonic distortion features according to the geomagnetic induced current frequency and the frequency-domain spectrum data; performing frequency-domain noise correction on the storm resonance enhancement factor and the storm harmonic distortion features according to the storm correction spectrum entropy features.

4. The charging pile signal transmission method according to claim 3, characterized in that, The highland environment interference features include a corona discharge index and an ultraviolet induction factor, and the highland environment data include a measured atmospheric pressure and an ultraviolet intensity; The step of generating the highland environment interference features for each interference node according to the frequency domain spectrum data, the highland environment data and the frequency domain interference features specifically includes: generating an air pressure correction factor according to the measured atmospheric pressure; generating the corona discharge index according to the frequency domain spectrum data and the air pressure correction factor; generating the ultraviolet induction factor according to the ultraviolet intensity and the average interference power of the interference node.

5. The charging pile signal transmission method according to claim 4, characterized in that, The highland space meteorological data further include an X-ray flux and a geomagnetic field change amount, and the electromagnetic interference analysis data include an interference source and a target interference degree; The step of determining the electromagnetic interference analysis data for each interference node according to the time domain interference features, the frequency domain interference features, the electromagnetic storm interference features and the highland environment interference features specifically includes: performing storm state analysis according to a geomagnetic activity index, an X-ray flux, a geomagnetic field change amount and a particle radiation flux in the highland space meteorological data; obtaining a reference spectrum entropy range according to the storm state; when the storm correction spectrum entropy feature is not located in the reference spectrum entropy range, re-obtaining the time domain waveform data and the frequency domain spectrum data of each interference node on the charging pile to perform feature calculation; when the storm correction spectrum entropy feature is located in the reference spectrum entropy range, determining the interference source and the initial interference degree according to a pulse storm coefficient; correcting the initial interference degree according to the interference source, the electromagnetic storm interference features and the highland environment interference features to generate target interference degrees of different interference sources; determining the target interference degrees of different interference sources as the electromagnetic interference analysis data.

6. The charging pile signal transmission method according to claim 5, characterized in that, The step of performing interference elimination processing for each interference node of the charging pile according to the electromagnetic interference analysis data specifically includes: determining an interference suppression mode according to the interference source of the interference node; when the interference suppression mode is a filtering suppression, determining a target filtering module according to the interference source; adjusting parameters of the target filtering module according to the target interference degree.

7. The charging pile signal transmission method according to any one of claims 1 to 6, characterized in that, When the charging pile is a charging pile for an unmanned observation equipment, and the highland space meteorological data show that an electromagnetic interference storm occurs, the step of determining at least one interference node of the charging pile according to the highland space meteorological data specifically includes: when the charging pile is a charging pile for an unmanned observation equipment, and the highland space meteorological data show that an electromagnetic interference storm occurs, determining a particle radiation flux in the highland space meteorological data; obtaining functional module information in the charging pile, and dividing functional modules in the functional module information into a particle locking module, a particle flipping module and a single particle burnout module; generating influence factors of the particle locking module, the particle flipping module and the single particle burnout module according to the particle radiation flux; determining a functional module with an influence factor greater than a preset value as an interference node of the charging pile.

8. An electromagnetic interference resistant charging station signal transmission device, comprising: The method comprises the steps of: an obtaining unit is configured to obtain highland environment data and highland space meteorological data collected by a highland meteorological station, the highland space meteorological data including electromagnetic storm data; The first determining unit is used to determine at least one interference node of the charging pile based on the plateau space meteorological data when the charging pile is charging the unmanned observation equipment and the plateau space meteorological data shows an electromagnetic interference storm. The acquisition unit is used to simultaneously acquire the time-domain waveform data and frequency-domain spectrum data of each interference node on the charging pile after the protection against emergency electromagnetic shock. The first generation unit is used to generate time-domain interference features and frequency-domain interference features for each interference node based on the plateau space meteorological data, the time-domain waveform data and the frequency-domain spectrum data. The second generation unit is used to generate electromagnetic storm interference features for the interference node based on the frequency domain spectrum data, the electromagnetic storm data, and the frequency domain interference features. The third generation unit is used to generate plateau environment interference features for each interference node based on the frequency domain spectrum data, the plateau environment data, and the frequency domain interference features. The second determining unit is used to determine electromagnetic interference analysis data for each interference node based on the time-domain interference characteristics, the frequency-domain interference characteristics, the electromagnetic storm interference characteristics, and the plateau environment interference characteristics. The interference elimination unit is used to perform interference elimination processing on each interference node of the charging pile based on the electromagnetic interference analysis data.

9. The charging pile signal transmission device according to claim 8, characterized in that, The plateau space meteorological data includes geomagnetic activity index, energy channel flux during non-storm periods, and triaxial magnetic field change rate matrix; the frequency domain interference feature is storm-corrected spectral entropy feature; and the time domain interference feature is pulse storm coefficient. The first generation unit specifically includes: Based on the geomagnetic activity index and the energy channel flux during non-storm periods, generate the energy channel flux during storm periods for each interfering node; Based on the energy channel flux during the storm, the background correction weight, and the frequency domain spectral data, the normalized power of each energy channel after background correction is generated for each interference node. Storm-corrected spectral entropy features are generated based on the normalized power of each energy channel after background correction. Real-time geomagnetic disturbance distance is generated based on the three-axis magnetic field change rate matrix under the geomagnetic activity index in plateau space meteorological data. A pulse storm coefficient is generated for each interference node based on the real-time geomagnetic disturbance distance, signal frame length, signal standard deviation, and time-domain waveform data.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains a program that, when executed on a computer, performs the charging pile signal transmission method as described in any one of claims 1 to 7.

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