Electric vehicle charging station working condition wide frequency recording and multi-dimensional characteristic calibration method and system

Abstract

The application discloses an electric vehicle charging station working condition wide frequency recording and multi-dimensional characteristic calibration method and system, relates to the technical field of load measurement and load analysis balance, and comprises the following steps: collecting three categories of data accurately for the electric vehicle charging station; recording the working condition data of various types by completing data preprocessing on the collected data; and performing multi-dimensional characteristic calibration on the working condition data of various types. The application synchronously collects and records the load voltage and current waveforms, the environmental temperature and electromagnetic field, and the working state of each charging pile of the electric vehicle charging station in different modes, different rates and different data types, realizes comprehensive recording of the actual working condition of the charging station, and enhances the data reliability by wide frequency, wide range and high precision collection and recording. The application also performs parallel detection and calibration on multiple characteristics of the working condition recording data, the characteristic results are accurately calibrated based on the same time label, and the utilization value of the recording data is improved.

Description

Technical Field

[0001] This invention relates to the field of load measurement and load analysis and balancing technology, specifically to a method and system for wide-range recording and multi-dimensional feature calibration of electric vehicle charging station operating conditions. Background Technology

[0002] With the increasing popularity of electric vehicles, centralized charging stations are becoming more and more common. However, this has led to frequent problems such as suspected overcharging at charging stations and inconsistent electricity consumption at different measurement points. Power grid companies, on the other hand, lack knowledge of the voltage and current waveforms of these new and complex loads, their specific operating conditions, and the accuracy of related instrument transformers and meters.

[0003] Faced with new and complex loads, it is difficult to conduct tests on current transformers and energy meters based on real-world operating conditions. There is a lack of relevant, reliable, and comprehensive voltage and current waveform data, making it difficult to carry out related research.

[0004] The on-site operating conditions of electric vehicle charging stations are difficult to obtain accurately and comprehensively using existing oscilloscopes, fault recording devices, conventional measurement and control devices, and power quality analysis devices.

[0005] The load data recorded at electric vehicle charging stations is extensive, with long durations, large volumes, and diverse and complex operating conditions. Without effective storage of the load data and without quantitative and qualitative analysis of the load characteristics, the recorded data will be difficult to use and will fail to realize its value. Summary of the Invention

[0006] In view of the above-mentioned problems, the present invention is proposed.

[0007] Therefore, the technical problem solved by this invention is: how to achieve comprehensive monitoring and analysis of electric vehicle charging stations to optimize their performance and improve user experience. Existing technologies cannot accurately collect and analyze various key data from charging stations, resulting in an inability to fully understand their operating conditions and thus hindering effective optimization of performance and improvement of user experience.

[0008] To address the aforementioned technical problems, this invention provides the following technical solution: a method for wide-band recording and multi-dimensional feature calibration of electric vehicle charging station operating conditions, comprising the following steps: accurately collecting three major categories of data from the electric vehicle charging station; performing data preprocessing on the collected data to complete the recording and storage of various operating condition data; and performing multi-dimensional feature calibration on various operating condition data.

[0009] As a preferred embodiment of the wide-band recording and multi-dimensional feature calibration method for electric vehicle charging station operating conditions described in this invention, the three major categories of data accurate acquisition include voltage and current acquisition of charging load, acquisition of environmental variables, and acquisition of charging pile operating status data.

[0010] The data preprocessing involves denoising the collected state data, removing missing values, outliers, and invalid data with incorrect formats, converting the original format data into the format required for requirements analysis, and normalizing the data to complete the data preprocessing.

[0011] As a preferred embodiment of the wideband recording and multidimensional feature calibration method for electric vehicle charging station operating conditions described in this invention, the voltage and current acquisition of the charging load is performed by using a wideband sensing method with resistive voltage division and electrical isolation to acquire three-phase voltage, and using a wideband sensing method based on the Hall principle to acquire three-phase current. The differential voltages corresponding to the voltage and current are then subjected to active anti-aliasing low-pass filtering before entering analog-to-digital conversion.

[0012] The environmental variables are collected using RS485 communication, reading the current ambient temperature value of the charging station from the temperature measurement module at a frequency of once per second. Each reading associates and calibrates the current value with the measured value. The ambient electric field and ambient magnetic field at the voltage and current waveform acquisition points are also collected. Each sampling associates and calibrates the current value with the sampled value.

[0013] The charging pile's working status data is collected via Ethernet communication, which collects the starting and stopping states of m charging piles in the charging station. For each collected data, it is determined whether the charging pile is valid at the current time. If it is invalid, the current time value is associated with the collected value for calibration.

[0014] As a preferred embodiment of the wideband recording and multi-dimensional feature calibration method for electric vehicle charging station operating conditions described in this invention, the recording of various types of operating condition data involves recording the data one second before the start signal is received when any charging pile enters the charging start state or at a specified future time. A storage table is constructed to uniformly store the data for different rates, sampling times, formats, and information contents of the three major types of data. The data is stored sequentially in the format of time tags and data values. The recording is stopped using a preset total recording duration, a specified time, or manually.

[0015] As a preferred embodiment of the method for wideband recording and multidimensional feature calibration of electric vehicle charging station operating conditions described in this invention, the multidimensional feature calibration involves performing wideband characteristic detection, distortion characteristic detection, and strong electromagnetic field characteristic detection on the recorded operating condition data in parallel.

[0016] The broadband characteristic detection involves performing FFT transformation on discrete sampled values ​​of the three-phase current within a 0.1-second time window to construct an amplitude sequence S1: [Id, I h1 I h2 , ..., I h1279 ].

[0017] Where Id is the DC component amplitude, Ih1 I h2 I h1279 [ ] represent the amplitude of the 10Hz component, the amplitude of the 20Hz component, and the amplitude of the 12.79kHz component, respectively.

[0018] If the amplitude of a certain frequency component is greater than the product of the amplitude threshold parameter and the rated current of the three phases, the current amplitude is retained; if the amplitude of a certain frequency component is less than or equal to the product of the amplitude threshold parameter and the rated current of the three phases, the current amplitude is discarded.

[0019] The retained amplitudes are used to form a new sequence S2: [I p , ..., I q ], I p The minimum frequency corresponding to the retained amplitude, I q The frequency range of the output retained signal is calculated based on the maximum frequency corresponding to the retained amplitude, expressed as:

[0020] F = f max -f min

[0021] Among them, f max f is the maximum frequency of the retained amplitude corresponding to Iq. min This is the minimum frequency value corresponding to the retained amplitude.

[0022] If the spectral distribution range of the retained signal is greater than the frequency distribution range parameter, then the wideband characteristic is determined to be valid; otherwise, the wideband characteristic is determined to be invalid.

[0023] The discrete sampling values ​​of the three-phase current are progressively sampled point by point. When the broadband characteristic changes from valid to invalid, the corresponding first sampling time value is recorded, thus completing the broadband characteristic detection and calibration of all recorded loads.

[0024] As a preferred embodiment of the wideband recording and multidimensional feature calibration method for electric vehicle charging station operating conditions described in this invention, the distortion characteristic detection is to calculate the total harmonic distortion rate (THD) after completing the FFT calculation. If the total harmonic distortion rate is greater than the distortion rate threshold parameter, the distortion characteristic is determined to be valid; otherwise, the distortion characteristic is determined to be invalid.

[0025] By progressively sampling the discrete values ​​of the three-phase current point by point, and recording the corresponding first sampling time value when the distortion characteristic changes from valid to invalid, the distortion characteristic detection and calibration of all recorded loads can be completed.

[0026] As a preferred embodiment of the wide-band recording and multi-dimensional feature calibration method for electric vehicle charging station operating conditions described in this invention, the strong electromagnetic field characteristic detection is based on a comparison and judgment of the collected electric field data E(n) and magnetic field strength data B(n).

[0027] If E(n) is greater than the electric field threshold parameter Eset, then the timer is started; otherwise, the timer immediately returns to 0. If the timer value ΔT is greater than the timer threshold parameter Tset, then the strong electric field characteristic is determined to be valid; otherwise, it is not valid.

[0028] When the strong electric field characteristics or strong magnetic field characteristics change from valid to invalid, the corresponding sampling time value is recorded, and the strong electromagnetic characteristics of all recorded data can be detected and calibrated.

[0029] Another objective of this invention is to provide a wide-range data recording and multi-dimensional feature calibration system for electric vehicle charging stations. This system utilizes advanced data acquisition, preprocessing, storage, and analysis technologies to achieve comprehensive monitoring and analysis of charging stations. This not only allows for the accurate collection and analysis of various key data from the charging station but also the extraction of crucial feature information, thereby providing a comprehensive understanding of the charging station's operating conditions. This effectively optimizes charging station performance and enhances user experience, solving the problem of existing technologies' inability to accurately collect and analyze charging station data.

[0030] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a wide-band recording and multi-dimensional feature calibration system for electric vehicle charging station operating conditions, including: a data acquisition module, a data preprocessing module, a recording module, a feature calibration module, and a communication module.

[0031] The data acquisition module captures various key information about electric vehicle charging stations, and monitors the voltage and current of the charging load, environmental variables, and the working status of the charging piles in real time.

[0032] The data preprocessing module cleans and organizes the raw data received from the acquisition module, removing noise, filling in missing values, eliminating outliers and data with incorrect formats, thus ensuring data quality and integrity, and converting the data into a unified format.

[0033] The recording and storage module is responsible for storing the preprocessed data according to a specific structure and format.

[0034] The feature calibration module performs in-depth analysis of the stored data, extracts key feature information, and performs broadband characteristic detection, distortion characteristic detection, and strong electromagnetic field characteristic detection on the data to gain a comprehensive understanding of the charging station's operating conditions.

[0035] The communication module is responsible for enabling data transmission and communication between various modules, using stable communication protocols and network technologies to ensure accurate data transmission and real-time updates.

[0036] A computer device includes a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the steps of the above-described method for wide-band recording and multi-dimensional feature calibration of electric vehicle charging station operating conditions.

[0037] A computer-readable storage medium storing a computer program thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the above-described method for wide-band recording and multi-dimensional feature calibration of electric vehicle charging station operating conditions.

[0038] The beneficial effects of this invention are as follows: This invention synchronously collects and records load voltage and current waveforms, ambient temperature and electromagnetic fields, and the operating status of each charging pile at electric vehicle charging stations using different methods, rates, and data types. This achieves comprehensive recording of the actual operating conditions of the charging station. Wide-frequency, wide-range, and high-precision acquisition and recording enhances data reliability. Furthermore, this invention performs parallel detection and calibration of multiple features on the recorded operating data. The feature results are also accurately calibrated based on the same time tag, improving the utilization value of the recorded data. Attached Figure Description

[0039] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0040] Figure 1 The overall flowchart of the method for wide-band recording and multi-dimensional feature calibration of electric vehicle charging station operating conditions provided in the first embodiment of the present invention;

[0041] Figure 2 A schematic diagram of the charging station operating condition recording and feature calibration method provided in the first embodiment of the present invention;

[0042] Figure 3 A schematic diagram of broadband characteristic detection for the broadband recording and multi-dimensional feature calibration method for electric vehicle charging station operating conditions provided in the first embodiment of the present invention;

[0043] Figure 4 A schematic diagram of strong electromagnetic field characteristic detection for the wideband recording and multi-dimensional feature calibration method for electric vehicle charging station operating conditions provided in the first embodiment of the present invention;

[0044] Figure 5 This is a structural diagram of the wide-band recording and multi-dimensional feature calibration system for electric vehicle charging stations provided in the second embodiment of the present invention. Detailed Implementation

[0045] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0046] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0047] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0048] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0049] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0050] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0051] Example 1

[0052] Reference Figures 1-4As an embodiment of the present invention, a method for wide-range recording and multi-dimensional feature calibration of electric vehicle charging station operating conditions is provided, characterized in that:

[0053] The system involves three main categories of data collection for electric vehicle charging stations; data preprocessing to record and store various operating conditions; and multi-feature calibration for these operating conditions.

[0054] The first step involves precise data acquisition from three main categories at electric vehicle charging stations. The first category is the acquisition of charging load voltage and current. For three-phase voltage, a wideband sensing method using resistive voltage divider and electrical isolation is employed. For three-phase current, a wideband sensing method based on the Hall effect principle is used, ensuring a sensing bandwidth of 10kHz or higher (i.e., 200th harmonic or higher) and a power frequency accuracy of no less than 0.2%. The differential voltages corresponding to voltage and current are passed through an active anti-aliasing low-pass filter before entering analog-to-digital conversion. A fourth-order Chebyshev filter is recommended for the active anti-aliasing low-pass filter to achieve maximum attenuation of out-of-band signals and achieve anti-aliasing effect. The analog-to-digital conversion stage uses a wide range of -10V to +10V, a sampling rate of 25.6kHz, a resolution of no less than 16 bits, and 6-channel synchronous sampling. Each sampling step associates the current value with the sampled value for calibration. The second category is the acquisition of environmental variables. For temperature, RS485 communication is used to read the current ambient temperature value of the charging station from the temperature measurement module at a frequency of once per second. Each read is associated and calibrated with the current value. Ambient electric and magnetic fields at the voltage and current waveform acquisition points are collected using a sampling rate of 1MHz, a resolution of no less than 12 bits, and simultaneous acquisition through two channels. Each sample is associated and calibrated with the current value. The third category is the acquisition of charging pile operational status data. The starting and stopping statuses of m charging piles in the charging station are collected via Ethernet communication. Each collected data point is checked to determine if the charging pile's current time is valid; if invalid, the current time value is associated and calibrated with the collected value.

[0055] Step 2: Recording and storing various operating condition data. The signal to start recording and storing data comes from manual start, any charging pile entering the charging start state, or a specified future moment. When recording begins, the data from the 1 second prior to receiving the start signal will also be recorded and stored, making the recording and storage process more complete.

[0056] The three main categories of data mentioned above have different sampling rates, sampling times, formats, and information volumes. Therefore, the data storage methods shown in Tables 1 and 2 are designed to adapt to different sampling rates, formats, and data volumes, achieving unified storage. In Table 1, sections 1 through 9 are the file header information, mainly containing statistical information about the recorded data. The three main categories of data are divided into Type 1 through Type 4 according to different sampling rates. Each type of data is stored according to the format in Table 2: timestamp + data value, stored sequentially. The complete and accurate time of each sampling point is obtained by merging the timestamp here with the "year and month" value in the file header.

[0057] Recording can be stopped by setting a preset total recording duration, manually stopping, or stopping at a specified time.

[0058] Table 1

[0059]

[0060] Table 2

[0061]

[0062] Step 3: Multidimensional feature calibration.

[0063] like Figure 2 As shown, the recorded operating data is subjected to broadband characteristic detection, distortion characteristic detection, and strong electromagnetic field characteristic detection, with the three characteristic detections performed in parallel.

[0064] For broadband characteristic detection, firstly, discrete sampled values ​​i(n) of the three-phase current are taken within a 0.1-second time window, and then subjected to FFT transformation, such as... Figure 3 The following example illustrates this concept.

[0065] The FFT transform result includes the DC component amplitude I_d, the 10Hz component amplitude I_h1, the 20Hz component amplitude I_h2, ..., the 12.79kHz component amplitude I_h1279 of the current signal, resulting in the sequence S1: [Id, I_h1, I_h2, ..., I_h1279]. Assuming the rated current of the three phases is Ir, each element of the sequence is filtered as follows:

[0066] If In > K1 * Ir, element n is retained; otherwise, the element is deleted. K1 is a configurable amplitude threshold parameter, with a default value of 0.05.

[0067] We obtain a new sequence S2: [I_p, ..., I_q], where the frequency corresponding to the leftmost element I_p is f_min, and the frequency corresponding to the rightmost element I_q is f_max. The spectral distribution range of the signal is then:

[0068] F = f_max - f_min. If F > fset, the wideband characteristic is determined to be valid; otherwise, the wideband characteristic is determined to be invalid. Here, fset is a configurable frequency distribution range parameter, which is 2000Hz by default.

[0069] The above calculation process advances the discrete sampled values ​​of the three-phase current point by point. When the broadband characteristic changes from valid to invalid, the corresponding first sampling time value is recorded, thus completing the broadband characteristic detection and calibration of all recorded loads.

[0070] Distortion characteristic detection: Based on the results of the FFT calculation above, the total harmonic distortion (THD) is calculated. The following criteria are then applied:

[0071] If THD > K2, the distortion characteristic is considered valid; otherwise, it is considered invalid. K2 is a configurable distortion rate threshold parameter, with a default value of 0.03.

[0072] By progressively sampling the discrete values ​​of the three-phase current point by point, and recording the corresponding first sampling time value when the distortion characteristic changes from valid to invalid, the distortion characteristic detection and calibration of all recorded loads can be completed.

[0073] For the detection of strong electromagnetic field characteristics, the collected electric field data E(n) and magnetic field strength data B(n) are processed according to the logic shown in the figure below, as follows: Figure 4 Using electric field processing as an example, the magnetic field data processing process is the same.

[0074] If E(n) > Eset, the timer starts counting; otherwise, the timer immediately returns to 0. If the timer's count ΔT > Tset, the strong electric field characteristic is confirmed; otherwise, it is not. Here, Eset is a configurable electric field threshold parameter, with a default value of 30V / m. ΔT is a configurable time parameter, with a default value of 0.5ms.

[0075] For magnetic field data B(n), the processing logic is the same as for electric field data. The corresponding magnetic field threshold parameter is set to 0.3uT by default, and the time parameter is set to 0.5ms by default.

[0076] When the strong electric field characteristics or strong magnetic field characteristics change from valid to invalid, the corresponding sampling time value is recorded, and the strong electromagnetic characteristics of all recorded data can be detected and calibrated.

[0077] Example 2

[0078] Reference Figure 2As an embodiment of the present invention, a system for wideband recording and storage of operating conditions and multidimensional feature calibration of electric vehicle charging stations is provided, characterized in that it includes a data acquisition module, a data preprocessing module, a recording and storage module, a feature calibration module and a communication module.

[0079] The data acquisition module captures various key information about electric vehicle charging stations, and monitors the voltage and current of the charging load, environmental variables, and the working status of the charging piles in real time.

[0080] The data preprocessing module cleans and organizes the raw data from the acquisition module, removing noise, filling in missing values, eliminating outliers and data with incorrect formats, thus ensuring data quality and integrity, and converting the data into a unified format.

[0081] The recording and storage module is responsible for storing the preprocessed data according to a specific structure and format.

[0082] The feature calibration module performs in-depth analysis of the stored data, extracts key feature information, and performs broadband characteristic detection, distortion characteristic detection, and strong electromagnetic field characteristic detection on the data to gain a comprehensive understanding of the charging station's operating conditions.

[0083] The communication module is responsible for enabling data transmission and communication between various modules, using stable communication protocols and network technologies to ensure accurate data transmission and real-time updates.

[0084] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion 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 invention. 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.

[0085] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0086] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0087] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0088] Example 3

[0089] In this embodiment, to verify the beneficial effects of the present invention, scientific demonstration is conducted through economic benefit calculations and simulation experiments. This embodiment presents experiments comparing both existing conventional methods and the method of this embodiment.

[0090] Multidimensional feature calibration.

[0091] The recorded operating data is subjected to broadband characteristic testing, distortion characteristic testing, and strong electromagnetic field characteristic testing, with the three characteristic tests performed in parallel.

[0092] For broadband characteristic detection, the discrete sampled values ​​i(n) of the three-phase current are first taken within a 0.1-second time window and then subjected to FFT transformation.

[0093] The FFT transform result includes the DC component amplitude I_d, the 10Hz component amplitude I_h1, the 20Hz component amplitude I_h2, ..., the 12.79kHz component amplitude I_h1279 of the current signal, resulting in the sequence S1: [Id, I_h1, I_h2, ..., I_h1279]. Assuming the rated current of the three phases is Ir, each element of the sequence is filtered as follows:

[0094] If In > K1 * Ir, element n is retained; otherwise, the element is deleted. K1 is a configurable amplitude threshold parameter, with a default value of 0.05.

[0095] We obtain a new sequence S2: [I_p, ..., I_q], where the frequency corresponding to the leftmost element I_p is f_min, and the frequency corresponding to the rightmost element I_q is f_max. The spectral distribution range of the signal is then:

[0096] F = f_max - f_min. If F > fset, the wideband characteristic is determined to be valid; otherwise, the wideband characteristic is determined to be invalid. Here, fset is a configurable frequency distribution range parameter, which is 2000Hz by default.

[0097] The above calculation process proceeds point by point through the discrete sampled values ​​of the three-phase current. When the broadband characteristic changes from valid to invalid, the corresponding first sampling time value is recorded. This completes the broadband characteristic detection and calibration of all recorded loads.

[0098] Amplitude sequence S1: [0.5, 1.2, 0.8, 1.5, 2.0, 1.8, 1.1, 0.9, 1.3, 1.7].

[0099] Amplitude threshold parameter: 1.0.

[0100] The retained amplitude sequence S2 is: [1.2, 1.5, 2.0, 1.8, 1.1, 1.3, 1.7].

[0101] Spectral distribution range F: 1.7-1.2=0.5.

[0102] Frequency distribution range parameter: 0.4.

[0103] Judgment: Wideband characteristic is valid.

[0104] Distortion characteristic detection: Based on the results of the FFT calculation above, the total harmonic distortion (THD) is calculated. The following criteria are then applied:

[0105] If THD > K2, the distortion characteristic is considered valid; otherwise, it is considered invalid. K2 is a configurable distortion rate threshold parameter, with a default value of 0.03.

[0106] By progressively sampling the discrete values ​​of the three-phase current point by point, and recording the corresponding first sampling time value when the distortion characteristic changes from valid to invalid, the distortion characteristic detection and calibration of all recorded loads can be completed.

[0107] Total harmonic distortion (THD): 8%.

[0108] Distortion rate threshold parameter: 10%.

[0109] Judgment: The distortion characteristic is not valid.

[0110] The strong electromagnetic field characteristics detection involves processing the collected electric field data E(n) and magnetic field strength data B(n) as shown in the figure below.

[0111] If E(n) > Eset, the timer starts counting; otherwise, the timer immediately returns to 0. If the timer's count ΔT > Tset, the strong electric field characteristic is confirmed; otherwise, it is not. Here, Eset is a configurable electric field threshold parameter, with a default value of 30V / m. ΔT is a configurable time parameter, with a default value of 0.5ms.

[0112] For magnetic field data B(n), the processing logic is the same as for electric field data. The corresponding magnetic field threshold parameter is set to 0.3uT by default, and the time parameter is set to 0.5ms by default.

[0113] Electric field data E(n): 5.

[0114] Electric field threshold parameter Eset: 4.

[0115] The timer's timing value ΔT: 3 seconds.

[0116] Timing threshold parameter Tset: 2 seconds.

[0117] Judgment: The strong electric field characteristic is valid.

[0118] When the strong electric field characteristics or strong magnetic field characteristics change from valid to invalid, the corresponding sampling time value is recorded, and the strong electromagnetic characteristics of all recorded data can be detected and calibrated.

[0119] The data results from multiple experiments are recorded in Table 1.

[0120] Table 1 Data Comparison Table

[0121] Charging efficiency 92.7 80.0 15.9 ％ Charging time (minutes) 40 49 18.4 min User satisfaction 88.6 79.0 12.2 ％ Voltage stability 95.4 89.0 7.2 ％ Current stability 94.1 87.0 8.2 ％ Environmental adaptability 90.3 82.0 10.1 ％ Data processing speed (seconds) 3.4 4.6 26.1 s System stability 93.7 88.0 6.5 ％ Fault detection and handling efficiency (seconds) 5.4 10.1 46.5 s Energy consumption (kilowatt-hours) 15.5 18.0 13.9 kWh

[0122] The table shows that the recharging efficiency of this invention reaches 92.7%, an improvement of 15.9% compared to the existing method's 80.0%. It can more effectively convert electrical energy into vehicle power, thereby reducing energy waste. Charging time is also significantly shortened, from 49 minutes in the existing method to 40 minutes, a reduction of 18.4%. This not only improves the user experience but also allows for faster service to more electric vehicles during peak hours.

[0123] In addition, user satisfaction has also improved significantly, from 79.0% to 88.6%, an increase of 12.2%, which better meets user needs and provides better services.

[0124] This invention also demonstrates excellent performance in voltage and current stability, achieving 95.4% and 94.1% respectively, representing improvements of 7.2% and 8.2% compared to existing methods. This enables a more stable supply of electrical energy to electric vehicles, reducing problems caused by voltage or current instability.

[0125] In terms of environmental adaptability, the present invention achieves 90.3%, which is 10.1% higher than the 82.0% of existing methods. This indicates that the present invention can better adapt to different environmental conditions and ensure the normal operation of charging stations.

[0126] In terms of data processing speed, this invention only requires 3.4 seconds, which is 26.1% faster than the existing method's 4.6 seconds. This enables faster data processing and provides more timely service.

[0127] In terms of system stability, the present invention achieved 93.7%, which is 6.5% higher than the 88.0% of the existing methods. This indicates that the system of the present invention is more stable and can better cope with various emergencies.

[0128] In terms of fault detection and handling efficiency, this invention only requires 5.4 seconds, which is 46.5% faster than the existing method's 10.1 seconds. This enables faster fault detection and handling, reducing the impact of faults on the operation of charging stations.

[0129] Finally, in terms of energy consumption, this invention requires only 15.5 kWh, which is 13.9% lower than the 18.0 kWh of the existing method. This demonstrates that this invention is more energy-efficient and reduces its environmental impact.

[0130] In summary, this invention demonstrates superior performance in terms of charging efficiency, charging time, user satisfaction, voltage stability, current stability, environmental adaptability, data processing speed, system stability, fault detection and handling efficiency, and energy consumption, representing a significant improvement over existing methods. These data fully demonstrate the beneficial effects of this invention and showcase its potential value and advantages in practical applications.

[0131] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for wide-range recording and multi-dimensional feature calibration of electric vehicle charging station operating conditions, characterized in that, include: Accurate data collection will be conducted on three main categories of data from electric vehicle charging stations; Data preprocessing is performed on the collected data to complete the recording and storage of various operating condition data; Multidimensional feature calibration is performed on various types of operating condition data; The recording of various types of working condition data involves recording the data one second before the start signal is received when any charging pile enters the charging start state or at a specified future time. Storage tables are constructed for different rates, sampling times, formats, and information amounts of the three major types of data to achieve unified storage. The data is stored sequentially in the format of time tags and data values. Recording can be stopped using a preset total recording time, a specified time, or manually. The multidimensional feature calibration involves performing broadband characteristic detection, distortion characteristic detection, and strong electromagnetic field characteristic detection in parallel on the recorded operating data. The broadband characteristic detection involves performing FFT transformation on discrete sampled values ​​of the three-phase current within a 0.1-second time window to construct an amplitude sequence S1: [Id, I h1 I h2 , ..., I h1279 ]; Where Id is the DC component amplitude, I h1 I h2 I h1279 [ ] represent the amplitude of the 10Hz component, the amplitude of the 20Hz component, and the amplitude of the 12.79kHz component, respectively; If the amplitude of a certain frequency component is greater than the product of the amplitude threshold parameter and the rated current of the three phases, the current amplitude is retained; if the amplitude of a certain frequency component is less than or equal to the product of the amplitude threshold parameter and the rated current of the three phases, the current amplitude is discarded. The retained amplitudes are used to form a new sequence S2: [I p , ..., I q ], I p The minimum frequency corresponding to the retained amplitude, I q The frequency range of the output retained signal is calculated based on the maximum frequency corresponding to the retained amplitude, expressed as: F = f max - f min Where, f max f is the maximum frequency of the retained amplitude corresponding to Iq. min This corresponds to the minimum frequency of the retained amplitude. If the spectral distribution range of the retained signal is greater than the frequency distribution range parameter, then the wideband characteristic is determined to be valid; otherwise, the wideband characteristic is determined to be invalid. The three-phase current discrete sampling values ​​are progressively advanced point by point. When the broadband characteristic changes from valid to invalid, the corresponding first sampling time value is recorded, thus completing the broadband characteristic detection and calibration of all recorded loads. The strong electromagnetic field characteristic detection is based on a comparison and judgment of the collected electric field data E(n) and magnetic field strength data B(n); If E(n) is greater than the electric field threshold parameter Eset, then the timer is started; otherwise, the timer immediately returns to 0. If the timer value ΔT is greater than the timer threshold parameter Tset, then the strong electric field characteristic is determined to be valid; otherwise, it is not valid. When the strong electric field characteristics or strong magnetic field characteristics change from valid to invalid, the corresponding sampling time value is recorded, and the strong electromagnetic characteristics of all recorded data can be detected and calibrated.

2. The method for wide-range recording and multi-dimensional feature calibration of electric vehicle charging station operating conditions as described in claim 1, characterized in that: The three main categories of data collection include voltage and current collection of charging load, collection of environmental variables, and collection of charging pile operating status data. The data preprocessing involves denoising the collected state data, removing missing values, outliers, and invalid data with incorrect formats, converting the original format data into the format required for requirements analysis, and normalizing the data to complete the data preprocessing.

3. The method for wide-range recording and multi-dimensional feature calibration of electric vehicle charging station operating conditions as described in claim 2, characterized in that: The voltage and current of the charging load are acquired by using a wideband sensing method with resistive voltage division and electrical isolation to acquire the three-phase voltage, and by using a wideband sensing method based on the Hall principle to acquire the three-phase current. The differential voltages corresponding to the voltage and current are then filtered by an active anti-aliasing low-pass filter before entering the analog-to-digital converter. The environmental variables are collected using RS485 communication. The current ambient temperature value of the charging station is read from the temperature measurement module once per second. Each reading is associated with and calibrated with the current value. The ambient electric field and ambient magnetic field at the voltage and current waveform acquisition points are collected. Each sampling is associated with and calibrated with the current value. The charging pile's working status data is collected via Ethernet communication, which collects the start-up and stop-charging status of m charging piles in the charging station. For each collected data, it is determined whether the charging pile is valid at the current time. If it is invalid, the current time value is associated with the collected value for calibration.

4. The method for wide-range recording and multi-dimensional feature calibration of electric vehicle charging station operating conditions as described in claim 3, characterized in that: The distortion characteristic detection is to calculate the total harmonic distortion rate (THD) after completing the FFT calculation. If the total harmonic distortion rate is greater than the distortion rate threshold parameter, the distortion characteristic is determined to be valid; otherwise, the distortion characteristic is determined to be invalid. By progressively sampling the discrete values ​​of the three-phase current point by point, and recording the corresponding first sampling time value when the distortion characteristic changes from valid to invalid, the distortion characteristic detection and calibration of all recorded loads can be completed.

5. A system employing the wide-range recording and multi-dimensional feature calibration method for electric vehicle charging station operating conditions as described in any one of claims 1 to 4, characterized in that: It includes a data acquisition module, a data preprocessing module, a recording and storage module, a feature calibration module, and a communication module; The data acquisition module captures various key information of electric vehicle charging stations and monitors the voltage and current of the charging load, environmental variables, and the working status of the charging piles in real time. The data preprocessing module cleans and organizes the raw data from the acquisition module, removing noise, filling in missing values, eliminating outliers and data with incorrect formats, thus ensuring data quality and integrity, and converting the data into a unified format. The recording and storage module is responsible for storing the preprocessed data according to a specific structure and format; The feature calibration module performs in-depth analysis of the stored data, extracts key feature information, and performs broadband characteristic detection, distortion characteristic detection, and strong electromagnetic field characteristic detection on the data to gain a comprehensive understanding of the charging station's operating conditions. The communication module is responsible for enabling data transmission and communication between various modules, using stable communication protocols and network technologies to ensure accurate data transmission and real-time updates.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the electric vehicle charging station operating condition wideband recording and multi-dimensional feature calibration method as described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the wide-band recording and multi-dimensional feature calibration method for electric vehicle charging station operating conditions as described in any one of claims 1 to 4.

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