A method for detecting cc1 of charging gun insertion and extraction

Abstract

The present application relates to a kind of charging gun insertion and the CC1 detection method of gun pulling.Wherein, the method includes: obtaining line impedance data and voltage data, determine effective connection in combination with preset correlation law, distinguish the key trigger state after insertion and output locking control signal;Dynamic update detection signal stability reference feature, start validity verification, determine gun pulling and trigger charging interruption protection;Impedance-voltage correlation matching model is constructed, and the connection reliability and button state are determined by multi-parameter fitting operation;Bidirectional signal feedback logic is constructed, signal stability is maintained by dynamic correction mechanism, trigger reverse verification signal sending process, in combination with charging gun end response and impedance voltage state determine gun pulling effective, start safety lock release process and record state switching data.The method improves detection accuracy and stability, guarantees charging safety.

Description

Technical Field

[0001] This invention belongs to the field of power system and electrical equipment technology, specifically relating to a CC1 detection method for plugging and unplugging a charging gun. Background Technology

[0002] With the rapid popularization of the electric vehicle industry, the safe operation and reliability of charging infrastructure have become core issues of concern to the industry. As a key connecting component between electric vehicles and charging equipment, the accurate detection of the charging gun's insertion and removal status is directly related to the electrical safety of the charging process. It is a core link to avoid safety hazards such as overcurrent, short circuit, and arc discharge. Among them, the detection accuracy of the CC1 signal (charging connection confirmation signal) is the key basis for determining whether the charging equipment starts power output and whether it triggers the safety interlock.

[0003] Existing CC1 detection methods for charging guns mainly rely on single voltage data threshold judgment or simple impedance matching mechanisms, which exposes many technical defects in practical applications: First, they have weak anti-interference capabilities. High-frequency electromagnetic interference and line contact jitter in charging scenarios can easily lead to signal distortion, resulting in misjudgment of the gun insertion status (such as judging a valid connection when it is not fully inserted) or missed detection of the gun removal signal (such as signal fluctuations during gun removal being misjudged as load changes); Second, the detection benchmark is fixed and cannot adapt to variables such as dynamic load changes, ambient temperature fluctuations, and line aging losses during charging, leading to a significant decrease in detection accuracy after long-term use. The problems are as follows: First, the voltage drop is significant. For example, a fixed voltage threshold makes it difficult to distinguish between signal changes caused by "plugging in the charging gun" and "line impedance drift". Second, the state judgment logic is simplistic and lacks multi-parameter collaborative verification and dynamic calibration mechanisms. For instance, judging the removal of the charging gun solely by a voltage surge is prone to false triggering due to instantaneous load shedding, and the lack of a bidirectional signal feedback mechanism makes it impossible to effectively verify the true state of the charging gun. Third, the recognition accuracy of the plugging in button trigger state is insufficient. Existing solutions mostly rely on a single step signal for judgment, without considering the continuous stability and matching degree analysis of the signal, leading to false button triggering or triggering delay, affecting user experience and the timeliness of safety control.

[0004] The aforementioned defects directly lead to safety hazards in charging equipment: misjudgments in gun insertion detection may result in risks such as live plugging and unplugging, or unexpected interruptions during charging; missed gun removal detection may cause the charging equipment to fail to cut off power output in time, leading to electric shock or equipment damage. Meanwhile, as charging protocols such as GB / T18487.1 place higher demands on the accuracy and response speed of connection detection, existing detection methods can no longer meet the safety compliance and high-performance operation requirements of charging equipment. Therefore, there is an urgent need to develop a CC1 detection method with strong anti-interference capabilities, good dynamic adaptability, and multi-parameter collaborative verification to solve technical problems such as inaccurate gun insertion / removal status recognition, unreliable button trigger judgment, and poor environmental adaptability in existing technologies, thereby ensuring the safety and stability of the electric vehicle charging process. Summary of the Invention

[0005] To address the aforementioned problems in the prior art, this invention provides a CC1 detection method for plugging and unplugging a charging gun.

[0006] The objective of this invention can be achieved through the following technical solution: a CC1 detection method for plugging in and unplugging a charging gun, comprising:

[0007] S1: Acquire the impedance and voltage data of the line, process them through signal filtering and steady-state calibration mechanism, dynamically adjust the impedance strategy of the detection line, and determine the formation of effective connection based on the preset correlation law. At the same time, distinguish the trigger state of the button after the gun is inserted according to the stability characteristics of the voltage data, and output a lock control signal.

[0008] S2: During the charging gun connection period, the stable reference characteristics of the detection signal are dynamically updated based on the impedance data and the voltage data. The dynamic changes of the impedance data and the voltage data are monitored in real time. The signal validity verification process is started. By verifying that the voltage data and impedance data continuously maintain the no-load reference characteristics and the state of no button trigger signal, it is determined that the gun removal operation is completed and the charging interruption protection mechanism is triggered.

[0009] S3: Construct an impedance-voltage correlation matching model to analyze the continuity and matching degree of impedance data and voltage data transitioning from no-load characteristics to connection characteristics. At the same time, identify impedance abrupt change characteristics during the transition process, and analyze the reliability of the plug connection and button status through multi-parameter fitting calculation.

[0010] S4: Construct a bidirectional signal feedback logic for the detection circuit. During the monitoring process, maintain the continuous stability of the detection signal through a dynamic correction mechanism. Based on the dynamic changes of the impedance data and the voltage data, trigger the reverse verification signal transmission process. Based on the effective response signal and impedance and voltage status at the charging gun end, determine that the gun removal operation is valid and start the safety interlock release process, and synchronously record the state switching data.

[0011] Specifically, the impedance strategy includes: dynamically configuring the impedance matching parameters of the detection line based on the real-time correlation between the impedance data and the voltage data of the line, establishing an impedance threshold range, wherein the boundary values ​​of the impedance threshold range are preset according to the rated operating parameters of the charging gun and the inherent impedance characteristics of the line, and after the signal filtering and steady-state calibration mechanism completes the data processing, the impedance threshold range is dynamically corrected based on the ratio of the processed impedance data to the voltage data. The impedance strategy also includes setting a buffer adjustment mechanism for the fluctuation characteristics of the impedance data.

[0012] Specifically, the signal filtering includes: first, identifying the typical frequency range of high-frequency electromagnetic interference in the detection signal, setting the cutoff frequency parameter of the low-pass filter accordingly, and performing low-pass filtering on the acquired impedance data and voltage data; then, extracting the processed data according to a preset sampling interval, using a moving average mechanism to perform rolling calculations on a continuously set number of data sets, and outputting smoothed data.

[0013] Specifically, the steady-state calibration mechanism includes: setting a fluctuation judgment threshold and a reference calibration period for the detection signal, monitoring the instantaneous fluctuation amplitude of the detection signal in real time, comparing it with the fluctuation judgment threshold, recording the initial signal reference value of the current line based on the comparison result, obtaining the continuous signal to calculate the average deviation, and correcting the initial signal reference value based on the average deviation.

[0014] Specifically, the process of distinguishing the trigger state of the button after insertion includes: setting a threshold for the step amplitude of voltage data, a threshold for the effective trigger duration, and a signal stability determination time; continuously monitoring the dynamic changes of voltage data after the insertion of the button to form an effective connection; recording the amplitude value and duration of the step change of voltage data in real time; comparing the recorded amplitude value with the threshold for the step amplitude and the duration with the threshold for the effective trigger duration; and simultaneously monitoring the stable state of the voltage data after the step change within the signal stability determination time. Based on the comparison results and monitoring results, the button is determined to be effectively triggered.

[0015] Specifically, the process of updating the stable reference characteristics of the detection signal is as follows: First, based on the impedance and voltage steady-state data after the plug-in forms an effective connection, an initial stable reference characteristic is established; then, the load change amplitude during the charging process and the deviation of the current impedance data and voltage data from the initial reference characteristic are monitored in real time. Based on the deviation of the load change amplitude, the reference update process is started, a set number of impedance and voltage steady-state data are continuously acquired, the statistical mean is calculated, and a new stable reference characteristic is generated.

[0016] Specifically, the signal validity verification process includes: continuously acquiring impedance and voltage data at set time intervals, constructing a time-series trend reference model, fitting the impedance and voltage data to the time series to generate actual change curves, and calculating the trend fit with the time-series trend reference model; simultaneously verifying the synchronicity of changes in impedance and voltage data, analyzing the correspondence between rises and falls and the matching degree of change rates; tracking the signal fluctuation trend using the sliding window method, and determining the signal validity based on the trend fit, the synchronicity of impedance and voltage changes, and the fluctuation trend.

[0017] Specifically, the construction process of the impedance-voltage correlation matching model is as follows: based on the impedance data and the voltage data, key correlation features are extracted to establish a feature dataset; the feature dataset is divided into a data subset and a verification subset according to a preset ratio; the correlation pattern of the data subset is fitted using a support vector regression mechanism; the performance of the fitted correlation pattern is verified using the verification subset data; the feature correlation accuracy and consistency of state judgment are calculated; and the feature selection dimension and mechanism parameters are iteratively adjusted according to the verification results to generate the impedance-voltage correlation matching model.

[0018] Specifically, the process of analyzing the continuity and matching degree of impedance and voltage data transitioning from no-load characteristics to connection characteristics is as follows: Real-time tracking of the dynamic change trajectory of the impedance and voltage data; extraction of continuous change rate, transition phase duration, and signal characteristic values ​​of key nodes starting from the transition initiation time; determination of signal trajectory based on continuity analysis and the gradual law of physical connection; comparison of change trend synergy based on matching degree analysis, combined with the normal transition characteristic law in the impedance-voltage correlation matching model, to comprehensively determine the degree of fit of the transition process.

[0019] Specifically, the multi-parameter fitting operation process is as follows: based on the impedance data and the voltage data, core parameters are extracted, and the influence weights of the core parameters on connection status determination and button trigger recognition are combined with preset weighting coefficients; then, linear fitting operation is performed on the parameter values ​​according to the preset weighting coefficients to generate an initial connection reliability score and an initial confidence level of button status; the feature rules in the impedance-voltage correlation matching model are called simultaneously to verify the initial operation results, and finally, the connection reliability score and button status confidence level are output.

[0020] Specifically, the bidirectional signal feedback logic includes: establishing a half-duplex communication link between the detection end and the charging gun end; the detection end sending a status query signal, including impedance detection command, voltage sampling command, and connection status verification command, to the charging gun end at a preset period; the charging gun end receiving the command and acquiring the corresponding signal data in real time; encapsulating the data according to a preset data frame format and adding a signal acquisition timestamp and device status identifier before feeding it back to the detection end; the detection end performing frame structure verification and CRC verification on the received feedback data; parsing the data based on the verification results and updating the local detection status; and the charging gun end receiving the status update signal from the detection end in real time and dynamically adjusting its own sampling frequency and data transmission strategy.

[0021] Specifically, the dynamic correction mechanism includes: real-time monitoring of the dynamic changes in ambient temperature, line aging loss coefficient, and deviations of current impedance and voltage data from stable reference characteristics; comparing these deviations with preset temperature change trigger intervals, loss coefficient thresholds, and allowable signal deviation ranges; initiating a dynamic correction process based on the comparison results; calculating the signal deviation correction amount according to preset influence weights based on the correlation between temperature, loss, and signal deviation in the impedance-voltage correlation matching model; generating correction coefficients; and adjusting the currently acquired impedance and voltage data in real time based on the correction coefficients, while synchronously updating the parameters of the stable reference characteristics.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] Enhanced safety performance: A multi-level safety protection system is constructed through impedance-voltage dual-parameter collaborative detection, signal validity timing verification, and bidirectional signal feedback logic. This effectively eliminates problems such as misjudgment of incomplete insertion of the charging gun and missed detection of removal signals, accurately identifies the true connection / disconnection status, and fundamentally blocks safety hazards such as live insertion / removal, arc discharge, and overcurrent, thereby improving the operational safety level of charging equipment.

[0024] Improved status recognition accuracy: Relying on impedance-voltage correlation matching model, dynamic benchmark update mechanism, and multi-condition collaborative judgment logic, it effectively filters high-frequency electromagnetic interference and line contact jitter. It can accurately distinguish between valid button activation and line impedance drift, and dynamic load changes and button removal operations, meeting the requirements of high-reliability charging scenarios.

[0025] Strong adaptability and high stability under various operating conditions: Through dynamic adaptation to the target charging protocol, real-time correction of ambient temperature and line aging losses, and load change-driven benchmark updates, the impact of extreme environments and long-term operating losses on detection accuracy is effectively offset; ensuring stable detection performance throughout the entire charging cycle and significantly extending the effective service life of charging equipment.

[0026] Strong compliance and controllable industrialization costs: Strictly follows mainstream charging protocol standards such as GB / T18487.1 to meet real-time requirements; achieves multi-protocol adaptation through algorithm optimization, reduces equipment upgrade and transformation costs, and helps products quickly pass compliance certification and be industrialized.

[0027] User experience and operational efficiency optimization: Accurately identify button trigger status to avoid charging interruptions or startup delays, ensuring convenient user operation; synchronously record key information such as status switching data and connection reliability scores to facilitate maintenance personnel in quickly locating line faults and signal anomalies, reducing the frequency of equipment calibration and lowering maintenance workload. Attached Figure Description

[0028] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0029] Figure 1 This is a flowchart of a CC1 detection method for inserting and removing a charging gun according to the present invention.

[0030] Figure 2 This is a schematic diagram of the CC1 detection method for inserting and removing a charging gun according to the present invention. Detailed Implementation

[0031] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0032] Please see Figures 1-2 A CC1 detection method for plugging in and unplugging a charging gun, comprising:

[0033] S1: Acquire the impedance and voltage data of the line, process them through signal filtering and steady-state calibration mechanism, dynamically adjust the impedance strategy of the detection line, and determine the formation of effective connection based on the preset correlation law. At the same time, distinguish the trigger state of the button after the gun is inserted according to the stability characteristics of the voltage data, and output a lock control signal.

[0034] S2: During the charging gun connection period, the stable reference characteristics of the detection signal are dynamically updated based on the impedance data and the voltage data. The dynamic changes of the impedance data and the voltage data are monitored in real time. The signal validity verification process is started. By verifying that the voltage data and impedance data continuously maintain the no-load reference characteristics and the state of no button trigger signal, it is determined that the gun removal operation is completed and the charging interruption protection mechanism is triggered.

[0035] S3: Construct an impedance-voltage correlation matching model to analyze the continuity and matching degree of impedance data and voltage data transitioning from no-load characteristics to connection characteristics. At the same time, identify impedance abrupt change characteristics during the transition process, and analyze the reliability of the plug connection and button status through multi-parameter fitting calculation.

[0036] S4: Construct a bidirectional signal feedback logic for the detection circuit. During the monitoring process, maintain the continuous stability of the detection signal through a dynamic correction mechanism. Based on the dynamic changes of the impedance data and the voltage data, trigger the reverse verification signal transmission process. Based on the effective response signal and impedance and voltage status at the charging gun end, determine that the gun removal operation is valid and start the safety interlock release process, and synchronously record the state switching data.

[0037] Specifically, the impedance strategy includes: dynamically configuring the impedance matching parameters of the detection line based on the real-time correlation between the impedance data and the voltage data of the line, establishing an impedance threshold range, wherein the boundary values ​​of the impedance threshold range are preset according to the rated operating parameters of the charging gun and the inherent impedance characteristics of the line, and after the signal filtering and steady-state calibration mechanism completes the data processing, the impedance threshold range is dynamically corrected based on the ratio of the processed impedance data to the voltage data. The impedance strategy also includes setting a buffer adjustment mechanism for the fluctuation characteristics of the impedance data.

[0038] Specifically, the signal filtering includes: first, identifying the typical frequency range of high-frequency electromagnetic interference in the detection signal, setting the cutoff frequency parameter of the low-pass filter accordingly, and performing low-pass filtering on the acquired impedance data and voltage data; then, extracting the processed data according to a preset sampling interval, using a moving average mechanism to perform rolling calculations on a continuously set number of data sets, and outputting smoothed data.

[0039] Specifically, the steady-state calibration mechanism includes: setting a fluctuation judgment threshold and a reference calibration period for the detection signal, monitoring the instantaneous fluctuation amplitude of the detection signal in real time, comparing it with the fluctuation judgment threshold, recording the initial signal reference value of the current line based on the comparison result, obtaining the continuous signal to calculate the average deviation, and correcting the initial signal reference value based on the average deviation.

[0040] Specifically, the process of distinguishing the trigger state of the button after insertion includes: setting a threshold for the step amplitude of voltage data, a threshold for the effective trigger duration, and a signal stability determination time; continuously monitoring the dynamic changes of voltage data after the insertion of the button to form an effective connection; recording the amplitude value and duration of the step change of voltage data in real time; comparing the recorded amplitude value with the threshold for the step amplitude and the duration with the threshold for the effective trigger duration; and simultaneously monitoring the stable state of the voltage data after the step change within the signal stability determination time. Based on the comparison results and monitoring results, the button is determined to be effectively triggered.

[0041] In this embodiment, for target charging protocol A (such as a DC charging protocol conforming to GB / T18487.1 standard), plug connection detection and button trigger determination are performed:

[0042] Signal processing parameters: The typical frequency range of high-frequency electromagnetic interference is F1-F2 (F1 < F2), the low-pass filter cutoff frequency is Fc (Fc < F1), the sampling interval is T, and the number of continuous moving average calculation groups is N;

[0043] Steady-state calibration parameters: voltage data fluctuation judgment threshold is ΔVt, impedance data fluctuation judgment threshold is ΔZt, and reference calibration period is Tc;

[0044] Impedance adjustment parameters: The target charging protocol A has a preset impedance threshold of Zr and a dynamic adjustment range of [Zm, ZM] (Zm < Zr < ZM).

[0045] Key trigger determination parameters: voltage step amplitude threshold is ΔVk, effective trigger duration threshold is Tk, and signal stability determination duration is Ts.

[0046] Signal acquisition and filtering:

[0047] After the charging gun is inserted into the charging interface, the impedance data Z(t) and voltage data V(t) of the line are collected in real time through the detection circuit.

[0048] First, identify the actual frequency range of high-frequency electromagnetic interference in the acquired signal as F1'-F2' (F1'∈[F1,F2], F2'∈[F1,F2]). Set the low-pass filter cutoff frequency to Fc' (Fc'<F1'). Perform low-pass filtering on Z(t) and V(t) to filter out high-frequency interference components.

[0049] The filtered signal data is extracted according to the preset sampling interval T. The moving average algorithm is used to perform rolling calculation on N consecutive sets of signal data (that is, after removing the earliest set of data for each new set of data, the mean of N sets of data is calculated), and the smoothed impedance data Zs(t) and voltage data Vs(t) are output.

[0050] Steady-state calibration processing:

[0051] Initiate a steady-state calibration mechanism to monitor the instantaneous fluctuation amplitude ΔZ of Zs(t) and the instantaneous fluctuation amplitude ΔV of Vs(t) in real time;

[0052] When ΔZ≤ΔZt and ΔV≤ΔVt, record the initial impedance reference value Zr0 and the initial voltage reference value Vr0 of the current line;

[0053] Within the reference calibration period Tc, continuous Zs(t) and Vs(t) data are acquired, and the average impedance deviation ΔZa (the average deviation of all Zs(t) and Zr0 within Tc) and the average voltage deviation ΔVa (the average deviation of all Vs(t) and Vr0 within Tc) are calculated.

[0054] Based on the deviation value, the initial reference value is corrected to obtain the calibrated impedance reference value Zr=Zr0+ΔZa and the voltage reference value Vr=Vr0+ΔVa.

[0055] Dynamic adjustment of line impedance:

[0056] Extract the preset impedance threshold Zr and the dynamic adjustment range [Zm, ZM] from the target charging protocol A;

[0057] The actual impedance parameter Zreal of the current test line is obtained in real time through the impedance detection module, and Zreal is compared with Zr:

[0058] If Zreal < Zm, control the variable impedance element (such as a digital potentiometer) to increase the impedance parameter until Zreal falls into the [Zm, Zr] interval;

[0059] If Zreal > ZM, control the variable impedance element to reduce the impedance parameter until Zreal falls into the [Zr, ZM] interval;

[0060] If Zm≤Zreal≤ZM, keep the parameters of the variable impedance element unchanged;

[0061] After impedance adjustment, continuously monitor the degree of fit between Zs(t) and Vs(t) (i.e. the synergy of their changing trends, determined by calculating the correlation coefficient R; fit is determined when R≥R0) to ensure that the line impedance matches the voltage data.

[0062] Valid connection determination and button trigger recognition:

[0063] Based on the preset correlation rules (such as Zr being in the connection impedance range specified in the protocol after calibration, and Vr being in the connection voltage range specified in the protocol), if Zs(t) is continuously stable within the range of Zr±ΔZt and Vs(t) is continuously stable within the range of Vr±ΔVt, and the duration is ≥ Tconn (the connection stabilization duration specified in the protocol), a valid connection is determined to be formed.

[0064] After a valid connection is established, the dynamic changes of Vs(t) are continuously monitored:

[0065] When a step change is detected in Vs(t), record the step amplitude ΔV_step = |Vs(t1) - Vs(t0)| (t0 is the time before the step, t1 is the time after the step) and the step duration T_step (the time from t1 until Vs(t) stops changing).

[0066] If ΔV step ≥ ΔVk and T step ≥ Tk, continue to monitor the fluctuation amplitude of Vs(t) after the step within the time Ts. If the fluctuation amplitude ≤ ΔVt, it is determined that the button is effectively triggered.

[0067] If ΔV< ΔVk, T< Tk, or the fluctuation amplitude within the time period Ts > ΔVt, it is determined to be an invalid trigger (such as line interference).

[0068] Once a valid connection is established and the button is successfully triggered, a locking control signal is output to the charging control unit to activate the mechanical locking device of the charging interface, thereby achieving a stable lock between the charging gun and the interface.

[0069] Specifically, the process of updating the stable reference characteristics of the detection signal is as follows: First, based on the impedance and voltage steady-state data after the plug-in forms an effective connection, an initial stable reference characteristic is established; then, the load change amplitude during the charging process and the deviation of the current impedance data and voltage data from the initial reference characteristic are monitored in real time. Based on the deviation of the load change amplitude, the reference update process is started, a set number of impedance and voltage steady-state data are continuously acquired, the statistical mean is calculated, and a new stable reference characteristic is generated.

[0070] Specifically, the signal validity verification process includes: continuously acquiring impedance and voltage data at set time intervals, constructing a time-series trend reference model, fitting the impedance and voltage data to the time series to generate actual change curves, and calculating the trend fit with the time-series trend reference model; simultaneously verifying the synchronicity of changes in impedance and voltage data, analyzing the correspondence between rises and falls and the matching degree of change rates; tracking the signal fluctuation trend using the sliding window method, and determining the signal validity based on the trend fit, the synchronicity of impedance and voltage changes, and the fluctuation trend.

[0071] In this embodiment, based on the same target charging protocol A (a DC charging protocol conforming to GB / T18487.1 standard), the baseline dynamic update and gun disconnection status detection are performed during the charging gun connection period:

[0072] The baseline update parameters are: load change amplitude threshold ΔZL, signal deviation threshold ΔD, number of steady-state signal acquisition groups N (consistent with the number of S1 moving average groups), and the baseline update trigger condition is "load change amplitude ≥ ΔZL or signal deviation ≥ ΔD".

[0073] Signal validity verification parameters: sampling interval T (consistent with S1 sampling interval), time series trend fitting window length L, synchronicity judgment threshold R0 (consistent with S1 signal fit judgment threshold), sliding window length W, trend fit qualification threshold Cfit.

[0074] No-load characteristic judgment parameters: The protocol specifies the no-load impedance range [Z_no-load min, Z_no-load max], the no-load voltage range [V_no-load min, V_no-load max], and the duration T of the signal continuously conforming to the no-load characteristics;

[0075] The following parameters are retained from S1: impedance fluctuation threshold ΔZt, voltage fluctuation threshold ΔVt (steady-state calibration parameter), button trigger voltage step threshold ΔVk, and effective trigger duration Tk (button judgment parameter).

[0076] Dynamically update the stable benchmark features of the detection signal:

[0077] After the charging gun forms an effective connection through step S1, it collects N sets of smoothed impedance steady-state data Zs and voltage steady-state data Vs (the signals are filtered and calibrated by S1). The statistical mean is calculated as the initial stable reference feature: Zr0=1 / N×ΣZs (i=1 to N), Vr0=1 / N×ΣVs (i=1 to N).

[0078] During charging, the detection module monitors in real time the load change amplitude ΔZnegative (the absolute value of the difference between the current load impedance and the initial load impedance), the deviation of the current smoothed impedance data Zs from Zr0 ΔZbias = |Zs - Zr0| / Zr0, and the deviation of the current smoothed voltage data Vs from Vr0 ΔVbias = |Vs - Vr0| / Vr0.

[0079] When ΔZnegative ≥ ΔZL, or ΔZbiased ≥ ΔD, or ΔVbiased ≥ ΔD, the benchmark update process is initiated: N sets of new stable Zs and stable Vs are continuously collected, and the statistical mean is calculated to generate new stable benchmark features: Zrnew = 1 / N × ΣZsnew (i = 1 to N), Vrnew = 1 / N × ΣVsnew (i = 1 to N), which replace the original benchmark features and are used continuously.

[0080] If the update trigger condition is not met, the current baseline characteristics remain unchanged, and the load and signal deviation status are continuously monitored.

[0081] Gun draw signal triggering and validity verification process:

[0082] During charging, Zs and Vs are continuously monitored. When a sudden increase in Vs is detected (the magnitude of a single voltage change is ≥ ΔVs, where ΔVs is the threshold for voltage change when the gun is removed as specified in the protocol), and Zs falls within the range of [Zmin, Zmax], a suspected signal for removing the gun is triggered, and the signal validity verification process is initiated.

[0083] L sets of Zs and Vs data are continuously collected at sampling interval T. Based on the no-load state signal characteristics of charging protocol A (such as impedance stabilizing at [Z_no_min, Z_no_max], voltage stabilizing at [V_no_min, V_no_max] and the rate of change being gradual), a time-series trend reference model M is constructed.

[0084] The collected L groups of Zs and Vs data are fitted according to the time series to generate actual change curves Zs curve and Vs curve. The fit degree Cz between Zs curve and impedance trend in M ​​parameter is calculated, and the fit degree Cv between Vs curve and voltage trend in M ​​parameter is calculated. The average of the two is taken as the total trend fit degree Ctotal = (Cz + Cv) / 2.

[0085] Verify the synchronicity of changes in Zs and Vs: Analyze the corresponding relationship between their rise and fall (e.g., voltage rises synchronously when impedance rises) and the ratio of their rates of change (must fall within the range of [Kmin, Kmax], where Kmin and Kmax are the synchronous rate range specified in the agreement), calculate the synchronicity coefficient R_same, and if R_same ≥ R0, the synchronicity is deemed qualified;

[0086] The sliding window method (window length W) is used to track the fluctuation trends of the Zs curve and the Vs curve. If the signal fluctuation amplitude within the window is ≤ ΔZt and ΔVt (using the S1 steady-state calibration fluctuation threshold), the fluctuation trend is determined to be stable.

[0087] If Ctotal ≥ Csum, the synchronization is qualified, and the fluctuation trend is stable, the signal is deemed valid; otherwise, it is deemed an invalid interference signal, and the system returns to continuous monitoring.

[0088] Gun removal detection and charging interruption protection:

[0089] After the signal validity verification is passed, continue to monitor Zs and Vs: if Zs continues to be maintained at [Z_empty_min, Z_empty_max] and Vs continues to be maintained at [V_empty_min, V_empty_max], and the duration is ≥ T_hold, and no button trigger signal is detected at the same time (the voltage data does not meet the step change of ΔVk and Tk, and ΔVk and Tk use the button judgment parameters of S1), it is determined that the gun-drawing operation is completed;

[0090] Immediately send a lock release signal (forming a logical closed loop with the S1 lock control signal) and a charging interruption protection signal to the charging control unit, control the main charging circuit to quickly cut off the power output, and simultaneously record status data such as the gun removal time, signal change trajectory, and reference update record to complete the charging interruption protection process.

[0091] Specifically, the construction process of the impedance-voltage correlation matching model is as follows: based on the impedance data and the voltage data, key correlation features are extracted to establish a feature dataset; the feature dataset is divided into a data subset and a verification subset according to a preset ratio; the correlation pattern of the data subset is fitted using a support vector regression mechanism; the performance of the fitted correlation pattern is verified using the verification subset data; the feature correlation accuracy and consistency of state judgment are calculated; and the feature selection dimension and mechanism parameters are iteratively adjusted according to the verification results to generate the impedance-voltage correlation matching model.

[0092] Specifically, the process of analyzing the continuity and matching degree of impedance and voltage data transitioning from no-load characteristics to connection characteristics is as follows: Real-time tracking of the dynamic change trajectory of the impedance and voltage data; extraction of continuous change rate, transition phase duration, and signal characteristic values ​​of key nodes starting from the transition initiation time; determination of signal trajectory based on continuity analysis and the gradual law of physical connection; comparison of change trend synergy based on matching degree analysis, combined with the normal transition characteristic law in the impedance-voltage correlation matching model, to comprehensively determine the degree of fit of the transition process.

[0093] Specifically, the multi-parameter fitting operation process is as follows: based on the impedance data and the voltage data, core parameters are extracted, and the influence weights of the core parameters on connection status determination and button trigger recognition are combined with preset weighting coefficients; then, linear fitting operation is performed on the parameter values ​​according to the preset weighting coefficients to generate an initial connection reliability score and an initial confidence level of button status; the feature rules in the impedance-voltage correlation matching model are called simultaneously to verify the initial operation results, and finally, the connection reliability score and button status confidence level are output.

[0094] This embodiment continues the target charging protocol A (a DC charging protocol conforming to GB / T18487.1 standard) and the preceding detection system architecture, and uses the core parameters already defined in S1 and S2. The preset new parameters and the parameters used are as follows:

[0095] Model building parameters: the feature dataset partitioning ratio is a preset ratio K (K is the proportion of the data subset), the support vector regression kernel function is a radial basis function, the feature association accuracy pass threshold P is set, and the state judgment consistency pass threshold Q is set.

[0096] Transition process analysis parameters: transition initiation judgment threshold (impedance shift from unloaded interval to connected interval ≥ ΔZ, voltage shift from unloaded interval to connected interval ≥ ΔV), key node feature value extraction time (transition initiation time, impedance change time, signal steady state time).

[0097] Multi-parameter fitting parameters: core parameter weighting coefficients (transition continuity score weight α, matching degree score weight β, impedance mutation feature weight γ, α+β+γ=1), connection reliability score pass threshold S, button state confidence pass threshold C.

[0098] The following parameters are retained: filtering parameters (low-pass filter cutoff frequency Fc, sampling interval T, number of moving average groups N), fluctuation threshold (ΔZt, ΔVt), idle characteristic interval [Z_empty_min, Z_empty_max], [V_empty_min, V_empty_max], connection characteristic interval [Z_connected_min, Z_connected_max], [V_connected_min, V_connected_max] (as specified in the protocol), and button trigger parameters (ΔVk, Tk).

[0099] Impedance-voltage correlation matching model construction:

[0100] Feature extraction and dataset construction: Based on historical plug-in detection data of charging protocol A, key correlation features of impedance data and voltage data are extracted, including the transition rate from no-load to connection, the correspondence between steady-state impedance and steady-state voltage, the step amplitude coordination features when the button is triggered, and the time difference between impedance change and voltage response, to construct a feature dataset containing a sufficient number of effective samples.

[0101] Dataset partitioning and model training: The feature dataset is divided into a data subset and a validation subset according to a preset ratio K. The data subset is fitted with the association pattern using the support vector regression mechanism. The radial basis function is selected as the kernel function. The initial penalty coefficient C_initial and the initial gamma parameter γ_initial are set to obtain the preliminary association pattern model.

[0102] Model validation and iterative optimization: Input the validation subset data into the preliminary model, calculate the feature association accuracy P (the matching ratio between predicted features and actual features) and the state judgment consistency Q (the matching ratio between the connection / button state determined by the model and the actual state); if P < P<Q ...

[0103] Model storage and retrieval: The final model is stored in the detection system's storage module for comparison of transitional features and verification of calculation results during the gun insertion process.

[0104] Analysis of the continuity and matching degree of the gun insertion transition process:

[0105] Signal preprocessing: After the insertion operation is started, the original impedance signal Z and the original voltage signal V are acquired in real time through the detection circuit. Following the signal processing flow of S1, the impedance data Zs and voltage data Vs are output after low-pass filtering (cutoff frequency Fc) and moving average (number of groups N).

[0106] Transition start determination: Real-time monitoring of the interval offset between Zs and Vs. When Zs shifts from [Z_empty_min, Z_empty_max] to [Z_connected_min, Z_connected_max] by ≥ΔZ_offset, and Vs shifts from [V_empty_min, V_empty_max] to [V_connected_min, V_connected_max] by ≥ΔV_offset, the gun insertion transition process is determined to have started, and the transition start time t_start is recorded.

[0107] Feature extraction: Starting from t, the dynamic change trajectories of Zs and Vs are tracked in real time to extract three core features: continuous change rate (the change rate of Zs vz=ΔZs / Δt, the change rate of Vs vv=ΔVs / Δt), transition phase duration t_over (the duration from the start of t to the signal entering steady state), and key node feature values ​​(Zs_start and Vs_start at the start of t, Zs_ ...

[0108] Continuity determination: Based on the gradual law of physical connection (the contact of the gun is gradual, the signal has no sudden change or the change amplitude is ≤ ΔZ_surge, ΔV_surge, ΔZ_surge = K_surge × Z_surge_max, ΔV_surge = K_surge × V_surge_max, K_surge is the preset change ratio coefficient), if the change trajectory of Zs and Vs does not have a sudden change that exceeds the threshold, and the continuous change rates vz and vv are both within the reasonable range [vzmin, vzmax] and [vvmin, vvmax] specified in the protocol, the transition continuity is determined to be qualified;

[0109] Matching degree determination: Compare the synergy of the changing trends of Zs and Vs (e.g., vz and vv change in the same direction, the ratio of the rate of change falls within the range of [Kmin, Kmax], where Kmin and Kmax are the synchronization rate range specified by the protocol), and at the same time call the impedance-voltage correlation matching model, compare the extracted feature values ​​with the normal transition feature patterns in the model. If the trend synergy is qualified and the feature comparison fit is ≥ the preset fit threshold, the transition matching degree is determined to be qualified.

[0110] Multi-parameter fitting calculation and state output:

[0111] Core Parameter Extraction: Based on the transient process analysis results, the core parameters are extracted as follows: Transient continuity score (full marks for passing, deductions for failing according to the degree of defect), Transient matching score (fitness × full marks), Impedance sudden change amplitude (|Zs sudden - Zs initial|), Voltage step amplitude (|Vs stable - Vs initial|), Signal steady-state fluctuation amplitude (Zs stable fluctuation within the T stable time period is ≤ΔZt for passing, otherwise it is failing; the same applies to Vs stable).

[0112] Linear fitting operation: The parameter values ​​are standardized according to the preset weighting coefficients α, β, and γ (uniformly mapped to the 0-full score range), and the linear fitting operation is performed: Initial connection reliability score Sinitial = α × continuity score + β × matching score + γ × (1 - impedance change amplitude / Zconnection max) × full score; Initial confidence of button state Cinitial = (voltage step amplitude ≥ ΔVk and duration ≥ Tk), high confidence value: low confidence value (meeting the button trigger condition is a high confidence value, otherwise it is a low confidence value);

[0113] Model validation and optimization: The impedance-voltage correlation matching model is invoked, and the initial calculation results are compared with the corresponding feature patterns of "connection state - button triggering" in the model. If S_initial ≥ S_closed and the model determines that the connection features match, the final connection reliability score S_final = S_initial; if S_initial < S_closed but the model determines that there are some effective features, the model weights are adjusted to S_final = S_initial × weight coefficient 1 + model matching score × weight coefficient 2 (weight coefficient 1 + weight coefficient 2 = 1); the button state confidence C_final = C_initial × button feature matching degree in the model. If C_final ≥ C_closed, the button is determined to be effectively triggered; otherwise, it is invalid.

[0114] Output results: Output the final connection reliability score S_final and the button status confidence C_final. When S_final ≥ S_closed and C_final ≥ C_closed, feed back a "plug connection is reliable and button is effectively triggered" signal to the charging control unit, forming a coordinated control logic with the locking control signal of S1.

[0115] Specifically, the bidirectional signal feedback logic includes: establishing a half-duplex communication link between the detection end and the charging gun end; the detection end sending a status query signal, including impedance detection command, voltage sampling command, and connection status verification command, to the charging gun end at a preset period; the charging gun end receiving the command and acquiring the corresponding signal data in real time; encapsulating the data according to a preset data frame format and adding a signal acquisition timestamp and device status identifier before feeding it back to the detection end; the detection end performing frame structure verification and CRC verification on the received feedback data; parsing the data based on the verification results and updating the local detection status; and the charging gun end receiving the status update signal from the detection end in real time and dynamically adjusting its own sampling frequency and data transmission strategy.

[0116] Specifically, the dynamic correction mechanism includes: real-time monitoring of the dynamic changes in ambient temperature, line aging loss coefficient, and deviations of current impedance and voltage data from stable reference characteristics; comparing these deviations with preset temperature change trigger intervals, loss coefficient thresholds, and allowable signal deviation ranges; initiating a dynamic correction process based on the comparison results; calculating the signal deviation correction amount according to preset influence weights based on the correlation between temperature, loss, and signal deviation in the impedance-voltage correlation matching model; generating correction coefficients; and adjusting the currently acquired impedance and voltage data in real time based on the correction coefficients, while synchronously updating the parameters of the stable reference characteristics.

[0117] This embodiment continues the target charging protocol A (a DC charging protocol conforming to GB / T18487.1 standard) and the detection system architecture of the preceding S1-S3. The preset new parameters and the parameters used are as follows:

[0118] Two-way communication parameters: The half-duplex communication link follows the interface specification of Protocol A. The status query signal transmission period is T. The data frame format includes instruction identifier, data length, signal data, timestamp, device status code, and CRC check segment. The frame structure verification rule is the protocol preset standard, and the CRC check polynomial is the type specified by the protocol.

[0119] Dynamic correction parameters: ambient temperature change trigger range [ΔT low, ΔT high], line aging loss coefficient threshold K_loss, signal deviation from allowable range [ΔZ_allowable, ΔV_allowable], influence weights (temperature weight αT, loss weight αK, signal deviation weight α_deviation, αT + αK + α_deviation = 1);

[0120] The parameters for gun removal verification are: impedance surge threshold ΔZ_surge (the offset relative to the current stable reference Zr), voltage regression no-load judgment condition (Vs falls into [V_empty_min, V_empty_max] and lasts ≥ T_regression), number of reverse verification signal transmissions N, response timeout T_over, and no-load state duration T_hold.

[0121] Using the previous parameters: smoothing signals Zs and Vs, stable references Zr and Vr, fluctuation thresholds ΔZt and ΔVt, no-load characteristic intervals [Z_no_load min, Z_no_load max] and [V_no_load min, V_no_load max], impedance-voltage correlation matching model, and S1 lockout control signal logic.

[0122] Bidirectional signal feedback logic construction and operation:

[0123] Link setup: Establish a half-duplex communication link between the detection end and the charging gun end, and configure basic parameters such as baud rate and interface type as agreed in Protocol A to ensure communication compatibility between the two ends;

[0124] Query signal transmission: The detection end sends a status query signal to the charging gun end according to the cycle T, which includes three types of instructions: impedance detection instruction (triggered acquisition Zs), voltage sampling instruction (triggered acquisition Vs), and connection status verification instruction (triggered feedback interface contact status).

[0125] Data feedback encapsulation: After receiving the command, the charging gun end collects the corresponding signal data in real time and encapsulates it according to the preset data frame format: the command identifier (corresponding to the query command), data length, original signal data, acquisition timestamp, device status code (such as connection status, working mode) are filled in sequentially, and finally a CRC check field is attached.

[0126] Detection end verification and update: After receiving feedback data, the detection end first performs frame structure verification (verifies whether the field format and length conform to the rules), and then performs CRC verification (calculates according to the protocol polynomial and compares the verification value); if both verifications pass, the data is parsed and the local detection status is updated (synchronizing the consistency of signals at both ends); if the verification fails, it is logged and the query signal is resent in the next cycle.

[0127] Charging gun end strategy adjustment: The charging gun end receives the status update signal from the detection end in real time (such as benchmark adjustment, acquisition accuracy requirements), and dynamically adjusts the sampling frequency (increases the frequency when the signal fluctuates and decreases it when it is stable) and the data transmission strategy (adjusts the transmission frequency as needed).

[0128] Dynamic correction mechanism execution:

[0129] Parameter monitoring and acquisition: During the monitoring process, the temperature change ΔT (the difference between the current temperature and the initial calibration temperature) is collected by the environmental sensor. The line aging loss coefficient K_old is calculated by the loss module (derived based on usage time and signal attenuation data). At the same time, the deviation of Zs relative to Zr ΔZ_bia = |Zs-Zr| and the deviation of Vs relative to Vr ΔV_bia = |Vs-Vr| are calculated.

[0130] Correction trigger judgment: Compare ΔT with [ΔT low, ΔT high], K old with K consumption, ΔZ bias with ΔZ allow, and ΔV bias with ΔV allow respectively; if any parameter exceeds the threshold, the dynamic correction process is initiated.

[0131] Correction amount and coefficient generation: Call the impedance-voltage correlation matching model, extract the correlation between temperature, loss and signal offset, calculate the impedance correction amount ΔZcorrection and voltage correction amount ΔVcorrection according to the weights αT, αK and αbias, and generate the correction coefficients KZ=1+ΔZcorrection / Zr and KV=1+ΔVcorrection / Vr.

[0132] Signal and reference adjustment: Adjust the current Zs and Vs according to KZ and KV to obtain the corrected signals Zs_corrected and Vs_corrected; synchronously update the stable reference to Zr_new = Zr + ΔZ_corrected and Vr_new = Vr + ΔV_corrected for subsequent detection.

[0133] Gun withdrawal reverse verification and safety lock release:

[0134] Suspected signal trigger: Continuously monitor Zs repair and Vs repair. When Zs repair suddenly increases by ≥ ΔZ, and Vs repair falls into [V empty min, V empty max] and continues to be ≥ T return, trigger the reverse verification process.

[0135] Reverse verification interaction: The detection end sends a reverse verification signal to the charging gun end N times (including the current Zs repair, Vs repair and no-load characteristic requirements); if the charging gun end is in the connected state, it needs to provide a valid response within T time (including its own collected signal and status code).

[0136] Validity determination of gun withdrawal: If the detection end does not receive a valid response (or the response verification fails), and the subsequent Zs repair is continuously in [Z empty min, Z empty max] and Vs repair is continuously in [V empty min, V empty max] and T holds, the gun withdrawal is determined to be valid;

[0137] Lockout Release and Data Recording: Send a safety lockout release signal to the charging control unit (closed loop with the S1 lock signal) to unlock the mechanical lockout device; synchronously record state switching data (gun removal time, signal characteristics, verification log, correction record, etc.) to complete the process.

[0138] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A CC1 detection method for plugging in and unplugging a charging gun, characterized in that, include: S1: Acquire the impedance and voltage data of the line, process them through signal filtering and steady-state calibration mechanism, dynamically adjust the impedance strategy of the detection line, and determine the formation of effective connection based on the preset correlation law. At the same time, distinguish the trigger state of the button after the gun is inserted according to the stability characteristics of the voltage data, and output a lock control signal. S2: During the charging gun connection period, the stable reference characteristics of the detection signal are dynamically updated based on the impedance data and the voltage data. The dynamic changes of the impedance data and the voltage data are monitored in real time. The signal validity verification process is started. By verifying that the voltage data and impedance data continuously maintain the no-load reference characteristics and the state of no button trigger signal, it is determined that the gun removal operation is completed and the charging interruption protection mechanism is triggered. S3: Construct an impedance-voltage correlation matching model to analyze the continuity and matching degree of impedance data and voltage data transitioning from no-load characteristics to connection characteristics. At the same time, identify impedance abrupt change characteristics during the transition process, and analyze the reliability of the plug connection and button status through multi-parameter fitting calculation. S4: Construct a bidirectional signal feedback logic for the detection circuit. During the monitoring process, maintain the continuous stability of the detection signal through a dynamic correction mechanism. Based on the dynamic changes of the impedance data and the voltage data, trigger the reverse verification signal transmission process. Based on the effective response signal and impedance and voltage status at the charging gun end, determine that the gun removal operation is valid and start the safety interlock release process, and synchronously record the state switching data.

2. The method according to claim 1, characterized in that, The impedance strategy specifically includes: dynamically configuring the impedance matching parameters of the detection line based on the real-time correlation between the impedance data and the voltage data of the line, establishing an impedance threshold range, wherein the boundary values ​​of the impedance threshold range are preset according to the rated operating parameters of the charging gun and the inherent impedance characteristics of the line, and after the signal filtering and steady-state calibration mechanism completes the data processing, the impedance threshold range is dynamically corrected based on the ratio of the processed impedance data to the voltage data. The impedance strategy also includes setting a buffer adjustment mechanism for the fluctuation characteristics of the impedance data.

3. The method according to claim 1, characterized in that, The signal filtering specifically includes: first, identifying the typical frequency range of high-frequency electromagnetic interference in the detection signal, setting the cutoff frequency parameter of the low-pass filter accordingly, and performing low-pass filtering on the acquired impedance data and voltage data; then, extracting the processed data according to a preset sampling interval, using a moving average mechanism to perform rolling calculations on a continuously set number of data sets, and outputting smoothed data.

4. The method according to claim 1, characterized in that, The steady-state calibration mechanism specifically includes: setting a fluctuation judgment threshold and a reference calibration period for the detection signal, monitoring the instantaneous fluctuation amplitude of the detection signal in real time, comparing it with the fluctuation judgment threshold, recording the initial signal reference value of the current line based on the comparison result, obtaining the continuous signal to calculate the average deviation, and correcting the initial signal reference value based on the average deviation.

5. The method according to claim 1, characterized in that, The specific process for distinguishing the trigger state of the button after inserting the gun includes: setting a threshold for judging the step amplitude of voltage data, a threshold for the effective trigger duration, and a signal stability judgment duration; continuously monitoring the dynamic changes of voltage data after the gun is connected effectively, recording the amplitude value and duration of the step change of voltage data in real time, comparing the recorded amplitude value with the threshold for judging the step amplitude, and comparing the duration with the threshold for the effective trigger duration, while simultaneously monitoring the stable state of the voltage data after the step change within the signal stability judgment duration, and determining that the button is effectively triggered based on the comparison results and monitoring results.

6. The method according to claim 1, characterized in that, The specific process of updating the stable reference characteristics of the detection signal is as follows: First, based on the impedance and voltage steady-state data after the plug-in forms an effective connection, an initial stable reference characteristic is established; then, the load change amplitude during the charging process and the deviation of the current impedance data and voltage data from the initial reference characteristic are monitored in real time. Based on the deviation of the load change amplitude, the reference update process is started, a set number of impedance and voltage steady-state data are continuously acquired, the statistical mean is calculated, and a new stable reference characteristic is generated.

7. The method according to claim 1, characterized in that, The signal validity verification process specifically includes: continuously acquiring impedance and voltage data at set time intervals, constructing a time-series trend reference model, fitting the impedance and voltage data into a time series to generate actual change curves, and calculating the trend fit with the time-series trend reference model; simultaneously verifying the synchronicity of changes in impedance and voltage data, analyzing the correspondence between rises and falls and the matching degree of change rates; tracking the signal fluctuation trend using the sliding window method, and determining the signal validity based on the trend fit, the synchronicity of impedance and voltage changes, and the fluctuation trend.

8. The method according to claim 1, characterized in that, The specific construction process of the impedance-voltage correlation matching model is as follows: Based on the impedance data and the voltage data, key correlation features are extracted to establish a feature dataset; the feature dataset is divided into a data subset and a verification subset according to a preset ratio; the correlation pattern of the data subset is fitted using a support vector regression mechanism; the performance of the fitted correlation pattern is verified using the verification subset data; the feature correlation accuracy and consistency of state judgment are calculated; the feature selection dimension and mechanism parameters are iteratively adjusted according to the verification results to generate the impedance-voltage correlation matching model.

9. The method according to claim 1, characterized in that, The specific process of analyzing the continuity and matching degree of impedance data and voltage data transitioning from no-load characteristics to connection characteristics is as follows: real-time tracking of the dynamic change trajectory of the impedance data and voltage data, starting from the transition start time, extracting the continuous change rate, transition stage duration and signal feature values ​​of key nodes; Based on continuity analysis and the gradual laws of physical connections, the signal trajectory is determined; based on matching degree analysis, the synergy of changing trends is compared, and combined with the normal transition characteristics in the impedance-voltage correlation matching model, the degree of fit of the transition process is comprehensively determined.

10. The method according to claim 1, characterized in that, The specific process of the multi-parameter fitting operation is as follows: Based on the impedance data and the voltage data, core parameters are extracted, and the influence weights of the core parameters on connection status determination and button trigger recognition are combined with preset weighting coefficients; then, linear fitting operation is performed on the parameter values ​​according to the preset weighting coefficients to generate an initial connection reliability score and an initial confidence level of button status; the feature rules in the impedance-voltage correlation matching model are called simultaneously to verify the initial operation results, and finally, the connection reliability score and button status confidence level are output.

11. The method according to claim 1, characterized in that, The bidirectional signal feedback logic specifically includes: establishing a half-duplex communication link between the detection end and the charging gun end; the detection end sending status query signals, including impedance detection commands, voltage sampling commands, and connection status verification commands, to the charging gun end at a preset period; the charging gun end receiving the commands and acquiring the corresponding signal data in real time; encapsulating the data according to a preset data frame format and adding a signal acquisition timestamp and device status identifier before feeding it back to the detection end; the detection end performing frame structure verification and CRC verification on the received feedback data; parsing the data based on the verification results and updating the local detection status; and simultaneously, the charging gun end receiving the status update signals from the detection end in real time and dynamically adjusting its own sampling frequency and data transmission strategy.

12. The method according to claim 1, characterized in that, The dynamic correction mechanism specifically includes: real-time monitoring of the dynamic changes in ambient temperature, line aging loss coefficient, and deviations of current impedance and voltage data from stable reference characteristics; comparing these deviations with preset temperature change trigger intervals, loss coefficient thresholds, and allowable signal deviation ranges; initiating the dynamic correction process based on the comparison results; calculating the signal deviation correction amount according to preset influence weights based on the correlation between temperature, loss, and signal deviation in the impedance-voltage correlation matching model; generating correction coefficients; and adjusting the currently acquired impedance and voltage data in real time based on the correction coefficients, while synchronously updating the parameters of the stable reference characteristics.

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