Large-current charging product capable of realizing secondary locking through key structure

Abstract

The invention relates to the technical field of high-current charging, and discloses a high-current charging product capable of realizing secondary locking through a key structure. The product comprises a key state detection module, a locking parameter optimization module, an environment adaptability adjustment module, a current threshold setting module, a charging state prediction module and a secondary locking feedback control module. A locking state value is obtained by detecting a key state, spring bolt parameters are optimized, a locking combination is adjusted by combining environmental factors, a current load threshold value is set, a charging state is predicted, spring bolt locking is adjusted through current and voltage error analysis based on a predicted value, and a secondary locking automatic regulation and control scheme is obtained. According to the product, through cooperation of multiple modules, dynamic adaptation of the locking state, the charging load and environmental factors is achieved, the safety and stability of the large-current charging process are improved, and the device is suitable for various large-current charging scenes.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of large-current charging, in particular to a large-current charging product with secondary locking through a key structure. BACKGROUND

[0002] In the context of rapid development of the new energy industry, the safety and stability of large-current charging products, as key equipment for energy supply, are increasingly concerned. Traditional large-current charging equipment mostly adopts single mechanical locking in the design of locking structure, relying only on the initial locking tongue position to fix the charging interface, which is difficult to meet the long-term use requirements in complex environments. In actual application, factors such as frequent current fluctuations, temperature and humidity changes during the charging process can cause the locking structure to loosen, thereby causing problems such as poor contact and increased resistance, which not only affects the charging efficiency, but also may cause safety hazards due to local overheating. In the prior art, some charging products attempt to increase the locking force to improve stability, but do not consider the influence of environmental factors on locking performance. For example, in high temperature and humidity environments, the metal locking tongue is prone to thermal expansion and contraction or rusting, resulting in imbalance between the extension stroke and the locking force, and over-loose or over-tight phenomena. Over-loose will cause poor contact of the interface, causing arc discharge; over-tight may cause accelerated mechanical wear of the locking tongue, shortening the service life of the equipment. At the same time, the traditional equipment lacks linkage control of the current and locking state during charging, and when the charging current fluctuates instantaneously, it cannot adjust the locking force according to the current load change, which is easy to cause uneven distribution of current due to changes in contact resistance, thereby causing charging interruption or equipment failure. In the prior art, the current threshold is mostly set based on fixed parameters without dynamic adjustment in combination with real-time environment and charging state. In low temperature environments, the resistance of the charging medium increases with decreasing temperature, and if the current threshold is still set according to the normal temperature parameters, the actual current is likely to exceed the carrying capacity of the equipment; in humid environments, the insulation performance decreases, and a fixed threshold may increase the risk of electric shock. At the same time, the traditional charging state monitoring only relies on real-time data acquisition of current and voltage, lacks prediction of fluctuation trend, and when abnormal current occurs, it cannot adjust the locking state in advance, and often only after the fault occurs can it be remedied, delaying the best processing opportunity. The secondary locking scheme on the market mostly adopts a single feedback mechanism, and only determines the locking state through a mechanical stroke sensor, without forming a closed-loop control with electrical parameters. When the current suddenly increases during the charging process, the mechanical locking structure cannot be adjusted in real time according to the electrical load, resulting in a mismatch between the locking force and the current load, which easily causes interface wear or overheating after long-term use. In addition, the fixed locking parameters have poor adaptability in cross-regional applications due to large differences in temperature and humidity in different regions, and need to be repeatedly adjusted manually, which increases the operation and maintenance cost and reduces the universality of the equipment. The existence of these problems makes it difficult for traditional large-current charging products to meet the development needs of the new energy industry in terms of safety, adaptability and intelligence, and there is an urgent need for a technical solution that can realize dynamic regulation and control of secondary locking. SUMMARY

[0003] The purpose of the present application is to provide a large-current charging product that realizes secondary locking through a key structure to solve the problems raised in the background art.

[0004] To achieve the above purpose, the present application provides a large-current charging product that realizes secondary locking through a key structure, which comprises: A key state detection module analyzes the difference between the mechanical trigger signal and the electrical induction signal based on the key insertion depth and the rotation angle, calculates the signal matching degree, integrates the key state parameter set, and obtains the key locking state value; A locking parameter optimization module extracts the parameter combination of the lock tongue extension stroke and the locking force based on the key locking state value, selects the optimal stroke and force value combination, and obtains the lock tongue locking parameter set; An environmental adaptability adjustment module extracts the current environmental temperature and humidity change based on the lock tongue locking parameter set, analyzes the relationship between the extension stroke and the locking force, matches the environmental factors and the locking combination, and obtains the environmental adaptation parameter set; A current threshold setting module extracts the current charging current fluctuation value based on the environmental adaptation parameter set, combines the real-time voltage data, distributes the current and voltage, sets the threshold value and applies it to the distribution of the charging load, and obtains the current load threshold value; A charging state prediction module captures the current data during the charging process based on the current load threshold value, infers the change trend of the current fluctuation combined with logical judgment, analyzes the state change corresponding to the current load, classifies and organizes the state change according to the inference result, logically adjusts and analyzes the classified data combined with the state change information, and obtains the charging state prediction value; A secondary locking feedback control module analyzes the error value of the current and voltage based on the charging state prediction value through real-time current and voltage data, adjusts the lock tongue locking combined with the error value, and obtains the secondary locking automatic regulation and control scheme of the charging product.

[0005] Preferably, the key locking state value includes an insertion depth parameter set, a rotation angle parameter set, and a signal matching degree parameter set, the lock tongue locking parameter set includes a stroke parameter and a force value parameter, the environmental adaptability parameter set includes a temperature and humidity change parameter and a stroke force value matching parameter, the current load threshold value includes a current fluctuation parameter, a voltage matching parameter, and a threshold setting parameter, the charging state prediction value includes a state trend analysis parameter and a current-voltage relationship parameter, and the charging product secondary locking automatic regulation scheme includes an error analysis parameter and a lock tongue adjustment parameter.

[0006] Preferably, the key state detection module includes: The signal acquisition submodule acquires real-time depth values and angle values based on key insertion depth and rotation angle, locates invalid signals, removes interference data, arranges the extracted depth values and angle values in time sequence, and generates a key mechanical signal data set. The signal matching submodule analyzes the insertion depth and rotation angle based on the key mechanical signal data set, calculates the signal parameter change ratio, sorts the signal matching degree by weight, marks the matching abnormal area, and obtains signal matching difference data. The state integration submodule calls the signal matching degree value based on the signal matching difference data, performs multi-dimensional summarization, screens state value differences, classifies them according to matching degree value size, and sequentially arranges the state values to generate a key locking state value.

[0007] Preferably, the locking parameter optimization module includes: The parameter extraction submodule identifies the extension state of each lock tongue based on the key locking state value, records the stroke and locking force of the lock tongue, standardizes the recorded data, classifies the standardized data by stroke and force value, and generates a lock tongue parameter data set. The locking parameter optimization submodule analyzes the stroke and force value in the data set according to the lock tongue parameter data set, screens parameter combinations with high matching degree with the locking state, records the matching results through pattern matching, adjusts the parameter combinations, and generates parameter combination optimization results. The parameter selection submodule retrieves the parameter combination optimization results, determines the optimal stroke and force value combination with the highest matching degree, adjusts the lock tongue control parameters, inputs control configurations, verifies the stability of the parameter set, and generates a lock tongue locking parameter set.

[0008] Preferably, the environmental adaptability adjustment module includes: The environmental factor analysis submodule collects key data including temperature, humidity, and vibration through environmental sensors based on the lock tongue locking parameter set, performs time series analysis on the data, eliminates outliers, processes the remaining data in partitions, and obtains environmental factor analysis data. The parameter matching sub-module analyzes the environmental factor analysis data to analyze the influence of environmental variables on the extension stroke and locking force of the lock tongue, calculates the influence degree of the change of each environmental factor on parameter adjustment, determines the optimal matching parameter setting according to the influence score, and captures the optimal combination by cyclically adjusting the parameters to obtain a parameter docking result. The response parameter integration sub-module selects a stroke and force value combination matching the current environmental condition from the parameter docking result, performs parameter adjustment test, optimizes the parameter setting through multiple adjustments and verifications, determines and solidifies the parameter as an operation standard, and generates an environmental adaptation parameter set.

[0009] Preferably, the current threshold setting module comprises: The current extraction sub-module positions a current fluctuation monitoring point based on the environmental adaptation parameter set, extracts a current fluctuation value in a monitoring area, continuously records a current change rate, extracts a plurality of key change nodes corresponding to the change rate, sorts the node values in order, and obtains a current current change characteristic value. The voltage matching sub-module analyzes the node change value and real-time voltage data based on the current current change characteristic value, calibrates according to a predetermined matching criterion, calls the matching criterion to perform distribution redistribution within a voltage interval, and obtains a current-voltage matching structure. The load distribution sub-module measures the distribution of current between voltage change nodes based on the current-voltage matching structure using a dynamic threshold adjustment method, sets an upper limit and a lower limit of the node threshold value, applies the threshold value to the charging load value, and distributes the charging load value to obtain a current load threshold value.

[0010] Preferably, the dynamic threshold adjustment method comprises the following steps: obtaining a distribution load value of a matching current change node, wherein the distribution load value is determined by the real-time current measured by the current node, the adjusted voltage matching value of the upstream node, the real-time voltage measured by the current node, the adjusted voltage matching value of the downstream node, the weight coefficient of the current, the weight coefficient of the voltage, the dynamic adjustment weight coefficient, and the threshold lower limit set by the node. The threshold lower limit is used to control the minimum load.

[0011] Preferably, the charging state prediction module comprises: The current data capture sub-module applies a logical judgment algorithm based on the current load threshold value to capture current data at sampling points, eliminates abnormal values and corrects errors, stores them in layers according to intervals, performs state processing, and generates a stateful current data set. The current load analysis sub-module divides intervals according to current load based on the stateful current data set, extracts change trends and fluctuation characteristics, and generates a current load and state change characteristic set. The state distribution inference sub-module adjusts feature parameters and calibrates trend data based on the current load and the set of state change features, extracts a distribution interval, and performs numerical prediction to obtain a state prediction value.

[0012] Preferably, the logic judgment algorithm comprises the following steps: calculating a current state value, and generating a state current data set, wherein the state current value is determined by the weight of each data point, the original current value of the sampling point, the voltage value of the sampling point, the load coefficient of the sampling point, and the total number of sampling points.

[0013] Preferably, the secondary locking feedback control module comprises: The error analysis sub-module extracts real-time current and voltage data based on the state prediction value, analyzes the real-time current value and the prediction value, corresponds the current difference value with the current voltage information, and generates a current voltage error value; The parameter adjustment sub-module sets the expansion adjustment parameters of the locking tongue based on the current voltage error value, sets the adjustment range for the area with large error, fine-tunes the low error area, screens and integrates the matched parameter set by comparing the locking effect, and generates a locking tongue adjustment parameter set; The locking control sub-module applies the adjustment parameters at each locking tongue position based on the locking tongue adjustment parameter set, implements expansion operation item by item, synchronously monitors the current and voltage, gradually adjusts the locking operation sequence of each area, and generates a secondary locking automatic control scheme for the charging product.

[0014] Compared with the prior art, the present application has the following advantages: The large current charging product realizes the secondary locking through the key structure, which breaks through the limitations of traditional charging equipment in multiple dimensions. From the perspective of structural design, it innovatively combines key state detection and locking tongue control, analyzes the mechanical and electrical signal differences of key insertion depth and rotation angle, and forms accurate locking state parameters. This dual signal verification mechanism can more comprehensively reflect the true situation of the locking state compared to the traditional single mechanical locking method, avoiding the problem of locking failure caused by mechanical wear or signal error, and fundamentally improving the reliability of the charging interface connection.

[0015] In terms of environmental adaptability, the scheme shows advantages. The environmental adaptability adjustment module can capture temperature and humidity changes in real time, match corresponding environmental factors and locking combination parameters by analyzing the dynamic relationship between the telescopic stroke and the locking force. This means that in a high-temperature environment, the device can automatically adjust the telescopic stroke of the lock tongue to compensate for the size changes caused by metal thermal expansion; in a high-humidity environment, it prevents water vapor from seeping into the interface gap by optimizing the locking force. This dynamic adaptation capability solves the problem of manual parameter adjustment of traditional devices in different regions and seasons, enabling the product to maintain stable locking performance in complex environments such as extreme cold, extreme heat, and high humidity along the coast, greatly expanding its application scenarios. From the perspective of electrical safety control, the cooperative work of the current threshold setting module and the charging state prediction module builds a more refined load management system. By combining environmental adaptation parameters with real-time current and voltage data, the device can dynamically allocate current load thresholds to avoid overload risks caused by current fluctuations. At the same time, the charging state prediction function can predict possible overcurrent, undervoltage, and other abnormal states through trend analysis of current data, and link the secondary locking feedback control module for preventive adjustment. This "prediction-adjustment" mechanism changes the traditional passive fault handling to active prevention, reducing safety hazards during charging and reducing downtime caused by sudden device failures. In terms of intelligent regulation, the secondary locking feedback control module forms a complete closed-loop control logic. By analyzing the error value of current and voltage in real time, the device can automatically adjust the locking force of the lock tongue, so that the locking state always matches the charging load. When the charging current increases suddenly, the lock tongue can increase the locking force to reduce the contact resistance; when the voltage fluctuates abnormally, it can also adjust the stroke to avoid interface overheating. This dynamic regulation capability not only reduces the invalid wear of mechanical structures, prolongs the service life of the device, but also reduces the dependence on manual maintenance and saves operation and maintenance costs. In addition, through data interaction and logic judgment of multiple modules, the scheme realizes full-process automatic control from key insertion to charging completion, improves operation convenience, and brings users a safer and more efficient charging experience. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 The working principle diagram of the large-current charging product with secondary locking realized by the key structure described in the present application; Figure 2 The working principle diagram of the current threshold setting module; Figure 3 The working principle diagram of the charging state prediction module; Figure 4 The working principle diagram of the secondary locking feedback control module. DETAILED DESCRIPTION

[0017] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0018] Please refer to Figures 1-4 The present application provides a large-current charging product with secondary locking achieved by a key structure, the product comprising: The key state detection module analyzes the difference between the mechanical trigger signal and the electrical induction signal based on the key insertion depth and the rotation angle, calculates the signal matching degree, integrates into a key state parameter set, and obtains a key locking state value; The locking parameter optimization module extracts the parameter combination of the lock tongue extension stroke and the locking force based on the key locking state value, screens the optimal stroke and force value combination, and obtains a lock tongue locking parameter set; The environmental adaptability adjustment module extracts the current environmental temperature and humidity change based on the lock tongue locking parameter set, analyzes the relationship between the extension stroke and the locking force, matches the environmental factors and the locking combination, and obtains an environmental adaptation parameter set; The current threshold setting module extracts the current charging current fluctuation value based on the environmental adaptation parameter set, combines the real-time voltage data, distributes the current and voltage, sets the threshold value and applies the threshold value to the distribution of the charging load, and obtains a current load threshold value; The charging state prediction module captures the current data of the charging process based on the current load threshold value, infers the change trend of the current fluctuation combined with logical judgment, analyzes the state change corresponding to the current load, classifies and organizes the state change according to the inference result, logically adjusts and analyzes the classified data combined with the state change information, and obtains a charging state prediction value; The secondary locking feedback control module analyzes the error value of the current and voltage based on the charging state prediction value through real-time current and voltage data, adjusts the lock tongue locking combined with the error value, and obtains a secondary locking automatic control scheme of the charging product.

[0019] Embodiment 1: The key locking state value is composed of an insertion depth parameter set, a rotation angle parameter set and a signal matching degree parameter set. The insertion depth parameter set covers the depth values of the key at different times from the initial insertion position to the complete insertion process. The rotation angle parameter set contains angle data of the key at each stage during the rotation process after insertion. The signal matching degree parameter set reflects the degree of agreement between the mechanical trigger signal and the electrical induction signal. The bolt locking parameter set includes a stroke parameter and a force value parameter. The stroke parameter records the distance change of the bolt extension and retraction, and the force value parameter corresponds to the locking force data of the bolt at different stroke positions. The environmental adaptation parameter set includes temperature and humidity change parameters and stroke force value matching parameters. The temperature and humidity change parameters capture the fluctuation of environmental temperature and humidity over time, and the stroke force value matching parameters reflect the adaptation relationship between the bolt stroke and the locking force under different temperature and humidity conditions. The current load threshold includes current fluctuation parameters, voltage matching parameters and threshold setting parameters. The current fluctuation parameter records the fluctuation of current during charging, the voltage matching parameter reflects the corresponding coordination relationship between current and voltage, and the threshold setting parameter specifies the limited range of current and voltage. The charging state prediction value includes state trend analysis parameters and current-voltage relationship parameters. The state trend analysis parameters are used to infer the development direction of the charging state, and the current-voltage relationship parameters deeply analyze the internal relationship between current and voltage in the change process. The secondary locking automatic control scheme of the charging product includes error analysis parameters and bolt adjustment parameters. The error analysis parameters quantify the deviation of real-time current and voltage from the expected value, and the bolt adjustment parameters specify the specific adjustment mode of the bolt under different deviation conditions.

[0020] The operation flow of the key state detection module is as follows: the signal acquisition submodule takes the key insertion depth and rotation angle as the monitoring object, starts the depth sensor and angle sensor deployed inside the keyhole, and performs real-time acquisition of depth value and angle value. During the acquisition process, invalid signals such as instantaneous no data output or value jump caused by poor sensor contact are identified through a pre-set signal filtering mechanism, and these interference data are excluded from the original acquisition results. For the extracted valid depth value and angle value, they are arranged in the order of their acquisition time, forming a key mechanical signal data set distributed along the time axis, which records the mechanical motion trajectory data of the key from insertion to rotation process.

[0021] The signal matching sub-module analyzes the correlation between the insertion depth and the rotation angle based on the generated key mechanical signal dataset. By calculating the signal parameter change ratio of the two at the same time node, the synchronization of depth change and angle change is determined. According to the weight distribution rule built in the system, the matching degree between signals at different time points is sorted by weight, and the abnormal recognition program is enabled to mark the matching abnormal area, such as the interval where the key rotation angle changes greatly while the depth value remains unchanged, or the period where the depth changes sharply while the angle does not adjust significantly. Through these operations, signal matching difference data is obtained, which records the specific time interval and numerical difference of various matching abnormalities in detail.

[0022] The state integration sub-module calls the historical signal matching degree values stored in the system database based on the obtained signal matching difference data, and performs multi-dimensional summarization, including different insertion depth intervals, different rotation angle ranges, and different operation periods. By using the numerical comparison tool to screen the differences between the current state values and the historical data, the matching degree values are divided into multiple level categories in order from high to low, for example, 90% or more for high matching, 60%-90% for medium matching, and 60% or less for low matching. The state values of each category are sequentially arranged, and finally a key locking state value is generated, which includes insertion depth, rotation angle, signal matching degree and other information. This state value fully reflects the current locking state characteristics of the key.

[0023] Embodiment 2: The locking parameter optimization module and the environmental adaptability adjustment module work together to optimize the locking parameter of the bolt and adjust the environmental adaptability.

[0024] The running process of the locking parameter optimization module is as follows: After the parameter extraction sub-module receives the key locking state value, it identifies the extension state of each bolt in real time through the displacement sensor and pressure sensor installed at the bolt position. The displacement sensor records the distance between the initial position and the terminal position after the bolt is extended or retracted, forming travel data; the pressure sensor measures the pressure generated by the bolt on the lock at different travel positions, forming locking force data. These recorded data are imported into the data processing unit for standardization processing. During the processing, the travel data is converted into a proportional value relative to the maximum extension travel of the bolt, and the locking force data is converted into a proportional value relative to the maximum rated locking force of the bolt, so that the travel and force value data of different bolts are under the same quantitative standard. After processing, the data is classified according to the travel value range and the force value range, for example, the travel proportional value is divided into five intervals of 0-20%, 20%-40%, 40%-60%, 60%-80% and 80%-100%, and the force proportional value is divided into intervals in the same way, generating a bolt parameter dataset that covers the standardized travel and force value information of all bolts in different states.

[0025] The locking parameter optimization submodule deeply analyzes the lock tongue parameter dataset, mines the corresponding relationship between the stroke and force value through a data correlation algorithm, such as the common force value range in a stroke interval or the typical stroke value corresponding to a force value. According to the locking demand reflected in the key locking state value, the parameter combinations with high matching degree to the current locking state are screened out, that is, those stroke and force value combinations that can meet the locking degree required by the current key insertion depth and rotation angle. These parameter combinations are compared with the standard locking mode preset by the system through a pattern matching algorithm. The standard mode includes stroke and force value combination examples verified for a long time under different locking states. The matching results of each comparison are recorded, including the parameter items that match successfully and the parameter items that have deviations. The parameter combinations are adjusted according to the matching results, for example, when the stroke value of a certain combination meets the standard but the force value is low, the force value parameter is appropriately increased, and the parameter combination optimization result is generated. The result includes multiple optimized stroke and force value combinations and their corresponding matching scores.

[0026] The parameter selection submodule traverses the parameter combination optimization result, calculates the matching degree score of each combination, and the score is based on the degree of agreement with the standard mode, the applicability under the current key locking state, etc. The optimal stroke and force value combination is determined according to the score, which can ensure the locking effect while avoiding mechanical wear caused by excessive stroke or force value. The lock tongue control parameters are adjusted according to the optimal combination, including the motor speed and working time of driving the lock tongue to stretch and retract, etc. The adjusted parameters are input into the lock tongue control configuration system. The stability of the parameter set under different working conditions is verified through multiple start and stop of the lock tongue action, such as whether the stroke and force value remain within the set range after continuous multiple operations, and the lock tongue locking parameter set is generated. The parameter set clearly defines the optimal stroke and force value parameters of each lock tongue under the current state.

[0027] The running process of the environmental adaptability adjustment module is as follows: The environmental factor analysis submodule takes the lock tongue locking parameter set as the benchmark, collects environmental temperature and humidity data through temperature and humidity sensors installed inside and outside the charging product, and collects vibration data during product operation through vibration sensors. Time series analysis is performed on the collected temperature and humidity and vibration data, and the data change is counted by hour. Data cleaning rules are used to eliminate abnormal values, such as instantaneous values that suddenly exceed the product working temperature range or negative values caused by humidity sensor failure. The remaining valid data is processed by 24 hours per day, with each hour as a data partition. Environmental factor analysis data is obtained, which reflects the environmental temperature and humidity and vibration change characteristics in different time periods.

[0028] The parameter matching submodule, based on environmental factor analysis data, constructs a correlation model between environmental variables and the bolt extension / retraction stroke and locking force. It analyzes the impact of temperature increases or decreases on the bolt material's expansion and contraction performance; for example, increased temperature may cause the bolt material to expand, requiring adjustment of the stroke parameters to compensate for dimensional changes. It also analyzes the impact of humidity changes on the bolt lubrication effect; for example, high humidity environments may lead to decreased lubrication performance, requiring appropriate adjustment of the force parameters to ensure smooth locking. The module calculates the degree of influence of each environmental factor on parameter adjustments, such as the percentage adjustment required for the stroke parameter for every 10°C temperature change, or the correction value for the force parameter for every 20% increase in humidity. Based on these impact scores, the optimal parameter settings are determined. By iteratively adjusting the stroke and force parameters, and detecting the locking effect after each adjustment, the module captures the parameter combination that achieves the best locking state under the current environmental conditions, obtaining the parameter matching results.

[0029] The response parameter integration submodule selects stroke and force combinations that match the current environmental conditions from the parameter docking results. Parameter adjustment tests are then conducted in a simulated environment chamber, which can simulate different temperature, humidity, and vibration conditions. Under each simulated environment, the locking action of the latch is run multiple times, recording the stroke execution accuracy and locking force stability. Based on the test results, the parameter settings are optimized; for example, the stroke is appropriately shortened in high-temperature environments to offset the effects of material expansion. After determining the final parameter values, they are solidified as environmental adaptation operating standards, clarifying the adjustment rules for the latch stroke and force within different temperature, humidity, and vibration ranges, and generating an environmental adaptation parameter set. This parameter set ensures that the latch maintains stable locking performance under various environmental conditions.

[0030] Example 3: The current threshold setting module operates around an environmental adaptation parameter set. Through multi-stage data processing and analysis, it achieves precise setting of the current load threshold. The current extraction submodule first determines the monitoring range based on the environmental adaptation parameter set. Multiple current fluctuation monitoring points are preset at the input, output, and key nodes of the charging circuit, each equipped with a high-precision current sensor. The sensor captures the current value flowing through that point in real time, acquiring current fluctuation values ​​through continuous sampling, and simultaneously recording the rate of current change. Through rate calculation, key change nodes where the current rate of change changes from positive to negative or vice versa are identified. These nodes often correspond to state transitions during the charging process. These node values ​​are arranged chronologically to form ordered current current change characteristic values, fully presenting the dynamic trajectory of current change over time.

[0031] After receiving the current current change characteristic value, the voltage matching submodule correlates the current change values ​​of each node with the real-time voltage data at the same moment. Based on the system's built-in matching criteria, it calibrates the correspondence between current and voltage to ensure coordination in their numerical changes. For example, when the current is rising, it checks whether the voltage shows a corresponding matching change; if a deviation exists, it corrects it. The matching criteria are then invoked to redistribute the current distribution within the voltage range, making the current distribution at different voltage levels more reasonable, ultimately forming a current-voltage matching structure that clearly reflects the current range and distribution ratio corresponding to different voltage ranges.

[0032] The load distribution submodule is based on a current-voltage matching structure and uses a dynamic threshold adjustment method to calculate the current distribution among various voltage change nodes. The dynamic threshold adjustment method determines the load distribution value by considering multiple parameters, specifically calculated using the following formula: in, Represents the allocated load value; The weighting coefficient representing the current has a range that is dynamically adjusted according to the charging stage. Represents the real-time current measured at the current node; Weighting coefficient representing upstream voltage; This represents the voltage matching value adjusted by the upstream node; The weighting coefficient representing the current voltage; Represents the real-time voltage measured at the current node; Weighting coefficients representing downstream voltage; This represents the adjusted voltage matching value at the downstream node; This represents a dynamically adjusted weighting coefficient that changes in real time based on the overall charging load. This represents the lower limit of the threshold set for the node.

[0033] The load allocation value calculated using this formula considers both the current voltage status and that of upstream and downstream components, as well as real-time current and dynamic adjustment requirements. Simultaneously, the lower threshold L0 ensures that each node receives a basic load allocation. The load allocation submodule sets upper and lower thresholds for each node based on the calculation results. The upper limit prevents current overload, while the lower limit guarantees basic operational needs. These thresholds are applied to the actual allocation of charging load, clarifying the current carrying capacity range for each time period and each node, ultimately obtaining the current load threshold. This threshold includes the allowable current fluctuation range, voltage matching standards, and specific threshold values ​​for each node.

[0034] Throughout the process, data transmission between submodules remains real-time, ensuring that the latest current change characteristics acquired by the current extraction submodule are processed promptly by the voltage matching submodule, and that the matching structure generated by the voltage matching submodule is quickly available for use by the load distribution submodule. The weighting coefficients in the dynamic threshold adjustment method are adaptively adjusted according to different stages of the charging process; for example, in the initial stage of charging, the current weighting coefficient... It may be set to a higher value to prioritize a stable increase in current; while in the near-full charge stage, the voltage weighting factor... , , This may be increased accordingly to prevent safety issues caused by excessive voltage. The lower limit of the threshold set for the node. It will be set according to the product's minimum operating current requirements to ensure that even during load distribution adjustments, each node will not stop working due to low load.

[0035] Example 4: The charging status prediction module operates based on the current load threshold. It generates a charging status prediction value by capturing, analyzing, and inferring current data during the charging process.

[0036] The current data capture submodule uses a current load threshold as a reference standard and applies a logical judgment algorithm to collect and process current data during the charging process. When the charging product is running, this submodule captures current data at fixed time intervals using current sensors deployed in the charging circuit. The sampling interval can be adjusted according to different charging stages; for example, the interval is shortened at the beginning and end of charging to obtain more dense data, while the interval is appropriately lengthened during the stable charging stage to reduce the amount of data. The captured raw current data may contain outliers caused by factors such as momentary sensor malfunctions and electromagnetic interference. These outliers are characterized by values ​​that deviate significantly from adjacent data points. The submodule identifies and removes such outliers by comparing the change in each data point with the adjacent data. For the data after removing outliers, if there are gaps due to missing data, error correction is performed using the average of adjacent valid data to maintain the continuity of the data sequence. Subsequently, the processed current data is stored hierarchically according to the current load threshold intervals. For example, current data in the range from the lower limit of the threshold to 1 / 3 of the threshold is classified as the low load layer, data in the range from 1 / 3 to 2 / 3 of the threshold is classified as the medium load layer, and data in the range from 2 / 3 to the upper limit of the threshold is classified as the high load layer. Each layer of data is then processed to be state-defined, that is, each layer of data is assigned a corresponding state label, such as "low load stable", "medium load fluctuating", "high load continuous", etc., to generate a state-defined current dataset. This dataset fully records the current state information of different time periods and different load intervals.

[0037] The current load analysis submodule performs in-depth analysis of the state-based current dataset, further subdividing it into intervals according to the magnitude of the current load. For example, the low load level is divided into two sub-intervals: extremely low load and low load; the medium load level is divided into three sub-intervals: medium-low load, medium load, and medium-high load; and the high load level is divided into two sub-intervals: high load and extremely high load. Each sub-interval corresponds to a specific range of current values. For each sub-interval, the module extracts the trend of current data changes, such as a gradual increase, a gradual decrease, or a relatively stable trend in current values ​​across multiple consecutive sampling points. It also analyzes the characteristics of current fluctuations, including the frequency (number of fluctuations per unit time) and amplitude (the difference between the maximum and minimum values). These trends and characteristics are correlated with the corresponding sub-intervals to generate a current load and state change feature set. This feature set clearly presents the state change patterns within different current load intervals. For example, the medium-high load sub-interval may exhibit characteristics of "medium fluctuation frequency and small amplitude," while the extremely high load sub-interval may exhibit characteristics of "high fluctuation frequency and large amplitude."

[0038] The state distribution inference submodule adjusts feature parameters based on the current load and state change feature set. For example, it changes the trend weight and fluctuation weight according to the importance of different intervals, making the analysis results more consistent with actual charging needs. Simultaneously, it calibrates the trend data to eliminate potential biases caused by different data collection periods. For instance, it compares trend data from the same time period on different dates to correct systematic biases. Statistical analysis extracts the distribution interval corresponding to each state feature, i.e., the distribution range of a certain trend and fluctuation feature in the current load value. For example, the feature of "upward trend and low fluctuation amplitude" is mainly distributed in the low-to-medium load range. Based on this, a time-series forecasting method is used to numerically predict the current load state over a future period. The prediction includes possible load intervals, changing trends, and fluctuation characteristics. These prediction results are integrated into a charging state prediction value, which can reflect potential state changes during the charging process in advance.

[0039] Logical judgment algorithms play a crucial role in current data capture, calculating state-defined current values ​​by integrating multiple factors. During calculation, each sampling point is first assigned a corresponding weight, determined by the charging stage at which it occurs; for example, sampling points in critical charging stages have higher weights. Then, by combining the original current value, the corresponding voltage value (synchronously acquired via a voltage sensor), the load factor of that sampling point (reflecting its load proportion in the entire charging circuit), and the total number of sampling points, multi-dimensional numerical integration transforms the raw current data into state-identified values, generating a state-defined current dataset. This converts the previously fragmented current data into structured information that directly reflects the charging state.

[0040] Example 5: The secondary locking feedback control module operates based on the predicted charging status value. By analyzing real-time current and voltage data, it achieves automatic adjustment of the secondary locking of the charging product.

[0041] The error analysis submodule uses the predicted charging state as a reference and extracts current and voltage data in real time during the charging process using current and voltage sensors deployed within the product. It compares the real-time collected current value with the predicted current portion of the charging state, calculating the difference between the two. This difference directly reflects the degree of deviation between the actual charging current and the expected current. Simultaneously, it correlates the current voltage data with this current difference to clarify the specific current deviation under specific voltage conditions, generating current and voltage error values. These error values ​​are arranged chronologically to form a continuous error sequence, comprehensively recording the deviation information between the actual and predicted charging states at different times.

[0042] After receiving the current and voltage error values, the parameter adjustment submodule determines the bolt extension and retraction adjustment parameters according to preset adjustment rules. For areas with large error values, such as current deviations exceeding 15% of the preset range, a larger bolt adjustment range is set, including increasing or decreasing the bolt extension and retraction stroke, and adjusting the bolt locking force to quickly correct the deviation. For areas with small error values, such as deviations within 5%, only fine adjustments are made to avoid new deviations caused by over-adjustment. By simulating the locking effect under different adjustment parameters, the current and voltage changes corresponding to each parameter combination are compared, and the parameter set with the highest matching degree to the current error state is selected. These parameters are then integrated to form a bolt adjustment parameter set containing the specific adjustment values ​​for each bolt.

[0043] The locking control submodule, based on a set of latch adjustment parameters, sends the corresponding adjustment parameters to the drive device of each latch via a drive circuit. The drive device performs extension and retraction operations at each latch position according to the parameter requirements. For example, for latches requiring increased locking force, the drive motor extends the latch a specific length; for latches requiring reduced locking force, the motor reverses to retract the latch by the corresponding distance. During operation, current and voltage sensors continuously monitor the current and voltage synchronously, recording the changes in current and voltage before and after adjustment. Based on the monitoring results, the locking operation sequence for each area is gradually adjusted. For example, areas with still large errors are processed first, followed by areas where the error has decreased, ensuring the efficiency of the adjustment process. Through this step-by-step operation and dynamic adjustment, an automatic secondary locking control scheme for charging products is ultimately generated. This scheme clarifies the specific adjustment method, timing, and expected state after adjustment for each latch, achieving precise control of secondary locking.

[0044] Throughout the process, real-time data exchange is maintained between the submodules. The error value generated by the error analysis submodule is promptly transmitted to the parameter adjustment submodule, and the adjustment parameters determined by the parameter adjustment submodule are quickly applied to the actual operation by the locking control submodule. Simultaneously, when implementing adjustments, the locking control submodule feeds back the real-time monitored current and voltage data to the error analysis submodule, forming a closed-loop control. This allows the error analysis submodule to recalculate the error value based on the latest data, ensuring the timeliness and accuracy of parameter adjustments. This closed-loop feedback mechanism is maintained throughout the operation of the secondary locking feedback control module, enabling the entire control process to dynamically adapt to changes in the actual charging state, thereby achieving effective control of the secondary locking of the charging product.

[0045] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0046] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A high-current charging product that achieves secondary locking via a key structure, characterized in that, The products include: The key status detection module analyzes the difference between the mechanical trigger signal and the electrical induction signal based on the key insertion depth and rotation angle, calculates the signal matching degree, integrates them into a key status parameter set, and obtains the key locking status value. Based on the key locking state value, the locking parameter optimization module extracts the parameter combination of bolt extension stroke and locking force, filters the optimal stroke and force value combination, and obtains the bolt locking parameter set. The environmental adaptability adjustment module extracts the changes in current ambient temperature and humidity based on the locking tongue parameter set, analyzes the relationship between the extension stroke and the locking force, matches environmental factors with the locking combination, and obtains the environmental adaptability parameter set. The current threshold setting module extracts the current charging current fluctuation value based on the environmental adaptation parameter set, combines it with real-time voltage data, allocates current and voltage, sets a threshold, and applies the threshold to the allocation of charging load to obtain the current load threshold. The charging status prediction module captures current data during the charging process based on the current load threshold, infers the trend of current fluctuations by combining logical judgments, analyzes the status changes corresponding to the current load, classifies and organizes the status changes according to the inference results, and performs logical adjustments and analysis on the classified data based on the status change information to obtain the charging status prediction value. The secondary locking feedback control module analyzes the error values ​​of current and voltage based on the predicted charging status value and real-time current and voltage data. It then adjusts the locking tongue based on the error value to obtain an automatic control scheme for the secondary locking of the charging product.

2. The high-current charging product that achieves secondary locking through a key structure according to claim 1, characterized in that: The key locking status value includes an insertion depth parameter set, a rotation angle parameter set, and a signal matching degree parameter set. The latch locking parameter set includes a travel parameter and a force parameter. The environmental adaptation parameter set includes temperature and humidity change parameters and travel-force matching parameters. The current load threshold includes current fluctuation parameters, voltage matching parameters, and threshold setting parameters. The charging status prediction value includes status trend analysis parameters and current-voltage relationship parameters. The automatic secondary locking control scheme for the charging product includes error analysis parameters and latch adjustment parameters.

3. A high-current charging product that achieves secondary locking via a key structure according to claim 1, characterized in that: The key status detection module includes: The signal acquisition submodule acquires the key insertion depth and rotation angle in real time, locates invalid signals, removes interference data, and arranges the extracted depth and angle values ​​in time series to generate a key mechanical signal dataset. Based on the key mechanical signal dataset, the signal matching submodule analyzes the insertion depth and rotation angle, calculates the ratio of signal parameter changes, sorts the matching degree between signals by weight, marks the matching abnormal area, and obtains signal matching difference data. Based on the signal matching difference data, the status integration submodule calls the signal matching degree value to perform multi-dimensional summarization, screens the status value differences, classifies them according to the size of the matching degree value, and arranges the status values ​​in an orderly manner to generate the key lock status value.

4. A high-current charging product that achieves secondary locking via a key structure according to claim 1, characterized in that: The locking parameter optimization module includes: The parameter extraction submodule identifies the extension and retraction state of each bolt based on the key locking state value, records the bolt travel and locking force, standardizes the recorded data, sorts the standardized data by travel and force value, and generates a bolt parameter dataset. The locking parameter optimization submodule analyzes the stroke and force values ​​in the locking tongue parameter dataset, filters parameter combinations with high matching degree with the locking state, records the matching results through pattern matching, adjusts the parameter combinations, and generates parameter combination optimization results. The parameter selection submodule retrieves the optimization results of the parameter combination, determines the optimal combination of stroke and force, adjusts the locking tongue control parameters, inputs the control configuration, verifies the stability of the parameter set, and generates the locking tongue locking parameter set.

5. A high-current charging product that achieves secondary locking via a key structure according to claim 1, characterized in that: The environmental adaptation adjustment module includes: The environmental factor analysis submodule, based on the locking parameter set of the locking tongue, collects key data through environmental sensors, including temperature, humidity and vibration, performs time series analysis on the data, removes outliers, partitions the remaining data, and obtains environmental factor analysis data. The parameter matching submodule analyzes the environmental factors analysis data to analyze the impact of environmental variables on the extension and retraction stroke and locking force of the locking tongue, calculates the degree of influence of each environmental factor change on parameter adjustment, determines the optimal matching parameter settings based on the influence score, iteratively adjusts the parameters to capture the optimal combination, and obtains the parameter matching results. The response parameter integration submodule selects the stroke and force combination that matches the current environmental conditions from the parameter docking results, conducts parameter adjustment tests, optimizes parameter settings through multiple adjustments and verifications, determines and solidifies the parameters as operating standards, and generates an environmental adaptation parameter set.

6. A high-current charging product that achieves secondary locking via a key structure according to claim 1, characterized in that: The current threshold setting module includes: Based on the environmental adaptation parameter set, the current extraction submodule locates the current fluctuation monitoring point, extracts the current fluctuation value in the monitoring area, continuously records the current change rate, extracts multiple key change nodes corresponding to the change rate, sorts the node values ​​in order, and obtains the current current change feature value. The voltage matching submodule analyzes the node change value and real-time voltage data based on the current current change characteristic value, performs calibration according to a predetermined matching criterion, and calls the matching criterion to perform distribution redistribution within the voltage range to obtain the current-voltage matching structure. Based on the current-voltage matching structure, the load distribution submodule uses a dynamic threshold adjustment method to calculate the current distribution among voltage change nodes, sets upper and lower limits for node thresholds, applies the thresholds to the charging load values, and distributes them to obtain the current load threshold.

7. A high-current charging product that achieves secondary locking via a key structure according to claim 6, characterized in that: The dynamic threshold adjustment method includes the following steps: obtaining the allocated load value of the matching current change node, wherein the allocated load value is determined by the real-time current measured by the current node, the voltage matching value adjusted by the upstream node, the real-time voltage measured by the current node, the voltage matching value adjusted by the downstream node, the weighting coefficient of the current, the weighting coefficient of the voltage, the dynamic adjustment weighting coefficient, and the lower limit of the threshold set by the node. The lower limit of the threshold is used to control the minimum load.

8. A high-current charging product that achieves secondary locking via a key structure according to claim 1, characterized in that: The charging state prediction module includes: Based on the current load threshold, the current data capture submodule uses a logical judgment algorithm to capture current data at sampling points, remove outliers and correct errors, store the data in a hierarchical manner according to intervals, perform stateful processing, and generate a stateful current dataset. The current load analysis submodule, based on the state-based current dataset, divides the current load into intervals, extracts the changing trends and fluctuation characteristics, and generates a current load and state change feature set. The state distribution inference submodule adjusts the feature parameters and calibrates the trend data based on the current load and state change feature set, extracts the distribution interval, and performs numerical prediction to obtain the predicted charging state value.

9. A high-current charging product that achieves secondary locking via a key structure according to claim 8, characterized in that: The logical judgment algorithm includes the following steps: calculating the state-state value of the current and generating a state-state current dataset, wherein the state-state current value is determined by the weight of each data point, the original current value of the sampling point, the voltage value of the sampling point, the load factor of the sampling point, and the total number of sampling points.

10. A high-current charging product that achieves secondary locking via a key structure according to claim 1, characterized in that: The secondary locking feedback control module includes: The error analysis submodule extracts real-time current and voltage data based on the predicted charging state value, analyzes the real-time current value and the predicted value, and correlates the current difference with the current voltage information to generate a current and voltage error value. The parameter adjustment submodule sets the extension and retraction adjustment parameters of the latch based on the current and voltage error values. It sets the adjustment range for areas with large errors and makes fine adjustments for areas with low errors. By comparing the locking effect, it filters and integrates the matching parameter set to generate the latch adjustment parameter set. Based on the set of locking tongue adjustment parameters, the locking control submodule applies adjustment parameters to each locking tongue position, performs extension and retraction operations item by item, synchronously monitors current and voltage, gradually adjusts the locking operation sequence of each area, and generates an automatic control scheme for secondary locking of charging products.

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