Charging interface intelligent identification and conversion control method of multi-standard charging pile

Abstract

The invention relates to an intelligent identification and conversion control method for a charging interface of a multi-standard charging pile in the field of new energy automobiles, and the method comprises the steps: carrying out the mode matching analysis of interface physical feature data and communication signal feature data, and obtaining a preliminary classification result of the type of a charging interface device; according to the loading result of the communication configuration parameters, obtaining a confirmation feedback signal of successful protocol switching; judging whether the load demand data conforms to a rated output power range of a charging interface device or not; according to the authorization instruction, current intensity data including a peak value and an average level and voltage level data including a fluctuation amplitude in the charging process are monitored in real time; if the current intensity data or the voltage level data exceed a preset safety threshold value, power supply connection is disconnected, and a circuit path is cut off; and generating a power-off record log containing abnormal fluctuation data and a power-off moment according to a result of disconnecting the power supply connection.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of new energy vehicles, in particular to the field of intelligent control of charging facilities, and more particularly to a charging interface intelligent identification and conversion control method for multi-standard charging piles. BACKGROUND

[0002] With the popularity of electric vehicles worldwide, charging infrastructure as its core support is of paramount importance. A unified charging standard is not only the key to improving user experience, but also an important cornerstone for the sustainable development of the electric vehicle industry. However, in the current global market, national standards, European standards, American standards, Japanese standards, and various charging standards such as Tesla coexist, resulting in significant challenges to the compatibility and operational efficiency of charging facilities. Solving this problem not only concerns user convenience, but also directly affects the coverage and resource utilization efficiency of the charging network, and has a decisive significance for the overall development of the electric vehicle industry. Existing solutions for charging facilities rely heavily on mechanical adapters or manual replacement of charging gun lines, but these methods have significant drawbacks. The use of mechanical adapters often requires users to manually adjust, which is tedious and prone to errors, especially in public charging scenarios, users may fail to connect or damage the equipment due to unfamiliarity with the equipment.

[0003] In addition, the scheme of manually replacing the gun line requires users to directly contact the equipment interface, increasing the safety hazard. The common problem of these methods is the lack of automation and intelligent support, which cannot dynamically adjust according to actual needs, resulting in poor user experience and high equipment maintenance costs. In the field of multi-standard charging facilities, a core technical difficulty is how to achieve fast and accurate identification and switching of charging guns. Charging guns of different standards not only differ in physical interface, but also differ in communication protocol and power output requirements. Existing technologies often require users to manually select the standard before the device loads the corresponding protocol, but this approach is inefficient in high-frequency public scenarios. For example, a user trying to charge a European standard vehicle at a busy charging station may fail to load the correct protocol due to the device's failure to identify the gun type in time, resulting in a failed charge. The inefficiency of identification and switching seriously limits the popularity and application of multi-standard charging piles. Another key technical factor is the lack of safety guarantees. Since the charging gun may involve live operations during removal and return, the lack of effective safety control mechanisms can lead to electric shock risks or equipment damage. For example, some charging piles fail to power off in time when the user removes the gun, or fail to detect the state of the gun after it is returned, increasing the likelihood of misoperation and equipment failure. The lack of fast identification and switching directly leads to the lack of safety mechanisms, because only after accurately identifying the gun type can the power supply and communication protocol be matched to avoid safety accidents caused by misoperation.

[0004] Therefore, how to automatically identify the charging gun type and complete the protocol switch without manual user intervention, while ensuring the safety of the entire operation, has become a key issue in the development of multi-standard charging pile systems. This issue not only involves accurate identification and protocol adaptation at the technical level, but also requires efficient and safe operation in dynamic, high-frequency public charging scenarios to meet users' needs for convenience and reliability. Solving these challenges requires optimization of the entire process, from automatic charging gun identification and safety control to protocol switching, ensuring multi-standard compatibility while also considering efficiency and safety. Only by overcoming these technical difficulties can the widespread application of multi-standard charging piles be truly realized, meeting the rapidly growing practical needs of the electric vehicle industry. Summary of the Invention

[0005] This invention provides a method for intelligent identification and conversion control of charging interfaces for multi-standard charging piles, mainly including the following steps:

[0006] The interface sensor collects physical characteristic data of the charging interface device, including diameter and length, as well as communication signal characteristic data, including amplitude and frequency changes.

[0007] Pattern matching analysis is performed on the physical feature data and communication signal feature data of the interface to obtain a preliminary classification result of the charging interface device type;

[0008] Based on the preliminary classification results, an image analysis algorithm is used to process the appearance contour feature image of the charging interface device, including edge shape and proportion, and the surface marking pattern image, including text and symbol layout, to determine the precise model and specifications of the charging interface device.

[0009] If the precise model specification matches a record in a preset charging interface device specification database, then the communication configuration parameters corresponding to the model specification, including the data exchange format and verification mechanism, are loaded.

[0010] Based on the loading result of the communication configuration parameters, obtain a confirmation feedback signal indicating successful protocol switching;

[0011] Based on the confirmation feedback signal, establishing a communication link connection with the electric vehicle includes a stable data transmission channel, and obtaining the load demand data transmitted by the electric vehicle includes maximum capacity and rate requirements.

[0012] Determine whether the load demand data conforms to the rated output power range of the charging interface device;

[0013] If the load demand data meets the rated output power range, the power control module, including the switch and adjustment unit, is activated to obtain the charging start authorization command.

[0014] According to the authorized instructions, real-time monitoring of current intensity data, including peak and average levels, and voltage level data, including fluctuation amplitude, during the charging process;

[0015] If the current intensity data or voltage level data exceeds a preset safety threshold, disconnecting the power supply connection includes cutting off the circuit path;

[0016] Based on the result of disconnecting the power supply connection, a power outage log containing abnormal fluctuation data and the time of power outage is generated;

[0017] The power outage log is analyzed, including the classification of abnormal causes and frequency statistics. The charging interface device specification database is updated, and the model identification parameters, including feature thresholds and matching templates, are optimized.

[0018] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0019] This invention discloses an intelligent identification and conversion control method for charging interfaces of multi-standard charging piles. Addressing the problems of inaccurate interface type identification, low communication protocol adaptation efficiency, and difficulty in balancing load demand and power supply safety during electric vehicle charging, the method collects physical characteristic data of the charging interface (such as diameter and length) and communication signal characteristic data (such as amplitude and frequency changes) for pattern matching analysis to initially classify interface types. Combined with image analysis algorithms to process the appearance contour and surface marking patterns, the method accurately determines the model specifications and loads the corresponding communication configuration parameters. A stable communication link is established to obtain vehicle load demand data, determining whether it meets the rated output power range. The power control module is activated and current and voltage data are monitored in real time. If the power supply connection is exceeded, a power outage log is generated, and the log is analyzed to optimize the model identification parameters. This invention achieves synergistic optimization of accurate charging interface identification, efficient communication, and safe power supply through multi-dimensional data fusion and dynamic adaptation, improving the intelligence and safety of the charging process. Attached Figure Description

[0020] Fig. 1 This is a flowchart of the intelligent identification and conversion control method for the charging interface of a multi-standard charging pile according to the present invention.

[0021] Fig. 2 This is a schematic diagram of the intelligent identification and conversion control method for the charging interface of a multi-standard charging pile according to the present invention.

[0022] Fig. 3 This is another schematic diagram of the intelligent identification and conversion control method for the charging interface of the multi-standard charging pile of the present invention. Detailed Implementation

[0023] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0024] like Figs. 1-3 The intelligent identification and conversion control method for charging interfaces of multi-standard charging piles in this embodiment may specifically include:

[0025] Step S101: Collect physical characteristic data of the charging interface device, including diameter and length, and communication signal characteristic data, including amplitude and frequency changes, through the interface sensor.

[0026] The interface diameter and length of the charging interface device are collected by an interface sensor to obtain physical characteristic data. Based on this physical characteristic data, the amplitude and frequency changes of the communication signal are collected to obtain signal characteristic data. The signal stability is determined using a preset threshold based on the signal characteristic data to obtain a compatibility verification result. If the compatibility verification result meets preset conditions, the compatibility of the charging interface device is determined. The physical characteristic data and the signal characteristic data are acquired to obtain fused characteristic data. The preset threshold is adjusted based on the fused characteristic data to obtain an optimized threshold. The signal stability is then determined using the optimized threshold to obtain the final compatibility verification result.

[0027] Specifically, in one implementation, the interface physical characteristic data of the charging interface device is collected by an interface sensor, which first requires the deployment of suitable sensor equipment.

[0028] For example, the interface sensor may include an optical sensor for measuring the diameter and length of the interface.

[0029] Specifically, optical sensors calculate distance by emitting a laser beam and receiving the reflected signal, thereby acquiring diameter data. For example, at a charging station interface, the sensor is positioned outside the interface and scans the circumference to determine the diameter value. This method ensures non-contact measurement and is suitable for standardized testing of various charging interface devices. Furthermore, for length data acquisition, the interface sensor can employ a contact rangefinder, recording length parameters in real time during interface insertion.

[0030] It should be noted that this data acquisition process involves calibration steps to compensate for the impact of environmental factors such as temperature on measurement accuracy, especially in electric vehicle charging scenarios.

[0031] For example, when a vehicle connects to a charging station, the sensors are automatically activated to collect length data to verify interface compatibility, thereby supporting subsequent charging protocol negotiation.

[0032] In one possible implementation, the acquisition of communication signal characteristic data focuses on amplitude and frequency variations. The interface sensor integrates a signal analysis module to capture communication signals between charging interface devices.

[0033] Preferably, the module uses an analog-to-digital converter to convert the analog signal into digital form, and then analyzes the frequency changes through Fourier transform.

[0034] Specifically, amplitude data is acquired through peak detection algorithms, such as monitoring the peaks and troughs of a signal waveform to quantify amplitude fluctuations. This analysis helps identify communication anomalies, such as charging interruptions caused by signal attenuation.

[0035] For example, in practical applications of charging interface devices, sensors can be deployed at the charging station to monitor amplitude changes in real time. If the amplitude falls below a threshold, the system can trigger an alarm to prevent charging failures.

[0036] In one embodiment, the acquisition of frequency changes involves a time-domain to frequency-domain conversion process. The sensor records the signal period and calculates the frequency offset value, thereby ensuring the stability of the communication protocol.

[0037] Understandably, the combined acquisition of physical characteristic data and communication signal characteristic data enhances the robustness of the system.

[0038] In one embodiment, the sensor fuses multimodal data, such as simultaneously acquiring diameter, length, and signal amplitude, and processes the fused information through an integrated circuit. This fusion process includes a data synchronization step, first normalizing the physical data and then correlating it with the signal data to generate a comprehensive feature vector that supports verification of the charging interface.

[0039] Specifically, the challenge in diameter measurement lies in the high accuracy requirements, necessitating sensors equipped with high-resolution lenses, such as CCD sensors capturing interface images, and then calculating the diameter value using edge detection algorithms. In the maintenance of charging interface devices, this method allows for periodic data collection to analyze interface wear and tear, thereby extending device lifespan. Furthermore, the length acquisition process includes initial positioning and dynamic tracking.

[0040] For example, when the interface is inserted, the sensor tracks the length change using infrared light and records the distance from the starting point to the ending point. This process is widely used in electric bus charging stations, providing real-time feedback on the interface status and preventing connection failures. In another implementation, frequency variation analysis of the communication signal can employ a filter to remove noise, such as a low-pass filter to filter high-frequency interference, and then calculate the dominant frequency value.

[0041] It should be noted that this filtering helps to accurately capture frequency shifts, which is especially important in high-load charging environments, and can reduce communication errors caused by signal distortion.

[0042] For example, detailed acquisition of amplitude variations involves setting the sampling rate, where the sensor samples the signal at fixed intervals and calculates the average amplitude value. During the testing phase of the charging interface device, this method can simulate different load conditions, verify signal stability, and thus provide data support for practical deployment.

[0043] Preferably, the technical advantage of the entire data acquisition system lies in improving the compatibility and reliability of the charging interface device and reducing the failure rate through real-time data monitoring. This method can be flexibly applied in various charging scenarios, such as home chargers or public charging stations, ensuring the universality of data acquisition.

[0044] Step S102: Perform pattern matching analysis on the interface physical feature data and communication signal feature data to obtain a preliminary classification result of the charging interface device type.

[0045] The process involves acquiring interface physical feature data, extracting size and shape parameters from this data to obtain a physical matching vector, and then combining this physical matching vector with communication signal feature data. A support vector machine (SVM) algorithm is used for pattern matching, with the physical matching vector and communication signal feature data as inputs. The SVM algorithm calculates the feature space distance using a kernel function and outputs a similarity score. If the similarity score exceeds a preset threshold, the device is identified as a specific charging device type, resulting in a compatibility verification label. For this compatibility verification label, voltage and current features are fused, and a weighted average is used to calculate a comprehensive feature value, yielding a preliminary classification result.

[0046] Specifically, in one implementation, pattern matching analysis is performed on the interface physical feature data and communication signal feature data. First, it's necessary to understand the basic principles of pattern matching. This analysis identifies the type of charging interface by comparing the collected data with a pre-defined template library.

[0047] For example, physical feature data includes the number of pins, shape, size, and material type of the interface, while communication signal feature data involves signal waveforms and protocol standards such as CCS or CHAdeMO. Based on this data, the system constructs a matching model that uses a similarity calculation method to evaluate the fit between the data and the template, thereby obtaining preliminary classification results. This method is suitable for interface detection scenarios in electric vehicle charging stations, ensuring compatibility between vehicles and charging piles. Furthermore, a feature vector-based comparison strategy can be adopted when performing pattern matching analysis.

[0048] Specifically, physical feature data, such as the measured length and width of the interface, is first extracted and converted into standardized vectors. Then, communication signal features are extracted, such as the frequency and amplitude of the signal. By inputting these vectors into the matching engine, the engine calculates Euclidean distance or cosine similarity to quantify the degree of matching.

[0049] For example, if the calculation result exceeds a preset threshold, it is initially classified as a specific type, such as a Type 2 interface. The implementation process of this strategy includes data preprocessing, feature normalization, and matching calculations to ensure the accuracy and efficiency of the analysis. In applications within the electric vehicle charging field, this analysis can quickly identify interface types, avoiding charging failures caused by incompatibility. Preferably...

[0050] In one possible implementation, pattern matching analysis can be combined with machine learning models to improve accuracy.

[0051] It's important to note that the machine learning model here refers to a pre-trained classifier that learns feature patterns for different interface types based on historical data. The training process involves collecting a large amount of charging interface sample data, including physical images and signal records, and then building the model using supervised learning algorithms such as support vector machines. In the analysis phase, newly collected data is input into the model, which outputs a probability distribution, thus yielding preliminary classification results.

[0052] For example, in a charging station maintenance scenario, this method can distinguish between Chinese standard interfaces and European standard interfaces, and the preliminary results are used for subsequent compatibility verification. This implementation demonstrates the flexibility of the technical solution within the same field, adapting to different charging environments through model optimization.

[0053] Specifically, matching communication signal characteristic data can be divided into two sub-steps: signal timing analysis and protocol parsing. Signal timing analysis examines the duration and intervals of the signal, while protocol parsing checks whether the data packet structure conforms to the standard. Through these steps, the system can identify whether the signal belongs to a specific charging protocol, thereby assisting in the matching of physical characteristics.

[0054] For example, in the application of electric bus charging stations, if the signal characteristics match the CHAdeMO protocol and the physical characteristics show a corresponding pin layout, it is initially classified as a fast charging interface type. This detailed sub-step description ensures the completeness of the analysis process and supports the core feature of pattern matching of data in the claims.

[0055] In one embodiment, pattern matching analysis under noise interference is considered.

[0056] For example, in an outdoor charging station environment, physical feature data may be affected by dust, and communication signals may experience electromagnetic interference. In this case, a filtering mechanism is introduced into the analysis process to first denoise the data before performing matching calculations. In this way, the system can maintain a high classification accuracy and obtain reliable preliminary results. This embodiment highlights the robustness of the technical solution in real-world charging scenarios. Furthermore, the output of the pattern matching analysis can be presented in the form of classification labels, such as "AC slow charging interface" or "DC fast charging interface." After analysis, the system triggers corresponding charging process control based on the preliminary classification results. This output mechanism ensures seamless integration from data analysis to application and is applicable to various scenarios in the electric vehicle charging field.

[0057] Understandably, in another embodiment, pattern matching can be extended to multimodal data fusion.

[0058] Specifically, physical and signal features are combined into a joint vector, which is then matched using a clustering algorithm. This fusion approach can handle complex interface types, such as hybrid charging interfaces, and provides more comprehensive preliminary classification results.

[0059] For example, in smart charging networks, this approach helps identify emerging interface standards and supports compatibility with future charging technologies.

[0060] Preferably, the analysis process can be optimized to a real-time mode to accommodate high-frequency charging demands. By accelerating the matching step through parallel computing, the system outputs results within seconds. This optimization is particularly useful in busy charging station scenarios, improving overall efficiency. In one implementation, a feedback mechanism can also be introduced for the initial classification of different charging interface devices. After analysis, if the initial results are uncertain, the system requests additional data for verification. This mechanism enhances the reliability of the classification and reduces misjudgments in practical applications.

[0061] For example, in the scenario of electric vehicle rental stations, pattern matching analysis directly impacts charging scheduling. Through the steps described above, the system quickly categorizes interface types, ensuring efficient operation.

[0062] Step S103: Based on the preliminary classification results, an image analysis algorithm is used to process the appearance contour feature image of the charging interface device, including edge shape and proportion, and the surface marking pattern image, including text and symbol layout, to determine the precise model and specifications of the charging interface device.

[0063] A device image is acquired, including contour edge shape and proportional size features. Key point coordinates are extracted from the proportional size features. The Canny algorithm is used with the key point coordinates as input to detect edges and obtain continuous edge curves. For the continuous edge curves, a feature vector matrix is ​​constructed by combining surface text markings and symbol layout patterns. Noise in the feature vector matrix is ​​filtered through preliminary image classification results to determine the pattern matching degree. If the pattern matching degree meets a preset threshold, the feature extraction method and pattern matching technology are integrated to determine the consistency of the device appearance analysis and obtain the accurate model specifications.

[0064] Specifically, in one implementation, the image of the charging interface device is further processed based on the preliminary classification results. First, an image of the appearance contour features is acquired, which captures the shape and proportion features of the outer edge of the charging interface.

[0065] For example, in the maintenance of electric vehicle charging stations, high-definition cameras are used to capture side and front views of the interface device to extract contour data. Image analysis algorithms, such as edge detection methods, are then used to identify the interface's geometry, including the curvature of circular or rectangular edges and its aspect ratio. These features help differentiate charging interfaces from different brands; for example, some models have unique elliptical contour proportions, thus narrowing down the model range. This step ensures the accurate continuation of the initial classification. Furthermore, aspect ratio calculations, such as aspect ratio values ​​ranging from 0.8 to 1.2, are used to match standard specifications in the database.

[0066] Specifically, image analysis algorithms process surface marking pattern images, including the layout of text and symbols.

[0067] For example, during the detection of charging station equipment, the algorithm first preprocesses the image, such as grayscale conversion and noise removal, and then extracts the text content and symbol positions.

[0068] For example, the relative position of the "Type2" text or lightning bolt symbol on the interface is identified, the text is analyzed using optical character recognition (OCR) technology, and the spacing and arrangement pattern between the symbols are calculated. This layout analysis can reveal specific identification rules for the model, such as certain interface specifications placing the symbol below the text to form a unique vertical layout. This can be cross-validated with the preliminary classification results to determine the precise model, such as "CCSCombo1" or "CHAdeMO". Preferably.

[0069] In one possible implementation, the algorithm integrates contour and identifier features to form a comprehensive feature vector. The specific process includes converting edge shape data into numerical vectors, such as shape descriptor vectors representing curvature features, and converting identifier layouts into coordinate matrices representing the two-dimensional positions of text and symbols. Then, this vector is compared with a pre-stored model database using a matching algorithm such as Euclidean distance calculation. If the matching degree exceeds a threshold, such as 0.9, the precise specification is output. This integration method, when applied to charging equipment production lines, can effectively process batch images and improve recognition efficiency. In practice, the algorithm can be deployed in embedded systems to process on-site captured images in real time, ensuring the reliability of model determination. Furthermore…

[0070] It should be noted that this image analysis algorithm can adapt to charging interface images under different lighting conditions.

[0071] For example, in outdoor charging station scenarios, the algorithm incorporates a lighting compensation step. It first assesses the image brightness distribution; if the average brightness is below 50, histogram equalization is applied to enhance contrast, followed by the extraction of contour and identification features. This adaptability ensures accuracy in rainy weather or nighttime environments, thereby expanding the application scope of the technical solution.

[0072] In one embodiment, for a wireless charging interface device, the algorithm focuses on processing its flat profile proportions and the layout of the surface QR code symbols.

[0073] Specifically, Canny edge detection is first used to extract the contour edges, and the ratio is calculated, for example, the width-to-height ratio is approximately 1.5. Then, the location of the QR code and embedded text, such as "Qi standard," are identified. The model is confirmed by parsing the QR code data. This method is applied in the detection of smart home charging pads and can distinguish devices with different power specifications.

[0074] Understandably, the algorithm's output includes the generation of reports with precise model specifications.

[0075] For example, the processed data generates a report containing model codes, compatible voltage and current parameters for device compatibility checks. In charging network management, this output helps to quickly match adapters and avoid connection errors.

[0076] For example, in the portable charger scenario, the algorithm processes the outline image of a small interface, identifies its compact shape with an aspect ratio of, for example, 0.5, and the horizontal layout of the USB symbol on the surface. By comparing it with a database, it determines the specifications such as "PD3.0", thereby supporting the model identification of mobile devices.

[0077] In one embodiment, multi-angle image processing is combined to further improve accuracy. The specific process involves acquiring multiple views of the interface, such as a 45-degree tilted view, extracting the contour proportions of each view, and fusing the positional information of the marker pattern. A weighted averaging method is used to integrate the multi-view data; for example, a higher weight of 0.6 is given to the front view to ensure robust recognition even under deformation or occlusion. This method, applied in industrial assembly lines, can handle charging interface images with complex postures.

[0078] Preferably, this technical solution achieves long-term applicability by regularly updating the model database.

[0079] For example, feature data of new charging interfaces is imported quarterly, and the algorithm automatically adjusts matching parameters to cover emerging specifications such as the latest standards for wireless charging. In practice, this implementation method enables accurate determination of charging interface device models, supporting applications such as device maintenance and compatibility verification.

[0080] Step S104: If the precise model specification matches a record in the preset charging interface device specification database, then load the communication configuration parameters corresponding to the model specification, including the data exchange format and verification mechanism.

[0081] The process involves obtaining model specifications and specification records, extracting fields matching the model specifications from the specification records, comparing the model specifications with the corresponding fields in the specification records, and determining the similarity between fields using a preset threshold. If the similarity is greater than the preset threshold, consistency is determined, and a communication configuration is obtained. For the communication configuration, the process integrates the structural elements of the data format with the rule set of the verification mechanism, generates a unified parameter set, loads the unified parameter set into the interface device, and determines the configuration fusion.

[0082] Specifically, in one implementation, if the precise model specification matches a record in a preset charging interface device specification database, the system loads the communication configuration parameters corresponding to that model specification. These parameters include data exchange format and verification mechanisms to ensure stable interaction between charging devices.

[0083] Specifically, the data exchange format refers to a standard protocol that defines the structure of data packets. For example, in the scenario of electric vehicle charging stations, it specifies the transmission order and encoding method of voltage and current information. By immediately retrieving these format parameters from the database after matching, the system can automatically configure the communication interface, avoiding errors caused by manual settings. Furthermore...

[0084] It should be noted that the verification mechanism involves the processes of identity authentication and data integrity checks.

[0085] For example, in charging station management, once the model matches, the system loads a verification mechanism, such as using digital signatures to verify the charger's authorization status. This includes generating key pairs and calculating checksums to ensure that transmitted data has not been tampered with. The loading process of this mechanism first queries pre-stored verification rules in the database and then applies them to the communication link, supporting real-time verification. Preferably...

[0086] In one possible implementation, the loading process is integrated into the embedded charging controller. The specific process includes comparing the model code, retrieving database records, and if the matching degree reaches a threshold, extracting the parameter file and injecting it into the communication module.

[0087] For example, in public charging station applications, the loaded data exchange format might use a JSON structure, defining fields such as "voltage range" and "current limit," while the verification mechanism checks the integrity of the data packets using the HMAC algorithm. This approach ensures device compatibility and reduces connection failures in multi-device environments.

[0088] For example, for portable charging interface devices, the parameters loaded after matching are adapted to mobile scenarios.

[0089] Specifically, the database records contain communication configurations specific to each model, such as the USB protocol format for small chargers, byte order, and frame structure for data exchange. The verification mechanism includes a challenge-response protocol, which is automatically executed upon system loading to confirm device identity. This implementation expands the application of the technology in outdoor or travel charging.

[0090] In one embodiment, the process of loading communication configuration parameters takes into account network latency factors.

[0091] Specifically, if a match is successful, the system first buffers the parameter data and then applies it to the communication stack step by step.

[0092] For example, in a wireless charging pad scenario, the data exchange format is defined as a binary stream, including a power negotiation field, while the authentication mechanism uses a certificate chain to ensure a secure connection. This buffered loading allows the system to maintain stability in weak signal environments.

[0093] Understandably, this method can handle the configuration of multiple charging interfaces.

[0094] For example, on a charging equipment production line, multiple devices are matched in model and their corresponding parameters are loaded simultaneously. The data exchange format is standardized to a standard protocol such as OCPP, while the verification mechanism involves batch key distribution. This parallel loading improves efficiency and supports large-scale deployment. Furthermore...

[0095] In one embodiment, loading is optimized by incorporating a user feedback mechanism. The specific process includes loading parameters after matching, monitoring communication performance, and rolling back to default settings if verification fails.

[0096] For example, in home chargers, this ensures the reliability of parameters and avoids charging interruptions due to mismatched configurations.

[0097] Preferably, this technical solution achieves dynamic loading of parameters by periodically updating the database.

[0098] For example, communication configurations for new models are synchronized monthly, and the system automatically selects the latest version during matching. This update mechanism extends the long-term applicability of the solution and maintains compatibility with emerging charging technologies such as fast charging.

[0099] Step S105: Obtain a confirmation feedback signal indicating successful protocol switching based on the loading result of the communication configuration parameters.

[0100] The protocol switching status is obtained based on the parameter loading result. Status indicators are extracted from the loading result, and it is determined whether the status indicators meet the preset switching criteria to obtain confirmation information. If the status indicators meet the preset switching criteria, signal features are extracted from the feedback channel (pre-established based on communication configuration parameters) according to the confirmation information. The matching degree between the signal features and the confirmation information is determined to obtain a feedback signal indicating successful protocol switching. This feedback signal is then used to verify the communication link originating from the protocol switching process, obtaining final confirmation of successful protocol switching.

[0101] Specifically, in one implementation, to obtain a confirmation feedback signal of successful protocol switching by loading the communication configuration parameters, it is first necessary to monitor the loading process.

[0102] Specifically, after receiving new protocol configuration parameters, the communication device will start a loading program to apply these parameters to the current communication stack. This process includes parameter parsing and verification.

[0103] For example, the system checks whether the parameters conform to predefined format standards to ensure the integrity of the loading. If the loading is successful, the system generates an internal status flag indicating that the parameters have been correctly integrated. Further, based on the loading result, the device sends a query signal to the peer device to confirm the synchronization of the protocol switch. In this step, the confirmation feedback signal is obtained by analyzing specific fields in the loading result.

[0104] For example, the loading result might contain a success code. If this code is a preset value, the switchover is considered successful, triggering a feedback signal. This mechanism helps maintain a stable connection in the communication network and avoids protocol incompatibility caused by parameter loading failure.

[0105] It should be noted that the confirmation signal for a successful protocol switch can take several forms.

[0106] In one possible implementation, the signal is represented as a data packet containing a timestamp and a handover identifier. Upon receiving the loading result, the device parses the result code. If the result code indicates no errors, a feedback signal is immediately constructed and sent via the communication link. This approach ensures the real-time nature of the handover process, making it particularly suitable for wireless communication applications such as 5G networks, as it allows for rapid response to network changes.

[0107] For example, in a mobile communication scenario, when a device switches from a 4G protocol to a 5G protocol, the loaded configuration parameters include frequency band allocation and modulation scheme. The loading result generates a log record, from which the system extracts a success confirmation message. If the loading result shows that all parameters have been applied correctly, a feedback signal is returned to the initiating end in the form of an ACK message. This feedback not only confirms the switch but also carries additional data such as signal strength indicators to support subsequent optimization and adjustments.

[0108] Preferably, to enhance robustness, a redundancy check mechanism can be introduced. After the loading results are obtained, if any abnormal loading of some parameters is detected, the system will automatically retry the loading process and only generate an acknowledgment feedback signal after the retry is successful.

[0109] For example, in IoT device communication, this mechanism prevents handover failures due to network fluctuations and ensures protocol consistency between devices.

[0110] Understandably, this technology is also used in the field of satellite communications.

[0111] In one embodiment, after the satellite terminal loads the configuration parameters sent by the ground station, it obtains a feedback signal through result analysis. If the loading result contains a checksum match, the signal confirms a successful handover, thereby maintaining the continuity of long-distance communication. This implementation demonstrates the versatility of the solution and its ability to adapt to the parameter loading requirements of different communication environments.

[0112] Specifically, the process of obtaining the confirmation feedback signal involves parsing the loading result bit by bit.

[0113] For example, the result data structure might include a header, a parameter list, and a tail checksum. The system first verifies the header identifier, then checks if the parameter list is complete. If everything is normal, a success flag is extracted from the tail as the basis for the feedback signal. This detailed parsing ensures the accuracy of the signal, which is particularly important in high-reliability communications such as aviation control systems. In another implementation, the acquisition of the feedback signal can be combined with a timer mechanism. After the loading result is generated, a timer is started. If no error is detected within a specified time, an acknowledgment signal is automatically generated and broadcast. This method is suitable for real-time communication systems, such as protocol switching in vehicle-to-everything (V2X) networks, ensuring rapid synchronization between vehicles without interrupting data transmission. Furthermore, the technical advantage of this scheme is that it improves the success rate of protocol switching and reduces the risk of communication interruption through precise processing of the loading result. In practical deployments, such as in enterprise-level wireless networks, this acknowledgment mechanism can significantly improve overall network stability without requiring additional hardware support.

[0114] For example, in industrial automation communication, when a device switches to a new encryption protocol, the loading parameters trigger the generation of a feedback signal. If the result shows that the encryption key has been loaded correctly, the signal confirms the switch is complete, thus ensuring secure data transmission. This versatility highlights the solution's flexibility in the communication field.

[0115] Step S106: Based on the confirmation feedback signal, establish a communication link connection with the electric vehicle terminal, including a stable data transmission channel, and obtain the load demand data transmitted by the electric vehicle terminal, including maximum capacity and rate requirements.

[0116] Upon confirming the feedback signal, a communication link connection is established with the electric vehicle. Link status parameters are obtained from this connection, and a connection stability threshold is determined based on these parameters. For this communication link connection, a stable data transmission channel is used to obtain load demand data from the electric vehicle. The transmission quality is assessed using the connection stability threshold, yielding a maximum capacity indicator. Based on the load demand data, a rate requirement is determined, and combined with the maximum capacity indicator, demand matching characteristics are obtained. From these characteristics, load demand priorities are determined, and a transmission allocation scheme for the electric vehicle is obtained based on these priorities, resulting in an optimized load demand data outcome.

[0117] Specifically, in one implementation, a communication link is first established with the electric vehicle based on the received confirmation feedback signal. This process is implemented through a wireless communication protocol, such as Wi-Fi or Bluetooth technology, to ensure the reliability of the connection.

[0118] Specifically, the system sends a connection request signal and initializes the data transmission channel upon receiving confirmation from the electric vehicle. This channel is designed to be stable, resistant to interference, and supports continuous data exchange. In electric vehicle charging scenarios, this connection helps monitor vehicle status in real time and prevents data loss. Furthermore, after establishing the communication link, the system acquires the load demand data transmitted from the electric vehicle. This data includes maximum capacity and rate requirements; for example, maximum capacity refers to the highest amount of electricity the vehicle battery can handle, while the rate requirement relates to charging speed limitations.

[0119] For example.

[0120] In one possible implementation, the system receives this data via an encryption protocol to ensure secure transmission.

[0121] It should be noted that the process of acquiring load demand data involves parsing messages sent by the vehicle, where the maximum capacity may be expressed in kilowatt-hours, and the rate requirement may be based on amperes or kilowatts. This method enables precise control of the charging process.

[0122] Preferably, in another implementation, the communication link connection can be extended to multiple electric vehicle scenarios, such as home charging piles or public charging stations. In the home scenario, the connection is established through the home network, and a stable channel employs a redundant backup mechanism to cope with signal fluctuations. When acquiring load demand data, the system first verifies the vehicle's identity and then extracts the maximum capacity data; for example, an electric car with a battery capacity of 60 kWh requires a speed not exceeding 22 kW. This design enhances the flexibility of the technical solution and can adapt to the needs of different vehicle types.

[0123] Specifically, establishing a stable data transmission channel involves link quality assessment. The system monitors signal strength and latency, and switches to a backup channel if they fall below a threshold. This assessment is based on real-time feedback to ensure the stability of the transmission channel. When acquiring load demand data, the data packets are further parsed to extract the maximum capacity and rate requirements.

[0124] For example, for an electric SUV, the maximum capacity might be 100 kWh, with a required charging rate of 50 kW. This detailed analysis helps optimize charging strategies and avoid the risk of overload.

[0125] In one embodiment, for commercial electric vehicles, such as electric buses, the communication link connection emphasizes high-bandwidth channels to support large amounts of data transmission. After acquiring load demand data, the system can adjust power supply parameters accordingly. This implementation demonstrates the versatility of the technology within the same field, enabling efficient energy management.

[0126] Step S107: Determine whether the load demand data conforms to the rated output power range of the charging interface device.

[0127] Load demand data is collected, and the rated output power range is obtained from the charging interface device. The load demand data is compared with the rated output power range to obtain an initial comparison value. Based on the initial comparison value, a preset threshold is used to check its boundary. If the initial comparison value exceeds the preset threshold, it is determined that the load demand data exceeds the rated output power range, and an adjustment signal is determined. The load demand data is corrected in real time using the adjustment signal to obtain corrected data. The corrected data is compared with the rated output power range again. If the corrected data conforms to the rated output power range, it is determined that the corrected data conforms to the rated output power range, and the final verification result is obtained.

[0128] Specifically, in one implementation, the charging interface device first collects load demand data, for example, by monitoring the battery status and required charging power of the electric vehicle through built-in sensors.

[0129] Specifically, the load demand data includes current and voltage requirements, which are obtained in real time from the vehicle's communication interface to ensure accurate judgment. The rated output power range is preset based on the device's specifications, for example, a maximum output power of 100 kW and a minimum of 20 kW. Furthermore, the judgment process involves comparing the load demand data with the rated output power range.

[0130] For example, if the calculated load demand power is 80 kW, a comparison algorithm is used to check whether it falls between 20 kW and 100 kW. This algorithm can employ a threshold comparison method, first calculating the value of the demand power, and then comparing it one by one with the upper and lower limits to determine compliance.

[0131] It should be noted that the calculation of load demand data can be adjusted for environmental factors. For example, in high-temperature environments, the demand data will be corrected according to the temperature coefficient.

[0132] Specifically, the temperature coefficient is defined as an empirically based multiplier. For example, when the ambient temperature exceeds 30 degrees Celsius, the required power is multiplied by 1.1 to compensate for heat loss. This adjustment process ensures that the judgment is more in line with the actual charging scenario and avoids misjudgments caused by external conditions.

[0133] In one possible implementation, the correction is performed through the device's control module, which integrates simple logic circuits to achieve automatic data updates.

[0134] Preferably, in a multi-vehicle scenario at a charging station, the determination can be extended to aggregated load requirements.

[0135] For example, when multiple electric vehicles connect simultaneously, the system aggregates all demand data. If the total power exceeds the device's rated range, an allocation mechanism is triggered, such as prioritizing charging for vehicles with lower demand. This mechanism is implemented through a central controller, which collects data from each interface and performs a total comparison, demonstrating the versatility of the technical solution.

[0136] In one embodiment, if the determination does not meet the requirements, the device will output a warning signal and suspend charging.

[0137] Specifically, non-compliance includes situations where the required power exceeds the upper limit, such as a calculated value of 120 kW exceeding the 100 kW threshold. In this case, the system logs the information and notifies the user to adjust the vehicle status. This process improves the safety of the charging process and enables equipment protection.

[0138] Understandably, the rated output power range can be flexibly defined based on the device model. For example, in portable charging ports, the range narrows to 5 kW to 20 kW, while in fixed charging stations it expands to 50 kW to 200 kW. These settings allow the technical solution to be applicable to charging applications of different scales. Furthermore, the data acquisition module can be integrated with the vehicle's battery management system to transmit demand data in real time. This integration is achieved through standard protocols, ensuring compatibility. After determining that the requirements are met, the device initiates output power matching, for example, gradually increasing the output to near the demand value to optimize charging efficiency.

[0139] For example, in a home charging scenario, load demand data comes from battery monitoring of household electric vehicles. The judgment process is simplified to a single-point comparison; if the conditions are met, power is supplied directly. This example demonstrates the application of the solution in a small environment. In another embodiment, considering dynamic load changes, time series analysis is incorporated into the judgment algorithm.

[0140] Specifically, the system monitors the fluctuation curve of demand data; if the average value within a short period falls within a specified range, it is considered valid. This analysis process involves collecting data from multiple time points and calculating the average value to ensure the robustness of the judgment and to address the challenges posed by power fluctuations during charging. Overall, these implementation methods cover the complete process from data acquisition to result processing, supporting the application of the technical features of the claims in various scenarios within the charging field.

[0141] Step S108: If the load demand data meets the rated output power range, then activate the power control module including the switch and adjustment unit to obtain the charging start authorization command.

[0142] The system acquires load demand data, which is used to establish the power supply path for the power control module. It then determines upper and lower limits that meet a threshold from a preset rated output power range. By comparing the load demand data with the upper and lower limits of the rated output power range, it determines the consistency of the demand and obtains a consistency result. If the load demand data meets the consistency result, it activates the switching unit in the power control module to obtain an initial power supply path. The system then adjusts the power supply parameters in the initial power supply path using the adjustment unit, and outputs the adjusted power supply parameters to the load device from the adjusted power supply path.

[0143] Specifically, in one implementation, the power management system first collects load demand data, which includes the current power demand value of the device and the expected load curve. This data is obtained through real-time monitoring by sensors.

[0144] For example, in electric vehicle charging scenarios, load demand data can come from the vehicle battery management system, reflecting the power level that the charging station needs to provide.

[0145] It should be noted that the rated output power range refers to the preset safe upper and lower limits of the power supply equipment, such as from 100 watts to 500 watts, to ensure that the equipment capacity is not exceeded to avoid overload. Furthermore, if the load demand data meets the rated output power range, the system activates the power control module. This module includes a switching unit and a regulating unit, where the switching unit is responsible for controlling the circuit's on / off state, and the regulating unit adjusts voltage or current parameters to match the load demand.

[0146] Specifically, the activation process involves sending an activation signal to the module, such as by issuing a command using a microcontroller to switch the switching unit from an open state to a closed state, while the regulating unit dynamically adjusts the output power based on demand data. This process ensures the stability and safety of the power supply, preventing equipment damage caused by sudden power fluctuations in home charging station implementations.

[0147] For example, in a public charging station scenario, the compliance determination of load demand data can be achieved through a comparison algorithm: the system compares the collected demand value with the rated range; if the demand value is within the range, the module is immediately activated. In one possible implementation, the switching unit uses a solid-state relay for fast response, while the regulating unit integrates pulse width modulation technology for fine-tuning the output. This design enhances the system's response speed, enabling it to quickly adapt to the parallel demands of multiple vehicles, for example, during peak charging periods.

[0148] Preferably, after activating the power control module, the system obtains a charging start authorization command. This command originates from an authorization server or the user-end application and is transmitted via an encrypted communication protocol to verify the user's identity and payment status.

[0149] Specifically, the acquisition process includes sending a request to the server, which verifies the request and then returns an authorization code, for example, using a token-based mechanism to ensure security.

[0150] In one embodiment, if the authorization command fails to be obtained, the system will suspend activation and notify the user. This mechanism is widely used in commercial charging networks to prevent unauthorized use.

[0151] Understandably, the power control module's switching and regulation units can be configured according to different load types. For example, in fast charging mode, the regulation unit prioritizes increasing current output, while in standard mode, it focuses on voltage stability. This flexibility demonstrates the versatility of the technical solution, covering both home and commercial scenarios within the same charging field. Furthermore, the entire process logic, starting from data acquisition, progresses to condition judgment, module activation, and authorization acquisition, ensuring seamless integration.

[0152] For example, in implementation, the system can integrate a feedback loop. If the output mismatch occurs after activation, the adjustment unit automatically corrects it, which improves overall efficiency. In another implementation, for high-load scenarios such as electric bus charging, the rated output power range can be extended to the kilowatt level. When activating the module, thermal management is emphasized, and the switching unit incorporates a heat dissipation mechanism to maintain stability.

[0153] Specifically, the regulating unit adjusts the power curve by monitoring temperature sensor data to avoid the risk of overheating. This feature ensures long-term reliable operation in practical applications.

[0154] It's worth noting that obtaining charging start authorization commands can be combined with blockchain technology to provide decentralized verification. For example, in a distributed charging network, users obtain commands through smart contracts. This approach enhances security and demonstrates efficiency in multi-site charging environments.

[0155] For example, the system can log data upon activation for subsequent analysis, such as tracking power usage patterns to optimize range settings. This objective recording helps maintain the lifespan of power supply equipment and brings practical technical benefits in daily charging operations, such as reducing the failure rate.

[0156] In one embodiment, the entire technical solution can be embedded in a smart grid framework, combining load demand data with grid load balancing, and considering real-time grid status when activating the module, thereby achieving more accurate power allocation.

[0157] Step S109: According to the authorized instruction, monitor the current intensity data, including peak and average levels, and the voltage level data, including fluctuation amplitude, during the charging process in real time.

[0158] Current intensity and voltage level data are collected by sensors during the charging process to obtain peak, average, and fluctuation amplitudes. Current anomalies are identified from the peak and average levels. If the peak exceeds a preset threshold or the average level deviates from the standard range, the charging rate is adjusted by reducing the input power to obtain a stable current distribution. Voltage stability is calculated based on the difference in fluctuation amplitude between consecutive sampling points. If the voltage stability is lower than a preset threshold, adjusted voltage level data is obtained from the stable current distribution. The fluctuation amplitude is determined from the adjusted voltage level data, and the stability of the fluctuation amplitude relative to the voltage level data is obtained. If the stability meets the preset threshold, the charging process is completed using the stable current distribution and the voltage level data.

[0159] Specifically, in one implementation, the charging monitoring system initiates a real-time monitoring process based on an authorized instruction. The authorized instruction can be understood as a signal from the control center, such as a start command received via a wireless communication module, used to guide the charging equipment into monitoring mode.

[0160] Specifically, when an electric vehicle is connected to a charging station, the system receives an authorization command and activates its built-in sensors to begin collecting data. This method ensures that monitoring is only performed under authorized conditions, avoiding unauthorized access or misoperation. Furthermore, real-time monitoring of current intensity data includes both peak and average levels.

[0161] For example, the system uses a current sensor to collect current values ​​multiple times per second and identifies instantaneous high-load conditions by calculating peak values. The peak value calculation process involves extracting the maximum value from the sequence of collected current samples, for example, finding the highest current intensity value within a consecutive 10-second window to reflect potential overload risk. The average value is obtained by calculating the arithmetic mean of all samples within the same window. This calculation helps assess the overall stability of the charging process; in electric vehicle charging scenarios, excessively high peak values ​​may indicate circuit faults, while the average value is used to monitor long-term current trends.

[0162] Preferably, for monitoring voltage level data, the system focuses on the fluctuation amplitude. The fluctuation amplitude can be understood as the maximum deviation of the voltage value over a period of time, which can be quantified, for example, by calculating the standard deviation or the maximum and minimum differences of the voltage series.

[0163] It should be noted that the voltage sensor records the voltage reading in real time, while the system uses a filtering algorithm to remove noise before calculating the amplitude. The specific process includes first acquiring the raw voltage data, then using a moving average filter to smooth the curve, and finally calculating the peak-to-peak value as an indicator of fluctuation amplitude. This method helps detect potential hazards caused by voltage instability during charging, such as battery damage.

[0164] In one possible implementation, the monitoring process is integrated into the embedded system of the charging station.

[0165] For example, once the authorization command is issued, the system generates a data report every minute, including peak current, average current, and voltage fluctuation. This data is uploaded to a cloud platform for remote analysis. In the scenario of electric bicycle charging stations, this monitoring ensures safety when multiple devices are charging simultaneously, triggering an alarm when the fluctuation exceeds a threshold.

[0166] Understandably, the above monitoring supports the universality of multiple charging scenarios.

[0167] For example, in home chargers, the system adjusts the monitoring frequency based on authorized instructions and calculates lower peak thresholds for slow charging modes, while in fast charging stations, emphasis is placed on voltage fluctuation control to prevent overheating. Through these implementations, the system achieves precise monitoring of the charging process, providing a data foundation for optimizing device performance.

[0168] Specifically, the calculation of peak and average current levels requires careful consideration of the sampling rate. A high sampling rate ensures accurate peak capture; for example, sampling once per millisecond, and then aggregating the data to calculate the average. This attention to detail improves monitoring reliability in practical operations, avoiding errors caused by low sampling rates. Furthermore, the assessment of voltage fluctuation amplitude involves threshold setting.

[0169] For example, the system presets a fluctuation range limit of 5%, and automatically adjusts the charging power when this limit is exceeded. This mechanism brings stability to the charging business and reduces the failure rate.

[0170] In one embodiment, the system combines current and voltage data to generate a comprehensive monitoring log for subsequent analysis. This integration demonstrates the versatility of the technology, adapting to different device types within the same charging field.

[0171] Step S1010: If the current intensity data or voltage level data exceeds a preset safety threshold, disconnecting the power supply connection includes cutting off the circuit path.

[0172] Current intensity data and voltage level data are acquired from sensors. An abnormal state is identified if the current intensity data and voltage level data exceed a preset safety threshold. If the abnormal state exists, the power supply connection is disconnected to achieve safety isolation. This safety isolation interrupts the transmission path of the current intensity data and voltage level data. The abnormal state is determined by comparing the current intensity data and voltage level data with a preset safety threshold. The preset safety threshold is pre-established for comparison of the current intensity data and voltage level data.

[0173] Specifically, in one implementation, the power supply system includes a monitoring module for real-time acquisition of current intensity and voltage level data. This data is acquired through sensors, such as current transformers to measure current intensity in the lines and voltage sensors to detect the supply voltage level.

[0174] Specifically, the monitoring module compares the collected data with preset safety thresholds. These thresholds can be preset based on system load and safety standards; for example, the current threshold is 120% of the rated value, and the voltage threshold is 110% of the standard voltage. Furthermore, if the current intensity data or voltage level data exceeds the preset safety threshold, a disconnection mechanism is activated. This mechanism involves cutting off the circuit path by controlling a relay or circuit breaker to disconnect the power supply connection.

[0175] For example, in high-voltage transmission line scenarios, when the current intensity is detected to exceed the threshold, the system immediately sends a signal to the circuit breaker to cut off the main circuit path to prevent equipment overheating or short circuit risks.

[0176] Preferably, in another embodiment, this security mechanism can be integrated into the smart grid control system. The monitoring module uses a digital signal processor to process the data, and the threshold comparison is performed through an embedded software algorithm. Specifically, the process involves first converting the analog signal into digital data, then comparing it with a threshold value; if the threshold is exceeded, a disconnect command is triggered. This approach ensures a fast response time, typically completing circuit disconnection within milliseconds.

[0177] It should be noted that this power disconnection is not limited to a single scenario. For example, in distributed photovoltaic power generation systems, this mechanism also applies when the voltage level rises abnormally. By cutting off the connection path between the inverter and the grid, damage to the system from reverse current is prevented. This versatility demonstrates the flexible application of the technology in the power sector.

[0178] For example, in industrial power environments, the system can incorporate temperature sensors for auxiliary judgment. If the current exceeds a threshold and the temperature is abnormal, a disconnection will be prioritized to enhance the protection effect. Based on the above steps, this mechanism can achieve effective safety management of the power system and ensure stable operation.

[0179] In one possible implementation, an automatic reset function can be configured after a disconnection, reclosing the circuit path when data recovers to within a safe threshold. This extension further improves system availability without altering the core safety logic.

[0180] Step S1011: Based on the result of disconnecting the power supply connection, generate a power outage log containing abnormal fluctuation data and the time of power outage.

[0181] Based on the power disconnection result, abnormal fluctuation data and power outage time are obtained from the abnormal fluctuation capture and power outage time markers to obtain the power outage record basis. For this power outage record basis, log data integration and result anomaly correlation methods are used to determine the correlation between abnormal fluctuation data and power outage time, and to determine the backup log path containing power supply result analysis. From the backup log path, the integrated data is obtained to obtain the final power outage record log.

[0182] Specifically, in one implementation, the power supply system includes a log generation module for automatically creating a power outage log after the power connection is disconnected. This module first collects abnormal fluctuation data, including specific numerical fluctuations in current intensity or voltage level.

[0183] For example, when the current is detected to rise sharply from the normal value to exceed the threshold, the data points of the fluctuation curve are recorded.

[0184] Specifically, abnormal fluctuation data is captured in real time by sensors and stored in logs in time-series format, ensuring that the process of the fluctuation can be traced during subsequent analysis. This approach helps system administrators identify potential sources of failure. Furthermore, the log generation process involves the precise marking of the power outage moment.

[0185] For example, the power outage time refers to the instant when the system activates the disconnection mechanism, recorded using a standard time format such as UTC. For instance, in a high-voltage transmission line, when an abnormal fluctuation in the voltage level causes a disconnection, the log will mark the time to the second, such as "2023-10-15 14:30:45".

[0186] It should be noted that this moment is obtained synchronously with the system clock to avoid time deviations affecting the accuracy of the record. When generating the log, the module associates the abnormal fluctuation data with the moment of power outage, forming structured entries to facilitate querying and auditing.

[0187] Preferably, in a distributed photovoltaic (PV) power generation system, log entries can be extended to include statistical summaries of fluctuation data. The specific process involves first summarizing the peak and average values ​​during the fluctuation period, such as the voltage trajectory as it rises from a standard 110% threshold to 120%, and then combining this with the time of the power outage to generate a complete log entry. This summary helps to quickly assess the severity of an event without having to sift through all the raw data.

[0188] Understandably, this mechanism is implemented through embedded software to ensure that logs are generated and stored in non-volatile memory immediately after a power outage.

[0189] In one possible implementation, the log format uses a standard structure, including a header, an exception data segment, and a timestamp segment. The header records the event type, such as "current overload disconnection"; the exception data segment details the sequence of fluctuating values, such as multiple sampling points that gradually increase from the normal current value to the peak value; and the timestamp segment independently indicates the disconnection time. This modular design improves the readability and compatibility of the log, making it suitable for different power subsystems.

[0190] For example, in industrial power environments, when a power outage is triggered by temperature-based detection, the log will include additional relevant fluctuation data, such as the combined current and temperature fluctuation curve, and will indicate the time of the power outage. This integration ensures the comprehensiveness of the log and supports post-outage fault diagnosis. Furthermore, an access interface can be set after the log is generated, allowing authorized users to retrieve specific power outage events. In this way, the system achieves effective management of historical records and improves overall safety response capabilities.

[0191] In one embodiment, the process of recording abnormal fluctuation data needs to take into account the data sampling frequency in detail.

[0192] For example, the monitoring module collects current and voltage data 10 times per second. When fluctuations exceed the threshold, these data points are serialized and stored in the log.

[0193] Specifically, the process first filters out noisy data, then calculates fluctuation amplitudes, such as the percentage change in current from its rated value, and finally links it to the timing of power outages, such as "interruption time accurate to milliseconds." This detailed recording helps analyze the causes of fluctuations, such as identifying whether they are caused by sudden changes in external load, thus providing a basis for system optimization. The application of this mechanism in the power supply sector demonstrates its practical value in maintaining stable operation.

[0194] Preferably, the log may include recovery-related information, such as a timestamp after data recovery to a safe threshold, but the core focus remains on recording the disconnection results.

[0195] For example, in smart grid control systems, log generation verifies data integrity to ensure that abnormal fluctuation data is complete and consistent with the time of power outage. This verification process is implemented through a checksum algorithm, which improves the reliability of the logs.

[0196] Step S1012: Analyze the power outage log, including the classification of abnormal causes and frequency statistics; update the charging interface device specification database; and optimize the model identification parameters, including feature thresholds and matching templates.

[0197] The system analyzes power outage logs to obtain anomaly classification summaries; extracts frequency statistics from these summaries to generate anomaly classification results; performs pattern induction on the categories in the anomaly classification summaries to obtain anomaly pattern distributions; updates the database specification refresh using log association mapping based on the anomaly pattern distributions; correlates the distribution items in the anomaly pattern distributions with the specification items in the database specification refresh to determine the charging interface verification standard; if the charging interface verification standard exceeds a preset threshold, optimizes model identification calibration through device specification comparison; applies the comparison results from device specification comparison to the identification items in the model identification calibration to obtain iterative matching templates after feature threshold fine-tuning.

[0198] Specifically, in one implementation, the process of analyzing the power outage log first involves collecting log data from the operating system of the charging interface device. This data includes the timestamp of the power outage, device status parameters, and relevant environmental factors, such as voltage fluctuations or interface connection status.

[0199] Specifically, the classification of anomaly causes can be achieved through a pre-defined rule engine that matches log entries with known anomaly patterns.

[0200] For example, if the log shows a sudden voltage drop to zero accompanied by an abnormal rise in interface temperature, it is classified as a power supply failure; if the log records multiple plugging and unplugging operations followed by power outages, it is classified as interface mechanical damage. Through this classification, the system can categorize power outage events into several types, such as electrical faults, mechanical damage, or external interference, thus providing a basis for subsequent maintenance. Frequency statistics, based on the classification results, count the number of occurrences of each type of anomaly within a specified time period, such as monthly statistics on the occurrence rate of power failures to identify high-frequency problem areas. This analysis helps charging station operators promptly identify potential equipment problems and ensure the stability of the charging process. Furthermore, the anomaly cause classification can be implemented using a decision tree model, which constructs a tree structure based on the multidimensional features in the logs.

[0201] It's important to note that decision trees classify data by recursively splitting it. For example, they first check if the voltage parameter is below a threshold; if so, they further examine the temperature data to determine if the power outage is caused by overheating. The frequency statistics process involves the application of aggregation functions, grouping the classified data by time windows, calculating the count value for each group, and generating a report. This method is commonly used in the charging equipment field, such as in public charging station networks, to monitor the power outage patterns of multiple devices, thereby optimizing the overall operation and maintenance strategy. Through these steps, the analysis results can be directly fed back into equipment management, improving fault response efficiency.

[0202] Preferably, the step of updating the charging interface device specification database is performed based on the analysis results.

[0203] Specifically, when log analysis identifies new causes of anomalies or changes in frequency, the system will trigger a database update mechanism.

[0204] For example, if frequency statistics show that a certain type of interface frequently experiences power outages due to specification incompatibility, the specification information for that model in the database is updated, such as adding compatible voltage ranges or interface size details. The update process includes a data verification step to ensure that the new specification data is consistent with existing standards and to avoid erroneous input. This type of update is applicable to various scenarios in the charging equipment field, such as updating the database of electric vehicle charging stations to adapt to the specification changes of new charging guns, thereby maintaining system compatibility.

[0205] In one possible implementation, the process of optimizing model recognition parameters focuses on adjusting feature thresholds and matching templates.

[0206] For example, a feature threshold refers to a critical value used to determine similarity in model identification. For instance, setting the Euclidean distance threshold for interface shape features to 0.05 means that a match is considered complete if the calculated distance is below this value. During optimization, the threshold can be iteratively adjusted using historical log data to gradually reduce the false recognition rate. The matching template is a predefined set of feature vectors used to compare input data. For example, the template might include vectors of the geometric dimensions and electrical parameters of a standard interface. Optimizing the template involves updating these vectors to incorporate features of the new model, thereby improving recognition accuracy. This optimization is crucial in model detection for charging interface devices, reducing power outages caused by incorrect model numbers.

[0207] Specifically, the detailed process of optimizing model recognition parameters includes first extracting failed recognition cases from power outage logs as an optimization dataset. Then, gradient descent is used to progressively adjust the feature thresholds; for example, an initial threshold of 0.1 is iterated and reduced to 0.03 based on error rate feedback to achieve higher matching accuracy. The matching templates are optimized using clustering algorithms, such as clustering similar interface features into new templates and merging them with existing templates. This method can be applied to different scenarios in the charging equipment field, such as optimizing the recognition of electric bicycle interfaces in smart charging piles or adjusting the matching of commercial vehicle models in high-voltage charging stations. Through these optimizations, the system can significantly improve the robustness of model recognition, reduce the frequency of abnormal power outages, and provide more reliable data support for database updates.

[0208] For example, in one embodiment of a charging station network, combining the above steps, firstly, analysis of power outage logs reveals that mechanical damage accounts for 30%, with frequency statistics showing it occurs 5 times per week. Then, the database is updated, adding specification enhancement information for this type of damage, such as adding a description of wear-resistant materials for the interface. Next, the identification parameters are optimized, adjusting the feature threshold from 0.04 to 0.02, and the matching template is updated to cover new interface shapes. This integrated application ensures the continuity and safety of the charging process.

[0209] It is understandable that the overall process of analysis and optimization described above is universal in the field of charging equipment. For example, in home charger systems, anomalies can also be classified and statistically analyzed through logs, and the specification database can be updated and identification parameters optimized accordingly to accommodate user-defined device models. This flexibility allows the technical solution to cover a variety of scenarios, from small to large charging facilities. Furthermore, in another embodiment, the anomaly cause classification can be extended to include environmental factors, such as integrating weather data from logs and classifying it as short-circuit power outages caused by rain, while frequency statistics are grouped by season. This extension optimizes the model identification parameters, allowing thresholds to be dynamically adjusted according to the environment, such as relaxing the electrical characteristic tolerance of the matching template under humid conditions, thereby improving the system's adaptability.

[0210] It should be noted that the database update process can also include a batch import mechanism, such as automatically extracting data from the manufacturer's specifications, verifying it, and then updating it to ensure real-time performance. When optimizing parameters, the generation of matching templates can be based on training with sample data; for example, collecting features from 100 interface instances and calculating the average vector as the template basis. This method can effectively reduce human error and improve overall device performance during the maintenance of charging interface devices.

[0211] For example, the effectiveness of the entire technical solution is reflected in reducing power outages. Through log analysis and parameter optimization, the availability of charging stations can be increased from 90% to 98%, which in actual business means lower maintenance costs and higher user satisfaction.

[0212] The above embodiments are merely one of the preferred embodiments of the present invention and should not be used to limit the scope of protection of the present invention. Any modifications or refinements made to the main design concept and spirit of the present invention that are not of substantial significance, but solve the same technical problem as the present invention, should be included within the scope of protection of the present invention.

Claims

1. A method for intelligent identification and conversion control of charging interfaces for multi-standard charging piles, characterized in that, The method includes the following steps: Step S101: Collect physical characteristic data of the charging interface device, including diameter and length, and communication signal characteristic data, including amplitude and frequency changes, through the interface sensor; Step S101 further includes: The interface diameter and length of the charging interface device are collected by the interface sensor to obtain physical feature data. The amplitude and frequency changes of the communication signal are collected based on the physical feature data to obtain signal feature data. The signal stability is judged by the preset threshold based on the signal feature data to obtain a compatibility verification result. If the compatibility verification result meets the preset conditions, the compatibility of the charging interface device is determined. The physical feature data and the signal feature data are obtained to obtain fused feature data. The preset threshold is adjusted based on the fused feature data to obtain an optimized threshold. The signal stability is judged by the optimized threshold to obtain the final compatibility verification result. Step S102: Perform pattern matching analysis on the interface physical feature data and communication signal feature data to obtain a preliminary classification result of the charging interface device type; Step S102 further includes: The interface physical feature data is acquired, and size and shape parameters are extracted from the interface physical feature data to obtain a physical matching vector. The physical matching vector is combined with communication signal feature data, and a support vector machine algorithm is used for pattern matching. The input of the support vector machine algorithm is the physical matching vector and the communication signal feature data. The feature space distance is calculated through a kernel function, and a similarity score is output. If the similarity score exceeds a preset threshold, it is determined to be a specific charging device type, and a compatibility verification label is obtained. For the compatibility verification label, voltage and current features are fused, and a comprehensive feature value is calculated by weighted averaging to obtain a preliminary classification result. Step S103: Based on the preliminary classification results, an image analysis algorithm is used to process the appearance contour feature image of the charging interface device, including edge shape and proportion, and the surface marking pattern image, including text and symbol layout, to determine the precise model and specifications of the charging interface device. Step S104: If the precise model specification matches a record in the preset charging interface device specification database, then load the communication configuration parameters corresponding to the model specification, including the data exchange format and verification mechanism. Step S105: Obtain a confirmation feedback signal indicating successful protocol switching based on the loading result of the communication configuration parameters; Step S105 further includes: The protocol switching status is obtained by loading the parameters, and status indicators are extracted from the loading results. It is then determined whether the status indicators meet the preset switching criteria to obtain confirmation information. If the status indicator meets the preset switching criteria, then according to the confirmation information, the signal features are extracted from the feedback channel. The feedback channel is pre-established based on communication configuration parameters to determine the matching degree between the signal features and the confirmation information, and a feedback signal of successful protocol switching is obtained. The feedback signal is used to verify the communication link, which originates from the protocol switching process, and to obtain final confirmation that the protocol switching was successful. Step S106: Based on the confirmation feedback signal, establish a communication link connection with the electric vehicle terminal, including a stable data transmission channel, and obtain the load demand data transmitted by the electric vehicle terminal, including maximum capacity and rate requirements. Step S107: Determine whether the load demand data conforms to the rated output power range of the charging interface device; Step S107 further includes: Collect load demand data, obtain the rated output power range from the charging interface device, and compare the load demand data with the rated output power range to obtain an initial comparison value. Based on the initial comparison value, a limit check is performed on the initial comparison value using a preset threshold. If the initial comparison value exceeds the preset threshold, it is determined that the load demand data exceeds the rated output power range, and an adjustment signal is determined. The load demand data is corrected in real time using the adjustment signal to obtain the corrected data. The corrected data is compared with the rated output power range again. If the corrected data conforms to the rated output power range, it is determined that the corrected data conforms to the rated output power range, and the final verification result is obtained. Step S108: If the load demand data meets the rated output power range, then activate the power control module including the switch and adjustment unit to obtain the charging start authorization command. Step S109: According to the authorized instruction, monitor the current intensity data, including peak and average levels, and the voltage level data, including fluctuation amplitude, during the charging process in real time. Step S10110: If the current intensity data or voltage level data exceeds a preset safety threshold, disconnecting the power supply connection includes cutting off the circuit path. Step S10111: Based on the result of disconnecting the power supply connection, generate a power outage log containing abnormal fluctuation data and the time of power outage; Step S10112: Analyze the power outage log, including the classification of abnormal causes and frequency statistics; update the charging interface device specification database; and optimize the model identification parameters, including feature thresholds and matching templates.

2. The intelligent identification and conversion control method for charging interfaces of multi-standard charging piles according to claim 1, characterized in that, Step S103 includes: Acquire a device image, the device image including contour edge shape and scale size features; Extract key point coordinates from the aforementioned scale features; The Canny algorithm is used to detect edges by taking the coordinates of the key points as input, and a continuous edge curve is obtained. For the continuous edge curve, a feature vector matrix is ​​constructed by combining surface text markings and symbol layout patterns; The noise in the feature vector matrix is ​​filtered out using the preliminary image classification results to determine the pattern matching degree; If the pattern matching degree meets the preset threshold, the feature extraction method and pattern matching technology are integrated to determine the consistency of the device appearance analysis and obtain the accurate model specifications.

3. The intelligent identification and conversion control method for charging interfaces of multi-standard charging piles according to claim 1, characterized in that, Step S104 includes: The model specification and specification record are obtained. Fields matching the model specification are extracted from the specification record. The model specification and the corresponding fields in the specification record are compared. A preset threshold is used to determine the similarity between the fields. If the similarity is greater than the preset threshold, consistency is determined and the communication configuration is obtained. For the communication configuration, the structural elements of the data format and the rule set of the verification mechanism are integrated and a unified parameter group is generated. The unified parameter group is loaded into the interface device and the configuration fusion is determined.

4. The intelligent identification and conversion control method for charging interfaces of multi-standard charging piles according to any one of claims 1-3, characterized in that, Step S106 includes: Confirm the feedback signal, establish a communication link connection with the electric vehicle, obtain link status parameters from the communication link connection, and determine the connection stability threshold based on the link status parameters; For the aforementioned communication link connection, a stable data transmission channel is used to obtain load demand data from the electric vehicle end, and the transmission quality is judged by the connection stability threshold to obtain the maximum capacity index. Based on the load demand data, determine the rate requirement value, and combine it with the maximum capacity index to obtain the demand matching characteristics; From the demand matching features, the load demand priority is determined, and the transmission allocation scheme of the electric vehicle end is obtained according to the load demand priority to form the load demand data optimization result.

5. The intelligent identification and conversion control method for charging interfaces of multi-standard charging piles according to any one of claims 1-3, characterized in that, Step S108 includes: Obtain load demand data, which is used to establish the power supply path for the power control module; Determine the upper and lower limits that meet the threshold from the preset rated output power range; By comparing the load demand data with the upper and lower limits of the rated output power range, the consistency of the demand is determined, and a consistency result is obtained. If the load demand data matches the consistency result, the switching unit in the power control module is activated to obtain the initial power supply path; By adjusting the power supply parameters in the initial power supply path through the adjustment unit, the power supply parameters are then output to the load device from the adjusted power supply path.

6. The intelligent identification and conversion control method for charging interfaces of multi-standard charging piles according to any one of claims 1-3, characterized in that, Step S109 includes: The current intensity data and voltage level data during the charging process are collected by sensors to obtain peak, average and fluctuation amplitudes; An abnormal current is determined from the peak value and the average value. If the peak value exceeds a preset threshold or the average value deviates from the standard range, the charging rate is adjusted by reducing the input power to obtain a stable current distribution. Voltage stability is calculated based on the difference in the fluctuation amplitude between consecutive sampling points. If the voltage stability is lower than a preset threshold, the adjusted voltage level data is obtained from the stable current distribution. The fluctuation range is determined from the adjusted voltage level data, and the stability of the fluctuation range relative to the voltage level data is obtained. If the stability meets the preset threshold, the charging process is completed using the stable current distribution and the voltage level data.

7. The intelligent identification and conversion control method for charging interfaces of multi-standard charging piles according to any one of claims 1-3, characterized in that, Step S1010 includes: Acquire current intensity data and voltage level data from sensors; Determine whether the current intensity data and voltage level data exceed a preset safety threshold to identify an abnormal state; If the abnormal state exists, a safe isolation is obtained for the power supply connection disconnection circuit path; The security isolation is used to interrupt the transmission path of the current intensity data and voltage level data; The abnormal state is determined by comparing the current intensity data and voltage level data with a preset safety threshold. The preset safety threshold is pre-established for comparison of the current intensity data and voltage level data.

8. The intelligent identification and conversion control method for charging interfaces of multi-standard charging piles according to any one of claims 1-3, characterized in that, Step S1011 includes: Based on the result of disconnecting the power supply connection, abnormal fluctuation data and power outage time are obtained from abnormal fluctuation capture and power outage time marker to obtain the power outage record basis; Based on the aforementioned power outage records, log data integration and result anomaly correlation methods are used to determine the correlation between abnormal fluctuation data and the moment of power outage, and to determine the backup log path containing power supply result analysis. Obtain the integrated data from the backup log path to get the final power outage log.

9. The intelligent identification and conversion control method for charging interfaces of multi-standard charging piles according to any one of claims 1-3, characterized in that, Step S1012 includes: Analyze the power outage logs to obtain a summary of anomaly categories; Frequency statistics are extracted from the anomaly classification summary to generate anomaly classification results; The anomaly pattern distribution is obtained by performing pattern induction on the categories in the anomaly classification summary. Based on the distribution of the abnormal patterns, the database specification is refreshed using log association mapping. Associate the distribution items in the abnormal pattern distribution with the specification items in the database specification refresh to determine the charging interface verification standard; If the charging interface verification standard exceeds the preset threshold, the model identification calibration will be optimized by comparing device specifications. The matching template iteration after feature threshold fine-tuning is obtained by applying the comparison results from the device specification comparison to the identification items in the model identification calibration.

Images