A charging protection method and a charging socket

Abstract

This application discloses a charging protection method, relating to the field of charging protection technology. The method includes: acquiring temperature data of a key heat-generating area monitored by a fiber optic temperature sensor in a charging socket; the fiber optic temperature sensor comprising a fiber Bragg grating temperature probe and a fiber Bragg grating demodulator; the fiber Bragg grating temperature probe comprising an aluminum nitride ceramic capillary, a fiber Bragg grating, a glass fiber sheath, and an FC / APC connector; the key heat-generating area being the location near the power supply copper sheet inside the charging socket; determining the operating state of the charging socket corresponding to the temperature data, and determining a control command for the charging socket based on the operating state; and adjusting the charging power of the charging socket based on the control command. In this way, the temperature data of the power supply copper sheet inside the charging socket can be accurately monitored, allowing for adjustment of the charging power based on the temperature data, thereby significantly improving charging safety.

Description

Technical Field

[0001] This application relates to the field of charging protection technology, and in particular to a charging protection method and a charging socket. Background Technology

[0002] During the charging process of high-power devices, the charging socket, as a key interface for power transmission, will generate heat due to the current passing through it. In particular, critical parts such as the power supply copper plates are prone to abnormal temperature rise due to the presence of resistance, especially during long-term or high-current charging. Excessive temperature will accelerate the aging of the internal insulation materials of the charging socket, reduce its insulation performance, increase the risk of short circuits and leakage, and in severe cases may even cause safety accidents such as fires, threatening personal and property safety.

[0003] However, traditional charging sockets typically use electrical temperature sensors such as negative temperature coefficient thermistors and PT100. While these sensors can monitor temperature to some extent, they are susceptible to electromagnetic interference. In the strong magnetic environment of a charging socket, the accuracy and reliability of the measurement results are easily affected. Furthermore, these sensors have relatively slow response times, making it difficult to capture rapid temperature changes in a timely manner. Their installation location and wiring methods are also limited, making it difficult to accurately monitor the temperature of critical heat-generating areas such as the power supply copper sheet. In addition, most traditional sensors are electrically connected, posing an electrical connection risk to high-voltage circuits, which is detrimental to safety monitoring. Summary of the Invention

[0004] This application provides a charging protection method and a charging socket to solve the following technical problem: how to accurately monitor the temperature data of the power supply copper sheet inside the charging socket, so as to adjust the charging power of the charging socket according to the temperature data, and significantly improve the charging safety.

[0005] In a first aspect, embodiments of this application provide a charging protection method, the method comprising: The temperature data of the key heat-generating area monitored by the fiber optic temperature sensor in the charging socket is obtained. The fiber optic temperature sensor includes a fiber optic grating temperature probe and a fiber optic grating demodulator. The fiber optic grating temperature probe includes an aluminum nitride ceramic capillary, a fiber optic grating, a glass fiber sheath, and an FC / APC connector. The key heat-generating area is the location near the power supply copper sheet inside the charging socket. Determine the operating state of the charging socket corresponding to the temperature data, and determine the control command for the charging socket based on the operating state of the charging socket; The charging power of the charging socket is adjusted based on the control command.

[0006] Secondly, this application embodiment also provides a charging socket, which includes a power supply copper sheet, an optical fiber temperature sensor, and a controller; The power supply copper sheet is used to electrically connect to the charging plug and transmit electrical energy. The fiber optic temperature sensor includes a fiber optic grating temperature probe and a fiber optic grating demodulator, used to monitor temperature data near the power supply copper sheet. The fiber optic grating temperature probe includes an aluminum nitride ceramic capillary, a fiber optic grating, a glass fiber sheath, and an FC / APC connector. The controller is used to determine the operating state of the charging socket based on temperature data near the power supply copper sheet monitored by the fiber optic temperature sensor in the charging socket, and to determine the control command of the charging socket based on the operating state of the charging socket, so as to adjust the charging power of the charging socket based on the control command.

[0007] The embodiments of this application have the following beneficial effects: First, a high-precision fiber Bragg grating temperature probe containing an aluminum nitride ceramic capillary (combined with a glass fiber sheath for thermal conductivity and FC / APC connector sealing) is used to accurately acquire temperature data of the critical heat-generating area near the power supply copper sheet, which is most prone to overheating. This solves the problems of electromagnetic interference, installation limitations, and monitoring lag associated with traditional electrical sensors, ensuring the real-time accuracy of temperature data and laying a reliable foundation for subsequent intelligent decision-making. Next, the temperature data is analyzed to accurately determine the current operating status of the charging socket. Based on the current operating status, appropriate control commands are dynamically generated to ensure that the charging socket operates within a safe temperature range while maximizing the use of charging resources, improving charging speed and energy utilization. This not only solves the technical bottleneck of high-temperature protection for charging sockets but also achieves a dual improvement in safety and user experience through intelligent adjustment. Attached Figure Description

[0008] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 A flowchart of a charging protection method provided in an embodiment of this application; Figure 2 This is a schematic diagram of a charging socket structure provided in an embodiment of this application; Figure 3 This is a schematic diagram showing the disassembled structure of each component of the charging socket provided in the embodiments of this application; Figure 4 This is a top view of the charging socket provided in the embodiment of this application; Figure 5 This is a schematic diagram of the internal structure of the fiber optic grating temperature probe provided in the embodiments of this application. Detailed Implementation

[0009] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0010] It is understood that in the embodiments of this disclosure, data related to user information (such as user accounts) is involved. When the embodiments of this disclosure are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data must comply with relevant laws, regulations and standards.

[0011] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. The terminology used herein is for the purpose of describing embodiments of this disclosure only and is not intended to be limiting of this disclosure.

[0012] In the following description, the terms “first, second, ...” are used merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that “first, second, ...” may be interchanged in a specific order or sequence where permitted, so that the embodiments of this disclosure described herein can be implemented in an order other than that illustrated or described herein.

[0013] During the charging process of high-power devices, the charging socket, as a key interface for power transmission, will generate heat due to the current passing through it. In particular, critical parts such as the power supply copper plates are prone to abnormal temperature rise due to the presence of resistance, especially during long-term or high-current charging. Excessive temperature will accelerate the aging of the internal insulation materials of the charging socket, reduce its insulation performance, increase the risk of short circuits and leakage, and in severe cases may even cause safety accidents such as fires, threatening personal and property safety.

[0014] However, traditional charging sockets typically use electrical temperature sensors such as negative temperature coefficient thermistors and PT100. While these sensors can monitor temperature to some extent, they are susceptible to electromagnetic interference. In the strong magnetic environment of a charging socket, the accuracy and reliability of the measurement results are easily affected. Furthermore, these sensors have relatively slow response times, making it difficult to capture rapid temperature changes in a timely manner. Their installation location and wiring methods are also limited, making it difficult to accurately monitor the temperature of critical heat-generating areas such as the power supply copper sheet. In addition, most traditional sensors are electrically connected, posing an electrical connection risk to high-voltage circuits, which is detrimental to safety monitoring.

[0015] Based on this, this application provides a charging protection method that can accurately monitor the temperature data of the power supply copper sheet inside the charging socket, and adjust the charging power of the charging socket according to the temperature data, thereby significantly improving the safety of charging.

[0016] The technical solutions proposed in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0017] Figure 1 This is a flowchart illustrating a charging protection method provided in an embodiment of this application. Figure 1 As shown in the figure, a charging protection method provided in this application embodiment specifically includes the following steps: Step 101: Obtain temperature data of the key heat-generating area monitored by the fiber optic temperature sensor in the charging socket.

[0018] Here, the fiber optic temperature sensor includes a fiber optic grating temperature probe and a fiber optic grating demodulator. The fiber optic grating temperature probe includes an aluminum nitride ceramic capillary, a fiber optic grating, a glass fiber sheath, and an FC / APC connector. The critical area for heat generation is the location near the power supply copper sheet inside the charging socket.

[0019] It should be noted that the aluminum nitride ceramic capillary is used to conduct the heat of the power supply copper sheet to the fiber Bragg grating. The fiber Bragg grating is used to sense temperature changes and generate corresponding wavelength signals. The glass fiber sheath is used to wrap the fiber Bragg grating and provide protection. The FC / APC connector is used to connect to the fiber Bragg grating demodulator. The fiber Bragg grating demodulator is used to emit broadband light to the fiber Bragg grating, receive the wavelength signals reflected by the fiber Bragg grating, and convert the wavelength signals into temperature data.

[0020] In some embodiments, step 101 described above can be implemented as follows: a broadband light beam is emitted to the fiber Bragg grating temperature probe through the fiber Bragg grating demodulator, and the reflected light of the broadband light at the fiber Bragg grating temperature probe is received by the fiber Bragg grating demodulator; based on the correspondence between the reflected wavelength and the temperature, the reflected wavelength of the reflected light is mapped to obtain the temperature data of the key heating area inside the charging socket.

[0021] Thus, by combining a fiber Bragg grating demodulator with a fiber Bragg grating temperature probe, and utilizing broadband light emission and reflection wavelength monitoring, temperature data of the critical heat-generating area (near the power supply copper plate) inside the charging socket can be acquired. This enables high-precision temperature measurement. Based on the stable correlation between reflected wavelength and temperature, the reflected wavelength can be accurately mapped to temperature data, precisely capturing temperature changes in the critical heat-generating area. This provides a solid and reliable data foundation for subsequently determining the charging socket's operating status. Furthermore, because optical fiber itself is insulated and resistant to electromagnetic interference, using optical fiber to transmit broadband and reflected light allows for measurement in the strong electromagnetic environment generated by the high current in the charging socket without being affected by electromagnetic interference, ensuring accurate measurement results. The fiber optic grating ensures high accuracy and reliability. Furthermore, its long-term stable performance makes it resistant to environmental factors such as humidity and dust, allowing for continuous and accurate temperature measurement during long-term operation of the charging socket. This reduces measurement deviations caused by changes in sensor performance, providing stable monitoring assurance for the long-term safe operation of the charging socket. Additionally, the fiber optic cable, acting as an insulator, avoids safety risks associated with electrical connections, preventing the introduction of additional safety hazards during monitoring and ensuring safety during both measurement and charging. Moreover, the fiber optic grating temperature probe's aluminum nitride ceramic capillary, glass fiber sheath, and FC / APC connector structure enable it to adapt to harsh environments such as high temperatures and humidity around the charging socket, ensuring stable and reliable operation.

[0022] It should be noted that the correspondence between the reflected wavelength and temperature can be a preset linear function mapping, or a nonlinear or other mapping relationship, without specific limitations here.

[0023] As an example, suppose the charging socket handles a high-current transmission task. During charging, the copper supply plate generates a significant amount of heat due to the current flow, making it a critical heat-generating area. A fiber Bragg grating (FBG) temperature probe is installed inside the charging socket, close to the copper supply plate. The FBG temperature probe comprises an aluminum nitride ceramic capillary, a fiber grating, a glass fiber sheath, and an FC / APC connector. The aluminum nitride ceramic capillary, in close contact with the copper supply plate, effectively conducts heat to the fiber grating. The glass fiber sheath protects the fiber grating. The FC / APC connector is used to connect external devices. The FBG demodulator is installed in a suitable location near the charging socket. The optical signal is connected to the FC / APC connector of the fiber optic temperature probe via an optical fiber to ensure stable transmission. At this time, the entire system is in standby mode, ready to monitor the temperature during the charging process. When the connection between the charging socket and the charging device is detected, the fiber optic demodulator emits a broadband light beam to the fiber optic temperature probe. This broadband light beam is transmitted through the optical fiber, enters the fiber optic temperature probe through the FC / APC connector, and finally reaches the fiber optic grating. The broadband light contains light of various wavelengths, providing rich spectral information for subsequent temperature measurement. Then, when the broadband light shines on the fiber optic grating, a portion of the light is reflected back. As charging is in progress, the copper power supply plate generates heat due to the current flowing through it. This heat is conducted to the fiber optic grating through the aluminum nitride ceramic capillary, causing a change in the grating's temperature. This results in a corresponding change in the wavelength of the reflected light. The reflected light returns along the original fiber path and is transmitted to the fiber optic demodulator via the FC / APC connector and the fiber. After receiving this reflected light, the demodulator maps the reflected wavelength based on the correlation between the reflected wavelength and temperature, obtaining the temperature data of the key emission area within the charging socket. For example, if the wavelength of the reflected light shifts by a specific value, the demodulator can calculate the corresponding temperature change based on calibration data. Combined with parameters such as the initial temperature of the environment where the charging socket is located, the accurate temperature data of the key heat-generating area (near the copper power supply plate) within the charging socket can be obtained. During the charging process, the fiber Bragg grating temperature probe continuously senses the temperature change of the power supply copper sheet. As charging progresses, the temperature of the power supply copper sheet may change continuously due to factors such as current magnitude and charging time. Whenever the temperature changes, the reflected wavelength of the fiber Bragg grating will also change accordingly. At this time, the fiber Bragg grating demodulator will continuously emit broadband light and receive the reflected light, repeatedly performing the above wavelength mapping and temperature calculation process to monitor the temperature data of the key heat-generating areas inside the charging socket in real time.

[0024] Step 102: Determine the working status of the charging socket corresponding to the temperature data.

[0025] It should be noted that the temperature data can be determined directly based on the preset correspondence between temperature data and working status, or it can be achieved through a pre-trained model; no specific limitations are made here.

[0026] In some embodiments, step 102 described above can be implemented as follows: mapping the temperature data to obtain a first score for the charging socket; obtaining the ambient temperature of the charging socket and mapping the ambient temperature to obtain a second score for the charging socket; weighting and summing the first score and the second score based on a preset first weight to obtain a comprehensive score for the charging socket; and mapping the comprehensive score of the charging socket based on a preset rule table of working states to obtain the working state of the charging socket.

[0027] Thus, the system first maps the charging socket's own temperature data and ambient temperature data to obtain a first score and a second score, respectively. This allows for independent consideration of the charging socket's internal heat generation and the impact of external ambient temperature, comprehensively and meticulously capturing key factors affecting the charging socket's operating status and avoiding the limitations of single-factor judgment. Then, based on a preset first weight, the two scores are weighted and summed to obtain a comprehensive score. This comprehensively score reasonably balances the influence of internal temperature and external ambient temperature on the charging socket's operating status, making it more scientific and accurate in reflecting the actual operating condition of the charging socket. Subsequently, the comprehensive score is mapped according to a preset rule table for operating status to determine the operating status. This transforms complex temperature data and overall conditions into clear and specific operating status categories, achieving an intuitive and efficient conversion from data to actual operating status. This facilitates the rapid implementation of targeted control measures based on different operating statuses, ensuring charging safety and efficiency. Furthermore, this rule-based judgment method is logically clear, easy to understand and implement, and can improve the system's stability and reliability. It effectively solves the problem that relying solely on a single temperature threshold to judge the operating status is not comprehensive or flexible enough, thereby improving the intelligence and accuracy of charging socket management.

[0028] As an example, suppose the charging socket detects a connection to the charging device, and the fiber optic temperature probe inside the charging socket monitors the temperature near the power supply copper plate in real time, obtaining a temperature of 60℃. According to a pre-set mapping rule between temperature and score, for example, a temperature between 40℃ and 50℃ scores 20 points, between 50℃ and 60℃ scores 40 points, and between 60℃ and 70℃ scores 60 points, the first score corresponding to the temperature data of 60℃ is 60 points. Next, the ambient temperature sensor installed in the charging station detects the current ambient temperature as 38℃. According to the preset mapping rule for ambient temperature, for example, an ambient temperature between 20℃ and 30℃ scores 10 points, between 30℃ and 40℃ scores 20 points, and between 40℃ and 50℃ scores 30 points. According to this rule, the second score corresponding to an ambient temperature of 38℃ is 20 points. Then, the preset first weight is obtained as 7:3. The first score and the second score are weighted and summed according to the first weight to obtain a comprehensive score of 48 points. After that, the working state corresponding to different comprehensive score ranges is determined according to the preset rule table. For example, a comprehensive score between 0 and 20 points is a low-power charging state, 20 to 40 points is the optimal charging state, 40 to 65 points is a high-power charging state, and above 65 points is a high-temperature warning charging state. According to the rule table, the working state of the charging socket at this time is a high-power charging state.

[0029] In some embodiments, step 102 described above can also be implemented by: extracting features from the temperature data to obtain a first feature of the charging socket; obtaining the ambient temperature of the charging socket and extracting features from the ambient temperature of the charging socket to obtain a second feature of the charging socket; and predicting the working state of the charging socket based on the first feature and the second feature to obtain the working state of the charging socket.

[0030] Thus, by extracting features from the charging socket's own temperature data and ambient temperature data, and predicting the charging socket's operating status based on these features, hidden information within the temperature data can be uncovered. Compared to directly using raw temperature data, the extracted features can more accurately and comprehensively reflect the charging socket's thermal state and operating status, providing a more representative basis for operating status prediction. Simultaneously considering ambient temperature characteristics allows for a comprehensive analysis of the impact of internal and external factors on the charging socket's operating status, avoiding the one-sidedness of relying solely on its own temperature and making the prediction results more consistent with reality. Furthermore, feature-based operating status prediction can utilize advanced machine learning or data analysis methods to uncover the complex relationship between features and operating status, achieving more accurate and intelligent predictions. This enables timely detection of potential temperature anomalies and risks, allowing for proactive responses and ensuring charging safety and efficiency. Moreover, this method has good scalability and adaptability, flexibly adjusting the feature extraction method and prediction model according to different charging scenarios and data characteristics, improving the intelligence and reliability of charging socket management.

[0031] As an example, suppose that when the charging socket detects a connection to a charging device, the fiber optic temperature probe inside the charging socket monitors the temperature near the power supply copper plate in real time, and the obtained temperature data is... By extracting features from the temperature data, the first feature of the charging socket can be obtained. Meanwhile, the ambient temperature sensor detected that the ambient temperature around the charging socket was... Furthermore, by extracting features from the ambient temperature, a second feature of the charging socket can be obtained. Subsequently, the trained work status prediction model is used to predict the first feature of the input. Second feature By predicting the working state of the charging socket, we can obtain the current working state of the charging socket.

[0032] In some embodiments, the above-mentioned prediction of the working state of the charging socket based on the first feature and the second feature can be achieved by: performing attention processing on the first feature and the second feature to obtain a second weight corresponding to each feature; fusing the first feature and the second feature based on the second weight to obtain a fused feature; performing a linear transformation on the fused feature to obtain a linear transformation result; performing activation processing on the linear transformation result to obtain the probability of the charging socket corresponding to different working states, and taking the working state corresponding to the highest probability as the working state of the charging socket.

[0033] Thus, by dynamically weighting and fusing the charging socket's own temperature characteristics with the first environmental characteristics through an attention mechanism, the model can automatically focus on the feature dimensions that have a greater impact on the current working state during processing. This overcomes the limitations of fixed weights or simple feature splicing, and improves the targeting and effectiveness of feature expression. Furthermore, after linear transformation and activation processing, the fused features output the probability distribution of each working state, which can realize a nonlinear mapping from multi-source features to working states. This can capture the complex correlations between features, improve the accuracy and robustness of prediction, and finally take the working state corresponding to the highest probability as the judgment result. This makes the decision-making process smoother, more continuous, and interpretable, avoiding the mechanical nature of traditional hard threshold judgment. It can predict the trend of state change in advance based on subtle feature changes, thereby maintaining efficient charging more flexibly while ensuring charging safety. This significantly improves the intelligence level and dynamic adaptability of charging socket working state prediction.

[0034] Continuing with the example above, suppose the first feature of the charging socket is obtained through feature extraction. Second feature The first feature can be obtained through attention calculation (such as self-attention). The corresponding second weight Second feature The corresponding second weight Next, the first weight and the second feature are weighted and fused according to the second weight to obtain the fused feature. for Subsequently, a fully connected layer is used to perform a linear transformation on the fused features to reduce the dimensionality of the fused features and obtain the linear transformation result. The linear transformation result is then activated by an activation function (such as the Sigmoid function) to obtain the probability of the charging socket corresponding to different working states, for example, the probability is {low efficiency charging state: 0.01, optimal charging state: 0.95, high efficiency charging state: 0.02, high temperature warning charging state: 0.02}.

[0035] In some embodiments, the operating state prediction is obtained through a trained prediction model. Before performing feature extraction on the temperature data to obtain the first feature of the charging socket, the following processing may also be performed: extracting features from historical temperature data of key heat-generating areas within the charging socket to obtain the third feature of the charging socket, and extracting features from historical ambient temperatures of the charging socket corresponding to the historical temperature data to obtain the fourth feature of the charging socket; using an initialized prediction model, predicting the operating state of the charging socket based on the third and fourth features to obtain the predicted probability of the charging socket for each operating state; determining the model loss based on the difference between the predicted probability of the charging socket for each operating state and the reference probability of the charging socket for each operating state, and updating the initialized prediction model based on the model loss to obtain the trained prediction model.

[0036] Thus, by extracting features from historical temperature data and ambient temperature data of the key heat-generating areas of the charging socket, the third and fourth features are obtained respectively. An initial prediction model is then used to predict the probability of each operating state based on these two sets of features. The model loss is calculated by combining the difference with the actual reference probability, and iterative updates are performed to obtain a trained prediction model. By utilizing historical operating data to mine long-term accumulated temperature change patterns and the influence of environmental conditions, the model can learn typical characteristic patterns under different operating conditions, improving prediction accuracy and generalization ability, avoiding occasional misjudgments caused by relying solely on real-time data. Feature extraction transforms raw data into more representative information, helping the model grasp the core factors affecting operating states and reduce noise. By mitigating acoustic interference and incorporating long-term environmental temperature characteristics, the model can gain a more comprehensive understanding of the impact of external conditions on the thermal state of the charging socket, enhancing its adaptability under complex seasonal and climatic conditions. Furthermore, by calculating the loss and updating the model using the difference between predicted and reference probabilities, data-driven continuous optimization can be achieved. The model can continuously improve itself over time, promptly reflecting new patterns brought about by equipment aging and environmental changes, ensuring the long-term reliability of prediction results. The final trained model can quickly and accurately output the probability distribution of each working state based on the real-time extracted third and fourth features, providing a reliable basis for the generation of subsequent control commands. This enables intelligent and refined management of the charging process, significantly improving charging safety, efficiency, and system operation and maintenance levels.

[0037] It should be noted that the model loss can be calculated using the cross-entropy loss function, log loss function, and focus loss function, and no specific limitation is made here.

[0038] As an example, suppose we obtain historical temperature data of the key heat-generating areas of the charging socket over a period of time. and the corresponding historical ambient temperature By analyzing historical temperature data and historical environmental temperature By performing feature extraction separately, the third feature of the charging socket can be obtained. and the fourth feature Subsequently, based on the third feature and the fourth feature Predicting the operating state of a charging socket yields the predicted probability for each operating state. Then, based on the predicted probability of each working state... Reference probability for each working state The difference between them, calculate the model loss Taking the cross-entropy loss function as an example, the model loss can be calculated using formula (1). Finally, the model parameters are updated by backpropagation based on the model loss, thus obtaining the trained prediction model.

[0039] (1) Where N represents the number of working states of the charging socket. Let be the reference probability that the charging socket is in the i-th working state. This represents the predicted probability that the charging socket is in the i-th working state.

[0040] Here's an explanation of backpropagation: Historical temperature data of the charging socket and historical ambient temperature are input into the input layer of the neural network model (prediction model), passing through the hidden layer, and finally reaching the output layer to output the result. This is the forward propagation process of the neural network model. Since there is an error between the output result of the neural network model and the actual result, the error between the calculated result and the actual value is propagated back from the output layer to the hidden layer until it reaches the input layer. During the backpropagation process, the values ​​of the model parameters are adjusted according to the error. The above process is iterated continuously until convergence.

[0041] Taking the cross-entropy loss function in this embodiment as an example, the server determines the model loss based on the cross-entropy loss function, backpropagates the model loss from the output layer in the prediction model, and backpropagates the model loss layer by layer. When the model loss reaches each layer, the gradient (that is, the partial derivative of the cross-entropy loss function with respect to the parameters of each layer) is solved in combination with the propagated model loss, and the corresponding gradient value of the parameters of each layer is updated.

[0042] In some embodiments, step 102 described above can also be implemented in the following ways: In response to the temperature data being less than or equal to a first threshold, the operating state of the charging socket is determined to be a low-power charging state, where the first threshold is the optimal operating temperature lower limit of the charging socket; in response to the temperature data being greater than or equal to a second threshold and less than a third threshold, the operating state of the charging socket is determined to be a high-power charging state, where the second threshold is the optimal operating temperature upper limit of the charging socket and the third threshold is the warning operating temperature value of the charging socket; in response to the temperature data being greater than the first threshold and less than the second threshold, the operating state of the charging socket is determined to be an optimal charging state; in response to the temperature data being greater than or equal to the third threshold, the operating state of the charging socket is determined to be a high-temperature warning charging state.

[0043] Thus, by setting a first threshold (lower limit of optimal operating temperature), a second threshold (upper limit of optimal operating temperature), and a third threshold (warning operating temperature value), the temperature data of the charging socket is divided into four intervals, which are mapped to four operating states: low power, high power, optimal, and high temperature warning. This transforms continuously changing temperature data into discrete and semantically clear operating states, making the control logic clear and intuitive, facilitating engineering implementation and on-site maintenance. Furthermore, the division into four intervals fully considers the performance and risk characteristics of the charging socket in different temperature ranges. When the temperature is below the optimal lower limit, it enters a low-power state to avoid material stress or efficiency reduction caused by low temperatures. When the temperature is within the optimal range, it enters the optimal state to maximize charging efficiency and equipment lifespan. When the temperature exceeds the optimal upper limit but does not reach the warning value, the system enters a high-power state to maintain relatively fast charging within a safe range. When the temperature reaches or exceeds the warning value, it immediately enters a high-temperature warning state to trigger protective measures such as power reduction, heat dissipation, or power outage. This achieves a balance between safety and efficiency across the entire range from low to high temperatures. Furthermore, by clearly defining the correspondence between thresholds and states, this application enables the system to adopt the most suitable operating strategy under different temperature conditions. This prevents charging efficiency loss caused by overly conservative power limiting and safety hazards caused by excessively high temperatures, thereby significantly improving the stability, intelligence, and user experience of the charging process. At the same time, the configurability of the threshold parameters provides good adaptability and scalability for charging sockets of different models and in different usage environments.

[0044] As an example, assuming the first threshold (lower limit of optimal operating temperature) is 25℃, the second threshold (upper limit of optimal operating temperature) is 45℃, and the third threshold (warning operating temperature) is 60℃, by monitoring the temperature data of the charging socket, when the temperature data is ≤25℃, the current operating state of the charging socket is determined to be low-power charging state; when 25℃ < temperature data < 45℃, the current operating state of the charging socket is determined to be optimal charging state; when 45℃ ≤ temperature data < 45℃, the current operating state of the charging socket is determined to be high-power charging state; and when the temperature data is ≥60℃, the current operating state of the charging socket is determined to be high-temperature warning charging state.

[0045] Step 103: Determine the control command for the charging socket based on its working state.

[0046] It should be noted that control commands refer to operation commands or signals generated by the control system and sent to the charging power adjustment unit to change or maintain charging behavior, such as increasing charging power or maintaining the current charging power.

[0047] In some embodiments, step 103 described above can be implemented in the following ways: in response to the charging socket being in a low-power charging state, increasing the charging power is used as a control command for the charging socket; in response to the charging socket being in a high-power charging state, decreasing the charging power is used as a control command for the charging socket; in response to the charging socket being in an optimal charging state, maintaining the current charging power is used as a control command for the charging socket; in response to the charging socket being in a high-temperature warning charging state, decreasing the charging power, initiating heat dissipation, or forcibly cutting off power is used as a control command for the charging socket.

[0048] In this way, by clearly defining the correspondence between working states and control commands, the operating strategy of the charging socket under different temperature conditions is transformed into specific executable hardware operations, realizing direct closed-loop control from state determination to action execution. When the temperature is below the optimal lower limit, the charging power is increased as a command, allowing the charging socket to gradually rise to the optimal operating range, thus avoiding long-term inefficient operation at low temperatures or damage caused by excessive cold resistance. When the temperature is within the optimal range, the current power is maintained to ensure that charging efficiency and equipment lifespan are maximized simultaneously. When the temperature is above the optimal upper limit but has not reached the warning value, the power is reduced to slow down the temperature rise. It maintains relatively fast charging within the safety boundary; when the temperature reaches or exceeds the warning value, it takes comprehensive measures such as reducing power, starting heat dissipation, or forcibly cutting off power to curb the risk of overheating in the first instance and prevent serious accidents such as insulation aging, short circuit or fire. Moreover, the control command design based on state mapping has clear logic, rapid response and strong executability. It can prevent the rigidity and lag of single threshold control and achieve a dynamic balance between safety and efficiency across the entire temperature range. It can significantly improve the intelligent management level and operational reliability of the charging socket, while providing a clear framework and configurability for future expansion of more state or command combinations.

[0049] As an example, when the charging socket is in a low-power charging state, the control instruction for the charging socket is determined to be to increase the charging power (e.g., increase the charging voltage, increase the charging current, etc.); when the charging socket is in an optimal charging state, the control instruction for the charging socket is determined to be to maintain the current charging power (e.g., keep the charging voltage and charging current unchanged); when the charging socket is in a high-power charging state, the control instruction for the charging socket is determined to be to decrease the charging power (e.g., decrease the charging voltage, decrease the charging current, etc.); when the charging socket is in a high-temperature warning charging state, the control instruction for the charging socket is determined to be to decrease the charging power and start heat dissipation or force power off.

[0050] Step 104: Adjust the charging power of the charging socket based on the control command.

[0051] It should be noted that the charging power of the charging socket can be adjusted by regulating the current and voltage of the charging socket according to the control command through algorithms such as PID control or fuzzy control.

[0052] As an example, assuming the control command is to increase charging power, the controller will control the charging socket to increase the output voltage from 200V to 300V and the output current from 10A to 16A, thus increasing the charging socket's power from 2kW to 4.8kW. Simultaneously, the system continuously monitors the temperature. As the power increases, the copper plates begin to heat up with the increased current. After 10 minutes of charging, the charging socket's operating state transitions to the optimal charging state. At this point, the control command is updated to maintain the current charging power. Therefore, the controller will stop increasing the voltage / current, maintaining an output power of 300V / 16A (4.8kW), ensuring charging continues within the optimal efficiency range. Assuming... As the afternoon temperature rises and the charging device approaches full charge, the increased charging current causes the temperature to rise. The charging socket switches to high-power charging mode, and the control command is updated to reduce the charging power. The controller then reduces the output voltage from 300V to 250V and the output current from 16A to 12A, reducing the charging power of the charging socket from 4.8kW to 3kW to slow down the temperature rise. If the temperature continues to rise, the charging socket switches to high-temperature warning charging mode. At this time, the control command is updated to reduce power, activate heat dissipation, and cut off power if necessary. The controller immediately reduces the charging power by 1.5kW and activates the cooling fan. If the temperature continues to rise, the charging circuit is cut off to ensure charging safety.

[0053] In some embodiments, after performing step 104 above, the following processing may also be performed: obtaining historical monitoring data of the charging socket; and performing a health assessment of the charging socket based on the historical monitoring data to obtain a health assessment report of the charging socket.

[0054] Thus, by acquiring historical monitoring data of charging sockets and conducting health assessments based on this data to generate health assessment reports, an upgrade from simple real-time control to long-term equipment status management can be achieved. Historical monitoring data covers temperature, power, environmental parameters, and protection trigger records during long-term operation, comprehensively reflecting the aging trend, potential defects, and performance degradation of charging sockets. This allows health assessments to diagnose based on long-term behavioral patterns rather than relying solely on instantaneous conditions, thereby identifying hidden problems such as gradual changes in contact resistance, insulation material fatigue, and decreased heat dissipation capacity earlier. Furthermore, assessments based on historical monitoring data can quantify the remaining lifespan, reliability level, and risk points of the sockets, generating structured health assessment reports that provide maintenance personnel with clear recommendations for maintenance, repair, or replacement. This drives a shift in management from "reactive maintenance" to "predictive maintenance," significantly reducing the rate of sudden failures and maintenance costs. Simultaneously, the assessment results can be fed back into control strategies, such as automatically adopting more conservative power curves for sockets with high aging risks, further improving the overall safety and operational efficiency of the system. This ensures that charging facilities maintain optimal performance throughout their entire lifecycle and supports charging network operators in asset optimization and resource planning.

[0055] It should be noted that historical monitoring data can include parameters such as voltage, current, and temperature, without any specific limitations.

[0056] As an example, the operation and maintenance system storage unit or cloud database retrieves historical monitoring data of the charging socket from the past 12 months, including temperature data (temperature curves near the power supply copper sheet during each charging process, including maximum temperature, average temperature, heating rate, temperature fluctuation amplitude, etc.), power and current / voltage data (output power changes, peak current, and voltage fluctuation records for each charging session), environmental and operating condition data (ambient temperature, humidity, charging duration, and charging frequency statistics during charging), and protection event records (time and reason for system-triggered power reduction, heat dissipation activation, power outage, and other protection actions). Then, a health assessment module performs a health assessment on the historical monitoring data. For example, analysis of temperature data reveals that in the past three months, under the same ambient temperature, the maximum temperature has increased by approximately 4°C compared to the average for the same period a year ago, and the temperature fluctuation amplitude has increased, suggesting that there may be slight oxidation or increased contact resistance at the contact points between the power supply copper sheet and the socket's interior. Analysis of power and current / voltage data shows that under full-load fast charging, the maximum stable output power has decreased from the initial 12kW to 11.2kW. Based on temperature trends, the heat dissipation capacity has slightly decreased. Analysis of protection event records shows that power reduction and heat dissipation trigger frequency have increased by 15% compared to the previous year, but the number of forced power outages has not increased, indicating that the safety protection system is still effective, but the equipment shows obvious signs of aging. Simultaneously, a lifespan and risk prediction for the charging socket indicates that if the current usage intensity is maintained, the frequency of high-temperature warnings may further increase within the next 6 months, and maintenance is recommended in the next quarter. The system will then integrate the above analysis results to generate a structured health assessment report, which may include: Current health level: rated as good, but showing signs of aging; Key findings: increased contact resistance of the power supply copper sheet, decreased heat dissipation efficiency, and increased number of high-temperature triggers; Remaining lifespan estimate: safe operation for approximately 6-9 months under current conditions; Maintenance recommendations: clean the contact surface of the power supply copper sheet, inspect the cooling fan and air duct, replace the aging fiber optic temperature probe sleeve, and schedule a comprehensive inspection within the next 3 months; Risk warning: if high-load fast charging continues during the summer high temperatures, it is recommended to appropriately reduce the power limit to extend lifespan. The above are embodiments of the method proposed in this application. Based on the same inventive concept, embodiments of this application also provide a charging socket, the structure of which is as follows: Figure 2 As shown.

[0057] Figure 2 This is a schematic diagram of a charging socket structure provided in an embodiment of this application. Figure 2 As shown, the charging socket 20 includes a power supply copper sheet 201, an optical fiber temperature sensor 202, and a controller 203; The power supply copper sheet 201 is used to electrically connect to the charging plug and transmit electrical energy. The fiber optic temperature sensor 202 is used to monitor the temperature data near the power supply copper sheet 201. The fiber optic temperature sensor 202 includes a fiber optic grating temperature probe 2021 and a fiber optic grating demodulator 2022. The fiber optic grating temperature probe 2021 includes an aluminum nitride ceramic capillary, a fiber optic grating, a glass fiber sheath, and an FC / APC connector. The controller 203 is used to determine the working state of the charging socket based on the temperature data of the location near the power supply copper plate 201 monitored by the fiber optic temperature sensor 202 in the charging socket, and to determine the control command of the charging socket based on the working state of the charging socket, so as to adjust the charging power of the charging socket based on the control command.

[0058] In some embodiments, see Figure 3 , Figure 3 This is a schematic diagram showing the disassembled structure of the components of the charging socket provided in the embodiments of this application, as shown below. Figure 3 As shown, the charging socket includes a fiber optic flange 301, a socket protective housing 302, a charging control module 303, an optical fiber 304, a fiber optic temperature probe 2021, and a clamping plate 305. The fiber optic flange 301 is used to connect the fiber optic temperature probe 2021 to the fiber optic demodulator in the charging control module 303. The charging control module 303 includes a power supply copper plate, a fiber optic demodulator, and a controller. The fiber optic temperature probe 2021 is fixed by the clamping plate 305.

[0059] In some embodiments, see Figure 4 , Figure 4 This is a top view of the charging socket provided in the embodiment of this application, as shown below. Figure 4 As shown, one end of the optical fiber 304 inside the optical fiber flange 301 is connected to the fiber grating demodulator inside the charging control module, and the other end is connected to the fiber grating temperature probe.

[0060] In some embodiments, see Figure 5 , Figure 5 This is a schematic diagram of the internal structure of the fiber Bragg grating temperature probe provided in the embodiments of this application, as shown below. Figure 5As shown, the fiber Bragg grating temperature probe consists of an aluminum nitride ceramic capillary 20211, a fiber Bragg grating 20213, a glass fiber sheath 20212, and an FC / APC connector 20214. The aluminum nitride ceramic capillary 20211 conducts heat from the power supply copper sheet to the fiber Bragg grating 20213. The fiber Bragg grating 20213 senses temperature changes and generates corresponding wavelength signals. The glass fiber sheath 20212 covers the fiber Bragg grating 20213 and provides protection. The FC / APC connector 20214 connects to a fiber Bragg grating demodulator. The fiber Bragg grating demodulator emits broadband light into the fiber Bragg grating 20213, receives the wavelength signals reflected by the fiber Bragg grating 20213, and converts the wavelength signals into temperature data.

[0061] In some embodiments, aluminum nitride ceramic capillaries are used. The theoretical thermal conductivity of aluminum nitride can reach 320 W / (m·K), while that of copper is approximately 401 W / (m·K). This material is insulating and has thermal conductivity similar to that of copper, quickly transferring heat from the surface of the copper power supply sheet to the internal fiber optic temperature sensing element. This ensures safety in the charging environment. Furthermore, aluminum nitride ceramics are non-magnetic and non-conductive, and they do not generate electromagnetic interference or are affected by strong external electromagnetic fields. This allows the entire temperature monitoring system to maintain stable and accurate measurement results even in the strong electromagnetic environment of high-power charging, providing a reliable data source for subsequent status judgment and power control. The fiber optic grating uses a 3mm long grating area for temperature measurement. This grating area occupies little space in the capillary ceramic tube, and the aluminum nitride ceramic capillary is easy to fix to the heating position of the socket. The opening of the aluminum nitride ceramic capillary is flared, and a glass fiber sleeve is inserted into the flared opening. The flared opening is sealed with high-temperature adhesive. In addition, the front panel of the charging socket has an opening for attaching a fiber optic flange. The jumper on the flange is used to connect to the fiber optic demodulator. If the fiber optic temperature probe is damaged, it can be replaced directly. It is plug-and-play. The fiber optic temperature probe is placed near the copper plate of the charging socket. If the charging current is too large and causes the socket to heat up, the temperature probe can detect the temperature change in time.

[0062] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments for IoT devices and media are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0063] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0064] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A charging protection method, characterized in that, The method includes: The temperature data of the key heat-generating area monitored by the fiber optic temperature sensor in the charging socket is obtained. The fiber optic temperature sensor includes a fiber optic grating temperature probe and a fiber optic grating demodulator. The fiber optic grating temperature probe includes an aluminum nitride ceramic capillary, a fiber optic grating, a glass fiber sheath, and an FC / APC connector. The key heat-generating area is the location near the power supply copper sheet inside the charging socket. Determine the operating state of the charging socket corresponding to the temperature data, and determine the control command for the charging socket based on the operating state of the charging socket; The charging power of the charging socket is adjusted based on the control command.

2. The method according to claim 1, characterized in that, The acquisition of temperature data of the key heat-generating area monitored by the fiber optic temperature sensor in the charging socket includes: A broadband light beam is emitted to the fiber optic temperature probe through the fiber optic demodulator, and the reflected light of the broadband light at the fiber optic temperature probe is received by the fiber optic demodulator. Based on the correspondence between reflected wavelength and temperature, the reflected wavelength of the reflected light is mapped to obtain the temperature data of the key heat-generating area inside the charging socket.

3. The method according to claim 1, characterized in that, Determining the operating state of the charging socket corresponding to the temperature data includes: The temperature data is mapped to obtain the first score of the charging socket; The ambient temperature of the charging socket is obtained and mapped to obtain a second score for the charging socket; Based on a preset first weight, the first score and the second score are weighted and summed to obtain the comprehensive score of the charging socket; Based on a preset rule table of working status, the comprehensive score of the charging socket is mapped to obtain the working status of the charging socket.

4. The method according to claim 1, characterized in that, Determining the operating state of the charging socket corresponding to the temperature data includes: Feature extraction is performed on the temperature data to obtain the first feature of the charging socket; The ambient temperature of the charging socket is obtained, and the ambient temperature of the charging socket is used to extract features to obtain the second feature of the charging socket; Based on the first feature and the second feature, the working state of the charging socket is predicted to obtain the working state of the charging socket.

5. The method according to claim 4, characterized in that, The step of predicting the working state of the charging socket based on the first feature and the second feature to obtain the working state of the charging socket includes: Attention processing is performed on the first feature and the second feature to obtain the second weight corresponding to each feature; Based on the second weight, the first feature and the second feature are fused to obtain the fused feature; The fused features are subjected to a linear transformation to obtain the linear transformation result; The linear transformation result is activated to obtain the probability of the charging socket corresponding to different working states, and the working state corresponding to the highest probability is taken as the working state of the charging socket.

6. The method according to claim 4, characterized in that, The working state prediction is obtained through a trained prediction model; Before performing feature extraction on the temperature data to obtain the first feature of the charging socket, the method further includes: Feature extraction is performed on the historical temperature data of the key heat-generating area inside the charging socket to obtain the third feature of the charging socket, and feature extraction is performed on the historical ambient temperature of the charging socket corresponding to the historical temperature data to obtain the fourth feature of the charging socket. Using the initialized prediction model, based on the third feature and the fourth feature, the working state of the charging socket is predicted to obtain the predicted probability of the charging socket for each working state. Based on the difference between the predicted probability of the charging socket for each working state and the reference probability of the charging socket for each working state, the model loss is determined, and the initialized prediction model is updated based on the model loss to obtain the trained prediction model.

7. The method according to claim 1, characterized in that, Determining the operating state of the charging socket corresponding to the temperature data includes: In response to the temperature data being less than or equal to a first threshold, the operating state of the charging socket is determined to be a low-power charging state, where the first threshold is the lower limit of the optimal operating temperature of the charging socket; In response to the temperature data being greater than or equal to a second threshold and the temperature data being less than a third threshold, the operating state of the charging socket is determined to be a high-power charging state, where the second threshold is the upper limit of the optimal operating temperature of the charging socket and the third threshold is the warning operating temperature of the charging socket. In response to the temperature data being greater than the first threshold and the temperature data being less than the second threshold, the working state of the charging socket is determined to be the optimal charging state; In response to the temperature data being greater than or equal to the third threshold, the operating state of the charging socket is determined to be a high-temperature warning charging state.

8. The method according to claim 1, characterized in that, The step of determining the control command for the charging socket based on its operating state includes: In response to the charging socket being in a low-power charging state, the charging power will be increased as a control command for the charging socket. In response to the charging socket being in a high-power charging state, a control command to reduce the charging power is given to the charging socket. In response to the charging socket being in the optimal charging state, the current charging power will be maintained as the control command for the charging socket. In response to the charging socket being in a high-temperature warning charging state, the control commands for the charging socket are to reduce the charging power, activate heat dissipation, or force power off.

9. A charging socket, characterized in that, The charging socket includes a power supply copper plate, an optical fiber temperature sensor, and a controller; The power supply copper sheet is used to electrically connect to the charging plug and transmit electrical energy. The fiber optic temperature sensor is used to monitor the temperature data near the power supply copper sheet. The fiber optic temperature sensor includes a fiber optic grating temperature probe and a fiber optic grating demodulator. The fiber optic grating temperature probe includes an aluminum nitride ceramic capillary, a fiber optic grating, a glass fiber sheath, and an FC / APC connector. The controller is used to determine the operating state of the charging socket based on temperature data near the power supply copper sheet monitored by the fiber optic temperature sensor in the charging socket, and to determine the control command of the charging socket based on the operating state of the charging socket, so as to adjust the charging power of the charging socket based on the control command.

10. The charging socket according to claim 9, characterized in that, The aluminum nitride ceramic capillary is used to conduct the heat of the power supply copper sheet to the fiber grating. The fiber grating is used to sense temperature changes and generate corresponding wavelength signals. The glass fiber sheath is used to cover the fiber grating and provide protection. The FC / APC connector is used to connect to the fiber grating demodulator. The fiber grating demodulator is used to emit broadband light to the fiber grating, receive the wavelength signals reflected by the fiber grating, and convert the wavelength signals into temperature data.

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