Battery charging control method and device for intelligent battery swap cabinet and intelligent battery swap cabinet

Abstract

The present invention provides a battery charging control method and device for an intelligent battery-swap cabinet, and an intelligent battery-swap cabinet, comprising: when the intelligent battery-swap cabinet detects that a battery is placed in a battery compartment for charging, the intelligent battery-swap cabinet adopts a preparatory charging mode to charge the battery and detects feedback data of the battery; obtains characteristic information of the battery, and obtains corresponding correction parameters based on the feedback data and the characteristic information; wherein the correction parameters are used to correct the preparatory charging mode; the preparatory charging mode is corrected based on the correction parameters to obtain a corrected charging mode, and the battery is charged in the corrected charging mode. In the present invention, the charging mode is dynamically adjusted based on battery feedback data and characteristic information, aiming to improve charging efficiency, ensure battery safety, and extend battery life.

Description

Technical Field

[0001] The present invention relates to the technical field of battery swap cabinets, and in particular to a battery charging control method and device for an intelligent battery swap cabinet and an intelligent battery swap cabinet. Background Art

[0002] In the field of smart battery swap cabinets, traditional battery charging control methods usually adopt a fixed charging strategy, that is, the same charging process and parameters are used for all batteries. Although this fixed charging method is simple and easy to use, there are many problems in practical applications. First, the characteristics of batteries of different brands and models, and even the same model of batteries in different use cycles, will vary. The fixed charging strategy cannot meet the optimal charging requirements of the battery, resulting in low charging efficiency and shortened battery life. Secondly, the battery will be affected by environmental factors such as temperature and humidity during use. The fixed charging strategy is difficult to adapt to these changes in real time, further affecting the charging effect. Summary of the invention

[0003] The main purpose of the present invention is to provide a battery charging control method, device and smart battery swap cabinet for an intelligent battery swap cabinet, aiming to overcome the defect that the current battery charging control method has no adaptive adjustment.

[0004] To achieve the above object, the present invention provides a battery charging control method for an intelligent battery swap cabinet, comprising the following steps:

[0005] When the intelligent battery-swapping cabinet detects that a battery is placed in the battery compartment for charging, it charges the battery in a preparatory charging mode and detects feedback data of the battery;

[0006] Acquire characteristic information of the battery, and obtain corresponding correction parameters based on the feedback data and the characteristic information; wherein the correction parameters are used to correct the preparatory charging mode;

[0007] The preparatory charging mode is corrected based on the correction parameter to obtain a corrected charging mode, and the battery is charged in the corrected charging mode.

[0008] Furthermore, the preparatory charging mode includes charging voltage, charging power and charging time; the feedback data includes voltage increment, internal temperature change, charging acceptance rate and battery resistance of the battery in the preparatory charging mode.

[0009] Further, obtaining corresponding correction parameters based on the feedback data and the characteristic information includes:

[0010] Inputting the characteristic information and feedback data into a pre-trained correction parameter prediction model; wherein the correction parameter prediction model includes a first prediction sub-model and a second prediction sub-model;

[0011] Analyzing the feature information based on the first prediction sub-model, and predicting a first correction parameter;

[0012] Analyzing the feedback data based on the second prediction sub-model, and predicting a second correction parameter;

[0013] The first correction parameter and the second correction parameter are fused and calculated to obtain the correction parameter.

[0014] Furthermore, obtaining characteristic information of the battery includes:

[0015] The charging interface of the battery is detected by an electrical signal probe on the intelligent battery exchange cabinet to obtain a detection signal, and characteristic information of the battery is obtained based on the detection signal.

[0016] Further, after charging the battery in the correction charging mode, the method further comprises:

[0017] Recording the change of the charge of the battery in the corrective charging mode in real time, and generating a charge change curve; Recording the change of the temperature of the battery in the corrective charging mode in real time, and generating a temperature change curve;

[0018] Based on the charge change curve and the temperature change curve, determining whether the battery is abnormally charged;

[0019] If the battery is charged abnormally, a preset adjustment strategy is used to adjust the corrective charging mode.

[0020] Further, after judging whether the battery is abnormally charged based on the power change curve and the temperature change curve, the method further includes:

[0021] If the battery is charged normally, the feedback data, the characteristic information and the correction parameters are combined into a data group;

[0022] Based on the power change curve and the temperature change curve, an encryption password is generated;

[0023] The data group is encrypted based on the encryption password and then stored in a database.

[0024] Further, generating an encryption password based on the power change curve and the temperature change curve includes:

[0025] Obtaining the total charging time of the battery, and obtaining a corresponding encrypted character data table based on the total charging time;

[0026] Based on the power variation curve, the encrypted character data table is corrected to obtain a corrected character data table; based on the corrected character data table and the temperature variation curve, the encrypted password is generated.

[0027] Further, generating an encryption password based on the power change curve and the temperature change curve includes:

[0028] Obtaining the total charging time of the battery, and obtaining a corresponding encrypted string based on the total charging time; evenly dividing the encrypted string into a first string and a second string;

[0029] According to a preset character layout rule, the characters in the first character string are sequentially added one by one to the power change curve, and the characters in the second character string are sequentially added one by one to the temperature change curve;

[0030] The power change curve and the temperature change curve after adding the characters are drawn in the same coordinate system, and the power change curve and the temperature change curve are translated so that their starting points overlap;

[0031] The intersection point of the power change curve and the temperature change curve is determined, and the characters on the intersection point are combined in sequence to obtain the encryption password.

[0032] The present invention also provides a battery charging control device for an intelligent battery exchange cabinet, comprising:

[0033] The detection unit is used for the intelligent battery-swap cabinet to charge the battery in a preparatory charging mode and detect feedback data of the battery when detecting that the battery is placed in the battery compartment for charging;

[0034] an acquisition unit, configured to acquire characteristic information of the battery, and obtain corresponding correction parameters based on the feedback data and the characteristic information; wherein the correction parameters are used to correct the preparatory charging mode;

[0035] The charging unit is used to correct the preparatory charging mode based on the correction parameter to obtain a corrected charging mode, and charge the battery in the corrected charging mode.

[0036] The present invention also provides an intelligent power exchange cabinet, comprising a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps of any of the above methods when executing the computer program.

[0037] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0038] The battery charging control method and device of the smart battery swap cabinet provided by the present invention and the smart battery swap cabinet include: when the smart battery swap cabinet detects that a battery is placed in a battery compartment for charging, the smart battery swap cabinet adopts a preparatory charging mode to charge the battery and detects feedback data of the battery; obtains characteristic information of the battery, and obtains corresponding correction parameters based on the feedback data and the characteristic information; wherein the correction parameters are used to correct the preparatory charging mode; the preparatory charging mode is corrected based on the correction parameters to obtain a corrected charging mode, and the battery is charged in the corrected charging mode. In the present invention, the charging mode is dynamically adjusted based on battery feedback data and characteristic information, aiming to improve charging efficiency, ensure battery safety, and extend battery life. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of the steps of a battery charging control method of an intelligent battery swap cabinet in one embodiment of the present invention;

[0040] Figure 2 It is a structural block diagram of a battery charging control device of an intelligent battery swap cabinet in one embodiment of the present invention;

[0041] Figure 3 It is a schematic block diagram of the structure of an intelligent battery exchange cabinet according to an embodiment of the present invention.

[0042] The implementation, functional features and advantages of the present invention will be further described in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0044] Reference Figure 1 In one embodiment of the present invention, a battery charging control method for an intelligent battery swap cabinet is provided, comprising the following steps:

[0045] Step S1, when the smart battery swap cabinet detects that a battery is placed in the battery compartment for charging, it charges the battery in a preparatory charging mode and detects feedback data of the battery;

[0046] Step S2, acquiring characteristic information of the battery, and obtaining corresponding correction parameters based on the feedback data and the characteristic information; wherein the correction parameters are used to correct the preparatory charging mode;

[0047] Step S3, correcting the preparatory charging mode based on the correction parameter to obtain a corrected charging mode, and charging the battery in the corrected charging mode.

[0048] In this embodiment, it specifically includes:

[0049] Step S1: When the user puts the battery into the battery compartment of the smart battery swap cabinet for charging, a preset basic charging mode, namely the preparatory charging mode, is first started. The main task of this stage is to start the preliminary charging of the battery and collect real-time feedback data of the battery during the charging process. These data include but are not limited to key indicators such as battery voltage, current, and temperature, which are used to evaluate the current state of the battery and the response during the charging process.

[0050] Step S2: After collecting the feedback data of the battery, the characteristic information of the battery will be further obtained, involving static data such as the brand, model, usage history, and health status of the battery. By combining real-time feedback data with battery characteristic information, a more comprehensive battery status model can be constructed. Based on this model, an algorithm model (such as a machine learning algorithm) is used to calculate a set of correction parameters. The role of these correction parameters is to fine-tune or significantly adjust the preparatory charging mode to better match the actual needs of the current battery, such as adjusting the charging current, charging voltage, or type of charging strategy.

[0051] Step S3: Finally, the preparatory charging mode is adjusted according to the calculated correction parameters to form a new correction charging mode. In this mode, the smart battery swap cabinet will restart charging the battery. The correction charging mode is customized according to the real-time status and historical data of the battery, so it can control the charging process more accurately, which not only improves the charging efficiency, but also effectively extends the service life of the battery and reduces the risk of overcharging or undercharging.

[0052] In summary, the above technical solution realizes the intelligent and personalized charging control of the battery in the smart battery swap cabinet by dynamically adjusting the charging strategy. It is an innovative application of modern Internet of Things technology and big data analysis in the field of battery management.

[0053] In one embodiment, the preparatory charging mode includes charging voltage, charging power and charging time; the feedback data includes voltage increment, internal temperature change, charging acceptance rate and battery resistance of the battery in the preparatory charging mode.

[0054] In this embodiment, the parameters of the preparatory charging mode are:

[0055] Charging voltage: refers to the voltage level provided by the smart battery swap cabinet to the battery in the initial stage, which is the basic driving force of the charging process.

[0056] Charging power: determined by charging voltage and charging current, indicating the amount of energy transferred to the battery per unit time.

[0057] Charging time: The length of time that the pre-charging mode lasts, which directly affects how much power the battery can receive.

[0058] Indicators of feedback data:

[0059] Voltage increment: monitors the increase in battery voltage in the pre-charging mode, reflecting the battery's charging efficiency and capacity.

[0060] Internal temperature changes: Records temperature changes during battery charging. Excessively high temperatures indicate that the battery is overheating and the charging strategy needs to be adjusted to prevent damage.

[0061] Charge Acceptance Rate: A measure of a battery’s ability to accept charging energy. A low acceptance rate means the battery is close to full charge or has health issues.

[0062] Battery Resistance: Changes in the internal resistance of a battery. High resistance results in reduced charging efficiency and indicates battery aging or failure.

[0063] In this embodiment, when the battery is placed in the battery compartment of the smart battery exchange cabinet and starts charging, it is first charged according to the predetermined charging voltage, power and duration. At the same time, the above feedback data is continuously monitored to evaluate the real-time status and charging effect of the battery. If problems such as unsatisfactory voltage increment, excessive temperature, low charging acceptance rate or increased battery resistance are found, this may mean that the preparatory charging mode is not fully suitable for the actual situation of the current battery.

[0064] At this time, these feedback data will be used in combination with the characteristic information of the battery (such as model, usage history, etc.) to adjust the charging strategy through the correction parameter prediction model introduced earlier. For example, if the battery temperature is detected to be too high, the charging power will be reduced or the charging voltage will be lowered to avoid overheating; if the battery acceptance rate is found to be low, the charging time needs to be extended or the charging voltage needs to be adjusted to ensure that the battery can be fully charged.

[0065] In this way, the smart battery swap cabinet can achieve refined and dynamic management of the battery charging process, which not only improves the charging efficiency and safety, but also optimizes the charging strategy according to the specific conditions of the battery and extends the battery life, reflecting the development trend of intelligence and personalization in modern battery management systems.

[0066] In one embodiment, obtaining corresponding correction parameters based on the feedback data and the characteristic information includes:

[0067] Inputting the characteristic information and feedback data into a pre-trained correction parameter prediction model; wherein the correction parameter prediction model includes a first prediction sub-model and a second prediction sub-model;

[0068] Analyzing the feature information based on the first prediction sub-model, and predicting a first correction parameter;

[0069] Analyzing the feedback data based on the second prediction sub-model, and predicting a second correction parameter;

[0070] The first correction parameter and the second correction parameter are fused and calculated to obtain the correction parameter.

[0071] In this embodiment, it specifically includes:

[0072] First prediction sub-model based on feature information: First, a machine learning model called the first prediction sub-model is used to process the feature information of the battery. The feature information here usually includes static attributes such as the brand, model, age, and health status of the battery. By analyzing these static data, the first prediction sub-model can predict a set of charging parameters suitable for this specific battery type, which are called the first correction parameters. The key to this step is that the model needs to be trained with a large amount of relevant data in advance to learn the correlation between different battery characteristics and ideal charging parameters.

[0073] Second prediction sub-model based on feedback data: At the same time, another independent second prediction sub-model focuses on the analysis of real-time feedback data. Dynamic data such as voltage, current, temperature, etc. collected during the charging process are input into this model, which can evaluate the current state of the battery and its performance during the charging process in real time, thereby predicting another set of parameters that adapt to the current charging conditions, namely the second correction parameters. The second prediction sub-model is also based on previous training data and learns how to adjust the charging strategy according to the real-time response of the battery.

[0074] Fusion calculation of correction parameters: After obtaining the first correction parameter and the second correction parameter, a fusion calculation step is performed to combine the information of the two and finally determine the most appropriate charging parameter, i.e., the "correction parameter". The fusion calculation can use weighted average, decision tree, neural network or other machine learning algorithms to ensure that the charging strategy takes into account the historical characteristics of the battery and fully adapts to the current charging environment and battery status, so as to achieve the best charging effect.

[0075] The core advantage of the entire technical solution is that it combines the static characteristics and dynamic feedback of the battery, and uses two specially trained prediction sub-models to calculate the best correction parameters from different angles, thus achieving highly personalized charging control, significantly improving charging efficiency and safety, and also extending the battery life. This dynamic and intelligent charging control method is an important innovation in modern battery management systems.

[0076] In one embodiment, obtaining characteristic information of the battery includes:

[0077] The charging interface of the battery is detected by an electrical signal probe on the intelligent battery exchange cabinet to obtain a detection signal, and characteristic information of the battery is obtained based on the detection signal.

[0078] In this embodiment, it specifically includes:

[0079] Electrical signal detection and characteristic information:

[0080] The electrical signal probe on the smart battery swap cabinet is responsible for contacting the battery's charging interface. By sending specific electrical signals and interacting with the response signals generated by the battery, a series of electrical characteristic parameters of the battery can be obtained as characteristic information. This information includes basic specifications such as the battery model, rated voltage, rated capacity, and maximum charging current, as well as operating status parameters such as the battery's current remaining power, health status, and internal resistance. The electrical signal detection method can directly reflect the electrical performance of the battery, which is crucial for formulating a reasonable charging strategy.

[0081] Based on the characteristic information obtained from electrical signal detection, the smart battery swap cabinet can build a complete characteristic information file of the battery. This file not only covers the electrical specifications and real-time status of the battery, but also includes its physical identification and historical records, providing a detailed data basis for subsequent charging strategy adjustments. By analyzing this characteristic information, the smart battery swap cabinet can determine whether the battery is suitable for charging, select the most appropriate charging mode, and monitor the battery status in real time during the charging process to ensure the safety and efficiency of the charging process.

[0082] In summary, the intelligent battery swap cabinet obtains the characteristic information of the battery through electrical signal detection, laying a solid data foundation for realizing intelligent and personalized charging control. This technical solution reflects the advancement and practicality of IoT devices in data collection and analysis, and is an indispensable key link in the modern intelligent battery swap system.

[0083] In one embodiment, after charging the battery in the correction charging mode, the method further comprises:

[0084] Record the charge change of the battery in the corrective charging mode in real time, and generate a charge change curve; record the temperature change of the battery in the corrective charging mode in real time, and generate a temperature change curve:

[0085] Based on the charge change curve and the temperature change curve, determining whether the battery is abnormally charged;

[0086] If the battery is charged abnormally, a preset adjustment strategy is used to adjust the corrective charging mode.

[0087] In this embodiment, it specifically includes:

[0088] Real-time recording of power and temperature changes: During the intelligent charging process, the battery power and temperature are continuously monitored and recorded in real time. Power changes reflect the efficiency and progress of battery charging, while temperature is an important indicator for measuring charging safety, because excessive temperature may cause thermal runaway of the battery, affecting battery life and even causing safety hazards.

[0089] Generate power change curve and temperature change curve:

[0090] The collected real-time data will be plotted into graphs, namely the power change curve and the temperature change curve. These curves intuitively show the changing trends of power and temperature over time during the charging process. By analyzing the curves, abnormal conditions during the charging process can be quickly identified, such as an abnormally slow increase in power or a sudden increase in temperature.

[0091] Determine whether the battery charging is abnormal: Set a series of thresholds and standard models to determine whether the charging process is normal. For example, if the power change curve shows that the battery power does not increase significantly after charging for a period of time, or the temperature change curve shows that the battery temperature exceeds the preset safety range, then the charging process will be determined to be abnormal.

[0092] Charging mode adjustment under abnormal conditions: Once a charging anomaly is detected, a preset adjustment strategy will be immediately activated to correct the charging mode. This includes reducing the charging current to reduce heat, changing the charging algorithm to optimize power growth, or suspending charging until the problem is resolved. The adjustment strategy is designed to protect the battery from damage while ensuring charging efficiency as much as possible.

[0093] In summary, the above technical solution realizes dynamic management and optimization of the charging process through real-time monitoring and intelligent analysis, effectively avoids battery damage caused by improper charging, and improves the safety and efficiency of charging. Especially in the fields of electric vehicles and drones that rely on high-performance batteries, the application of this solution can significantly improve the reliability of the equipment and user experience, and also provides an important reference for the development of battery management systems.

[0094] In one embodiment, after judging whether the battery is abnormally charged based on the power change curve and the temperature change curve, the method further includes:

[0095] If the battery is charged normally, the feedback data, the characteristic information and the correction parameters are combined into a data group;

[0096] Based on the power change curve and the temperature change curve, an encryption password is generated;

[0097] The data group is encrypted based on the encryption password and then stored in a database.

[0098] In this embodiment, it specifically includes:

[0099] When it is determined that the battery charging process is normal (that is, the change in charge and temperature are within the preset safety range), it will combine the "feedback data", "characteristic information" and "correction parameters" related to this charging into a data group. "Feedback data" includes the start and end time of charging, charging rate, final charge level, etc. "Characteristic information" may cover battery type, battery health status, charging environment conditions, etc. "Correction parameters" refer to any adjustments made according to battery performance during the charging process, such as adjusting the charging current, voltage or the decision to suspend charging. These data are crucial for subsequent analysis of battery performance and optimization of charging algorithms.

[0100] Generation of encryption password: To ensure data security, a unique encryption password is generated based on the charge change curve and temperature change curve during the charging process. This password is generated based on the characteristic points of the curve, the rate of change, or the average value within a specific time period, so that each charging process will generate a different encryption password. This method of generating passwords based on real-time data increases the difficulty of cracking and improves the security of data storage.

[0101] Encryption and storage of data groups: After obtaining the encryption password, the previously constructed data groups are encrypted. Encryption is a technology that converts raw data into an unreadable form that can only be restored using the correct decryption key. The encrypted data groups are then stored in the database, ensuring that even if the database is unauthorized, attackers cannot easily decipher useful information.

[0102] The above technical solution not only ensures the security of data by encrypting and storing key data during the charging process, but also provides a solid foundation for future data analysis and charging strategy optimization. The stored data can be used to monitor the long-term health of the battery, identify potential improvements in charging patterns, and predict the remaining service life of the battery. In addition, encryption measures strengthen data protection, which is particularly important for charging applications involving sensitive information.

[0103] In one embodiment, generating an encryption password based on the power change curve and the temperature change curve includes:

[0104] Obtain the total charging time of the battery, and obtain the corresponding encrypted character data table based on the total charging time:

[0105] Based on the power variation curve, the encrypted character data table is corrected to obtain a corrected character data table; based on the corrected character data table and the temperature variation curve, the encrypted password is generated.

[0106] In this embodiment, it specifically includes:

[0107] First, record the total charging time of the battery, and match the corresponding encrypted character data table according to the mapping relationship between the total charging time and the data table. This encrypted character data table can be understood as a list or matrix containing various characters (letters, numbers, special symbols, etc.), and each specific total charging time corresponds to a different encrypted character data table. This design enables different charging times to be mapped to different character sequences, thereby adding randomness and complexity to the generated password.

[0108] Correction based on the power change curve: Next, the power change curve is used to correct the initially selected encrypted character data. The power change curve reflects the trend of the battery's power percentage over time during the entire charging process. Analyze certain features of this curve, such as the slope of the curve, the position of the inflection point, the maximum power value, etc., and then adjust the initial character data table based on these features. For example, the slope of the curve can determine how the character order is adjusted, and the inflection point position can be used to select a specific character replacement rule. In this way, the power change curve becomes a dynamic influencing factor in password generation, further enhancing the unpredictability of the password.

[0109] Generate encryption password by combining temperature change curve: Finally, the corrected character data table is combined with the temperature change curve to generate the final encryption password. The temperature change curve records the temperature change over time during battery charging. It also contains rich information, such as the temperature rise rate, the highest temperature point, etc. Use the properties of the temperature change curve to further adjust the corrected character data. For example, the temperature peak can determine the position of special characters in the password, and the temperature change rate can affect the rotation or shift operation of the characters. In this way, the temperature change curve is also cleverly integrated into the password generation mechanism, making the final generated password a coded expression of the unique characteristics of the charging process.

[0110] Overall, this technical solution of generating encryption passwords based on the power change curve and temperature change curve cleverly transforms the physical characteristics of the battery charging process into control parameters for password generation, which not only improves the randomness and complexity of the password, but also ensures that each generated password is unique, greatly enhancing data security. This method is particularly suitable for scenarios where battery charging-related data needs to be frequently stored and transmitted, and can effectively prevent data leakage and tampering, and protect user privacy and system security.

[0111] Specifically, based on the power change curve, the encrypted character data table is corrected to obtain a corrected character data table; based on the corrected character data table and the temperature change curve, the encrypted password is generated, including:

[0112] According to a preset superposition rule, the power change curve is superimposed on the encrypted character data table, so that the power change curve intersects with the cells in the encrypted character data table;

[0113] In the encrypted character data table, a cell intersecting with the electric quantity change curve is used as a target cell, characters in the target cell are extracted, and all of them are inserted into the head of the encrypted character data table, and the characters in the encrypted character data table are sequentially shifted backward and filled in completely to obtain the corrected character data table;

[0114] According to a preset superposition rule, the temperature change curve is superimposed on the correction character data table, so that the temperature change curve intersects with the cells in the correction character data table;

[0115] In the correction character data table, the characters in the cells intersecting with the temperature change curve are combined in sequence to obtain the encrypted password.

[0116] In this embodiment, it specifically includes:

[0117] Intersection of the curve and table under the superposition rule: First, the power change curve is superimposed on the encrypted character data table according to the preset superposition rule. The superposition rule here includes the setting of the position, angle or other parameters of the curve relative to the table to ensure that the curve intersects with the cells in the table in a predictable but unique way.

[0118] Identification and character processing of target cells: Identify cells that intersect the charge change curve, namely target cells. Then, it extracts the characters in these target cells and inserts all of them into the head of the encrypted character data table. Subsequently, the original character list is shifted backward in sequence to fill the vacancies caused by the insertion of new characters at the head. This process forms a new, adjusted character data table, namely the corrected character data table.

[0119] Interaction between temperature variation curve and correction character data table:

[0120] Superposition and intersection of temperature change curves: Next, use the preset superposition rules again, this time superimposing the temperature change curve on the correction character data table. Similarly, this superposition process will cause the temperature change curve to intersect with the cells in the table.

[0121] Combination of intersecting cell characters: Identify cells that intersect with the temperature change curve and combine the characters in these cells in sequence. The order of these characters is followed by the order in which the curve intersects with the cells, and finally forms an encrypted password. This password is dynamically generated based on the specific interaction between the temperature change curve and the correction character data table, so it has high security and randomness.

[0122] Innovations of technical solutions:

[0123] Dynamic encryption mechanism: By combining the changes in physical quantities (electricity and temperature) with the encryption character data table, the dynamic generation of encryption passwords is achieved, which improves the security and unpredictability of the passwords.

[0124] Multiple encryption factors: The scheme not only considers the single power change curve, but also introduces the temperature change curve. Through double superposition and character processing, the complexity and difficulty of password generation are increased.

[0125] Adaptive adjustment: The encrypted character data table is first corrected through the power change curve, and then the temperature change curve is used to generate the final password. This adaptive adjustment mechanism ensures that a valid encryption password can be generated even under different conditions.

[0126] The above technical solution is particularly suitable for scenarios that require high security, such as IoT device communications, smart grid management, data encryption storage, etc., and can effectively improve the security level of data transmission and storage.

[0127] In one embodiment, generating an encryption password based on the power change curve and the temperature change curve includes:

[0128] Obtaining the total charging time of the battery, and obtaining a corresponding encrypted string based on the total charging time; evenly dividing the encrypted string into a first string and a second string;

[0129] According to a preset character layout rule, the characters in the first character string are sequentially added one by one to the power change curve, and the characters in the second character string are sequentially added one by one to the temperature change curve;

[0130] The power change curve and the temperature change curve after adding the characters are drawn in the same coordinate system, and the power change curve and the temperature change curve are translated so that their starting points overlap;

[0131] The intersection point of the power change curve and the temperature change curve is determined, and the characters on the intersection point are combined in sequence to obtain the encryption password.

[0132] In this embodiment, it specifically includes:

[0133] Get the encrypted string and split it: First, record the total charging time of the battery, and extract an encrypted string from the preset encrypted character set based on this time. This string consists of a series of characters, which can be letters, numbers or special symbols. Then, split the encrypted string evenly into two parts: the first string and the second string. This segmentation ensures that the two groups of characters can work independently but interrelatedly in the subsequent processing process.

[0134] Add characters to the curve:

[0135] Next, the system will add the characters in the first string to the power change curve one by one in sequence according to the preset character layout rules, and add the characters in the second string to the temperature change curve one by one in sequence. The sequence here means that the order of adding characters follows the order in which the characters appear in the string, and the preset character layout rules involve how to position each character on the curve. For example, the placement of the character can be determined based on the length, slope or specific time point of the curve. This process links the originally abstract characters with specific changes in physical quantities (power and temperature), laying the foundation for subsequent password generation. Specifically, the above curve can be evenly divided, and a rectangular box can be added at the division point, and the characters in the first string and the second string are added to the rectangular box on the above curve in sequence.

[0136] Draw curves and overlap starting points: Draw the power change curve and temperature change curve with characters added in the same coordinate system. To facilitate subsequent operations, the two curves will be translated so that their starting points overlap. This operation ensures that the two curves start at the same time starting point, which facilitates subsequent cross and overlap analysis.

[0137] Determine the intersection point and combine characters: Finally, the intersection point of the power change curve and the temperature change curve will be found. At these intersection points, there is a character on each curve. The characters on these intersection points are combined in sequence to form the final encrypted password. This method of combining characters based on the intersection points of the curves cleverly utilizes the natural intersection points of the power and temperature change curves, ensuring that the generated password has a high degree of randomness and complexity. It should be noted that if a rectangular frame is added to the above-mentioned curve, the intersection of the two curves also includes the case where one curve passes through the rectangular frame of the other curve, and the characters in the rectangular frame are the characters corresponding to the intersection point, which are used for the subsequent generation of the encrypted password. If there is no rectangular frame at the position where the two curves intersect, the characters in the rectangular frame closest to the intersection point are used as the characters of the intersection point. In other embodiments, the characters at the intersection point of the above-mentioned two curves can also be removed from the above-mentioned first character string and the second character string, and the removed characters are combined in sequence to obtain the above-mentioned encrypted password.

[0138] In summary, the above technical solution creates a new encryption password generation method by combining the encrypted string with the charge change curve and temperature change curve during the battery charging process. This method not only uses the physical characteristics of the charging process as the basis for password generation, but also realizes dynamic password generation and high security through the character combination of the intersection points of the curves. This encryption method is particularly suitable for data transmission and storage scenarios that require high-intensity encryption protection, such as smart grids, IoT device communications, etc., and can effectively prevent unauthorized access and data theft.

[0139] Reference Figure 2 In another embodiment of the present invention, a battery charging control device for an intelligent battery swap cabinet is provided, comprising:

[0140] The detection unit is used for the intelligent battery-swap cabinet to charge the battery in a preparatory charging mode and detect feedback data of the battery when detecting that the battery is placed in the battery compartment for charging;

[0141] an acquisition unit, configured to acquire characteristic information of the battery, and obtain corresponding correction parameters based on the feedback data and the characteristic information; wherein the correction parameters are used to correct the preparatory charging mode;

[0142] The charging unit is used to correct the preparatory charging mode based on the correction parameter to obtain a corrected charging mode, and charge the battery in the corrected charging mode.

[0143] In this embodiment, for the specific implementation of each unit in the above device embodiment, please refer to the above method embodiment, which will not be repeated here.

[0144] Reference Figure 3In an embodiment of the present invention, a smart power exchange cabinet is also provided. The internal structure of the smart power exchange cabinet can be as follows: Figure 3 As shown. The intelligent power exchange cabinet includes a processor, a memory, a display unit, an input device, a network interface and a database connected through a system bus. Among them, the computer-designed processor is used to provide computing and control capabilities. The memory of the intelligent power exchange cabinet includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the intelligent power exchange cabinet is used to store the corresponding data in this embodiment. The network interface of the intelligent power exchange cabinet is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0145] Those skilled in the art will understand that Figure 3 The structure shown in is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the smart battery swap cabinet to which the solution of the present invention is applied.

[0146] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0147] In summary, the battery charging control method, device and smart battery swap cabinet provided in the embodiments of the present invention include: when the smart battery swap cabinet detects that a battery is placed in a battery compartment for charging, the smart battery swap cabinet adopts a preparatory charging mode to charge the battery and detects feedback data of the battery; obtains characteristic information of the battery, and obtains corresponding correction parameters based on the feedback data and the characteristic information; wherein the correction parameters are used to correct the preparatory charging mode; the preparatory charging mode is corrected based on the correction parameters to obtain a corrected charging mode, and the battery is charged in the corrected charging mode. In the present invention, the charging mode is dynamically adjusted based on battery feedback data and characteristic information, aiming to improve charging efficiency, ensure battery safety, and extend battery life.

[0148] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0149] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0150] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A battery charging control method for an intelligent battery swap cabinet, characterized in that: The following steps are involved: When the intelligent battery-swapping cabinet detects that a battery is placed in the battery compartment for charging, it charges the battery in a preparatory charging mode and detects feedback data of the battery; Acquire characteristic information of the battery, and obtain corresponding correction parameters based on the feedback data and the characteristic information; wherein the correction parameters are used to correct the preparatory charging mode; the feedback data includes voltage increment, internal temperature change, charging acceptance rate, and battery resistance of the battery in the preparatory charging mode; the characteristic information includes static data of battery brand, model, usage history, and health status; Correcting the preparatory charging mode based on the correction parameter to obtain a corrected charging mode, and charging the battery in the corrected charging mode; The obtaining corresponding correction parameters based on the feedback data and the characteristic information includes: Inputting the characteristic information and feedback data into a pre-trained correction parameter prediction model; wherein the correction parameter prediction model includes a first prediction sub-model and a second prediction sub-model; Analyzing the feature information based on the first prediction sub-model, and predicting a first correction parameter; Analyzing the feedback data based on the second prediction sub-model, and predicting a second correction parameter; The first correction parameter and the second correction parameter are fused and calculated to obtain the correction parameter.

2. The battery charging control method of the intelligent battery swap cabinet according to claim 1 is characterized in that: The preparatory charging mode includes charging voltage, charging power and charging time.

3. The battery charging control method of the intelligent battery swap cabinet according to claim 1 is characterized in that: Acquiring characteristic information of the battery, including: The charging interface of the battery is detected by an electrical signal probe on the intelligent battery exchange cabinet to obtain a detection signal, and characteristic information of the battery is obtained based on the detection signal.

4. The battery charging control method of the intelligent battery swap cabinet according to claim 1 is characterized in that: After charging the battery in the correction charging mode, the method further comprises: Recording the change of the charge of the battery in the corrective charging mode in real time, and generating a charge change curve; Recording the change of the temperature of the battery in the corrective charging mode in real time, and generating a temperature change curve; Based on the charge change curve and the temperature change curve, determining whether the battery is abnormally charged; If the battery is charged abnormally, a preset adjustment strategy is used to adjust the corrective charging mode.

5. The battery charging control method of the intelligent battery exchange cabinet according to claim 4 is characterized in that: After judging whether the battery is abnormally charged based on the power change curve and the temperature change curve, the method further includes: If the battery is charged normally, the feedback data, the characteristic information and the correction parameters are combined into a data group; Based on the power change curve and the temperature change curve, an encryption password is generated; The data group is encrypted based on the encryption password and then stored in a database.

6. The battery charging control method of the intelligent battery exchange cabinet according to claim 5 is characterized in that: The step of generating an encryption password based on the power change curve and the temperature change curve includes: Obtaining the total charging time of the battery, and obtaining a corresponding encrypted character data table based on the total charging time; Based on the power variation curve, the encrypted character data table is corrected to obtain a corrected character data table; based on the corrected character data table and the temperature variation curve, the encrypted password is generated.

7. The battery charging control method of the intelligent battery exchange cabinet according to claim 5 is characterized in that: The step of generating an encryption password based on the power change curve and the temperature change curve includes: Obtaining the total charging time of the battery, and obtaining a corresponding encrypted string based on the total charging time; evenly dividing the encrypted string into a first string and a second string; According to a preset character layout rule, the characters in the first character string are sequentially added one by one to the power change curve, and the characters in the second character string are sequentially added one by one to the temperature change curve; The power change curve and the temperature change curve after adding the characters are drawn in the same coordinate system, and the power change curve and the temperature change curve are translated so that their starting points overlap; The intersection point of the power change curve and the temperature change curve is determined, and the characters on the intersection point are combined in sequence to obtain the encryption password.

8. A battery charging control device for an intelligent battery exchange cabinet, characterized in that: include: The detection unit is used for the intelligent battery-swap cabinet to charge the battery in a preparatory charging mode and detect feedback data of the battery when detecting that the battery is placed in the battery compartment for charging; an acquisition unit, configured to acquire characteristic information of the battery, and obtain corresponding correction parameters based on the feedback data and the characteristic information; wherein the correction parameters are used to correct the preparatory charging mode; the feedback data includes a voltage increment, internal temperature change, charging acceptance rate, and battery resistance of the battery in the preparatory charging mode; and the characteristic information includes static data of the battery brand, model, usage history, and health status; a charging unit, configured to correct the preparatory charging mode based on the correction parameter to obtain a corrected charging mode, and charge the battery in the corrected charging mode; The obtaining corresponding correction parameters based on the feedback data and the characteristic information includes: Inputting the characteristic information and feedback data into a pre-trained correction parameter prediction model; wherein the correction parameter prediction model includes a first prediction sub-model and a second prediction sub-model; Analyzing the feature information based on the first prediction sub-model, and predicting a first correction parameter; Analyzing the feedback data based on the second prediction sub-model, and predicting a second correction parameter; The first correction parameter and the second correction parameter are fused and calculated to obtain the correction parameter.

9. An intelligent power exchange cabinet, comprising a memory and a processor, wherein a computer program is stored in the memory, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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