A method and apparatus for detecting mobile power banks

Abstract

This application relates to the technical field of power source detection, and in particular to a method for detecting a mobile power source, comprising: identifying a target mobile power source; detecting the target mobile power source using a quantum sensor; acquiring atomic dynamic information of the target mobile power source based on the detection results; generating a first-stage microscopic dataset based on the atomic dynamic information; constructing a target topology structure corresponding to the first-stage microscopic dataset in the target topology space; inputting the target topology structure into a topology recognition model for recognition; generating a second-stage topology analysis result; invoking a pre-designed encrypted communication mechanism to encrypt and transmit the data in the second-stage topology analysis result, generating third-stage encrypted secure data; generating virtual data corresponding to the third-stage encrypted secure data based on a generative adversarial network; and generating a target detection report based on the fused virtual data and the third-stage encrypted secure data. This application can improve the accuracy of mobile power source detection.

Description

Technical Field

[0001] This application relates to the technical field of power supply detection, and in particular to a method and apparatus for detecting a mobile power supply. Background Technology

[0002] Existing mobile power bank testing methods suffer from significant shortcomings in the accuracy of data acquisition and analysis. Traditional sensors and detection methods, due to technological limitations, cannot provide high-resolution data, resulting in inaccurate assessments of the battery's internal state. This low-precision data fails to accurately reflect the battery's actual health condition, particularly in early fault detection and performance degradation identification.

[0003] This low-precision testing method makes it difficult for existing technologies to comprehensively and accurately assess the overall performance and potential failure risks of power banks. Due to the lack of detailed data support, existing test results are often unreliable and cannot provide users with detailed performance evaluations and effective fault warnings, thus affecting the safety and lifespan of the device.

[0004] As can be seen from the above, there is still a problem to be solved in order to improve the accuracy of power bank detection. Summary of the Invention

[0005] To improve the accuracy of power bank testing, this application provides a power bank testing method and apparatus.

[0006] Firstly, this application provides a method for detecting a mobile power bank, employing the following technical solution:

[0007] A method for detecting a mobile power bank includes: identifying a target mobile power bank; detecting the microscopic charge distribution inside the target mobile power bank using a quantum sensor through quantum tunneling and quantum confinement effects; acquiring atomic dynamic information of the battery of the target mobile power bank during charging and discharging based on the detection results; generating a first-stage microscopic dataset based on the atomic dynamic information; mapping the first-stage microscopic dataset to a corresponding target topological space; constructing a target topological structure corresponding to the first-stage microscopic dataset in the target topological space; retrieving a pre-trained topological recognition model; inputting the target topological structure into the topological recognition model for recognition; and generating a second-stage topological analysis result, wherein the target topological structure includes... The second-stage topology analysis results, including simple complex and persistent cohomology, include potential failure modes. A pre-designed encrypted communication mechanism based on a chaotic system is invoked. Based on the randomness and unpredictability of the encrypted communication mechanism, the data in the second-stage topology analysis results is encrypted and transmitted, generating third-stage encrypted security data. Virtual data corresponding to the third-stage encrypted security data is generated based on a generative adversarial network. The virtual data is then fused with the third-stage encrypted security data. Based on the fused virtual data and the third-stage encrypted security data, a corresponding target detection report is generated. The target detection report includes target mobile power supply performance evaluation, failure prediction, security level assessment, and personalized usage suggestions.

[0008] By employing the above technical solutions, the operating parameters of the quantum sensor are dynamically adjusted under different charging and discharging states to ensure the acquisition of high-precision microscopic charge distribution data. Topological data analysis technology is used to deeply mine complex patterns and potential faults in the data, generating detailed topological analysis results. A chaotic encryption mechanism is employed to ensure the security and integrity of data transmission. Furthermore, virtual data is generated and fused with real data through generative adversarial networks to produce a comprehensive and personalized inspection report. This method, through multi-stage precise data acquisition, advanced data analysis, and secure data processing, significantly improves the accuracy of power bank health status assessment, enabling earlier detection of potential faults and providing reliable maintenance recommendations, thereby greatly improving the overall accuracy of power bank testing.

[0009] Optionally, during the process of the quantum sensor acquiring atomic dynamic information, the method further includes: determining the current charging and discharging state of the target mobile power source; retrieving a preset working parameter mapping table of the quantum sensor; and determining the corresponding sensor working parameters based on the charging and discharging state and the working parameter mapping table, wherein the sensor working parameters include sampling frequency, gain coefficient, and threshold setting.

[0010] By employing the above technical solution, during the acquisition of atomic dynamic information by the quantum sensor, the current charging and discharging state of the target mobile power source is determined, and a preset working parameter mapping table is invoked. The working parameters of the quantum sensor (such as sampling frequency, gain coefficient, and threshold setting) are dynamically adjusted according to the charging and discharging state, thereby ensuring high-precision data acquisition under different working conditions. This method can significantly improve the sensitivity and resolution of the detection, ensuring more accurate and reliable acquisition of microscopic charge distribution data, and thus improving the overall accuracy of the detection results.

[0011] Optionally, during encrypted transmission, the method further includes: real-time monitoring of the real-time response status of the protection circuit in the target mobile power bank, the real-time response status including current, voltage, and temperature; determining the corresponding security threat level based on the real-time response status; and adjusting the corresponding encryption strategy based on the security threat level, wherein the encryption strategy includes adjusting the generation frequency of the encryption key, modifying the parameters of the encryption algorithm, and changing the size of the encrypted data block.

[0012] By employing the above technical solution, the security threat level is dynamically assessed through real-time monitoring of the current, voltage, and temperature response status of the protection circuit in the target mobile power bank. Based on this assessment, encryption strategies are flexibly adjusted, such as changing the encryption key generation frequency, modifying encryption algorithm parameters, and adjusting the size of encrypted data blocks. This method effectively enhances the security of data transmission, ensuring that data maintains a high degree of confidentiality and integrity under various security threats, thereby significantly improving the overall reliability and security of the detection system.

[0013] Optionally, during encrypted transmission, the method further includes: real-time monitoring of the real-time response status of the protection circuit in the target mobile power bank, the real-time response status including current, voltage, and temperature; determining the corresponding security threat level based on the real-time response status; and adjusting the corresponding encryption strategy based on the security threat level, wherein the encryption strategy includes adjusting the generation frequency of the encryption key, modifying the parameters of the encryption algorithm, and changing the size of the encrypted data block.

[0014] By employing the above technical solution, the current security threat level is dynamically assessed through real-time monitoring of parameters such as current, voltage, and temperature in the protection circuit of the target mobile power bank. Based on this assessment, encryption strategies are intelligently adjusted, such as optimizing the encryption key generation frequency, modifying encryption algorithm parameters, and adjusting the size of encrypted data blocks. This adaptive security mechanism maintains a high degree of data confidentiality and integrity under different threat environments, significantly improving data transmission security and overall system reliability.

[0015] Optionally, during the detection process, the method further includes: acquiring topology data corresponding to the target power bank at different time periods; comparing and analyzing the topology data at different time periods based on time series analysis, identifying the battery performance trend of the target power bank over time based on the analysis results, including changes in charge distribution and ion migration paths; identifying potential faults or performance degradation problems based on the battery performance trend, and generating a corresponding battery report.

[0016] By employing the above technical solution, topology data of the target mobile power source is acquired at different time intervals, and time series analysis is used to compare these data to identify trends in battery performance over time, such as changes in charge distribution and ion migration paths. Based on these trend analyses, potential faults or performance degradation issues can be detected in advance, and detailed battery health reports can be generated. This method significantly improves the accuracy of battery health status assessment and fault prediction capabilities, making maintenance work more targeted and proactive, thereby extending battery life and improving system reliability.

[0017] Optionally, the method further includes: real-time monitoring of the state parameters of the target mobile power supply, including charging / discharging status, temperature, voltage, and current; retrieving a preset detection task priority rule, prioritizing each detection task based on the detection task priority rule, wherein high-priority tasks are processed first; determining the execution order and time of each detection task according to the state parameters of the target mobile power supply battery and the priority of each detection task; and adjusting the execution order and time of each detection task during the detection process based on the real-time monitored state changes and the completion status of the detection tasks.

[0018] By adopting the above technical solution, and through real-time monitoring of the target mobile power source's status parameters (such as charging / discharging status, temperature, voltage, and current), combined with preset detection task priority rules, the execution order and time of each detection task are dynamically adjusted. This method ensures that high-priority tasks are processed first, and flexibly adjusts the arrangement of subsequent tasks based on real-time status changes and task completion status, thereby improving the overall efficiency and response speed of the detection process. This enables the system to respond more agilely to changes in battery status, ensuring timely detection and handling of potential problems.

[0019] Secondly, this application provides a mobile power bank detection device, which adopts the following technical solution:

[0020] A mobile power bank detection device, comprising:

[0021] The first-stage micro-dataset generation module identifies the corresponding target mobile power source, detects the micro-charge distribution inside the target mobile power source using quantum sensors through quantum tunneling and quantum confinement effects, obtains atomic dynamic information of the battery of the target mobile power source during the charging and discharging process based on the detection results, and uses the atomic dynamic information to generate the first-stage micro-dataset.

[0022] The second-stage topology analysis result production module maps the first-stage micro dataset to the corresponding target topology space, constructs the target topology structure corresponding to the first-stage micro dataset in the target topology space, retrieves the pre-trained topology recognition model, and inputs the target topology structure into the topology recognition model for recognition to generate the second-stage topology analysis results. The target topology structure includes simple complexes and persistent cohomology, and the second-stage topology analysis results include potential failure modes.

[0023] The target detection report generation module invokes a pre-designed encrypted communication mechanism based on a chaotic system. Based on the randomness and unpredictability of this mechanism, it encrypts and transmits the data from the second-stage topology analysis results, generating third-stage encrypted security data. It then generates virtual data corresponding to the third-stage encrypted security data using a generative adversarial network. The virtual data is then fused with the third-stage encrypted security data. Finally, the fused virtual data and the third-stage encrypted security data are used to generate a corresponding target detection report. This target detection report includes target mobile power bank performance evaluation, fault prediction, security level assessment, and personalized usage suggestions.

[0024] Thirdly, this application provides a mobile power bank detection device, which adopts the following technical solution:

[0025] A power bank detection device includes a processor, wherein the processor runs a program for the power bank detection method described in any one of the above-mentioned methods.

[0026] Fourthly, this application provides a storage medium, which adopts the following technical solution:

[0027] A storage medium storing a program for the mobile power bank detection method described in any one of the above.

[0028] In summary, this application includes at least one of the following beneficial technical effects:

[0029] First, the operating parameters are dynamically adjusted using quantum sensors under different charge and discharge states to ensure the acquisition of high-precision microscopic charge distribution data. Then, topological data analysis techniques are used to delve into the complex patterns and potential faults within the data, generating detailed topological analysis results. Combined with time-series analysis, the trends in battery performance over time are identified, allowing for the early detection of potential faults or performance degradation. This method, through multi-stage precise data acquisition, advanced data analysis, and adaptive security encryption mechanisms, significantly improves the accuracy of mobile power bank health status assessment, making the detection results more reliable and comprehensive.

[0030] Furthermore, by monitoring the target power bank's status parameters (such as charging / discharging status, temperature, voltage, and current) in real time and combining this with preset detection task priority rules, the system dynamically adjusts the execution order and timing of each detection task. This intelligent scheduling mechanism not only ensures the timely processing of high-priority tasks but also flexibly adjusts subsequent task arrangements based on real-time status changes, improving the overall efficiency and response speed of the detection process. Through these optimization measures, the system can respond more agilely to changes in battery status, ensuring timely detection and handling of potential problems, further enhancing the overall accuracy and reliability of power bank detection. Attached Figure Description

[0031] Figure 1 This is a flowchart illustrating a mobile power bank detection method according to an exemplary embodiment.

[0032] Figure 2 This is a structural block diagram of a mobile power supply detection device according to an exemplary embodiment. Detailed Implementation

[0033] The embodiments of this application are described in detail below, and examples of the embodiments are shown in the accompanying drawings.

[0034] In the description of this specification, the references to "certain embodiments," "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples" refer to specific features, structures, materials, or characteristics described in connection with the described embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0035] This application discloses a method for detecting a mobile power bank, referring to... Figure 1 ,include:

[0036] S100: Determine the corresponding target mobile power source. Based on the quantum sensor, detect the microscopic charge distribution inside the target mobile power source through quantum tunneling and quantum confinement effects. Based on the detection results, obtain the atomic dynamic information of the battery of the target mobile power source during the charging and discharging process. Generate the first-stage microscopic dataset based on the atomic dynamic information.

[0037] The process begins by identifying the target mobile power source to be tested using device identifiers (such as serial numbers or unique IDs). This step ensures that subsequent operations are performed on the correct device. The quantum dot sensor is then prepared, including calibration and preheating. The calibration process ensures that the sensor can accurately measure the microscopic charge distribution, while the preheating process brings the sensor to its optimal working state.

[0038] It should be noted that, by utilizing the quantum tunneling effect, electrons can pass through the potential barrier, thereby providing high-resolution information on charge distribution; by restricting the movement of electrons within a specific region, the measurement accuracy of charge distribution can be further improved.

[0039] During the charging and discharging process of the battery, the atomic dynamic information inside the battery is collected in real time, including charge density, ion migration path, etc. This information reflects the changes in the microstructure of the battery under different states. Based on the atomic dynamic information collected above, the first stage micro dataset is generated, which contains high-precision micro charge distribution information.

[0040] The acquisition of high-precision microscopic charge distribution data by quantum sensors provides high-quality basic data for subsequent analysis, significantly improving the sensitivity and resolution of detection and ensuring the accuracy of subsequent analysis.

[0041] S110: Map the first-stage micro dataset to the corresponding target topology space, construct the target topology structure corresponding to the first-stage micro dataset in the target topology space, call the pre-trained topology recognition model, input the target topology structure into the topology recognition model for recognition, and generate the second-stage topology analysis results.

[0042] In this process, the first-stage micro-dataset is mapped to the corresponding target topological space. It should be noted that a topological space is a mathematical structure used to represent the relationships between data.

[0043] Constructing topological structures includes simple complexes and persistent cohomology. Simple complexes connect data points into simple geometric shapes (such as triangles) to capture local structures in the data. Persistent cohomology extracts persistent features from the data by calculating cohomology groups at different scales, reflecting the changing trends of the microstructure inside the battery.

[0044] Furthermore, a pre-trained topology recognition model is retrieved, and the target topology is input into the model for identification. The topology recognition model analyzes the complex patterns in the topology and generates a second-stage topology analysis result, which includes potential fault modes. Based on the output of the topology recognition model, potential fault modes inside the battery are identified, such as electrode material aging and internal short circuits.

[0045] By using topology data analysis technology to delve into complex patterns and potential faults in the data and generate detailed topology analysis results, we can more accurately identify changes in the microstructure inside the battery, discover potential fault modes in advance, and improve the accuracy of fault prediction.

[0046] S120: Invoke the pre-designed encrypted communication mechanism based on the chaotic system, encrypt and transmit the data in the second-stage topology analysis results based on the randomness and unpredictability of the encrypted communication mechanism, generate the third-stage encrypted security data, generate virtual data corresponding to the third-stage encrypted security data based on the generative adversarial network, fuse the virtual data with the third-stage encrypted security data, and generate the corresponding target detection report based on the fused virtual data and the third-stage encrypted security data.

[0047] This involves retrieving a pre-designed encrypted communication mechanism based on a chaotic system. It's important to note that chaotic systems, with their randomness and unpredictability, are suitable for encryption. Depending on the current security threat level, an appropriate encryption strategy is selected, such as adjusting the encryption key generation frequency, modifying encryption algorithm parameters, and changing the size of the encrypted data block.

[0048] Then, the data in the second-stage topology analysis results are encrypted and transmitted using the characteristics of chaotic systems to generate the third-stage encrypted secure data. During the encrypted transmission process, the response status (such as current, voltage, and temperature) of the protection circuit in the target mobile power supply is monitored in real time. The security threat level is dynamically assessed based on these statuses, and the encryption strategy is adjusted accordingly to ensure the security and integrity of the data.

[0049] Furthermore, based on Generative Adversarial Network (GAN) technology, virtual data corresponding to the third-stage encrypted security data is generated. The generator is responsible for generating realistic virtual data, while the discriminator is responsible for distinguishing between real and generated data. Through adversarial training, the generator gradually generates virtual data that increasingly approximates the distribution of real data.

[0050] The generated virtual data is merged with real, third-stage encrypted security data to form a larger and more diverse dataset, enhancing its diversity and richness. Based on this merged dataset, a performance evaluation report for the target power bank is generated.

[0051] The target testing report includes performance evaluation of the target power bank, fault prediction, safety level assessment, and personalized usage suggestions. Fault prediction uses fused data to predict potential fault modes and their probability of occurrence. The safety level assessment generates a safety level assessment report based on safety ratings across multiple test scenarios. Personalized usage suggestions generate personalized optimization recommendations based on the user's specific needs and historical data to help the user better manage and maintain the power bank device.

[0052] By generating virtual data using generative adversarial networks and fusing it with real data, a comprehensive and personalized detection report is produced. This method not only enhances the diversity and richness of the dataset but also provides more reference information, making the detection results more comprehensive and reliable, and improving the overall accuracy and reliability of the detection.

[0053] It should be noted that the methods used in acquiring atomic dynamics information by quantum sensors also include:

[0054] S131, determine the current charging / discharging state of the target power bank.

[0055] This involves real-time monitoring of the target mobile power source's charging and discharging status, typically achieved by connecting to a Battery Management System (BMS). The BMS provides real-time charging and discharging status information. Parameters such as battery voltage, current, and temperature are acquired from the BMS, helping to determine whether the battery is currently charging or discharging. Based on these collected parameters, the specific charging and discharging state of the battery (e.g., fast charging, slow charging, normal discharging, deep discharging, etc.) is confirmed. These states are then used to adjust the operating parameters of the quantum sensor.

[0056] S132, retrieve the preset operating parameter mapping table of the quantum sensor.

[0057] Specifically, a working parameter mapping table containing various charge and discharge states and corresponding sensor operating parameters is pre-established. This mapping table is usually generated by experimental data and theoretical models to ensure its accuracy. The working parameter mapping table is stored in the system's database or memory for quick retrieval when needed.

[0058] Based on the requirements of the current detection task, a preset working parameter mapping table is retrieved. This working parameter mapping table contains the optimal combination of sensor working parameters for different charge and discharge states. By calling the preset working parameter mapping table, the system can quickly find and apply the sensor working parameters most suitable for the current charge and discharge state, thereby improving the efficiency and accuracy of data acquisition.

[0059] S133, determine the corresponding sensor operating parameters based on the charging / discharging state and operating parameter mapping table.

[0060] Specifically, a matching algorithm is used to match the currently detected charging / discharging state with the state in the operating parameter mapping table to find the closest match. Based on the matching result, the corresponding sensor operating parameters are extracted from the mapping table, including but not limited to sampling frequency, gain coefficient, and threshold setting.

[0061] Sampling frequency: Select an appropriate sampling frequency based on the battery's charging and discharging rate to ensure that key information can be captured even under rapidly changing conditions; Gain coefficient: Adjust the gain coefficient based on the signal strength inside the battery to optimize the sensor's sensitivity and avoid information loss caused by excessively strong or weak signals; Threshold setting: Set an appropriate threshold based on the battery's operating environment and state to ensure that the sensor can work stably under different conditions.

[0062] The selected sensor operating parameters are then applied to the quantum sensor to ensure its efficient operation under the current conditions. By dynamically adjusting the quantum sensor's operating parameters, high-precision microscopic charge distribution data can be obtained under different charge and discharge states, thereby improving detection sensitivity and resolution, ensuring more accurate and reliable data, and ultimately enhancing the overall accuracy of the detection results.

[0063] In addition, the method also includes the following during encrypted transmission:

[0064] S141, real-time monitoring of the real-time response status of the protection circuit in the target mobile power supply.

[0065] Among these measures, current, voltage, and temperature sensors are deployed in the target mobile power supply to ensure that key parameters of the protection circuit can be monitored in real time. These sensors collect data such as current, voltage, and temperature of the protection circuit in real time, which reflects the working status of the battery and its protection circuit.

[0066] The collected data is then transmitted to a central control system or a dedicated security monitoring module, typically via wired or wireless communication protocols (such as I2C, SPI, Bluetooth, etc.). The collected data undergoes preliminary processing, including filtering, noise reduction, and normalization, to ensure its accuracy and consistency.

[0067] S142, determine the corresponding security threat level based on the real-time response status.

[0068] This involves pre-setting normal operating ranges for current, voltage, and temperature, as well as thresholds for various safety threat levels. For example, different temperature ranges are set to distinguish between normal, warning, and dangerous states. Real-time collected data is compared and analyzed against the preset thresholds. If a parameter exceeds its normal range, the corresponding safety threat level assessment is triggered.

[0069] A specially designed threat assessment algorithm can be used to determine the current security threat level by comprehensively considering the changing trends and interrelationships of multiple parameters. For example, changes in current and temperature can be combined to determine whether there is a risk of overheating or short circuit. A classification report is generated based on the assessment results, clearly indicating the current security threat level (e.g., low, medium, high).

[0070] S143, adjust the corresponding encryption strategy based on the security threat level, wherein the encryption strategy includes adjusting the generation frequency of the encryption key, modifying the parameters of the encryption algorithm, and changing the size of the encryption data block.

[0071] Specifically, a mapping table containing different security threat levels and corresponding encryption strategies is pre-established. This mapping table defines the encryption strategies to be adopted under different threat levels, such as adjusting the generation frequency of encryption keys, modifying the parameters of encryption algorithms, and changing the size of encryption data blocks. Based on the security threat level assessed in real time, the corresponding encryption strategy is retrieved from the mapping table.

[0072] At high threat levels, increase the frequency of encryption key generation and speed up key updates to reduce the risk of key breaches. Modify specific parameters of the encryption algorithm according to the threat level, such as adjusting the number of rounds in block ciphers or the initialization vector of stream ciphers, to enhance encryption strength. At high threat levels, reduce the size of encrypted data blocks to decrease the amount of data transmitted in a single transaction and reduce the possibility of data leakage. Apply the selected encryption strategy to the data transmission process to ensure that the confidentiality and integrity of data are maintained at a high level under different threat levels.

[0073] Let's say we have a power bank charging a phone, with built-in sensors monitoring current, voltage, and temperature in real time. The currently monitored data is: current 2A, voltage 3.7V, and temperature 35℃, all within the normal range.

[0074] During the real-time monitoring phase, the system collects key parameters of the power bank, such as current, voltage, and temperature, through sensors and compares this data with preset thresholds. In this example, all parameters are within the normal range (low threat level), indicating that the battery is currently in a healthy state and poses no significant safety risk. This real-time monitoring provides firsthand information, ensuring that any potential safety issues can be detected promptly.

[0075] Based on the assessed low threat level, the system retrieves the corresponding encryption strategy from a pre-established mapping table: generating an encryption key every hour, using default encryption algorithm parameters, and encrypting data blocks of 1KB. This method ensures that data transmission maintains a high degree of confidentiality and integrity even in low-threat environments. If parameters are detected to be outside the normal range (e.g., current exceeding 4A or temperature exceeding 50°C), the system automatically adjusts the encryption strategy to enhance security, such as increasing the key generation frequency and the number of encryption rounds, thereby effectively protecting data under different threat levels.

[0076] In this embodiment of the application, the method further includes:

[0077] S151, during the detection process, monitor the target state changes of the target mobile power supply in real time.

[0078] The target mobile power bank is equipped with a variety of sensors to monitor key parameters such as charging and discharging status, temperature changes, voltage fluctuations, and current intensity in real time. The current sensor is used to monitor the charging and discharging current of the battery, the voltage sensor is used to monitor the voltage level of the battery, and the temperature sensor is used to monitor the operating temperature of the battery.

[0079] These sensors collect battery status parameters in real time and transmit the data to a central control system or a dedicated monitoring module. By monitoring key battery status parameters in real time, they provide first-hand information on battery health, providing a basis for subsequent threshold comparisons and triggering of specific status changes, ensuring that potential problems can be detected in a timely manner.

[0080] S152, retrieve the corresponding target state change threshold based on the target state change, and check whether the magnitude of the target state change exceeds the target state change threshold.

[0081] This involves pre-setting threshold values ​​for various state parameters. For example:

[0082] Charge / discharge status: A charging rate exceeding 3A or a discharging rate exceeding 5A is considered abnormal;

[0083] Temperature changes: Temperature changes exceeding 5°C per minute are considered abnormal;

[0084] Voltage fluctuation: Voltage fluctuations exceeding ±0.1V are considered abnormal;

[0085] Current intensity: Current intensity exceeding the design limit (e.g., 6A) is considered abnormal;

[0086] These thresholds are stored in the system's database or memory for quick retrieval when needed. Based on real-time monitored state changes, the corresponding threshold is retrieved from the database for comparative analysis with the current state parameters. By setting and invoking different state change thresholds, the system can accurately determine whether the battery's state exceeds the normal range, thus providing a scientific basis for identifying specific state changes and ensuring the precise triggering of detection steps.

[0087] S153, if so, then determine the corresponding specific state change and trigger the corresponding target detection step based on the specific state change.

[0088] The process involves comparing real-time monitored state changes with preset thresholds. If the change in a certain state parameter exceeds the corresponding threshold, it is determined to be a specific state change.

[0089] For example, if the battery temperature is detected to rise by 7°C per minute, exceeding the preset threshold of 5°C, it is identified as a specific state change. Once identified, the system automatically triggers a series of target detection steps, including: quantum sensing data acquisition: using quantum sensors to acquire microscopic charge distribution data inside the battery; topological data analysis and evaluation: mapping the collected data to a topological space, constructing and identifying the topological structure, and generating detailed topological analysis results; chaotic encryption security detection: using a chaotic system to encrypt and transmit the topological analysis results, ensuring data security and integrity; generative adversarial network-assisted report: generating virtual data based on generative adversarial networks and fusing it with real data to generate a comprehensive and personalized detection report, providing detailed performance evaluation, fault prediction, and maintenance recommendations.

[0090] Through dynamic monitoring and threshold comparison, the system can promptly detect and respond to specific state changes in the battery, triggering corresponding detection steps to ensure high-quality detection data under different conditions and generate comprehensive detection reports. This method not only improves the sensitivity and accuracy of detection but also enhances the overall reliability of the system, making maintenance work more targeted and proactive.

[0091] Using the same scenario as above, suppose we have a power bank charging a mobile phone. Built-in sensors monitor the charging / discharging status, temperature changes, voltage fluctuations, and current intensity in real time. Currently monitored data shows: charging current 2A, battery voltage 3.7V, and temperature 35℃, all within normal ranges. During real-time monitoring, the system collects key status parameters of the power bank through various sensors and compares this data with preset thresholds. For example, the set temperature change threshold is no more than 5℃ per minute; the current temperature change is 1℃ per minute, which is within the threshold. If any parameter (such as temperature) exceeds the preset threshold (e.g., a rise of 6℃ per minute), the system will immediately identify this specific status change. This real-time monitoring provides firsthand information on the battery's health, ensuring that any potential safety issues can be detected promptly.

[0092] Once the system detects a specific state change (such as an abnormal increase in temperature), it automatically triggers a series of target detection steps. First, it uses quantum sensors to acquire data on the microscopic charge distribution inside the battery; then, it performs topological data analysis and evaluation, constructing the topological structure and identifying potential fault modes; next, it uses a chaotic encryption mechanism to securely transmit the analysis results; finally, it generates virtual data based on a generative adversarial network and merges it with real data to generate a comprehensive and personalized detection report. This method not only improves the sensitivity and accuracy of detection but also enhances the overall reliability of the system, making maintenance work more targeted and proactive, thereby extending battery life and improving system safety.

[0093] Furthermore, the detection process also includes the following methods:

[0094] S161, acquire the topology data corresponding to the target mobile power supply in different time periods.

[0095] Specifically, 't' involves developing a detailed data acquisition plan to determine when to acquire topological data (e.g., hourly, daily, or weekly), typically based on battery usage frequency and expected performance change cycles. At these predetermined time points, quantum sensors are used to acquire data on the microscopic charge distribution within the battery, reflecting information such as charge distribution and ion migration paths over a specific time period.

[0096] By acquiring topological data at different time points, the microscopic state information of the battery at each time point is provided, which provides basic data for subsequent time series analysis and ensures a comprehensive understanding of the battery's health status change trend.

[0097] S162, Based on time series analysis, the topological structure data of different time periods are compared and analyzed, and the battery performance of the target mobile power supply is identified over time based on the analysis results. The battery performance trend includes changes in charge distribution and ion migration path.

[0098] The topological data from different time periods are arranged chronologically to form a time-series dataset, which contains microscopic state information of the battery at each time point. Time-series analysis algorithms (such as Autoregressive Integral Moving Average (ARIMA) and Long Short-Term Memory (LSTM) networks) are used to compare and analyze the time-series data, which helps to identify the trend of battery performance changes over time.

[0099] Key features, such as the rate of change in charge distribution and the changing patterns of ion migration paths, are extracted from time-series data. These features will be used for subsequent fault prediction and performance evaluation. Time-series analysis techniques can deeply explore the trends in battery performance over time, identify potential performance degradation issues, and thus improve the accuracy of fault prediction.

[0100] S163 identifies potential faults or performance degradation issues based on battery change trends and generates corresponding battery reports.

[0101] Based on the extracted key features, pre-trained fault identification models (such as Support Vector Machine (SVM) and Random Forest (RF)) are used to identify potential fault modes within the battery. For example, uneven charge distribution may lead to localized overheating, and abnormal ion migration paths may cause battery capacity degradation. Time series analysis results are then used to predict battery performance over a future period. This helps to identify potential fault risks in advance and take corresponding preventative measures.

[0102] Based on the above analysis results, a detailed battery report is generated. The report includes, but is not limited to: Battery performance assessment: the current overall health status and performance indicators of the battery; Fault prediction: possible future fault modes and their probability of occurrence; Maintenance recommendations: specific maintenance suggestions and optimization solutions for current and potential future problems; Personalized usage recommendations: personalized battery usage recommendations based on user habits and historical data to help users better manage and maintain battery devices.

[0103] By identifying battery trends and generating detailed battery reports, the system can proactively detect potential faults or performance degradation and provide targeted maintenance recommendations. This approach not only improves the efficiency and effectiveness of battery management but also extends battery life and enhances the overall reliability of the system.

[0104] In this embodiment of the application, the method further includes:

[0105] S171 monitors the status parameters of the target mobile power supply in real time.

[0106] The status parameters include charge / discharge status, temperature, voltage, and current; the system continuously updates the battery status information and stores it in the database for subsequent analysis and retrieval.

[0107] S172, retrieve the preset detection task priority rules, sort the detection tasks according to the priority rules, and process the high-priority tasks first.

[0108] This involves pre-setting a set of priority rules for detection tasks, categorizing them according to their importance and urgency. For example: high-priority tasks include battery overheating alarms and voltage anomaly detection; medium-priority tasks include periodic performance evaluations and charging efficiency testing; and low-priority tasks include long-term trend analysis and historical data backup.

[0109] These priority rules are stored in the system's database or memory for quick retrieval when needed. Based on current testing requirements and battery status parameters, the system retrieves the corresponding priority rules from the database to prioritize various testing tasks. By setting and invoking these priority rules, the system can allocate resources rationally, ensuring that high-priority tasks are processed promptly, thus improving the overall efficiency and effectiveness of the testing work.

[0110] S173, determine the execution order and time of each detection task based on the state parameters of the target mobile power battery and the priority of each detection task.

[0111] The task list generation feature lists all pending detection tasks and marks their priorities. For example: high-priority tasks: overheat alarm detection, voltage anomaly detection; medium-priority tasks: periodic performance evaluation, charging efficiency detection; low-priority tasks: long-term trend analysis, historical data backup.

[0112] The tasks in the task list are sorted according to priority rules, ensuring that high-priority tasks are listed first and processed first. An appropriate time window is allocated to each task to ensure that it can be completed within the specified time. For example, high-priority tasks may be assigned shorter but more frequent time windows, while low-priority tasks are assigned longer but less frequent time windows.

[0113] S174, During the detection process, the execution order and time of each detection task are adjusted according to the real-time monitored status changes and the completion status of the detection tasks.

[0114] The system continuously monitors battery status parameters, captures any abnormal changes in real time, tracks the completion status of each detection task, and records completed and incomplete tasks. Based on the real-time monitored status changes and task completion status, the system dynamically adjusts the execution order and timing of tasks. For example, if a high-priority task fails to complete on time, its time window is extended or resources are increased; if a low-priority task has been completed, the next similar task is terminated early or skipped.

[0115] By dynamically adjusting the execution order and timing of tasks, the system can flexibly respond to real-time changes in status parameters and task completion, ensuring the efficiency and accuracy of detection work and improving the overall reliability and response speed of the system.

[0116] Based on the same case background, as mentioned above (and will not be repeated here), during the real-time monitoring phase, the system collects key status parameters of the power bank through various sensors and compares and analyzes this data with preset detection task priority rules. For example, high-priority tasks include overheat alarm detection and voltage anomaly detection, while medium-priority tasks include periodic performance evaluation and charging efficiency detection. If a key parameter (such as temperature) exceeds a preset threshold (e.g., an increase of 5°C per minute), the system will immediately identify this change and prioritize the relevant high-priority tasks. This real-time monitoring provides firsthand information on the battery's health status, ensuring that potential problems can be detected and responded to promptly.

[0117] Based on real-time monitored status parameters and task priorities, the system dynamically adjusts the execution order and timing of each detection task. For example, when the battery temperature suddenly rises to 40°C, the system will prioritize triggering the overheat alarm detection task and postpone other low-priority tasks. As the battery temperature returns to normal, the system will readjust the task order and continue executing any unfinished low- to medium-priority tasks. Through this dynamic adjustment mechanism, the system can efficiently utilize resources under different conditions, quickly respond to potential problems, and ensure the efficiency and accuracy of the detection work. This method not only improves the flexibility and adaptability of the detection but also enhances the overall reliability of the system and extends the battery's lifespan.

[0118] This case study demonstrates how real-time monitoring and dynamic adjustment of the testing task sequence can ensure the health status assessment of power banks, effectively addressing potential problems and providing high-quality test results under various circumstances. This method significantly improves the accuracy and efficiency of the testing process.

[0119] This application discloses a mobile power bank detection device, referring to... Figure 2 The device includes, but is not limited to:

[0120] The first-stage micro-dataset generation module 200 determines the corresponding target mobile power source, detects the micro-charge distribution inside the target mobile power source through quantum tunneling and quantum confinement effects using quantum sensors, obtains atomic dynamic information of the battery of the target mobile power source during the charging and discharging process based on the detection results, and uses the atomic dynamic information to generate the first-stage micro-dataset.

[0121] The second-stage topology analysis result production module 210 maps the first-stage micro dataset to the corresponding target topology space, constructs the target topology structure corresponding to the first-stage micro dataset in the target topology space, calls the pre-trained topology recognition model, inputs the target topology structure into the topology recognition model for recognition, and generates the second-stage topology analysis results. The target topology structure includes simple complexes and persistent cohomology, and the second-stage topology analysis results include potential failure modes.

[0122] The target detection report generation module 220 invokes a pre-designed encrypted communication mechanism based on a chaotic system. Based on the randomness and unpredictability of the encrypted communication mechanism, it encrypts and transmits the data in the second-stage topology analysis results to generate third-stage encrypted security data. It generates virtual data corresponding to the third-stage encrypted security data based on a generative adversarial network. It merges the virtual data with the third-stage encrypted security data and uses the merged virtual data and the third-stage encrypted security data to generate a corresponding target detection report. The target detection report includes target mobile power bank performance evaluation, fault prediction, security level assessment, and personalized usage suggestions.

[0123] This application also discloses a mobile power bank detection device, including a processor, wherein the processor runs a program of any one of the mobile power bank detection methods described above.

[0124] This application also discloses a storage medium storing a program for the mobile power bank detection method described in any one of the above embodiments.

[0125] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for detecting a mobile power bank, characterized in that, include: The corresponding target mobile power source is identified, and the microscopic charge distribution inside the target mobile power source is detected by quantum sensors through quantum tunneling and quantum confinement effects. Based on the detection results, the atomic dynamic information of the battery of the target mobile power source during the charging and discharging process is obtained, and the first-stage microscopic dataset is generated based on the atomic dynamic information. The first-stage micro dataset is mapped to the corresponding target topology space. The target topology structure corresponding to the first-stage micro dataset is constructed in the target topology space. A pre-trained topology recognition model is retrieved, and the target topology structure is input into the topology recognition model for recognition, generating the second-stage topology analysis results. The target topology structure includes simple complexes and persistent cohomology, and the second-stage topology analysis results include potential failure modes. The system retrieves a pre-designed encrypted communication mechanism based on a chaotic system. Based on the randomness and unpredictability of this mechanism, it encrypts and transmits the data from the second-stage topology analysis results, generating third-stage encrypted security data. It then generates virtual data corresponding to the third-stage encrypted security data using a generative adversarial network. This virtual data is then fused with the third-stage encrypted security data. Finally, a corresponding target detection report is generated based on the fused virtual data and the third-stage encrypted security data. The target detection report includes target mobile power bank performance evaluation, fault prediction, security level assessment, and personalized usage suggestions.

2. The mobile power bank detection method according to claim 1, characterized in that, In the process of acquiring atomic dynamic information by the quantum sensor, the method further includes: Determine the current charging / discharging state of the target power bank; Retrieve the preset operating parameter mapping table of the quantum sensor; The corresponding sensor operating parameters are determined based on the charging / discharging state and the operating parameter mapping table, wherein the sensor operating parameters include sampling frequency, gain coefficient and threshold setting.

3. The mobile power bank detection method according to claim 1, characterized in that, The method also includes the following during encrypted transmission: Real-time monitoring of the real-time response status of the protection circuit in the target mobile power supply, including current, voltage and temperature; The corresponding security threat level is determined based on the real-time response status. The corresponding encryption strategy is adjusted based on the security threat level. The encryption strategy includes adjusting the generation frequency of the encryption key, modifying the parameters of the encryption algorithm, and changing the size of the encrypted data block.

4. The mobile power bank detection method according to claim 1, characterized in that, The method also includes: During the detection process, the target state changes of the target mobile power supply are monitored in real time, including charging and discharging status, temperature changes, voltage fluctuations and current intensity. Based on the target state change, retrieve the corresponding target state change threshold, and check whether the magnitude of the target state change exceeds the target state change threshold. If so, the corresponding specific state change is determined, and the corresponding target detection steps are triggered based on the specific state change. The target detection steps include quantum sensing data acquisition, topology data analysis and evaluation, chaotic encryption security detection, and generation of adversarial network auxiliary reports.

5. The mobile power bank detection method according to claim 1, characterized in that, The method also includes the following during the detection process: Acquire the topology data of the target power bank at different time periods; Based on time series analysis, the topological structure data of different time periods are compared and analyzed. Based on the analysis results, the battery performance of the target mobile power bank is identified as a trend of battery change over time. The battery change trend includes changes in charge distribution and ion migration path. Based on the battery change trend, potential faults or performance degradation issues are identified, and corresponding battery reports are generated.

6. The mobile power bank detection method according to claim 4, characterized in that, The method also includes: Real-time monitoring of the target mobile power supply's status parameters, including charging / discharging status, temperature, voltage, and current; Retrieve the preset detection task priority rules, and sort the detection tasks according to the priority rules, with high-priority tasks being processed first. The execution order and time of each detection task are determined based on the state parameters of the target mobile power bank battery and the priority of each detection task. During the testing process, the execution order and timing of each testing task are adjusted based on real-time monitoring of status changes and task completion.

7. A mobile power bank detection device, characterized in that, include: The first-stage micro-dataset generation module identifies the corresponding target mobile power source, detects the micro-charge distribution inside the target mobile power source using quantum sensors through quantum tunneling and quantum confinement effects, obtains atomic dynamic information of the battery of the target mobile power source during the charging and discharging process based on the detection results, and uses the atomic dynamic information to generate the first-stage micro-dataset. The second-stage topology analysis result production module maps the first-stage micro dataset to the corresponding target topology space, constructs the target topology structure corresponding to the first-stage micro dataset in the target topology space, retrieves the pre-trained topology recognition model, and inputs the target topology structure into the topology recognition model for recognition to generate the second-stage topology analysis results. The target topology structure includes simple complexes and persistent cohomology, and the second-stage topology analysis results include potential failure modes. The target detection report generation module invokes a pre-designed encrypted communication mechanism based on a chaotic system. Based on the randomness and unpredictability of this mechanism, it encrypts and transmits the data from the second-stage topology analysis results, generating third-stage encrypted security data. It then generates virtual data corresponding to the third-stage encrypted security data using a generative adversarial network. The virtual data is then fused with the third-stage encrypted security data. Finally, the fused virtual data and the third-stage encrypted security data are used to generate a corresponding target detection report. This target detection report includes target mobile power bank performance evaluation, fault prediction, security level assessment, and personalized usage suggestions.

8. A mobile power bank detection device, characterized in that, Includes a processor, wherein the processor runs a program for the mobile power bank detection method as described in any one of claims 1-6.

9. A storage medium, characterized in that, The device stores a program for a mobile power bank detection method as described in any one of claims 1-6.

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