Battery management method, terminal device, and storage medium

Abstract

This application provides a battery management method, terminal device, and storage medium. The method manages at least one start-stop power supply, which supplies power to preset devices. The method includes: acquiring real-time battery data, power information, and corresponding device information of the preset devices from the start-stop power supply; the real-time battery data includes at least one parameter data characterizing battery operating parameters; determining the data type corresponding to each parameter data; determining monitoring conditions corresponding to each data type based on the power information, device information, and data type; and monitoring the corresponding parameter data in the real-time battery data according to each monitoring condition to complete the battery management of the start-stop power supply. This battery management method achieves efficient and intelligent management of one or more start-stop power supplies by centrally acquiring and processing data from distributed battery packs, combining power information and device information, and dynamically adjusting monitoring conditions.

Description

Technical Field

[0001] This application relates to the field of battery management technology, and in particular to a battery management method, terminal device and storage medium. Background Technology

[0002] Battery safety is a critical issue. Problems such as overheating, overcharging, and short circuits can lead to battery explosions or fires. Lack of targeted battery management can result in battery devices being less durable. Therefore, safer technologies are needed to ensure battery safety. In modern industrial and smart device environments, the application of Battery Management Systems (BMS) is becoming increasingly widespread, especially in applications that rely on stable power sources, such as electric vehicles, energy storage systems, and portable electronic devices.

[0003] Traditional battery management systems mainly rely on local monitoring and alarm mechanisms. While these can ensure battery safety and lifespan to a certain extent, localization has many limitations when facing the monitoring needs of a large number of distributed battery packs, such as difficulties in centralized management, limited data processing capabilities, and high maintenance costs.

[0004] Therefore, a battery management method is urgently needed to solve at least one of the above problems. Summary of the Invention

[0005] This application provides a battery management method, terminal device, and storage medium, aiming to solve the problem that traditional battery management systems mainly rely on local monitoring and alarm mechanisms. Although these mechanisms can guarantee battery safety and lifespan to a certain extent, they have many limitations when facing the monitoring needs of a large number of distributed battery packs, such as difficulties in centralized management, limited data processing capabilities, and high maintenance costs.

[0006] In a first aspect, this application provides a battery management method for managing at least one start-stop power supply, which supplies power to a preset device; the method includes:

[0007] Acquire real-time battery data, power information, and corresponding preset device information for power supply start-stop; real-time battery data includes at least one parameter data characterizing battery operating parameters;

[0008] Determine the data type corresponding to each parameter;

[0009] Determine the monitoring conditions corresponding to each data type based on power information, device information, and data type.

[0010] The system monitors the corresponding parameters in the real-time battery data according to each monitoring condition to complete the battery management of the start-stop power supply.

[0011] Secondly, this application provides a battery management device for managing at least one start-stop power supply, the start-stop power supply being used to power a preset device; the device includes:

[0012] The data acquisition unit is used to acquire real-time battery data, power information and corresponding preset device information of the start-stop power supply; the real-time battery data includes at least one parameter data for characterizing battery operating parameters;

[0013] The type determination unit is used to determine the data type corresponding to each parameter data.

[0014] The condition determination unit is used to determine the monitoring conditions corresponding to each data type based on power information, device information, and data type.

[0015] The management completion unit is used to monitor the corresponding parameter data in the real-time battery data according to each monitoring condition, so as to complete the battery management of the start-stop power supply.

[0016] Thirdly, this application also provides a terminal device, including: a memory and a processor;

[0017] The memory is used to store computer programs;

[0018] The processor is configured to execute the computer program and, in executing the computer program, implement the steps of the battery management method as described in the first aspect above.

[0019] Fourthly, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to implement the steps of the battery management method described in the first aspect above.

[0020] This application provides a battery management method, terminal device, and storage medium, aiming to optimize the limitations of traditional battery management systems for start-stop power supplies when dealing with a large number of distributed battery packs. The specific technical content is as follows: First, real-time battery data from the start-stop power supply is acquired. This data includes, but is not limited to, battery operating parameters such as voltage, current, temperature, and charging status. Power supply information from the start-stop power supply is acquired, such as power type, model, location, and usage time. Device information of the preset devices powered by the start-stop power supply is acquired, such as device type, power consumption, and operating status. Then, the data type of each parameter is analyzed and determined for better management and monitoring. Next, based on the power supply information, device information, and data type, monitoring conditions corresponding to each data type are determined. Monitoring conditions may include thresholds, anomaly detection rules, and early warning mechanisms. Finally, based on the determined monitoring conditions, the corresponding parameter data in the real-time battery data is monitored to ensure the normal operation and safety of the battery. The monitoring results can be used for real-time alarms, fault diagnosis, and maintenance suggestions.

[0021] Furthermore, by centrally acquiring and processing data from distributed battery packs, the system overcomes the difficulties of centralized management inherent in traditional localized management methods, thus improving management efficiency. Simultaneously, utilizing advanced data processing technology enables efficient processing and analysis of data from numerous distributed battery packs corresponding to multiple start-stop power supplies, enhancing the system's data processing capabilities. Real-time monitoring and fault diagnosis allow for the early detection of potential problems and preventative maintenance, reducing maintenance costs and the risk of equipment failure. Real-time monitoring of battery parameters enables timely detection of anomalies and responsiveness, improving battery safety and reducing the risk of accidents. Furthermore, precise monitoring and management of battery operating parameters optimizes battery usage conditions and extends battery life. This method dynamically adjusts monitoring conditions based on different power sources and equipment information, improving system flexibility and adaptability. Through data type determination and monitoring condition setting, the system can more accurately diagnose and provide early warnings, enhancing its reliability and stability.

[0022] In summary, this battery management method achieves efficient and intelligent management of start-up and shutdown power supplies by centrally acquiring and processing data from distributed battery packs, combining power and equipment information, and dynamically adjusting monitoring conditions. This method not only improves management efficiency and data processing capabilities but also reduces maintenance costs and enhances battery safety and lifespan, demonstrating broad application prospects.

[0023] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a schematic flowchart illustrating the steps of a battery management method provided in an embodiment of this application;

[0026] Figure 2 This is a schematic diagram illustrating the principle of a battery management method provided in an embodiment of this application;

[0027] Figure 3 This is a schematic diagram of the structure of a battery management device provided in an embodiment of this application;

[0028] Figure 4 This is a schematic block diagram of the structure of a terminal device provided in an embodiment of this application.

[0029] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Detailed Implementation

[0030] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0031] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0032] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.

[0033] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0034] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0035] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0036] Battery safety is a critical issue. Problems such as overheating, overcharging, and short circuits can lead to battery explosions or fires. Lack of targeted battery management can result in battery devices being less durable. Therefore, safer technologies are needed to ensure battery safety. In modern industrial and smart device environments, the application of Battery Management Systems (BMS) is becoming increasingly widespread, especially in applications that rely on stable power sources, such as electric vehicles, energy storage systems, and portable electronic devices.

[0037] Traditional battery management systems mainly rely on local monitoring and alarm mechanisms. While these can ensure battery safety and lifespan to a certain extent, localization has many limitations when facing the monitoring needs of a large number of distributed battery packs, such as difficulties in centralized management, limited data processing capabilities, and high maintenance costs.

[0038] Therefore, a battery management method is urgently needed to solve at least one of the above problems.

[0039] To resolve the above issues, please refer to [link / reference]. Figure 1 , Figure 1 This is a schematic flowchart of a battery management method provided in one embodiment of this application. The method is used to manage at least one start-stop power supply, which is used to supply power to a preset device. This battery management method can be implemented by a terminal device, which can be deployed on a single server or a server cluster. It can also be deployed on a handheld terminal, laptop, wearable device, or robot, etc.

[0040] It should be noted that the acquisition of any information mentioned in the provided method, such as power information and device information, is carried out in accordance with relevant regulations and with the consent of the corresponding users, and will not infringe on user privacy or violate relevant laws and regulations.

[0041] To solve the above problem, please refer to Figure 1 and Figure 2 Specifically, such as Figure 1 As shown, the provided battery management method includes steps S101 to S106. Details are as follows:

[0042] Step S101. Obtain real-time battery data, power information, and corresponding preset device information of the start-stop power supply; the real-time battery data includes at least one parameter data used to characterize the battery's operating parameters.

[0043] Specifically, terminal devices can send relevant real-time battery data, power information, and device information through the Battery Management System (BMS) integrated into the start-stop power supply. Real-time battery data includes, but is not limited to, parameters such as the start-stop power supply's voltage, current, temperature, state of charge (SOC), state of health (SOH), internal resistance, self-discharge rate, cycle count, and charge / discharge rate. These parameters comprehensively characterize the battery's operating status. Power information includes, but is not limited to, the start-stop power supply's model, capacity, production date, usage history, manufacturer information, and maintenance history. This information helps in understanding the start-stop power supply's basic characteristics and historical status. Device information includes, but is not limited to, the model, usage scenario, power requirements, operating mode, and operating environment of the preset devices powered by the start-stop power supply. This information is used to determine the specific application requirements of the battery within the devices.

[0044] It should be noted that, as Figure 2 As shown, the method provided in this application allows the terminal device to simultaneously manage the battery of multiple start-stop power supplies. The preset devices mounted on these start-stop power supplies include, but are not limited to, electric vehicles, energy storage devices, and portable electronic devices. The method provided in this application can manage start-stop power supplies mounted on any preset device. Specific embodiments illustrating real-time battery data, power information, and device information when the preset devices are electric vehicles, energy storage devices, and portable electronic devices are now provided:

[0045] When the preset device is an electric vehicle, real-time battery data can include the battery pack's voltage, current, and temperature. Power information can include the battery pack's model, capacity, and production date. Device information can include the vehicle's brand, model, and mileage.

[0046] When the preset device is an energy storage device, real-time battery data can include the energy storage device's voltage, current, charging status, and temperature. Power information can include the battery pack's production date and usage history. Device information can include the energy storage device's total capacity and operating environment.

[0047] When the preset device is a portable electronic device, real-time battery data can include the battery's state of charge (SOC) and state of health (SOH). Power information can include the battery model and capacity. Device information can include the portable electronic device's brand, model, and usage scenario.

[0048] In summary, step S101, by acquiring various types of real-time data, provides a comprehensive understanding of the battery's operating status and enables timely detection of potential problems. Acquiring power supply and device information allows for more precise battery management of the power supply, avoiding a one-size-fits-all approach. Simultaneously, the acquisition and processing of real-time data allows for rapid response to changes in battery status, improving system efficiency and responsiveness. The abundant data collected in this step can support subsequent fault analysis and optimization.

[0049] Step S102. Determine the data type corresponding to each parameter.

[0050] Specifically, based on the nature of the parameter data, it is classified into different data types, such as voltage data, current data, temperature data, SOC data, SOH data, internal resistance data, self-discharge rate data, cycle count data, charge / discharge rate data, etc.

[0051] Classification criteria can be based on the units or dimensions corresponding to each data type. For example, voltage is measured in volts (V), current in amperes (A), and temperature in degrees Celsius (°C), and classification is based on the symbols corresponding to these units. Alternatively, identifiers can be set for each data type using units or dimensions (e.g., current is set to A) and matched with various parameter data to determine the appropriate data type based on the matching results.

[0052] In summary, step 102 categorizes large amounts of real-time data to facilitate subsequent processing and analysis. Categorization allows for faster identification of specific data types, improving the monitoring system's response speed. Categorized data is more structured, facilitating data mining and fault analysis. Management and monitoring strategies for different data types can be more refined and targeted, simplifying overall management complexity.

[0053] Step S103. Determine the monitoring conditions corresponding to each data type based on the power information, device information, and data type.

[0054] Specifically, this application determines whether the battery is in normal working condition by setting different monitoring thresholds or rules for different types of parameter data. Monitoring conditions may include, but are not limited to, the following:

[0055] 1. Threshold setting: Set the threshold according to the normal range of parameter data, such as the normal range of voltage being 3.0V to 3.5V, and the normal range of temperature being 0℃ to 45℃, etc.

[0056] 2. Dynamic Adjustment: By combining power supply and equipment information, monitoring conditions are dynamically adjusted. For example, based on the battery model and capacity, as well as the power requirements and operating environment of the equipment, temperature and voltage thresholds are dynamically adjusted.

[0057] 3. Rule setting: Set more complex monitoring rules, such as triggering an alarm when the battery temperature and voltage exceed a certain range at the same time, or adjusting the charging and discharging rate under specific operating conditions.

[0058] 4. Multi-level early warning: Set multiple early warning levels. When the data approaches or exceeds a certain threshold, different levels of alarms or protective measures will be triggered.

[0059] When the preset device is an electric vehicle, the normal range for voltage data can be set to 3.0V to 3.5V based on the battery pack model and capacity. The normal range for temperature data can be set to 0℃ to 45℃ based on the vehicle's brand and model. The SOC threshold is dynamically adjusted based on the vehicle's mileage and usage records to prevent over-discharge of the battery after high mileage or prolonged use.

[0060] When the preset device is an energy storage device, the depth of discharge threshold can be dynamically adjusted based on the energy storage unit's production date and ambient temperature to prevent over-discharge of the battery under extreme conditions. A threshold for the charge / discharge rate can be set based on the system's total capacity and usage records to prevent damage caused by excessively rapid charging and discharging of the battery.

[0061] When the preset device is a portable electronic device, the normal range of SOC can be set from 15% to 90% according to the brand and model of the device. The temperature threshold can be dynamically adjusted according to the usage scenario of the device (such as home, outdoors, etc.) to adapt to different environmental conditions.

[0062] Furthermore, step S103 sets different monitoring conditions based on the characteristics of different devices and power supplies, making management more personalized and precise. By dynamically adjusting the monitoring conditions according to the specific needs of the equipment and its operating environment, the overall performance of the equipment can be optimized.

[0063] Step S104. Monitor the corresponding parameter data in the real-time battery data according to each monitoring condition to complete the battery management of the start-stop power supply.

[0064] Specifically, by comparing parameter data with corresponding monitoring conditions in real time, if the parameter data exceeds the set threshold or does not conform to the rules, the corresponding alarm or protection mechanism is triggered. The method continuously collects and processes real-time data through terminal devices and compares it with preset monitoring conditions. When data anomalies are detected, the system immediately takes measures. When a parameter exceeds a set threshold, the system issues an alarm to the user through various means (such as sound, light, SMS, APP notification, etc.). Different protection measures are taken according to the type of anomaly, such as stopping charging, cutting off power, and reducing device power. Detailed information on each anomaly detection and protection measure is recorded for subsequent analysis and optimization. When an anomaly occurs in one of the multiple battery groups of any start-stop power supply, the terminal device can control the BMS system of that start-stop power supply to isolate it, preventing it from affecting the normal operation of other battery groups.

[0065] When the preset device is an electric vehicle, if the real-time voltage exceeds 3.5V, the system will trigger an alarm and automatically stop charging. If the real-time temperature exceeds 45℃, the system will issue an alarm and activate the cooling system. If the SOC is below 15%, the system will issue a low battery alarm and recommend charging.

[0066] When the preset device is an energy storage device, if the real-time temperature data exceeds the monitoring temperature corresponding to the monitoring conditions, the terminal device will issue an alarm and activate heat dissipation measures. If the real-time charge / discharge rate exceeds the monitoring rate corresponding to the monitoring conditions, the terminal device will issue an alarm and reduce the charge / discharge rate. If the depth of discharge exceeds the set threshold, the terminal device will issue an alarm and stop discharging to prevent over-discharge of the battery.

[0067] When the preset device is a portable electronic device, if the real-time temperature data is lower than the monitoring temperature corresponding to the monitoring conditions, the terminal device will issue an alarm and reduce the device's power consumption. If the State of Charge (SOC) is lower than the monitoring SOC corresponding to the monitoring conditions, the terminal device will issue a low battery alarm and suggest charging. If the battery's internal resistance exceeds the monitoring internal resistance corresponding to the monitoring conditions, the terminal device will issue an alarm and suggest that the user replace the battery.

[0068] Step S104, through real-time monitoring, enables timely detection and handling of abnormal situations, reducing the occurrence of battery accidents. Reasonable management strategies can effectively extend battery life and improve the overall performance and reliability of the equipment. Timely alarm and protection mechanisms provide a better user experience and avoid equipment malfunctions or safety risks caused by battery problems. Simultaneously, terminal devices can continuously optimize monitoring conditions and protection measures through log recording and data analysis, improving the level of intelligent management.

[0069] In summary, the battery management method provided in this application achieves comprehensive, real-time, and precise battery management by acquiring comprehensive real-time data on power supply start-up and shutdown, detailed power information, and device information, classifying the data, and setting personalized monitoring conditions. This method not only effectively addresses the limitations of traditional battery management systems in terms of centralized management, data processing, and maintenance costs, but also promptly detects and handles battery anomalies, extends battery life, and ensures device safety and reliability. The specific technical content and examples corresponding to the above steps demonstrate the practical operation and effects of this method in different application scenarios, further proving its importance and effectiveness in modern industry and intelligent devices.

[0070] In some embodiments, before monitoring the corresponding parameter data in the real-time battery data according to each monitoring condition, the method further includes: generating a data storage area corresponding to each start-stop power supply according to multiple data types; the data storage area includes multiple parameter storage areas, each parameter storage area being used to store one parameter data; constructing a real-time battery database according to the data storage area corresponding to each start-stop power supply, so as to obtain each monitoring condition from the real-time battery database to monitor the corresponding parameter data in the real-time battery data.

[0071] This embodiment generates a data storage area for each start-stop power supply. This storage area includes multiple parameter storage areas, each used to store a specific type of parameter data. Such storage areas may include, but are not limited to, voltage parameter storage areas, current parameter storage areas, temperature parameter storage areas, SOC parameter storage areas, SOH parameter storage areas, internal resistance parameter storage areas, self-discharge rate parameter storage areas, cycle count parameter storage areas, and charge / discharge rate parameter storage areas.

[0072] By integrating the data storage areas of each start-stop power supply, a unified real-time battery database is constructed to store and manage real-time data from all batteries. Terminal devices store the real-time battery data into the corresponding parameter storage area. A unique index is generated for each start-stop power supply and parameter storage area to facilitate quick data retrieval and access. The database is updated in real-time to ensure data timeliness and accuracy.

[0073] Then, based on the monitoring requirements, the terminal device retrieves relevant parameter data from the real-time battery database. The retrieved parameter data is compared with preset monitoring conditions to determine if it exceeds thresholds or violates rules. When abnormal data is detected, corresponding alarms or protection measures are triggered. The user is notified through various means (such as sound, light, SMS, and app notifications). Simultaneously, protective measures such as stopping charging, cutting off power, and reducing device power can also be taken. The terminal device records detailed information for each anomaly detection and protection measure, facilitating subsequent analysis and optimization.

[0074] By generating multiple parameter storage areas for each start / stop power supply, data is categorized and stored, making the data structured and organized. This structuring facilitates data management and retrieval, improving the management efficiency of terminal devices. The real-time battery database updates data in real time, ensuring that terminal devices can obtain the latest battery status information promptly. Through real-time data, terminal devices can quickly respond to changes in battery status and handle abnormal situations promptly.

[0075] Meanwhile, all parameter data is categorized and stored in different parameter storage areas to ensure data integrity. Real-time data updating and storage mechanisms ensure data accuracy, reducing false alarms caused by missing or inaccurate data. Furthermore, by centrally storing data from multiple start-stop power supplies in a real-time battery database, centralized management and analysis are facilitated. Through a unified database, terminal devices can achieve cross-device monitoring of start-stop power supplies across multiple devices, improving management efficiency. Detailed records of each anomaly detection and protection measure are maintained, providing data support for subsequent fault analysis. Data analysis can identify potential problems with start-stop power supplies, allowing for timely optimization of monitoring conditions and protection measures to prevent fault occurrence.

[0076] In summary, this embodiment enhances the data processing capabilities and real-time performance of the battery management system by generating a data storage area corresponding to each start-stop power supply and constructing a real-time battery database. This method not only effectively addresses the limitations of traditional battery management systems in terms of centralized management, data processing, and maintenance costs, but also improves data integrity and accuracy through categorized storage and real-time updates. Multi-level early warning and fault isolation mechanisms further enhance system security and reliability, while log recording and data-driven optimization measures provide strong support for subsequent fault analysis and prevention. Through these improvements, terminal devices can better adapt to the complex needs of modern industry and intelligent devices, improving overall device performance and user experience.

[0077] For example, after completing the battery management of the start-stop power supply, the method further includes: upon receiving updated real-time battery data from the start-stop power supply, obtaining the data type corresponding to each parameter data of the updated real-time battery data; storing each parameter data in the corresponding parameter storage area according to the updated data type, so as to complete the battery management of each start-stop power supply based on the updated real-time battery database.

[0078] In this example, when the terminal device receives new real-time battery data, it first identifies the data type corresponding to each parameter to ensure the accuracy of data classification. The updated parameter data is then stored in the corresponding parameter storage area. For example:

[0079] If new voltage data is received, it is stored in the voltage parameter storage area.

[0080] If new temperature data is received, it is stored in the temperature parameter storage area.

[0081] If new SOC data is received, it is stored in the SOC parameter storage area.

[0082] The real-time battery database is updated in real time to ensure that the data is always up-to-date. Based on the updated data in the real-time battery database, monitoring and management are re-implemented to ensure that the latest battery status is addressed promptly. The latest parameter data is retrieved from the updated database. This latest parameter data is compared with preset monitoring conditions to determine if thresholds are exceeded or rules are not followed. If an anomaly is detected, appropriate alarms or protective measures are triggered.

[0083] Continuous updates to the real-time battery database ensure that terminal devices are always monitoring and managing based on the latest data, improving data real-time performance and accuracy. Terminal devices can quickly respond to changes in battery status, promptly handle anomalies, and reduce battery accidents. New data undergoes data type identification before storage to ensure consistent and accurate data classification, avoiding data confusion. Consequently, terminal devices can dynamically store new data, flexibly responding to changes in battery status and enhancing their adaptability and flexibility. Based on updated data, terminal devices can dynamically adjust monitoring conditions and protection measures to better meet actual needs.

[0084] In summary, by re-identifying the data type and storing it in the corresponding parameter storage area upon receiving new data, the real-time battery database is ensured to always be up-to-date. This process not only improves the real-time performance and accuracy of the data but also enhances the flexibility and reliability of the terminal device. Through continuous data updates and monitoring, the terminal device can promptly detect and handle battery anomalies, extend battery life, provide a better user experience, and reduce maintenance costs. This effectively improves the level of intelligence in battery management.

[0085] It should be noted that, in some embodiments, the identification information corresponding to the parameter storage area is the data type corresponding to the stored parameter data; storing each parameter data into the corresponding parameter storage area according to the updated data type includes: matching the updated data type with the identification information; sending the parameter data to the parameter storage area corresponding to the successfully matched identification information, and determining the data type that fails to match as the target data type; generating a target storage area in the data storage area according to the target data type, for storing the parameter data corresponding to the target data type.

[0086] By matching the updated data types with the identification information, it is ensured that each parameter data is stored in the corresponding data type storage area, avoiding data confusion and classification errors. Each parameter storage area has clear identification information, facilitating management by terminal devices and querying by users.

[0087] When new data types emerge (e.g., when a new sensor on the power supply measures parameters of a new data type), the terminal device can automatically identify and generate a corresponding target storage area, ensuring the system can adapt to the new data type. The terminal device can dynamically expand the data storage area without manual configuration, improving system flexibility and scalability. Regardless of whether the data type already exists, the terminal device ensures complete storage of all real-time data, preventing data loss. The terminal device can update the data storage area in real time, ensuring data up-to-dateness and accuracy, improving monitoring effectiveness.

[0088] By matching the updated data type with the identifier information of the parameter storage area, it is ensured that each parameter data is accurately stored in the corresponding data type storage area. If a match fails, the terminal device automatically generates a target storage area for the new data type, thereby achieving dynamic data management and expansion. This method not only improves the accuracy and consistency of data storage but also enhances the system's flexibility and scalability, reduces maintenance costs, and improves user experience and system reliability. Through these improvements, the battery management system can better adapt to the diversity and complexity of modern industry and smart devices, providing a more efficient, safer, and more reliable battery management solution.

[0089] It should be noted that in some embodiments, the real-time battery database is a Prometheus database. Prometheus is a time-series database characterized by high concurrency and large data volume, making it suitable for scenarios involving start-stop power supplies and other devices deployed with a large number of pre-installed devices. Users can store the real-time battery data reported by the BMS system corresponding to each start-stop power supply into the Prometheus database to achieve monitoring functions for specific scenarios. The specific data reporting process corresponding to the database may include:

[0090] The BMS system for starting and stopping the power supply reports real-time battery data (battery power, battery voltage, battery temperature, ignition data, etc.) of the equipment operation through communication modules such as 4G / WIFI / Bluetooth.

[0091] The terminal device parses the data reported by the BMS through a preset connection protocol. The terminal device creates multiple Prometheus indicator buckets (equivalent to parameter storage areas) and uploads the corresponding indicators (equivalent to parameter data) to the corresponding indicator buckets through the interface.

[0092] By connecting to the Prometheus database using tools such as Grafana, and employing PromQL queries and corresponding indicator bucket data, the operational status of various metrics for power supply start-up and shutdown can be displayed. The displayed charts can include information such as battery performance trends and current health status, facilitating in-depth analysis and decision-making.

[0093] Correspondingly, PromQL statements or custom algorithms can be used to identify issues such as battery health, performance degradation, charging / discharging status, and abnormal temperatures. Alarms can be triggered to notify the relevant users of any anomalies.

[0094] Meanwhile, the connection between the terminal equipment and the start / stop power supply can be achieved through the following steps:

[0095] The device corresponding to the power supply has remote 4G communication function, which can maintain a real-time communication connection with the terminal device. Users can activate the power supply by operating the screen on it and clicking the screen button to display an activation QR code.

[0096] The terminal device receives the activation QR code request sent by the BMS system that controls power-on and power-off. It then dynamically generates encrypted QR code data in real time using the device's IMEI number and returns it to the corresponding BMS system. The BMS system displays the received QR code data on the screen for the user to scan.

[0097] Users can activate the device by scanning a QR code directly using a mobile phone scanning function. The mobile phone has internet access and sends an activation request to the terminal device through the QR code scanning address. After receiving the activation request, the terminal device performs device activation judgment and related processing.

[0098] Authentication is performed on activation requests to prevent unauthorized activation. Transmitted data is encrypted to protect the security of terminal devices and user information. For data encryption, the HTTPS protocol can be used to ensure data security during transmission. The encryption algorithm can be either AES (Advanced Encryption Standard) or RSA (Rivest-Shamir-Adleman).

[0099] In some embodiments, power information includes one or more of battery type, battery capacity, battery pack configuration, battery specifications, and battery usage count; device information includes one or more of device type, device power consumption, device operating environment, and device protection requirements; determining monitoring conditions corresponding to each data type based on power information, device information, and data type includes: inputting power information, device information, and data type into a pre-trained condition determination model, and outputting monitoring conditions corresponding to the data type based on the condition determination model.

[0100] The acquired power information, device information, and data type are input into a pre-trained conditional determination model. This model can be a machine learning model (such as a neural network or decision tree) or a rule-based system. Based on the various input information, the conditional determination model outputs monitoring conditions corresponding to each data type. These monitoring conditions may include: voltage range (e.g., maximum and minimum allowable voltage); current range (e.g., maximum and minimum allowable current); temperature range (e.g., maximum and minimum allowable temperature); charge / discharge rate (e.g., maximum allowable charging and discharging rates); and protection thresholds (e.g., specific thresholds for triggering overheat protection and overcharge protection).

[0101] By using a pre-trained condition determination model, suitable monitoring conditions can be dynamically generated based on different battery types, device types, and usage environments. This method is more flexible and accurate than monitoring with fixed thresholds, and can better adapt to the needs of different scenarios. Traditional battery management systems often require manual setting of monitoring conditions, while the condition determination model provided in this embodiment can automatically generate these conditions, reducing the need for human intervention and improving the system's automation level.

[0102] The specific steps for playing the game in this embodiment can be:

[0103] Terminal devices (such as handheld terminals, laptops, wearable devices, etc.) collect real-time battery data, power information, and device information. For example, they collect data on battery voltage, current, temperature, battery type, battery capacity, device type, and device power consumption. The collected data is cleaned and preprocessed to ensure accuracy and consistency. This includes removing outliers and filling in missing values. The preprocessed power information, device information, and data type are then input into a pre-trained conditional determination model. This model can be a deep learning model based on neural networks, or a machine learning model based on decision trees or support vector machines. Based on the input information, the conditional determination model outputs monitoring conditions corresponding to each data type. For example, it outputs the safe range for battery voltage and temperature. Based on the generated monitoring conditions, the terminal device monitors the corresponding parameters in the real-time battery data. If a parameter exceeds the safe range, the system automatically triggers an alarm and takes corresponding protective measures, such as stopping charging or activating cooling devices. Problems discovered during monitoring and the measures taken are recorded, and the model is regularly optimized and adjusted. Through continuous learning and optimization, the accuracy and reliability of the model are improved.

[0104] In summary, by utilizing a pre-trained condition determination model, dynamic generation and personalized management of battery management monitoring conditions were achieved. This method not only improves the system's monitoring accuracy but also reduces the need for human intervention, optimizes battery life, enhances system safety, and improves response speed, making battery management more efficient and intelligent.

[0105] For example, before inputting power information, equipment information, and data type into the pre-trained condition determination model, the method further includes: acquiring historical monitoring data of the power supply, which includes historical monitoring conditions and corresponding historical power information, historical operating information, and historical data type; inputting the historical power information, historical operating information, and historical data type into the condition determination model to be trained, which outputs predicted monitoring conditions; and completing the training of the condition determination model based on the historical monitoring conditions and the predicted monitoring conditions.

[0106] First, monitoring data for power supply start-up and shutdown needs to be obtained from historical records. This data includes: historical monitoring conditions: monitoring thresholds and rules set in the terminal device in the past; historical power information: battery type, battery capacity, battery pack configuration, battery specifications, number of battery uses, etc.; historical operating information: device type, device power consumption, device operating environment, and device protection requirements, etc.; and historical data types: parameter data used to characterize battery operating parameters, such as voltage, current, and temperature. The obtained historical power information, historical operating information, and historical data types are then input into the conditional determination model to be trained. This model can be a machine learning model (such as a neural network, decision tree, support vector machine, etc.) or a rule-based terminal device.

[0107] The training condition determination model generates predicted monitoring conditions based on historical input data. These predicted conditions will be the model's recommendations for battery management during the current training phase. The predicted monitoring conditions generated by the model are compared with the actual historical monitoring conditions to calculate the model's prediction error. Based on these errors, the model's parameters are adjusted to optimize its predictive ability. The training process can employ supervised learning, optimizing the model by minimizing the discrepancy between the predicted and actual conditions.

[0108] By training with historical monitoring data, the model can learn the optimal monitoring conditions for different battery types, device types, and usage environments. This enables the model to generate monitoring conditions more accurately in practical applications, improving the precision of battery management. Historical monitoring data covers a variety of usage scenarios; by learning from this data, the model can better adapt to various complex operating conditions, improving the robustness and reliability of the provided method. Traditional battery management terminal devices require manual setting of initial monitoring conditions, while with pre-trained models, the terminal device can generate reasonable monitoring conditions in a short time, significantly reducing the time and workload of initial configuration. The model can dynamically adjust monitoring conditions based on information such as the number of times the battery has been used and its internal resistance from historical data, thereby more effectively protecting the battery and extending its lifespan.

[0109] The specific steps corresponding to this example may include:

[0110] Retrieve monitoring data for power supply start-up and shutdown from historical databases, including historical monitoring conditions, historical power supply information, historical operating information, and historical data types. Clean and preprocess the collected historical data to ensure accuracy and consistency. This includes removing outliers, imputing missing values, and formatting the data. Input the preprocessed historical power supply information, historical operating information, and historical data types into a condition determination model to be trained. The model type can be a neural network, decision tree, support vector machine, etc., depending on the specific application scenario and data characteristics. The condition determination model generates predicted monitoring conditions based on the input historical data. These predicted conditions include voltage range, current range, temperature range, etc. Compare the predicted monitoring conditions generated by the model with the actual historical monitoring conditions to calculate the prediction error. For example, use mean squared error (MSE) or other appropriate loss functions to measure the difference between the predicted and actual conditions. Adjust the model parameters based on the calculated prediction error to optimize its predictive ability. Use algorithms such as gradient descent and random forest to adjust the parameters and gradually reduce the prediction error. After adjusting the model parameters, validate the model using a portion of historical data not used in training to ensure the model's accuracy and reliability. The model's performance is evaluated using methods such as cross-validation. Once the model is trained and validated, it is deployed to actual battery management terminal devices to generate new monitoring conditions. During the operation of the terminal devices, the model can continuously learn from new monitoring data and gradually optimize the monitoring conditions.

[0111] In summary, by using historical monitoring data to train the condition determination model, battery management terminal devices can generate monitoring conditions more accurately, improving the adaptability and reliability of the devices. This method not only reduces the time and workload of initial configuration but also achieves more efficient battery management, extends battery life, and improves safety through continuous learning and optimization.

[0112] In some embodiments, power information includes one or more of battery type, battery capacity, battery pack configuration, battery specifications, and battery usage count; device information includes one or more of device type, device power consumption, device operating environment, and device protection requirements; determining monitoring conditions corresponding to each data type based on power information, device information, and data type includes: inputting power information, device information, and data type into a pre-trained condition determination model; outputting monitoring conditions corresponding to the data type from the condition determination model; sending the power information, device information, and data type to the user terminal; and receiving the monitoring conditions corresponding to the data type returned by the user terminal.

[0113] The system transmits acquired power information, device information, and data type to a user terminal. The user terminal can be any device capable of communicating with the system, such as a mobile phone, tablet, or laptop. The user on the user terminal side can be a relevant maintenance personnel / expert. After receiving the data, the user terminal can manually or through on-device tools set monitoring conditions for each data type, based on the specific battery type, device type, and usage environment. The user terminal returns the set monitoring conditions to the terminal device. Upon receiving the monitoring conditions from the user terminal, the terminal device compares and integrates these conditions with the output of a pre-trained condition determination model to ultimately determine the monitoring conditions for each data type. For example, if the pre-trained model and the user-set monitoring conditions differ, the system can use a weighted average or select more stringent conditions to determine the final monitoring conditions.

[0114] Furthermore, users can adjust and optimize the monitoring conditions generated by the method based on their specific needs and experience. The user-participatory model allows monitoring conditions to be dynamically adjusted according to actual user usage, enhancing the method's flexibility and adaptability. Through user feedback, terminal devices can continuously learn and optimize, gradually improving the accuracy of monitoring conditions. Simultaneously, user participation serves as a supplementary and verification method, ensuring the rationality of the monitoring conditions. Through user experience and feedback, terminal devices can promptly identify and correct potential problems, improving system reliability.

[0115] Specific implementation steps may include:

[0116] The preprocessed power information, device information, and data types are sent to the user terminal via secure communication protocols (such as HTTPS, TLS / SSL). The user terminal can be a mobile device or computer with a dedicated application installed. After receiving the data, the user can set monitoring conditions for each data type either through the application or manually. For example, the user can set safe ranges for voltage, temperature, etc., in the application. The user terminal returns the user-set monitoring conditions to the terminal device via a secure communication protocol. Upon receiving the monitoring conditions returned by the user terminal, the terminal device compares and fuses them with the output of a pre-trained condition determination model. Using methods such as weighted averaging and selecting stricter conditions, the final monitoring conditions are generated. Based on the generated final monitoring conditions, the terminal device monitors the corresponding parameters in the real-time battery data. For example, it monitors parameters such as battery voltage, current, and temperature. If any parameter exceeds the safe range, the system will automatically trigger an alarm and take corresponding protective measures. The terminal device records problems found during monitoring and the measures taken, and periodically optimizes and adjusts the model. Through user feedback and system logs, the predictive ability of the model is continuously improved, enhancing the overall performance of the system.

[0117] By involving users in determining monitoring conditions through their terminals, not only is user participation and transparency increased, but the flexibility and personalization of monitoring conditions are also enhanced. This approach optimizes battery lifespan and safety, improves the overall user experience, and makes the provided battery management methods more intelligent and efficient.

[0118] In some embodiments, the data type includes at least a voltage data type; the monitoring conditions include at least voltage monitoring conditions; monitoring the corresponding parameter data in the real-time battery data according to each monitoring condition includes: monitoring the parameter data corresponding to the voltage data type according to the voltage monitoring conditions.

[0119] Voltage monitoring conditions are applied to monitor corresponding voltage parameters in real-time battery data. For example, upper and lower voltage thresholds can be set; when the battery voltage exceeds these thresholds, alarms or protective measures are triggered. For instance, the upper limit of the battery voltage can be set to 4.2V, and the lower limit to 3.0V. By setting specific voltage monitoring conditions, changes in battery voltage can be monitored more accurately, ensuring the battery operates within a safe range. Voltage monitoring conditions can promptly detect abnormal fluctuations in battery voltage, such as overvoltage or undervoltage, allowing for proactive measures to prevent battery damage in start-stop power supplies. Voltage monitoring is a crucial indicator of battery safety; accurate voltage monitoring can effectively prevent safety hazards such as overcharging and over-discharging. By monitoring changes in battery voltage, a better understanding of the start-stop power supply's battery operating status can be achieved, allowing for optimization of charging and discharging strategies and improving battery performance and lifespan.

[0120] In some embodiments, the data type includes at least a voltage data type; the monitoring conditions include at least a voltage drop monitoring condition; and monitoring the corresponding parameter data in the real-time battery data according to each monitoring condition includes: monitoring the parameter data corresponding to the voltage data type according to the voltage monitoring condition.

[0121] Voltage drop monitoring is achieved by applying conditions to the corresponding voltage parameters in real-time battery data. A rate threshold for voltage drop is set, triggering an alarm or protective measure when the voltage drops too rapidly within a short period. For example, an alarm could be triggered if the voltage drops by more than 0.5V within one minute. Voltage drop monitoring can detect rapid changes in battery voltage, helping to identify internal battery anomalies such as short circuits or poor connections. By monitoring voltage drop, early warnings can be issued at the initial stage of battery failure, allowing for timely intervention to prevent further damage. Voltage drop monitoring provides dynamic information about the battery during charging, helping to optimize charging strategies and prevent voltage fluctuations from damaging the battery. Voltage drop monitoring is a crucial indicator in battery management systems, improving overall system reliability and reducing the probability of failure.

[0122] For example, the battery pack of the start-stop power supply includes multiple batteries; monitoring the parameter data corresponding to the voltage data type according to the voltage drop monitoring conditions includes: parsing the parameter data corresponding to the voltage data type to obtain the voltage value of each battery in the battery pack; calculating the voltage drop information corresponding to the voltage values ​​of every two adjacent batteries in the battery pack based on the multiple voltage values; monitoring each voltage drop information according to the voltage drop monitoring conditions, so that if any voltage drop information does not meet the voltage drop monitoring conditions, abnormal monitoring information is generated to prompt maintenance of the battery pack.

[0123] Voltage drop monitoring conditions include the voltage drop variation range, typically expressed as the percentage change in battery voltage over a certain period. The terminal device needs to acquire battery voltage data in real time and calculate the voltage drop information at each point in time. Voltage drop information can be the amount of change in battery voltage over a period of time (e.g., 1 minute or 10 seconds). Each voltage drop information is compared to the set voltage drop variation range. If any voltage drop information is outside the set range, an abnormal monitoring message is generated.

[0124] Anomaly monitoring information can include the specific value of the voltage drop exceeding the range, a timestamp, and possible causes. Generated anomaly monitoring information can trigger alarms or protective measures, such as stopping charging, disconnecting the load, or initiating diagnostic procedures.

[0125] By setting specific voltage drop ranges (e.g., 5% to 25%), the system can detect and address abnormal voltage drops in the early stages, preventing potential faults from escalating. Voltage drops exceeding the normal range may indicate internal short circuits, poor contact, or other faults within the battery. Timely detection and handling of these anomalies can effectively prevent safety hazards such as battery overheating and explosions. During charging, voltage drop monitoring provides real-time information on the battery's internal state, helping to optimize charging strategies and prevent voltage fluctuations from damaging the battery. Early detection and handling of abnormal voltage drops can reduce battery operating time under abnormal conditions, thereby extending battery life. As a crucial indicator of the battery management system, voltage drop monitoring improves overall system reliability and reduces the probability of failures. Anomaly monitoring information includes not only the specific value and timestamp of the voltage drop exceeding the range but also possible causes, helping technicians quickly locate and resolve faults.

[0126] The specific steps corresponding to this example may include:

[0127] The system acquires battery voltage data in real time from the battery management system. The data acquisition frequency can be set according to actual needs, such as acquiring voltage data every 10 seconds or every minute. It calculates the voltage drop information at each time point, i.e., the change in battery voltage over a period of time. For example, it calculates the difference between the current voltage and the voltage one minute ago and converts it to a percentage change. The voltage drop variation range is set according to battery type, device type, and operating environment. For example, the voltage drop variation range is set to 5% to 25%. Each calculated voltage drop information is compared with the set voltage drop variation range. If the voltage drop information exceeds the range (less than 5% or greater than 25%), an anomaly monitoring message is generated. The anomaly monitoring message includes: Voltage drop value: the specific voltage drop value that exceeds the range; Timestamp: the time point when the voltage drop value exceeds the range; Possible causes: based on past experience and data analysis, it provides possible causes of the fault, such as internal short circuits, poor contact, etc.

[0128] In summary, by setting a voltage drop variation range (e.g., 5% to 25%), precise monitoring of battery voltage drop can be achieved. This method not only enables early detection of battery anomalies but also improves system safety, optimizes charging strategies, extends battery life, and enhances system reliability. With detailed fault diagnosis information, the system can more quickly locate and resolve potential problems, providing more intelligent and efficient battery management.

[0129] In some embodiments, the data type includes at least a temperature data type; the monitoring conditions include at least temperature monitoring conditions; and monitoring the corresponding parameter data in the real-time battery data according to each monitoring condition includes: monitoring the parameter data corresponding to the temperature data type according to the temperature monitoring conditions.

[0130] Temperature monitoring conditions are applied to monitor corresponding temperature parameters in real-time battery data. For example, upper and lower temperature thresholds can be set, triggering alarms or protective measures when the battery temperature exceeds these thresholds. By setting temperature monitoring conditions, overheating can be detected promptly, allowing cooling measures to be taken to prevent safety accidents caused by overheating. Batteries age faster when operating at high temperatures; temperature monitoring conditions ensure the battery operates within a suitable temperature range, thereby extending its lifespan. For example, the cooling system can be activated when the battery temperature exceeds 45°C, and the heating system can be activated when the battery temperature is below 5°C. Real-time monitoring of battery temperature allows for a better understanding of the battery's thermal management needs and optimization of the cooling system's operating strategy. Temperature is a crucial indicator of battery safety; accurate temperature monitoring can effectively prevent safety hazards such as overheating and thermal runaway.

[0131] In some embodiments, the data type includes at least a power data type; the monitoring conditions include at least power monitoring conditions; and monitoring the corresponding parameter data in the real-time battery data according to each monitoring condition includes: monitoring the parameter data corresponding to the power data type according to the power monitoring conditions.

[0132] This involves applying power monitoring conditions to the corresponding power parameters in real-time battery data. For example, setting upper and lower thresholds for SOC (State of Charge) can trigger alarms or protective measures when the battery level exceeds these thresholds.

[0133] By setting power monitoring conditions, the charging and discharging process of batteries can be better managed, preventing overcharging and over-discharging, and improving battery efficiency. Overcharging or over-discharging accelerates battery aging; power monitoring ensures the battery operates within an appropriate power range, thus extending its lifespan. For example, charging can be stopped when the battery's SOC exceeds 90%; when the SOC falls below 20%, protective measures are activated and the user is reminded to charge. Power monitoring provides accurate information about the remaining battery power, helping users better plan device usage and improving the user experience. Overcharging and over-discharging are significant battery safety hazards; accurate power monitoring can effectively prevent these risks and ensure safe battery use.

[0134] In some embodiments, if it is confirmed that the corresponding parameter data in the real-time battery data is abnormal according to any monitoring condition, the method further includes: obtaining abnormal information corresponding to the abnormal parameter data; generating dynamic adjustment information based on the abnormal information; sending the dynamic adjustment information to the battery management system and / or preset device of the power supply, and the battery management system and / or preset device making adjustments according to the dynamic adjustment information.

[0135] This embodiment is used to generate dynamic adjustment information and transmit it to the battery management system and / or preset devices for adjustment after confirming abnormal battery parameter data. The system confirms whether the corresponding parameter data in the real-time battery data is abnormal based on any monitoring condition (such as voltage monitoring condition, voltage drop monitoring condition, temperature monitoring condition, power monitoring condition, etc.). For example, if the voltage data exceeds a set threshold (4.2V to 3.0V), or the voltage drop exceeds a set range (5% to 25%), or the temperature exceeds a set range (5℃ to 45℃), or the power exceeds a set range (20% to 90%), then the parameter data is considered abnormal. Once the abnormal parameter data is confirmed, the terminal device obtains detailed information about the abnormal parameter data.

[0136] Abnormal information includes, but is not limited to, the specific values ​​of abnormal parameters (such as abnormal voltage values, voltage drop values, temperature values, or power values), the timestamp of the abnormality (recording the specific time when the abnormality occurred), the type of abnormal parameter (clearly specifying whether it is voltage, voltage drop, temperature, or power parameter data), and possible causes of the abnormality (providing possible causes of the fault based on monitoring conditions and experience, such as internal short circuits, poor contact, etc.).

[0137] Dynamic adjustment information is generated based on the acquired anomaly information. This dynamic adjustment information may include:

[0138] 1. Adjustment suggestions: such as stopping charging, reducing discharge current, starting the cooling system, starting the heating system, etc.

[0139] 2. Adjust parameters: Specific parameter values ​​to adjust, such as reducing the charging current from 5A to 3A, or starting the cooling fan, etc.

[0140] 3. Timing of adjustment: It is recommended to adjust at certain times, such as immediately or at the beginning of the next charging cycle.

[0141] The terminal device sends the generated dynamic adjustment information to the battery management system and / or preset device for power supply startup and shutdown. Upon receiving the dynamic adjustment information, the battery management system and / or preset device make corresponding adjustments based on the information content. For example, the battery management system may immediately stop charging, or the preset device may activate the cooling system. The battery management system and / or preset device adjust the battery's operating state according to the dynamic adjustment information. Adjustments can be immediate or performed at suggested times.

[0142] Dynamic adjustment information for abnormal parameter data can be immediately transmitted to the battery management system and / or preset devices, enabling the system to respond quickly to abnormal situations and prevent further escalation of the fault. Through dynamic adjustment, timely measures can be taken, such as stopping charging or discharging, activating the cooling or heating system, effectively preventing safety hazards such as overvoltage, overtemperature, and overheating, and protecting the safety of the battery and equipment. Dynamic adjustment information provides specific adjustment suggestions and parameters, helping the battery management system and preset devices better manage the battery's operating state, optimize charging and discharging strategies, and improve battery efficiency and lifespan. Real-time monitoring and dynamic adjustment can reduce equipment downtime caused by faults, lowering maintenance costs. Dynamic adjustment information also provides detailed fault information, enabling maintenance personnel to quickly locate and repair faults, improving maintenance efficiency.

[0143] Specifically, the steps corresponding to this embodiment may include:

[0144] The system acquires real-time battery voltage, voltage drop, temperature, and charge data from the battery management system (BMS). Based on pre-defined monitoring conditions, it monitors the corresponding parameters in the real-time battery data. For example, it checks whether the voltage exceeds the range of 4.2V to 3.0V, the voltage drop exceeds the range of 5% to 25%, the temperature exceeds the range of 5℃ to 45℃, and the charge exceeds the range of 20% to 90%. Upon detecting any abnormal parameter data, it immediately acquires the anomaly information, including the specific value of the abnormal parameter, the timestamp of the anomaly, the parameter type, and possible causes. Based on the anomaly information, it generates specific dynamic adjustment information. This dynamic adjustment information may include adjustment suggestions, adjustment parameters, and adjustment timing. The dynamic adjustment information is sent to the BMS and / or preset devices via a communication interface (such as CAN bus, Ethernet, etc.). For example, it sends a message via the CAN bus instructing the BMS to immediately stop charging. Upon receiving the dynamic adjustment information, the BMS and / or preset devices adjust accordingly. For example, upon receiving the command to stop charging, the BMS immediately disconnects the charging circuit. Upon receiving the command to start the cooling system, the preset devices immediately start the cooling fan. Record all operations and results during the adjustment process for subsequent analysis and fault diagnosis. Adjustment information and results can be recorded via log files or a database. Provide feedback on the adjustment results to users or maintenance personnel to ensure users are aware of the system's current status.

[0145] By generating dynamic adjustment information upon confirming abnormal battery parameter data and sending it to the battery management system and / or preset devices, real-time battery management and adjustment are achieved. This method not only improves system responsiveness and safety but also optimizes battery management strategies, reduces maintenance costs, and enhances user experience. Ultimately, it enables a more intelligent and efficient battery management system, improving the overall performance and reliability of the device.

[0146] For example, generating dynamic adjustment information based on abnormal information includes: parsing the abnormal information to obtain the abnormal data type corresponding to the abnormal data and the deviation information between the abnormal data and the monitoring conditions; generating dynamic adjustment information based on the abnormal data type and the deviation information; the dynamic adjustment information includes at least one or more of temperature adjustment information, load adjustment information and backup power switching information.

[0147] Upon receiving abnormal monitoring information, the terminal device first parses the information and extracts key data. Then, it determines the specific type of the abnormal data, such as voltage, voltage drop, temperature, or power consumption. Next, it obtains the deviation between the abnormal data and the monitoring conditions, i.e., the difference between the actual value and the set range. Finally, based on the parsed abnormal data type and deviation information, it generates corresponding dynamic adjustment information. This dynamic adjustment information includes, but is not limited to:

[0148] 1. Temperature Regulation Information: If the abnormal data type is temperature, generate temperature regulation information. For example, if the temperature is too high, generate a command to start the cooling system; if the temperature is too low, generate a command to start the heating system.

[0149] 2. Load Adjustment Information: If the abnormal data type is voltage or voltage drop, generate load adjustment information. For example, if the voltage is too high or the voltage drop is too large, generate a command to reduce the load current; if the voltage is too low or the voltage drop is too small, generate a command to increase the load current.

[0150] 3. Backup Power Switching Information: If the abnormal data type is voltage or power, generate backup power switching information. For example, if the main battery voltage is too low or the power is insufficient, generate a command to switch to the backup power source.

[0151] By analyzing anomaly information, the type of abnormal data can be accurately identified, enabling more targeted adjustments and avoiding blind operations. Dynamic adjustment information can generate various adjustment measures based on different anomaly data types, such as temperature regulation, load adjustment, and backup power switching, ensuring the system's comprehensiveness and flexibility. Temperature regulation information can effectively prevent battery safety issues caused by excessively high or low temperatures, such as overheating, explosion, or performance degradation. Load adjustment information can prevent battery damage caused by excessively high or low voltage, such as overcharging or over-discharging. Backup power switching information ensures the system can continue operating when the main battery is faulty, avoiding equipment failure and data loss due to sudden power outages. Temperature regulation information can optimize the battery's operating environment, improving battery performance and lifespan. Load adjustment information ensures the battery operates within a safe voltage range, reducing battery stress and extending its lifespan. Backup power switching information ensures the equipment can still operate normally when the main battery fails, improving the overall reliability of the system. Through various dynamic adjustment measures, equipment downtime and maintenance frequency due to battery failures can be reduced, lowering maintenance costs. The dynamic adjustment information provides specific adjustment measures and parameters, enabling maintenance personnel to locate and resolve problems more quickly and improve maintenance efficiency.

[0152] In summary, by analyzing anomaly information and generating various dynamic adjustment information (such as temperature regulation, load adjustment, and backup power switching information) based on the anomaly type and deviation information, precise monitoring and intelligent management of the start-stop power supply battery are achieved. This method not only improves the system's safety and reliability but also optimizes battery performance and lifespan, reduces maintenance costs, and enhances the user experience.

[0153] In some embodiments, the method further includes: generating a visualization interface corresponding to multiple start-stop power supplies; and displaying the parameter data corresponding to each real-time battery data and the monitoring results of monitoring each parameter data in the visualization interface.

[0154] A corresponding visualization interface is generated for each start-stop power supply. This visualization interface can be a web-based interface, a mobile application interface, or a dedicated hardware display interface (such as a visualization interface providing maintenance personnel with visual dashboards and regular battery status reports via tools like Grafana). The interface can include information such as battery performance trends and current health status for each start-stop power supply, enabling in-depth analysis and decision-making.

[0155] Meanwhile, the visual interface can also be developed using Web technologies (such as HTML, CSS, JavaScript) or mobile application technologies (such as React Native, Flautter), and this application embodiment does not impose any restrictions on this.

[0156] The terminal device collects parameter data for each battery in real time from the battery management system, including voltage, voltage drop, temperature, and charge level. This real-time data is transmitted to the terminal device via a communication interface (such as CAN bus, Ethernet, or Wi-Fi). The terminal device displays the parameter data corresponding to each real-time battery in a visual interface. Parameter data can be displayed in various formats, such as charts, numbers, and color coding, to provide users with an intuitive understanding of the battery's status. For example, voltage can be displayed as numbers and line graphs, temperature as numbers and thermometer graphs, and charge level as numbers and bar graphs. The visual interface also displays the monitoring results for each parameter. These results can be categorized as normal, warning, and abnormal states, distinguished by different colors or icons. For example, a normal state can be displayed in green, a warning state in yellow, and an abnormal state in red, along with specific anomaly information and suggested actions. The visual interface provides interactive functions, allowing users to view historical data, set monitoring conditions, and adjust parameters. For example, users can view the voltage change trend over the past 24 hours, set new temperature monitoring conditions, or manually activate the cooling system. When monitoring results reach a warning or abnormal state, the visual interface can trigger alarm functions, notifying users through various means such as sound, pop-ups, and emails. For example, when the battery temperature exceeds 45°C, the interface can display a red warning box and send an email to maintenance personnel. Ensure the interface layout is clear and aesthetically pleasing, with intuitive information display that is easy for users to understand and operate.

[0157] By generating multiple visual interfaces corresponding to start / stop power supplies and displaying parameter data and monitoring results for each real-time battery, intuitive monitoring and management of battery status is achieved. This method not only improves system transparency and security but also optimizes battery management strategies, reduces maintenance costs, and enhances user experience.

[0158] Please see Figure 3 As shown, Figure 3 This is a schematic diagram of the structure of the battery management device 200 provided in the embodiments of this application. The battery management device 200 is used to perform the steps of the battery management method shown in the above embodiments. The battery management device 200 can be a single server or a server cluster, or the battery management device 200 can be a terminal, such as a handheld terminal, a laptop computer, a wearable device, or a robot.

[0159] like Figure 3 As shown, the battery management device 200 includes:

[0160] The data acquisition unit 201 is used to acquire real-time battery data, power information and corresponding preset device information of the start-stop power supply; the real-time battery data includes at least one parameter data used to characterize the battery's operating parameters.

[0161] The type determination unit 202 is used to determine the data type corresponding to each parameter data.

[0162] The condition determination unit 203 is used to determine the monitoring conditions corresponding to each data type based on power information, device information and data type.

[0163] The management completion unit 204 is used to monitor the corresponding parameter data in the real-time battery data according to each monitoring condition, so as to complete the battery management of the start-stop power supply.

[0164] It should be noted that those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the battery management device and its modules described above can be referred to the corresponding processes in the battery management method embodiments described above, and will not be repeated here.

[0165] The battery management method described above can be implemented as a computer program, which can be used in, for example... Figure 3 It runs on the device shown.

[0166] Please see Figure 4 , Figure 4 This is a schematic block diagram of the structure of a terminal device provided in an embodiment of this application. The terminal device includes a processor, a memory, and a network interface connected via a device bus, wherein the memory may include a storage medium and internal memory.

[0167] The storage medium may store operating devices and computer programs. The computer program includes program instructions that, when executed, cause the processor to perform any battery management method.

[0168] The processor provides computing and control capabilities to support the operation of the entire terminal device.

[0169] Internal memory provides an environment for the execution of computer programs on non-volatile storage media, which, when executed by a processor, enable the processor to perform any battery management method.

[0170] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 4The structure shown is merely a block diagram of a portion of the structure related to the solution of this application and does not constitute a limitation on the terminal to which the solution of this application is applied. Specific terminal devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0171] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0172] In one embodiment, the processor is configured to run a computer program stored in memory to perform the following steps:

[0173] Acquire real-time battery data, power information, and corresponding preset device information for power supply start-up and shutdown; real-time battery data includes at least one parameter data used to characterize battery operating parameters.

[0174] Determine the data type corresponding to each parameter.

[0175] The monitoring conditions for each data type are determined based on power information, device information, and data type.

[0176] The system monitors the corresponding parameters in the real-time battery data according to each monitoring condition to complete the battery management of the start-stop power supply.

[0177] It should be noted that those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the steps implemented by the processor described above can be referred to the corresponding process in the battery management method embodiments described above, and will not be repeated here.

[0178] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the steps of the battery management method provided in any embodiment of this application.

[0179] The computer-readable storage medium can be an internal storage unit of the terminal device described in the foregoing embodiments, such as the hard disk or memory of the terminal device. Alternatively, the computer-readable storage medium can be an external storage device of the terminal device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., provided on the terminal device.

[0180] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A battery management method, characterized in that, The method is used to manage at least one start-stop power supply, which supplies power to preset devices; the method includes: The system acquires real-time battery data, power information, and corresponding device information of the preset device for the start-stop power supply; the real-time battery data includes at least one parameter data for characterizing battery operating parameters. Determine the data type corresponding to each of the parameter data; Based on the power information, device information, and data type, determine the monitoring conditions corresponding to each data type; The corresponding parameter data in the real-time battery data is monitored according to each monitoring condition to complete the battery management of the start-stop power supply.

2. The method according to claim 1, characterized in that, Before monitoring the corresponding parameter data in the real-time battery data according to each of the monitoring conditions, the method further includes: Each start / stop power supply is generated according to multiple data types; the data storage area includes multiple parameter storage areas, each parameter storage area is used to store one parameter data; A real-time battery database is constructed based on the data storage area corresponding to each of the start-stop power supplies, so as to obtain each of the monitoring conditions in the real-time battery database and monitor the corresponding parameter data in the real-time battery data.

3. The method according to claim 2, characterized in that, After completing the battery management of the start-stop power supply, the method further includes: Upon receiving the real-time battery data updated by the power supply, obtain the data type corresponding to each parameter data of the updated real-time battery data; Each parameter data is stored in the corresponding parameter storage area according to the updated data type, so as to complete the battery management of each start-stop power supply according to the updated real-time battery database.

4. The method according to claim 1, characterized in that, The power information includes one or more of the following: battery type, battery capacity, battery pack configuration, battery specifications, and number of battery uses; the device information includes one or more of the following: device type, device power consumption, device operating environment, and device protection requirements; determining the monitoring conditions corresponding to each data type based on the power information, device information, and data type includes: The power information, device information, and data type are input into a pre-trained condition determination model, which outputs the monitoring conditions corresponding to the data type; and / or, The power information, device information, and data type are sent to the user terminal, and the monitoring conditions corresponding to the data type are received from the user terminal.

5. The method according to claim 4, characterized in that, Before inputting the power information, device information, and data type into the pre-trained conditional determination model, the method further includes: Acquire historical monitoring data of the start-stop power supply, the historical monitoring data including historical monitoring conditions and historical power supply information, historical working information and historical data types corresponding to the historical monitoring conditions; The historical power information, historical operating information, and historical data type are input into the condition determination model to be trained, and the condition determination model outputs the predicted monitoring conditions. The training of the condition determination model is completed based on the historical monitoring conditions and the predicted monitoring conditions.

6. The method according to claim 1, characterized in that, The data type includes at least one or more of voltage data type, temperature data type, and power data type; the monitoring conditions include at least one or more of voltage drop monitoring conditions, voltage monitoring conditions, temperature monitoring conditions, and power monitoring conditions. The step of monitoring the corresponding parameter data in the real-time battery data according to each of the monitoring conditions includes: Monitor the parameter data corresponding to the voltage data type according to the voltage monitoring conditions; And / or, Monitor the parameter data corresponding to the voltage data type according to the voltage drop monitoring conditions; and / or, Monitor the parameter data corresponding to the temperature data type according to the temperature monitoring conditions; and / or, The parameter data corresponding to the power data type is monitored according to the power monitoring conditions.

7. The method according to claim 6, characterized in that, The battery pack of the start-stop power supply includes multiple batteries; the monitoring of parameter data corresponding to the voltage data type according to the voltage drop monitoring conditions includes: Parse the parameter data corresponding to the voltage data type to obtain the voltage value of each battery in the battery pack; Calculate the voltage drop information corresponding to the voltage values ​​of each two adjacent batteries in the battery pack based on the multiple voltage values; Each voltage drop information is monitored according to the voltage drop monitoring conditions, so that if any voltage drop information does not meet the voltage drop monitoring conditions, abnormal monitoring information is generated to prompt the battery pack to be repaired.

8. The method according to claim 1, characterized in that, If, based on any of the monitoring conditions, it is confirmed that the corresponding parameter data in the real-time battery data is abnormal, the method further includes: Obtain the exception information corresponding to the abnormal parameter data; Dynamic adjustment information is generated based on the aforementioned anomaly information; The dynamic adjustment information is sent to the battery management system of the start-stop power supply and / or the preset device, and the battery management system and / or the preset device make adjustments according to the dynamic adjustment information.

9. The method according to claim 8, characterized in that, The step of generating dynamic adjustment information based on the abnormal information includes: Parse the abnormal information to obtain the abnormal data type corresponding to the abnormal data and the deviation information between the abnormal data and the monitoring conditions; The dynamic adjustment information is generated based on the abnormal data type and the deviation information; the dynamic adjustment information includes at least one or more of the following: temperature regulation information, load adjustment information, and backup power switching information.

10. The method according to claim 1, characterized in that, The method further includes: Generate multiple visual interfaces corresponding to the aforementioned start / stop power supplies; The parameter data corresponding to each of the real-time battery data and the monitoring results of monitoring each of the parameter data are displayed in the visualization interface.

11. A terminal device, characterized in that, The terminal device includes a memory and a processor; The memory is used to store computer programs; The processor is configured to execute the computer program and, in executing the computer program, implement the method as described in any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, causes the processor to implement the method as described in any one of claims 1 to 9.

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