Vehicle storage battery monitoring method and device based on cloud platform and electronic equipment

Abstract

The invention discloses a vehicle storage battery monitoring method and device based on a cloud platform and electronic equipment, and the method comprises the steps: obtaining the monitoring data of each vehicle storage battery from the cloud platform, and enabling the monitoring data to comprise the environment data of the vehicle storage battery, the accumulated use time, and the voltage of the storage battery, a DC converter output voltage corresponding to the vehicle storage battery; dynamically determining an abnormality judgment condition for each vehicle storage battery based on the environment data and the accumulated use duration of each vehicle storage battery; based on the storage battery voltage of each vehicle storage battery and the corresponding DC converter output voltage, whether each vehicle storage battery is abnormal or not is judged through the abnormity judgment condition; and if each vehicle storage battery is abnormal, generating early warning information, and pushing the early warning information to a user terminal corresponding to each vehicle storage battery. Through the technical scheme provided by the invention, the misjudgment rate of vehicle storage battery monitoring can be reduced.

Description

Technical Field

[0001] This application belongs to the field of vehicle battery monitoring technology, and in particular relates to a vehicle battery monitoring method, device and electronic equipment based on a cloud platform. Background Technology

[0002] With the rapid development of vehicle electrification and connectivity, monitoring the operating condition of vehicle batteries has become a crucial aspect of ensuring stable vehicle operation. Currently, cloud platforms are widely used for remote monitoring of vehicle batteries to promptly detect any abnormalities. However, in practical applications, the misjudgment rate of vehicle battery monitoring is relatively high. Batteries operating under normal conditions are frequently identified as abnormal, triggering unnecessary warnings and interference, or batteries that are actually abnormal are identified as normal, creating potential safety hazards such as vehicle breakdowns and electrical system malfunctions.

[0003] Therefore, reducing the false alarm rate of vehicle battery monitoring has become an urgent technical problem to be solved. Summary of the Invention

[0004] The embodiments of this application provide a vehicle battery monitoring method, device, computer program product, computer-readable storage medium and electronic device based on a cloud platform, which can at least reduce the misjudgment rate of vehicle battery monitoring to a certain extent.

[0005] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0006] According to a first aspect of the embodiments of this application, a vehicle battery monitoring method based on a cloud platform is provided. The method includes: acquiring monitoring data of each vehicle battery from the cloud platform, the monitoring data including environmental data, cumulative usage time, battery voltage, and DC-DC converter output voltage corresponding to the vehicle battery; dynamically determining anomaly judgment conditions for each vehicle battery based on the environmental data and cumulative usage time of each vehicle battery; determining whether each vehicle battery has an anomaly based on the battery voltage and the corresponding DC-DC converter output voltage of each vehicle battery, according to the anomaly judgment conditions; if each vehicle battery has an anomaly, generating a warning message and pushing the warning message to the user terminal corresponding to each vehicle battery.

[0007] In some embodiments of this application, based on the foregoing scheme, the method further includes: after obtaining monitoring data of each vehicle battery from the cloud platform, obtaining operating condition data of each vehicle battery from the cloud platform, the operating condition data including power mode, battery status, battery charge, DC-DC converter operating status, and operating condition duration; and filtering monitoring data of vehicle batteries that do not meet preset operating condition conditions from the monitoring data of each vehicle battery according to the operating condition data, the preset operating condition conditions including the power mode being a preset operating mode, the battery status being a preset state, the battery charge being greater than a set charge threshold, the DC-DC converter operating status being a preset output state, and the operating condition duration being greater than or equal to a set stable duration.

[0008] In some embodiments of this application, based on the aforementioned scheme, the environmental data includes the ambient temperature and humidity of the area where the vehicle is located. The dynamic determination of the anomaly determination conditions for each vehicle battery includes: determining a battery voltage threshold and a DC-DC converter output voltage threshold for each vehicle battery based on the ambient temperature and humidity, wherein both the battery voltage threshold and the DC-DC converter output voltage threshold are positively correlated with the ambient temperature and negatively correlated with the ambient humidity; and correcting the battery voltage threshold and the DC-DC converter output voltage threshold for each vehicle battery based on the cumulative usage time of each vehicle battery to obtain the anomaly determination conditions for each vehicle battery.

[0009] In some embodiments of this application, based on the foregoing scheme, the step of correcting the battery voltage threshold and DC-DC converter output voltage threshold of each vehicle battery based on the cumulative usage time of each vehicle battery includes: determining a first correction value for the battery voltage threshold and a second correction value for the DC-DC converter output voltage threshold based on the cumulative usage time, wherein both the first correction value and the second correction value are negatively correlated with the cumulative usage time, and the range of the first correction value and the second correction value is set to [-1, 1]; correcting the battery voltage threshold of each vehicle battery based on the first correction value; and correcting the DC-DC converter output voltage threshold of each vehicle battery based on the second correction value.

[0010] In some embodiments of this application, based on the aforementioned scheme, determining whether each vehicle battery is abnormal by the abnormality determination condition includes: if the battery voltage is less than or equal to the battery voltage threshold, or the DC-DC converter output voltage is less than or equal to the DC-DC converter output voltage threshold, then determining that each vehicle battery is abnormal.

[0011] In some embodiments of this application, based on the foregoing scheme, generating early warning information includes: calculating the voltage ratio between the battery voltage and the battery voltage threshold; generating early warning information of a corresponding level according to the voltage ratio, wherein the level of the early warning information is negatively correlated with the voltage ratio, and when the voltage ratio is equal to 1, the level of the early warning information is level 1 early warning.

[0012] In some embodiments of this application, based on the aforementioned scheme, generating early warning information includes: fitting a battery voltage trend curve based on time-series data of the battery voltage within a preset historical period; calculating a monthly average voltage change value based on the battery voltage trend curve; and generating early warning information of a corresponding level based on the monthly average voltage change value, wherein the level of the early warning information is negatively correlated with the monthly average voltage change value, and when the monthly average voltage change value is greater than or equal to 0, the level of the early warning information is a level 1 early warning.

[0013] In some embodiments of this application, based on the aforementioned scheme, the warning information includes N levels, and each level of warning information includes a recommended battery replacement period, wherein the recommended battery replacement period for warning information from level 1 to level N decreases sequentially.

[0014] According to a second aspect of the embodiments of this application, a vehicle battery monitoring device based on a cloud platform is provided. The device includes: an acquisition unit, configured to acquire monitoring data of each vehicle battery from the cloud platform, the monitoring data including environmental data, cumulative usage time, battery voltage, and DC-DC converter output voltage corresponding to the vehicle battery; a determination unit, configured to dynamically determine anomaly judgment conditions for each vehicle battery based on the environmental data and cumulative usage time of each vehicle battery; a judgment unit, configured to determine whether each vehicle battery is abnormal based on the battery voltage and the corresponding DC-DC converter output voltage of each vehicle battery, according to the anomaly judgment conditions; and a generation unit, configured to generate early warning information if each vehicle battery is abnormal, and push the early warning information to the user terminal corresponding to each vehicle battery.

[0015] According to a third aspect of the embodiments of this application, a computer program product is provided, the computer program product including computer instructions stored in a computer-readable storage medium and adapted to be read and executed by a processor to cause a computer device having the processor to perform an operation as described in any of the first aspects above.

[0016] According to a fourth aspect of the embodiments of this application, a computer-readable storage medium is provided, the computer-readable storage medium storing at least one computer program instruction, the at least one computer program instruction being loaded and executed by a processor to perform the operation as described in any of the first aspects above.

[0017] According to a fifth aspect of the present application, an electronic device is provided, the electronic device including one or more processors and one or more memories, the one or more memories storing at least one computer program instruction, the at least one computer program instruction being loaded and executed by the one or more processors to perform the operation as described in any of the first aspects above.

[0018] Based on the technical solution proposed in this application, comprehensive monitoring data obtained from the cloud platform ensures a comprehensive and reliable data foundation for anomaly detection. By dynamically determining personalized anomaly detection conditions based on environmental data and cumulative usage time, the limitations of uniform detection standards in existing technologies are overcome. This makes anomaly detection more closely aligned with the actual usage of each battery, effectively reducing the misjudgment rate caused by differences in environment and usage time. By generating and pushing timely warning information to user terminals, users can be informed of abnormal battery status immediately, facilitating timely repair and replacement measures. This prevents vehicle breakdowns, electrical system malfunctions, and other safety hazards caused by battery problems, ensuring the stability and safety of the entire vehicle operation and improving user satisfaction.

[0019] 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

[0020] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings: Figure 1 A flowchart of a cloud-based vehicle battery monitoring method according to an embodiment of this application is shown; Figure 2 A block diagram of a cloud-based vehicle battery monitoring device according to an embodiment of this application is shown; Figure 3 A schematic diagram of the structure of an electronic device in an embodiment of this application is shown. Detailed Implementation

[0021] 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, and 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.

[0022] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0023] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices. It should also be noted that, for the sake of simplicity, certain components in the drawings that do not affect the interpretation of the technical solution of this application have been appropriately omitted.

[0024] 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 described order. For example, some operations / steps can be broken down, while others can be combined or partially combined. Therefore, the actual execution order may change depending on the actual situation.

[0025] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "multiple" means two or more.

[0026] Currently, with the rapid development of vehicle electrification and connectivity, vehicle batteries, as the core energy storage component of the vehicle's electrical system, directly affect the stable operation of the entire vehicle. Cloud platforms, with their powerful data storage and remote access capabilities, are now widely used in remote monitoring of vehicle batteries, aiming to promptly detect abnormal battery conditions and proactively mitigate risks such as vehicle breakdowns and electrical system malfunctions.

[0027] However, the applicant discovered that existing vehicle battery monitoring methods have significant flaws: due to differences in the usage environment and duration of different vehicles, using uniform monitoring standards and anomaly detection criteria to monitor all vehicle batteries leads to a high false alarm rate. On the one hand, batteries operating under normal conditions are frequently identified as abnormal, causing unnecessary warning interference and affecting user experience; on the other hand, batteries that are actually abnormal may be identified as normal, failing to detect potential faults in a timely manner and creating safety hazards such as vehicle breakdowns and electrical system failures.

[0028] In this context, this application proposes a cloud-based vehicle battery monitoring solution to reduce the misjudgment rate of vehicle battery monitoring.

[0029] Next, this application will elaborate on the proposed cloud-based vehicle battery monitoring solution. (Refer to...) Figure 1 The diagram illustrates a flowchart of a cloud-based vehicle battery monitoring method according to an embodiment of this application. This cloud-based vehicle battery monitoring method can be executed by a device with computing processing capabilities, such as... Figure 1 As shown, this cloud-based vehicle battery monitoring method includes at least steps 110 to 140, which are detailed below: Step 110: Obtain monitoring data of each vehicle battery from the cloud platform. The monitoring data includes environmental data of the vehicle battery, cumulative usage time, battery voltage, and the output voltage of the DC-DC converter corresponding to the vehicle battery.

[0030] Step 120: Based on the environmental data and cumulative usage time of each vehicle battery, dynamically determine the abnormal judgment conditions for each vehicle battery.

[0031] Step 130: Based on the battery voltage of each vehicle battery and the corresponding DC-DC converter output voltage, determine whether each vehicle battery has an abnormality by using the abnormality determination conditions.

[0032] Step 140: If any of the vehicle batteries is abnormal, a warning message is generated and pushed to the user terminal corresponding to each vehicle battery.

[0033] In this application, the monitoring data of the vehicle battery can be used to reflect various data reflecting the battery's own condition, usage, and the working status of related components, serving as the basis for subsequent anomaly determination. For example, it can include environmental data reflecting the external environment in which the battery operates, cumulative usage time reflecting the degree of battery wear and tear, battery voltage directly reflecting the battery's energy output capability, and DC-DC converter output voltage reflecting the working status of the core components that charge the battery, which can indirectly relate to the battery's charging efficiency and health status.

[0034] In this application, the abnormality determination criteria refer to the standards set individually for each vehicle battery to determine whether there is an abnormality. These criteria are not fixed, but are dynamically adjusted based on the actual usage environment and usage time of the battery to adapt to the different conditions of different batteries.

[0035] In this application, the user terminal refers to the electronic device used by the user corresponding to the vehicle battery, such as a smartphone or in-vehicle terminal, which is used to receive early warning information so that the user can know the battery status in a timely manner and take appropriate measures.

[0036] In this application, firstly, monitoring data of each vehicle battery can be acquired through a cloud platform. This ensures that the data covers the battery's external environment, usage time, its own electrical state, and the working status of related components, providing comprehensive data support for subsequent accurate judgment. Next, for each vehicle battery, specific anomaly judgment conditions are dynamically constructed based on its own environmental data and cumulative usage time, avoiding judgment bias caused by using a uniform standard. Then, the acquired battery voltage and DC-DC converter output voltage are compared with the dynamically determined anomaly judgment conditions to determine if the battery is abnormal. Finally, if the judgment result is abnormal, a corresponding warning message is generated and pushed to the user terminal corresponding to the battery, achieving timely notification of the anomaly.

[0037] Based on the technical solution proposed in this application, comprehensive monitoring data obtained from the cloud platform ensures a comprehensive and reliable data foundation for anomaly detection. By dynamically determining personalized anomaly detection conditions based on environmental data and cumulative usage time, the limitations of uniform detection standards in existing technologies are overcome. This makes anomaly detection more closely aligned with the actual usage of each battery, effectively reducing the misjudgment rate caused by differences in environment and usage time. By generating and pushing timely warning information to user terminals, users can be informed of abnormal battery status immediately, facilitating timely repair and replacement measures. This prevents vehicle breakdowns, electrical system malfunctions, and other safety hazards caused by battery problems, ensuring the stability and safety of the entire vehicle operation and improving user satisfaction.

[0038] In this application, as Figure 1After step 110, i.e., after obtaining the monitoring data of each vehicle's battery from the cloud platform, steps 111 to 112 can be performed as follows: Step 111: Obtain the operating condition data of each vehicle battery from the cloud platform. The operating condition data includes power mode, battery status, battery charge, DC-DC converter operating status, and operating condition duration.

[0039] Step 112: Based on the operating condition data, filter the monitoring data of vehicle batteries that do not meet the preset operating condition conditions from the monitoring data of each vehicle battery. The preset operating condition conditions include the power mode being a preset working mode, the battery status being a preset state, the battery charge being greater than a set charge threshold, the DC-DC converter working state being a preset output state, and the operating condition duration being greater than or equal to a set stable duration.

[0040] In this application, the operating condition data of the vehicle battery refers to data reflecting the vehicle's operating state, its own operating state, and the duration of operation when the vehicle battery is working. Specifically, it may include power mode (i.e., the operating state of the vehicle power supply, such as starting, stopping, standby, etc.), battery state (i.e., the operating mode of the battery, such as charging, discharging, resting, etc.), battery charge (i.e., the current energy storage level of the battery), DC-DC converter operating state (i.e., whether the DC-DC converter is in the operating state of outputting electrical energy to the battery), and operating condition duration (i.e., the continuous holding time of the current operating condition).

[0041] In this application, the preset operating conditions refer to the pre-set operating condition standards that can ensure the validity and representativeness of the monitoring data. Only monitoring data that meets these conditions can be used for subsequent anomaly judgment, thus avoiding misjudgment caused by unstable operating conditions or unrepresentative data.

[0042] In this application, the set power threshold refers to the minimum value of the battery power that is preset. A value higher than this indicates that the battery has sufficient energy storage, and the monitoring data at this time can better reflect the true health status of the battery.

[0043] In this application, the setting of a stable duration refers to a pre-set minimum duration of the operating condition to ensure that the operating condition is in a stable state and to avoid distortion of monitoring data due to instantaneous fluctuations in the operating condition.

[0044] In practice, after obtaining monitoring data of each vehicle battery from the cloud platform, corresponding operating condition data can be further obtained from the cloud platform to supplement the background information of the monitoring data. Subsequently, the monitoring data is filtered according to preset operating condition conditions, eliminating monitoring data that does not meet the conditions, and retaining only monitoring data that meets the following criteria: power mode is a preset working mode, battery status is a preset state, battery power is greater than a set power threshold, DC-DC converter is in a preset output state, and operating condition duration is greater than or equal to a set stable duration. The filtered valid monitoring data is used for subsequent anomaly detection.

[0045] For example, in a specific embodiment, the preset operating conditions may include the following: the power mode is startup mode, the battery status is charging state, the battery charge is greater than or equal to 75%, the DC-DC converter is in output state, and the operating condition duration is greater than or equal to 20 seconds.

[0046] Furthermore, in this embodiment, it is assumed that the monitoring data of vehicle A is a battery voltage of 11.3V and a DC-DC converter output voltage of 11.2V. The corresponding operating conditions are: power mode is standby mode, battery status is idle, battery charge is 60%, DC-DC converter is off, and operating condition duration is 30 seconds. It is assumed that the monitoring data of vehicle B is a battery voltage of 11.6V and a DC-DC converter output voltage of 11.5V. The corresponding operating conditions are: power mode is start mode, battery status is charging, battery charge is 80%, DC-DC converter is output, and operating condition duration is 25 seconds.

[0047] As can be seen, because the operating conditions of vehicle A do not meet the preset conditions of power mode being start-up mode, battery status being charging state, battery charge being greater than 75%, and DC-DC converter operating state being output state, its corresponding monitoring data is filtered out. However, the operating conditions of vehicle B meet all the preset conditions, and its corresponding monitoring data is retained for subsequent anomaly detection.

[0048] Based on the technical solutions in steps 111 to 112 above, supplementing the acquired operating condition data can provide a basis for filtering the validity of monitoring data. By setting preset operating condition conditions and filtering monitoring data that do not meet the conditions, distorted monitoring data caused by unstable operating conditions, insufficient battery energy storage, or malfunction of related components can be eliminated, ensuring that the monitoring data used for anomaly determination is stable and valid, further improving the accuracy of anomaly determination and reducing the false judgment rate. Simultaneously, it can avoid interference from invalid data in the determination process, reduce data processing volume, improve the execution efficiency of the monitoring method, and ensure that anomaly determination can be completed quickly and accurately, providing a guarantee for timely push of warning information, thereby better ensuring the safety of vehicle operation and user experience.

[0049] In this application, after obtaining the monitoring data of each vehicle battery from the cloud platform, it is also possible to filter out some invalid values ​​in the monitoring data, such as monitoring data where the battery voltage is 0 and the DC-DC converter output voltage is 0.

[0050] Furthermore, after filtering out these invalid values, they can be repaired. For example, by using interpolation, based on valid data within a preset time window before and after these invalid values, the corresponding reasonable values ​​can be calculated to replace the original invalid values.

[0051] In this application, the environmental data may include the ambient temperature and humidity of the area where the vehicle is located.

[0052] In such Figure 1 In step 120, the dynamic determination of the abnormality judgment conditions for each vehicle battery can be performed according to the following steps 121 to 122: Step 121: Based on the ambient temperature and the ambient humidity, determine the battery voltage threshold and the DC-DC converter output voltage threshold for each vehicle battery. The battery voltage threshold and the DC-DC converter output voltage threshold are both positively correlated with the ambient temperature and negatively correlated with the ambient humidity.

[0053] Step 122: Based on the cumulative usage time of each vehicle battery, adjust the battery voltage threshold and DC-DC converter output voltage threshold of each vehicle battery to obtain the abnormal judgment conditions for each vehicle battery.

[0054] In this application, ambient temperature refers to the real-time atmospheric temperature of the area where the vehicle is located, which is a key environmental factor affecting the electrochemical reaction rate and internal resistance of the battery. Ambient humidity refers to the real-time atmospheric humidity of the area where the vehicle is located, which mainly affects the battery's operating status indirectly by influencing the battery's sealing performance and terminal condition.

[0055] In this application, the battery voltage threshold is a critical value used to determine whether the battery voltage is abnormal. If it is lower than this value, it indicates that the battery voltage may be abnormal. Similarly, the DC-DC converter output voltage threshold is a critical value used to determine whether the DC-DC converter output voltage is abnormal. If it is lower than this value, it indicates that the DC-DC converter output may be abnormal, which in turn relates to abnormal battery charging.

[0056] In this application, both the battery voltage threshold and the DC-DC converter output voltage threshold are positively correlated with the ambient temperature, that is, the higher the ambient temperature, the higher the battery voltage threshold and the DC-DC converter output voltage threshold; both the battery voltage threshold and the DC-DC converter output voltage threshold are negatively correlated with the ambient humidity, that is, the higher the ambient humidity, the lower the battery voltage threshold and the DC-DC converter output voltage threshold.

[0057] In practical applications, the environmental data, including ambient temperature and humidity, is first defined. Based on these two key environmental parameters, and considering the battery's operating characteristics under different temperature and humidity conditions, initial battery voltage thresholds and DC-DC converter output voltage thresholds are determined. Higher ambient temperatures lead to more complete electrochemical reactions in the battery, lower internal resistance, and consequently, higher voltage thresholds. Higher ambient humidity may cause battery seal failure or terminal oxidation, resulting in lower voltage thresholds. Subsequently, considering the cumulative usage time of each vehicle's battery and the natural aging process that leads to decreased energy storage and voltage output capabilities, the initially determined battery voltage thresholds and DC-DC converter output voltage thresholds are revised. This ultimately yields personalized anomaly detection conditions tailored to the specific battery.

[0058] For example, in a specific embodiment, the association rules between temperature / humidity and thresholds are set as follows: For every 10°C increase in ambient temperature, the battery voltage threshold increases by 0.2V, and the DC-DC converter output voltage threshold increases by 0.15V. For every 20% increase in ambient humidity, the battery voltage threshold decreases by 0.1V, and the DC-DC converter output voltage threshold decreases by 0.08V.

[0059] Assuming vehicle C's environmental data is an ambient temperature of 35℃ and an ambient humidity of 60%, based on the above rules, the initially determined battery voltage threshold is 11.4V, and the DC-DC converter output voltage threshold is 11.3V. Assuming vehicle D's environmental data is an ambient temperature of 15℃ and an ambient humidity of 40%, the initially determined battery voltage threshold is 11.0V, and the DC-DC converter output voltage threshold is 10.9V.

[0060] Based on the technical solutions in steps 121 and 122 above, clarifying the specific composition of environmental data provides a clear basis for the dynamic determination of anomaly judgment conditions. Determining the initial threshold based on the positive correlation between ambient temperature and the threshold, and the negative correlation between ambient humidity and the threshold, fully considers the impact of temperature and humidity on the battery's operating state, making the initial threshold more closely match the battery's actual operating environment. Further refining the initial threshold by incorporating cumulative usage time takes into account the battery's natural aging characteristics, ensuring that the final anomaly judgment conditions adapt to both environmental differences and usage wear differences. This further refines the personalization of the anomaly judgment conditions, significantly improves the accuracy of anomaly judgment, effectively reduces the risk of misjudgment due to environmental changes or aging, lays a solid foundation for accurately determining whether the battery is abnormal, and ultimately better ensures the stable operation of the vehicle's electrical system.

[0061] Specifically, in step 121 above, the step of adjusting the battery voltage threshold and DC-DC converter output voltage threshold of each vehicle battery based on the cumulative usage time of each vehicle battery can be performed according to steps 1211 to 1213 as follows: Step 1211: Based on the cumulative usage time, determine a first correction value for the battery voltage threshold and a second correction value for the DC-DC converter output voltage threshold. Both the first correction value and the second correction value are negatively correlated with the cumulative usage time. The range of the first correction value and the second correction value is set to [-1, 1].

[0062] Step 1212: Based on the first correction value, correct the battery voltage threshold of each vehicle battery.

[0063] Step 1213: Based on the second correction value, correct the DC-DC converter output voltage threshold of each vehicle battery.

[0064] In this application, the first correction value refers to a parameter used to correct the battery voltage threshold. Its value is determined by the cumulative usage time and reflects the degree of decrease in the battery's voltage output capability due to increased usage time. The second correction value refers to a parameter used to correct the DC-DC converter output voltage threshold. Its value is also determined by the cumulative usage time and reflects the degree of change in the DC-DC converter's output voltage adaptability after battery aging.

[0065] In practical applications, the correction value rules are first established based on the cumulative usage time of each vehicle battery. Since a longer cumulative usage time indicates more severe battery aging, resulting in poorer voltage output capability and adaptability to the DC-DC converter's output voltage, both the first and second correction values ​​are negatively correlated with the cumulative usage time; that is, the longer the cumulative usage time, the smaller the correction value. Simultaneously, limiting the range of the first and second correction values ​​to [-1, 1] avoids over-correction. Subsequently, the determined first correction value is calculated against the initial battery voltage threshold to correct the battery voltage threshold. Similarly, the determined second correction value is calculated against the initial DC-DC converter output voltage threshold to correct the DC-DC converter's output voltage threshold.

[0066] For example, in a specific embodiment, the correspondence between cumulative usage time and correction value can be set as follows: The cumulative usage time is 0-1 year, with a first correction value of 0.1V and a second correction value of 0.08V; With a cumulative usage time of 1-3 years, the first correction value is 0V, and the second correction value is 0V. With a cumulative usage time of 3-5 years, the first correction value is -0.2V, and the second correction value is -0.15V; For cumulative usage of 5 years or more, the first correction value is -0.3V and the second correction value is -0.25V.

[0067] Assuming vehicle E has been used for a cumulative period of 4 years, the initial battery voltage threshold is 11.4V, and the initial DC-DC converter output voltage threshold is 11.3V, the first correction value is determined to be -0.2V, and the second correction value is -0.15V, according to the rules. The thresholds for vehicle E are then corrected as follows: Battery voltage threshold = 11.4V + (-0.2V) = 11.2V, DC-DC converter output voltage threshold = 11.3V + (-0.15V) = 11.15V.

[0068] Assuming vehicle F has been in use for 6 years, its initial battery voltage threshold is 11.5V, and its initial DC-DC converter output voltage threshold is 11.4V, the first correction value is determined to be -0.3V, and the second correction value is -0.25V. The thresholds for vehicle F are then adjusted as follows: Battery voltage threshold = 11.5V + (-0.3V) = 11.2V, DC-DC converter output voltage threshold = 11.4V + (-0.25V) = 11.15V.

[0069] Based on the technical solutions in steps 1212 to 1213 above, by clarifying the negative correlation between the first and second correction values ​​and the cumulative usage time, the impact of battery aging on the voltage threshold can be accurately reflected, making the threshold correction more scientific. By limiting the range of correction values, excessive correction amplitude due to excessive cumulative usage time can be avoided, ensuring that the threshold is always within a reasonable range and guaranteeing the accuracy of anomaly detection. By specifically correcting the battery voltage threshold and the DC-DC converter output voltage threshold separately, the personalized adaptability of anomaly detection conditions can be further refined, effectively solving the problem of inapplicable judgment standards caused by battery aging, reducing the misjudgment rate of aging batteries, ensuring accurate identification of abnormal states of aging batteries, and providing stronger support for the accurate monitoring of vehicle batteries.

[0070] In such Figure 1 In step 130, the step of determining whether each vehicle battery is abnormal based on the anomaly determination conditions can be specifically performed as follows: step 131: Step 131: If the battery voltage is less than or equal to the battery voltage threshold, or the DC-DC converter output voltage is less than or equal to the DC-DC converter output voltage threshold, then it is determined that each vehicle battery is abnormal.

[0071] In this application, after determining the anomaly criteria, the battery voltage after operating condition screening can be compared with a battery voltage threshold to determine if it is less than or equal to the threshold. Simultaneously, the DC-DC converter output voltage after operating condition screening can be compared with a DC-DC converter output voltage threshold to determine if it is less than or equal to the threshold. If at least one of the above two comparison results is yes, the vehicle battery is determined to be abnormal. If both comparison results are no, the battery is determined to be normal.

[0072] In one specific embodiment, for example, the anomaly detection conditions for vehicle G are a battery voltage threshold of 11.0V and a DC-DC converter output voltage threshold of 10.9V. After operating condition screening, the monitoring data shows a battery voltage of 10.8V and a DC-DC converter output voltage of 11.0V. Therefore, it can be determined that the battery of vehicle G is abnormal. As another example, the anomaly detection conditions for vehicle H are a battery voltage threshold of 11.3V and a DC-DC converter output voltage threshold of 11.2V. After operating condition screening, the monitoring data shows a battery voltage of 11.4V and a DC-DC converter output voltage of 11.3V. Therefore, it can be determined that the battery of vehicle H is normal.

[0073] In this application, based on the technical solution in step 130 above, the two key abnormal scenarios of abnormal battery voltage and abnormal DC-DC converter output voltage can be comprehensively covered, ensuring that no possible situation that may cause battery malfunction is missed, thus improving the comprehensiveness of abnormality identification. At the same time, the judgment logic is simple and clear, facilitating rapid execution and effectively improving the efficiency of abnormality judgment, ensuring timely judgment after acquiring monitoring data, and providing a guarantee for the rapid push of subsequent early warning information. Furthermore, since the abnormality judgment conditions for each vehicle battery are individually defined, this judgment logic can accurately identify the abnormal state of each battery, further reducing the false judgment rate, ensuring that users can promptly know and handle battery abnormalities, avoiding vehicle malfunctions caused by undetected abnormalities, and ensuring the safety and stability of the entire vehicle operation.

[0074] In such Figure 1 In step 140 shown, the generation of early warning information can be performed according to the following steps 141 to 142: Step 141: Calculate the voltage ratio between the battery voltage and the battery voltage threshold.

[0075] Step 142: Generate a warning message of the corresponding level based on the voltage ratio. The level of the warning message is negatively correlated with the voltage ratio. When the voltage ratio is equal to 1, the warning message is a level 1 warning.

[0076] In this application, the voltage ratio refers to the ratio of the actual battery voltage to the battery voltage threshold. This ratio can intuitively reflect the degree of deviation of the actual battery voltage from the threshold. The smaller the ratio, the greater the deviation and the more serious the battery abnormality.

[0077] In this application, the warning information level refers to the warning grade divided according to the severity of the anomaly. Different levels correspond to different levels of urgency and handling suggestions, making it easier for users to take appropriate measures according to the level. The level is negatively correlated with the voltage ratio, that is, the smaller the voltage ratio, the higher the warning level and the stronger the urgency.

[0078] In practical applications, after determining that a vehicle battery is malfunctioning, the actual battery voltage and the corresponding battery voltage threshold can be obtained, and their ratio can be calculated (i.e., voltage ratio = actual battery voltage / battery voltage threshold). Subsequently, based on a preset correspondence between voltage ratios and warning levels, the warning level corresponding to that voltage ratio can be determined. The voltage ratio and warning level are negatively correlated; the smaller the voltage ratio, the higher the warning level. Finally, a warning message corresponding to the level is generated, containing prompts and handling suggestions matching the level.

[0079] In a specific embodiment, for example, the preset warning levels are divided into 4 levels, with the following correspondence: when the voltage ratio is ≥1, level 1 warning; when the voltage ratio is 0.95≤1, level 2 warning; when the voltage ratio is 0.9≤0.95, level 3 warning; when the voltage ratio is <0.9, level 4 warning.

[0080] Based on the technical solutions in steps 141 and 142 above, the voltage ratio can be calculated to accurately quantify the degree of battery voltage abnormality, providing an objective and accurate basis for classifying warning levels. Classifying warning levels based on the rule of a negative correlation between the voltage ratio and the warning level allows the warning information to intuitively reflect the urgency of the abnormality, facilitating users' quick understanding of the severity of the situation. Different levels of warning information can provide users with differentiated handling guidance, preventing users from taking inappropriate measures due to an inability to judge the urgency of the abnormality. This ensures that serious abnormalities are handled promptly while avoiding excessive user anxiety caused by minor abnormalities, further improving the user experience. It also provides the after-sales department with accurate service guidelines, enabling them to rationally allocate service resources according to the warning level and improve service efficiency.

[0081] In such Figure 1 In step 140 shown, the generation of warning information can also be performed according to steps 143 to 145 as follows: Step 143: Fit a battery voltage trend curve based on the time-series data of the battery voltage within a preset historical time period.

[0082] Step 144: Calculate the monthly average voltage change value based on the battery voltage trend curve.

[0083] Step 145: Generate a warning message of a corresponding level based on the monthly average voltage change value. The level of the warning message is negatively correlated with the monthly average voltage change value. When the monthly average voltage change value is greater than or equal to 0, the warning message is a Level 1 warning.

[0084] In this application, the preset historical duration refers to the pre-set historical data time range used to analyze the trend of battery voltage change, such as 3 months, 6 months, etc., to ensure that there is enough historical data to support the trend analysis and make the trend curve more representative.

[0085] In this application, the battery voltage trend curve refers to a curve obtained by fitting algorithm based on battery voltage time series data within a preset historical period, which reflects the change law of battery voltage over time and can intuitively show the trend of battery voltage change, such as stable, slow decrease, rapid decrease, etc.

[0086] In this application, the monthly average voltage change value refers to the monthly average voltage change of the battery calculated based on the voltage trend curve. A positive number indicates a voltage increase, zero indicates a stable voltage, and a negative number indicates a voltage decrease. The greater the decrease, the faster the battery ages and the higher the risk of abnormality.

[0087] In practical applications, after determining that a battery is abnormal, all battery voltage time-series data (i.e., battery voltage data at different points in time) within a preset historical period can be obtained. A suitable fitting algorithm (such as linear fitting, polynomial fitting, etc.) is used to fit this time-series data to obtain a battery voltage trend curve. Then, based on this trend curve, the monthly voltage change, i.e., the monthly average voltage change value, is calculated. Further, according to a preset correspondence between the monthly average voltage change value and the warning level, the corresponding warning level can be determined. The monthly average voltage change value and the warning level are negatively correlated; the greater the monthly average voltage drop (the smaller the monthly average voltage change value), the higher the warning level. Finally, the corresponding warning information is generated.

[0088] In a specific embodiment, for example, the preset historical duration is set to 6 months, and the warning level is divided into 4 levels, with the following correspondence: A Level 1 warning is issued when the monthly average voltage change is ≥0V; a Level 2 warning is issued when the monthly average voltage change is -0.05V ≤ the monthly average voltage change <0V; a Level 3 warning is issued when the monthly average voltage change is -0.1V ≤ the monthly average voltage change < -0.05V; and a Level 4 warning is issued when the monthly average voltage change is < -0.1V.

[0089] Based on the technical solutions in steps 143 to 145 above, by analyzing voltage time-series data within a preset historical time period and fitting trend curves, the long-term variation patterns of battery voltage can be deeply explored, and the aging trend and abnormal risks of the battery can be predicted in advance. This extends the monitoring from anomaly identification to trend warning, further enhancing the forward-looking nature of the monitoring. Classifying warning levels based on monthly average voltage changes can accurately reflect the aging rate and severity of anomalies, providing users with more targeted handling suggestions. This allows users to plan battery maintenance or replacement in advance, avoiding vehicle breakdowns due to sudden battery failure. Simultaneously, this solution enriches the basis for generating warning information, making the warning level classification more comprehensive and scientific, further reducing the risk of misjudgment, improving the reliability and practicality of the monitoring method, and providing users with a better user experience.

[0090] In this application, the warning information may include N levels, and each level of warning information includes a recommended battery replacement period, wherein the recommended battery replacement period for warning information from level 1 to level N decreases sequentially.

[0091] In this application, it can refer to the number of warning levels set according to actual monitoring needs, such as level 2, level 3, level 4, etc. The specific number can be adjusted according to the application scenario to ensure that different degrees of severity of anomalies can be accurately distinguished.

[0092] In this application, the recommended battery replacement period refers to the latest time limit for users to replace the battery based on the warning level. The higher the warning level (the more severe the abnormality), the shorter the recommended replacement period, so as to ensure that users can complete the replacement before the battery completely fails and avoid vehicle failure.

[0093] In practical applications, the number of preset warning message levels is N (e.g., N=4), and a corresponding battery replacement recommendation period is set for each level. The higher the level, the more urgent the recommended replacement time. After determining the warning level, the warning message content is improved according to the corresponding replacement recommendation period, ensuring that the warning message includes both an anomaly warning and a clear notification of the recommended replacement period to the user. Finally, the warning message containing the replacement recommendation period can be pushed to the user's terminal.

[0094] In a specific implementation, for example, if N=4, the recommended replacement period for each warning level is as follows: The recommended replacement period for a Level 1 warning is within 6 months; for a Level 2 warning, it is within 3 months; for a Level 3 warning, it is within 1 month; and for a Level 4 warning, it is within 7 days.

[0095] For example, if vehicle I calculates a voltage ratio of 0.98, corresponding to a Level 2 warning, the generated warning message would be: "Your vehicle's battery has an abnormality. It is recommended to replace the battery at an after-sales service center within 3 months to avoid affecting the normal use of the vehicle." As another example, if vehicle J calculates a monthly average voltage change of -0.12V, corresponding to a Level 4 warning, the generated warning message would be: "Your vehicle's battery is aging rapidly and has a serious abnormality. It is recommended to replace the battery at an after-sales service center within 7 days, otherwise it may cause the vehicle to break down."

[0096] This application sets N warning levels and configures progressively decreasing battery replacement recommendation deadlines for each level, making the warning information more practical and instructive. Users can clearly understand the urgency and timeframes for handling abnormalities, avoiding delays in repair or replacement due to a lack of understanding of the deadlines, and effectively reducing the risk of vehicle malfunctions caused by battery problems. Simultaneously, the clearly defined replacement recommendation deadlines provide clear service guidance for after-sales departments, facilitating their advance preparation of relevant parts and service resources, and improving service response speed and quality. Furthermore, this approach enriches the content of warning information, enhancing its reference value, and creating a complete closed loop from anomaly detection to warning delivery. This comprehensively ensures the normal operation of the vehicle battery and the stable operation of the entire vehicle, significantly improving user satisfaction and trust in the vehicle brand.

[0097] The following describes an embodiment of the apparatus described in this application, which can be used to execute the cloud-based vehicle battery monitoring method described in the above embodiments of this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the cloud-based vehicle battery monitoring method described above.

[0098] See Figure 2 The diagram shows a block diagram of a cloud-based vehicle battery monitoring device according to an embodiment of this application.

[0099] like Figure 2 As shown, the vehicle battery monitoring device 200 based on a cloud platform according to an embodiment of this application includes: an acquisition unit 201, a determination unit 202, a judgment unit 203, and a generation unit 204.

[0100] The system includes: an acquisition unit 201, used to acquire monitoring data of each vehicle battery from a cloud platform, including environmental data, cumulative usage time, battery voltage, and the output voltage of the DC-DC converter corresponding to the vehicle battery; a determination unit 202, used to dynamically determine anomaly judgment conditions for each vehicle battery based on the environmental data and cumulative usage time; a judgment unit 203, used to determine whether each vehicle battery is abnormal based on the battery voltage and the corresponding DC-DC converter output voltage, according to the anomaly judgment conditions; and a generation unit 204, used to generate early warning information if each vehicle battery is abnormal, and push the early warning information to the user terminal corresponding to each vehicle battery.

[0101] In some embodiments of this application, based on the foregoing solution, the device further includes: a filtering unit, configured to, after acquiring monitoring data of each vehicle battery from the cloud platform, acquire operating condition data of each vehicle battery from the cloud platform, the operating condition data including power mode, battery status, battery charge, DC-DC converter operating status, and operating condition duration; and, based on the operating condition data, filter monitoring data of vehicle batteries that do not meet preset operating condition conditions from the monitoring data of each vehicle battery, the preset operating condition conditions including a preset operating mode for the power mode, a preset state for the battery status, a battery charge greater than a set charge threshold, a preset output state for the DC-DC converter, and an operating condition duration greater than or equal to a set stable duration.

[0102] In some embodiments of this application, based on the aforementioned scheme, the environmental data includes the ambient temperature and humidity of the area where the vehicle is located. The determining unit 202 is configured to: determine a battery voltage threshold and a DC-DC converter output voltage threshold for each vehicle battery based on the ambient temperature and the ambient humidity, wherein both the battery voltage threshold and the DC-DC converter output voltage threshold are positively correlated with the ambient temperature and negatively correlated with the ambient humidity; and correct the battery voltage threshold and the DC-DC converter output voltage threshold for each vehicle battery based on the cumulative usage time of each vehicle battery to obtain anomaly determination conditions for each vehicle battery.

[0103] In some embodiments of this application, based on the foregoing scheme, the determining unit 202 is configured to: determine a first correction value for the battery voltage threshold and a second correction value for the DC-DC converter output voltage threshold based on the cumulative usage time, wherein both the first correction value and the second correction value are negatively correlated with the cumulative usage time, and the range of the first correction value and the second correction value is set to [-1, 1]; correct the battery voltage threshold of each vehicle battery based on the first correction value; and correct the DC-DC converter output voltage threshold of each vehicle battery based on the second correction value.

[0104] In some embodiments of this application, based on the foregoing scheme, the determination unit 203 is configured to: determine that each vehicle battery is abnormal if the battery voltage is less than or equal to the battery voltage threshold, or if the DC-DC converter output voltage is less than or equal to the DC-DC converter output voltage threshold.

[0105] In some embodiments of this application, based on the foregoing scheme, the generation unit 204 is configured to: calculate the voltage ratio between the battery voltage and the battery voltage threshold; generate warning information of a corresponding level according to the voltage ratio, wherein the level of the warning information is negatively correlated with the voltage ratio, wherein when the voltage ratio is equal to 1, the level of the warning information is a level 1 warning.

[0106] In some embodiments of this application, based on the foregoing scheme, the generation unit 204 is configured to: fit a battery voltage trend curve based on the time-series data of the battery voltage within a preset historical period; calculate the monthly average voltage change value based on the battery voltage trend curve; and generate a warning message of a corresponding level based on the monthly average voltage change value, wherein the level of the warning message is negatively correlated with the monthly average voltage change value, and when the monthly average voltage change value is greater than or equal to 0, the level of the warning message is a level 1 warning.

[0107] In some embodiments of this application, based on the aforementioned scheme, the warning information includes N levels, and each level of warning information includes a recommended battery replacement period, wherein the recommended battery replacement period for warning information from level 1 to level N decreases sequentially.

[0108] Based on the same inventive concept, embodiments of this application provide a computer program product, the computer program product including computer instructions stored in a computer-readable storage medium and adapted to be read and executed by a processor, so as to cause a computer device having the processor to perform the operations performed by the cloud platform-based vehicle battery monitoring method as described above.

[0109] Based on the same inventive concept, embodiments of this application provide a computer-readable storage medium storing at least one computer program instruction, which is loaded and executed by a processor to perform the operations described above for the vehicle battery monitoring method based on a cloud platform.

[0110] Based on the same inventive concept, this application also provides an electronic device, see reference. Figure 3 The diagram shows a schematic of the structure of an electronic device in an embodiment of this application. The electronic device includes one or more memories 304, one or more processors 302, and at least one computer program (computer program instruction) stored in the memory 304 and executable on the processor 302. When the processor 302 executes the computer program, it implements the vehicle battery monitoring method based on the cloud platform as described above.

[0111] Among them, Figure 3In this document, a bus architecture (represented by bus 300) is used. Bus 300 may include any number of interconnected buses and bridges, linking various circuits including one or more processors represented by processor 302 and memory represented by memory 304. Bus 300 may also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 305 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 may be the same element, i.e., a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 302 is responsible for managing bus 300 and general processing, while memory 304 can be used to store data used by processor 302 during operation.

[0112] The functions described herein can be implemented in hardware, software executed by a processor, firmware, or any combination thereof. When implemented in software executed by a processor, the functions can be stored as one or more instructions or codes on or transmitted via a computer-readable medium. Other examples and embodiments are within the scope and spirit of this application and the appended claims. For example, due to the nature of software, the functions described above can be implemented using software executed by a processor, hardware, firmware, hardwired, or any combination thereof. Furthermore, the functional units can be integrated into a single processing unit, or each unit can exist physically separately, or two or more units can be integrated into a single unit.

[0113] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0114] The units described as separate components may or may not be physically separate. Similarly, the components of the control device may or may not be physical units; they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0115] When the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing computer program instructions, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0116] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A vehicle battery monitoring method based on a cloud platform, characterized in that, The method includes: The monitoring data of each vehicle battery is obtained from the cloud platform. The monitoring data includes environmental data of the vehicle battery, cumulative usage time, battery voltage, and the output voltage of the DC-DC converter corresponding to the vehicle battery. Based on the environmental data and cumulative usage time of each vehicle battery, the abnormal judgment conditions for each vehicle battery are dynamically determined. Based on the battery voltage of each vehicle battery and the corresponding DC-DC converter output voltage, the abnormality determination criteria are used to determine whether each vehicle battery is abnormal. If any of the vehicle batteries is found to be faulty, a warning message is generated and pushed to the user terminal corresponding to each vehicle battery.

2. The method according to claim 1, characterized in that, The method further includes: After obtaining the monitoring data of each vehicle battery from the cloud platform, the operating condition data of each vehicle battery is obtained from the cloud platform. The operating condition data includes power mode, battery status, battery power, DC-DC converter operating status, and operating condition duration. Based on the operating condition data, the monitoring data of vehicle batteries that do not meet the preset operating condition conditions are filtered from the monitoring data of each vehicle battery. The preset operating condition conditions include the power mode being a preset working mode, the battery status being a preset state, the battery charge being greater than a set charge threshold, the DC-DC converter operating status being a preset output state, and the operating condition duration being greater than or equal to a set stable duration.

3. The method according to claim 2, characterized in that, The environmental data includes the ambient temperature and humidity of the area where the vehicle is located. The dynamic determination of the anomaly detection conditions for each vehicle battery includes: Based on the ambient temperature and the ambient humidity, a battery voltage threshold and a DC-DC converter output voltage threshold are determined for each vehicle battery. Both the battery voltage threshold and the DC-DC converter output voltage threshold are positively correlated with the ambient temperature and negatively correlated with the ambient humidity. Based on the cumulative usage time of each vehicle battery, the battery voltage threshold and DC-DC converter output voltage threshold of each vehicle battery are adjusted to obtain the abnormal judgment conditions for each vehicle battery.

4. The method according to claim 3, characterized in that, The step of adjusting the battery voltage threshold and DC-DC converter output voltage threshold for each vehicle battery based on the cumulative usage time of each vehicle battery includes: Based on the cumulative usage time, a first correction value for the battery voltage threshold and a second correction value for the DC-DC converter output voltage threshold are determined. Both the first correction value and the second correction value are negatively correlated with the cumulative usage time. The range of the first correction value and the second correction value is set to [-1, 1]. Based on the first correction value, the battery voltage threshold of each vehicle battery is corrected. Based on the second correction value, the DC-DC converter output voltage threshold of each vehicle battery is corrected.

5. The method according to claim 4, characterized in that, The step of determining whether each vehicle battery is abnormal based on the anomaly detection criteria includes: If the battery voltage is less than or equal to the battery voltage threshold, or the DC-DC converter output voltage is less than or equal to the DC-DC converter output voltage threshold, then each vehicle battery is determined to be abnormal.

6. The method according to claim 5, characterized in that, The generation of early warning information includes: Calculate the voltage ratio between the battery voltage and the battery voltage threshold. Based on the voltage ratio, a corresponding level of early warning information is generated. The level of the early warning information is negatively correlated with the voltage ratio. When the voltage ratio is equal to 1, the level of the early warning information is level 1.

7. The method according to claim 5, characterized in that, The generation of early warning information includes: Based on the time-series data of the battery voltage within a preset historical period, fit a battery voltage trend curve; Based on the battery voltage trend curve, calculate the monthly average voltage change value; Based on the monthly average voltage change value, a corresponding level of early warning information is generated. The level of the early warning information is negatively correlated with the monthly average voltage change value. Specifically, when the monthly average voltage change value is greater than or equal to 0, the level of the early warning information is Level 1.

8. The method according to claim 6 or 7, characterized in that, The warning information includes N levels, and each level of warning information includes a recommended battery replacement period. The recommended battery replacement period decreases sequentially from level 1 to level N.

9. A vehicle battery monitoring device based on a cloud platform, characterized in that, The device includes: The acquisition unit is used to acquire monitoring data of each vehicle battery from the cloud platform. The monitoring data includes environmental data of the vehicle battery, cumulative usage time, battery voltage, and the output voltage of the DC-DC converter corresponding to the vehicle battery. The determination unit is used to dynamically determine the abnormal judgment conditions for each vehicle battery based on the environmental data and cumulative usage time of each vehicle battery. The determination unit is used to determine whether each vehicle battery is abnormal based on the battery voltage of each vehicle battery and the corresponding DC-DC converter output voltage, according to the abnormality determination conditions. The generation unit is used to generate a warning message if each vehicle battery has an abnormality, and push the warning message to the user terminal corresponding to each vehicle battery.

10. An electronic device, characterized in that, The electronic device includes one or more processors and one or more memories, wherein at least one piece of program code is stored in the one or more memories, and the at least one piece of program code is loaded and executed by the one or more processors to implement the method as described in any one of claims 1 to 8.

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