Remote diagnostic management methods, systems and equipment for electric bicycle battery faults

Abstract

This application relates to the field of charging management technology, and provides a method, system, and device for remote diagnosis and management of electric bicycle battery faults. The management method includes: determining whether there is an anomaly in the current charging process by analyzing the discharge data of the target charging pile; if an anomaly is detected and the target charging pile is checked for anomalies, obtaining the corresponding status data of the target charging pile; performing anomaly checks on the target charging pile based on the status data; and determining that the vehicle-side anomaly is present if the anomaly check result indicates that the target charging pile is not abnormal. By adopting the above technical solution, when an anomaly is detected in the charging process based on discharge data, further anomaly checks can be performed on the charging pile based on the charging pile status data. If the anomaly check result indicates that the charging pile is not abnormal, the vehicle-side anomaly can be directly determined. This indirectly achieves the judgment of vehicle-side anomalies, thereby realizing precise management of electric vehicle charging safety.

Description

Technical Field

[0001] This application relates to the field of charging management technology, and in particular to remote diagnosis and management methods, systems and equipment for electric bicycle battery faults. Background Technology

[0002] Electric bicycles, as an environmentally friendly and convenient mode of transportation, have been widely used for short-distance urban travel. With the rapid increase in the number of electric bicycles, the construction of charging infrastructure has also become increasingly sophisticated, greatly enhancing the user experience and safety.

[0003] In related technologies, to ensure the safety of the charging process, the status of the charging pile can be monitored during the charging process. If an abnormality is detected in the status of the charging pile (such as an increase in temperature), corresponding measures can be taken for the charging pile.

[0004] However, during the charging process, abnormalities may occur on the vehicle side (such as the charger and battery), leading to safety risks. The management methods in related technologies cannot detect these abnormalities, resulting in inadequate safety management. Summary of the Invention

[0005] To enhance the safety management of electric bicycle charging, this application provides a method, system, and equipment for remote diagnosis and management of electric bicycle battery faults.

[0006] Firstly, this application provides a remote diagnostic management method for electric bicycle battery faults, employing the following technical solution:

[0007] A remote diagnostic and management method for electric bicycle battery faults, the method comprising:

[0008] The discharge data of the target charging station is determined. The target charging station provides a power interface. The electric bicycle is electrically connected to the power interface of the target charging station through a charger so that the electric bicycle can be charged through the target charging station.

[0009] Based on the discharge data, determine whether there are any abnormalities in this charging process;

[0010] If an anomaly is determined in the current charging process, it is determined whether to perform an anomaly check on the target charging station.

[0011] If it is determined that the target charging pile is to be anomaly checked, the status data corresponding to the target charging pile shall be obtained.

[0012] Anomaly verification is performed on the target charging pile based on the status data;

[0013] If the anomaly verification result indicates that the target charging pile is not abnormal, then the vehicle-side anomaly is determined.

[0014] By adopting the above technical solution, when an anomaly is detected in the charging process based on discharge data, the charging pile can be further checked for anomalies based on the charging pile status data. If the anomaly check result indicates that there is no anomaly in the charging pile, the vehicle-side anomaly can be directly determined. In this way, the judgment of vehicle-side anomalies can be indirectly realized, thereby accurately identifying different types of anomalies, which can help to achieve precise management of electric vehicle charging safety.

[0015] Optionally, the step of performing anomaly verification on the target charging pile based on the status data includes:

[0016] Based on the status data, determine whether the target charging pile has any abnormalities;

[0017] If it is determined from the status data that there is no abnormality in the target charging pile, the historical discharge data corresponding to the target charging pile is obtained;

[0018] The target charging pile is checked for anomalies by combining the historical discharge data.

[0019] By adopting the above technical solution, when it is determined from the status data that there is no abnormality in the target charging pile, the anomaly verification of the target charging pile can be further combined with historical charging data. This can help to take into account the influencing factors that have not yet caused the anomaly during the anomaly verification process, thereby helping to improve the accuracy of the anomaly verification results.

[0020] Optionally, the step of performing anomaly verification on the target charging pile based on the historical discharge data includes:

[0021] Based on the historical discharge data, determine whether the target charging pile has any abnormalities;

[0022] If the target charging station is determined to be normal based on the historical data, the health threshold for this use is determined based on the discharge data.

[0023] Determine whether the target health status corresponding to the target charging pile is less than the health status threshold;

[0024] If the target health level is less than the health level threshold, an anomaly verification result is generated indicating that the target charging pile is abnormal.

[0025] By adopting the above technical solution, when it is determined that there are no abnormalities in the target charging pile based on historical discharge data, a health threshold can be further determined by combining the actual discharge situation. If the target health of the target charging pile is less than the health threshold, an anomaly verification result is generated indicating that the target charging pile is abnormal. In this way, the anomaly verification of the target charging pile can be carried out in combination with the actual discharge situation, which can help improve the accuracy of the anomaly verification result.

[0026] Optionally, determining the health threshold to be used this time based on the discharge data includes:

[0027] The health requirement parameters corresponding to the discharge data are determined based on the magnitude of the discharge data.

[0028] The basic health score is adjusted based on the health score requirement parameters to obtain the health score threshold.

[0029] By adopting the above technical solution, the basic health level can be adjusted based on the actual discharge data to obtain the health level threshold, which can help improve the accuracy of the determined health level threshold.

[0030] Optionally, the method further includes:

[0031] The discharge adjustment method is determined based on the historical discharge data.

[0032] The target health level is adjusted based on the aforementioned discharge adjustment method.

[0033] By adopting the above technical solution, the target health level of the target charging pile can be adjusted by combining historical discharge data, thereby improving the accuracy of the determined target health level, which in turn helps to improve the accuracy of the anomaly verification results.

[0034] Optionally, the step of performing anomaly verification on the target charging pile based on the historical discharge data includes:

[0035] Determine the maximum cumulative fluctuation amplitude corresponding to each historical discharge cycle in the historical discharge data;

[0036] The presence of any abnormality in the target charging pile is determined based on the change in the maximum cumulative fluctuation amplitude over time.

[0037] By adopting the above technical solution, the changes in the maximum cumulative fluctuation range can be determined by combining historical discharge data, and the abnormality of the target charging pile can be determined based on the changes, which can help improve the accuracy of the abnormality verification results.

[0038] Optionally, after determining that the vehicle-side is abnormal, the process further includes:

[0039] Acquire infrared image data corresponding to the target charging pile, wherein the image area corresponding to the infrared image data includes the target vehicle parking area corresponding to the target charging pile;

[0040] Based on the infrared image data, determine whether there are any locations with abnormal temperatures within the parking area of ​​the target vehicle;

[0041] If there is a temperature anomaly location within the target vehicle parking area, determine the anomaly location type corresponding to the temperature anomaly location, where the anomaly location type includes chargers and vehicle batteries;

[0042] The cause of the anomaly is determined based on the type of anomaly location.

[0043] By adopting the above technical solution, when an anomaly is determined at the vehicle end, the cause of the anomaly can be further determined by analyzing the infrared image data corresponding to the target charging pile. This can help distinguish between vehicle battery anomalies and charger anomalies, and thus help to handle anomalies during the charging process.

[0044] Optionally, if there is a location with an abnormal temperature within the target vehicle parking area, the method further includes:

[0045] Determine whether the temperature at the location of the temperature anomaly is greater than a preset temperature threshold;

[0046] If the temperature at the location is not greater than the preset temperature threshold, the target charging pile is controlled to reduce its discharge power, and the change in the temperature at the location is monitored.

[0047] If the temperature at the location is detected to be rising, the target charging station will be controlled to stop discharging.

[0048] Secondly, this application provides an electric bicycle charging management system, which adopts the following technical solution:

[0049] An electric bicycle charging management system, the system including a charging pile and a server that is communicatively connected to the charging pile;

[0050] The charging station provides a power interface for supplying power to electric bicycles and sending power supply data to the server.

[0051] The server is used to execute any of the electric bicycle management methods provided in the first aspect.

[0052] Thirdly, this application provides an electronic device that adopts the following technical solution:

[0053] An electronic device, the electronic device comprising:

[0054] At least one processor;

[0055] Memory;

[0056] At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: execute any of the remote diagnostic management methods for electric bicycle battery faults provided in the first aspect.

[0057] In summary, this application includes at least one of the following beneficial technical effects:

[0058] 1. If an anomaly is detected in the charging process based on the discharge data, the charging pile status data can be used to further verify the anomaly of the charging pile. If the anomaly verification result indicates that there is no anomaly in the charging pile, the vehicle-side anomaly can be directly determined. This can indirectly achieve the judgment of the vehicle-side anomaly, thereby accurately identifying different types of anomalies, which can help to achieve precise management of electric vehicle charging safety.

[0059] 2. If the target charging pile is determined to be free of anomalies based on status data, further anomaly verification can be performed by combining historical charging data. This can help to take into account factors that have not yet caused anomalies during the anomaly verification process, thereby improving the accuracy of the anomaly verification results. Attached Figure Description

[0060] Figure 1 This is a flowchart illustrating a remote diagnosis and management method for electric bicycle battery faults provided in an embodiment of this application.

[0061] Figure 2 This is a flowchart illustrating a charging pile anomaly verification method provided in an embodiment of this application;

[0062] Figure 3 This is a flowchart illustrating another charging pile anomaly verification method provided in this application embodiment;

[0063] Figure 4 This is a flowchart illustrating an abnormal cause determination method provided in an embodiment of this application;

[0064] Figure 5 This application provides an embodiment of an electric bicycle charging management system;

[0065] Figure 6 This is a schematic diagram of the structure of an electronic bicycle charging management system provided in an embodiment of this application. Detailed Implementation

[0066] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figure 1-6 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0067] This application discloses a remote diagnostic and management method for electric bicycle battery faults. (Refer to...) Figure 1 The remote diagnostic management method for electric bicycle battery faults includes the following steps:

[0068] Step 101: Determine the discharge data of the target charging station.

[0069] In this embodiment, the target charging station provides a power interface, and the electric bicycle is electrically connected to the power interface of the target charging station through a charger so that the electric bicycle can be charged through the target charging station.

[0070] Accordingly, discharge data is used to indicate the discharge status of the target charging station. In one example, discharge data includes current magnitude, continuous operating time, etc.

[0071] In one example, a current transformer and a voltage sensor are installed in the wiring inside the target charging station. Specifically, the current transformer is wound around a power supply coil, allowing for real-time current acquisition during use.

[0072] Step 102: Determine whether there are any abnormalities in the current charging process based on the discharge data.

[0073] Specifically, determining whether there are any abnormalities in the current charging process based on the discharge data includes: determining whether there are any abnormalities in the current charging process based on the magnitude and / or changes in the discharge data.

[0074] In one example, the fluctuation of the current is monitored during the discharge process. If the cumulative fluctuation of the current within a reference time period is found to be greater than a preset fluctuation threshold, it is determined that there is an anomaly in the charging process.

[0075] The length of the reference time period is fixed. For example, the length of the reference time period is 1 minute.

[0076] The cumulative fluctuation amplitude refers to the sum of the differences between adjacent current peaks and troughs within a reference time period. In one example, let's consider current values ​​collected within a reference time period as 1.2, 1.1, 1.3, 1.5, 1.8, 1.0, 1.5, 1.1, 1.6, and 1.7. In this case, there are 4 peaks and 3 troughs within the reference time period. Correspondingly, the cumulative fluctuation amplitude is 0.1 + 0.7 + 0.8 + 0.5 + 0.4 + 0.6, which equals 3.1. If the fluctuation threshold is 3, then an anomaly can be identified.

[0077] In another example, if the cumulative fluctuation amplitude within the reference time period exceeds a preset fluctuation amplitude threshold, waveform analysis can be performed on the current changes within the reference time period to determine whether there is any abnormality in the current charging process.

[0078] In one example, waveform analysis is performed on the current changes within a reference time period, including: analyzing the current changes within the reference time period in the time domain to obtain at least one waveform characteristic parameter; determining whether the waveform parameter is greater than the corresponding parameter threshold; if so, determining that there is an anomaly in the current charging process.

[0079] The waveform parameters include at least one of the following: waveform factor, peak factor, etc. Different waveform parameters have pre-set threshold values. Specifically, the waveform factor describes the shape of the waveform. For example, the waveform factor can be the ratio of the root mean square (RMS) to the average value of the current values ​​within a reference time period, or it can be the ratio of the RMS to the mean absolute deviation of the current values ​​within the reference time period.

[0080] The climax factor is used to describe the peak characteristics of a waveform. For example, it is the ratio of the peak value of the current to the root mean square value within a reference time period.

[0081] In another example, waveform analysis is performed on the current changes within a reference time period, including: performing a Fast Fourier Transform (FFT) on the current changes within the reference time period to obtain the corresponding frequency domain signal; analyzing the frequency domain signal to obtain frequency domain features; and determining whether there are any abnormalities in the current charging process based on the frequency domain features.

[0082] The frequency domain features include at least one of the following parameters: spectral energy, main frequency component, spectral entropy, and spectral distortion. Specifically, spectral energy indicates the energy distribution of each frequency component; for example, the sum of the energy of each frequency component (generally the square of the amplitude of the frequency component) is determined as the spectral energy. The main frequency component indicates the main frequency component of the signal. Spectral entropy indicates the complexity of the signal spectrum; for example, the ratio of the energy of each frequency component to the total energy is calculated, and the corresponding information entropy is calculated by treating the ratio as a probability distribution to obtain the spectral entropy.

[0083] Accordingly, determining whether there are any abnormalities in the current charging process based on frequency domain characteristics includes any of the following methods:

[0084] The first type includes the main frequency component. In this case, if the difference between the main frequency component and the reference frequency component is greater than a preset difference threshold, it is determined that there is an abnormality in the charging process.

[0085] The second type involves frequency characteristics, including spectral energy distribution. In this case, if the score of the spectral energy analysis appears at non-major frequencies and / or the spectral energy is dispersed, it is determined that there is an anomaly in the charging process.

[0086] The third method involves frequency characteristics, including spectral entropy. If the difference between the spectral entropy and the reference value exceeds a preset threshold, an anomaly is identified in the charging process. Further, this difference can be categorized into two cases: spectral entropy greater than the reference value and spectral entropy less than the reference value. A greater spectral entropy indicates increased randomness in current changes, while a less than reference value indicates increased regularity in current changes. This can be used to help determine the cause of the anomaly.

[0087] Step 103: If an anomaly is found in the current charging process, determine whether to perform an anomaly verification on the target charging station.

[0088] Specifically, if there is an abnormality in the charging process, the abnormality may be caused by the charging pile or by the vehicle (electric bicycle or charger). Since the data of the vehicle is difficult to obtain, it is difficult to judge the abnormality of the vehicle. In this embodiment, when an abnormality occurs, the charging pile is judged first.

[0089] In one example, determining whether to perform anomaly verification on the target charging station includes: determining whether the interval since the last anomaly verification is greater than an interval duration threshold; if yes, then determining to perform anomaly verification on the target charging station; if no, then determining not to perform anomaly verification on the target charging station.

[0090] The interval duration threshold can be preset or dynamically determined by factors such as the service life and location of the charging pile. For example, the longer the service life of the charging pile, the smaller the interval duration threshold. The interval duration threshold for charging piles installed in sheds is greater than the interval duration threshold for charging piles installed in open-air environments. This embodiment does not limit the method of determining the interval duration threshold.

[0091] In another example, determining whether to perform anomaly verification on the target charging pair includes: determining whether the cumulative discharge duration of the charging pile since the last anomaly verification has reached the discharge duration threshold; if yes, then determining to verify the target charging pile; if no, then determining not to perform anomaly verification on the target charging pile.

[0092] The method for determining the discharge duration threshold is analogous to the method for determining the interval duration threshold mentioned above, and will not be repeated here.

[0093] Furthermore, if the above two examples show that no anomaly verification is performed on the target charging pile, it indicates that the probability of an anomaly in the charging pile is low, and the anomaly on the vehicle side can be directly determined.

[0094] Step 104: If it is determined that the target charging pile is to be checked for anomalies, obtain the status data corresponding to the target charging pile.

[0095] Among them, the status data is used to indicate the status of the target charging pile.

[0096] In one example, status data is collected through status sensors. Accordingly, acquiring the status data corresponding to the target charging station includes obtaining the status data collected by the status sensors. For example, the status data may include temperature data, and the corresponding status sensors may include temperature sensors.

[0097] In practical implementation, status data can also be recorded in the background. For example, status data includes the continuous operating time of the target charging pile. Specifically, continuous operating time refers to the continuous duration for which the charging pile's output power is greater than a preset minimum power. Furthermore, if the output power falls below a preset power threshold, the recording of the interruption duration can begin. If the interruption duration does not reach the interruption duration threshold and the output power recovers to above the preset minimum power, the continuous operating time is accumulated. If the interruption duration reaches the interruption duration threshold and the output power does not recover to above the preset minimum power, the calculation of the continuous operating time stops. This avoids the impact of short-term fluctuations in output power on the continuous operating time statistics, thus helping to improve the accuracy of the continuous operating time.

[0098] Step 105: Perform anomaly verification on the target charging pile based on the status data.

[0099] Optionally, the target charging pile is verified based on the status data, including: determining whether the status data is greater than the corresponding data threshold; if so, generating an abnormal verification result indicating that the target charging pile has an anomaly; if not, generating an abnormal verification result indicating that the target charging pile does not have an anomaly.

[0100] The data threshold can be preset or determined in combination with other state data. For example, the temperature threshold corresponding to temperature can be determined in combination with the continuous working time. As the continuous working time increases, the temperature threshold can also increase accordingly. In this way, the data threshold can be dynamically determined in combination with the actual situation, which can help improve the accuracy of anomaly verification results.

[0101] Step 106: If the anomaly verification result indicates that there is no anomaly in the target charging pile, then determine that there is an anomaly on the vehicle side.

[0102] If the anomaly verification result indicates that the target charging pile is abnormal, the charging pile is determined to be abnormal.

[0103] Optionally, if the verification result indicates that there is no abnormality in the target charging pile, the target charging pile is controlled to reduce its output power and continue to perform abnormality judgment until no abnormality is determined or the output power is 0; if the verification result indicates that there is an abnormality in the charging pile, the target charging pile is disabled.

[0104] Furthermore, corresponding exception prompts can be generated based on the exception verification results and output through the alarm component, or sent to users and administrators via SMS, in-app messages, etc., so that exceptions can be handled in a timely manner.

[0105] In some implementations, if the anomaly verification result indicates that the target charging pile is not abnormal, determining the vehicle-side anomaly includes: if the anomaly verification result indicates that the target charging pile is not abnormal, acquiring monitoring image data corresponding to the target charging pile, wherein the monitoring area corresponding to the monitoring image data includes the area where the target charging pile is located; determining whether there is a contact anomaly based on the monitoring image data; determining the vehicle-side anomaly if it is determined that there is no contact anomaly; and determining the contact anomaly if it is determined that there is a contact anomaly, and further controlling the target charging pile to stop working. This can accurately identify anomalies caused by poor contact, thereby improving the accuracy of the anomaly determination result.

[0106] In this context, "abnormal contact" refers to the charger plug not being fully inserted into the power interface of the target charging station. In practice, the location of the target power interface can be captured from monitoring image data, and the presence of abnormal contact can be determined through machine recognition or manual identification.

[0107] Furthermore, if the anomaly verification result indicates that there is no anomaly in the target charging pile, the monitoring image data corresponding to the target charging pile is obtained, including: if the anomaly verification result indicates that there is no anomaly in the target charging pile, determining whether there is a risk of contact anomaly based on the discharge data; if the risk of contact anomaly is determined based on the discharge data, obtaining the monitoring image data corresponding to the target charging pile; if the risk of contact anomaly is determined based on the discharge data, directly determining that there is no risk of contact anomaly.

[0108] Specifically, considering that the power supply to the target charging station will be frequently interrupted in the event of contact anomaly, before making a contact anomaly judgment, it is possible to first determine whether there is a contact anomaly risk based on the discharge data, that is, to determine whether the current in the discharge data is frequently interrupted (for example, the number of interruptions within the reference period is greater than the abnormal number threshold). If so, there is a contact anomaly risk; if not, it is determined that there is no contact anomaly risk.

[0109] The implementation principle of a remote diagnostic management method for electric bicycle battery faults according to an embodiment of this application is as follows: The discharge data of the target charging pile is determined. The target charging pile provides a power interface. The electric bicycle is electrically connected to the power interface of the target charging pile through a charger to charge the electric bicycle. Based on the discharge data, it is determined whether there is an abnormality in the charging process. If an abnormality is determined, it is determined whether to perform an anomaly check on the target charging pile. If an anomaly check is performed, the corresponding status data of the target charging pile is obtained. An anomaly check is performed on the target charging pile based on the status data. If the anomaly check result indicates that there is no abnormality in the target charging pile, an abnormality on the vehicle side is determined. In the above technical solution, if an abnormality is detected in the charging process based on the discharge data, an anomaly check can be further performed on the charging pile based on the charging pile status data. If the anomaly check result indicates that there is no abnormality in the charging pile, an abnormality on the vehicle side can be directly determined. This indirectly achieves the judgment of vehicle-side anomalies, thereby accurately identifying different types of anomalies, which helps to achieve precise management of electric vehicle charging safety.

[0110] In some implementations, further reference is made. Figure 2 Step 105 above, which performs anomaly verification on the target charging pile based on the status data, includes the following steps:

[0111] Step 201: Determine whether there is any abnormality in the target charging pile based on the status data.

[0112] Specifically, the method of determining whether the target charging pile is abnormal based on the status data can be compared with the specific implementation of step 105 above, which verifies the anomaly of the target charging pile based on the status data, and will not be repeated here.

[0113] Step 202: If it is determined from the status data that there is no abnormality in the target charging pile, obtain the historical discharge data corresponding to the target charging pile.

[0114] The historical discharge data includes discharge data from at least one historical discharge cycle. Specifically, a discharge cycle corresponds to a charging process, with one discharge cycle corresponding to one charging process. The historical discharge cycle refers to the discharge cycle of the charging pile prior to the current discharge. In one example, the number of historical discharge data points is three.

[0115] Step 203: Perform anomaly verification on the target charging pile by combining historical discharge data.

[0116] Specifically, considering that the anomaly of the target charging pile may be caused by the accumulation of different risk factors during historical use, although the anomaly of the target charging pile is not yet reflected in the status, it will still have a significant impact on the charging process. Based on this, when it is determined from the status data that there is no anomaly of the target charging pile, the anomaly verification of the target charging pile is carried out by combining historical discharge data. This can help improve the accuracy of the anomaly verification results of the charging pile.

[0117] In one example, anomaly verification of the target charging station is performed using historical discharge data. This includes: determining the maximum cumulative fluctuation amplitude corresponding to each historical discharge cycle in the historical discharge data; and determining whether the target charging station is abnormal based on the change of the maximum cumulative fluctuation amplitude over time. For example, if the maximum cumulative fluctuation amplitude is continuously uploaded over time, it is determined that the target charging station is abnormal.

[0118] The maximum cumulative fluctuation amplitude refers to the maximum value of the cumulative fluctuation amplitude within the reference duration of the discharge cycle. For example, if the discharge cycle is fifty minutes and the reference duration is five minutes, then the cumulative fluctuation amplitude corresponding to the five minutes with the largest cumulative fluctuation amplitude within the discharge cycle is determined as the maximum cumulative fluctuation amplitude.

[0119] Furthermore, if the maximum cumulative fluctuation amplitude increases continuously over time, determine whether the maximum cumulative fluctuation amplitude corresponding to the discharge data is greater than the abnormal amplitude threshold; if so, determine that the target charging pile is abnormal; if not, determine that the target charging pile is not abnormal.

[0120] The abnormal amplitude threshold is greater than the fluctuation amplitude threshold. For example, the abnormal amplitude threshold is 1.2 times the fluctuation amplitude threshold.

[0121] In practical implementation, when the maximum cumulative fluctuation amplitude continuously increases over time, waveform analysis can be performed on the current changes in each historical discharge cycle in the historical discharge data to obtain at least one waveform parameter and / or frequency domain feature. Based on the changes in the waveform parameter and / or frequency domain feature over time, it can be further determined whether the target charging pile has an anomaly, thereby improving the accuracy of the anomaly verification results. The method for determining the waveform parameter and frequency domain feature is described in the implementation method corresponding to step 102 above, and will not be repeated here. For example, taking further verification based on waveform parameters as an example, if the waveform parameter increases over time, it can be determined that the target charging pile has an anomaly.

[0122] In the above implementation, if it is determined that there is no abnormality in the target charging pile based on the status data, the target charging pile is further checked for abnormality by combining historical charging data. This can help to take into account the influencing factors that have not yet caused the abnormality during the abnormality check process, thereby helping to improve the accuracy of the abnormality check results.

[0123] Based on the above technical solution, and further, referring to... Figure 3 Step 203 above, which combines historical discharge data to perform anomaly verification on the target charging pile, includes:

[0124] Step 301: Determine whether there is any abnormality in the target charging pile based on historical discharge data.

[0125] Specifically, the method of determining whether the target charging pile is abnormal based on historical discharge data can be compared with the specific implementation of step 203 above, and will not be repeated here.

[0126] Step 302: If it is determined from historical data that there are no abnormalities in the target charging pile, determine the health threshold for this use based on the discharge data.

[0127] Specifically, considering that the requirements for target charging piles may differ depending on the discharge intensity—for example, the requirements for high-power discharge may be higher than those for low-power discharge—it is necessary to dynamically determine the health threshold based on the actual discharge data. This can help improve the accuracy of anomaly verification results.

[0128] Optionally, the health threshold to be used in this case is determined based on the discharge data, including: determining the health requirement parameter corresponding to the discharge data based on the numerical value of the discharge data; and adjusting the basic health based on the health requirement parameter to obtain the health threshold.

[0129] The correspondence between the values ​​of the discharge data and the health requirement parameters is preset. In one example, the range of discharge data values ​​is divided into at least two value intervals, and preset requirement parameters are set for different value intervals. In this case, the preset requirement parameters corresponding to the value interval to which the discharge data value belongs can be determined as the health requirement parameters corresponding to the discharge data value.

[0130] The basic health level is preset. In one example, the basic health level is a preset value. In actual implementation, the basic health level can be set in conjunction with at least one factor that may affect the safety of discharge, such as the hardware configuration of the charging pile and the deployment environment.

[0131] Step 303: Determine whether the target health level corresponding to the target charging pile is less than the health level threshold.

[0132] Among them, the target health level is used to indicate the current health status of the target charging pile, and the target health level is dynamically determined.

[0133] In one example, the target health level decreases continuously as the usage time increases. In this case, the electric vehicle management method provided in this embodiment further includes: determining the health level change value based on a reference usage time and a reference adjustment coefficient; and determining the difference between the reference health level and the health level change value as the target health level.

[0134] The reference usage time is the usage time of the charging station after the reference health level is determined. For example, the reference health level is the initial health level, and the corresponding reference usage time is the cumulative usage time of the charging station. Alternatively, during the charging station's use, the health level may be calibrated periodically or irregularly using other methods; the health level after each calibration becomes the reference health level. This ensures the accuracy of the health level to a certain extent while reducing the computational workload in determining the health level.

[0135] In another example, the remote diagnostic management method for electric bicycle battery faults also includes: determining the discharge adjustment method based on the historical discharge data of the target charging station; and adjusting the target health status based on the discharge adjustment method.

[0136] The discharge adjustment method includes adjustment direction, such as increasing or decreasing the target's health. Furthermore, the discharge adjustment method can also include the adjustment range of the target's health. In actual implementation, the adjustment range of the target's health can also be preset.

[0137] Optionally, discharge adjustment parameters are determined based on the historical discharge data of the target charging pile, including: determining discharge index values ​​based on historical discharge data, and determining discharge adjustment parameters based on the relationship between the discharge index values ​​and the corresponding benchmark index values.

[0138] In one example, the discharge index value is the average discharge power. Accordingly, if the average discharge power is greater than the reference discharge power, the adjustment direction is determined to decrease the target health level; if the average discharge power is less than the reference discharge power, the adjustment direction is determined to increase the target health level. Furthermore, the adjustment range can be determined based on the ratio of the absolute value of the difference between the discharge index value and the corresponding reference index value to the reference index value, as well as a preset adjustment coefficient.

[0139] In practical implementation, the methods in the two examples above can also be combined. That is, the reference health is adjusted based on the reference usage time during use, and the target health is adjusted based on historical discharge data when the preset calibration conditions are met (such as reaching the preset calibration interval time) to obtain a new reference health.

[0140] Step 304: If the target health level is less than the health level threshold, generate an anomaly verification result indicating that the target charging pile is abnormal.

[0141] Optionally, if the target health level is greater than or equal to the health level threshold, an anomaly verification result indicating that the target charging pile does not have any anomalies can be generated.

[0142] In the above implementation, if it is determined that the target charging pile is not abnormal based on historical discharge data, a health threshold can be further determined by combining the actual discharge situation. If the target health of the target charging pile is less than the health threshold, an anomaly verification result is generated indicating that the target charging pile is abnormal. In this way, the target charging pile can be anomaly verified by combining the actual discharge situation, which can help improve the accuracy of the anomaly verification result.

[0143] In some implementations, reference Figure 4 In step 106, if an abnormality is determined at the vehicle departure end, the following steps are also included:

[0144] Step 401: Obtain the infrared image data corresponding to the target charging pile.

[0145] The image area corresponding to the infrared image data includes the parking area of ​​the target vehicle corresponding to the target charging pile.

[0146] In one example, infrared image data is acquired via an infrared camera near the target charging station.

[0147] Step 402: Determine whether there are any abnormal temperature locations within the target vehicle parking area based on infrared image data.

[0148] In one example, determining whether there are abnormal temperature locations within the target vehicle's parking area based on infrared image data includes: dividing the image region corresponding to the infrared image data into several sub-regions; for each sub-region, determining the average temperature corresponding to that sub-region based on the image data; and identifying sub-regions whose average temperature is significantly higher than that of their neighboring sub-regions as abnormal locations. For example, sub-regions whose average temperature difference with their neighboring sub-regions is greater than a preset temperature difference threshold are identified as abnormal locations. In one example, the temperature difference threshold is 10 degrees Celsius.

[0149] In another example, if an abnormal subregion is identified where the average temperature is significantly higher than that of its neighboring subregions, the abnormal subregion can be further divided into cell regions. Then, based on the same method as for the subregions, abnormal cell subregions with abnormal temperatures can be selected from each cell region and identified as abnormal locations. This can help narrow down the range of abnormal locations.

[0150] In actual implementation, the location of temperature anomalies can also be obtained by directly performing image analysis on infrared image data. This embodiment does not limit the method of determining the location of temperature anomalies.

[0151] Step 403: If there is an abnormal temperature location within the target vehicle parking area, determine the abnormal location type corresponding to the abnormal temperature location.

[0152] Among them, abnormal location types include chargers and vehicle batteries.

[0153] In one example, determining the abnormal location type corresponding to the abnormal temperature location includes: determining whether the abnormal temperature location is located on an electric bicycle; if not, then determining the abnormal location type as a charger.

[0154] Furthermore, if the abnormal temperature location is on the electric bicycle, determine whether the abnormal location is located in the battery area of ​​the electric bicycle; if not, determine the abnormal location type as the charger; if so, determine the abnormal location type as the vehicle battery.

[0155] The battery area includes the area below the footrest and the area below the seat.

[0156] Furthermore, determine whether the location temperature at the abnormal temperature location is greater than a preset temperature threshold; if the location temperature is not greater than the preset temperature threshold, control the target charging pile to reduce the discharge power and start monitoring the changes in location temperature; if a rise in location temperature is detected, control the target charging pile to stop discharging.

[0157] One way to control the target charging pile to reduce its discharge power is to control the target charging pile to reduce its discharge power to a preset protection power; or it is to control the dynamic reduction of the discharge power of the target charging pile, such as reducing the discharge power to half of the current discharge power.

[0158] In one example, the preset temperature threshold corresponds to the anomaly location type of the temperature anomaly location, and the preset temperature threshold may differ for different anomaly location types.

[0159] Optionally, if no temperature rise is detected, the target charging station can be controlled to continue discharging at a reduced discharge power.

[0160] Furthermore, before determining whether the temperature at the location of the temperature anomaly is greater than a preset temperature threshold, the process also includes: determining whether the discharge power of the target charging pile is greater than a preset minimum judgment power value; if so, then the step of determining whether the temperature at the location of the temperature anomaly is greater than a preset temperature threshold is executed; if not, then the target charging pile is directly controlled to stop discharging.

[0161] Step 404: Determine the cause of the anomaly based on the anomaly location type.

[0162] Specifically, based on the type of abnormal location, the exact location of the abnormality on the vehicle can be determined, which can then help determine the cause of the abnormality.

[0163] Optionally, if the abnormal location includes the vehicle battery, the cause of the abnormality is determined to be a vehicle battery abnormality; if the abnormal location includes the charger, the cause of the abnormality is determined to be a charger abnormality.

[0164] In the above technical solution, when an abnormality is determined at the vehicle end, it is possible to further determine whether there is a temperature abnormality location in the parking area of ​​the target vehicle based on the infrared image data corresponding to the target charging pile. If a temperature abnormality location exists, the cause of the abnormality can be determined based on the abnormality location type corresponding to the temperature abnormality location. This can help distinguish between vehicle battery abnormalities and charger abnormalities, and thus help to handle abnormalities during the charging process.

[0165] This application also provides an electric bicycle charging management system, see reference. Figure 5 The system includes a charging pile 510 and a server that communicates with the charging pile 520.

[0166] Charging station 510 provides a power interface for supplying power to electric bicycles and sending power supply data to server 520.

[0167] The server 520 is used to execute any of the electric bicycle management methods provided in the above method embodiments.

[0168] This application also provides an electronic device, such as... Figure 6 As shown, Figure 6 The illustrated electronic device 600 includes a processor 601 and a memory 603. The processor 601 and the memory 603 are connected, for example, via a bus 602. Optionally, the electronic device 600 may also include a transceiver 604. It should be noted that in practical applications, the transceiver 604 is not limited to one type, and the structure of this electronic device 600 does not constitute a limitation on the embodiments of this application.

[0169] Processor 601 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 601 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0170] Bus 602 may include a pathway for transmitting information between the aforementioned components. Bus 602 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 602 may be divided into address bus, data bus, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0171] The memory 603 may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0172] The memory 603 stores application code that executes the scheme of this application, and its execution is controlled by the processor 601. The processor 601 executes the application code stored in the memory 603 to implement the content shown in the foregoing method embodiments.

[0173] Electronic devices include, but are not limited to: mobile terminals such as mobile phones, laptops, PDAs (personal digital assistants), and PADs (tablet computers), as well as fixed terminals such as digital TVs and desktop computers. They can also serve as server-side components. Figure 6 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0174] It should be understood that although the steps in the flowcharts in the accompanying drawings are shown sequentially as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise expressly stated herein, there is no strict order in which these steps are performed, and they may be performed in other orders.

[0175] The above are only some embodiments of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A remote diagnosis and management method for electric bicycle battery faults, characterized in that, The method includes: The discharge data of the target charging station is determined. The target charging station provides a power interface. The electric bicycle is electrically connected to the power interface of the target charging station through a charger so that the electric bicycle can be charged through the target charging station. Based on the discharge data, determine whether there are any abnormalities in this charging process; If an anomaly is determined in the current charging process, it is determined whether to perform an anomaly check on the target charging station. If it is determined that the target charging pile is to be anomaly checked, the status data corresponding to the target charging pile shall be obtained. Anomaly verification is performed on the target charging pile based on the status data; If the anomaly verification result indicates that the target charging pile is not abnormal, then the vehicle-side anomaly is determined; The anomaly verification of the target charging pile based on the status data includes: Based on the status data, determine whether the target charging pile has any abnormalities; If it is determined from the status data that there is no abnormality in the target charging pile, the historical discharge data corresponding to the target charging pile is obtained; The target charging pile is checked for anomalies by combining the historical discharge data. The step of performing anomaly verification on the target charging pile based on the historical discharge data includes: Based on the historical discharge data, determine whether the target charging pile has any abnormalities; If the target charging station is determined to be normal based on the historical discharge data, the health threshold for this use is determined based on the discharge data. Determine whether the target health status corresponding to the target charging pile is less than the health status threshold; If the target health level is less than the health threshold, an anomaly verification result is generated indicating that the target charging pile is abnormal. The process of determining the health threshold to be used in this instance based on the discharge data includes: The health requirement parameters corresponding to the discharge data are determined based on the magnitude of the discharge data. The basic health score is adjusted based on the health score requirement parameters to obtain the health score threshold.

2. The method according to claim 1, characterized in that, The method further includes: The discharge adjustment method is determined based on the historical discharge data. The target health level is adjusted based on the aforementioned discharge adjustment method.

3. The method according to claim 1, characterized in that, The step of performing anomaly verification on the target charging pile based on the historical discharge data includes: Determine the maximum cumulative fluctuation amplitude corresponding to each historical discharge cycle in the historical discharge data; The presence of any abnormality in the target charging pile is determined based on the change in the maximum cumulative fluctuation amplitude over time.

4. The method according to claim 1, characterized in that, After determining the vehicle-side anomaly, the process also includes: Acquire infrared image data corresponding to the target charging pile, wherein the image area corresponding to the infrared image data includes the target vehicle parking area corresponding to the target charging pile; Based on the infrared image data, determine whether there are any locations with abnormal temperatures within the parking area of ​​the target vehicle; If there is a temperature anomaly location within the target vehicle parking area, determine the anomaly location type corresponding to the temperature anomaly location, where the anomaly location type includes chargers and vehicle batteries; The cause of the anomaly is determined based on the type of anomaly location.

5. The method according to claim 4, characterized in that, In the event that an abnormal temperature location exists within the target vehicle parking area, the following additional steps are also included: Determine whether the temperature at the location of the temperature anomaly is greater than a preset temperature threshold; If the temperature at the location is not greater than the preset temperature threshold, the target charging pile is controlled to reduce its discharge power, and the change in the temperature at the location is monitored. If the temperature at the location is detected to be rising, the target charging station will be controlled to stop discharging.

6. An electric bicycle charging management system, characterized in that, The system includes a charging pile and a server that is communicatively connected to the charging pile. The charging pile provides a power interface for supplying power to electric bicycles and sending power supply data to the server. The server is used to execute the remote diagnosis and management method for electric bicycle battery faults as described in any one of claims 1-5.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; Memory; At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, said at least one application being configured to: perform the remote diagnostic management method for electric bicycle battery faults as described in any one of claims 1 to 5.

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