Fault prediction method and device of relay, electronic equipment and storage medium

Abstract

The invention relates to a fault prediction method and device of a relay, electronic equipment and a storage medium, and belongs to the technical field of high-voltage energy management. The method comprises the following steps: acquiring an average closing time length of a relay of each vehicle in a unit time length; determining a target vehicle based on the average closing duration of each vehicle in the first preset duration; and performing fault prediction on the relay of the target vehicle based on the average closing duration of the target vehicle in the second preset duration. Therefore, according to the scheme, the average closing duration of the relay of each vehicle in the unit duration is obtained, and the target vehicle is determined from the vehicles in the first preset duration. And then fault prediction is carried out on the relay of the target vehicle based on the average closing duration of the target vehicle in the second preset duration, so that the relay closed loop fault caused by various factors can be effectively identified in advance, early warning can be conveniently carried out on a user in advance, the user experience is improved, and the after-sales operation and maintenance efficiency is improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of high-voltage energy management, and in particular to a fault prediction method, device, electronic device and storage medium for a relay. Background Art

[0002] At present, the fault diagnosis of relays still relies on calculating the current and voltage values ​​of the diagnostic circuit to determine whether the relay is faulty. And it is limited to real-time diagnosis, and cannot warn of possible faults in advance, which in turn affects the user's driving of the vehicle. Summary of the invention

[0003] In order to overcome the problems existing in the related art, the present disclosure provides a fault prediction method, device, electronic device and storage medium for a relay to solve the problem that faults cannot be warned in advance. The technical solution of the present disclosure is as follows:

[0004] According to a first aspect of an embodiment of the present disclosure, a method for predicting faults of a relay is provided, comprising: obtaining an average closing time of a relay of each vehicle within a unit time length; determining a target vehicle based on the average closing time of each vehicle within a first preset time length; and predicting faults of a relay of the target vehicle based on the average closing time of the target vehicle within a second preset time length.

[0005] In one embodiment of the present disclosure, determining the target vehicle based on the average closing time of each vehicle within a first preset time period includes: acquiring a first vehicle of the same model, and forming a first set based on the average closing time of the first vehicle within the first preset time period; determining outliers in the first set based on the average closing time in the first set; and determining the target vehicle from the first vehicles based on the outliers.

[0006] In one embodiment of the present disclosure, determining the target vehicle from the first vehicles based on the outliers includes: for each of the first vehicles, determining the number of outliers belonging to the first vehicle; if the number of outliers of the first vehicle is greater than a set number, determining the first vehicle as the target vehicle.

[0007] In one embodiment of the present disclosure, the determining of outliers in the first set based on the average closing time in the first set includes: obtaining the average of the average closing time in the first set as the global average closing time; determining the upper 1 / 4 quartile value and the lower 1 / 4 quartile value based on the global average closing time, and obtaining the quartile distance based on the upper 1 / 4 quartile value and the lower 1 / 4 quartile value; determining outlier identification conditions based on the upper 1 / 4 quartile value, the lower 1 / 4 quartile value and the quartile distance; and determining the average closing time that meets the outlier identification conditions from the average closing time in the first set as the outlier.

[0008] In one embodiment of the present disclosure, the fault prediction of the relay of the target vehicle based on the average closing time of the target vehicle within a second preset time includes: obtaining the determination time of each of the average closing time within the second preset time; performing linear fitting to obtain a target fitting curve based on the average closing time within the second preset time and the determination time of each of the average closing time; determining the maximum average closing time from the average closing time within the second preset time; predicting the time interval from the current time to the occurrence of a fault of the relay of the target vehicle based on the slope and intercept of the target fitting curve, as well as the maximum average closing time and the second preset time; and identifying the fault of the relay of the target vehicle based on the time interval.

[0009] In one embodiment of the present disclosure, the time interval is determined using the following formula: Wherein, N is the time interval, M is the second preset time length, and y is max is the maximum average closing time, c is the intercept, and k is the slope.

[0010] In one embodiment of the present disclosure, the fault identification of the relay of the target vehicle based on the time interval includes: if the time interval is greater than or equal to the set time interval, determining that the relay of the target vehicle has a closing failure, and generating warning information.

[0011] In one embodiment of the present disclosure, the fault identification of the relay of the target vehicle is performed based on the time interval, including: if the time interval is less than the set time interval, determining the fit of the target fitting curve; if the fit is greater than the set threshold, determining that the relay of the target vehicle has a closing failure, and generating warning information.

[0012] In one embodiment of the present disclosure, determining the degree of fit of the target fitting curve includes: based on the target fitting curve, obtaining the fitted average closing time corresponding to each of the determined times; and determining the degree of fit of the target fitting curve based on the fitted average closing time corresponding to the determined time and the average closing time.

[0013] In one embodiment of the present disclosure, the following formula is used to determine the degree of fit: Among them, y fit (i) is the fitted average closing time corresponding to the i-th determined time; avg(y) is the average of the average closing times within the second preset time; y(i) is the average closing time corresponding to the i-th determined time within the second preset time.

[0014] According to a second aspect of an embodiment of the present disclosure, a fault prediction device for a relay is provided, comprising: an acquisition module for acquiring an average closing time of the relay of each vehicle within a unit time length; a determination module for determining a target vehicle based on the average closing time of each vehicle within a first preset time length; and a prediction module for predicting a fault of the relay of the target vehicle based on the average closing time of the target vehicle within a second preset time length.

[0015] According to a third aspect of an embodiment of the present disclosure, there is provided an electronic device, comprising a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to implement the steps of the method described in the first aspect of the embodiment of the present disclosure.

[0016] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which computer program instructions are stored. When the program instructions are executed by an electronic device, the steps of the method described in the first aspect of the embodiment of the present disclosure are implemented.

[0017] According to a fifth aspect of an embodiment of the present disclosure, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by an electronic device of a vision detection device, the steps of the method described in the first aspect of the embodiment of the present disclosure are implemented.

[0018] The technical solution provided by the embodiments of the present disclosure may include the following beneficial effects: by obtaining the average closing time of the relay of each vehicle within a unit time, a target vehicle with a possible relay problem can be determined from the vehicles within a first preset time. Furthermore, based on the average closing time of the target vehicle within a second preset time, the fault prediction of the relay of the target vehicle can be performed, which can effectively identify the relay closing circuit failure caused by various factors in advance, facilitate early warning to the user, and prompt the user to return the vehicle for repair and after-sales spare parts, thereby avoiding the situation where the user cannot use the vehicle normally when a relay failure occurs, improving the user experience, and improving the efficiency of after-sales operation and maintenance.

[0019] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0021] Figure 1 The present invention is a flowchart of a method for predicting faults of a relay according to some embodiments of the present invention.

[0022] Figure 2 is a structural diagram of a high-voltage battery system for a vehicle according to some embodiments of the present disclosure.

[0023] Figure 3 The present invention is a flowchart of another relay fault prediction method according to some embodiments of the present disclosure.

[0024] Figure 4 The present invention is a flowchart of another relay fault prediction method according to some embodiments of the present disclosure.

[0025] Figure 5 It is a diagram showing the effect of fault prediction for a relay of a target vehicle according to some embodiments of the present disclosure.

[0026] Figure 6 The present invention is a flowchart of another relay fault prediction method according to some embodiments of the present disclosure.

[0027] Figure 7 The present invention is a flowchart showing fault prediction of a relay according to some embodiments of the present disclosure.

[0028] Figure 8 It is a block diagram of a fault prediction device for a relay according to some embodiments of the present disclosure.

[0029] Fig. 9It is a block diagram of an electronic device according to an exemplary embodiment of the present disclosure.

[0030] Fig.10 It is a structural block diagram of an electronic device according to an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0031] Some embodiments of the present disclosure will be described in detail here, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. Various changes, modifications and equivalents of the methods, devices and / or systems described herein will become apparent after understanding the present disclosure. For example, the order of operations described herein is merely an example and is not limited to those orders set forth herein, but can be changed as becomes apparent after understanding the present disclosure, except for operations that must be performed in a specific order. In addition, for clarity and brevity, descriptions of features known in the art may be omitted.

[0032] The embodiments described in some embodiments of the present disclosure below do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0033] Figure 1 is a flow chart of a relay fault prediction method according to some embodiments of the present disclosure, such as Figure 1 As shown, the process of the relay fault prediction method includes but is not limited to the following steps:

[0034] S101, obtaining the average closing time of the relay of each vehicle within a unit time.

[0035] It should be noted that the execution subject of the fault prediction method of the relay in the embodiment of the present disclosure is an electronic device, which may be an intelligent platform, a server, etc. Optionally, the intelligent platform may be an artificial intelligence development platform, a data analysis platform, a cloud computing platform, etc. Optionally, the server includes but is not limited to a cloud server, a network server, an application server, and may also be a server of a distributed system, or a server combined with a blockchain, etc. The embodiment of the present disclosure is not specifically limited.

[0036] It is understandable that in the high-voltage battery system of the vehicle, the high-voltage circuit can be turned on and off by a relay. The types of relays are divided into main positive relays, main negative relays and pre-charge relays. Among them, the function of the pre-charge relay is to charge the large capacitor of the whole vehicle in advance to reduce spark arcing when the contactor contacts, reduce impact and increase safety. The main positive and main negative relays are relays that need to be closed when the high-voltage circuit is in normal circulation. The disclosed embodiments do not specifically limit the type of relay.

[0037] In some implementations, the average closing time of the relay in a unit time can be obtained based on the vehicle's own battery management system (BMS). The BMS records the closing time when the relay is closed and uploads it to the cloud server, which can then calculate the average closing time in a unit time.

[0038] Optionally, a day may be used as a unit of duration, and the cloud server may calculate the average closing duration of the relay of each vehicle in a day, and the average closing duration may be recorded as T_avg.

[0039] For example, Figure 2 The schematic diagram of the high-voltage battery system of the vehicle shown in the figure. The relay is connected to the high-voltage load and the battery pack through a high-voltage line to realize the on-off of the high-voltage circuit. The BMS controls the relay switch through a low-voltage signal. At the same time, the BMS collects the closing time of the relay and forwards it to the telematics box (TBOX) through the vehicle control unit (VCU), and finally uploads it to the cloud server through the network protocol. The cloud server calculates the average closing time of the relay in the unit time based on the received closing time of the relay.

[0040] S102: Determine a target vehicle based on an average closing time of each vehicle within a first preset time.

[0041] In some implementations, the target vehicle may be determined based on outliers of the average closing time of each vehicle within a first preset time. The outliers may refer to points with a larger average closing time. The first preset time may be one week. Optionally, the outliers of the average closing time may be calculated using an interquartile range (IQR).

[0042] Furthermore, the target vehicle can be determined from each vehicle based on the number of outliers. Optionally, a threshold for the number of outliers can be set. If the number of outliers of any vehicle is greater than or equal to the threshold for the number of outliers, the vehicle is determined to be the target vehicle. For example, if the threshold for the number of outliers is 2, and the number of outliers of vehicle A is 3, which is greater than 2, vehicle A is determined to be the target vehicle.

[0043] S103: Predicting a fault of a relay of the target vehicle based on an average closing time of the target vehicle within a second preset time.

[0044] In some implementations, the average closing time of the target vehicle within the second preset time length can be fitted to obtain a target fitting curve, and based on the target fitting curve, the fault prediction of the relay of the target vehicle is performed. Optionally, the average closing time length of the target vehicle within the second preset time length can be linearly fitted to obtain a target fitting curve. For example, a target fitting curve of the form y=kx+c is obtained, where k is the slope of the target fitting curve and c is the intercept of the target fitting curve.

[0045] Optionally, the time interval from the current time to the occurrence of a fault of the target vehicle's relay can be predicted based on the target fitting curve, and then based on the time interval, it is determined whether the target vehicle's relay is faulty. If the time interval is greater than or equal to a set time interval threshold, it is determined that the target vehicle's relay is faulty.

[0046] Optionally, the degree of fit of the target fitting curve may be determined based on the target fitting curve, and then based on the time interval and the degree of fit, it is determined whether the relay of the target vehicle is faulty. If the time interval is less than a set time interval threshold and the degree of fit is greater than a set degree of fit threshold, it is determined that the relay of the target vehicle is faulty.

[0047] In some implementations, when it is determined that the relay of the target vehicle is faulty, the cloud server generates a warning message of the relay failure to prompt the user to repair it, avoid the situation where the vehicle cannot be driven normally, and improve the user experience.

[0048] In the relay fault prediction method provided in the embodiment of the present disclosure, by obtaining the average closing time of the relay of each vehicle within a unit time, a target vehicle with a possible relay problem can be determined from the vehicles within a first preset time. Furthermore, based on the average closing time of the target vehicle within a second preset time, the fault prediction of the relay of the target vehicle can effectively identify the relay closed circuit failure caused by various factors in advance, so as to facilitate early warning to the user, prompt the user to return the vehicle for repair and after-sales spare parts, avoid the situation where the user cannot use the vehicle normally when a relay failure occurs, improve the user experience, and improve the efficiency of after-sales operation and maintenance.

[0049] Figure 3 is a flow chart of a relay fault prediction method according to some embodiments of the present disclosure, such as Figure 3 As shown, the process of the relay fault prediction method includes but is not limited to the following steps:

[0050] S301, obtaining the average closing time of the relay of each vehicle within a unit time.

[0051] In the embodiment of the present disclosure, the implementation method of step S301 can be implemented by any method in the embodiments of the present disclosure, which is not limited here and will not be repeated.

[0052] S302, obtaining a first vehicle of the same model, and forming a first set based on an average closing time of the first vehicle within a first preset time.

[0053] It is understandable that the average closing time of the first vehicle of the same model within the first preset time is used to form the first set, which can better understand the overall performance and trend of the relay of the model and help to find potential problems. Since the models are the same, the first vehicles may have similar working environments and system structures, and the data in the first set is more reliable, reducing the potential deviations caused by different models.

[0054] In some implementations, the model of each vehicle can be determined based on the vehicle model code, vehicle identification number, etc. The average closing time of first vehicles of the same model within a first preset time is obtained, and the average closing time is formed into a first set. The first set includes the average closing time of all first vehicles within the first preset time. Among them, the number of first vehicles must be greater than a set threshold. For example, the number of first vehicles must be greater than 1,000.

[0055] S303: Determine outliers in the first set based on the average closure duration in the first set.

[0056] In some implementations, outliers in the first set can be calculated according to the IQR based on the average closure duration in the first set.

[0057] Optionally, the average of the average closing times in the first set is obtained as the global average closing time, and then the upper 1 / 4 quartile value and the lower 1 / 4 quartile value are determined based on the global average closing time, and the intra-quartile distance is obtained based on the upper 1 / 4 quartile value and the lower 1 / 4 quartile value.

[0058] As an example, the upper 1 / 4 quartile value of the global average closing time is recorded as Q3, and the lower 1 / 4 quartile value of the global average closing time is recorded as Q1, then the intra-quartile range IQR=Q3-Q1.

[0059] Optionally, the outlier identification condition can be determined based on the upper 1 / 4 quartile value, the lower 1 / 4 quartile value and the quartile distance. The outlier identification condition is that the average closing time is greater than the sum of the 1 / 4 quartile value and 1.5 times the quartile distance. Then, the average closing time that meets the outlier identification condition can be determined from the average closing time in the first set as the outlier.

[0060] For example, assuming that the average closing time is T_avg, the outlier identification condition is T_avg>Q3+1.5*IQR. If the average closing time of any vehicle in the first set is greater than Q3+1.5*IQR, the average closing time is determined to be an outlier.

[0061] Optionally, all outliers of each first vehicle may be stored as an outlier list, such as List1, so as to process and analyze the outliers and further determine the target vehicle.

[0062] S304: Determine a target vehicle from the first vehicles based on the outliers.

[0063] In some implementations, a target vehicle may be determined from the first vehicles based on the number of outliers. For each first vehicle, the number of outliers belonging to the first vehicle is determined. If the number of outliers of the first vehicle is greater than a set number, the first vehicle is determined to be a target vehicle. For example, assuming that the set number of outliers is 2, if the number of outliers of any first vehicle is 3, which is greater than 2, the first vehicle is determined to be a target vehicle.

[0064] Optionally, all target vehicles may be stored as a vehicle list, such as List2, so that any target vehicle in the list can be quickly accessed and searched, facilitating prediction of relay failure conditions of the target vehicle.

[0065] S305: Based on the average closing time of the target vehicle within a second preset time, a fault prediction is performed on the relay of the target vehicle.

[0066] In the embodiment of the present disclosure, the implementation method of step S305 can be implemented by any method in the embodiments of the present disclosure, which is not limited here and will not be repeated.

[0067] In the relay fault prediction method provided in the embodiment of the present disclosure, the average closing time of the relay of each vehicle within a unit time is obtained, and the average closing time of the first vehicle of the same model within the first preset time is taken as the first set, and the target vehicle is selected from the first vehicle based on the outlier of the average closing time in the first set. Furthermore, based on the average closing time of the target vehicle within the second preset time, the fault prediction of the relay of the target vehicle is performed, which can effectively identify the relay closed circuit failure caused by various factors in advance, facilitate early warning to the user, prompt the user to return the vehicle for repair and after-sales spare parts, avoid the situation where the user cannot use the vehicle normally when a relay failure occurs, improve the user experience, and improve the efficiency of after-sales operation and maintenance.

[0068] In the relay fault prediction method provided in the embodiment of the present disclosure, by obtaining the average closing time of the relay of each vehicle within a unit time, a target vehicle with a possible relay problem can be determined from the vehicles within a first preset time. Furthermore, based on the average closing time of the target vehicle within a second preset time, the fault prediction of the relay of the target vehicle can effectively identify the relay closed circuit failure caused by various factors in advance, so as to facilitate early warning to the user, prompt the user to return the vehicle for repair and after-sales spare parts, avoid the situation where the user cannot use the vehicle normally when a relay failure occurs, improve the user experience, and improve the efficiency of after-sales operation and maintenance.

[0069] Figure 4 is a flow chart of a relay fault prediction method according to some embodiments of the present disclosure, such as Figure 4 As shown, the process of the relay fault prediction method includes but is not limited to the following steps:

[0070] S401, obtaining the average closing time of the relay of each vehicle within a unit time.

[0071] In the embodiment of the present disclosure, the implementation method of step S401 can be implemented by any method in the embodiments of the present disclosure, which is not limited here and will not be repeated.

[0072] S402: Determine a target vehicle based on an average closing time of each vehicle within a first preset time.

[0073] In the embodiment of the present disclosure, the implementation method of step S402 can be implemented by any method in the embodiments of the present disclosure, which is not limited here and will not be repeated.

[0074] S403, obtaining the determination time of each average closing duration within the second preset duration.

[0075] In some implementations, for each target vehicle, the average closing time of each target vehicle within the second preset time and the corresponding determination time of the average closing time can be obtained. The determination time of each average closing time is a day vector and is less than or equal to the second preset time. Optionally, the second preset time can be set to 7 days, 15 days, etc.

[0076] For example, assuming that any target vehicle is vehicle B, and the second preset time is 7 days, the average closing time of vehicle B within 7 days is T_avg1, T_avg2, T_avg3, T_avg4, T_avg5, T_avg6, and T_avg7. The determined time of T_avg1 is 1, the determined time of T_avg2 is 2, the determined time of T_avg3 is 3, the determined time of T_avg4 is 4, the determined time of T_avg5 is 5, the determined time of T_avg6 is 6, and the determined time of T_avg7 is 7.

[0077] S404: Perform linear fitting based on the average closing duration within the second preset duration and the respective determined times of the average closing duration to obtain a target fitting curve.

[0078] In some implementations, a target fitting curve may be obtained by performing a linear fit on the average closing duration and the respective determined time of the average closing duration within the second preset duration. Optionally, a linear fit may be performed based on y=kx+c to calculate the values ​​of k and c, thereby obtaining the target fitting curve. Optionally, the values ​​of the parameters may be calculated based on the least squares method. The formula for calculating the values ​​of the parameters k and c is as follows:

[0079]

[0080] Among them, x T It refers to the transpose of vector x, where x is the day vector, y is the average closure duration vector, parameter k is the slope of the target fitting curve, and parameter c is the intercept of the target fitting curve.

[0081] S405: Determine the maximum average closing time from the average closing time within the second preset time.

[0082] In some implementations, by comparing the average closing durations within the second preset duration, the largest average closing duration is selected as the maximum average closing duration, and the maximum average closing duration is set as y max The maximum average closing time refers to the longest closing time allowed by the relay. If it exceeds this value, it is considered that the relay has failed to close, that is, a fault has occurred.

[0083] S406, based on the slope and intercept of the target fitting curve, as well as the maximum average closing time and the second preset time, predict the time interval from the current time to the occurrence of a fault of the relay of the target vehicle.

[0084] In some implementations, the time interval from the current time to the occurrence of the fault of the relay of the target vehicle can be predicted based on the difference between the maximum average closing time minus the ratio of the intercept of the target fitting curve to the slope of the target fitting curve and the second preset time. The formula for calculating the time interval N is as follows:

[0085]

[0086] Wherein, N is the time interval, M is the second preset time length, and y max is the maximum average closure time, c is the intercept, and k is the slope.

[0087] S407, based on the time interval, performing fault identification on the relay of the target vehicle.

[0088] In some implementations, the target vehicle's relay may be identified for fault based on a set time interval. If the time interval is greater than or equal to the set time interval, it is determined that the target vehicle's relay has a closing failure, and a warning message is generated.

[0089] In some implementations, if the time interval is less than the set time interval, the fit of the target fitting curve is determined, and then based on the fit, the fault of the target vehicle's relay is identified. By setting a fit threshold, if the fit is greater than the set threshold, it is determined that the target vehicle's relay has a closing failure, and a warning message is generated.

[0090] For example, if the set time interval is 15 days and the set threshold is 0.8, if the time interval from the current time to the occurrence of the fault of the target vehicle's relay is 10 days, the fit is 1, which is less than the set time interval and greater than the set threshold, it is determined that the target vehicle's relay has a closing failure and a warning message is generated.

[0091] Optionally, the average closing time corresponding to each determined time can be obtained based on the target fitting curve. Then, the degree of fit of the target fitting curve is determined based on the average closing time corresponding to the determined time and the average closing time. The formula for calculating the degree of fit is as follows:

[0092]

[0093] Among them, y fit(i) is the fitted average closing time corresponding to the i-th determined time. avg(y) is the average of the average closing time within the second preset time. y(i) is the average closing time corresponding to the i-th determined time within the second preset time. fit (i) = kx(i) + c.

[0094] like Figure 5 The effect diagram of fault prediction of the relay of the target vehicle is shown. The horizontal axis in the figure is the number of days, and the vertical axis is the average closing time, in milliseconds (ms). The second preset time is 15 days. Based on the average closing time of the target vehicle within 15 days, the target fitting curve is determined, and the relay is predicted to have a fault according to the target fitting curve, so as to issue early warning information.

[0095] In the relay fault prediction method provided in the embodiment of the present disclosure, by obtaining the average closing time of the relay of each vehicle within a unit time, the target vehicle with a possible relay problem can be determined from the vehicles within a first preset time. Furthermore, based on the average closing time of the target vehicle within a second preset time and the corresponding determination time, a target fitting curve is obtained, and the time interval from the current time to the occurrence of the fault of the target vehicle's relay is predicted, and the fitting degree of the target fitting curve is determined, and then the fault prediction of the target vehicle's relay can be carried out, which can effectively identify the relay closed circuit failure caused by various factors in advance, facilitate early warning to the user, prompt the user to return for repair and after-sales spare parts, avoid the situation where the user cannot use the vehicle normally when a relay failure occurs, improve the user experience, and improve the efficiency of after-sales operation and maintenance.

[0096] Figure 6 is a flow chart of a relay fault prediction method according to some embodiments of the present disclosure, such as Figure 6 As shown, the process of the relay fault prediction method includes but is not limited to the following steps:

[0097] S601, obtaining the average closing time of the relay of each vehicle within a unit time.

[0098] S602, obtaining a first vehicle of the same model, and forming a first set based on an average closing time of the first vehicle within a first preset time.

[0099] S603: Determine outliers in the first set based on the average closure duration in the first set.

[0100] S604: Determine a target vehicle from the first vehicles based on the outliers.

[0101] S605, obtaining the determined time of each average closing duration of the target vehicle within the second preset duration.

[0102] S606: Perform linear fitting based on the average closing duration within the second preset duration and the respective determined times of the average closing duration to obtain a target fitting curve.

[0103] S607: Determine the maximum average closing time from the average closing time within the second preset time.

[0104] S608, based on the slope and intercept of the target fitting curve, as well as the maximum average closing time and the second preset time, predict the time interval from the current time to the occurrence of a fault of the relay of the target vehicle.

[0105] S609: If the time interval is greater than or equal to the set time interval, it is determined that the relay of the target vehicle fails to close, and a warning message is generated.

[0106] S610: If the time interval is less than the set time interval, determine the fitting degree of the target fitting curve.

[0107] S611, if the degree of fit is greater than the set threshold, it is determined that the target vehicle's relay fails to close, and a warning message is generated.

[0108] In the relay fault prediction method provided in the embodiment of the present disclosure, by obtaining the average closing time of the relay of each vehicle within a unit time, a target vehicle with a possible relay problem can be determined from the vehicles within a first preset time. Furthermore, based on the average closing time of the target vehicle within a second preset time, the fault prediction of the relay of the target vehicle can effectively identify the relay closed circuit failure caused by various factors in advance, so as to facilitate early warning to the user, prompt the user to return the vehicle for repair and after-sales spare parts, avoid the situation where the user cannot use the vehicle normally when a relay failure occurs, improve the user experience, and improve the efficiency of after-sales operation and maintenance.

[0109] Figure 7The flowchart of fault prediction for relays is shown. The battery management system (BMS) records the closing time of each relay and uploads it to the cloud server. The cloud server calculates the average closing time T_avg of the relay with a daily cycle. Assuming the first preset time is 7 days, the first vehicle of the same model is obtained, and the first set is formed based on the average closing time of the first vehicle within 7 days. If the first vehicle is greater than 1000, the outliers are determined from the first set. If the number of outliers of any vehicle is greater than 2, the vehicle is determined to be the target vehicle. Assuming the second preset time is M days, a linear fit is performed based on T_avg within M days to obtain the target fitting curve, and the fitting degree R2 and the predicted time interval N are calculated based on the target fitting curve. If the fitting degree R2 and the predicted time interval N of any target vehicle satisfy N<15 and R2>0.8, the vehicle is warned. By repeating the above steps every day, the vehicle is warned with a sliding window, so as to achieve early warning to users and improve the user experience.

[0110] Figure 8 is a block diagram of a relay fault prediction device according to some embodiments of the present disclosure. Figure 8 The device includes an acquisition module 801, a determination module 802 and a prediction module 803.

[0111] The acquisition module 801 is used to acquire the average closing time of the relay of each vehicle within a unit time.

[0112] The determination module 802 is used to determine the target vehicle based on the average closing time of each vehicle within a first preset time.

[0113] The prediction module 803 is used to predict the fault of the relay of the target vehicle based on the average closing time of the target vehicle within a second preset time.

[0114] In one embodiment of the present disclosure, the determination module 802 is also used to: obtain a first vehicle of the same model, and form a first set based on the average closing time of the first vehicle within the first preset time; determine outliers in the first set based on the average closing time in the first set; and determine the target vehicle from the first vehicles based on the outliers.

[0115] In one embodiment of the present disclosure, the determination module 802 is further used to: determine, for each of the first vehicles, the number of outliers belonging to the first vehicle; if the number of outliers of the first vehicle is greater than a set number, determine that the first vehicle is the target vehicle.

[0116] In one embodiment of the present disclosure, the determination module 802 is further used to: obtain the average value of the average closing time in the first set as the global average closing time; determine the upper 1 / 4 quartile value and the lower 1 / 4 quartile value based on the global average closing time, and obtain the quartile point distance based on the upper 1 / 4 quartile value and the lower 1 / 4 quartile value; determine the outlier identification condition based on the upper 1 / 4 quartile value, the lower 1 / 4 quartile value and the quartile point distance; determine the average closing time that meets the outlier identification condition from the average closing time in the first set as the outlier.

[0117] In one embodiment of the present disclosure, the prediction module 803 is also used to: obtain the determined time of each of the average closing times within the second preset time length; perform linear fitting to obtain a target fitting curve based on the average closing time length within the second preset time length and the determined time of each of the average closing times; determine the maximum average closing time length from the average closing time length within the second preset time length; predict the time interval from the current time to the occurrence of a fault of the relay of the target vehicle based on the slope and intercept of the target fitting curve, as well as the maximum average closing time length and the second preset time length; and perform fault identification on the relay of the target vehicle based on the time interval.

[0118] In one embodiment of the present disclosure, the time interval is determined using the following formula: Wherein, N is the time interval, M is the second preset time length, and y is max is the maximum average closing time, c is the intercept, and k is the slope.

[0119] In one embodiment of the present disclosure, the prediction module 803 is further used to: if the time interval is greater than or equal to a set time interval, determine that a closing failure occurs in the relay of the target vehicle, and generate a warning message.

[0120] In one embodiment of the present disclosure, the prediction module 803 is also used to: if the time interval is less than the set time interval, determine the fit of the target fitting curve; if the fit is greater than the set threshold, determine that the relay of the target vehicle has a closing failure, and generate warning information.

[0121] In one embodiment of the present disclosure, the prediction module 803 is also used to: obtain the fitted average closing time corresponding to each of the determined times based on the target fitting curve; and determine the degree of fit of the target fitting curve based on the fitted average closing time corresponding to the determined time and the average closing time.

[0122] In one embodiment of the present disclosure, the following formula is used to determine the degree of fit: Among them, y fit (i) is the fitted average closing time corresponding to the i-th determined time; avg(y) is the average of the average closing times within the second preset time; y(i) is the average closing time corresponding to the i-th determined time within the second preset time.

[0123] In the relay fault prediction method provided in the embodiment of the present disclosure, by obtaining the average closing time of the relay of each vehicle within a unit time, a target vehicle with a possible relay problem can be determined from the vehicles within a first preset time. Furthermore, based on the average closing time of the target vehicle within a second preset time, the fault prediction of the relay of the target vehicle can effectively identify the relay closed circuit failure caused by various factors in advance, so as to facilitate early warning to the user, prompt the user to return the vehicle for repair and after-sales spare parts, avoid the situation where the user cannot use the vehicle normally when a relay failure occurs, improve the user experience, and improve the efficiency of after-sales operation and maintenance.

[0124] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0125] Fig. 9 It is a block diagram of an electronic device according to an exemplary embodiment. Fig. 9 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0126] like Fig. 9 As shown, the electronic device 900 includes a processor 901, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 902 or the program loaded from the memory 906 to the random access memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the electronic device 900 are also stored. The processor 901, the ROM 902 and the RAM 903 are connected to each other through a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0127] The following components are connected to the I / O interface 905: a memory 906 including a hard disk, etc.; and a communication part 907 including a network interface card such as a local area network (LAN) card, a modem, etc., and the communication part 907 performs communication processing via a network such as the Internet; a drive 908 is also connected to the I / O interface 905 as needed.

[0128] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 907. When the computer program is executed by the processor 901, the above-mentioned functions defined in the method of the present disclosure are executed.

[0129] In an exemplary embodiment, a storage medium including instructions is also provided, such as a memory including instructions, and the instructions can be executed by the processor 901 of the electronic device 900 to complete the above method. Optionally, the storage medium can be a non-transitory computer-readable storage medium, for example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0130] In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0131] Fig.10 It is a structural block diagram of an electronic device according to an exemplary embodiment. Fig.10 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure. Fig.10As shown, the electronic device 1000 includes a processor 1001 and a memory 1002. The memory 1002 is used to store program codes, and the processor 1001 is connected to the memory 1002 and is used to read the program codes from the memory 1002 to implement the fault prediction method of the relay in the above embodiment.

[0132] Optionally, the number of processors 1001 may be one or more.

[0133] Optionally, the electronic device may further include an interface 1003, and the number of the interface 1003 may be multiple. The interface 1003 may be connected to an application program and may receive data from an external device such as a sensor.

[0134] The present disclosure also provides a computer-readable storage medium having computer program instructions stored thereon, and when the program instructions are executed by a processor, the steps of the relay fault prediction method provided by the present disclosure are implemented.

[0135] In addition, the word "exemplary" is used herein to indicate serving as an example, instance, or diagram. Any aspect or design described as "exemplary" in this article is not necessarily understood to be advantageous compared to other aspects or designs. On the contrary, the use of the word exemplary is intended to present concepts in a concrete way. As used herein, the term "or" is intended to represent an inclusive "or" rather than an exclusive "or". That is, unless otherwise specified or clear from the context, "X applies A or B" is intended to represent any one of the natural inclusive arrangements. That is, if X applies A; X applies B; or X applies both A and B, "X applies A or B" is satisfied under any of the aforementioned examples. In addition, unless otherwise specified or clearly pointed to a singular form from the context, the articles "one" and "an" as used in this disclosure and the appended claims are generally understood to mean "one or more".

[0136] Likewise, although the present disclosure has been shown and described with respect to one or more implementations, equivalent variations and modifications will occur to those skilled in the art after reading and understanding the specification and drawings. The present disclosure includes all such modifications and variations and is limited only by the scope of the claims. In particular, with respect to the various functions performed by the components (e.g., elements, resources, etc.) described above, unless otherwise noted, the terms used to describe such components are intended to correspond to any component (functionally equivalent) that performs the specific functions of the described components, even if the structure is not equivalent to the disclosed structure. In addition, although the specific features of the present disclosure may have been disclosed with respect to only one of several implementations, such features may be combined with one or more other features of other implementations as may be desired and conducive to any given or specific application. In addition, with respect to "including", "having", "having", "having", or variations thereof used in the specific embodiments or claims, such terms are intended to be inclusive in a manner similar to the term "comprising".

[0137] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0138] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

[0139] In the above detailed description, reference is made to the accompanying drawings, which illustrate specific aspects of the present disclosure that can be practiced by way of illustration. In this regard, terms such as "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicating directions or representing positional relationships, can be used with reference to the orientation of the described figures. Since the components of the described device can be positioned in a plurality of different orientations, directional terms can be used for illustrative purposes rather than restrictive. It should be understood that other aspects can be utilized and structural or logical changes can be made without departing from the concepts of the present disclosure. Therefore, the following detailed description should not be considered in a limiting sense.

[0140] It should be understood that, unless otherwise specifically noted, the features of some embodiments of the various present disclosures described herein may be combined with each other. As used herein, the term "and / or" includes any one of the related listed items and any combination of any two or more; similarly, "at least one of . . . " includes any one of the related listed items and any combination of any two or more.

[0141] It should be understood that, unless otherwise clearly specified and limited, the terms "joining", "attaching", "installing", "connecting", "connecting", "fixing" and the like used in the embodiments of the present disclosure should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral one; it can be a mechanical connection, an electrical connection, or communication with each other; it can be a direct connection, or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly limited. For ordinary technicians in this field, the specific meanings of the above terms in this article can be understood according to specific circumstances.

[0142] In addition, the term "above" used in relation to a component, element or material layer formed "above" or located "above" a surface may be used herein to indicate that the component, element or material layer is "indirectly" positioned (e.g., placed, formed, deposited, etc.) on the surface such that one or more additional components, elements or layers are arranged between the surface and the component, element or material layer. However, the term "above" used in relation to a component, element or material layer formed "above" or located "above" a surface may also optionally have a specific meaning: the component, element or material layer is "directly" positioned (e.g., placed, formed, deposited, etc.) on the surface, such as in direct contact with the surface.

[0143] Although terms such as "first", "second" and "third" can be used herein to describe various components, parts, regions, layers or sections, these components, parts, regions, layers or sections are not limited to these terms. On the contrary, these terms are only used to distinguish one component, component, region, layer or section from another component, component, region, layer or section. Therefore, without departing from the teachings of each example, the first component, component, region, layer or section mentioned in the examples described herein may also be referred to as the second component, component, region, layer or section. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Thus, the features defined as "first" and "second" may expressly or implicitly include at least one of the features. In the description herein, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0144] It should be understood that spatially relative terms such as "above", "upper", "below", and "lower" are used herein to describe the relationship of one element to another element shown in the figures. In addition to the orientation depicted in the drawings, such spatially relative terms are also intended to include different orientations of the device in use or operation. For example, if the device in the drawings is turned over, the element described as being "above" or "upper" relative to another element will be "below" or "lower" relative to the other element. Therefore, depending on the spatial orientation of the device, the term "above" includes both upper and lower orientations. The device may have other orientations (e.g., rotated 90 degrees or in other orientations), and the spatially relative terms used herein should be interpreted accordingly.

Claims

1. A method for predicting relay failure, characterized in that: The method comprises: Get the average closing time of the relay of each vehicle per unit time; Determining a target vehicle based on the average closing time of each vehicle within a first preset time; Based on the average closing time of the target vehicle within a second preset time, a fault prediction is performed on the relay of the target vehicle.

2. The method according to claim 1, characterized in that The determining of the target vehicle based on the average closing time of each vehicle within the first preset time comprises: Acquire a first vehicle of the same vehicle model, and form a first set based on the average closing time of the first vehicle within the first preset time; Determining outliers in the first set based on the average closure duration in the first set; The target vehicle is determined from the first vehicles based on the outliers.

3. The method according to claim 2, characterized in that The step of determining the target vehicle from the first vehicles based on the outliers includes: For each of the first vehicles, determining a number of outliers belonging to the first vehicle; If the number of outliers of the first vehicle is greater than a set number, the first vehicle is determined to be the target vehicle.

4. The method according to claim 2, characterized in that: The determining of outliers in the first set based on the average closure duration in the first set includes: Obtaining an average of the average closing durations in the first set as a global average closing duration; Based on the global average closure time, an upper 1 / 4 quartile value and a lower 1 / 4 quartile value are determined, and based on the upper 1 / 4 quartile value and the lower 1 / 4 quartile value, an intraquartile distance is obtained; Determining an outlier identification condition based on the upper 1 / 4 quartile value, the lower 1 / 4 quartile value, and the interquartile range; From the average closing durations in the first set, an average closing duration that satisfies the outlier identification condition is determined as the outlier.

5. The method according to claim 1, characterized in that The performing fault prediction on the relay of the target vehicle based on the average closing time of the target vehicle within the second preset time includes: Obtaining the determined time of each of the average closing durations within the second preset duration; Based on the average closing duration within the second preset duration and the respective determined times of the average closing duration, linear fitting is performed to obtain a target fitting curve; Determining a maximum average closing time from the average closing time within the second preset time; Based on the slope and intercept of the target fitting curve, as well as the maximum average closing time and the second preset time, predict the time interval from the current time to the occurrence of a fault of the relay of the target vehicle; Based on the time interval, fault identification is performed on the relay of the target vehicle.

6. The method according to claim 5, characterized in that The time interval is determined using the following formula: Wherein, N is the time interval, M is the second preset time length, and y is max is the maximum average closing time, c is the intercept, and k is the slope.

7. The method according to claim 5 or 6, characterized in that: The step of performing fault identification on the relay of the target vehicle based on the time interval includes: If the time interval is greater than or equal to the set time interval, it is determined that a closing failure occurs in the relay of the target vehicle, and a warning message is generated.

8. The method according to claim 5 or 6, characterized in that: The step of performing fault identification on the relay of the target vehicle based on the time interval includes: If the time interval is less than the set time interval, determining the fitting degree of the target fitting curve; If the degree of fit is greater than a set threshold, it is determined that a closing failure occurs in the relay of the target vehicle, and a warning message is generated.

9. The method according to claim 8, characterized in that Determining the degree of fit of the target fitting curve includes: Based on the target fitting curve, obtaining the fitting average closing time corresponding to each of the determined times; The fitting degree of the target fitting curve is determined based on the fitted average closing time corresponding to the determined time and the average closing time.

10. The method according to claim 9, characterized in that The goodness of fit is determined using the following formula: Among them, y fit (i) is the fitted average closing time corresponding to the i-th determined time; avg(y) is the average of the average closing times within the second preset time; y(i) is the average closing time corresponding to the i-th determined time within the second preset time.

11. A relay fault prediction device, characterized in that: The device comprises: An acquisition module is used to acquire the average closing time of the relay of each vehicle within a unit time; A determination module, configured to determine a target vehicle based on the average closing time of each vehicle within a first preset time; The prediction module is used to predict the fault of the relay of the target vehicle based on the average closing time of the target vehicle within a second preset time.

12. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to: Implement the steps of the method according to any one of claims 1 to 10.

13. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the program instructions are executed by a processor, the steps of the method described in any one of claims 1 to 10 are implemented.

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