Charging station abnormal alarm method, device, equipment and medium

Abstract

The present disclosure proposes a charging seat abnormality alarm method, device, equipment and medium, the method comprising: obtaining a first candidate abnormal vehicle set with charging abnormalities in an initial vehicle set; filtering the first candidate abnormal vehicle set with abnormal charging piles to obtain a filtered second candidate abnormal vehicle set; obtaining fitting parameters of each second candidate abnormal vehicle in the second candidate abnormal vehicle set; in response to identifying a target charging seat abnormal vehicle with a charging seat abnormality in the second candidate abnormal vehicle set according to the fitting parameters, an abnormal alarm is issued for the target charging seat abnormal vehicle. The manual dependence, labor cost and operating cost of abnormal vehicle alarms are reduced, and the target charging seat abnormal vehicle is determined by fitting parameters, which improves the timeliness and accuracy of vehicle charging abnormality alarms, thereby improving the safety of vehicle driving and optimizing the user's driving experience.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing, and in particular to a charging stand abnormal alarm method, device, equipment and medium. Background Art

[0002] With the development of technology, more and more people choose electric vehicles as their means of transportation. During the charging process of electric vehicles, charging abnormalities may occur, which may lead to safety hazards of electric vehicles.

[0003] In the related art, the temperature of the charging equipment in the electric vehicle can be monitored. When the temperature of the charging equipment is detected to be abnormal, an abnormal charging alarm of the electric vehicle is issued. The timeliness of the vehicle charging abnormality is poor, which has a certain degree of impact on the user experience. Summary of the invention

[0004] The present disclosure aims to solve one of the technical problems in the related art at least to some extent.

[0005] To this end, the first aspect of the present disclosure proposes a charging stand abnormality alarm method.

[0006] A second aspect of the present disclosure provides a charging stand abnormality alarm device.

[0007] A third aspect of the present disclosure provides a vehicle.

[0008] A fourth aspect of the present disclosure provides an electronic device.

[0009] A fifth aspect of the present disclosure provides a computer-readable storage medium.

[0010] In a first aspect, the present disclosure proposes a charging seat abnormality alarm method, the method comprising: obtaining a first candidate abnormal vehicle set with charging abnormalities in an initial vehicle set; filtering the first candidate abnormal vehicle set with abnormal charging piles to obtain a filtered second candidate abnormal vehicle set; obtaining fitting parameters of each second candidate abnormal vehicle in the second candidate abnormal vehicle set; in response to identifying a target charging seat abnormal vehicle with a charging seat abnormality in the second candidate abnormal vehicle set according to the fitting parameters, issuing an abnormality alarm for the target charging seat abnormal vehicle.

[0011] In addition, the charging stand abnormality alarm method proposed in the first aspect of the present disclosure may also have the following additional technical features:

[0012] According to one embodiment of the present disclosure, the method of obtaining a first candidate abnormal vehicle set with charging abnormalities in an initial vehicle set includes: obtaining a first target analysis data group set of the initial vehicle set, and obtaining a set of fragment temperature rise rates in each first target analysis data group; and obtaining the first candidate abnormal vehicle set with charging abnormalities from the initial vehicle set based on the set of fragment temperature rise rates of each first target analysis data group.

[0013] According to one embodiment of the present disclosure, the method of obtaining the first target analysis data group set of the initial vehicle set includes: obtaining a first charging data set of each initial vehicle, and integrating each first charging data set to obtain a total second charging data set; obtaining a first grouping item set of the two charging data sets, and grouping the second charging data set according to the first grouping item set to obtain a third charging data set combination under each first grouping item; obtaining a first candidate analysis data group set of the initial vehicle set according to the third charging data set combination under each first grouping item; obtaining a first fragment number of each first candidate analysis data group, and obtaining the first target analysis data group set from the first candidate analysis data group set according to the first fragment number.

[0014] According to an embodiment of the present disclosure, the first candidate analysis data group set of the initial vehicle set is obtained based on the combination of the third charging data sets under each first grouping item, including: obtaining the fourth charging data set under each first grouping item from each third charging data set combination, wherein, for any first grouping item, the fourth charging data set under the first grouping item is any data set in the combination of the third charging data sets under the first grouping item; obtaining the first candidate analysis data group obtained by combining the fourth charging data sets under each first grouping item to obtain the first candidate analysis data group set.

[0015] According to an embodiment of the present disclosure, the method of obtaining the first fragment number of each first candidate analysis data group and obtaining the first target analysis data group set from the first candidate analysis data group set according to the first fragment number includes: for any first candidate analysis data group, obtaining the first data fragment set in the first candidate analysis data group, and obtaining the first fragment number according to the first data fragment set, wherein, for any first data fragment, each charging data in the first data fragment is generated based on the same charging behavior; in response to the first fragment number being greater than or equal to a preset first fragment number threshold, determining that the first candidate analysis data group is the first target analysis data group; and obtaining the first target analysis data group set of the initial vehicle set according to each first target analysis data group.

[0016] According to one embodiment of the present disclosure, obtaining a set of fragment temperature rise rates in each first target analysis data group includes: obtaining a set of second data fragments in each first target analysis data group; for any first target analysis data group, obtaining the fragment temperature rise rate of each second data fragment in the set of second data fragments of the first target analysis data group to obtain the set of fragment temperature rise rates of the first target analysis data group.

[0017] According to an embodiment of the present disclosure, the first candidate abnormal vehicle set with charging abnormality is obtained from the initial vehicle set based on the fragment temperature rise rate set of each first target analysis data group, including: for any first target analysis data group, obtaining the first quarter quartile value and the second quarter quartile value of the first target analysis data group to obtain the interquartile range parameter of the first target analysis data group; obtaining an outlier temperature rise rate reference value based on the second quarter quartile value and the interquartile range parameter; obtaining an outlier fragment temperature rise rate set in each fragment temperature rise rate set based on the outlier temperature rise rate reference value, wherein any outlier fragment temperature rise rate in the outlier fragment temperature rise rate set is less than or equal to the outlier temperature rise rate reference value; obtaining the vehicle identification of each outlier fragment temperature rise rate set, and obtaining the first candidate abnormal vehicle of each vehicle identification in the initial vehicle set to obtain the first candidate abnormal vehicle set.

[0018] According to an embodiment of the present disclosure, the filtering of the first candidate abnormal vehicle set by abnormal charging piles to obtain the filtered second candidate abnormal vehicle set includes: obtaining the charging pile identification of each first candidate abnormal vehicle in the first candidate abnormal vehicle set to obtain the number of identifications of each charging pile identification; obtaining a third candidate abnormal vehicle set with abnormal charging piles in the first candidate abnormal vehicle set according to the number of identifications of each charging pile identification; filtering the first candidate abnormal vehicle set by abnormal charging piles according to the third candidate abnormal vehicle set to obtain the remaining abnormal vehicle set in the first candidate abnormal vehicle set except the third candidate abnormal vehicle set as the second candidate abnormal vehicle set.

[0019] According to an embodiment of the present disclosure, the method of obtaining a third candidate abnormal vehicle set with charging pile abnormalities in the first candidate abnormal vehicle set based on the number of identifications of each charging pile identification includes: for any charging pile identification, in response to the number of identifications of the charging pile identification being greater than or equal to a preset identification number threshold, determining the vehicle corresponding to the charging pile identification in the first candidate abnormal vehicle set as the third candidate abnormal vehicle with charging pile abnormalities.

[0020] According to one embodiment of the present disclosure, the method of obtaining the fitting parameters of each second candidate abnormal vehicle in the second candidate abnormal vehicle set includes: obtaining a set of second target analysis data groups of the second candidate abnormal vehicle set; obtaining the fitting slope and fitting degree of each second target analysis data group to obtain the fitting parameters of each second candidate abnormal vehicle.

[0021] According to one embodiment of the present disclosure, the method of obtaining the second target analysis data group set of the second candidate abnormal vehicle set includes: obtaining the fifth charging data set of each second candidate abnormal vehicle; obtaining the second grouping item set of the fifth charging data set, and grouping each fifth charging data set according to the second grouping item set to obtain a sixth charging data set combination of each fifth charging data set under each second grouping item; obtaining a second candidate analysis data group set of each second candidate abnormal vehicle according to the sixth charging data set combination under each second grouping item; obtaining a third data segment set of each second candidate analysis data group, and obtaining the second target analysis data group set from the second candidate analysis data group set according to the third data segment set.

[0022] According to an embodiment of the present disclosure, the method of obtaining the third data segment set of each second candidate analysis data group, and obtaining the second target analysis data group set from the second candidate analysis data group set based on the third data segment set, includes: for any second candidate analysis data group, obtaining the fourth data segment set that does not belong to the preset abnormal charging pile list from the third data segment set of the second candidate analysis data group; obtaining the third candidate analysis data group set based on the fourth data segment set in each second candidate analysis data group; for any third candidate analysis data group, in response to the number of second segments in the fourth data segment set in the third candidate analysis data group being greater than or equal to a preset second segment number threshold, determining the third candidate analysis data group as the second target analysis data group to obtain the second target analysis data group set.

[0023] According to one embodiment of the present disclosure, in response to identifying a target charging seat abnormal vehicle with a charging seat abnormality in the second candidate abnormal vehicle set according to the fitting parameters, an abnormality alarm is issued for the target charging seat abnormal vehicle, including: obtaining the fitting slope and the degree of fit in the fitting parameters of each second target analysis data group; for any second target analysis data group, in response to the fitting slope of the second target analysis data group being greater than or equal to a preset fitting slope threshold, and the degree of fit of the second target analysis data group being greater than or equal to a preset degree of fit threshold, determining that the second target analysis data group is a target charging seat abnormal data group with a charging seat abnormality; obtaining the corresponding vehicle of the target charging seat abnormal data group in the second candidate abnormal vehicle set as the target charging seat abnormal vehicle with a charging seat abnormality, and issuing an abnormality alarm for the target charging seat abnormal vehicle.

[0024] According to a second aspect of the present disclosure, a charging seat abnormality alarm device is proposed, which includes: a first acquisition module, used to acquire a first candidate abnormal vehicle set with charging abnormalities in an initial vehicle set; a filtering module, used to filter the abnormal charging piles of the first candidate abnormal vehicle set to obtain a filtered second candidate abnormal vehicle set; a second acquisition module, used to acquire fitting parameters of each second candidate abnormal vehicle in the second candidate abnormal vehicle set; and an alarm module, used to respond to identifying a target charging seat abnormal vehicle with a charging seat abnormality in the second candidate abnormal vehicle set according to the fitting parameters, and to issue an abnormal alarm for the target charging seat abnormal vehicle.

[0025] In addition, the charging stand abnormality alarm device proposed in the second aspect of the present disclosure may also have the following additional technical features:

[0026] According to one embodiment of the present disclosure, the first acquisition module is further used to: acquire a first target analysis data group set of the initial vehicle set, and acquire a set of fragment temperature rise rates in each first target analysis data group; and acquire the first candidate abnormal vehicle set with charging abnormalities from the initial vehicle set based on the set of fragment temperature rise rates of each first target analysis data group.

[0027] According to one embodiment of the present disclosure, the first acquisition module is further used to: acquire a first charging data set of each initial vehicle, and integrate each first charging data set to obtain a total second charging data set; acquire a first grouping item set of the two charging data sets, and group the second charging data set according to the first grouping item set to obtain a third charging data set combination under each first grouping item; obtain a first candidate analysis data group set of the initial vehicle set according to the third charging data set combination under each first grouping item; acquire a first fragment number of each first candidate analysis data group, and acquire the first target analysis data group set from the first candidate analysis data group set according to the first fragment number.

[0028] According to one embodiment of the present disclosure, the first acquisition module is further used to: acquire the fourth charging data set under each first grouping item from each third charging data set combination, wherein, for any first grouping item, the fourth charging data set under the first grouping item is any data set in the third charging data set combination under the first grouping item; acquire the first candidate analysis data group obtained by combining the fourth charging data sets under each first grouping item to obtain the first candidate analysis data group set.

[0029] According to one embodiment of the present disclosure, the first acquisition module is further used to: for any first candidate analysis data group, acquire a set of first data fragments in the first candidate analysis data group, and obtain the first fragment quantity based on the first data fragment set, wherein, for any first data fragment, each charging data in the first data fragment is generated based on the same charging behavior; in response to the first fragment quantity being greater than or equal to a preset first fragment quantity threshold, determine that the first candidate analysis data group is a first target analysis data group; and obtain the first target analysis data group set of the initial vehicle set based on each first target analysis data group.

[0030] According to one embodiment of the present disclosure, the first acquisition module is further used to: acquire a set of second data segments in each first target analysis data group; and for any first target analysis data group, acquire a segment temperature rise rate of each second data segment in the set of second data segments of the first target analysis data group to obtain the segment temperature rise rate set of the first target analysis data group.

[0031] According to one embodiment of the present disclosure, the first acquisition module is also used to: for any first target analysis data group, acquire the first quarter quartile value and the second quarter quartile value of the first target analysis data group to obtain the interquartile range parameter of the first target analysis data group; acquire the outlier temperature rise rate reference value according to the second quarter quartile value and the interquartile range parameter; acquire the outlier fragment temperature rise rate set in each fragment temperature rise rate set according to the outlier temperature rise rate reference value, wherein any outlier fragment temperature rise rate in the outlier fragment temperature rise rate set is less than or equal to the outlier temperature rise rate reference value; acquire the vehicle identification of each outlier fragment temperature rise rate set, and acquire the first candidate abnormal vehicle of each vehicle identification in the initial vehicle set to obtain the first candidate abnormal vehicle set.

[0032] According to one embodiment of the present disclosure, the filtering module is also used to: obtain the charging pile identification of each first candidate abnormal vehicle in the first candidate abnormal vehicle set to obtain the identification number of each charging pile identification; obtain a third candidate abnormal vehicle set with abnormal charging piles in the first candidate abnormal vehicle set according to the identification number of each charging pile identification; filter the first candidate abnormal vehicle set for abnormal charging piles according to the third candidate abnormal vehicle set, and obtain the remaining abnormal vehicle set in the first candidate abnormal vehicle set except the third candidate abnormal vehicle set as the second candidate abnormal vehicle set.

[0033] According to one embodiment of the present disclosure, the filtering module is also used to: for any charging pile identifier, in response to the number of identifiers of the charging pile identifier being greater than or equal to a preset identifier number threshold, determine the vehicle corresponding to the charging pile identifier in the first candidate abnormal vehicle set as a third candidate abnormal vehicle with charging pile abnormality.

[0034] According to one embodiment of the present disclosure, the second acquisition module is further used to: obtain a second target analysis data group set of the second candidate abnormal vehicle set; obtain the fitting slope and fitting degree of each second target analysis data group to obtain the fitting parameters of each second candidate abnormal vehicle.

[0035] According to one embodiment of the present disclosure, the second acquisition module is also used to: acquire the fifth charging data set of each second candidate abnormal vehicle; acquire the second grouping item set of the fifth charging data set, and group each fifth charging data set according to the second grouping item set to obtain a sixth charging data set combination of each fifth charging data set under each second grouping item; obtain a second candidate analysis data group set of each second candidate abnormal vehicle according to the sixth charging data set combination under each second grouping item; acquire the third data segment set of each second candidate analysis data group, and acquire the second target analysis data group set from the second candidate analysis data group set according to the third data segment set.

[0036] According to one embodiment of the present disclosure, the second acquisition module is further used to: for any second candidate analysis data group, obtain a fourth data segment set that does not belong to a preset list of abnormal charging piles from the third data segment set of the second candidate analysis data group; obtain a third candidate analysis data group set based on the fourth data segment set in each second candidate analysis data group; for any third candidate analysis data group, in response to the number of second segments in the fourth data segment set in the third candidate analysis data group being greater than or equal to a preset second segment number threshold, determine that the third candidate analysis data group is the second target analysis data group to obtain the second target analysis data group set.

[0037] According to one embodiment of the present disclosure, the alarm module is also used to: obtain the fitting slope and fitting degree in the fitting parameters of each second target analysis data group; for any second target analysis data group, in response to the fitting slope of the second target analysis data group being greater than or equal to a preset fitting slope threshold, and the fitting degree of the second target analysis data group being greater than or equal to a preset fitting degree threshold, determine that the second target analysis data group is a target charging seat abnormal data group with an abnormal charging seat; obtain the corresponding vehicle of the target charging seat abnormal data group in the second candidate abnormal vehicle set as the target charging seat abnormal vehicle with an abnormal charging seat, and issue an abnormal alarm for the target charging seat abnormal vehicle.

[0038] A third aspect of the present disclosure provides a vehicle, which is used to implement the charging seat abnormality alarm method provided in the first aspect.

[0039] The fourth aspect of the present disclosure proposes an electronic device, comprising: a processor; a memory for storing executable instructions of the processor; wherein the processor is configured to execute instructions to implement the charging stand abnormality alarm method proposed in the first aspect above.

[0040] The fifth aspect of the present disclosure proposes a computer-readable storage medium. When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device can execute the charging stand abnormal alarm method proposed in the first aspect above.

[0041] The charging seat abnormality alarm method, device, equipment and medium proposed in the present disclosure obtain a first candidate abnormal vehicle set with charging abnormalities in the initial vehicle set, filter the first candidate abnormal vehicle set for abnormal charging piles, and obtain a second candidate abnormal vehicle set. Further, the fitting parameters of each second candidate abnormal vehicle are obtained, and the target charging seat abnormal vehicle is identified in the second candidate abnormal vehicle set according to the fitting parameters. When the target charging seat abnormal vehicle is identified, an abnormal alarm of the target charging seat abnormal vehicle is issued. In the present disclosure, the target charging seat abnormal vehicle is identified from the second candidate abnormal vehicle set after the abnormal charging pile is filtered, without relying on staff for confirmation, thereby reducing the degree of manual dependence, labor costs and operating costs of abnormal vehicle alarms. The target charging seat abnormal vehicle is determined by fitting parameters. Compared with the related art of issuing an abnormal alarm after the temperature abnormality of the charging equipment through temperature monitoring, the timeliness and accuracy of the vehicle charging abnormality alarm are improved, thereby improving the safety of vehicle driving and optimizing the user's driving experience.

[0042] It should be understood that the contents described in the present disclosure are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The above and / or additional aspects and advantages of the present disclosure will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0044] Figure 1 This is a flow chart of a charging stand abnormality alarm method according to an embodiment of the present disclosure;

[0045] Figure 2 It is a flowchart of a charging stand abnormality alarm method according to another embodiment of the present disclosure;

[0046] Figure 3 A schematic diagram of charging data collection according to an embodiment of the present disclosure;

[0047] Figure 4 It is a flowchart of a charging stand abnormality alarm method according to another embodiment of the present disclosure;

[0048] Figure 5 This is a schematic diagram of the structure of a charging stand abnormality alarm device according to an embodiment of the present disclosure;

[0049] Figure 6A block diagram of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0050] Embodiments of the present disclosure are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0051] A charging stand abnormality alarm method, device, equipment and medium proposed in an embodiment of the present disclosure will be described below with reference to the accompanying drawings.

[0052] Figure 1 FIG. 1 is a flow chart of a charging stand abnormality alarm method according to an embodiment of the present disclosure. Figure 1 As shown, the method includes:

[0053] S101, obtaining a first candidate abnormal vehicle set with charging abnormalities in an initial vehicle set.

[0054] In the disclosed embodiment, when the vehicle is charging, it is necessary to connect the charging seat on the vehicle to the charging pile. In this scenario, any abnormality in any device in the charging seat or the charging pile may cause abnormal charging of the vehicle, thereby posing a potential safety hazard during the charging process.

[0055] In this scenario, the historical charging data of the vehicle can be used to identify whether the charging seat configured on the vehicle is abnormal, wherein the vehicle that needs charging abnormality identification can be marked as an initial vehicle, thereby obtaining an initial vehicle set consisting of multiple initial vehicles.

[0056] It should be noted that for any initial vehicle set, each initial vehicle in the initial vehicle set can be a vehicle of the same vehicle model, or a vehicle equipped with a charging base of the same model, and no specific limitation is made here.

[0057] In the disclosed embodiment, historical charging data of each initial vehicle in the initial vehicle set can be obtained, and abnormal data analysis and processing can be performed on the acquired historical charging data according to the data analysis method in the relevant technology, and then some vehicles with charging abnormalities are identified from the initial vehicle set according to the analysis results, and these some vehicles are marked as first candidate abnormal vehicles, thereby obtaining a first candidate abnormal vehicle set consisting of at least one first candidate abnormal vehicle.

[0058] S102, filtering abnormal charging piles from the first candidate abnormal vehicle set to obtain a filtered second candidate abnormal vehicle set.

[0059] In the disclosed embodiment, the first candidate abnormal vehicle set includes vehicles with charging abnormalities in the initial vehicle set, that is, the first candidate abnormal vehicle set may include vehicles with charging abnormalities caused by abnormal charging piles.

[0060] In order to implement abnormal alarm for the vehicle's charging seat, the first candidate abnormal vehicle set can be screened to obtain some vehicles in the first candidate abnormal vehicle set that have charging abnormalities due to abnormal charging piles, wherein these some vehicles are the charging pile abnormal vehicles in the first candidate abnormal vehicle set.

[0061] Optionally, this part of the vehicles can be filtered and deleted from the first candidate abnormal vehicle set to achieve abnormal charging pile filtering of the first candidate abnormal vehicle set, and the remaining vehicles after filtering and deletion can be marked as at least one second candidate abnormal vehicle in the first candidate abnormal vehicle set, thereby obtaining a second candidate abnormal vehicle set composed of the at least one second candidate abnormal vehicle.

[0062] S103: Obtain fitting parameters of each second candidate abnormal vehicle in the second candidate abnormal vehicle set.

[0063] In the disclosed embodiment, the historical charging data of each second candidate abnormal vehicle in the second candidate abnormal vehicle set may be acquired, and data analysis may be performed on the historical charging data of each second candidate abnormal vehicle.

[0064] Optionally, for the historical charging data of any second candidate abnormal vehicle, the historical charging data can be algorithmically processed according to the fitting parameter acquisition algorithm in the relevant technology, and then the fitting parameters of the historical charging data can be obtained based on the results of the algorithm processing. The fitting parameters can be marked as the fitting parameters of the second candidate abnormal vehicle.

[0065] S104, in response to identifying a target abnormal charging seat vehicle with an abnormal charging seat in the second candidate abnormal vehicle set according to the fitting parameters, issuing an abnormality alarm for the target abnormal charging seat vehicle.

[0066] In the disclosed embodiment, it is possible to identify whether there are vehicles in the second candidate abnormal vehicle set whose charging abnormality is caused by an abnormal charging seat based on the fitting parameters, and such vehicles can be marked as target abnormal charging seat vehicles in the second candidate abnormal vehicle set.

[0067] Optionally, the abnormality judgment condition corresponding to the fitting parameter can be obtained, and each fitting parameter can be compared with the abnormality judgment condition respectively. Among them, for any fitting parameter, when the fitting parameter meets the abnormality judgment condition, the second candidate abnormal vehicle corresponding to the fitting parameter can be determined as the target charging seat abnormal vehicle with abnormal charging seat.

[0068] Furthermore, an abnormal alarm of a vehicle with an abnormal target charging seat can be performed based on a preset alarm method, wherein the vehicle information of the vehicle with an abnormal target charging seat can be sent to relevant staff through a preset communication method to achieve an abnormal alarm of a vehicle with an abnormal target charging seat, or an abnormal alarm of the charging seat can be issued to the driver of the vehicle through the sound and light alarm device configured on the vehicle, without specific limitation here.

[0069] The charging seat abnormality alarm method proposed in the present disclosure obtains a first candidate abnormal vehicle set with charging abnormalities in an initial vehicle set, filters the first candidate abnormal vehicle set for abnormal charging piles to obtain a second candidate abnormal vehicle set, and further obtains fitting parameters of each second candidate abnormal vehicle, and identifies whether there is a target charging seat abnormal vehicle in the second candidate abnormal vehicle set based on the fitting parameters, and when the target charging seat abnormal vehicle is identified, an abnormal alarm for the target charging seat abnormal vehicle is issued. In the present disclosure, the target charging seat abnormal vehicle is identified from the second candidate abnormal vehicle set after filtering the abnormal charging piles, without relying on staff for confirmation, thereby reducing the degree of manual dependence, labor costs and operating costs of abnormal vehicle alarms, and determining the target charging seat abnormal vehicle through fitting parameters. Compared with the related art of issuing an abnormal alarm after a temperature abnormality occurs in the charging equipment through temperature monitoring, the timeliness and accuracy of the vehicle charging abnormality alarm are improved, thereby improving the safety of vehicle driving and optimizing the user's driving experience.

[0070] In the above embodiment, the abnormal alarm of the target charging station abnormal vehicle can be combined with Figure 2 Further understanding, Figure 2 FIG. 1 is a flow chart of a charging stand abnormality alarm method according to another embodiment of the present disclosure. Figure 2 As shown, the method includes:

[0071] S201, obtaining a set of first target analysis data groups of an initial vehicle set, and obtaining a set of segment temperature rise rates in each first target analysis data group.

[0072] Optionally, a first charging data set of each initial vehicle is obtained, and each first charging data set is integrated to obtain a total second charging data set.

[0073] In the embodiment of the present disclosure, the historical charging data of the initial vehicle set can be used to identify whether there is a target abnormal charging seat vehicle with an abnormal charging seat in the initial vehicle set, wherein the historical charging data can be marked as the first charging data set of the initial vehicle set.

[0074] Optionally, historical charging data may be collected based on the vehicle-side system and uploaded to the cloud, and then the first charging data set of each initial vehicle may be obtained from the cloud.

[0075] As an example, Figure 3 As shown, when the charging system is connected to the charging pile to charge the vehicle, Figure 3 The battery management system shown can collect charging data in the charging system, wherein the charging data can also be collected by Figure 3 The temperature acquisition module shown collects the temperature data of the charging base. Further, the collected charging data is transmitted to the Figure 3 In the vehicle controller shown, the collected data is transmitted through the vehicle controller Figure 3 The shown connected vehicle system is uploaded to the cloud.

[0076] The first charging data sets may be merged and integrated, and the data set obtained after the merging and integration is marked as the second charging data set.

[0077] Optionally, a first grouping item set of the second charging data set is obtained, and the second charging data set is grouped according to the first grouping item set to obtain a third charging data set combination under each first grouping item.

[0078] In the disclosed embodiment, the second charging data set includes multiple charging data items, such as SOC, ambient temperature, starting charging seat temperature, and charging current, etc. The multiple items can be marked as first group items of the second charging data set, and the set composed of the multiple first group items is the first group item set.

[0079] In this scenario, the second charging data set can be grouped according to each first grouping item, and multiple charging data sets obtained after grouping under each first grouping item are obtained from the second charging data set, and marked as multiple third charging data sets under each first grouping item, thereby obtaining a third charging data set combination composed of multiple third charging data sets under each first grouping item.

[0080] As an example, taking the SOC and ambient temperature included in the first grouping item set as an example, the second charging data set can be grouped based on the SOC and the ambient temperature, and the second charging data set can be grouped according to the grouping strategy corresponding to the SOC item to obtain a third charging data set under the SOC item.

[0081] And, the second charging data set is grouped according to the grouping strategy corresponding to the ambient temperature item to obtain a third charging data set combination under the ambient temperature item.

[0082] Optionally, the acquisition of the third charging data set combination may be understood in combination with the following contents:

[0083] The grouping strategies of the respective first grouping item sets may be obtained to group the second charging data set combinations to obtain the third charging data set combinations under the first grouping items.

[0084] In the embodiment of the present disclosure, the grouping strategies of the first grouping items in the first grouping item set are different. In this scenario, the grouping strategies under the first grouping items can be obtained, and the second charging data set combinations can be grouped according to the obtained grouping strategies.

[0085] As an example, taking the ambient temperature as the first grouping item as an example, the grouping strategy corresponding to the ambient temperature item is set to group into a low ambient temperature interval and a normal ambient temperature interval based on a preset ambient temperature threshold.

[0086] Among them, the preset ambient temperature threshold can be 0 degrees. In this example, the charging data in the second charging data set with an ambient temperature lower than or equal to 0 degrees can be divided into one data set as the third charging data set in the low ambient temperature range, and the charging data in the second charging data set with an ambient temperature higher than 0 degrees can be divided into one data set as the third charging data set in the normal ambient temperature range, thereby obtaining the third charging data set combination under the first grouping item corresponding to the ambient temperature.

[0087] As another example, taking the starting charging seat temperature as an example, the grouping strategy for setting the starting charging seat temperature is to group the starting charging seat temperature into a low temperature starting charging seat temperature range and a normal starting charging seat temperature range based on a preset starting charging seat temperature threshold.

[0088] Among them, the preset starting charging seat temperature threshold can be 0 degrees. In this example, the charging data in the second charging data set with a starting charging seat temperature lower than or equal to 0 degrees can be divided into one data set as the third charging data set in the low-temperature starting charging seat temperature range, and the charging data in the second charging data set with a starting charging seat temperature higher than 0 degrees can be divided into one data set as the third charging data set in the normal starting charging seat temperature range, thereby obtaining the third charging data set combination under the first grouping item corresponding to the starting charging seat temperature.

[0089] As another example, taking the average charging current as an example, the grouping strategy of the average charging current set as the first grouping item is to group the average charging current into a low rate interval, a medium rate interval and a high rate interval based on a preset average charging current threshold.

[0090] Among them, the average charging current threshold for grouping between the low rate interval and the medium rate interval is 1 times the capacity rate (1C), and the average charging current threshold for grouping between the medium rate interval and the high rate interval is 2 times the capacity rate (2C).

[0091] In this example, the charging data in the second charging data set with an average charging current lower than or equal to 1 times the capacity multiple can be divided into one data set as the third charging data set in the low rate range. Also, the charging data in the second charging data set with an average charging current higher than 1 times the capacity multiple and lower than 2 times the capacity multiple can be divided into one data set as the third charging data set in the medium rate range. Also, the charging data in the second charging data set with an average charging current higher than or equal to 2 times the capacity multiple can be divided into one data set as the third charging data set in the high rate range, thereby obtaining the third charging data set combination under the first grouping item corresponding to the average charging current.

[0092] Optionally, a first candidate analysis data group set of the initial vehicle set is obtained according to the combination of the third charging data sets under each first grouping item.

[0093] Among them, from each third charging data set combination, the fourth charging data set under each first grouping item is obtained, wherein, for any first grouping item, the fourth charging data set under the first grouping item is any data set in the third charging data set combination under the first grouping item, and the first candidate analysis data group is obtained by combining the fourth charging data sets under each first grouping item to obtain a first candidate analysis data group set.

[0094] In the disclosed embodiment, any third charging data set can be obtained from the combination of third charging data sets under each first grouping item as the fourth charging data set under each first grouping item, and the fourth charging data sets under each first grouping item are combined and processed according to the data set combination method in the related art to obtain a combined data group, and the data group is marked as the first candidate analysis data group.

[0095] As an example, it can be seen from the above example that the ambient temperature in the first group item set includes a third charging data set in the low temperature ambient temperature range and a third charging data set in the normal ambient temperature range, totaling 2 third charging data sets, the starting charging seat temperature includes a third charging data set in the low temperature starting charging seat temperature range and a third charging data set in the normal starting charging seat temperature, totaling 2 third charging data sets, and the average charging current includes a third charging data set in the low rate range, a third charging data set in the medium rate range and a third charging data set in the high rate range, totaling 3 third charging data sets.

[0096] In this example, any third charging data set can be obtained from the two third charging data sets included in the ambient temperature as the fourth charging data set at the ambient temperature, and any third charging data set can be obtained from the two third charging data sets included in the starting charging seat temperature as the fourth charging data set at the starting charging seat temperature, and any third charging data set can be obtained from the three third charging data sets included under the average charging current as the fourth charging data set under the average charging current, and the three fourth charging data sets can be combined to obtain a combined data group, which can be marked as a first candidate analysis data group obtained in this example.

[0097] It should be noted that the number of first candidate analysis data groups in this example may be 2×2×3=12. In this example, the first candidate analysis data group set of the initial vehicle set can be obtained based on the 12 first candidate analysis data groups.

[0098] Optionally, the number of first segments of each first candidate analysis data group is obtained, and the first target analysis data group set is obtained from the first candidate analysis data group set according to the number of first segments.

[0099] For any first candidate analysis data group, a first data segment set in the first candidate analysis data group is obtained, and the first segment quantity is obtained according to the first data segment set, wherein, for any first data segment, each charging data in the first data segment is generated based on the same charging behavior.

[0100] In an embodiment of the present disclosure, for any first candidate analysis data group, the first candidate analysis data group may include multiple charging data segments, which can be marked as first data segments, wherein for any first data segment, the charging data included in the first data segment are all generated based on the same charging behavior, and it can be understood that this part of the charging data is data generated by the same charging seat and the same charging pile being connected within the same time range.

[0101] In this scenario, the number of all first data fragments included in the first data fragment set can be obtained according to a counting method in the relevant technology, and this number is the number of first fragments in the first data fragment set.

[0102] Optionally, for any first candidate analysis data group, in response to the number of first segments in the first data segment set in the first candidate analysis data group being greater than or equal to a preset first segment number threshold, the first candidate analysis data group is determined to be the first target analysis data group.

[0103] In the embodiment of the present disclosure, when there are fewer charging data segments in the first candidate analysis data group, there is a possibility that the analysis result may be inaccurate. In this scenario, the number of first segments in each first data segment set can be obtained respectively, and the number of first segments can be used to identify whether the first candidate analysis data group can meet the requirements of charging stand abnormality analysis.

[0104] Among them, the number of first fragments of any first candidate analysis data group can be compared with a preset first fragment number threshold. For any first candidate analysis data group, when the number of first fragments of the first data fragments in the first candidate analysis data group is greater than or equal to the first fragment number threshold, the first candidate analysis data group can be determined as the first target analysis data group.

[0105] Optionally, a set of first target analysis data groups for the initial vehicle set is obtained based on each first target analysis data group.

[0106] Among them, each first target analysis data group can be constructed and processed according to a set construction method in the relevant technology, and then a first target analysis data group set constructed by each first target analysis data group is obtained according to the processing result.

[0107] Optionally, a set of second data segments in each first target analysis data group is obtained, and for any first target analysis data group, a segment temperature rise rate of each second data segment in the set of second data segments of the first target analysis data group is obtained to obtain a set of segment temperature rise rates of the first target analysis data group.

[0108] In the disclosed embodiments, a set of data segments in each first target analysis data group can be obtained and marked as a set of second data segments, and each second data segment can be algorithmically processed according to the temperature rise rate acquisition algorithm in the related technology, and then the temperature rise rate of each second data segment can be obtained according to the result of the algorithm processing, which is marked as the segment temperature rise rate of each second data segment.

[0109] As an example, the calculation formula of the segment temperature rise rate of each second data segment is as follows:

[0110]

[0111] In the above formula, V represents the segment temperature rise rate of the second data segment, dT represents the temperature change value in the time period T, and t represents the charging time corresponding to the second data segment.

[0112] S202, obtaining a first candidate abnormal vehicle set with charging abnormality from an initial vehicle set according to a segment temperature rise rate set of each first target analysis data group.

[0113] Optionally, for any first target analysis data group, a first quarter quartile value and a second quarter quartile value of the first target analysis data group are obtained to obtain an interquartile range parameter of the first target analysis data group.

[0114] In the disclosed embodiment, for any first target analysis data group, the fragment temperature rise rate of each set of second data fragments in the first target analysis data group can be obtained, and the fragment temperature rise rate of each second data fragment can be algorithmically processed according to the quarter quantile value acquisition algorithm in the related technology, and the quarter quantile value of the first target analysis data group can be obtained through the result of the algorithm processing.

[0115] Optionally, the first target analysis data group may include two quartile values, which may be labeled as a first quartile value and a second quartile value, respectively, wherein the first quartile value may be an upper quartile value and the second quartile value may be a lower quartile value.

[0116] In this scenario, the interquartile range parameter of the first target analysis data group may be calculated according to a preset algorithm, wherein the algorithm formula may be as follows:

[0117] IQR=Q3-Q1

[0118] In the above formula, IQR represents the interquartile range parameter of the first target analysis data group, Q3 represents the first quarter quartile value, and Q1 represents the second quarter quartile value.

[0119] Optionally, based on the second quarter quartile value and the interquartile range parameter, an outlier temperature rise rate reference value is obtained, and based on the outlier temperature rise rate reference value, an outlier fragment temperature rise rate set in each fragment temperature rise rate set is obtained, wherein any outlier fragment temperature rise rate in the outlier fragment temperature rise rate set is less than or equal to the outlier temperature rise rate reference value.

[0120] In the disclosed embodiment, for any first target analysis data group, there may be outlier data segments in the second data segment set in the first target analysis data group, wherein the outlier data segment is the data segment of abnormal charging data in the first target analysis data group.

[0121] Optionally, the second quarter quartile value and interquartile range parameter can be algorithmically calculated according to a preset algorithm, and the judgment value of the outlier data segment of the first target analysis data group is obtained through the result of the algorithm calculation, and the value is marked as the outlier temperature rise rate reference value.

[0122] The algorithm formula can be shown as follows:

[0123] Q4=Q1-1.5×IQR

[0124] In the above formula, Q4 represents the outlier temperature rise rate reference value, Q1 represents the second quarter quantile value, and IQR represents the interquartile range parameter of the first target analysis data group.

[0125] Optionally, for any first target analysis data group, the fragment temperature rise rate of each second data fragment in the first target analysis data group is obtained, and each fragment temperature rise rate is compared with the outlier temperature rise rate reference value corresponding to the first target analysis data group, and at least one fragment temperature rise rate that is less than or equal to the outlier temperature rise rate reference value is obtained from the fragment temperature rise rates of each second data fragment, and the at least one fragment temperature rise rate is marked as at least one outlier fragment temperature rise rate in the first target analysis data group.

[0126] Further, a set of outlier fragment temperature rise rates measured in the first target analysis data set is constructed according to the at least one outlier fragment temperature rise rate.

[0127] Optionally, the first vehicle identification of each of the outlier segment temperature rise rate sets is obtained, and the first candidate abnormal vehicle of each first vehicle identification in the initial vehicle set is obtained to obtain the first candidate abnormal vehicle set.

[0128] In the disclosed embodiment, the vehicle identification corresponding to each outlier temperature rise rate fragment in the outlier temperature rise rate fragment set can be obtained and marked as the first vehicle identification of each outlier fragment temperature rise rate, thereby obtaining at least one vehicle in the initial vehicle set having the same identification as each first vehicle identification. The at least one vehicle can be determined as at least one first candidate abnormal vehicle in the initial vehicle set, thereby obtaining a first candidate abnormal vehicle set consisting of the at least one first candidate abnormal vehicle.

[0129] S203, filtering abnormal charging piles on the first candidate abnormal vehicle set to obtain a filtered second candidate abnormal vehicle set.

[0130] Optionally, the charging pile identification of each first candidate abnormal vehicle in the first candidate abnormal vehicle set is obtained to obtain the identification number of each charging pile identification, and, based on the identification number of each charging pile identification, a third candidate abnormal vehicle set with abnormal charging piles in the first candidate abnormal vehicle set is obtained.

[0131] In the embodiment of the present disclosure, each first candidate abnormal vehicle in the first candidate abnormal vehicle set has a charging pile connected thereto, wherein the character number of the charging pile can be marked as the charging pile identifier of each first candidate abnormal vehicle.

[0132] In this scenario, the number of occurrences of the charging data fragments of each charging pile identification in all the charging data fragments in the first candidate abnormal vehicle set can be counted according to the counting method in the relevant technology, so as to obtain the identification number of each charging pile identification.

[0133] Optionally, for any charging pile identifier, in response to the identifier quantity of the charging pile identifier being greater than or equal to a preset identifier quantity threshold, the vehicle corresponding to the charging pile identifier in the first candidate abnormal vehicle set is determined as a third candidate abnormal vehicle with charging pile abnormality.

[0134] In the disclosed embodiment, a preset threshold value of the number of charging pile identifiers is obtained, and at least one charging pile identifier that is greater than or equal to the threshold value of the number of identifiers is obtained from the number of identifiers of each charging pile identifier, wherein the corresponding vehicle of each of the at least one charging pile identifiers in the first candidate abnormal vehicle set can be determined as a vehicle with an abnormal charging pile, and can be marked as at least one third candidate abnormal vehicle with an abnormal charging pile, thereby obtaining a third candidate abnormal vehicle set consisting of at least one third candidate abnormal vehicle.

[0135] Optionally, the first candidate abnormal vehicle set is filtered for abnormal charging piles according to the third candidate abnormal vehicle set to obtain the remaining abnormal vehicle set in the first candidate abnormal vehicle set except the third candidate abnormal vehicle set as the second candidate abnormal vehicle set.

[0136] In the disclosed embodiment, the third candidate abnormal vehicle set can be filtered and deleted from the first candidate abnormal vehicle set, wherein the processing process can be understood as filtering the abnormal charging piles of the first candidate abnormal vehicle set, and then the remaining vehicle set in the first candidate abnormal vehicle set except the third candidate abnormal vehicle is obtained, and this part of the remaining vehicle set can be determined as the second candidate abnormal vehicle set.

[0137] S204, obtaining a second target analysis data group set of the second candidate abnormal vehicle set.

[0138] Optionally, a fifth charging data set of each second candidate abnormal vehicle is obtained.

[0139] In the disclosed embodiment, a charging data set of each second candidate abnormal vehicle within a set historical time range may be obtained, or a charging data set of each second candidate abnormal vehicle at a preset number of charging times may be obtained and marked as the fifth charging data set of each second candidate abnormal vehicle.

[0140] Optionally, a second grouping item set of the fifth charging data sets is obtained, and item groups are respectively performed on each of the fifth charging data sets according to the second grouping item set to obtain a sixth charging data set combination of each of the fifth charging data sets under each of the second grouping items.

[0141] In the embodiment of the present disclosure, the specific content of grouping the items of each fifth charging data set according to the second grouping item set can be understood in conjunction with the specific content of grouping the items of the second charging data set according to the first grouping item set proposed in the above embodiment, and will not be repeated here.

[0142] It should be noted that the second grouping items included in the second grouping item set are the same as the first grouping items included in the first grouping item set.

[0143] Optionally, a second candidate analysis data group set of each second candidate abnormal vehicle is obtained according to the combination of the sixth charging data sets under each second grouping item.

[0144] In the embodiment of the present disclosure, the specific content of the second candidate analysis data group set for each second candidate abnormal vehicle obtained by combining the sixth charging data set under each second grouping item can be understood in conjunction with the specific content of the first candidate analysis data group set obtained by combining the third charging data set under each first grouping item proposed in the above embodiment, and will not be repeated here.

[0145] Optionally, a third data segment set of each second candidate analysis data group is obtained, and a second target analysis data group set is obtained from the second candidate analysis data group set according to the third data segment set.

[0146] In the embodiment of the present disclosure, the charging data segments in the second candidate analysis data group may be marked as third data segments, thereby obtaining a third data segment set consisting of at least one third data segment in the second candidate analysis data group.

[0147] Optionally, for any second candidate analysis data group, a fourth data segment set that does not belong to a preset abnormal charging pile list is obtained from the third data segment set of the second candidate analysis data group.

[0148] In the disclosed embodiment, the charging pile information carried in each third data segment can be obtained, and the obtained charging pile information can be compared with the abnormal charging pile information included in a preset abnormal charging pile list, wherein, for any third data segment, when the comparison result indicates that the charging pile information carried by the third data segment does not have matching abnormal charging pile information in the abnormal charging pile list, it can be determined that the third data segment is a data segment that does not belong to the abnormal charging pile list, wherein the data segment can be marked as the fourth data segment in the third data segment set.

[0149] Further, according to at least one fourth data segment included in the third data segment set, a fourth data segment set in the third data segment set is obtained.

[0150] It should be noted that the list of abnormal charging piles proposed in the embodiments of the present disclosure can be constructed based on the charging pile identifications of each third candidate abnormal vehicle in the set of third candidate abnormal vehicles with abnormal charging piles proposed in the above embodiments, or it can be obtained based on statistics using other methods, which is not specifically limited here.

[0151] Optionally, a third candidate analysis data set set is obtained according to the fourth data segment sets in each second candidate analysis data set.

[0152] In the disclosed embodiment, for any second candidate analysis data group, a fourth data segment set in the second candidate analysis data group can be obtained, and the data segments in the fourth data segment set can be combined to obtain a combined data group, which is then marked as the third candidate analysis data group corresponding to the second candidate analysis data group.

[0153] Furthermore, the third candidate analysis data groups of each second candidate analysis data group are obtained, and the set consisting of the part of the third candidate analysis data groups is marked as a third candidate analysis data group set.

[0154] Optionally, for any third candidate analysis data group, in response to the number of second fragments in the fourth data fragment set in the third candidate analysis data group being greater than or equal to a preset second fragment number threshold, the third candidate analysis data group is determined to be the second target analysis data group to obtain a third target analysis data group set.

[0155] In the embodiment of the present disclosure, it is necessary to further screen the number of data segments in the fourth data segment set in the third candidate analysis data group, wherein, for any third candidate analysis data group, the number of segments in the fourth data segment set included in the third candidate analysis data group can be obtained and marked as the second number of segments.

[0156] Furthermore, a second fragment number threshold corresponding to the second fragment number is obtained. When the second fragment number is greater than or equal to the second fragment number threshold, the third candidate analysis data group can be determined as the second target analysis data group, thereby obtaining a second target analysis data group set consisting of multiple second target analysis data groups.

[0157] S205, obtaining the fitting slope and fitting degree of each second target analysis data group to obtain the fitting parameters of each second candidate abnormal vehicle.

[0158] In the disclosed embodiment, each second target analysis data group can be processed by a fitting slope algorithm and a fitting degree algorithm in the related technology, and then the fitting slope and fitting degree of each second target analysis data group can be obtained according to the results of the algorithm processing.

[0159] As an example, the formula for obtaining the fitting slope can be as follows:

[0160]

[0161] In the above formula, k represents the fitting slope, x represents the day vector corresponding to the charging data segment, which can be [1, 2, 3, ..., L], y represents the historical charging rate data of the second candidate abnormal vehicle for the last L vehicle charges, and x T Represents the transpose of x, c is the operation parameter, where y=kx+c.

[0162] And the formula for obtaining the degree of fit can be shown as follows:

[0163]

[0164] In the above formula, R represents the degree of fit, y fit (i) represents the fitted value y corresponding to the i-th x, where y fit (i) =kx(i)+c, where c is the operation parameter, avg(y) represents the average value of set y, and y(i) represents the i-th value in set y.

[0165] Further, for any second target analysis data set, the fitting slope and fitting degree of the second target analysis data set are determined as fitting parameters of the second target analysis data set.

[0166] S206 , in response to identifying a target abnormal charging seat vehicle with an abnormal charging seat in the second candidate abnormal vehicle set according to the fitting parameters, issuing an abnormality alarm for the target abnormal charging seat vehicle.

[0167] Optionally, the fitting slope and fitting degree in the fitting parameters of each second target analysis data group are obtained. For any second target analysis data group, in response to the fitting slope of the second target analysis data group being greater than or equal to a preset fitting slope threshold, and the fitting degree of the second target analysis data group being greater than or equal to a preset fitting degree threshold, the second target analysis data group is determined to be a target charging seat abnormality data group with an abnormal charging seat.

[0168] In the disclosed embodiment, for the fitting slope and fit degree in the fitting parameters of any second target analysis data group, a preset fitting slope threshold corresponding to the fitting slope and a preset fit degree threshold corresponding to the fit degree can be obtained, and the fitting slope can be compared with the corresponding fitting slope threshold, and the fit degree can be compared with the corresponding fit degree threshold.

[0169] Among them, when the fitting slope is greater than or equal to the fitting slope threshold, and the degree of fit is greater than or equal to the degree of fit threshold, it can be determined that the second target analysis data group is a charging data group with an abnormal charging seat, and the second target analysis data group is marked as a target charging seat abnormal data group.

[0170] Optionally, a corresponding vehicle of the target charging seat abnormal data group in the second candidate abnormal vehicle set is obtained as the target charging seat abnormal vehicle with charging seat abnormality, and an abnormal alarm is issued for the target charging seat abnormal vehicle.

[0171] In the disclosed embodiment, the target vehicle identification corresponding to the charging data in the target charging seat abnormal data group can be obtained, and the target vehicle identification can be compared with the abnormal vehicle identifications of each second candidate abnormal vehicle in the second candidate abnormal vehicle set, and the abnormal vehicle identification that matches the target vehicle identification can be obtained from the abnormal vehicle identifications of each second candidate abnormal vehicle, and the second candidate abnormal vehicle corresponding to the abnormal vehicle identification can be determined as the target charging seat abnormal vehicle with the charging seat abnormality.

[0172] Furthermore, based on a preset alarm strategy, an abnormal alarm is issued to the abnormal vehicle at the target charging station.

[0173] The charging seat abnormality alarm method proposed in the present invention identifies the target charging seat abnormal vehicle from the second candidate abnormal vehicle set after the abnormal charging piles are filtered, without relying on staff for confirmation, reducing the degree of manual dependence, labor cost and operating cost of abnormal vehicle alarms, and determining the target charging seat abnormal vehicle by fitting parameters. Compared with the related technology of using temperature monitoring to issue an abnormal alarm after the temperature abnormality of the charging equipment, the timeliness and accuracy of the vehicle charging abnormality alarm are improved, thereby improving the safety of vehicle driving and optimizing the user's driving experience.

[0174] To better understand the above embodiments, Figure 4 , Figure 4 FIG. 1 is a flow chart of a charging stand abnormality alarm method according to another embodiment of the present disclosure. Figure 4 As shown, the method includes:

[0175] S401, acquiring a first charging data set of an initial vehicle set to obtain a second charging data set obtained by merging and integrating the first charging data sets.

[0176] In the disclosed embodiment, the charging data of the vehicle can be recorded by the battery management system, and the recorded charging data can be uploaded to the cloud through the data interaction link between the vehicle and the cloud.

[0177] In this scenario, a first charging data set of an initial vehicle set may be obtained from historical charging data stored in the cloud, and the first charging data sets of each initial vehicle may be merged to obtain a second charging data set.

[0178] S402, grouping the second charging data set by items to obtain a third charging data set combination under each first grouping item to obtain a first candidate analysis array set.

[0179] In the disclosed embodiment, the second charging data set can be grouped according to the first grouping item set to obtain a third charging data set combination under each first grouping item, and then any data set can be obtained from the third charging data set combination under each first grouping item as the fourth charging data set under each first grouping item, and the fourth charging data sets can be combined to obtain a first candidate analysis data group.

[0180] Furthermore, a first candidate analysis data group set is obtained through a plurality of first candidate analysis data groups.

[0181] S403: For any first candidate analysis data group, identify whether the first segment quantity of the first data segments in the first candidate analysis data group is greater than or equal to a first segment quantity threshold.

[0182] When the first segment quantity is greater than or equal to a first segment quantity threshold, the first candidate analysis data group can be determined as the first target analysis data group.

[0183] And, when the number of first fragments is less than the first fragment number threshold, the charging pile identification information corresponding to the first data fragment can be obtained, and the charging pile identification information is stored in Figure 4 The list of abnormal charging piles is shown.

[0184] S404: When it is identified that the first segment quantity is greater than or equal to the first segment quantity threshold, determine the first candidate analysis data group as the first target analysis data group to obtain a first target analysis data group set.

[0185] The first segment quantity threshold may be set to 100 or other values, which are not specifically limited here.

[0186] S405, obtaining a set of segment temperature rise rates of each first target analysis data group.

[0187] Among them, each first target analysis data group can be algorithmically processed according to the temperature rise rate algorithm proposed in the above embodiment, and then the fragment temperature rise rate of each second data fragment in each first target analysis data group can be obtained according to the result of the algorithm processing, so as to obtain the fragment temperature rise rate set of each first target analysis data group.

[0188] S406: According to the segment temperature rise rate set, a first candidate abnormal vehicle set with charging abnormality is obtained from the initial vehicle set.

[0189] In the disclosed embodiment, for any first target analysis data group, an outlier data segment in the first target analysis data group can be obtained through a segment temperature rise rate set of the first target analysis data group, wherein the outlier data segment is a charging data segment corresponding to the charging anomaly.

[0190] Optionally, an outlier temperature rise rate reference value of the first target analysis data group can be obtained based on the fragment temperature rise rate set, and the fragment temperature rise rates of each second data fragment can be compared with the outlier temperature rise rate reference value. For any fragment temperature rise rate, when the fragment temperature rise rate is less than or equal to the outlier temperature rise rate reference value, it can be determined that the fragment temperature rise rate is an outlier fragment temperature rise rate, and the data fragment corresponding to the outlier fragment temperature rise rate is the outlier data fragment in the first target analysis data group.

[0191] Further, a vehicle corresponding to the first vehicle identifier corresponding to the outlier data segment in the initial vehicle set is determined as a first candidate abnormal vehicle.

[0192] S407, filtering abnormal charging piles on the first candidate abnormal vehicle set to obtain a filtered second candidate abnormal vehicle set.

[0193] In the embodiment of the present disclosure, a third candidate abnormal vehicle set with abnormal charging piles in the first candidate abnormal vehicle set can be obtained, such as Figure 4 As shown, the abnormal charging pile information of each third candidate abnormal vehicle in the third candidate abnormal vehicle set can be stored in Figure 4 The list of abnormal charging piles is shown.

[0194] S408, obtaining a second target analysis data group for each second candidate abnormal vehicle.

[0195] Optionally, the most recent M historical charging data of each second candidate abnormal vehicle can be obtained, and the second target analysis data group of each second candidate abnormal vehicle can be obtained from the M historical charging data, where M can be 50 or other values, which are not specifically limited here.

[0196] The fifth charging data set of each second candidate abnormal vehicle may be obtained, and each fifth charging data set may be grouped according to the second grouping item set to obtain a sixth charging data set combination of each fifth charging data set under each second grouping item.

[0197] Optionally, for any fifth charging data set, according to the sixth charging data set combination under each second grouping item of the fifth charging data set, a second candidate analysis data set screened out from the fifth charging data set is obtained.

[0198] Furthermore, the charging pile information of each of the third data segments in each second candidate analysis data group is obtained to obtain a set of fourth data segments in each second candidate analysis data group that do not belong to the abnormal charging pile list, thereby obtaining each third candidate analysis data group.

[0199] Optionally, at least some data groups whose second fragment number is greater than or equal to a second fragment number threshold are obtained from each third candidate analysis data group, thereby obtaining a second target analysis data group of the second candidate abnormal vehicle corresponding to the fifth charging data set.

[0200] S409, obtaining the fitting slope and fitting degree of each second target analysis data group.

[0201] The algorithm proposed in the above embodiment may be used to perform algorithm processing on each second target analysis data group to obtain the fitting slope and fitting degree of each second target analysis data group.

[0202] S410: For any second target analysis data set, identify whether the fitting slope of the second target analysis data set is greater than 0, and whether the fitting degree is greater than 0.9.

[0203] As an example, the fitting slope threshold corresponding to the fitting slope is set to 0, and the fitting degree threshold corresponding to the fitting degree is set to 0.9. Then, the fitting slope and fitting degree of each second target analysis data group can be compared and identified with their respective thresholds.

[0204] S411, when it is identified that the fitting slope is greater than 0 and the fitting degree is greater than 0.9, determine the target charging station abnormal vehicle according to the second target analysis data group.

[0205] It can be understood that when the fitting slope of any second target analysis data group is greater than 0 and the degree of fitting is greater than 0.9, the fitting temperature rise rate result of the second target analysis data group shows an upward trend, and it can be determined that the charging seat of the vehicle corresponding to the second target analysis data group may have an abnormality.

[0206] In this scenario, it can be determined that the vehicle is an abnormal vehicle in the target charging station.

[0207] S412, issuing an abnormality alarm to the target charging station abnormal vehicle.

[0208] In the disclosed embodiment, an abnormal alarm for a vehicle with an abnormal target charging seat can be issued according to a preset alarm strategy, wherein information alarms can be issued to users of vehicles with abnormal target charging seats and back-end staff through a reserved communication method, or the user can be alerted through an alarm method configured on the vehicle side, without specific limitation here.

[0209] The charging seat abnormality alarm method proposed in the present invention identifies the target charging seat abnormal vehicle from the second candidate abnormal vehicle set after the abnormal charging piles are filtered, without relying on staff for confirmation, reducing the degree of manual dependence, labor cost and operating cost of abnormal vehicle alarms, and determining the target charging seat abnormal vehicle by fitting parameters. Compared with the related technology of using temperature monitoring to issue an abnormal alarm after the temperature abnormality of the charging equipment, the timeliness and accuracy of the vehicle charging abnormality alarm are improved, thereby improving the safety of vehicle driving and optimizing the user's driving experience.

[0210] Corresponding to the charging seat abnormal alarm methods proposed in the above-mentioned embodiments, an embodiment of the present disclosure further proposes a charging seat abnormal alarm device. Since the charging seat abnormal alarm device proposed in the embodiment of the present disclosure corresponds to the charging seat abnormal alarm methods proposed in the above-mentioned embodiments, the implementation method of the above-mentioned charging seat abnormal alarm method is also applicable to the charging seat abnormal alarm device proposed in the embodiment of the present disclosure, and will not be described in detail in the following embodiments.

[0211] Figure 5 FIG. 1 is a schematic diagram of a charging stand abnormality alarm device according to an embodiment of the present disclosure. Figure 5 As shown, the charging stand abnormality alarm device 500 includes a first acquisition module 51, a filtering module 52, a second acquisition module 53 and an alarm module 54, wherein:

[0212] A first acquisition module 51 is used to acquire a first candidate abnormal vehicle set with charging abnormalities in the initial vehicle set;

[0213] A filtering module 52 is used to filter the first candidate abnormal vehicle set for abnormal charging piles to obtain a filtered second candidate abnormal vehicle set;

[0214] A second acquisition module 53 is used to acquire the fitting parameters of each second candidate abnormal vehicle in the second candidate abnormal vehicle set;

[0215] The alarm module 54 is used for, in response to identifying a target abnormal charging seat vehicle with an abnormal charging seat in the second candidate abnormal vehicle set according to the fitting parameters, issuing an abnormal alarm for the target abnormal charging seat vehicle.

[0216] In the disclosed embodiment, the first acquisition module 51 is also used to: obtain a first target analysis data group set of the initial vehicle set, and obtain a set of fragment temperature rise rates in each first target analysis data group; and obtain a first candidate abnormal vehicle set with charging abnormalities from the initial vehicle set based on the set of fragment temperature rise rates of each first target analysis data group.

[0217] In the disclosed embodiment, the first acquisition module 51 is further used to: acquire a first charging data set of each initial vehicle, and integrate each first charging data set to obtain a total second charging data set; acquire a first grouping item set of the two charging data sets, and group the second charging data set according to the first grouping item set to obtain a third charging data set combination under each first grouping item; obtain a first candidate analysis data set set of the initial vehicle set according to the third charging data set combination under each first grouping item; acquire a first fragment number of each first candidate analysis data set, and acquire a first target analysis data set set from the first candidate analysis data set set according to the first fragment number.

[0218] In the disclosed embodiment, the first acquisition module 51 is further used to: acquire the fourth charging data set under each first grouping item from each third charging data set combination, wherein, for any first grouping item, the fourth charging data set under the first grouping item is any data set in the third charging data set combination under the first grouping item; acquire the first candidate analysis data group obtained by combining the fourth charging data sets under each first grouping item to obtain a first candidate analysis data group set.

[0219] In the disclosed embodiment, the first acquisition module 51 is further used to: for any first candidate analysis data group, acquire a set of first data fragments in the first candidate analysis data group, and obtain a first fragment number based on the first data fragment set, wherein, for any first data fragment, each charging data in the first data fragment is generated based on the same charging behavior; in response to the first fragment number being greater than or equal to a preset first fragment number threshold, determine that the first candidate analysis data group is a first target analysis data group; and obtain a first target analysis data group set of the initial vehicle set based on each first target analysis data group.

[0220] In the disclosed embodiment, the first acquisition module 51 is further used to: acquire a set of second data segments in each first target analysis data group; and for any first target analysis data group, acquire the segment temperature rise rate of each second data segment in the set of second data segments of the first target analysis data group to obtain a set of segment temperature rise rates of the first target analysis data group.

[0221] In the disclosed embodiment, the first acquisition module 51 is also used to: for any first target analysis data group, obtain the first quarter quartile value and the second quarter quartile value of the first target analysis data group to obtain the interquartile range parameter of the first target analysis data group; obtain the outlier temperature rise rate reference value according to the second quarter quartile value and the interquartile range parameter; obtain the outlier fragment temperature rise rate set in each fragment temperature rise rate set according to the outlier temperature rise rate reference value, wherein any outlier fragment temperature rise rate in the outlier fragment temperature rise rate set is less than or equal to the outlier temperature rise rate reference value; obtain the vehicle identification of each outlier fragment temperature rise rate set, and obtain the first candidate abnormal vehicle of each vehicle identification in the initial vehicle set to obtain the first candidate abnormal vehicle set.

[0222] In the disclosed embodiment, the filtering module 52 is further used to: obtain the charging pile identification of each first candidate abnormal vehicle in the first candidate abnormal vehicle set to obtain the identification number of each charging pile identification; obtain a third candidate abnormal vehicle set with abnormal charging piles in the first candidate abnormal vehicle set according to the identification number of each charging pile identification; filter the first candidate abnormal vehicle set for abnormal charging piles according to the third candidate abnormal vehicle set, and obtain the remaining abnormal vehicle set in the first candidate abnormal vehicle set except the third candidate abnormal vehicle set as the second candidate abnormal vehicle set.

[0223] In the disclosed embodiment, the filtering module 52 is also used to: for any charging pile identifier, in response to the number of charging pile identifiers being greater than or equal to a preset identifier number threshold, determine the vehicle corresponding to the charging pile identifier in the first candidate abnormal vehicle set as a third candidate abnormal vehicle with an abnormal charging pile.

[0224] In the disclosed embodiment, the second acquisition module 53 is further used to: obtain a second target analysis data group set of the second candidate abnormal vehicle set; obtain the fitting slope and fitting degree of each second target analysis data group to obtain the fitting parameters of each second candidate abnormal vehicle.

[0225] In the disclosed embodiment, the second acquisition module 53 is also used to: acquire the fifth charging data set of each second candidate abnormal vehicle; acquire the second grouping item set of the fifth charging data set, and group each fifth charging data set according to the second grouping item set to obtain a sixth charging data set combination of each fifth charging data set under each second grouping item; acquire a second candidate analysis data group set of each second candidate abnormal vehicle according to the sixth charging data set combination under each second grouping item; acquire a third data segment set of each second candidate analysis data group, and acquire the second target analysis data group set from the second candidate analysis data group set according to the third data segment set.

[0226] In the disclosed embodiment, the second acquisition module 53 is further used to: acquire a fourth data segment set that does not belong to a preset list of abnormal charging piles from the third data segment set of the second candidate analysis data group; obtain a third candidate analysis data group set based on the fourth data segment set in each second candidate analysis data group; for any third candidate analysis data group, in response to the number of second segments in the fourth data segment set in the third candidate analysis data group being greater than or equal to a preset second segment number threshold, determine that the third candidate analysis data group is the second target analysis data group to obtain a second target analysis data group set.

[0227] In the disclosed embodiment, the alarm module 54 is also used to: obtain the fitting slope and fitting degree in the fitting parameters of each second target analysis data group; for any second target analysis data group, in response to the fitting slope of the second target analysis data group being greater than or equal to a preset fitting slope threshold, and the fitting degree of the second target analysis data group being greater than or equal to a preset fitting degree threshold, determine that the second target analysis data group is a target charging seat abnormal data group with an abnormal charging seat; obtain the corresponding vehicle of the target charging seat abnormal data group in the second candidate abnormal vehicle set as the target charging seat abnormal vehicle with an abnormal charging seat, and issue an abnormal alarm for the target charging seat abnormal vehicle.

[0228] The charging seat abnormality alarm device proposed in the present disclosure obtains a first candidate abnormal vehicle set with charging abnormalities in an initial vehicle set, filters the first candidate abnormal vehicle set for abnormal charging piles, and obtains a second candidate abnormal vehicle set. Further, the fitting parameters of each second candidate abnormal vehicle are obtained, and the target charging seat abnormal vehicle is identified in the second candidate abnormal vehicle set based on the fitting parameters. When the target charging seat abnormal vehicle is identified, an abnormal alarm of the target charging seat abnormal vehicle is issued. In the present disclosure, the target charging seat abnormal vehicle is identified from the second candidate abnormal vehicle set after the abnormal charging piles are filtered, without relying on staff for confirmation, thereby reducing the degree of manual dependence, labor costs and operating costs of abnormal vehicle alarms. The target charging seat abnormal vehicle is determined by fitting parameters. Compared with the related art of issuing an abnormal alarm after the temperature abnormality of the charging equipment through temperature monitoring, the timeliness and accuracy of the vehicle charging abnormality alarm are improved, thereby improving the safety of vehicle driving and optimizing the user's driving experience.

[0229] In order to achieve the above-mentioned embodiments, the present disclosure also provides a vehicle, wherein the vehicle is used to implement the charging seat abnormality alarm method proposed in the above-mentioned embodiments.

[0230] To achieve the above embodiments, the present disclosure also provides an electronic device, a computer-readable storage medium, and a computer program product.

[0231] Figure 6is a block diagram of an electronic device 600 according to an embodiment of the present disclosure, as shown in Figure 6 As shown, the electronic device 600 includes a memory 601, a processor 602, and a computer program stored in the memory 601 and executable on the processor 602. When the processor 602 executes the program instructions, the abnormal alarm method for the charging stand provided in the above embodiment is implemented.

[0232] The charging seat abnormality alarm method proposed in the present disclosure obtains a first candidate abnormal vehicle set with charging abnormalities in an initial vehicle set, filters the first candidate abnormal vehicle set for abnormal charging piles to obtain a second candidate abnormal vehicle set, and further obtains fitting parameters of each second candidate abnormal vehicle, and identifies whether there is a target charging seat abnormal vehicle in the second candidate abnormal vehicle set based on the fitting parameters, and when the target charging seat abnormal vehicle is identified, an abnormal alarm for the target charging seat abnormal vehicle is issued. In the present disclosure, the target charging seat abnormal vehicle is identified from the second candidate abnormal vehicle set after filtering the abnormal charging piles, without relying on staff for confirmation, thereby reducing the degree of manual dependence, labor costs and operating costs of abnormal vehicle alarms, and determining the target charging seat abnormal vehicle through fitting parameters. Compared with the related art of issuing an abnormal alarm after a temperature abnormality occurs in the charging equipment through temperature monitoring, the timeliness and accuracy of the vehicle charging abnormality alarm are improved, thereby improving the safety of vehicle driving and optimizing the user's driving experience.

[0233] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0234] The program code for implementing the method itself can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0235] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0236] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0237] The systems and techniques described herein can be implemented in a computing system that includes a backend component (e.g., as a data server), or a computing system that includes a middleware component (e.g., an application server), or a computing system that includes a frontend component (e.g., a user computer with a graphical user interface or a grid browser through which a user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by digital data communication of any form or medium (e.g., a communication grid). Examples of communication grids include: a local area network (LAN), a wide area network (WAN), the Internet, and a blockchain grid.

[0238] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication grid. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server for a distributed system, or a server combined with a blockchain.

[0239] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0240] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present disclosure, "plurality" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0241] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present disclosure includes additional implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present disclosure belong.

[0242] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or otherwise processing in a suitable manner if necessary, and then stored in a computer memory.

[0243] It should be understood that the various parts of the present disclosure can be implemented in hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0244] A person skilled in the art may understand that all or part of the steps in the above-mentioned embodiment method may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0245] In addition, each functional unit in each embodiment of the present disclosure may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0246] The storage medium mentioned above may be a read-only memory, a disk or an optical disk, etc. Although the embodiments of the present disclosure have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations of the present disclosure. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present disclosure.

[0247] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0248] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A charging stand abnormal alarm method, characterized in that: The method comprises: Obtain a first candidate abnormal vehicle set with charging abnormality in the initial vehicle set; Filtering the first candidate abnormal vehicle set for abnormal charging piles to obtain a filtered second candidate abnormal vehicle set; Obtaining fitting parameters of each second candidate abnormal vehicle in the second candidate abnormal vehicle set; In response to identifying a target abnormal charging seat vehicle with an abnormal charging seat in the second candidate abnormal vehicle set according to the fitting parameters, issuing an abnormality alarm for the target abnormal charging seat vehicle; The step of obtaining a first candidate abnormal vehicle set of charging abnormalities in the initial vehicle set includes: Acquire a set of first target analysis data groups of the initial vehicle set, and acquire a set of segment temperature rise rates in each first target analysis data group; For any first target analysis data group, obtaining a first quarter quartile value and a second quarter quartile value of the first target analysis data group to obtain an interquartile range parameter of the first target analysis data group; Obtaining an outlier temperature rise rate reference value according to the second quarter quantile value and the interquartile range parameter; According to the outlier temperature rise rate reference value, an outlier segment temperature rise rate set in each segment temperature rise rate set is acquired, wherein any outlier segment temperature rise rate in the outlier segment temperature rise rate set is less than or equal to the outlier temperature rise rate reference value; The vehicle identification of each of the outlier segment temperature rise rate sets is obtained, and the first candidate abnormal vehicle of each vehicle identification in the initial vehicle set is obtained to obtain the first candidate abnormal vehicle set.

2. The method according to claim 1, characterized in that The step of obtaining a first target analysis data set of the initial vehicle set includes: Acquire a first charging data set of each initial vehicle, and integrate each first charging data set to obtain a total second charging data set; Acquire a first grouping item set of the two charging data sets, and group the second charging data sets according to the first grouping item set to obtain a third charging data set combination under each first grouping item; According to the combination of the third charging data sets under each first grouping item, a first candidate analysis data set set of the initial vehicle set is obtained; The first segment quantity of each first candidate analysis data group is obtained, and the first target analysis data group set is obtained from the first candidate analysis data group set according to the first segment quantity.

3. The method according to claim 2, characterized in that The step of obtaining a first candidate analysis data group set of the initial vehicle set according to the combination of the third charging data sets under each first grouping item includes: Acquire a fourth charging data set under each first grouping item from each third charging data set combination, wherein, for any first grouping item, the fourth charging data set under the first grouping item is any data set in the third charging data set combination under the first grouping item; A first candidate analysis data group is obtained by combining the fourth charging data sets under each first grouping item to obtain the first candidate analysis data group set.

4. The method according to claim 2, characterized in that: The obtaining of the first segment number of each first candidate analysis data group, and obtaining the first target analysis data group set from the first candidate analysis data group set according to the first segment number, includes: For any first candidate analysis data group, obtaining a first data segment set in the first candidate analysis data group, and obtaining the first segment quantity according to the first data segment set, wherein for any first data segment, each charging data in the first data segment is generated based on the same charging behavior; In response to the first segment number being greater than or equal to a preset first segment number threshold, determining the first candidate analysis data group as a first target analysis data group; According to each first target analysis data set, the first target analysis data set of the initial vehicle set is obtained.

5. The method according to claim 1, characterized in that The step of obtaining a set of segment temperature rise rates in each first target analysis data group includes: Acquire a set of second data segments in each first target analysis data group; For any first target analysis data group, the segment temperature rise rate of each second data segment in the second data segment set of the first target analysis data group is obtained to obtain the segment temperature rise rate set of the first target analysis data group.

6. The method according to claim 1, characterized in that The filtering of the first candidate abnormal vehicle set by abnormal charging piles to obtain a filtered second candidate abnormal vehicle set includes: Obtaining the charging pile identification of each first candidate abnormal vehicle in the first candidate abnormal vehicle set to obtain the identification quantity of each charging pile identification; According to the number of identifications of each charging pile identification, a third candidate abnormal vehicle set with abnormal charging piles in the first candidate abnormal vehicle set is obtained; The first candidate abnormal vehicle set is filtered for abnormal charging piles according to the third candidate abnormal vehicle set, and the remaining abnormal vehicle set in the first candidate abnormal vehicle set except the third candidate abnormal vehicle set is obtained as the second candidate abnormal vehicle set.

7. The method according to claim 6, characterized in that The step of obtaining, according to the number of identifications of each charging pile identification, a third candidate abnormal vehicle set having abnormal charging piles in the first candidate abnormal vehicle set includes: For any charging pile identifier, in response to the identifier quantity of the charging pile identifier being greater than or equal to a preset identifier quantity threshold, a vehicle corresponding to the charging pile identifier in the first candidate abnormal vehicle set is determined as a third candidate abnormal vehicle for the charging pile abnormality.

8. The method according to claim 1, characterized in that The obtaining of the fitting parameters of each second candidate abnormal vehicle in the second candidate abnormal vehicle set includes: Acquire a second target analysis data group set of the second candidate abnormal vehicle set; The fitting slope and fitting degree of each second target analysis data group are obtained to obtain the fitting parameters of each second candidate abnormal vehicle.

9. The method according to claim 8, characterized in that The step of obtaining a second target analysis data set of the second candidate abnormal vehicle set includes: Acquire a fifth charging data set of each second candidate abnormal vehicle; Acquire a second grouping item set of the fifth charging data set, and group each of the fifth charging data sets into items according to the second grouping item set to obtain a sixth charging data set combination of each of the fifth charging data sets under each of the second grouping items; According to the combination of the sixth charging data sets under each second grouping item, a second candidate analysis data group set of each second candidate abnormal vehicle is obtained; A third data segment set of each second candidate analysis data group is obtained, and the second target analysis data group set is obtained from the second candidate analysis data group set according to the third data segment set.

10. The method according to claim 9, characterized in that The step of acquiring the third data segment set of each second candidate analysis data group, and acquiring the second target analysis data group set from the second candidate analysis data group set according to the third data segment set, comprises: For any second candidate analysis data group, obtaining a fourth data segment set that does not belong to a preset abnormal charging pile list from the third data segment set of the second candidate analysis data group; Obtaining a third candidate analysis data group set according to the fourth data segment set in each second candidate analysis data group; For any third candidate analysis data group, in response to the number of second fragments in the fourth data fragment set in the third candidate analysis data group being greater than or equal to a preset second fragment number threshold, the third candidate analysis data group is determined to be the second target analysis data group to obtain the second target analysis data group set.

11. The method according to claim 1, characterized in that: In response to identifying, according to the fitting parameters, that there is a target abnormal charging seat vehicle with an abnormal charging seat in the second candidate abnormal vehicle set, issuing an abnormality alarm for the target abnormal charging seat vehicle, comprising: Obtaining the fitting slope and fitting degree in the fitting parameters of each second target analysis data group; For any second target analysis data group, in response to the fitting slope of the second target analysis data group being greater than or equal to a preset fitting slope threshold, and the fitting degree of the second target analysis data group being greater than or equal to a preset fitting degree threshold, determining that the second target analysis data group is a target charging seat abnormal data group with an abnormal charging seat; The corresponding vehicle of the target charging seat abnormal data group in the second candidate abnormal vehicle set is obtained as the target charging seat abnormal vehicle with charging seat abnormality, and an abnormal alarm is issued for the target charging seat abnormal vehicle.

12. A charging stand abnormality alarm device, characterized in that: The device comprises: A first acquisition module is used to acquire a first candidate abnormal vehicle set with charging abnormalities in the initial vehicle set; A filtering module, configured to filter the first candidate abnormal vehicle set for abnormal charging piles to obtain a filtered second candidate abnormal vehicle set; A second acquisition module, used for acquiring fitting parameters of each second candidate abnormal vehicle in the second candidate abnormal vehicle set; An alarm module is configured to, in response to identifying a target abnormal charging seat vehicle with an abnormal charging seat in the second candidate abnormal vehicle set according to the fitting parameters, issue an abnormal alarm for the target abnormal charging seat vehicle; The step of obtaining a first candidate abnormal vehicle set of charging abnormalities in the initial vehicle set includes: Acquire a set of first target analysis data groups of the initial vehicle set, and acquire a set of segment temperature rise rates in each first target analysis data group; According to the segment temperature rise rate sets of each first target analysis data group, the first candidate abnormal vehicle set with charging abnormality is obtained from the initial vehicle set, including: For any first target analysis data group, obtaining a first quarter quartile value and a second quarter quartile value of the first target analysis data group to obtain an interquartile range parameter of the first target analysis data group; Obtaining an outlier temperature rise rate reference value according to the second quarter quantile value and the interquartile range parameter; According to the outlier temperature rise rate reference value, an outlier segment temperature rise rate set in each segment temperature rise rate set is acquired, wherein any outlier segment temperature rise rate in the outlier segment temperature rise rate set is less than or equal to the outlier temperature rise rate reference value; The vehicle identification of each of the outlier segment temperature rise rate sets is obtained, and the first candidate abnormal vehicle of each vehicle identification in the initial vehicle set is obtained to obtain the first candidate abnormal vehicle set.

13. A vehicle, characterized in that: The vehicle is used to implement the method according to any one of claims 1 to 11.

14. An electronic device, characterized in that: include: processor; a memory for storing executable instructions for the processor; The processor is configured to execute instructions to implement the method as claimed in any one of claims 1 to 11.

15. A computer-readable storage medium, when instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the method according to any one of claims 1 to 11.

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