Working state early warning method and device of battery replacing trolley and electronic equipment

Abstract

The invention relates to a working state early warning method and device of a battery replacing trolley, a storage medium and electronic equipment. The working state early warning method of the battery replacing trolley comprises the steps of performing data processing on action associated data generated by the battery replacing trolley at each action point through a data processing model, identifying a stable data segment and a fluctuation data segment in the action associated data, and performing early warning on the working state of the battery replacing trolley. Performing fault identification on action associated data corresponding to the over-threshold action points in the stable data segment, and determining non-fault action points in the over-threshold action points; and at least adjusting the early warning threshold value corresponding to one target non-fault action point based on the action associated data corresponding to the non-fault action points in the over-threshold action points, so that the non-fault action points needing early warning threshold value adjustment can be judged, the accuracy of fault early warning through the early warning threshold values is improved, and the fault early warning efficiency is improved. And abnormal conditions can be handled in time, and the stable operation quality of the equipment is improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of new energy vehicles, and in particular to a working status warning method for a battery-swap vehicle, a working status warning device for a battery-swap vehicle, a storage medium and an electronic device. Background Art

[0002] With the rapid development of new energy vehicles, there are more and more electric vehicle battery swap stations. Battery swap stations are energy stations that provide rapid replacement of power batteries for electric vehicles. However, the current warning threshold setting of battery swap equipment in battery swap stations is not accurate, which makes it difficult to predict equipment failures and unable to handle abnormal situations in a timely manner, seriously affecting the stable operation of the equipment and the service quality of the entire new energy vehicle charging network. Summary of the invention

[0003] In view of this, the embodiments of the present disclosure hope to provide a working status warning method for a battery-swap vehicle, a working status warning device for a battery-swap vehicle, a storage medium and an electronic device.

[0004] The technical solution of the present disclosure is achieved as follows:

[0005] In a first aspect, the present disclosure provides a working status warning method for a battery-swap vehicle.

[0006] The working state warning method of the battery-swapping vehicle provided by the embodiment of the present disclosure includes:

[0007] Obtain action-related data generated at each action point during the battery-swapping vehicle's battery-swapping action;

[0008] Based on the data processing model, the action-related data generated by the battery-swapping vehicle at each action point is processed to identify the stable data segments and the fluctuating data segments in the action-related data; wherein the data processing model is obtained by training a machine learning model based on sample data with data feature markers, and the data feature markers are used to distinguish the stable data segments and the fluctuating data segments in the sample data by data features;

[0009] Performing fault identification on the over-threshold action points in the stable data segment, and determining non-fault action points among the over-threshold action points;

[0010] Based on the action association data corresponding to the non-fault action points among the super-threshold action points, adjusting at least one warning threshold corresponding to a target non-fault action point;

[0011] Based on the warning threshold corresponding to each action point after the threshold adjustment, a fault warning is given to the working status of the battery swapping vehicle during the battery swapping process.

[0012] In some embodiments, adjusting at least one warning threshold corresponding to a target non-fault action point based on the action association data corresponding to the non-fault action point in the super-threshold action point includes:

[0013] Acquire action association data corresponding to n non-fault action points adjacent to the target non-fault action point among the super-threshold action points;

[0014] Based on the action association data corresponding to n non-fault action points adjacent to the target non-fault action point among the super-threshold action points, the warning threshold corresponding to the target non-fault action point is adjusted; wherein n≥2.

[0015] In some embodiments, adjusting the warning threshold corresponding to the target non-fault action point based on the action association data corresponding to the n non-fault action points adjacent to the target non-fault action point among the super-threshold action points includes:

[0016] Based on the action association data corresponding to n non-fault action points adjacent to the target non-fault action point among the super-threshold action points, obtaining percentages of the action association data corresponding to the n non-fault action points deviating from their respective corresponding current warning thresholds;

[0017] Based on the percentages by which the action-related data corresponding to the n non-fault action points deviate from their respective corresponding current warning thresholds, and the percentages by which the action-related data corresponding to the target non-fault action point deviate from the current warning threshold corresponding to the target non-fault action point, data averaging or data fitting is performed to obtain the target warning threshold to which the target non-fault action point needs to be adjusted.

[0018] In some embodiments, before obtaining the action-related data generated at each action point during the battery-swapping vehicle performing the battery-swapping action, the method includes:

[0019] According to the physical action mechanism of the battery-swap vehicle, the collected sample data are analyzed in segments, and the stable data segments corresponding to the stable operation time periods of the battery-swap vehicle and the fluctuating data segments corresponding to the fluctuating operation time periods of the battery-swap vehicle in the sample data are calibrated.

[0020] In a second aspect, the present disclosure provides a working status warning device for a battery-swapping vehicle, comprising:

[0021] A data acquisition module is used to acquire action-related data generated at each action point during the battery-swapping vehicle's battery-swapping action;

[0022] A data segment identification module is used to process the action-related data generated by the battery-swapping vehicle at each action point based on a data processing model, and identify stable data segments and fluctuating data segments in the action-related data; wherein the data processing model is obtained by training a machine learning model based on sample data with data feature markers, and the data feature markers are used to distinguish the stable data segments and fluctuating data segments in the sample data by data features;

[0023] A fault identification module, used for performing fault identification on the over-threshold action points in the stable data segment, and determining non-fault action points among the over-threshold action points;

[0024] A threshold adjustment module, configured to adjust a warning threshold corresponding to at least one target non-fault action point based on the action association data corresponding to the non-fault action point among the super-threshold action points;

[0025] The fault warning module is used to provide fault warning for the working status of the battery swapping vehicle during the battery swapping process based on the warning threshold corresponding to each action point after the threshold adjustment.

[0026] In some embodiments, the threshold adjustment module is used to

[0027] Acquire action association data corresponding to n non-fault action points adjacent to the target non-fault action point among the super-threshold action points;

[0028] Based on the action association data corresponding to n non-fault action points adjacent to the target non-fault action point among the super-threshold action points, the warning threshold corresponding to the target non-fault action point is adjusted; wherein n≥2.

[0029] In some embodiments, the threshold adjustment module is used to

[0030] Based on the action association data corresponding to n non-fault action points adjacent to the target non-fault action point among the super-threshold action points, obtaining percentages of the action association data corresponding to the n non-fault action points deviating from their respective corresponding current warning thresholds;

[0031] Based on the percentages by which the action-related data corresponding to the n non-fault action points deviate from their respective corresponding current warning thresholds, and the percentages by which the action-related data corresponding to the target non-fault action point deviate from the current warning threshold corresponding to the target non-fault action point, data averaging or data fitting is performed to obtain the target warning threshold to which the target non-fault action point needs to be adjusted.

[0032] In some embodiments, the device includes a data marking module; before obtaining the action-related data generated at each action point during the battery-swapping vehicle performs the battery-swapping action, the data marking module is used to

[0033] According to the physical action mechanism of the battery-swap vehicle, the collected sample data are analyzed in segments, and the stable data segments corresponding to the stable operation time periods of the battery-swap vehicle and the fluctuating data segments corresponding to the fluctuating operation time periods of the battery-swap vehicle in the sample data are calibrated.

[0034] In a third aspect, the present disclosure provides a computer-readable storage medium on which a working status warning program for a battery swapping vehicle is stored. When the working status warning program for the battery swapping vehicle is executed by a processor, the working status warning method for the battery swapping vehicle described in the first aspect above is implemented.

[0035] In a fourth aspect, the present disclosure provides an electronic device comprising a memory, a processor, and a working status warning program for a battery swap vehicle stored in the memory and executable on the processor. When the processor executes the working status warning program for the battery swap vehicle, the working status warning method for the battery swap vehicle described in the first aspect above is implemented.

[0036] According to the working status warning method of the battery-swapping vehicle in the embodiment of the present disclosure, it includes obtaining action-related data generated at each action point when the battery-swapping vehicle performs a battery-swapping action; based on the data processing model, the action-related data generated at each action point by the battery-swapping vehicle is processed, and stable data segments and fluctuating data segments in the action-related data are identified; wherein the data processing model is obtained by training a machine learning model based on sample data with data feature markers, and the data feature markers are used to distinguish the stable data segments and fluctuating data segments in the sample data by data features; fault identification is performed on the action-related data corresponding to the super-threshold action points in the stable data segments, and non-fault action points among the super-threshold action points are determined; based on the action-related data corresponding to the non-fault action points among the super-threshold action points, at least one warning threshold value corresponding to a target non-fault action point is adjusted; based on the warning threshold value corresponding to each action point after the threshold adjustment, a fault warning is performed on the working status of the battery-swapping vehicle during the battery-swapping action. In the present application, a data processing model is used to process the action-related data generated by the battery-swap vehicle at each action point, identify the stable data segments and the fluctuating data segments in the action-related data, perform fault identification on the action-related data corresponding to the super-threshold action points in the stable data segments, and determine the non-fault action points in the super-threshold action points; based on the action-related data corresponding to the non-fault action points in the super-threshold action points, at least one warning threshold value corresponding to a target non-fault action point is adjusted, which is helpful to determine the non-fault action points that need to adjust the warning threshold value, thereby improving the accuracy of fault warning through the warning threshold value, and further facilitating the timely handling of abnormal situations, improving the stable operation of the equipment and the service quality of the entire new energy vehicle charging network.

[0037] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will be obvious from the following description or learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a flow chart of a working status warning method of a battery-swapping vehicle according to an exemplary embodiment;

[0039] Figure 2 is a schematic diagram of action-related data of a battery-swapping vehicle according to an exemplary embodiment;

[0040] Figure 3 It is a schematic diagram of the structure of a working status warning device for a battery-swapping vehicle according to an exemplary embodiment. DETAILED DESCRIPTION

[0041] Embodiments of the present disclosure are described in detail below, examples of which 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.

[0042] With the rapid development of new energy vehicles, there are more and more electric vehicle battery swap stations. Battery swap stations are energy stations that provide rapid replacement of power batteries for electric vehicles. However, the current warning threshold setting of battery swap equipment in battery swap stations is not accurate, which makes it difficult to predict equipment failures and unable to handle abnormal situations in a timely manner, seriously affecting the stable operation of the equipment and the service quality of the entire new energy vehicle charging network.

[0043] In view of the above situation, the present invention provides a working status warning method for a battery-swap vehicle. Figure 1 FIG. 1 is a flow chart of a working status warning method of a battery-swapping vehicle according to an exemplary embodiment. Figure 1 As shown, the working status early warning method of the battery-swapping vehicle includes:

[0044] Step 10: Obtain action-related data generated at each action point during the battery-swapping vehicle's battery-swapping action;

[0045] Step 11: Based on the data processing model, the action-related data generated by the battery-swapping vehicle at each action point is processed to identify the stable data segments and the fluctuating data segments in the action-related data; wherein the data processing model is obtained by training a machine learning model based on sample data with data feature markers, and the data feature markers are used to distinguish the stable data segments and the fluctuating data segments in the sample data by data features;

[0046] Step 12, performing fault identification on the over-threshold action points in the stable data segment, and determining non-fault action points among the over-threshold action points;

[0047] Step 13: based on the action association data corresponding to the non-fault action points among the super-threshold action points, adjusting at least one warning threshold corresponding to the target non-fault action point;

[0048] Step 14: Based on the warning threshold corresponding to each action point after the threshold adjustment, a fault warning is issued for the working status of the battery swapping vehicle during the battery swapping operation.

[0049] In an exemplary embodiment, the action-related data generated at each action point may include motor torque data, current data, voltage data, etc. generated at each action point. When the battery swapping vehicle performs battery swapping, multiple execution actions may be performed, and one execution action may be composed of multiple action points, and one action point corresponds to one data collection. Figure 2 FIG. 1 is a schematic diagram of action-related data of a battery-swapping vehicle according to an exemplary embodiment. Figure 2 As shown, the horizontal axis is the sampling time, and the vertical axis is the motor torque data. Sampling is performed once every 20ms to obtain the action-related data corresponding to an action point. Among them, the stable data segment contains a large amount of action-related data corresponding to the action point. Among them, the warning threshold is not set in the fluctuating data segment.

[0050] In an exemplary embodiment, based on a data processing model, data processing is performed on the action-related data generated by the battery-swapping vehicle at each action point. After the stable data segment and the fluctuating data segment in the action-related data are identified, fault identification can be performed on the super-threshold action point in the stable data segment to determine the non-fault action point in the super-threshold action point. Among them, by determining the working state of the battery-swapping vehicle when the super-threshold action point is executed, it is determined whether the super-threshold action point is a non-fault action point or a fault action point. For example, if a fault occurs when the battery-swapping vehicle performs an action at the super-threshold action point, the super-threshold action point is a fault action point. If no fault occurs when the battery-swapping vehicle performs an action at the super-threshold action point, the super-threshold action point is a non-fault action point.

[0051] In an exemplary embodiment, the data processing model can be obtained by training a machine learning model with sample data marked with data features. The data feature marking can distinguish the stable data segments and the fluctuating data segments in the sample data by data features. For example, the stable data segments in the sample data can be marked with a first data feature, and the fluctuating data segments in the sample data can be marked with a second data feature; wherein the first data feature marker is different from the second data feature marker. In this way, the data processing model can be used to process the action-related data generated by the collected battery-swapping vehicle at each action point, and identify the stable data segments and fluctuating data segments in the action-related data.

[0052] In an exemplary embodiment, after identifying the stable data segment and the fluctuating data segment in the action-associated data, fault identification is performed on the action-associated data corresponding to the super-threshold action point in the stable data segment, and the non-fault action point in the super-threshold action point is determined; based on the action-associated data corresponding to the non-fault action point in the super-threshold action point, at least one warning threshold corresponding to the target non-fault action point is adjusted, which is conducive to determining the non-fault action point that needs to adjust the warning threshold, thereby improving the accuracy of fault warning through the warning threshold, and further facilitating timely handling of abnormal situations, improving the stable operation of the equipment and the service quality of the entire new energy vehicle charging network.

[0053] In some embodiments, adjusting at least one warning threshold corresponding to a target non-fault action point based on the action association data corresponding to the non-fault action point in the super-threshold action point includes:

[0054] Acquire action association data corresponding to n non-fault action points adjacent to the target non-fault action point among the super-threshold action points;

[0055] Based on the action association data corresponding to n non-fault action points adjacent to the target non-fault action point among the super-threshold action points, the warning threshold corresponding to the target non-fault action point is adjusted; wherein n≥2.

[0056] In an exemplary embodiment, there are non-fault action points and fault action points among the super-threshold action points, and there are situations where the non-fault action points are adjacent to each other. When adjusting the warning threshold corresponding to one of the target non-fault action points, the adjustment can be made in combination with the action association data corresponding to the n non-fault action points adjacent to the target non-fault action point.

[0057] In an exemplary embodiment, adjusting the warning threshold corresponding to the target non-fault action point based on the action association data corresponding to n non-fault action points adjacent to the target non-fault action point among the super-threshold action points includes:

[0058] Based on the action association data corresponding to n non-fault action points adjacent to the target non-fault action point among the super-threshold action points, obtaining percentages of the action association data corresponding to the n non-fault action points deviating from their respective corresponding current warning thresholds;

[0059] Based on the percentages by which the action-related data corresponding to the n non-fault action points deviate from their respective corresponding current warning thresholds, and the percentages by which the action-related data corresponding to the target non-fault action point deviate from the current warning threshold corresponding to the target non-fault action point, data averaging or data fitting is performed to obtain the target warning threshold to which the target non-fault action point needs to be adjusted.

[0060] In an exemplary embodiment, for example, the percentages of the action-related data corresponding to the three non-fault action points adjacent to the target non-fault action point deviating from their respective corresponding current warning thresholds are +10%, +20%, and +30%, respectively, and the percentage of the action-related data corresponding to the target non-fault action point deviating from the current warning threshold corresponding to the target non-fault action point is 30%, then the data is averaged, and +22.5% is obtained by adding +10%, +20%, +30%, and +30% and dividing by 4. In this way, the target warning threshold to which the target non-fault action point needs to be adjusted is 22.5% higher than the original warning threshold, that is, 122.5% of the original warning threshold.

[0061] In an exemplary embodiment, the step of adjusting at least one warning threshold value corresponding to a target non-fault action point based on the action association data corresponding to the non-fault action point in the super-threshold action point includes:

[0062] Based on multiple action association data corresponding to a target non-fault action point among the super-threshold action points, the warning threshold corresponding to the target non-fault action point is adjusted. For example, the percentages of multiple action association data corresponding to a target non-fault action point deviating from the current warning threshold corresponding to the target non-fault action point can be averaged to obtain the target warning threshold to which the target non-fault action point needs to be adjusted.

[0063] In some embodiments, before obtaining the action-related data generated at each action point during the battery-swapping vehicle performing the battery-swapping action, the method includes:

[0064] According to the physical action mechanism of the battery-swap vehicle, the collected sample data are analyzed in segments, and the stable data segments corresponding to the stable operation time periods of the battery-swap vehicle and the fluctuating data segments corresponding to the fluctuating operation time periods of the battery-swap vehicle in the sample data are calibrated.

[0065] In an exemplary embodiment, the battery swap vehicle operation phase includes a startup phase (generating Figure 2 The fluctuation data segment shown in the figure) and the stable working stage. Among them, the action-related data in the startup stage has large fluctuations, but in most cases it does not belong to a fault, so this part of the fluctuation data segment can be not used for fault warning analysis. Therefore, according to the physical action mechanism of the battery-swapping vehicle, the collected sample data can be segmented and analyzed, and the stable data segment corresponding to the stable operation period of the battery-swapping vehicle and the fluctuation data segment corresponding to the fluctuation operation period of the battery-swapping vehicle in the sample data can be calibrated. Then, the machine learning model is trained based on the sample data with data feature markers to obtain a data processing model. The data processing model is then used to process the action-related data generated by the currently collected battery-swapping vehicle at each action point, and the stable data segment and the fluctuation data segment in the action-related data are identified. The fluctuation data segment is discarded, and the action-related data corresponding to the super-threshold action point in the stable data segment is fault identified to determine the non-fault action point in the super-threshold action point.

[0066] Figure 3 FIG. 1 is a schematic diagram of a working status warning device for a battery-swapping vehicle according to an exemplary embodiment. Figure 3 As shown, the working status warning device of the battery-swapping vehicle includes:

[0067] The data acquisition module 30 is used to acquire the action-related data generated at each action point during the battery-swapping vehicle performs the battery-swapping action;

[0068] The data segment identification module 31 is used to process the action-related data generated by the battery-swapping vehicle at each action point based on a data processing model, and identify the stable data segments and fluctuating data segments in the action-related data; wherein the data processing model is obtained by training a machine learning model based on sample data with data feature markers, and the data feature markers are used to distinguish the stable data segments and fluctuating data segments in the sample data by data features;

[0069] A fault identification module 32 is used to identify faults of the over-threshold action points in the stable data segment and determine non-fault action points among the over-threshold action points;

[0070] A threshold adjustment module 33, configured to adjust at least one warning threshold corresponding to a target non-fault action point based on the action association data corresponding to the non-fault action point in the super-threshold action point;

[0071] The fault warning module 34 is used to issue a fault warning for the working status of the battery swapping vehicle during the battery swapping operation based on the warning threshold corresponding to each action point after the threshold adjustment.

[0072] In an exemplary embodiment, the action-related data generated at each action point may include motor torque data, current data, voltage data, etc. generated at each action point. When the battery swapping vehicle is performing battery swapping, multiple execution actions can be performed. One execution action can be composed of multiple action points, and one action point corresponds to one data collection. As shown in the figure, sampling is performed once every 20ms to obtain action-related data corresponding to one action point. The stable data segment contains a large amount of action-related data corresponding to action points.

[0073] In an exemplary embodiment, the data processing model can be obtained by training a machine learning model with sample data marked with data features. The data feature marking can distinguish the stable data segments and the fluctuating data segments in the sample data by data features. For example, the stable data segments in the sample data can be marked with a first data feature, and the fluctuating data segments in the sample data can be marked with a second data feature; wherein the first data feature marker is different from the second data feature marker. In this way, the data processing model can be used to process the action-related data generated by the collected battery-swapping vehicle at each action point, and identify the stable data segments and fluctuating data segments in the action-related data.

[0074] In an exemplary embodiment, after identifying the stable data segment and the fluctuating data segment in the action-associated data, fault identification is performed on the action-associated data corresponding to the super-threshold action point in the stable data segment, and the non-fault action point in the super-threshold action point is determined; based on the action-associated data corresponding to the non-fault action point in the super-threshold action point, at least one warning threshold corresponding to the target non-fault action point is adjusted, which is conducive to determining the non-fault action point that needs to adjust the warning threshold, thereby improving the accuracy of fault warning through the warning threshold, and further facilitating timely handling of abnormal situations, improving the stable operation of the equipment and the service quality of the entire new energy vehicle charging network.

[0075] In some embodiments, the threshold adjustment module is used to

[0076] Acquire action association data corresponding to n non-fault action points adjacent to the target non-fault action point among the super-threshold action points;

[0077] Based on the action association data corresponding to n non-fault action points adjacent to the target non-fault action point among the super-threshold action points, the warning threshold corresponding to the target non-fault action point is adjusted; wherein n≥2.

[0078] In an exemplary embodiment, there are non-fault action points and fault action points among the super-threshold action points, and there are situations where the non-fault action points are adjacent to each other. When adjusting the warning threshold corresponding to one of the target non-fault action points, the adjustment can be made in combination with the action association data corresponding to the n non-fault action points adjacent to the target non-fault action point.

[0079] In an exemplary embodiment, the threshold adjustment module is used to

[0080] Based on the action association data corresponding to n non-fault action points adjacent to the target non-fault action point among the super-threshold action points, obtaining percentages of the action association data corresponding to the n non-fault action points deviating from their respective corresponding current warning thresholds;

[0081] Based on the percentages by which the action-related data corresponding to the n non-fault action points deviate from their respective corresponding current warning thresholds, and the percentages by which the action-related data corresponding to the target non-fault action point deviate from the current warning threshold corresponding to the target non-fault action point, data averaging or data fitting is performed to obtain the target warning threshold to which the target non-fault action point needs to be adjusted.

[0082] In an exemplary embodiment, for example, the percentages of the action-related data corresponding to the three non-fault action points adjacent to the target non-fault action point deviating from their respective corresponding current warning thresholds are +10%, +20%, and +30%, respectively, and the percentage of the action-related data corresponding to the target non-fault action point deviating from the current warning threshold corresponding to the target non-fault action point is 30%, then the data is averaged, and +22.5% is obtained by adding +10%, +20%, +30%, and +30% and dividing by 4. In this way, the target warning threshold to which the target non-fault action point needs to be adjusted is 22.5% higher than the original warning threshold, that is, 122.5% of the original warning threshold.

[0083] In some embodiments, the device includes a data marking module; before obtaining the action-related data generated at each action point during the battery-swapping vehicle performs the battery-swapping action, the data marking module is used to

[0084] According to the physical action mechanism of the battery-swap vehicle, the collected sample data are analyzed in segments, and the stable data segments corresponding to the stable operation time periods of the battery-swap vehicle and the fluctuating data segments corresponding to the fluctuating operation time periods of the battery-swap vehicle in the sample data are calibrated.

[0085] In an exemplary embodiment, the operation phase of the battery swap car includes a startup phase and a stable working phase. Among them, the action-related data in the startup phase has large fluctuations, but in most cases it does not belong to a fault, so this part of the fluctuating data segment can not be used for fault warning analysis. Therefore, according to the physical action mechanism of the battery swap car, the collected sample data can be segmented and analyzed, and the stable data segment corresponding to the stable operation period of the battery swap car and the fluctuating data segment corresponding to the fluctuating operation period of the battery swap car in the sample data can be calibrated. Then, the machine learning model is trained based on the sample data with data feature markers to obtain a data processing model. Then, the action-related data generated by the currently collected battery swap car at each action point is processed by the data processing model to identify the stable data segment and the fluctuating data segment in the action-related data. The fluctuating data segment is discarded, and the action-related data corresponding to the super-threshold action point in the stable data segment is fault identified to determine the non-fault action point in the super-threshold action point.

[0086] The present disclosure provides a computer-readable storage medium, on which a working status warning program of a battery swapping vehicle is stored. When the working status warning program of the battery swapping vehicle is executed by a processor, the working status warning method of the battery swapping vehicle described in the above embodiments is implemented.

[0087] The present disclosure provides an electronic device, including a memory, a processor, and a working status warning program for a battery swapping vehicle stored in the memory and executable on the processor. When the processor executes the working status warning program for the battery swapping vehicle, the working status warning method for the battery swapping vehicle described in the above-mentioned embodiments is implemented.

[0088] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented 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 of computer-readable media (a non-exhaustive list) include the following: an electrical connection portion with one or more wirings (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.

[0089] 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.

[0090] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" 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 representation of the above terms does 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.

[0091] In the description of the present disclosure, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present disclosure and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present disclosure.

[0092] In addition, the terms "first", "second", etc. used in the embodiments of the present disclosure are only used for descriptive purposes and should not be understood as indicating or implying relative importance, or implicitly indicating the number of technical features indicated in the embodiments. Therefore, the features defined by the terms "first", "second", etc. in the embodiments of the present disclosure may explicitly or implicitly indicate that at least one of the features is included in the embodiment. In the description of the present disclosure, the word "multiple" means at least two or two or more, such as two, three, four, etc., unless otherwise clearly and specifically defined in the embodiments.

[0093] In the present disclosure, unless otherwise clearly specified or limited in the embodiments, the terms "installed", "connected", "connected" and "fixed" etc. appearing in the embodiments should be understood in a broad sense. For example, the connection can be a fixed connection, a detachable connection, or an integrated connection. It can be understood that it can also be a mechanical connection, an electrical connection, etc.; of course, it can also be a direct connection, or an indirect connection through an intermediate medium, or it can be the internal connection of two elements, or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present disclosure can be understood according to the specific implementation situation.

[0094] In the present disclosure, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature being "above", "above" or "above" a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being "below", "below" or "below" a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0095] Although the embodiments of the present disclosure have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present disclosure. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present disclosure.

Claims

1. A working status warning method for a battery-swapping vehicle, characterized in that: include: Obtain action-related data generated at each action point during the battery-swapping vehicle's battery-swapping action; Based on the data processing model, the action-related data generated by the battery-swapping vehicle at each action point is processed to identify the stable data segments and the fluctuating data segments in the action-related data; wherein the data processing model is obtained by training a machine learning model based on sample data with data feature markers, and the data feature markers are used to distinguish the stable data segments and the fluctuating data segments in the sample data by data features; Performing fault identification on the over-threshold action points in the stable data segment, and determining non-fault action points among the over-threshold action points; Based on the action association data corresponding to the non-fault action points among the super-threshold action points, adjusting at least one warning threshold corresponding to a target non-fault action point; Based on the warning threshold corresponding to each action point after the threshold adjustment, a fault warning is given to the working status of the battery swapping vehicle during the battery swapping process.

2. The working status warning method of the battery-swapping vehicle according to claim 1 is characterized in that: The step of adjusting at least one warning threshold value corresponding to a target non-fault action point based on the action association data corresponding to the non-fault action point among the super-threshold action points comprises: Acquire action association data corresponding to n non-fault action points adjacent to the target non-fault action point among the super-threshold action points; Based on the action association data corresponding to n non-fault action points adjacent to the target non-fault action point among the super-threshold action points, the warning threshold corresponding to the target non-fault action point is adjusted; wherein n≥2.

3. The working status warning method of the battery-swapping vehicle according to claim 2 is characterized in that: The adjusting the warning threshold corresponding to the target non-fault action point based on the action association data corresponding to the n non-fault action points adjacent to the target non-fault action point among the super-threshold action points includes: Based on the action association data corresponding to n non-fault action points adjacent to the target non-fault action point among the super-threshold action points, obtaining percentages of the action association data corresponding to the n non-fault action points deviating from their respective corresponding current warning thresholds; Based on the percentages by which the action-related data corresponding to the n non-fault action points deviate from their respective corresponding current warning thresholds, and the percentages by which the action-related data corresponding to the target non-fault action point deviate from the current warning threshold corresponding to the target non-fault action point, data averaging or data fitting is performed to obtain the target warning threshold to which the target non-fault action point needs to be adjusted.

4. The working status warning method of the battery-swapping vehicle according to claim 1 is characterized in that: Before obtaining the action-related data generated at each action point during the battery-swapping vehicle performing the battery-swapping action, the method includes: According to the physical action mechanism of the battery-swap vehicle, the collected sample data are analyzed in segments, and the stable data segments corresponding to the stable operation time periods of the battery-swap vehicle and the fluctuating data segments corresponding to the fluctuating operation time periods of the battery-swap vehicle in the sample data are calibrated.

5. A working status warning device for a battery-swapping vehicle, characterized in that: include: A data acquisition module is used to acquire action-related data generated at each action point during the battery-swapping vehicle's battery-swapping action; A data segment identification module is used to process the action-related data generated by the battery-swapping vehicle at each action point based on a data processing model, and identify stable data segments and fluctuating data segments in the action-related data; wherein the data processing model is obtained by training a machine learning model based on sample data with data feature markers, and the data feature markers are used to distinguish the stable data segments and fluctuating data segments in the sample data by data features; A fault identification module, used for performing fault identification on the over-threshold action points in the stable data segment, and determining non-fault action points among the over-threshold action points; A threshold adjustment module, configured to adjust a warning threshold corresponding to at least one target non-fault action point based on the action association data corresponding to the non-fault action point among the super-threshold action points; The fault warning module is used to provide fault warning for the working status of the battery swapping vehicle during the battery swapping process based on the warning threshold corresponding to each action point after the threshold adjustment.

6. The working status warning device of the battery-swapping vehicle according to claim 5 is characterized in that: The threshold adjustment module is used to Acquire action association data corresponding to n non-fault action points adjacent to the target non-fault action point among the super-threshold action points; Based on the action association data corresponding to n non-fault action points adjacent to the target non-fault action point among the super-threshold action points, the warning threshold corresponding to the target non-fault action point is adjusted; wherein n≥2.

7. The working status warning device of the battery-swapping vehicle according to claim 6 is characterized in that: The threshold adjustment module is used to Based on the action association data corresponding to n non-fault action points adjacent to the target non-fault action point among the super-threshold action points, obtaining percentages of the action association data corresponding to the n non-fault action points deviating from their respective corresponding current warning thresholds; Based on the percentages by which the action-related data corresponding to the n non-fault action points deviate from their respective corresponding current warning thresholds, and the percentages by which the action-related data corresponding to the target non-fault action point deviate from the current warning threshold corresponding to the target non-fault action point, data averaging or data fitting is performed to obtain the target warning threshold to which the target non-fault action point needs to be adjusted.

8. The working status warning device of the battery-swapping vehicle according to claim 5 is characterized in that: The device includes a data marking module; before obtaining the action-related data generated at each action point during the battery-swapping vehicle performs the battery-swapping action, the data marking module is used to According to the physical action mechanism of the battery-swap vehicle, the collected sample data are analyzed in segments, and the stable data segments corresponding to the stable operation time periods of the battery-swap vehicle and the fluctuating data segments corresponding to the fluctuating operation time periods of the battery-swap vehicle in the sample data are calibrated.

9. A computer-readable storage medium, characterized in that: A working status warning program of the battery-swap vehicle is stored thereon. When the working status warning program of the battery-swap vehicle is executed by the processor, the working status warning method of the battery-swap vehicle described in any one of claims 1-4 is implemented.

10. An electronic device, characterized in that: It includes a memory, a processor, and a working status warning program of the battery-swap vehicle stored in the memory and executable on the processor. When the processor executes the working status warning program of the battery-swap vehicle, the working status warning method of the battery-swap vehicle described in any one of claims 1 to 4 is implemented.

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