Vehicle anchoring early warning method and device, electronic equipment and storage medium

Abstract

The invention discloses a vehicle anchor dropping early warning method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining various anchor dropping related data of a target vehicle, the various anchoring related data comprises at least one of historical maintenance data of the target vehicle, data of an external environment where the target vehicle is located, and vehicle operation data of the target vehicle; respectively acquiring data features corresponding to the various anchoring related data; performing anchoring prediction based on the data features corresponding to the various anchoring related data to obtain an anchoring prediction result; wherein the anchoring prediction result comprises a first prediction result, and the first prediction result is used for representing whether the target vehicle has an anchoring risk or not. Through the above mode, the accuracy of vehicle anchoring prediction can be improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a vehicle breakdown warning method, device, electronic device and storage medium. Background Art

[0002] At present, the risk of vehicle breakdown is mainly predicted by monitoring the sensor data of the vehicle's battery system (such as the battery's voltage, current, temperature and other sensor data); however, the accuracy of the risk of breakdown prediction is low. How to accurately and effectively predict the risk of vehicle breakdown has become an urgent problem to be solved. Summary of the invention

[0003] The present application at least provides a vehicle breakdown warning method, device, electronic device and storage medium.

[0004] A first aspect of the present application provides a vehicle breakdown warning method, the method comprising: obtaining a variety of breakdown-related data of a target vehicle, wherein the variety of breakdown-related data comprises historical maintenance data of the target vehicle, at least one of external environment data of the target vehicle, and vehicle operation data of the target vehicle; respectively obtaining data features corresponding to the variety of breakdown-related data; performing breakdown prediction based on the data features corresponding to the variety of breakdown-related data to obtain a breakdown prediction result; wherein the breakdown prediction result comprises a first prediction result, and the first prediction result is used to characterize whether the target vehicle has a breakdown risk.

[0005] Therefore, the data features corresponding to the breakdown-related data can express the relationship between the breakdown-related data and the vehicle breakdown in a more concrete or explicit way, and based on the data features corresponding to the breakdown-related data, it can accurately predict whether the target vehicle has a breakdown risk; in addition, the breakdown prediction is performed based on the data features corresponding to a variety of breakdown-related data, which can more comprehensively capture the key information that may cause the target vehicle to have a breakdown risk, thereby more accurately predicting whether the target vehicle has a breakdown risk, and further identifying the breakdown risk and issuing a breakdown risk warning before the target vehicle breaks down (e.g., a few hours before the breakdown occurs, a few days before the breakdown occurs, etc.), so that users can intervene in time to ensure driving safety.

[0006] Among them, the vehicle operation data includes at least one of the following: battery cell voltage data, battery cell current data, vehicle temperature data, battery pack charge status data, and vehicle operation data; the historical maintenance data includes at least one of the following: maintenance time, vehicle breakdown reason, maintenance content, vehicle status data before and after the failure, and the external environment conditions of the target vehicle when it broke down; the external environment data includes at least one of weather data and geographic data; among them, the vehicle operation data and weather data are time series data.

[0007] Therefore, the vehicle operation data of the target vehicle directly reflects the real-time status and current health status of the target vehicle. The early signals of vehicle breakdown can be captured through the vehicle operation data of the target vehicle, so as to predict whether the target vehicle may break down in the future; therefore, the target vehicle can be predicted to have a breakdown risk based on the vehicle operation data of the target vehicle. In addition, the vehicle operation data can be flexibly set.

[0008] The historical maintenance data of the target vehicle reveals the "health file" of the target vehicle. Through the historical maintenance data of the target vehicle, the systemic defects, component aging trends or hidden risk chains of the target vehicle can be revealed, so as to predict whether the target vehicle may break down in the future; therefore, it can be predicted whether the target vehicle has a risk of breaking down based on the historical maintenance data of the target vehicle. In addition, the historical maintenance data can be flexibly set.

[0009] The external environment data of the target vehicle reflects the current environment of the target vehicle. Through the external environment data of the target vehicle, the impact of the current environment of the target vehicle on the vehicle performance can be quantified, and high-risk scenarios that may cause the vehicle to break down can be identified, so as to predict whether the target vehicle may break down in the future; therefore, it can be predicted whether the target vehicle has a risk of breaking down based on the external environment data of the target vehicle. In addition, the external environment data of the target vehicle can be flexibly set.

[0010] Among them, the battery cell voltage data includes the voltage value of each battery cell in the battery pack of the target vehicle at different times; the battery cell current data includes the charging and discharging current of each battery cell at different times; the vehicle temperature data includes at least one of the following: the battery cell temperature at different times, the internal ambient temperature of the target vehicle at different times; the whole vehicle operation data includes at least one of the following: the vehicle speed of the target vehicle at different times, the power output of the target vehicle at different times; the vehicle status data before and after the fault includes at least one of the following: the vehicle operation status before and after the fault, the vehicle sensor data before and after the fault; the weather data includes at least one of the following: the external ambient temperature corresponding to different times, the external ambient humidity corresponding to different times; the geographical data includes road condition information.

[0011] Therefore, the voltage of the battery cell at different times is a direct representation of the battery health status, balance and potential faults. The abnormal fluctuation, imbalance or degradation trend of the battery cell voltage can reflect the failure risk of the battery system of the target vehicle. The failure of the battery system of the target vehicle may cause the target vehicle to break down. Therefore, it is subsequently possible to predict whether the target vehicle has a risk of breaking down based on the voltage values ​​of each battery cell in the battery pack of the target vehicle at different times.

[0012] The charge and discharge current of the battery cells at different times is a direct reflection of the energy flow of the target vehicle's battery system. Abnormal current patterns of the battery cells (such as overcurrent, imbalance, fluctuation, etc.) may indicate battery cell aging, which may cause a sudden drop in battery life and power interruption, leading to the target vehicle breaking down. Therefore, it is subsequently possible to predict whether the target vehicle is at risk of breaking down based on the charge and discharge current of each battery cell in the battery pack of the target vehicle at different times.

[0013] The cell temperature at different times is an indicator of the health status, safety and reliability of the cell. Abnormal cell temperature (such as local overheating, etc.) may cause cell failure or protective power-off of the target vehicle's battery system, causing the target vehicle to break down. Therefore, based on the cell temperature of each cell in the target vehicle's battery pack at different times, it is possible to predict whether the target vehicle is at risk of breaking down. The internal ambient temperature is an influencing factor of the target vehicle's battery system performance, safety and life. Abnormal temperature rise, temperature drop or uneven temperature distribution may cause cell failure and power limitation in the battery pack, causing the target vehicle to break down. Therefore, based on the internal ambient temperature of the target vehicle at different times, it is possible to predict whether the target vehicle is at risk of breaking down.

[0014] The target vehicle's speed at different times is a direct reflection of the target vehicle's power system load, component wear and driving behavior. Abnormal speed patterns (such as continuous high speed, frequent rapid acceleration / deceleration, sudden speed drops, etc.) may indicate a power system failure, energy management imbalance or hidden component failure of the target vehicle, which may cause the target vehicle to break down. Therefore, the target vehicle can be predicted to be at risk of breaking down based on its speed. The target vehicle's power output at different times is a direct reflection of the target vehicle's power system health status. Abnormal power fluctuations, sudden drops or continuous over-limit may reveal potential problems such as aging of the target vehicle's battery pack, motor failure of the target vehicle, and transmission system. These potential problems may cause power interruption and breakdown. Therefore, the target vehicle can be predicted to be at risk of breaking down based on its power output at different times.

[0015] The changes in the vehicle's operating status before and after the fault reveal the evolution of the target vehicle's component degradation, system imbalance or hidden failure. Therefore, it is possible to predict whether the target vehicle is at risk of breaking down based on the conditions of the target vehicle's components and systems. The physical signals captured by the vehicle's sensors can reflect the evolution of the target vehicle's component degradation, system imbalance or sudden failure. Therefore, by analyzing the vehicle's sensor data before and after the fault, it is possible to predict whether the target vehicle is at risk of breaking down.

[0016] The external environment temperature will directly affect the material properties, chemical process stability and system thermal management efficiency of the target vehicle, thereby indirectly accelerating the aging of the target vehicle's components, thereby increasing the target vehicle's risk of breaking down; therefore, it is possible to subsequently predict whether the target vehicle is at risk of breaking down based on the external environment temperature corresponding to different times. The external environment humidity indirectly causes component failure or system malfunction by affecting metal corrosion, electrical system reliability, sensor accuracy and material aging speed, thereby increasing the target vehicle's risk of breaking down.

[0017] Road conditions directly affect the target vehicle's mechanical load, component wear rate and system stability. Specific road conditions (such as bumps, slopes, wetness, etc.) will accelerate the aging of key components of the target vehicle or cause instantaneous overloads, thereby causing the vehicle to break down. Therefore, the subsequent prediction of whether the target vehicle is at risk of breaking down can be made based on the target vehicle's road condition information.

[0018] Among them, data features corresponding to multiple types of anchoring-related data are obtained respectively, including: taking at least one type of anchoring-related data as first anchoring-related data, and performing statistics on the first anchoring-related data to obtain data features of the first anchoring-related data; and / or taking at least one type of anchoring-related data as second anchoring-related data, and directly taking the second anchoring-related data as data features of second anchoring-related data.

[0019] Therefore, by converting the "implicit signal" of the target vehicle's breakdown - the first anchor-related data, into the "explicit indicator" that can be identified by the prediction model - the data characteristics of the first anchor-related data, the data characteristics of the first anchor-related data can express the relationship between the first anchor-related data and the vehicle breakdown in a more concrete or explicit way. Subsequently, based on the data characteristics of the first anchor-related data, it is possible to more accurately predict whether the target vehicle is at risk of breaking down.

[0020] Since the second breakdown-related data itself is able to concretely or explicitly express the relationship between itself and the vehicle breakdown, it is directly used as its corresponding data feature.

[0021] Among them, the multiple types of breakdown-related data include vehicle operation data; when the vehicle operation data includes battery cell voltage data, the battery cell voltage data is the first breakdown-related data, and the data characteristics of the battery cell voltage data include at least one of the following: a first battery cell voltage difference, a second battery cell voltage difference, a voltage fluctuation characterization value corresponding to each battery cell, and the number of voltage abnormalities of the battery pack within a first time window, wherein the first battery cell voltage difference is the voltage difference between different battery cells, and the second battery cell voltage difference is the difference between the sum of the voltages of each battery cell and the total voltage of the battery pack; when the vehicle operation data includes battery cell current data, the battery cell current data is the first breakdown-related data, and the data characteristics of the battery cell current data include at least one of the following: a discharge current fluctuation characterization value corresponding to each battery cell, a discharge current fluctuation characterization value of the battery pack in the second The number of abnormal charging currents within the time window, and the correlation between the current and voltage of each battery cell; when the vehicle operation data includes vehicle temperature data, the vehicle temperature data is the first breakdown-related data, and the data characteristics of the vehicle temperature data include at least one of the following: the difference in temperature distribution of each battery cell, the temperature fluctuation characterization value of each battery cell, and the number of temperature abnormalities of the battery pack within the third time window; when the vehicle operation data includes the state of charge data of the battery pack, the state of charge data is the first breakdown-related data, and the data characteristics of the state of charge data include at least one of the following: the charge fluctuation characterization value of the battery pack, the relationship between the charge consumption value of the battery pack and the mileage of the target vehicle; when the vehicle operation data includes the whole vehicle operation data, the whole vehicle operation data is the second breakdown-related data.

[0022] Therefore, the data characteristics of the battery cell voltage data, the battery cell current data, the vehicle temperature data, and the battery pack state of charge data can be flexibly set.

[0023] Among them, the vehicle operation data is time series data, and the data features corresponding to each vehicle operation data are time series feature sequences, among which the time series feature sequences corresponding to the battery cell voltage data include the first battery cell voltage difference, the second battery cell voltage difference, the voltage fluctuation characterization value corresponding to each battery cell and the number of voltage anomalies at different times; the time series feature sequences corresponding to the battery cell current data include the discharge current fluctuation characterization value corresponding to different times, the number of charging current anomalies, and the correlation between the current and voltage of each battery cell; the time series feature sequences corresponding to the vehicle temperature data include the temperature distribution difference, the temperature fluctuation characterization value and the number of temperature anomalies corresponding to different times; the time series feature sequences corresponding to the state of charge data include the temperature distribution difference, the temperature fluctuation characterization value and the number of temperature anomalies corresponding to different times. The sequence characteristic sequence includes the relationship between the charge fluctuation characterization values ​​corresponding to different moments, the charge value of the battery pack and the mileage of the target vehicle; and / or, the voltage fluctuation characterization value corresponding to the battery cell includes the voltage change rate of the battery cell; and / or, the first battery cell voltage difference includes the voltage difference between the maximum voltage battery cell and the minimum voltage battery cell; and / or, the discharge current fluctuation characterization value corresponding to the battery cell includes the discharge current change rate of the battery cell; and / or, the temperature distribution difference of the battery cell includes the temperature difference between the maximum temperature battery cell and the minimum temperature battery cell; and / or, the temperature fluctuation characterization value of the battery cell includes the temperature change rate of the battery cell; and / or, the charge fluctuation characterization value of the battery pack includes the charge decrease rate of the battery pack.

[0024] Therefore, the voltage fluctuation characterization value corresponding to the battery cell, the first battery cell voltage difference, the discharge current fluctuation characterization value corresponding to the battery cell, the temperature distribution difference of the battery cell, the temperature fluctuation characterization value of the battery cell and the charge fluctuation characterization value of the battery pack can be flexibly set.

[0025] Among them, when the anchoring-related data includes external environment data, and the external environment data includes external environment temperature, the external environment temperature is the first anchoring-related data, and the data characteristics of the external environment temperature include at least one of the following: external environment temperature central tendency statistical value, external environment temperature fluctuation characterization value, external environment temperature distribution within the fourth time window; when the anchoring-related data includes external environment data, and the external environment data includes external environment humidity, the external environment humidity is the first anchoring-related data, and the data characteristics of the external environment humidity include at least one of the following: external environment humidity central tendency statistical value, external environment humidity fluctuation characterization value; when the anchoring-related data includes historical maintenance data of the target vehicle, the historical maintenance data is the first anchoring-related data, and the data characteristics of the historical maintenance data include at least one of the following: the total number of historical anchoring times of the target vehicle, the number of times the target vehicle is anchored due to battery cell failure, the statistical results of parts replacement of the target vehicle, the fault code of the fault that caused the target vehicle to be anchored, the anchoring interval of the target vehicle, the vehicle operation data of the target vehicle before the anchoring, and the vehicle operation status of the target vehicle after the anchoring repair.

[0026] Therefore, the data characteristics of the external environment temperature, the external environment humidity, and the historical maintenance data can be flexibly set.

[0027] Among them, the external ambient temperature and the external ambient humidity are time series data, and the data features corresponding to the external ambient temperature and the external ambient humidity are time series feature sequences, wherein the time series feature sequence corresponding to the external ambient temperature includes the external ambient temperature central trend statistical values ​​corresponding to different moments, the external ambient temperature fluctuation characterization values, and the external ambient temperature distribution; the time series feature sequence corresponding to the external ambient humidity includes the external ambient humidity central trend statistical values ​​and the external ambient humidity fluctuation characterization values ​​corresponding to different moments; and / or, the external ambient temperature fluctuation characterization values ​​include the external ambient temperature change rate; and / or, the external ambient temperature distribution includes the highest temperature and the lowest temperature within the fourth time window; and / or, the external ambient humidity fluctuation characterization values ​​include the external ambient humidity change rate.

[0028] Therefore, the external environment temperature fluctuation characterization value, the external environment temperature distribution and the external environment humidity fluctuation characterization value can be flexibly set.

[0029] Among them, before performing a breakdown prediction based on the data features corresponding to a plurality of breakdown-related data and obtaining the breakdown prediction result, the vehicle breakdown warning method also includes at least one of the following steps: from the data features corresponding to the plurality of breakdown-related data, finding an associated feature group whose feature correlation meets a preset correlation condition, and selecting a representative feature from the associated feature group, and deleting other data features in the associated feature group except the representative feature; finding each time series feature sequence from the data features corresponding to the plurality of breakdown-related data, and combining each time series feature sequence into a time series data feature.

[0030] Therefore, the feature correlation between each data feature in the associated feature group meets the preset correlation condition, indicating that the data features in the associated feature group have a strong correlation and there may be information overlap. Therefore, representative features are selected from the associated feature group and retained, and other data features in the associated feature group are deleted to effectively reduce the redundant features in the data features corresponding to the various anchor-related data, while retaining the key features in the data features corresponding to the various anchor-related data. In other words, redundant features are identified and deleted by quantifying the feature correlation between the data features corresponding to the various anchor-related data.

[0031] Combining the time series feature sequences can enhance the ability to capture hidden relationships in the time series data, thereby improving the accuracy of subsequent vehicle breakdown prediction based on the time series data features.

[0032] Among them, the data features corresponding to the various anchoring-related data include time series data features and non-time series data features; anchoring prediction is performed based on the data features corresponding to the various anchoring-related data to obtain anchoring prediction results, including: using a time series processing model to detect the time series dependency of time series data features to obtain time-dependent features; and using a decision model to identify the association between non-time series data features and anchoring to obtain anchoring-related features; using time-dependent features and anchoring-related features to perform predictions to obtain anchoring prediction results.

[0033] Therefore, the time-dependent feature is obtained by using the time series processing model to detect the time series data feature's time series dependency, which can reflect the long-term attenuation trend (such as battery capacity decline, etc.) and short-term abnormal fluctuations (such as battery temperature surge, etc.) of the target vehicle's time series data, and model the dynamic evolution law; the anchorage-related feature is obtained by using the decision model to identify the association between non-time series data features and anchorage, which can mine the correlation of discrete events. Therefore, using time-dependent features and anchorage-related features for prediction, while covering the risks of "progressive aging" and "sudden impact", avoiding the blind spots of a single data source, can improve the accuracy of vehicle anchorage risk prediction.

[0034] Among them, the time series processing model is a long short-term memory network model, and the decision model is a random forest model; and / or, using time-dependent features and anchoring-related features to make predictions to obtain anchoring prediction results, including: fusing time-dependent features and anchoring-related features to obtain fused features; making predictions based on fused features to obtain anchoring prediction results.

[0035] Therefore, the timing processing model and decision model can be set flexibly.

[0036] By fusing time-dependent features with anchoring-related features to construct a multi-dimensional and multi-granular anchoring risk characterization system, the accuracy of anchoring risk prediction can be improved.

[0037] Among them, multiple types of breakdown-related data include time series data and non-time series data; before respectively obtaining the data features corresponding to the multiple types of breakdown-related data, the vehicle breakdown warning method also includes: performing a first preprocessing on the time series data; wherein the first preprocessing includes at least one of the following: denoising, missing data completion, abnormal data processing, inconsistent data processing, data alignment; and, performing a second preprocessing on the non-time series data; wherein the second preprocessing includes at least one of the following: data format unification processing, missing data completion, data alignment.

[0038] Therefore, a first preprocessing is performed on the time series data among the multiple anchorage-related data, and a second preprocessing is performed on the non-time series data among the multiple anchorage-related data, so as to convert the time series data and the non-time series data among the multiple anchorage-related data into high-quality data. Subsequently, anchorage prediction is performed based on the preprocessed multiple anchorage-related data, which can improve the accuracy of anchorage prediction.

[0039] Among them, the anchorage-related data includes the vehicle operation data of the target vehicle, and the vehicle operation data is time series data; the time series data is first preprocessed, including: denoising the vehicle operation data, completing missing data, processing abnormal data, processing inconsistent data, and aligning data time; and / or, the anchorage-related data includes the historical maintenance data of the target vehicle, and the historical maintenance data is non-time series data; the non-time series data is second preprocessed, including: unifying the data format and completing missing data of the historical maintenance data of the target vehicle; and / or, the anchorage-related data includes the vehicle operation data of the target vehicle and the external environment data of the target vehicle, and the external environment data includes weather data and geographic data, the vehicle operation data and weather data are time series data, and the geographic data is non-time series data; the step of preprocessing the anchorage-related data also includes: aligning the vehicle operation data with the weather data in time; and aligning the vehicle operation data with the geographic data in space.

[0040] Therefore, the preprocessing of the vehicle operation data of the target vehicle, the historical maintenance data of the target vehicle and the external environment data of the target vehicle can be flexibly set.

[0041] Among them, the first prediction result is used to characterize that the target vehicle has a risk of breaking down, and the anchorage prediction result also includes a second prediction result, and the second prediction result includes at least one of the following: the predicted occurrence time of the anchorage risk, the predicted cause of the anchorage risk, and the solution to the anchorage risk; and / or, after performing an anchorage prediction based on data features corresponding to a plurality of anchorage-related data and obtaining the anchorage prediction result, the vehicle anchorage warning method also includes: feeding back at least part of the information in the anchorage prediction result to the user, wherein the feedback method includes at least one of the following: displaying at least part of the information on the display screen of the target vehicle, playing at least part of the information on the target vehicle through audio, and sending at least part of the information to an associated terminal.

[0042] Therefore, when the breakdown prediction is performed based on the data features corresponding to a variety of breakdown-related data and it is determined that the target vehicle is at risk of breakdown, the time of occurrence of the breakdown risk, the cause of the breakdown risk, the solution measures for the breakdown risk and other related information will also be predicted; that is, the breakdown prediction results are diverse and rich.

[0043] There are various ways to feed back at least part of the information in the anchor prediction result to the user, and the feedback method can be flexibly selected.

[0044] A second aspect of the present application provides a vehicle breakdown warning device, which includes a first acquisition module, a second acquisition module and a prediction module; the first acquisition module is used to acquire multiple breakdown-related data of a target vehicle, wherein the multiple breakdown-related data include historical maintenance data of the target vehicle, at least one of external environment data of the target vehicle, and vehicle operation data of the target vehicle; the second acquisition module is used to respectively acquire data features corresponding to the multiple breakdown-related data; the prediction module is used to perform breakdown prediction based on the data features corresponding to the multiple breakdown-related data to obtain a breakdown prediction result; wherein the breakdown prediction result includes a first prediction result, and the first prediction result is used to characterize whether the target vehicle has a breakdown risk.

[0045] A third aspect of the present application provides an electronic device, which includes a memory and a processor, the memory stores program instructions, and the processor is used to execute the program instructions to implement the above-mentioned vehicle breakdown warning method.

[0046] A fourth aspect of the present application provides a computer-readable storage medium, which is used to store program instructions, and the program instructions can be executed to implement the above-mentioned vehicle breakdown warning method.

[0047] In the above technical scheme, the data features corresponding to the breakdown-related data can express the relationship between the breakdown-related data and the vehicle breakdown in a more concrete or explicit way, and based on the data features corresponding to the breakdown-related data, it is possible to accurately predict whether the target vehicle has a breakdown risk; in addition, the breakdown prediction is performed based on the data features corresponding to a variety of breakdown-related data, which can more comprehensively capture key information that may cause the target vehicle to have a breakdown risk, thereby being able to more accurately predict whether the target vehicle has a breakdown risk, and further, being able to identify the breakdown risk and issue a breakdown risk warning before the target vehicle breaks down (e.g., a few hours before the breakdown occurs, a few days before the breakdown occurs, etc.), so that the user can intervene in time to ensure driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a flow chart of an embodiment of a vehicle breakdown warning method provided by the present application; Figure 2 yes Figure 1 The flowchart of step S13 is shown as an embodiment; Figure 3 It is a structural schematic diagram of an embodiment of a vehicle breakdown warning device provided by the present application; Figure 4 It is a structural schematic diagram of an embodiment of an electronic device provided by the present application; Figure 5 It is a structural schematic diagram of an embodiment of a computer-readable storage medium provided by the present application. DETAILED DESCRIPTION

[0049] The scheme of the embodiment of the present application is described in detail below in conjunction with the drawings of the specification.

[0050] In the following description, for the purpose of explanation rather than limitation, specific details such as specific system structures, interfaces, and technologies are provided to facilitate a thorough understanding of the present application.

[0051] The term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the objects associated before and after are in an "or" relationship. In addition, "many" in this article means two or more than two. In addition, the term "at least one" in this article means any combination of at least two of any one or more of a plurality of, for example, including at least one of A, B, and C, can mean including any one or more elements selected from the set consisting of A, B, and C.

[0052] See also Figure 1 , Figure 1 1 is a flow chart of an embodiment of a vehicle breakdown warning method provided by the present application. It should be noted that if there are substantially the same results, this embodiment is not used. Figure 1 The process sequence shown is limited. Figure 1 As shown, this embodiment includes: Step S11: Acquire various breakdown-related data of the target vehicle.

[0053] In this embodiment, a variety of anchoring-related data of the target vehicle are obtained; wherein, the variety of anchoring-related data include historical maintenance data of the target vehicle, at least one of the external environment data of the target vehicle, and vehicle operation data of the target vehicle. The vehicle operation data of the target vehicle directly reflects the real-time status and current health status of the target vehicle. The early signals of vehicle anchoring can be captured through the vehicle operation data of the target vehicle, so as to predict whether the target vehicle may anchor in the future; therefore, it is possible to subsequently predict whether the target vehicle is at risk of anchoring based on the vehicle operation data of the target vehicle. The historical maintenance data of the target vehicle reveals the "health file" of the target vehicle. The historical maintenance data of the target vehicle can reveal the systematic defects, component aging trends or hidden risk chains of the target vehicle, so as to predict whether the target vehicle may anchor in the future; therefore, it is possible to subsequently predict whether the target vehicle is at risk of anchoring based on the historical maintenance data of the target vehicle. The external environment data of the target vehicle reflects the environmental conditions of the target vehicle's current environment. Through the external environment data of the target vehicle, it is possible to quantify the impact of the target vehicle's current environment on the vehicle performance, identify high-risk scenarios that may cause the vehicle to break down, and thus predict whether the target vehicle may break down in the future; therefore, it is possible to subsequently predict whether the target vehicle has a risk of breaking down based on the external environment data of the target vehicle.

[0054] There is no limitation on the types of various breakdown-related data obtained for the target vehicle, and the types can be specifically set according to actual use needs.

[0055] For example, the acquired target vehicle's multiple breakdown-related data include the target vehicle's vehicle operation data and the target vehicle's historical maintenance data. For another example, the acquired target vehicle's multiple breakdown-related data include the target vehicle's vehicle operation data and the target vehicle's external environment data.

[0056] For another example, the various types of target vehicle breakdown-related data obtained include the target vehicle's vehicle operation data, the target vehicle's historical maintenance data, and the target vehicle's external environment data. When the various types of target vehicle breakdown-related data obtained include the target vehicle's vehicle operation data, the target vehicle's historical maintenance data, and the target vehicle's external environment data, a panoramic portrait of the target vehicle is constructed from the three dimensions of the target vehicle's real-time status, health records, and environmental adaptability, which can more comprehensively capture the subtle signs before the target vehicle breaks down, or in other words, can more comprehensively capture the key information that may cause the target vehicle to have a breakdown risk; further, subsequent breakdown prediction based on the target vehicle's vehicle operation data, the target vehicle's historical maintenance data, and the target vehicle's external environment data can more accurately predict whether the target vehicle has a breakdown risk, so that the target vehicle can identify the breakdown risk and issue a breakdown risk warning before the target vehicle breaks down (e.g., a few hours before the breakdown occurs, a few days before the breakdown occurs, etc.), so that the user can intervene in time to ensure driving safety.

[0057] In one embodiment, the vehicle operation data includes at least one of the following: battery cell voltage data, battery cell current data, vehicle temperature data, battery pack state of charge data, and vehicle operation data. The battery cell voltage data, battery cell current data, vehicle temperature data, battery pack state of charge data, and vehicle operation data are essentially the "vital signs" of the target vehicle power chain, directly reflecting the real-time status and current health status of the target vehicle. Through the vehicle operation data such as battery cell voltage data, battery cell current data, vehicle temperature data, battery pack state of charge data, and vehicle operation data, early signals of vehicle breakdown can be captured, thereby predicting whether the target vehicle may break down in the future.

[0058] In a specific embodiment, the cell voltage data includes the voltage value of each cell in the battery pack of the target vehicle at different times. The voltage of the cell at different times is a direct representation of the battery health status, balance and potential faults. The abnormal fluctuation, imbalance or degradation trend of the cell voltage can reflect the failure risk of the battery system of the target vehicle. The failure of the battery system of the target vehicle may cause the target vehicle to break down. For example, a sudden drop or imbalance in the cell voltage may trigger a protective power outage of the BMS, causing the vehicle to break down instantly; for another example, the degradation of the cell voltage indicates capacity attenuation, and a cliff-like drop in cruising range may cause the vehicle to break down midway. Therefore, it is subsequently possible to predict whether the target vehicle is at risk of breaking down based on the voltage values ​​of each cell in the battery pack of the target vehicle at different times.

[0059] In a specific embodiment, the cell current data includes the charge and discharge current of each cell at different times. The charge and discharge current of the cell at different times is a direct reflection of the energy flow of the battery system of the target vehicle. The abnormal current pattern of the cell (such as overcurrent, imbalance, fluctuation, etc.) may indicate cell aging, and cell aging may cause a sudden drop in endurance and power interruption, causing the target vehicle to break down. Therefore, it is subsequently possible to predict whether the target vehicle is at risk of breaking down based on the charge and discharge current of each cell in the battery pack of the target vehicle at different times.

[0060] In a specific embodiment, the vehicle temperature data includes at least one of the following: the cell temperature at different times, the internal ambient temperature of the target vehicle at different times. For the cell temperature at different times, the cell temperature at different times is an indicator of the health status, safety and reliability of the cell. Abnormal cell temperature (such as local overheating, etc.) may cause cell failure or protective power failure of the battery system of the target vehicle, thereby causing the target vehicle to break down; therefore, it is subsequently possible to predict whether the target vehicle has a risk of breaking down based on the cell temperature of each cell in the battery pack of the target vehicle at different times. The internal ambient temperature of the target vehicle at different times can be regarded as the internal ambient temperature of the battery pack. The internal ambient temperature is an influencing factor of the battery system performance, safety and life of the target vehicle. Abnormal temperature rise, temperature drop or uneven temperature distribution may cause cell failure, power limitation, etc. in the battery pack, thereby causing the target vehicle to break down; therefore, it is subsequently possible to predict whether the target vehicle has a risk of breaking down based on the internal ambient temperature of the target vehicle at different times.

[0061] In a specific embodiment, the vehicle operation data includes at least one of the following: the vehicle speed of the target vehicle at different times, and the power output of the target vehicle at different times. For the vehicle speed of the target vehicle at different times, the vehicle speed of the target vehicle at different times is a direct reflection of the target vehicle's power system load, component wear and driving behavior, etc. Abnormal speed patterns (such as continuous high speed, frequent rapid acceleration / deceleration, speed drops, etc.) may indicate a power system failure, energy management imbalance or hidden component failure of the target vehicle, etc., which may cause the target vehicle to break down. For the power output of the target vehicle at different times, the power output of the target vehicle at different times is a direct reflection of the health status of the target vehicle's power system. Abnormal power fluctuations, sudden drops or continuous over-limits may reveal potential problems such as aging of the battery pack of the target vehicle, motor failure of the target vehicle, and transmission system. These potential problems may cause power interruption, etc., and cause breakdown.

[0062] In a specific implementation, the vehicle operation data of the target vehicle may be obtained from an on-board diagnostics system (OBD) or a battery management system (BMS).

[0063] In a specific embodiment, the vehicle operation data of the target vehicle in the on-board diagnostic system or the battery management system may be detected by sensors set on the target vehicle. For example, the target vehicle operation data includes the voltage value of each battery cell in the battery pack of the target vehicle at different times, the charge and discharge current of each battery cell at different times, the battery cell temperature of each battery cell at different times, the internal ambient temperature of the target vehicle at different times, the vehicle speed of the target vehicle at different times, the power output of the target vehicle at different times, the acceleration of the target vehicle at different times, the vibration frequency of the target vehicle at different times, and the charge state data of the battery pack of the target vehicle. Among them, the voltage value of each cell in the battery pack of the target vehicle at different times is detected by the voltage sensor installed on the target vehicle; the charging and discharging current of each cell in the battery pack of the target vehicle at different times is detected by the current sensor installed on the target vehicle; the cell temperature of each cell in the battery pack of the target vehicle at different times is detected by the first temperature sensor installed on the target vehicle; the internal ambient temperature of the target vehicle at different times is detected by the second temperature sensor installed on the target vehicle; the vibration frequency of the target vehicle at different times is detected by the vibration sensor installed on the target vehicle; the acceleration of the target vehicle at different times is detected by the acceleration sensor installed on the target vehicle; the vehicle speed of the target vehicle at different times is detected by the vehicle speed sensor installed on the target vehicle; the power output of the target vehicle at different times is detected by the torque sensor installed on the target vehicle; the charge state data of the battery pack of the target vehicle is detected by the current sensor installed on the target vehicle.

[0064] In one embodiment, the historical maintenance data includes at least one of the following: maintenance time, vehicle breakdown reason (such as battery failure, sensor failure, software problem, etc.), maintenance content (such as replaced parts, repair measures, software update, etc.), vehicle status data before and after the breakdown, and the external environment of the target vehicle when the breakdown occurs. The historical maintenance data of the target vehicle, such as the maintenance time of the target vehicle, the vehicle breakdown reason, maintenance content, vehicle status data before and after the breakdown, and the external environment of the target vehicle when the breakdown occurs, constitute the "health file" of the target vehicle, and the "health file" of the target vehicle reveals the systematic defects, component aging trends or hidden risk chains of the target vehicle, so it is possible to predict whether the target vehicle has a breakdown risk based on the historical maintenance data of the target vehicle, such as the maintenance time of the target vehicle, the vehicle breakdown reason, maintenance content, vehicle status data before and after the breakdown, and the external environment of the target vehicle when the breakdown occurs.

[0065] Of course, in other implementations, the historical maintenance data may also include maintenance frequency, maintenance cycle, etc., which are not limited here.

[0066] In a specific embodiment, the vehicle status data before and after the failure includes at least one of the following: the vehicle operating status before and after the failure, and the vehicle sensor data before and after the failure. For the vehicle operating status before and after the failure, the change in the vehicle operating status before and after the failure reveals the evolution of component degradation, system imbalance or hidden failure of the target vehicle, so it is possible to predict whether the target vehicle is at risk of breaking down through the components, systems, etc. of the target vehicle. For the vehicle sensor data before and after the failure, the physical signals captured by the vehicle sensors can reflect the evolution of component degradation, system imbalance or sudden failure of the target vehicle, so by analyzing the vehicle sensor data before and after the failure, it is possible to predict whether the target vehicle is at risk of breaking down.

[0067] In a specific implementation, the historical maintenance data of the target vehicle may be obtained from the after-sales service system of the target vehicle, the maintenance report of the target vehicle, the fault diagnosis system (eg, OBD), the vehicle owner, and the like.

[0068] In one embodiment, the external environment data includes at least one of weather data and geographic data. For weather data, weather is an important external variable that affects the operating reliability of the target vehicle. Extreme or special weather conditions will aggravate the component wear, material aging or system performance degradation of the target vehicle, thereby increasing the target vehicle's risk of breaking down; therefore, it is subsequently possible to predict whether the target vehicle has a risk of breaking down based on the weather data of the target vehicle. For geographic data, the geographical environment directly or indirectly causes mechanical wear, thermal management failure or energy demand imbalance by affecting the physical load of the target vehicle's components, material aging rate and system adaptability, thereby increasing the risk of breaking down; therefore, it is subsequently possible to predict whether the target vehicle has a risk of breaking down based on the geographic data of the target vehicle.

[0069] In a specific embodiment, the weather data includes at least one of the following: external environmental temperatures corresponding to different times, and external environmental humidity corresponding to different times. For external environmental temperatures corresponding to different times, the external environmental temperature will directly affect the material properties, chemical process stability, and system thermal management efficiency of the target vehicle, thereby indirectly accelerating the aging of components of the target vehicle, thereby increasing the risk of the target vehicle being stranded; therefore, it is subsequently possible to predict whether the target vehicle is at risk of being stranded based on the external environmental temperatures corresponding to different times. For external environmental humidity corresponding to different times, the external environmental humidity will indirectly cause component failure or system malfunction by affecting metal corrosion, electrical system reliability, sensor accuracy, and material aging speed, thereby increasing the risk of the target vehicle being stranded.

[0070] Of course, in other specific implementations, the weather data also includes whether there is rain in the external environment where the target vehicle is located, whether there is snow in the external environment where the target vehicle is located, the wind speed of the external environment corresponding to different times, etc., which are not limited here and can be specifically set according to actual use needs.

[0071] In a specific implementation, the geographic data includes road condition information. Road conditions directly affect the mechanical load, component wear rate and system stability of the target vehicle. Specific road conditions (such as bumps, slopes, slippery, etc.) will accelerate the aging of key components of the target vehicle or cause instantaneous overload, etc., thus causing the vehicle to break down. Therefore, it is possible to subsequently predict whether the target vehicle is at risk of breaking down based on the road condition information of the target vehicle.

[0072] The road condition information may include whether the road is slippery, whether the road has a slope, whether the road has a curve, etc., which are not limited here.

[0073] In a specific implementation, the external environment data includes weather data, and specifically the weather data of the target vehicle can be acquired from a weather API service.

[0074] In a specific embodiment, the external environment data includes geographic data. Specifically, the vehicle position of the target vehicle can be obtained through the GPS of the target vehicle, and then the vehicle position of the target vehicle is matched with a high-precision map to determine the geographic data of the vehicle position of the target vehicle.

[0075] It should be noted that the vehicle operation data and weather data of the target vehicle are time series data. Time series data refers to a series of data points recorded in chronological order. Each data point has a timestamp (Timestamp) that is used to indicate the state or measurement value of the data at a certain point in time. The characteristic of time series data is that there is a temporal dependency between the data, that is, the current data may be affected by the past data, and the data may show trends, periodicity or random fluctuations over time.

[0076] Non-time series data refers to data that is not recorded in time order, and there is no strict time sequence between data points, or the time dimension is not considered as the main factor in the analysis. This type of data usually reflects static attributes, discrete events or fixed states, rather than dynamic processes that change over time.

[0077] Step S12: respectively obtaining data features corresponding to a plurality of anchoring-related data.

[0078] In this embodiment, data features corresponding to multiple types of anchoring-related data are obtained respectively. That is to say, the "implicit signal" of the target vehicle anchoring - multiple types of anchoring-related data of the target vehicle - are converted into "explicit indicators" that can be identified by the prediction model - data features corresponding to multiple types of anchoring-related data. The data features corresponding to the anchoring-related data can express the relationship between the anchoring-related data and the vehicle anchoring in a more concrete or explicit way. Subsequently, based on the data features corresponding to the multiple types of anchoring-related data, it is possible to more accurately predict whether the target vehicle has an anchoring risk, thereby identifying the anchoring risk and issuing an anchoring risk warning before the target vehicle anchors (e.g., several hours before the anchoring occurs, several days before the anchoring occurs, etc.), so that the user can intervene in time to ensure driving safety.

[0079] In one embodiment, the data features corresponding to the multiple types of anchoring-related data are obtained respectively, specifically: at least one anchoring-related data is respectively used as the first anchoring-related data, and the first anchoring-related data is statistically analyzed to obtain the data features of the first anchoring-related data. That is to say, for some anchoring-related data, data statistics are performed on them, and then the statistical results are used as the corresponding data features. By converting the "implicit signal" of the target vehicle anchoring-the first anchoring-related data, into the "explicit indicator" recognizable by the prediction model-the data features of the first anchoring-related data, the data features of the first anchoring-related data can express the relationship between the first anchoring-related data and the vehicle anchoring in a more concrete or explicit way. Subsequently, based on the data features of the first anchoring-related data, it is possible to more accurately predict whether the target vehicle has an anchoring risk, so that the anchoring risk can be identified before the target vehicle anchors (e.g., a few hours before the anchoring occurs, a few days before the anchoring occurs, etc.) and an anchoring risk warning is issued, so that the user can intervene in time to ensure driving safety.

[0080] In a specific embodiment, the multiple types of breakdown-related data include vehicle operation data; when the vehicle operation data includes battery cell voltage data, the battery cell voltage data is the first breakdown-related data, and the data characteristics of the battery cell voltage data include at least one of the following: a first battery cell voltage difference, a second battery cell voltage difference, a voltage fluctuation characterization value corresponding to each battery cell, and the number of voltage abnormalities of the battery pack within a first time window, wherein the first battery cell voltage difference is the voltage difference between different battery cells, and the second battery cell voltage difference is the difference between the sum of the voltages of each battery cell and the total voltage of the battery pack.

[0081] The "implicit signal" of the target vehicle's breakdown - the cell voltage data, is converted into an "explicit indicator" identifiable by the prediction model - the first cell voltage difference, the second cell voltage difference, the voltage fluctuation characterization value corresponding to each cell, and the number of voltage anomalies of the battery pack in the first time window. The first cell voltage difference, the second cell voltage difference, the voltage fluctuation characterization value corresponding to each cell, and the number of voltage anomalies of the battery pack in the first time window can more concretely or more explicitly express the relationship between the cell voltage data and the vehicle breakdown. Subsequently, based on the first cell voltage difference, the second cell voltage difference, the voltage fluctuation characterization value corresponding to each cell, and the number of voltage anomalies of the battery pack in the first time window, it is possible to more accurately predict whether the target vehicle has a risk of breakdown, thereby identifying the risk of breakdown and issuing a warning of the risk of breakdown before the target vehicle breaks down (e.g., a few hours before the breakdown occurs, a few days before the breakdown occurs, etc.), so that the user can intervene in time to ensure driving safety.

[0082] The size of the first time window is not limited and can be set according to actual use needs. In addition, it should be noted that the voltage abnormality of the battery pack in the first time window can be understood as the voltage being too low or too high, such as the voltage being higher than the first threshold and the voltage being lower than the second threshold.

[0083] In a specific implementation, the voltage fluctuation characterization value corresponding to the battery cell includes the voltage change rate of the battery cell.

[0084] In one specific embodiment, the first cell voltage difference includes a voltage difference between a cell with a maximum voltage and a cell with a minimum voltage. Of course, in other specific embodiments, the first cell voltage difference may also be a voltage difference between any two cells, which is not limited here.

[0085] In a specific embodiment, the multiple types of breakdown-related data include vehicle operation data; when the vehicle operation data includes battery cell current data, the battery cell current data is the first breakdown-related data, and the data characteristics of the battery cell current data include at least one of the following: a discharge current fluctuation characterization value corresponding to each battery cell, the number of abnormal charging currents of the battery pack within the second time window, and the correlation between the current and voltage of each battery cell.

[0086] Converting the "implicit signal" of the target vehicle's breakdown - the cell current data, into an "explicit indicator" identifiable by the prediction model - the discharge current fluctuation characterization value corresponding to each cell, the number of abnormal charging current of the battery pack in the second time window, and the correlation between the current and voltage of each cell, can express the relationship between the cell current data and the vehicle breakdown in a more concrete or explicit way. Subsequently, based on the discharge current fluctuation characterization value corresponding to each cell, the number of abnormal charging current of the battery pack in the second time window, and the correlation between the current and voltage of each cell, it can more accurately predict whether the target vehicle has a breakdown risk, thereby identifying the breakdown risk and issuing a breakdown risk warning before the target vehicle breaks down (e.g., a few hours before the breakdown occurs, a few days before the breakdown occurs, etc.), so that users can intervene in time to ensure driving safety.

[0087] Among them, the size of the second time window is not limited and can be specifically set according to actual use needs. In addition, it should be noted that the abnormal charging current of the battery pack in the second time window can be understood as the charging current is too low or too high, such as the charging current is higher than the third threshold and the charging current is lower than the fourth threshold. Furthermore, the correlation between the current and voltage of each battery cell can be represented by whether it is positively correlated or negatively correlated, and the corresponding correlation coefficient.

[0088] In a specific implementation, the discharge current fluctuation characterization value corresponding to the battery cell includes the discharge current change rate of the battery cell.

[0089] In a specific embodiment, the multiple types of breakdown-related data include vehicle operation data; when the vehicle operation data includes vehicle temperature data, the vehicle temperature data is the first breakdown-related data, and the data characteristics of the vehicle temperature data include at least one of the following: the temperature distribution difference of each battery cell, the temperature fluctuation characterization value of each battery cell, and the number of temperature abnormalities of the battery pack within a third time window.

[0090] Converting the "implicit signal" of the target vehicle's breakdown - vehicle temperature data, into "explicit indicators" identifiable by the prediction model - the temperature distribution differences of each battery cell, the temperature fluctuation characterization value of each battery cell, and the number of temperature anomalies of the battery pack within the third time window, can more concretely or explicitly express the relationship between the vehicle temperature data and the vehicle breakdown. Subsequently, based on the temperature distribution differences of each battery cell, the temperature fluctuation characterization value of each battery cell, and the number of temperature anomalies of the battery pack within the third time window, it can more accurately predict whether the target vehicle has a risk of breakdown, thereby identifying the risk of breakdown and issuing a warning of the risk of breakdown before the target vehicle breaks down (e.g., a few hours before the breakdown occurs, a few days before the breakdown occurs, etc.), so that users can intervene in time to ensure driving safety.

[0091] The size of the third time window is not limited and can be set according to actual use needs. In addition, it should be noted that the abnormal temperature of the battery pack in the third time window can be understood as the battery pack temperature being too low or too high, such as the temperature being higher than the fifth threshold and the temperature being lower than the sixth threshold.

[0092] In one specific embodiment, the temperature distribution difference of the battery cells includes the temperature difference between the battery cell with the maximum temperature and the battery cell with the minimum temperature. Of course, in other specific embodiments, the temperature distribution difference of the battery cells may also be the temperature difference between any two battery cells, which is not limited here.

[0093] In a specific implementation, the temperature fluctuation characterization value of the battery cell includes the temperature change rate of the battery cell.

[0094] In a specific embodiment, the multiple types of breakdown-related data include vehicle operation data; when the vehicle operation data includes charge state data of the battery pack, the charge state data is the first breakdown-related data, and the data characteristics of the charge state data include at least one of the following: the relationship between the charge fluctuation characterization value of the battery pack, the charge consumption value of the battery pack and the mileage of the target vehicle.

[0095] Converting the "implicit signal" of the target vehicle's breakdown - the charge state data of the battery pack - into an "explicit indicator" recognizable by the prediction model - the relationship between the charge fluctuation characterization value of the battery pack and the charge consumption value of the battery pack and the mileage of the target vehicle, can express the relationship between the charge state data of the battery pack and the vehicle breakdown in a more concrete or explicit way. Subsequently, based on the relationship between the charge fluctuation characterization value of the battery pack and the charge consumption value of the battery pack and the mileage of the target vehicle, it can more accurately predict whether the target vehicle has a breakdown risk, thereby identifying the breakdown risk and issuing a breakdown risk warning before the target vehicle breaks down (e.g., a few hours before the breakdown occurs, a few days before the breakdown occurs, etc.), so that users can intervene in time to ensure driving safety.

[0096] In a specific embodiment, the charge fluctuation characterization value of the battery pack includes a charge decrease rate of the battery pack.

[0097] In a specific embodiment, the vehicle operation data is time series data, and the data features corresponding to each vehicle operation data are time series feature sequences, wherein the time series feature sequence corresponding to the battery cell voltage data includes the first battery cell voltage difference, the second battery cell voltage difference, the voltage fluctuation characterization value corresponding to each battery cell and the number of voltage anomalies at different times; the time series feature sequence corresponding to the battery cell current data includes the discharge current fluctuation characterization value corresponding to different times, the number of charging current anomalies, and the correlation between the current and voltage of each battery cell; the time series feature sequence corresponding to the vehicle temperature data includes the temperature distribution difference, the temperature fluctuation characterization value, and the number of temperature anomalies corresponding to different times; the time series feature sequence corresponding to the charge state data includes the charge fluctuation characterization value corresponding to different times, and the relationship between the charge value of the battery pack and the mileage of the target vehicle.

[0098] In a specific embodiment, when the anchoring-related data includes external environment data, and the external environment data includes external environment temperature, the external environment temperature is the first anchoring-related data, and the data characteristics of the external environment temperature include at least one of the following: a statistical value of the central trend of the external environment temperature, a characterization value of the external environment temperature fluctuation, and the distribution of the external environment temperature within a fourth time window.

[0099] Converting the "implicit signal" of the target vehicle's breakdown - the external ambient temperature, into an "explicit indicator" identifiable by the prediction model - the external ambient temperature central trend statistical value, the external ambient temperature fluctuation characterization value and the external ambient temperature distribution within the fourth time window, can express the relationship between the external ambient temperature and the vehicle breakdown in a more concrete or explicit way. Subsequently, based on the external ambient temperature central trend statistical value, the external ambient temperature fluctuation characterization value and the external ambient temperature distribution within the fourth time window, it can more accurately predict whether the target vehicle has a breakdown risk, thereby identifying the breakdown risk and issuing a breakdown risk warning before the target vehicle breaks down (e.g., a few hours before the breakdown occurs, a few days before the breakdown occurs, etc.), so that the user can intervene in time to ensure driving safety.

[0100] There is no limitation on the size of the fourth time window, and it can be set according to actual use needs.

[0101] In a specific implementation, the external environment temperature fluctuation characterization value includes the external environment temperature change rate.

[0102] In a specific implementation, the external environment temperature distribution includes a maximum temperature and a minimum temperature within the fourth time window.

[0103] In one specific implementation, the external environment temperature central tendency statistical value may be the average value of the external environment temperature, that is, the external average environment temperature. Of course, in other specific implementations, the external environment temperature central tendency statistical value may also be the median value of the external environment temperature, which is not limited here.

[0104] In a specific embodiment, when the anchoring-related data includes external environment data, and the external environment data includes external environment humidity, the external environment humidity is the first anchoring-related data, and the data characteristics of the external environment humidity include at least one of the following: a statistical value of the central trend of the external environment humidity, and a characterization value of the fluctuation of the external environment humidity.

[0105] Converting the "implicit signal" of target vehicle breakdown - external environmental humidity, into "explicit indicators" recognizable by the prediction model - external environmental humidity central trend statistical values ​​and external environmental humidity fluctuation characterization values, can express the relationship between external environmental humidity and vehicle breakdown in a more concrete or explicit way. Subsequently, based on the external environmental humidity central trend statistical values ​​and external environmental humidity fluctuation characterization values, it can more accurately predict whether the target vehicle has a breakdown risk, thereby identifying the breakdown risk and issuing a breakdown risk warning before the target vehicle breaks down (e.g., a few hours before the breakdown occurs, a few days before the breakdown occurs, etc.), so that users can intervene in time to ensure driving safety.

[0106] In a specific implementation, the external environment humidity fluctuation characterization value includes the external environment humidity change rate.

[0107] In one specific implementation, the external environment humidity central tendency statistical value may be the average value of the external environment humidity, that is, the external average environment humidity. Of course, in other specific implementations, the external environment humidity central tendency statistical value may also be the median value of the external environment humidity, which is not limited here.

[0108] In a specific embodiment, the external ambient temperature and the external ambient humidity are time series data, and the data features corresponding to the external ambient temperature and the external ambient humidity are time series feature sequences, wherein the time series feature sequence corresponding to the external ambient temperature includes the external ambient temperature central trend statistical values ​​corresponding to different moments, the external ambient temperature fluctuation characterization values, and the external ambient temperature distribution; the time series feature sequence corresponding to the external ambient humidity includes the external ambient humidity central trend statistical values ​​corresponding to different moments, the external ambient temperature fluctuation characterization values.

[0109] In a specific embodiment, when the breakdown-related data includes historical maintenance data of the target vehicle, the historical maintenance data is the first breakdown-related data, and the data characteristics of the historical maintenance data include at least one of the following: the total number of historical breakdowns of the target vehicle, the number of times the target vehicle has broken down due to battery cell failures, the statistical results of parts replacement of the target vehicle, the fault code of the fault that caused the target vehicle to break down, the breakdown interval of the target vehicle, the vehicle operation data of the target vehicle before the breakdown, and the vehicle operation status of the target vehicle after the breakdown and repair.

[0110] The "implicit signal" of the target vehicle's breakdown - historical maintenance data, is converted into an "explicit indicator" that can be identified by the prediction model - the target vehicle's total historical breakdown times, the number of target vehicle breakdowns caused by battery cell failures, the target vehicle's parts replacement statistics, the fault code of the fault that caused the target vehicle to break down, the target vehicle's breakdown interval, the target vehicle's vehicle operation data before the breakdown, and the target vehicle's vehicle operation status after the breakdown repair. This can more concretely or explicitly express the relationship between historical maintenance data and vehicle breakdowns. Subsequently, based on the target vehicle's total historical breakdown times, the number of target vehicle breakdowns caused by battery cell failures, the target vehicle's parts replacement statistics, the fault code of the fault that caused the target vehicle to break down, the target vehicle's breakdown interval, the target vehicle's vehicle operation data before the breakdown, and the target vehicle's vehicle operation status after the breakdown repair, it can more accurately predict whether the target vehicle has a breakdown risk, thereby identifying the breakdown risk and issuing a breakdown risk warning before the target vehicle breaks down (e.g., a few hours before the breakdown occurs, a few days before the breakdown occurs, etc.), so that users can intervene in time to ensure driving safety.

[0111] It should be noted that the statistical results of parts replacement of the target vehicle may include whether key components such as batteries, battery cells, and BMS have been replaced.

[0112] Of course, in other implementations, all anchoring related data may be respectively taken as first anchoring related data, and statistics may be performed on the first anchoring related data to obtain data features of the first anchoring related data.

[0113] In one embodiment, data features corresponding to a plurality of types of breakdown-related data are obtained respectively, specifically: at least one type of breakdown-related data is used as the second breakdown-related data, and the second breakdown-related data is directly used as the data feature of the second breakdown-related data. Since the second breakdown-related data itself can be visualized or explicitly express the relationship between it and the breakdown of the vehicle, it is directly used as its corresponding data feature.

[0114] In a specific implementation, the multiple types of anchoring-related data include vehicle operation data; when the vehicle operation data include whole vehicle operation data, the whole vehicle operation data is the second anchoring-related data. The whole vehicle operation data of the target vehicle can itself visualize or explicitly express the relationship between the whole vehicle operation data and the vehicle anchoring. Subsequently, based on the whole vehicle operation data, it can accurately predict whether the target vehicle has an anchoring risk, thereby identifying the anchoring risk and issuing an anchoring risk warning before the target vehicle anchors (e.g., several hours before the anchoring occurs, several days before the anchoring occurs, etc.), so that the user can intervene in time to ensure driving safety.

[0115] In a specific embodiment, the multiple types of anchoring-related data include external environment data. When the external environment data include geographic data, the geographic data is the second anchoring-related data. The geographic data of the target vehicle can itself visualize or explicitly express the relationship between the geographic data of the target vehicle and the vehicle anchoring. Subsequently, based on the geographic data of the target vehicle, it is possible to accurately predict whether the target vehicle has an anchoring risk, thereby identifying the anchoring risk and issuing an anchoring risk warning before the target vehicle anchors (e.g., several hours before the anchoring occurs, several days before the anchoring occurs, etc.), so that the user can intervene in time to ensure driving safety.

[0116] Of course, in other implementations, all anchoring related data may be respectively used as second anchoring related data, and the second anchoring related data may be directly used as the data feature of the second anchoring related data.

[0117] In one embodiment, multiple anchor-related data include time series data and non-time series data; before respectively obtaining the data features corresponding to the multiple anchor-related data, the time series data will be subjected to a first preprocessing; wherein the first preprocessing includes at least one of the following: denoising, missing data completion, abnormal data processing, inconsistent data processing, and data alignment; and, the non-time series data will be subjected to a second preprocessing; wherein the second preprocessing includes at least one of the following: data format unification processing, missing data completion, and data alignment.

[0118] That is to say, a first preprocessing is performed on the time series data among the multiple anchorage-related data, and a second preprocessing is performed on the non-time series data among the multiple anchorage-related data, so as to convert the time series data and non-time series data among the multiple anchorage-related data into high-quality data, and subsequently anchorage prediction is performed based on the preprocessed multiple anchorage-related data, which can improve the accuracy of anchorage prediction.

[0119] In a specific embodiment, the breakdown-related data includes vehicle operation data of the target vehicle, and the vehicle operation data is time series data; a first preprocessing is performed on the time series data, specifically: denoising, missing data completion, abnormal data processing, inconsistent data processing, and data time alignment are performed on the vehicle operation data.

[0120] In a specific implementation, a signal processing algorithm (eg, moving average, Kalman filter, etc.) may be used to perform denoising on the vehicle operation data.

[0121] In a specific implementation, for a small amount of randomly missing data, interpolation or average value can be used to complete the missing data. For data that is continuously missing or has large gaps, a regression model can be used to predict and fill in the missing values ​​to complete the missing data.

[0122] In a specific implementation, statistical methods (such as Zscore, IQR, etc.) can be used to detect outliers in vehicle operation data and process the outliers. The processing of outliers can be to delete the outliers; or to adjust the outliers to the mean, median, etc.; or to mark the outliers and not use the marked outliers in the future.

[0123] In a specific implementation, a logical consistency check is performed on the operation data of different vehicles in the same time period, and the logically inconsistent data is processed, wherein the processing of the logically inconsistent data may be to delete the logically inconsistent data, or to adjust the logically inconsistent data to logically consistent data.

[0124] For example, in a battery system, the battery pack of the target vehicle is composed of multiple battery cells, and the total voltage of the battery pack should theoretically be equal to the sum of the voltages of each battery cell.

[0125] For another example, according to the battery charging and discharging principle, when the battery is charging, current flows into the battery, and the battery's SOC will increase as the charging time increases; when the battery is discharging, current flows out of the battery, and the battery's SOC will gradually decrease as the discharge process progresses. Moreover, during the charging and discharging process, the magnitude of the current is related to the rate of change of the SOC. For example, in the early stages of charging, the larger the current, the faster the SOC rises; as the current gradually fills up, the current will gradually decrease, and the rate of increase of the SOC will also slow down. The opposite is true during the discharge process, where the larger the discharge current, the faster the SOC decreases. Therefore, the change in current should theoretically match the trend of the change in SOC.

[0126] In a specific implementation, the vehicle operation data with different time stamps may be time aligned using methods such as interpolation and resampling to ensure that different vehicle operation data are comparable at the same time point.

[0127] In a specific implementation, the breakdown-related data includes historical maintenance data of the target vehicle, which is non-time series data; a second preprocessing is performed on the non-time series data, specifically: the historical maintenance data of the target vehicle is processed in a unified data format and missing data is completed.

[0128] Since the source channels of different historical maintenance data of the target vehicle are different (for example, the source channel may be an after-sales service system, a maintenance report, a fault diagnosis system, etc.), the recording formats of different historical maintenance data may be different. Therefore, it is necessary to unify the data format of the historical maintenance data.

[0129] In a specific implementation, the missing historical maintenance data can be supplemented by using technologies such as time series prediction and maintenance record correlation analysis. In addition, for the missing of important historical maintenance data, such as the cause of vehicle breakdown or maintenance measures, it can be inferred by combining historical data of similar faults to complete the data.

[0130] In a specific implementation, the anchor-related data includes vehicle operation data of the target vehicle and external environment data of the target vehicle, the external environment data includes weather data and geographic data, the vehicle operation data and weather data are time series data, and the geographic data is non-time series data; the step of preprocessing the anchor-related data is specifically: aligning the vehicle operation data with the weather data in time; and aligning the vehicle operation data with the geographic data in data space. That is, aligning the weather data with the vehicle operation data in time, and aligning the geographic data with the vehicle operation data in data space, to ensure that the external environment data of the target vehicle and the vehicle operation data of the target vehicle are aligned in the same time and space.

[0131] Since the characteristic scales of the data features corresponding to the various anchoring-related data may be different, in one embodiment, after respectively acquiring the data features corresponding to the various anchoring-related data, the data features corresponding to the various anchoring-related data are standardized or normalized.

[0132] Step S13: performing anchoring prediction based on data features corresponding to a plurality of anchoring-related data to obtain an anchoring prediction result.

[0133] In this embodiment, based on the data features corresponding to the various types of anchor-related data, anchor-down prediction is performed to obtain an anchor-down prediction result; wherein, the anchor-down prediction result includes a first prediction result, and the first prediction result is used to characterize whether the target vehicle has an anchor-down risk. The data features corresponding to the anchor-down related data can express the relationship between the anchor-down related data and the vehicle anchor-down in a more concrete or explicit way, and based on the data features corresponding to the anchor-down related data, it is possible to accurately predict whether the target vehicle has an anchor-down risk; in addition, anchor-down prediction is performed based on the data features corresponding to the various types of anchor-related data, and it is possible to more comprehensively capture key information that may cause the target vehicle to have an anchor-down risk, thereby being able to more accurately predict whether the target vehicle has an anchor-down risk, and then being able to identify the anchor-down risk and issue an anchor-down risk warning before the target vehicle anchors (e.g., a few hours before the anchor occurs, a few days before the anchor occurs, etc.), so that the user can intervene in time to ensure driving safety.

[0134] In one embodiment, the first prediction result is used to characterize that the target vehicle has a risk of breaking down, and the prediction result also includes a second prediction result, and the second prediction result includes at least one of the following: the predicted time of occurrence of the risk of breaking down, the predicted cause of the risk of breaking down, and the solution to the risk of breaking down. That is, when the prediction of breaking down is performed based on the data features corresponding to the various data related to breaking down, and it is determined that the target vehicle has a risk of breaking down, the time of occurrence of the risk of breaking down, the cause of the risk of breaking down, the solution to the risk of breaking down, and other related information are also predicted. That is, the prediction result of breaking down is diverse and rich.

[0135] In one embodiment, after performing a prediction of a breakdown based on data features corresponding to a plurality of breakdown-related data and obtaining a breakdown prediction result, at least part of the information in the breakdown prediction result is fed back to the user, wherein the feedback method includes at least one of the following: displaying at least part of the information on a display screen of the target vehicle, playing at least part of the information in an audio on the target vehicle, and sending at least part of the information to an associated terminal. There are various ways to feed back at least part of the information in the breakdown prediction result to the user, and the feedback method can be flexibly selected.

[0136] When the feedback method is to display at least part of the information on the display screen of the target vehicle, at least part of the information in the breakdown prediction result can be presented in a visual manner, so as to display at least part of the information in the breakdown prediction result to the user in a concise and intuitive manner, so that the user can quickly understand the potential risks of the target vehicle and take corresponding measures in time to ensure driving safety.

[0137] In which, at least part of the information in the breakdown prediction result fed back to the user is not limited. For example, the first prediction result is used to characterize that the target vehicle has a breakdown risk, and the first prediction result and the second prediction result are fed back to the user, and the second prediction result includes the predicted occurrence time of the breakdown risk, the predicted cause of the breakdown risk, and the solution to the breakdown risk.

[0138] In one embodiment, before performing anchoring prediction based on data features corresponding to multiple anchoring-related data and obtaining the anchoring prediction result, an associated feature group whose feature correlation meets a preset correlation condition is found from the data features corresponding to the multiple anchoring-related data, and a representative feature is selected from the associated feature group, and other data features in the associated feature group except the representative feature are deleted.

[0139] The feature correlation between each data feature in the associated feature group meets the preset correlation condition, indicating that the data features in the associated feature group have a strong correlation and there may be information overlap. Therefore, representative features are selected from the associated feature group and retained, and other data features in the associated feature group are deleted to effectively reduce the redundant features in the data features corresponding to the various anchor-related data, while retaining the key features in the data features corresponding to the various anchor-related data. In other words, redundant features are identified and deleted by quantifying the feature correlation between the data features corresponding to the various anchor-related data.

[0140] The preset relevance condition is not limited. For example, the preset relevance condition is greater than or equal to a relevance threshold.

[0141] In a specific implementation, clustering may be used to find out the associated feature group whose feature correlation meets the preset correlation condition.

[0142] In other specific implementations, the feature correlation between each pair of data features may be calculated, and then an associated feature group whose feature correlation meets a preset correlation condition may be determined.

[0143] Among them, the MIC (Maximal Information Coefficient) value between the two data features can be calculated as the feature correlation between the two data features.

[0144] In one embodiment, before performing a breakdown prediction based on data features corresponding to a plurality of breakdown-related data and obtaining a breakdown prediction result, each time series feature sequence is also found from the data features corresponding to the plurality of breakdown-related data, and each time series feature sequence is combined into a time series data feature. In other words, the time series feature sequences in the data features corresponding to the plurality of breakdown-related data are combined to obtain a time series data feature to construct a combined feature. Combining the time series feature sequences can enhance the ability to capture hidden relationships in the time series data, thereby improving the accuracy of subsequent vehicle breakdown prediction based on the time series data features.

[0145] In a specific implementation, the time series feature sequence includes time series data corresponding to different moments. The time series data corresponding to the same moment can also be combined as the time series data feature corresponding to the moment. In this case, multiple combined features will be constructed (one time series data feature corresponds to one moment).

[0146] In one embodiment, before performing a breakdown prediction based on data features corresponding to a plurality of breakdown-related data and obtaining a breakdown prediction result, non-time series data is searched from the data features corresponding to the plurality of breakdown-related data, and the non-time series data is combined to obtain non-time series data features. Combining non-time series data can enhance the ability to capture hidden relationships in non-time series data, thereby improving the accuracy of subsequent vehicle breakdown prediction based on non-time series data features.

[0147] In one embodiment, the vehicle breakdown warning method provided by the present application can be executed locally. Of course, in other embodiments, the vehicle breakdown warning method provided by the present application can also be executed in the cloud, and the cloud service can also provide nationwide vehicle breakdown risk prediction analysis to help enterprises or service providers understand the vehicle health status in advance.

[0148] See also Figure 2 , Figure 2 yes Figure 1 It should be noted that if there is substantially the same result, this embodiment does not use Figure 2 The process sequence shown is limited. Figure 2 As shown, the data features corresponding to the various anchor-related data include time series data features and non-time series data features. This embodiment includes: Step S21: using a time series processing model to detect the time series dependency of time series data features to obtain time-dependent features.

[0149] In this embodiment, the time series processing model is used to detect the time series data features’ time series dependencies to obtain time-dependent features. That is, the time series processing model is used to model the input time series data features, extract the high-order representation that can reflect the dynamic change law of the time dimension, namely the time-dependent features, to capture the long-term time series data features’ time series dependencies, thereby providing key feature support for subsequent vehicle breakdown prediction.

[0150] In one embodiment, the time series processing model is a long short-term memory network model (Long Short-Term Memory, LSTM). Of course, in other embodiments, the time series processing model can also be other models, which are not limited here.

[0151] Step S22: using the decision model to identify the association between the non-time series data features and the anchoring to obtain the anchoring association features.

[0152] In this embodiment, the decision model is used to identify the association between non-time series data features and the breakdown to obtain the breakdown-related features. In other words, the decision model can identify the breakdown-related features associated with the breakdown risk from the non-time series data features, that is, the decision model can accurately locate the breakdown risk factor from the non-time series data features, thereby providing key feature support for subsequent vehicle breakdown prediction.

[0153] In one embodiment, a single anchoring-related feature that is most associated with anchoring risk may be identified from time series data features using a decision model. Of course, in other embodiments, multiple anchoring-related features associated with anchoring risk may be identified from time series data features using a decision model, which is not limited here.

[0154] In one embodiment, the decision model is a random forest model (RF). Of course, in other embodiments, the decision model may also be a LASSO model, which is not limited here.

[0155] Step S23: Use the time-dependent features and the anchoring-related features to make predictions and obtain anchoring prediction results.

[0156] In this embodiment, the time-dependent features and the anchoring-related features are used for prediction to obtain the anchoring prediction results. The time-dependent features are obtained by using the time series processing model to detect the time series data features’ dependencies on the time series, which can reflect the long-term attenuation trend (such as battery capacity decline, etc.) and short-term abnormal fluctuations (such as battery temperature surge, etc.) of the target vehicle’s time series data, and model the dynamic evolution law; the anchoring-related features are obtained by using the decision model to identify the association between non-time series data features and anchoring, which can mine the correlation between discrete events. Therefore, using the time-dependent features and the anchoring-related features for prediction, while covering the risks of “progressive aging” and “sudden impact”, avoiding the blind spots of a single data source, can improve the accuracy of vehicle anchoring risk prediction.

[0157] In one embodiment, a fully connected layer is used to perform prediction based on time-dependent features and anchor-related features to obtain an anchor prediction result.

[0158] In one embodiment, the time-dependent feature and the anchoring-related feature are used for prediction to obtain the anchoring prediction result, specifically: the time-dependent feature and the anchoring-related feature are fused to obtain the fused feature; and the anchoring prediction result is obtained by prediction based on the fused feature. That is, the time-dependent feature and the anchoring-related feature are fused, and anchoring prediction is performed based on the fused feature obtained after fusion, so as to obtain the anchoring prediction result. By fusing the time-dependent feature with the anchoring-related feature, a multi-dimensional and multi-granular anchoring risk characterization system is constructed, so that the accuracy of anchoring risk prediction can be improved.

[0159] In a specific implementation, the fusion of the time-dependent feature and the anchor-related feature may be to concatenate the time-dependent feature and the anchor-related feature.

[0160] In a specific embodiment, the time series processing model is a long short-term memory network model; the decision model is a random forest model; the fully connected layer is used to predict based on time-dependent features and anchoring-related features to obtain anchoring prediction results. The long short-term memory network model and the random forest model are trained separately, wherein a binary cross entropy loss function is used to adjust the model's hyperparameters through grid search, such as the number of layers and hidden units of the long short-term memory network model, the number of trees of the random forest model, etc., to improve the model performance; then, the performance of the long short-term memory network model and the random forest model is evaluated using accuracy, recall rate, F1 score, etc., and the model is verified using cross-validation to prevent overfitting. After completing the training of the long short-term memory network model and the random forest model, the fully connected layer is trained.

[0161] In other embodiments, a multi-input-output model can also be used to perform anchor prediction based on data features corresponding to a variety of anchor-related data to obtain an anchor prediction result. Specifically, the multi-input-output model includes a time-series branch, a non-time-series branch, and a prediction branch. The time-series branch, the non-time-series branch, and the prediction branch work together to form a multi-input-output model that can perform anchor prediction based on data features corresponding to a variety of anchor-related data. The time-series branch can be regarded as a time-series processing layer for processing time-series data in the multi-input-output model. The time-series branch detects the time-series dependency of the time-series data features to obtain time-dependent features; the non-time-series branch can be regarded as a non-time-series processing layer for processing non-time-series data in the multi-input-output model. The non-time-series branch identifies the association between non-time-series data features and anchoring to obtain anchor-related features; the prediction branch can be regarded as a prediction layer for anchor prediction in the multi-input-output model. The prediction branch uses time-dependent features and anchor-related features for prediction to obtain anchor prediction results.

[0162] Among them, the time series processing layer can be an LSTM layer, the non-time series processing layer can be an RF layer, and the prediction layer can be a fully connected layer.

[0163] See also Figure 3 , Figure 3 It is a structural diagram of an embodiment of a vehicle breakdown warning device provided by the present application. The vehicle breakdown warning device 30 includes a first acquisition module 31, a second acquisition module 32 and a prediction module 33. The first acquisition module 31 is used to acquire a variety of breakdown-related data of the target vehicle, wherein the multiple breakdown-related data include historical maintenance data of the target vehicle, at least one of the external environment data of the target vehicle, and vehicle operation data of the target vehicle; the second acquisition module 32 is used to respectively acquire data features corresponding to the multiple breakdown-related data; the prediction module 33 is used to perform breakdown prediction based on the data features corresponding to the multiple breakdown-related data to obtain a breakdown prediction result; wherein the breakdown prediction result includes a first prediction result, and the first prediction result is used to characterize whether the target vehicle has a breakdown risk.

[0164] Among them, the above-mentioned vehicle operation data includes at least one of the following: battery cell voltage data, battery cell current data, vehicle temperature data, battery pack charge status data, and vehicle operation data; the historical maintenance data includes at least one of the following: maintenance time, vehicle breakdown reason, maintenance content, vehicle status data before and after the failure, and the external environment conditions of the target vehicle when it breaks down; the external environment data includes at least one of weather data and geographic data; among them, the vehicle operation data and weather data are time series data.

[0165] Among them, the above-mentioned battery cell voltage data includes the voltage value of each battery cell in the battery pack of the target vehicle at different times; the battery cell current data includes the charging and discharging current of each battery cell at different times; the vehicle temperature data includes at least one of the following: the battery cell temperature at different times, the internal ambient temperature of the target vehicle at different times; the whole vehicle operation data includes at least one of the following: the vehicle speed of the target vehicle at different times, the power output of the target vehicle at different times; the vehicle status data before and after the fault includes at least one of the following: the vehicle operation status before and after the fault, the vehicle sensor data before and after the fault; the weather data includes at least one of the following: the external ambient temperature corresponding to different times, the external ambient humidity corresponding to different times; the geographical data includes road condition information.

[0166] Among them, the second acquisition module 32 is used to respectively acquire data features corresponding to multiple types of anchoring-related data, including: taking at least one anchoring-related data as first anchoring-related data, and performing statistics on the first anchoring-related data to obtain data features of the first anchoring-related data; and / or, the second acquisition module 32 is used to respectively take at least one anchoring-related data as second anchoring-related data, and directly take the second anchoring-related data as data features of second anchoring-related data.

[0167] Among them, the above-mentioned multiple types of breakdown-related data include vehicle operation data; in the case that the vehicle operation data includes battery cell voltage data, the battery cell voltage data is the first breakdown-related data, and the data characteristics of the battery cell voltage data include at least one of the following: a first battery cell voltage difference, a second battery cell voltage difference, a voltage fluctuation characterization value corresponding to each battery cell, and the number of voltage abnormalities of the battery pack within a first time window, wherein the first battery cell voltage difference is the voltage difference between different battery cells, and the second battery cell voltage difference is the difference between the sum of the voltages of each battery cell and the total voltage of the battery pack; in the case that the vehicle operation data includes battery cell current data, the battery cell current data is the first breakdown-related data, and the data characteristics of the battery cell current data include at least one of the following: a discharge current fluctuation characterization value corresponding to each battery cell, a discharge current fluctuation characterization value of the battery pack in the first The data characteristics of the vehicle temperature data include at least one of the following: the difference in temperature distribution of each battery cell, the temperature fluctuation characterization value of each battery cell, and the number of temperature anomalies of the battery pack in the third time window; When the vehicle operation data includes the state of charge data of the battery pack, the state of charge data is the first anchor-related data, and the data characteristics of the state of charge data include at least one of the following: the charge fluctuation characterization value of the battery pack, the relationship between the charge consumption value of the battery pack and the mileage of the target vehicle; When the vehicle operation data includes the whole vehicle operation data, the whole vehicle operation data is the second anchor-related data.

[0168] Among them, the above-mentioned vehicle operation data is time series data, and the data features corresponding to each vehicle operation data are time series feature sequences, among which the time series feature sequences corresponding to the battery cell voltage data include the first battery cell voltage difference, the second battery cell voltage difference, the voltage fluctuation characterization value corresponding to each battery cell and the number of voltage anomalies at different times; the time series feature sequences corresponding to the battery cell current data include the discharge current fluctuation characterization value corresponding to different times, the number of charging current anomalies, and the correlation between the current and voltage of each battery cell; the time series feature sequences corresponding to the vehicle temperature data include the temperature distribution difference, the temperature fluctuation characterization value and the number of temperature anomalies corresponding to different times; the time series feature sequences corresponding to the state of charge data include the temperature distribution difference, the temperature fluctuation characterization value and the number of temperature anomalies corresponding to different times. The timing feature sequence includes the relationship between the charge fluctuation characterization values ​​corresponding to different moments, the charge value of the battery pack and the mileage of the target vehicle; and / or, the voltage fluctuation characterization value corresponding to the battery cell includes the voltage change rate of the battery cell; and / or, the first battery cell voltage difference includes the voltage difference between the maximum voltage battery cell and the minimum voltage battery cell; and / or, the discharge current fluctuation characterization value corresponding to the battery cell includes the discharge current change rate of the battery cell; and / or, the temperature distribution difference of the battery cell includes the temperature difference between the maximum temperature battery cell and the minimum temperature battery cell; and / or, the temperature fluctuation characterization value of the battery cell includes the temperature change rate of the battery cell; and / or, the charge fluctuation characterization value of the battery pack includes the charge decrease rate of the battery pack.

[0169] Among them, when the anchoring-related data includes external environment data, and the external environment data includes the external environment temperature, the above-mentioned external environment temperature is the first anchoring-related data, and the data characteristics of the external environment temperature include at least one of the following: the external environment temperature central tendency statistical value, the external environment temperature fluctuation characterization value, and the external environment temperature distribution within the fourth time window; when the anchoring-related data includes the external environment data, and the external environment data includes the external environment humidity, the external environment humidity is the first anchoring-related data, and the data characteristics of the external environment humidity include at least one of the following: the external environment humidity central tendency statistical value, the external environment humidity fluctuation characterization value; when the anchoring-related data includes the historical maintenance data of the target vehicle, the historical maintenance data is the first anchoring-related data, and the data characteristics of the historical maintenance data include at least one of the following: the total number of historical anchoring times of the target vehicle, the number of times the target vehicle is anchored due to cell failure, the statistical results of parts replacement of the target vehicle, the fault code of the fault that caused the target vehicle to be anchored, the anchoring interval of the target vehicle, the vehicle operation data of the target vehicle before the anchoring, and the vehicle operation status of the target vehicle after the anchoring repair.

[0170] Among them, the above-mentioned external environment temperature and external environment humidity are time series data, and the data characteristics corresponding to the external environment temperature and the external environment humidity are time series feature sequences, wherein the time series feature sequence corresponding to the external environment temperature includes the external environment temperature central trend statistical values ​​corresponding to different moments, the external environment temperature fluctuation characterization values, and the external environment temperature distribution; the time series feature sequence corresponding to the external environment humidity includes the external environment humidity central trend statistical values ​​and the external environment humidity fluctuation characterization values ​​corresponding to different moments; and / or, the external environment temperature fluctuation characterization values ​​include the external environment temperature change rate; and / or, the external environment temperature distribution includes the highest temperature and the lowest temperature within the fourth time window; and / or, the external environment humidity fluctuation characterization values ​​include the external environment humidity change rate.

[0171] Among them, the prediction module 33 is also used to perform anchoring prediction based on the data features corresponding to the multiple anchoring-related data. Before obtaining the anchoring prediction result, from the data features corresponding to the multiple anchoring-related data, find out the associated feature group whose feature correlation meets the preset correlation condition, select the representative feature from the associated feature group, and delete other data features in the associated feature group except the representative feature; find out each time series feature sequence from the data features corresponding to the multiple anchoring-related data, and combine each time series feature sequence into a time series data feature.

[0172] Among them, the data features corresponding to the above-mentioned multiple anchoring-related data respectively include time series data features and non-time series data features; the prediction module 33 is used to perform anchoring prediction based on the data features corresponding to the multiple anchoring-related data to obtain anchoring prediction results, including: using a time series processing model to detect the time series dependency of time series data features to obtain time-dependent features; and using a decision model to identify the association between non-time series data features and anchoring to obtain anchoring-related features; using time-dependent features and anchoring-related features to perform predictions to obtain anchoring prediction results.

[0173] Among them, the above-mentioned time series processing model is a long short-term memory network model, and the decision model is a random forest model; and / or, the prediction module 33 is used to use time-dependent features and anchoring-related features to make predictions to obtain the anchoring prediction results, including: fusing time-dependent features and anchoring-related features to obtain fused features; making predictions based on fused features to obtain anchoring prediction results.

[0174] Among them, the above-mentioned multiple anchor-related data include time series data and non-time series data; the second acquisition module 32 is also used to perform a first preprocessing on the time series data before respectively acquiring the data features corresponding to the multiple anchor-related data; wherein the first preprocessing includes at least one of the following: denoising, missing data completion, abnormal data processing, inconsistent data processing, data alignment; and, performing a second preprocessing on the non-time series data; wherein the second preprocessing includes at least one of the following: data format unification processing, missing data completion, data alignment.

[0175] Among them, the above-mentioned anchoring-related data includes vehicle operation data of the target vehicle, and the vehicle operation data is time series data; the second acquisition module 32 is used to perform a first preprocessing on the time series data, including: denoising the vehicle operation data, completing missing data, processing abnormal data, processing inconsistent data, and aligning data time; and / or, the above-mentioned anchoring-related data includes historical maintenance data of the target vehicle, and the historical maintenance data is non-time series data; the second acquisition module 32 is used to perform a second preprocessing on the non-time series data, including: performing data format unification processing and missing data completion on the historical maintenance data of the target vehicle respectively; and / or, the above-mentioned anchoring-related data includes vehicle operation data of the target vehicle and external environment data of the target vehicle, and the external environment data includes weather data and geographic data, the vehicle operation data and weather data are time series data, and the geographic data is non-time series data; the second acquisition module 32 is used to perform a step of preprocessing the anchoring-related data, and also includes: aligning the vehicle operation data with the weather data in data time; and aligning the vehicle operation data with the geographic data in data space.

[0176] Among them, the above-mentioned first prediction result is used to characterize that the target vehicle has a risk of breaking down, and the anchorage prediction result also includes a second prediction result, and the second prediction result includes at least one of the following: the predicted occurrence time of the anchorage risk, the predicted cause of the anchorage risk, and the solution to the anchorage risk; and / or, the vehicle anchorage warning device 30 also includes a feedback module 34, and the feedback module 34 is used to perform an anchorage prediction based on data features corresponding to a variety of anchorage-related data, and after obtaining the anchorage prediction result, feedback at least part of the information in the anchorage prediction result to the user, wherein the feedback method includes at least one of the following: displaying at least part of the information on the display screen of the target vehicle, playing at least part of the information on the target vehicle through audio, and sending at least part of the information to an associated terminal.

[0177] See also Figure 4 , Figure 44 is a schematic diagram of the structure of an embodiment of an electronic device provided by the present application. The electronic device 40 includes a memory 41 and a processor 42 coupled to each other, and the processor 42 is used to execute program instructions stored in the memory 41 to implement the steps of any of the above-mentioned vehicle breakdown warning method embodiments. In a specific implementation scenario, the electronic device 40 may include but is not limited to: a microcomputer, a server, and in addition, the electronic device 40 may also include a mobile device such as a laptop computer and a tablet computer, which is not limited here.

[0178] Specifically, the processor 42 is used to control itself and the memory 41 to implement the steps of any of the above-mentioned vehicle breakdown warning method embodiments. The processor 42 can also be called a CPU (Central Processing Unit). The processor 42 may be an integrated circuit chip with signal processing capabilities. The processor 42 can also be a general-purpose processor, a digital signal processor (Digital Signal Processor, DSP), an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field-programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. In addition, the processor 42 can be implemented by an integrated circuit chip.

[0179] See also Figure 5 , Figure 5 It is a schematic diagram of the structure of an embodiment of a computer-readable storage medium provided by the present application. The computer-readable storage medium 50 of the embodiment of the present application stores a program instruction 51, and when the program instruction 51 is executed, it implements the method provided by any embodiment of the vehicle breakdown warning method of the present application and any non-conflicting combination. Among them, the program instruction 51 can form a program file and be stored in the above-mentioned computer-readable storage medium 50 in the form of a software product, so that a computer device (which can be a personal computer, a server, or a network device, etc.) executes all or part of the steps of the methods of each implementation method of the present application. The aforementioned computer-readable storage medium 50 includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a disk or an optical disk, or a terminal device such as a computer, a server, a mobile phone, and a tablet.

[0180] The above description is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly used in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A vehicle breakdown warning method, characterized in that: The method comprises: Acquiring a plurality of breakdown-related data of a target vehicle, wherein the plurality of breakdown-related data includes historical maintenance data of the target vehicle, at least one of external environment data of the target vehicle, and vehicle operation data of the target vehicle; respectively obtaining data features corresponding to the plurality of anchoring-related data; An anchoring prediction is performed based on data features corresponding to the multiple anchoring-related data to obtain an anchoring prediction result; wherein the anchoring prediction result includes a first prediction result, and the first prediction result is used to characterize whether the target vehicle has an anchoring risk.

2. The method according to claim 1, characterized in that The vehicle operation data includes at least one of the following: battery cell voltage data, battery cell current data, vehicle temperature data, battery pack charge state data, and vehicle operation data; The historical maintenance data includes at least one of the following: maintenance time, vehicle breakdown reason, maintenance content, vehicle status data before and after the failure, and the external environment of the target vehicle when the vehicle broke down; The external environment data includes at least one of weather data and geographic data; Among them, the vehicle operation data and weather data are time series data.

3. The method according to claim 2, characterized in that The battery cell voltage data includes the voltage value of each battery cell in the battery pack of the target vehicle at different times; The battery cell current data includes the charge and discharge current of each battery cell at different times; The vehicle temperature data includes at least one of the following: the battery core temperature at different times, the internal environment temperature of the target vehicle at different times; The whole vehicle operation data includes at least one of the following: the vehicle speed of the target vehicle at different times, the power output of the target vehicle at different times; The vehicle status data before and after the failure includes at least one of the following: vehicle operating status before and after the failure, vehicle sensor data before and after the failure; The weather data includes at least one of the following: external environment temperatures corresponding to different times, and external environment humidity corresponding to different times; The geographic data includes road condition information.

4. The method according to any one of claims 1 to 3, characterized in that: The respectively obtaining data features corresponding to the multiple anchoring related data includes: taking at least one of the anchoring-related data as first anchoring-related data, and performing statistics on the first anchoring-related data to obtain data features of the first anchoring-related data; and / or, At least one of the anchoring-related data is used as the second anchoring-related data, and the second anchoring-related data is directly used as the data feature of the second anchoring-related data.

5. The method according to claim 4, characterized in that The plurality of breakdown-related data include the vehicle operation data; In the case where the vehicle operation data includes battery cell voltage data, the battery cell voltage data is the first breakdown-related data, and data features of the battery cell voltage data include at least one of the following: a first battery cell voltage difference, a second battery cell voltage difference, a voltage fluctuation characterization value corresponding to each battery cell, and the number of voltage anomalies of the battery pack within a first time window, wherein the first battery cell voltage difference is a voltage difference between different battery cells, and the second battery cell voltage difference is a difference between the sum of the voltages of each battery cell and the total voltage of the battery pack; In the case where the vehicle operation data includes battery cell current data, the battery cell current data is the first breakdown related data, and data features of the battery cell current data include at least one of the following: a discharge current fluctuation characterization value corresponding to each battery cell, the number of abnormal charging currents of the battery pack within the second time window, and a correlation between the current and the voltage of each battery cell; In the case where the vehicle operation data includes vehicle temperature data, the vehicle temperature data is the first breakdown-related data, and data features of the vehicle temperature data include at least one of the following: a temperature distribution difference of each battery cell, a temperature fluctuation characterization value of each battery cell, and a number of abnormal temperature occurrences of the battery pack within a third time window; In the case where the vehicle operation data includes state of charge data of a battery pack, the state of charge data is the first anchor-related data, and data characteristics of the state of charge data include at least one of the following: a charge fluctuation characterization value of the battery pack, a relationship between a charge consumption value of the battery pack and a mileage of the target vehicle; In a case where the vehicle operation data includes whole vehicle operation data, the whole vehicle operation data is the second breakdown-related data.

6. The method according to claim 5, characterized in that The vehicle operation data is time series data, and the data features corresponding to each of the vehicle operation data are time series feature sequences, wherein the time series feature sequences corresponding to the battery cell voltage data include the first battery cell voltage difference, the second battery cell voltage difference, the voltage fluctuation characterization value corresponding to each battery cell and the number of voltage anomalies corresponding to different moments; the time series feature sequences corresponding to the battery cell current data include the discharge current fluctuation characterization value, the number of charging current anomalies and the correlation between the current and voltage of each battery cell corresponding to different moments; the time series feature sequences corresponding to the vehicle temperature data include the temperature distribution difference, the temperature fluctuation characterization value and the number of temperature anomalies corresponding to different moments; the time series feature sequences corresponding to the state of charge data include the charge fluctuation characterization value corresponding to different moments, the relationship between the charge value of the battery pack and the mileage of the target vehicle; And / or, the voltage fluctuation characterization value corresponding to the battery cell includes the voltage change rate of the battery cell; And / or, the first cell voltage difference includes a voltage difference between a maximum voltage cell and a minimum voltage cell; And / or, the discharge current fluctuation characterization value corresponding to the battery cell includes the discharge current change rate of the battery cell; And / or, the temperature distribution difference of the battery cells includes a temperature difference between a battery cell with a maximum temperature and a battery cell with a minimum temperature; And / or, the temperature fluctuation characterization value of the battery cell includes the temperature change rate of the battery cell; And / or, the charge fluctuation characterization value of the battery pack includes a charge decrease rate of the battery pack.

7. The method according to claim 4, characterized in that In the case where the anchoring-related data includes external environment data, and the external environment data includes external environment temperature, the external environment temperature is the first anchoring-related data, and the data characteristics of the external environment temperature include at least one of the following: a statistical value of a central tendency of the external environment temperature, a characteristic value of a fluctuation of the external environment temperature, and a distribution of the external environment temperature in a fourth time window; In the case where the anchoring-related data includes external environment data, and the external environment data includes external environment humidity, the external environment humidity is the first anchoring-related data, and the data characteristics of the external environment humidity include at least one of the following: a statistical value of a central tendency of the external environment humidity, and a characteristic value of a fluctuation of the external environment humidity; In the case where the breakdown-related data includes historical maintenance data of the target vehicle, the historical maintenance data is the first breakdown-related data, and data features of the historical maintenance data include at least one of the following: the total number of historical breakdowns of the target vehicle, the number of times the target vehicle has broken down due to battery cell failure, statistical results of parts replacement of the target vehicle, the fault code of the fault that caused the target vehicle to break down, the interval between breakdowns of the target vehicle, the vehicle operation data of the target vehicle before the breakdown, and the vehicle operation status of the target vehicle after the breakdown and repair.

8. The method according to claim 7, characterized in that The external environment temperature and the external environment humidity are time series data, and the data features corresponding to the external environment temperature and the external environment humidity are time series feature sequences, wherein the time series feature sequence corresponding to the external environment temperature includes the external environment temperature central tendency statistical values ​​corresponding to different moments, the external environment temperature fluctuation characterization values, and the external environment temperature distribution; the time series feature sequence corresponding to the external environment humidity includes the external environment humidity central tendency statistical values ​​corresponding to different moments, the external environment humidity fluctuation characterization values; And / or, the external environment temperature fluctuation characterization value includes the external environment temperature change rate; And / or, the external environment temperature distribution includes the highest temperature and the lowest temperature in a fourth time window; And / or, the external environment humidity fluctuation characterization value includes the external environment humidity change rate.

9. The method according to any one of claims 1 to 3, characterized in that: Before performing anchoring prediction based on the data features corresponding to the multiple anchoring-related data to obtain an anchoring prediction result, at least one of the following steps is also included: Finding, from the data features respectively corresponding to the multiple anchoring-related data, a correlation feature group whose feature correlation meets a preset correlation condition, selecting a representative feature from the correlation feature group, and deleting other data features in the correlation feature group except the representative feature; Find out each time series feature sequence from the data features respectively corresponding to the multiple anchoring related data, and combine each of the time series feature sequences into a time series data feature.

10. The method according to any one of claims 1 to 3, characterized in that: The data features corresponding to the multiple anchoring-related data include time series data features and non-time series data features; The performing anchoring prediction based on the data features corresponding to the multiple anchoring related data to obtain an anchoring prediction result includes: Using a time series processing model to detect the time series dependency of the time series data features in time series to obtain time-dependent features; and Using a decision model to identify the association between the non-time series data feature and the anchoring to obtain an anchoring association feature; The time-dependent feature and the anchoring-related feature are used to perform prediction to obtain the anchoring prediction result.

11. The method according to claim 10, characterized in that The time series processing model is a long short-term memory network model, and the decision model is a random forest model; And / or, the using the time-dependent feature and the anchoring-related feature to perform prediction to obtain the anchoring prediction result includes: Fusing the time-dependent feature and the anchor-related feature to obtain a fused feature; Prediction is performed based on the fusion features to obtain the anchor prediction result.

12. The method according to claim 1, characterized in that The multiple types of anchor-related data include time series data and non-time series data; before respectively obtaining data features corresponding to the multiple types of anchor-related data, the method further includes: Performing a first preprocessing on the time series data; wherein the first preprocessing includes at least one of the following: denoising, missing data completion, abnormal data processing, inconsistent data processing, and data alignment; And, performing a second preprocessing on the non-time series data; wherein the second preprocessing includes at least one of the following: data format unification processing, missing data completion, and data alignment.

13. The method according to claim 12, characterized in that The breakdown-related data includes vehicle operation data of the target vehicle, and the vehicle operation data is the time series data; the first preprocessing of the time series data includes: Respectively performing denoising, missing data completion, abnormal data processing, inconsistent data processing, and data time alignment on the vehicle operation data; And / or, the breakdown-related data includes historical maintenance data of the target vehicle, and the historical maintenance data is the non-time series data; and the second preprocessing of the non-time series data includes: The historical maintenance data of the target vehicle is processed in a unified data format and missing data is supplemented; And / or, the breakdown-related data includes vehicle operation data of the target vehicle and external environment data of the target vehicle, the external environment data includes weather data and geographic data, the vehicle operation data and the weather data are the time series data, and the geographic data is the non-time series data; the step of preprocessing the breakdown-related data also includes: Performing data time alignment on the vehicle operation data and the weather data; and, The vehicle operation data is spatially aligned with the geographic data.

14. The method according to claim 1, characterized in that The first prediction result is used to characterize that the target vehicle has a risk of breaking down, and the breakdown prediction result also includes a second prediction result, and the second prediction result includes at least one of the following: a predicted occurrence time of the breakdown risk, a predicted cause of the breakdown risk, and a solution to the breakdown risk; And / or, after performing anchoring prediction based on the data features corresponding to the multiple anchoring-related data to obtain an anchoring prediction result, the method further includes: Feedback at least part of the information in the breakdown prediction result to the user, wherein the feedback method includes at least one of the following: displaying at least part of the information on a display screen of the target vehicle, playing at least part of the information as audio on the target vehicle, and sending at least part of the information to an associated terminal.

15. A vehicle breakdown warning device, characterized in that: The device comprises: A first acquisition module is used to acquire a plurality of breakdown-related data of a target vehicle, wherein the plurality of breakdown-related data include historical maintenance data of the target vehicle, at least one of external environment data of the target vehicle, and vehicle operation data of the target vehicle; A second acquisition module is used to respectively acquire data features corresponding to the multiple types of anchoring related data; A prediction module is used to perform a breakdown prediction based on data features corresponding to the multiple breakdown-related data to obtain a breakdown prediction result; wherein the breakdown prediction result includes a first prediction result, and the first prediction result is used to characterize whether the target vehicle has a breakdown risk.

16. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores program instructions, and the processor is used to execute the program instructions to implement the vehicle breakdown warning method as described in any one of claims 1-14.

17. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store program instructions, and the program instructions can be executed to implement the vehicle breakdown warning method as described in any one of claims 1-14.

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