A method, device and medium for detecting and saving low-voltage abnormal power failure
Patent Information
- Application Number
- CN202611021493.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-09
- Publication Date
- 2026-11-06
AI Technical Summary
Existing technologies cannot effectively detect and save critical data when a vehicle experiences a low-voltage power outage, leading to data loss. Furthermore, existing solutions are costly, lack real-time performance, and are not reliable enough.
By collecting the voltage at the vehicle-side MCU power supply port in real time, performing weighted moving average filtering, and extracting multi-dimensional voltage features such as short-term slope, long-term slope, average rate of change, and curvature accumulation, abnormal power failure is determined by combining the comprehensive confidence level, and non-volatile memory is triggered to write during the determination.
It enables efficient and reliable detection and storage of critical data during low-voltage power outages without the need for additional hardware, reducing hardware costs, avoiding storage device lifespan degradation, and ensuring data integrity and reliability.
Smart Images
Figure CN123046747A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of power outage diagnosis technology, and in particular to a method, device and medium for detecting and storing low-voltage abnormal power outages. Background Technology
[0002] During vehicle operation, factors such as loose power connectors, poor contact, or human error may cause an abnormal low-voltage power outage before the key is turned off. For example, a 12V system may drop below 9V, or a 24V system below 18V, leading to premature MCU failure. This results in the loss of data such as the SOC, critical system operating status, and important fault codes, ultimately manifesting as inaccurate driving range and abnormal system operation, severely impacting the user experience. Current mainstream solutions fall into three categories: First, using hardware current-carrying and voltage-regulating circuits such as supercapacitors or button batteries to achieve delayed power-off, but this requires hardware modifications, increases costs, and raises design complexity. Second, using triggered EEPROM storage combined with cloud backup, but the storage condition may not be triggered at the moment of power failure, and cloud data is lagging, still failing to guarantee the integrity of the latest data. Third, continuously writing data to non-volatile memory, but frequent erasure and write operations significantly shorten device lifespan. It is evident that existing technologies struggle to balance cost, real-time performance, reliability, and durability, necessitating a solution that can reliably and efficiently detect critical data during low-voltage power outages without requiring significant hardware modifications. Summary of the Invention
[0003] To address the aforementioned issues, this application proposes a method for detecting and storing low-voltage abnormal power outages, comprising: real-time voltage acquisition of the power supply port of the vehicle-side MCU according to a pre-set sampling frequency to obtain an original voltage sequence; performing weighted moving average filtering on the original voltage sequence to obtain a smoothed voltage sequence; traversing the smoothed voltage sequence according to a pre-established sliding time window to extract multi-dimensional voltage features in any sliding window, the voltage features including time-domain dynamic features and mathematical statistical features, the time-domain dynamic features including short-time slope, long-time slope, average rate of change, curvature, and cumulative curvature value; determining a comprehensive confidence level based on the multi-dimensional voltage features, comparing the comprehensive confidence level with a pre-set threshold; if the comprehensive confidence level is greater than or equal to the threshold, an abnormal power outage is determined, triggering a write operation to the non-volatile memory.
[0004] In one example, the original voltage sequence is subjected to a weighted moving average filter to obtain a smoothed voltage sequence. Specifically, the expression for the weighted moving average filter is:
[0005] in, This represents the filtered output value corresponding to the k-th data point. Represents the original voltage sequence. The width of the filter window. Let be the weight of the i-th position window.
[0006] In one example, determining the overall confidence level based on the multidimensional voltage features specifically includes: normalizing each voltage feature and then calculating the overall confidence level using a weighted summation method. The formula for calculating the overall confidence level is as follows:
[0007] in, To assess the overall confidence level, The weights corresponding to each feature satisfy the following conditions: , This represents multidimensional voltage characteristics.
[0008] In one example, the method further includes: determining the instantaneous difference between the current sampling point and the previous sampling point to obtain the short-time slope, wherein the formula for calculating the short-time slope is:
[0009] in, The short-time slope, The sampled value is the value at the sampling time.
[0010] In one example, the method further includes: selecting multiple consecutive valid sampling points preceding the current sampling point to form a local voltage subset, and using the least squares method to perform linear fitting on the local voltage subset to obtain the slope of the fitted line, thereby obtaining the long-term slope, wherein the long-term slope is calculated using the following formula:
[0011] in, The long-term slope, Indicates time, Indicates voltage. Represents the average over time. , This represents the average voltage. .
[0012] In one example, the method further includes: tracing back multiple consecutive sampling points to calculate the average voltage difference value within the corresponding time period, thereby determining the average rate of change, wherein the formula for calculating the average rate of change is:
[0013] in, This represents the average rate of change. The sampled value is the value at the sampling time.
[0014] In one example, the method further includes: the curvature is an approximate curvature obtained by engineering simplification using second-order voltage difference, and the formula for calculating the curvature is:
[0015] in, The curvature is an engineering approximation.
[0016] In one example, the method further includes: the curvature accumulation value is a feature value obtained by accumulating the curvature within a preset accumulation length window, and the formula for calculating the curvature accumulation value is:
[0017] in, The cumulative value of the curvature is the engineering approximation, with an accumulation length window of L and n being the sampling time.
[0018] On the other hand, this application also proposes a low-voltage abnormal power failure detection and storage device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the low-voltage abnormal power failure detection and storage device to perform: the method described in any of the examples above.
[0019] On the other hand, this application also proposes a non-volatile computer storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured to be the method described in any of the examples above.
[0020] This application eliminates the need for additional hardware freewheeling circuits such as supercapacitors, farad capacitors, or button batteries, and requires no modification to the hardware circuitry. Abnormal power failure detection can be achieved solely through MCU application layer software, reducing hardware costs and avoiding increased hardware design complexity. Secondly, preprocessing the original voltage sequence using weighted moving average filtering effectively suppresses acquisition noise and random fluctuations, preserving the true trend of voltage changes and providing a reliable data foundation for subsequent feature extraction. Thirdly, this scheme constructs a multi-dimensional voltage feature system, encompassing short-term slope, long-term slope, average rate of change, curvature, and curvature accumulation value—time-domain dynamic features. It comprehensively characterizes the dynamic characteristics of the voltage drop process from multiple dimensions, including instantaneous abrupt changes, local trends, stage average speed, curvature degree, and cumulative effects, overcoming the shortcomings of single-feature susceptibility to interference and low recognition accuracy. Furthermore, through a normalized weighted summation comprehensive confidence judgment mechanism, which considers both instantaneous abrupt changes and trend changes, the accuracy and anti-interference capability of abnormal power failure identification are significantly improved. Finally, this solution only performs non-volatile memory write operations when an abnormal power failure is detected, avoiding the wear and tear on the storage device caused by continuous and frequent erase and write operations. At the same time, the millisecond-level response speed ensures that critical data is reliably saved before the power supply is completely interrupted, achieving a good balance between cost, reliability and durability. Attached Figure Description
[0021] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a flowchart illustrating a method for detecting and storing low-voltage abnormal power outages in an embodiment of this application. Figure 2 This is a schematic diagram of the power supply voltage response curve during an abnormal power outage in an embodiment of this application; Figure 3 This is a schematic diagram illustrating the changes in short-term slope and curvature with voltage during abnormal power outages in embodiments of this application; Figure 4 This is a schematic diagram of a low-voltage abnormal power failure detection and storage device in an embodiment of this application. Detailed Implementation
[0022] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0023] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.
[0024] like Figure 1 As shown, in order to solve the above problems, this application provides a method for detecting and storing low-voltage abnormal power outages, the method including: S101. The voltage at the power supply port of the vehicle-end MCU is collected in real time according to the preset sampling frequency to obtain the original voltage sequence.
[0025] During normal vehicle operation, factors such as loose or poorly connected power connectors at the vehicle end, or human error, may cause a low-voltage power outage before the key is turned off. Taking a commercial vehicle's 24V power supply as an example, the normal operating range of a 12V power supply is 9V-16V, and the normal operating range of a 24V power supply is 18V-32V. If the voltage falls below the lower limit, it may cause an overall low-voltage power supply anomaly, preventing the MCU from functioning properly. Consequently, data such as the SOC, critical system operating status, and important fault codes may be lost due to lack of time to be saved. To address this, this application first collects the voltage of the vehicle-side MCU's power supply port in real time according to a pre-set sampling frequency to obtain the original voltage sequence.
[0026] Specifically, abnormal power outage scenarios can be simulated on hardware-in-the-loop (HIL) test benches, bench tests, and in the entire vehicle, with real-time acquisition of the power supply port voltage at a fixed sampling frequency. The sampling frequency is no less than 1kHz to ensure that the rapid voltage drop details during the abnormal power outage can be captured. The original voltage sequence is recorded. Where n is the number of sampling points. For example, when the sampling frequency is 1kHz, one voltage data point is collected every millisecond; when the abnormal power outage process lasts for about 50ms, about 50 raw voltage data points can be collected, providing a sufficient data foundation for subsequent filtering and feature extraction.
[0027] S102. Perform weighted moving average filtering on the original voltage sequence to obtain a smooth voltage sequence.
[0028] Original voltage sequence The original voltage sequence may contain acquisition noise and random fluctuations, which could lead to misjudgments if feature extraction is performed directly based on the raw data. Therefore, this application applies a weighted moving average filter to the original voltage sequence to obtain a smoothed voltage sequence V[k], thereby effectively suppressing high-frequency noise while preserving the voltage change trend. Specifically, the expression for the weighted moving average filter is:
[0029] in, This represents the filtered output value corresponding to the k-th data point. Represents the original voltage sequence. The width of the filter window is half its width, and the total length of the filter window is 2m+1. Let the weight of the i-th position window satisfy the following condition: The weight of the center point is the largest, decreasing towards both sides, to reflect that the contribution of the current sampling point to the filtering result should be greater than that of the far sampling points. For example, when m=2, the filter window length is 5, and the weight sequences w[-2], w[-1], w[0], w[1], and w[2] can be set to 0.1, 0.2, 0.4, 0.2, and 0.1 respectively, so that the weight of the center point is the largest and the weights on both sides decrease symmetrically. Through the above weighted moving average filtering process, the random fluctuations in the original voltage sequence can be effectively smoothed, and a smooth voltage sequence V[k] that more realistically reflects the voltage change trend can be obtained, providing a reliable data foundation for subsequent multidimensional voltage feature extraction.
[0030] S103. The smoothed voltage sequence is traversed according to a pre-established sliding time window to extract multi-dimensional voltage features in any sliding window. The voltage features include time-domain dynamic features and mathematical statistical features. The time-domain dynamic features include short-time slope, long-time slope, average rate of change, curvature, and curvature accumulation value.
[0031] like Figure 2 The figure shows the 24V power supply voltage response curve during an abnormal power outage. The 24V power supply voltage response curve during an abnormal power outage can be roughly divided into four parts: 101 is the initial stable region, belonging to the normal operation stage, where the voltage is approximately stable at 24V; 102 is the inflection point transition region, where the voltage begins to decline; 103 is the sharp drop region, where the voltage begins to drop sharply; and 104 is the slow decay region, where the voltage declines from a low level. To accurately identify this dynamic change process, a fixed-length sliding time window is constructed, and the smoothed voltage sequence V[k] is traversed in a time-series sliding manner. Multidimensional voltage features are extracted within each sliding window, covering both time-domain dynamic features and mathematical statistical features. Among these, the core time-domain dynamic features include at least short-term slope, long-term slope, average rate of change, curvature, and curvature accumulation.
[0032] In one embodiment, the short-time slope is used to calculate the instantaneous difference between the current sampling point and the previous sampling point, characterizing the magnitude of the instantaneous voltage change. When an abnormal power outage occurs, the voltage drops sharply within a very short time, and the short-time slope can sensitively capture this instantaneous change. Specifically, the instantaneous difference between the current sampling point and the previous sampling point is determined to obtain the short-time slope. The formula for calculating the short-time slope is:
[0033] in, Let V[n] be the short-time slope, and V[n] be the sampled value at the nth sampling time. In the initial stable region 101, the voltage fluctuation is minimal, and the short-time slope is close to 0; in the inflection transition region 102, the voltage begins to decline, and the short-time slope changes from 0 to negative; in the sharp drop region 103, the short-time slope quickly reaches a negative peak; in the slow decay region 104, the short-time slope gradually decreases and tends to stabilize.
[0034] In one embodiment, the long-time slope is used to characterize the overall rate of change of the local voltage. Unlike the short-time slope, which focuses on instantaneous abrupt changes, the long-time slope reflects the trend of voltage change over a longer time scale by performing a linear fit on multiple consecutive valid sampling points preceding the current sampling point. Specifically, a subset of the local voltage is formed by selecting M consecutive valid sampling points preceding the current sampling point. The slope of the fitted line obtained by performing a linear fit on the local voltage subset using the least squares method is used to obtain the long-time slope. The formula for calculating the long-time slope is:
[0035] in, The long-term slope, Indicates time, Indicates voltage. Represents the average over time. , This represents the average voltage. The long-term slope is close to 0 in the initial stable region, begins to turn negative in the inflection transition region, and reaches a negative peak in the sharp drop region, which can reflect the severity of the voltage drop trend from a macroscopic perspective.
[0036] In one embodiment, the average rate of change is used to characterize the phased average rate of change of voltage over a specific time period. By calculating the average difference value by looking back at multiple consecutive sampling points, the average rate of voltage decrease over a period of time can be reflected. Specifically, by looking back at N consecutive sampling points, the average voltage difference value for the corresponding time period is calculated to determine the average rate of change. The formula for calculating the average rate of change is:
[0037] in, This represents the average rate of change. The sampled value is the value at the sampling time. The average rate of change provides a measure of change at an intermediate scale between the short-time slope and the long-time slope, which helps to more comprehensively characterize the dynamic characteristics of the voltage drop process.
[0038] In one embodiment, curvature is used to characterize the degree of bending of the voltage drop curve. During an abnormal power outage, the voltage curve transitions from a stable region through an inflection point transition region to a region of sharp drop, and the degree of bending of the curve changes significantly. The curvature feature can effectively capture this change. Specifically, the curvature is an approximate curvature obtained by engineering simplification calculations using the second-order voltage difference. When the slope is small, the denominator of the curvature is... Since the value is close to 1, we can simply calculate the second difference of the numerator as an approximate quantification of the curvature. The formula for calculating curvature is:
[0039] in, The curvature is an engineering approximation. In the initial stable region (101), the curvature is close to 0; in the inflection transition region (102), the curvature begins to increase; in the sharp drop region (103), the curvature reaches its maximum value; and in the slow decay region (104), the curvature gradually decreases. The curvature characteristics can clearly show the degree of bending and the acceleration of change during the voltage decay process.
[0040] In one embodiment, the curvature accumulation value is used to accurately characterize the curvature, concavity, and acceleration of the voltage drop curve during an abnormal power outage, capturing the dynamic trend of voltage change rate. The curvature at a single moment may be affected by noise interference, while the curvature accumulation value enhances feature stability by accumulating the curvature within a preset accumulation length window. Specifically, the curvature accumulation value is a feature value obtained by accumulating the curvature within a preset accumulation length window, and the formula for calculating the curvature accumulation value is:
[0041] in, The cumulative curvature value is an engineering approximation, with an accumulation length window of L, meaning the total window length is 2L+1, and n is the sampling time. The cumulative curvature value comprehensively reflects the cumulative bending effect of the voltage curve over a large time range, and can more robustly characterize the overall dynamic characteristics of voltage drop during abnormal power outages.
[0042] In one embodiment, the five time-domain dynamic features constructed above can be used to quantitatively characterize voltage changes during an abnormal power outage in multiple dimensions. The voltage change process throughout the abnormal power outage can be clearly shown by features such as slope and curvature, revealing its attenuation trend. For example... Figure 3The diagram illustrates the short-term slope and curvature changes with voltage during an abnormal power outage. In the initial stable region (101), voltage fluctuations are minimal, and the slope and curvature are close to zero. In the inflection transition region (102), the voltage begins to decline, the slope changes from zero to negative, and the curvature begins to increase. In the sharp drop region (103), the slope reaches its peak, and the curvature also reaches its maximum value. In the slow decay region (104), the slope gradually decreases, and the curvature also begins to decline. The entire voltage change process during the abnormal power outage can be observed, and the decay trend can be clearly shown through features such as slope and curvature.
[0043] S104. Determine the comprehensive confidence level based on the multidimensional voltage characteristics, and compare the comprehensive confidence level with a preset threshold.
[0044] To avoid the limitations of relying on single features and improve the accuracy and anti-interference capabilities of abnormal power outage identification, a method of calculating the comprehensive confidence score after normalization is adopted, taking into account both instantaneous changes and trend variations. Specifically, after normalizing the voltage features of each dimension, a weighted summation method is used to calculate the comprehensive confidence score. The formula for calculating the comprehensive confidence score is as follows:
[0045] in, To assess the overall confidence level, The weights corresponding to each feature satisfy the following conditions: , It represents multidimensional voltage characteristics, including short-time slope, long-time slope, average rate of change, curvature, and cumulative curvature value.
[0046] In one embodiment, the normalization of voltage characteristics in each dimension can be achieved by mapping each characteristic value to the [0,1] interval to eliminate differences in dimensions and numerical ranges between different characteristics. The weights of each characteristic can be set according to the actual application scenario and the importance of the characteristic. For example, higher weights can be assigned to the short-term slope and curvature, which change most drastically during an abnormal power outage; and relatively lower weights can be assigned to the long-term slope and average rate of change, which change relatively gently. The specific values of each characteristic weight can be determined through statistical analysis and optimization of a large amount of experimental data, so that the overall confidence level can rise rapidly when an abnormal power outage occurs and remain at a low level under normal operating conditions.
[0047] In one embodiment, the calculated overall confidence level P is compared with a pre-set threshold P_thr. The selection of the threshold P_thr should take into account both detection sensitivity and false alarm resistance. It can be determined by conducting a large number of abnormal power failure simulation experiments on HIL test benches, bench tests, and whole vehicles, and statistically analyzing the distribution range of the overall confidence level under normal operating conditions and abnormal power failure conditions.
[0048] S105. If the overall confidence level is greater than or equal to the threshold, it is determined to be an abnormal power failure, triggering a write operation to the non-volatile memory.
[0049] If the overall confidence level P ≥ P_thr, the judgment condition is met, confirming an abnormal power failure and immediately triggering emergency storage. The entire low-voltage abnormal power failure process is very short, for example, around 50ms. Therefore, both the abnormal triggering mode and data storage must achieve millisecond-level response. Currently, the commonly used trigger-based storage method supporting non-volatile storage in automotive controllers is Electrically Erasable Programmable Read-Only Memory (EEPROM). When writing a single page (256 bytes), it takes approximately 4-5ms; if multiple pages are written, the storage time may double when page flipping is involved. Therefore, to ensure timely data storage during a low-voltage abnormal power failure, a high-priority EEPROM page needs to be found and the write operation performed. After the write is completed, a write completion status flag can be returned to confirm the success of the storage operation.
[0050] In one embodiment, it should be noted that EEPROM is not the only storage solution. Other storage methods, such as non-volatile memories like NAND Flash, can also be used as long as storage speed and reliability can be guaranteed. In practical implementation, as much time as possible should be reserved for data storage in non-volatile memory; otherwise, storage failure may occur.
[0051] Furthermore, to avoid affecting the normal use of EEPROM pages, the low-voltage power failure criterion, i.e., the comprehensive confidence level P ≥ P_thr, can be used as one of the conditions for triggering storage of relevant important variables, and then ORed with other triggering storage conditions. That is: Storage trigger condition = Low-voltage power failure criterion OR Other triggering storage condition 1 OR Other triggering storage condition 2 OR ... This compatible design ensures that relevant data can be successfully stored under both normal operation and abnormal power outage conditions, guaranteeing reliable system operation.
[0052] like Figure 4 As shown in the illustration, this application also provides a device for detecting and storing low-voltage abnormal power outages, including: At least one processor; and, A memory that is communicatively connected to at least one processor; wherein, The memory stores instructions that can be executed by at least one processor to enable a low-voltage abnormal power failure detection and preservation device to perform the method as described in any of the embodiments above.
[0053] This application also provides a non-volatile computer storage medium storing computer-executable instructions, which are configured as described in any of the above embodiments.
[0054] In the 1990s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many methodological improvements today can be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that a methodological improvement cannot be implemented using hardware physical modules. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program and "integrate" a digital system onto a PLD themselves, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. Similar to the software compiler used in program development, the original code before compilation must also be written in a specific programming language, called a Hardware Description Language (HDL). There are many HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also understand that by simply performing some logic programming on the method flow using one of these hardware description languages and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.
[0055] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.
[0056] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.
[0057] For ease of description, the above devices are described in terms of function, divided into various units. Of course, in implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware components.
[0058] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device and medium embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the description of the method embodiments.
[0059] The devices and media provided in this application are one-to-one with the methods. Therefore, the devices and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.
[0060] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0061] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0062] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0063] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0064] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.
[0065] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.
[0066] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.
[0067] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.
Claims
1. A method of detection and preservation of low voltage abnormal power outage, characterized by, include: The voltage at the power supply port of the vehicle-side MCU is collected in real time according to a pre-set sampling frequency to obtain the original voltage sequence; The original voltage sequence is subjected to a weighted moving average filter to obtain a smoothed voltage sequence; The smooth voltage sequence is traversed according to a pre-established sliding time window to extract multi-dimensional voltage features in any sliding window. The voltage features include time-domain dynamic features and mathematical statistical features. The time-domain dynamic features include short-time slope, long-time slope, average rate of change, curvature, and curvature accumulation. A comprehensive confidence level is determined based on the multidimensional voltage characteristics, and the comprehensive confidence level is compared with a pre-set threshold. If the overall confidence level is greater than or equal to the threshold, it is determined to be an abnormal power failure, triggering a write operation to the non-volatile memory.
2. The method of claim 1, wherein, The original voltage sequence is subjected to a weighted moving average filter to obtain a smoothed voltage sequence, specifically including: The expression for the weighted moving average filter is: wherein, represents the filtered output value corresponding to the kth data point, represents the original voltage sequence, is the half-width of the filter window, is the weight of the ith position window, satisfying .
3. The method of claim 1, wherein, The comprehensive confidence level is determined based on the multidimensional voltage characteristics, specifically including: After normalizing the voltage characteristics of each dimension, a weighted summation method is used to calculate the overall confidence level. The formula for calculating the overall confidence level is as follows: wherein, is the comprehensive confidence, is the weight corresponding to each feature, satisfying , represents the multi-dimensional voltage feature.
4. The method of claim 1, wherein, The method further includes: The instantaneous difference between the current sampling point and the previous sampling point is determined to obtain the short-time slope. The formula for calculating the short-time slope is as follows: wherein is the short-time slope, is the sample value at the sampling instant.
5. The method of claim 1, wherein, The method further includes: A local voltage subset is formed by selecting multiple consecutive valid sampling points preceding the current sampling point. The slope of the fitted line obtained by linearly fitting the local voltage subset using the least squares method is used to obtain the long-term slope. The formula for calculating the long-term slope is as follows: wherein is the long-time slope, denotes time, denotes voltage, denotes the mean value of time, , denotes the mean value of voltage, .
6. The method of claim 1, wherein, The method further includes: By tracing back multiple consecutive sampling points, the average voltage difference value within the corresponding time period is calculated to determine the average rate of change. The formula for calculating the average rate of change is as follows: wherein denotes the average rate of change, is the sample value at the sampling instant.
7. The method of claim 1, wherein, The method further includes: The curvature is an approximate curvature obtained by engineering simplification using the second-order voltage difference, and the formula for calculating the curvature is: in, The curvature is an engineering approximation.
8. The method according to claim 1, characterized in that, The method further includes: The curvature accumulation value is a feature value obtained by accumulating the curvature within a preset accumulation length window. The formula for calculating the curvature accumulation value is as follows: in, The cumulative value of the curvature is the engineering approximation, with an accumulation length window of L and n being the sampling time.
9. A device for detecting and storing data on low-voltage abnormal power outages, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the low-voltage abnormal power failure detection and storage device to perform the method as described in any one of claims 1-8.
10. A non-volatile computer storage medium storing computer-executable instructions, characterized in that, The computer-executable instructions are configured to be the method as described in any one of claims 1-8.