A passenger car random driving cycle construction method, device, equipment and medium

Abstract

This invention relates to a method, apparatus, device, and medium for constructing random driving conditions for passenger vehicles. The method includes the following steps: collecting actual driving condition data and extracting short-distance segments according to a test plan; clustering the short-distance segments to construct clusters corresponding to different driving condition categories; randomly selecting and combining short-distance segments from the clusters corresponding to different driving condition categories to construct random driving conditions. When randomly selecting short-distance segments, the proportion of the total time length of the short-distance segments in the cluster corresponding to each driving condition category to the total time length of all short-distance segments is calculated. Based on this proportion, the minimum allowable movement time length for each driving condition category is calculated to determine whether to stop randomly selecting short-distance segments from the cluster corresponding to that driving condition category and adding them to the driving condition sequence. Compared with the prior art, the random driving conditions generated by this invention have the advantage of reflecting both randomness and macroscopic realism.

Description

Technical Field

[0001] This invention relates to the field of passenger vehicle driving performance testing technology, and in particular to a method, apparatus, equipment and medium for constructing random driving conditions for passenger vehicles. Background Technology

[0002] In passenger vehicle performance testing, driving conditions are a crucial standard. Currently, the main methods for constructing driving conditions include: I. Typical Operating Condition Construction Method Based on Operating Condition Segment Library: The aim is to construct deterministic typical operating conditions. Its core technology is to extract several of the most representative operating condition segments from massive amounts of real driving data and construct them into a standard operating condition library. When generating a complete test operating condition, the system deterministically selects and combines these typical segments from the library according to preset targets (such as duration and speed range), and finally forms a standardized and reproducible operating condition curve, which is mainly used for scenarios that require fixed benchmarks, such as regulatory certification.

[0003] II. Probabilistic Model Working Condition Synthesis Method Based on Markov Analysis: This method aims to synthesize randomized statistical working conditions. Its core technology is to establish a probabilistic model of driving state or segment through statistical analysis (especially the state transition probability matrix). When generating a new working condition, the algorithm performs continuous state transitions based on this probabilistic model, synthesizing speed-time curves segment by segment that are consistent with the original data distribution in terms of macroscopic statistical characteristics, but have infinitely random microscopic sequences. It is mainly used for scenarios that require a large number of variations, such as reliability testing and edge case mining.

[0004] Problems with current mainstream technologies: For typical driving condition construction methods, such as the vehicle driving condition construction method based on the improved short-stroke method disclosed in Chinese patent CN114943296A, the idling data is processed through intermediate processing steps, which improves the quality of driving condition data and the representativeness of data segments. However, it has insufficient randomness.

[0005] The core of the Markov analysis method is to establish a state transition probability matrix between different road categories or different kinematic segments. Then, based on the generated state transition probability matrix, driving conditions are randomly constructed from the perspective of the occurrence probability of different road categories or different kinematic segments. The driving conditions generated by this method also extract and combine kinematic segments according to road type. Its randomness is affected by road type and cannot reflect the randomness of real driving.

[0006] Currently published or released methods for constructing vehicle driving conditions suffer from insufficient randomness when processing urban vehicle driving condition data, leading to two problems: first, a significant discrepancy exists between announced energy consumption and actual user energy consumption; second, due to the fixed operating conditions, manufacturers may target these conditions through specific control strategies. This is especially true for passenger vehicles, where there are coupling relationships during operation—the frequency, intensity, and operating range of each component are greatly influenced by control strategies and operating conditions. Without considering randomness, the final test results will be difficult to accurately reflect reality. Therefore, there is an urgent need for a driving condition construction method that can fully consider randomness. Summary of the Invention

[0007] The purpose of this invention is to overcome the deficiencies of the prior art by providing a method, apparatus, device, and medium for constructing random driving conditions for passenger vehicles. This invention rationally classifies urban driving conditions of passenger vehicles, completes the classification of different categories of driving conditions, and randomly selects representative driving condition data from the classification results based on category time ratio constraints to construct random driving conditions. This ensures that the generated conditions are not a simple accumulation of fragments, but a statistical reproduction of the driving time structure in the real world.

[0008] The objective of this invention can be achieved through the following technical solutions: According to a first aspect of the present invention, a method for constructing random driving conditions for a passenger vehicle is provided, the method comprising the following steps: According to the test plan, actual driving condition data are collected and short-stroke segments are extracted. The short stroke is defined as the movement process of the vehicle from the start of movement to the start of the next idle speed. The short-stroke segments are clustered to construct clusters corresponding to different operating conditions; Random driving conditions are constructed by randomly selecting short-distance segments from clusters corresponding to different working condition categories and randomly combining them. The method for randomly selecting short-distance segments is as follows: Calculate the proportion of the total time length of short-stroke segments in the cluster corresponding to each working condition category to the total time length of all short-stroke segments. Based on the proportion and the preset minimum allowable time length of random driving conditions, calculate the minimum allowable motion time length for each working condition category. For any working condition category, initialize a working condition sequence. Each time, randomly select a short-stroke segment from the cluster corresponding to the working condition category and add it to the end of the working condition sequence. Calculate whether the total time length of the working condition sequence after adding is less than the minimum allowable motion time length for the working condition category. If so, continue to randomly select short-stroke segments and add them to the working condition sequence. Otherwise, output the working condition sequence after the current addition as the result of the random selection of short-stroke segments.

[0009] The random combination specifically refers to: The results of random sampling of short-stroke segments of all working condition categories are aggregated into a pool. All working condition sequences in the pool are randomly sorted. The working condition sequences are then spliced ​​together according to the sorting results to generate the final random driving working condition curve.

[0010] Before extracting short-stroke segments, the collected actual driving condition data is preprocessed, including: Clear the data of short-run segments whose running time is less than the preset length; Identify short travel segments with missing or intermittent data collection and delete them as invalid data; Data showing abnormally sudden changes in vehicle speed is identified and deleted as abnormal data.

[0011] The clustering specifically includes the following steps: Calculate the characteristic parameters of short-stroke segments; Principal component analysis was performed on the short-stroke segments based on the aforementioned feature parameters to extract principal component scores. The number of clusters is determined based on the elbow rule and the silhouette coefficient; Short-stroke segments are clustered according to principal component scores, and the segments are divided into different clusters based on the determined number of clusters.

[0012] The characteristic parameters include at least the average vehicle speed, maximum vehicle speed, running time, running distance, minimum deceleration, maximum acceleration, average deceleration during the deceleration phase, and average acceleration during the acceleration phase.

[0013] The cluster analysis used was K-means cluster analysis.

[0014] The method further includes: When collecting actual driving condition data according to the test plan, the driving condition type is recorded or labeled according to the actual traffic scenario to verify the clustering results.

[0015] According to a second aspect of the present invention, a passenger vehicle random driving condition construction apparatus is provided, the apparatus comprising: Data acquisition and short-stroke segment extraction module: used to collect actual driving condition data and extract short-stroke segments according to the test plan, wherein the short stroke is defined as the movement process of the vehicle from the start of movement to the start of the next idle speed; Clustering module: used to cluster the short-stroke segments and construct clusters corresponding to different working condition categories; Random driving condition construction module: This module is used to randomly extract short-distance segments from clusters corresponding to different driving condition categories and randomly combine them to construct random driving conditions. The method for randomly extracting short-distance segments is as follows: Calculate the proportion of the total time length of short-stroke segments in the cluster corresponding to each working condition category to the total time length of all short-stroke segments. Based on the proportion and the preset minimum allowable time length of random driving conditions, calculate the minimum allowable motion time length for each working condition category. For any working condition category, initialize a working condition sequence. Each time, randomly select a short-stroke segment from the cluster corresponding to the working condition category and add it to the end of the working condition sequence. Calculate whether the total time length of the working condition sequence after adding is less than the minimum allowable motion time length for the working condition category. If so, continue to randomly select short-stroke segments and add them to the working condition sequence. Otherwise, output the working condition sequence after the current addition as the result of the random selection of short-stroke segments.

[0016] According to a third aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described thereon.

[0017] According to a fourth aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method described thereon.

[0018] Compared with the prior art, the present invention has the following beneficial effects: After clustering, this invention calculates the total time length of all short-distance segments in each driving condition category and its proportion of the total time length of all short-distance segments. This proportion macroscopically represents the probability or time share of a driver being in that driving mode (such as a cluster) in the traffic environment of the target city (megacity). Subsequently, based on this proportion, the minimum allowable movement time length for the corresponding driving condition type is calculated, setting a mandatory time quota for each segment. Finally, based on this time quota, random segments are randomly selected and spliced ​​to obtain the final random driving condition. This process cleverly introduces the core idea of ​​"time proportion constraint." The constructed random driving condition maintains the randomness and dynamic changes of the segments at the micro level, simulating the uncertainty of actual driving; while at the macro level, it accurately reproduces the statistical weight of various driving scenarios in real traffic flow. The test conditions generated in this way are highly representative and realistic, enabling more accurate and fair evaluation of vehicle energy consumption, emissions, and component durability in real-world use. They provide reference data for component fatigue durability testing, thus offering a powerful tool for vehicle development and standards setting. Furthermore, they are significant for the research and evaluation of the driving range and energy consumption characteristics of new energy vehicles. The randomness of the constructed test conditions also effectively prevents the application of specific strategies. Attached Figure Description

[0019] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a schematic diagram illustrating the determination of the optimal number of clusters using the elbow rule in one embodiment; Figure 3 This is a schematic diagram illustrating the determination of the optimal number of clusters using the contour coefficient in one embodiment. Figure 4 This is a schematic diagram of the clustering effect in one embodiment (based on maximum acceleration, maximum deceleration, and average vehicle speed). Figure 5 This is a schematic diagram of the clustering effect in one embodiment (based on average acceleration and average deceleration). Figure 6 This is a schematic diagram of the clustering effect in one embodiment (based on runtime and average speed). Figure 7 This is a schematic diagram of the cluster results after clustering driving conditions in one embodiment; Figure 8 This is an example of a random driving condition for a passenger vehicle constructed in one embodiment; Figure 9 This represents the vehicle speed probability distribution of all test segments in the original data in one embodiment. Figure 10 This is a vehicle speed probability distribution for random operating conditions constructed according to the present invention in one embodiment; Figure 11 A radar chart comparing all test segments with the characteristic parameters of random operating conditions in one embodiment; Figure 12 This is a schematic diagram of the device structure of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0021] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms “a,” “an,” “an,” “the,” and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, products, or devices. The terms “connected,” “linked,” “coupled,” and similar words used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following objects are in an "or" relationship. The terms "first," "second," and "third" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects.

[0022] This embodiment first provides a method for constructing random driving conditions for passenger vehicles, such as... Figure 1 As shown, the method includes the following steps: S1, collect actual driving condition data and extract short-stroke segments according to the test plan.

[0023] First, it is necessary to conduct research on the road traffic network and traffic flow information of megacities. The research information includes the distribution of various road types and networks, the mileage of various roads, the time periods of morning and evening rush hours, and the traffic nodes prone to congestion during morning and evening rush hours.

[0024] Based on the research, and according to the road network distribution of various road types in the city, as well as information such as morning and evening rush hours and traffic nodes prone to congestion, a route and plan for collecting actual road driving condition data were formulated. R&D personnel or test engineers accompanied the vehicle to collect road driving condition data. While recording the vehicle driving condition data, the accompanying personnel recorded or marked the driving condition type according to the actual traffic scenario, such as recording and marking highway driving conditions, urban road driving conditions, and congested driving conditions.

[0025] After completing the pre-collection of actual driving condition data, the collected raw data is preprocessed. The preprocessing work includes: a. Clear the data of short-run segments with a runtime of less than 5 seconds; b. Identify short travel segments where the collected data (vehicle speed data) is missing or intermittent. These short travel segments are considered invalid data and are deleted. c. Identify data with abnormal speed changes. In this example, speed data with acceleration greater than 10 m / s² or deceleration less than -13 m / s² is considered abnormal and is deleted.

[0026] Extract short-stroke operating condition data (defining the movement process of the vehicle from the start of movement to the start of the next idle speed as one segment). Save each extracted short-stroke segment data separately as a separate data file to form a short-stroke segment database.

[0027] S2, cluster the short-stroke segments to construct clusters corresponding to different working condition categories.

[0028] This step addresses the problem of how to scientifically and effectively classify massive amounts of short-stroke segments with varying characteristics. Through a rigorous data science process, the seemingly chaotic raw data is transformed into several distinct and internally consistent operating condition categories.

[0029] S21. Calculate the characteristic parameters of the short-stroke operating condition data. The characteristic parameters should include at least the average vehicle speed, maximum vehicle speed, running time, running distance, minimum deceleration, maximum acceleration, average deceleration during deceleration, and average acceleration during acceleration. Perform statistical analysis on the above characteristic parameters for each operating condition category, and extract the main statistical features of each parameter.

[0030] S22, Based on the characteristic parameters of the short-stroke segment, principal component analysis is performed on the short-stroke segment to extract the principal component scores. In this embodiment, for each principal component, its eigenvalue, the difference between its eigenvalue and that of the next principal component, its contribution rate, and its cumulative contribution rate are provided.

[0031] After calculating the feature parameters, principal component analysis (data dimensionality reduction) is performed on the multiple original feature parameters to extract several comprehensive indicators (principal components) that best represent the differences between all segments and are mutually independent. Subsequent cluster analysis will no longer be based on the original dozen or so potentially related features, but on the scores of these N mutually independent principal components. This eliminates redundant information, making the measurement of differences between segments more accurate and clear.

[0032] In principal component analysis, a cumulative contribution rate coverage of 90% is considered valid. Principal components with a cumulative contribution rate coverage of 90% are selected for subsequent cluster analysis.

[0033] S23, determine the number of clusters based on the elbow rule and the profile coefficient.

[0034] Elbow Rule: This method calculates the total sum of squared errors (WCSS) for different K values. As K increases, the WCSS decreases. The ideal K value is located at the elbow—the inflection point where the rate of error decrease slows down. This point means that further increasing the number of classes no longer yields significant benefits. Figure 2 As shown.

[0035] Silhouette coefficient: This method measures the similarity of each data point to other points within its own category, as well as its dissimilarity to points in other categories. The coefficient ranges from -1 to 1; a larger value indicates better clustering (closer clustering within the same category and more separation between dissimilar categories). The ideal K value should maximize the average silhouette coefficient, such as... Figure 3 As shown.

[0036] This method does not rely on a single indicator, but rather uses both the elbow rule and the silhouette coefficient. This double validation avoids the limitations of a single method (e.g., the inflection point of the elbow rule may not be obvious), thus selecting the statistically robust and explanatory number of clusters. This ensures that the final classification result is not an artificially imposed division, but a natural manifestation of the data's inherent structure. For example, in one embodiment, a candidate K value can be determined first based on the elbow rule, then several values ​​near the candidate K value can be selected as candidates, and the candidate K value and the K value with the largest and positive silhouette coefficient among these values ​​can be selected as the final number of clusters.

[0037] S24. Perform cluster analysis on short-stroke segments according to principal component scores, and divide the short-stroke segments into different clusters according to the determined number of clusters.

[0038] After determining the principal components and the optimal K value, standard K-means clustering is performed. This embodiment uses the K-means clustering analysis method. The basic K-means algorithm is implemented as follows: (a) Select K points as initial centroids; (b) Assign each point to the nearest centroid to form K clusters; (c) Recalculate the centroid of each cluster; (d) Reassign each point to the nearest centroid to form new K clusters; (e) Repeat steps (c) and (d) until the centroids converge or do not change. Finally, each short-stroke segment is assigned to a cluster with a different index, and each cluster represents a working condition type.

[0039] At this point, each short-stroke segment is assigned a unique cluster label (e.g., cluster 1, cluster 2... cluster K), and each cluster represents a driving mode with unique motion characteristics. For example... Figure 4As shown, K=7 has been set for analysis. This is an example of vehicle driving condition classification using a multi-parameter feature deviation identification method. The classification results are displayed from three feature parameter dimensions (maximum acceleration, maximum deceleration, and average vehicle speed). The classification results are presented from the feature parameter dimensions of average acceleration and average deceleration, and the dimensions of running time and average speed. The results are shown below. Figure 5 and Figure 6 As shown.

[0040] The results of cluster analysis show that the clusters differ across the three feature parameter dimensions (maximum acceleration, maximum deceleration, and average vehicle speed) as follows: Figure 7 As shown.

[0041] In one preferred embodiment, when collecting actual driving condition data according to the test plan, the driving condition type can be recorded or labeled according to the actual traffic scenario, which can be used to verify the clustering results in this step and improve the reliability of the classification results.

[0042] S3: Randomly select short-distance segments from clusters corresponding to different working condition categories and randomly combine them to construct random driving conditions.

[0043] This step addresses the problem of how to assemble the categorized short-trip segments into a driving condition curve that reflects both randomness and macroscopic realism. It ensures that the generated driving condition is not a simple accumulation of segments, but a statistical reproduction of the structure of real-world driving time.

[0044] Limitations of traditional methods: Ignoring macroscopic statistical accuracy: Traditional random splicing methods may only focus on the random selection of segments or only roughly control the number of segments in each category. However, the average duration of segments in different categories varies greatly (for example, a high-speed segment may last 10 minutes, while a congestion segment may only last 2 minutes). Simply controlling the number cannot guarantee that the proportion of time occupied by each driving mode in the total driving conditions corresponds to the actual situation.

[0045] Insufficient representativeness: If the proportion of high-speed driving time in the generated test conditions is much higher than in reality, then the energy consumption or emissions results of the vehicle tested using it will be too optimistic and will not reflect the frequent traffic congestion that vehicles encounter in real cities.

[0046] Based on this, this step innovatively proposes the following solution: First, calculate the time percentage for each category—establishing time quotas. After clustering is complete, calculate the time quota for each category. iThe total time length of all short-trip segments is calculated, and its proportion to the total time length of the entire short-trip segment database is determined. This proportion macroscopically represents the probability or time share of a driver being in that type of driving mode (such as a cluster) in the traffic environment of the target city (megacity).

[0047] Next, minimum motion time is allocated—translating the proportion into a task. Users preset the minimum allowable time length for the entire random condition to be generated, based on testing requirements. Then, calculations are performed: for each category... i This defines the minimum allowable movement time for each type of segment within the construction process. This sets a time quota that must be completed for each segment.

[0048] Finally, perform category-based filling and random sorting: a. Fill by category to meet quotas: Perform the following operations for each category: Initialize an empty sequence to store fragments of this category.

[0049] Fragments are continuously and randomly extracted from the fragment library for that category and added to the end of the sequence, while the total time of the sequence is accumulated. Extraction stops when the accumulated time reaches or just exceeds the time quota for that category. In this way, a fragment sequence that meets the time requirements is generated for each category.

[0050] b. Random sorting to form a curve: All fragment sequences generated by all categories (sequences categorized by cluster) are aggregated into a pool. Then, all fragments in this pool are randomly sorted.

[0051] c. Final synthesis: By splicing together all the randomly sorted segments in sequence, the final random driving condition curve is formed.

[0052] This algorithm cleverly incorporates the core idea of ​​time-proportion constraints. The randomized driving scenarios it constructs maintain the randomness and dynamic changes of the segments at the micro level, simulating the uncertainties of actual driving; while at the macro level, it accurately reproduces the statistical weights of various driving scenarios in real traffic flow. The resulting test scenarios are highly representative and realistic, enabling more accurate and equitable evaluation of vehicle energy consumption, emissions, and component durability in real-world use, providing a powerful tool for vehicle development and standards setting.

[0053] Specifically, it includes the following steps: S31, the user pre-sets the minimum allowable time length for generating random driving conditions according to their own needs. T tar .

[0054] S32, calculate the proportion of the total time length of short-stroke segments in the cluster corresponding to each working condition category to the total time length of all short-stroke segments: ; in, Indicates the type of working condition i The proportion, Indicates the type of working condition i The total time length of the short-distance segments, This represents the total time length of all short-stroke segments.

[0055] S33, Calculate the minimum allowable movement time for each condition category based on the stated proportion and the preset minimum allowable time for random driving conditions: in, Indicates the type of working condition i The minimum allowable motion time.

[0056] S34, for any operating condition category i Perform the following operations; a. Initialize a working condition sequence and initialize the sequence motion time length. ; b. Each time from this working condition category i Randomly select a cluster of length from the corresponding cluster. Short-stroke segments are added to the operating condition sequence. At the end of the sequence, and calculate the total time length of the added working condition sequence (i.e. To update ; Determine the updated Is it less than the minimum allowable motion time for this work condition category? If yes, return to step b and continue randomly selecting short-stroke segments and adding them to the work condition sequence; otherwise, output the work condition sequence after the current addition. This is the result of random sampling of short-stroke segments.

[0057] S35: Randomly sample short-stroke segments from all operating condition categories and aggregate them into a pool. Randomly sort all operating condition sequences in the pool, and then concatenate the first and last sequences according to the sorting results to generate the final random driving operating condition curve, such as... Figure 8 As shown.

[0058] To verify the statistical representativeness of the random working conditions constructed in this scheme, Figure 9 and Figure 10The document presents the vehicle speed probability distributions for all test segments in the original segment library and the vehicle speed probability distributions for a random driving scenario with a target duration of 1800 seconds generated according to this scheme. The overall consistency in the distribution patterns of the two indicates that the constructed scenario effectively reproduces the macroscopic statistical characteristics of actual traffic flow. The numerical differences in different speed segments reflect the randomness of the random scenario and highlight the random construction characteristics of this scheme.

[0059] Figure 11 A multi-dimensional radar chart was used to visually compare the key kinematic features of the original database (all test segments) and the constructed driving condition (1800 seconds of random driving). The selected comparison dimensions included: maximum acceleration, maximum deceleration, average acceleration, average deceleration, maximum vehicle speed, and average vehicle speed. The core of this method lies in generating random driving conditions with realistic statistical fluctuations. Figure 11 The data clearly demonstrates the quantitative manifestation of this randomness across specific parameters. All parameters have been normalized; "overall data" represents the overall statistical baseline of the original database, while "random data" represents the results of a single randomly generated operating condition.

[0060] Analysis shows that the generated random driving conditions did not aim to perfectly match the overall baseline values, but rather exhibited natural fluctuations resulting from random sampling and splicing. For example, in terms of extreme dynamic indicators, the maximum acceleration (0.94), maximum deceleration (0.81), and maximum speed (0.88) of the random driving conditions were all lower than the overall baseline, indicating that a single random sequence may have smoothed out occasional extreme driving events. Meanwhile, its average performance indicators, such as average acceleration (1.00) and average speed (1.04), oscillated slightly around the baseline values. This pattern of difference is not a bias, but rather direct evidence of the inherent nature of randomness: with different random seeds, each generated driving condition produces unique and reasonable fluctuations in these parameters.

[0061] Therefore, the primary purpose of this comparison is not to verify consistency, but to demonstrate the output characteristics of the random algorithm—it can generate a series of different, nondeterministic driving curves under statistical constraints, each of which carries reasonable random fluctuations, thus providing a basis for evaluating vehicle performance in various possible driving scenarios.

[0062] This embodiment also provides a device for constructing random driving conditions for passenger vehicles, such as... Figure 12 As shown, the device includes: Data acquisition and short-stroke segment extraction module: used to collect actual driving condition data and extract short-stroke segments according to the test plan, wherein the short stroke is defined as the movement process of the vehicle from the start of movement to the start of the next idle speed; Clustering module: used to cluster the short-stroke segments and construct clusters corresponding to different working condition categories; Random driving condition construction module: This module is used to randomly extract short-distance segments from clusters corresponding to different driving condition categories and randomly combine them to construct random driving conditions. The method for randomly extracting short-distance segments is as follows: Calculate the proportion of the total time length of short-stroke segments in the cluster corresponding to each working condition category to the total time length of all short-stroke segments. Based on the proportion and the preset minimum allowable time length of random driving conditions, calculate the minimum allowable motion time length for each working condition category. For any working condition category, initialize a working condition sequence. Each time, randomly select a short-stroke segment from the cluster corresponding to the working condition category and add it to the end of the working condition sequence. Calculate whether the total time length of the working condition sequence after adding is less than the minimum allowable motion time length for the working condition category. If so, continue to randomly select short-stroke segments and add them to the working condition sequence. Otherwise, output the working condition sequence after the current addition as the result of the random selection of short-stroke segments.

[0063] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the described module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0064] The electronic device of this invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) or loaded from a storage unit into random access memory (RAM). The RAM may also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0065] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0066] The processing unit executes the various methods and processes described above, such as methods S1 to S3. For example, in some embodiments, methods S1 to S3 may be implemented as computer software programs tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of methods S1 to S3 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute methods S1 to S3 by any other suitable means (e.g., by means of firmware).

[0067] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0068] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0069] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0070] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for constructing random driving conditions for passenger vehicles, characterized in that, The method includes the following steps: According to the test plan, actual driving condition data are collected and short-stroke segments are extracted. The short stroke is defined as the movement process of the vehicle from the start of movement to the start of the next idle speed. The short-stroke segments are clustered to construct clusters corresponding to different operating conditions; Random driving conditions are constructed by randomly selecting short-distance segments from clusters corresponding to different working condition categories and randomly combining them. The method for randomly selecting short-distance segments is as follows: Calculate the proportion of the total time length of short-stroke segments in the cluster corresponding to each working condition category to the total time length of all short-stroke segments. Based on the proportion and the preset minimum allowable time length of random driving conditions, calculate the minimum allowable motion time length for each working condition category. For any working condition category, initialize a working condition sequence. Each time, randomly select a short-stroke segment from the cluster corresponding to the working condition category and add it to the end of the working condition sequence. Calculate whether the total time length of the working condition sequence after adding is less than the minimum allowable motion time length for the working condition category. If so, continue to randomly select short-stroke segments and add them to the working condition sequence. Otherwise, output the working condition sequence after the current addition as the result of the random selection of short-stroke segments.

2. The method for constructing random driving conditions for passenger vehicles according to claim 1, characterized in that, The random combination specifically refers to: The results of random sampling of short-stroke segments of all working condition categories are aggregated into a pool. All working condition sequences in the pool are randomly sorted. The working condition sequences are then spliced ​​together according to the sorting results to generate the final random driving working condition curve.

3. The method for constructing random driving conditions for passenger vehicles according to claim 1, characterized in that, Before extracting short-stroke segments, the collected actual driving condition data is preprocessed, including: Clear the data of short-run segments whose running time is less than the preset length; Identify short travel segments with missing or intermittent data collection and delete them as invalid data; Data showing abnormally sudden changes in vehicle speed is identified and deleted as abnormal data.

4. The method for constructing random driving conditions for passenger vehicles according to claim 1, characterized in that, The clustering specifically includes the following steps: Calculate the characteristic parameters of short-stroke segments; Principal component analysis was performed on the short-stroke segments based on the aforementioned feature parameters to extract principal component scores. The number of clusters is determined based on the elbow rule and the silhouette coefficient; Short-stroke segments are clustered according to principal component scores, and the segments are divided into different clusters based on the determined number of clusters.

5. The method for constructing random driving conditions for passenger vehicles according to claim 4, characterized in that, The characteristic parameters include at least the average vehicle speed, maximum vehicle speed, running time, running distance, minimum deceleration, maximum acceleration, average deceleration during the deceleration phase, and average acceleration during the acceleration phase.

6. The method for constructing random driving conditions for a passenger vehicle according to claim 4, characterized in that, The cluster analysis used was K-means cluster analysis.

7. The method for constructing random driving conditions for a passenger vehicle according to claim 4, characterized in that, The method further includes: When collecting actual driving condition data according to the test plan, the driving condition type is recorded or labeled according to the actual traffic scenario to verify the clustering results.

8. A device for constructing random driving conditions for a passenger vehicle, characterized in that, The device includes: Data acquisition and short-stroke segment extraction module: used to collect actual driving condition data and extract short-stroke segments according to the test plan, wherein the short stroke is defined as the movement process of the vehicle from the start of movement to the start of the next idle speed; Clustering module: used to cluster the short-stroke segments and construct clusters corresponding to different working condition categories; Random driving condition construction module: This module is used to randomly extract short-distance segments from clusters corresponding to different driving condition categories and randomly combine them to construct random driving conditions. The method for randomly extracting short-distance segments is as follows: Calculate the proportion of the total time length of short-stroke segments in the cluster corresponding to each working condition category to the total time length of all short-stroke segments. Based on the proportion and the preset minimum allowable time length of random driving conditions, calculate the minimum allowable motion time length for each working condition category. For any working condition category, initialize a working condition sequence. Each time, randomly select a short-stroke segment from the cluster corresponding to the working condition category and add it to the end of the working condition sequence. Calculate whether the total time length of the working condition sequence after adding is less than the minimum allowable motion time length for the working condition category. If so, continue to randomly select short-stroke segments and add them to the working condition sequence. Otherwise, output the working condition sequence after the current addition as the result of the random selection of short-stroke segments.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 7.

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