Electric vehicle test condition construction method, device, storage medium and processor

Abstract

The present invention discloses a method, device, storage medium and processor for constructing a test condition for an electric vehicle. The method comprises: obtaining a plurality of condition set information corresponding to different condition types, wherein the condition set information includes a plurality of condition fragment information of the same condition type; determining the weight of each condition set information based on the number of condition fragment information contained in each condition set information; determining the first-order condition of the test condition based on the weight; performing Markov calculation on the first-order condition to determine the second-order condition; cyclically performing Markov calculation on the obtained second-order condition until the ranking of all condition set information is determined to obtain the arrangement order of the test condition; constructing the test condition of the target vehicle based on the plurality of condition set information and the arrangement order. The present invention solves the technical problem in the prior art that the test condition is constructed without specifying the test operation order between the plurality of conditions according to the actual driving habits of the user.

Description

Technical Field

[0001] The present invention relates to the field of vehicle testing technology, and in particular to a method, device, storage medium and processor for constructing a test condition for an electric vehicle. Background Art

[0002] With the development of technology and the continuous improvement of user needs, the requirements for new energy vehicles are becoming more and more stringent. New energy vehicles need to undergo extensive testing to ensure that their performance meets the requirements. When testing new energy vehicles, it is necessary to construct electric drive test conditions. The current method for constructing electric drive test conditions usually only stipulates the content of different conditions, and there are no clear regulations and research on the test operation sequence between multiple conditions. In the test, each condition is run independently and is not combined together. This cannot more realistically reflect the user's actual driving habits, and thus cannot achieve the goal of truly constructing test conditions that fit the actual scenario.

[0003] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0004] Embodiments of the present invention provide a method, device, storage medium, and processor for constructing an electric vehicle test condition, to at least solve the technical problem in the prior art that the test operation sequence between multiple conditions is not specified according to the user's actual driving habits when constructing the test condition.

[0005] According to one aspect of an embodiment of the present invention, a method for constructing a test working condition of an electric vehicle is provided, comprising: obtaining a plurality of working condition set information corresponding to different working condition types, wherein the working condition set information includes a plurality of working condition fragment information of the same working condition type, and the working condition fragment information includes at least one of the following: the current working condition of the electric vehicle and the speed of the electric vehicle under the current working condition; determining the weight of each working condition set information based on the number of working condition fragment information contained in each working condition set information; determining a first-order working condition of the test working condition based on the weight; performing Markov calculation on the first-order working condition to determine a second-order working condition; cyclically performing Markov calculation on the obtained second-order working condition until the ranking of all the working condition set information is determined to obtain an arrangement order of the test working condition; and constructing a test working condition of a target vehicle based on the plurality of working condition set information and the arrangement order.

[0006] Optionally, a plurality of working condition set information corresponding to different working condition types are obtained, including: obtaining vehicle cloud data, wherein the vehicle cloud data is obtained by collecting the actual driving process of multiple electric vehicles; and classifying and processing the vehicle cloud data to obtain a plurality of working condition set information corresponding to different working condition types.

[0007] Optionally, determining the first-order working condition of the test working condition based on the weight includes: taking the one with the largest weight in the working condition set information as the first-order working condition of the test working condition.

[0008] Optionally, a Markov calculation is performed on the first-order operating condition to determine the second-order operating condition, including: performing a Markov calculation on the first-order operating condition to obtain the Markov transition probability of the operating condition set information other than the operating condition set information corresponding to the first-order operating condition; and determining that the operating condition set information with the largest Markov transition probability in this calculation is the second-order operating condition.

[0009] Optionally, a Markov calculation is performed on the first-order operating condition to obtain the Markov transition probability of the operating condition set information other than the operating condition set information corresponding to the first-order operating condition, including: performing Markov calculation on multiple operating condition fragment information contained in the first-order operating condition separately in sequence to obtain the same multiple calculation operating conditions, wherein the calculation operating condition is one of the operating condition set information other than the operating condition set information corresponding to the first-order operating condition; classifying the multiple calculation operating conditions according to the operating condition type; and obtaining the Markov transition probability of the operating condition set information other than the operating condition set information corresponding to the first-order operating condition based on the weight of the classified calculation operating conditions.

[0010] Optionally, a test condition is constructed based on multiple condition set information and arrangement order, including: obtaining multiple typical conditions corresponding one-to-one to the multiple condition set information, wherein each typical condition is selected through multiple condition fragment information in the corresponding condition set information; determining the number of cycles of each typical condition based on the multiple condition set information and the multiple typical conditions; determining the test order of each typical condition based on the arrangement order; and constructing the test condition based on the test order of each typical condition and the number of cycles of each typical condition.

[0011] Optionally, the number of cycles of each typical working condition is determined based on multiple working condition set information and multiple typical working conditions, including: calculating the total damage value of the target vehicle corresponding to the working condition set information based on each working condition set information, wherein the total damage value is used to characterize the degree of damage to the target vehicle after the target vehicle is tested in sequence through all the working condition fragment information in the working condition set information; calculating the fragment damage value of the target vehicle corresponding to the typical working condition based on each typical working condition, wherein the fragment damage value is used to characterize the degree of damage to the target vehicle after the target vehicle is tested through a single typical working condition test; and determining the number of cycles of each typical working condition based on the calculated multiple total damage values ​​and the calculated multiple fragment damage values.

[0012] According to another aspect of an embodiment of the present invention, a device for constructing an electric vehicle test condition is also provided, including: an acquisition unit for acquiring a plurality of condition set information corresponding to different condition types, wherein the condition set information includes a plurality of condition fragment information of the same condition type, and the condition fragment information includes at least one of the following: the current condition of the electric vehicle and the speed of the electric vehicle under the current condition; a first determination unit for determining the weight of each condition set information based on the number of condition fragment information contained in each condition set information; a second determination unit for determining a first-order condition of the test condition based on the weight; a first calculation unit for performing Markov calculation on the first-order condition to determine the second-order condition; a second calculation unit for repeatedly performing Markov calculation on the obtained second-order condition until the ranking of all the condition set information is determined to obtain the arrangement order of the test condition; a construction unit for constructing the test condition of the target vehicle based on the plurality of condition set information and the arrangement order.

[0013] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the above method.

[0014] According to another aspect of an embodiment of the present invention, a processor is further provided, and the processor is configured to run a program, wherein the above method is executed when the program is run.

[0015] In an embodiment of the present invention, a method of determining the first-order working condition of the test working condition according to the weight of the working condition set information is adopted. By performing Markov calculation on the first-order working condition, the second-order working condition is determined, and the Markov calculation is cyclically performed on the second-order working condition, and finally the arrangement order of the test working condition is obtained, thereby achieving the purpose of sorting multiple different working conditions according to the user's actual driving habits, thereby achieving the technical effect of constructing a test working condition that is more in line with the real scene, and further solving the technical problem in the prior art that the test operation order between multiple working conditions is not specified according to the user's actual driving habits when constructing the test working condition. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0017] Figure 1 1 is a hardware structure block diagram of a computer terminal according to an optional electric vehicle test condition construction method of an embodiment of the present invention;

[0018] Figure 2is a flow chart of an optional method for constructing an electric vehicle test condition according to an embodiment of the present invention;

[0019] Figure 3 is a flow chart of an optional method for constructing an electric vehicle test condition according to an embodiment of the present invention;

[0020] Figure 4 This is a schematic diagram of operating condition set information obtained after classifying and processing vehicle cloud data according to an optional electric vehicle test operating condition construction method in an embodiment of the present invention;

[0021] Figure 5 2 is a schematic diagram of Markov transition probability results of an optional electric vehicle test condition construction method according to an embodiment of the present invention;

[0022] Figure 6 This is a module block diagram of an optional electric vehicle test condition construction device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0024] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] According to an embodiment of the present invention, a method embodiment of a method for constructing an electric vehicle test condition is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0026] The method embodiment can be executed in an electronic device or similar computing device in a vehicle that includes a memory and a processor. For example, Figure 1 As shown, the electronic device of the vehicle may include one or more processors 102 (the processor may include but is not limited to a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processing (DSP) chip, a microprocessor (MCU), a field-programmable logic device (FPGA), a neural network processor (NPU), a tensor processing unit (TPU), an artificial intelligence (AI) type processor, etc.) and a memory 104 for storing data. Optionally, the electronic device of the above-mentioned car may also include a transmission device 106 for communication functions, an input and output device 108, and a display 110. It will be understood by those skilled in the art that Figure 1 The structure shown is for illustration only and does not limit the structure of the electronic device of the vehicle. For example, the electronic device of the vehicle may include more or fewer components than those described above, or have a configuration different from that described above.

[0027] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the electric vehicle test condition construction method in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, implementing the above-mentioned electric vehicle test condition construction method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely located relative to the processor 102, and these remote memories may be connected to the mobile terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0028] The transmission device 106 is used to receive or send data via a network. A specific example of the aforementioned network may include a wireless network provided by the mobile terminal's communications provider. In one embodiment, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In one embodiment, the transmission device may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0029] The display 110 may be a touch screen liquid crystal display (LCD). The LCD may enable a user to interact with a user interface of the mobile terminal. In some embodiments, the mobile terminal may include a graphical user interface (GUI), and a user may interact with the GUI by finger contact and / or gestures on a touch-sensitive surface. The human-computer interaction functions herein may include the following: creating web pages, drawing, word processing, making electronic documents, gaming, video conferencing, instant messaging, sending and receiving emails, call interfaces, playing digital videos, playing digital music, and / or web browsing. Executable instructions for executing the above-mentioned human-computer interaction functions are configured / stored in a computer program product or readable storage medium executable by one or more processors.

[0030] In the existing technology, testing of electric vehicles requires the construction of test conditions. The existing technology only places the electric vehicle under a single condition for testing, without considering the order of experiments between each single condition. As a result, the overall test does not conform to the user's actual driving habits and scenarios. Therefore, there is an urgent need for a method that can better determine the test order of each condition and then generate test conditions.

[0031] This embodiment provides a method for running the above-mentioned electric vehicle test condition construction method, Figure 2 FIG. 1 is a flow chart of a method for constructing an electric vehicle test condition according to one embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0032] Step S102: Acquire a plurality of sets of operating condition information corresponding to different operating condition types, wherein the operating condition set information includes a plurality of operating condition segment information of the same operating condition type, and the operating condition segment information includes at least one of the following: a current operating condition of the electric vehicle and a speed of the electric vehicle under the current operating condition;

[0033] It should be noted that the operating condition set information is obtained by collecting and processing the actual driving behaviors of other electric vehicles except the target vehicle, that is, the operating condition set information is determined by pre-built user big data. The vehicle side communicates with the remote big database through wireless or wired communication to obtain user big data, and obtains the operating condition set information based on the user big data processing.

[0034] Step S103: determining the weight of each operating condition set information based on the number of operating condition fragment information contained in each operating condition set information;

[0035] For example, multiple operating condition sets include: first operating condition information and second operating condition information. The first operating condition information includes 100 operating condition segments, and the second operating condition information includes 50 operating condition segments. Based on the number of operating condition segments included in the operating condition set, the weight of the first operating condition information is determined to be 100 / (100+50), that is, 2 / 3.

[0036] Step S104, determining a first order operating condition of the test operating condition based on the weight;

[0037] Step S105, performing Markov calculation on the first order operating condition to determine the second order operating condition;

[0038] Step S106, cyclically performing Markov calculation on the obtained second order operating conditions until the ranking of all the operating condition set information is determined to obtain the arrangement order of the test operating conditions;

[0039] Step S107: constructing a test operating condition for the target vehicle based on the multiple operating condition set information and arrangement order.

[0040] Through the above steps, a method of determining the first order working condition of the test working condition according to the weight of the working condition set information can be implemented. By performing Markov calculation on the first order working condition, the second order working condition is determined, and the Markov calculation is cyclically performed on the second order working condition, and finally the order of the test working condition is obtained, achieving the purpose of sorting multiple different working conditions according to the user's actual driving habits, thereby achieving the technical effect of constructing a test working condition that is more in line with the real scene, and thus solving the technical problem that the test working condition in the prior art does not specify the test running order between multiple working conditions according to the user's actual driving habits when constructing the test working condition. In fact, the present invention provides a method for confirming the running order of electric vehicle test working conditions and a test working condition construction method based on this order. It can determine the order between each single working condition in the test working condition based on the working condition order reflected by the big data collected from the user's actual driving behavior, and then construct a test working condition containing all single working conditions based on the principle of damage consistency. Compared with the existing single working condition test method, the present invention can more reasonably sort each single working condition based on user big data, so that the entire test working condition is closer to the actual usage of the target user.

[0041] Optionally, a plurality of working condition set information corresponding to different working condition types are obtained, including: obtaining vehicle cloud data, wherein the vehicle cloud data is obtained by collecting the actual driving process of multiple electric vehicles; and classifying and processing the vehicle cloud data to obtain a plurality of working condition set information corresponding to different working condition types.

[0042] Vehicle cloud data is the user big data collected in advance and stored in the database. Figure 4The diagram shows the result of classifying 30 vehicle cloud data, where the vehicle cloud data with segment number 1 is classified into working condition 1, and the vehicle cloud data with segment number 2 is classified into working condition 2. Figure 4 All the fragments with the same number corresponding to the cluster belong to the same working condition, that is, the working condition set information under the same working condition type. The classification processing includes the K-means clustering method. The big data platform should include various platforms that can download user data in batches, rather than specifically referring to a specific platform. There are many ways to classify user data (i.e., vehicle cloud data), which are not limited to K-means clustering, but the use of different classification methods should not be a difference from this method. This embodiment mainly emphasizes the processing of data and the output of the fragment set representing different working conditions (i.e., working condition set information), and does not impose special restrictions on the method. It should be noted that in this application, a fragment set represents a working condition set information, and the multiple fragments included in a fragment set are respectively multiple working condition fragment information included in a working condition set information.

[0043] Optionally, determining the first-order working condition of the test working condition based on the weight includes: taking the one with the largest weight in the working condition set information as the first-order working condition of the test working condition. In other words, taking the one with the largest number of occurrences in the working condition set information as the first-order working condition.

[0044] Optionally, a Markov calculation is performed on the first-order operating condition to determine the second-order operating condition, including: performing a Markov calculation on the first-order operating condition to obtain the Markov transition probability of the operating condition set information other than the operating condition set information corresponding to the first-order operating condition; and determining that the operating condition set information with the largest Markov transition probability in this calculation is the second-order operating condition.

[0045] Optionally, performing a Markov calculation on the first-order operating condition to obtain Markov transition probabilities for operating condition set information other than the operating condition set information corresponding to the first-order operating condition includes: sequentially performing Markov calculations on multiple operating condition segment information included in the first-order operating condition to obtain the same multiple calculation operating conditions, wherein the calculation operating condition is one of the operating condition set information other than the operating condition set information corresponding to the first-order operating condition; classifying the multiple calculation operating conditions according to operating condition type; and obtaining Markov transition probabilities for the operating condition set information other than the operating condition set information corresponding to the first-order operating condition based on the weights of the classified calculation operating conditions. For example, if the first-order operating condition includes 1000 operating condition segment information, Markov calculations are performed on each of the 1000 operating condition segment information to obtain 1000 calculation operating conditions. If, in addition to the first-order operating condition, three other operating condition set information corresponding to different operating conditions are included, the Markov transition probabilities are determined based on the ratio of the number of occurrences of each of the other three operating condition set information in the 1000 calculation operating conditions to 1000. The method for performing Markov calculation on the first-order operating condition includes: calculating the first-order operating condition according to a transition probability matrix.

[0046] Optionally, constructing a test condition based on multiple condition set information and arrangement order includes: obtaining multiple typical conditions corresponding to the multiple condition set information, wherein each typical condition is selected through multiple condition fragment information in the corresponding condition set information; determining the number of cycles of each typical condition based on the multiple condition set information and the multiple typical conditions; determining the test order of each typical condition based on the arrangement order; and constructing the test condition based on the test order of each typical condition and the number of cycles of each typical condition. For example, if the arrangement order is condition 1, condition 2, condition 4, and condition 3, then the first typical condition corresponding to condition 1, the second typical condition corresponding to condition 2, the fourth typical condition corresponding to condition 4, and the third typical condition corresponding to condition 3 are selected at a time, and the test condition is finally formed according to the arrangement order and the number of typical conditions.

[0047] A method for selecting a typical operating condition for a set of operating condition information includes: setting a determination condition for the operating condition corresponding to the set of operating condition information, and determining the operating condition segment that best meets the determination condition as the typical operating condition. For example, if a set of operating condition information includes one hundred operating condition segments, and the operating condition type corresponding to the set of operating condition information is a highway section, the determination condition may be that the difference between the speed and a preset value is minimized, and the operating condition segment that best meets the determination condition is the typical operating condition. For example, if a set of operating condition information includes one hundred operating condition segments, and the operating condition type corresponding to the set of operating condition information is a bumpy condition, the determination condition may be that the suspension movement is minimized, and the operating condition segment that best meets the determination condition is the typical operating condition.

[0048] Optionally, the number of cycles for each typical operating condition is determined based on multiple operating condition set information and multiple typical operating conditions, including: calculating a total damage value corresponding to the operating condition set information for the target vehicle based on each operating condition set information, wherein the total damage value is used to represent the extent of damage to the target vehicle after the target vehicle is sequentially tested through all the operating condition segment information in the operating condition set information; calculating a segment damage value corresponding to the target vehicle for each typical operating condition, wherein the segment damage value is used to represent the extent of damage to the target vehicle after the target vehicle is tested through a single typical operating condition; and determining the number of cycles for each typical operating condition based on the calculated multiple total damage values ​​and the calculated multiple segment damage values. Calculating the total damage value corresponding to the operating condition set information for the target vehicle based on each operating condition set information, or calculating the segment damage value corresponding to the typical operating condition for the target vehicle based on each typical operating condition, requires selecting the damaged component and type. In this embodiment, the damaged component and type can be selected based on the actual situation. The use of different damage values ​​for calculation should not be a difference from the present invention.

[0049] As a specific embodiment, the method for constructing an electric vehicle test condition of the present invention includes the following steps:

[0050] Step 1: Download the user big data of the required classification from a specific big data platform to the local computer;

[0051] Step 2: Cut the user big data to be classified into segments and classify them according to certain rules to form a set of M segments with working condition classification information. The number of segments in each category is m1, m2, ...m. M ; At the same time, extract the representative fragments in the working condition (x1, x2, ... x M ) as the basis for subsequent working condition construction; the above two steps correspond to Figure 3 The user big data in S1 and S2 are segmented and classified to form a set of working condition segments.

[0052] Step 3: Calculate the total damage D1, D2, ... D for each working condition M , and lesions d1, d2, ...d representing the segments M ; Divide the total damage of the corresponding working condition by the damage of the representative segment N1, N2, ... N M , get the number of cycles required for each working condition, that is

[0053] Step 4: extract the common divisor of the number of fragment cycles to make it a cycle combination of a smaller working condition, where the common divisor is the total number of cycles N, and the result after dividing the total number of fragments by the common divisor is used as the number of fragments of each category in a single cycle n1, n2...n M ;

[0054] Step 5: Calculate the proportion of each working condition segment in a single cycle The working condition with the largest proportion is selected as the initial working condition; in another optional calculation method, the proportion of each working condition can be calculated by dividing the number of fragments in each category in step 2 by the total number of fragments in each category.

[0055] Step 6: Calculate the Markov transition probability for the entire segment set to obtain the Markov transition matrix;

[0056] A transfer probability matrix is ​​a matrix in which all elements are non-negative, the sum of all row elements is 1, and each element is represented by a probability. Under certain conditions, these elements can be transferred to each other, hence the name "transition probability matrix." In this method, the probability of each classification condition occurring is independent of the previous condition, so the transfer matrix method can be used to determine the order of the previous and subsequent conditions.

[0057] Step 7: According to the probability of occurrence of each working condition in the transfer matrix, the working condition with the highest probability of occurrence after the current working condition is sorted as the next working condition, and finally the completed test condition sequence x'1, x'2...x' is formed. M , the number of cycles are n'1, n'2...n' M Then the single cycle working condition is constructed, and then combined with the number of cycles N in step 3, the working condition construction of the whole process cycle is finally completed. The final constructed working conditions are as follows: (1) Working condition x'1 runs n'1 times, working condition x'2 runs n'2 times, ... working condition x' M Run n' M (2) Repeat (1) N times. It should be noted that the implementation of the model and algorithm in steps 1 to 7 is based on a user big data platform that supports batch operations and can cover all platform users.

[0058] As another specific embodiment of the present application, step 1 is to download the user big data required for classification to a local computer. The big data must at least include the user's speed information at each moment. The big data comes from a specific online big data platform.

[0059] Step 2: Segment the user big data to be classified and classify it according to certain rules to form a set of segments with working condition classification information. In this example, the K-means clustering method is used for classification, and the number of categories is 4. The working conditions are named working condition 1, working condition 2, working condition 3, and working condition 4. The number of segments for working condition 1 is 8332, the number of segments for working condition 2 is 18832, the number of segments for working condition 3 is 42674, and the number of segments for working condition 4 is 47497. The subsequent working condition construction process uses typical segments to represent the working condition. For example, working condition 1 is an urban road section, working condition 2 is a suburban road section, working condition 3 is a highway section, and working condition 4 is a bumpy road section.

[0060] Step 3: Calculate the damage values ​​for the four working conditions. In this example, shaft damage is used as the damage basis. The total damage for working condition 1 is 1.44E-8, the total damage for working condition 2 is 3.85E-8, the total damage for working condition 3 is 4.76E-7, and the total damage for working condition 4 is 7.54E-8. The damage for typical segment 1 is 1.81E-11, the damage for typical segment 2 is 6.42E-11, the damage for typical segment 3 is 3.18E-11, and the damage for typical segment 4 is 6.28E-12. The total number of cycles for the four working conditions is 797, 599, 15023, and 10012, respectively. To simplify the calculation, the number of cycles is rounded to 800, 600, 15000, and 10000, respectively.

[0061] Step 4: Extract the common divisor of the fragment set to make it a cycle combination of a smaller working condition. In this example, the common divisor N = 100. After removing the common divisor, the number of cycles of the four working conditions in the small cycle are 8, 6, 150, and 100 respectively.

[0062] In step 5, the proportion of each working condition in the entire fragment set is calculated, and the working condition with the largest proportion is selected as the initial working condition. In this example, the result of step 2 shows that working condition 4 has the largest proportion, so working condition 4 is selected as the first-order working condition. In other words, the number of fragments in working condition 4 is 47,497, which has the largest ratio to the number of fragments in the four working conditions, that is, it has the largest proportion.

[0063] Step 6: Perform Markov calculation on the entire set of segments to obtain the transition probability result. Since there is no obvious correlation between the user's previous and subsequent working conditions, the Markov method can be used to calculate the transition probability. In this embodiment, ncode software is used for auxiliary calculation. The result after the transfer probability calculation is as follows: Figure 5 As shown;

[0064] Step 7: According to the transition probability, the working conditions with the highest probability of appearing after the current working condition are sorted as the next working conditions, and finally the completed test working condition sequence is formed. In this embodiment, according to step 3, working condition 4 is the first order; the probability of working condition 3 appearing after working condition 4 is the highest, so working condition 3 is the second order; the probability of working condition 2 appearing after working condition 3 is the highest, so working condition 2 is the third order, and finally working condition 1 is the fourth order. In the subsequent test process, the test working conditions are finally determined as follows: (1) Typical segment 4 is run 100 times, typical segment 3 is run 150 times, typical segment 2 is run 6 times, and typical segment 1 is run 8 times; (2) the above (1) is run 100 times.

[0065] By adopting the technical solution of the present application, the order of the test conditions can be determined based on the condition sequence of the big data collected from the user's actual driving behavior, and then a large condition containing all single conditions can be constructed based on the principle of damage consistency. It should be noted that the sorting basis for the fragment set is the damage consistency of the condition and the Markov transition probability matrix under the big data medium. Therefore, this arrangement order can truly reflect the user's driving habits and driving scene changes in the big data, thereby improving the authenticity of the test conditions.

[0066] The principle of damage consistency is illustrated by an example: a working condition set information includes 100 working condition segment information. The damage caused to the target vehicle after testing the 100 working condition segment information in the working condition set information in sequence is the same as the damage caused by testing the target vehicle using the number of cycles corresponding to the typical working condition corresponding to the working condition set information.

[0067] The embodiment of the present application also provides a device for constructing an electric vehicle test condition. Figure 6 This is a structural diagram of the construction device of the electric vehicle test condition, such as Figure 6 As shown, the device for constructing an electric vehicle test condition includes: an acquisition unit 40, which is used to obtain a plurality of condition set information corresponding to different condition types, wherein the condition set information includes a plurality of condition fragment information of the same condition type, and the condition fragment information includes at least one of the following: the current condition of the electric vehicle and the speed of the electric vehicle under the current condition; a first determination unit 42, which is used to determine the weight of each condition set information based on the number of condition fragment information contained in each condition set information; a second determination unit 44, which is used to determine the first-order condition of the test condition based on the weight; a first calculation unit 46, which is used to perform Markov calculation on the first-order condition to determine the second-order condition; a second calculation unit 48, which is used to repeatedly perform Markov calculation on the obtained second-order condition until the ranking of all the condition set information is determined to obtain the arrangement order of the test condition; a construction unit 50, which constructs the test condition of the target vehicle based on the plurality of condition set information and the arrangement order.

[0068] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0069] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0070] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0071] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0072] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0073] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc. Various media that can store program codes.

[0074] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for constructing an electric vehicle test condition, characterized in that: include: Acquire a plurality of operating condition set information corresponding to different operating condition types, wherein the operating condition set information includes a plurality of operating condition segment information of the same operating condition type, and the operating condition segment information includes at least one of the following: a current operating condition of the electric vehicle and a speed of the electric vehicle under the current operating condition; determining a weight of each piece of the operating condition set information based on the number of the operating condition fragment information contained in each piece of the operating condition set information; determining a first order operating condition of the test operating condition based on the weights; Performing a Markov calculation on the first sequential operating condition to determine a second sequential operating condition; cyclically performing Markov calculation on the obtained second sequence operating conditions until the ranking of all the operating condition set information is determined to obtain the arrangement order of the test operating conditions; Constructing the test operating condition of the target vehicle based on the plurality of operating condition set information and the arrangement order; Constructing the test working condition based on the plurality of working condition set information and the arrangement order includes: Acquire multiple typical operating conditions corresponding one-to-one to the multiple operating condition set information, wherein each of the typical operating conditions is obtained by selecting multiple operating condition fragment information in the corresponding operating condition set information; Determining the number of cycles of each of the typical operating conditions based on the plurality of operating condition set information and the plurality of typical operating conditions; Determining a test order for each of the typical working conditions based on the arrangement order; The test conditions are constructed based on the test sequence of each typical working condition and the number of cycles of each typical working condition; Determining the number of cycles of each typical operating condition based on the plurality of operating condition set information and the plurality of typical operating conditions includes: Calculating a total damage value of the target vehicle corresponding to the operating condition set information based on each set of operating condition information, wherein the total damage value is used to represent the extent of damage to the target vehicle after the target vehicle is sequentially tested with all of the operating condition segment information in the operating condition set information; Calculating a segment damage value of the target vehicle corresponding to each typical operating condition based on each typical operating condition, wherein the segment damage value is used to represent the extent of damage to the target vehicle after the target vehicle undergoes a single test under the typical operating condition; The number of cycles of each of the typical working conditions is determined based on the calculated multiple total damage values ​​and the calculated multiple segment damage values.

2. The method according to claim 1, characterized in that Get multiple sets of working condition information corresponding to different working condition types, including: Acquiring vehicle cloud data, wherein the vehicle cloud data is obtained by collecting actual driving processes of multiple electric vehicles; The vehicle cloud data is classified and processed to obtain a plurality of pieces of operating condition set information corresponding to different operating condition types.

3. The method according to claim 1, characterized in that Determining a first order operating condition of the test operating condition based on the weights includes: The one with the largest weight in the operating condition set information is used as the first sequential operating condition of the test operating condition.

4. The method according to claim 1, wherein Performing a Markov calculation on the first sequential operating condition to determine the second sequential operating condition includes: Performing a Markov calculation on the first sequential operating condition to obtain a Markov transition probability of the operating condition set information other than the operating condition set information corresponding to the first sequential operating condition; The operating condition set information with the maximum Markov transition probability in this calculation is determined to be the second-order operating condition.

5. The method according to claim 4, characterized in that Performing a Markov calculation on the first sequential operating condition to obtain the Markov transition probability of the operating condition set information other than the operating condition set information corresponding to the first sequential operating condition includes: Performing Markov calculations on the plurality of operating condition fragments included in the first sequential operating condition to obtain the same plurality of calculated operating conditions, wherein the calculated operating condition is one of the operating condition set information other than the operating condition set information corresponding to the first sequential operating condition; Classify the plurality of calculation working conditions according to the working condition types; Based on the weights of the calculated operating conditions after classification processing, the Markov transition probabilities of the operating condition set information other than the operating condition set information corresponding to the first sequential operating condition are obtained.

6. A device for constructing an electric vehicle test condition, characterized in that: The construction device is used to perform the method according to any one of claims 1 to 5, comprising: an acquiring unit, configured to acquire a plurality of pieces of operating condition set information corresponding to different operating condition types, wherein the operating condition set information includes a plurality of pieces of operating condition segment information having the same operating condition type, and the operating condition segment information includes at least one of the following: a current operating condition of the electric vehicle and a speed of the electric vehicle under the current operating condition; a first determining unit, configured to determine a weight of each piece of operating condition set information based on the number of operating condition fragment information contained in each piece of operating condition set information; a second determining unit, configured to determine a first sequence operating condition of the test operating condition based on the weight; a first calculation unit, configured to perform a Markov calculation on the first sequential operating condition to determine a second sequential operating condition; a second calculation unit, configured to repeatedly perform Markov calculation on the obtained second sequence operating conditions until the ranking of all the operating condition set information is determined to obtain the arrangement order of the test operating conditions; A construction unit constructs the test working condition of the target vehicle based on the plurality of working condition set information and the arrangement order.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 5.

8. A processor, characterized in that: The processor is configured to run a program, wherein the program executes the method according to any one of claims 1 to 5 when running.

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