Real world emissions test method and electronics

Abstract

The application provides a kind of actual road emission test method and electronic equipment, it is related to road emission test technical field.The method can select the highest score vehicle trip chain from multiple vehicle trip chains The journey trajectory of the vehicle trip chain is used as the simulation route of the simulated actual road emission test.In this way, the vehicle trip chain with the highest similarity of natural environmental conditions, the average driving speed corresponding to the road type of each road section and the target test area can be obtained.Furthermore, the actual road emission test can be carried out on the simulation route.If the actual road emission test on the simulation route fails, since the simulation area is near the vehicle production site, the adjustment can be directly made near the vehicle production site, which is low in cost and saves time;and when the test is successful, the success rate of putting the vehicle into the target test area for actual road emission test is high, and there is no need to return to the vehicle production site for adjustment.

Description

Technical Field

[0001] This application relates to the field of road emission testing technology, and in particular to a method and electronic device for actual road emission testing. Background Technology

[0002] Before vehicles are put on the market, they need to undergo Real Driving Emissions (RDE) testing. RDE testing refers to measuring the emission levels of exhaust pollutants (such as nitrogen oxides NOx and particulate matter PN) when the vehicle is driven on actual roads. As global vehicle emission requirements become increasingly stringent, major automobile export markets have fully implemented Real Driving Emissions (RDE) testing to bridge the gap between laboratory testing and real-world usage scenarios.

[0003] In the compliance verification of exported vehicle models, it is usually necessary to conduct on-site RDE testing in the target export market region to ensure that local requirements are met. If the exported vehicle fails the on-site RDE test in the target export market region, it needs to be returned to the production site for adjustments, and then the on-site RDE test in the target export market region needs to be conducted again, and so on. This results in high costs and lengthy cycles for conducting on-site RDE testing in the target export market region. Summary of the Invention

[0004] This application provides a real-road emissions testing method and electronic device to solve the problem of high cost and long cycle of conducting on-site RDE testing in target export market areas in the prior art.

[0005] Firstly, this application provides a method for testing emissions in real-world road conditions, comprising:

[0006] Obtain the primary natural environmental characteristics of the target test area during the target test time period;

[0007] From the pre-set simulated regions, determine the candidate sub-regions with the highest similarity between the second natural environmental feature and the first natural environmental feature;

[0008] From the preset database, obtain multiple vehicle travel chains associated with candidate sub-regions within the target test time period. Each vehicle travel chain includes the travel trajectory of a trip where the interval between the previous shutdown time and the current time exceeds a preset time threshold, the road segments traversed, and the vehicle's speed on each road segment.

[0009] Based on the travel trajectories of vehicles in each road segment, determine the average travel speed corresponding to each road segment.

[0010] For each road segment, the road type is determined based on the speed range to which the average driving speed of the road segment belongs;

[0011] The score for each vehicle travel chain is determined based on the average speed of vehicles on each road segment in each vehicle travel chain, the road type of each road segment, and the baseline average speed corresponding to each road type associated with the target test area.

[0012] From multiple vehicle travel chains, the travel trajectory of the vehicle travel chain with the highest score is selected as the simulation route for simulating real-world road emissions tests.

[0013] In some implementations, the first natural environmental feature includes a first temperature distribution feature and a first elevation distribution feature, and the second natural environmental feature includes a second temperature distribution feature and a second elevation distribution feature. From a preset simulated region, the candidate sub-region with the highest similarity between the second natural environmental feature and the first natural environmental feature is determined, including:

[0014] Determine the first similarity between the first temperature distribution characteristics and the second temperature distribution characteristics of each sub-region in the preset simulated area, and the second similarity between the first elevation distribution characteristics and the second elevation characteristics of each sub-region;

[0015] From multiple sub-regions in the simulated region, select multiple sub-regions to be filtered out where both the first similarity and the second similarity are greater than a preset similarity threshold;

[0016] Determine the absolute value of the difference between the first average elevation value and the second average elevation value of the second elevation distribution characteristics for each sub-region to be screened;

[0017] The score for each sub-region to be selected is determined based on the absolute value of the first similarity and the difference corresponding to each sub-region to be selected;

[0018] The sub-region with the highest score is selected as the candidate sub-region.

[0019] In some implementations, a score for each sub-region to be selected is determined based on the absolute value of the first similarity and the difference corresponding to each sub-region to be selected, including:

[0020] According to the formula Determine the score for each sub-region to be filtered, where, The score of the sub-region to be filtered. The first similarity score, The absolute value of the difference. The first weighting coefficient is preset. This is the preset second weighting coefficient.

[0021] In some implementations, the first temperature distribution feature is a first temperature probability density function of a sub-region during the target test time period, and the second temperature distribution feature is a second temperature probability density function of the target test region during the target test time period. Determining the first similarity between the first temperature distribution feature and the second temperature distribution feature of each sub-region in the preset simulated region includes:

[0022] Determine the relative entropy between the first temperature probability density function and the second temperature probability density function; wherein, the relative entropy is used to characterize the first similarity, and the relative entropy is negatively correlated with the first similarity.

[0023] In some implementations, a score for each vehicle travel chain is determined based on the average speed of vehicles on each road segment within each vehicle travel chain, the road type of each road segment, and the baseline average speed corresponding to each road type associated with the target test area, including:

[0024] Based on the average travel speed of vehicles on each road segment in each vehicle travel chain, determine the average travel speed associated with the road type to which each road segment belongs;

[0025] Determine the average driving speed associated with each road type for each vehicle travel chain, and the root mean square error of the basic average driving speed associated with each road type for the target test area.

[0026] For each vehicle trip chain, the score of the vehicle trip chain is determined based on the root mean square error corresponding to the vehicle trip chain, and the root mean square error is negatively correlated with the score of the vehicle trip chain.

[0027] In some implementations, a score for each vehicle travel chain is determined based on the average speed of vehicles on each road segment within each vehicle travel chain, the road type of each road segment, and the baseline average speed corresponding to each road type associated with the target test area, including:

[0028] Based on the average speed of vehicles on each road segment in each vehicle travel chain, determine the average speed associated with the road type to which each road segment belongs, and determine the first average speed of each road segment in each vehicle travel chain for each day during the target test period.

[0029] Determine the average driving speed associated with each road type for each vehicle travel chain, and the root mean square error of the basic average driving speed associated with each road type for the target test area.

[0030] The speed fluctuation of each road segment is determined based on the first average speed of each day and the preset second average speed during the target test period.

[0031] For each vehicle travel chain, the average speed fluctuation of each road segment in the vehicle travel chain is determined as the average speed fluctuation of the vehicle travel chain.

[0032] The score for each vehicle travel chain is determined based on the root mean square error and the average speed fluctuation of each vehicle travel chain.

[0033] In some implementations, a score for each vehicle trip chain is determined based on the root mean square error and the average speed fluctuation of each trip chain, including:

[0034] According to the formula Determine the score for each vehicle travel chain, among which, To score points, The root mean square error, The average velocity fluctuation, The third weighting coefficient is preset. This is the preset fourth weighting coefficient.

[0035] In some implementations, the speed fluctuation of each road segment is determined based on a first average speed for each day and a preset second average speed over the target test period, including:

[0036] According to the formula Determine the speed fluctuation of each road segment, where, Here, n represents the speed fluctuation of a single road segment, and n is the number of days within the target test period. The first average speed of each road segment on day i. The second average speed of each road segment during the target test period;

[0037] For each vehicle travel chain, the average speed fluctuation of each road segment within the vehicle travel chain is determined as the average speed fluctuation of the vehicle travel chain, including:

[0038] According to the formula Determine the average speed fluctuation of the vehicle travel chain, whereby... Let m represent the average speed fluctuation of the vehicle travel chain, and m be the number of road segments in the vehicle travel chain. Let be the speed fluctuation of the j-th road segment in the vehicle travel chain.

[0039] In some implementations, after obtaining multiple vehicle travel chains associated with candidate sub-regions within the target testing time period, the method provided in this application further includes:

[0040] From multiple vehicle travel chains, those that do not meet the preset conditions are eliminated.

[0041] In a second aspect, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the electronic device performs the method provided in the first aspect of this application.

[0042] Thirdly, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the computer to perform the method provided in the first aspect.

[0043] Fourthly, this application also provides a computer program product, including a computer program that, when run, causes a computer to perform the method provided in the first aspect.

[0044] This application provides a method and electronic device for real-world road emissions testing, which acquires a first natural environmental characteristic of a target test area during a target test time period; and determines candidate sub-regions from a preset simulated area whose second natural environmental characteristic has the highest similarity to the first natural environmental characteristic. The simulated area is located near the vehicle production site. This allows for the acquisition of multiple candidate sub-regions with natural environmental characteristics similar to the target test area.

[0045] From a pre-defined database, retrieve multiple vehicle travel chains associated with candidate sub-regions within the target testing time period. Based on the vehicle speeds along each road segment in the travel trajectories of each travel chain, determine the average speed for each road segment. For each road segment, determine the road type based on the speed range to which the average speed of the road segment belongs.

[0046] The score for each vehicle travel chain is determined based on the average vehicle speed on each road segment within each travel chain, the road type of each road segment, and the baseline average speed corresponding to each road type associated with the target test area. This allows us to obtain the similarity (i.e., score) between the average speed corresponding to each road type and the baseline average speed corresponding to each road type associated with the target test area.

[0047] From multiple vehicle travel chains, the trajectory of the highest-scoring travel chain is selected as the simulation route for real-world road emissions testing. This allows us to obtain the vehicle travel chain with the highest similarity to the target test area, considering both the natural environmental conditions and the average driving speed corresponding to the road type for each road segment.

[0048] This allows users to select the route of the vehicle travel chain with the highest score as the simulated route for real-world emissions testing, and then conduct real-world emissions tests on the simulated route.

[0049] If the simulated route fails the actual road emissions test, adjustments can be made directly at the vehicle manufacturing site since the simulated area is near the vehicle's production location, which is cost-effective and saves time. Furthermore, by obtaining the vehicle travel chain with the highest similarity to the target test area in terms of natural environmental conditions and average driving speeds for each road segment, a high success rate can be achieved when the simulated test is successful, allowing for a high success rate when the vehicle is deployed to the target test area for actual road emissions testing, eliminating the need to return to the vehicle manufacturing site for adjustments. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 A flowchart illustrating the actual road emission testing method provided in this application embodiment;

[0052] Figure 2 for Figure 1 The detailed flowchart of S103 in the document;

[0053] Figure 3 for Figure 1 The detailed flowchart of S106 in the document;

[0054] Figure 4 A functional block diagram of the actual road emission testing device provided in the embodiments of this application. Detailed Implementation

[0055] Embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0056] The accompanying drawings illustrate various structural schematics according to embodiments of the present disclosure. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0057] In the context of this disclosure, when a layer / element is referred to as being "above" another layer / element, the layer / element may be directly above the other layer / element, or there may be an intermediate layer / element between them. Additionally, if a layer / element is "above" another layer / element in one orientation, then when the orientation is reversed, the layer / element may be "below" the other layer / element.

[0058] The technical solutions of this application and how they solve the aforementioned technical problems will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0059] Please see Figure 1 This application provides a method for testing emissions on actual roads, applied to a server. The method provided in this application includes:

[0060] S101: Obtain the first natural environmental features of the target test area during the target test time period.

[0061] For example, the first natural environmental feature may include, but is not limited to, the first temperature distribution feature and the first elevation distribution feature (including altitude distribution feature) of the target test area.

[0062] For example, the target test area can be city A in country A, the target test period can be from April to June, the average temperature value of the first temperature distribution characteristic is between 14℃ and 26℃, and the altitude distribution characteristic of city A in country A corresponds to an altitude range of 20m-50m.

[0063] S102: From the preset simulated regions, determine the candidate sub-regions with the highest similarity between the second natural environmental feature and the first natural environmental feature.

[0064] For example, the simulated region can be a specific area within a country. The second natural environmental feature may include, but is not limited to, the second temperature distribution feature and the second elevation distribution feature (including altitude distribution feature) of the simulated region.

[0065] For example, when the first natural environmental feature includes a first temperature distribution feature and a first elevation distribution feature, the second natural environmental feature includes a second temperature distribution feature and a second elevation distribution feature. Figure 2 As shown, S103 can be specifically implemented as follows:

[0066] S201: Determine the first similarity between the first temperature distribution feature and the second temperature distribution feature of each sub-region in the preset simulated area, and the second similarity between the first elevation distribution feature and the second elevation distribution feature of each sub-region.

[0067] For example, the first temperature distribution feature is the first temperature probability density function of the sub-region during the target test time period, and the second temperature distribution feature is the second temperature probability density function of the target test region during the target test time period. The relative entropy (i.e., KL divergence) between the first and second temperature probability density functions can be determined; the relative entropy is used to characterize the first similarity, and is negatively correlated with the first similarity (details omitted here). Furthermore, the absolute value of the difference between the first average elevation value of the first elevation distribution feature and the second average elevation value of the second elevation distribution feature for each sub-region to be screened can be determined; this absolute value is used to characterize the second similarity, and is negatively correlated with the second similarity.

[0068] For example, the simulated region can be a simulated region of country B. In the meteorological database of country B, the second temperature distribution characteristics corresponding to each candidate sub-region (such as a prefecture-level city) from April to June can be traversed, and the KL divergence between the second temperature distribution characteristics and the first temperature distribution characteristics can be calculated.

[0069] S202: From multiple sub-regions in the simulated region, select multiple sub-regions to be filtered out, where both the first similarity and the second similarity are greater than the preset similarity threshold.

[0070] For example, candidate sub-regions with KL divergence below a preset threshold (such as 0.05) can be selected.

[0071] S203: Determine the absolute value of the difference between the first average elevation value and the second average elevation value of the second elevation distribution characteristics for each sub-region to be screened.

[0072] S204: Determine the score of each sub-region to be selected based on the absolute value of the first similarity and the difference corresponding to each sub-region to be selected.

[0073] For example, S204 can be, but is not limited to, based on the formula This determines the score for each sub-region to be filtered. The score of the sub-region to be filtered. This represents the first similarity (i.e., the first KL divergence). The absolute value of the difference. The first weighting coefficient is preset. This is the preset second weighting coefficient. In this way, the score for each sub-region to be filtered can be accurately obtained.

[0074] S205: The sub-region with the highest score is identified as the candidate sub-region.

[0075] S103: Obtain multiple vehicle travel chains associated with candidate sub-regions within the target test time period from the preset database.

[0076] Each vehicle travel chain includes the travel trajectory of a trip where the interval between the previous shutdown time and the current time exceeds a preset time threshold (e.g., 30 minutes), the road segments traversed, and the vehicle's speed on each road segment.

[0077] For example, Table 1 is a content table of the vehicle travel chain, and the vehicle travel chain may also include the content shown in Table 1 below.

[0078] Table 1 - Contents of Vehicle Travel Chain

[0079]

[0080] Furthermore, vehicle travel chains that do not meet the preset conditions can be eliminated from multiple vehicle travel chains.

[0081] For example, it is possible to collect the proportion of the total mileage of each type of road segment in each vehicle travel chain, the proportion of the total time of each type of road segment, the altitude difference between the starting point and the ending point, the positive gain of altitude per 100 kilometers, and the order of road segments corresponding to each road type in the vehicle travel chain.

[0082] For any vehicle travel chain, the vehicle travel chain is removed if the following conditions are met: the proportion of the mileage of each type of road segment to the total mileage is not within the preset proportion threshold range; the proportion of the travel time of each type of road segment to the total time is not within the preset ratio threshold range; the average travel speed of each type of road segment; the altitude difference between the starting point and the ending point is greater than the set altitude difference threshold; the positive gain per 100 kilometers of altitude is greater than the set gain threshold; the average travel speed of each type of road segment is not within the corresponding speed range; or the order of road segments corresponding to each road type in the vehicle travel chain does not meet the preset order (e.g., suburban road segments must appear after urban road segments).

[0083] For example, if the total duration of a vehicle travel chain is greater than 120 minutes, the travel time of any type of road segment accounts for more than 40% of the total duration, the urban road segment accounts for less than 30%, the altitude difference between the starting point and the ending point is greater than 100m, or the positive gain of altitude per 100 kilometers is greater than 120m, the vehicle travel chain will be removed.

[0084] S104: Determine the average speed corresponding to each road segment based on the travel speed of vehicles in each road segment in the travel trajectory of each vehicle travel chain.

[0085] For example, for road segment A, the travel speed of road segment A corresponding to vehicle travel chain A is X; the travel speed of road segment A corresponding to vehicle travel chain B is Y; and the travel speed of road segment A corresponding to vehicle travel chain C is Z. Then the average travel speed corresponding to road segment A is (X+Y+Z) / 3.

[0086] S105: For each road segment, determine the road type based on the speed range to which the average driving speed of the road segment belongs.

[0087] For example, if the average driving speed of a road segment is less than 40 km / h, the road type is urban; if the average driving speed of a road segment is between 40 km / h and 80 km / h, the road type is suburban; if the average driving speed of a road segment is between 80 km / h and 120 km / h, the road type is highway.

[0088] S106: Determine the score for each vehicle travel chain based on the average speed of vehicles on each road segment in each vehicle travel chain, the road type of each road segment, and the baseline average speed corresponding to each road type associated with the target test area.

[0089] In some implementations, such as Figure 3 As shown, S106 can be specifically implemented as follows:

[0090] S301: Based on the average speed of vehicles on each road segment in each vehicle travel chain, determine the average speed associated with the road type to which each road segment belongs, and determine the first average speed of each road segment in each vehicle travel chain for each day during the target test period.

[0091] S302: Determine the average driving speed associated with each road type for each vehicle travel chain, and the root mean square error of the basic average driving speed associated with each road type for the target test area.

[0092] S303: Determine the speed fluctuation of each road segment based on the first average speed for each day and the preset second average speed within the target test period.

[0093] For example, according to the formula Determine the speed fluctuation of each road segment. Here, n represents the speed fluctuation of a single road segment, and n is the number of days within the target test period. The first average speed of each road segment on day i. The second average speed for each road segment during the target test period.

[0094] S304: For each vehicle travel chain, the average speed fluctuation of each road segment in the vehicle travel chain is determined as the average speed fluctuation of the vehicle travel chain.

[0095] For example, it can be based on the formula To determine the average speed fluctuation of the vehicle travel chain. Let m represent the average speed fluctuation of the vehicle travel chain, and m be the number of road segments in the vehicle travel chain. Let be the speed fluctuation of the j-th road segment in the vehicle travel chain.

[0096] S305: Determine the score for each vehicle travel chain based on the root mean square error and the average speed fluctuation of each vehicle travel chain.

[0097] For example, it can be based on the formula This determines the score for each vehicle travel chain. Among them, To score points, The root mean square error, The average velocity fluctuation, The third weighting coefficient is preset. This is the preset fourth weighting coefficient. Understandably, the score for each vehicle travel chain determined by the above formula is highly reliable.

[0098] In other implementations, S106 may also be implemented as follows: determining the average driving speed associated with each road type of each road segment based on the average driving speed of vehicles on each road segment in each vehicle travel chain; determining the root mean square error between the average driving speed associated with each road type of each vehicle travel chain and the basic average driving speed associated with each road type of the target test area; for each vehicle travel chain, determining the score of the vehicle travel chain based on the root mean square error of the vehicle travel chain, wherein the root mean square error is negatively correlated with the score of the vehicle travel chain.

[0099] S107: Select the travel trajectory of the vehicle travel chain with the highest score from multiple vehicle travel chains as the simulation route for simulating real-world road emissions tests.

[0100] Through experimentation, the actual road emission test route in the target test area is route A in city A of country A. Table 2 shows the parameter relationship between route A and route B. When the simulated area is route B in city B of country B, the relationship between route A and route B can be shown in Table 2 below.

[0101] Table 2 - Parameter Relationship Table

[0102]

[0103] In summary, this application provides a method for real-world road emissions testing, which can acquire the first natural environmental characteristics of a target test area during a target test time period; and determine candidate sub-regions from a preset simulated area whose second natural environmental characteristics have the highest similarity to the first natural environmental characteristics. The simulated area is located near the vehicle production site. This allows for the acquisition of multiple candidate sub-regions with natural environmental characteristics similar to the target test area.

[0104] From a pre-defined database, retrieve multiple vehicle travel chains associated with candidate sub-regions within the target testing time period. Based on the vehicle speeds along each road segment in the travel trajectories of each travel chain, determine the average speed for each road segment. For each road segment, determine the road type based on the speed range to which the average speed of the road segment belongs.

[0105] The score for each vehicle travel chain is determined based on the average vehicle speed on each road segment within each travel chain, the road type of each road segment, and the baseline average speed corresponding to each road type associated with the target test area. This allows us to obtain the similarity (i.e., score) between the average speed corresponding to each road type and the baseline average speed corresponding to each road type associated with the target test area.

[0106] From multiple vehicle travel chains, the trajectory of the highest-scoring travel chain is selected as the simulation route for real-world road emissions testing. This allows us to obtain the vehicle travel chain with the highest similarity to the target test area, considering both the natural environmental conditions and the average driving speed corresponding to the road type for each road segment.

[0107] This allows users to select the route of the vehicle travel chain with the highest score as the simulated route for real-world emissions testing, and then conduct real-world emissions tests on the simulated route.

[0108] If the simulated route fails the actual road emissions test, adjustments can be made directly at the vehicle manufacturing site since the simulated area is near the vehicle's production location, resulting in low cost and time savings. Furthermore, by obtaining the vehicle travel chain with the highest similarity to the target test area in terms of natural environmental conditions and average driving speed for each road segment, a successful test can lead to a high success rate when the vehicle is deployed to the target test area for actual road emissions testing, eliminating the need to return to the vehicle manufacturing site for adjustments, further reducing costs and saving time.

[0109] Additionally, please see Figure 4This application also provides a real-world road emission testing device. It should be noted that the real-world road emission testing device provided in this application has the same basic principle and technical effects as the above embodiments. For the sake of brevity, any parts not mentioned in this application can be referred to the corresponding content in the above embodiments. The device provided in this application includes a data acquisition unit, a sub-region acquisition unit, a travel chain acquisition unit, a speed determination unit, a road type determination unit, a score determination unit, and a simulated route determination unit, wherein...

[0110] The data acquisition unit is used to acquire the first natural environmental characteristics of the target test area during the target test time period.

[0111] The sub-region acquisition unit is used to determine the candidate sub-region with the highest similarity between the second natural environment feature and the first natural environment feature from the preset simulated region.

[0112] The travel chain acquisition unit is used to obtain multiple vehicle travel chains associated with candidate sub-regions within the target test time period from a preset database. Each vehicle travel chain includes the travel trajectory of a trip where the interval between the previous shutdown time and the current time exceeds a preset time threshold, the road segments traversed, and the vehicle's speed on each road segment.

[0113] The speed determination unit is used to determine the average speed corresponding to each road segment based on the travel speed of vehicles in each road segment in the travel trajectory of each vehicle travel chain.

[0114] The road type determination unit is used to determine the road type of each road segment based on the speed range to which the average driving speed of the road segment belongs.

[0115] The scoring unit is used to determine the score of each vehicle travel chain based on the average speed of vehicles on each road segment in each vehicle travel chain, the road type of each road segment, and the baseline average speed corresponding to each road type associated with the target test area.

[0116] The simulation route determination unit is used to select the travel trajectory of the vehicle travel chain with the highest score from multiple vehicle travel chains as the simulation route for simulating actual road emissions tests.

[0117] In addition, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the electronic device performs the method provided in the above embodiments of this application.

[0118] In addition, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the computer to perform the method provided in the above embodiments of this application.

[0119] Fourthly, embodiments of this application also provide a computer program product, including a computer program that, when run, causes a computer to perform the methods provided in the above embodiments of this application.

[0120] The above description does not provide detailed technical specifications regarding the structure of each layer. However, those skilled in the art should understand that layers and regions of desired shapes can be formed using various technical means. Furthermore, to form the same structure, those skilled in the art can also design methods that are not entirely identical to those described above. Additionally, although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be advantageously combined.

[0121] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0122] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method of real road emissions testing, characterized in that, The method comprises: acquiring a first natural environment feature of a target test region in a target test time period; determining, from a preset simulation region, a candidate sub-region with the highest similarity of a second natural environment feature to the first natural environment feature; acquiring, from a preset database, a plurality of vehicle trip chains associated with the candidate sub-region in the target test time period, wherein each vehicle trip chain comprises a trip trajectory of a trip with an interval time length from a previous engine-off time exceeding a preset time length threshold, a passed road segment, and a driving speed of the vehicle on each road segment; determining an average driving speed corresponding to each road segment according to the driving speed of the vehicle on each road segment in the trip trajectory of each vehicle trip chain; determining a road type of each road segment according to a speed interval to which the average driving speed of the road segment belongs; determining a score of each vehicle trip chain according to the average driving speed of the vehicle on each road segment in each vehicle trip chain, the road type of each road segment, and a reference average driving speed corresponding to each road type associated with the target test region; selecting a trip trajectory of a vehicle trip chain with the highest score from the plurality of vehicle trip chains as a simulation route for a simulation actual road emission test.

2. The method of claim 1, wherein, The first natural environment feature comprises a first air temperature distribution feature and a first elevation distribution feature, and the second natural environment feature comprises a second air temperature distribution feature and a second elevation distribution feature. The determining, from a preset simulation region, a candidate sub-region with the highest similarity of a second natural environment feature to the first natural environment feature comprises: determining a first similarity of the first air temperature distribution feature of each sub-region in the preset simulation region to the second air temperature distribution feature, and a second similarity of the first elevation distribution feature of each sub-region to the second elevation distribution feature; screening a plurality of to-be-screened sub-regions with both the first similarity and the second similarity greater than a preset similarity threshold from a plurality of sub-regions in the simulation region; determining an absolute value of a difference between a first average elevation value of each to-be-screened sub-region and a second average elevation value of the second elevation distribution feature; determining a score of each to-be-screened sub-region according to the first similarity and the absolute value of the difference corresponding to each to-be-screened sub-region; determining the to-be-screened sub-region with the highest score as the candidate sub-region.

3. The method of claim 2, wherein, The determining a score of each to-be-screened sub-region according to the first similarity and the absolute value of the difference corresponding to each to-be-screened sub-region comprises: According to the formula , a score of each of the to-be-screened sub-regions is determined, wherein, is the score of the to-be-screened sub-region, is the first similarity, is an absolute value of the difference value, is a preset first weighting coefficient, is a preset second weighting coefficient.

4. The method of claim 2, wherein, The first air temperature distribution feature is a first air temperature probability density function of the sub-region in the target test time period, the second air temperature distribution feature is a second air temperature probability density function of the target test region in the target test time period, and the determining a first similarity of the first air temperature distribution feature of each sub-region in the preset simulation region to the second air temperature distribution feature comprises: determine a relative entropy of the first air temperature probability density function and a second air temperature probability density function; wherein the relative entropy is used to represent the first similarity, and the relative entropy is negatively correlated with the first similarity.

5. The method of claim 1, wherein, The score of each of the vehicle trip chains is determined according to the average travel speed of vehicles on each road segment in each of the vehicle trip chains, the road type of each road segment, and the baseline average travel speed corresponding to each of the road types associated with the target test area. The average travel speed of vehicles on each road segment in each of the vehicle trip chains is determined according to the average travel speed of vehicles on each road segment in each of the vehicle trip chains. The root mean square error of the average travel speed corresponding to each of the road types of each of the vehicle trip chains and the baseline average travel speed corresponding to each of the road types associated with the target test area is determined. For each of the vehicle trip chains, the score of the vehicle trip chain is determined according to the root mean square error corresponding to the vehicle trip chain, and the root mean square error is negatively correlated with the score of the vehicle trip chain.

6. The method of claim 1, wherein, The score of each of the vehicle trip chains is determined according to the average travel speed of vehicles on each road segment in each of the vehicle trip chains, the road type of each road segment, and the baseline average travel speed corresponding to each of the road types associated with the target test area. The average travel speed of vehicles on each road segment in each of the vehicle trip chains is determined according to the average travel speed of vehicles on each road segment in each of the vehicle trip chains. The root mean square error of the average travel speed corresponding to each of the road types of each of the vehicle trip chains and the baseline average travel speed corresponding to each of the road types associated with the target test area is determined. The speed fluctuation of each road segment is determined according to the first average speed of each day corresponding to each road segment and a preset second average speed in the target test time period. For each of the vehicle trip chains, the average speed fluctuation of the vehicle trip chain is determined as the average value of the speed fluctuation of each road segment in the vehicle trip chain. The score of each of the vehicle trip chains is determined according to the root mean square error and the average speed fluctuation of each of the vehicle trip chains.

7. The method of claim 6, wherein, The score of each of the vehicle trip chains is determined according to the root mean square error and the average speed fluctuation of each of the vehicle trip chains. According to the formula , the score of each vehicle trip chain is determined, wherein, is the score, is the root mean square error, is the average speed fluctuation, is a preset third weighting coefficient, is a preset fourth weighting coefficient.

8. The method of claim 6, wherein, The speed fluctuation of each road segment is determined according to the first average speed of each day corresponding to each road segment and a preset second average speed in the target test time period. According to the formula , the speed fluctuation degree of each road section is determined, wherein, is the speed fluctuation degree of a single road section, and n is the number of days in the target test period, is the first average speed of each road section on the ith day, is the second average speed of each road section in the target test period. For each of the vehicle trip chains, the average speed fluctuation of the vehicle trip chain is determined as the average value of the speed fluctuation of each road segment in the vehicle trip chain. According to the formula , the average speed fluctuation degree of the vehicle trip chain is determined, wherein, is the average speed fluctuation degree of the vehicle trip chain, m is the number of each road section of the vehicle trip chain, is the speed fluctuation degree of the jth road section in the vehicle trip chain.

9. The method of claim 1, wherein, After the candidate sub-region associated with the plurality of vehicle trip chains in the target test time period is obtained, the method further comprises: From the plurality of vehicle trip chains, a vehicle trip chain that does not satisfy a preset specified condition is eliminated.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program, so that the electronic device executes the method in any one of claims 1 to 9.

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