Function self-adaptive test method and system for electro-tricycle controller

Abstract

The invention provides a function adaptive test method and system for an electro-tricycle controller, and relates to the technical field of tricycle function test, and the method comprises the steps: carrying out the scene analysis according to a road surface impact factor and an environment impact factor, and constructing a plurality of scene parameter groups; performing simulation modeling according to the vehicle attribute information and the plurality of scene parameter groups to generate a plurality of driving twin models; executing vehicle load simulation by using the plurality of driving twinborn models to meet a predetermined performance index and a predetermined risk threshold as a constraint, and determining a plurality of maximum adaptive loads; and constructing a scene-load matching library to execute a subsequent freight task. According to the invention, the technical problem that the maximum load of the tricycle cannot be accurately analyzed in combination with an application scene in an existing freight tricycle function test method, so that the adaptation degree of the maximum load and the scene is low, and the freight safety of the tricycle is influenced can be solved, and the purposes of ensuring the operation stability and safety of the tricycle and goods and improving the freight safety are achieved. And the service life of the vehicle is prolonged.

Description

Technical Field

[0001] The present application relates to the technical field of tricycle function testing, and in particular to a function adaptive testing method and system for an electric tricycle controller. Background Art

[0002] With the acceleration of urbanization, freight tricycles are playing an increasingly important role in urban logistics and distribution. They play an important role in delivering goods, takeout and express delivery, especially in densely populated and traffic-congested urban environments.

[0003] At present, there are technical problems in the existing functional testing methods for freight tricycles, such as the inability to accurately analyze the maximum load of the tricycle in combination with actual application scenarios, resulting in a low degree of adaptability between the maximum load of the tricycle and the scenario, which affects the safety of tricycle freight. Summary of the invention

[0004] The purpose of this application is to provide a functional adaptive testing method and system for an electric tricycle controller, so as to solve the technical problem that the maximum load of the tricycle cannot be accurately analyzed in combination with the actual application scenario in the existing functional testing method of the freight tricycle, resulting in a low degree of adaptability between the maximum load of the tricycle and the scenario, thus affecting the freight safety of the tricycle.

[0005] In view of the above problems, the present application provides a functional adaptive testing method and system for an electric tricycle controller.

[0006] In the first aspect, the present application provides a functional adaptive testing method for an electric tricycle controller, which is implemented through a functional adaptive testing system for an electric tricycle controller, including: collecting vehicle attribute information of a target tricycle to determine road surface influencing factors and environmental influencing factors; performing scenario analysis based on the road surface influencing factors and the environmental influencing factors to construct a scenario simulation space, wherein the scenario simulation space includes multiple scenario parameter groups; based on digital twins, simulation modeling is performed based on the vehicle attribute information and the multiple scenario parameter groups to generate multiple driving twin models; using the multiple driving twin models, with the constraints of meeting predetermined performance indicators and predetermined risk thresholds, vehicle load simulation is performed to determine multiple maximum adaptive loads; based on the classification decision principle, a scenario-load matching library is constructed based on the multiple scenario parameter groups and the multiple maximum adaptive loads, and subsequent freight tasks of the target tricycle are performed according to the scenario-load matching library.

[0007] In the second aspect, the present application also provides a functional adaptive testing system for an electric tricycle controller, which is used to execute a functional adaptive testing method for an electric tricycle controller as described in the first aspect, including: an information acquisition module, which is used to collect vehicle attribute information of the target tricycle and determine road surface influencing factors and environmental influencing factors; a scenario analysis module, which is used to perform scenario analysis based on the road surface influencing factors and the environmental influencing factors, and construct a scenario simulation space, wherein the scenario simulation space includes multiple scenario parameter groups; a driving twin model generation module, which is used to perform simulation modeling based on the vehicle attribute information and the multiple scenario parameter groups based on the digital twin, and generate multiple driving twin models; a vehicle load simulation module, which is used to use the multiple driving twin models to perform vehicle load simulation and determine multiple maximum adaptive loads based on the constraints of meeting predetermined performance indicators and predetermined risk thresholds; a scenario-load matching library construction module, which is used to construct a scenario-load matching library based on the classification decision principle, according to the multiple scenario parameter groups and the multiple maximum adaptive loads, and execute subsequent freight tasks of the target tricycle according to the scenario-load matching library.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: By collecting the vehicle attribute information of the target tricycle, the road surface influencing factors and environmental influencing factors are determined at the same time; then, scenario analysis is performed based on the road surface influencing factors and environmental influencing factors to determine multiple scenario parameter groups; then, based on the digital twin, simulation modeling is performed based on the vehicle attribute information and multiple scenario parameter groups to generate multiple driving twin models; further using multiple driving twin models, with the predetermined performance indicators and predetermined risk thresholds as constraints, vehicle load simulation is performed to determine multiple maximum adaptive loads; then, based on the classification decision principle, a scenario-load matching library is constructed according to multiple scenario parameter groups and the maximum adaptive load; finally, the subsequent freight tasks of the target tricycle are performed according to the scenario-load matching library; the precision and accuracy of the maximum load analysis of the tricycle can be improved, so that matching load constraints can be set based on the actual application scenarios of the tricycle, so as to achieve the technical effect of ensuring the stability and safety of the vehicle and cargo operation and extending the service life of the vehicle.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0011] Figure 1 A flowchart of a functional adaptive testing method for an electric tricycle controller is provided in the present application.

[0012] Figure 2 A schematic diagram of a flow chart for determining multiple scene parameter groups in a functional adaptive testing method for an electric tricycle controller in the present application.

[0013] Figure 3 This is a structural schematic diagram of a functional adaptive testing system for an electric tricycle controller according to the present application.

[0014] Description of reference numerals: Information collection module 11, scenario analysis module 12, driving twin model generation module 13, vehicle load simulation module 14, scenario-load matching library construction module 15. DETAILED DESCRIPTION

[0015] This application provides a function adaptive testing method and system for an electric tricycle controller, which solves the technical problem in the existing freight tricycle function testing method that the maximum load of the tricycle cannot be accurately analyzed in combination with the actual application scenario, resulting in a low degree of adaptation between the maximum load of the tricycle and the scenario, which affects the freight safety of the tricycle. The precision and accuracy of the maximum load analysis of the tricycle can be improved, so that matching load constraints can be set based on the actual application scenario of the tricycle, achieving the technical effect of ensuring the stability and safety of the vehicle and cargo operation and extending the service life of the vehicle.

[0016] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0017] For example, please refer to the attached Figure 1 The present application provides a function adaptive testing method for an electric tricycle controller, which is applied to a function adaptive testing system for an electric tricycle controller, and specifically comprises the following steps: Step 1: Collect vehicle attribute information of the target tricycle and determine the road surface influencing factors and environmental influencing factors.

[0018] Specifically, first, the vehicle attribute information of the target tricycle is collected, and the vehicle attribute information includes the basic specifications, performance parameters, technical characteristics, etc. of the vehicle, which is an important basis for evaluating the vehicle performance. On the other hand, the road surface influencing factors and environmental influencing factors are determined. The road surface influencing factors refer to the factors affecting the vehicle performance and driving safety during the vehicle driving process, such as road surface type, road surface flatness, road surface slope and other characteristics; the environmental influencing factors refer to the environmental factors that affect the vehicle performance and driving safety during the vehicle driving process, such as weather type, ambient temperature, ambient humidity, wind speed, etc.

[0019] By obtaining vehicle attribute information, road influencing factors and environmental influencing factors, it provides support for the next step of vehicle driving scenario analysis and construction of a driving twin model.

[0020] Step 2: Perform scenario analysis based on the road surface influencing factors and the environmental influencing factors to construct a scenario simulation space, wherein the scenario simulation space includes a plurality of scenario parameter groups.

[0021] Specifically, the factor thresholds of the road surface influencing factor and the environmental influencing factor are obtained, wherein the factor threshold refers to the fluctuation range of each influencing factor, which can be set according to the actual situation. The road surface influencing factor threshold includes the road surface type threshold, the road surface slope threshold and the turning feature threshold, and the environmental influencing factor threshold includes the weather type threshold, the ambient temperature threshold, the ambient humidity threshold and the wind speed threshold. Then, based on the road surface type threshold, the road surface slope threshold, the turning feature threshold, the weather type threshold, the ambient temperature threshold, the ambient humidity threshold and the wind speed threshold, the scene enumeration is performed, that is, a parameter is randomly selected from multiple thresholds for combination to generate the first scene parameter group, and the same method is used for iterative combination to obtain multiple scene parameter groups, and the scene simulation space is constructed according to the multiple scene parameter groups.

[0022] By randomly combining multiple influencing factor thresholds and generating multiple scenario parameter groups, the precision of the tricycle application scenario analysis can be improved, and the usage scenarios of tricycles in different environments can be more comprehensively simulated, thereby improving the accuracy of subsequent maximum simulated load analysis of tricycles in different scenarios.

[0023] Step three: Based on the digital twin, simulation modeling is performed according to the vehicle attribute information and the multiple scenario parameter groups to generate multiple driving twin models.

[0024] Specifically, digital twin technology is an advanced technology that uses physical models, sensors, operation history and other data to complete mapping in virtual space by integrating multi-disciplinary, multi-physical quantity, multi-scale, and multi-probability simulation processes, thereby reflecting the entire life cycle of the corresponding physical equipment. Based on digital twins, simulation modeling is performed according to the vehicle attribute information and the multiple scene parameter groups, that is, using digital twin technology, a virtual tricycle model is created according to the vehicle attribute information, and the model can simulate the behavior and performance of the vehicle in the actual environment; then the generated multiple scene parameter groups are input into the digital twin model, where each scene parameter group corresponds to a specific driving twin model, and multiple driving twin models are obtained.

[0025] By conducting simulation modeling based on digital twin technology and constructing multiple driving twin models, digital twin technology has many advantages such as real-time, interoperability, and fidelity. Therefore, it can improve the accuracy and authenticity of the simulated driving of the tricycle in different scenarios, thereby more accurately predicting the performance of the tricycle in different scenarios.

[0026] Step 4: Utilize the multiple driving twin models to perform vehicle load simulation and determine multiple maximum adaptive loads, subject to the constraints of meeting predetermined performance indicators and predetermined risk thresholds.

[0027] Specifically, predetermined performance indicators and predetermined risk thresholds are obtained, where the predetermined performance indicators refer to the expected performance requirements of the target tricycle during driving, including acceleration performance indicators, steering performance indicators, braking performance indicators and handling stability indicators; the predetermined risk threshold refers to the expected safety requirements of the tricycle during driving, including vehicle safety and cargo operation safety, and can be set according to actual conditions.

[0028] Next, with the predetermined performance indicators as constraints, multiple vehicle load simulations are performed using the multiple driving twin models to determine multiple simulated load sets, wherein the simulated load sets correspond one to one to the driving twin models; then, based on a predetermined risk threshold, the multiple simulated load sets are screened, and the maximum simulated load among the multiple simulated load sets that meet the predetermined risk threshold is selected, and is set as the maximum adaptive load to obtain multiple maximum adaptive loads.

[0029] By using multiple driving twin models to simulate vehicle load and screening the load simulation results based on predetermined performance indicators and predetermined risk thresholds, multiple maximum adaptive loads can be obtained. This can improve the adaptability of the adaptive load and the scenario parameter group, improve the accuracy and practicality of the adaptive load setting, and ensure the safety and reliability of vehicle driving in various scenarios.

[0030] Step 5: Based on the classification decision principle, a scene-load matching library is constructed according to the multiple scene parameter groups and multiple maximum adaptive loads, and subsequent freight tasks of the target tricycle are performed according to the scene-load matching library.

[0031] Specifically, the classification decision principle is a decision-making method that is based on classifying and evaluating decision objects to determine the best decision. In the classification decision principle, decision makers usually divide decision objects into different categories according to certain classification criteria or characteristics, and then evaluate each category to determine the best decision. Based on the classification decision principle, the scene parameter group is set as a sub-node, where the scene parameter group includes road surface type, road surface slope, turning characteristics, weather type, ambient temperature, ambient humidity and wind speed; then the maximum adaptive load corresponding to the scene parameter group is used as the leaf node of the sub-node, and a scene-load matching library is constructed according to the multiple scene parameter groups and multiple maximum adaptive loads.

[0032] By building a scenario-load matching library based on the classification decision principle, operators can quickly determine the maximum adaptive load of electric tricycles in different scenarios in actual use, thereby improving the accuracy and efficiency of adaptive load matching.

[0033] Finally, when the target tricycle performs the freight task, the real-time scene data is collected and input into the scene-load matching library, and the real-time adaptive load is output; and the freight task of the target tricycle is performed with the constraint condition of being less than the real-time adaptive load.

[0034] The functional adaptive testing method for an electric tricycle controller is applied to a functional adaptive testing system for an electric tricycle controller, which can solve the technical problem in the existing freight tricycle functional testing method that the maximum load of the tricycle cannot be accurately analyzed in combination with the actual application scenario, resulting in a low degree of adaptability between the maximum load of the tricycle and the scenario, thus affecting the freight safety of the tricycle. By collecting the vehicle attribute information of the target tricycle, the road surface influencing factors and environmental influencing factors are determined at the same time; then, scenario analysis is performed based on the road surface influencing factors and environmental influencing factors to determine multiple scenario parameter groups; then, based on the digital twin, simulation modeling is performed based on the vehicle attribute information and multiple scenario parameter groups to generate multiple driving twin models; further using multiple driving twin models, with the predetermined performance indicators and predetermined risk thresholds as constraints, vehicle load simulation is performed to determine multiple maximum adaptive loads; then, based on the classification decision principle, a scenario-load matching library is constructed according to multiple scenario parameter groups and the maximum adaptive load; finally, the subsequent freight tasks of the target tricycle are performed according to the scenario-load matching library; the precision and accuracy of the maximum load analysis of the tricycle can be improved, so that matching load constraints can be set based on the actual application scenarios of the tricycle, so as to achieve the technical effect of ensuring the stability and safety of the vehicle and cargo operation and extending the service life of the vehicle.

[0035] Further, the vehicle attribute information of the target tricycle is collected to determine the road surface influencing factor and the environmental influencing factor. Step 1 of this application includes: The vehicle attribute information includes body structure, powertrain and handling characteristics; the road surface influencing factors include at least road surface type, road surface slope and turning characteristics, wherein the turning characteristics include curve radius and inclination angle; the environmental influencing factors include at least weather type, ambient temperature, ambient humidity and wind speed.

[0036] Specifically, the vehicle attribute information includes body structure, powertrain and handling characteristics. The body structure includes frame material, frame design, body size and cargo space, etc.; the powertrain includes motor type, motor power, battery type, etc.; the handling characteristics include steering system, braking system, suspension system, etc.

[0037] The road surface influencing factors include at least road surface type, road surface slope and turning characteristics. Technical personnel in this field can add additional influencing factors according to actual conditions, where turning characteristics include curve radius and inclination angle; road surface types include asphalt road surface, cement road surface, gravel road surface and dirt road, etc.; the environmental influencing factors include at least weather type, ambient temperature, ambient humidity and wind speed, and weather types include sunny, light rain, moderate rain, heavy rain, light snow, moderate snow, etc., which can be set according to actual conditions.

[0038] Further, a scenario analysis is performed based on the road surface influencing factors and the environmental influencing factors, as shown in the attached Figure 2 As shown, step 2 of this application includes: A positive sample search is performed with the road surface type as a constraint to determine a road surface feature set, wherein the road surface features include road surface flatness, road surface roughness and road surface hardness; a positive sample search is performed with the road surface type and the weather type as constraints to determine a road surface slippery coefficient set, wherein the road surface slippery coefficient includes slippery coefficients of multiple different road surface types; a road surface slope interval, a curve radius interval, an inclination angle interval, an ambient temperature interval, an ambient humidity interval and a wind speed interval are divided according to a predetermined multivariate step size to determine a road surface slope set, a curve radius set, an inclination angle set, an ambient temperature set, an ambient humidity set and a wind speed set; based on the road surface feature set, the road surface slippery coefficient set, the road surface slope set, the curve radius set, the inclination angle set, the ambient temperature set, the ambient humidity set and the wind speed set, scene enumeration is performed to determine multiple scene parameter groups.

[0039] Specifically, positive sample retrieval is a method for retrieving relevant samples in a specific task or field, which have similar features or attributes to the samples to be queried; taking the road type as a retrieval constraint, positive sample retrieval is performed based on big data technology, such as selecting a suitable similarity measurement method, such as Euclidean distance, cosine similarity, etc., to calculate the similarity between the sample to be queried and each sample in the retrieval database; then the features of the sample to be queried are compared with the features of each sample in the retrieval database, and based on the similarity measurement results, positive samples that meet a predetermined similarity threshold with the sample to be queried are retrieved; a road feature set is determined, wherein the road feature includes road flatness, road roughness, and road hardness. On the other hand, positive sample retrieval is performed with the road type and the weather type as constraints to determine a road slip coefficient set, wherein the road slip coefficient includes slip coefficients of multiple different road types.

[0040] Obtain a predetermined multivariate step length, the predetermined multivariate step length includes a road surface slope step length, a curve radius step length, etc., and corresponds to an influencing factor interval one by one, wherein the predetermined multivariate step length and the influencing factor interval can be set according to actual conditions; then divide the road surface slope interval, the curve radius interval, the tilt angle interval, the ambient temperature interval, the ambient humidity interval and the wind speed interval according to the predetermined multivariate step length, and determine a road surface slope set, a curve radius set, a tilt angle set, an ambient temperature set, an ambient humidity set and a wind speed set. Further, based on the road surface feature set, the road surface slip coefficient set, the road surface slope set, the curve radius set, the tilt angle set, the ambient temperature set, the ambient humidity set and the wind speed set, scene enumeration is performed, that is, a parameter is randomly selected from the road surface feature set, the road surface slip coefficient set, the road surface slope set, the curve radius set, the tilt angle set, the ambient temperature set, the ambient humidity set and the wind speed set to combine, and multiple scene parameter groups are obtained, each of which includes road surface features, road surface slip coefficient, road surface slope, curve radius, tilt angle, ambient temperature, ambient humidity and wind speed.

[0041] Furthermore, the positive sample retrieval is performed with the road surface type as a constraint to determine the road surface feature set. The present application further includes the following steps: A first road surface type is selected, and a positive sample search is performed based on big data with the first road surface type as a constraint to obtain a sample data set, and a first sample data is randomly selected, wherein the first sample data includes a first sample road surface flatness, a first sample road surface roughness, and a first sample road surface hardness; a deviation calculation is performed between the first sample road surface flatness and other sample road surface flatnesses in the sample data set, and the number of sample data less than the flatness deviation threshold is counted, which is recorded as a first criticality; if the first criticality is greater than a predetermined threshold, a deviation calculation is performed between the first sample road surface roughness and other sample road surface roughnesses in the sample data set, and the number of sample data less than the roughness deviation threshold is counted, which is recorded as a second criticality; if the second criticality is greater than a predetermined threshold, a deviation calculation is performed between the first sample road surface hardness and other sample road surface hardnesses in the sample data set, and the number of sample data less than the hardness deviation threshold is counted, which is recorded as a third criticality; if the third criticality is greater than a predetermined threshold, the first sample data is added to the key sample data set, the mean calculation is performed on the key sample data set, a first road feature is determined, and the first road feature is added to the road feature set.

[0042] Specifically, first, a first road surface type is selected from the road surface types, wherein the first road surface type is any one of multiple road surface types; then, with the first road surface type as a constraint, positive sample retrieval is performed based on big data to obtain a sample data set, and any sample data is randomly selected from the sample data set as the first sample data, wherein the first sample data includes a first sample road surface flatness, a first sample road surface roughness, and a first sample road surface hardness.

[0043] Then, the deviation between the first sample road surface flatness and other sample road surface flatness in the sample data set is calculated to determine multiple flatness deviations; then, based on multiple flatness deviations, the number of sample data that is less than the flatness deviation threshold is counted. The flatness deviation threshold can be set according to the number of data and actual conditions, and the number of sample data is recorded as the first critical degree. The more sample data, the greater the critical degree. Then, the first critical degree is judged based on a predetermined threshold. If the first critical degree is greater than the predetermined threshold, the deviation between the first sample road surface roughness and other sample road surface roughness in the sample data set is calculated, and the number of sample data that is less than the roughness deviation threshold is counted, which is recorded as the second critical degree; then, if the second critical degree is greater than the predetermined threshold, the deviation between the first sample road surface hardness and other sample road surface hardness in the sample data set is calculated, and the number of sample data that is less than the hardness deviation threshold is counted, which is recorded as the third critical degree; finally, if the third critical degree is greater than the predetermined threshold, the first sample data is added to the critical sample data set.

[0044] Among them, if the first criticality of the first sample data is less than or equal to the predetermined threshold, or the second criticality is less than or equal to the predetermined threshold, or the third criticality is less than or equal to the predetermined threshold, the first sample data is discarded; iterative analysis is continuously performed until all sample data are traversed to obtain a key sample data set. Finally, the mean of multiple sample road surface flatness, multiple sample road surface roughness, and multiple sample road surface hardness in the key sample data set is calculated, and the mean calculation result is used as the first road surface feature and added to the road surface feature set to obtain the road surface feature set.

[0045] The method for obtaining the road surface slippery coefficient set is consistent with the method for obtaining the road surface feature set. The road surface slippery coefficient is the average of multiple key slippery coefficients after screening, which will not be elaborated here.

[0046] Further, multiple maximum adaptive loads are determined. Step 4 of this application includes: A first driving twin model is randomly selected from the multiple driving twin models, and multiple vehicle load simulations are performed under the constraint of meeting predetermined performance indicators, and a first simulated load set is output, wherein the predetermined performance indicators include acceleration performance indicators, steering performance indicators, braking performance indicators and handling stability indicators; the first simulated load set is arranged from large to small according to the load to generate a simulated load sequence; the simulated load sequence is screened based on the predetermined risk threshold, and a first maximum adaptive load is output, which is added to the multiple maximum adaptive loads.

[0047] Specifically, first, predetermined performance indicators are obtained, where the predetermined performance indicators include acceleration performance indicators, steering performance indicators, braking performance indicators and handling stability indicators, which can be set according to actual needs. For example, steering performance indicators include steering response time, steering body stability, etc., and braking performance indicators include braking distance, braking body stability, etc.; then, any one of the multiple driving twin models is randomly selected and set as the first driving twin model, and then, with the predetermined performance indicators being met as a constraint, the first driving twin model is used to perform multiple vehicle load simulations, and a first simulated load set is output.

[0048] The first simulated load set is further arranged from large to small according to the load to generate a simulated load sequence; then the simulated load sequence is screened based on the predetermined risk threshold, and the maximum simulated load that meets the predetermined risk threshold is selected as the first maximum adaptive load, which is added to the multiple maximum adaptive loads. By using multiple driving twin models to simulate vehicle loads, and screening the load simulation results based on predetermined performance indicators and predetermined risk thresholds to obtain multiple maximum adaptive loads, the adaptability of the adaptive load and the scenario parameter group can be improved, the accuracy and practicality of the adaptive load setting can be improved, and the safety and reliability of the vehicle driving in various scenarios can be ensured.

[0049] Further, the simulated load sequence is screened based on the predetermined risk threshold, and the present application further comprises the following steps: A first simulated load is selected from the simulated load sequence, wherein the first simulated load is the maximum simulated load of the simulated load sequence; a first scenario parameter group of the first driving twin model is obtained, and a positive sample search is performed with the first simulated load, the vehicle attribute information and the first scenario parameter group as constraints to obtain a first sample driving event set; based on predetermined abnormal indicators, abnormal event proportion statistics are performed on the first sample driving event set to determine a first abnormal proportion, wherein the predetermined abnormal indicators at least include vehicle abnormalities and cargo abnormalities; a first risk coefficient is determined based on the first abnormal proportion matching, and if the first risk coefficient is less than the predetermined risk threshold, the first simulated load is set to the first maximum adaptive load; if the first risk coefficient is greater than or equal to the predetermined risk threshold, a second simulated load is selected for risk analysis until a simulated load less than the predetermined risk threshold is obtained, which is set as the first maximum adaptive load.

[0050] Specifically, first, the first simulated load is selected in the simulated load sequence, wherein the first simulated load is the maximum simulated load of the simulated load sequence; then, the first scenario parameter group of the first driving twin model is obtained, and then positive sample retrieval is performed with the first simulated load, the vehicle attribute information and the first scenario parameter group as constraints to obtain the first sample driving event set.

[0051] Obtain a predetermined abnormal index, which includes at least vehicle abnormality and cargo abnormality, wherein vehicle abnormality includes events such as vehicle failure, and cargo abnormality includes events such as cargo damage; then, based on the predetermined abnormal index, perform abnormal event proportion statistics on the first sample driving event set to determine the first abnormal proportion, wherein the first abnormal proportion is the ratio of the number of abnormal events to the total number of events in the first sample driving event set. Then, determine the first risk coefficient based on the first abnormal proportion matching, wherein the first risk coefficient is positively correlated with the first abnormal proportion, i.e., the larger the first abnormal proportion, the larger the first risk coefficient, and a proportion-risk matching table can be constructed based on the sample data to match the risk coefficient.

[0052] The first risk coefficient is further judged based on a predetermined risk threshold. If the first risk coefficient is less than the predetermined risk threshold, the first simulated load is set to the first maximum adaptive load. If the first risk coefficient is greater than or equal to the predetermined risk threshold, a second simulated load is selected for risk analysis, and the second simulated load is a simulated load adjacent to the first simulated load in the simulated load sequence. The same method is used for iterative analysis until a simulated load less than the predetermined risk threshold is obtained, and the load is set as the first maximum adaptive load.

[0053] Further, according to the scenario-load matching library, the subsequent freight task of the target tricycle is performed, and step five of this application includes: Collect real-time road features and real-time environmental features of the freight task; input the real-time road features and the real-time environmental features into the scene-load matching library for matching, and output the real-time adaptive load; and execute the freight task of the target tricycle with the constraint of being less than the real-time adaptive load.

[0054] Specifically, when the target tricycle performs a freight task, real-time road characteristics and real-time environmental characteristics of the freight task are collected, including real-time road type, real-time road slope, real-time turning characteristics, real-time weather type, real-time ambient temperature, real-time ambient humidity and real-time wind speed; then the real-time road characteristics and the real-time environmental characteristics are input into the scene-load matching library for matching, and the real-time adaptive load is output; finally, the freight task of the target tricycle is performed with the constraint condition of being less than the real-time adaptive load.

[0055] In summary, the functional adaptive testing method of an electric tricycle controller provided by the present application has the following technical effects: 1. By collecting the vehicle attribute information of the target tricycle, the road surface influencing factor and the environmental influencing factor are determined at the same time; then, the scenario analysis is performed according to the road surface influencing factor and the environmental influencing factor to determine multiple scenario parameter groups; then, based on the digital twin, simulation modeling is performed according to the vehicle attribute information and multiple scenario parameter groups to generate multiple driving twin models; further using multiple driving twin models, with the predetermined performance indicators and predetermined risk thresholds as constraints, vehicle load simulation is performed to determine multiple maximum adaptive loads; then, based on the classification decision principle, a scenario-load matching library is constructed according to multiple scenario parameter groups and the maximum adaptive load; finally, the subsequent freight tasks of the target tricycle are performed according to the scenario-load matching library; the precision and accuracy of the maximum load analysis of the tricycle can be improved, so that matching load constraints can be set based on the actual application scenarios of the tricycle, so as to achieve the technical effect of ensuring the stability and safety of the vehicle and cargo operation and extending the service life of the vehicle.

[0056] 2. By using multiple driving twin models to simulate vehicle load and screening the load simulation results based on predetermined performance indicators and predetermined risk thresholds, multiple maximum adaptive loads can be obtained, which can improve the adaptability of the adaptive load and the scenario parameter group, improve the accuracy and practicality of the adaptive load setting, and ensure the safety and reliability of vehicle driving in various scenarios.

[0057] Embodiment 2: Based on the functional adaptive testing method of an electric tricycle controller in the above embodiment, the present application also provides a functional adaptive testing system for an electric tricycle controller, see the attached Figure 3 ,include: An information collection module 11 is used to collect vehicle attribute information of the target tricycle and determine road surface influencing factors and environmental influencing factors; a scenario analysis module 12 is used to perform scenario analysis based on the road surface influencing factors and the environmental influencing factors, and construct a scenario simulation space, wherein the scenario simulation space includes multiple scenario parameter groups; a driving twin model generation module 13 is used to perform simulation modeling based on the vehicle attribute information and the multiple scenario parameter groups based on digital twins, and generate multiple driving twin models; a vehicle load simulation module 14 is used to use the multiple driving twin models to perform vehicle load simulation and determine multiple maximum adaptive loads based on constraints of meeting predetermined performance indicators and predetermined risk thresholds; a scenario-load matching library construction module 15 is used to construct a scenario-load matching library based on the classification decision principle, according to the multiple scenario parameter groups and the multiple maximum adaptive loads, and execute subsequent freight tasks of the target tricycle according to the scenario-load matching library.

[0058] Further, the functional adaptive test system of the electric tricycle controller is also used for: The vehicle attribute information includes body structure, powertrain and handling characteristics; the road surface influencing factors include at least road surface type, road surface slope and turning characteristics, wherein the turning characteristics include curve radius and inclination angle; the environmental influencing factors include at least weather type, ambient temperature, ambient humidity and wind speed.

[0059] Further, the functional adaptive test system of the electric tricycle controller is also used for: A positive sample search is performed with the road surface type as a constraint to determine a road surface feature set, wherein the road surface features include road surface flatness, road surface roughness and road surface hardness; a positive sample search is performed with the road surface type and the weather type as constraints to determine a road surface slippery coefficient set, wherein the road surface slippery coefficient includes slippery coefficients of multiple different road surface types; a road surface slope interval, a curve radius interval, an inclination angle interval, an ambient temperature interval, an ambient humidity interval and a wind speed interval are divided according to a predetermined multivariate step size to determine a road surface slope set, a curve radius set, an inclination angle set, an ambient temperature set, an ambient humidity set and a wind speed set; based on the road surface feature set, the road surface slippery coefficient set, the road surface slope set, the curve radius set, the inclination angle set, the ambient temperature set, the ambient humidity set and the wind speed set, scene enumeration is performed to determine multiple scene parameter groups.

[0060] Further, the functional adaptive test system of the electric tricycle controller is also used for: A first road surface type is selected, and a positive sample search is performed based on big data with the first road surface type as a constraint to obtain a sample data set, and a first sample data is randomly selected, wherein the first sample data includes a first sample road surface flatness, a first sample road surface roughness, and a first sample road surface hardness; a deviation calculation is performed between the first sample road surface flatness and other sample road surface flatnesses in the sample data set, and the number of sample data less than the flatness deviation threshold is counted, which is recorded as a first criticality; if the first criticality is greater than a predetermined threshold, a deviation calculation is performed between the first sample road surface roughness and other sample road surface roughnesses in the sample data set, and the number of sample data less than the roughness deviation threshold is counted, which is recorded as a second criticality; if the second criticality is greater than a predetermined threshold, a deviation calculation is performed between the first sample road surface hardness and other sample road surface hardnesses in the sample data set, and the number of sample data less than the hardness deviation threshold is counted, which is recorded as a third criticality; if the third criticality is greater than a predetermined threshold, the first sample data is added to the key sample data set, the mean calculation is performed on the key sample data set, a first road feature is determined, and the first road feature is added to the road feature set.

[0061] Further, the functional adaptive test system of the electric tricycle controller is also used for: A first driving twin model is randomly selected from the multiple driving twin models, and multiple vehicle load simulations are performed under the constraint of meeting predetermined performance indicators, and a first simulated load set is output, wherein the predetermined performance indicators include acceleration performance indicators, steering performance indicators, braking performance indicators and handling stability indicators; the first simulated load set is arranged from large to small according to the load to generate a simulated load sequence; the simulated load sequence is screened based on the predetermined risk threshold, and a first maximum adaptive load is output, which is added to the multiple maximum adaptive loads.

[0062] Further, the functional adaptive test system of the electric tricycle controller is also used for: A first simulated load is selected from the simulated load sequence, wherein the first simulated load is the maximum simulated load of the simulated load sequence; a first scenario parameter group of the first driving twin model is obtained, and a positive sample search is performed with the first simulated load, the vehicle attribute information and the first scenario parameter group as constraints to obtain a first sample driving event set; based on predetermined abnormal indicators, abnormal event proportion statistics are performed on the first sample driving event set to determine a first abnormal proportion, wherein the predetermined abnormal indicators at least include vehicle abnormalities and cargo abnormalities; a first risk coefficient is determined based on the first abnormal proportion matching, and if the first risk coefficient is less than the predetermined risk threshold, the first simulated load is set to the first maximum adaptive load; if the first risk coefficient is greater than or equal to the predetermined risk threshold, a second simulated load is selected for risk analysis until a simulated load less than the predetermined risk threshold is obtained, which is set as the first maximum adaptive load.

[0063] Further, the functional adaptive test system of the electric tricycle controller is also used for: Collect real-time road features and real-time environmental features of the freight task; input the real-time road features and the real-time environmental features into the scene-load matching library for matching, and output the real-time adaptive load; and execute the freight task of the target tricycle with the constraint of being less than the real-time adaptive load.

[0064] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The functional adaptive testing method and specific examples of an electric tricycle controller in the aforementioned embodiment 1 are also applicable to a functional adaptive testing system for an electric tricycle controller in this embodiment. Through the aforementioned detailed description of the functional adaptive testing method for an electric tricycle controller, those skilled in the art can clearly understand the functional adaptive testing system for an electric tricycle controller in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method section description.

[0065] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0066] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A functional adaptive test method for an electric tricycle controller. It is characterized in that include: Collect vehicle attribute information of the target tricycle and determine road surface influencing factors and environmental influencing factors; Performing scenario analysis according to the road surface influencing factors and the environmental influencing factors to construct a scenario simulation space, wherein the scenario simulation space includes a plurality of scenario parameter groups; Based on the digital twin, simulation modeling is performed according to the vehicle attribute information and the multiple scene parameter groups to generate multiple driving twin models; Using the multiple driving twin models, performing vehicle load simulation and determining multiple maximum adaptive loads with the predetermined performance index and the predetermined risk threshold as constraints; Based on the classification decision principle, a scenario-load matching library is constructed according to the multiple scenario parameter groups and the multiple maximum adaptive loads, and the subsequent freight task of the target tricycle is performed according to the scenario-load matching library.

2. According to the function adaptive testing method of the electric tricycle controller described in claim 1, It is characterized in that Collect vehicle attribute information of the target tricycle and determine road surface influencing factors and environmental influencing factors, including: The vehicle attribute information includes body structure, powertrain and handling characteristics; The road surface influencing factors at least include road surface type, road surface slope and turning characteristics, wherein the turning characteristics include curve radius and inclination angle; The environmental influencing factors include at least weather type, ambient temperature, ambient humidity and wind speed.

3. According to the function adaptive testing method of the electric tricycle controller described in claim 2, It is characterized in that Performing scenario analysis according to the road surface influencing factor and the environmental influencing factor includes: Performing positive sample retrieval with the road surface type as a constraint to determine a road surface feature set, wherein the road surface features include road surface flatness, road surface roughness, and road surface hardness; Performing positive sample retrieval with the road surface type and the weather type as constraints to determine a road surface slippery coefficient set, wherein the road surface slippery coefficient includes slippery coefficients of multiple different road surface types; Dividing the road surface slope interval, the curve radius interval, the inclination angle interval, the ambient temperature interval, the ambient humidity interval and the wind speed interval according to a predetermined multivariate step length to determine a road surface slope set, a curve radius set, an inclination angle set, an ambient temperature set, an ambient humidity set and a wind speed set; Based on the road feature set, road slip coefficient set, road slope set, curve radius set, inclination angle set, ambient temperature set, ambient humidity set and wind speed set, scene enumeration is performed to determine multiple scene parameter groups.

4. According to claim 3, a function adaptive testing method for an electric tricycle controller, It is characterized in that The positive sample retrieval is performed with the road surface type as a constraint to determine the road surface feature set, including: Selecting a first road surface type, taking the first road surface type as a constraint, performing positive sample retrieval based on big data, acquiring a sample data set, and randomly selecting first sample data, wherein the first sample data includes a first sample road surface flatness, a first sample road surface roughness, and a first sample road surface hardness; Calculate the deviation between the first sample road surface flatness and other sample road surface flatness in the sample data set, and count the number of sample data that are less than the flatness deviation threshold, which is recorded as the first criticality; If the first criticality is greater than a predetermined threshold, the first sample road surface roughness is calculated to be different from other sample road surface roughnesses in the sample data set, and the number of sample data less than the roughness deviation threshold is counted and recorded as the second criticality; If the second criticality is greater than a predetermined threshold, the first sample road surface hardness is calculated to be different from the other sample road surface hardnesses in the sample data set, and the number of sample data less than the hardness deviation threshold is counted and recorded as the third criticality; If the third criticality is greater than a predetermined threshold, the first sample data is added to the key sample data set, the mean of the key sample data set is calculated, the first road surface feature is determined, and the first road surface feature is added to the road surface feature set.

5. According to claim 1, a function adaptive testing method for an electric tricycle controller, It is characterized in that Determine multiple maximum adaptable loads, including: Randomly selecting a first driving twin model from the multiple driving twin models, performing multiple vehicle load simulations to meet predetermined performance indicators as a constraint, and outputting a first simulated load set, wherein the predetermined performance indicators include acceleration performance indicators, steering performance indicators, braking performance indicators, and handling stability indicators; Arranging the first simulated load sets in descending order of load to generate a simulated load sequence; The simulated load sequence is screened based on the predetermined risk threshold, and a first maximum adaptive load is output and added to the plurality of maximum adaptive loads.

6. A function adaptive testing method for an electric tricycle controller according to claim 5, It is characterized in that Screening the simulated load sequence based on the predetermined risk threshold comprises: Selecting a first simulated load in the simulated load sequence, wherein the first simulated load is a maximum simulated load in the simulated load sequence; Obtaining a first scenario parameter group of the first driving twin model, performing positive sample retrieval with the first simulated load, the vehicle attribute information, and the first scenario parameter group as constraints, and obtaining a first sample driving event set; Based on a predetermined abnormality index, performing abnormal event proportion statistics on the first sample driving event set to determine a first abnormality proportion, wherein the predetermined abnormality index includes at least vehicle abnormality and cargo abnormality; Determine a first risk coefficient based on the first abnormality proportion matching, and if the first risk coefficient is less than the predetermined risk threshold, set the first simulated load as the first maximum adaptive load; If the first risk coefficient is greater than or equal to the predetermined risk threshold, a second simulated load is selected for risk analysis until a simulated load less than the predetermined risk threshold is obtained and set as the first maximum adaptive load.

7. According to claim 1, a function adaptive testing method for an electric tricycle controller, It is characterized in that The subsequent freight tasks of the target tricycle are performed according to the scenario-load matching library, including: Collect real-time road characteristics and real-time environmental characteristics of freight tasks; Inputting the real-time road surface features and the real-time environment features into the scene-load matching library for matching, and outputting the real-time adapted load; The freight mission of the target tricycle is performed with the load being less than the real-time adaptive load as a constraint.

8. A functional adaptive test system for electric tricycle controller, It is characterized in that The steps for implementing the function adaptive testing method of an electric tricycle controller as described in any one of claims 1 to 7 include: An information collection module is used to collect vehicle attribute information of the target tricycle and determine road surface influencing factors and environmental influencing factors; A scenario analysis module, used to perform scenario analysis according to the road surface influencing factors and the environmental influencing factors, and construct a scenario simulation space, wherein the scenario simulation space includes a plurality of scenario parameter groups; A driving twin model generation module, used to generate multiple driving twin models by performing simulation modeling based on the digital twin and according to the vehicle attribute information and the multiple scene parameter groups; A vehicle load simulation module, configured to utilize the plurality of driving twin models to perform vehicle load simulation and determine a plurality of maximum adaptive loads, subject to constraints of satisfying predetermined performance indicators and predetermined risk thresholds; The scenario-load matching library construction module is used to construct the scenario-load matching library based on the classification decision principle, according to the multiple scenario parameter groups and multiple maximum adaptive loads, and execute the subsequent freight task of the target tricycle according to the scenario-load matching library.

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