A simulation test system, method, device and storage medium for automobile electrical components

Abstract

The embodiments of the present specification provide an automotive electrical component simulation test system, method, device and storage medium, the system including a test box, a conveying module, a heating module, an environmental control module, an environmental monitoring module and a processor; the conveying module is mechanically connected to the test box, and is configured to convey the component to be tested to the test box; the heating module is configured to heat the component to be tested; the environmental control module is configured to control the test environment in the test box based on environmental parameters; the environmental monitoring module is configured to monitor environmental data in the test box; the processor is configured to: control the heating module to heat the component to be tested based on the heating parameters; determine the environmental parameters based on the environmental data, and send the environmental parameters to the environmental control module to control the test environment.

Description

Technical Field

[0001] The present invention relates to the field of vehicle manufacturing, and in particular to a simulation test system, method, device and storage medium for automotive electrical components. Background Art

[0002] Existing automotive electrical component tests usually involve automotive durability tests, such as salt spray tests, high temperature or cold environment tests, and component fatigue tests. These tests usually use test devices to simulate the alternating changes of parameters such as temperature, humidity, time, and force to evaluate the performance of different automotive components.

[0003] However, existing test methods still have some limitations. In actual test scenarios, existing tests are usually conducted based on preset test procedures, and valid test results can only be obtained when the test is completed or manually terminated. This method is inefficient and cannot provide timely feedback and adjust the test process. In addition, existing test methods are difficult to accurately simulate the temperature impact of the car on electrical components during actual driving. This may result in a poor match between the test results and actual usage, thereby limiting the accuracy and reliability of the test.

[0004] Therefore, it is necessary to provide an automotive electrical component testing system, method, device and storage medium, which can monitor and adjust the test conditions in real time to better simulate the working environment of automotive electrical components in actual driving, improve the efficiency, accuracy and reliability of the test, and ensure the performance and durability of automotive electrical components. Summary of the invention

[0005] In order to solve the problem of low efficiency of existing automotive electrical component testing methods, one or more embodiments of this specification provide an automotive electrical component simulation testing system, method, device and storage medium.

[0006] One or more embodiments of the present specification provide an automotive electrical component simulation test system, which includes: a test box, a conveying module, a heating module, an environmental control module, an environmental monitoring module and a processor; the conveying module is mechanically connected to the test box, and is configured to convey the component to be tested to the test box; the heating module is configured to heat the component to be tested; the environmental control module is configured to control the test environment in the test box based on environmental parameters; the environmental monitoring module is configured to monitor environmental data in the test box; the processor is configured to: control the heating module to heat the component to be tested based on the heating parameters; determine the environmental parameters based on the environmental data, and send the environmental parameters to the environmental control module to control the test environment.

[0007] One or more embodiments of the present specification provide a simulation test method for automotive electrical components, which includes: controlling a heating module to heat a component to be tested based on heating parameters; determining environmental parameters based on environmental data, and sending the environmental parameters to an environmental control module to control the test environment.

[0008] One or more embodiments of the present specification provide an automotive electrical component simulation test device, including a processor, wherein the processor is used to execute an automotive electrical component simulation test method.

[0009] One or more embodiments of the present specification provide a computer-readable storage medium, wherein the storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes a simulation test method for automotive electrical components. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0011] Figure 1 is a schematic diagram of the structure of an automotive electrical component simulation test system according to some embodiments of this specification;

[0012] Figure 2 is an exemplary schematic diagram of a threshold determination model according to some embodiments of this specification;

[0013] Figure 3 is an exemplary flow chart of cyclic heating according to some embodiments of the present specification;

[0014] Figure 4 It is a schematic diagram of determining the working parameters of the component to be tested according to some embodiments of this specification. DETAILED DESCRIPTION

[0015] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0016] It should be understood that the "system", "device", "unit" and / or "module" used herein are a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0017] As shown in this specification and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "comprise" and "include" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0018] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed precisely in order. Instead, the steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0019] Figure 1 It is a structural schematic diagram of an automotive electrical component simulation test system according to some embodiments of this specification.

[0020] like Figure 1 As shown, the automobile electrical component simulation test system 10 includes a test box 110 , a transmission module 130 , a heating module 140 , an environment monitoring module 150 , an environment control module 160 and a processor 170 .

[0021] The test box 110 refers to a box for placing the component to be tested 120. In some embodiments, an experimental space for experiments is formed inside the test box 110, and salt spray can be arranged in the experimental space. In some embodiments, at least one side of the test box 110 is provided with an opening that can be opened or closed. In some embodiments, the experimental space can be sealed to be isolated from the external environment.

[0022] The component under test 120 refers to a test object of the system. In some embodiments, the component under test 120 may be at least one electrical component of a car.

[0023] The conveying module 130 refers to a structure having a transport function. In some embodiments, the conveying module 130 is mechanically connected to the test box 110 and is configured to convey the components to be tested 120 to the test box 110. In some embodiments, the conveying module 130 can dock with the opening of the test box 110 to transport the components to be tested 120 into the test box 110. In some embodiments, the conveying module 130 can include at least one of a rotatable conveyor belt, a conveyor chain, etc. In some embodiments, the conveying module 130 can control the speed of transporting the components to be tested 120.

[0024] Since salt mist is harmful to the human body, if the test component 120 is manually sent into the test box, it will bring health risks to the tester. By using the conveying module 130 to convey the test component 120 to the test box 110, the influence of salt mist on the tester can be avoided. At the same time, by speeding up the conveying speed of the conveying module 130, fast-paced and multi-frequency testing can be achieved.

[0025] The heating module 140 refers to a structure that can provide thermal energy to heat other substances (for example, the air in the heating chamber, a solid structure in contact with the heating module 140, etc.). In some embodiments, the heating module 140 is configured to heat the component to be tested 120. In some embodiments, the heating module 140 can heat at least one local position of the component to be tested 120. For example, the position of the component to be tested 120 close to the heat source of the car (for example, the battery assembly, drive system, etc. of the car) is heated to simulate the actual operation of the local position of the component to be tested 120 due to the proximity to the heat source of the car. In some embodiments, the heating module 140 may include at least one of an electric heating wire, an electric heating tube, an infrared heating tube, etc.

[0026] In some embodiments, the component to be tested 120 may include a cooling system (not shown in the figure). A cooling system refers to a structure that can be used to reduce the temperature of other objects. In some embodiments, the cooling system can be used to reduce the temperature of the component to be tested 120. In some embodiments, the cooling system can be at least one of a water cooling system and an air cooling system. Among them, the air cooling system can control the flow of air so that the flowing air contacts the component to be tested 120 to exchange heat, so that the heat energy of the component to be tested 120 is transferred to the flowing air, and the flowing air can take away the heat energy of the component to be tested 120, so that the temperature of the component to be tested 120 is reduced.

[0027] The environmental monitoring module 150 refers to a module capable of monitoring environmental data. In some embodiments, the environmental monitoring module 150 is configured to monitor environmental data within the test box 110. In some embodiments, the environmental monitoring module 150 may include at least one of a temperature sensor, a humidity sensor, and a salt spray concentration sensor. The temperature sensor and the humidity sensor may be used to monitor the temperature data and humidity data within the test box 110, respectively. The salt spray concentration sensor may be used to monitor the salt spray concentration data within the test box 110 (e.g., the concentration of the sodium chloride solution, the humidity of 80 cm 2 In some embodiments, the environment monitoring module 150 may continuously monitor and record the environment data at preset time intervals.

[0028] Environmental data refers to data obtained by the environmental monitoring module 150 monitoring the environment in the test box 110. In some embodiments, the environmental data may include at least one of temperature data, humidity data, salt spray concentration data, etc. In some embodiments, the environmental data may be represented by numerical values. For example, the temperature is 50°C, the humidity is 20%, the concentration of the salt solution is 50g / L, and the concentration of the salt solution is 80cm 2 The horizontal plane and average sedimentation rate are 1.5ml / h, etc.

[0029] The environmental control module 160 refers to a module capable of controlling the environment. In some embodiments, the environmental control module 160 is configured to control the test environment in the test box 110 based on environmental parameters. In some embodiments, the environmental control module 160 may include at least one of a temperature control device, a humidity control device, a salt spray concentration control device, and the like. Among them, the temperature control device, the humidity control device, and the salt spray concentration control device can be used to control the temperature data, humidity data, and salt spray concentration data in the test box 110, respectively, to the standard test requirements. The temperature control device may include an air conditioner, etc. The humidity control device may include at least one of a humidifier, a dehumidifier, etc. The salt spray concentration control device may include a salt spray sprayer, etc. The standard test requirements refer to the specific values ​​that the environmental data in the test box 110 are required to reach during each test.

[0030] Environmental parameters refer to working parameters when the environmental control module 160 controls environmental data. Environmental parameters may include cooling / heating power of air conditioners, humidification power of humidifiers, dehumidification power of dehumidifiers, salt spray power of salt spray sprayers, and the like.

[0031] In some embodiments, the environmental control module 160 can adjust the environmental data based on the environmental parameters to adjust the environmental data to be consistent with the standard test requirements. For example, the temperature requirement corresponding to the standard test requirements is 50°C, and the temperature corresponding to the environmental data in the test box 110 is 20°C. At this time, the heating power of the air conditioner in the environmental control module 160 is 300W, and the environmental parameters may include a heating power of 1000W. The processor controls the air conditioner in the environmental control module 160 to adjust the heating power to 1000W, so that the temperature in the test box 110 rises to 50°C.

[0032] In some embodiments, each test may correspond to an environmental parameter, and the environmental parameters corresponding to different tests may be the same or different. In some embodiments, the environmental parameter may be obtained based on a variety of methods. For example, based on at least one of experience, historical data, table lookup, and calculation by a first preset algorithm. Among them, the first preset algorithm may be, environmental parameter = (standard test requirements - environmental data) * coefficient. Among them, the coefficient may be obtained based on at least one of experience, historical data, table lookup, and the like.

[0033] The test environment refers to the environment required in the test box 110 when testing the component to be tested 120. In some embodiments, after the environment control module 160 controls the environmental data in the test box 110 based on the environmental parameters, the environment formed is the test environment.

[0034] In some embodiments, the automotive electrical component simulation test system 10 may further include a power drive module. The power drive module refers to a module that can provide electrical energy to other structures. The power drive module is configured to provide electrical energy to the automotive electrical component simulation test system 10. In some embodiments, the power drive module may be electrically connected to the transmission module 130, the heating module 140, the environmental control module 160, the environmental monitoring module 150, and the processor 170, respectively.

[0035] The processor 170 can be used to collect, store, analyze, and process data, and the processor can also issue control instructions to the actuator to enable the actuator to perform corresponding actions or functions. In some embodiments, the processor 170 can be respectively connected to the test box 110, the transmission module 130, the heating module 140, the environmental control module 160, and the environmental monitoring module 150. Among them, the processor 170 can issue a control instruction to the test box 110 to enable the test box 110 to perform at least one function of opening an opening, closing an opening, etc.

[0036] The processor 170 may send a control instruction to the transmission module 130, so that the transmission module 130 performs at least one function of starting, shutting down, changing the transmission speed, etc. The processor 170 may send a control instruction to the heating module 140, so that the heating module 140 performs at least one function of starting, shutting down, changing the temperature, etc.

[0037] The processor 170 may send a control instruction to the environment monitoring module 150, so that the environment monitoring module 150 performs at least one function of starting, shutting down, monitoring environmental data, etc. The processor 170 may send a control instruction to the environment control module 160, so that the environment control module 160 performs at least one function of starting, shutting down, regulating the environment, etc.

[0038] In some embodiments, the processor 170 is configured to: control the heating module 140 to heat the component to be tested 120 based on the heating parameters; determine the environmental parameters based on the environmental data, and send the environmental parameters to the environmental control module to control the test environment. In some embodiments, the processor can determine the environmental parameters based on the environmental data. For more information about determining the environmental parameters, please refer to the relevant description above.

[0039] The heating parameters refer to the parameters related to the heating module 140 when heating other objects (e.g., the component to be tested 120, etc.), such as heating power, heating time, etc. In some embodiments, the heating parameters can be preset. In some embodiments, the heating parameters can be obtained in a variety of ways. For example, based on at least one of experience, historical data, table lookup, preset algorithm calculation, etc.

[0040] In some embodiments, the processor may use the heating parameters with accurate test results in historical data as preset heating parameters. For example, the tested salt spray corrosion resistance is consistent with the salt spray corrosion resistance in actual use. In some embodiments, the processor may use the heating parameters of vehicles with good user reputation during salt spray testing as preset heating parameters. Among them, the reputation may be positively correlated with the degree of consistency between the heating parameters during the test phase and the data actually experienced by the user.

[0041] In some embodiments, the automotive electrical component simulation test system 10 may further include a state monitoring component (not shown). For more information about the state monitoring component, please refer to Figure 2 Related description.

[0042] During the test, the component to be tested 120 is placed on the conveying module 130, and the processor 170 can control the conveying module 130 to start and control the opening of the test box 110 to open. After the conveying module 130 transports the component to be tested 120 into the test box 110, the processor 170 controls the conveying module 130 to close and controls the opening of the test box 110 to close.

[0043] The processor 170 controls the heating module 140 to heat the component to be tested 120 based on the heating parameters. The processor 170 controls the environment monitoring module 150 to monitor the environmental data (e.g., temperature data, humidity data, salt spray concentration data, etc.) in the test box 110. Then, the environmental parameters are determined based on the environmental data, and the environmental parameters are sent to the environment control module 160. The environment control module 160 adjusts the environmental data to the environmental parameters based on the environmental parameters. The desired test environment is formed in the test box 110, and the component to be tested 120 is kept in the test environment for a preset test time.

[0044] The automotive electrical component simulation test system provided in some embodiments of this specification can realize the function of fully automated testing and can automatically adjust the test environment so that the components to be tested can be tested in a test environment that meets the preset requirements, thereby improving the efficiency and accuracy of the test.

[0045] In some embodiments, the system further includes a condition monitoring component.

[0046] The state monitoring component can be used to monitor the state data of the component to be tested during the test process. In some embodiments, the state monitoring component includes a weight sensor and an image sensor. Among them, the weight sensor can be used to monitor the weight change of the component to be tested during the test process. The image sensor can be used to monitor the appearance change of the component to be tested during the test process. In some embodiments, the test process can include a salt spray corrosion test process.

[0047] The state data refers to data used to characterize the salt spray corrosion condition of the tested component. In some embodiments, the state data includes weight data and image data.

[0048] Weight data refers to data related to the weight of the component to be tested during the salt spray corrosion test. In some embodiments, the weight change of the component to be tested during the salt spray corrosion test can be used to reflect the situation of the component being corroded by salt spray. For example, the weight of the component to be tested will increase accordingly after being corroded by salt spray.

[0049] In some embodiments, the weight data can be acquired by a weight sensor based on real-time or preset time intervals, wherein the preset time interval can be manually preset or set by system default. For example, the weight sensor can be set to measure the weight of the component to be tested every 10 seconds.

[0050] Image data refers to data related to the appearance image of the component to be tested during the salt spray corrosion test. For example, views of the component to be tested from different directions. In some embodiments, the processor can obtain the specific location and area of ​​the component corroded by salt spray by comparing the image data of the component to be tested during the salt spray corrosion process with the initial image data of the component.

[0051] In some embodiments, the image data may be acquired by an image sensor in real time or based on a preset time interval, wherein the preset time interval may be manually preset or set by a system default, for example, 10 seconds.

[0052] In some embodiments, the processor is configured to dynamically acquire status data monitored by the status monitoring component; terminate the test in response to the status data satisfying a test termination condition, and determine the salt spray resistance performance of the component to be tested based on the status data.

[0053] Dynamic acquisition refers to obtaining status data in real time as needed during the salt spray corrosion test.

[0054] In some embodiments, the processor may dynamically obtain the status data of the component to be tested during the salt spray corrosion test by connecting with the status monitoring component.

[0055] The test termination condition refers to a set of specific parameters or data, and when the state data meets these conditions, the test process automatically terminates. In some embodiments, the test termination condition can be manually pre-set in the system.

[0056] In some embodiments, the termination test condition includes that the difference between at least one of the weight data and the image data and the initial state data reaches a corrosion threshold. For detailed descriptions of the weight data and the image data, refer to the above content.

[0057] The initial state data refers to the state data of the component to be tested before the test is performed. In some embodiments, the initial state data of the component to be tested can be obtained by a state monitoring component before the test.

[0058] The corrosion threshold refers to the difference between the state data of the component to be tested and the initial state data when the test termination condition is met. In some embodiments, the processor can determine the corrosion threshold based on at least one of historical data, table lookup, etc.

[0059] In some embodiments, the corrosion threshold may include a weight threshold and an image threshold, wherein the weight threshold and the image threshold refer to the difference between the weight data and the image data and the initial state data when the termination test condition is met.

[0060] In some embodiments, the corrosion threshold can be determined based on the environmental data sequence, heating parameters, and test duration. For more information on heating parameters, see Figure 3 Related content.

[0061] In some embodiments, different environmental data sequences, heating parameters, and test durations have different salt spray corrosion conditions on the tested component, and thus different requirements for the salt spray corrosion resistance of the tested component. The processor can pre-set different corrosion thresholds based on historical record data according to different environmental data sequences, heating parameters, and test durations to accept different corrosion conditions of the weight data and image data fed back. Among them, the corrosion threshold can be preset manually, and the specific description of the salt spray corrosion resistance can be found below.

[0062] Environmental data sequence refers to a sequence formed by a series of environmental data. For more information about environmental data, please refer to Figure 1 The corresponding description.

[0063] In some embodiments, the environmental data sequence may include a sequence formed by at least one environmental data. In some embodiments, the processor may sort one or more environmental data to obtain the environmental data sequence.

[0064] The test duration refers to the total duration of the test process. In some embodiments, the processor can calculate the test duration by pre-setting the test start time point and the test end time point. In some embodiments, the processor can also directly set a specific test duration.

[0065] In some embodiments of the present specification, the processor can determine the severity of salt spray corrosion of the component to be tested by determining whether the difference between at least one of the weight data and the image data and the initial state data reaches a corrosion threshold, thereby further evaluating whether the salt spray resistance of the component to be tested meets expectations.

[0066] Salt spray resistance refers to the performance of the component to be tested against salt spray corrosion. In some embodiments, the processor can determine the salt spray resistance based on the difference between the weight data and the expected weight data, and the difference between the image data and the expected image data. For example, the greater the difference between the weight data and the expected weight data, and the greater the difference between the image data and the expected image data, the worse the salt spray resistance of the component to be tested.

[0067] The expected weight data and expected image data refer to the weight data and image data of the anti-salt spray performance of the tested component under an ideal state. The expected weight data and expected image data can be measured and obtained in advance or preset manually. The difference between the weight data and the expected weight data, and the difference between the image data and the expected image data can be determined by the processor based on the weight data and image data obtained by the state monitoring component and the expected weight data and expected image data stored in the system. The processor can calculate the difference between the image data and the expected image data that can reflect the area and depth of the corrosion area and other related data (such as the pixel value of the corrosion area) through image recognition technology.

[0068] In some embodiments, determining the corrosion threshold may include: obtaining a completed cyclic heating process; and determining the corrosion threshold through a threshold determination model based on the completed cyclic heating process, an environmental data sequence, and multiple sets of heating parameters.

[0069] In some embodiments, the processor can control the heating module to perform cyclic heating on the tested component based on the heating parameters. The completed cyclic heating process can include one or more rounds of cyclic heating. For more information about heating parameters and cyclic heating, please refer to Figure 3 Related content.

[0070] The cyclic heating process refers to a repeated heating process. In some embodiments, the cyclic heating process includes multiple sets of heating parameters, specific cyclic heating times, test duration, etc. For example, the current cyclic heating process may include: 10 rounds of heating based on the 10th set of heating parameters, and the current estimated cumulative time is 1 hour.

[0071] In some embodiments, the processor may obtain the cyclic heating progress based on historical data statistics.

[0072] In some embodiments, the cyclic heating process includes alternating heating and cooling phases.

[0073] In some embodiments, based on different heating parameters, number of cycle heating times, and heating time, different cycle heating processes have different effects on the salt spray corrosion of the test components, and thus different requirements for the salt spray corrosion resistance of the test components. Therefore, the processor needs to determine the corresponding corrosion threshold when the test termination condition is met according to different cycle heating processes.

[0074] In some embodiments, the processor may determine the corrosion threshold based on a threshold determination model 240. The threshold determination model 240 may be a machine learning model, such as a Convolutional Neural Networks (CNN) model.

[0075] like Figure 2As shown, the input of the threshold determination model 240 may include a cyclic heating process 210, an environmental data sequence 220, and multiple sets of heating parameters 230; the output may include a corrosion threshold 250. The specific contents of the cyclic heating process, the environmental data sequence, and the corrosion threshold can be found in the above description, and the specific contents of the heating parameters can be found in Figure 3 Related instructions.

[0076] In some embodiments, the processor may train the threshold confirmation module 240 based on a large number of first samples with first labels. The first sample may include a sample cycle heating process, a sample environmental data sequence, and multiple sets of sample heating parameters. The processor may determine the corresponding actual use scenario based on the use scenario simulated by the first sample, and determine the first label based on the actual use scenario. The first label may include the statistical values ​​(e.g., average value, mode, etc.) of actual corrosion data of a large number of vehicles with qualified salt spray corrosion resistance in actual use scenarios during the test time of equivalent duration. The statistical values ​​of the actual corrosion data serve as training data to reflect the corrosion threshold of the component to be tested in different actual use scenarios.

[0077] The equivalent duration refers to the actual driving duration equivalent to the duration of the test under normal use determined by the conversion relationship. In some embodiments, the conversion relationship can be that 1 hour of test duration is equivalent to 10-100 hours in the natural environment (refer to "GB-T 10125 Artificial atmosphere corrosion test salt spray test").

[0078] In some embodiments of the present specification, the processor determines the corrosion threshold through a threshold determination model, thereby determining the termination test condition, which can more accurately simulate and evaluate the corrosion resistance of the component to be tested under actual working conditions.

[0079] In some embodiments of the present specification, the state monitoring component can more accurately evaluate the salt spray resistance of the component to be tested by dynamically acquiring the state data during the test by monitoring the component to be tested. The processor makes a decision to terminate the test in a timely manner in response to the state data satisfying the test termination condition, which helps to monitor the test process in real time and respond to changes quickly.

[0080] Figure 3 is an exemplary flow chart of cyclic heating according to some embodiments of this specification. Figure 3 As shown, process 300 includes the following steps.

[0081] Step 310, obtaining a driving scenario of the vehicle, and generating simulated driving data of the vehicle based on the driving scenario.

[0082] The driving scene refers to information related to the driving environment of the car. In some embodiments, the driving scene may include at least one of the driving road (e.g., urban road, highway, country road, etc.), driving distance, traffic jam (e.g., urban road has more traffic jams), etc. In some embodiments, the processor may obtain the driving scene based on the car recorder of the car.

[0083] Driving data refers to data related to the vehicle driving in a driving scene. In some embodiments, the driving data may include at least one of driving speed data, driving steering data, driving environment data, and the like.

[0084] Driving speed data refers to data related to the driving speed of the car. Driving speed data can be related to actual driving conditions such as traffic jams, waiting for traffic lights, and pedestrian avoidance. For example, when actual driving conditions such as traffic jams, waiting for traffic lights, and pedestrian avoidance occur, the driving speed of the car will gradually decrease to 0. The change in driving speed can affect and reflect the heating conditions of relevant parts of the car. In some embodiments, the driving speed data can be represented by a speed curve.

[0085] Driving steering data refers to data related to the driving direction of the car. In some embodiments, the driving steering data may include the number of turns of the car per unit time. In some embodiments, the driving steering data may be used to distinguish different driving scenarios. For example, urban roads are straight and there are fewer turns. In some embodiments, the driving steering data may be positively correlated to the heat generated by related components (e.g., components to be tested, etc.). For example, the more turns per unit time, the greater the heat generated by the related components.

[0086] The driving environment data refers to data related to the driving environment of the automobile. In some embodiments, the driving environment data may include at least one of ambient temperature, ambient humidity, ambient salt spray concentration, ambient wind force, and the like.

[0087] The simulated driving data refers to the driving data of the car during the simulated driving of the driving scene. In some embodiments, the simulated driving data may include at least one of simulated driving speed data, simulated driving steering data, simulated driving environment data, etc.

[0088] In some embodiments, the processor may determine the simulated driving data based on the driving scenario by a variety of methods. For example, by at least one of historical data, table lookup, and preset algorithm calculation. In some embodiments, the processor may select a number of historical driving data from a historical driving database as the simulated driving data. For more information about the historical driving database and historical driving data, please refer to the relevant description below.

[0089] In some embodiments, the processor 170 can obtain a historical driving database corresponding to multiple driving scenarios, where the historical driving database is obtained based on driving records of multiple existing vehicles of the same type; based on the driving scenario, determine the reference driving data corresponding to the driving scenario; cluster the historical driving data based on the reference driving data to obtain multiple scenario driving data clusters; and filter the covering number of driving data in each scenario driving data cluster to obtain multiple groups of simulated driving data.

[0090] The historical driving database refers to a database composed of historical driving data. The historical driving data refers to the driving data generated by the car in the actual driving scene. In some embodiments, the historical driving data can be obtained based on a driving recorder. In some embodiments, the historical driving database can be stored in the processor 170. For more information about the driving data, please refer to the relevant description above.

[0091] Reference driving data refers to typical driving data corresponding to different driving scenarios. In some embodiments, the processor may obtain reference driving data through a second preset algorithm based on historical driving data. For example, the second preset algorithm may be to average the driving data collected by the car in different scenarios as reference driving data. For more information about driving data, please refer to the relevant description above.

[0092] In some embodiments, the processor may cluster the historical driving data in the historical driving database using a clustering algorithm, taking the reference driving data corresponding to each driving scene as the initial clustering center, to obtain a scene driving data cluster corresponding to each scene. There may be multiple types of clustering algorithms, for example, the clustering algorithm may include K-Means clustering, density-based clustering method (DBSCAN), etc.

[0093] The coverage number refers to the number of representative historical driving data in the historical driving data corresponding to a certain driving scene. In some embodiments, the coverage number of different driving scenes may be different. In some embodiments, the coverage number may be obtained based on at least one method of experience, historical data, and calculation by a third preset algorithm. For example, the third preset algorithm is equal to the ratio of the total number of historical driving data to the coefficient. The coefficient may be determined based on at least one of experience, historical data, and the like.

[0094] In some embodiments, the processor can screen the driving data with the same coverage number in each scene driving data cluster based on the coverage number. Equidistant means that the difference in the sequence numbers of the two adjacent driving data screened out (the larger sorting value minus the smaller sorting value, which is a natural number) is the same. Among them, the difference in sequence numbers can be equal to the coefficient. For example, the scene driving data cluster corresponding to a certain driving scene includes 50 historical driving data, the determination coefficient is 10, and the coverage number = 50 ÷ 10. Then, these 50 historical driving data are sorted according to the preset sorting rules, and selected according to the difference in sequence numbers of 10. The 5th, 15th, 25th, 35th, and 45th historical driving data can be selected as the simulated driving data for the driving scene. The 1st, 11th, 21st, 31st, and 41st historical driving data can also be selected.

[0095] In some embodiments, the preset sorting rule refers to a preset rule for sorting historical driving data. For example, the preset sorting rule may be at least one of sorting driving speed from fast to slow, sorting the number of stops from more to less, sorting driving time from long to short, sorting the number of turns from more to less, etc.

[0096] The simulated driving data of different driving scenarios are selected from the historical driving database through clustering and used to determine the heating parameters later. This can more realistically restore the heating conditions of the vehicle's tested components during actual driving and improve the accuracy of determining the heating parameters.

[0097] In some embodiments, the coverage quantity is related to the proportion of the amount of data contained in a row of scene data cluster in the amount of data contained in the historical driving database. The larger the proportion, the more likely the vehicle encounters the driving scene, and the more comprehensive the coverage is, and the larger the coverage quantity is.

[0098] For example, a historical driving database includes five scene driving data clusters, and the historical driving database includes 500 historical driving data. Among them, the first scene driving data cluster includes 200 historical driving data, and the historical driving data of the first scene driving data cluster accounts for 40%. The total coverage quantity of the historical driving database is determined to be 50. The coverage quantity of the first scene driving data cluster is 50×40%=20. In some embodiments, the total coverage quantity = the total number of historical driving data ÷ coefficient. Among them, the coefficient can be obtained based on at least one of experience and historical data.

[0099] Making the coverage quantity related to the proportion of the data volume of the row scene data cluster in the historical driving database can improve the accuracy of the determined coverage quantity, thereby improving the accuracy of the filtered driving data.

[0100] Step 320 , determining heating parameters based on the simulated driving data.

[0101] In some embodiments, the heating parameters may include a heating power curve.

[0102] The heating power curve refers to a curve consisting of heating power data and changing with time. In some embodiments, the abscissa of the heating power curve can be time, and the ordinate can be heating power.

[0103] Since different simulated driving data correspond to different heating conditions, the heating of related components may change over time during driving. Therefore, during the entire test process, the heating power of the heating module cannot be a fixed power, but the heating power varies at different time periods to simulate the heating condition of the entire tested component 120 during actual driving. For more information about heating parameters, please refer to Figure 1 In some embodiments, the processor may match historical driving data with the same heating condition from the historical database, and determine the historical heating parameter corresponding to the historical driving data as the heating parameter. For more information about the historical database, please refer to the relevant description above.

[0104] In some embodiments, the processor may determine the heating parameters based on a heating parameter model.

[0105] The heating parameter model refers to a model used to determine the heating parameters. The heating parameter model can be a machine learning model, for example, a neural network (NN).

[0106] In some embodiments, the inputs to the heating parameter model may include simulated driving data, temperature curves, and environmental parameters, and the outputs may include heating parameters.

[0107] For more information about temperature curves, see Figure 4 Related instructions.

[0108] In some embodiments, the heating parameter model can be obtained by training multiple second training samples with second labels. Multiple second training samples with second labels can be input into the initial heating parameter model, and a loss function is constructed by the second label and the result of the initial heating parameter model, and the parameters of the initial heating parameter model are iteratively updated based on the loss function. When the loss function of the initial heating parameter model meets the preset conditions, the model training is completed and the trained heating parameter model is obtained. The preset conditions may be that the loss function converges, the number of iterations reaches a threshold, etc.

[0109] In some embodiments, the second training sample can be the actual heating environment parameters in the historical test records, the temperature curve during the heating process, and the simulated driving data corresponding to the temperature curve. The second label can be the actual heating parameters corresponding to the second training sample in the historical test records. In some embodiments, the processor can match the simulated driving data corresponding to the temperature curve from the temperature database based on the temperature curve. In some embodiments, the temperature curve, the simulated driving data, and the heating parameters can be matched in the temperature database. For more information about the temperature database, see Figure 4 Related instructions.

[0110] Determining the heating parameters through a heating parameter model can improve the efficiency and accuracy of determining the heating parameters.

[0111] In step 330 , the processor controls the heating module to perform cyclic heating on the component to be tested based on the heating parameters.

[0112] Cyclic heating means that the heating module heats the component to be tested multiple times continuously according to at least one set of heating parameters. Cyclic heating can be used to simulate the repeated use of a car every day in an actual driving scenario. For example, the heating parameters may include R1, R2, R3... Wherein, R1, R2, R3 respectively correspond to a set of heating parameters. When the heating module heats the component to be tested, one round of heating according to R1, R2, R3... is a cycle. After one round of heating, the component to be tested is cooled, and after cooling, it is heated again according to R1, R2, R3..., and the heating is repeated multiple times. In some embodiments, the number of cyclic heating times can be preset based on experience, historical data, etc.

[0113] By using multiple sets of heating parameters to perform cyclic heating tests on the test components, the driving scenarios of the test components can be more realistically simulated, making the test results more representative of the actual driving conditions.

[0114] Figure 4 It is a schematic diagram of determining the working parameters of the component to be tested according to some embodiments of this specification.

[0115] In some embodiments, the processor may determine a temperature curve 430 of the vehicle under the simulated driving data based on the simulated driving data 410 ; and determine an operating parameter 440 of the component to be tested based on the temperature curve 430 .

[0116] The working parameters 440 refer to a set of parameters used to describe and control the working state of the component to be tested. For example, the component to be tested is a cooling system of a car. The working parameters may include ventilation power and ventilation volume. For example, when the temperature of the relevant components of the car (such as the battery, the drive system) rises to a preset temperature, the cooling system inhales the cold air in the test box according to the preset ventilation power and ventilation volume for cooling.

[0117] In some embodiments, the processor can determine the working parameters 440 of the component to be tested under different temperature conditions in a variety of ways based on the temperature curve 430. For example, it can be determined by obtaining user input, or by obtaining historical data in the system, etc. The user refers to an authorized vehicle user, a staff member who tests the automotive electrical component simulation test system.

[0118] The temperature curve 430 refers to a curve reflecting the temperature change of the automobile-related components during driving. The automobile-related components may include components to be tested, such as a battery, a drive system, etc.

[0119] In some embodiments, the processor may determine the temperature curve 430 of the vehicle under the simulated driving data in a variety of ways based on the simulated driving data 410. For example, a preset table may be constructed based on the simulated driving data and its corresponding temperature curve, and the temperature curve of the vehicle under different simulated driving data may be determined by looking up the table, and the temperature curves corresponding to different simulated driving data may be pre-recorded in the comparison table.

[0120] In some embodiments, the processor may determine the temperature curve 430 by matching the temperature database 420 based on the simulated driving data.

[0121] The temperature database 420 refers to a database constructed from historical driving data and temperature change data of a large number of vehicles during actual driving. In some embodiments, any data in the temperature database includes a set of historical driving data and a temperature curve determined by the temperature change data corresponding to the set of historical driving data. For more information about historical driving data, please refer to Figure 3 Related content.

[0122] In some embodiments, historical driving data 420-1 and temperature change data 420-2 during actual driving can be obtained after obtaining authorization from the vehicle user, wherein the obtained data only involves numerical values ​​and does not contain sensitive privacy information such as specific location; they can also be collected by testers driving the vehicle in different driving scenarios.

[0123] The temperature change data refers to the temperature data of the relevant parts of the automobile during the actual driving process. In some embodiments, the temperature change data can be collected and obtained by a temperature sensor.

[0124] In some embodiments, the processor can calculate the similarity between the simulated driving data and the historical driving data of each data in the temperature database, determine the historical driving data that meets the similarity threshold as the reference historical driving data, and determine the temperature curve corresponding to the reference historical driving data as the target temperature curve, and obtain the temperature curve corresponding to the simulated driving data by weighted summing the target temperature curve. Among them, the temperature data corresponding to the same time in multiple target temperature curves constitute the same group of temperature data. The processor can perform weighted summation on each group of temperature data, and a group of summation results is used as the temperature data of the group at the corresponding time, and then the temperature curve formed by each group of weighted summation results is used as the temperature curve corresponding to the simulated driving data. For specific instructions on simulated driving data, please refer to Figure 3 Related content.

[0125] In some embodiments of the present specification, by simulating driving data and temperature change data of actual driving scenarios during the test process, test conditions that are closer to actual driving can be provided, thereby improving the reliability and validity of the test results; the temperature curve generated based on the actual driving data can make the test of the components to be tested more relevant to the actual driving scenarios, so that the test results can better reflect the performance and durability of the components in actual use.

[0126] In some embodiments, the similarity threshold is related to the amount of data in the scene driving data cluster corresponding to the simulated driving data, and the weight is related to the similarity between the simulated driving data and the historical driving data in the temperature database. For more information about the scene driving data cluster, see Figure 3 Related instructions.

[0127] In some embodiments, the similarity threshold may be positively correlated with the amount of data in the scene driving data cluster. For example, the larger the amount of data in the scene driving data cluster, the more historical driving data of the scene. At this time, the processor may set a higher similarity threshold to determine the historical driving data that is more similar to the simulated driving data, thereby obtaining a more accurate temperature curve.

[0128] In some embodiments, when the similarity meets the similarity threshold, the weight of the weighted sum of the temperature curves is positively correlated to the similarity between the simulated driving data and the historical driving data in the temperature database. Exemplarily, the greater the similarity, the greater the weight.

[0129] In some embodiments of this specification, by setting similarity thresholds and weights, the processor can select more representative and relevant data for analysis, thereby improving the reliability of the test results.

[0130] In some embodiments of the present specification, the processor can determine the temperature curve through the temperature database based on the simulated driving data, so as to more accurately simulate the temperature change of the car under the actual driving data and provide a more realistic test environment. The processor determines the working parameters of the components to be tested based on the temperature curve, which helps to simulate the conditions of different components during normal operation, thereby ensuring that the test is more realistic.

[0131] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0132] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures or characteristics in one or more embodiments of this specification can be appropriately combined.

[0133] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0134] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0135] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of this specification are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0136] Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, documents, etc., cited in this specification are hereby incorporated by reference in their entirety. Except for application history documents that are inconsistent with or conflicting with the contents of this specification, documents that limit the broadest scope of the claims of this specification (currently or later attached to this specification) are also excluded. It should be noted that if the descriptions, definitions, and / or use of terms in the materials attached to this specification are inconsistent or conflicting with the contents described in this specification, the descriptions, definitions, and / or use of terms in this specification shall prevail.

[0137] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A simulation test system for automotive electrical components, characterized in that: The system comprises: a test box, a transmission module, a heating module, an environment control module, an environment monitoring module, a state monitoring component and a processor; The conveying module is mechanically connected to the test box and is configured to convey the component to be tested to the test box; The heating module is configured to heat the component to be tested; The environmental control module is configured to control the test environment in the test box based on environmental parameters; The environmental monitoring module is configured to monitor environmental data within the test box; The state monitoring component is configured to monitor state data of the component to be tested during the test process, wherein the state data includes at least one of weight data and image data; The processor is configured to: Controlling the heating module to heat the component to be tested based on the heating parameters; Determine the environmental parameters based on the environmental data, and send the environmental parameters to the environmental control module to perform test environment control; Dynamically acquiring the status data monitored by the status monitoring component; In response to the state data satisfying a test termination condition, terminating the test, and determining the salt spray resistance performance of the component to be tested based on the state data, wherein the test termination condition includes that a difference between at least one of the weight data and the image data and the initial state data reaches a corrosion threshold; The step of controlling the heating module to heat the component to be tested based on the heating parameter includes: Acquire a driving scene of a car, and generate simulated driving data of the car based on the driving scene; determining the heating parameter based on the simulated driving data; Controlling the heating module to perform cyclic heating on the component to be tested based on the heating parameters; The step of generating the simulated driving data of the automobile comprises: Acquire a historical driving database corresponding to the driving scenario, wherein the historical driving database is acquired based on driving records of multiple existing vehicles of the same type; Based on the driving scenario, determining reference driving data corresponding to the driving scenario; Clustering the historical driving data based on the reference driving data to obtain multiple scene driving data clusters; Filtering the historical driving data with a coverage number in each of the scene driving data clusters to obtain multiple groups of the simulated driving data, wherein the coverage number is related to the proportion of the data volume of the scene driving data cluster in the historical driving database; The corrosion threshold is determined based on the environmental data sequence, the heating parameters, and the test duration, including: Get the completed cycle heating process; Based on the completed cyclic heating process, the environmental data sequence, and the heating parameters, the corrosion threshold is determined by a threshold determination model, and the threshold determination model is a machine learning model.

2. The system according to claim 1, characterized in that The processor is further configured to: Based on the simulated driving data, determining a temperature curve of the vehicle under the simulated driving data; Based on the temperature curve, determining the operating parameters of the component to be tested; Determining the temperature curve of the automobile under the simulated driving data includes: Based on the simulated driving data, matching is performed through a temperature database, and weighted summation is performed on the temperature curves corresponding to the historical driving data whose similarity meets a similarity threshold, so as to obtain the temperature curve of the automobile under the simulated driving data; The similarity threshold is related to the amount of data in the scenario driving data cluster corresponding to the simulated driving data, and the weight of the weighted sum is related to the similarity between the simulated driving data and the historical driving data in the temperature database.

3. A method for simulating and testing automotive electrical components, implemented based on the processor of the automotive electrical component simulation and testing system according to claim 1, characterized in that: include: Controlling the heating module to heat the component to be tested based on the heating parameters; Determine environmental parameters based on environmental data, and send the environmental parameters to an environmental control module to perform test environment control; Dynamically obtain status data monitored by status monitoring components; In response to the state data satisfying a test termination condition, terminating the test, and determining the salt spray resistance of the component to be tested based on the state data, wherein the test termination condition includes that a difference between at least one of the weight data and the image data and the initial state data reaches a corrosion threshold; The step of controlling the heating module to heat the component to be tested based on the heating parameters includes: Acquire a driving scene of a car, and generate simulated driving data of the car based on the driving scene; determining the heating parameter based on the simulated driving data; Controlling the heating module to perform cyclic heating on the component to be tested based on the heating parameters; The step of generating the simulated driving data of the automobile comprises: Acquire a historical driving database corresponding to the driving scenario, wherein the historical driving database is acquired based on driving records of multiple existing vehicles of the same type; Based on the driving scenario, determining reference driving data corresponding to the driving scenario; Clustering the historical driving data based on the reference driving data to obtain multiple scene driving data clusters; Filtering the historical driving data with a coverage number in each of the scene driving data clusters to obtain multiple groups of the simulated driving data, wherein the coverage number is related to the proportion of the data volume of the scene driving data cluster in the historical driving database; The corrosion threshold is determined based on the environmental data sequence, the heating parameters, and the test duration, including: Get the completed cycle heating process; Based on the completed cyclic heating process, the environmental data sequence, and the heating parameters, the corrosion threshold is determined by a threshold determination model, and the threshold determination model is a machine learning model.

4. The method according to claim 3, characterized in that: The method further comprises: Based on the simulated driving data, determining a temperature curve of the vehicle under the simulated driving data; Based on the temperature curve, determining the operating parameters of the component to be tested; Determining the temperature curve of the automobile under the simulated driving data includes: Based on the simulated driving data, matching is performed through a temperature database, and weighted summation is performed on the temperature curves corresponding to the historical driving data whose similarity meets a similarity threshold, so as to obtain the temperature curve of the automobile under the simulated driving data; The similarity threshold is related to the amount of data in the scenario driving data cluster corresponding to the simulated driving data, and the weight of the weighted sum is related to the similarity between the simulated driving data and the historical driving data in the temperature database.

5. An automobile electrical component simulation test device, comprising a processor, wherein the processor is used to execute the automobile electrical component simulation test method according to any one of claims 3 to 4.

6. A computer-readable storage medium, wherein the storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the automotive electrical component simulation test method according to any one of claims 3 to 4.

Images