Vehicle headlamp detection method and device and storage medium

Abstract

The invention discloses a vehicle headlamp detection method and device and a storage medium, and relates to the technical field of headlamp detection, and the method comprises the steps: constructing a headlamp detection scene, and carrying out the headlamp control of a vehicle in a vehicle positioning platform of the headlamp detection scene, so as to obtain the actual light type of the headlamp of the vehicle; obtaining identification information of the vehicle, and matching the standard parameter database according to the identification information to obtain a standard light type parameter; and determining a detection result of the headlamp of the vehicle according to the actual light type of the headlamp and the standard light type parameters. Therefore, according to the method, the headlamp is detected and evaluated based on the actual light pattern and the standard light pattern parameters collected by the headlamp detection scene, the uniformity of quality standards can be realized, the detection efficiency and the detection precision of the headlamp are improved, and the input demand on human resources is reduced.

Description

Technical Field

[0001] This application relates to the field of headlight testing technology, and more particularly to a method, testing device, and storage medium for testing vehicle headlights. Background Technology

[0002] In related technologies, the detection of vehicle headlights is carried out through manual identification and evaluation. This method is highly dependent on human experience and has a large degree of subjectivity, resulting in poor evaluation consistency and significant fluctuations in product quality. This leads to an unstable defective product interception rate and the risk of products being leaked. Summary of the Invention

[0003] This application aims to at least partially address one of the technical problems in the related art. Therefore, the first objective of this application is to propose a method for detecting vehicle headlights, which evaluates headlights based on actual and standard light pattern parameters collected from a headlight detection scenario. This method achieves uniformity in quality standards, improves headlight detection efficiency and accuracy, and reduces the need for human resources.

[0004] The second objective of this application is to provide a headlight detection device for a vehicle.

[0005] The third objective of this application is to propose an electronic device.

[0006] The fourth objective of this application is to provide a computer-readable storage medium.

[0007] To achieve the above objectives, the first aspect of this application proposes a method for detecting vehicle headlights. The method includes: constructing a headlight detection scenario and controlling the headlights of a vehicle in a vehicle positioning platform within the headlight detection scenario to obtain the actual headlight beam pattern of the vehicle; obtaining the vehicle's identification information and matching it with a standardized parameter database to obtain standard beam pattern parameters; and determining the headlight detection result of the vehicle based on the actual headlight beam pattern and the standard beam pattern parameters.

[0008] According to the vehicle headlight detection method of this application embodiment, firstly, a headlight detection scenario is constructed, and headlight control is performed on the vehicle in the vehicle positioning platform within the headlight detection scenario to obtain the actual light pattern of the vehicle's headlights. Then, the vehicle's identification information is obtained, and a standardized parameter database is matched based on the identification information to obtain standard light pattern parameters. The headlight detection result of the vehicle is determined based on the actual light pattern and the standard light pattern parameters. Thus, this method detects and evaluates headlights based on the actual light pattern and standard light pattern parameters collected in the headlight detection scenario, which can achieve uniformity of quality standards, improve the detection efficiency and accuracy of headlights, and reduce the need for human resources.

[0009] In addition, the vehicle headlight detection method according to the above embodiments of this application may also have the following additional technical features: According to one embodiment of this application, the identification information includes one or more of vehicle identification code, vehicle model, and vehicle type information. Obtaining the vehicle identification information includes: identifying the vehicle identification code of the vehicle through a scanning module in a headlight detection scenario; and / or, acquiring image information of the vehicle through a vision module in a headlight detection scenario, identifying the vehicle model of the vehicle by recognizing the image information; and / or receiving vehicle type information issued by the vehicle production line control system.

[0010] According to one embodiment of this application, the standard beam pattern parameter includes a beam pattern standard range. The headlight test result of the vehicle is determined based on the actual beam pattern of the headlight and the standard beam pattern parameter, including: when the actual beam pattern of the headlight is within the beam pattern standard range, the headlight test of the vehicle is determined to be qualified; when the actual beam pattern of the headlight is not within the beam pattern standard range, the headlight test of the vehicle is determined to be unqualified.

[0011] According to one embodiment of this application, the headlight detection method for the vehicle further includes: obtaining the actual beam pattern of historical headlights that have passed the headlight detection within a first preset time period; and adjusting the beam pattern standard range based on the historical headlight actual beam pattern data.

[0012] According to one embodiment of this application, the headlight detection method for a vehicle further includes: obtaining headlight detection results within a preset time period; if, based on the headlight detection results, the failure rate within a second preset time period is greater than the historical average failure rate, and the difference between the failure rate and the historical average pass rate is greater than a preset value, obtaining the headlight traceability information corresponding to the headlight detection results; identifying traceability overlap areas based on the headlight traceability information, and generating corresponding fault diagnosis suggestions based on the traceability overlap areas.

[0013] According to one embodiment of this application, after determining the headlight detection result of a vehicle based on the actual headlight beam pattern and standard beam pattern parameters, the headlight detection method for the vehicle further includes: determining the risk level based on the headlight detection result; and generating a corresponding control signal based on the risk level and the current test scenario.

[0014] According to one embodiment of this application, a corresponding control signal is generated based on the risk level and the current test scenario, including: when the current test scenario is a vehicle production scenario, generating a corresponding production line control signal based on the risk level; and when the current test scenario is a vehicle after-sales scenario, generating a corresponding after-sales guidance signal based on the risk level.

[0015] According to one embodiment of this application, after constructing a headlight detection scenario, the headlight detection method for the vehicle further includes: in the headlight detection scenario, using a high-precision machine vision system to acquire optimal light pattern images of the headlights of multiple test vehicles; defining upper and lower limit values ​​of the corresponding headlights in the horizontal and vertical directions based on the optimal light pattern images to determine the standard range of light patterns of the headlights of the multiple test vehicles; and constructing a standardized parameter database based on the standard range of light patterns of the headlights of the multiple vehicles.

[0016] To achieve the above objectives, a second aspect of this application provides a vehicle headlight detection device, comprising: a construction module for constructing a headlight detection scenario; an acquisition module for controlling the headlights of a vehicle in a vehicle positioning platform within the headlight detection scenario to acquire the actual headlight beam pattern of the vehicle; a matching module for acquiring vehicle identification information and matching it against a standardized parameter database to obtain standard beam pattern parameters; and a detection module for determining the headlight detection result of the vehicle based on the actual headlight beam pattern and the standard beam pattern parameters.

[0017] According to the vehicle headlight detection device of this application embodiment, a headlight detection scenario is constructed by a construction module, and the headlights of vehicles in the vehicle positioning platform within the headlight detection scenario are controlled by an acquisition module to obtain the actual beam pattern of the vehicle's headlights. The vehicle's identification information is obtained by a matching module, and a standardized parameter database is matched based on the identification information to obtain standard beam pattern parameters. The detection module determines the headlight detection result of the vehicle based on the actual beam pattern and the standard beam pattern parameters. Therefore, this device detects and evaluates headlights based on the actual beam pattern and standard beam pattern parameters collected in the headlight detection scenario, achieving uniformity in quality standards while improving headlight detection efficiency and accuracy, and reducing the need for human resource input.

[0018] To achieve the above objectives, a third aspect of this application provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the above-described vehicle headlight detection method.

[0019] According to the embodiments of this application, a computer-readable storage medium storing a computer program thereon implements the steps of the above-described vehicle headlight detection method when executed by a processor. Based on the above-described headlight detection method, the uniformity of quality standards is achieved, the detection efficiency and accuracy of headlights are improved, and the input of human resources is reduced.

[0020] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0021] Figure 1 This is a flowchart of a vehicle headlight detection method according to an embodiment of this application; Figure 2 This is a connection diagram of a vehicle headlight detection device according to an embodiment of this application; Figure 3 This is a block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0022] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0023] The following description, with reference to the accompanying drawings, outlines a method, apparatus, electronic device, and storage medium for detecting vehicle headlights according to embodiments of this application.

[0024] The current headlight testing method has the following problems: 1. Highly dependent on human experience, resulting in poor consistency and large quality fluctuations: Specifically, different operators at headlight manufacturers have subjective differences in their understanding of "qualified." A slight deviation that worker A considers acceptable might be deemed unqualified by worker B. Even the same worker's judgment standards may fluctuate at different times of the day (e.g., when fatigued). This leads to inconsistent product quality standards, making true "standardization" impossible, and resulting in inconsistent headlight quality among the products that leave the factory.

[0025] 2. The defective product interception rate is unstable, posing a risk of outflow: Specifically, due to human fatigue, negligence, or inconsistent standards, "missed inspections" and "incorrect inspections" are inevitable. Some products with slight deviations in adjustment range or flaws in light pattern may be mistakenly judged as qualified and released (missed inspection), while some qualified products may be misjudged due to stringent judgment standards (incorrect inspection). This creates loopholes in product quality control and increases the risk of large deviations in light height and width.

[0026] 3. High personnel training costs, and difficulty in solidifying and passing on skills: Specifically, training a qualified lighting inspector requires a significant amount of time and practice, as the core skill is "experience." Once an employee leaves, their accumulated experience is lost, and the company needs to reinvest in training new personnel. Current technological methods cannot digitize and solidify the experience and judgment criteria of outstanding employees and replicate them for all operators.

[0027] 4. The headlights on the dynamic inspection line in the final assembly workshop need to be manually adjusted. The initial values ​​of the lights on incoming parts have large deviations, resulting in wasted time.

[0028] To address at least one of the aforementioned technical problems, this application proposes a method for testing vehicle headlights. The method assesses and tests the headlights based on the actual beam pattern collected from a headlight testing scenario and the standard beam pattern parameters from a standardized parameter database. This achieves uniformity in quality standards, improves testing efficiency and accuracy, and reduces the need for human resources. The headlight testing method of this application will be described in detail below with reference to the accompanying drawings.

[0029] Figure 1 This is a flowchart of a vehicle headlight detection method according to an embodiment of this application; like Figure 1 As shown, the headlight detection method for a vehicle according to an embodiment of this application may include: S1. Construct a headlight detection scenario and control the headlights of vehicles in the vehicle positioning platform within the headlight detection scenario to obtain the actual headlight beam pattern of the vehicles.

[0030] Specifically, the headlight detection scenario may include the following components: 1. Vehicle positioning platform: Equipped with precision stops and wheel alignment devices to ensure that the vehicle is parked in the same position and orientation every time.

[0031] 2. Standard detection screen: Located at a fixed distance (3 meters) directly in front of the vehicle on the vehicle positioning platform, the screen has a clear coordinate grid.

[0032] 3. Industrial camera and image processing unit: Headlight detector, fixedly installed in front of or above the side of the detection screen to ensure clear capture of the entire light pattern.

[0033] 4. Control cabinet (PLC (Programmable Logic Controller)): Receives decision signals and controls the start, stop, and branching of the production line rollers.

[0034] 5. Server / Industrial PC: Performs headlight quality inspection, including standard parameter database, image processing algorithm, and judgment logic.

[0035] 6. Information identification device: The DSA device obtains the vehicle's VIN code by scanning the vehicle's VIN code with a barcode scanner.

[0036] During headlight testing, the vehicle is driven onto a vehicle positioning platform, and the headlights are controlled accordingly. Taking low beam testing as an example, the adjustment range for the low beam during the testing process is: horizontal beam illumination position -100~100mm; vertical beam illumination position -140~-100mm. Then, a camera and computer are used to replace the human eye for identification and measurement to obtain the actual beam pattern of the low beam. The adjustment range refers to the allowable adjustable angle range of the low beam in both the horizontal and vertical directions to ensure that the vehicle can maintain compliant lighting under different load conditions. Specific settings can be adjusted according to actual circumstances.

[0037] S2, obtain the vehicle's identification information, and match the identification information with a standardized parameter database to obtain standard light pattern parameters.

[0038] Specifically, by establishing a digital database of standardized data parameters, subjective judgments relying on individual worker experience are replaced, achieving absolute uniformity in testing standards. The standardized parameter database includes standard beam pattern parameters corresponding to different vehicles. Vehicle identification information can include the vehicle's VIN (Vehicle Identification Number), vehicle model, etc., with no specific restrictions. Continuing with the example of low beam headlight testing, based on the identified vehicle's identification information, the corresponding standard beam pattern parameters are queried from the standardized parameter database for headlight beam pattern evaluation, thus addressing the problem of inconsistent and subjective headlight adjustment standards.

[0039] In some embodiments of this application, the identification information includes one or more of vehicle identification code, vehicle model, and vehicle type information. Obtaining the vehicle identification information includes: identifying the vehicle identification code of the vehicle through a scanning module in a headlight detection scenario; and / or, acquiring image information of the vehicle through a vision module in a headlight detection scenario, identifying the vehicle model of the vehicle through the image information; and / or receiving vehicle type information issued by the vehicle production line control system.

[0040] Specifically, taking the vehicle identification information, specifically the VIN code / model code, as an example, firstly, the abstract, experience-based headlight qualification standards are transformed into a set of digital parameters strongly associated with the specific vehicle identification (VIN code) or model code, which can be directly read and executed by machines. This constructs a standardized parameter database. Then, during actual testing, the corresponding standard light pattern parameters are automatically invoked based on the vehicle identification information. The VIN code may contain information such as model, year, and assembly plant.

[0041] In addition to using the vehicle's VIN code as identification information to call standard parameters, vehicle model information (such as the front grille and logo) can be visually identified. Image recognition algorithms can then be used to determine the vehicle type, which is used as identification information to call standard optical parameters. Alternatively, the production line control system (PLC) can directly send vehicle type information to the detection system, using this information as identification to call standard optical parameters, thus eliminating the need for front-end identification. Furthermore, to further improve detection accuracy, multiple features mentioned above can be used as vehicle identification information, allowing standard optical parameters to be called based on these features.

[0042] In one embodiment of this application, after constructing a headlight detection scenario, the headlight detection method for the vehicle further includes: in the headlight detection scenario, using a high-precision machine vision system to acquire optimal light pattern images of the headlights of multiple test vehicles; defining upper and lower limit values ​​of the corresponding headlights in the horizontal and vertical directions based on the optimal light pattern images to determine the standard range of light patterns of the headlights of the multiple test vehicles; and constructing a standardized parameter database based on the standard range of light patterns of the headlights of the multiple vehicles.

[0043] Specifically, the construction of a standardized parameter database may include the following steps: 1. Data Acquisition and Modeling: For a specific vehicle model's compliant headlights, under standard experimental conditions, a high-precision machine vision system is used to acquire its optimal light pattern image.

[0044] 2. Parameter definition: Define the upper and lower limits of the horizontal (Y) and vertical (Z) adjustment range of the headlights, and formulate the standard range (Y-value (-400~300mm) Z-value (-440~-300mm).

[0045] The establishment of a standardized parameter database in this embodiment solves the problem of missed or incorrect inspections of defective products caused by human factors (fatigue, negligence), and improves the accuracy of quality inspection by setting standard ranges for judgment.

[0046] S3 determines the headlight test results of the vehicle based on the actual beam pattern and standard beam pattern parameters of the headlights.

[0047] In other words, the light pattern image captured by machine vision is extracted into quantifiable feature values ​​(such as horizontal / height angle, inflection point sharpness, etc.) using a specific algorithm, and then immediately automatically compared with standard light pattern parameters in a database. Finally, the system makes a final judgment of "pass / fail," thereby realizing image processing. The integrated closed-loop process of feature quantification, automatic comparison, and intelligent judgment improves detection efficiency.

[0048] In one embodiment of this application, the standard beam pattern parameter includes a beam pattern standard range. The headlight test result of the vehicle is determined based on the actual beam pattern of the headlight and the standard beam pattern parameter, including: when the actual beam pattern of the headlight is within the beam pattern standard range, the headlight test of the vehicle is determined to be qualified; when the actual beam pattern of the headlight is not within the beam pattern standard range, the headlight test of the vehicle is determined to be unqualified.

[0049] In other words, the standard beam pattern parameters include the standard beam pattern range, such as the horizontal and vertical range of the beam pattern. First, the beam pattern image captured by machine vision is subjected to image recognition to extract quantifiable beam pattern feature values ​​(horizontal, height angle, inflection point sharpness, etc.) to determine the actual beam pattern of the headlight. Then, the actual beam pattern of the headlight is compared with the standard beam pattern range. If the actual beam pattern of the headlight is completely within the standard beam pattern range, the headlight is considered to have passed the test; if the actual beam pattern of the headlight is not completely within the standard beam pattern range, the headlight is considered to have failed the test.

[0050] In one embodiment of this application, the headlight detection method for the vehicle further includes: obtaining the actual beam pattern of historical headlights that have passed the headlight detection within a first preset time period; and adjusting the beam pattern standard range based on the historical headlight actual beam pattern data.

[0051] In other words, in addition to using a preset, fixed digital standard range as the standard optical pattern parameter, an adaptive learning mechanism is also introduced. Specifically, based on data from a large number of qualified products within a certain period (the first preset time), the center value or tolerance of the standard range can be dynamically fine-tuned to make the standard more closely match actual production capacity and environmental changes. Thus, in this embodiment, the standard optical pattern parameter not only covers static standards but also dynamic standards with self-optimization capabilities. The specific duration of the first preset time period can be selected according to the actual situation, and the first time period is determined based on the current moment.

[0052] In some embodiments of this application, the headlight detection method further includes: obtaining headlight detection results within a second preset time period; if, based on the headlight detection results, the failure rate within the second preset time period is greater than the historical average failure rate, and the difference between the failure rate and the historical average pass rate is greater than a preset value, obtaining the headlight traceability information corresponding to the headlight detection results; identifying traceability overlap areas based on the headlight traceability information, and generating corresponding fault diagnosis suggestions based on the traceability overlap areas. The second preset time period can be set according to actual conditions.

[0053] Specifically, based on the headlight inspection results within the second preset time period, the headlight defect rate within that period is statistically analyzed. If the headlight defect rate abnormally increases within the second preset time period, the defective headlights are traced and identified, and overlapping traceability areas are statistically analyzed. Based on these overlapping areas, corresponding fault diagnosis suggestions are generated. For example, if the system dashboard reveals an abnormally high defect rate for "low height angle" on a given day, a related query through the MES (Manufacturing Execution System) reveals that these vehicles are all equipped with "an adjustable motor from a certain supplier with batch number XYZ". At this point, corresponding fault diagnosis suggestions are generated to notify the quality department to isolate and inspect the motors in batch XYZ, achieving "feedback prevention" and preventing the production of more vehicles equipped with defective motors.

[0054] Therefore, the "multi-dimensional traceability and feedforward control" method based on defective product inspection data links and stores the detailed out-of-tolerance data of each defective product with its production data (such as component batch, assembly station, timestamp), and uses this linked data to conduct root cause analysis, thereby realizing quality early warning (feedforward control) to the supply chain or upstream process, thus linking inspection data with production data to achieve quality traceability and early warning.

[0055] In some embodiments of this application, after determining the headlight detection result of the vehicle based on the actual headlight beam pattern and standard beam pattern parameters, the headlight detection method of the vehicle further includes: determining the risk level based on the headlight detection result; and generating a corresponding control signal based on the risk level and the current test scenario.

[0056] In other words, in addition to distinguishing between qualified and unqualified headlights, the system further introduces risk level determination and generates corresponding control signals based on the current test scenario. For example, when the current test scenario is a vehicle production line, the control signals may include hardware control signals of the vehicle production line, reminder signals, etc.; when the current test scenario is an after-sales maintenance scenario, the control signals may include reminder trigger signals, which will provide corresponding reminders through the control display screen.

[0057] For example, the risk level of the test results can be divided into "qualified", "slightly out of tolerance (requiring prompt for retesting)" and "severely out of tolerance (requiring interception)". Based on the test accuracy requirements in different test scenarios, different control signals can be generated according to the test results under different risk levels to meet the needs of different scenarios.

[0058] In one embodiment of this application, generating corresponding control signals based on risk level and current test scenario includes: generating corresponding production line control signals based on risk level when the current test scenario is a vehicle production scenario; and generating corresponding after-sales guidance signals based on risk level when the current test scenario is a vehicle after-sales scenario.

[0059] In other words, when the headlight inspection is conducted on the vehicle production line, the inspection results are deeply integrated with the Production Execution System (PLC / MES) and a "hard interception" mechanism is specified. Specifically, the automatic judgment result is not only displayed as information, but also directly transmitted as a control signal to the production line control system (PLC) or manufacturing execution system (MES), thereby triggering physical actions (such as release, alarm, and guidance to the rework area) to achieve automatic isolation of defective products (hard interception). In addition, for minor deviations, the system may not force interception, but will record and prompt the operator, providing a certain degree of flexibility.

[0060] In after-sales scenarios, the assessment results do not directly control the hardware, but rather drive guiding operations. For example, on the repair technician's handheld device, AR (Augmented Reality) arrows intuitively guide him to adjust the screws in which direction and how many turns.

[0061] As a specific embodiment of this application, taking the application of this testing process to the vehicle dynamic testing line in the final assembly workshop as an example, the headlight testing method realizes the automated and standardized testing of headlights of off-line vehicles and effectively prevents defective products from leaving the line.

[0062] First, construct a headlight detection scenario, including: 1. Vehicle positioning platform: Equipped with precision stops and wheel alignment devices to ensure that the vehicle is parked in the same position and orientation every time.

[0063] 2. Standard inspection screen: Located at a fixed distance (3 meters) directly in front of the vehicle, the screen has a clear coordinate grid.

[0064] 3. Industrial camera and image processing unit: Headlight detector, fixedly installed in front of or above the side of the detection screen to ensure clear capture of the entire light pattern.

[0065] 4. Control cabinet: Receives judgment signals and controls the start, stop and branching of the production line roller bed.

[0066] 5. Server / Industrial PC: Runs the headlight detection program, including a standard parameter database, image processing algorithms, and judgment logic.

[0067] 6. Information identification device: Scans the vehicle's VIN code using a barcode scanner.

[0068] Then, prepare the software and data: 1. Establish a standardized parameter database: The standard parameters of the target vehicle model to be tested have been determined through previous experiments and stored in the database, and are associated with the VIN code.

[0069] 2. Determine the standard range of the parameters: horizontal position of the low beam illumination -100~100mm; height position of the low beam illumination -140~-100mm.

[0070] During the headlight inspection process, the vehicle to be inspected is driven to the vehicle positioning platform, and the headlights are controlled accordingly to obtain the actual headlight beam pattern. The actual headlight beam pattern is then compared with the standard beam pattern parameters in the standardized parameter database to generate a headlight inspection report, which is then fed back to the staff through the terminal.

[0071] Assuming the report clearly indicates "height angle too low -400mm," staff will focus on checking and adjusting the height adjustment screws to quickly complete the repair. Simultaneously, the quality engineer, through the system dashboard, notices an abnormally high defect rate for "height angle too low" that day. By querying the MES system, they discover that these vehicles are all equipped with "an adjustable motor from a certain supplier with batch number XYZ." The engineer immediately notifies the quality department to isolate and inspect the motors in batch XYZ, achieving "feedback prevention" and preventing the production of more vehicles equipped with defective motors.

[0072] Therefore, the above-mentioned headlight detection method has the following technical advantages: 1. Achieve absolute uniformity in quality standards: Transform abstract "experience" and visual standards into concrete, machine-readable and executable data instructions as the benchmark for judgment.

[0073] 2. Facilitates process optimization: This digital parameter library provides authoritative basis for precise process standards, design, and new product launch.

[0074] 3. Construct a comprehensive and traceable defective product control system: "Real-time interception" enables in-process control; "data traceability" enables rapid post-event location; and "feedback control" enables pre-event prevention. This forms a complete PDCA (Plan-Do-Check-Act) quality closed loop.

[0075] 4. Significantly reduced quality costs: By reducing the number of defective products leaving the factory, preparation and maintenance costs are lowered. Simultaneously, precise traceability reduces large-scale rework and component scrapping, thus lowering internal quality costs.

[0076] 5. Facilitates continuous optimization of production processes: It provides engineers with a refined data perspective, enabling them to make effective process improvement decisions based on data.

[0077] Therefore, the headlight testing method disclosed in this application can solve the following technical problems: 1. Solve the problem of inconsistent headlight adjustment standards: By establishing a digital standard parameter library, subjective judgments that rely on workers' personal experience can be replaced, achieving absolute uniformity in testing standards.

[0078] 2. Solve the problems of complex causes of defective products, difficulty in tracing and effective prevention: By linking defective product information with production data, rapid source tracing and statistical analysis of quality problems can be achieved, transforming "post-event remediation" into "pre-event prevention" and "in-process control".

[0079] 3. Resolve the problem of missed or incorrect inspections of defective products due to human factors (fatigue, negligence).

[0080] In summary, the vehicle headlight detection method according to the embodiments of this application firstly constructs a headlight detection scenario and controls the headlights of vehicles in the vehicle positioning platform within the headlight detection scenario to obtain the actual light pattern of the vehicle's headlights. Then, it acquires the vehicle's identification information and matches it with a standardized parameter database to obtain standard light pattern parameters. Finally, it determines the vehicle's headlight detection result based on the actual light pattern and the standard light pattern parameters. Thus, this method detects and evaluates headlights based on the actual light pattern and standard light pattern parameters collected in the headlight detection scenario, achieving uniformity in quality standards while improving the detection efficiency and accuracy of headlights and reducing the need for human resources.

[0081] Corresponding to the above embodiments, this application also proposes a headlight detection device for vehicles.

[0082] like Figure 2 As shown, the vehicle headlight detection device of this application embodiment includes: a construction module 10, an acquisition module 20, a matching module 30, and a detection module 40.

[0083] The construction module 10 is used to construct a headlight detection scenario; the acquisition module 20 is used to control the headlights of vehicles in the vehicle positioning platform within the headlight detection scenario to obtain the actual headlight beam pattern of the vehicles; the matching module 30 is used to acquire the vehicle's identification information and match it with a standardized parameter database to obtain standard beam pattern parameters; and the detection module 40 is used to determine the headlight detection result of the vehicles based on the actual headlight beam pattern and the standard beam pattern parameters.

[0084] According to one embodiment of this application, the identification information includes one or more of vehicle identification code, vehicle model, and vehicle type information. The acquisition module 20 acquires the vehicle identification information specifically for: identifying the vehicle identification code of the vehicle through the scanning module in the headlight detection scenario; and / or, acquiring the image information of the vehicle through the vision module in the headlight detection scenario, identifying the vehicle model of the vehicle by recognizing the image information; and / or receiving the vehicle type information issued by the vehicle production line control system.

[0085] According to one embodiment of this application, the standard beam pattern parameter includes a beam pattern standard range. The detection module 40 determines the headlight detection result of the vehicle based on the actual beam pattern of the headlight and the standard beam pattern parameter. Specifically, it is used to: determine that the headlight detection of the vehicle is qualified when the actual beam pattern of the headlight is within the beam pattern standard range; and determine that the headlight detection of the vehicle is unqualified when the actual beam pattern of the headlight is not within the beam pattern standard range.

[0086] According to one embodiment of this application, the detection module 40 is further configured to: obtain the actual beam pattern of historical headlights that have passed the headlight detection within a first preset time period; and adjust the beam pattern standard range based on the historical headlight actual beam pattern data.

[0087] According to one embodiment of this application, the detection module 40 is further configured to: obtain headlight detection results within a second preset time period; if, based on the headlight detection results, the failure rate within the second preset time period is greater than the historical average failure rate, and the difference between the failure rate and the historical average pass rate is greater than a preset value, obtain the headlight traceability information corresponding to the headlight detection results; identify traceability overlap areas based on the headlight traceability information, and generate corresponding fault diagnosis suggestions based on the traceability overlap areas.

[0088] According to one embodiment of this application, after the detection module 40 determines the headlight detection result of the vehicle based on the actual headlight beam pattern and standard beam pattern parameters, it is further configured to: determine the risk level based on the headlight detection result; and generate a corresponding control signal based on the risk level and the current test scenario.

[0089] According to one embodiment of this application, the detection module 40 generates corresponding control signals based on the risk level and the current test scenario, specifically for: generating corresponding production line control signals based on the risk level when the current test scenario is a vehicle production scenario; and generating corresponding after-sales guidance signals based on the risk level when the current test scenario is a vehicle after-sales scenario.

[0090] According to one embodiment of this application, after constructing the headlight detection scenario, the construction module 10 is further configured to: acquire optimal light pattern images of the headlights of multiple test vehicles using a high-precision machine vision system in the headlight detection scenario; define upper and lower limit values ​​of the corresponding headlights in the horizontal and vertical directions based on the optimal light pattern images to determine the standard range of light patterns of the headlights of multiple test vehicles; and construct a standardized parameter database based on the standard range of light patterns of the headlights of multiple vehicles.

[0091] It should be noted that for details not disclosed in the vehicle headlight detection device of the embodiments of this application, please refer to the details disclosed in the vehicle headlight detection method of the above embodiments of this application, which will not be repeated here.

[0092] According to the vehicle headlight detection device of this application embodiment, a headlight detection scenario is constructed by a construction module, and the headlights of vehicles in the vehicle positioning platform within the headlight detection scenario are controlled by an acquisition module to obtain the actual beam pattern of the vehicle's headlights. The vehicle's identification information is obtained by a matching module, and a standardized parameter database is matched based on the identification information to obtain standard beam pattern parameters. The detection module determines the headlight detection result of the vehicle based on the actual beam pattern and the standard beam pattern parameters. Therefore, this device detects and evaluates headlights based on the actual beam pattern and standard beam pattern parameters collected in the headlight detection scenario, achieving uniformity in quality standards while improving headlight detection efficiency and accuracy, and reducing the need for human resource input.

[0093] Corresponding to the above embodiments, this application also proposes an electronic device.

[0094] like Figure 3 As shown, the electronic device 100 of this application embodiment includes: a memory 110 for storing a computer program; and a processor 120 for executing the computer program to implement the steps of the above-described vehicle headlight detection method.

[0095] According to the embodiments of this application, when the processor executes the computer program stored in its memory, the electronic device implements the steps of the above-described vehicle headlight detection method. Based on the above-described headlight detection method, the uniformity of quality standards is achieved, the detection efficiency and accuracy of headlights are improved, and the input of human resources is reduced.

[0096] Corresponding to the above embodiments, this application also proposes a computer-readable storage medium.

[0097] The computer-readable storage medium of this application embodiment stores a computer program, wherein when the computer program is executed by a processor, it implements the steps of the above-described vehicle headlight detection method.

[0098] According to the embodiments of this application, a computer-readable storage medium storing a computer program thereon implements the steps of the above-described vehicle headlight detection method when executed by a processor. Based on the above-described headlight detection method, the uniformity of quality standards is achieved, the detection efficiency and accuracy of headlights are improved, and the input of human resources is reduced.

[0099] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0100] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0101] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0102] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0103] In this application, unless otherwise expressly specified and limited, the terms "installation," "connection," "joining," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise expressly limited. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0104] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for detecting headlights of a vehicle, characterized in that, The method includes: A headlight detection scenario is constructed, and headlight control is performed on vehicles in the vehicle positioning platform within the headlight detection scenario to obtain the actual headlight beam pattern of the vehicles. Obtain the vehicle's identification information and match it with a standardized parameter database to obtain standard light pattern parameters; The headlight test results of the vehicle are determined based on the actual beam pattern of the headlight and the standard beam pattern parameters.

2. The headlight detection method according to claim 1, characterized in that, The identification information includes one or more of the following: vehicle identification number, vehicle model, and vehicle type information. Obtaining the vehicle identification information includes: The vehicle identification number (VIN) of the vehicle is identified by the scanning module in the headlight detection scenario; and / or, The vehicle's image information is acquired by a vision module in the headlight detection scenario, and the vehicle model is determined by identifying the image information; and / or Receive vehicle model information from the vehicle production line control system.

3. The headlight detection method according to claim 1, characterized in that, The standard beam pattern parameters include a standard beam pattern range. The headlight test results of the vehicle are determined based on the actual beam pattern of the headlight and the standard beam pattern parameters, including: When the actual beam pattern of the headlight is within the standard range of the beam pattern, the headlight of the vehicle is deemed to have passed the inspection. If the actual beam pattern of the headlight is not within the standard range, the headlight of the vehicle is deemed to have failed the test.

4. The headlight detection method according to claim 3, characterized in that, The method further includes: Obtain the actual beam pattern of historical headlights that passed the headlight inspection within the first preset time period; The standard range of the beam pattern is adjusted based on the actual beam pattern data of the historical headlights.

5. The headlight detection method according to claim 1, characterized in that, The method further includes: Obtain the headlight detection results within the second preset time period; If, based on the headlight test results, the failure rate within the second preset time period is determined to be greater than the historical average failure rate, and the difference between the failure rate and the historical average pass rate is greater than a preset value, then the headlight traceability information corresponding to the headlight test results is obtained. Identify overlapping areas in the headlight tracing information and generate corresponding fault diagnosis suggestions based on these overlapping areas.

6. The headlight detection method according to claim 1, characterized in that, After determining the headlight test results of the vehicle based on the actual beam pattern and the standard beam pattern parameters, the method further includes: The risk level is determined based on the headlight test results. Generate corresponding control signals based on the risk level and the current test scenario.

7. The headlight detection method according to claim 6, characterized in that, Based on the risk level and the current test scenario, corresponding control signals are generated, including: When the current test scenario is a vehicle production scenario, a corresponding production line control signal is generated according to the risk level; When the current test scenario is a vehicle after-sales scenario, a corresponding after-sales guidance signal is generated based on the risk level.

8. The headlight detection method according to claim 1, further comprising, after constructing the headlight detection scenario: In the headlight detection scenario, a high-precision machine vision system is used to acquire optimal light pattern images of the headlights of multiple test vehicles; Based on the optimal light pattern image, the upper and lower limit values ​​of the corresponding headlights in the horizontal and vertical directions are defined to determine the standard range of light patterns for the headlights of the multiple test vehicles. The standardized parameter database is constructed based on the standard range of light patterns of the headlights of the various vehicles.

9. A headlight detection device for a vehicle, characterized in that, The device includes: The building module is used to construct the headlight detection scenario; The acquisition module is used to control the headlights of vehicles in the vehicle positioning platform in the headlight detection scenario in order to obtain the actual light pattern of the vehicle's headlights. The matching module is used to obtain the vehicle's identification information and match it with a standardized parameter database to obtain standard light pattern parameters. The detection module is used to determine the headlight detection result of the vehicle based on the actual beam pattern of the headlight and the standard beam pattern parameters.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, it implements the steps of the headlight detection method for a vehicle as described in any one of claims 1 to 8.

Images