An intelligent high temperature warning monitoring method and system for LED car light integrated circuit modules

Abstract

The present invention proposes an intelligent high-temperature early warning monitoring method and system for an LED car lamp integrated circuit module, which relates to the technical field of high-temperature early warning monitoring of car lamps. The method and system obtain the status of the car lamps, collect electrical data of the integrated circuit modules, establish a data correspondence, train a temperature prediction model for the car lamps, compare the predicted temperature with the average temperature of the car lamps, monitor and annotate the corresponding car lamps according to the comparison results, and obtain the annotated information of the monitored car lamps; draw a temperature monitoring diagram of the monitored car lamps according to the annotated information of the monitored car lamps, obtain the abnormal temperature of the car lamps, calculate and obtain the temperature adjustment value information according to the temperature information of the car lamps; obtain the abnormal linkage relationship of the car lamp temperatures, calculate the linkage early warning value, and perform corresponding early warning operations. The present invention can realize real-time monitoring and prediction of the temperature of the car lamps, comprehensively consider the linkage relationship between multiple car lamps, provide a specific temperature adjustment plan, and perform accurate early warning operations, thereby effectively improving the performance and safety of the car lamps.

Description

Technical Field

[0001] The present invention provides an intelligent high temperature early warning monitoring method and system for an LED vehicle lamp integrated circuit module, and relates to the technical field of high temperature early warning monitoring for vehicle lamps. Background Art

[0002] With the rapid development of automobile technology, headlights, as an important part of automobiles, are attracting more and more attention for their performance and safety. The temperature state of headlights is directly related to their working effect and life. Since headlights may have large temperature changes in a short period of time, real-time control may not be able to avoid losses. Therefore, real-time monitoring and prediction of headlight temperature, as well as timely adjustment according to temperature state, are of great significance to ensure vehicle driving safety and increase the service life of headlights. At present, there are some systems for monitoring headlight temperature on the market, but these systems can only realize temperature monitoring of a single headlight and cannot fully consider the mutual influence and linkage between multiple headlights. At the same time, these systems usually only consider data at a single time point when predicting temperature, lack of comprehensive analysis of historical data and environmental factors, resulting in low prediction accuracy. In addition, for the processing of abnormal temperature, existing systems can often only provide simple alarm prompts and cannot provide specific quantitative early warning solutions. Summary of the invention

[0003] The present invention provides an intelligent high temperature early warning monitoring method and system for LED lamp integrated circuit modules, which is used to solve the problems that the temperature of the lamp may change greatly in a short period of time, and the real-time control may not be able to avoid losses. The traditional lamp temperature monitoring method can only realize the temperature monitoring of a single lamp, and cannot fully consider the mutual influence and linkage relationship between multiple lamps, and it is difficult to perform quantitative early warning.

[0004] The present invention proposes an intelligent high temperature early warning monitoring method and system for an LED vehicle lamp integrated circuit module, the method comprising:

[0005] S1. Collect and analyze the temperature of the vehicle lamp to obtain the vehicle lamp status, collect electrical data of the integrated circuit module according to the vehicle lamp status, and obtain electrical processing data;

[0006] S2, establishing a data correspondence relationship according to the lamp state, training a lamp temperature prediction model, obtaining a predicted temperature, comparing the predicted temperature with an average lamp temperature, monitoring and labeling the corresponding lamp according to the comparison result, and obtaining monitoring lamp labeling information;

[0007] S3, drawing a temperature monitoring diagram of the monitored lamp according to the labeled information of the monitored lamp, obtaining abnormal lamp temperature, and calculating and obtaining temperature adjustment value information according to the lamp temperature information;

[0008] S4. Obtain the abnormal linkage relationship of the vehicle lamp temperature, calculate the linkage warning value, and perform corresponding warning operations.

[0009] Further, the S1 includes:

[0010] Acquire lamp temperature data collection information, and determine the lamp state according to the lamp temperature data collection information; the lamp state includes an abnormal state and a normal state;

[0011] When the vehicle light is in a normal state, collecting electrical data of the integrated circuit module, preprocessing the electrical data, and obtaining electrical processing data;

[0012] When the status of the headlight is abnormal, the abnormal information is obtained to perform targeted maintenance on the headlight.

[0013] Further, the S2 includes:

[0014] When the lamp state is normal, a data correspondence is established for the lamp temperature data collection information, the electrical processing data and the environmental collection data according to the time correlation, training data is generated, a lamp temperature prediction model is trained according to the training data, and a predicted temperature is output;

[0015] The predicted temperature is compared with the average temperature of the headlights. When the predicted temperature is greater than the average temperature of the headlights, the corresponding headlights are monitored and marked to obtain monitoring headlight marking information. When the predicted temperature is less than or equal to the average temperature of the headlights, the corresponding headlights are not monitored and marked.

[0016] Further, the S3 includes:

[0017] With the predicted temperature as the ordinate and the time as the abscissa, a temperature monitoring graph is established for each headlight with a monitoring mark, a horizontal line perpendicular to the ordinate is drawn with the temperature threshold on the ordinate as the origin, and the corresponding headlight with a corresponding curve having a predicted temperature greater than the temperature threshold is obtained, and an abnormal mark is made for it to obtain the abnormal headlight mark and the corresponding abnormal headlight temperature;

[0018] The temperature adjustment value marked on each abnormal headlight is calculated by combining the abnormal headlight temperature, the temperature threshold and the headlight temperature data collection information to obtain the headlight temperature adjustment value information.

[0019] Further, the S4 includes:

[0020] Acquire the abnormal linkage relationship of the lamp temperature according to the lamp temperature adjustment value information; calculate the linkage warning value by combining the lamp temperature adjustment value with the abnormal linkage relationship of the lamp temperature;

[0021] According to the linkage warning value, the preset warning level is called up and the corresponding warning operation is performed.

[0022] Furthermore, the system comprises:

[0023] A data processing module, used to collect and analyze the temperature of the headlight, obtain the headlight status, collect electrical data of the integrated circuit module according to the headlight status, and obtain electrical processing data;

[0024] A prediction and labeling module is used to establish a data correspondence according to the state of the headlight, train a headlight temperature prediction model, obtain a predicted temperature, compare the predicted temperature with the average temperature of the headlight, monitor and label the corresponding headlight according to the comparison result, and obtain monitoring headlight labeling information;

[0025] A monitoring and adjustment module is used to draw a temperature monitoring diagram of the monitored lamp according to the marked information of the monitored lamp, obtain abnormal lamp temperature, and calculate and obtain temperature adjustment value information according to the lamp temperature information;

[0026] The linkage warning module is used to obtain the abnormal linkage relationship of the vehicle lamp temperature, calculate the linkage warning value, and perform corresponding warning operations.

[0027] Furthermore, the data processing module includes:

[0028] A state determination module, used to obtain the lamp temperature data collection information, and determine the lamp state according to the lamp temperature data collection information; the lamp state includes an abnormal state and a normal state;

[0029] A determination processing module, used for collecting electrical data of the integrated circuit module when the state of the vehicle lamp is normal, pre-processing the electrical data, and obtaining electrical processing data;

[0030] When the status of the headlight is abnormal, the abnormal information is obtained to perform targeted maintenance on the headlight.

[0031] Furthermore, the prediction and labeling module includes:

[0032] A training prediction module is used to establish a data correspondence between the lamp temperature data collection information, the electrical processing data and the environmental collection data according to the time correlation when the lamp state is normal, generate training data, train the lamp temperature prediction model according to the training data, and output the predicted temperature;

[0033] The comparison and marking module is used to compare the predicted temperature with the average temperature of the headlights. When the predicted temperature is greater than the average temperature of the headlights, the corresponding headlights are monitored and marked to obtain monitoring headlight marking information. When the predicted temperature is less than or equal to the average temperature of the headlights, the corresponding headlights are not monitored and marked.

[0034] Furthermore, the monitoring and adjustment module includes:

[0035] The abnormal marking module is used to establish a temperature monitoring diagram for each headlight with monitoring markings, using the predicted temperature as the ordinate and the time as the abscissa, draw a horizontal line perpendicular to the ordinate with the temperature threshold on the ordinate as the origin, obtain the corresponding headlight with the corresponding curve whose predicted temperature is greater than the temperature threshold, mark it as abnormal, and obtain the abnormal headlight marking and the corresponding abnormal headlight temperature;

[0036] The adjustment calculation module is used to calculate the temperature adjustment value marked on each abnormal headlight by combining the abnormal headlight temperature, the temperature threshold and the headlight temperature data collection information to obtain the headlight temperature adjustment value information.

[0037] Furthermore, the linkage warning module includes:

[0038] A linkage module, used for obtaining a linkage relationship of abnormal lamp temperature according to the lamp temperature adjustment value information; and calculating a linkage warning value by combining the lamp temperature adjustment value with the lamp temperature abnormal linkage relationship;

[0039] The early warning module is used to call the preset early warning level according to the linkage early warning value and perform corresponding early warning operations.

[0040] Beneficial effects of the present invention: By collecting the temperature data of the headlights, the temperature of the headlights can be monitored in real time and accurately. The method can not only capture the subtle changes in the temperature of the headlights, but also reveal the working status of the headlights through data analysis. Collecting the electrical data of the integrated circuit module and obtaining the electrical processing data can help to gain a deep understanding of the working status and performance of the headlights. These data can provide detailed information about the current of the headlights, thereby helping to analyze the working efficiency and potential problems of the headlights. By processing the electrical data, the working performance of the headlights can be further optimized and the overall safety of the vehicle can be improved. By establishing a data correspondence according to the headlight status and training the headlight temperature prediction model, the temperature of the headlights can be accurately predicted. The prediction model can predict the future temperature change trend of the headlights, providing strong support for early warning and adjustment. By comparing the predicted temperature with the average temperature of the headlights, monitoring and marking the headlights that may have abnormally increased temperatures, and drawing a temperature monitoring diagram, the temperature distribution of the headlights can be intuitively displayed, which not only helps to quickly locate abnormal headlights, but also provides important reference information for subsequent maintenance and replacement. At the same time, the temperature monitoring diagram can also serve as an important basis for vehicle maintenance, helping car owners and managers to better understand the working status of the vehicle. This method can realize dynamic predictive temperature monitoring of car lights that may have abnormalities, while avoiding the waste of unnecessary monitoring resources. It calculates the temperature adjustment value based on the temperature information of the car lights, obtains the linkage relationship of the abnormal temperature of the car lights, calculates the linkage warning value, and performs corresponding warning operations. This method can promptly and effectively respond to abnormal temperature problems of car lights, and prevent failures and safety accidents caused by excessively high or low temperatures. At the same time, the warning operation can be quantitatively warned by quantifying the warning value, reminding car owners and managers to pay attention to and deal with car light problems in a timely manner to ensure the safe operation of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A schematic diagram of an intelligent high temperature early warning monitoring method for an LED vehicle lamp integrated circuit module;

[0042] Figure 2 This is a schematic diagram of the headlight temperature monitoring diagram. DETAILED DESCRIPTION

[0043] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0044] In one embodiment of the present invention, a method and system for intelligent high temperature early warning monitoring of an LED vehicle lamp integrated circuit module is provided, the method comprising:

[0045] S1. Collect and analyze the temperature of the vehicle lamp to obtain the vehicle lamp status, collect electrical data of the integrated circuit module according to the vehicle lamp status, and obtain electrical processing data;

[0046] S2, establishing a data correspondence relationship according to the lamp state, training a lamp temperature prediction model, obtaining a predicted temperature, comparing the predicted temperature with an average lamp temperature, monitoring and labeling the corresponding lamp according to the comparison result, and obtaining monitoring lamp labeling information;

[0047] S3, drawing a temperature monitoring diagram of the monitored lamp according to the labeled information of the monitored lamp, obtaining abnormal lamp temperature, and calculating and obtaining temperature adjustment value information according to the lamp temperature information;

[0048] S4. Obtain the abnormal linkage relationship of the vehicle lamp temperature, calculate the linkage warning value, and perform corresponding warning operations.

[0049] The working principle of the above technical solution is as follows: first, the temperature of the headlight is collected and analyzed to obtain the headlight status, and the electrical data of the integrated circuit module is collected according to the headlight status to obtain the electrical processing data; then, a data correspondence is established according to the headlight status, a headlight temperature prediction model is trained to obtain the predicted temperature, the predicted temperature is compared with the average headlight temperature, and the corresponding headlight is monitored and labeled according to the comparison result to obtain the monitoring headlight labeling information; a temperature monitoring diagram of the monitoring headlight is drawn according to the monitoring headlight labeling information, such as Figure 2 As shown, the abnormal headlight temperature is obtained, and the temperature adjustment value information is calculated and obtained according to the headlight temperature information; finally, the abnormal linkage relationship of the headlight temperature is obtained, the linkage warning value is calculated, and the corresponding warning operation is performed.

[0050] The technical effect of the above technical solution is: by collecting the temperature data of the headlights, the temperature of the headlights can be monitored in real time and accurately. This method can not only capture subtle changes in the temperature of the headlights, but also reveal the working status of the headlights through data analysis. Collecting the electrical data of the integrated circuit module and obtaining the electrical processing data can help to gain a deeper understanding of the working status and performance of the headlights. These data can provide detailed information about the current of the headlights, thereby helping to analyze the working efficiency and potential problems of the headlights. By processing the electrical data, the working performance of the headlights can be further optimized and the overall safety of the vehicle can be improved. By establishing a data correspondence based on the status of the headlights and training the headlight temperature prediction model, accurate prediction of the headlight temperature can be achieved. The prediction model can predict the future temperature change trend of the headlights, providing strong support for early warning and adjustment. By comparing the predicted temperature with the average temperature of the headlights, the headlights that may have abnormally high temperatures are monitored and marked, and a temperature monitoring graph is drawn, such as Figure 2As shown, the temperature distribution of the headlights can be intuitively displayed, which not only helps to quickly locate abnormal headlights, but also provides important reference information for subsequent repairs and replacements. At the same time, the temperature monitoring chart can also serve as an important basis for vehicle maintenance, helping car owners and managers to better understand the working status of the vehicle. This method can realize dynamic predictive temperature monitoring of headlights that may have abnormalities, while avoiding the waste of unnecessary monitoring resources. The temperature adjustment value is calculated based on the temperature information of the headlights, and the abnormal linkage relationship of the headlight temperature is obtained, the linkage warning value is calculated, and the corresponding warning operation is performed. This method can promptly and effectively respond to abnormal headlight temperature problems and prevent failures and safety accidents caused by excessively high or low temperatures. At the same time, the warning operation can be quantitatively warned by quantifying the warning value, reminding car owners and managers to pay attention to and deal with headlight problems in a timely manner to ensure the safe operation of the vehicle.

[0051] In one embodiment of the present invention, the S1 includes:

[0052] Acquire lamp temperature data collection information, and determine the lamp state according to the lamp temperature data collection information; the lamp state includes an abnormal state and a normal state;

[0053] When the vehicle light is in a normal state, collecting electrical data of the integrated circuit module, preprocessing the electrical data, and obtaining electrical processing data;

[0054] When the status of the headlight is abnormal, the abnormal information is obtained to perform targeted maintenance on the headlight.

[0055] The working principle of the above technical solution is: obtain the temperature data collection information of the headlights, and determine the status of the headlights according to the temperature data collection information of the headlights; the headlight status includes abnormal status and normal status; when all the data of the headlights are within the normal range, it is a normal status, and when part or all of the data of the headlights are outside the normal range, it is an abnormal status. When the headlight status is normal, collect the electrical data of the integrated circuit module, pre-process the electrical data, and obtain electrical processing data; temperature prediction is required only in normal status, and real-time fault processing is performed in abnormal status. The electrical data includes current data, voltage data, and temperature data of the integrated circuit module. When the headlight status is abnormal, obtain abnormal information and perform targeted maintenance on the headlights.

[0056] The technical effect of the above technical solution is: by acquiring the lamp temperature data collection information, the lamp status can be accurately determined, including the normal status and the abnormal status. In the normal state, the system collects the electrical data of the integrated circuit module and performs preprocessing to obtain the electrical processing data. The optimization of this process effectively improves the quality and availability of the data. In the normal state, the system predicts the lamp temperature through the electrical processing data, and can predict the predicted temperature corresponding to the electrical data at a certain time in the future, obtain the corresponding relationship, so as to timely discover potential problems and realize predictive maintenance. This maintenance method can not only extend the service life of the lamp, but also reduce the parking time and maintenance costs caused by faults. When the lamp status is determined to be abnormal, the system can immediately obtain the abnormal information and perform targeted maintenance on the lamp. This rapid response mechanism can quickly solve the fault, restore the normal operation of the lamp, and ensure the safe operation of the vehicle. By optimizing the data collection, processing, prediction and fault handling processes, the efficiency of the entire system has been significantly improved. This not only improves the efficiency and accuracy of lamp maintenance, but also reduces maintenance costs and improves the overall performance and reliability of the vehicle.

[0057] In one embodiment of the present invention, S2 includes:

[0058] When the lamp state is normal, a data correspondence is established for the lamp temperature data collection information, the electrical processing data and the environmental collection data according to the time correlation, training data is generated, a lamp temperature prediction model is trained according to the training data, and a predicted temperature is output;

[0059] The predicted temperature is compared with the average temperature of the headlights. When the predicted temperature is greater than the average temperature of the headlights, the corresponding headlights are monitored and marked to obtain monitoring headlight marking information. When the predicted temperature is less than or equal to the average temperature of the headlights, the corresponding headlights are not monitored and marked.

[0060] The working principle of the above technical solution is: when the state of the headlight is normal, the headlight temperature data collection information, electrical processing data and environmental collection data are established according to the time correlation to generate training data, and the headlight temperature prediction model is trained according to the training data to output the predicted temperature; the headlight temperature data collection information, electrical processing data and environmental collection data at the same time are a data correspondence relationship. When the state of the headlight is normal, the environmental data is collected to obtain the environmental collection data. The environmental collection data includes environmental temperature data, environmental data and environmental wind data. The predicted temperature is compared with the average temperature of the headlight. When the predicted temperature is greater than the average temperature of the headlight, the corresponding headlight is monitored and marked to obtain the monitoring headlight marking information. When the predicted temperature is less than or equal to the average temperature of the headlight, the corresponding headlight is not monitored and marked.

[0061] The technical effect of the above technical solution is: by establishing a time-related data correspondence between the lamp temperature data collection information, the electrical processing data and the environmental collection data, generating training data, and then training the lamp temperature prediction model, the system can achieve accurate prediction of the lamp temperature. This prediction takes into account a variety of influencing factors, including the state of the lamp itself and environmental factors, so the prediction result is more accurate and reliable. By comparing the predicted temperature with the average temperature of the lamp, the system can automatically monitor and mark the lamps with higher predicted temperatures. This intelligent monitoring and marking method not only improves the efficiency and accuracy of monitoring, but also can timely discover potential temperature problems, providing important reference information for subsequent maintenance and management. When the predicted temperature is greater than the average temperature of the lamp, it is not necessarily an abnormal state, but it is more likely to develop into a high temperature trend than less than the average temperature of the lamp. When monitoring low temperatures, the method is the opposite. By monitoring the marked lamps, the system can timely discover those lamps with higher predicted temperatures and respond to abnormalities in a timely manner. This timely problem discovery and early warning mechanism helps prevent lamp failures or safety accidents caused by excessive temperature, and improves the overall safety of the vehicle. By establishing a time-related data correspondence and taking into account a variety of data factors, the system can make full use of data resources to improve the model's training effect and prediction accuracy. At the same time, this data utilization method also helps to improve the generalization ability of the model, enabling it to adapt to the prediction of headlight temperature under different environments and conditions. Through accurate headlight temperature prediction and intelligent monitoring and labeling, the system can guide maintenance personnel to perform headlight maintenance more accurately, avoiding unnecessary inspections and repairs. This not only improves maintenance efficiency, but also reduces maintenance costs and improves the overall operating efficiency of the vehicle.

[0062] In one embodiment of the present invention, S3 includes:

[0063] With the predicted temperature as the ordinate and the time as the abscissa, a temperature monitoring graph is established for each headlight with a monitoring mark, a horizontal line perpendicular to the ordinate is drawn with the temperature threshold on the ordinate as the origin, and the corresponding headlight with a corresponding curve having a predicted temperature greater than the temperature threshold is obtained, and an abnormal mark is made for it to obtain the abnormal headlight mark and the corresponding abnormal headlight temperature;

[0064] The temperature adjustment value marked on each abnormal headlight is calculated by combining the abnormal headlight temperature, the temperature threshold and the headlight temperature data collection information to obtain the headlight temperature adjustment value information.

[0065] The working principle of the above technical solution is as follows: using the predicted temperature as the ordinate and the time as the abscissa, a temperature monitoring graph is established for each headlight with a monitoring mark, a horizontal line perpendicular to the ordinate is drawn with the temperature threshold on the ordinate as the origin, the corresponding headlight with the corresponding curve having a predicted temperature greater than the temperature threshold is obtained, and it is marked abnormally to obtain the abnormal headlight mark and the corresponding abnormal headlight temperature; an abnormal headlight may have multiple abnormal headlight temperatures. The predicted temperature curves of multiple headlights can be drawn in the same temperature monitoring graph, and the temperature threshold is a preset value. The temperature adjustment value of each abnormal headlight mark is calculated by combining the abnormal headlight temperature, the temperature threshold and the headlight temperature data collection information to obtain the headlight temperature adjustment value information. The headlight temperature adjustment value information includes the temperature adjustment value information of multiple abnormal headlight marks. The abnormal headlight temperature is the predicted temperature of the abnormal headlight.

[0066] The calculation formula of the lamp temperature adjustment value is:

[0067]

[0068] Among them, W tdz The abnormal lamp temperature marked by the abnormal lamp, that is, the abnormal predicted temperature adjustment rate (including the temperature adjustment value), that is, how much time to adjust the temperature value at the current time interval, to achieve slow temperature adjustment and ensure the stability of temperature adjustment, W yd is the abnormal lamp temperature, Y wd is the temperature threshold, S wd is the current temperature of the headlight, H yd is the prediction time of abnormal vehicle lamp temperature, H wd The time for the current lamp temperature.

[0069] The technical effect of the above technical scheme is as follows: this method is an early warning of the predicted temperature. When the predicted temperature at the predicted time point is predicted to be an abnormal temperature, the temperature is adjusted in advance to avoid losses caused by excessive temperature adjustment. The closer the predicted time point is to the current time point, the more accurate the effect. By establishing a temperature monitoring graph with the predicted temperature as the ordinate and the time as the abscissa, the system can intuitively display the changes in the predicted temperature of each headlight with monitoring annotations over time. This visual display method not only facilitates personnel to understand and analyze the changing trend of the headlight temperature, but also quickly locates the abnormal headlights and their corresponding abnormal temperature points. By setting the temperature threshold in the temperature monitoring graph, the system can accurately identify the abnormal headlights whose predicted temperature exceeds the threshold and their corresponding abnormal temperatures. This identification method is based on a large amount of data and model prediction, has high accuracy and reliability, and can effectively avoid underreporting and false alarms. By abnormally marking the abnormal headlights, the system can quickly locate the abnormal headlights that need to be processed, thereby improving the efficiency of abnormal processing. At the same time, by combining the headlight temperature data collection information to calculate the temperature adjustment value of each abnormal headlight, the system can provide maintenance personnel with specific maintenance plans, further shortening the maintenance time. By using the formula The temperature adjustment rate from the abnormal lamp temperature to the current lamp temperature can be calculated by using the formula The adjustment time of the abnormal lamp temperature adjustment value to the normal value can be calculated by the formula The temperature adjustment rate of abnormal lamp temperature can be calculated, that is, starting from the current time, how much time is required to adjust the temperature downward, so as to achieve slow temperature adjustment and ensure the stability of temperature adjustment. By obtaining the lamp temperature adjustment value information, the system can guide maintenance personnel to perform targeted temperature adjustment on the lamp, thereby optimizing the performance of the lamp. This optimization not only helps to improve the service life and stability of the lamp, but also improves the overall performance and safety of the vehicle. By collecting and organizing information such as lamp temperature monitoring diagrams, abnormal lamp annotations, and temperature adjustment values, the system can provide rich data support for subsequent data analysis and model optimization. These data can be used to evaluate the accuracy of model predictions, analyze the laws of lamp temperature changes, and optimize temperature adjustment strategies, thereby continuously improving the performance and effectiveness of the system.

[0070] In one embodiment of the present invention, the S4 includes:

[0071] The abnormal linkage relationship of the headlight temperature is obtained according to the headlight temperature adjustment value information; the linkage warning value is calculated by combining the headlight temperature adjustment value with the abnormal linkage relationship of the headlight temperature; the preset warning level is retrieved according to the linkage warning value, and the corresponding warning operation is performed.

[0072] The working principle of the above technical solution is: obtaining the abnormal linkage relationship of the headlight temperature according to the headlight temperature adjustment value information; the abnormal linkage relationship of the headlight temperature includes multi-level linkage, when the abnormal headlight is marked as one in the headlight temperature adjustment information, it is a first-level linkage, when the abnormal headlight is marked as n in the headlight temperature adjustment information, it is an n-level linkage; calculating the linkage warning value through the headlight temperature adjustment value combined with the abnormal linkage relationship of the headlight temperature; calling the preset warning level according to the linkage warning value, and performing the corresponding warning operation.

[0073] The calculation formula of the linkage warning value is:

[0074]

[0075] Among them, Z lyj is the linkage warning value, D cs T is the number of abnormal lights marked. i+1 is the predicted temperature of the i+1th abnormal headlight at the same prediction time, T i It is the predicted temperature of the i-th abnormal headlight at the same prediction time.

[0076] The technical effect of the above technical solution is: this method can accurately identify the abnormal linkage relationship of the headlight temperature according to the headlight temperature adjustment value information, including primary linkage and multi-level linkage. This identification method can fully consider the mutual influence and correlation between multiple headlights, avoid the situation of focusing only on the abnormality of a single headlight, and improve the comprehensiveness and accuracy of abnormality processing. By calculating the linkage warning value, the system can quantitatively evaluate the degree and potential risk of the headlight temperature abnormality. By using the formula The inverse of the sum of the abnormal temperature deviation values ​​marked by all abnormal lamps can be calculated. The linkage warning value can be calculated. When the number of abnormal lights is greater, the warning value is greater. The larger the value, the smaller the warning value. This quantitative evaluation method ensures that the warning measures match the degree of abnormality. The warning level is quantified by quantifying the warning value, so that the warning level increases with the increase of the warning value. According to the linkage warning value, the system can intelligently call the preset warning level and perform the corresponding warning operation. This intelligent warning mechanism can ensure the timeliness and accuracy of the warning and avoid delays or misjudgments caused by human factors. By combining the temperature adjustment value information of the headlights and the linkage relationship of the abnormal temperature of the headlights, the system can achieve more efficient and accurate warning. This warning method not only considers the abnormal situation of a single headlight, but also comprehensively considers the linkage relationship between multiple headlights, thereby improving the pertinence and effectiveness of the warning. By identifying and warning the linkage relationship of abnormal headlight temperature, the system can provide maintenance personnel with more comprehensive and accurate maintenance information. This helps to optimize maintenance strategies, reasonably allocate maintenance resources, and improve maintenance efficiency and quality. By timely warning and processing the linkage relationship of abnormal headlight temperature, the system can ensure that the vehicle's headlight system is always in good working condition. This helps to improve the overall safety and reliability of the vehicle and reduce the safety risks caused by headlight failures.

[0077] In one embodiment of the present invention, the system comprises:

[0078] A data processing module, used to collect and analyze the temperature of the headlight, obtain the headlight status, collect electrical data of the integrated circuit module according to the headlight status, and obtain electrical processing data;

[0079] A prediction and labeling module is used to establish a data correspondence according to the state of the headlight, train a headlight temperature prediction model, obtain a predicted temperature, compare the predicted temperature with the average temperature of the headlight, monitor and label the corresponding headlight according to the comparison result, and obtain monitoring headlight labeling information;

[0080] A monitoring and adjustment module is used to draw a temperature monitoring diagram of the monitored lamp according to the marked information of the monitored lamp, obtain abnormal lamp temperature, and calculate and obtain temperature adjustment value information according to the lamp temperature information;

[0081] The linkage warning module is used to obtain the abnormal linkage relationship of the vehicle lamp temperature, calculate the linkage warning value, and perform corresponding warning operations.

[0082] The working principle of the above technical solution is as follows: the data processing module is used to collect and analyze the temperature of the headlights to obtain the headlight status, collect the electrical data of the integrated circuit module according to the headlight status, and obtain the electrical processing data;

[0083] The prediction and labeling module is used to establish a data correspondence according to the state of the headlights, train a headlight temperature prediction model, obtain a predicted temperature, compare the predicted temperature with the average temperature of the headlights, monitor and label the corresponding headlights according to the comparison results, and obtain monitoring headlight labeling information; the monitoring and adjustment module is used to draw a temperature monitoring diagram for the monitoring headlights according to the monitoring headlight labeling information, obtain abnormal headlight temperature, and calculate and obtain temperature adjustment value information according to the headlight temperature information; the linkage warning module is used to obtain the abnormal linkage relationship of the headlight temperature, calculate the linkage warning value, and perform corresponding warning operations.

[0084] The technical effect of the above technical scheme is: the system realizes real-time and accurate monitoring of the temperature of the headlights by collecting the temperature data of the headlights. Collecting the electrical data of the integrated circuit module and obtaining the electrical processing data is helpful to deeply understand the working status and performance of the headlights. By processing the electrical data, the working performance of the headlights can be further optimized and the overall safety of the vehicle can be improved. By establishing a data correspondence according to the headlight status and training the headlight temperature prediction model, the temperature of the headlights can be accurately predicted. The prediction model can predict the future temperature change trend of the headlights, providing strong support for early warning and adjustment. By comparing the predicted temperature with the average temperature of the headlights, the headlights that may have abnormal temperature increases are monitored and marked, and a temperature monitoring diagram is drawn, which can intuitively show the temperature distribution of the headlights, which is helpful to quickly locate abnormal headlights. At the same time, the temperature monitoring diagram can also serve as an important basis for vehicle maintenance, helping car owners and managers to better understand the working status of the vehicle. This method can realize dynamic predictive temperature monitoring of headlights that may have abnormalities, while avoiding the waste of unnecessary monitoring resources, calculating the temperature adjustment value according to the headlight temperature information, and obtaining the abnormal linkage relationship of the headlight temperature, calculating the linkage warning value, and performing corresponding warning operations. The method can promptly and effectively deal with abnormal temperature problems of vehicle lamps, and prevent malfunctions and safety accidents caused by excessively high or low temperatures.

[0085] In one embodiment of the present invention, the data processing module includes:

[0086] A state determination module, used to obtain the lamp temperature data collection information, and determine the lamp state according to the lamp temperature data collection information; the lamp state includes an abnormal state and a normal state;

[0087] A determination processing module, used for collecting electrical data of the integrated circuit module when the state of the vehicle lamp is normal, pre-processing the electrical data, and obtaining electrical processing data;

[0088] When the status of the headlight is abnormal, the abnormal information is obtained to perform targeted maintenance on the headlight.

[0089] The working principle of the above technical solution is as follows: the state determination module is used to obtain the temperature data collection information of the headlights, and determine the state of the headlights according to the temperature data collection information of the headlights; the headlight state includes abnormal state and normal state; when all the data of the headlights are within the normal range, it is a normal state, and when part or all of the data of the headlights are outside the normal range, it is an abnormal state. The determination processing module is used to collect the electrical data of the integrated circuit module when the headlight state is normal, pre-process the electrical data, and obtain electrical processing data; temperature prediction is required only in normal state, and real-time fault processing is performed in abnormal state. The electrical data includes current data, voltage data, and temperature data of the integrated circuit module. When the headlight state is abnormal, abnormal information is obtained to perform targeted maintenance on the headlights.

[0090] The technical effect of the above technical solution is: by acquiring the lamp temperature data collection information, the lamp status can be accurately determined, including the normal status and the abnormal status. In the normal state, the system collects the electrical data of the integrated circuit module and performs preprocessing to obtain the electrical processing data. The optimization of this process effectively improves the quality and availability of the data. In the normal state, the system predicts the lamp temperature through the electrical processing data, and can predict the predicted temperature corresponding to the electrical data at a certain time in the future, obtain the corresponding relationship, so as to timely discover potential problems and realize predictive maintenance. This maintenance method can not only extend the service life of the lamp, but also reduce the parking time and maintenance costs caused by faults. When the lamp status is determined to be abnormal, the system can immediately obtain the abnormal information and perform targeted maintenance on the lamp. This rapid response mechanism can quickly solve the fault, restore the normal operation of the lamp, and ensure the safe operation of the vehicle. By optimizing the data collection, processing, prediction and fault handling processes, the efficiency of the entire system has been significantly improved. This not only improves the efficiency and accuracy of lamp maintenance, but also reduces maintenance costs and improves the overall performance and reliability of the vehicle.

[0091] In one embodiment of the present invention, the prediction and labeling module includes:

[0092] A training prediction module is used to establish a data correspondence between the lamp temperature data collection information, the electrical processing data and the environmental collection data according to the time correlation when the lamp state is normal, generate training data, train the lamp temperature prediction model according to the training data, and output the predicted temperature;

[0093] The comparison and marking module is used to compare the predicted temperature with the average temperature of the headlights. When the predicted temperature is greater than the average temperature of the headlights, the corresponding headlights are monitored and marked to obtain monitoring headlight marking information. When the predicted temperature is less than or equal to the average temperature of the headlights, the corresponding headlights are not monitored and marked.

[0094] The working principle of the above technical solution is: the training prediction module is used to establish a data correspondence between the lamp temperature data collection information, electrical processing data and environmental collection data according to the time correlation when the lamp state is normal, generate training data, train the lamp temperature prediction model according to the training data, and output the predicted temperature; the lamp temperature data collection information, electrical processing data and environmental collection data at the same time are a data correspondence relationship, when the lamp state is normal, collect environmental data to obtain environmental collection data. The environmental collection data includes environmental temperature data, environmental data and environmental wind data, etc. The comparison and annotation module is used to compare the predicted temperature with the average lamp temperature. When the predicted temperature is greater than the average lamp temperature, the corresponding lamp is monitored and annotated to obtain monitoring lamp annotation information. When the predicted temperature is less than or equal to the average lamp temperature, the corresponding lamp is not monitored and annotated.

[0095] The technical effect of the above technical solution is: by establishing a time-related data correspondence between the lamp temperature data collection information, the electrical processing data and the environmental collection data, generating training data, and then training the lamp temperature prediction model, the system can achieve accurate prediction of the lamp temperature. This prediction takes into account a variety of influencing factors, including the state of the lamp itself and environmental factors, so the prediction result is more accurate and reliable. By comparing the predicted temperature with the average temperature of the lamp, the system can automatically monitor and mark the lamps with higher predicted temperatures. This intelligent monitoring and marking method not only improves the efficiency and accuracy of monitoring, but also can timely discover potential temperature problems, providing important reference information for subsequent maintenance and management. When the predicted temperature is greater than the average temperature of the lamp, it is not necessarily an abnormal state, but it is more likely to develop into a high temperature trend than less than the average temperature of the lamp. When monitoring low temperatures, the method is the opposite. By monitoring the marked lamps, the system can timely discover those lamps with higher predicted temperatures and respond to abnormalities in a timely manner. This timely problem discovery and early warning mechanism helps prevent lamp failures or safety accidents caused by excessive temperature, and improves the overall safety of the vehicle. By establishing a time-related data correspondence and taking into account a variety of data factors, the system can make full use of data resources to improve the model's training effect and prediction accuracy. At the same time, this data utilization method also helps to improve the generalization ability of the model, enabling it to adapt to the prediction of headlight temperature under different environments and conditions. Through accurate headlight temperature prediction and intelligent monitoring and labeling, the system can guide maintenance personnel to perform headlight maintenance more accurately, avoiding unnecessary inspections and repairs. This not only improves maintenance efficiency, but also reduces maintenance costs and improves the overall operating efficiency of the vehicle.

[0096] In one embodiment of the present invention, the monitoring and regulating module includes:

[0097] The abnormal marking module is used to establish a temperature monitoring diagram for each headlight with monitoring markings, using the predicted temperature as the ordinate and the time as the abscissa, draw a horizontal line perpendicular to the ordinate with the temperature threshold on the ordinate as the origin, obtain the corresponding headlight with the corresponding curve whose predicted temperature is greater than the temperature threshold, mark it as abnormal, and obtain the abnormal headlight marking and the corresponding abnormal headlight temperature;

[0098] The adjustment calculation module is used to calculate the temperature adjustment value marked on each abnormal headlight by combining the abnormal headlight temperature, the temperature threshold and the headlight temperature data collection information to obtain the headlight temperature adjustment value information.

[0099] The working principle of the above technical solution is as follows: the abnormal marking module is used to establish a temperature monitoring graph for each headlight with monitoring markings, using the predicted temperature as the ordinate and the time as the abscissa, and draw a horizontal line perpendicular to the ordinate with the temperature threshold on the ordinate as the origin, obtain the corresponding headlight with the corresponding curve having a predicted temperature greater than the temperature threshold, mark it as abnormal, and obtain the abnormal headlight marking and the corresponding abnormal headlight temperature; an abnormal headlight may have multiple abnormal headlight temperatures. The predicted temperature curves of multiple headlights can be drawn in the same temperature monitoring graph, and the temperature threshold is a preset value. The adjustment calculation module is used to calculate the temperature adjustment value of each abnormal headlight marking by combining the abnormal headlight temperature, temperature threshold and headlight temperature data collection information to obtain the headlight temperature adjustment value information. The headlight temperature adjustment value information includes the temperature adjustment value information of multiple abnormal headlight markings.

[0100] The calculation formula of the lamp temperature adjustment value is:

[0101]

[0102] Among them, W tdz The abnormal lamp temperature marked by the abnormal lamp, that is, the abnormal predicted temperature adjustment rate (including the temperature adjustment value), that is, how much time to adjust the temperature value at the current time interval, to achieve slow temperature adjustment and ensure the stability of temperature adjustment, W yd is the abnormal lamp temperature, Y wd is the temperature threshold, S wd is the current temperature of the headlight, H yd is the prediction time of abnormal vehicle lamp temperature, H wd The time for the current lamp temperature.

[0103] The technical effect of the above technical scheme is as follows: this method is an early warning of the predicted temperature. When the predicted temperature at the predicted time point is predicted to be an abnormal temperature, the temperature is adjusted in advance to avoid losses caused by excessive temperature adjustment. The closer the predicted time point is to the current time point, the more accurate the effect. By establishing a temperature monitoring graph with the predicted temperature as the ordinate and the time as the abscissa, the system can intuitively display the changes in the predicted temperature of each headlight with monitoring annotations over time. This visual display method not only facilitates personnel to understand and analyze the changing trend of the headlight temperature, but also can quickly locate the abnormal headlight and its corresponding abnormal temperature point. By setting the temperature threshold in the temperature monitoring graph, the system can accurately identify the abnormal headlight whose predicted temperature exceeds the threshold and its corresponding abnormal temperature. This identification method is based on a large amount of data and model prediction, has high accuracy and reliability, and can effectively avoid missed reports and false alarms. By abnormally marking the abnormal headlights, the system can quickly locate the abnormal headlights that need to be processed, thereby improving the efficiency of abnormal processing. At the same time, by combining the headlight temperature data collection information to calculate the temperature adjustment value of each abnormal headlight, the system can provide maintenance personnel with a specific maintenance plan, further shortening the maintenance time. By obtaining the temperature adjustment value information of the headlights, the system can guide maintenance personnel to perform targeted temperature adjustment on the headlights, thereby optimizing the performance of the headlights. This optimization not only helps to increase the service life and stability of the headlights, but also improves the overall performance and safety of the vehicle. By collecting and organizing information such as headlight temperature monitoring diagrams, abnormal headlight annotations, and temperature adjustment values, the system can provide rich data support for subsequent data analysis and model optimization. These data can be used to evaluate the accuracy of model predictions, analyze the laws of headlight temperature changes, and optimize temperature adjustment strategies, thereby continuously improving the performance and effectiveness of the system.

[0104] In one embodiment of the present invention, the linkage warning module includes:

[0105] A linkage module is used to obtain a linkage relationship of abnormal lamp temperature according to the lamp temperature adjustment value information; the abnormal lamp temperature linkage relationship includes multi-level linkage, when the lamp temperature adjustment information contains one abnormal lamp, it is a first-level linkage, when the lamp temperature adjustment information contains n abnormal lamps, it is an n-level linkage; a linkage warning value is calculated by combining the lamp temperature adjustment value with the abnormal lamp temperature linkage relationship;

[0106] The early warning module is used to call the preset early warning level according to the linkage early warning value and perform corresponding early warning operations.

[0107] The working principle of the above technical solution is: the linkage module is used to obtain the abnormal linkage relationship of the headlight temperature according to the headlight temperature adjustment value information; the abnormal linkage relationship of the headlight temperature includes multi-level linkage, when the abnormal headlight is marked as one in the headlight temperature adjustment information, it is a first-level linkage, when the abnormal headlight is marked as n in the headlight temperature adjustment information, it is an n-level linkage; the linkage warning value is calculated by combining the headlight temperature adjustment value with the abnormal linkage relationship of the headlight temperature; the warning module is used to call the preset warning level according to the linkage warning value, and perform corresponding warning operations.

[0108] The calculation formula of the linkage warning value is:

[0109]

[0110] Among them, Z lyj is the linkage warning value, D cs T is the number of abnormal lights marked. i+1 is the predicted temperature of the i+1th abnormal headlight at the same prediction time, T i It is the predicted temperature of the i-th abnormal headlight at the same prediction time.

[0111] The technical effect of the above technical solution is: the method can accurately identify the abnormal linkage relationship of the headlight temperature according to the headlight temperature adjustment value information, including primary linkage and multi-level linkage. This identification method can fully consider the mutual influence and association between multiple headlights, avoid the situation of focusing only on the abnormality of a single headlight, and improve the comprehensiveness and accuracy of abnormality processing. By calculating the linkage warning value, the system can quantitatively evaluate the degree and potential risk of the abnormal temperature of the headlight. This quantitative evaluation method ensures that the warning measures match the degree of abnormality. According to the linkage warning value, the system can intelligently call the preset warning level and perform the corresponding warning operation. This intelligent warning mechanism can ensure the timeliness and accuracy of the warning, and avoid the delay or misjudgment of the warning caused by human factors. By combining the headlight temperature adjustment value information and the abnormal linkage relationship of the headlight temperature, the system can achieve more efficient and accurate warning. This warning method not only considers the abnormal situation of a single headlight, but also comprehensively considers the linkage relationship between multiple headlights, thereby improving the pertinence and effectiveness of the warning. By identifying and warning the abnormal linkage relationship of the headlight temperature, the system can provide maintenance personnel with more comprehensive and accurate maintenance information. This helps to optimize maintenance strategies, reasonably allocate maintenance resources, and improve maintenance efficiency and quality. By timely warning and handling the abnormal temperature linkage of the headlights, the system can ensure that the vehicle's headlight system is always in good working condition. This helps to improve the overall safety and reliability of the vehicle and reduce the safety risks caused by headlight failures.

[0112] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An intelligent high temperature early warning monitoring method for LED vehicle lamp integrated circuit modules, characterized in that: The method comprises: S1. Collect and analyze the temperature of the vehicle lamp to obtain the vehicle lamp status, collect electrical data of the integrated circuit module according to the vehicle lamp status, and obtain electrical processing data; S2, establishing a data correspondence relationship according to the lamp state, training a lamp temperature prediction model, obtaining a predicted temperature, comparing the predicted temperature with an average lamp temperature, monitoring and labeling the corresponding lamp according to the comparison result, and obtaining monitoring lamp labeling information; Wherein, the S2 includes: When the lamp state is normal, a data correspondence is established for the lamp temperature data collection information, the electrical processing data and the environmental collection data according to the time correlation, training data is generated, a lamp temperature prediction model is trained according to the training data, and a predicted temperature is output; The predicted temperature is compared with the average temperature of the headlights. When the predicted temperature is greater than the average temperature of the headlights, the corresponding headlights are monitored and marked to obtain monitoring headlight marking information. When the predicted temperature is less than or equal to the average temperature of the headlights, the corresponding headlights are not monitored and marked. S3, drawing a temperature monitoring diagram of the monitored lamp according to the labeled information of the monitored lamp, obtaining abnormal lamp temperature, and calculating and obtaining temperature adjustment value information according to the lamp temperature information; Wherein, the S3 includes: With the predicted temperature as the ordinate and the time as the abscissa, a temperature monitoring graph is established for each headlight with a monitoring mark, a horizontal line perpendicular to the ordinate is drawn with the temperature threshold on the ordinate as the origin, and the corresponding headlight with a corresponding curve having a predicted temperature greater than the temperature threshold is obtained, and an abnormal mark is made for it to obtain the abnormal headlight mark and the corresponding abnormal headlight temperature; Calculate the temperature adjustment value marked on each abnormal headlight by combining the abnormal headlight temperature, the temperature threshold and the headlight temperature data collection information to obtain the headlight temperature adjustment value information; The calculation formula of the lamp temperature adjustment value is: ， in, The abnormal lamp temperature marked for the abnormal lamp is the abnormal predicted temperature adjustment rate, that is, how much time to adjust the temperature value at the current time interval, to achieve slow temperature adjustment and ensure the stability of temperature adjustment. For abnormal headlight temperature, is the temperature threshold, is the current temperature of the headlight, is the predicted time of abnormal vehicle lamp temperature, The time for the current lamp temperature; S4, obtaining the abnormal linkage relationship of the vehicle lamp temperature, calculating the linkage warning value, and performing corresponding warning operations; Wherein, the S4 includes: Acquire the abnormal linkage relationship of the lamp temperature according to the lamp temperature adjustment value information; calculate the linkage warning value by combining the lamp temperature adjustment value with the abnormal linkage relationship of the lamp temperature; The calculation formula of the linkage warning value is: ， in, is the linkage warning value, Mark the number of abnormal lights. is the predicted temperature of the i+1th abnormal headlight at the same prediction time, The predicted temperature of the i-th abnormal headlight at the same prediction time; According to the linkage warning value, the preset warning level is called up and the corresponding warning operation is performed.

2. According to claim 1, a method for intelligent high temperature early warning monitoring of LED vehicle lamp integrated circuit modules, characterized in that: The S1 includes: Acquire lamp temperature data collection information, and determine the lamp state according to the lamp temperature data collection information; the lamp state includes an abnormal state and a normal state; When the vehicle light is in a normal state, collecting electrical data of the integrated circuit module, preprocessing the electrical data, and obtaining electrical processing data; When the status of the headlight is abnormal, the abnormal information is obtained to perform targeted maintenance on the headlight.

3. An intelligent high temperature warning monitoring system for LED vehicle lamp integrated circuit modules, characterized in that: The system comprises: A data processing module, used to collect and analyze the temperature of the headlight, obtain the headlight status, collect electrical data of the integrated circuit module according to the headlight status, and obtain electrical processing data; A prediction and labeling module is used to establish a data correspondence according to the state of the headlight, train a headlight temperature prediction model, obtain a predicted temperature, compare the predicted temperature with the average temperature of the headlight, monitor and label the corresponding headlight according to the comparison result, and obtain monitoring headlight labeling information; Wherein, the prediction and labeling module includes: A training prediction module is used to establish a data correspondence between the lamp temperature data collection information, the electrical processing data and the environmental collection data according to the time correlation when the lamp state is normal, generate training data, train the lamp temperature prediction model according to the training data, and output the predicted temperature; A comparison and marking module, used for comparing the predicted temperature with the average temperature of the headlights, and when the predicted temperature is greater than the average temperature of the headlights, monitoring and marking the corresponding headlights are performed to obtain monitoring headlight marking information; when the predicted temperature is less than or equal to the average temperature of the headlights, monitoring and marking the corresponding headlights are not performed; A monitoring and adjustment module is used to draw a temperature monitoring diagram of the monitored lamp according to the marked information of the monitored lamp, obtain abnormal lamp temperature, and calculate and obtain temperature adjustment value information according to the lamp temperature information; Wherein, the monitoring and adjustment module includes: The abnormal marking module is used to establish a temperature monitoring diagram for each headlight with monitoring markings, using the predicted temperature as the ordinate and the time as the abscissa, draw a horizontal line perpendicular to the ordinate with the temperature threshold on the ordinate as the origin, obtain the corresponding headlight with the corresponding curve whose predicted temperature is greater than the temperature threshold, mark it as abnormal, and obtain the abnormal headlight marking and the corresponding abnormal headlight temperature; An adjustment calculation module, used to calculate the temperature adjustment value marked on each abnormal headlight by combining the abnormal headlight temperature, the temperature threshold and the headlight temperature data collection information, and obtain the headlight temperature adjustment value information; The calculation formula of the lamp temperature adjustment value is: ， in, The abnormal lamp temperature marked for the abnormal lamp is the abnormal predicted temperature adjustment rate, that is, how much time to adjust the temperature value at the current time interval, to achieve slow temperature adjustment and ensure the stability of temperature adjustment. For abnormal headlight temperature, is the temperature threshold, is the current temperature of the headlight, is the predicted time of abnormal vehicle lamp temperature, The time for the current lamp temperature; The linkage warning module is used to obtain the abnormal linkage relationship of the lamp temperature, calculate the linkage warning value, and perform corresponding warning operations; Wherein, the linkage warning module includes: A linkage module, used for obtaining a linkage relationship of abnormal lamp temperature according to the lamp temperature adjustment value information; and calculating a linkage warning value by combining the lamp temperature adjustment value with the lamp temperature abnormal linkage relationship; The calculation formula of the linkage warning value is: ， in, is the linkage warning value, Mark the number of abnormal lights. is the predicted temperature of the i+1th abnormal headlight at the same prediction time, The predicted temperature of the i-th abnormal headlight at the same prediction time; The early warning module is used to call the preset early warning level according to the linkage early warning value and perform corresponding early warning operations.

4. According to claim 3, the LED vehicle lamp integrated circuit module intelligent high temperature warning monitoring system is characterized in that: The data processing module comprises: A state determination module, used to obtain the lamp temperature data collection information, and determine the lamp state according to the lamp temperature data collection information; the lamp state includes an abnormal state and a normal state; A determination processing module, used for collecting electrical data of the integrated circuit module when the state of the vehicle lamp is normal, pre-processing the electrical data, and obtaining electrical processing data; When the status of the headlight is abnormal, the abnormal information is obtained to perform targeted maintenance on the headlight.

Images