LED display screen driving parameter adjustment method and system

Abstract

The present application relates to the technical field of LED display screen driving parameter adjustment, and particularly relates to an LED display screen driving parameter adjustment method and system, the method comprising the following steps: continuously collecting operation parameters of each local area of the display screen; calculating the cumulative heat pressure of each local area in real time; when the cumulative heat pressure of the local area is equal to or greater than a warning threshold, identifying the local area as an overheating risk area; adjusting the driving parameters of the overheating risk area, the adjustment including reducing the peak driving current of the LED lamp beads of the local area or adjusting the pulse width modulation duty cycle of the peak driving current of the LED lamp beads of the local area to reduce the instantaneous heat load of the local area; when the actual energy consumption exceeds the energy consumption benchmark, combining the data of the cumulative heat pressure of the local area to optimize the driving strategy of the local area. Through real-time monitoring and dynamic adjustment of the driving parameters, the energy consumption is optimized, thereby prolonging the service life of the display screen and improving the operation stability.

Description

Technical Field

[0001] This invention relates to the technical field of LED display screen driving parameter adjustment, and specifically to an LED display screen driving parameter adjustment method and system. Background Technology

[0002] Outdoor LED displays play a vital role in information transmission in urban environments, making their stable and reliable operation crucial. To ensure optimal visual performance and extended lifespan under various conditions, their internal cooling systems are typically meticulously designed, incorporating appropriate temperature protection mechanisms. However, in actual long-term operation, the complex outdoor environment often presents unexpected challenges that can lead to decreased cooling system efficiency, thereby impacting the overall performance and lifespan of the display. Summary of the Invention

[0003] The purpose of this invention is to address the aforementioned shortcomings by proposing a method and system for adjusting the driving parameters of an LED display screen.

[0004] The present invention adopts the following technical solution:

[0005] A method for adjusting driving parameters of an LED display screen, the method comprising the following steps:

[0006] The system continuously collects the operating parameters of each local area of ​​the display screen, including the operating temperature of the driver integrated circuit, the driving current flowing through the LED bead cluster, and the cumulative operating time of each local area.

[0007] Based on the operating parameters and the calculation rules for the physical characteristics of the components, the cumulative heat pressure of each local area is calculated in real time.

[0008] The cumulative heat pressure of each local area is compared with the warning threshold. When the cumulative heat pressure of a local area is equal to or greater than the warning threshold, the local area is identified as an overheating risk zone.

[0009] Adjust the driving parameters of the overheat risk zone, including reducing the peak driving current of the LED beads in the local area or adjusting the pulse width modulation duty cycle of the peak driving current of the LED beads in the local area, in order to reduce the instantaneous heat load of the local area.

[0010] Continuously monitor the changes in actual energy consumption in each local area of ​​the display screen. When the actual energy consumption exceeds the energy consumption benchmark, optimize the driving strategy of the local area by combining the data of the accumulated heat pressure in the local area.

[0011] This technical solution enables refined management of the operating status of local areas of LED displays. By monitoring and dynamically adjusting driving parameters in real time, it effectively avoids accelerated aging and performance degradation of components caused by local overheating, while optimizing energy consumption, thereby extending the service life of the display and improving operational stability.

[0012] Furthermore, the steps for obtaining the early warning threshold include:

[0013] The additional thermal resistance of the heat dissipation path in the local area is calculated based on the internal junction temperature of the component, the external heat dissipation surface temperature, and the instantaneous power consumption of the component in the local area.

[0014] The warning threshold is dynamically adjusted based on the additional thermal resistance of the heat dissipation path in the local area.

[0015] Furthermore, continuously monitoring the actual energy consumption changes in each local area of ​​the display screen, and when the actual energy consumption exceeds the energy consumption baseline, the steps to optimize the driving strategy for the local area by combining the data of the accumulated heat pressure of the local area include:

[0016] Continuously monitor changes in actual energy consumption in local areas;

[0017] Based on reference conditions, including ambient temperature and brightness distribution of the displayed content, the actual energy consumption of a local area is periodically collected when the display is in low brightness or in a specific test mode.

[0018] The actual energy consumption of the collected local area is correlated with the current reference conditions;

[0019] Trend analysis is performed on the actual energy consumption of a local area collected periodically to identify the long-term drift trend of the energy consumption benchmark.

[0020] The energy consumption benchmark curve is corrected based on the long-term drift trend of the energy consumption benchmark.

[0021] When the corrected energy consumption baseline curve shows that the actual energy consumption exceeds the energy consumption baseline, the driving strategy for the local area is optimized by combining the data of the cumulative heat pressure in the local area.

[0022] Furthermore, based on operating parameters and the calculation rules for the physical characteristics of components, the steps for calculating the cumulative heat pressure of each local area in real time include:

[0023] Real-time acquisition of electrical parameters of components in a local area;

[0024] Based on the electrical parameters of the components in a local area, the aging status of the components can be estimated.

[0025] The calculation rules for the physical properties of components are dynamically adjusted based on their aging status.

[0026] Based on the adjusted calculation rules for the physical characteristics of the components and combined with the operating parameters, the cumulative heat pressure of each local area is calculated in real time.

[0027] Furthermore, the method also includes modifying the operating parameters:

[0028] Periodically, under a preset low-load operating mode, the operating temperature of the driver integrated circuit in a local area and the driving current flowing through the LED bead cluster are collected.

[0029] Simultaneously collect environmental microclimate parameters of the local area;

[0030] Based on the local environmental microclimate parameters, assess the aging rate and drift trend of sensors in the local area;

[0031] Based on the aging rate and drift trend of the sensor in a local area, a personalized calibration coefficient is dynamically generated for each local area.

[0032] The operating temperature of the driver integrated circuit and the driving current flowing through the LED bead cluster are corrected in real time using personalized calibration coefficients collected in local areas.

[0033] The operating parameters of the local area are periodically sampled and compared using an external reference sensor, and the personalized calibration coefficients are adjusted based on the comparison results.

[0034] Furthermore, the step of dynamically generating personalized calibration coefficients for each local region based on the aging rate and drift trend of the sensor in that local region includes:

[0035] Continuously monitor the instantaneous changes in local environmental microclimate parameters;

[0036] When changes in local environmental microclimate parameters exceeding preset fluctuation thresholds are detected, a reassessment of the aging rate and drift trend of the sensors in that local area is triggered.

[0037] Based on the reassessed aging rate and drift trend of the local sensor area, the personalized calibration coefficients of the local area are updated in real time.

[0038] Furthermore, the steps of periodically sampling and comparing the operating parameters of a local area using an external reference sensor, and adjusting the personalized calibration coefficients based on the comparison results, include:

[0039] The performance degradation of the external reference sensor is assessed based on the microclimate parameters of the environment in which the external reference sensor is located.

[0040] When the performance degradation of an external reference sensor exceeds a preset degradation threshold, a cross-comparison of multiple external reference sensors is triggered, and the data of the external reference sensor with degraded performance is corrected based on the cross-comparison results.

[0041] The personalized calibration coefficients are adjusted based on the comparison results of the corrected external reference sensor data and the operating parameters of the local area.

[0042] Furthermore, the step of dynamically generating personalized calibration coefficients for each local region based on the aging rate and drift trend of the sensor in that local region includes:

[0043] Identify the type, batch, and manufacturer information of sensors in the local area;

[0044] Based on the type, batch, and manufacturer information of the sensors in the local area, the corresponding inherent performance parameters and aging characteristic curves are retrieved from the preset sensor characteristic database;

[0045] Based on the aging rate and drift trend of the sensor in the local area, personalized calibration coefficients are generated by combining them with the retrieved inherent performance parameters and aging characteristic curves.

[0046] Furthermore, after generating personalized calibration coefficients, the following steps are further included:

[0047] When the display screen is in low brightness or a specific test mode, collect the actual operating parameters of a local area;

[0048] The actual operating parameters of the local area are compared with the operating temperature of the driver integrated circuit and the driving current flowing through the LED bead cluster after correction based on the personalized calibration coefficient.

[0049] When the deviation of the comparison result exceeds the preset accuracy threshold, the personalized calibration coefficient is iteratively corrected according to the direction and magnitude of the deviation until the deviation converges to within the preset accuracy threshold.

[0050] Record the changes in the personalized calibration coefficients before and after correction, as well as the corresponding local environmental microclimate parameters.

[0051] This application also discloses an LED display screen driving parameter adjustment system, applied to the above-mentioned LED display screen driving parameter adjustment method, the system comprising:

[0052] The parameter acquisition module is used to continuously collect the operating parameters of each local area of ​​the display screen. The operating parameters include the operating temperature of the driver integrated circuit, the driving current flowing through the LED bead cluster, and the cumulative working time of each local area.

[0053] The calculation module calculates the cumulative heat pressure of each local area in real time based on the operating parameters and the calculation rules of the physical characteristics of the components.

[0054] The risk identification module compares the cumulative heat pressure of each local area with the warning threshold. When the cumulative heat pressure of a local area is equal to or greater than the warning threshold, the local area is identified as an overheating risk zone.

[0055] The adjustment module is used to adjust the driving parameters of the overheat risk zone. The adjustment includes reducing the peak driving current of the LED beads in the local area or adjusting the pulse width modulation duty cycle of the peak driving current of the LED beads in the local area to reduce the instantaneous heat load of the local area.

[0056] The processing module continuously monitors the actual energy consumption changes of each local area of ​​the display screen. When the actual energy consumption exceeds the energy consumption benchmark, it optimizes the driving strategy of the local area by combining the data of the accumulated heat pressure of the local area.

[0057] This technical solution provides a system for implementing the aforementioned LED display screen driving parameter adjustment method. Through modular design, it enables fully automated management of the entire process, including the acquisition of display screen operating parameters, heat pressure calculation, risk identification, parameter adjustment, and energy consumption optimization, thereby improving the system's integration and operating efficiency.

[0058] This application, by comprehensively considering thermal pressure and energy consumption data, enables more intelligent and refined drive strategy optimization, ensuring display quality and extending component lifespan while avoiding unnecessary energy consumption increases.

[0059] To further understand the features and technical content of the present invention, please refer to the following detailed description and drawings of the present invention. However, the drawings provided are for reference and illustration only and are not intended to limit the present invention. Attached Figure Description

[0060] Figure 1 This is a flowchart of a method for adjusting LED display driving parameters according to the present invention;

[0061] Figure 2 This is a schematic diagram of the structure of an LED display screen driving parameter adjustment system according to the present invention. Detailed Implementation

[0062] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the content disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention. Furthermore, the accompanying drawings of the present invention are for simple illustrative purposes only and are not depictions of actual dimensions; this is stated in advance. The following embodiments will further describe the relevant technical content of the present invention in detail, but the disclosed content is not intended to limit the scope of protection of the present invention.

[0063] This embodiment provides a method and system for adjusting LED display screen driving parameters, combined with... Figure 1 and Figure 2 As shown.

[0064] refer to Figure 1 A method for adjusting driving parameters of an LED display screen, the method comprising the following steps:

[0065] The system continuously collects the operating parameters of each local area of ​​the display screen, including the operating temperature of the driver integrated circuit, the driving current flowing through the LED bead cluster, and the cumulative operating time of each local area.

[0066] Based on the operating parameters and the calculation rules for the physical characteristics of the components, the cumulative heat pressure of each local area is calculated in real time.

[0067] The cumulative heat pressure of each local area is compared with the warning threshold. When the cumulative heat pressure of a local area is equal to or greater than the warning threshold, the local area is identified as an overheating risk zone.

[0068] Adjust the driving parameters of the overheat risk zone, including reducing the peak driving current of the LED beads in the local area or adjusting the pulse width modulation duty cycle of the peak driving current of the LED beads in the local area, in order to reduce the instantaneous heat load of the local area.

[0069] Continuously monitor the changes in actual energy consumption in each local area of ​​the display screen. When the actual energy consumption exceeds the energy consumption benchmark, optimize the driving strategy of the local area by combining the data of the accumulated heat pressure in the local area.

[0070] Here, "local area" refers to several smaller, independent physical or logical units divided on the LED display panel. Each local area typically contains a cluster of LED beads and a corresponding driver integrated circuit, whose operating parameters and thermal status can be monitored and managed independently.

[0071] "Operating parameters" are key indicators reflecting the working status of a local area, including the operating temperature of the driver integrated circuit, the driving current flowing through the LED bead cluster, and the cumulative operating time of each local area. These parameters are the basis for assessing the heat load and aging status of the local area.

[0072] "Component physical property calculation rules" refers to mathematical models or algorithms established based on the material properties, structural design and aging models of components such as LED beads and driver integrated circuits to estimate their thermal behavior and performance degradation.

[0073] "Accumulated heat pressure" is a comprehensive indicator that measures the heat load borne by a local area over a period of time. It not only considers the instantaneous temperature, but also combines the heat accumulation effect and the heat resistance limit of the components, and can more accurately reflect the thermal stress state of the components.

[0074] The "early warning threshold" is a critical value used to determine whether a local area is at risk of overheating. When the accumulated heat pressure reaches or exceeds this threshold, the system will trigger an early warning and take corresponding adjustment measures.

[0075] "Peak drive current" refers to the maximum instantaneous current value of the current pulse when the LED bead is driven in pulse width modulation (PWM) mode.

[0076] "Pulse Width Modulation Duty Cycle" refers to the ratio of the duration of the current pulse to the time of a complete cycle in PWM drive mode. It directly affects the average brightness and instantaneous heat load of LED beads.

[0077] "Energy consumption benchmark" refers to the expected energy consumption level of a local area of ​​an LED display screen under normal and healthy operating conditions. The deviation between the actual energy consumption and the energy consumption benchmark can be used as a basis for judging system abnormalities or efficiency decline.

[0078] The core of the LED display screen driving parameter adjustment method proposed in this application lies in the fine management and dynamic adjustment of the operating status of each local area of ​​the display screen.

[0079] First, this method continuously collects operating parameters for each local area of ​​the display screen. These parameters form the basis for assessing the thermal state and aging degree of each local area. For example, the operating temperature can be acquired in real time by placing miniature temperature sensors near the driver integrated circuit in each local area. The drive current flowing through the LED cluster can be measured using a current detection module integrated into the driver circuit. The cumulative operating time of each local area can be recorded using an internal system timer. As an implementation, a high-precision thermistor or thermocouple can be used to measure the operating temperature of the driver integrated circuit, with a data acquisition frequency set to once per second to capture rapid temperature changes. The drive current can be acquired using a Hall effect sensor or a shunt resistor, ensuring measurement accuracy within ±1%. The cumulative operating time is recorded using non-volatile memory to prevent data loss in the event of power failure.

[0080] Secondly, based on the collected operating parameters and the calculation rules for the physical characteristics of the components, the cumulative heat pressure of each local area is calculated in real time. The calculation rules for the physical characteristics of the components can be a pre-established thermal model that considers factors such as the thermal conductivity, heat capacity, packaging structure, and heat dissipation path of the LED chips and driver integrated circuits. By inputting the real-time collected temperature, current, and operating time into this model, the junction temperature and heat accumulation inside the components can be dynamically calculated, thus obtaining the cumulative heat pressure. For example, a heat conduction model based on finite element analysis can be established, which can simulate the heat distribution and accumulation inside the LED chips and driver integrated circuits under different driving currents and ambient temperatures. The real-time collected operating parameters serve as the input to the model, and through iterative calculation, the cumulative heat pressure of each local area is output.

[0081] Next, the cumulative heat pressure of each local area is compared with a warning threshold. The warning threshold is a preset value representing the safe upper limit of the heat load of a local area. When the cumulative heat pressure of a local area is equal to or greater than the warning threshold, the system identifies that local area as an overheating risk zone. For example, the warning threshold can be set according to the maximum allowable junction temperature and safety margin provided by the component manufacturer, and fine-tuned according to the actual application scenario.

[0082] Subsequently, the driving parameters for the overheating risk zone are adjusted. This adjustment aims to reduce the instantaneous heat load of the local area, thereby mitigating the overheating risk. Specific adjustments include reducing the peak drive current of the LED chips in the local area, or adjusting the pulse width modulation (PWM) duty cycle of the peak drive current of the LED chips in the local area. Reducing the peak drive current directly reduces the power consumption of the LED chips, thus reducing heat generation. Adjusting the PWM duty cycle can reduce the average current and average power consumption by shortening the current conduction time without changing the peak current. For example, when a local area is identified as an overheating risk zone, the system can automatically reduce the peak drive current of that area by 5% to 10%, or adjust the PWM duty cycle from 90% to 80%, to observe changes in its accumulated heat pressure.

[0083] Finally, the system continuously monitors the actual energy consumption changes in each local area of ​​the display screen. When the actual energy consumption exceeds the energy consumption benchmark, the driving strategy for that local area is optimized by combining the data of the accumulated heat pressure of that local area. The energy consumption benchmark can be obtained by long-term statistical analysis and modeling of the energy consumption of each local area under normal display conditions. When the actual energy consumption is significantly higher than the energy consumption benchmark, it may indicate a decrease in efficiency or abnormal heat generation. At this time, by combining the accumulated heat pressure data, the health status of the local area can be more comprehensively assessed, and the driving strategy can be optimized accordingly. For example, while ensuring display quality, the driving current or duty cycle can be further fine-tuned to achieve the dual purpose of energy saving and cooling. For example, a high-precision power metering chip can be used to collect the instantaneous power of each local area and accumulate and calculate the energy consumption. When it is found that the energy consumption of a certain local area is more than 15% higher than the energy consumption benchmark under the same display content and brightness, the system will combine the accumulated heat pressure data of that area to determine whether it is necessary to further reduce the driving current or adjust the display content allocation to optimize the overall energy consumption.

[0084] The steps to obtain the early warning threshold include:

[0085] The additional thermal resistance of the heat dissipation path in the local area is calculated based on the internal junction temperature of the component, the external heat dissipation surface temperature, and the instantaneous power consumption of the component in the local area.

[0086] The warning threshold is dynamically adjusted based on the additional thermal resistance of the heat dissipation path in the local area.

[0087] The internal junction temperature of a component refers to the temperature of the semiconductor junction inside core heat-generating components such as LED beads or driver integrated circuits, directly reflecting the actual thermal state of the component. The external heat dissipation surface temperature refers to the temperature of the component package surface or the heat dissipation structure (e.g., heat sink) in direct contact with it, which can be measured using infrared sensors or thermocouples. The instantaneous power consumption of components in a local area refers to the total power input of all heat-generating components in that local area at a given moment, which can be obtained by monitoring voltage and current and multiplying them. Using these parameters, the additional thermal resistance of the heat dissipation path in the local area can be accurately calculated. This thermal resistance reflects changes in heat dissipation performance, such as increased thermal resistance due to dust accumulation, cooling fan failure, or aging of thermally conductive materials.

[0088] The steps for continuously monitoring the actual energy consumption changes in each local area of ​​the display screen, and optimizing the driving strategy for the local area based on the data of the accumulated heat pressure of the local area when the actual energy consumption exceeds the energy consumption baseline, include:

[0089] Continuously monitor changes in actual energy consumption in local areas;

[0090] Based on reference conditions, including ambient temperature and brightness distribution of the displayed content, the actual energy consumption of a local area is periodically collected when the display is in low brightness or in a specific test mode.

[0091] The actual energy consumption of the collected local area is correlated with the current reference conditions;

[0092] Trend analysis is performed on the actual energy consumption of a local area collected periodically to identify the long-term drift trend of the energy consumption benchmark.

[0093] The energy consumption benchmark curve is corrected based on the long-term drift trend of the energy consumption benchmark.

[0094] When the corrected energy consumption baseline curve shows that the actual energy consumption exceeds the energy consumption baseline, the driving strategy for the local area is optimized by combining the data of the cumulative heat pressure in the local area.

[0095] Specifically, continuous monitoring of actual energy consumption changes in a localized area refers to acquiring real-time power consumption data of that area under normal operating conditions through sensors integrated into the localized area of ​​the display screen or through measurement functions within the driver integrated circuit. This data can include parameters such as voltage and current, and instantaneous or average energy consumption can be calculated. In conjunction with reference conditions, including ambient temperature and display content brightness distribution, the actual energy consumption of the localized area is periodically collected when the display screen is in low brightness or a specific test mode. These reference conditions aim to capture key external and internal factors affecting energy consumption benchmarks. Ambient temperature can be obtained through environmental sensors, and display content brightness distribution can be obtained through the display screen's image processing unit. Periodic collection can be performed at preset time intervals (e.g., hourly, daily, or weekly), and selecting low brightness or a specific test mode is to acquire energy consumption data under relatively stable and controllable conditions, reducing interference from the complexity of the display content on energy consumption measurement, thereby more accurately reflecting the energy consumption characteristics of the components themselves. For example, in low brightness mode, the driving current of LED beads is lower, and the heat load is smaller, making the collected energy consumption data more valuable. Specific test modes can refer to displaying a solid color image or a specific pattern to standardize the energy consumption measurement process. In practical applications, correlating the actual energy consumption of a localized area with current reference conditions means binding and storing the periodically collected actual energy consumption data with reference conditions such as ambient temperature and the brightness distribution of the displayed content. This correlation helps in subsequent analysis of the relationship between energy consumption and these conditions, thereby more accurately understanding the reasons for changes in energy consumption.

[0096] Furthermore, trend analysis is performed on the actual energy consumption of periodically collected local areas to identify long-term drift trends in the energy consumption benchmark. Trend analysis can employ statistical methods, such as moving averages, regression analysis, or machine learning algorithms, to detect slow, continuous changes in the energy consumption benchmark over time. This drift may be caused by long-term effects such as component aging and material performance degradation. Therefore, the energy consumption benchmark curve is corrected based on the long-term drift trend. The energy consumption benchmark curve is a dynamic reference standard used to determine whether actual energy consumption is abnormal. By identifying the long-term drift trend, the benchmark curve can be adjusted in real-time or periodically to better reflect the current operating state and aging level of the display screen. For example, if the trend analysis indicates a slow upward trend in the energy consumption benchmark, the energy consumption benchmark curve can be adjusted accordingly to avoid frequent false alarms due to an excessively low benchmark. Finally, when the corrected energy consumption benchmark curve shows an increase in actual energy consumption exceeding the benchmark, the driving strategy for the local area is optimized by combining data on the accumulated heat pressure of the local area. This means that the optimization of the driving strategy will only be triggered when an abnormal increase is determined under the corrected, more accurate energy consumption benchmark. At this point, by combining the accumulated heat pressure data, the health status of the local area can be assessed more comprehensively, thereby formulating a more reasonable and effective driving strategy. For example, when energy consumption increases abnormally and the accumulated heat pressure is high, priority can be given to reducing the peak driving current or adjusting the pulse width modulation duty cycle to alleviate the heat load and extend the life.

[0097] In some preferred embodiments, it is assumed that an LED display screen operates outdoors for extended periods. First, the system continuously monitors the actual energy consumption changes in each local area of ​​the display screen. To more accurately identify energy consumption anomalies, the system periodically (e.g., at 3 AM daily, when the display screen is in low-brightness standby mode) collects the actual energy consumption of each local area. Simultaneously, the current ambient temperature (e.g., obtained via an environmental sensor built into the display screen) and the brightness distribution of the displayed content (e.g., in a completely black or low-brightness standby mode) are recorded. This collected energy consumption data and reference conditions are correlated and stored. Over time, the system accumulates a large amount of periodically collected data. For example, by performing a moving average analysis on the energy consumption data collected at 3 AM over the past 30 days, the system finds that the average energy consumption of a certain local area shows a slow upward trend. This may indicate slight aging of the LED chips or driver ICs in that area, leading to a slight increase in energy consumption under the same conditions. Based on this long-term drift trend, the system automatically corrects the energy consumption baseline curve for that local area, fine-tuning it upwards to reflect the actual aging state of the components. Under the corrected energy consumption baseline, if the system detects a sudden and significant increase in the actual energy consumption of a local area during normal operation (e.g., energy consumption is 10% higher than the corrected baseline when displaying content of the same brightness), and the accumulated heat pressure data of that local area also indicates a high risk, the system will determine that there is an overheating risk and abnormal energy consumption in that area. At this time, the system will immediately optimize the driving strategy for that local area, for example, reducing the peak driving current of the LED chips in that area by 5%, or adjusting the pulse width modulation duty cycle from 80% to 75%, to reduce the instantaneous heat load, thereby effectively mitigating the overheating risk and controlling the abnormal increase in energy consumption, ensuring the stable operation of the display screen and extending its service life.

[0098] Based on operating parameters and the calculation rules for the physical characteristics of components, the steps for real-time calculation of the cumulative heat pressure in each local area include:

[0099] Real-time acquisition of electrical parameters of components in a local area;

[0100] Based on the electrical parameters of the components in a local area, the aging status of the components can be estimated.

[0101] The calculation rules for the physical properties of components are dynamically adjusted based on their aging status.

[0102] Based on the adjusted calculation rules for the physical characteristics of the components and combined with the operating parameters, the cumulative heat pressure of each local area is calculated in real time.

[0103] Specifically, real-time acquisition of electrical parameters of components in a localized area can include, but is not limited to, acquiring parameters such as voltage, current, resistance, and capacitance. Changes in these electrical parameters are often closely related to the degradation of the component's internal structure or material properties, serving as crucial evidence for assessing the component's aging state. Calculating the aging state of components based on their electrical parameters in a localized area involves analyzing the trends of these parameters over time or their deviations from initial values ​​to determine the degree of performance degradation. For example, the forward voltage of an LED chip may increase with aging under the same current, or its luminous efficiency may decrease. The calculated aging state can be a quantifiable indicator, such as an aging coefficient or a percentage of remaining lifespan. In practical applications, dynamically adjusting the calculation rules for the physical characteristics of components based on their aging state involves real-time correction of the physical models or parameters used to calculate cumulative heat pressure based on the calculated aging state. For example, the thermal resistance model, thermal capacity parameters, or power loss coefficient of the component can be adjusted to more accurately reflect the actual thermal behavior of the aging component. This adjustment can be based on a preset aging model curve or on learning and predicting from historical data using machine learning algorithms. Therefore, based on the adjusted calculation rules for the physical characteristics of the components and combined with the operating parameters, the cumulative heat pressure of each local area is calculated in real time, ensuring that the calculation results of the cumulative heat pressure still have high accuracy and reliability even under the background of component aging.

[0104] As a specific implementation method, suppose that in a certain local area of ​​an LED display screen, the forward voltage of the LED cluster increases slightly under the same driving current after long-term operation, while its luminous efficiency decreases. The system collects the electrical parameters of these LEDs in real time, such as their forward voltage at a specific current. By comparing this data with the initial factory data or a preset aging model, the system calculates that the LEDs in this local area have entered a moderate aging state. Based on this aging state, the system dynamically adjusts the calculation rules for the physical characteristics of the components used to calculate the accumulated heat pressure in this area. For example, it increases the thermal resistance coefficient of the LEDs by 5% or adjusts their electro-optical conversion efficiency parameters. Thus, even under the same operating parameters, the adjusted calculation rules more accurately reflect the actual heat generated by the aging LEDs and the changes in their heat dissipation capacity, making the calculation results of the accumulated heat pressure closer to reality and avoiding misjudgment or delayed response of overheating risk due to the neglect of aging effects.

[0105] The method for adjusting LED display driver parameters also includes correcting operating parameters:

[0106] Periodically, under a preset low-load operating mode, the operating temperature of the driver integrated circuit in a local area and the driving current flowing through the LED bead cluster are collected.

[0107] Simultaneously collect environmental microclimate parameters of the local area;

[0108] Based on the local environmental microclimate parameters, assess the aging rate and drift trend of sensors in the local area;

[0109] Based on the aging rate and drift trend of the sensor in a local area, a personalized calibration coefficient is dynamically generated for each local area.

[0110] The operating temperature of the driver integrated circuit and the driving current flowing through the LED bead cluster are corrected in real time using personalized calibration coefficients collected in local areas.

[0111] The operating parameters of the local area are periodically sampled and compared using an external reference sensor, and the personalized calibration coefficients are adjusted based on the comparison results.

[0112] Specifically, the correction of operating parameters aims to ensure the high accuracy and reliability of the raw data used for thermal pressure calculation and energy consumption monitoring. The "preset low-load operating mode" refers to the operating mode where the display screen operates at low brightness, with minimal changes in displayed content, or under specific test pattern display conditions. In this mode, the driving current of the LED cluster and the operating temperature of the driving integrated circuit are relatively stable, facilitating the acquisition and comparison of benchmark data. The synchronously acquired "environmental microclimate parameters" can include local temperature, humidity, airflow velocity, etc., which are of significant reference value for assessing sensor performance degradation and drift.

[0113] Furthermore, "assessing the aging rate and drift trend of sensors in a local area" refers to quantifying sensor performance changes by analyzing the deviations between sensor readings in low-load mode and theoretical or historical benchmark values ​​under different environmental microclimate parameters. Based on this assessment result, a "personalized calibration coefficient" is dynamically generated. This coefficient is used to compensate for the inherent errors and time-varying drift of the sensor within a specific local area. In practical applications, this calibration coefficient can be a multiplicative factor, an additive offset, or a combination of both, with the aim of making the corrected operating parameters closer to the true values.

[0114] Furthermore, an "external reference sensor" refers to a measurement device with higher accuracy and stability, independent of the internal sensors of the display screen, such as a high-precision thermocouple or ammeter. By periodically sampling and comparing the operating parameters of a local area with the measurement results of the external reference sensor, the generated personalized calibration coefficients can be verified and further adjusted, ensuring the accuracy and long-term effectiveness of the calibration.

[0115] In some preferred embodiments, suppose that after long-term operation, the temperature sensor in a certain local area of ​​an LED display screen begins to drift continuously due to aging; that is, when the actual temperature is 60°C, the sensor only displays 58°C. According to the LED display screen drive parameter adjustment method described above, the system periodically collects the operating temperature of the driver integrated circuit and environmental microclimate parameters of this local area under low-load operation mode. For example, in an ambient temperature of 25°C and low brightness mode, the theoretical operating temperature of the driver integrated circuit should be 40°C, but the sensor continuously displays 38°C. Simultaneously, the system evaluates the aging rate and drift trend of the sensor in this local area, identifying a systematic deviation of -2°C. Based on this evaluation, the system dynamically generates a personalized calibration coefficient for this local area, such as an additive correction value of +2°C. Subsequently, all operating temperatures of the driver integrated circuit collected from this sensor are corrected by adding 2°C in real time. Furthermore, the system periodically samples and compares the actual temperature of this local area using a high-precision external reference temperature sensor. If the external reference sensor displays an actual temperature of 60°C, and the corrected internal sensor reading is also 60°C, then the calibration coefficient is valid. If the comparison still reveals a deviation, for example, the external reference sensor displays 60°C, while the corrected internal sensor reading is 59°C, the system will fine-tune the personalized calibration coefficient based on the comparison results, for example, adjusting the correction value to +3°C, until the deviation converges within the preset accuracy threshold. In this way, even if the sensor experiences aging drift, the system can ensure accurate operating parameters, thereby guaranteeing the accuracy of accumulated heat pressure calculation and effectively preventing overheating risks.

[0116] This application further proposes a step for dynamically generating personalized calibration coefficients for each local region based on the aging rate and drift trend of the sensor in that local region, including:

[0117] Continuously monitor the instantaneous changes in local environmental microclimate parameters;

[0118] When changes in local environmental microclimate parameters exceeding preset fluctuation thresholds are detected, a reassessment of the aging rate and drift trend of the sensors in that local area is triggered.

[0119] Based on the reassessed aging rate and drift trend of the local sensor area, the personalized calibration coefficients of the local area are updated in real time.

[0120] Specifically, continuous monitoring of instantaneous changes in local environmental microclimate parameters refers to the system continuously collecting and analyzing real-time data on environmental factors such as temperature, humidity, and airflow speed within a local area. Its purpose is to promptly capture external environmental disturbances that may affect sensor performance. Environmental microclimate parameters can be understood as local environmental conditions that affect the sensor's operating state, such as local temperature, humidity, air pressure, light intensity, and the concentration of potentially corrosive gases.

[0121] Furthermore, when changes in local environmental microclimate parameters exceed preset fluctuation thresholds, a reassessment of the aging rate and drift trend of the sensor in that area is triggered. This means the system does not perform assessments at fixed time intervals, but rather determines whether a reassessment is necessary based on dynamic environmental changes. The preset fluctuation thresholds can be set according to the sensor's characteristics, application scenario, and accuracy requirements. For example, a reassessment can be triggered when the local temperature changes by more than 5 degrees Celsius or the humidity changes by more than 10% RH within a short period. The reassessment aims to make a more accurate judgment on the sensor's current aging status and drift trend based on the latest environmental conditions.

[0122] Therefore, based on the reassessed aging rate and drift trend of the sensor in the local area, the personalized calibration coefficients for that local area are updated in real time. This means that once the sensor performance evaluation is triggered and completed, the corresponding calibration coefficients are immediately adjusted to ensure they match the actual performance of the sensor in the current environment. The purpose of real-time updates is to minimize measurement errors caused by environmental changes or accelerated sensor performance degradation.

[0123] In some preferred embodiments, it is assumed that an LED display screen is installed outdoors, with localized sensors used to measure the ambient temperature. Under normal circumstances, the ambient temperature changes slowly, and the personalized calibration coefficients are updated based on periodic evaluations. However, on a summer afternoon, a localized thunderstorm suddenly hits the area, causing the ambient temperature to plummet from 35 degrees Celsius to 25 degrees Celsius in a short period, while the humidity rises sharply.

[0124] In this scenario, the proposed solution continuously monitors the instantaneous changes in local environmental microclimate parameters (such as temperature and humidity). When the system detects a temperature change (10 degrees Celsius) exceeding a preset fluctuation threshold (e.g., 5 degrees Celsius), or a humidity change exceeding a preset fluctuation threshold, the system immediately triggers a reassessment of the aging rate and drift trend of the sensor in that local area. Through this reassessment, the system may discover that the sensor's response characteristics or zero-point drift have undergone instantaneous changes after experiencing rapid temperature changes. Based on the results of this reassessment, the system updates the personalized calibration coefficients for that local area in real time. For example, if a specific drift is detected in the sensor under low temperature and high humidity conditions, the calibration coefficients will be adjusted to compensate for this drift. In this way, even under extreme conditions of drastic environmental changes, the operating parameters such as the operating temperature of the driver integrated circuit and the driving current flowing through the LED bead cluster, collected by the sensor, can be corrected in a timely and accurate manner, thereby avoiding measurement errors caused by sudden environmental changes and ensuring the accuracy and reliability of the LED display drive parameter adjustment.

[0125] This application further proposes a step of periodically sampling and comparing the operating parameters of a local area using an external reference sensor, and adjusting the personalized calibration coefficients based on the comparison results, including:

[0126] The performance degradation of the external reference sensor is assessed based on the microclimate parameters of the environment in which the external reference sensor is located.

[0127] When the performance degradation of an external reference sensor exceeds a preset degradation threshold, a cross-comparison of multiple external reference sensors is triggered, and the data of the external reference sensor with degraded performance is corrected based on the cross-comparison results.

[0128] The personalized calibration coefficients are adjusted based on the comparison results of the corrected external reference sensor data and the operating parameters of the local area.

[0129] Specifically, in the process of periodically sampling and comparing the operating parameters of a local area using an external reference sensor, the first step is to assess the performance degradation of the external reference sensor based on the collected microclimate parameters of its environment. These microclimate parameters can include ambient temperature, humidity, and light intensity, which significantly impact the long-term stability and accuracy of the sensor. The assessment of performance degradation can be achieved by comparing the current sensor output with historical baseline data, or by analyzing trends in parameters such as response time and noise level. The aim is to promptly identify potential performance degradation in the external reference sensor and prevent data errors from affecting subsequent calibration processes.

[0130] Furthermore, when the assessed performance degradation of an external reference sensor exceeds a preset degradation threshold, a cross-comparison of multiple external reference sensors will be triggered, and the data of the degraded external reference sensor will be corrected based on the cross-comparison results. The preset degradation threshold can be set according to the sensor specifications, application scenario, and expected accuracy requirements. Cross-comparison refers to mutually verifying the data of one or more potentially degraded external reference sensors with data from other external reference sensors considered reliable. For example, measurement deviations and consistency indices between different sensors can be calculated. Based on the cross-comparison results, specific degraded sensors can be identified, and their data can be corrected, for example, through weighted averaging, deviation compensation, or direct replacement, to ensure that the external reference data used for comparison is accurate and reliable.

[0131] Finally, the personalized calibration coefficients are adjusted based on a comparison of the corrected external reference sensor data and the operating parameters of the local area. The corrected external reference sensor data is considered a reference benchmark that is closer to the true value. By comparing the operating parameters of the local area (such as the operating temperature of the driver IC and the drive current flowing through the LED cluster) with this corrected reference data, the deviation of the sensor in the local area can be calculated more accurately. Based on this accurate deviation information, the personalized calibration coefficients will be adjusted accordingly to compensate for the drift or aging of the sensor in the local area, thereby improving its measurement accuracy.

[0132] In some preferred embodiments, assuming a large LED display system is deployed outdoors, the sensors within its internal local areas may drift over time and due to environmental changes. To calibrate these internal sensors, the system is equipped with three external reference temperature sensors A, B, and C, which are placed near the display to periodically collect ambient temperature data as a reference.

[0133] In a periodic calibration task, the system first collected microclimate parameters of the environment in which external reference sensors A, B, and C were located, such as ambient temperature and humidity. By analyzing the historical data and current response characteristics of sensor A, the system assessment found that the performance degradation of sensor A had exceeded the preset degradation threshold (e.g., its response time has slowed down significantly, or there is a persistent deviation from the historical baseline data).

[0134] At this point, the system will trigger a cross-comparison of sensors A, B, and C. For example, the system can calculate the temperature reading differences between sensors A and B, A and C, and B and C. If it is found that the reading of sensor A consistently deviates from the average value of sensors B and C, and the deviation exceeds a preset consistency threshold, it can be determined that there is a problem with the data of sensor A. Based on the cross-comparison results, the system can correct the data of sensor A, for example, by using the average value of sensors B and C as the corrected reference temperature, or by compensating the reading of sensor A through an algorithm based on a historical deviation model.

[0135] Subsequently, the system compares the corrected external reference sensor data (e.g., the accurate ambient temperature represented by the average of sensors B and C) with the operating temperature collected by the sensors within the local area. If there is a deviation between the operating temperature reported by the sensors within the local area and the corrected external reference temperature, the system adjusts the personalized calibration coefficient for that local area based on this deviation. For example, if the internal sensor readings are generally too high, the calibration coefficient will be adjusted to correct the readings downwards to make them closer to the true value. In this way, even if the external reference sensor itself experiences performance degradation, the scheme of this application can ensure the accuracy and reliability of the calibration process.

[0136] The steps for dynamically generating personalized calibration coefficients for each local area of ​​the sensor, based on the aging rate and drift trend of the local area, include:

[0137] Identify the type, batch, and manufacturer information of sensors in the local area;

[0138] Based on the type, batch, and manufacturer information of the sensors in the local area, the corresponding inherent performance parameters and aging characteristic curves are retrieved from the preset sensor characteristic database;

[0139] Based on the aging rate and drift trend of the sensor in the local area, personalized calibration coefficients are generated by combining them with the retrieved inherent performance parameters and aging characteristic curves.

[0140] Specifically, identifying the type, batch, and manufacturer information of sensors in a localized area refers to obtaining detailed information such as the specific model, production batch, and manufacturer of the sensor installed in that localized area of ​​the display screen by reading the sensor's own identifier, scanning a QR code, or accessing system configuration data. This information forms the basis for subsequent retrieval of the sensor's inherent characteristics.

[0141] Based on the type, batch, and manufacturer information of the sensors in a local area, the system retrieves the corresponding inherent performance parameters and aging characteristic curves from a pre-defined sensor characteristic database. This can be understood as the system maintaining a database containing detailed technical specifications and long-term operational performance data for various types of sensors. Once the specific information of the sensor in the local area is obtained, the system queries this database to retrieve the initial performance parameters of that type of sensor under ideal conditions (e.g., accuracy, range, response time, etc.) and its typical aging patterns or degradation curves under different environmental conditions and workloads. This data provides a benchmark for evaluating the current state of the sensor.

[0142] In practical applications, personalized calibration coefficients are generated by combining the aging rate and drift trend of the sensor in a local area with retrieved inherent performance parameters and aging characteristic curves. Specifically, this involves comprehensively analyzing the real-time assessed aging rate and drift trend of the sensor in a local area with the inherent performance parameters and aging characteristic curves of that type of sensor obtained from a database. For example, if the database shows that a batch of sensors typically exhibits positive drift after a certain operating time, and the current assessment results also indicate similar drift in this local area, then combining the two can more accurately predict its current deviation and generate a personalized calibration coefficient for that specific sensor and its specific aging state. This calibration coefficient is used to correct the operating parameters collected by the sensor, making them closer to the true values.

[0143] The above technical solution overcomes the coarseness of traditional methods in assessing sensor aging characteristics and avoids calibration inaccuracies caused by individual sensor differences. By finely identifying sensor information and combining it with its inherent characteristics, the generated personalized calibration coefficients are more targeted and accurate. This ensures that the collected data on operating parameters such as the operating temperature of the driver integrated circuit and the driving current flowing through the LED bead cluster are more realistic and reliable. This provides a solid data foundation for subsequent calculations of accumulated heat pressure and adjustments to driving parameters, further improving the stability and safety of the LED display screen.

[0144] After generating personalized calibration coefficients, the following steps are further included:

[0145] When the display screen is in low brightness or a specific test mode, collect the actual operating parameters of a local area;

[0146] The actual operating parameters of the local area are compared with the operating temperature of the driver integrated circuit and the driving current flowing through the LED bead cluster after correction based on the personalized calibration coefficient.

[0147] When the deviation of the comparison result exceeds the preset accuracy threshold, the personalized calibration coefficient is iteratively corrected according to the direction and magnitude of the deviation until the deviation converges to within the preset accuracy threshold.

[0148] Record the changes in the personalized calibration coefficients before and after correction, as well as the corresponding local environmental microclimate parameters.

[0149] Specifically, when the display is in low brightness or a specific test mode, actual operating parameters of a local area are collected. The purpose is to provide a relatively stable and controllable operating environment to reduce the impact of external interference on the accuracy of parameter acquisition. Low brightness mode or specific test mode usually means that the power consumption and heat generation of the display are at a low level. At this time, the sensor measurements are relatively stable and more suitable as a calibration benchmark. The actual operating parameters of the local area refer to the operating temperature of the driver integrated circuit and the driving current flowing through the LED chip cluster, which are directly measured by the sensor in the local area under the current operating conditions.

[0150] The comparison between the actual operating parameters of the local area and the operating temperature of the driver IC and the driving current flowing through the LED cluster, corrected based on personalized calibration coefficients, can be understood as verifying the effectiveness of the calibration coefficients. The parameters corrected based on personalized calibration coefficients are expected values ​​obtained by processing the original acquired data according to previously generated calibration coefficients, while the actual operating parameters are the original, uncalibrated measurements. By comparing these two, the correction effect of the calibration coefficients can be quantified, and whether any residual errors exist.

[0151] In practical applications, when the deviation of the comparison results exceeds a preset accuracy threshold, the personalized calibration coefficients are iteratively corrected based on the direction and magnitude of the deviation until the deviation converges within the preset accuracy threshold. The accuracy threshold is a pre-defined acceptable error range used to determine whether the calibration meets the requirements. Iterative correction is an optimization process that gradually approaches the target value. Based on the sign (direction) and magnitude (amplitude) of the deviation, the calibration coefficients are systematically adjusted, for example, through gradient descent or other optimization algorithms, to reduce the difference between the actual value and the corrected value. This process continues until the deviation falls within the acceptable accuracy threshold, ensuring the accuracy of the calibration coefficients.

[0152] In addition, recording the changes in personalized calibration coefficients before and after correction, along with the corresponding local environmental microclimate parameters, aims to establish calibration history data. This data can be used to analyze the changes in calibration coefficients over time, environmental conditions, and sensor aging, providing a basis for future calibration strategy optimization and aiding in the diagnosis of potential system problems.

[0153] This application's solution effectively addresses the issue of insufficient calibration accuracy that may arise from relying solely on one-time generation or periodic external comparisons by introducing a verification and iterative correction mechanism for personalized calibration coefficients. First, actual operating parameters are collected under controlled low-brightness or specific test modes, providing a stable benchmark for verifying the calibration coefficients. Second, comparing these actual parameters with the parameters corrected by the current calibration coefficients intuitively reveals the accuracy of the current calibration coefficients. When a comparison deviation exceeds a preset accuracy threshold, the system can perform fine-grained iterative adjustments to the personalized calibration coefficients based on the direction and magnitude of the deviation. This iterative correction process allows the calibration coefficients to gradually approach the true values, significantly improving the accuracy of the corrected operating parameters. Finally, by recording the changes in calibration coefficients and environmental microclimate parameters before and after correction, valuable data support is provided for subsequent calibration model optimization and fault diagnosis, forming a closed-loop adaptive calibration system.

[0154] Through the above technical solution, this application can significantly improve the accuracy and reliability of LED display screen operating parameter correction. By verifying and iteratively correcting personalized calibration coefficients under controlled conditions, residual deviations caused by sensor aging, environmental changes, or model errors can be effectively eliminated, ensuring that key operating parameters such as the operating temperature of the driver integrated circuit and the driving current flowing through the LED bead cluster are accurately corrected. This high-precision parameter correction makes subsequent cumulative heat pressure calculation and overheat risk identification more accurate, thereby enabling more timely and precise adjustment of driving parameters, effectively reducing the instantaneous heat load in local areas, and preventing premature aging of components. In addition, by recording calibration data, a data foundation is provided for the long-term optimization and adaptive capability of the calibration model, further extending the service life of the LED display screen and improving its operational stability and energy efficiency.

[0155] As a specific implementation, suppose that after long-term operation, the temperature and current sensors of a certain LED display screen may experience slight drift in a local area. Based on the aforementioned LED display screen drive parameter adjustment method, the system has generated a preliminary personalized calibration coefficient for this local area. To verify and optimize this coefficient, the system can collect the actual operating temperature of the driver integrated circuit and the drive current flowing through the LED cluster in this local area when the display screen is in a low-brightness nighttime display mode (e.g., brightness set to 10%) or enters a preset diagnostic test mode. For example, the actual measured operating temperature is 55.2℃, and the drive current is 1.5A. Simultaneously, the system will use the existing personalized calibration coefficient to correct the theoretical or historical data, obtaining a corrected expected operating temperature of 54.8℃ and a corrected expected drive current of 1.48A.

[0156] Subsequently, the system compares the actual collected values ​​(55.2℃, 1.5A) with the corrected expected values ​​(54.8℃, 1.48A). If the preset accuracy thresholds are that the temperature deviation should not exceed 0.2℃ and the current deviation should not exceed 0.01A, then the current temperature deviation is 0.4℃ and the current deviation is 0.02A, both exceeding the preset thresholds. At this point, the system will initiate an iterative correction algorithm based on the direction (higher temperature, higher current) and magnitude of the deviation. For example, the algorithm may fine-tune the personalized calibration coefficients to more accurately predict the actual values ​​in the next correction. This process will be repeated, for example, by collecting and comparing data again in the next low-brightness cycle or test mode, until the temperature deviation and current deviation both converge to within 0.2℃ and 0.01A, respectively. After each iterative correction, the system records the change in the calibration coefficients and the environmental microclimate parameters (such as ambient temperature and humidity) at that time, to facilitate subsequent analysis of the evolution of the calibration coefficients and the impact of environmental factors. Through this iterative verification and correction, the long-term accuracy and adaptability of the personalized calibration coefficients are ensured.

[0157] refer to Figure 2 This application further proposes an LED display screen driving parameter adjustment system, applied to an LED display screen driving parameter adjustment method, the system comprising:

[0158] The parameter acquisition module is used to continuously collect the operating parameters of each local area of ​​the display screen. The operating parameters include the operating temperature of the driver integrated circuit, the driving current flowing through the LED bead cluster, and the cumulative working time of each local area.

[0159] The calculation module calculates the cumulative heat pressure of each local area in real time based on the operating parameters and the calculation rules of the physical characteristics of the components.

[0160] The risk identification module compares the cumulative heat pressure of each local area with the warning threshold. When the cumulative heat pressure of a local area is equal to or greater than the warning threshold, the local area is identified as an overheating risk zone.

[0161] The adjustment module is used to adjust the driving parameters of the overheat risk zone. The adjustment includes reducing the peak driving current of the LED beads in the local area or adjusting the pulse width modulation duty cycle of the peak driving current of the LED beads in the local area to reduce the instantaneous heat load of the local area.

[0162] The processing module continuously monitors the actual energy consumption changes of each local area of ​​the display screen. When the actual energy consumption exceeds the energy consumption benchmark, it optimizes the driving strategy of the local area by combining the data of the accumulated heat pressure of the local area.

[0163] Specifically, the parameter acquisition module can be understood as various sensors and data interfaces integrated inside or outside the display screen. Its purpose is to acquire key operational data of various local areas of the display screen in real time and accurately. For example, the operating temperature of the driver integrated circuit can be measured using a thermistor or integrated temperature sensor; the driving current flowing through the LED bead cluster can be monitored using a current sensor or shunt resistor; and the cumulative operating time of a local area can be recorded using an internal system timer or software counter. This data forms the basis for subsequent thermal stress calculations and risk assessments.

[0164] The calculation module, which can be a dedicated microcontroller, digital signal processor, or embedded system, is configured to receive data from the parameter acquisition module. This module internally stores calculation rules for the physical characteristics of components, such as heat accumulation models based on thermal resistance models, thermal capacity models, or empirical formulas. By substituting the acquired operating parameters into these rules, the calculation module can calculate the cumulative heat pressure of each local area in real time, thereby quantifying its thermal load status.

[0165] In practical applications, the risk identification module can be integrated into the calculation module or exist as a separate software or hardware unit. Its function is to compare the accumulated heat pressure output by the calculation module with a preset warning threshold. When the accumulated heat pressure reaches or exceeds the warning threshold, the module can accurately identify the localized area with overheating risk and issue a corresponding risk signal. The warning threshold can be set according to the LED display's design specifications, component tolerance limits, and the actual application scenario.

[0166] Furthermore, the adjustment module can be a drive controller or power management unit configured to receive risk signals from the risk identification module. When an overheating risk zone is identified, the adjustment module can dynamically adjust the drive parameters for that zone. Specifically, heat generation can be directly reduced by decreasing the peak drive current of the LED chips, or the effective brightness of the LEDs can be indirectly reduced by adjusting the pulse width modulation duty cycle of the peak drive current of the LED chips. These adjustments aim to quickly and effectively alleviate localized overheating conditions.

[0167] Furthermore, the processing module can be a central control unit or intelligent management system configured to continuously monitor the actual energy consumption of each local area of ​​the display screen. When an increase in actual energy consumption exceeding the energy consumption baseline is detected, the processing module will optimize the driving strategy of the local area by combining the accumulated heat pressure data provided by the computing module. This optimization may include adjusting brightness, refresh rate, color correction parameters, etc., to reduce overall energy consumption and avoid potential overheating risks while ensuring display quality.

[0168] This application's solution visualizes the various logical steps in the LED display driver parameter adjustment method as collaborative hardware or software modules, thereby achieving automated, real-time, and systematic execution of the method. The parameter acquisition module, as the data input, ensures continuous acquisition of operating parameters; the calculation module is responsible for the core thermal pressure assessment, transforming raw data into meaningful risk indicators; the risk identification module makes intelligent judgments based on this, promptly identifying potential problems; the adjustment module acts as the actuator, providing immediate intervention to identified risk areas; and finally, the processing module, through continuous monitoring of energy consumption and strategy optimization, achieves macro-control and long-term maintenance of the entire display's operating status. It is precisely this modular design and collaborative work that enables the complex driver parameter adjustment method to be applied efficiently and stably to the operation and management of actual displays.

[0169] Through the above technical solution, this application provides an LED display screen driver parameter adjustment system with a clear structure and well-defined functions, effectively solving problems such as execution efficiency, real-time response, and system integration that may be encountered in practical applications when relying solely on methodologies. This system ensures the automation and intelligence of the LED display screen's operating parameter adjustment process, significantly improving the display screen's operational reliability and lifespan. Furthermore, through modular design, the system possesses excellent scalability and maintainability, facilitating upgrades and optimizations according to actual needs, thus providing solid hardware and software support for the long-term stable operation of the LED display screen.

[0170] In some preferred embodiments, the LED display drive parameter adjustment system can be integrated into a central control unit (CCU). The parameter acquisition module can consist of multiple temperature sensors, current sensors, and timers integrated into the driver chip, distributed on the display backplane. These sensors transmit data to the CCU via I2C or SPI bus. The functions of the calculation module and risk identification module are executed by a microprocessor within the CCU. This microprocessor runs pre-installed firmware, processes sensor data in real time, and performs thermal pressure calculations and risk assessments. The adjustment module can be a programmable logic controller (PLC) or a dedicated driver IC controller. It receives adjustment commands from the CCU and controls the peak drive current or duty cycle of the LED beads through PWM signals or voltage regulation. The processing module can be implemented by advanced management software within the CCU. This software not only monitors energy consumption but also interacts with a cloud platform for remote monitoring and policy updates. For example, when the operating temperature of the driver IC in a certain local area continuously rises and reaches a warning threshold, the risk identification module immediately notifies the adjustment module. The adjustment module then reduces the peak drive current of the LED beads in that area to quickly reduce the instantaneous heat load and prevent component damage. Meanwhile, the processing module will record this event and, in subsequent low-brightness modes, reassess the energy consumption baseline of the area by combining ambient temperature and the brightness distribution of the displayed content, and optimize its long-term driving strategy to achieve more efficient and stable operation.

[0171] The content disclosed above is only a preferred and feasible embodiment of the present invention, and is not intended to limit the scope of protection of the present invention. Therefore, all equivalent technical changes made based on the content of the present invention specification and drawings are included within the scope of protection of the present invention. Furthermore, the elements therein can be updated as technology develops.

Claims

1. A method for adjusting driving parameters of an LED display screen, characterized in that, The method includes the following steps: The system continuously collects operating parameters for each local area of ​​the display screen, including the operating temperature of the driver integrated circuit, the driving current flowing through the LED cluster, and the cumulative operating time of each local area. Based on these operating parameters and the calculation rules for the physical characteristics of the components, the system calculates the cumulative heat pressure of each local area in real time. The system compares the cumulative heat pressure of each local area with a warning threshold. When the cumulative heat pressure of a local area is equal to or greater than the warning threshold, the local area is identified as an overheating risk zone. The driving parameters for the overheating risk zone are adjusted, including reducing the peak driving current of the LEDs in the local area or adjusting the LEDs in the local area... The pulse width modulation duty cycle of the peak drive current of the LED beads is used to reduce the instantaneous heat load in local areas; the actual energy consumption of each local area of ​​the display screen is continuously monitored, and when the actual energy consumption exceeds the energy consumption benchmark, the driving strategy of the local area is optimized by combining the data of the accumulated heat pressure of the local area; the steps of continuously monitoring the actual energy consumption of each local area of ​​the display screen and optimizing the driving strategy of the local area by combining the data of the accumulated heat pressure of the local area include: continuously monitoring the actual energy consumption of the local area; periodically collecting the actual energy consumption of the local area when the display screen is in a low brightness or specific test mode, in conjunction with reference conditions, including ambient temperature and display content brightness distribution; correlating the collected actual energy consumption of the local area with the current reference conditions; performing trend analysis on the periodically collected actual energy consumption of the local area to identify the long-term drift trend of the energy consumption benchmark; correcting the energy consumption benchmark curve according to the long-term drift trend of the energy consumption benchmark; and optimizing the driving strategy of the local area by combining the data of the accumulated heat pressure of the local area when the corrected energy consumption benchmark curve shows that the actual energy consumption exceeds the energy consumption benchmark.

2. The LED display screen driving parameter adjusting method of claim 1, wherein, The steps for obtaining the warning threshold include: calculating the additional thermal resistance of the heat dissipation path in the local area based on the internal junction temperature of the component, the external heat dissipation surface temperature, and the instantaneous power consumption of the component in the local area; and dynamically adjusting the warning threshold based on the additional thermal resistance of the heat dissipation path in the local area.

3. The LED display screen driving parameter adjusting method of claim 1, wherein, The steps for calculating the cumulative heat pressure of each local area in real time, based on operating parameters and the calculation rules for the physical characteristics of components, include: real-time acquisition of the electrical parameters of components in the local area; estimation of the aging state of components based on the electrical parameters of components in the local area; dynamic adjustment of the calculation rules for the physical characteristics of components based on the aging state of components; and real-time calculation of the cumulative heat pressure of each local area based on the adjusted calculation rules for the physical characteristics of components and the operating parameters.

4. The LED display screen driving parameter adjusting method of claim 1, wherein, The method also includes correcting the operating parameters: periodically collecting the operating temperature of the driver integrated circuit and the driving current flowing through the LED bead cluster in a preset low-load operating mode; simultaneously collecting the environmental microclimate parameters of the local area; and evaluating the aging rate and drift trend of the sensor in the local area based on the environmental microclimate parameters of the local area. Based on the aging rate and drift trend of the sensor in a local area, a personalized calibration coefficient is dynamically generated for each local area. The operating temperature of the driver integrated circuit and the driving current flowing through the LED bead cluster are corrected in real time using personalized calibration coefficients collected in the local area; the operating parameters of the local area are periodically sampled and compared using external reference sensors, and the personalized calibration coefficients are adjusted according to the comparison results.

5. The method for adjusting LED display screen driving parameters as described in claim 4, characterized in that, The steps for dynamically generating personalized calibration coefficients for each local area based on the aging rate and drift trend of the local area sensors include: continuously monitoring the instantaneous changes of the local area's environmental microclimate parameters; when the environmental microclimate parameters of the local area are found to have changed beyond a preset fluctuation threshold, triggering a reassessment of the aging rate and drift trend of the local area sensors; and updating the personalized calibration coefficients of the local area sensors in real time based on the reassessed aging rate and drift trend of the local area sensors.

6. The method of claim 4, wherein the LED display screen driving parameter adjustment method is characterized in that, The steps of periodically sampling and comparing the operating parameters of a local area using an external reference sensor, and adjusting the personalized calibration coefficients based on the comparison results, include: assessing the performance degradation of the external reference sensor based on the microclimate parameters of the environment where the external reference sensor is located; triggering a cross-comparison of multiple external reference sensors when the performance degradation of the external reference sensor exceeds a preset degradation threshold, and correcting the data of the external reference sensor with performance degradation based on the cross-comparison results; and adjusting the personalized calibration coefficients based on the comparison results of the corrected external reference sensor data and the operating parameters of the local area.

7. The method of claim 4, wherein the LED display screen driving parameter adjustment method is characterized in that, The steps for dynamically generating personalized calibration coefficients for each local area sensor based on the aging rate and drift trend of the local area sensor include: identifying the type, batch, and manufacturer information of the local area sensor; retrieving the corresponding inherent performance parameters and aging characteristic curves from a preset sensor characteristic database based on the type, batch, and manufacturer information of the local area sensor; and combining the retrieved inherent performance parameters and aging characteristic curves with the aging rate and drift trend of the local area sensor to generate personalized calibration coefficients.

8. The method of claim 4, wherein the LED display screen driving parameter adjustment method is characterized in that, After generating the personalized calibration coefficients, the following steps are further included: When the display screen is in low brightness or a specific test mode, collect the actual operating parameters of the local area; compare the actual operating parameters of the local area with the operating temperature of the driver integrated circuit and the driving current flowing through the LED bead cluster after correction based on the personalized calibration coefficients; when the deviation of the comparison result exceeds a preset accuracy threshold, iteratively correct the personalized calibration coefficients according to the direction and magnitude of the deviation until the deviation converges to within the preset accuracy threshold; record the change in the personalized calibration coefficients before and after correction and the corresponding environmental microclimate parameters of the local area.

9. An LED display screen driving parameter adjusting system applied to the LED display screen driving parameter adjusting method of claim 1, characterized in that, The system includes: a parameter acquisition module for continuously acquiring operating parameters of each local area of ​​the display screen, including the operating temperature of the driver integrated circuit, the driving current flowing through the LED bead cluster, and the cumulative operating time of each local area; a calculation module for calculating the cumulative heat pressure of each local area in real time based on the operating parameters and the calculation rules of the component physical characteristics; a risk identification module for comparing the cumulative heat pressure of each local area with a warning threshold, identifying the local area as an overheating risk zone when the cumulative heat pressure of a local area is equal to or greater than the warning threshold; and an adjustment module for adjusting the driving parameters of the overheating risk zone, including reducing the peak driving current of the LED beads in the local area or adjusting the LEDs in the local area. The pulse width modulation duty cycle of the peak drive current of the LED beads is used to reduce the instantaneous heat load in local areas. A processing module continuously monitors the actual energy consumption changes in each local area of ​​the display screen. When the actual energy consumption exceeds the energy consumption benchmark, the driving strategy for the local area is optimized based on the accumulated heat pressure data of the local area. The processing module includes: continuously monitoring the actual energy consumption changes in local areas; periodically collecting the actual energy consumption of local areas when the display screen is in low brightness or a specific test mode, based on reference conditions including ambient temperature and display content brightness distribution; correlating the collected actual energy consumption of local areas with the current reference conditions; performing trend analysis on the periodically collected actual energy consumption of local areas to identify the long-term drift trend of the energy consumption benchmark; correcting the energy consumption benchmark curve based on the long-term drift trend of the energy consumption benchmark; and optimizing the driving strategy for the local area when the corrected energy consumption benchmark curve shows an increase in actual energy consumption exceeding the energy consumption benchmark, based on the accumulated heat pressure data of the local area.

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