A temperature monitoring and adjustment method for display screen

Abstract

The present invention relates to the field of temperature control technology, specifically to a temperature monitoring and adjustment method for a display screen, comprising the following steps: using a temperature sensor to capture the temperature data of the display screen in real time, using information entropy to evaluate the difference and complexity of the temperature data, obtaining an entropy analysis result, identifying abnormal fluctuations in the data according to the entropy analysis result, and generating abnormal fluctuation monitoring results. In the present invention, by introducing information entropy to evaluate the difference and complexity of temperature data, abnormal fluctuations can be accurately identified, the output of the heat dissipation device can be adjusted in time, the response speed and prediction ability of the temperature management of the display screen can be improved, the temperature control accuracy can be significantly enhanced, and the stability of the device in a changing environment can be ensured. The designed temperature trend prediction algorithm uses historical and current data to predict future changes, enhances adaptability, reduces maintenance costs, optimizes energy use, reduces energy consumption, and continuously protects device performance by dynamically adjusting the control strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature control, and in particular to a temperature monitoring and regulating method for a display screen. Background Art

[0002] The field of temperature control technology covers a range of methods and devices for monitoring and regulating the temperature of equipment, environments or systems. The field is particularly critical for applications that are extremely sensitive to temperature, such as electronic equipment, chemical processes and biotechnology. Temperature control technology can be divided into two methods: active and passive. Active temperature control technology uses external energy to increase or decrease the temperature of the system, such as heaters, fans, refrigeration systems, etc. Passive temperature control relies on the thermal properties of materials to maintain temperature, such as using insulation materials or thermal barrier technology. Modern temperature control technology also combines sensor technology and intelligent algorithms, such as PID controllers or fuzzy logic, which can adjust control strategies based on real-time data to optimize the efficiency and effectiveness of temperature regulation.

[0003] Among them, the temperature monitoring and adjustment method for display screens is a temperature management technology specifically for electronic display devices. Display screens generate heat during operation. If not controlled, excessive temperature will damage the performance and life of the display screen. By using temperature sensors to monitor the real-time temperature of the display screen, and starting the corresponding heat dissipation or heating equipment according to the temperature data, the display screen is kept within an ideal operating temperature range. The application of such technology is crucial to improving the stability and reliability of display devices, especially in those application scenarios that are sensitive to ambient temperature changes or require long-term operation, such as outdoor advertising screens, vehicle information display screens, and medical monitoring equipment.

[0004] Existing technologies rely on more traditional sensors and reactive adjustment mechanisms for temperature control, which limits their effectiveness in dealing with rapidly changing or unforeseen environmental conditions. For example, traditional methods cannot adjust in time when rapid temperature changes occur because they only react when the temperature exceeds a preset threshold, causing the display to be exposed to unsuitable operating temperatures before timely adjustments. Existing technologies have static heat dissipation strategies and lack dynamic adjustment mechanisms, which limits flexibility and efficiency in temperature control. This lack of predictive and adaptive temperature management leads to energy waste, maintaining high-power heat dissipation when not needed, or insufficient response when needed. Existing technologies fail to fully utilize data analysis to optimize temperature management, resulting in unstable performance of equipment under extreme or unusual environmental conditions, increasing failure rates and maintenance costs. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a temperature monitoring and adjustment method for a display screen.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a method for temperature monitoring and adjustment of a display screen, comprising the following steps:

[0007] S1: Capture the temperature data of the display screen in real time through a temperature sensor, use information entropy to evaluate the difference and complexity of the temperature data, obtain entropy analysis results, identify abnormal fluctuations in the data according to the entropy analysis results, and generate abnormal fluctuation monitoring results;

[0008] S2: Based on the abnormal fluctuation monitoring result, evaluate whether the temperature of the current display screen exceeds the safe operating range, adjust the heat dissipation output according to the evaluation result, and perform temperature control to obtain a heat dissipation configuration adjustment result, optimize the temperature control scheme according to the heat dissipation configuration adjustment result, and generate a temperature control optimization result;

[0009] S3: designing a temperature trend prediction algorithm according to the temperature control optimization result, using historical and current temperature data to predict temperature changes in a future time period, obtaining a future temperature prediction model, evaluating temperature fluctuations in a future time period according to the future temperature prediction model, and generating a future temperature control strategy;

[0010] S4: According to the future temperature control strategy, the operating parameters of the heat dissipation device are automatically adjusted to match the predicted temperature changes, the configuration of the heat dissipation device is updated, and a heat dissipation configuration update instruction is obtained. According to the heat dissipation configuration update instruction, the deviation between the real-time temperature data and the predicted data is continuously monitored to generate a dynamic temperature control strategy adjustment result.

[0011] As a further solution of the present invention, the step of obtaining the abnormal fluctuation monitoring result is specifically as follows:

[0012] S111: capturing temperature data of the display screen in real time through a temperature sensor, recording the temperature data in real time, and forming a time-series temperature data set, wherein the time-series temperature data set includes a temperature value of each measurement point and a corresponding timestamp;

[0013] S112: Perform entropy analysis on the time-series temperature data set to evaluate the difference and complexity of the data, using the formula:

[0014]

[0015] By adding the regularization of the logarithmic term in the form of weighted sum, the entropy value analysis result is obtained, where p i represents the probability of the ith temperature value, H a Indicates the entropy analysis result;

[0016] S113: Based on the entropy analysis result, an entropy threshold is set, and temperature data points exceeding the threshold are identified. If the entropy analysis result is greater than the entropy threshold, it is marked as abnormal, abnormal fluctuations in the data set are identified, and abnormal fluctuation monitoring results are generated.

[0017] As a further solution of the present invention, the step of obtaining the heat dissipation configuration adjustment result is specifically:

[0018] S211: According to the abnormal fluctuation monitoring result, the real-time temperature data of the display screen is captured and compared with the set upper limit of the safe temperature, and the degree to which the temperature exceeds the safe range is calculated using the formula:

[0019] E b =T b -θ b

[0020] Generates temperature deviation evaluation results, where T b Indicates the real-time monitored display temperature, θ b is the safety temperature threshold, E b Indicates the temperature deviation evaluation result;

[0021] S212: Based on the temperature deviation evaluation result, determine whether the heat dissipation output needs to be adjusted, using a nonlinear adjustment formula:

[0022]

[0023] Generate the cooling output adjustment index, where k b is the sensitivity adjustment coefficient, m b is the deviation median adjustment point, E b Indicates the temperature deviation evaluation result, A b Indicates the heat dissipation output adjustment index;

[0024] S213: According to the heat dissipation output adjustment index, the performance of the current heat dissipation device is evaluated to determine the current heat dissipation efficiency and existing problems, and the operating parameters of the heat dissipation fan are adjusted using the formula:

[0025] C b =max(0,min(A b ,1))

[0026] Get the cooling configuration adjustment result, where A b is the heat dissipation output adjustment index, C b It is the result of cooling configuration adjustment.

[0027] As a further solution of the present invention, the step of obtaining the temperature control optimization result is specifically:

[0028] S221: According to the cooling configuration adjustment result, current temperature data is captured and analyzed, and real-time performance of the cooling configuration is determined by calculating the deviation between the current temperature data and the target safety temperature using the formula:

[0029] D c =arg(T c )-θ c

[0030] Generates temperature deviation evaluation results, where T c Represents the continuously monitored temperature data, θ c is the predetermined safety temperature threshold, D c Indicates the temperature deviation evaluation result;

[0031] S222: According to the temperature deviation evaluation result, a nonlinear response formula is used to optimize the heat dissipation effect, by formula:

[0032]

[0033] Generate a heat dissipation efficiency adjustment index, where λ c is the adjustment factor of heat dissipation efficiency, n c Indicates the sensitivity to temperature deviation, D c is the temperature deviation evaluation result, E c Indicates the heat dissipation efficiency adjustment index;

[0034] S223: Determine the optimal configuration of temperature control by using the heat dissipation efficiency adjustment index, and apply the formula:

[0035] O c =max(0,min(1,C c +E c ))

[0036] The temperature control optimization result is obtained, where E c Adjustment index for heat dissipation efficiency, C c is the current cooling configuration, O c Indicates the temperature control optimization result.

[0037] As a further solution of the present invention, the step of acquiring the future temperature prediction model is specifically as follows:

[0038] S311: Analyze the temperature control optimization result, extract historical temperature data and current temperature data, and use weighted average method to calculate the fusion value of historical and current temperature, through the formula:

[0039] A d =α d avg(H d )+(1-α d)·C d

[0040] Generate temperature analysis results, where H d Represents historical temperature data, C d is the current temperature data, α d Adjust the weight of historical and current data, A d is the temperature analysis result;

[0041] S312: Based on the temperature analysis result, the temperature in the future time period is predicted using the exponential smoothing method, using the formula:

[0042] F d =β d ·A d +(1-β d )·P d

[0043] Generate temperature prediction index, where P d is the previous prediction result, β d is the smoothing coefficient, A d is the temperature analysis result, F d represents the temperature prediction index;

[0044] S313: Combining the temperature prediction index and the associated parameters, by formula:

[0045] M d =F d +γ d ·(δ d -F d )

[0046] Optimize the temperature prediction in the future time period and obtain the future temperature prediction model, where γ d is the adjustment factor, δ d represents the long-term temperature target, F d represents the temperature prediction index, M d It is a future temperature prediction model.

[0047] As a further solution of the present invention, the steps of obtaining the future temperature control strategy are specifically as follows:

[0048] S321: Analyze the future temperature prediction model, evaluate the predicted temperature fluctuation, and use the standard deviation formula:

[0049]

[0050] The future temperature fluctuation assessment results are obtained, where T h,i represents a single temperature value predicted by the model, is the average temperature value, nh is the total number of data points, V h is the result of the assessment of future temperature fluctuations;

[0051] S322: Compare the future temperature fluctuation assessment result with the set safety threshold, using the difference analysis formula:

[0052] D h =V h -T h

[0053] If D h If it is a positive value, it means that the temperature fluctuation exceeds the threshold, and the difference between the temperature fluctuation and the safety threshold is obtained, where V h is the result of the future temperature fluctuation assessment, T h is the predetermined safety threshold, D h Represents the difference between temperature fluctuation and safety threshold;

[0054] S323: Based on the difference between the temperature fluctuation and the safety threshold, if the difference is greater than zero, increase the heat dissipation intensity; otherwise, maintain the current state and obtain a future temperature control strategy.

[0055] As a further solution of the present invention, the step of obtaining the heat dissipation configuration update instruction is specifically as follows:

[0056] S411: According to the future temperature control strategy, the predicted temperature data is captured, using the formula:

[0057] T adj =(T pred +E)·(1+Δt)

[0058] Introduce environmental impact factors and time difference correction coefficients to obtain the adjusted target temperature, where T pred is the predicted future temperature, E is the environmental impact factor, Δt is the time difference correction coefficient, T adj is the adjusted target temperature;

[0059] S412: Using the adjusted target temperature to adjust the operating parameters of the heat dissipation device, according to the current device state and device performance, using the formula:

[0060]

[0061] Calculate the parameters of the new cooling device, where P old is the current operating parameter of the cooling device, T current is the current temperature, T max is the maximum adjustable temperature range, η is the device efficiency, P new It is the parameter of the new cooling device;

[0062] S413: Update the configuration of the heat dissipation device according to the new heat dissipation device parameters, using the formula:

[0063] C new =C old +ΔC·V

[0064] Introduce the configuration version change factor to generate the cooling configuration update instruction, where C old is the current cooling configuration, ΔC is the configuration change calculated based on the new parameters, V is the configuration version change factor, and C new It is the instruction to update the cooling configuration.

[0065] As a further solution of the present invention, the step of obtaining the dynamic temperature control strategy adjustment result is specifically as follows:

[0066] S421: According to the heat dissipation configuration update instruction, the ambient temperature and the predicted temperature are captured in real time, using the formula:

[0067]

[0068] Calculate the deviation between the real-time temperature and the predicted temperature to obtain the temperature deviation data, where: Indicates the current temperature monitored in real time. Indicates the predicted temperature in the future time period, ΔT v Indicates temperature deviation data;

[0069] S422: Analyze the temperature deviation data, calculate the average deviation and standard deviation of multiple temperature points, analyze the fluctuation range of the data, determine the matching adjustment threshold according to the statistical distribution of the deviation, and optimize the temperature control using the formula:

[0070]

[0071] Calculate and generate adjustment threshold data, where ΔT v is the temperature deviation data, α v is the influence coefficient, β v is the equilibrium constant, Θ v It is to adjust the threshold data;

[0072] S423: Based on the adjustment threshold data and the real-time temperature data, apply the formula:

[0073]

[0074] Calculate the new temperature target and generate the dynamic temperature control strategy adjustment result, where Θ v is to adjust the threshold data, is the current temperature monitored in real time, ΔT v Indicates temperature deviation data, Rv It is the result of dynamic temperature control strategy adjustment.

[0075] Compared with the prior art, the advantages and positive effects of the present invention are:

[0076] In the present invention, by introducing information entropy to evaluate the difference and complexity of temperature data, it is possible to accurately identify abnormal fluctuations in the data, and adjust the output of the heat dissipation device according to the fluctuations. By early identification and response to abnormal fluctuations, the response speed and prediction ability of the display screen temperature management are improved. The accuracy of temperature control is significantly improved, thereby ensuring the reliability and stability of the display device in various environments. The design of the temperature trend prediction algorithm allows the use of historical and current temperature data to predict future temperature changes, enhance the adaptability and foresight of the configuration, reduce maintenance costs and potential equipment failure risks, and through this active temperature adjustment strategy, not only the heat dissipation efficiency is improved, but also the energy use is optimized, and energy consumption is significantly reduced. The deviation between real-time and predicted data is continuously monitored, and its control strategy is dynamically adjusted to ensure long-term and continuous equipment performance protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 It is a schematic diagram of the main steps of the present invention;

[0078] Figure 2 It is a flow chart of abnormal fluctuation monitoring results in the present invention;

[0079] Figure 3 A flow chart showing the heat dissipation configuration adjustment result in the present invention;

[0080] Figure 4 It is a flow chart of the temperature control optimization results in the present invention;

[0081] Figure 5 It is a flow chart of the future temperature prediction model in the present invention;

[0082] Figure 6 is a flow chart of the future temperature control strategy in the present invention;

[0083] Figure 7 A flow chart of the heat dissipation configuration update instruction in the present invention;

[0084] Figure 8 This is a flow chart of the dynamic temperature control strategy adjustment results in the present invention. DETAILED DESCRIPTION

[0085] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0086] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0087] Embodiment 1

[0088] See also Figure 1 The present invention provides a technical solution: a method for temperature monitoring and adjustment of a display screen, comprising the following steps:

[0089] S1: Use the temperature sensor to capture the temperature data of the display screen in real time, use information entropy to evaluate the difference and complexity of the temperature data, obtain the entropy analysis results, identify abnormal fluctuations in the data based on the entropy analysis results, and generate abnormal fluctuation monitoring results;

[0090] S2: Based on the abnormal fluctuation monitoring results, evaluate whether the temperature of the current display screen exceeds the safe operating range, adjust the heat dissipation output according to the evaluation results, and perform temperature control to obtain the heat dissipation configuration adjustment results. Based on the heat dissipation configuration adjustment results, optimize the temperature control plan and generate the temperature control optimization results;

[0091] S3: Based on the temperature control optimization results, a temperature trend prediction algorithm is designed. The temperature changes in the future time period are predicted using the historical and current temperature data to obtain a future temperature prediction model. Based on the future temperature prediction model, the temperature fluctuations in the future time period are evaluated to generate a future temperature control strategy.

[0092] S4: According to the future temperature control strategy, the operating parameters of the heat dissipation device are automatically adjusted to match the predicted temperature changes, the configuration of the heat dissipation device is updated, and the heat dissipation configuration update instruction is obtained. According to the heat dissipation configuration update instruction, the deviation between the real-time temperature data and the predicted data is continuously monitored to generate the dynamic temperature control strategy adjustment result.

[0093] The entropy analysis results include the entropy standard deviation, time series variation coefficient, and data discreteness. The abnormal fluctuation monitoring results include abnormal frequency, fluctuation abnormality index, and abnormal duration. The heat dissipation configuration adjustment results include the number of heat dissipation channels, adjustment accuracy, and response speed. The temperature control optimization results include the optimized energy efficiency ratio, maintenance temperature deviation, and control system stability. The future temperature prediction model includes the model response speed, historical data fitness, and prediction error range. The future temperature control strategy includes the strategy matching change capability, execution condition strictness, and strategy update frequency. The heat dissipation configuration update instructions include update trigger conditions, update execution speed, and efficiency improvement after update. The dynamic temperature control strategy adjustment results include the adjusted temperature fluctuation range, adjustment reaction time, and adjustment environmental adaptability.

[0094] See also Figure 2 , the specific steps for obtaining abnormal fluctuation monitoring results are:

[0095] S111: Capturing temperature data of the display screen in real time through a temperature sensor, recording the temperature data in real time, and forming a time-series temperature data set, where the time-series temperature data set includes a temperature value of each measurement point and a corresponding timestamp;

[0096] S112: Perform entropy analysis on the time-series temperature data set to evaluate the difference and complexity of the data, using the formula:

[0097]

[0098] By adding the regularization of the logarithmic term in the form of weighted sum, the entropy value analysis result is obtained, where p i represents the probability of the ith temperature value, H a Indicates the entropy analysis result;

[0099] S113: Based on the entropy analysis result, an entropy threshold is set, and temperature data points exceeding the threshold are identified. If the entropy analysis result is greater than the entropy threshold, it is marked as abnormal, abnormal fluctuations in the data set are identified, and abnormal fluctuation monitoring results are generated.

[0100] The formula for entropy analysis results is:

[0101]

[0102] p i : The probability of the i-th temperature value appearing in the data set;

[0103] Set the data set D obtained from the temperature sensor a There are 100 data points, 20 of which are recorded at 30°C and 80 are recorded at 31°C. Based on the data, the probability of occurrence of each temperature value can be calculated:

[0104] p(30°C)=0.2

[0105] p(31°C)=0.8

[0106] Calculate the entropy contribution H at 30℃ and 31℃ a :

[0107] For 30℃:

[0108] -(0.2log(0.2)+0.8log(0.8))=-(0.2×(-2.3219)+0.8×(-0.3219))≈0.57

[0109] For 31°C:

[0110] -(0.8log(0.8)+0.2log(0.2))=-(0.8×(-0.3219)+0.2×(-2.3219))≈0.57

[0111] The logarithmic function uses the natural logarithm;

[0112] Calculate the total entropy from all data points: H a = 0.5795 (since each temperature value has the same entropy contribution, the total entropy is also this value);

[0113] The final calculated entropy value H a ≈0.5795 represents the randomness and complexity of the data set. Lower entropy values ​​indicate lower uncertainty in the data and more consistent changes.

[0114] See also Figure 3 , the specific steps for obtaining the cooling configuration adjustment results are:

[0115] S211: Based on the abnormal fluctuation monitoring results, capture the real-time temperature data of the display screen and compare it with the set safety temperature upper limit to calculate the degree to which the temperature exceeds the safety range, using the formula:

[0116] E b =T b -θ b

[0117] Generates temperature deviation evaluation results, where T b Indicates the real-time monitored display temperature, θ b is the safety temperature threshold, E b Indicates the temperature deviation evaluation result;

[0118] S212: Based on the temperature deviation evaluation result, determine whether the heat dissipation output needs to be adjusted, using a nonlinear adjustment formula:

[0119]

[0120] Generate the cooling output adjustment index, where k b is the sensitivity adjustment coefficient, m b is the deviation median adjustment point, E b Indicates the temperature deviation evaluation result, A b Indicates the heat dissipation output adjustment index;

[0121] S213: According to the heat dissipation output adjustment index, the performance of the current heat dissipation device is evaluated to determine the current heat dissipation efficiency and existing problems, and the operating parameters of the heat dissipation fan are adjusted using the formula:

[0122] C b =max(0,min(A b ,1))

[0123] Get the cooling configuration adjustment result, where A b is the heat dissipation output adjustment index, C b It is the result of cooling configuration adjustment.

[0124] The formula for the temperature deviation evaluation result is:

[0125] E b =T b -θ b

[0126] T b : The current display screen temperature, set the actual monitoring value to 45℃;

[0127] θ b : Safety temperature upper limit, set this value to 40℃ according to the manufacturer's specifications;

[0128] Calculate the temperature deviation:

[0129] E b =45-40=5℃

[0130] Temperature deviation E b =5℃ means the current temperature exceeds the safety threshold by 5℃ and cooling adjustment is required.

[0131] Heat dissipation output adjustment index formula:

[0132]

[0133] Set k b =0.1, the value obtained from the response curve analysis, used to adjust the response speed of the heat dissipation device;

[0134] Set m b =3, set this as the median adjustment point set during design;

[0135] Calculate the nonlinear response required for thermal adjustment:

[0136]

[0137] Cooling output adjustment index A b ≈0.45 means that the cooling system needs to be adjusted to approximately 45% of its maximum output to cope with the current temperature exceedance.

[0138] The formula for the cooling configuration adjustment result is:

[0139] C b =max(0,min(A b ,1))

[0140] A b : The heat dissipation output adjustment index calculated from the previous step, 0.45;

[0141] Ensure that the cooling configuration is within the effective range:

[0142] C b =max(0,min(0.45,1))=0.45

[0143] Cooling configuration adjustment result C b =0.45 means the cooling system will be adjusted to 45% of the maximum output to maintain the temperature of the display within a safe range.

[0144] See also Figure 4 , the specific steps for obtaining the temperature control optimization results are:

[0145] S221: Based on the cooling configuration adjustment result, capture and analyze the current temperature data, and determine the real-time performance of the cooling configuration by calculating the deviation between the current temperature data and the target safe temperature, using the formula:

[0146] D c =arg(T c )-θ c

[0147] Generates temperature deviation evaluation results, where T c Represents the continuously monitored temperature data, θ c is the predetermined safety temperature threshold, D c Indicates the temperature deviation evaluation result;

[0148] S222: Based on the temperature deviation evaluation results, a nonlinear response formula is used to optimize the heat dissipation effect, using the formula:

[0149]

[0150] Generate a heat dissipation efficiency adjustment index, where λ c is the adjustment factor of heat dissipation efficiency, n c Indicates the sensitivity to temperature deviation, D c is the temperature deviation evaluation result, E c Indicates the heat dissipation efficiency adjustment index;

[0151] S223: Use the heat dissipation efficiency adjustment index to determine the optimal configuration of temperature control, and apply the formula:

[0152] O c =max(0,min(1,C c +E c ))

[0153] The temperature control optimization result is obtained, where E c Adjustment index for heat dissipation efficiency, C c is the current cooling configuration, O c Indicates the temperature control optimization result.

[0154] The formula for the temperature deviation evaluation result is:

[0155] D c =arg(T c )-θ c

[0156] T c : represents the temperature data sequence collected from the temperature sensor in real time. For example, if the temperature values ​​collected within a certain period of time are [38℃, 42℃, 45℃];

[0157] θ c : A predetermined safety temperature threshold, for example, set to 40°C;

[0158] Compute the mean of a temperature series:

[0159]

[0160] Calculate the temperature deviation:

[0161] D c =41.67-40=1.67℃

[0162] Temperature deviation D c =1.67°C means that the current operating temperature is 1.67°C higher than the safety threshold, indicating that the cooling configuration needs to be adjusted.

[0163] The formula for adjusting the heat dissipation efficiency index is:

[0164]

[0165] Let λ c=0.5 is the heat dissipation efficiency adjustment factor, which is the standard value determined through historical data analysis;

[0166] Assume n c =2 is the exponent of nonlinear response, which enhances the sensitivity of the model to temperature changes;

[0167] Apply the formula to calculate the cooling efficiency adjustment index:

[0168]

[0169] Heat dissipation efficiency adjustment index E c ≈0.2205 indicates the degree to which adjustment of the cooling configuration is needed, and lower values ​​indicate that the temperature is slightly exceeded but does not require major adjustments.

[0170] The formula for temperature control optimization result is:

[0171] O c =max(0,min(1,C c +E c ))

[0172] C c : Current heat dissipation configuration adjustment result, set the current heat dissipation configuration to 0.6;

[0173] Calculate the final temperature control optimization results:

[0174] O c =max(0,min(1,0.6+0.2205))=max(0,min(1,0.8205))=0.8205

[0175] Temperature control optimization results c =0.8205 indicates that the heat dissipation configuration is appropriately increased to maintain the device within the ideal operating temperature range.

[0176] See also Figure 5 , the specific steps for obtaining the future temperature prediction model are:

[0177] S311: Analyze the temperature control optimization results, extract historical temperature data and current temperature data, and use the weighted average method to calculate the fusion value of historical and current temperatures through the formula:

[0178] A d =α d avg(H d )+(1-α d )·C d

[0179] Generate temperature analysis results, where H d Represents historical temperature data, C dis the current temperature data, α d Adjust the weight of historical and current data, A d is the temperature analysis result;

[0180] S312: Based on the temperature analysis results, the exponential smoothing method is used to predict the temperature in the future time period, through the formula:

[0181] F d =β d ·A d +(1-β d )·P d

[0182] Generate temperature prediction index, where P d is the previous prediction result, β d is the smoothing coefficient, A d is the temperature analysis result, F d represents the temperature prediction index;

[0183] S313: Combine temperature prediction index and associated parameters, through the formula:

[0184] M d =F d +γ d ·(δ d -F d )

[0185] Optimize the temperature prediction in the future time period and obtain the future temperature prediction model, where γ d is the adjustment factor, δ d represents the long-term temperature target, F d represents the temperature prediction index, M d It is a future temperature prediction model.

[0186] The formula for temperature analysis results is:

[0187] A d =α d avg(H d )+(1-α d )·C d

[0188] H d : Historical temperature data, extracted from the device's temperature sensor history record. For example, the average temperature data for the past week is set to [35℃, 36℃, 37℃];

[0189] C d : Current temperature data, obtained directly from the current temperature reading of the device, such as 38°C;

[0190] α d: Balance coefficient, set according to the importance of historical data and set to 0.7;

[0191] Calculate the average of historical temperatures:

[0192]

[0193] Apply the formula to calculate the comprehensive temperature analysis results:

[0194] A d =0.7·36+0.3·38=25.2+11.4=36.6℃

[0195] Comprehensive temperature analysis results A d =36.6℃ represents the weighted average temperature after considering historical and current data.

[0196] The formula for the temperature prediction index is:

[0197] F d =β d ·A d +(1-β d )·P d

[0198] β d : Smoothing coefficient, set to 0.6, to reflect the impact of current data on the prediction;

[0199] P d : The last prediction result is set to 35℃;

[0200] Use the A obtained in the previous step d and P d Perform the calculation:

[0201] F d =0.6·36.6+0.4·35=21.96+14=35.96

[0202] Temperature prediction index F d =35.96℃ represents the new value for the predicted future temperature, combining historical and current temperature data.

[0203] The formula for the future temperature prediction model is:

[0204] M d =F d +γ d ·(δ d -F d )

[0205] γ d : Adjustment factor, set to 0.1, indicating the adjustment sensitivity to the long-term target;

[0206] δ d : Long-term temperature target, set at 37°C;

[0207] According to F d and target δ d Calculate the final temperature prediction model:

[0208] M d =35.96+0.1·(37-35.96)=35.96+0.104=36.064℃

[0209] Future temperature prediction model M d = 36.064°C represents the future temperature predicted by the device, taking into account the current trend and adjustment factors.

[0210] See also Figure 6 , the specific steps for obtaining the future temperature control strategy are:

[0211] S321: Analyze the future temperature prediction model and evaluate the predicted temperature fluctuations using the standard deviation formula:

[0212]

[0213] The future temperature fluctuation assessment results are obtained, where T h,i represents a single temperature value predicted by the model, is the average temperature value, n h is the total number of data points, V h is the result of the assessment of future temperature fluctuations;

[0214] S322: Compare the future temperature fluctuation assessment results with the set safety threshold, using the difference analysis formula:

[0215] D h =V h -T h

[0216] If D h If it is a positive value, it means that the temperature fluctuation exceeds the threshold, and the difference between the temperature fluctuation and the safety threshold is obtained, where V h is the result of the future temperature fluctuation assessment, T h is the predetermined safety threshold, D h Represents the difference between temperature fluctuation and safety threshold;

[0217] S323: Based on the difference between the temperature fluctuation and the safety threshold, if the difference is greater than zero, increase the heat dissipation intensity; otherwise, maintain the current state and obtain a future temperature control strategy.

[0218] The formula for evaluating the results of future temperature fluctuations is:

[0219]

[0220] T h,i :By predicting the temperature model M h The specific future predicted temperature values ​​obtained, for example, the predicted temperature data for a week is [22℃, 24℃, 23℃, 25℃, 24℃];

[0221] The arithmetic mean of the temperature values, i.e.

[0222] n h : The total number of data points, 5 in this case;

[0223] Compute the sum of the squares of the deviations of each data point from the mean:

[0224]

[0225] Apply the formula to calculate the standard deviation:

[0226]

[0227] Calculated temperature fluctuation V h =1.14℃ represents the standard deviation of temperature within a week, reflecting the degree of dispersion of temperature prediction. The formula for the difference between temperature fluctuation and safety threshold is:

[0228] D h =V h -T h

[0229] T h : The set safety threshold is determined according to the specific application environment and is set to 2°C;

[0230] Use the V calculated in the previous step h And the given T h For comparison:

[0231] D h =1.14-2=-0.86

[0232] The difference D h =-0.86℃ means the actual temperature fluctuation is lower than the safety threshold, indicating that the temperature fluctuation is within the safety range;

[0233] According to the evaluation results of future temperature fluctuations, if D h If it is greater than zero, the heat dissipation needs to be increased. Otherwise, the current state is maintained. h =-0.86℃, select "maintain current state";

[0234] The final future temperature control strategy Sh is to maintain the current cooling settings because the temperature fluctuations do not exceed the preset safety threshold.

[0235] See also Figure 7 , the specific steps for obtaining the cooling configuration update instruction are:

[0236] S411: Capture predicted temperature data based on future temperature control strategies using the formula:

[0237] T adj =(T pred +E)·(1+Δt)

[0238] Introduce environmental impact factors and time difference correction coefficients to obtain the adjusted target temperature, where T pred is the predicted future temperature, E is the environmental impact factor, Δt is the time difference correction coefficient, T adj is the adjusted target temperature;

[0239] S412: Use the adjusted target temperature to adjust the operating parameters of the heat dissipation device according to the current device status and device performance, using the formula:

[0240]

[0241] Calculate the parameters of the new cooling device, where P old is the current operating parameter of the cooling device, T current is the current temperature, T max is the maximum adjustable temperature range, η is the device efficiency, P new It is the parameter of the new cooling device;

[0242] S413: Update the configuration of the cooling device according to the new cooling device parameters, using the formula:

[0243] C new =C old +ΔC·V

[0244] Introduce the configuration version change factor to generate the cooling configuration update instruction, where C old is the current cooling configuration, ΔC is the configuration change calculated based on the new parameters, V is the configuration version change factor, and C new It is the instruction to update the cooling configuration.

[0245] Adjusted target temperature formula:

[0246] T adj =(T pred +E)·(1+Δt)

[0247] T pred: Predicted future temperature, assuming that according to the meteorological model, today's future temperature is 35℃;

[0248] E: Environmental impact factor. Based on the analysis of the historical temperature data of the region, this factor is set to 2°C, indicating that the actual temperature will be 2°C higher than the predicted temperature. This value is obtained based on the average deviation between the actual temperature and the predicted temperature in the same period of the past few years.

[0249] Δt: time difference correction coefficient. If the time difference between the prediction and the actual time is 6 hours, this coefficient is set to 0.05 through historical data analysis, which means that the predicted temperature deviation increases by 5% for every hour that passes;

[0250] Calculate T adj :

[0251] T adj =(35+2)·(1+0.05)=37·1.05=38.85

[0252] Final result adj =38.85°C, representing the adjusted target temperature, which is the actual predicted temperature after correction based on environmental influences and time differences.

[0253] Formula for new cooling device parameters:

[0254]

[0255] P old : The current operating parameters of the cooling device are obtained through the device monitoring system and are set to 150W;

[0256] T current : The current temperature is obtained in real time through the temperature sensor and is set to 32°C;

[0257] T max : The maximum adjustable temperature range is set to 10°C based on the equipment design parameters;

[0258] η: Equipment efficiency, obtained through equipment performance testing, with a set value of 0.8, indicating equipment operating efficiency;

[0259] Calculate P new :

[0260]

[0261] Final result new =278.4, representing the updated cooling device parameters.

[0262] The formula for the cooling configuration update instruction is:

[0263] C new =C old+ΔC·V

[0264] C old : The current cooling configuration, obtained from the system configuration record, is set to version 1.0;

[0265] ΔC: Configuration change calculated based on the new parameters, P has been calculated in the previous formula new , where ΔC is set to P new With P old The difference is divided by 100 to simplify the processing;

[0266] V: Configuration version change factor, which is set to 0.1 considering the frequency of configuration updates;

[0267] Calculate C new :

[0268] C new =1.0+((278.4-150) / 100)·0.1

[0269] =1.0+(1.284)·0.1=1.0+0.1284=1.1284

[0270] Final result C new =1.1284, representing the updated cooling configuration version, which means the device has been reconfigured.

[0271] See also Figure 8 , the specific steps for obtaining the dynamic temperature control strategy adjustment results are:

[0272] S421: Update instructions based on cooling configuration, capture ambient temperature and predicted temperature in real time, using the formula:

[0273]

[0274] Calculate the deviation between the real-time temperature and the predicted temperature to obtain the temperature deviation data, where: Indicates the current temperature monitored in real time. Indicates the predicted temperature in the future time period, ΔT v Indicates temperature deviation data;

[0275] S422: Analyze the temperature deviation data, calculate the average deviation and standard deviation of multiple temperature points, analyze the fluctuation range of the data, determine the matching adjustment threshold according to the statistical distribution of the deviation, and optimize the temperature control using the formula:

[0276]

[0277] Calculate and generate adjustment threshold data, where ΔT v is the temperature deviation data, αv is the influence coefficient, β v is the equilibrium constant, Θ v It is to adjust the threshold data;

[0278] S423: Based on the adjustment threshold data and the real-time temperature data, apply the formula:

[0279]

[0280] Calculate the new temperature target and generate the dynamic temperature control strategy adjustment result, where Θ v is to adjust the threshold data, is the current temperature monitored in real time, ΔT v Indicates temperature deviation data, R v It is the result of dynamic temperature control strategy adjustment.

[0281] Temperature deviation data formula:

[0282]

[0283] The current temperature is monitored in real time and collected in real time by environmental sensors. For example, the current temperature measured at a specific moment is 32°C.

[0284] The predicted future temperature is obtained based on the meteorological model prediction, and the predicted temperature at the same time is set to 30℃; calculate ΔT v :

[0285] ΔT v =32-30=2℃

[0286] This means that the real-time temperature is 2°C higher than the predicted temperature, which is an important difference data and will be used for further adjustment of the dynamic temperature control strategy.

[0287] Adjust the threshold data formula:

[0288]

[0289] α v : influence coefficient, set to 0.5 to reflect the sensitivity of adjusting the threshold;

[0290] β v : equilibrium constant, set to 0.1, to balance the nonlinear response of the threshold;

[0291] ΔT v : The temperature difference calculated from the previous step is 2°C;

[0292] Calculate Θ v :

[0293]

[0294] This calculated adjustment threshold Θ v =0.8333 is used to adjust the temperature control strategy and display the calculation results of the temperature difference adjustment threshold.

[0295] The formula for dynamic temperature control strategy adjustment result is:

[0296]

[0297] The real-time temperature obtained from the previous step, 32°C;

[0298] Θ v : The adjusted threshold calculated from the previous step, 0.8333;

[0299] ΔT v : The temperature difference calculated from the previous step, 2°C;

[0300] Calculate R v :

[0301] R v =32+0.8333·sign(2)=32+0.8333=32.8333

[0302] This means that the new temperature target is 32.8333°C, which is used to adjust the actual temperature control to cope with the deviation between the real-time and predicted temperatures.

[0303] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for temperature monitoring and adjustment of a display screen, characterized in that: The following steps are involved: The temperature data of the display screen is captured in real time by a temperature sensor, and the difference and complexity of the temperature data are evaluated by using information entropy to obtain an entropy value analysis result. The abnormal fluctuations in the data are identified according to the entropy value analysis result to generate an abnormal fluctuation monitoring result; According to the abnormal fluctuation monitoring result, evaluate whether the temperature of the current display screen exceeds the safe operating range, adjust the heat dissipation output according to the evaluation result, and perform temperature control to obtain a heat dissipation configuration adjustment result, optimize the temperature control scheme according to the heat dissipation configuration adjustment result, and generate a temperature control optimization result; According to the temperature control optimization result, a temperature trend prediction algorithm is designed, and the temperature change in the future time period is predicted by using the historical and current temperature data to obtain a future temperature prediction model. According to the future temperature prediction model, the temperature fluctuation in the future time period is evaluated to generate a future temperature control strategy; According to the future temperature control strategy, the operating parameters of the heat dissipation device are automatically adjusted to match the predicted temperature change, the configuration of the heat dissipation device is updated, and a heat dissipation configuration update instruction is obtained. According to the heat dissipation configuration update instruction, the deviation between the real-time temperature data and the predicted data is continuously monitored to generate a dynamic temperature control strategy adjustment result; The steps for obtaining the heat dissipation configuration update instruction are specifically as follows: According to the future temperature control strategy, the predicted temperature data is captured, using the formula: Introducing environmental impact factors and time difference correction coefficients, we get the adjusted target temperature, where: is the predicted future temperature, It is the environmental influencing factor. is the time difference correction factor, is the adjusted target temperature; The adjusted target temperature is used to adjust the operating parameters of the heat dissipation device according to the current device status and device performance, using the formula: The new heat dissipation equipment parameters are calculated, where: are the current operating parameters of the cooling device. is the current temperature, is the maximum adjustable temperature range, It is the equipment performance. It is the parameter of the new cooling device; Update the configuration of the heat sink according to the new heat sink parameters, using the formula: Introduce the configuration version change factor and generate the cooling configuration update instruction, where: is the current cooling configuration, is the configuration change calculated based on the new parameters, is the configuration version change factor, It is the instruction to update the cooling configuration.

2. The temperature monitoring and adjustment method for a display screen according to claim 1, characterized in that: The steps for obtaining the abnormal fluctuation monitoring results are specifically as follows: Capturing the temperature data of the display screen in real time through a temperature sensor, recording the temperature data in real time, and forming a time-series temperature data set, wherein the time-series temperature data set includes the temperature value of each measurement point and the corresponding timestamp; The entropy value analysis of the time-series temperature data set is performed to evaluate the difference and complexity of the data, using the formula: By adding the regularization of the logarithmic term in the form of weighted sum, the entropy analysis result is obtained, where: Indicates The probability of a temperature value, Indicates the entropy analysis result; Based on the entropy analysis result, an entropy threshold is set, and temperature data points exceeding the threshold are identified. If the entropy analysis result is greater than the entropy threshold, it is marked as abnormal, abnormal fluctuations in the data set are identified, and abnormal fluctuation monitoring results are generated.

3. The temperature monitoring and adjustment method for a display screen according to claim 2, characterized in that: The steps for obtaining the heat dissipation configuration adjustment result are specifically as follows: According to the abnormal fluctuation monitoring results, the real-time temperature data of the display screen is captured and compared with the set upper limit of the safe temperature to calculate the degree to which the temperature exceeds the safe range, using the formula: Generates temperature deviation evaluation results, where Indicates the display screen temperature monitored in real time. is the safety temperature threshold, Indicates the temperature deviation evaluation result; Based on the temperature deviation evaluation result, it is determined whether the heat dissipation output needs to be adjusted, using a nonlinear adjustment formula: Generate a heat dissipation output adjustment index, where is the sensitivity adjustment factor, is the deviation median adjustment point, Indicates the temperature deviation evaluation result, Indicates the heat dissipation output adjustment index; According to the heat dissipation output adjustment index, the performance of the current heat dissipation equipment is evaluated to determine the current heat dissipation efficiency and existing problems, and the operating parameters of the heat dissipation fan are adjusted using the formula: Get the cooling configuration adjustment result, where: Adjust the index for heat dissipation output, It is the result of cooling configuration adjustment.

4. The temperature monitoring and adjustment method for a display screen according to claim 3, characterized in that: The steps for obtaining the temperature control optimization result are specifically as follows: According to the cooling configuration adjustment result, the current temperature data is captured and analyzed, and the real-time performance of the cooling configuration is determined by calculating the deviation between the current temperature data and the target safety temperature using the formula: Generates temperature deviation evaluation results, where Represents continuously monitored temperature data, is the predetermined safety temperature threshold, Indicates the temperature deviation evaluation result; According to the temperature deviation evaluation result, a nonlinear response formula is used to optimize the heat dissipation effect, through the formula: Generate a heat dissipation efficiency adjustment index, where is the adjustment factor of the heat dissipation efficiency, Indicates the sensitivity to temperature deviation. is the temperature deviation evaluation result, Indicates the heat dissipation efficiency adjustment index; Using the heat dissipation efficiency adjustment index, determine the optimal configuration of temperature control and apply the formula: The temperature control optimization results are obtained, where Adjust the index for heat dissipation efficiency, is the current cooling configuration, Indicates the temperature control optimization result.

5. The temperature monitoring and adjustment method for a display screen according to claim 4, characterized in that: The steps for obtaining the future temperature prediction model are specifically as follows: Analyze the temperature control optimization results, extract historical temperature data and current temperature data, and use the weighted average method to calculate the fusion value of historical and current temperatures, through the formula: Generates temperature analysis results where: Represents historical temperature data, is the current temperature data, Adjust the weights of historical and current data, is the temperature analysis result; Based on the temperature analysis results, the exponential smoothing method is used to predict the temperature in the future time period, through the formula: Generate temperature prediction indicators, where is the previous prediction result. is the smoothing coefficient, is the temperature analysis result, represents the temperature prediction index; Combining the temperature prediction index and associated parameters, the formula is: Optimize the temperature prediction in the future time period to obtain the future temperature prediction model, where: is the adjustment factor, represents the long-term temperature target, represents the temperature prediction index, It is a future temperature prediction model.

6. The temperature monitoring and adjustment method for a display screen according to claim 5, characterized in that: The steps for obtaining the future temperature control strategy are specifically as follows: The future temperature prediction model is analyzed to evaluate the predicted temperature fluctuations, using the standard deviation formula: The future temperature fluctuation assessment results are obtained, among which, represents a single temperature value predicted by the model, is the average temperature value, is the total number of data points, is the result of the assessment of future temperature fluctuations; The future temperature fluctuation assessment result is compared with the set safety threshold, using the difference analysis formula: like If it is a positive value, it means that the temperature fluctuation exceeds the threshold, and the difference between the temperature fluctuation and the safety threshold is obtained, where is the result of the future temperature fluctuation assessment, is a predetermined safety threshold, Represents the difference between temperature fluctuation and safety threshold; According to the difference result between the temperature fluctuation and the safety threshold, if the difference result is greater than zero, the heat dissipation intensity is increased, otherwise, the current state is maintained to obtain the future temperature control strategy.

7. The temperature monitoring and adjustment method for a display screen according to claim 1, characterized in that: The steps for obtaining the dynamic temperature control strategy adjustment result are specifically as follows: According to the cooling configuration update instruction, the ambient temperature and the predicted temperature are captured in real time, using the formula: Calculate the deviation between the real-time temperature and the predicted temperature to obtain the temperature deviation data, where: Indicates the current temperature monitored in real time. It indicates the temperature prediction for the future time period. Indicates temperature deviation data; Analyze the temperature deviation data, calculate the average deviation and standard deviation of multiple temperature points, analyze the fluctuation range of the data, determine the matching adjustment threshold according to the statistical distribution of the deviation, and optimize the temperature control using the formula: Calculate and generate adjustment threshold data, where: is the temperature deviation data, is the influence coefficient, is the equilibrium constant, It is to adjust the threshold data; Based on the adjustment threshold data and the real-time temperature data, the formula is applied: Calculate the new temperature target and generate the dynamic temperature control strategy adjustment result, where: is to adjust the threshold data, is the current temperature monitored in real time. Indicates temperature deviation data, It is the result of dynamic temperature control strategy adjustment.

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