A low-power heat dissipation method and system for an infrared thermal imaging module

Abstract

This invention relates to the field of infrared temperature measurement technology, and discloses a low-power heat dissipation method and system for an infrared thermal imaging core. The method includes acquiring temperature distribution data, workload, and ambient temperature, preprocessing them, and constructing a matrix to obtain a temperature distribution matrix; judging and classifying the temperature distribution matrix to obtain normal temperature region data and abnormal temperature region data; constructing a gray dataset based on the abnormal temperature region data and predicting it to obtain an abnormal temperature prediction sequence; calculating dynamic operating parameters based on the workload and abnormal temperature prediction sequence and matching them to obtain a dynamic operating mode for the abnormal temperature region; matching the normal temperature region data to obtain a low-performance operating mode for the normal temperature region; and when the temperature change rate exceeds a preset temperature change rate threshold, re-executing all the above steps and using a model predictive control algorithm for rolling optimization. This method can dynamically switch operating modes.

Description

Technical Field

[0001] This invention relates to the field of infrared temperature measurement technology, and in particular to a low-power heat dissipation method and system for an infrared thermal imaging module. Background Technology

[0002] Infrared thermal imaging modules play a crucial role in industrial inspection and medical diagnostics. By detecting the intensity of thermal radiation from objects, they can quickly and non-contactly acquire information about the temperature distribution of a scene. In the industrial sector, infrared thermal imaging technology is widely used for equipment condition monitoring and fault early warning. In manufacturing, abnormal temperatures in mechanical transmission components (such as bearings and gearboxes) on production lines may indicate wear or lubrication failure; infrared thermal imaging technology can help achieve predictive maintenance and reduce unplanned downtime losses. Furthermore, in industries such as chemical and metallurgical manufacturing, monitoring the surface temperature of high-temperature reactors or furnaces is critical for safe production; infrared thermal imaging modules can provide accurate, non-contact temperature data, avoiding the safety hazards of manual inspections.

[0003] Existing technologies capture the thermal radiation signals of target objects using infrared detectors. When the ambient temperature rises or the workload increases, the sensor needs to increase the chip's operating frequency and voltage to meet the real-time processing requirements of hotspot areas, thus completing non-contact temperature detection and analysis. However, existing infrared thermal imaging sensor technologies typically operate at a constant power, maintaining full-speed heat dissipation even when the ambient temperature is low or the sensor load is light. Therefore, existing technologies cannot dynamically adjust their operating mode according to changes in ambient temperature or workload.

[0004] In summary, existing technologies cannot dynamically adjust their operating modes according to changes in the scenario, leading to an increase in overall power consumption. Summary of the Invention

[0005] This invention provides a low-power heat dissipation method and system for infrared thermal imaging cores, in order to solve the problem that existing infrared thermal imaging core detection technologies cannot dynamically adjust the working mode according to changes in the scene, which leads to an increase in overall power consumption.

[0006] In a first aspect, to solve the above-mentioned technical problems, the present invention provides a low-power heat dissipation method for an infrared thermal imaging module, comprising:

[0007] Acquire temperature distribution data, workload, and ambient temperature of the infrared thermal imaging core;

[0008] The temperature distribution data is preprocessed and a temperature matrix is ​​constructed to obtain the temperature distribution matrix.

[0009] Based on the temperature distribution matrix, a judgment is made based on a preset temperature threshold matrix, and the region is classified according to the judgment result to obtain normal temperature region data and abnormal temperature region data.

[0010] A gray dataset is constructed based on the abnormal temperature region data, and an abnormal temperature prediction sequence is obtained by using a gray prediction algorithm.

[0011] Dynamic operating point calculation is performed based on the workload and the abnormal temperature prediction sequence to obtain dynamic operating parameters for the abnormal temperature region.

[0012] The working mode is matched with the preset dynamic working threshold based on the dynamic working parameters to obtain the dynamic working mode of the abnormal temperature region.

[0013] The normal temperature range data is matched with a preset normal temperature threshold to obtain a low-performance operating mode for the normal temperature range, thereby controlling the low-performance operation of the infrared thermal imaging core.

[0014] The temperature change rate is calculated based on the ambient temperature and the temperature distribution data. When the temperature change rate is greater than the preset temperature change rate threshold, all the above steps are repeated, and the abnormal temperature area data is optimized by a model predictive control algorithm to obtain optimized abnormal temperature area data, so as to construct a gray dataset based on the optimized abnormal temperature area data.

[0015] In one optional implementation, the step of preprocessing the temperature distribution data and constructing a temperature matrix to obtain the temperature distribution matrix includes:

[0016] The temperature distribution data is denoised to obtain noise-free temperature distribution data.

[0017] The noise-free temperature distribution data is validated to obtain complete temperature distribution data;

[0018] A temperature matrix is ​​constructed based on the complete temperature distribution data to obtain the temperature distribution matrix;

[0019] The temperature distribution matrix is ​​as follows:

[0020]

[0021] In the formula, The temperature distribution matrix is... The element in the first row and first column of the temperature distribution matrix. The first row of the temperature distribution matrix Column elements, The temperature distribution matrix is ​​the first The element in the first column of the row, The temperature distribution matrix is ​​the first Line 1 Column elements, Let x be the horizontal dimension of the temperature distribution matrix. This represents the vertical dimension of the temperature distribution matrix.

[0022] In one optional implementation, the step of judging based on the temperature distribution matrix using a preset temperature threshold matrix, and classifying regions based on the judgment result to obtain normal temperature region data and abnormal temperature region data, includes:

[0023] When an element of the temperature distribution matrix is ​​greater than an element of the preset temperature threshold matrix, it is determined to be abnormal temperature data.

[0024] When the elements of the temperature distribution matrix are less than the elements of the preset temperature threshold matrix, the temperature data is determined to be normal.

[0025] A temperature block matrix is ​​constructed based on the abnormal temperature data and the normal temperature data to obtain normal temperature region data and abnormal temperature region data.

[0026] The temperature block matrix is ​​as follows:

[0027]

[0028] In the formula, This is a temperature block matrix. Data is for the normal temperature range. This data represents an abnormal temperature region.

[0029] In one optional implementation, the step of using a grey prediction algorithm to obtain an abnormal temperature prediction sequence includes:

[0030] Based on the gray dataset, data cleaning and preprocessing are performed to obtain gray training set data;

[0031] The accumulated sequence is constructed based on the gray training set data to obtain gray accumulated data;

[0032] The mean sequence is constructed based on the gray training set data to obtain gray mean data;

[0033] A gray differential equation is constructed based on the gray cumulative data and the gray mean data, and the gray differential equation is solved using the least squares method to obtain the whitening equation.

[0034] The predicted values ​​are restored based on the whitening equation to obtain the abnormal temperature prediction sequence.

[0035] In one optional implementation, the step of calculating the dynamic operating point based on the workload and the abnormal temperature prediction sequence to obtain the dynamic operating parameters for the abnormal temperature region includes:

[0036] The workload increment is obtained by performing incremental calculations based on the workload.

[0037] Dynamic operating point calculation is performed based on the workload increment and the abnormal temperature prediction sequence to obtain dynamic operating parameters for the abnormal temperature region.

[0038] The workload includes the operating frequency and the operating voltage;

[0039] The workload increment includes the operating frequency increment and the operating voltage increment;

[0040] The operating frequency increment is calculated using the following formula:

[0041]

[0042] In the formula, For the operating frequency increment, For the first One operating frequency, For the first One operating frequency, For the first Each time value, For the first Each time value;

[0043] The operating voltage increment is calculated using the following formula:

[0044]

[0045] In the formula, This is the operating voltage increment. For the first One operating voltage, For the first One operating voltage;

[0046] The dynamic operating parameters are calculated using the following formula:

[0047]

[0048] In the formula, For dynamic operating parameters, For the first A predicted sequence of abnormal temperatures. The base of the natural logarithm, The length of the abnormal temperature prediction sequence.

[0049] In one optional implementation, the step of matching the operating mode based on the dynamic operating parameters and a preset dynamic operating threshold to obtain the dynamic operating mode for the abnormal temperature region includes:

[0050] When the dynamic operating parameter is greater than or equal to the preset first dynamic operating threshold, the dynamic operating mode of the abnormal temperature region is the preset first operating mode.

[0051] When the dynamic working parameter is greater than or equal to the preset second dynamic working threshold and less than the preset first dynamic working threshold, the dynamic working mode of the abnormal temperature region is the preset second working mode.

[0052] When the dynamic operating parameter is greater than or equal to the preset third dynamic operating threshold and less than the preset second dynamic operating threshold, the dynamic operating mode of the abnormal temperature region is the preset third operating mode.

[0053] When the dynamic operating parameters are less than the preset fourth dynamic operating threshold, the dynamic operating mode of the abnormal temperature region is the preset fourth operating mode.

[0054] The preset dynamic working thresholds include a first dynamic working threshold, a second dynamic working threshold, a third dynamic working threshold, and a fourth dynamic working threshold;

[0055] The dynamic working modes include a first working mode, a second working mode, a third working mode, and a fourth working mode.

[0056] In one optional implementation, the step of matching the normal temperature range data with a preset normal temperature threshold to obtain a low-performance operating mode for the normal temperature range, in order to control the low-performance operation of the infrared thermal imaging module, includes:

[0057] When the normal temperature range data is less than or equal to a preset first normal temperature threshold and greater than a preset second normal temperature threshold, the low-performance working mode of the normal temperature range is an ultra-low performance mode.

[0058] When the normal temperature range data is less than or equal to the preset second normal temperature threshold, the low-performance working mode of the normal temperature range is the extremely low-performance mode.

[0059] The preset normal temperature threshold includes a first normal temperature threshold and a second normal temperature threshold.

[0060] The low-performance operating modes include ultra-low performance mode and extremely low performance mode.

[0061] In one optional implementation, calculating the temperature change rate based on the ambient temperature and the temperature distribution data includes:

[0062] The rate of temperature change is calculated using the following formula:

[0063]

[0064] In the formula, For the rate of temperature change, For the first An ambient temperature, For the first Temperature distribution data, For the first Each time value, For the first Each time value.

[0065] In one optional implementation, the step of using a model predictive control algorithm to perform rolling optimization on the abnormal temperature region data to obtain optimized abnormal temperature region data includes:

[0066] An optimized dataset is constructed based on the abnormal temperature region data, and data cleaning and preprocessing are performed to obtain the rolling optimized training set data.

[0067] A rolling optimization model is constructed based on the rolling optimization training set data. The prediction error is minimized as the optimization objective. The rolling optimization model is solved using nonlinear programming to obtain the rolling optimization test set data.

[0068] The root mean square error is calculated based on the rolling optimization test set data. When the root mean square error is less than a preset error threshold, the first data point of the rolling optimization test set data is taken, and the rolling optimization model is solved repeatedly to obtain the optimized abnormal temperature region data.

[0069] Secondly, the present invention provides a low-power heat dissipation system for an infrared thermal imaging module, comprising:

[0070] The data acquisition module is used to acquire temperature distribution data, workload, and ambient temperature of the infrared thermal imaging core.

[0071] The temperature distribution matrix construction module is used to preprocess the temperature distribution data and construct a temperature matrix to obtain the temperature distribution matrix.

[0072] The temperature region data classification module is used to make judgments based on the temperature distribution matrix and a preset temperature threshold matrix, and to classify regions according to the judgment results to obtain normal temperature region data and abnormal temperature region data.

[0073] An abnormal temperature sequence prediction module is used to construct a gray dataset based on the abnormal temperature region data and to perform prediction using a gray prediction algorithm to obtain an abnormal temperature prediction sequence.

[0074] The dynamic operating parameter calculation module is used to calculate the dynamic operating point based on the workload and the abnormal temperature prediction sequence to obtain the dynamic operating parameters of the abnormal temperature region.

[0075] The dynamic working mode matching module is used to match the working mode with the dynamic working parameters and the preset dynamic working threshold to obtain the dynamic working mode of the abnormal temperature region.

[0076] The low-performance operating mode matching module is used to match the normal temperature range data with a preset normal temperature threshold to obtain the low-performance operating mode of the normal temperature range, so as to control the low-performance operation of the infrared thermal imaging core.

[0077] The model predictive control optimization module is used to calculate the temperature change rate based on the ambient temperature and the temperature distribution data. When the temperature change rate is greater than the preset temperature change rate threshold, all the above steps are re-executed, and the model predictive control algorithm is used to perform rolling optimization on the abnormal temperature area data to obtain optimized abnormal temperature area data, so as to construct a gray dataset based on the optimized abnormal temperature area data.

[0078] Thirdly, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the low-power heat dissipation method of the infrared thermal imaging core described in any one of the above.

[0079] Fourthly, the present invention also provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the low-power heat dissipation method of the infrared thermal imaging core described in any one of the above.

[0080] Compared with the prior art, the present invention has the following beneficial effects:

[0081] (1) The present invention preprocesses and constructs a matrix for the temperature distribution data of the infrared thermal imaging core. All the original temperature distribution data have been denoised, verified and constructed into a temperature matrix to ensure the integrity, consistency and accuracy of the data. Through the standardization of the data, the system can perform subsequent classification, analysis and storage operations more efficiently and accurately.

[0082] (2) The present invention classifies and divides the data into regions based on the temperature distribution matrix to obtain normal temperature region data and abnormal temperature region data. Through this classification and regional division process, all temperature distribution data can be stored in the database by region, thereby achieving efficient retrieval and management.

[0083] (3) The present invention can dynamically adjust the working mode of the infrared thermal imaging core according to the real-time changes of ambient temperature and workload, effectively reduce the overall power consumption and extend the service life of the equipment. It is especially suitable for complex environments such as outdoor and industrial environments, with significant energy-saving effect and strong adaptability.

[0084] (4) This invention combines the gray prediction algorithm with the model prediction control algorithm for the prediction and optimization of abnormal temperature area data. This not only improves the accuracy of temperature prediction, but also realizes the accurate prediction and advance adjustment of future heat dissipation needs, avoids the impact of abnormal temperature on equipment performance, and ensures the stable operation of the infrared thermal imaging core in different scenarios. Attached Figure Description

[0085] Figure 1 This is a schematic diagram of a low-power heat dissipation method for an infrared thermal imaging core provided in the first embodiment of the present invention;

[0086] Figure 2 This is a schematic diagram of a low-power heat dissipation system for an infrared thermal imaging core provided in the second embodiment of the present invention. Detailed Implementation

[0087] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0088] Reference Figure 1 The first embodiment of the present invention provides a low-power heat dissipation method for an infrared thermal imaging core, comprising the following steps:

[0089] S11, acquires temperature distribution data, workload and ambient temperature of the infrared thermal imaging core;

[0090] S12, Preprocess the temperature distribution data and construct a temperature matrix to obtain the temperature distribution matrix;

[0091] S13, make a judgment based on the temperature distribution matrix and a preset temperature threshold matrix, and classify the regions according to the judgment results to obtain normal temperature region data and abnormal temperature region data.

[0092] S14, construct a gray dataset based on the abnormal temperature area data, and use a gray prediction algorithm to make predictions to obtain an abnormal temperature prediction sequence;

[0093] S15, calculate the dynamic operating point based on the workload and the abnormal temperature prediction sequence to obtain the dynamic operating parameters of the abnormal temperature region;

[0094] S16, Match the working mode with the preset dynamic working threshold according to the dynamic working parameters to obtain the dynamic working mode of the abnormal temperature region.

[0095] S17, Match the normal temperature range data with the preset normal temperature threshold to obtain the low-performance working mode of the normal temperature range, so as to control the low-performance operation of the infrared thermal imaging core.

[0096] S18, calculate the temperature change rate based on the ambient temperature and the temperature distribution data. When the temperature change rate is greater than the preset temperature change rate threshold, repeat all the above steps and use the model predictive control algorithm to perform rolling optimization on the abnormal temperature area data to obtain optimized abnormal temperature area data, so as to construct a gray dataset based on the optimized abnormal temperature area data.

[0097] In step S11, the temperature distribution data, workload, and ambient temperature of the infrared thermal imaging core are acquired.

[0098] It should be noted that the workload of an infrared thermal imaging module includes its operating voltage and operating frequency. To acquire temperature distribution data, an infrared detector captures infrared radiation from the scene, converts it into an electrical signal, and then processes the signal to obtain the temperature distribution data. The infrared detector is installed at the front end of the module and can collect real-time temperature distribution data of the scene. For example, in industrial inspection, the infrared detector can capture 30 frames of infrared images per second. Each frame contains rich temperature information, and the data processing system converts this image data into a temperature distribution matrix for subsequent analysis and processing.

[0099] It should be noted that the operating voltage of the infrared thermal imaging module is obtained by directly measuring the output voltage of the internal power module using a high-precision voltage sensor. The voltage sensor is installed on the power supply line between the power module and the chip, enabling real-time monitoring of voltage changes. For example, during module operation, the voltage sensor records the voltage value every 0.1 seconds and transmits the data to the data acquisition system. The data processing system analyzes voltage fluctuations to ensure the chip operates under a stable voltage.

[0100] It should be noted that to obtain the operating frequency of the infrared thermal imaging chip, professional equipment such as a frequency counter or oscilloscope is used to measure the chip's operating frequency. The frequency counter is connected to the chip's clock signal output terminal and can accurately measure and display the current operating frequency. For example, in chip performance testing, the frequency counter can capture changes in the operating frequency in real time with millisecond-level accuracy. The data acquisition system records this frequency data to analyze the chip's operating status under different loads.

[0101] It should be noted that, for acquiring ambient temperature, a temperature sensor (such as a thermistor or thermocouple) is used to measure the ambient temperature around the movement. The temperature sensor is installed near the movement's casing or heat sink and can monitor changes in ambient temperature in real time. For example, the resistance of a thermistor changes with temperature; by measuring its resistance, the ambient temperature can be calculated. The data acquisition system records the temperature value at regular intervals (e.g., every minute) to analyze the impact of ambient temperature on the movement's performance.

[0102] In step S12, the temperature distribution data is preprocessed and a temperature matrix is ​​constructed to obtain the temperature distribution matrix.

[0103] The temperature distribution data is denoised to obtain noise-free temperature distribution data.

[0104] The noise-free temperature distribution data is validated to obtain complete temperature distribution data;

[0105] A temperature matrix is ​​constructed based on the complete temperature distribution data to obtain the temperature distribution matrix;

[0106] The data verification includes outlier removal and missing data insertion.

[0107] The temperature distribution matrix is ​​as follows:

[0108]

[0109] In the formula, The temperature distribution matrix is... The element in the first row and first column of the temperature distribution matrix. The first row of the temperature distribution matrix Column elements, The temperature distribution matrix is ​​the first The element in the first column of the row, The temperature distribution matrix is ​​the first Line 1 Column elements, Let x be the horizontal dimension of the temperature distribution matrix. This represents the vertical dimension of the temperature distribution matrix.

[0110] It should be noted that the acquired temperature distribution data from the infrared thermal imaging sensor is first denoised to remove interference signals and random noise, resulting in noise-free temperature distribution data. In practical applications, temperature data acquired by infrared thermal imaging systems can be affected by environmental noise, sensor accuracy limitations, or signal transmission interference, leading to noise in the data. For example, in industrial equipment surface temperature monitoring, electromagnetic interference generated during equipment operation or thermal radiation from the surrounding environment can cause high-frequency noise or random fluctuations in the acquired temperature data. Gaussian filtering can be used to smooth the original temperature distribution data. Gaussian filtering is a linear filtering algorithm based on the Gaussian function, which can effectively remove Gaussian noise from images or data while preserving the main features of the data. By setting appropriate filter kernel size and standard deviation parameters, convolution operations are performed on the temperature distribution data to reduce the impact of noise. For example, setting the filter kernel size to 3×3 and the standard deviation to 1, Gaussian filtering of the acquired temperature data can significantly reduce random fluctuations in the data, resulting in smooth, noise-free temperature distribution data.

[0111] It's important to note that the denoised temperature distribution data undergoes data validation to remove outliers and fill in missing values, thus obtaining complete temperature distribution data. In real-world scenarios, outliers or missing data can occur in temperature data due to sensor malfunctions, obstructions, or data transmission errors. For example, when monitoring the temperature of a building's exterior walls, some areas may be obstructed, preventing the normal collection of certain temperature data, resulting in missing data; or sensor malfunctions may cause temperature values ​​in certain areas to be abnormally high or low, exceeding the normal range. A statistical method is used to detect outliers in the noise-free temperature distribution data. By calculating the mean and standard deviation of the data, a threshold range for outliers is set. For example, temperature values ​​exceeding ±3 times the standard deviation of the mean are identified as outliers and removed. For missing data, an interpolation algorithm is used to fill in the gaps. By utilizing the temperature values ​​of four known points surrounding the missing data and their spatial relationships, the temperature value of the missing point is calculated, thus achieving data completeness.

[0112] It should be noted that the final step is to construct a temperature matrix based on the complete temperature distribution data. A temperature matrix is ​​a two-dimensional array structure used to represent the spatial characteristics of temperature distribution. The construction process involves arranging the complete temperature distribution data according to their spatial relationships to form an m×n matrix, where m represents the horizontal dimension and n represents the vertical dimension. For example, when monitoring the temperature of the surface of industrial equipment, the surface is divided into a 10×10 grid area, with each grid point corresponding to a temperature value. These temperature values ​​are then arranged sequentially according to their grid positions to form a 10×10 temperature distribution matrix.

[0113] In step S13, the temperature distribution matrix is ​​judged based on the preset temperature threshold matrix, and the region is classified according to the judgment result to obtain normal temperature region data and abnormal temperature region data.

[0114] When an element of the temperature distribution matrix is ​​greater than an element of the preset temperature threshold matrix, it is determined to be abnormal temperature data.

[0115] When the elements of the temperature distribution matrix are less than the elements of the preset temperature threshold matrix, the temperature data is determined to be normal.

[0116] A temperature block matrix is ​​constructed based on the abnormal temperature data and the normal temperature data to obtain normal temperature region data and abnormal temperature region data.

[0117] The temperature block matrix is ​​as follows:

[0118]

[0119] In the formula, This is a temperature block matrix. Data is for the normal temperature range. This data represents an abnormal temperature region.

[0120] For example, taking a third-order square matrix as an example, when the temperature distribution matrix... At that time, preset temperature threshold matrix At this time, the temperature block matrix obtained by classification is .

[0121] In step S14, a gray dataset is constructed based on the abnormal temperature region data, and a gray prediction algorithm is used to make predictions to obtain an abnormal temperature prediction sequence.

[0122] Based on the gray dataset, data cleaning and preprocessing are performed to obtain gray training set data;

[0123] The accumulated sequence is constructed based on the gray training set data to obtain gray accumulated data;

[0124] The mean sequence is constructed based on the gray training set data to obtain gray mean data;

[0125] A gray differential equation is constructed based on the gray cumulative data and the gray mean data, and the gray differential equation is solved using the least squares method to obtain the whitening equation.

[0126] The predicted values ​​are restored based on the whitening equation to obtain the abnormal temperature prediction sequence.

[0127] It should be noted that the gray dataset is constructed by sorting the abnormal temperature region data by timestamp to form a time series data group. Then, for each abnormal temperature region... It is obtained by extracting temperature data from K consecutive sampling periods. .

[0128] It should be noted that the gray accumulated data is calculated using the following formula:

[0129]

[0130] In the formula, The data is accumulated in gray. For the first A gray training set of data, The sample length;

[0131] It should be noted that the gray mean data is calculated using the following formula:

[0132]

[0133] In the formula, The data is gray mean. For the first A gray training set of data, For the first A gray training set of data;

[0134] It should be noted that the grey differential equation is:

[0135]

[0136] In the formula, The coefficients are for the gray mean data. The constants of the grey differential equation;

[0137] It should be noted that the least squares solution formula is as follows:

[0138]

[0139]

[0140] In the formula, The mean of the gray training set data. This represents the mean of the gray mean data.

[0141] It should be noted that the whitening equation is:

[0142]

[0143] In the formula, The derivative of the gray accumulated data;

[0144] It should be noted that the abnormal temperature prediction sequence is calculated using the following formula:

[0145]

[0146] In the formula, This is a predicted sequence for abnormal temperatures.

[0147] For example, when the abnormal temperature region data is {1, 2, 3, 4, 5, 6}, the result is calculated according to the formula model. , The predicted abnormal temperature sequence is {7, 8, 9, 10, 11, 12}.

[0148] In step S15, dynamic operating point calculation is performed based on the workload and the abnormal temperature prediction sequence to obtain dynamic operating parameters for the abnormal temperature region.

[0149] The workload increment is obtained by performing incremental calculations based on the workload.

[0150] Dynamic operating point calculation is performed based on the workload increment and the abnormal temperature prediction sequence to obtain dynamic operating parameters for the abnormal temperature region.

[0151] The workload includes the operating frequency and the operating voltage;

[0152] The workload increment includes the operating frequency increment and the operating voltage increment;

[0153] The operating frequency increment is calculated using the following formula:

[0154]

[0155] In the formula, For the operating frequency increment, For the first One operating frequency, For the first One operating frequency, For the first Each time value, For the first Each time value;

[0156] The operating voltage increment is calculated using the following formula:

[0157]

[0158] In the formula, This is the operating voltage increment. For the first One operating voltage, For the first One operating voltage;

[0159] The dynamic operating parameters are calculated using the following formula:

[0160]

[0161] In the formula, For dynamic operating parameters, For the first A predicted sequence of abnormal temperatures. The base of the natural logarithm, The length of the abnormal temperature prediction sequence.

[0162] It should be noted that, This represents the ratio of the operating voltage increment to the operating frequency increment. This ratio reflects the voltage change per unit frequency change and can be used as an indicator of workload variation. If the operating voltage increases accordingly with increasing operating frequency, this ratio indicates the voltage's sensitivity to frequency changes. An exponential function is used to adjust this ratio of workload increments. When the average value of the abnormal temperature prediction sequence is large, the exponential function value will be small, indicating that under high temperatures, dynamic operating parameters should be reduced to decrease power consumption and heat dissipation requirements. Conversely, when the average value of the abnormal temperature prediction sequence is small, the exponential function value will be large, indicating that under low temperatures, dynamic operating parameters should be increased to improve performance.

[0163] For example, when the operating frequency Operating voltage When the time interval is 1 second, the calculation is obtained. , , .

[0164] In step S16, the working mode is matched with the preset dynamic working threshold according to the dynamic working parameters to obtain the dynamic working mode of the abnormal temperature region.

[0165] When the dynamic operating parameter is greater than or equal to the preset first dynamic operating threshold, the dynamic operating mode of the abnormal temperature region is the preset first operating mode.

[0166] When the dynamic working parameter is greater than or equal to the preset second dynamic working threshold and less than the preset first dynamic working threshold, the dynamic working mode of the abnormal temperature region is the preset second working mode.

[0167] When the dynamic operating parameter is greater than or equal to the preset third dynamic operating threshold and less than the preset second dynamic operating threshold, the dynamic operating mode of the abnormal temperature region is the preset third operating mode.

[0168] When the dynamic operating parameters are less than the preset fourth dynamic operating threshold, the dynamic operating mode of the abnormal temperature region is the preset fourth operating mode.

[0169] The preset dynamic working thresholds include a first dynamic working threshold, a second dynamic working threshold, a third dynamic working threshold, and a fourth dynamic working threshold;

[0170] The dynamic working modes include a first working mode, a second working mode, a third working mode, and a fourth working mode.

[0171] For example, when the first dynamic working threshold, the second dynamic working threshold, the third dynamic working threshold, and the fourth dynamic working threshold are 0.005, 0.004, 0.003, and 0.002, respectively, the above calculation yields... If the temperature exceeds the preset third dynamic working threshold but is less than the preset second dynamic working threshold, the dynamic working mode of the abnormal temperature region is the preset third working mode.

[0172] In step S17, the normal temperature range data is matched with a preset normal temperature threshold to obtain a low-performance operating mode for the normal temperature range, so as to control the low-performance operation of the infrared thermal imaging core.

[0173] When the normal temperature range data is less than or equal to a preset first normal temperature threshold and greater than a preset second normal temperature threshold, the low-performance working mode of the normal temperature range is an ultra-low performance mode.

[0174] When the normal temperature range data is less than or equal to the preset second normal temperature threshold, the low-performance working mode of the normal temperature range is the extremely low-performance mode.

[0175] The preset normal temperature threshold includes a first normal temperature threshold and a second normal temperature threshold.

[0176] The low-performance operating modes include ultra-low performance mode and extremely low performance mode.

[0177] For example, when the first normal temperature threshold and the second normal temperature threshold are 10 and 5 respectively, the normal temperature range data... When the temperature is below the preset first normal temperature threshold and above the preset second normal temperature threshold, the low-performance operating mode in the normal temperature range is the ultra-low performance mode.

[0178] In step S18, the temperature change rate is calculated based on the ambient temperature and the temperature distribution data. When the temperature change rate is greater than the preset temperature change rate threshold, all the above steps are repeated, and the model predictive control algorithm is used to perform rolling optimization on the abnormal temperature area data to obtain optimized abnormal temperature area data, so as to construct a gray dataset based on the optimized abnormal temperature area data.

[0179] The rate of temperature change is calculated using the following formula:

[0180]

[0181] In the formula, For the rate of temperature change, For the first An ambient temperature, For the first Temperature distribution data, For the first Each time value, For the first Each time value.

[0182] An optimized dataset is constructed based on the abnormal temperature region data, and data cleaning and preprocessing are performed to obtain the rolling optimized training set data.

[0183] A rolling optimization model is constructed based on the rolling optimization training set data. The prediction error is minimized as the optimization objective. The rolling optimization model is solved using nonlinear programming to obtain the rolling optimization test set data.

[0184] The root mean square error is calculated based on the rolling optimization test set data. When the root mean square error is less than a preset error threshold, the first data point of the rolling optimization test set data is taken, and the rolling optimization model is solved repeatedly to obtain the optimized abnormal temperature region data.

[0185] It should be noted that when re-executing all steps, rolling optimization using a model predictive control algorithm is required. The rolling optimization model employs a quadratic programming method, with the optimization objective being to minimize the sum of squared prediction errors. Model predictive control consists of three steps: data prediction, rolling optimization, and feedback correction. After determining the objective function, the nonlinear programming algorithm in the Cplex solver is used to solve the rolling optimization model, thus completing the model solution. When the error meets the requirements, the first data point of the rolling optimization test set is taken, and the rolling optimization model is solved repeatedly to obtain the optimized abnormal temperature region data.

[0186] It should be noted that the formula for calculating the root mean square error is:

[0187]

[0188] In the formula, The root mean square error, For the sample size, For the first One predicted value data, For the first One actual value data.

[0189] For example, when the original abnormal temperature region data is {0.98, 1.99, 3.09, 4.01, 5, 6.02}, after rolling optimization, the optimized abnormal temperature region data is {1, 2, 3, 4, 5, 6}.

[0190] In summary, this invention discloses a low-power heat dissipation method for an infrared thermal imaging module, comprising: acquiring temperature distribution data, workload, and ambient temperature of the infrared thermal imaging module; preprocessing the temperature distribution data and constructing a temperature matrix to obtain a temperature distribution matrix; judging based on a preset temperature threshold matrix based on the temperature distribution matrix, and classifying regions according to the judgment results to obtain normal temperature region data and abnormal temperature region data; constructing a gray dataset based on the abnormal temperature region data, and using a gray prediction algorithm to predict abnormal temperatures to obtain an abnormal temperature prediction sequence; and calculating the dynamic operating point based on the workload and the abnormal temperature prediction sequence to obtain an abnormal temperature prediction sequence. Dynamic operating parameters for the temperature range; matching the operating modes with the dynamic operating parameters and preset dynamic operating thresholds to obtain the dynamic operating mode for the abnormal temperature range; matching the normal temperature range data with preset normal temperature thresholds to obtain the low-performance operating mode for the normal temperature range, thereby controlling the low-performance operation of the infrared thermal imaging core; calculating the temperature change rate based on the ambient temperature, and when the temperature change rate is greater than a preset temperature change rate threshold, re-executing all the above steps, and using a model predictive control algorithm to perform rolling optimization on the abnormal temperature range data to obtain optimized abnormal temperature range data, thereby constructing a gray dataset based on the optimized abnormal temperature range data. The method first acquires temperature distribution data, workload, and ambient temperature to construct a temperature matrix, obtaining a temperature distribution matrix. Then, based on the temperature distribution matrix and a preset temperature threshold matrix, it performs judgment and region classification to obtain normal temperature region data and abnormal temperature region data. Next, it constructs a gray dataset based on the abnormal temperature region data and uses a gray prediction algorithm to predict abnormal temperatures, obtaining an abnormal temperature prediction sequence. Then, it calculates dynamic operating points based on the workload and the abnormal temperature prediction sequence to obtain dynamic operating parameters for the abnormal temperature regions. Subsequently, it matches the dynamic operating parameters with a preset dynamic operating threshold to obtain the dynamic operating mode for the abnormal temperature regions. Simultaneously, it matches the normal temperature region data with a preset normal temperature threshold to obtain a low-performance operating mode for the normal temperature regions, controlling the low-performance operation of the infrared thermal imaging core. Finally, it calculates the temperature change rate based on the ambient temperature. When the temperature change rate exceeds a preset temperature change rate threshold, all the above steps are repeated, and a model predictive control algorithm is used for rolling optimization to obtain optimized abnormal temperature region data, which is then used to construct a gray dataset. This method can dynamically adjust the operating mode according to changes in the scene, reducing the overall power consumption of the infrared thermal imaging core.

[0191] Reference Figure 2 The second embodiment of the present invention provides a low-power heat dissipation system for an infrared thermal imaging core, comprising:

[0192] The data acquisition module is used to acquire temperature distribution data, workload, and ambient temperature of the infrared thermal imaging core.

[0193] The temperature distribution matrix construction module is used to preprocess the temperature distribution data and construct a temperature matrix to obtain the temperature distribution matrix.

[0194] The temperature region data classification module is used to make judgments based on the temperature distribution matrix and a preset temperature threshold matrix, and to classify regions according to the judgment results to obtain normal temperature region data and abnormal temperature region data.

[0195] An abnormal temperature sequence prediction module is used to construct a gray dataset based on the abnormal temperature region data and to perform prediction using a gray prediction algorithm to obtain an abnormal temperature prediction sequence.

[0196] The dynamic operating parameter calculation module is used to calculate the dynamic operating point based on the workload and the abnormal temperature prediction sequence to obtain the dynamic operating parameters of the abnormal temperature region.

[0197] The dynamic working mode matching module is used to match the working mode with the dynamic working parameters and the preset dynamic working threshold to obtain the dynamic working mode of the abnormal temperature region.

[0198] The low-performance operating mode matching module is used to match the normal temperature range data with a preset normal temperature threshold to obtain the low-performance operating mode of the normal temperature range, so as to control the low-performance operation of the infrared thermal imaging core.

[0199] The model predictive control optimization module is used to calculate the temperature change rate based on the ambient temperature and the temperature distribution data. When the temperature change rate is greater than the preset temperature change rate threshold, all the above steps are re-executed, and the model predictive control algorithm is used to perform rolling optimization on the abnormal temperature area data to obtain optimized abnormal temperature area data, so as to construct a gray dataset based on the optimized abnormal temperature area data.

[0200] It should be noted that the low-power heat dissipation system for an infrared thermal imaging core provided in this embodiment of the invention is used to execute all the process steps of the low-power heat dissipation method for an infrared thermal imaging core in the above embodiment. The working principles and beneficial effects of the two are one-to-one, so they will not be described again.

[0201] This invention also provides an electronic device. The electronic device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a low-power heat dissipation program for an infrared thermal imaging module. When the processor executes the computer program, it implements the steps in the various embodiments of the low-power heat dissipation methods for infrared thermal imaging modules described above, for example... Figure 1The step S11 shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above-described device embodiments, such as the data acquisition module.

[0202] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device.

[0203] The electronic device may be a desktop computer, laptop, handheld computer, or smart tablet, etc. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. It may include more or fewer components than described above, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0204] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the electronic device, connecting all parts of the electronic device via various interfaces and lines.

[0205] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0206] Wherein, if the modules / units integrated in the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0207] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0208] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A low-power heat dissipation method for an infrared thermal imaging module, characterized in that, include: Acquire temperature distribution data, workload, and ambient temperature of the infrared thermal imaging core; The temperature distribution data is preprocessed and a temperature matrix is ​​constructed to obtain the temperature distribution matrix. Based on the temperature distribution matrix, a judgment is made based on a preset temperature threshold matrix, and the region is classified according to the judgment result to obtain normal temperature region data and abnormal temperature region data. A gray dataset is constructed based on the abnormal temperature region data, and an abnormal temperature prediction sequence is obtained by using a gray prediction algorithm. Dynamic operating point calculation is performed based on the workload and the abnormal temperature prediction sequence to obtain dynamic operating parameters for the abnormal temperature region. The working mode is matched with the preset dynamic working threshold based on the dynamic working parameters to obtain the dynamic working mode of the abnormal temperature region. The normal temperature range data is matched with a preset normal temperature threshold to obtain a low-performance operating mode for the normal temperature range, thereby controlling the low-performance operation of the infrared thermal imaging core. The temperature change rate is calculated based on the ambient temperature and the temperature distribution data. When the temperature change rate is greater than the preset temperature change rate threshold, all the above steps are repeated, and the abnormal temperature area data is optimized by a model predictive control algorithm to obtain optimized abnormal temperature area data. A gray dataset is then constructed based on the optimized abnormal temperature area data. The step of calculating the dynamic operating point based on the workload and the abnormal temperature prediction sequence to obtain the dynamic operating parameters for the abnormal temperature region includes: The workload increment is obtained by performing incremental calculations based on the workload. Dynamic operating point calculation is performed based on the workload increment and the abnormal temperature prediction sequence to obtain dynamic operating parameters for the abnormal temperature region. The workload includes the operating frequency and the operating voltage; The workload increment includes the operating frequency increment and the operating voltage increment.

2. The low-power heat dissipation method for the infrared thermal imaging core according to claim 1, characterized in that, The step of preprocessing the temperature distribution data and constructing a temperature matrix to obtain the temperature distribution matrix includes: The temperature distribution data is denoised to obtain noise-free temperature distribution data. The noise-free temperature distribution data is validated to obtain complete temperature distribution data; A temperature matrix is ​​constructed based on the complete temperature distribution data to obtain the temperature distribution matrix; The temperature distribution matrix is ​​as follows: In the formula, The temperature distribution matrix is... The element in the first row and first column of the temperature distribution matrix. The first row of the temperature distribution matrix Column elements, The temperature distribution matrix is ​​the first The element in the first column of the row, The temperature distribution matrix is ​​the first Line 1 Column elements, Let x be the horizontal dimension of the temperature distribution matrix. This represents the vertical dimension of the temperature distribution matrix.

3. The low-power heat dissipation method for the infrared thermal imaging core according to claim 1, characterized in that, The step involves judging based on the temperature distribution matrix using a preset temperature threshold matrix, and classifying regions according to the judgment results to obtain normal temperature region data and abnormal temperature region data, including: When an element of the temperature distribution matrix is ​​greater than an element of the preset temperature threshold matrix, it is determined to be abnormal temperature data. When the elements of the temperature distribution matrix are less than the elements of the preset temperature threshold matrix, the temperature data is determined to be normal. A temperature block matrix is ​​constructed based on the abnormal temperature data and the normal temperature data to obtain normal temperature region data and abnormal temperature region data. The temperature block matrix is ​​as follows: In the formula, This is a temperature block matrix. Data is for the normal temperature range. This data represents an abnormal temperature region.

4. The low-power heat dissipation method for the infrared thermal imaging module according to claim 1, characterized in that, The process of using a grey prediction algorithm to obtain an abnormal temperature prediction sequence includes: Based on the gray dataset, data cleaning and preprocessing are performed to obtain gray training set data; The accumulated sequence is constructed based on the gray training set data to obtain gray accumulated data; The mean sequence is constructed based on the gray training set data to obtain gray mean data; A gray differential equation is constructed based on the gray cumulative data and the gray mean data, and the gray differential equation is solved using the least squares method to obtain the whitening equation. The predicted values ​​are restored based on the whitening equation to obtain the abnormal temperature prediction sequence.

5. The low-power heat dissipation method for the infrared thermal imaging module according to claim 1, characterized in that, The operating frequency increment is calculated using the following formula: In the formula, For the operating frequency increment, For the first One operating frequency, For the first One operating frequency, For the first Each time value, For the first Each time value; The operating voltage increment is calculated using the following formula: In the formula, This is the operating voltage increment. For the first One operating voltage, For the first One operating voltage; The dynamic operating parameters are calculated using the following formula: In the formula, For dynamic operating parameters, For the first A predicted sequence of abnormal temperatures. The base of the natural logarithm, The length of the abnormal temperature prediction sequence.

6. The low-power heat dissipation method for the infrared thermal imaging module according to claim 1, characterized in that, The step of matching the working mode based on the dynamic working parameters and the preset dynamic working threshold to obtain the dynamic working mode of the abnormal temperature region includes: When the dynamic operating parameter is greater than or equal to the preset first dynamic operating threshold, the dynamic operating mode of the abnormal temperature region is the preset first operating mode. When the dynamic working parameter is greater than or equal to the preset second dynamic working threshold and less than the preset first dynamic working threshold, the dynamic working mode of the abnormal temperature region is the preset second working mode. When the dynamic operating parameter is greater than or equal to the preset third dynamic operating threshold and less than the preset second dynamic operating threshold, the dynamic operating mode of the abnormal temperature region is the preset third operating mode. The preset dynamic working threshold includes a first dynamic working threshold, a second dynamic working threshold, and a third dynamic working threshold; The dynamic working modes include a first working mode, a second working mode, and a third working mode.

7. The low-power heat dissipation method for the infrared thermal imaging module according to claim 1, characterized in that, The step of matching the normal temperature range data with a preset normal temperature threshold to obtain a low-performance operating mode for the normal temperature range, in order to control the low-performance operation of the infrared thermal imaging core, includes: When the normal temperature range data is less than or equal to a preset first normal temperature threshold and greater than a preset second normal temperature threshold, the low-performance working mode of the normal temperature range is an ultra-low performance mode. When the normal temperature range data is less than or equal to the preset second normal temperature threshold, the low-performance working mode of the normal temperature range is the extremely low-performance mode. The preset normal temperature threshold includes a first normal temperature threshold and a second normal temperature threshold. The low-performance operating modes include ultra-low performance mode and extremely low performance mode.

8. The low-power heat dissipation method for the infrared thermal imaging core according to claim 1, characterized in that, The calculation of the temperature change rate based on the ambient temperature and the temperature distribution data includes: The rate of temperature change is calculated using the following formula: In the formula, For the rate of temperature change, For the first An ambient temperature, For the first Temperature distribution data, For the first Each time value, For the first Each time value.

9. The low-power heat dissipation method for the infrared thermal imaging core according to claim 1, characterized in that, The step of using a model predictive control algorithm to perform rolling optimization on the abnormal temperature region data to obtain optimized abnormal temperature region data includes: An optimized dataset is constructed based on the abnormal temperature region data, and data cleaning and preprocessing are performed to obtain the rolling optimized training set data. A rolling optimization model is constructed based on the rolling optimization training set data. The prediction error is minimized as the optimization objective. The rolling optimization model is solved using nonlinear programming to obtain the rolling optimization test set data. The root mean square error is calculated based on the rolling optimization test set data. When the root mean square error is less than a preset error threshold, the first data point of the rolling optimization test set data is taken, and the rolling optimization model is solved repeatedly to obtain the optimized abnormal temperature region data.

10. A low-power heat dissipation system for an infrared thermal imaging module, used to implement the low-power heat dissipation method for the infrared thermal imaging module as described in claim 1, characterized in that, include: The data acquisition module is used to acquire temperature distribution data, workload, and ambient temperature of the infrared thermal imaging core. The temperature distribution matrix construction module is used to preprocess the temperature distribution data and construct a temperature matrix to obtain the temperature distribution matrix. The temperature region data classification module is used to make judgments based on the temperature distribution matrix and a preset temperature threshold matrix, and to classify regions according to the judgment results to obtain normal temperature region data and abnormal temperature region data. An abnormal temperature sequence prediction module is used to construct a gray dataset based on the abnormal temperature region data and to perform prediction using a gray prediction algorithm to obtain an abnormal temperature prediction sequence. The dynamic operating parameter calculation module is used to calculate the dynamic operating point based on the workload and the abnormal temperature prediction sequence to obtain the dynamic operating parameters of the abnormal temperature region. The dynamic working mode matching module is used to match the working mode with the dynamic working parameters and the preset dynamic working threshold to obtain the dynamic working mode of the abnormal temperature region. The low-performance operating mode matching module is used to match the normal temperature range data with a preset normal temperature threshold to obtain the low-performance operating mode of the normal temperature range, so as to control the low-performance operation of the infrared thermal imaging core. The model predictive control optimization module is used to calculate the temperature change rate based on the ambient temperature and the temperature distribution data.

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