Low-power-consumption heat dissipation method and system for infrared thermal imaging movement

Abstract

The invention relates to the technical field of infrared temperature measurement, and discloses a low-power-consumption heat dissipation method and system for an infrared thermal imaging machine core, and the method comprises the steps: obtaining temperature distribution data, a working load and an environment temperature, carrying out the preprocessing, and constructing a matrix, thereby obtaining a temperature distribution matrix; judging and classifying according to the temperature distribution matrix to obtain normal temperature area data and abnormal temperature area data; constructing a gray data set according to the abnormal temperature region data, and performing prediction to obtain an abnormal temperature prediction sequence; performing calculation according to the workload and the abnormal temperature prediction sequence to obtain dynamic working parameters, and performing matching to obtain a dynamic working mode of the abnormal temperature region; performing matching according to the normal temperature region data to obtain a low-performance working mode of the normal temperature region; when the temperature change rate is larger than a preset temperature change rate threshold value, all the steps are executed again, and rolling optimization is conducted through a model prediction control algorithm. According to the method, the working modes can be dynamically switched.

Description

Technical Field

[0001] The present invention relates to the technical field of infrared temperature measurement, and particularly to a low-power heat dissipation method and system for an infrared thermal imaging module. Background Art

[0002] Infrared thermal imaging modules play an important role in fields such as industrial inspection and medical diagnosis. By detecting the thermal radiation intensity of an object, they can quickly and non-contactingly obtain the temperature distribution information of a scene. In the industrial field, infrared thermal imaging technology is widely used in equipment status monitoring and fault warning. In the manufacturing industry, abnormal temperatures of mechanical transmission components (such as bearings and gearboxes) on a production line may indicate wear or lubrication failure, and infrared thermal imaging technology can help achieve predictive maintenance and reduce unplanned downtime losses. In addition, in industries such as chemical engineering and metallurgy, surface temperature monitoring of high-temperature reaction kettles or furnaces is crucial for safe production, and infrared thermal imaging modules can provide accurate non-contact temperature data to avoid safety hazards of manual inspections.

[0003] The existing technology captures the thermal radiation signal of a target object through an infrared detector. When the ambient temperature rises or the workload increases, the module needs to increase the working frequency and voltage of the chip to meet the real-time processing requirements of the hot spot area, so as to complete non-contact temperature detection and analysis. However, the existing infrared thermal imaging module detection technology usually operates at a constant power, and even when the ambient temperature is low or the module load is light, it still maintains full-speed heat dissipation. Therefore, the existing technology cannot dynamically adjust the working mode when the ambient temperature or workload changes.

[0004] In summary, the existing technology has the problem that it cannot dynamically adjust the working mode according to the changes of the scene, resulting in an increase in the overall power consumption. Summary of the Invention

[0005] The present invention provides a low-power heat dissipation method and system for an infrared thermal imaging module to solve the problem in the existing infrared thermal imaging module detection technology that the working mode cannot be dynamically adjusted according to the changes of the scene, which will lead to an increase in the overall power consumption.

[0006] In a first aspect, to solve the above technical problem, the present invention provides a low-power heat dissipation method for an infrared thermal imaging module, including: Obtaining the 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 according to the temperature distribution matrix, and performing region classification according to the judgment result to obtain normal temperature region data and abnormal temperature region data; Construct a grey data set based on the abnormal temperature region data, and use the grey prediction algorithm for prediction to obtain an abnormal temperature prediction sequence; Perform dynamic operating point calculation based on the workload and the abnormal temperature prediction sequence to obtain dynamic operating parameters for the abnormal temperature region; Perform operating mode matching based on the dynamic operating parameters and a preset dynamic operating threshold to obtain a dynamic operating mode for the abnormal temperature region; Match the normal temperature region data with a preset normal temperature threshold to obtain a low-performance operating mode for the normal temperature region, so as to control the low-performance operation of the infrared thermal imaging module; Calculate the temperature change rate according to the ambient temperature and the temperature distribution data. When the temperature change rate is greater than a preset temperature change rate threshold, re-execute all the above steps, and use the model predictive control algorithm to perform rolling optimization on the abnormal temperature region data to obtain optimized abnormal temperature region data, so as to construct a grey data set based on the optimized abnormal temperature region data.

[0007] In an alternative embodiment, the preprocessing the temperature distribution data and constructing a temperature matrix to obtain a temperature distribution matrix includes: Perform noise removal on the temperature distribution data to obtain noise-free temperature distribution data; Perform data verification on the noise-free temperature distribution data to obtain complete temperature distribution data; Construct a temperature matrix based on the complete temperature distribution data to obtain a temperature distribution matrix; Wherein, the temperature distribution matrix is: In the formula, is the temperature distribution matrix, is the element in the first row and first column of the temperature distribution matrix, is the element in the first row and the th column of the temperature distribution matrix, is the element in the th row and first column of the temperature distribution matrix, is the element in the th row and the th column of the temperature distribution matrix, is the horizontal vector dimension of the temperature distribution matrix, is the vertical vector dimension of the temperature distribution matrix.

[0008] In an alternative embodiment, the judging based on the temperature distribution matrix and a preset temperature threshold matrix, and performing region classification according to the judgment result to obtain normal temperature region data and abnormal temperature region data includes: When an element of the temperature distribution matrix is greater than an element of a preset temperature threshold matrix, it is determined as abnormal temperature data; When an element of the temperature distribution matrix is less than an element of a preset temperature threshold matrix, it is determined as normal temperature data; Construct a temperature block matrix based on the abnormal temperature data and the normal temperature data to obtain normal temperature region data and abnormal temperature region data; Among them, the temperature block matrix is: In the formula, is the temperature block matrix, is the normal temperature region data, is the abnormal temperature region data.

[0009] In an alternative embodiment, the gray prediction algorithm is used for prediction to obtain an abnormal temperature prediction sequence, including: Perform data cleaning and preprocessing on the gray data set to obtain gray training set data; Construct an accumulation sequence based on the gray training set data to obtain gray accumulation data; Construct a mean sequence based on the gray training set data to obtain gray mean data; Construct a gray differential equation based on the gray accumulation data and the gray mean data, and solve the gray differential equation using the least squares method to obtain a whiting equation; Restore the predicted value according to the whiting equation to obtain an abnormal temperature prediction sequence.

[0010] In an alternative embodiment, the dynamic operating point is calculated according to the workload and the abnormal temperature prediction sequence to obtain dynamic operating parameters for the abnormal temperature region, including: Perform an incremental calculation based on the workload to obtain a workload increment; Calculate the dynamic operating point according to 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 an operating frequency increment and an operating voltage increment; The operating frequency increment is calculated by the following formula: In the formula, is the operating frequency increment, is the th operating frequency, is the A working frequency, is the th moment value, is the th moment value; The increment of the working voltage is calculated by the following formula: In the formula, is the increment of the working voltage, is the th working voltage, is the th working voltage; The dynamic working parameter is calculated by the following formula: In the formula, is the dynamic working parameter, is the th abnormal temperature prediction sequence value, is the base of the natural logarithm, is the length of the abnormal temperature prediction sequence.

[0011] In an optional implementation manner, the matching of the working mode according to the dynamic working parameter and the preset dynamic working threshold to obtain the dynamic working mode of the abnormal temperature region includes: When the dynamic working parameter is greater than or equal to the preset first dynamic working threshold, the dynamic working mode of the abnormal temperature region is the preset first working 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 working parameter is greater than or equal to the preset third dynamic working threshold and less than the preset second dynamic working threshold, the dynamic working mode of the abnormal temperature region is the preset third working mode; When the dynamic working parameter is less than the preset fourth dynamic working threshold, the dynamic working mode of the abnormal temperature region is the preset fourth working mode; 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; The dynamic working modes include a first working mode, a second working mode, a third working mode, and a fourth working mode.

[0012] In an optional implementation manner, the matching of the normal temperature region data with the preset normal temperature threshold to obtain the low-performance working mode of the normal temperature region to control the low-performance operation of the infrared thermal imaging module includes: When the data in the normal temperature region 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 in the normal temperature region is the ultra-low-performance mode; When the data in the normal temperature region is less than or equal to the preset second normal temperature threshold, the low-performance working mode in the normal temperature region 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 working mode includes the ultra-low-performance mode and the extremely low-performance mode.

[0013] In an alternative embodiment, the calculating the temperature change rate according to the ambient temperature and the temperature distribution data includes: The temperature change rate is calculated by the following formula: In the formula, is the temperature change rate, is the th ambient temperature, is the th temperature distribution data, is the th moment value, is the th moment value.

[0014] In an alternative embodiment, the using the model predictive control algorithm to perform rolling optimization on the abnormal temperature region data to obtain optimized abnormal temperature region data includes: Constructing an optimization data set according to the abnormal temperature region data, and performing data cleaning and preprocessing to obtain rolling optimization training set data; Constructing a rolling optimization model according to the rolling optimization training set data, taking minimizing the prediction error as the optimization goal, and using nonlinear programming to solve the rolling optimization model to obtain rolling optimization test set data; Calculating the root mean square error according to the rolling optimization test set data. When the root mean square error is less than a preset error threshold, taking the first data of the rolling optimization test set data, and repeating to solve the rolling optimization model to obtain optimized abnormal temperature region data.

[0015] In a second aspect, the present invention provides a low-power heat dissipation system for an infrared thermal imaging module, including: A data acquisition module, configured to acquire the temperature distribution data, workload, and ambient temperature of the infrared thermal imaging module; A temperature distribution matrix construction module, configured to preprocess the temperature distribution data and construct a temperature matrix to obtain a temperature distribution matrix; A temperature region data classification module, configured to make a judgment based on the temperature distribution matrix and a preset temperature threshold matrix, and perform region classification according to the judgment result to obtain normal temperature region data and abnormal temperature region data; An abnormal temperature sequence prediction module, configured to construct a grey data set based on the abnormal temperature region data and perform prediction using a grey prediction algorithm to obtain an abnormal temperature prediction sequence; A dynamic working parameter calculation module, configured to calculate a dynamic working point based on the workload and the abnormal temperature prediction sequence to obtain dynamic working parameters for the abnormal temperature region; A dynamic working mode matching module, configured to perform working mode matching based on the dynamic working parameters and a preset dynamic working threshold to obtain a dynamic working mode for the abnormal temperature region; A low-performance working mode matching module, configured to perform matching based on the normal temperature region data and a preset normal temperature threshold to obtain a low-performance working mode for the normal temperature region, so as to control the infrared thermal imaging core to operate with low performance; A model predictive control optimization module, configured to calculate a temperature change rate based on the environmental temperature and the temperature distribution data. When the temperature change rate is greater than a preset temperature change rate threshold, re-execute all the above steps, and perform rolling optimization on the abnormal temperature region data using a model predictive control algorithm to obtain optimized abnormal temperature region data, so as to construct a grey data set based on the optimized abnormal temperature region data.

[0016] In a third aspect, the present invention further 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. When the processor executes the computer program, the low-power heat dissipation method of the infrared thermal imaging core described in any one of the above is implemented.

[0017] In a fourth aspect, the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute the low-power heat dissipation method of the infrared thermal imaging core described in any one of the above.

[0018] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention preprocesses the temperature distribution data of the infrared thermal imaging core and constructs a matrix. All the original temperature distribution data has undergone noise removal, data verification, and construction of a temperature matrix to ensure the integrity, consistency, and accuracy of the data. Through the normalization process of the data, the system can perform subsequent classification, analysis, and storage operations more efficiently and accurately. (2) The present invention classifies and divides the data 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 the temperature distribution data can be stored in the database by region, thereby achieving efficient retrieval and management. (3) The present invention can dynamically adjust the working mode of the infrared thermal imaging core according to the real-time changes in the ambient temperature and workload, effectively reducing the overall power consumption and extending the service life of the device. It is especially suitable for complex environments such as outdoors and industry, with significant energy-saving effects and strong adaptability. (4) The present invention combines the grey prediction algorithm with the model predictive control algorithm for the prediction and optimization of abnormal temperature region data, not only improving the accuracy of temperature prediction but also achieving precise estimation and early adjustment of future heat dissipation requirements, avoiding the impact of temperature anomalies on the device performance, and ensuring the stable operation of the infrared thermal imaging core in different scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a schematic flowchart of a low-power heat dissipation method for an infrared thermal imaging core provided by the first embodiment of the present invention; Figure 2 is a schematic structural diagram of a low-power heat dissipation system for an infrared thermal imaging core provided by the second embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0021] Referring to Figure 1 , the first embodiment of the present invention provides a low-power heat dissipation method for an infrared thermal imaging core, including the following steps: S11, obtaining the temperature distribution data, workload, and ambient temperature of the infrared thermal imaging core; S12, preprocessing the temperature distribution data according to the temperature distribution data and constructing a temperature matrix to obtain a temperature distribution matrix; S13. Judge based on the preset temperature threshold matrix according to the temperature distribution matrix, and perform area classification according to the judgment result to obtain normal temperature area data and abnormal temperature area data; S14. Construct a grey data set according to the abnormal temperature area data, and use the grey prediction algorithm for prediction to obtain an abnormal temperature prediction sequence; S15. Calculate the dynamic operating point according to the workload and the abnormal temperature prediction sequence to obtain the dynamic operating parameters of the abnormal temperature area; S16. Perform a working mode matching according to the dynamic operating parameters and the preset dynamic operating threshold to obtain the dynamic working mode of the abnormal temperature area; S17. Match the normal temperature area data with the preset normal temperature threshold to obtain the low-performance working mode of the normal temperature area, so as to control the low-performance operation of the infrared thermal imaging core; S18. Calculate the temperature change rate according to the ambient temperature and the temperature distribution data. When the temperature change rate is greater than the preset temperature change rate threshold, re-execute 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 grey data set according to the optimized abnormal temperature area data.

[0022] In step S11, obtain the temperature distribution data, workload and ambient temperature of the infrared thermal imaging core.

[0023] It should be noted that the workload of the infrared thermal imaging core includes the working voltage and the working frequency. For obtaining the temperature distribution data of the infrared thermal imaging core, the infrared detector captures the infrared radiation in the scene, converts it into an electrical signal, and obtains the temperature distribution data after signal processing. The infrared detector is installed at the front end of the core and can collect the temperature distribution in the scene in real time. For example, in industrial inspection, the infrared detector can capture 30 infrared images per second, and each image contains rich temperature information. The data processing system converts these image data into a temperature distribution matrix for subsequent analysis and processing.

[0024] It should be noted that for obtaining the working voltage of the infrared thermal imaging core, the output voltage of the internal power supply module of the core is directly measured by a high-precision voltage sensor. The voltage sensor is installed on the power supply line between the power supply module and the chip and can monitor the change of the working voltage in real time. For example, during the operation of the core, the voltage sensor records the voltage value every 0.1 second and transmits the data to the data acquisition system. The data processing system analyzes the voltage fluctuation to ensure that the chip works under a stable voltage.

[0025] It should be noted that for obtaining the operating frequency of the infrared thermal imaging module, the operating frequency of the chip is measured by professional equipment such as a frequency meter or an oscilloscope. The frequency meter is connected to the clock signal output terminal of the chip and can accurately measure and display the current operating frequency. For example, in the chip performance test, the frequency meter can capture the change of the operating frequency in real time with millisecond-level accuracy, and the data acquisition system records these frequency data for analyzing the operating state of the chip under different loads.

[0026] It should be noted that for obtaining the ambient temperature, the ambient temperature around the module is measured by a temperature sensor (such as a thermistor or a thermocouple). The temperature sensor is installed near the housing or heat sink of the module and can monitor the change of the ambient temperature in real time. For example, the resistance value of the thermistor changes with the temperature, and the ambient temperature can be calculated by measuring its resistance value. The data acquisition system records the temperature value at regular intervals (such as every 1 minute) for analyzing the influence of the ambient temperature on the performance of the module.

[0027] In step S12, preprocessing is performed on the temperature distribution data and a temperature matrix is constructed to obtain a temperature distribution matrix.

[0028] Noise removal is performed on the temperature distribution data to obtain noise-free temperature distribution data; Data verification is performed on the noise-free temperature distribution data to obtain complete temperature distribution data; A temperature matrix is constructed based on the complete temperature distribution data to obtain a temperature distribution matrix; Among them, the data verification includes removing data outliers and supplementing missing data values; Among them, the temperature distribution matrix is: In the formula, is the temperature distribution matrix, is the element in the first row and first column of the temperature distribution matrix, is the element in the first row and the th column of the temperature distribution matrix, is the element in the th row and first column of the temperature distribution matrix, is the element in the th row and the th column of the temperature distribution matrix, is the horizontal vector dimension of the temperature distribution matrix, is the vertical vector dimension of the temperature distribution matrix.

[0029] It should be noted that the temperature distribution data of the infrared thermal imaging core obtained is first de-noised to remove the interference signal and random noise therein, and obtain noise-free temperature distribution data. In practical applications, the temperature data collected by the infrared thermal imaging system will be affected by environmental noise, sensor accuracy limitation or signal transmission interference, resulting in noise in the data. For example, in the surface temperature monitoring of industrial equipment, due to the electromagnetic interference generated by the equipment during operation or the thermal radiation of the surrounding environment, the collected temperature data contains high-frequency noise or random fluctuations. The Gaussian filter algorithm can be used to smooth the original temperature distribution data. Gaussian filtering is a linear filtering algorithm based on Gaussian function, which can effectively remove Gaussian noise in images or data while retaining the main features of the data. By setting appropriate filter kernel size and standard deviation parameters, the temperature distribution data is convolved to reduce the impact of noise. For example, setting the filter kernel size to 3×3 and the standard deviation to 1, the collected temperature data is subjected to Gaussian filtering, which can significantly reduce the random fluctuations in the data and obtain smooth noise-free temperature distribution data.

[0030] It should be noted that the denoised temperature distribution data is subjected to data verification to remove outliers and supplement missing values, thereby obtaining complete temperature distribution data. In actual scenarios, due to sensor failure, occlusion or data transmission errors, outliers or missing data may appear in the temperature data. For example, when monitoring the temperature of the exterior wall of a building, some temperature data cannot be collected normally due to the occlusion of some areas, resulting in missing data; or due to sensor failure, the temperature values ​​in some areas are abnormally high or low, exceeding the normal range. A statistical method is used to detect outliers in noise-free temperature distribution data, and the threshold range of outliers is set by calculating the mean and standard deviation of the data. For example, temperature values ​​exceeding the mean ±3 times the standard deviation are judged as outliers and are removed. For missing data, an interpolation algorithm is used to supplement it. By using the temperature values ​​of four known points around the missing data, the temperature value of the missing point is calculated according to their spatial position relationship, thereby achieving data integrity.

[0031] It should be noted that the temperature matrix is ​​finally constructed based on the complete temperature distribution data to obtain the temperature distribution matrix. The temperature matrix is ​​a two-dimensional array structure used to represent the spatial characteristics of temperature distribution. The construction process of the temperature distribution matrix is ​​to arrange the complete temperature distribution data according to the spatial position relationship 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 an industrial equipment, the surface of the equipment is divided into a 10×10 grid area, each grid point corresponds to a temperature value, and these temperature values ​​are arranged in sequence according to the grid position to form a 10×10 temperature distribution matrix.

[0032] In step S13, based on the temperature distribution matrix, a judgment is made according to a preset temperature threshold matrix, and region classification is performed according to the judgment result to obtain normal temperature region data and abnormal temperature region data.

[0033] When an element of the temperature distribution matrix is greater than an element of the preset temperature threshold matrix, it is determined as abnormal temperature data; When an element of the temperature distribution matrix is less than an element of the preset temperature threshold matrix, it is determined as normal temperature data; Based on the abnormal temperature data and the normal temperature data, a temperature block matrix is constructed to obtain normal temperature region data and abnormal temperature region data; Among them, the temperature block matrix is: In the formula, is the temperature block matrix, is the normal temperature region data, is the abnormal temperature region data.

[0034] Exemplarily, taking a third-order square matrix as an example, when the temperature distribution matrix and the preset temperature threshold matrix , the temperature block matrix obtained by classification at this time is .

[0035] In step S14, a grey data set is constructed based on the abnormal temperature region data, and grey prediction algorithm is used for prediction to obtain an abnormal temperature prediction sequence.

[0036] Based on the grey data set, data cleaning and preprocessing are performed to obtain grey training set data; Based on the grey training set data, an accumulated sequence is constructed to obtain grey accumulated data; Based on the grey training set data, a mean sequence is constructed to obtain grey mean data; Based on the grey accumulated data and the grey mean data, a grey differential equation is constructed, and the grey differential equation is solved by the least square method to obtain a white equation; Based on the white equation, the predicted value is restored to obtain an abnormal temperature prediction sequence.

[0037] It should be noted that the grey data set is obtained by sorting the abnormal temperature region data according to the time stamp to construct a time series data group, and then, for each abnormal temperature region , the temperature data of its continuous K sampling periods is extracted. .

[0038] It should be noted that the gray cumulative data is calculated by the following formula: In the formula, is the gray cumulative data, is the th gray training set data, is the sample length; It should be noted that the gray mean data is calculated by the following formula: In the formula, is the gray mean data, is the th gray training set data, is the th gray training set data; It should be noted that the gray differential equation is: In the formula, is the coefficient of the gray mean data, is the constant of the gray differential equation; It should be noted that the least squares method solving formula is: In the formula, is the mean of the gray training set data, is the mean of the gray mean data; It should be noted that the whiting equation is: In the formula, is the differential of the gray cumulative data; It should be noted that the abnormal temperature prediction sequence is calculated by the following formula: In the formula, is the abnormal temperature prediction sequence.

[0039] Exemplarily, when the abnormal temperature region data is {1, 2, 3, 4, 5, 6}, according to the formula model calculation, , , and the predicted abnormal temperature prediction sequence is {7, 8, 9, 10, 11, 12}.

[0040] In step S15, according to the workload and the abnormal temperature prediction sequence, dynamic operating point calculation is performed to obtain the dynamic operating parameters of the abnormal temperature region.

[0041] Perform incremental calculation based on the workload to obtain a workload increment; Perform dynamic operating point calculation based on the workload increment and the abnormal temperature prediction sequence to obtain dynamic operating parameters for the abnormal temperature region; The workload includes an operating frequency and an operating voltage; The workload increment includes an operating frequency increment and an operating voltage increment; The operating frequency increment is calculated by the following formula: where is the operating frequency increment, is the th operating frequency, is the th operating frequency, is the th moment value, is the th moment value; The operating voltage increment is calculated by the following formula: where is the operating voltage increment, is the th operating voltage, is the th operating voltage; The dynamic operating parameters are calculated by the following formula: where is the dynamic operating parameter, is the th abnormal temperature prediction sequence value, is the base of the natural logarithm, is the length of the abnormal temperature prediction sequence.

[0042] It should be noted that Represents the ratio of the working voltage increment to the working frequency increment. This ratio reflects the change in voltage under a unit change in frequency and can be used as an indicator of the change in the working load. When the working frequency increases, if the working voltage also increases accordingly, then this ratio represents the sensitivity of the voltage to the change in frequency. The exponential function is used to adjust the ratio of the working load increment. When the average value of the abnormal temperature prediction sequence is large, the value of the exponential function is small, indicating that under high-temperature conditions, the dynamic working parameters should be reduced to lower power consumption and heat dissipation requirements. Conversely, when the average value of the abnormal temperature prediction sequence is small, the value of the exponential function is large, indicating that under low-temperature conditions, the dynamic working parameters are increased to improve performance.

[0043] Exemplarily, when the working frequency , the working voltage , at a time interval of 1 s, it is calculated that , , .

[0044] In step S16, the working mode is matched according to the dynamic working parameter and the preset dynamic working threshold to obtain the dynamic working mode of the abnormal temperature region.

[0045] When the dynamic working parameter is greater than or equal to the preset first dynamic working threshold, the dynamic working mode of the abnormal temperature region is the preset first working 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 working parameter is greater than or equal to the preset third dynamic working threshold and less than the preset second dynamic working threshold, the dynamic working mode of the abnormal temperature region is the preset third working mode; When the dynamic working parameter is less than the preset fourth dynamic working threshold, the dynamic working mode of the abnormal temperature region is the preset fourth working mode; The preset dynamic working thresholds include the first dynamic working threshold, the second dynamic working threshold, the third dynamic working threshold, and the fourth dynamic working threshold; The dynamic working modes include the first working mode, the second working mode, the third working mode, and the fourth working mode.

[0046] Exemplarily, 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 obtains Greater than the preset third dynamic working threshold and less than the preset second dynamic working threshold, the dynamic working mode of the abnormal temperature region is the preset third working mode.

[0047] In step S17, the low-performance working mode of the normal temperature region is obtained by matching the normal temperature region data with the preset normal temperature threshold to control the low-performance operation of the infrared thermal imaging module.

[0048] When the normal temperature region data is less than or equal to the preset first normal temperature threshold and greater than the preset second normal temperature threshold, the low-performance working mode of the normal temperature region is the ultra-low performance mode; When the normal temperature region data is less than or equal to the preset second normal temperature threshold, the low-performance working mode of the normal temperature region 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 working mode includes an ultra-low performance mode and an extremely low performance mode.

[0049] Exemplarily, when the first normal temperature threshold and the second normal temperature threshold are 10 and 5 respectively, the normal temperature region data Less than the preset first normal temperature threshold and greater than the preset second normal temperature threshold, the low-performance working mode of the normal temperature region is the ultra-low performance mode.

[0050] In step S18, the temperature change rate is calculated according to 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 executed again, and the model predictive control algorithm is used to perform rolling optimization on the abnormal temperature region data to obtain optimized abnormal temperature region data, so as to construct a gray data set according to the optimized abnormal temperature region data.

[0051] The temperature change rate is calculated by the following formula: In the formula, is the temperature change rate, is the th ambient temperature, is the th temperature distribution data, is the th moment value, is the th moment value.

[0052] An optimized data set is constructed according to the abnormal temperature region data, and data cleaning and preprocessing are performed to obtain rolling optimization training set data; Construct a rolling optimization model based on the rolling optimization training set data, minimize the prediction error as the optimization objective, and use nonlinear programming to solve the rolling optimization model to obtain the rolling optimization test set data; Calculate the root mean square error based on the rolling optimization test set data. When the root mean square error is less than the preset error threshold, take the first data of the rolling optimization test set data, and repeat the solution of the rolling optimization model to obtain the optimized abnormal temperature region data.

[0053] It should be noted that when all steps are re-executed, the model predictive control algorithm needs to be used for rolling optimization. The rolling optimization model adopts the quadratic programming method, and the optimization objective is to minimize the sum of the squares of the prediction errors. Model predictive control is divided into three steps: data prediction, rolling optimization, and feedback correction. After determining the objective function, use the nonlinear programming algorithm in the Cplex solver to solve the rolling optimization model to complete the solution of the model. And when the error is satisfied, take the first data of the rolling optimization test set data and repeat the solution of the rolling optimization model to obtain the optimized abnormal temperature region data.

[0054] It should be noted that the formula for calculating the root mean square error is: In the formula, is the root mean square error, is the number of samples, is the th predicted value data, is the th actual value data.

[0055] Exemplarily, when the original abnormal temperature region data is {0.98, 1.99, 3.09, 4.01, 5, 6.02}, after rolling optimization, the obtained optimized abnormal temperature region data is {1, 2, 3, 4, 5, 6}.

[0056] In summary, the present invention discloses a low-power heat dissipation method for an infrared thermal imaging module, which includes obtaining 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 according to the temperature distribution matrix, and performing region classification according to the judgment result to obtain normal temperature region data and abnormal temperature region data; constructing a grey data set according to the abnormal temperature region data, and performing prediction using a grey prediction algorithm to obtain an abnormal temperature prediction sequence; calculating a dynamic operating point according to the workload and the abnormal temperature prediction sequence to obtain dynamic operating parameters for the abnormal temperature region; performing a working mode matching according to the dynamic operating parameters and a preset dynamic operating threshold to obtain a dynamic working mode for the abnormal temperature region; matching the normal temperature region data with a preset normal temperature threshold to obtain a low-performance working mode for the normal temperature region, so as to control the low-performance operation of the infrared thermal imaging module; calculating a temperature change rate according to 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 performing rolling optimization on the abnormal temperature region data using a model predictive control algorithm to obtain optimized abnormal temperature region data, so as to construct a grey data set according to the optimized abnormal temperature region data. The method first obtains temperature distribution data, workload, and ambient temperature and constructs a temperature matrix to obtain a temperature distribution matrix, then judges and performs region classification based on a preset temperature threshold matrix according to the temperature distribution matrix to obtain normal temperature region data and abnormal temperature region data, then constructs a grey data set according to the abnormal temperature region data and performs prediction using a grey prediction algorithm to obtain an abnormal temperature prediction sequence, then calculates a dynamic operating point according to the workload and the abnormal temperature prediction sequence to obtain dynamic operating parameters for the abnormal temperature region, then performs a working mode matching according to the dynamic operating parameters and a preset dynamic operating threshold to obtain a dynamic working mode for the abnormal temperature region, and at the same time matches the normal temperature region data with a preset normal temperature threshold to obtain a low-performance working mode for the normal temperature region to control the low-performance operation of the infrared thermal imaging module, and finally calculates a temperature change rate according to the ambient temperature, and when the temperature change rate is greater than a preset temperature change rate threshold, re-execute all the above steps and perform rolling optimization using a model predictive control algorithm to obtain optimized abnormal temperature region data, so as to construct a grey data set according to the optimized abnormal temperature region data. The method can dynamically adjust the working mode according to the change of the scene, and reduce the overall power consumption of the infrared thermal imaging module.

[0057] Referring to Figure 2 , the second embodiment of the present invention provides a low-power heat dissipation system for an infrared thermal imaging module, including: A data acquisition module, configured to acquire temperature distribution data, workload, and ambient temperature of the infrared thermal imaging module; A temperature distribution matrix construction module, configured to preprocess the temperature distribution data and construct a temperature matrix to obtain a temperature distribution matrix; A temperature region data classification module, configured to make a judgment based on the temperature distribution matrix and a preset temperature threshold matrix, and perform region classification according to the judgment result to obtain normal temperature region data and abnormal temperature region data; An abnormal temperature sequence prediction module, configured to construct a grey data set based on the abnormal temperature region data and perform prediction using a grey prediction algorithm to obtain an abnormal temperature prediction sequence; A dynamic working parameter calculation module, configured to perform dynamic working point calculation based on the workload and the abnormal temperature prediction sequence to obtain dynamic working parameters for the abnormal temperature region; A dynamic working mode matching module, configured to perform working mode matching based on the dynamic working parameters and a preset dynamic working threshold to obtain a dynamic working mode for the abnormal temperature region; A low-performance working mode matching module, configured to perform matching based on the normal temperature region data and a preset normal temperature threshold to obtain a low-performance working mode for the normal temperature region, so as to control the low-performance operation of the infrared thermal imaging core; A model predictive control optimization module, configured to calculate a temperature change rate based on the ambient temperature and the temperature distribution data. When the temperature change rate is greater than a preset temperature change rate threshold, re-execute all the above steps, and perform rolling optimization on the abnormal temperature region data using a model predictive control algorithm to obtain optimized abnormal temperature region data, so as to construct a grey data set based on the optimized abnormal temperature region data.

[0058] It should be noted that a low-power heat dissipation system for an infrared thermal imaging core provided in an embodiment of the present invention is used to execute all the process steps of a low-power heat dissipation method for an infrared thermal imaging core in the above embodiment. The working principles and beneficial effects of the two correspond one by one, and thus will not be elaborated herein.

[0059] An embodiment of the present invention further 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 core. When the processor executes the computer program, the steps in the above embodiments of the low-power heat dissipation method for an infrared thermal imaging core are implemented, such as Figure 1 step S11 shown. Alternatively, when the processor executes the computer program, the functions of each module / unit in the above device embodiments are implemented, such as the data acquisition module.

[0060] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program in the electronic device.

[0061] The electronic device may be a computing device such as a desktop computer, a notebook, a palm computer, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above components are only examples of the electronic device and do not constitute a limitation on the electronic device. It may include more or fewer components than the above, or combine some components, or different components. For example, the electronic device may further include input / output devices, network access devices, a bus, etc.

[0062] The so-called processor may be a central processing unit (CPU), or may also be 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. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the electronic device and connects various parts of the entire electronic device through various interfaces and circuits.

[0063] The memory can be used to store the computer programs and / or modules. By running or executing the computer programs and / or modules stored in the memory and invoking the data stored in the memory, the processor realizes various functions of the electronic device. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage devices.

[0064] Among them, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above method embodiments of the present invention, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above method embodiments can be realized. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0065] 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 separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the attached drawings of the device embodiments provided by the present invention, the connection relationships between the modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0066] The specific embodiments described above further elaborate on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. In particular, for those skilled in the art, any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A low power dissipation method for infrared thermal imaging core, characterized in that: include: Obtain temperature distribution data, workload and ambient temperature of infrared thermal imaging core; Preprocessing is performed according to the temperature distribution data and a temperature matrix is ​​constructed to obtain a temperature distribution matrix; According to the temperature distribution matrix, a judgment is made based on a preset temperature threshold matrix, and regional classification is performed according to the judgment result to obtain normal temperature regional data and abnormal temperature regional data; Constructing a grey data set according to the abnormal temperature area data, and using a grey prediction algorithm to perform prediction to obtain an abnormal temperature prediction sequence; Perform dynamic operating point calculation according to the workload and the abnormal temperature prediction sequence to obtain dynamic operating parameters in the abnormal temperature region; Matching the working mode according to the dynamic working parameter and the preset dynamic working threshold value to obtain the dynamic working mode of the abnormal temperature area; According to the normal temperature area data, the normal temperature threshold is matched to obtain a low-performance working mode in the normal temperature area to control the infrared thermal imaging core to operate at a low performance; The temperature change rate is calculated according to the ambient temperature and the temperature distribution data. When the temperature change rate is greater than a preset temperature change rate threshold, all the above steps are re-executed, and the abnormal temperature area data is rolling optimized using a model predictive control algorithm to obtain optimized abnormal temperature area data, so as to construct a gray data set based on the optimized abnormal temperature area data.

2. The low power consumption heat dissipation method of the infrared thermal imaging core according to claim 1, characterized in that: The preprocessing is performed according to the temperature distribution data and a temperature matrix is ​​constructed to obtain a temperature distribution matrix, including: De-noising the temperature distribution data to obtain noise-free temperature distribution data; Performing data verification on the noise-free temperature distribution data to obtain complete temperature distribution data; Constructing a temperature matrix according to the complete temperature distribution data to obtain a temperature distribution matrix; Wherein, the temperature distribution matrix is: In the formula, is the temperature distribution matrix, is the first row and first column element of the temperature distribution matrix, The first row of the temperature distribution matrix Column elements, is the temperature distribution matrix The row and column 1 element, is the temperature distribution matrix Line Column elements, is the lateral dimension of the temperature distribution matrix, is the longitudinal dimension of the temperature distribution matrix.

3. The low power consumption heat dissipation method of the infrared thermal imaging core according to claim 1, characterized in that: The method of performing judgment based on the temperature distribution matrix and the preset temperature threshold matrix, and classifying regions according to the judgment result to obtain normal temperature region data and abnormal temperature region data, includes: When the element of the temperature distribution matrix is ​​greater than the element of the preset temperature threshold matrix, it is determined to be abnormal temperature data; When the element of the temperature distribution matrix is ​​less than the element of the preset temperature threshold matrix, it is determined to be normal temperature data; Constructing a temperature block matrix according to the abnormal temperature data and the normal temperature data to obtain normal temperature area data and abnormal temperature area data; Wherein, the temperature block matrix is: In the formula, is the temperature block matrix, For normal temperature area data, This is the data for abnormal temperature area.

4. The low power consumption heat dissipation method of the infrared thermal imaging core according to claim 1, characterized in that: The gray prediction algorithm is used to predict and obtain the abnormal temperature prediction sequence, including: According to the grey data set, data cleaning and preprocessing are performed to obtain grey training set data; Constructing a cumulative sequence according to the grey training set data to obtain grey cumulative data; Constructing a mean sequence according to the grey training set data to obtain grey mean data; A grey differential equation is constructed according to the grey accumulated data and the grey mean data, and the grey differential equation is solved by a least square method to obtain a whitening equation; The predicted values ​​are restored according to the whitening equation to obtain an abnormal temperature prediction sequence.

5. The low power consumption heat dissipation method of the infrared thermal imaging core according to claim 1, characterized in that: The step of performing dynamic operating point calculation according to the workload and the abnormal temperature prediction sequence to obtain dynamic operating parameters in the abnormal temperature region includes: Performing incremental calculation according to the workload to obtain a workload increment; Perform dynamic operating point calculation according to the workload increment and the abnormal temperature prediction sequence to obtain dynamic operating parameters in the abnormal temperature region; The workload includes an operating frequency and an operating voltage; The workload increment includes an operating frequency increment and an operating voltage increment; The operating frequency increment is calculated by the following formula: In the formula, is the operating frequency increment, For the The operating frequency, For the The operating frequency, For the A moment value, For the A moment value; The working voltage increment is calculated by the following formula: In the formula, is the operating voltage increment, For the Working voltage, For the Working voltage; The dynamic working parameters are calculated by the following formula: In the formula, is the dynamic working parameter, For the abnormal temperature prediction sequence values, is the base of natural logarithm, The length of the sequence for abnormal temperature prediction.

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

7. The low power consumption heat dissipation method of the infrared thermal imaging core according to claim 1, characterized in that: The method of matching the normal temperature area data with a preset normal temperature threshold to obtain a low-performance working mode in the normal temperature area to control the infrared thermal imaging core to operate at a low performance level includes: When the normal temperature region 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 in the normal temperature region is an ultra-low performance mode; When the normal temperature region data is less than or equal to a preset second normal temperature threshold, the low performance working mode in the normal temperature region is an extremely low performance mode; The preset normal temperature threshold includes a first normal temperature threshold and a second normal temperature threshold; The low performance working mode includes an ultra-low performance mode and an extremely low performance mode.

8. The low power consumption heat dissipation method of the infrared thermal imaging core according to claim 1, characterized in that: The calculating the temperature change rate according to the ambient temperature and the temperature distribution data comprises: The temperature change rate is calculated by the following formula: In the formula, is the temperature change rate, For the Ambient temperature, For the Temperature distribution data, For the A moment value, For the A moment value.

9. The low power consumption heat dissipation method of the infrared thermal imaging core according to claim 1, characterized in that: The method 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: Constructing an optimized data set according to the abnormal temperature area data, and performing data cleaning and preprocessing to obtain rolling optimized training set data; A rolling optimization model is constructed according to the rolling optimization training set data, minimizing the prediction error is taken as the optimization goal, and the rolling optimization model is solved by 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 of the rolling optimization test set data is taken, and the rolling optimization model is repeatedly solved to obtain the optimized abnormal temperature area data.

10. A low power consumption heat dissipation system for an infrared thermal imaging core, characterized in that: include: A data acquisition module, used to obtain temperature distribution data, workload and ambient temperature of the infrared thermal imaging core; A temperature distribution matrix construction module is used to preprocess the temperature distribution data and construct a temperature matrix to obtain a temperature distribution matrix; A temperature region data classification module is used to make a judgment based on the temperature distribution matrix and a preset temperature threshold matrix, and to classify regions according to the judgment result to obtain normal temperature region data and abnormal temperature region data; The abnormal temperature sequence prediction module is used to construct a grey data set according to the abnormal temperature area data, and use a grey prediction algorithm to perform prediction to obtain an abnormal temperature prediction sequence; A dynamic operating parameter calculation module, used to perform dynamic operating point calculation according to the workload and the abnormal temperature prediction sequence to obtain dynamic operating parameters in the abnormal temperature area; A dynamic working mode matching module, used to match the working mode according to the dynamic working parameter and the preset dynamic working threshold value, so as to obtain the dynamic working mode of the abnormal temperature area; A low-performance working mode matching module is used to match the normal temperature area data with a preset normal temperature threshold to obtain a low-performance working mode in the normal temperature area to control the infrared thermal imaging core to operate at a low performance; 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 a 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 data set based on the optimized abnormal temperature area data.

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