Control method and device of intelligent terminal

Abstract

The invention discloses a control method and device of an intelligent terminal, and the method comprises the steps: obtaining the current acceleration information of the intelligent terminal in a current time period; whether a started radiator is installed on the intelligent terminal is judged based on the current acceleration information of the intelligent terminal; if it is determined that the started radiator is installed on the intelligent terminal based on the current acceleration information of the intelligent terminal, the performance parameters of the intelligent terminal are improved to preset performance parameters. According to the intelligent terminal, whether the opened heat dissipation back splint is installed or not is determined through the acceleration information of the intelligent terminal, then the performance of the intelligent terminal is automatically adjusted according to the working state of the heat dissipation back splint, whether the heat dissipation back splint exists or not can be intelligently sensed, the control effect of the intelligent terminal is improved, and the performance of the intelligent terminal is exerted to the maximum extent.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a control method and device for an intelligent terminal. Background Art

[0002] When users play games, their phones may heat up, which can affect performance and feel. The heat dissipation back clip is a device that is attached to the back of the phone and can cool the phone. After installing and turning on the heat dissipation back clip, users manually turn the phone's performance to the maximum. After turning off the heat dissipation back clip, users manually reduce the phone's performance. The smart terminal cannot automatically adjust its own performance according to the working status of the heat dissipation back clip, resulting in poor control of the smart terminal.

[0003] That is, the control effect of the intelligent terminal in the prior art is relatively poor. Summary of the invention

[0004] The embodiments of the present application provide a method and device for controlling a smart terminal, which can improve the control effect of the smart terminal.

[0005] In a first aspect, the present application provides a control method for a smart terminal, comprising:

[0006] Obtain the current acceleration information of the smart terminal in the current period;

[0007] Determining whether an activated radiator is installed on the smart terminal based on the current acceleration information of the smart terminal;

[0008] If it is determined based on the current acceleration information of the smart terminal that an activated radiator is installed on the smart terminal, the performance parameter of the smart terminal is increased to a preset performance parameter.

[0009] In a second aspect, the control device of the intelligent terminal provided by the present application includes:

[0010] An acquisition module is used to acquire the current acceleration information of the smart terminal in the current period;

[0011] A judging module, configured to judge whether an activated radiator is installed on the smart terminal based on the current acceleration information of the smart terminal;

[0012] The performance adjustment module is used to increase the performance parameter of the smart terminal to a preset performance parameter if it is determined based on the current acceleration information of the smart terminal that an activated radiator is installed on the smart terminal.

[0013] In a third aspect, the smart terminal provided in the present application includes a memory and a processor, the memory stores a computer program, and the processor is used to run the computer program in the memory to implement the steps in the control method of the smart terminal provided in the present application.

[0014] In a fourth aspect, the computer-readable storage medium provided in the present application stores a plurality of instructions, which are suitable for loading by a processor to implement the steps in the control method of the smart terminal provided in the present application.

[0015] In a fifth aspect, the computer program product provided in the present application includes a computer program or instructions, which, when executed by a processor, implement the steps in the control method of the smart terminal provided in the present application.

[0016] In this application, compared with the related art, the current acceleration information of the smart terminal in the current period is obtained; based on the current acceleration information of the smart terminal, it is determined whether an activated radiator is installed on the smart terminal; if it is determined based on the current acceleration information of the smart terminal that an activated radiator is installed on the smart terminal, the performance parameters of the smart terminal are increased to preset performance parameters. The smart terminal of this application uses its own acceleration information to determine whether an activated heat dissipation back clip is installed, and then automatically adjusts its own performance according to the working state of the heat dissipation back clip. It can intelligently sense whether there is a heat dissipation back clip, improve the control effect of the smart terminal, and maximize the performance of the smart terminal. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 is a schematic diagram of a scenario of a control system of an intelligent terminal provided in an embodiment of the present application;

[0019] Figure 2 It is a flowchart of an embodiment of a control method of an intelligent terminal provided in an embodiment of the present application;

[0020] Figure 3 is a schematic diagram of three axes of the smart terminal provided in an embodiment of the present application;

[0021] Figure 4 It is a schematic diagram of changes in three-axis acceleration when no activated radiator is installed on the smart terminal in an embodiment of the control method of the smart terminal provided by the embodiment of the present application;

[0022] Figure 5 It is a schematic diagram of changes in three-axis acceleration when an activated radiator is installed on the smart terminal in one embodiment of the control method of the smart terminal provided by the embodiment of the present application;

[0023] Figure 6 is a schematic diagram of a neural network classification model in an embodiment of a control method for an intelligent terminal provided in an embodiment of the present application;

[0024] Figure 7 is a flow chart of another embodiment of the control method of the intelligent terminal provided in the embodiment of the present application;

[0025] Figure 8 is a schematic diagram of obtaining current acceleration information of the smart terminal in the current time period according to a preset period in an embodiment of the control method of the smart terminal provided by the embodiment of the present application;

[0026] Fig. 9 A schematic diagram of the structure of a control device for an intelligent terminal provided in an embodiment of the present application;

[0027] Fig.10 It is a schematic diagram of the structure of the intelligent terminal provided in the embodiment of the present application. DETAILED DESCRIPTION

[0028] It should be noted that the principles of the present application are illustrated by implementing them in an appropriate computing environment. The following description is based on the illustrated specific embodiments of the present application and should not be considered as limiting other specific embodiments of the present application that are not described in detail herein.

[0029] In the following description of the present application, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it can be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0030] In the following description of the present application, the terms "first\second\third" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described here can be implemented in an order other than that illustrated or described here.

[0031] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0032] In order to improve the control effect of the smart terminal, the embodiments of the present application provide a control method of the smart terminal, a control device of the smart terminal, a smart terminal, a computer-readable storage medium, and a computer program product. The control method of the smart terminal can be executed by the control device of the smart terminal, or by the smart terminal integrated with the control device of the smart terminal.

[0033] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0034] Please refer to Figure 1 , the present application also provides a control system for an intelligent terminal, such as Figure 1 As shown, the control system of the smart terminal includes a smart terminal 100, and the smart terminal 100 is integrated with the control device of the smart terminal provided by the present application.

[0035] Among them, the smart terminal 100 can be any device equipped with a processor and has processing capabilities, such as smart phones, tablet computers, PDAs, laptops, smart speakers and other mobile smart terminals with processors, or desktop computers, televisions, servers, industrial equipment and other fixed smart terminals with processors.

[0036] In addition, if Figure 1 As shown, the control system of the intelligent terminal may further include a memory 200 for storing original data, intermediate data and result data.

[0037] In the embodiment of the present application, the memory 200 can be a cloud storage. Cloud storage is a new concept extended and developed from the concept of cloud computing. A distributed cloud storage system (hereinafter referred to as the storage system) refers to a storage system that uses cluster applications, grid technology, and distributed storage file systems to bring together a large number of different types of storage devices (storage devices are also called storage nodes) in the network through application software or application interfaces to work together and provide external data storage and business access functions.

[0038] At present, the storage method of the storage system is: create a logical volume, and when creating a logical volume, allocate physical storage space for each logical volume. The physical storage space may be composed of disks of a storage device or several storage devices. The client stores data on a logical volume, that is, stores the data on the file system. The file system divides the data into many parts, each of which is an object. The object contains not only data but also additional information such as data identification (ID, ID entity). The file system writes each object into the physical storage space of the logical volume, and the file system records the storage location information of each object, so that when the client requests to access the data, the file system can allow the client to access the data according to the storage location information of each object.

[0039] The process of the storage system allocating physical storage space to a logical volume is as follows: based on the estimated capacity of the objects stored in the logical volume (this estimate often has a large margin relative to the actual capacity of the objects to be stored) and the grouping of independent redundant disk arrays (RAID, Redundant Array of Independent Disks), the physical storage space is pre-divided into stripes. A logical volume can be understood as a stripe, thereby allocating physical storage space to the logical volume.

[0040] It should be noted that Figure 1 The scenario diagram of the control system of the smart terminal shown is only an example. The control system and scenario of the smart terminal described in the embodiment of the present application are intended to more clearly illustrate the technical solution of the embodiment of the present application, and do not constitute a limitation on the technical solution provided in the embodiment of the present application. A person of ordinary skill in the art can know that with the evolution of the control system of smart terminals and the emergence of new business scenarios, the technical solution provided in the embodiment of the present application is also applicable to similar technical problems.

[0041] It should be noted that the serial numbers of the following embodiments are not intended to limit the preferred order of the embodiments.

[0042] Please refer to Figure 2 , Figure 2 FIG. 1 is a flow chart of an embodiment of a control method of an intelligent terminal provided in an embodiment of the present application. Figure 2 As shown, the process of the control method of the intelligent terminal provided by this application is as follows:

[0043] 201. Obtain current acceleration information of the smart terminal in the current time period.

[0044] The current acceleration information includes multiple linear accelerations in the current period. The current period can be 0.1s, 1s, etc., which can be set according to the specific situation. For example, the current acceleration information includes 400 linear accelerations in 1s. The number of linear accelerations included in the current acceleration information can be 200, 300, etc., which can be set according to the specific situation.

[0045] In a specific embodiment, multiple linear accelerations of the smart terminal in the current time period are collected according to a preset period; the multiple linear accelerations of the smart terminal in the current time period are determined as the current acceleration information of the smart terminal in the current time period. Among them, the preset period can be 0.1s, 0.2s, etc., which can be set according to the specific situation. For example, the preset period is 0.1s, and the current time period is within the time range from 0.9 seconds to 1 seconds. The present application uses a sliding window solution to collect 400 linear accelerations within 1s every 0.1 seconds. At 1.0 seconds, the 0th second to the 1st second is determined as the current time period, and the current acceleration information from 0th second to 1 second is collected; at 1.1 seconds, the 0.1th second to 1.1 seconds is determined as the current time period, and the current acceleration information from 0.1 seconds to 1.1 seconds is collected.

[0046] In a specific embodiment, a linear acceleration sensor is used to obtain the current acceleration information of the smart terminal in the current time period. The linear acceleration sensor is referred to as LA-sensor. The linear acceleration sensor is the data obtained by subtracting the influence of gravity from the acceleration sensor. The unit is m / s^2, and the coordinate system is the same as that of the acceleration sensor. The calculation formulas of the acceleration sensor, gravity sensor and linear acceleration sensor are as follows:

[0047] Sensor acceleration = gravity acceleration + linear acceleration.

[0048] In a specific embodiment, Sensor.TYPE_ACCELEROMETER is used to obtain the sensor acceleration information of the acceleration sensor of the smart terminal. Due to the influence of gravity, the sensor acceleration information has the components of gravity acceleration on three axes added to it. That is, when placed flat on a horizontal table, the accelerometer data should be gravity acceleration, that is, 9.8m / s^2. The gravity acceleration information in the sensor acceleration information is removed to obtain the current acceleration information of the smart terminal in the current period.

[0049] In the embodiment of the present application, the current acceleration information includes multiple Z-axis linear accelerations in the current period. The Z-axis linear acceleration is the component of the linear acceleration in the Z-axis direction. Figure 3 As shown, the Z axis of the smart terminal is perpendicular to the display screen of the smart terminal, and the X axis and Y axis of the smart terminal are parallel to the display screen of the smart terminal. In other embodiments, the current acceleration information includes a set of acceleration components of multiple linear accelerations in the current period, and the acceleration component set is at least one of the X-axis linear acceleration, the Y-axis linear acceleration, and the Z-axis linear acceleration.

[0050] For example, the current acceleration information includes a set of acceleration components of multiple linear accelerations in the current period, and the current acceleration information includes 400 linear accelerations in 1 second, so the current acceleration information can be represented by a 3*400 matrix. The 400 acceleration components of the smart terminal in three directions in the current period.

[0051] like Figure 4 and Figure 5 As shown, the current acceleration information includes multiple acceleration component sets in the current time period, and the acceleration component sets include X-axis linear acceleration linacc_X, Y-axis linear acceleration linacc_Y, and Z-axis linear acceleration linacc_Z. Figure 4 It shows the temporal changes of the X-axis linear acceleration linacc_X, the Y-axis linear acceleration linacc_Y, and the Z-axis linear acceleration linacc_Z when the intelligent terminal is not equipped with an activated radiator. Figure 5 The graph shows the temporal changes of the X-axis linear acceleration linacc_X, the Y-axis linear acceleration linacc_Y, and the Z-axis linear acceleration linacc_Z when a powered-on radiator is installed on the smart terminal.

[0052] 202. Determine whether an activated radiator is installed on the smart terminal based on current acceleration information of the smart terminal.

[0053] The radiator may be a heat dissipation clip that is detachably mounted on the back of the smart terminal.

[0054] In an embodiment of the present application, determining whether an activated radiator is installed on the smart terminal based on the current acceleration information of the smart terminal includes: determining whether there is a linear acceleration greater than a preset acceleration value in the current acceleration information; if there is no linear acceleration greater than the preset acceleration value in the current acceleration information, determining whether an activated radiator is installed on the smart terminal based on the current acceleration information of the smart terminal.

[0055] The preset acceleration value may be 2m / s^2 or other values, depending on the specific situation.

[0056] In a specific embodiment, judging whether an activated radiator is installed on the smart terminal based on the current acceleration information of the smart terminal includes:

[0057] (1) The current acceleration information of the smart terminal is input into a pre-trained neural network classification model for classification to obtain a current predicted classification result of the current acceleration information.

[0058] Among them, the current prediction classification result is that an activated radiator is installed on the smart terminal or an activated radiator is not installed on the smart terminal.

[0059] The neural network classification model may be Resnet50 or other classification models, which are not limited here.

[0060] It should be noted that artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technology in computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence. Artificial intelligence is to study the design principles and implementation methods of various intelligent machines so that machines have the functions of perception, reasoning and decision-making.

[0061] Artificial intelligence technology is a comprehensive discipline that covers a wide range of fields, including both hardware-level and software-level technologies. The basic technologies of artificial intelligence generally include sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics and other technologies. Artificial intelligence software technology mainly includes machine learning (ML) technology, among which deep learning (DL) is a new research direction in machine learning. It is introduced into machine learning to make it closer to the original goal, namely artificial intelligence. At present, deep learning is mainly used in the fields of machine vision and natural speech processing.

[0062] Deep learning is the process of learning the inherent laws and representation levels of sample data. The information obtained in the learning process is very helpful for interpreting data such as text, images, and sound. Using deep learning technology and corresponding sample sets, network models that can achieve different functions can be trained.

[0063] (2) Determine whether an activated radiator is installed on the smart terminal based on the current prediction classification result.

[0064] Specifically, if the current predicted classification result of the current acceleration information is that an activated radiator is installed on the smart terminal, it is determined that an activated radiator is installed on the smart terminal; if the current predicted classification result of the current acceleration information is that an activated radiator is not installed on the smart terminal, it is determined that an activated radiator is not installed on the smart terminal.

[0065] In another specific embodiment, in order to improve the prediction accuracy, judging whether an activated radiator is installed on the smart terminal based on the current acceleration information of the smart terminal includes:

[0066] (1) Obtain the current voice data of the smart terminal in the current time period.

[0067] Specifically, the microphone of the smart terminal generates data at a preset frequency. The preset frequency can be 16kHz, 32kHz, 48kHz, etc. For example, the preset frequency is 16kHz, at which the data volume is the smallest, and the sound quality has met the recognition requirements. Under the condition of meeting the accuracy, the calculation amount of this frequency is the smallest.

[0068] Specifically, the preset frequency is 16kHz, and the current voice data in the current period includes 16,000 voice signal values ​​within 1 second. The number of voice signal values ​​included in the current voice data can be 32,000, 48,000, or other values, which can be set according to specific circumstances.

[0069] (2) The current acceleration information and current voice data of the smart terminal are input into a pre-trained neural network classification model for classification to obtain a current predicted classification result of the current acceleration information.

[0070] like Figure 6 As shown, in a specific embodiment, the neural network classification model includes a Mel spectrum coefficient extraction module and a speech feature extraction module. A deceleration feature extraction module and a classification module. The current acceleration information and current voice data of the intelligent terminal are input into the pre-trained neural network classification model for classification to obtain the current predicted classification result of the current acceleration information, including: inputting the current voice data into the Mel spectrum coefficient extraction module to obtain the Mel spectrum coefficient feature; inputting the Mel spectrum coefficient feature into the speech feature extraction module to obtain the current speech feature; inputting the current acceleration information into the acceleration feature extraction module to obtain the current acceleration feature; fusing the current acceleration feature and the current speech feature and inputting them into the classification module to obtain the current predicted classification result of the current acceleration information.

[0071] The Mel spectrum coefficient extraction module converts speech data into the frequency domain to obtain more significant speech features. This is a common and general practice in the speech field and will not be repeated here. The basic process is to perform signal pre-emphasis on the speech data, frame, window, Fourier transform to obtain the power spectrum, filter the Mel filter bank to obtain the Mel spectrum graph, and use DCT to obtain the Mel spectrum coefficient. Finally, a 96*64 matrix output is obtained. This step can be understood as obtaining a set of feature data in a standard format through the industry-common Mel spectrum coefficient extraction method. The speech feature extraction module is a deep neural network composed of convolutional layers and fully connected layers. Its input is a 96*64 speech Mel spectrum coefficient feature and its output is a 1024-size data. The acceleration feature extraction module is also a deep neural network composed of convolutional layers and fully connected layers. Its input is 3*400 acceleration information and its output is a 1024-size data. The input of the classification module is 1024-sized data, which is the sum of the output of the speech feature extraction module and the output of the acceleration feature extraction module. The output is a value in the range of (0, 1), which represents the probability of installing an activated radiator on the smart terminal based on the current speech data and the current acceleration information.

[0072] (3) Determine whether an activated radiator is installed on the smart terminal based on the current prediction classification result.

[0073] 203. If it is determined based on the current acceleration information of the smart terminal that an activated radiator is installed on the smart terminal, the performance parameter of the smart terminal is increased to a preset performance parameter.

[0074] In the embodiment of the present application, the preset performance parameter can be set according to the specific situation. For example, the preset performance parameter is that the image quality is the highest level of image quality, etc.

[0075] In the embodiment of the present application, if it is determined based on the current acceleration information of the smart terminal that an activated radiator is installed on the smart terminal, the performance parameters of the smart terminal are increased to preset performance parameters.

[0076] Furthermore, in order to more accurately determine whether an activated radiator is installed on the smart terminal, if it is determined that an activated radiator is installed on the smart terminal based on the terminal acceleration information of the smart terminal, it is determined whether the temperature of the smart terminal decreases by a preset temperature value within a preset historical time period; if the temperature of the smart terminal decreases by a preset temperature value within a preset historical time period, the performance parameters of the smart terminal are increased to preset performance parameters. The preset historical time period may be 90s, 60s, 50s, etc., and the preset temperature value may be 10 degrees, 20 degrees, etc. For example, if the temperature of the smart terminal decreases by 20 degrees within 90s, the performance parameters of the smart terminal are increased to preset performance parameters.

[0077] See also Figure 7 , Figure 7 FIG. 1 is a flow chart of another embodiment of the control method of the intelligent terminal provided in the embodiment of the present application. Figure 7 As shown, the control method of the intelligent terminal includes:

[0078] 301. Obtain current acceleration information of the smart terminal in a current time period according to a preset period.

[0079] The current acceleration information includes multiple linear accelerations in the current period. The current period can be 0.1s, 1s, etc., which can be set according to the specific situation. For example, the current acceleration information includes 400 linear accelerations in 1s. The number of linear accelerations included in the current acceleration information can be 200, 300, etc., which can be set according to the specific situation.

[0080] Among them, the preset period can be 0.1s, 0.2s, etc., which can be set according to the specific situation. For example, the preset period is 0.1s, and the current time period is within the time range from the 9th second to the 10th second. The present application uses a sliding window solution to collect 400 linear accelerations within 1s every 0.1 seconds. At 1.0 seconds, the 0th second to the 1st second is determined as the current time period, and the current acceleration information from the 0th second to the 1st second is collected; at 1.1 seconds, the 0.1st second to the 1.1st second is determined as the current time period, and the current acceleration information from the 0.1st second to the 1.1st second is collected.

[0081] like Figure 8 As shown, a sliding window is used to move along the time axis, and the acceleration information in the sliding window is collected every 40ms.

[0082] In a specific embodiment, a linear acceleration sensor is used to obtain current acceleration information of the smart terminal in the current time period.

[0083] In a specific embodiment, the sensor acceleration information of the acceleration sensor of the smart terminal is obtained by using Sensor.TYPE_ACCELEROMETER, and the gravity acceleration information in the sensor acceleration information is removed to obtain the current acceleration information of the smart terminal in the current period.

[0084] In the embodiment of the present application, the current acceleration information includes a plurality of acceleration component sets within the current time period, and the acceleration component sets include X-axis linear acceleration, Y-axis linear acceleration, and Z-axis linear acceleration.

[0085] 302. Determine whether there is a linear acceleration greater than a preset acceleration value in the current acceleration information.

[0086] The preset acceleration value may be 2m / s^2 or other values, depending on the specific situation.

[0087] 303. Input the current acceleration information of the smart terminal into a pre-trained neural network classification model for classification to obtain a current predicted classification result of the current acceleration information.

[0088] Specifically, if there is no linear acceleration greater than the preset acceleration value in the current acceleration information, the current acceleration information of the smart terminal is input into the pre-trained neural network classification model for classification to obtain the current predicted classification result of the current acceleration information. If there is a linear acceleration greater than the preset acceleration value in the current acceleration information, no classification is performed.

[0089] Among them, the current prediction classification result is that an activated radiator is installed on the smart terminal or an activated radiator is not installed on the smart terminal.

[0090] The radiator may be a heat dissipation clip that can be detachably mounted on the back of the smart terminal. The neural network classification model may be Resnet50 or other classification models, which are not limited here.

[0091] 304. Obtain at least one historical prediction classification result corresponding to at least one historical acceleration information obtained by inputting at least one historical acceleration information into a pre-trained neural network classification model for classification.

[0092] Among them, at least one historical acceleration information is collected by the smart terminal in at least one historical period.

[0093] Specifically, the current acceleration information of the smart terminal in the current period is obtained according to the preset period, and the acceleration information in each sampling period can be collected. At least one historical period is a period before the current period. At least one historical period has the same period length as the current period, and the period end time of at least one historical period and the period end time of the current period are arranged at equal intervals on the time axis according to the preset period. For example, the period lengths of at least one historical period and the current period are both 1s.

[0094] Further, it is determined whether there is a second preset number of historical acceleration information. If there is a second preset number of historical acceleration information, the second preset number of historical acceleration information is respectively input into the pre-trained neural network classification model for classification to obtain a second preset number of historical prediction classification results corresponding to the second preset number of historical acceleration information. The second preset number can be 9, 8, 7, etc., which is set according to the specific situation.

[0095] For example, the current time is 1.9s, and the current period is from 0.9s to 1.9s. The second preset number is 9, then at least one historical period is 9 historical periods, and the 9 historical periods are, in order, from 0s to 1s; from 0.1s to 1.1s; from 0.2s to 1.2s; from 0.3s to 1.3s; from 0.4s to 1.4s; from 0.5s to 1.5s; from 0.6s to 1.6s; from 0.7s to 1.7s; and from 0.8s to 1.8s.

[0096] For example, during the period from 0 to 1 second, since it is less than 1 second, all the data of the current period cannot be collected, so the model is not run. At 1.0 second, the 400 frames of data generated during the period from 0 to 1 second are sent to the pre-trained neural network classification model for classification to obtain the predicted classification result. If the return value corresponding to the predicted classification result is 1, it is considered that the predicted classification result is that a started radiator is installed on the smart terminal. If the return value corresponding to the predicted classification result is 0, it is considered that the predicted classification result is that a started radiator is not installed on the smart terminal. Then, wait until 1.1 second, send the data from 0.1 to 1.1 seconds into the pre-trained neural network classification model, and continue the same steps. Then, by analogy, at 1.2 seconds, send the data from 0.2 to 1.2 seconds into the pre-trained neural network classification model, at 1.3 seconds, send the data from 0.3 to 1.3 seconds into the pre-trained neural network classification model, and so on. According to the same principle, when the current time is 1.9s, 9 prediction classification results corresponding to 9 historical time periods can be obtained.

[0097] 305. Determine whether the number of prediction classification results belonging to the intelligent terminal having an activated radiator installed therein in at least one historical prediction classification result and a current prediction classification result exceeds a first preset number.

[0098] Among them, the first preset number is 5. Of course, the first preset number can also be 6, 7, 8, etc., which can be set according to the specific situation.

[0099] For example, the first preset number is 5, and 6 of at least one historical prediction classification result are that an activated radiator is installed on the smart terminal, and the current prediction classification result is also that an activated radiator is installed on the smart terminal. Then, a total of 7 of the 10 prediction classification results are prediction classification results that an activated radiator is installed on the smart terminal, which exceeds 5.

[0100] 306. Determine, based on current acceleration information of the smart terminal, whether an activated radiator is installed on the smart terminal.

[0101] In an embodiment of the present application, if the number of predicted classification results belonging to an activated radiator installed on the smart terminal in at least one historical predicted classification result and the current predicted classification result exceeds a first preset number, it is determined that an activated radiator is installed on the smart terminal.

[0102] For example, 7 out of 10 predicted classification results belong to the predicted classification results that the smart terminal is installed with an activated radiator, which exceeds 5, indicating that most of the data predicts that the smart terminal is installed with an activated radiator. Based on the current acceleration information of the smart terminal, it is determined that the smart terminal is installed with an activated radiator.

[0103] In another specific embodiment, an acceleration sampling time corresponding to at least one historical prediction classification result and an acceleration sampling time corresponding to a current prediction classification result are obtained, wherein the acceleration sampling time is the start time of a sampling period of the acceleration information corresponding to the prediction classification result, and a weight coefficient of each prediction classification result is determined based on the acceleration sampling time corresponding to at least one historical prediction classification result and the acceleration sampling time corresponding to the current prediction classification result, wherein the later the sampling time, the larger the weight coefficient, and the weight coefficient is greater than 0 and less than 1, and the weight coefficients of the prediction classification results belonging to the intelligent terminal having an activated radiator installed in at least one historical prediction classification result and the current prediction classification result are added to obtain the sum of the weight coefficients, and if the sum of the weight coefficients exceeds a first preset number, it is determined that an activated radiator is installed on the intelligent terminal.

[0104] 307. Improve the performance parameters of the intelligent terminal to preset performance parameters.

[0105] In the embodiment of the present application, if it is determined based on the current acceleration information of the smart terminal that an activated radiator is installed on the smart terminal, the performance parameters of the smart terminal are increased to preset performance parameters.

[0106] In the embodiment of the present application, the preset performance parameter can be set according to the specific situation. For example, the preset performance parameter is that the image quality is the highest level of image quality, etc.

[0107] 308. When it is determined based on the current acceleration information of the smart terminal that no activated radiator is installed on the smart terminal, the performance parameters of the smart terminal are restored from the preset performance parameters to the performance parameters before the improvement.

[0108] In the embodiment of the present application, when it is determined based on the current acceleration information of the smart terminal that there is no activated radiator installed on the smart terminal, it includes: obtaining the third preset number of historical prediction classification results within the first preset time length, if the third preset number of historical prediction classification results within the first preset time length are all that there is no activated radiator installed on the smart terminal, then it is determined based on the current acceleration information of the smart terminal that there is no activated radiator installed on the smart terminal, and the performance parameters of the smart terminal are restored from the preset performance parameters to the performance parameters before the improvement, so as to avoid overheating of the smart terminal. Among them, the first preset time length can be 60s, etc., and the first preset time length and the third preset number can be set according to the specific situation.

[0109] Furthermore, the maximum temperature within the second preset time period is obtained according to a preset cycle, and if the maximum temperature is higher than the preset temperature value, the performance parameter of the smart terminal is restored from the preset performance parameter to the performance parameter before the improvement. The second preset time period can be 90s, 60s, etc., and the preset temperature value can be 40 degrees, 45 degrees, etc., which can be set according to the specific situation.

[0110] In order to better implement the control method of the smart terminal provided in the embodiment of the present application, the embodiment of the present application also provides a control device of the smart terminal based on the control method of the smart terminal. The meanings of the terms are the same as those in the control method of the smart terminal. For specific implementation details, please refer to the description in the above method embodiment.

[0111] Please refer to Fig. 9 , Fig. 9 This is a schematic diagram of the structure of a control device for an intelligent terminal provided in an embodiment of the present application. The control device for the intelligent terminal may include an acquisition module 701, a judgment module 702, and a performance adjustment module 703, wherein:

[0112] An acquisition module is used to acquire the current acceleration information of the smart terminal in the current period;

[0113] A judgment module, used for judging whether a started radiator is installed on the smart terminal based on the current acceleration information of the smart terminal;

[0114] The performance adjustment module is used to increase the performance parameters of the smart terminal to preset performance parameters if it is determined based on the current acceleration information of the smart terminal that an activated radiator is installed on the smart terminal.

[0115] The specific implementation of each of the above modules can be found in the previous embodiments and will not be described in detail here.

[0116] An embodiment of the present application further provides an intelligent terminal, comprising a memory and a processor, wherein the processor is used to execute the steps in the control method of the intelligent terminal provided in the embodiment by calling a computer program stored in the memory.

[0117] Please refer to Fig.10 , Fig.10 A schematic diagram of the structure of the smart terminal provided in an embodiment of the present application.

[0118] The smart terminal may include one or more processing cores of the processor 101, one or more computer-readable storage media of the memory 102, a power supply 103, an input unit 104 and other components. Those skilled in the art will appreciate that the smart terminal structure shown in the figure does not constitute a limitation on the smart terminal, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. Among them:

[0119] The processor 101 is the control center of the smart terminal. It uses various interfaces and lines to connect various parts of the entire smart terminal. It executes various functions of the smart terminal and processes data by running or executing software programs and / or modules stored in the memory 102, and calling data stored in the memory 102. Optionally, the processor 101 may include one or more processing cores; optionally, the processor 101 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 101.

[0120] The memory 102 can be used to store software programs and modules. The processor 101 executes various functional applications and data processing by running the software programs and modules stored in the memory 102. The memory 102 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the smart terminal, etc. In addition, the memory 102 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory 102 may also include a memory controller to provide the processor 101 with access to the memory 102.

[0121] The intelligent terminal also includes a power supply 103 for supplying power to each component. Optionally, the power supply 103 can be logically connected to the processor 101 through a power management system, so as to manage charging, discharging, and power consumption through the power management system. The power supply 103 can also include any components such as one or more DC or AC power supplies, recharging systems, power failure detection circuits, power converters or inverters, and power status indicators.

[0122] The intelligent terminal may further include an input unit 104, which may be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0123] Although not shown, the smart terminal may also include a display unit, an image acquisition component, etc., which will not be described in detail here. Specifically in this embodiment, the processor 101 in the smart terminal will load the executable code corresponding to one or more computer programs into the memory 102 according to the following instructions, and the processor 101 will execute the steps in the control method of the smart terminal provided in this application, such as:

[0124] Obtain the current acceleration information of the smart terminal in the current period;

[0125] Determining whether an activated radiator is installed on the smart terminal based on current acceleration information of the smart terminal;

[0126] If it is determined based on the current acceleration information of the smart terminal that an activated radiator is installed on the smart terminal, the performance parameter of the smart terminal is increased to a preset performance parameter.

[0127] It should be noted that the smart terminal provided in the embodiment of the present application and the control method of the smart terminal in the above embodiment belong to the same concept, and the specific implementation process is detailed in the above related embodiments and will not be repeated here.

[0128] The present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program stored therein is executed on the processor of the smart terminal provided in the embodiment of the present application, the processor of the smart terminal executes the steps in the control method of the smart terminal provided in the present application. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0129] The present application also provides a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes various optional implementations of the control method of the above-mentioned intelligent terminal.

[0130] The above is a detailed introduction to a control method and device for an intelligent terminal provided by the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

[0131] It should be noted that when the above embodiments of the present application are applied to specific products or technologies, the relevant data of the user is involved, and the user's permission or consent is required, and the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

Claims

1. A control method for an intelligent terminal, characterized in that: include: Obtain the current acceleration information of the smart terminal in the current period; Determining whether an activated radiator is installed on the smart terminal based on the current acceleration information of the smart terminal; If it is determined based on the current acceleration information of the smart terminal that an activated radiator is installed on the smart terminal, the performance parameter of the smart terminal is increased to a preset performance parameter.

2. The control method of the intelligent terminal according to claim 1, characterized in that: The determining, based on the current acceleration information of the smart terminal, whether an activated radiator is installed on the smart terminal includes: Inputting the current acceleration information of the smart terminal into a pre-trained neural network classification model for classification, and obtaining a current predicted classification result of the current acceleration information, wherein the current predicted classification result is that an activated radiator is installed on the smart terminal or that an activated radiator is not installed on the smart terminal; Based on the current prediction classification result, it is determined whether an activated radiator is installed on the smart terminal.

3. The control method of the intelligent terminal according to claim 2, characterized in that: The determining whether an activated radiator is installed on the smart terminal based on the current prediction classification result includes: Obtaining at least one historical prediction classification result corresponding to at least one historical acceleration information obtained by inputting at least one historical acceleration information into a pre-trained neural network classification model for classification, wherein at least one historical acceleration information is collected by the smart terminal within at least one historical period; Based on at least one of the historical prediction classification results and the current prediction classification result, it is determined whether an activated radiator is installed on the smart terminal.

4. The control method of the intelligent terminal according to claim 3, characterized in that: The determining whether an activated radiator is installed on the smart terminal based on at least one of the historical prediction classification results and the current prediction classification result includes: Determine whether the number of prediction classification results belonging to the activated radiator installed on the smart terminal in at least one of the historical prediction classification results and the current prediction classification result exceeds a first preset number; If the number of prediction classification results belonging to the smart terminal having an activated radiator installed in at least one of the historical prediction classification results and the current prediction classification results exceeds a first preset number, it is determined that the smart terminal has an activated radiator installed based on the terminal acceleration information of the smart terminal.

5. The control method of the intelligent terminal according to claim 1, characterized in that: The obtaining of current acceleration information of the smart terminal in the current period includes: Collecting multiple linear accelerations of the smart terminal in the current time period according to a preset period; A plurality of linear accelerations of the smart terminal in the current time period are determined as the current acceleration information of the smart terminal in the current time period.

6. The control method of the intelligent terminal according to claim 5, characterized in that: The determining, based on the current acceleration information of the smart terminal, whether an activated radiator is installed on the smart terminal includes: Determining whether there is a linear acceleration greater than a preset acceleration value in the current acceleration information; If there is no linear acceleration greater than a preset acceleration value in the current acceleration information, it is determined whether an activated radiator is installed on the smart terminal based on the current acceleration information of the smart terminal.

7. A control device for an intelligent terminal, characterized in that: include: An acquisition module is used to acquire the current acceleration information of the smart terminal in the current period; A determination module, configured to determine whether an activated radiator is installed on the smart terminal based on the current acceleration information of the smart terminal; The performance adjustment module is used to increase the performance parameter of the smart terminal to a preset performance parameter if it is determined based on the current acceleration information of the smart terminal that an activated radiator is installed on the smart terminal.

8. An intelligent terminal, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program, and the processor is used to run the computer program in the memory to execute the steps in the control method of the intelligent terminal according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute the steps in the control method of the intelligent terminal according to any one of claims 1 to 6.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps in the control method of the intelligent terminal described in any one of claims 1 to 6 are implemented.

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