Temperature drift detection method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a temperature drift detection method and device, electronic equipment and a storage medium, and relates to the technical field of temperature drift detection. The method comprises the following steps: determining that target equipment meets a calibration condition, wherein the target equipment is provided with an acceleration sensor; the calibration condition comprises equipment static; adjusting the power consumption of the target device to change the device temperature of the target device; and obtaining the equipment temperature and the acceleration of the target equipment at a certain moment, and calculating the temperature drift of the acceleration sensor based on the equipment temperature and the acceleration. According to the embodiment of the invention, the interference of acceleration change caused by equipment movement on temperature drift detection can be eliminated, the target equipment does not need to be heated through a heat preservation box, and the temperature drift detection efficiency is improved.

Description

Technical Field

[0001] This application relates to the technical field of temperature drift detection, and in particular, to a temperature drift detection method and device, an electronic device, and a storage medium. Background Art

[0002] Acceleration sensors generally have errors caused by changes in material properties due to temperature, namely temperature drift. Devices equipped with acceleration sensors need to detect temperature drift before leaving the factory to calibrate the acceleration sensors, improve the measurement accuracy of the acceleration sensors, and ensure that accurate acceleration values can be output under different temperature conditions. In the related art, before the device leaves the factory, the device needs to be placed in a temperature chamber to obtain the temperature drift of the acceleration sensor at each temperature to correct the error of the acceleration sensor. However, this process takes a long time and affects the production capacity of the device. Summary of the Invention

[0003] The main purpose of the embodiments of this application is to propose a temperature drift detection method and device, an electronic device, and a storage medium, aiming to improve the efficiency of temperature drift detection.

[0004] To achieve the above object, a first aspect of the embodiments of this application proposes a temperature drift detection method, and the method includes:

[0005] Determine that the target device meets the calibration conditions, and the target device is equipped with an acceleration sensor; the calibration conditions include that the device is stationary;

[0006] Adjust the power consumption of the target device to change the device temperature of the target device;

[0007] Obtain the device temperature and acceleration of the target device at a certain moment, and determine the temperature drift of the acceleration sensor based on the device temperature and acceleration.

[0008] In some embodiments, determining that the target device meets the calibration conditions includes:

[0009] The target device obtains the first surrounding environment image and the second surrounding environment image at different moments;

[0010] Extract the edge features of the first surrounding environment image to obtain the first edge feature map;

[0011] Extract the edge features of the second surrounding environment image to obtain the second edge feature map;

[0012] Compare the differences between the first edge feature map and the second edge feature map to obtain the feature map difference data;

[0013] Determine that the target device meets the calibration conditions according to the feature map difference data.

[0014] In some embodiments, the number of first peripheral environment images is multiple; extracting the edge features of the first peripheral environment images to obtain a first edge feature map includes:

[0015] Extracting the edge features of the multiple first peripheral environment images to obtain multiple intermediate edge feature maps;

[0016] Superimposing the multiple intermediate edge feature maps to obtain a first edge feature map.

[0017] In some embodiments, superimposing the multiple intermediate edge feature maps to obtain a first edge feature map includes:

[0018] Performing line detection on the multiple intermediate edge feature maps to obtain multiple line feature maps;

[0019] Superimposing the multiple line feature maps to obtain a first edge feature map.

[0020] In some embodiments, the first edge feature map includes a first line, and the second edge feature map includes a second line; comparing the differences between the first edge feature map and the second edge feature map to obtain feature map difference data includes:

[0021] Calculating the included angle between the first lines to obtain a first angle;

[0022] Calculating the included angle between the second lines to obtain a second angle;

[0023] Calculating the difference between the first angle and the second angle as the feature map difference data.

[0024] In some embodiments, adjusting the power consumption of the target device to change the device temperature of the target device includes:

[0025] Obtaining the temperature limit value and the real-time temperature of the target device;

[0026] Adjusting the power consumption of the target device according to the difference between the temperature limit value and the real-time temperature to change the device temperature of the target device.

[0027] In some embodiments, the calibration conditions further include: the device is connected to an external interface; determining that the target device meets the calibration conditions includes:

[0028] Obtaining the interface access status of the target device, and determining that the target device meets the calibration conditions according to the interface access status.

[0029] To achieve the above object, a second aspect of the embodiments of the present application proposes a temperature drift detection device, and the device includes:

[0030] A determination module, configured to determine that the target device meets the calibration conditions, where the target device is provided with an acceleration sensor; the calibration conditions include that the device is stationary;

[0031] A power consumption adjustment module for adjusting the power consumption of a target device to change the device temperature of the target device;

[0032] A temperature drift calculation module for obtaining the device temperature and acceleration of the target device at a certain moment, and determining the temperature drift of the acceleration sensor based on the device temperature and acceleration.

[0033] To achieve the above object, a third aspect of the embodiments of the present application proposes an electronic device, which includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the method described in the first aspect above is implemented.

[0034] To achieve the above object, a fourth aspect of the embodiments of the present application proposes a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect above is implemented.

[0035] The temperature drift detection method and device, electronic device, and storage medium proposed in the present application can, after determining that the target device is in a stationary state, adjust the power consumption of the target device to change the device temperature of the target device, calculate the temperature drift of the acceleration sensor based on the device temperature and acceleration, exclude the interference of acceleration changes caused by device movement on temperature drift detection, and at the same time do not need to heat the target device through an incubator, improve the temperature drift detection efficiency, and improve the production efficiency of the device. Description of the Drawings

[0036] Figure 1 is a flowchart of the temperature drift detection method provided by the embodiments of the present application;

[0037] Figure 2 is Figure 1 a flowchart of step S101 in

[0038] Figure 3 is Figure 1 a flowchart of step S101 in

[0039] Figure 4 is Figure 3 a flowchart of step S302 in

[0040] Figure 5 is Figure 4 a flowchart of step S402 in

[0041] Figure 6 is Figure 5 a flowchart of step S304 in

[0042] Figure 7 is Figure 1Flow chart of step S102 in

[0043] Figure 8 Schematic structural diagram of the temperature drift detection device provided by the embodiment of the present application;

[0044] Figure 9 Schematic hardware structure diagram of the electronic device provided by the embodiment of the present application. Detailed implementation manners

[0045] In order to make the objectives, technical solutions and advantages of the present application more comprehensible, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application.

[0046] It should be noted that although functional module division is performed in the device schematic diagram and the logical order is shown in the flow chart, in some cases, the steps shown or described may be executed in a different order from the module division in the device or the order in the flow chart. Terms such as "first" and "second" in the specification, claims and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence.

[0047] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application, and are not intended to limit the present application.

[0048] First, several nouns involved in the present application are analyzed:

[0049] Acceleration sensor (Acceleromete, ACC): An acceleration sensor is a sensor that can measure acceleration. It is usually composed of a mass block, a damper, an elastic element, a sensitive element, an adaptation circuit, etc. During the acceleration process of the sensor, the acceleration value is obtained by measuring the inertial force on the mass block and using Newton's second law. According to the different sensitive elements of the sensor, common acceleration sensors include capacitive, inductive, strain gauge, piezoresistive, piezoelectric, etc.

[0050] Temperature Drift (TD); it refers to the change in the output value of a machine that is not related to the input value under the influence of external factors. The temperature drift of a sensor usually refers to the change in the output value of the sensor due to temperature or temperature-related factors. The temperature drift of a sensor is usually expressed in percentage (%) and degrees Celsius (°C), and the specific form is: when the temperature increases / decreases by X °C, the output value of the sensor changes by Y%. The main reason for the temperature drift is the change in parameters of semiconductor devices under the influence of temperature. Other common influencing factors include the structure, resistance, and thermal expansion coefficient of the sensor. The temperature drift of a sensor can be divided into zero-point temperature drift and sensitivity temperature drift. Common reasons for zero-point drift include: aging of circuit components, fluctuations in power supply voltage, and changes in semiconductor devices with temperature. Temperature drift is an important indicator to measure the performance of a sensor. The level of temperature drift is directly related to the stability and accuracy of the sensor. The higher the accuracy of the sensor, the higher the requirement for temperature drift.

[0051] Based on this, the embodiments of this application provide a temperature drift detection method, device, electronic device, and storage medium, aiming to improve the efficiency of temperature drift detection.

[0052] The temperature drift detection method, device, electronic device, and storage medium provided by the embodiments of this application will be specifically described through the following embodiments. First, the temperature drift detection method in the embodiments of this application will be described.

[0053] The embodiments of this application can obtain and process relevant data based on artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technology, and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results.

[0054] The basic technologies of artificial intelligence generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, and mechatronics. The software technologies of artificial intelligence mainly include several major directions such as computer vision technology, robotics, biometric technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0055] The temperature drift detection method provided by the embodiments of this application relates to the technical field of temperature drift detection. The temperature drift detection method provided by the embodiments of this application can be applied to a terminal, a server, or software running on a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the temperature drift detection method, etc., but is not limited to the above forms.

[0056] This application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and so on. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This application can also be practiced in a distributed computing environment where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0057] It should be noted that in each specific embodiment of this application, when it comes to relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, user permission or consent will be obtained first. Moreover, the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiments of this application need to obtain sensitive personal information of users, the user's separate permission or separate consent will be obtained through pop-up windows or by redirecting to a confirmation page. After clearly obtaining the user's separate permission or separate consent, the necessary user-related data for the normal operation of the embodiments of this application will be obtained.

[0058] Figure 1 is an optional flowchart of the temperature drift detection method provided by the embodiments of this application. Figure 1 The method in may include but is not limited to steps S101 to S103.

[0059] Step S101: Determine that the target device meets the calibration conditions. The target device is equipped with an acceleration sensor. The calibration conditions include that the device is stationary.

[0060] Step S102: Adjust the power consumption of the target device to change the device temperature of the target device.

[0061] Step S103: Obtain the device temperature and acceleration of the target device at a certain moment. Based on the device temperature and acceleration, determine the temperature drift of the acceleration sensor.

[0062] In steps S101 to S103 shown in the embodiments of the present application, after determining that the target device is in a stationary state, adjust the power consumption of the target device to change the device temperature of the target device. Based on the device temperature and acceleration, calculate the temperature drift of the acceleration sensor, which can exclude the interference of acceleration changes caused by device movement on temperature drift detection. At the same time, there is no need to heat the target device through an incubator, improving the temperature drift detection efficiency and the production efficiency of the device.

[0063] In step S101 of some embodiments, the target device can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc., and is not limited thereto.

[0064] In step S101 of some embodiments, the acceleration can be collected by the acceleration sensor, and according to the acceleration value or the acceleration change value in a certain period, it is judged whether the device is in a stationary state; it is also possible to judge whether the device is in a stationary state by obtaining target images including the target device at different times, and is not limited thereto.

[0065] In step S102 of some embodiments, adjusting the power consumption of the target device can be to increase or decrease the tasks executed by the processor in the target device; it can also be to change the power management strategy of the target device, such as controlling the charging efficiency of the target device; and is not limited thereto.

[0066] In step S103 of some embodiments, the relationship between the device temperature and the acceleration output by the acceleration sensor can be obtained by polynomial fitting to obtain a temperature drift calculation model, so as to determine the temperature drift of the acceleration sensor; it is also possible to obtain the relationship between the device temperature and the acceleration output by the acceleration sensor through neural network learning to obtain a temperature drift calculation model, so as to determine the temperature drift of the acceleration sensor; and is not limited thereto.

[0067] Please refer to Figure 2 , in some embodiments, the calibration conditions further include: the device is connected to an external interface. Step S101 may include but is not limited to steps S201 to S202:

[0068] Step S201: Obtain the interface access status of the target device.

[0069] Step S202: Determine that the target device meets the calibration conditions according to the interface access status.

[0070] In step S201 of some embodiments, the interface access status may be a wired interface such as a power interface, a data transmission interface, a PCIe interface, etc. A wired interface is an interface that realizes communication or power supply between devices through physical connections (such as cables, plugs, sockets, etc.).

[0071] In step S202 of some embodiments, if the interface access status of any one of the wired interfaces is connected, it is determined that the target device meets the calibration conditions; or, when the interface access status of the wired interfaces exceeding the data threshold is all connected, it is determined that the target device meets the calibration conditions; not limited to this.

[0072] It is easy to understand that when the interface access status is connected, it means that the target device is in a state such as charging and data transmission. The target device is likely to be in a stationary state and a large amount of heat will be generated by the device. By setting up dual calibration conditions of device stationary judgment and interface access status, the accuracy of temperature drift detection can be further improved.

[0073] Please refer to Figure 3 , in some embodiments, step S101 may include but is not limited to steps S301 to S305:

[0074] Step S301: The target device acquires the first surrounding environment image and the second surrounding environment image at different times;

[0075] Step S302: Extract the edge features of the first surrounding environment image to obtain the first edge feature map;

[0076] Step S303: Extract the edge features of the second surrounding environment image to obtain the second edge feature map;

[0077] Step S304: Compare the differences between the first edge feature map and the second edge feature map to obtain the feature map difference data;

[0078] Step S305: Determine that the target device meets the calibration conditions according to the feature map difference data.

[0079] In step S302 of some embodiments, the edge can be determined by calculating the intensity changes (i.e., gradients) of the image pixels in the horizontal and vertical directions of the first surrounding environment image and the second surrounding environment image through a gradient-based edge detection method to obtain edge features; or, an edge detection method based on wavelet transform can be used to reconstruct the image using the approximation coefficients of wavelet transform, and then an edge detection operator is used to detect the edges in the reconstructed image; not limited to this.

[0080] Similarly, in step S303 of some embodiments, the edge features of the second surrounding environment image can be extracted by the above-mentioned edge detection method.

[0081] In step S304 of some embodiments, the pixel difference between the first edge feature map and the second edge feature map can be directly calculated to obtain the feature map difference data, such as the absolute value difference of the corresponding pixels; or the similarity index (SSIM) between the first edge feature map and the second edge feature map can be calculated as the feature map difference data; the above is not limiting.

[0082] In step S305 of some embodiments, a pixel difference threshold can be set, such as the non-zero pixel ratio or the total intensity of the difference pixels. When the feature map difference data is lower than the pixel difference threshold, it is determined that the target device is stationary; or a similarity threshold can be set. When the feature map difference data is higher than the similarity threshold, it is determined that the target device is stationary; the above is not limiting.

[0083] Steps S301 to S305 illustrated in the embodiments of the present application obtain the surrounding environment images at different times through the target device, extract and calculate the edge feature differences of the surrounding environment images. The edge feature differences reflect the movement of the target device relative to the external environment and are not affected by the environmental light changes. Based on this difference, it is determined whether the target device is in a stationary state. Compared with determining whether the target device is in a stationary state through an acceleration sensor with measurement errors, it can more accurately detect whether the device is in a stationary state to improve the accuracy of temperature drift detection.

[0084] Please refer to Figure 4 , in some embodiments, step S302 may include but is not limited to steps S401 to S402:

[0085] Step S401, extracting edge features from multiple first surrounding environment images to obtain multiple intermediate edge feature maps;

[0086] Step S402, superimposing multiple intermediate edge feature maps to obtain the first edge feature map.

[0087] Similarly, step S303 may include but is not limited to: extracting edge features from multiple second surrounding environment images to obtain multiple intermediate edge feature maps; superimposing multiple intermediate edge feature maps to obtain the second edge feature map.

[0088] In some embodiments, the multiple intermediate edge feature maps can be fused by averaging to obtain the first edge feature map; or the multiple intermediate edge feature maps can be fused by taking the maximum value to obtain the first edge feature map; the above is not limiting.

[0089] Steps S401 to S402 shown in the embodiments of the present application suppress random interference factors in the environment by superimposing intermediate edge feature maps, making the obtained edge feature maps more stable and reliable.

[0090] Please refer to Figure 5 , in some embodiments, step S402 may include but is not limited to steps S501 to S502:

[0091] Step S501, perform line detection on multiple intermediate edge feature maps to obtain multiple line feature maps;

[0092] Step S502, superimpose multiple line feature maps to obtain a first edge feature map.

[0093] Similarly, superimposing multiple intermediate edge feature maps to obtain a second edge feature map includes: performing line detection on multiple intermediate edge feature maps to obtain multiple line feature maps; superimposing multiple line feature maps to obtain a second edge feature map.

[0094] In step S501 of some embodiments, line detection can be performed by Hough transform; it can also be performed by a line segment detector LSD (Line Segment Detector); it is not limited to this.

[0095] After step S501 of some embodiments, the temperature drift detection method further includes: post-processing the line feature map, such as deleting low-intensity edges and retaining significant edges.

[0096] Considering that stationary reference objects such as buildings and furniture in the surrounding environment usually have straight outlines, steps S501 to S502 shown in the embodiments of the present application further perform line detection on the intermediate edge feature maps to extract the main structural information of the stationary reference objects in the environmental image, improve the accuracy of judging the stillness of the target object, and simplify the complexity of subsequent feature map comparison.

[0097] Please refer to Figure 6 , in some embodiments, step S304 may include but is not limited to steps S601 to S603:

[0098] Step S601, calculate the angle between the first lines to obtain a first angle;

[0099] Step S602, calculate the angle between the second lines to obtain a second angle;

[0100] Step S603, calculate the difference between the first angle and the second angle as the feature map difference data.

[0101] In step S601 of some embodiments, for all the first straight lines in the first edge feature map, the included angle between each pair of straight lines can be calculated to obtain a first angle matrix; alternatively, the first straight lines in the first edge feature map can be screened, and for the remaining straight lines, the included angle between each pair of straight lines can be calculated to obtain a first angle matrix; the method is not limited thereto. Similarly, based on the second straight lines in the second edge feature map, a second angle matrix can be calculated.

[0102] In step S603 of some embodiments, an element-by-element difference calculation is performed on the first angle matrix and the second angle matrix to obtain a difference matrix as the feature map difference data; an angle threshold is set, and if all the angles in the difference matrix do not exceed the angle threshold, it is considered that the target device is in a stationary state.

[0103] In steps S601 to S603 illustrated in the embodiments of the present application, the included angle difference between the edge feature maps at different times is calculated as the feature map difference data to determine whether the device is stationary. The included angle difference can sensitively reflect the change in the straight line direction in the image, and even a small displacement or rotation can be reflected by the change in the included angle, so that the non-stationary state of the device can be detected with high sensitivity, ensuring that the temperature drift detection is performed when the device is truly stationary.

[0104] Please refer to Figure 7 , in some embodiments, step S102 may include but is not limited to steps S701 to S702:

[0105] Step S701, obtaining the temperature limit value and the real-time temperature of the target device;

[0106] Step S702, adjusting the power consumption of the target device according to the difference between the temperature limit value and the real-time temperature to change the device temperature of the target device.

[0107] In step S702 of some embodiments, a temperature threshold is set. When the difference is less than the temperature threshold, the power consumption of the target device is controlled to be a first power consumption value; when the difference is greater than the temperature threshold, the power consumption of the target device is controlled to be a second power consumption value; the first power consumption value is greater than the second power consumption value.

[0108] In steps S701 to S702 illustrated in the embodiments of the present application, the power consumption is dynamically adjusted according to the difference between the temperature limit value and the real-time temperature, and the change range and scope of the device temperature are controlled, so that the temperature drift detection can be performed under different temperature conditions, and it is ensured that the temperature drift detection is performed within a safe temperature range of the device.

[0109] The specific implementation manner of this temperature drift detection device is basically the same as the specific embodiments of the above temperature drift detection method, and will not be elaborated herein.

[0110] The embodiments of the present application also provide an electronic device, which includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the above temperature drift detection method is implemented. The electronic device can be any intelligent terminal including a tablet computer, an in-vehicle computer, etc.

[0111] Please refer to Figure 9 , Figure 9 which schematically shows the hardware structure of an electronic device according to another embodiment. The electronic device includes:

[0112] A processor 901, which can be implemented in ways such as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided by the embodiments of the present application;

[0113] A memory 902, which can be implemented in forms such as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an operating system and other application programs. When implementing the technical solutions provided by the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 902 and are called by the processor 901 to execute the temperature drift detection method of the embodiments of the present application;

[0114] An input / output interface 903, which is used to implement information input and output;

[0115] A communication interface 904, which is used to implement communication interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.);

[0116] A bus 905, which transmits information between various components of the device (such as the processor 901, the memory 902, the input / output interface 903, and the communication interface 904);

[0117] Among them, the processor 901, the memory 902, the input / output interface 903, and the communication interface 904 are communicatively connected to each other inside the device through the bus 905.

[0118] The embodiments of the present application also provide a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the above temperature drift detection method is implemented.

[0119] The memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory may optionally include memories remotely provided with respect to the processor, and these remote memories can be connected to the processor through a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0120] The temperature drift detection method, temperature drift detection device, electronic device, and storage medium provided by the embodiments of the present application adjust the power consumption of the target device to change the device temperature of the target device after determining that the target device is in a stationary state, and calculate the temperature drift of the acceleration sensor based on the device temperature and acceleration, which can eliminate the interference of acceleration changes caused by device movement on temperature drift detection. At the same time, it is not necessary to heat the target device through an incubator, improving the temperature drift detection efficiency and the production efficiency of the device.

[0121] The embodiments described in the embodiments of the present application are for more clearly explaining the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art can know that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.

[0122] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than those shown in the figures, or combine certain steps, or different steps.

[0123] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, that is, they may be located in one place, or may be 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.

[0124] Those of ordinary skill in the art can understand that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices can be implemented as software, firmware, hardware, and appropriate combinations thereof.

[0125] In the description of the present application and the above-mentioned drawings, the terms "first", "second", "third", "fourth", etc. (if any) are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0126] It should be understood that in the present application, "at least one (item)" means one or more, and "a plurality" means two or more. "And / or" is used to describe the association relationship of associated objects and indicates that three relationships may exist. For example, "A and / or B" may mean: only A exists, only B exists, and both A and B exist simultaneously. Among them, A and B can be singular or plural. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. "At least one (one) of the following" or its similar expression refers to any combination of these items, including any combination of single items (ones) or plural items (ones). For example, at least one (one) of a, b, or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0127] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the above-mentioned division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces, and the indirect coupling or communication connection of devices or units can be in electrical, mechanical, or other forms.

[0128] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0129] In addition, in each embodiment of the present application, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0130] If the integrated unit 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 this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes multiple instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in the various embodiments of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store programs.

[0131] The preferred embodiments of the embodiments of the present application have been described above with reference to the accompanying drawings, and thus do not limit the scope of the rights of the embodiments of the present application. Any modifications, equivalent replacements, and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of the present application shall be within the scope of the rights of the embodiments of the present application.

Claims

1. A temperature drift detection method, characterized in that: The method comprises: Determining that the target device meets the calibration conditions, wherein the target device is provided with an acceleration sensor; the calibration conditions include that the device is stationary; adjusting the power consumption of the target device to change the device temperature of the target device; The device temperature and acceleration of the target device at a certain moment are acquired, and the temperature drift of the acceleration sensor is determined based on the device temperature and the acceleration.

2. The method according to claim 1, characterized in that The step of determining that the target device meets the calibration condition includes: The target device acquires a first surrounding environment image and a second surrounding environment image at different times; Extracting edge features of the first surrounding environment image to obtain a first edge feature map; Extracting edge features of the second surrounding environment image to obtain a second edge feature map; Comparing the difference between the first edge feature map and the second edge feature map to obtain feature map difference data; According to the characteristic map difference data, it is determined that the target device meets the calibration conditions.

3. The method according to claim 2, characterized in that The number of the first surrounding environment images is multiple; and the step of extracting edge features of the first surrounding environment images to obtain a first edge feature map includes: Extracting edge features from the plurality of first surrounding environment images to obtain a plurality of intermediate edge feature maps; A plurality of the intermediate edge feature maps are superimposed to obtain the first edge feature map.

4. The method according to claim 3, characterized in that The step of superimposing a plurality of the intermediate edge feature maps to obtain the first edge feature map comprises: Performing straight line detection on the plurality of intermediate edge feature maps to obtain a plurality of straight line feature maps; The plurality of straight line feature maps are superimposed to obtain the first edge feature map.

5. The method according to claim 4, characterized in that The first edge feature map includes a first straight line, and the second edge feature map includes a second straight line; and comparing the difference between the first edge feature map and the second edge feature map to obtain feature map difference data includes: Calculating the angle between the first straight lines to obtain a first angle; Calculate the angle between the second straight lines to obtain a second angle; A difference between the first angle and the second angle is calculated as the feature map difference data.

6. The method according to any one of claims 1 to 5, characterized in that: The adjusting the power consumption of the target device to change the device temperature of the target device includes: Obtaining the temperature limit and real-time temperature of the target device; According to the difference between the temperature limit and the real-time temperature, the power consumption of the target device is adjusted to change the device temperature of the target device.

7. The method according to any one of claims 1 to 5, characterized in that: The calibration condition also includes: the device is connected to an external interface; and the determination that the target device meets the calibration condition includes: Get the interface access status of the target device; According to the interface access status, it is determined that the target device meets the calibration conditions.

8. A temperature drift detection device, characterized in that: The device comprises: A determination module, used to determine whether a target device meets a calibration condition, wherein the target device is provided with an acceleration sensor; the calibration condition includes that the device is stationary; A power consumption adjustment module, used for adjusting the power consumption of the target device to change the device temperature of the target device; The temperature drift calculation module is used to obtain the device temperature and acceleration of the target device at a certain moment, and determine the temperature drift of the acceleration sensor based on the device temperature and the acceleration.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the temperature drift detection method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the temperature drift detection method according to any one of claims 1 to 7 is implemented.

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