State detection method, device, apparatus and computer storage medium

Abstract

Embodiments of the present application provide a device state detection method and device, apparatus and computer storage medium, wherein the device state detection method comprises: acquiring at least two environment images of the surrounding environment of the device, the shooting angles of the environment images being the same and the shooting time difference of the environment images satisfying a set condition; performing image recognition on the environment images, determining a reference region corresponding to a stationary object in the environment images according to the recognition result, the stationary object being an object whose state or form does not change with the shooting time; acquiring the similarity of the reference regions of the environment images adjacent in shooting time; and if the similarity is greater than or equal to a preset similarity threshold, determining that the device is in a stationary state.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of data processing, and in particular, to a device state detection method and device, apparatus, and computer storage medium. BACKGROUND

[0002] With the development of electronic information technology, many devices are installed with application programs with map navigation functions, such as map application programs, online car-hailing application programs, or life service application programs. These application programs need to detect whether a device is in a moving state or a stationary state when implementing certain functions, such as implementing an AR navigation function.

[0003] When detecting a device state in the prior art, data output by sensors mounted on the device is generally used for state detection. The sensors include an inertial measurement unit (IMU), an acceleration gyroscope, a global positioning system (GPS), and the like. Since the sensors themselves have detection errors, the output data is not very accurate. Therefore, state detection based on the data output by the sensors has the problem of inaccurate state detection, thereby affecting the implementation of the corresponding function. SUMMARY

[0004] Therefore, embodiments of the present application provide a device state detection method and device, apparatus, and computer storage medium to solve some or all of the above problems.

[0005] According to a first aspect of embodiments of the present application, a device state detection method is provided, which includes: acquiring at least two environment images of a surrounding environment of the device that are captured, the environment images having the same shooting angle and a shooting time difference satisfying a set condition; performing image recognition on the environment images, and determining a reference region corresponding to a stationary object in the environment images according to a recognition result, the stationary object being an object whose state or form does not change with the shooting time; acquiring a similarity of the reference regions of the environment images adjacent in shooting time; and if the similarities are all greater than or equal to a preset similarity threshold, determining that the device is in a stationary state.

[0006] According to a second aspect of the embodiments of the present application, a device state detection apparatus is provided, comprising: an acquisition module configured to acquire at least two environment images of a surrounding environment of the device, the environment images being captured at the same angle of view and having a time difference satisfying a preset condition; an identification module configured to perform image recognition on the environment images, and determine a reference region corresponding to a static object in the environment images according to a recognition result, the static object being an object whose state or form does not change with the capturing time; a calculation module configured to acquire a similarity of the reference regions of the environment images captured at adjacent times; and a determination module configured to determine that the device is in a static state if the similarities are all greater than or equal to a preset similarity threshold.

[0007] According to a third aspect of the embodiments of the present application, an electronic device is provided, comprising: a processor, a memory, a communication interface and a communication bus, the processor, the memory and the communication interface performing communication with each other through the communication bus; and the memory is configured to store at least one executable instruction, the executable instruction causing the processor to perform operations corresponding to the device state detection method of the first aspect.

[0008] According to a fourth aspect of the embodiments of the present application, a computer storage medium is provided, and the computer storage medium stores a computer program, the program being executed by a processor to implement the device state detection method of the first aspect.

[0009] The device state detection method, apparatus, device and computer storage medium provided by the embodiments of the present application acquire at least two environment images of a surrounding environment of the device, the environment images being captured at the same angle of view and having a time difference satisfying a preset condition; perform image recognition on the environment images, and determine a reference region corresponding to a static object in the environment images according to a recognition result, the static object being an object whose state or form does not change with the capturing time; acquire a similarity of the reference regions of the environment images captured at adjacent times; and determine that the device is in a static state if the similarities are all greater than or equal to a preset similarity threshold. The embodiments of the present application determine whether the device is static by using the similarity of the environment images, reduce errors caused by hardware, and improve the accuracy of device state detection. BRIEF DESCRIPTION OF DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed in the embodiments or the prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments described in the embodiments of the present application, and other drawings can also be obtained by those skilled in the art according to these drawings.

[0011] Figure 1A scene schematic diagram of a device state detection method provided by Embodiment One of the present application is shown in FIG. 1.

[0012] Figure 2 A flowchart of a device state detection method provided by Embodiment One of the present application is shown in FIG. 2.

[0013] Figure 3 A similarity calculation effect schematic diagram provided by Embodiment One of the present application is shown in FIG. 3.

[0014] Figure 4 A structural diagram of a device state detection apparatus provided by Embodiment Two of the present application is shown in FIG. 4.

[0015] Figure 5 A structural schematic diagram of an electronic device provided by Embodiment Three of the present application is shown in FIG. 5. DETAILED DESCRIPTION

[0016] In order to make personnel in the art better understand the technical solutions in the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art should belong to the scope of protection of the embodiments of the present application.

[0017] The specific implementation of the embodiments of the present application will be further described below in conjunction with the drawings in the embodiments of the present application.

[0018] Embodiment One

[0019] Embodiment One of the present application provides a device state detection method applied to an electronic device. In order to facilitate understanding, the application scenario of the device state detection method provided by Embodiment One of the present application is described with reference to FIG. 1. Figure 1 Figure 1 A scene schematic diagram of a device state detection method provided by Embodiment One of the present application is shown in FIG. 1. Figure 1 The scene shown in FIG. 1 includes an electronic device 101 and a device 102. The electronic device 101 can be a device that executes the device state detection method provided by Embodiment One of the present application.

[0020] The electronic device 101 can be a terminal device such as a smart phone, a tablet computer, a notebook computer, etc. The electronic device 101 can also be a network device such as a server, and the present application does not limit this.

[0021] ​The electronic device 101 can access a network, connect to the cloud through the network, and perform data interaction. In this application, the network includes a local area network (Local Area Network, LAN), a wide area network (Wide Area Network, WAN), a mobile communication network, such as the World Wide Web (World Wide Web, WWW), a Long Term Evolution (Long Term Evolution, LTE) network, a 2G network (2th Generation Mobile Network), a 3G network (3th Generation Mobile Network), a 5G network (5th Generation Mobile Network), and the like. Of course, this is only an example and does not limit the application.

[0022] Figure 1 For example, the device is a vehicle, the electronic device obtains at least two environment images photographed at a preset photographing angle, the environment image can indicate the environment around the device, the preset photographing angle is unchanged relative to the device within a preset period of time, that is, the photographing angle of the environment image is unchanged relative to the device within a preset period of time, and a reference area corresponding to a stationary object is identified in the environment image. The stationary object can include objects whose position and appearance do not change relative to the ground in the environment image. For example, utility poles, buildings, and station signs. These stationary objects are objects that do not change easily in a short period of time. Therefore, if the device is stationary, these stationary objects in the environment image should have a very high similarity, and if the device is moving, these stationary objects will appear differently in different environment images. For example, trees and traffic lights are not suitable as stationary objects because the leaves of the trees will sway in the wind and the traffic lights will change according to the time. Therefore, even if the device does not move, the images of these objects in the environment image will change. Calculating the similarity of the reference area of each environment image and the corresponding area in the previous environment image can obtain at least one similarity. If all the at least one similarity is greater than or equal to a similarity threshold, it indicates that the environment around the device has not changed relative to the device, and it can be determined that the device is stationary. If there is a value less than the similarity threshold among the at least one similarity, it indicates that the environment around the device has changed relative to the device. Because these stationary objects do not change relative to the ground, it can be determined that the device has moved relative to the surrounding environment, so it can be determined that the device is not stationary.

[0023] In combination Figure 1 The device state detection method provided by the first embodiment of the application is described in detail in the scenario shown. It should be noted that Figure 1Just one application scenario of the device state detection method provided in Embodiment I of the present application, and does not mean that the device state detection method must be applied to Figure 1 The device state detection method provided in the present application can be applied to an electronic device, i.e., the electronic device is the execution subject of the device state detection method provided in the present application. The electronic device can be a terminal device such as a smart phone, a tablet computer, a notebook computer, etc., and the electronic device can also be a network device such as a server, etc., as shown in Figure 2 , Figure 2 A flowchart of the device state detection method provided in Embodiment I of the present application is shown in FIG. 2. The method includes the following steps:

[0024] Step 201: Obtain at least two environment images of the environment around the device that are captured, the shooting angles of the environment images are the same, and the time difference of capturing the environment images meets a preset condition.

[0025] The device can be any object to be detected for speed, for example, the device can be a vehicle, a person, a bicycle, etc. The environment image is used to indicate the environment around the device, and the preset shooting angle is fixed relative to the device. If the shooting angle changes, two images with high similarity cannot determine whether the device moves or not. Therefore, the preset shooting angle of the at least two environment images must be fixed relative to the device, and the time difference of capturing the environment images meets a preset condition. Exemplarily, the device can be a vehicle, and the preset shooting angle can include an angle towards the front of the vehicle, an angle towards the back of the vehicle, etc. The time difference can be a preset time period, which can be set by a person skilled in the art as needed.

[0026] It should be noted that, in one example, the environment image can be captured by an electronic device, or can be captured by other devices and then transmitted to the electronic device. In another example, the environment image can be an image frame in a video stream within a preset time period, or can be at least two images captured separately within a preset time period.

[0027] For example, in a specific example, obtaining at least two environment images includes: obtaining a video stream captured by an environment of the device; and extracting at least two image frames from the video stream as the at least two environment images. The video stream can be obtained by continuously capturing the environment around the device according to a preset shooting angle, and the environment images extracted from the video stream can ensure better real-time performance.

[0028] Optionally, in one embodiment, obtaining at least two environment images captured by the environment around the device includes: obtaining a measured speed of the device measured by a speed measurement module; and obtaining the at least two environment images when the measured speed of the device is less than or equal to a preset speed value.

[0029] Before acquiring the environment image, it can be determined that the measured speed measured by the speed measurement module is high. Although there is an error in the hardware of the measurement module, it is sufficient to prove that the device is not in a static state. When the speed is high, the error in the hardware has little effect on the size of the measured speed, and the environment image can not be acquired to detect the speed. When the speed of the device is low, the error in the measurement speed is relatively high, and the result is affected. At this time, the environment image can be acquired to detect the speed. The preset speed value can be any speed value greater than 0 km / h and less than or equal to 5 km / h. Of course, this is only an example. When the measured speed is less than or equal to the preset speed value, the environment image is acquired to reduce the data operation amount.

[0030] Step 202, image recognition is performed on the environment image, and a reference region corresponding to a static object in the environment image is determined according to a recognition result. The static object refers to an object whose state or form does not change with the shooting time.

[0031] The static object includes an object whose position and appearance do not change relative to the ground. For example, the static object can include a power pole, a building, a signal tower, and the like. For another example, a tree, a traffic light, and the like are not suitable for being a reference image because they are easily changed. The static object can also be a part of an object whose position and appearance do not change relative to the ground. For example, the static object can include a trunk of a tree, a support rod of a traffic light, and the like.

[0032] The image recognition on the environment image can have various implementation manners, and two specific examples are listed here for description.

[0033] In the first example, the image recognition on the environment image, and the reference region corresponding to the static object in the environment image is determined according to a recognition result, includes:

[0034] The change object in the environment image is identified by using the image recognition model. The change object refers to an object whose state or form changes with the shooting time. The environment image is segmented, and the area occupied by the change object is removed. The remaining area is determined as the reference region.

[0035] The reference region is determined by cutting out the area occupied by the change object in the embodiment of the application, so that the reference region can be as large as possible, and the accuracy of the similarity calculation can be improved.

[0036] In the second example, the image recognition on the environment image, and the reference region corresponding to the static object in the environment image is determined according to a recognition result, includes:

[0037] The image recognition model is used to identify a fixed object in the environment image as a static object; and the environment image is segmented to determine a region occupied by the fixed object as the reference region.

[0038] The reference region is determined by segmenting the region occupied by the fixed object, so that the reference region can be quickly obtained, and the algorithm is simple.

[0039] In step 203, the similarity of the reference regions of the environment images adjacent in shooting time is obtained.

[0040] In the present application, in an embodiment, the similarity of the reference regions of the environment images adjacent in shooting time is obtained, including:

[0041] The environment images adjacent in shooting time are obtained; for each of the environment images, the reference region of the environment image is divided into at least two image units; the similarity between the corresponding image units of the environment images adjacent in shooting time is calculated, wherein the corresponding image units refer to the image units having the same position in the environment images; and the similarity of the reference regions of the environment images adjacent in shooting time is determined based on the similarity between the image units of the environment images.

[0042] As shown in Figure 3 , a similarity calculation effect diagram provided by an embodiment of the present application is shown in Figure 3 , and environment image A and environment image B are shown in Figure 3 . The reference region in the environment image A is the region occupied by a building, N image units a are determined in the reference region of the environment image A, and one image unit can include at least one pixel. N image units b corresponding to the image units in the environment image A are determined in the environment image B. Each pair of position corresponding image units can form a group of image units, thereby forming N groups of image units. The similarity between the image units a and the image units b in each group of image units is calculated, so that the similarity of the N groups of image units is obtained. The similarity is calculated by determining N image units in the reference region of the environment image, rather than calculating the entire reference region, so that the amount of calculation is reduced and the speed of calculating the similarity is improved.

[0043] In step 204, if the similarity is greater than or equal to a preset similarity threshold, it is determined that the device is in a static state.

[0044] It should be noted that the similarity can be expressed in percentage, for example, 100% represents complete identity, and the similarity threshold can be set to any value between 80% and 100%; for example, 100 represents complete identity, and the similarity threshold can be set to any value between 80 and 100. Of course, this is only an example.

[0045] Optionally, in the first example, the similarity is calculated every time an environment image is obtained. The electronic device periodically obtains an environment image taken at a preset shooting angle, identifies the current period environment image, determines the reference area corresponding to the stationary object in the current period environment image according to the identification result, calculates the similarity between the reference area of the current period environment image and the corresponding area in the last environment image, and if the K period similarity is greater than or equal to the similarity threshold, it is determined that the device is stationary. K is the number of environment images obtained in the preset time period in step 201. Real-time similarity calculation can determine whether the device is stationary in real time, and the response speed is faster in scenarios that require real-time detection.

[0046] Optionally, in the second example, at least two environment images of the device taken at a preset shooting angle in the past can be obtained at the current time, and the at least two environment images can be sorted by time. The reference area corresponding to the stationary object is determined by image recognition for each environment image. For each environment image, the similarity between the reference area of the environment image and the corresponding area in the last environment image is calculated, and if the similarity is greater than or equal to the similarity threshold, it is determined that the device is stationary in the preset time period, otherwise, it can be determined that the device is not stationary in the preset time period.

[0047] Optionally, in a specific application scenario, taking augmented reality (English: Augmented Reality, AR) navigation as an example, the method further comprises: after determining that the device is stationary in the preset time period, generating an augmented reality AR navigation instruction according to the collected information; based on the AR navigation instruction, navigation guidance is performed.

[0048] The device state detection method provided in the embodiments of the present application comprises the following steps: acquiring at least two environment images of the surrounding environment of the device, the shooting angles of the environment images being the same and the shooting time difference of the environment images satisfying a set condition; performing image recognition on the environment images, determining a reference area corresponding to a static object in the environment images according to the recognition result, the static object being an object whose state or form does not change with the shooting time; acquiring the similarity of the reference areas of the environment images adjacent in shooting time; and determining that the device is in a static state if the similarity is greater than or equal to a preset similarity threshold. The embodiments of the present application determine whether the device is static by using the similarity of the environment images, thereby reducing the error caused by hardware and improving the accuracy of device state detection.

[0049] Embodiment two

[0050] Based on the method described in the above embodiment one, the second embodiment of the present application provides a device state detection apparatus for executing any of the methods described in the above embodiment one, referring to the apparatus 40 shown in the figure, the device state detection apparatus 40 comprises: Figure 4

[0051] The acquisition module 401 is configured to acquire at least two environment images of the surrounding environment of the device, the shooting angles of the environment images being the same and the shooting time difference of the environment images satisfying a set condition.

[0052] The recognition module 402 is configured to perform image recognition on the environment images, and determine a reference area corresponding to a static object in the environment images according to the recognition result, the static object being an object whose state or form does not change with the shooting time.

[0053] The calculation module 403 is configured to acquire the similarity of the reference areas of the environment images adjacent in shooting time.

[0054] The determination module 404 is configured to determine that the device is in a static state if the similarity is greater than or equal to a preset similarity threshold.

[0055] ​The device status detection apparatus provided in this application acquires at least two environmental images of the surrounding environment of the device, wherein the environmental images are captured from the same viewing angle and the time difference between their captures meets a set condition; performs image recognition on the environmental images, and determines a reference region corresponding to a stationary object in the environmental images based on the recognition results. A stationary object refers to an object whose state or form does not change with the capture time; acquires the similarity of the reference regions of environmental images captured at adjacent times; if the similarity is greater than or equal to a preset similarity threshold, the device is determined to be stationary. This application embodiment uses the similarity of environmental images to determine whether the device is stationary, reducing hardware-induced errors and improving the accuracy of device status detection.

[0056] Example 3

[0057] Based on the method described in Embodiment 1 above, Embodiment 3 of this application provides an electronic device for executing the method described in Embodiment 1 above, with reference to... Figure 5 As shown, Figure 5 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of this application. The specific embodiments of this application do not limit the specific implementation of the electronic device.

[0058] like Figure 5 As shown, the electronic device may include: a processor 502, a communications interface 504, a memory 506, and a communications bus 508.

[0059] in:

[0060] The processor 502, communication interface 504, and memory 506 communicate with each other via communication bus 508.

[0061] Communication interface 504 is used to communicate with other electronic devices such as terminal devices or servers.

[0062] The processor 502 is used to execute program 510, specifically the relevant steps in the above method embodiments.

[0063] Specifically, program 510 may include program code that includes computer operation instructions.

[0064] The processor 502 can be a CPU, or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement one or more embodiments of the present application. The one or more processors included in the electronic device can be the same type of processor, such as one or more CPUs; or different types of processors, such as one or more CPUs and one or more ASICs.

[0065] The memory 506 is configured to store a program 510. The memory 506 can include a high-speed RAM memory, and can further include a non-volatile memory, such as at least one disk memory.

[0066] The program 510 can be specifically configured to enable the processor 502 to perform any of the methods in the foregoing embodiments.

[0067] The specific implementation of each step in the program 510 can refer to the corresponding description in the corresponding steps and units in the foregoing device state detection method embodiments, and will not be described herein. It can be clearly understood by those skilled in the art that, for the convenience and brevity of description, the specific working processes of the devices and modules described above can refer to the corresponding process descriptions in the foregoing method embodiments, and will not be described herein.

[0068] The electronic device provided in the embodiments of the present application acquires at least two environment images of the surrounding environment of the device, the shooting angles of the environment images are the same, and the time difference between the shooting of the environment images satisfies a set condition; performs image recognition on the environment images, determines a reference region corresponding to a stationary object in the environment images according to a recognition result, the stationary object refers to an object whose state or form does not change with the shooting time; acquires the similarity of the reference regions of the environment images adjacent in shooting time; and if the similarity is greater than or equal to a preset similarity threshold, it is determined that the device is in a stationary state. The embodiments of the present application determine whether the device is stationary by using the similarity of the environment images, reduce the error caused by hardware, and improve the accuracy of device state detection.

[0069] Embodiment Four

[0070] Based on the method described in the foregoing embodiment one, the fourth embodiment of the present application provides a computer storage medium, which stores a computer program, and the program is executed by a processor to implement the method described in the embodiment one.

[0071] It should be noted that, according to the needs of implementation, each component / step described in the embodiments of the present application can be split into more components / steps, or two or more components / steps or part operations of the components / steps can be combined into a new component / step, to achieve the purpose of the embodiments of the present application.

[0072] The method according to the embodiments of the present application described above can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium such as a CD ROM, a RAM, a floppy disk, a hard disk or an optical disk, or be downloaded through a network originally stored in a remote recording medium or a non-transitory machine readable medium and stored in a local recording medium, so that the method described herein can be processed by such software using a general purpose computer, a special purpose processor or programmable or special hardware such as an ASIC or an FPGA. It can be understood that the computer, processor, microprocessor controller or programmable hardware includes a storage component (for example, RAM, ROM, flash memory, etc.) that can store or receive software or computer code, when the software or computer code is accessed and executed by the computer, processor or hardware, the device state detection method described herein is implemented. In addition, when a general purpose computer accesses the code for implementing the device state detection method shown herein, the execution of the code will convert the general purpose computer into a special purpose computer for executing the device state detection method shown herein.

[0073] Those of ordinary skill in the art can realize that the units and method steps of each example described in connection with the embodiments disclosed herein can be realized by electronic hardware, or a combination of computer software and electronic hardware. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of the present application.

[0074] The above embodiments are only used to illustrate the present application, and not to limit the present application, and those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present application, therefore all equivalent technical solutions also belong to the scope of the present application, the patent protection scope of the present application should be defined by the claims.

Claims

1. A device state detection method, comprising: obtaining at least two environment images of a surrounding environment of the device, the environment images being captured at a constant angle of view relative to the device and at a time difference satisfying a preset condition; performing image recognition on the environment images to determine a reference region corresponding to a static object in the environment images based on a recognition result, the static object being an object whose state or form does not change with the capturing time; obtaining a similarity of the reference regions of the environment images captured at adjacent times; if the similarity is greater than or equal to a preset similarity threshold, determining that the device is in a static state.

2. The method of claim 1, wherein, The image recognition on the environment images to determine the reference region corresponding to the static object in the environment images based on the recognition result comprises: identifying a changeable object in the environment images using an image recognition model, the changeable object being an object whose state or form changes with the capturing time; segmenting the environment images to exclude a region occupied by the changeable object, and determining a remaining region as the reference region.

3. The method of claim 1, wherein, The image recognition on the environment images to determine the reference region corresponding to the static object in the environment images based on the recognition result comprises: identifying a fixed object in the environment images as the static object using an image recognition model; segmenting the environment images to determine a region occupied by the fixed object as the reference region.

4. The method of claim 1, wherein, The obtaining of the similarity of the reference regions of the environment images captured at adjacent times comprises: obtaining environment images captured at adjacent times; dividing the reference region of each of the environment images into at least two image units; calculating a similarity between corresponding image units of the environment images captured at adjacent times, the corresponding image units being image units having the same position in the environment images; and determining the similarity of the reference regions of the environment images captured at adjacent times based on the similarity between the image units of the environment images.

5. The method of any of claims 1-4, wherein, The obtaining of the at least two environment images of the surrounding environment of the device comprises: obtaining a video stream captured by the device; extracting at least two image frames from the video stream as the at least two environment images.

6. The method of claim 1, wherein, The obtaining of the at least two environment images of the surrounding environment of the device comprises: obtaining a measured speed of the device measured by a speed measurement module; obtaining the at least two environment images when the measured speed of the device is less than or equal to a preset speed value.

7. The method of any one of claims 1-4, wherein, After determining that the device is in the static state, the method further comprises: generating an augmented reality (AR) navigation instruction based on the collected information; and performing navigation guidance based on the AR navigation instruction. 8.A device state detection apparatus, comprising: an obtaining module configured to obtain at least two environment images of a surrounding environment of the device, the environment images being captured at a constant angle of view relative to the device and at a time difference satisfying a preset condition. An identification module is configured to perform image recognition on the environment image, and determine a reference region corresponding to a static object in the environment image according to a recognition result, the static object being an object whose state or form does not change with a shooting time. A calculation module is configured to acquire a similarity of the reference region of the environment image adjacent in the shooting time. A determination module is configured to determine that the device is in a static state if the similarity is greater than or equal to a preset similarity threshold.

9. An electronic device comprising: A processor, a memory, a communication interface and a communication bus, the processor, the memory and the communication interface complete communication with each other through the communication bus; The memory is configured to store at least one executable instruction, and the executable instruction causes the processor to perform an operation corresponding to the device state detection method in any one of claims 1-7.

10. A computer storage medium having a computer program stored thereon, the program being executed by a processor to implement the device state detection method in any one of claims 1-7.

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