Method, model and apparatus for identifying device state of communication base station

Abstract

The application provides a device state recognition method, model and device for a communication base station, wherein the method comprises calculating a similarity measurement value of a first picture and a second picture, the first picture being a picture containing an image of a first device at a first time, the second picture being a picture containing an image of the first device at a second time, the second time being after the first time; and determining whether the state of the first device has changed after the first time according to the similarity measurement value. The application determines the similarity measurement value of the pictures containing the device images at different times, thereby determining whether the state of the device of the communication base station has changed, including whether the appearance state and the circuit state of the device have changed, which is beneficial to the safety and stable operation of the device in the base station.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to a method, model and apparatus for identifying the status of communication base stations. Background Technology

[0002] Communication base stations are critical infrastructure in the communications field, housing various devices whose status is crucial to the base station's security and affects the overall stability of the communication network. Current technologies rely on sensors to measure surface temperature and humidity of equipment within the base station to determine malfunctions. However, this method struggles to identify surface damage, thus compromising the security and stable operation of the equipment within the base station. Summary of the Invention

[0003] This application provides a method, model, and apparatus for identifying the status of equipment in a communication base station, in order to solve the problem that in the prior art, it is difficult to identify damage to the surface of equipment when using sensors to identify equipment faults, which is detrimental to the safety and stable operation of equipment in the base station.

[0004] To solve the above-mentioned technical problems, this application is implemented as follows;

[0005] In a first aspect, embodiments of this application provide a method for identifying the device status of a communication base station, the method comprising:

[0006] Calculate the similarity score between the first image and the second image, where the first image is an image containing the first device at a first time point, and the second image is an image containing the first device at a second time point, where the second time point is after the first time point;

[0007] Based on the similarity measure, it is determined whether the state of the first device has changed after the first moment.

[0008] In a second aspect, embodiments of this application also provide a device status identification model for a communication base station, used to perform the method as described in the first aspect, wherein the device status identification model for a communication base station includes;

[0009] The target detection sub-model is used to detect images at the first time step and images at the second time step. If an image containing a device within a communication base station is detected, the images at the first time step and the images at the second time step are cropped, and each cropped image contains at least one device.

[0010] The image calibration sub-model is used to perform angle calibration and scale calibration on the cropped image.

[0011] The image difference comparison sub-model is used to calculate the similarity measure between images containing images from the same device after the angle calibration and scale calibration processes.

[0012] Thirdly, embodiments of this application also provide a device for identifying the status of a communication base station, the device for identifying the status of a communication base station comprising:

[0013] The first calculation module is used to calculate the similarity measure between the first image and the second image. The first image is an image containing the image of the first device at a first time, and the second image is an image containing the image of the first device at a second time, with the second time being after the first time.

[0014] The first determining module determines, based on the similarity measurement value, whether the state of the first device has changed after the first moment.

[0015] Fourthly, embodiments of this application also provide an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the device status identification method for a communication base station as described in the first aspect.

[0016] Fifthly, embodiments of this application also provide a readable storage medium storing a program or instructions that, when executed by a processor, implement the steps of the device status identification method for a communication base station as described in the first aspect.

[0017] In this embodiment, a similarity measure is calculated between a first image and a second image. The first image is an image containing the first device at a first moment, and the second image is an image containing the first device at a second moment, where the second moment is after the first moment. Based on the similarity measure of the first and second images, it can be determined whether the state of the first device has changed after the first moment, where the device state includes its appearance and circuitry. This embodiment determines whether the state of the communication base station's equipment has changed, including its appearance and circuitry, by determining the similarity measure of images containing the device at different times. This is beneficial for the security and stable operation of equipment within the base station. Attached Figure Description

[0018] Figure 1 A flowchart illustrating a device status identification method for a communication base station provided in this application embodiment;

[0019] Figure 2A schematic diagram of a device status identification model for a communication base station provided in an embodiment of this application;

[0020] Figure 3 A schematic diagram illustrating the use of a Siamese network to determine similarity metrics in an image difference pairing sub-model provided in this application embodiment;

[0021] Figure 4 A structural diagram of a device status identification device for a communication base station provided in an embodiment of this application;

[0022] Figure 5 This is a structural diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0023] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] Communication base stations are critical infrastructure in the communications field, housing various devices including servers, cabinets, batteries, and air conditioners. The status of these devices is crucial to the security of the communication base station and affects the overall operational stability of the communication network. Existing technologies include methods that use sensors to obtain surface temperature and ambient humidity data of equipment within the base station to determine if a fault has occurred, and methods that use historical alarm data from the communication base station to train models for predicting equipment faults. However, both methods require a wide variety of data, making unified acquisition difficult, and they often struggle to identify surface damage to equipment, thus hindering the security and stable operation of equipment within the base station. To address these problems in existing technologies, the inventors of this application propose a method, model, and apparatus for identifying the status of equipment in communication base stations. This method determines whether the status of equipment in the communication base station has changed, including changes in the equipment's appearance and circuitry, thereby improving the security and stable operation of equipment within the base station.

[0025] The following describes the device status identification method for communication base stations provided in the embodiments of this application.

[0026] See Figure 1 , Figure 1 This is a flowchart of a device status identification method for a communication base station provided in an embodiment of this application, such as... Figure 1 As shown, the above method includes:

[0027] Step 101: Calculate the similarity score between the first image and the second image. The first image is the image containing the first device at a first time point, and the second image is the image containing the first device at a second time point, which is after the first time point.

[0028] Step 102: Determine whether the state of the first device has changed after the first moment based on the similarity measurement value.

[0029] In step 101, the device status recognition model for the communication base station (hereinafter referred to as the "device status recognition model") calculates the similarity measure value between the first image and the second image. This similarity measure value can be used to measure the similarity between the images in the first image and the images in the second image. The first image is an image containing the first device at a first time point, and the second image is an image containing the first device at a second time point, which is after the first time point. The first time point is a historical time point, and the second time point can be the current time point or any time point after the first time point. The first device is a device within the communication base station, such as an air conditioner, battery, or cabinet within the base station.

[0030] In step 102, the device state is the state obtainable from the device image, including the device's appearance and circuit connection status, such as surface damage. The device state recognition model determines whether the state of the first device has changed after the first moment based on a similarity measure value. Specifically, a similarity measure value threshold can be preset; if the similarity measure value is lower than or higher than the threshold, it is determined that the state of the first device has changed after the first moment.

[0031] In existing technologies, there are methods to determine whether equipment malfunctions occur by acquiring surface temperature and ambient humidity of equipment within a base station using sensors, and methods to predict equipment malfunctions by training models using historical alarm data from communication base stations. Both of these methods require a wide variety of data, making unified acquisition difficult, and they often struggle to identify surface damage to equipment. In this application embodiment, by calculating the similarity measure of images containing equipment images at different times, it is possible to determine whether the status of the communication base station equipment has changed. On one hand, compared to the numerous sensor and alarm data in existing technologies, the aforementioned images containing equipment images are easier to obtain. On the other hand, the equipment status in this application embodiment refers to the equipment status obtainable from the equipment images, including the equipment's appearance and circuit connection status. This application embodiment determines whether the status of the communication base station equipment has changed, including whether changes have occurred in the equipment's appearance and circuit status, where the appearance status includes surface damage. This application's method of determining whether the status of the communication base station equipment has changed is beneficial for judging whether there are potential hazards in the equipment, thereby contributing to the safety and stable operation of equipment within the base station.

[0032] Optionally, before calculating the similarity measure between the first image and the second image, the above method further includes:

[0033] Acquire the third image taken at the first moment and the fourth image taken at the second moment;

[0034] If it is detected that both the third and fourth images contain images of the first device, the third and fourth images are cropped to obtain two images containing images of the first device.

[0035] Angle and scale calibration processes are performed on the two images to obtain the first and second images.

[0036] The device status recognition model acquires a third image taken at the first moment and a fourth image taken at the second moment. The third and fourth images are both from the same space within the communication base station. These images can be taken by a pan-tilt-zoom (PTZ) camera within the base station. The PTZ camera can move according to a pre-set shooting point, thus enabling the capture of images of the same space at different times.

[0037] The device status recognition model detects images. If it detects that the third image contains an image of the first device and the fourth image does not contain an image of the first device, or if it detects that the third image does not contain an image of the first device and the fourth image contains an image of the first device, it can be directly determined that the status of the first device has changed, specifically, that the physical location of the first device has changed.

[0038] If both the third and fourth images are detected to contain images of the first device, the third and fourth images are cropped to obtain two images containing images of the first device, thereby reducing interference from irrelevant images.

[0039] The third and fourth images, captured by a camera at different times in the same space, may have angular discrepancies. When detecting and cropping the image of the first device within the third and fourth images, the resulting two images may exhibit scale discrepancies. Angle and scale calibration processing is performed on the two images to match their angles and scales. After calibration, the first and second images are obtained. It should be noted that the calibration can be performed based on one of the two images, or on both images based on a pre-set initial image.

[0040] In this embodiment, two images of the same space at different times are captured by a gimbal camera. The images in the two images are then detected, and the images containing the device are cropped out, which helps to reduce interference from irrelevant images. The two images containing the device image are calibrated to ensure that the angles and scales of the two images are more consistent.

[0041] The first and second images may contain only images of the first device, allowing for the determination of whether the state of the first device has changed based on the similarity score between the first and second images. In some embodiments, the first and second images may contain not only images of the first device but also images related to the first device, such as images of a certain area surrounding the device. Changes in the state of the area surrounding the device may affect its safety, and based on the positional relationship between the images of the area surrounding the device and the image of the device, it is also possible to determine whether the physical location of the device and the physical location of objects within the surrounding area have changed. The following describes an embodiment where the first and second images may contain more than just images of the first device.

[0042] Optionally, the first image also includes an image of a preset perimeter of the first device at a first moment, and the second image also includes an image of the preset perimeter of the first device at a second moment;

[0043] The above methods also include:

[0044] Based on the similarity measurement value, determine whether the state of the preset surrounding area of ​​the first device has changed after the first moment.

[0045] In this embodiment, the first image further includes an image of a preset perimeter of the first device at a first moment, and the second image further includes an image of the preset perimeter of the first device at a second moment. After determining the similarity measure value between the first image and the second image, the similarity between the image of the first device and the preset perimeter at the first moment and the image of the first device and the preset perimeter at the second moment can be measured based on the similarity measure value. This further allows for the determination of whether the state of the first device has changed after the first moment, and whether the state of the preset perimeter of the first device has changed after the first moment.

[0046] Changes in the surrounding environment of equipment can have a certain impact on its safety. The surrounding environment includes the state of objects around the equipment, and changes in the state of these objects may introduce potential hazards to the equipment. For example, if the position or state of objects around the equipment changes, or if fragile or flammable materials or other hazardous materials appear nearby, it will threaten the equipment's safety. Similarly, damage to the surface of objects around the equipment, such as a damaged fire extinguisher, can also pose a threat to its safety.

[0047] Based on the relative positional relationship between the image of the device and the image of the preset area surrounding the device, it can be determined whether the positional status of the device and the preset area has changed. Whether the positional status of the device and the preset area has changed can be determined based on position parameters.

[0048] Based on the similarity score of the two images, it can be determined whether the device status and the status of the preset area surrounding the device have changed. If the status has changed, the specific type of change can be determined by directly observing the information on the images, i.e., determining whether the status change is a change in appearance, circuit connection, or location.

[0049] It should be noted that, to save manpower, when determining the specific type of state change, changes in position state can be determined through the equipment state recognition model. If the specific type of state change is determined to be a position state change, the result of the position state change can be directly output. If the position state has not changed, the state change can be directly determined to be either a change in appearance or a change in circuit connection.

[0050] The following provides a detailed explanation of how to determine whether a change in the state of a device is a change in its position state.

[0051] Optionally, if it is determined that the state of the first device has changed after the first moment, and it is determined that the state of the preset surrounding area of ​​the first device has not changed after the first moment, the above method further includes:

[0052] Based on the first image and the second image, calculate the first position parameter of the first device relative to the preset surrounding range at the first moment, and the second position parameter of the first device relative to the preset surrounding range at the second moment;

[0053] Based on the first position parameter and the second position parameter, determine whether the position state of the first device has changed after the first moment.

[0054] In this embodiment, the device state recognition model calculates a first position parameter of the first device relative to a preset surrounding area at a first moment, and a second position parameter of the first device relative to the preset surrounding area at a second moment, based on the first and second images. Based on the first and second position parameters, the device state recognition model can determine whether the position state of the first device has changed after the first moment, which helps save manpower.

[0055] Based on the first position parameter and the second position parameter, determine whether the position state of the first device has changed after the first moment. This can be done by directly determining that the position state of the first device has changed after the first moment when the first position parameter and the second position parameter are different, or by pre-setting a range of change, determining that the position state of the first device has changed after the first moment when the change in the second position parameter exceeds the preset range.

[0056] Optionally, if it is determined that the position state of the first device has not changed after the first moment, it is determined that the appearance state or circuit state of the first device has changed after the first moment.

[0057] If the device state recognition model determines that the state of the first device has changed after the first moment, and the position state of the first device has not changed after the first moment, it can directly determine that the appearance state or circuit state of the first device has changed after the first moment.

[0058] Optionally, based on the similarity metric, determine whether the state of the preset surrounding area of ​​the first device has changed after the first moment, including:

[0059] If the image of the second device is found to exist in the first image, the state of the second device is determined based on the similarity measurement value to determine whether the state of the second device has changed after the first moment. The image of the second device is the image in the first image that is located within a preset perimeter of the image of the first device.

[0060] In this embodiment, the image of the second device is an image located within a preset perimeter of the image of the first device in the first image. The image of the second device can be a complete image of the second device or a partial image of the second device.

[0061] In this embodiment of the application, if it is found that there is also an image of the second device in the first image, it is also possible to determine whether the state of the second device has changed after the first moment based on the similarity measurement value.

[0062] This application also provides a device status identification model for communication base stations, used to execute the device status identification method for communication base stations described in the above embodiments, such as... Figure 2 As shown, the model includes;

[0063] The target detection sub-model 201 is used to detect images at the first time and the second time. When images containing devices within the communication base station are detected, the images at the first time and the second time are cropped, and each cropped image contains at least one device.

[0064] Image calibration sub-model 202 is used to perform angle calibration and scale calibration on cropped images;

[0065] Image difference comparison sub-model 203 is used to calculate the similarity measure between images containing images from the same device after angle calibration and scale calibration.

[0066] like Figure 2 As shown, the object detection sub-model 201 detects images at different times. When an image containing an image of a device within a communication base station is detected, the images from the first and second times are cropped, ensuring that each cropped image contains at least one device. The image calibration sub-model 202 performs angle and scale calibration on the cropped images to match the angles and scales of the two images from the first and second times that contain the same device, after cropping by the object detection sub-model 201. The image difference comparison sub-model 203 calculates the similarity measure between images containing the same device after angle and scale calibration. The images containing the same device are the two images from the first and second times. By calculating the similarity measure of these two images, it is possible to determine whether the state of the same device has changed from the first time to the second time.

[0067] Optionally, the object detection sub-model 201 uses the YOLO algorithm;

[0068] Image calibration sub-model 202 employs a scale-invariant feature transformation algorithm;

[0069] Image difference comparison sub-model 203 uses a Siamese network to calculate similarity metrics between images containing images from the same device.

[0070] In the embodiments of this application, each sub-model adopts an algorithm from deep learning, which makes it easier to implement the functions of each sub-model.

[0071] The object detection sub-model can use the YOLO algorithm, specifically YOLO V5.

[0072] Image calibration sub-model 202 can adopt a scale-invariant feature transformation algorithm. This algorithm can find extreme points in the spatial scale and perform feature description. Then, by matching feature points, it can find the correspondence between two images, thereby realizing angle calibration and scale calibration.

[0073] Image difference comparison sub-model 203 uses a Siamese network to calculate the similarity measure between images containing images of the same device, where the images containing the same device image are two images from the first time step and the second time step. In other words, image difference comparison sub-model 203 calculates the similarity measure between two images containing the same device image at the first time step and the second time step. Figure 3 As shown, the image difference comparison sub-model 203 takes two images as input, extracts features from both images through a Siamese network with identical structure and shared weights, and measures their similarity using a distance metric or other function, thus outputting a loss function value, which is the similarity measure. It should be noted that the Siamese network is trainable; training samples can be created in advance to train the Siamese network using a pre-trained model, making the similarity measure calculated by the image difference comparison sub-model 203 more accurate.

[0074] It should be noted that the device status identification model for communication base stations in this application embodiment is used to execute the various processes of the above-described device status identification method embodiment for communication base stations, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0075] This application also provides a device for identifying the status of a communication base station, such as... Figure 4 As shown, the device 300 includes:

[0076] The first calculation module 301 is used to calculate the similarity measurement value between the first image and the second image. The first image is an image containing the image of the first device at a first time, and the second image is an image containing the image of the first device at a second time. The second time is after the first time.

[0077] The first determining module 302 is used to determine whether the state of the first device has changed after the first moment based on the similarity measurement value.

[0078] Optionally, the device status identification device 300 for a communication base station also includes:

[0079] The first acquisition module is used to acquire the third image taken at the first moment and the fourth image taken at the second moment;

[0080] The first detection module, when it detects that both the third image and the fourth image contain images of the first device, crops the third image and the fourth image to obtain two images containing images of the first device.

[0081] The first calibration module is used to perform angle calibration and scale calibration on the two images to obtain the first image and the second image.

[0082] Optionally, the first image further includes an image of a preset perimeter of the first device at the first moment, and the second image further includes an image of a preset perimeter of the first device at the second moment;

[0083] The device status identification device 300 for communication base stations also includes:

[0084] The second determining module is used to determine, based on the similarity measurement value, whether the state of the preset surrounding area of ​​the first device has changed after the first moment.

[0085] Optionally, the device status identification device 300 for a communication base station also includes:

[0086] The second calculation module, when it is determined that the state of the first device has changed after the first moment and the state of the preset surrounding range of the first device has not changed after the first moment, is used to calculate the first position parameter of the first device relative to the preset surrounding range at the first moment and the second position parameter of the first device relative to the preset surrounding range at the second moment based on the first image and the second image.

[0087] The third determining module is used to determine whether the position state of the first device has changed after the first moment, based on the first position parameter and the second position parameter.

[0088] Optionally, the device status identification device 300 for a communication base station also includes:

[0089] The fourth determining module is used to determine whether the appearance or circuit state of the first device has changed after the first moment, provided that the position state of the first device has not changed after the first moment.

[0090] Optionally, the second determining module is specifically used to determine, based on a similarity measure, whether the state of the second device has changed after the first moment when the image of the second device is identified as also existing in the first image. The image of the second device is an image in the first image located within a preset perimeter of the image of the first device.

[0091] It should be noted that the device status identification device for communication base stations provided in this application embodiment can implement all the processes of the above-described device status identification method embodiment for communication base stations and achieve the same technical effect. To avoid repetition, it will not be described again here.

[0092] This application also provides an electronic device 400, see [link to previous document]. Figure 5 It includes at least one processor 401, a memory 402, and a computer program stored in the memory 402 and executable on the processor 401. The computer program is executed by at least one processor 401 to implement the various processes of the above-described embodiment of the device status identification method for a communication base station, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0093] This application also provides a computer-readable storage medium storing a computer program. This computer program is executed by a processor 401 to implement the various processes described in the embodiments of the device status identification method for communication base stations, achieving the same technical effects. To avoid repetition, these processes will not be repeated here. The computer-readable storage medium includes read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, etc.

[0094] It should be noted that the terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same class, not limited in number; for example, a first object can be one or more. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatus in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0095] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for identifying the status of equipment in a communication base station, characterized in that, The method includes: Calculate the similarity score between the first image and the second image, where the first image is an image containing the first device at a first time point, and the second image is an image containing the first device at a second time point, where the second time point is after the first time point; Based on the similarity measure, determine whether the state of the first device has changed after the first moment; Before calculating the similarity measure between the first image and the second image, the method further includes: Acquire the third image taken at the first moment and the fourth image taken at the second moment; If it is detected that both the third image and the fourth image contain an image of the first device, the third image and the fourth image are cropped to obtain two images containing an image of the first device; The two images are subjected to angle calibration and scale calibration to obtain the first image and the second image; The first image also includes an image of a preset perimeter of the first device at the first moment, and the second image also includes an image of a preset perimeter of the first device at the second moment; The method further includes: Based on the similarity measurement value, determine whether the state of the preset surrounding area of ​​the first device has changed after the first moment; Determining whether the state of the preset surrounding area of ​​the first device has changed after the first moment based on the similarity measurement value includes: If the image of the second device is detected in the first image, the similarity measurement value is used to determine whether the state of the second device has changed after the first moment. The image of the second device is the image in the first image that is located within a preset perimeter of the image of the first device. If it is determined that the state of the first device has changed after the first moment, and it is determined that the state of the preset surrounding area of ​​the first device has not changed after the first moment, the method further includes: Based on the first image and the second image, calculate the first position parameter of the first device relative to the preset surrounding range at the first time, and the second position parameter of the first device relative to the preset surrounding range at the second time; Based on the first position parameter and the second position parameter, determine whether the position state of the first device has changed after the first moment; The method further includes: If it is determined that the positional state of the first device has not changed after the first moment, it is determined that the appearance or circuit state of the first device has changed after the first moment.

2. A device status identification model for communication base stations, characterized in that, The model is used to perform the method as described in claim 1, wherein the model comprises; The target detection sub-model is used to detect images at a first time step and images at a second time step. If images containing devices within a communication base station are detected, the images at the first time step and the images at the second time step are cropped, and each cropped image contains at least one device. The image also contains an image of a preset surrounding area of ​​the device. The image calibration sub-model is used to perform angle calibration and scale calibration on the cropped image. The image difference comparison sub-model is used to calculate the similarity measure between images containing images from the same device after the angle calibration and scale calibration processes.

3. The device status identification model for communication base stations according to claim 2, characterized in that, The target detection sub-model uses the YOLO algorithm; The image calibration sub-model employs a scale-invariant feature transformation algorithm. The image difference comparison sub-model calculates a similarity measure between images containing images of the same device using a Siamese network.

4. A device for identifying the status of a communication base station, characterized in that, The device status identification device for communication base stations includes: The first calculation module is used to calculate the similarity measure between the first image and the second image. The first image is an image containing the image of the first device at a first time, and the second image is an image containing the image of the first device at a second time, with the second time being after the first time. The first determining module is used to determine, based on the similarity measurement value, whether the state of the first device has changed after the first moment; Before calculating the similarity measure between the first image and the second image, the device further includes: The first acquisition module is used to acquire the third image taken at the first moment and the fourth image taken at the second moment; The first detection module is used to crop the third image and the fourth image when it is detected that both the third image and the fourth image contain the image of the first device, so as to obtain two images containing the image of the first device; The first calibration module is used to perform angle calibration and scale calibration on the two images to obtain the first image and the second image. The first image also includes an image of a preset perimeter of the first device at the first moment, and the second image also includes an image of a preset perimeter of the first device at the second moment; The device further includes: The second determining module is used to determine, based on the similarity measurement value, whether the state of the preset surrounding area of ​​the first device has changed after the first moment; The second determining module is specifically used to determine, based on the similarity measurement value, whether the state of the second device has changed after the first moment when the image of the second device is identified as still existing in the first image; the image of the second device is an image in the first image located within a preset periphery of the image of the first device. The second calculation module, when it is determined that the state of the first device has changed after the first moment and the state of the preset surrounding range of the first device has not changed after the first moment, is used to calculate the first position parameter of the first device relative to the preset surrounding range at the first moment and the second position parameter of the first device relative to the preset surrounding range at the second moment based on the first image and the second image. The third determining module is used to determine whether the position state of the first device has changed after the first moment, based on the first position parameter and the second position parameter. The fourth determining module is used to determine whether the appearance or circuit state of the first device has changed after the first moment, provided that the position state of the first device has not changed after the first moment.

5. An electronic device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the device status identification method for a communication base station as described in claim 1.

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