Mine water level monitoring method, device and equipment based on AI identification and medium

Abstract

The invention relates to a mine water level monitoring method, device and equipment based on AI recognition and a medium, and relates to the technical field of mine water level monitoring, the method comprises the steps that image information containing a water level reference object in a monitoring area is acquired through an image acquisition device, and the water level reference object is a fixed component with a height mark; the real-time image information is analyzed through an AI processing unit, and water level data are obtained through calculation according to the corresponding relation between the calibrated reference object height and image pixels; and driving the drainage unit to start / stop based on a comparison result of the water level data and a preset threshold value. Special sensors and mine water level measurement are not needed, so that the hardware cost is low; manual observation is replaced by AI analysis, personal errors are avoided, and all-weather monitoring is supported.

Description

Technical Field

[0001] This invention relates to the field of mine water level monitoring technology, and in particular to a mine water level monitoring method, device, equipment, and medium based on AI recognition. Background Technology

[0002] Currently, water level monitoring in mines mainly relies on water level sensors (such as pressure type, ultrasonic type, and radar type) or manual observation. Sensor monitoring requires separate power supply and equipment installation for each detection point. For underground scenarios, this presents challenges due to the large workload and high cost. Furthermore, it is susceptible to inaccurate detection caused by silt layers, installation blind spots, and water impurities. Underground scenarios also pose a safety risk of sensor leakage. Manual observation, on the other hand, cannot achieve 24 / 7 uninterrupted monitoring, is easily affected by weather and human error, and cannot simultaneously retain on-site image data corresponding to water levels, making it difficult to trace and analyze the causes of water level anomalies. In addition, existing visual monitoring technologies have weak anti-interference capabilities, with significantly reduced recognition accuracy in complex environments such as rain, fog, nighttime, and water surface reflections. Moreover, they mostly only output water level values, lacking the collaborative support of data and images, and cannot meet the requirements for accurate, efficient, and safe water level monitoring. Summary of the Invention

[0003] This invention provides a mine water level monitoring method based on AI recognition, which solves the technical problem that current mine water level monitoring relies on water level sensors or manual labor, resulting in high installation and maintenance costs.

[0004] In a first aspect, the present invention provides a method for monitoring mine water levels based on AI recognition, the method comprising: Image information containing water level reference objects within the monitoring area is acquired through an image acquisition device. The water level reference objects are fixed components with height markings. The real-time image information is analyzed by the AI ​​processing unit, and the water level data is calculated based on the correspondence between the height of the calibrated reference object and the image pixels. Based on the comparison between the water level data and the preset threshold, the drainage unit is driven to start / stop.

[0005] Secondly, the present invention provides a mine water level monitoring device based on AI recognition, comprising: The acquisition unit is used to acquire image information containing water level reference objects within the monitoring area through an image acquisition device. The water level reference objects are fixed components with height markings. The analysis unit is used to analyze the real-time image information through the AI ​​processing unit and calculate the water level data based on the correspondence between the height of the calibrated reference object and the image pixels. The driving unit is used to drive the drainage unit to start / stop based on the comparison result between the water level data and the preset threshold.

[0006] Thirdly, an electronic device is provided, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a program stored in memory, it implements the steps of the AI-based mine water level monitoring method described in any embodiment of the first aspect.

[0007] Fourthly, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the AI-based mine water level monitoring method as described in any embodiment of the first aspect.

[0008] The technical solutions provided in the embodiments of the present invention have the following advantages compared with the prior art: First, it reduces costs by utilizing existing cameras, eliminating the need for complex civil engineering and separate power supplies, thus lowering hardware and maintenance costs compared to traditional sensors. Second, it improves accuracy by reducing detection errors in complex environments through calibration and multi-step image analysis, resulting in higher accuracy than traditional vision equipment. Third, it provides intelligent feedback, enabling automatic early warning or drainage when water levels exceed limits, reducing manual intervention. Fourth, it provides data traceability by linking and storing water level data with on-site images, providing a basis for anomaly analysis. The overall solution is highly adaptable and stable, meeting the needs of underground water level monitoring. Attached Figure Description

[0009] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A flowchart illustrating an AI-based method for monitoring mine water levels, provided as an embodiment of the present invention. Figure 2 A schematic diagram of a sub-process of a mine water level monitoring method based on AI recognition provided in an embodiment of the present invention; Figure 3 A structural block diagram of a mine water level monitoring device based on AI recognition provided in an embodiment of the present invention; Figure 4 A schematic diagram of the structure of a computer device provided in an embodiment of the present invention; Figure 5 A schematic diagram of another mine water level monitoring device based on AI recognition provided in an embodiment of the present invention. Detailed Implementation

[0012] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0013] Example Figure 1 This is a flowchart illustrating a mine water level monitoring method based on AI recognition, provided in an embodiment of the present invention. Specifically, this embodiment proposes a mine water level monitoring method based on AI recognition; see below. Figure 1 The AI-based method for monitoring mine water levels includes the following steps S101-S103.

[0014] S101, image information containing water level reference objects within the monitoring area is acquired through an image acquisition device, wherein the water level reference objects are fixed components with height markings.

[0015] S102, the AI ​​processing unit analyzes the real-time image information and calculates the water level data based on the correspondence between the height of the calibrated reference object and the image pixels.

[0016] S103, based on the comparison result between the water level data and the preset threshold, drive the drainage unit to start / stop.

[0017] In a specific embodiment, the image acquisition device includes a camera (which can utilize existing monitoring equipment). In this embodiment, an industrial-grade high-definition camera (supporting night vision) and an edge computing terminal (with a computing power ≥ 2 TOPS) are selected. The terminal adopts an IP65-level protection design and is suitable for working environments from -30℃ to 70℃. It is used to acquire images containing water level reference objects. The water level reference objects include physical water gauges, rulers with markings, or fixed marker poles (including scales, preset markers, and other height markings). The AI ​​processing unit uses an edge computing terminal and has data parsing, storage, and signal transmission functions. The preset thresholds include warning water levels and safe water levels, which can be customized according to the scenario.

[0018] See Figure 5 , Figure 5This is a schematic diagram of another mine water level monitoring device based on AI recognition provided in an embodiment of the present invention. A scale with clear graduations and markings is fixed at a suitable position in the middle of the water pit. A float moves up and down along the scale. A camera installed at the top of the roadway transmits video information to the AI ​​analysis box. The AI ​​analysis box periodically uses the markings on the scale to calibrate the height and analyzes the video information. It then transmits the real-time water level information to the automatic control unit. The automatic control unit judges the water level information. If the set warning height is reached, it drives the drainage unit to drain water. When the safe water level is reached, it drives the drainage unit to stop draining water.

[0019] Specific example: A marker with 10cm intervals is fixed next to a water pit in the mine. The viewing angle of an existing surveillance camera near the mine is adjusted to cover both the marker and the water surface. The AI ​​processing unit uses a mine-grade explosion-proof edge computing terminal, connected to the camera via a dedicated underground wired network. The preset warning water level for the water pit is 1.6m, and the safe water level is 1.0m. After the camera captures images, they are transmitted to the AI ​​processing unit. The AI ​​processing unit calculates the water level based on the "marker-pixel" calibration relationship, and triggers a preset feedback action when the water level reaches 1.6m.

[0020] There is no need to deploy dedicated sensors and well logging; existing equipment can be used to further reduce hardware costs. AI analysis replaces manual observation, avoiding human error, and supports all-weather monitoring.

[0021] In one embodiment, the calibration step of the water level reference includes: measuring the physical dimensions of the height marker of the fixed component, establishing the correspondence between the actual physical height of the height marker and the imaging pixels of the image acquisition device, forming calibration parameters and storing them in the AI ​​processing unit.

[0022] In a specific embodiment, the calibration process can be carried out using measuring tools such as a tape measure or a laser rangefinder to obtain the actual physical size of the height marker. The AI ​​processing unit needs to have a parameter storage module to save the correspondence between "actual height and pixels" (e.g., 1cm actual height corresponds to 5 pixels in the image).

[0023] Specific example: The water level reference is a ruler with marked points (marked points spaced 10cm apart, each marked point indicating the actual elevation); before installation, a laser rangefinder is used to measure the actual distance between adjacent marked points, and the camera angle is adjusted to ensure the ruler is fully imaged, taking multiple images of the ruler; the images are analyzed in the AI ​​processing unit, the pixel distance between adjacent marked points in the image is calculated (e.g., adjacent marked points correspond to 80 pixels), a correspondence of "10cm actual height = 80 pixels" is established, calibration parameters are generated and stored.

[0024] By establishing a precise calibration relationship through physical measurements and pixel correlation, a benchmark is provided for subsequent water level calculations, avoiding detection errors caused by camera imaging ratio deviations and improving the accuracy of water level data.

[0025] In one embodiment, the calibration step of the water level reference further includes: periodically checking whether the imaging angle of the image acquisition device has shifted; if the angle has shifted, re-measuring the correspondence between the physical height of the height marker and the imaging pixels, and updating the calibration parameters.

[0026] In a specific embodiment, the AI ​​processing unit determines whether the viewing angle has shifted by comparing the changes in the pixel positions of the height markers in the periodically collected ruler images.

[0027] Specific example: Set the camera's viewing angle to be calibrated every 7 days; the AI ​​processing unit retrieves the water level gauge image collected that day and compares it with the initial calibration image. If the X-axis coordinate of the "10m" mark on the water level gauge is found to be offset by more than 20 pixels in the image, it is determined that the viewing angle has shifted; the maintenance personnel are notified to bring a laser rangefinder to the site to remeasure the actual height and corresponding pixel distance of the key marks on the water level gauge, update the calibration parameters in the AI ​​processing unit, and ensure the accuracy of subsequent water level calculations.

[0028] In one embodiment, Figure 2 This is a schematic diagram of a sub-process of a mine water level monitoring method based on AI recognition provided in an embodiment of the present invention. Specifically, step S102 above includes steps S201-S203: S201, the image information is preprocessed by denoising and illumination compensation.

[0029] S202, the height marking area of ​​the water level reference object is located through the target detection algorithm.

[0030] S203, based on semantic segmentation algorithm to identify the boundary line between water bodies and non-water bodies.

[0031] In a specific embodiment, the AI ​​processing unit incorporates an image preprocessing module (including Gaussian filtering for noise reduction and histogram equalization for illumination compensation), an object detection module (based on the YOLOv7 framework), and a semantic segmentation module (based on a convolutional neural network CNN), which respectively achieve image optimization, reference object localization, and water body boundary recognition.

[0032] Specific example: On rainy days, the images captured by the camera have noise due to rain interference, and insufficient light on cloudy days causes the water level gauge markings to be blurry; the AI ​​processing unit first removes image noise through Gaussian filtering, and then improves image brightness and contrast through histogram equalization; then it calls the YOLOv7 algorithm to accurately select the water level gauge marking area in the image; finally, it uses the CNN semantic segmentation algorithm to distinguish between water bodies and non-water bodies in the image, identify the boundary line where the water surface and the water level gauge are in contact, and eliminate the interference of floating objects on the boundary judgment.

[0033] By eliminating interference from rain, fog, light, and floating objects through multi-step analysis, the accuracy is improved compared to traditional visual monitoring equipment, solving the problem of difficult water level identification in complex environments.

[0034] In one embodiment, step S102 above further includes: calling pre-stored calibration parameters to convert the pixel position of the boundary line on the height identifier into the actual physical height.

[0035] In a specific embodiment, the AI ​​processing unit has a built-in data conversion module that can call the stored "actual height-pixel" calibration parameters and back-calculate the actual height by the pixel position (e.g., the pixel position of the boundary line in the water level gauge image corresponding to the "1.5m" scale is converted into the actual height of 1.5m by combining the calibration parameters).

[0036] Specific example: The water boundary line identified by the semantic segmentation algorithm corresponds to the 500th pixel in the water level gauge image; the AI ​​processing unit calls the pre-stored calibration parameter "1cm actual height = 5 pixels" and calculates that the actual height corresponding to the boundary line is 500÷5=100cm=1m, that is, the current water level is 1m.

[0037] In one embodiment, the preset feedback action includes: controlling the drainage unit to drain water when the water level reaches a warning threshold, and controlling the drainage unit to stop operating when the water level drops to a safe threshold.

[0038] In a specific embodiment, the drainage unit is an electric drainage pump, and the AI ​​processing unit can output start / stop control commands through a wired / wireless connection with the drainage unit; the preset threshold is set by the user in the AI ​​processing unit, including a warning threshold (triggering drainage) and a safety threshold (stopping drainage).

[0039] Specific example: Set the warning threshold to 80% of the water level (actual height 1.6m) and the safety threshold to 50% of the water level (actual height 1.0m); when the AI ​​processing unit calculates that the water level reaches 1.6m, it controls the explosion-proof drainage pump in the well to start drainage via a wired signal; when the water level drops to 1.0m, the AI ​​processing unit outputs a stop command and the drainage pump shuts down.

[0040] It enables automated drainage when water levels exceed limits, eliminating the need for manual on-site operation. This solves the problem of untimely drainage in underground and remote areas, while also avoiding energy waste caused by excessive drainage and improving the level of intelligence in water level control.

[0041] In one embodiment, the AI-based mine water level monitoring method further includes: associating the water level data with image information of the corresponding time period; and storing the association marker information and the water level data to the AI ​​processing unit.

[0042] In a specific embodiment, the AI ​​processing unit has a built-in association tagging module and a storage module. The association tagging module can bind water level data (including collection time and value) with corresponding image information (including shooting timestamp). The storage module uses local hard disk or cloud storage to support long-term data retention.

[0043] Specific example: The AI ​​processing unit collects water level data every 5 minutes (e.g., water level is 1.2m at 14:00), and at the same time retrieves the river surface image taken at 14:00. The water level value and timestamp are superimposed on the image to form the association tag information of "14:00-1.2m-corresponding image". This information is stored on the local hard drive of the AI ​​processing unit and retained for ≥1 year for easy subsequent query.

[0044] This system enables a combined log of on-site water level data and images, addressing the problem that traditional sensors only output numerical values ​​and cannot trace the cause of anomalies. For example, images can be used to determine whether high water levels are caused by rainfall or upstream water, providing a more comprehensive basis for decision-making.

[0045] The embodiments of the present invention can achieve the following advantages: First, it reduces costs by utilizing existing cameras, eliminating the need for complex civil engineering and separate power supplies, thus lowering hardware and maintenance costs compared to traditional sensors. Second, it improves accuracy by reducing detection errors in complex environments through calibration and multi-step image analysis, resulting in higher accuracy than traditional vision equipment. Third, it provides intelligent feedback, enabling automatic early warning or drainage when water levels exceed limits, reducing manual intervention. Fourth, it provides data traceability by linking and storing water level data with on-site images, providing a basis for anomaly analysis. The overall solution is highly adaptable and stable, meeting the needs of underground water level monitoring.

[0046] See Figure 3 This invention also provides an AI-based mine water level monitoring device 400, which includes a data acquisition unit 401, a data analysis unit 402, a drive unit 403, and a drainage unit 404.

[0047] The acquisition unit 401 is used to acquire image information containing water level reference objects within the monitoring area through an image acquisition device. The water level reference objects are fixed components with height markings.

[0048] In one embodiment, the method further includes a calibration step for a water level reference: The height markings of the fixed components are physically measured to establish the correspondence between the actual physical height of the height markings and the imaging pixels of the image acquisition device, forming calibration parameters and storing them in the AI ​​processing unit.

[0049] In one embodiment, the calibration step of the water level reference further includes: Periodically check whether the imaging angle of the image acquisition device has shifted; If the viewing angle shifts, remeasure the correspondence between the physical height of the height marker and the imaging pixels, and update the calibration parameters.

[0050] The parsing unit 402 is used to parse the real-time image information through the AI ​​processing unit and calculate the water level data based on the correspondence between the height of the calibrated reference object and the image pixels.

[0051] In one embodiment, the step of parsing the real-time image information by the AI ​​processing unit includes: The image information is preprocessed with noise reduction and illumination compensation. The height marker area of ​​the water level reference object is located using a target detection algorithm; Identify the boundary lines between water bodies and non-water bodies based on semantic segmentation algorithms.

[0052] In one embodiment, the step of calculating the water level data includes: The pre-stored calibration parameters are called to convert the pixel position of the boundary line on the height marker into the actual physical height.

[0053] The drive unit 403 is used to drive the drainage unit 404 to start / stop based on the comparison result between the water level data and the preset threshold.

[0054] The drainage unit 404 is used to receive the control signal from the drive unit 403 to start or stop the drainage operation.

[0055] In one embodiment, the start / stop of the drive drainage unit 404 includes: When the water level reaches the warning threshold, the drainage unit is controlled to drain water; when the water level drops to the safety threshold, the drainage unit is controlled to stop operating.

[0056] In one embodiment, it further includes: The water level data is associated and labeled with the image information of the corresponding time period; The associated marker information and the water level data are stored in the AI ​​processing unit.

[0057] like Figure 4 As shown, Figure 4 This is a schematic block diagram of a computer device provided in an embodiment of this application. The computer device 500 can be a terminal or a server. The terminal can be an electronic device with communication functions, such as a smartphone, tablet, laptop, desktop computer, personal digital assistant, or wearable device. The server can be a standalone server or a server cluster composed of multiple servers.

[0058] The computer device 500 includes a processor 502, a memory, and a network interface 505 connected via a system bus 501. The memory may include a non-volatile storage medium 503 and internal memory 504.

[0059] The non-volatile storage medium 503 can store an operating system 5031 and a computer program 5032. When the computer program 5032 is executed, it enables the processor 502 to execute a mine water level monitoring method based on AI recognition.

[0060] The processor 502 provides computing and control capabilities to support the operation of the entire computer device 500.

[0061] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute a mine water level monitoring method based on AI recognition.

[0062] The network interface 505 is used for network communication with other devices. Those skilled in the art will understand that the above structure is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device 500 to which the present application is applied. A specific computer device 500 may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements.

[0063] It should be understood that in the embodiments of this application, the processor 502 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0064] It will be understood by those skilled in the art that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program may be stored in a storage medium, which is a computer-readable storage medium. The computer program is executed by at least one processor in the computer system to implement the process steps of the embodiments of the above methods.

[0065] Therefore, the present invention also provides a storage medium. This storage medium can be a computer-readable storage medium. The storage medium stores a computer program.

[0066] The storage medium is a physical, non-transient storage medium, such as a USB flash drive, external hard drive, read-only memory (ROM), magnetic disk, or optical disk, or any other physical storage medium capable of storing program code. The computer-readable storage medium can be non-volatile or volatile.

[0067] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented 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 implementations should not be considered beyond the scope of this invention.

[0068] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of each unit is merely a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0069] The steps in the method of this invention can be adjusted, merged, or reduced in order according to actual needs. The units in the device of this invention can be merged, divided, or reduced according to actual needs. Furthermore, the functional units in the various embodiments of this invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0070] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention, 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a terminal, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0071] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0072] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Since these modifications and variations fall within the scope of the claims and their equivalents, this invention also intends to include these modifications and variations.

[0073] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for monitoring mine water levels based on AI recognition, characterized in that, The method includes: Image information containing water level reference objects within the monitoring area is acquired through an image acquisition device. The water level reference objects are fixed components with height markings. The real-time image information is analyzed by the AI ​​processing unit, and the water level data is calculated based on the correspondence between the height of the calibrated reference object and the image pixels. Based on the comparison between the water level data and the preset threshold, the drainage unit is driven to start / stop.

2. The method according to claim 1, characterized in that, It also includes the calibration steps for water level reference points: The height markings of the fixed components are physically measured to establish the correspondence between the actual physical height of the height markings and the imaging pixels of the image acquisition device, forming calibration parameters and storing them in the AI ​​processing unit.

3. The method according to claim 2, characterized in that, The calibration steps for the water level reference also include: Periodically check whether the imaging angle of the image acquisition device has shifted; If the viewing angle shifts, remeasure the correspondence between the physical height of the height marker and the imaging pixels, and update the calibration parameters.

4. The method according to claim 1, characterized in that, The step of parsing the real-time image information using the AI ​​processing unit includes: The image information is preprocessed with noise reduction and illumination compensation. The height marker area of ​​the water level reference object is located using a target detection algorithm; Identify the boundary lines between water bodies and non-water bodies based on semantic segmentation algorithms.

5. The method according to claim 4, characterized in that, The steps for calculating the water level data include: The pre-stored calibration parameters are called to convert the pixel position of the boundary line on the height marker into the actual physical height.

6. The method according to claim 1, characterized in that, The start / stop of the drive drainage unit includes: When the water level reaches the warning threshold, the drainage unit is controlled to drain water; when the water level drops to the safety threshold, the drainage unit is controlled to stop operating.

7. The method according to claim 1, characterized in that, Also includes: The water level data is associated and labeled with the image information of the corresponding time period; The associated marker information and the water level data are stored in the AI ​​processing unit.

8. A mine water level monitoring device based on AI recognition, characterized in that, include: The acquisition unit is used to acquire image information containing water level reference objects within the monitoring area through an image acquisition device. The water level reference objects are fixed components with height markings. The analysis unit is used to analyze the real-time image information through the AI ​​processing unit and calculate the water level data based on the correspondence between the height of the calibrated reference object and the image pixels. The driving unit is used to drive the drainage unit to start / stop based on the comparison result between the water level data and the preset threshold.

9. A computer device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-7.

Images