Electronic device management method, tool management cabinet, device, and storage medium

Abstract

The application relates to the technical field of equipment management, and discloses an electronic equipment management method, a tool management cabinet, equipment and a storage medium. The method solves the problems of missing equipment installation authenticity verification, insufficient dynamic tracking capability and difficult image forgery identification through identity verification, dynamic shooting guidance, multi-layer anti-forgery identification and smart contract generation technology, and improves equipment management safety and standardization.

Description

Technical Field

[0001] This invention relates to the field of equipment management technology, specifically to an electronic equipment management method, a tool management cabinet, equipment, and storage medium. Background Technology

[0002] While existing tool and electronic equipment management cabinet systems provide basic functions such as storage compartment switching control and borrowing / returning management, they suffer from several technical shortcomings in practical applications. First, these systems lack an effective verification mechanism to ensure the authenticity of equipment installation, making it impossible to guarantee that borrowed electronic equipment is correctly installed at the usage site. Second, the systems lack dynamic tracking capabilities for the usage of borrowed equipment, making it difficult to monitor the actual usage status of the equipment in real time.

[0003] Furthermore, the existing system has vulnerabilities in user authentication; relying solely on initial identity verification is insufficient to prevent identity theft during subsequent use. Regarding image verification, the traditional system's static image upload method has significant security vulnerabilities, failing to effectively identify forged images manipulated by Photoshop or reused historical images, posing serious security risks to device management. These issues frequently lead to situations where devices are fraudulently claimed, returned with forgeries, or used improperly in actual operation, severely impacting the standardization and security of device management. Summary of the Invention

[0004] This application proposes an electronic device management method, a tool management cabinet, equipment, and storage medium, aiming to solve the problem that existing tool management cabinets cannot perform follow-up tracking and maintenance of borrowed equipment.

[0005] In a first aspect, the present invention provides an electronic device management method applied to a tool and equipment management cabinet, the method comprising:

[0006] When a user initiates a borrowing request through a mobile terminal with the APP installed or by triggering the tool and equipment management cabinet, the user's identity information is verified. After the verification is successful, the candidate electronic device directory corresponding to the user's permissions is retrieved and displayed. The APP is a device management application that matches the borrowing and returning control program of the tool and equipment management cabinet.

[0007] In response to the user's selection operation of the candidate electronic device catalog, the target electronic device is determined, and the smart lock of the storage compartment corresponding to the target electronic device is opened;

[0008] After removing the target electronic device, a prompt for installation and photo authentication is sent to the mobile terminal;

[0009] The system receives a response message from the mobile terminal based on the installation photo authentication prompt, and extracts a first photo of the target electronic device installed on-site from the response message. Based on the first photo, the system uses a preset AI guidance model to remotely control the APP to enter the AI ​​shooting guidance interface. The AI ​​shooting guidance interface displays the shooting angle, target features of the target electronic device, and anti-counterfeiting marks.

[0010] After identifying a target power device that meets preset conditions in the AI ​​shooting guidance interface, a second photo is taken, wherein the second photo is a sequence of images with specific dynamic effects;

[0011] The graph sequence is subjected to multi-layer anti-counterfeiting identification. If the identification is successful, a complete borrowing contract is generated.

[0012] In another embodiment of the first aspect, the step of remotely controlling the APP to enter the AI ​​shooting guidance interface based on the first photo using a preset AI guidance model includes:

[0013] The first photo is used to extract features and calculate the shooting angle using an AI image analysis model, resulting in at least one shooting angle and target device features associated with each shooting angle.

[0014] Using computer vision and scene recognition technology, the outline and anti-counterfeiting mark of each target device feature are constructed and fused with the background in the first photo, and a matching photo interface is generated;

[0015] A photo-taking scheduling instruction is generated based on the matching photo-taking interface and sent to the mobile terminal to schedule the mobile terminal's photo-taking function. The AI-guided shooting interface that uses the matching photo-taking interface as the display screen to start the photo-taking function is also provided.

[0016] In another embodiment of the first aspect, the step of using an AI image analysis model to extract features from the first photograph and calculate the shooting angle to obtain at least one shooting angle and target device features associated with each shooting angle includes:

[0017] The AI ​​image analysis model is used to identify whether the first photo contains key morphological features of the target electronic device;

[0018] If included, the orientation information of the target power equipment in the first photograph is identified;

[0019] Based on the posture information, at least one shooting angle is selected using a random algorithm, and the target device features are extracted from each shooting angle.

[0020] In another embodiment of the first aspect, after selecting at least one shooting angle based on the pose information using a random algorithm and extracting the target device features at each shooting angle, the method further includes:

[0021] Using each selected shooting angle as the shake base point, a shake anti-spoofing logic calculation is performed using a random algorithm combined with the posture information to obtain a shake direction combination sequence of the camera. The shake direction combination sequence is used to control the camera of the mobile terminal to take a second photo.

[0022] In another embodiment of the first aspect, the step of taking a second photo after recognizing a target electrical device that meets preset conditions on the AI ​​shooting guidance interface includes:

[0023] Detect whether the target power device located within the AI ​​shooting guidance interface is aligned with the outline at the shooting angle;

[0024] If the detection is not aligned, a prompt will be displayed on the AI ​​shooting guidance interface to adjust the shooting angle;

[0025] If alignment is detected, the camera on the mobile terminal is controlled to take a second photo based on the jitter direction combination sequence.

[0026] In another embodiment of the first aspect, controlling the camera on the mobile terminal to capture a second photo based on the jitter direction combination sequence includes:

[0027] If only one shooting angle is selected, the camera on the mobile terminal is controlled to continuously shake and capture multiple image frames based on the shaking direction combination sequence, and the multiple image frames are fused to generate an image sequence with dynamic effects.

[0028] If at least two shooting angles are selected, at the first shooting angle, the camera on the mobile terminal is controlled to shake and capture N image frames based on the first N shaking angles of the shaking direction combination sequence; after switching to the next shooting angle and aligning, the camera on the mobile terminal is controlled to shake and capture N image frames based on the shaking angles after the (N+1)th one in the shaking direction combination sequence, until the last shooting angle is reached and N image frames are captured; the N image frames at each shooting angle are sorted and fused according to time to generate a sequence of images with dynamic effects.

[0029] In another embodiment of the first aspect, the step of performing multi-layer anti-counterfeiting identification on the graph sequence, and generating a complete borrowing contract if the identification is successful, includes:

[0030] The graph sequence is subjected to anti-counterfeiting verification in sequence from the dimensions of data layer, visual layer and interaction layer to obtain the corresponding verification score;

[0031] Based on the weight coefficients of each dimension and the verification scores of each dimension, the anti-counterfeiting result of the graph sequence is determined.

[0032] If the anti-counterfeiting result is successful, a complete borrowing contract will be generated using the user's identity information, the target electronic device's information, and the installation authentication result.

[0033] Secondly, the present invention provides a tool and equipment management cabinet, comprising: multiple storage compartments and a main unit, each storage compartment being equipped with a smart lock, and the main unit being connected to the smart locks of each storage compartment via a bus; the main unit is specifically used for:

[0034] When a user initiates a borrowing request through a mobile terminal with the APP installed or by triggering the tool and equipment management cabinet, the user's identity information is verified. After the verification is successful, the candidate electronic device directory corresponding to the user's permissions is retrieved and displayed. The APP is a device management application that matches the borrowing and returning control program of the tool and equipment management cabinet.

[0035] In response to the user's selection operation of the candidate electronic device catalog, the target electronic device is determined, and the smart lock of the storage compartment corresponding to the target electronic device is opened;

[0036] After removing the target electronic device, a prompt for installation and photo authentication is sent to the mobile terminal;

[0037] The system receives a response message from the mobile terminal based on the installation photo authentication prompt, and extracts a first photo of the target electronic device installed on-site from the response message. Based on the first photo, the system uses a preset AI guidance model to remotely control the APP to enter the AI ​​shooting guidance interface. The AI ​​shooting guidance interface displays the shooting angle, target features of the target electronic device, and anti-counterfeiting marks.

[0038] After identifying a target power device that meets preset conditions in the AI ​​shooting guidance interface, a second photo is taken, wherein the second photo is a sequence of images with specific dynamic effects;

[0039] The graph sequence is subjected to multi-layer anti-counterfeiting identification. If the identification is successful, a complete borrowing contract is generated.

[0040] Thirdly, the present invention provides an electronic device including a processor and a memory, the memory storing machine-executable instructions that can be executed by the processor, the processor executing the machine-executable instructions to implement the electronic device management method provided above.

[0041] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement the electronic device management method provided above.

[0042] The embodiments of the present invention bring the following beneficial effects:

[0043] This invention provides an electronic device management method, tool management cabinet, device, and storage medium. Through identity verification, dynamic shooting guidance, multi-layer anti-counterfeiting identification, and smart contract generation technology, it solves the problems of lack of verification of device installation authenticity, insufficient dynamic tracking capability, and difficulty in identifying image forgery, thereby improving the security and standardization of device management.

[0044] Other features and advantages of the invention will be set forth in the following description, or some features and advantages may be inferred from the description or determined without doubt, or may be learned by practicing the techniques described above.

[0045] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0046] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0047] Figure 1 A flowchart of an electronic device management method provided in an embodiment of the present invention;

[0048] Figure 2 This is a schematic diagram of a tool and equipment management cabinet provided in an embodiment of the present invention;

[0049] Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0050] 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, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0051] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0052] In related technologies, tool or electronic equipment management cabinet systems mainly control the opening and closing of storage compartments and manage borrowing and returning operations. However, they lack dynamic verification mechanisms for the authenticity of equipment installation, post-borrowing usage, and user identity. Existing systems rely on a single image upload verification method, which struggles to identify forged or duplicate images, leading to frequent problems such as equipment being fraudulently claimed, returned falsely, or used improperly. For example, in the scenario of borrowing power equipment, users may upload pre-taken images to forge the equipment installation status or impersonate others to borrow high-privilege equipment. Existing technologies cannot effectively verify the actual on-site situation.

[0053] To address the aforementioned issues, the inventors discovered that a key flaw in existing technologies lies in the static and singular nature of the verification process. Analysis of the device management process revealed that fraudulent claims often occur due to a disconnect between identity verification and device status verification, with forged images frequently utilizing fixed angles or repetitive materials. Further consideration was given to combining identity verification, device status verification, and dynamic anti-counterfeiting measures. The proposed solution involves introducing an installation photo authentication process after the device is removed, incorporating an AI-guided model to ensure the standardization of shooting angles and features. To solve the image forgery problem, a dynamic image sequence shooting and multi-layered anti-counterfeiting recognition mechanism are employed, enhancing authentication reliability through multi-dimensional verification.

[0054] Please see Figure 1 A specific embodiment of the electronic device management method provided by the present invention includes the following steps:

[0055] 110. When a user initiates a borrowing request through a mobile terminal with the APP installed or by triggering a borrowing request on the tool management cabinet, the user's identity information is identified and verified. After the identification and verification are successful, the candidate electronic device directory corresponding to the user's permissions is retrieved and displayed.

[0056] It should be noted that when a user initiates a borrowing request, they can do so by clicking the "Borrow Equipment" button on the operation interface of the mobile terminal with the APP installed, or by clicking the "Borrow Equipment" button on the operation panel of the tool and equipment management cabinet.

[0057] In practical applications, when using mobile terminals to trigger borrowing requests, the app typically requires the user to enter a pre-set login account and password after launch. To enhance security, multi-factor authentication can be used, such as combining SMS verification codes. The system sends an SMS containing a verification code to the user's registered mobile phone number, and the user must enter the correct verification code in a designated input field within the app. Alternatively, biometric technologies, such as fingerprint or facial recognition, can be utilized. The user completes the corresponding biometric data collection on the mobile terminal, and the app compares the collected information with a pre-stored user biometric template. Upon successful verification, the app retrieves and displays a list of candidate electronic devices corresponding to the user's permissions.

[0058] When using the control panel to trigger a borrowing request, the user places their ID card on the reader, and the system reads the ID card information. Simultaneously, a camera on the management cabinet captures the user's facial image and compares it with the ID card photo to ensure identity verification. A fingerprint recognition module can also be installed, allowing users to authenticate their identity by pressing their fingerprint. Upon successful verification, a directory of candidate electronic devices corresponding to the user's permissions is retrieved and displayed.

[0059] In this embodiment, the step of retrieving and displaying the candidate electronic device directory corresponding to the user's permissions after successful identification and verification includes:

[0060] After successful identification and verification, the user's borrowing permission level and task type are determined based on the user's identity information;

[0061] Based on the borrowing permission level and the task type, the corresponding equipment information is matched from the equipment library in the tool management cabinet, and a candidate equipment list is generated.

[0062] The list of candidate devices is presented in the form of a categorized menu on the display screen of the mobile terminal or the tool management cabinet.

[0063] Understandably, determining the user's borrowing permission level and task type based on the user's identity information includes:

[0064] Based on the user's identity information, query historical borrowing records and task scheduling records, and construct a user behavior model based on the historical borrowing records and task scheduling records;

[0065] The user's reputation score is calculated using the behavioral model, and an initial borrowing permission level matching the reputation score is determined by combining it with a preset permission mapping table.

[0066] Extract the device functional requirements from the borrowing request, perform semantic processing to obtain the task type, and adjust the initial borrowing permission level based on the task type to obtain the borrowing permission level.

[0067] In practical applications, the borrowing permission levels for users are actually pre-set. During the system initialization phase, the administrator assigns different permission levels to each user based on factors such as their job position and responsibilities. For example, ordinary repair personnel may only have permission to borrow commonly used repair tools, while senior engineers may be able to borrow more specialized and high-value electronic equipment. Permission information is stored in the host's database and linked to the user's identity information.

[0068] Once the user's identity information is verified, the tool management cabinet retrieves a list of candidate electronic devices from the database based on the user's permission level. For example, for a user with a basic maintenance worker permission level, the system only retrieves information on common maintenance tools such as screwdrivers and wrenches; for a user with a senior R&D worker permission level, the system retrieves information from a catalog that includes high-precision testing instruments and specialized development equipment.

[0069] Furthermore, when presenting the candidate device list in the form of a categorized menu on the display screen of the mobile terminal or the tool management cabinet, the specific steps include:

[0070] If displayed on a mobile app, the retrieved list of candidate electronic devices will be encrypted and transmitted to the user's mobile app. The app displays the device name, model, brief description, and other information in a list format on the user interface, and users can browse the list by swiping the screen. The app also supports a search function, allowing users to quickly locate the desired device by entering keywords.

[0071] If displayed in the tool and equipment management cabinet, the candidate electronic equipment catalog will be displayed on the cabinet's touchscreen in a combination of text and images. Each device will show an image, name, and brief parameters. Users can select devices and learn more details by touching the screen.

[0072] 120, responding to the user's selection operation on the candidate electronic device catalog, determines the target electronic device and controls the smart lock of the storage compartment corresponding to the target electronic device to open.

[0073] In this embodiment, the host of the tool management cabinet sends an unlocking command to the corresponding smart lock; after receiving the unlocking command, the control unit of the smart lock verifies the consistency of the device number; when the number is consistent and the lock status feedback is normal, the unlocking action is performed, and the device is automatically reset to the closed state after being removed.

[0074] In practical applications, users select from a list of candidate electronic devices on the user interface. The host responds to the user's selection command in real time, parses the user's selection intent, and determines the target electronic device selected by the user. During this process, the host ensures that the electronic device selected by the user is within its authorized scope and that the device status is available (e.g., not borrowed, not under maintenance, etc.).

[0075] After identifying the target electronic device, the host performs the following operations to control the smart lock of the corresponding storage compartment to open:

[0076] An unlocking command is sent via bus to the smart lock of the storage compartment containing the target electronic device. This command contains a unique identifier for the target storage compartment and an unlocking password (or encrypted command), ensuring that only authorized operations can unlock the door.

[0077] Upon receiving an unlocking command, the smart lock on the storage compartment first verifies the command's legality and validity. If verification is successful, the smart lock executes the unlocking action, releasing the storage compartment's latch, allowing the user to open the compartment and retrieve the target electronic device.

[0078] As the door lock is opened, the system records detailed information about the borrowing operation, including user identity, borrowing time, and information about the target electronic device, for subsequent auditing and tracking.

[0079] 130. After removing the target electronic device, send an installation photo authentication prompt to the mobile terminal.

[0080] Specifically, after a preset time has elapsed following the completion of the borrowing process for the target electronic device, a prompt to install photo authentication is sent to the user's mobile terminal or account. This prompt includes a detailed description of the photo-taking task and a link to access the photo-taking guide interface.

[0081] In another embodiment, sending an installation photo authentication prompt to the mobile terminal after removing the target electronic device includes:

[0082] After the target electronic device is detected to be removed, the time for sending the prompt message is calculated based on the type of the target electronic device and the borrowing time in the borrowing request, using a preset installation authentication time limit rule.

[0083] After the sending time arrives, the system generates the shooting location requirements, lighting conditions, and angle suggestions for the first photo based on the appearance and structure of the target electronic device and the preset installation scenario, and generates an installation photo authentication prompt.

[0084] The process of generating the installation photo authentication prompt includes: calling the corresponding installation template parameters according to the type of the target electronic device; performing simulation analysis on the installation environment carried in the borrowing request based on an AI scene prediction model to determine the lighting direction and occlusion factor within the photo area; generating a photo composition diagram based on the lighting direction, the occlusion factor, and the device size, setting the photo composition diagram to a semi-transparent overlay mode, adding it to the message template, and generating the installation photo authentication prompt.

[0085] 140. Receive the response message returned by the mobile terminal based on the installation photo authentication prompt, and extract the first photo of the target electronic device installed on site carried in the response message. Based on the first photo, use the preset AI guidance model to remotely control the APP to enter the AI ​​shooting guidance interface. The AI ​​shooting guidance interface displays the shooting angle, target features of the target electronic device, and anti-counterfeiting mark.

[0086] In this embodiment, this step includes the following three process steps:

[0087] Step 1: Receive the response message and extract the first photo;

[0088] Specifically, after the host completes the borrowing process for the target electronic device, it will send an installation photo authentication prompt to the user's mobile terminal or account at a preset time. Upon receiving the prompt, the user will follow the instructions and return a response message to the host. The host receives this response message through its built-in communication module, ensuring the integrity and timeliness of the message.

[0089] Understandably, the response message carries a first photograph taken by the user of the target electronic device installed on-site. After receiving the response message, the host computer parses the message using its built-in message parsing module and extracts the first photograph.

[0090] Step 2: Generate an AI-guided shooting interface based on the first photo;

[0091] Specifically, after receiving the first photo, the host computer uses a pre-set AI image model to extract features from the photo. These features include, but are not limited to, key information such as the shape, position, and background elements of the target electronic device. Through feature extraction, the AI ​​model can accurately identify key elements in the photo, providing a basis for guiding subsequent shooting.

[0092] Based on the extracted photo features, the AI ​​model randomly selects multiple shooting angles from the first photo using a random algorithm, and then selects a target feature related to each shooting angle from the extracted features. This process ensures the diversity of shooting angles and the relevance of target features, improving the accuracy and effectiveness of subsequent shooting.

[0093] Using computer vision and scene recognition technologies, the AI ​​model fuses selected target features with the background in the first photo, constructing a contour where the target features and background are blended. Based on these contours, the AI ​​model generates a matching shooting interface, which includes multiple sub-interfaces, each corresponding to a shooting angle. In each sub-interface, the user can clearly see the blending of the target features and background, as well as the required shooting angle and position.

[0094] Step 3: Remotely control the APP to enter the AI ​​shooting guidance interface;

[0095] Specifically, the main unit establishes a communication connection with the corresponding APP installed on the mobile terminal through its built-in communication module. This process ensures that the main unit can remotely control the APP, enabling the display and operation of the shooting guidance interface.

[0096] After establishing a communication connection, the host computer sends the generated matching photo interface data to the mobile app. Upon receiving the data, the app displays it on the user interface, forming an AI-guided shooting interface. Through this interface, users can clearly see the target features and background blending at each shooting angle, as well as the shooting guidelines to follow.

[0097] In the AI-guided shooting interface, the system continuously monitors whether the user's photo matches the target's feature contours. If a mismatch is detected (such as angle deviation, occlusion, or abnormal background), the system will provide guidance to the user via voice or image overlay, such as "Please adjust the angle," "Please move to a well-lit area," or "Please keep the tool fully within the frame." These prompts help users adjust their shooting posture and lighting conditions in a timely manner, improving the quality of uploaded images and recognition accuracy.

[0098] 150. After recognizing a target power device that meets the preset conditions in the AI ​​shooting guidance interface, a second photo is taken, which is a sequence of images with specific dynamic effects.

[0099] Specifically, following the prompts on the AI-guided shooting interface, adjust the shooting angle and position to ensure the target electrical equipment is clearly visible in the camera's field of view. The AI ​​model analyzes the camera's captured image in real time to identify whether there are target electrical devices in the image that meet preset conditions. These preset conditions include the device's shape characteristics, positional relationship, and background blending.

[0100] After recognizing that preset conditions are met, the AI ​​continuously captures multiple frames from each shooting angle according to a preset dynamic change logic. For example, at a certain angle, the AI ​​might ask the user to first take a photo of the device in a stationary state, then take a photo of the device rotated a certain angle, and finally take a photo of the device after performing a certain action. In this way, three or more photos will be obtained from each angle. After capturing photos from all shooting angles, the mobile app will merge the multiple photos into a sequence of images with specific dynamic effects according to the shooting order. Then, the app will transmit the sequence of images to the host computer for anti-counterfeiting identification and subsequent processing.

[0101] 160. Perform multi-layer anti-counterfeiting identification on the graph sequence. If the identification is successful, a complete borrowing contract is generated.

[0102] Specifically, after receiving the image sequence, the host computer performs anti-counterfeiting identification through an anti-counterfeiting module. The identification process includes verifying the temporal, spatial, and texture consistency of the image sequence, as well as making an anti-counterfeiting judgment based on the image elements, their positions, and dynamic changes within the sequence. Only image sequences that pass the anti-counterfeiting identification are considered valid photographs.

[0103] After successful anti-counterfeiting verification, the system will prompt the user to verify their identity and complete the necessary procedures according to the terms of the loan agreement. Simultaneously, the system will record all data and information during the recording process, creating a complete record of the electronic device's borrowing, providing a basis for subsequent accountability and management.

[0104] Through the above technical solution, this embodiment aims to prevent identity theft and ensure that the equipment installation location matches the declared scenario. By combining dynamic image sequence shooting with multiple anti-counterfeiting identification methods, the difficulty of image forgery is significantly increased. Furthermore, an AI-guided shooting interface standardizes the shooting process, preventing verification failures due to improper angles. In power equipment management scenarios, this method can accurately identify unauthorized equipment lending and falsified installation sites, improving the security and reliability of tool management.

[0105] In one specific implementation, the step 140, which involves remotely controlling the APP to enter the AI ​​shooting guidance interface based on the first photo using a preset AI guidance model, can be achieved through the following steps:

[0106] The first photo is used to extract features and calculate the shooting angle using an AI image analysis model, resulting in at least one shooting angle and target device features associated with each shooting angle.

[0107] Using computer vision and scene recognition technology, the outline and anti-counterfeiting mark of each target device feature are constructed and fused with the background in the first photo, and a matching photo interface is generated;

[0108] A photo-taking scheduling instruction is generated based on the matching photo-taking interface and sent to the mobile terminal to schedule the mobile terminal's photo-taking function. The AI-guided shooting interface that uses the matching photo-taking interface as the display screen to start the photo-taking function is also provided.

[0109] It should be noted that this AI image analysis model refers to a convolutional neural network trained through a deep learning framework, specifically a ResNet50 or VGG16 model, used to identify the geometric features and spatial relationships of target devices in an image.

[0110] The computer vision and scene recognition technology mentioned here refers to the edge detection algorithm implemented based on the OpenCV library. Specifically, it can be implemented by combining the Canny operator with the Hough transform method to construct the fusion boundary between the target device and the background environment.

[0111] The matching photo interface refers to a dynamically generated graphical user interface, which can be implemented using HTML5 and WebGL technologies to display the superimposed effect of the shooting angle guide line and the anti-counterfeiting mark in real time.

[0112] Specifically, after the mobile terminal uploads the first photo, the AI ​​image analysis model first extracts the outline features and 3D pose information of the target device, and calculates the shooting angles of the front, side, and top of the device through spatial coordinate transformation. The computer vision algorithm performs edge matching between the device outline and the background, generating a virtual overlay layer containing the anti-counterfeiting watermark. After receiving the photo-taking command, the mobile terminal automatically switches to the shooting interface containing the virtual layer. The user adjusts the shooting position according to the angle guide lines on the interface to ensure that the target device features and anti-counterfeiting marks are fully presented within the viewfinder.

[0113] In another embodiment, generating the photo-taking scheduling instruction based on the matching photo-taking interface includes:

[0114] A unique photo task token matching the photo-taking interface is generated using a random seed generation algorithm;

[0115] The unique photo-taking task token and the matching photo-taking interface are combined with the call instruction template of the AI ​​guidance model to generate a photo-taking scheduling instruction.

[0116] In one specific implementation, the step of using an AI image analysis model to extract features from the first photo and calculate the shooting angle to obtain at least one shooting angle and target device features associated with each shooting angle includes:

[0117] The AI ​​image analysis model is used to identify whether the first photo contains key morphological features of the target electronic device;

[0118] If included, the orientation information of the target power equipment in the first photograph is identified;

[0119] Based on the posture information, at least one shooting angle is selected using a random algorithm, and the target device features are extracted from each shooting angle.

[0120] It should be noted that the key morphological features here refer to the unique identifier of the target electronic device in its physical structure. Specifically, this can be achieved through edge detection and feature matching algorithms to verify whether the device actually exists.

[0121] The attitude information here refers to the position and angle parameters of the device in three-dimensional space, which can be calculated by stereo vision algorithms to determine the adjustment range of the shooting angle.

[0122] This random algorithm refers to a method for generating unpredictable numerical sequences. Specifically, it can be implemented using a pseudo-random number generator to increase the diversity of shooting angles and prevent forgery.

[0123] Specifically, after a user uploads the first photo, the AI ​​image analysis model first detects whether the image contains preset features of the target electronic device, such as interfaces or logos of a specific shape. If valid features are detected, the system further analyzes the device's three-dimensional pose in the image, such as the tilt angle between the device's surface and the camera. Based on the pose data, the system generates multiple candidate shooting angles using a random algorithm, such as top-down or side-view angles, and extracts corresponding device features at each angle, such as the texture of specific components or the clarity of logos. This process ensures that the subsequent shooting guidance interface can cover the multi-dimensional features of the device, while increasing the difficulty of forgery through randomness.

[0124] In another feasible implementation, the step of selecting at least one shooting angle based on the attitude information using a random algorithm and extracting target device features at each shooting angle includes:

[0125] Based on the attitude information, the key point distribution matrix of the target electronic device in the first image is extracted;

[0126] Based on the key point distribution matrix, the three-dimensional attitude angle of the target electronic device is calculated using matrix projection;

[0127] The three-dimensional attitude angle is passed as an input parameter to a random angle generator to generate multiple shooting angles;

[0128] For each of the aforementioned shooting angles, extract the corresponding feature point clusters and fuse them to obtain the target device features.

[0129] In this embodiment, the random angle selection mechanism based on the actual device posture prevents fraud through pre-stored images or fixed-angle shooting, ensuring the uniqueness of each authentication process. Multi-angle feature extraction further enhances the accuracy of device identity verification, avoiding misjudgments caused by missing information from a single perspective.

[0130] In another embodiment, after selecting at least one shooting angle based on the attitude information using a random algorithm and extracting the target device features at each shooting angle, the method further includes:

[0131] Using each selected shooting angle as the shake base point, a shake anti-spoofing logic calculation is performed using a random algorithm combined with the posture information to obtain a shake direction combination sequence of the camera. The shake direction combination sequence is used to control the camera of the mobile terminal to take a second photo.

[0132] The jitter reference point refers to the selected shooting angle as the starting position of the camera's motion trajectory. Specifically, it can be implemented using Euler angle parameters in a three-dimensional coordinate system, and the orientation data of the device in space can be obtained through an attitude sensor.

[0133] The jitter anti-counterfeiting logic calculation refers to the process of dynamically generating anti-counterfeiting verification rules based on the device's posture. Specifically, it can use a convolutional neural network to analyze the correlation between the device's contour features and the background environment to generate anti-counterfeiting verification parameters that match the current scene.

[0134] Specifically, after determining the shooting angle of the target electronic device, the system uses the three-dimensional coordinates corresponding to each angle as a reference point. The tilt and rotation angles of the device in space are collected in real time by an attitude sensor, and a three-dimensional spatial model is constructed by combining this data with the device's surface texture features. A random algorithm generates a sequence of jitter parameters including horizontal, vertical, and rotational offsets based on the device's current posture. For example, a random offset within ±5° is generated in the X-axis direction, and a random offset within ±3° is generated in the Y-axis direction. The anti-counterfeiting logic calculation module matches and verifies the jitter parameters with the device's contour features. When the offset between the device edge and the background contour exceeds a preset threshold, the offset values ​​in the jitter parameter sequence are automatically adjusted to ensure that the generated jitter direction combination sequence contains both randomness and conforms to the actual shape characteristics of the device.

[0135] This embodiment dynamically generates a random three-dimensional spatial jitter sequence, ensuring that each captured image sequence contains unique spatial position parameters and motion trajectory features, effectively preventing fraudulent activities through image duplication or pre-stored photos. Simultaneously, it adjusts the jitter parameters based on the actual device posture to avoid image distortion caused by mechanical shaking.

[0136] Understandably, the step of using each selected shooting angle as a shake reference point and employing a random algorithm combined with the posture information to perform shake anti-spoofing logic calculations to obtain a sequence of camera shake direction combinations includes:

[0137] Based on each selected shooting angle, calculate the relative displacement vector between the target electronic device and the background under the posture information;

[0138] The relative displacement vector is jittered using a random algorithm to generate a three-dimensional direction matrix, wherein the three-dimensional direction matrix includes the amplitude, direction and duration of each jitter displacement;

[0139] The three-dimensional direction matrix is ​​decomposed into multiple jitter command sequences, and these sequences are sorted in a clockwise order to obtain a jitter direction combination sequence.

[0140] In one specific implementation, the step of taking a second photo after recognizing a target power device that meets preset conditions on the AI ​​shooting guidance interface includes:

[0141] The system detects whether the target power device located within the AI ​​shooting guidance interface at the shooting angle is aligned with the outline. The alignment at the shooting angle means that the position of the target power device in the shooting interface completely coincides with the preset outline boundary. Specifically, it can be achieved by using an edge matching algorithm in computer vision, and the alignment status is determined by calculating the overlap between the device edge and the outline.

[0142] If the detection is not aligned, a prompt will be displayed on the AI ​​shooting guidance interface to adjust the shooting angle;

[0143] If alignment is detected, the camera on the mobile terminal is controlled to take a second photo based on the jitter direction combination sequence. The jitter direction combination sequence refers to a pre-set camera movement trajectory, which can be generated by a random algorithm to include a sequence of instructions containing multiple directional changes, used to control the camera to make small displacements in a specific order during the shooting process.

[0144] In practical applications, after the user completes the installation of the target electronic device, the system launches an AI-powered image guidance interface and loads a preset device outline. The camera captures images in real time, and an edge matching algorithm calculates the overlap between the device's edges and the outline. If the overlap is below a threshold, angle adjustment prompts, such as arrows or highlighted areas, are displayed on the interface to guide the user to reposition the device. When the overlap reaches the threshold, the system calls a pre-generated sequence of jitter directions, controlling the camera to perform continuous micro-movements in combinations of up / down, left / right, or rotational directions, while simultaneously capturing multiple image frames. These image frames are timestamped and dynamically fused to generate a sequence of images showing the device's multi-angle state changes.

[0145] This embodiment ensures that the device is in the correct installation position through real-time alignment detection, and combines dynamic shaking shooting to force the generation of image data with spatiotemporal continuity, effectively blocking forgery using static images or repeated frames.

[0146] In another embodiment, detecting whether the target electrical device located within the AI ​​shooting guidance interface at the shooting angle is aligned with the outline includes:

[0147] Calculate the similarity value between the outline of the target electronic device in the real-time captured image and the outline displayed on the interface;

[0148] If the similarity value is lower than a preset threshold, a prompt message to adjust the angle will be output;

[0149] The superimposed auxiliary lines are calculated based on the real-time shooting footage and displayed on the AI ​​shooting guidance interface to guide the user to adjust the camera direction;

[0150] If the similarity value is not lower than a preset threshold, it is determined to be aligned, and the angle is automatically locked to execute the step of taking a second photo.

[0151] In one specific embodiment, controlling the camera on the mobile terminal to capture a second photo based on the jitter direction combination sequence includes:

[0152] If only one shooting angle is selected, the camera on the mobile terminal is controlled to continuously shake and capture multiple image frames based on the shaking direction combination sequence, and the multiple image frames are fused to generate an image sequence with dynamic effects.

[0153] If at least two shooting angles are selected, at the first shooting angle, the camera on the mobile terminal is controlled to shake and capture N image frames based on the first N shaking angles of the shaking direction combination sequence; after switching to the next shooting angle and aligning, the camera on the mobile terminal is controlled to shake and capture N image frames based on the shaking angles after the (N+1)th one in the shaking direction combination sequence, until the last shooting angle is reached and N image frames are captured; the N image frames at each shooting angle are sorted and fused according to time to generate a sequence of images with dynamic effects.

[0154] The aforementioned fusion processing refers to integrating multiple frames of image data into a single visual carrier. Specifically, weighted averaging or feature matching alignment techniques can be used to eliminate random noise while retaining key anti-counterfeiting information.

[0155] In practical applications, when the system detects that the user only needs to capture images from a single angle, the camera continuously shifts according to a preset shaking sequence, for example, completing rapid micro-movements in four directions (up, down, left, and right) within 0.5 seconds, simultaneously capturing multiple frames of images. These images are then processed using an edge detection algorithm to extract device contour features, and then used to generate a dynamic image sequence by overlaying them along a timeline. When multi-angle verification is required, the system guides the user to adjust to different viewpoints sequentially, executing an independent shaking sequence for each angle. Finally, the frames captured from each angle are stitched together according to their timestamps to form a composite dynamic sequence that includes spatial dimension changes.

[0156] Compared to existing technologies, this approach introduces a random jitter algorithm and a multi-angle dynamic sequence fusion mechanism to simultaneously increase the complexity of data acquisition in both the time and spatial dimensions, making it difficult for attackers to forge compliant verification materials using simple image processing methods.

[0157] In another specific embodiment, step 160, which involves performing multi-layer anti-counterfeiting identification on the graph sequence, and generating a complete borrowing contract if the identification passes, includes:

[0158] The graph sequence is subjected to anti-counterfeiting verification in sequence from the dimensions of data layer, visual layer and interaction layer to obtain the corresponding verification score;

[0159] Based on the weight coefficients of each dimension and the verification scores of each dimension, the anti-counterfeiting result of the graph sequence is determined.

[0160] If the anti-counterfeiting result is successful, a complete borrowing contract will be generated using the user's identity information, the target electronic device's information, and the installation authentication result.

[0161] It is understandable that the data layer anti-counterfeiting verification refers to verifying the authenticity of image metadata. Specifically, this can be achieved by extracting EXIF ​​time, GPS location, and hardware ID and performing cross-comparison. For example, it can be achieved by verifying whether the shooting time is within the equipment borrowing time range, whether the GPS matches the location of the tool management cabinet, and whether the hardware ID is consistent with the mobile terminal registration information.

[0162] The visual layer anti-counterfeiting verification refers to the consistency detection of image content. Specifically, it can be achieved by analyzing image edge features, texture continuity, and illumination distribution. For example, edge detection algorithms can be used to identify whether an image has been spliced ​​or tampered with, texture analysis can be used to determine whether there are duplicate areas, and illumination models can be used to verify whether the shadow distribution conforms to physical laws.

[0163] The anti-counterfeiting verification of the interaction layer refers to verifying the dynamic parameters of the shooting process. Specifically, it can be achieved by extracting the camera offset trajectory and response time and comparing them with the preset shaking logic. For example, the trajectory matching algorithm can be used to verify whether the camera movement path is consistent with the shaking sequence issued by the system, and the timestamp analysis can be used to determine whether the operation interval is in line with human reaction speed.

[0164] Specifically, after receiving the image sequence uploaded by the user, the system first extracts the EXIF ​​metadata from the data layer and performs time window verification against the device borrowing records. Simultaneously, it compares the GPS coordinates with the preset geofence range of the tool management cabinet. In the visual layer processing, a convolutional neural network is used to detect abnormal edge transition regions in the image, frequency domain analysis is used to determine if image compression artifacts exist, and an illumination consistency model is used to verify whether the light source direction of multiple images is consistent. In the interaction layer verification stage, the system dynamically matches the actual jitter trajectory coordinates captured by the camera with the sent jitter direction combination sequence, while simultaneously calculating the relationship between the response time of each offset operation and a preset threshold. The verification results from the three layers are input into a weighted judgment model, which calculates a comprehensive score based on preset weights of 40% for the data layer, 35% for the visual layer, and 25% for the interaction layer. When the total score exceeds 85 points, the anti-counterfeiting is deemed successful, and an electronic contract containing the device serial number, user ID, and installation location coordinates is generated.

[0165] This embodiment addresses the problem of fraudulent claims caused by the single dimension of image verification in existing electronic equipment management systems. It ensures the authenticity of on-site equipment installation photos and the legality of the operational process through a multi-layered anti-counterfeiting verification mechanism. This solution can effectively identify forged images generated by image processing software, reused historical photos, and fake images taken off-site, thereby improving the tool management cabinet's ability to track and verify loaned equipment, ensuring the controllability of equipment usage and the reliability of management data.

[0166] In this embodiment, the step of performing anti-counterfeiting verification on the graph sequence sequentially from the dimensions of the data layer, visual layer, and interaction layer to obtain the corresponding verification score includes:

[0167] Extract the EXIF ​​time, GPS location, and hardware ID from the data layer of the graph sequence, and perform cross-comparison and verification on the EXIF ​​time, GPS location, and hardware ID;

[0168] Extract image edge features, texture continuity, and illumination distribution from the visual layer of the image sequence, and perform anomaly detection on the image edge features, texture continuity, and illumination distribution;

[0169] The interaction parameters of the interaction layer in the graph sequence are extracted, and the consistency analysis of the interaction parameters with the shake logic when taking the second photo is performed. The interaction parameters include the camera offset trajectory and the response time for each offset.

[0170] The three-layer verification results are input into the weighted judgment model, and the final anti-counterfeiting result is output.

[0171] It should be noted that the data layer cross-comparison verification here refers to multi-dimensional matching verification by extracting timestamps, geographical locations, and device identification information from image metadata. Specifically, it can be achieved by using an EXIF ​​parsing tool in conjunction with a list of valid device IDs stored in a database, to verify the temporal consistency between the shooting time and the operation record, and the spatial consistency between the device location and the preset installation area.

[0172] Visual layer anomaly detection here refers to analyzing the continuity of object edges, the regularity of surface texture, and the rationality of light distribution in an image. Specifically, it can be achieved by using a convolutional neural network to extract high-frequency component features and combining them with an illumination model for calculation, in order to identify whether an image has been spliced ​​or tampered with later.

[0173] The interaction layer consistency analysis here refers to comparing the physical movement trajectory of the camera during the shooting process with the preset jitter logic sequence. Specifically, it can be implemented by using a time alignment algorithm between gyroscope sensor data and the preset jitter direction sequence to verify whether the shooting process is a real physical operation rather than a simulation.

[0174] Specifically, when the system receives the second photo containing the sequence of dynamic effect images, it first parses the EXIF ​​metadata of each frame, compares the shooting time with the timestamp of the borrowing operation, matches the GPS coordinates with the preset installation area of ​​electronic equipment in the tool and equipment management cabinet, and then verifies whether the hardware ID of the mobile terminal is in the list of authorized devices. Next, it performs multi-scale edge detection on the image frames, calculates the displacement of edge features between adjacent frames to determine whether there are any abnormal abrupt changes, analyzes whether the changes in the surface texture of the equipment during dynamic shooting conform to physical laws, and verifies whether the brightness gradient of each area is natural and continuous based on the ambient lighting model. At the same time, it extracts the offset trajectory data of the camera during the shooting process, performs time axis alignment analysis with the shake direction combination sequence issued by the system, and calculates whether the response delay of each shake action conforms to human operation characteristics. Finally, it inputs the temporal matching degree of the data layer, the tampering probability value of the visual layer, and the operation authenticity of the interaction layer into a linear weighted model with preset weights. When the comprehensive score exceeds the safety threshold, the anti-counterfeiting verification is deemed successful.

[0175] Furthermore, determining the anti-counterfeiting result of the graph sequence based on the weight coefficients and verification scores of each dimension includes:

[0176] The anti-counterfeiting risk score of the graph sequence is calculated based on the counterfeiting comprehensive judgment function, wherein the counterfeiting comprehensive judgment function is R=w1·Ad+w2·Bv+w3·Ci, Ad is the data layer authenticity score, Bv is the visual layer consistency score, Ci is the interaction layer score, and w1, w2, and w3 are the weight coefficients of the corresponding dimensions.

[0177] If the anti-counterfeiting risk score is not lower than the security threshold, the anti-counterfeiting verification is passed.

[0178] The forgery comprehensive judgment function refers to a mathematical model that integrates multi-dimensional verification results through linear weighting. Specifically, it can be trained using a preset weight allocation algorithm combined with historical verification data to quantitatively assess the forgery risk of image sequences.

[0179] The authenticity score of this data layer refers to the quantitative result of cross-validation by extracting timestamps, geographical locations and device identifiers from image metadata. Specifically, it can be generated by comparing the hash verification algorithm with database records.

[0180] The visual layer consistency score refers to the anomaly detection score generated by analyzing image edge features, texture continuity, and illumination distribution. Specifically, it can be calculated by using a convolutional neural network to extract multi-scale features and then inputting them into a classifier.

[0181] The interaction layer score refers to a dynamic behavior score generated by comparing the consistency between the camera offset trajectory and the preset jitter logic. Specifically, it can be calculated using a time series matching algorithm combined with a response delay threshold.

[0182] The weighting coefficient refers to the adjustment parameter that reflects the importance of the data layer, visual layer and interaction layer in anti-counterfeiting verification. Specifically, it can be dynamically adjusted based on counterfeiting cases in different scenarios through machine learning models.

[0183] This security threshold refers to the critical value at which anti-counterfeiting verification is deemed successful. It can be set differently based on device type, user credit rating, or historical borrowing and returning records.

[0184] Specifically, after completing multi-dimensional verification of the data layer, visual layer, and interaction layer, the scores of each dimension are input into a weighted comprehensive judgment function for forgery calculation. The data layer authenticity score is generated by verifying the deviation between EXIF ​​time and system time, the distance between GPS location and the coordinates of the tool management cabinet, and the matching degree between hardware ID and equipment entry records. The visual layer consistency score is generated by detecting abnormal features such as edge abrupt changes, texture breaks, or lighting abrupt changes in the image sequence. The interaction layer score is generated by analyzing the consistency between the camera offset trajectory and the combination sequence of jitter directions, as well as the conformity of each offset response time with a preset threshold. Finally, the scores of the three dimensions are weighted and summed according to preset weight coefficients. If the calculation result exceeds a security threshold, the anti-counterfeiting verification is deemed successful.

[0185] Through the above technical solution, this application solves the problem of low anti-counterfeiting recognition rate caused by the single image verification dimension in the prior art, effectively reducing the risk of device fraud or counterfeit return. By dynamically adjusting the weight coefficients and security thresholds, it can adapt to the needs of different security level scenarios, improving the flexibility and reliability of the verification mechanism.

[0186] Furthermore, to address the issue that existing tool and equipment management cabinets cannot continuously verify the authenticity of borrowed equipment, after generating the complete borrowing contract, the following steps are also included:

[0187] Based on the return time in the borrowing contract, a timed check-in strategy is generated in conjunction with the borrowing tracking mechanism. The timed check-in strategy involves multiple photo authentications, and the photo angles for each photo authentication are different.

[0188] Based on the timed check-in strategy, an AI-guided photo is taken using an AI-powered photo-taking interface, and a check-in verification is performed to maintain the validity of the borrowing contract. The logic for each photo authentication is similar to the installation photo authentication logic described above, except that the angle and photo of the previous authentication cannot be the same as those of the next authentication. The different setting method is to use a random algorithm to combine the photos obtained from previous authentications with the first photo uploaded at the moment.

[0189] It should be noted that this timed check-in strategy refers to automatically planning multiple authentication nodes based on the return time. Specifically, it can be implemented by using a time interval allocation algorithm combined with a device usage cycle prediction model. For example, the return period can be divided into three equal nodes and the authentication frequency can be dynamically adjusted to ensure continuous verification of authenticity during device use.

[0190] The use of random algorithms to generate different angles here refers to extracting angle parameters from previous photos and generating differential offsets through a probability distribution model. Specifically, the Markov chain Monte Carlo method can be used to randomly sample historical shooting angles, thereby avoiding the malicious use of duplicate angles.

[0191] The AI-guided shooting interface here takes a third photo, which can be understood as using the dynamic shooting rules from the installation phase, but regenerating the guidance parameters each time it is triggered. Specifically, this can be achieved by overlaying real-time generated anti-counterfeiting marks and angle indicator boxes on the guidance interface to ensure that each shot has unique characteristics.

[0192] Specifically, after the loan agreement takes effect, the system automatically calculates the number of check-in verifications and the timing based on the return period. For example, if the return period is three days, the system can be set to trigger a check-in task at 10:00 AM every day. When the scheduled time arrives, the mobile terminal receives the authentication command and launches the AI ​​shooting guidance interface. At this time, the system calls the device angle information recorded in the historical shooting data and generates shooting angle parameters different from the previous one through a random algorithm. For example, the original pitch angle of 30 degrees is adjusted to 45 degrees and an additional 5 degrees of left and right offset is added. The user needs to adjust the device position according to the interface instructions. When the device is detected to be aligned with the guidance frame, the camera takes multiple frames of images based on the newly generated shaking sequence and synthesizes them into a dynamic image sequence. The system performs the same multi-layer anti-counterfeiting verification as in the installation phase on this image sequence. If it passes, the contract status is updated to valid; otherwise, an early warning mechanism is triggered.

[0193] Furthermore, the step of generating a timed check-in strategy based on the return time in the borrowing contract and in conjunction with the borrowing tracking mechanism includes:

[0194] Read the return time and task duration from the loan contract;

[0195] The check-in interval is dynamically set based on the frequency of equipment use.

[0196] The initial angle and direction for each photo authentication are generated using a random algorithm, thus creating a timed check-in strategy.

[0197] In summary, to address the problem of insufficient anti-counterfeiting capabilities caused by the single image verification method in existing electronic equipment management systems, this application achieves triple verification of the authenticity of the equipment installation site, the authenticity of the image shooting process, and the continuity of equipment use through AI-guided shooting and multi-layered anti-counterfeiting mechanisms. This effectively prevents fraudulent activities such as fraudulent claims, image reuse, and virtual operations, and improves the security and reliability of the tool and equipment borrowing and returning management system.

[0198] The electronic device management method in the embodiments of the present invention has been described above. The tool and equipment management cabinet in the embodiments of the present invention is described below; please refer to... Figure 2 One embodiment of the tool and equipment management cabinet in this invention includes: multiple storage compartments 220 and a main unit 210. Each storage compartment 220 is equipped with a smart lock 230, and the main unit 210 is connected to the smart locks 230 of each storage compartment 220 via a bus. The main unit 210 is specifically used for:

[0199] When a user initiates a borrowing request through a mobile terminal with the APP installed or by triggering the tool and equipment management cabinet, the user's identity information is verified. After the verification is successful, the candidate electronic device directory corresponding to the user's permissions is retrieved and displayed. The APP is a device management application that matches the borrowing and returning control program of the tool and equipment management cabinet.

[0200] In response to the user's selection operation on the candidate electronic device catalog, the target electronic device is determined, and the smart lock 230 of the storage compartment 220 corresponding to the target electronic device is opened;

[0201] After removing the target electronic device, a prompt for installation and photo authentication is sent to the mobile terminal;

[0202] The system receives a response message from the mobile terminal based on the installation photo authentication prompt, and extracts a first photo of the target electronic device installed on-site from the response message. Based on the first photo, the system uses a preset AI guidance model to remotely control the APP to enter the AI ​​shooting guidance interface. The AI ​​shooting guidance interface displays the shooting angle, target features of the target electronic device, and anti-counterfeiting marks.

[0203] After identifying a target power device that meets preset conditions in the AI ​​shooting guidance interface, a second photo is taken, wherein the second photo is a sequence of images with specific dynamic effects;

[0204] The graph sequence is subjected to multi-layer anti-counterfeiting identification. If the identification is successful, a complete borrowing contract is generated.

[0205] Optionally, the host 210 is specifically used for:

[0206] The first photo is used to extract features and calculate the shooting angle using an AI image analysis model, resulting in at least one shooting angle and target device features associated with each shooting angle.

[0207] Using computer vision and scene recognition technology, the outline and anti-counterfeiting mark of each target device feature are constructed and fused with the background in the first photo, and a matching photo interface is generated;

[0208] A photo-taking scheduling instruction is generated based on the matching photo-taking interface and sent to the mobile terminal to schedule the mobile terminal's photo-taking function. The AI-guided shooting interface that uses the matching photo-taking interface as the display screen to start the photo-taking function is also provided.

[0209] Optionally, the host 210 is specifically used for:

[0210] The AI ​​image analysis model is used to identify whether the first photo contains key morphological features of the target electronic device;

[0211] If included, the orientation information of the target power equipment in the first photograph is identified;

[0212] Based on the posture information, at least one shooting angle is selected using a random algorithm, and the target device features are extracted from each shooting angle.

[0213] Optionally, the host 210 is further configured to:

[0214] Using each selected shooting angle as the shake base point, a shake anti-spoofing logic calculation is performed using a random algorithm combined with the posture information to obtain a shake direction combination sequence of the camera. The shake direction combination sequence is used to control the camera of the mobile terminal to take a second photo.

[0215] Optionally, the host 210 is specifically used for:

[0216] Detect whether the target power device located within the AI ​​shooting guidance interface is aligned with the outline at the shooting angle;

[0217] If the detection is not aligned, a prompt will be displayed on the AI ​​shooting guidance interface to adjust the shooting angle;

[0218] If alignment is detected, the camera on the mobile terminal is controlled to take a second photo based on the jitter direction combination sequence.

[0219] Optionally, the host 210 is specifically used for:

[0220] If only one shooting angle is selected, the camera on the mobile terminal is controlled to continuously shake and capture multiple image frames based on the shaking direction combination sequence, and the multiple image frames are fused to generate an image sequence with dynamic effects.

[0221] If at least two shooting angles are selected, at the first shooting angle, the camera on the mobile terminal is controlled to shake and capture N image frames based on the first N shaking angles of the shaking direction combination sequence; after switching to the next shooting angle and aligning, the camera on the mobile terminal is controlled to shake and capture N image frames based on the shaking angles after the (N+1)th one in the shaking direction combination sequence, until the last shooting angle is reached and N image frames are captured; the N image frames at each shooting angle are sorted and fused according to time to generate a sequence of images with dynamic effects.

[0222] Optionally, the host 210 is specifically used for:

[0223] The graph sequence is subjected to anti-counterfeiting verification in sequence from the dimensions of data layer, visual layer and interaction layer to obtain the corresponding verification score;

[0224] Based on the weight coefficients of each dimension and the verification scores of each dimension, the anti-counterfeiting result of the graph sequence is determined.

[0225] If the anti-counterfeiting result is successful, a complete borrowing contract will be generated using the user's identity information, the target electronic device's information, and the installation authentication result.

[0226] In this embodiment, by using identity verification, dynamic shooting guidance, multi-layer anti-counterfeiting identification, and smart contract generation technology, the problems of lack of verification of the authenticity of equipment installation, insufficient dynamic tracking capability, and difficulty in identifying image forgery are solved, which has the advantages of improving the security and standardization of equipment management.

[0227] above Figure 2 The tool management cabinet in this embodiment of the invention will be described in detail from the perspective of modular functional entities. The electronic equipment in this embodiment of the invention will be described in detail from the perspective of hardware processing.

[0228] Figure 3 This is a schematic diagram of the structure of an electronic device 300 provided in an embodiment of the present invention. The electronic device 300 can vary significantly due to differences in configuration or performance. It may include one or more central processing units (CPUs) 310 (e.g., one or more processors) and a memory 320, and one or more storage media 330 (e.g., one or more mass storage devices) for storing application programs 333 or data 332. The memory 320 and storage media 330 can be temporary or persistent storage. The program stored in the storage media 330 may include one or more modules (not shown in the diagram), each module including a series of instruction operations on the electronic device 300. Furthermore, the processor 310 may be configured to communicate with the storage media 330 and execute a series of instruction operations on the storage media 330 on the electronic device 300 to implement the electronic device management method provided in the above embodiment.

[0229] Electronic device 300 may also include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input / output interfaces 360, and / or one or more operating systems 331, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that... Figure 3 The illustrated electronic device structure does not constitute a limitation on the electronic device provided by the present invention, and may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0230] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the electronic device management method provided in the above embodiments.

[0231] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system, device, or unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0232] 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 computer-readable 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, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0233] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An electronic equipment management method, applied to a tool and equipment management cabinet, characterized in that, The method includes: When a user initiates a borrowing request through a mobile terminal with the APP installed or by triggering the tool and equipment management cabinet, the user's identity information is verified. After the verification is successful, the candidate electronic device directory corresponding to the user's permissions is retrieved and displayed. The APP is a device management application that matches the borrowing and returning control program of the tool and equipment management cabinet. In response to the user's selection operation of the candidate electronic device catalog, the target electronic device is determined, and the smart door lock of the storage compartment corresponding to the target electronic device is opened; After removing the target electronic device, a prompt for installation and photo authentication is sent to the mobile terminal; The system receives a response message from the mobile terminal based on the installation photo authentication prompt, and extracts a first photo of the target electronic device installed on-site from the response message. Based on the first photo, the system uses a preset AI guidance model to remotely control the APP to enter the AI ​​shooting guidance interface. The AI ​​shooting guidance interface displays the shooting angle, target features of the target electronic device, and anti-counterfeiting marks. After identifying a target electronic device that meets preset conditions in the AI ​​shooting guidance interface, a second photo is taken, wherein the second photo is a sequence of images with specific dynamic effects; The graph sequence is subjected to multi-layer anti-counterfeiting identification. If the identification is successful, a complete borrowing contract is generated. The step of remotely controlling an APP to enter an AI shooting guidance interface based on the first photo using a preset AI guidance model includes: using an AI image analysis model to extract features from the first photo and calculate the shooting angle to obtain at least one shooting angle and target device features associated with each shooting angle; using computer vision and scene recognition technology to construct the outline and anti-counterfeiting mark of each target device feature blended with the background in the first photo, and generating a matching shooting interface; generating a shooting scheduling command based on the matching shooting interface, sending it to the mobile terminal to schedule the shooting function of the mobile terminal, and starting the AI ​​shooting guidance interface for the shooting function with the matching shooting interface as the display screen.

2. The electronic device management method according to claim 1, characterized in that, The step of using an AI image analysis model to extract features from the first photo and calculate the shooting angle to obtain at least one shooting angle and target device features associated with each shooting angle includes: The AI ​​image analysis model is used to identify whether the first photo contains key morphological features of the target electronic device; If included, the pose information of the target electronic device in the first photograph is identified; Based on the posture information, at least one shooting angle is selected using a random algorithm, and the target device features are extracted from each shooting angle.

3. The electronic device management method according to claim 2, characterized in that, After selecting at least one shooting angle based on the pose information using a random algorithm and extracting the target device features at each shooting angle, the method further includes: Using each selected shooting angle as the shake base point, a shake anti-spoofing logic calculation is performed using a random algorithm combined with the posture information to obtain a shake direction combination sequence of the camera. The shake direction combination sequence is used to control the camera of the mobile terminal to take a second photo.

4. The electronic device management method according to claim 3, characterized in that, The step of taking a second photo after recognizing a target electronic device that meets preset conditions on the AI ​​shooting guidance interface includes: Detect whether the target electronic device located within the AI ​​shooting guidance interface is aligned with the outline at the shooting angle; If the detection is not aligned, a prompt will be displayed on the AI ​​shooting guidance interface to adjust the shooting angle; If alignment is detected, the camera on the mobile terminal is controlled to take a second photo based on the jitter direction combination sequence.

5. The electronic device management method according to claim 4, characterized in that, The step of controlling the camera on the mobile terminal to take a second photo based on the jitter direction combination sequence includes: If only one shooting angle is selected, the camera on the mobile terminal is controlled to continuously shake and capture multiple image frames based on the shaking direction combination sequence, and the multiple image frames are fused to generate an image sequence with dynamic effects. If at least two shooting angles are selected, at the first shooting angle, the camera on the mobile terminal is controlled to shake and capture N image frames based on the first N shaking angles of the shaking direction combination sequence; after switching to the next shooting angle and aligning, the camera on the mobile terminal is controlled to shake and capture N image frames based on the shaking angles after the (N+1)th one in the shaking direction combination sequence, until the last shooting angle is reached and N image frames are captured; the N image frames at each shooting angle are sorted and fused according to time to generate a sequence of images with dynamic effects.

6. The electronic device management method according to any one of claims 1-5, characterized in that, The process involves performing multi-layer anti-counterfeiting verification on the graph sequence. If the verification passes, a complete borrowing contract is generated, including: The graph sequence is subjected to anti-counterfeiting verification in sequence from the dimensions of data layer, visual layer and interaction layer to obtain the corresponding verification score; Based on the weight coefficients of each dimension and the verification scores of each dimension, the anti-counterfeiting result of the graph sequence is determined. If the anti-counterfeiting result is successful, a complete borrowing contract will be generated using the user's identity information, the target electronic device's information, and the installation authentication result.

7. A tool and equipment management cabinet, characterized in that, include: The system comprises multiple storage compartments and a main unit, each storage compartment equipped with a smart lock. The main unit is connected to the smart locks of each storage compartment via a bus. Specifically, the main unit is used for: When a user initiates a borrowing request through a mobile terminal with the APP installed or by triggering the tool and equipment management cabinet, the user's identity information is verified. After the verification is successful, the candidate electronic device directory corresponding to the user's permissions is retrieved and displayed. The APP is a device management application that matches the borrowing and returning control program of the tool and equipment management cabinet. In response to the user's selection operation of the candidate electronic device catalog, the target electronic device is determined, and the smart door lock of the storage compartment corresponding to the target electronic device is opened; After removing the target electronic device, a prompt for installation and photo authentication is sent to the mobile terminal; The system receives a response message from the mobile terminal based on the installation photo authentication prompt, and extracts a first photo of the target electronic device installed on-site from the response message. Based on the first photo, the system uses a preset AI guidance model to remotely control the APP to enter the AI ​​shooting guidance interface. The AI ​​shooting guidance interface displays the shooting angle, target features of the target electronic device, and anti-counterfeiting marks. After identifying a target electronic device that meets preset conditions in the AI ​​shooting guidance interface, a second photo is taken, wherein the second photo is a sequence of images with specific dynamic effects; The graph sequence is subjected to multi-layer anti-counterfeiting identification. If the identification is successful, a complete borrowing contract is generated. The step of remotely controlling an APP to enter an AI shooting guidance interface based on the first photo using a preset AI guidance model includes: using an AI image analysis model to extract features from the first photo and calculate the shooting angle to obtain at least one shooting angle and target device features associated with each shooting angle; using computer vision and scene recognition technology to construct the outline and anti-counterfeiting mark of each target device feature blended with the background in the first photo, and generating a matching shooting interface; generating a shooting scheduling command based on the matching shooting interface, sending it to the mobile terminal to schedule the shooting function of the mobile terminal, and starting the AI ​​shooting guidance interface for the shooting function with the matching shooting interface as the display screen.

8. An electronic device, characterized in that, The device includes a processor and a memory, the memory storing machine-executable instructions that can be executed by the processor, the processor executing the machine-executable instructions to implement the electronic device management method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement the electronic device management method according to any one of claims 1 to 6.

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