Application method of fingerprint identification in collaborative office process approval

Abstract

The application relates to an application method of fingerprint identification in a cooperative office process approval, which comprises the following steps: a target user initiates a sensitive business operation in a system, triggers fingerprint identification to perform fingerprint instrument connection confirmation, sends a prompt if the connection fails, otherwise, target user fingerprint information is judged; if the target user fingerprint information does not exist, a fingerprint input process is performed, if the input fails, input error prompt information is fed back for re-input, otherwise, a fingerprint identification process is entered; if the target user fingerprint information exists, a fingerprint identification process is performed; the system receives the identification result, if the identification is successful, the sensitive business operation is executed, otherwise, the system feeds back identification error prompt information for re-identification.

Description

Technical Field

[0001] The present invention relates to an application method of fingerprint recognition in collaborative office process approval, belonging to the technical field of office automation. Background Art

[0002] In the current digital office environment, the collaborative office platform has become an important tool for internal business management and coordination within an organization. On this platform, process approval, as one of the crucial links, involves a large amount of sensitive information and key business operations. Its security and credibility directly affect the operation efficiency and data security of an enterprise.

[0003] However, most current collaborative office platforms lack effective identity authentication measures in the process approval link, especially in links such as filling in opinions and signing. This leads to the inability to confirm the true identity of the filler during the approval process, and there are risks such as tampering and forgery. For example, an employee may leak their account password to others, allowing others to impersonate their identity for filling; or unauthorized personnel can use system vulnerabilities for illegal operations, thereby undermining the credibility and security of the approval.

[0004] The "Office Automation Method and System Based on Security Authentication" with the application (patent) number CN202111583898.6 mentions monitoring the temporary fingerprint data of the user's left index finger and the temporary fingerprint data of the user's right index finger. When the temporary fingerprint data does not match the preset fingerprint data, the communication connection between the keyboard main board and the computer main board is disconnected, and the keyboard cannot input characters to the computer. There are limitations in fingerprint recognition. Compared with this patent, the advantages of this patent are: 1. It can store and learn the fingerprints of the initiator, and the recognition is more accurate;

[0005] 2. Each sensitive business operation can be associated with the fingerprint information of a specific user, enhancing the traceability and security of the approval process, which is beneficial for subsequent auditing and supervision work. Summary of the Invention

[0006] In order to solve the problems existing in the above-mentioned prior art, the present invention proposes an application method of fingerprint recognition in collaborative office process approval.

[0007] The technical solution of the present invention is as follows:

[0008] On the one hand, the present invention provides an application method of fingerprint recognition in collaborative office process approval, including the following steps:

[0009] The target user initiates a sensitive business operation in the system, triggering fingerprint recognition to confirm the connection of the fingerprint instrument. If the connection fails, a prompt is sent; otherwise, the fingerprint information of the target user is judged;

[0010] If the target user's fingerprint information is not found, the fingerprint enrollment process proceeds. This process includes: the system collecting the target user's fingerprint image, extracting fingerprint features to obtain fingerprint feature information from the image; processing the fingerprint feature information to generate a corresponding fingerprint information string, and encrypting it; transmitting the encrypted fingerprint information string and fingerprint image to the user's fingerprint database; preprocessing the data in the user's fingerprint database to form a training set; constructing a ResNet model based on a residual neural network; training the ResNet model using the training set to obtain a trained fingerprint recognition model; if enrollment fails, an error message is displayed for re-enrollment; otherwise, the fingerprint recognition process continues.

[0011] If the target user's fingerprint information exists, the fingerprint recognition process is initiated. The fingerprint recognition process includes: the target user places the finger with the registered fingerprint information on the fingerprint scanner, the system acquires the target fingerprint image, extracts the fingerprint feature information from the fingerprint image, and generates the corresponding fingerprint information string; the fingerprint information string is input into the fingerprint recognition model to obtain the recognition result.

[0012] The system receives the recognition result. If the recognition is successful, it performs sensitive business operations; otherwise, the system sends out a recognition error message and performs a re-recognition.

[0013] Preferably, the ResNet model consists of an input layer, a convolutional layer, a group of residual blocks, a global average pooling layer, and a fully connected layer.

[0014] Input layer: Inputs the training set data into the convolutional layer;

[0015] Convolutional layer: Receives data from the input layer, performs convolution calculations, and obtains the fingerprint image features after convolution processing; each unit in the convolutional layer is activated using the ReLU activation function after convolution;

[0016] Residual block group: Each residual block group contains multiple residual blocks. Each residual block group further extracts features from the fingerprint image features extracted from the previous group to obtain a fingerprint image feature map.

[0017] Global average pooling layer: performs dimensionality reduction on each feature map;

[0018] Fully connected layer: Receives the output of the global average pooling layer and generates the output through linear transformation and activation function.

[0019] Preferably, the specific steps of the encryption process include: performing SHA-256 hash processing on the target user's identity information; taking the username as input, generating a 256-bit hash value using the SHA-256 algorithm; encrypting the hashed target user's fingerprint information using the AES symmetric encryption algorithm, generating a 128-bit key, and using CBC mode to encrypt the data in blocks to generate a unique encryption key; extracting fingerprint image feature data from the trained model and encrypting it using the RSA asymmetric encryption algorithm, and encrypting the fingerprint features using the user's public key to generate a digital signature.

[0020] Preferably, the fingerprint feature extraction includes: fingerprint image preprocessing, edge detection, thinning, Minutiae detection, and feature representation;

[0021] The fingerprint image preprocessing involves grayscale processing, image denoising, and image enhancement of the fingerprint image; the edge detection involves detecting the fingerprint ridge contours in the fingerprint image; the thinning involves thinning the coarse fingerprint ridge contours into fine lines; the minutiae detection involves detecting intersections and endpoints in the fingerprint image; and the feature representation involves describing the detected intersections and endpoints using coordinates and directions to obtain fingerprint feature information in the fingerprint image.

[0022] On the other hand, the present invention also provides an application system for fingerprint recognition in collaborative office workflow approval, comprising the following steps:

[0023] The operation initiation module triggers fingerprint recognition to confirm the fingerprint device connection when the target user initiates a sensitive business operation in the system. If the connection fails, a prompt is sent; otherwise, the target user's fingerprint information is judged.

[0024] The fingerprint enrollment module, if the target user's fingerprint information is not found, proceeds with the fingerprint enrollment process, which includes: the system collecting the target user's fingerprint image, extracting fingerprint features to obtain fingerprint feature information from the fingerprint image; processing the fingerprint feature information to generate a corresponding fingerprint information string, and encrypting it; transmitting the encrypted fingerprint information string and fingerprint image to the user's fingerprint database; preprocessing the data in the user's fingerprint database, forming a training set from the preprocessed data, constructing a ResNet model based on a residual neural network, and training the ResNet model using the training set to obtain a trained fingerprint recognition model; if enrollment fails, an error message is displayed for re-enrollment; otherwise, the fingerprint recognition process continues.

[0025] The fingerprint recognition module performs a fingerprint recognition process if the target user's fingerprint information is present. The fingerprint recognition process includes: the target user places the finger with the registered fingerprint information on the fingerprint scanner, the system acquires the target fingerprint image, extracts the fingerprint feature information from the fingerprint image, and generates a corresponding fingerprint information string; the fingerprint information string is input into the fingerprint recognition model to obtain the recognition result.

[0026] The operation execution module receives the recognition result. If the recognition is successful, the system performs sensitive business operations; otherwise, the system sends out an error message and performs a re-recognition.

[0027] Preferably, the ResNet model consists of an input layer, a convolutional layer, a group of residual blocks, a global average pooling layer, and a fully connected layer.

[0028] Input layer: Inputs the training set data into the convolutional layer;

[0029] Convolutional layer: Receives data from the input layer, performs convolution calculations, and obtains the fingerprint image features after convolution processing; each unit in the convolutional layer is activated using the ReLU activation function after convolution;

[0030] Residual block group: Each residual block group contains multiple residual blocks. Each residual block group further extracts features from the fingerprint image features extracted from the previous group to obtain a fingerprint image feature map.

[0031] Global average pooling layer: performs dimensionality reduction on each feature map;

[0032] Fully connected layer: Receives the output of the global average pooling layer and generates the output through linear transformation and activation function.

[0033] Preferably, the specific steps of the encryption process include: performing SHA-256 hash processing on the target user's identity information; taking the username as input, generating a 256-bit hash value using the SHA-256 algorithm; encrypting the hashed target user's fingerprint information using the AES symmetric encryption algorithm, generating a 128-bit key, and using CBC mode to encrypt the data in blocks to generate a unique encryption key; extracting fingerprint image feature data from the trained model and encrypting it using the RSA asymmetric encryption algorithm, and encrypting the fingerprint features using the user's public key to generate a digital signature.

[0034] Preferably, the fingerprint feature extraction includes: fingerprint image preprocessing, edge detection, thinning, Minutiae detection, and feature representation;

[0035] The fingerprint image preprocessing involves grayscale processing, image denoising, and image enhancement of the fingerprint image; the edge detection involves detecting the fingerprint ridge contours in the fingerprint image; the thinning involves thinning the coarse fingerprint ridge contours into fine lines; the minutiae detection involves detecting intersections and endpoints in the fingerprint image; and the feature representation involves describing the detected intersections and endpoints using coordinates and directions to obtain fingerprint feature information in the fingerprint image.

[0036] The present invention has the following beneficial effects:

[0037] 1. This invention enhances the accuracy and traceability of the approval process. Fingerprint recognition technology identifies and analyzes fingerprint features in real time, ensuring the accuracy and uniqueness of user identity during the approval process. Each sensitive business operation can be associated with the fingerprint information of a specific user, enhancing the traceability and security of the approval process and facilitating subsequent auditing and supervision.

[0038] 2. This invention enhances information security. The application of fingerprint recognition technology strengthens the security of sensitive information. During the approval process, it effectively prevents unauthorized users from accessing confidential information, thereby protecting the organization's information assets. Attached Figure Description

[0039] Figure 1 This is a flowchart of the technical solution of the present invention. Detailed Implementation

[0040] 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. 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.

[0041] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.

[0042] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0043] The terms “comprising” and “including” indicate the presence of the described feature, whole, step, operation, element and / or component, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0044] The term “and / or” refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes these combinations.

[0045] Example 1:

[0046] See Figure 1 This embodiment provides a method for applying fingerprint recognition in collaborative office workflow approval, including the following steps:

[0047] S10. When the target user initiates a sensitive business operation in the system, fingerprint recognition is triggered to confirm the fingerprint device connection. If the connection fails, a prompt is sent; otherwise, the target user's fingerprint information is judged.

[0048] S20. If the target user's fingerprint information does not exist, the fingerprint enrollment process is initiated. The fingerprint enrollment process includes: the system collects the target user's fingerprint image, extracts fingerprint features, and obtains fingerprint feature information from the fingerprint image; the fingerprint feature information is processed to generate a corresponding fingerprint information string, which is then encrypted; the encrypted fingerprint information string and fingerprint image are transmitted to the user's fingerprint database; the data in the user's fingerprint database is preprocessed, and the preprocessed data information forms a training set; a ResNet model is constructed based on a residual neural network; the ResNet model is trained using the training set to obtain a trained fingerprint recognition model; if the enrollment fails, an error message is displayed for re-enrollment; otherwise, the fingerprint recognition process continues.

[0049] S30. If the target user's fingerprint information exists, proceed with the fingerprint recognition process; the fingerprint recognition process includes: the target user places the finger with the recorded fingerprint information on the fingerprint scanner, the system acquires the target fingerprint image, extracts the fingerprint feature information from the fingerprint image, and generates the corresponding fingerprint information string; the fingerprint information string is input into the fingerprint recognition model to obtain the recognition result;

[0050] S40. The system receives the recognition result. If the recognition is successful, the system performs sensitive business operations. Otherwise, the system sends out an error message and performs a re-recognition.

[0051] In a preferred embodiment of this example, in steps S10-S40, a sensitive business operation is initiated: After logging into the collaborative office system and logging in through authentication, the user performs a series of sensitive operations involving confidential information or core business in the approval process, including approving key documents, issuing important notices, and signing for consent, which will trigger fingerprint recognition.

[0052] Fingerprint sensor connection confirmation: In this stage, the system will check the connection status of the fingerprint sensor through a series of operations, including physical connection and driver loading, to ensure that the fingerprint sensor is successfully connected to the system. If the connection fails, the system will prompt the user to perform fingerprint sensor connection operations, including re-plugging the device or installing the corresponding driver.

[0053] User fingerprint information verification: In this step, the system first checks whether the current user has already registered their fingerprint. The system queries the user fingerprint database to determine if the current user has any registered fingerprint information. If the system does not detect any registered fingerprint information, it will guide the user to perform the fingerprint registration operation. If fingerprint information already exists, the system backend program will obtain the existing fingerprint information string of the current user and enter the fingerprint recognition process.

[0054] Fingerprint Enrollment: Following system prompts, the user places their finger on the fingerprint scanner to complete fingerprint enrollment. During the enrollment process, the system gradually collects fingerprint features and ensures the accuracy of the enrolled fingerprint information. This may include scanning the same fingerprint multiple times to improve accuracy, while the system uses specific algorithms for real-time feature recognition and analysis. After enrollment, the system generates a corresponding fingerprint information string and encrypts it to ensure security. The encrypted fingerprint information string is then transmitted to the backend processing program and stored in the user's fingerprint database for subsequent identification. If the system detects an enrollment failure during the fingerprint enrollment process, it will immediately display a corresponding error message to the user. Enrollment failure may be due to incorrect finger placement, poor fingerprint quality, or other technical issues. In this case, the system will prompt the user to reposition their finger and try again. The system may also provide guidance, such as requiring the user to ensure their finger is clean and avoid movement to ensure accurate fingerprint enrollment. If multiple attempts still fail, the system may suggest that the user contact technical support for further assistance and may perform inspection and maintenance of the fingerprint scanner.

[0055] Fingerprint Recognition: Following system prompts, the user places their finger on the fingerprint scanner. The system acquires the fingerprint information to be recognized and generates a corresponding fingerprint string. Simultaneously, the system also acquires the user's existing fingerprint string and transmits both to the fingerprint scanner SDK. The fingerprint scanner SDK compares these two fingerprint information sets to verify their authenticity and match. During the comparison process, the system uses a specific algorithm to analyze the fingerprint information. Finally, the fingerprint scanner SDK returns the fingerprint recognition result for subsequent operations.

[0056] Fingerprint Recognition Result Processing: After receiving the fingerprint recognition result, the system will execute pre-set follow-up operations based on the result. If recognition is successful, the system will execute the initiated sensitive business operation. Conversely, if recognition fails, it may be due to incorrect finger positioning, poor fingerprint quality, or other technical issues. The system will temporarily suspend the initiated sensitive business operation and immediately display the corresponding error message to the user, suggesting that the user try placing their finger again for fingerprint recognition. Simultaneously, the system may provide guidance, such as ensuring the finger is clean and avoiding movement, to improve fingerprint recognition accuracy. If multiple attempts still fail, the system may prompt the user to contact technical support for further assistance.

[0057] Fingerprint enrollment and recognition employ a deep learning model. The data preparation phase includes collecting a large number of fingerprint images (primarily from the public database FVC2004) and manually annotating them to ensure accuracy. The annotation process is done manually using professional image annotation tools to mark key feature points on the fingerprint images, such as start points, bifurcation points, and end points. Simultaneously, data augmentation techniques (rotation, scaling, translation, etc.) are used to increase sample diversity. In the deep learning model design, a pre-trained ResNet model is selected, and the hierarchical structure is customized according to requirements, including adding additional convolutional and pooling layers, adjusting the number and size of layers, and introducing new activation functions. Furthermore, residual connections and attention mechanisms are introduced to further optimize the model's performance in fingerprint image feature extraction. During model training, the model parameters are optimized using the backpropagation algorithm with labeled data. In the feature extraction and optimization phase, appropriate feature extraction layers are determined, and their outputs are converted into one-dimensional feature vectors and normalized to eliminate the influence of scale and dimensions. Finally, in the fingerprint matching and verification phase, a cosine similarity matching algorithm is selected, and the matching threshold is dynamically set to meet the fingerprint recognition accuracy requirements of different systems. Finally, the matching accuracy of the model was validated on a separate test set.

[0058] Fingerprint Encryption Storage and Association Scheme: During fingerprint enrollment, the user's identity information is first hashed using SHA-256. This process takes the username as input and generates a 256-bit hash value using the SHA-256 algorithm. This process is irreversible, meaning the original identity information cannot be recovered from the hash value, thus protecting user privacy and data security. Next, the hashed fingerprint information is encrypted using the AES symmetric encryption algorithm. During encryption, a 128-bit key is generated, and the data is encrypted in blocks using CBC (Cipher Block Chaining) mode. Each user generates a unique encryption key, which is securely generated and managed by the system, ensuring the security and independence of the encrypted data. For the fingerprint data, feature data is extracted using a trained model and encrypted using the RSA asymmetric encryption algorithm. During encryption, the user's public key is used to encrypt the fingerprint features, generating a digital signature. Each user has a pair of RSA keys (public and private keys), which are generated and managed by the system, ensuring the security and independence of the keys. A series of rigorous measures are taken to ensure the secure storage and protection of encrypted fingerprint data. First, a highly secure database storage mechanism was implemented, including encrypted storage, access control, and audit log functions, to ensure data confidentiality and integrity. Second, a strict access control policy was adopted, allowing only authorized users to access and operate the database, thus guaranteeing data security and privacy. A dedicated key management system was also established to ensure the secure generation, storage, and distribution of keys, and to implement strict access control and monitoring mechanisms to prevent key leakage and misuse. Furthermore, regular audits and monitoring are conducted to track and record database access and operations, promptly identifying and responding to potential security incidents and violations. Simultaneously, the stored encrypted fingerprint data is regularly backed up and protected with the same encryption measures to ensure system availability and continuity. Finally, the storage system is regularly updated and maintained, including database software, operating system, and security patches, to improve the system's resilience and stability, ensuring it has the latest security and performance optimizations.

[0059] Example 2:

[0060] This embodiment provides an application system for fingerprint recognition in collaborative office workflow approval, including the following steps:

[0061] The operation initiation module is used when a target user initiates a sensitive business operation in the system, triggering fingerprint recognition to confirm the fingerprint device connection. If the connection fails, a prompt is sent; otherwise, the target user's fingerprint information is judged. This module is used to implement the function of step S1 in the above implementation one, and the details will not be elaborated here.

[0062] The fingerprint enrollment module, if the target user's fingerprint information is not found, proceeds with the fingerprint enrollment process. This process includes: the system collecting the target user's fingerprint image, extracting fingerprint features to obtain fingerprint feature information from the image; processing the fingerprint feature information to generate a corresponding fingerprint information string, and encrypting it; transmitting the encrypted fingerprint information string and fingerprint image to the user's fingerprint database; preprocessing the data in the user's fingerprint database to form a training set; constructing a ResNet model based on a residual neural network; training the ResNet model using the training set to obtain a trained fingerprint recognition model; if enrollment fails, an error message is displayed for re-enrollment; otherwise, the fingerprint recognition process continues. This module implements the function of step S2 in implementation one above, and will not be elaborated further.

[0063] The fingerprint recognition module performs a fingerprint recognition process if the target user's fingerprint information exists. The fingerprint recognition process includes: the target user places the finger with the registered fingerprint information on the fingerprint scanner, the system acquires the target fingerprint image, extracts the fingerprint feature information from the fingerprint image, and generates a corresponding fingerprint information string; the fingerprint information string is input into the fingerprint recognition model to obtain the recognition result; this module is used to implement the function of step S3 in the above implementation one, and the details will not be elaborated further.

[0064] The operation execution module receives the recognition result. If the recognition is successful, the system performs sensitive business operations; otherwise, the system sends out an error message and performs a re-recognition. This module is used to implement the function of step S4 in the above implementation one, and will not be described in detail here.

[0065] Example 3:

[0066] This embodiment provides a computer-readable medium having a computer program stored thereon. When the computer program is executed by a processor, it implements a fingerprint recognition application method in collaborative office workflow approval as described in any embodiment of the present invention.

[0067] Example 4:

[0068] This embodiment provides a storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement a fingerprint recognition application method in collaborative office workflow approval as described in any embodiment of the present invention.

[0069] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, A and B simultaneously, or B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0070] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments disclosed herein can be implemented using electronic hardware, computer software, or a combination of electronic hardware and software. 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 implementation should not be considered beyond the scope of this application.

[0071] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0072] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a 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 this application. 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.

[0073] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for applying fingerprint recognition in collaborative office workflow approval, characterized in that, Includes the following steps: When a target user initiates a sensitive business operation in the system, fingerprint recognition is triggered to confirm the connection of the fingerprint scanner. If the connection fails, a prompt is sent; otherwise, the target user's fingerprint information is judged. If the target user's fingerprint information is not found, the fingerprint enrollment process proceeds. This process includes: the system collecting the target user's fingerprint image, extracting fingerprint features to obtain fingerprint feature information from the image; processing the fingerprint feature information to generate a corresponding fingerprint information string, and encrypting it; transmitting the encrypted fingerprint information string and fingerprint image to the user's fingerprint database; preprocessing the data in the user's fingerprint database to form a training set; constructing a ResNet model based on a residual neural network; training the ResNet model using the training set to obtain a trained fingerprint recognition model; if enrollment fails, an error message is displayed for re-enrollment; otherwise, the fingerprint recognition process continues. If the target user's fingerprint information exists, the fingerprint recognition process is initiated. The fingerprint recognition process includes: the target user places the finger with the registered fingerprint information on the fingerprint scanner, the system acquires the target fingerprint image, extracts the fingerprint feature information from the fingerprint image, and generates the corresponding fingerprint information string; the fingerprint information string is input into the fingerprint recognition model to obtain the recognition result. The system receives the recognition result. If the recognition is successful, it performs sensitive business operations; otherwise, the system sends out a recognition error message and performs a re-recognition.

2. The method for applying fingerprint recognition in collaborative office workflow approval according to claim 1, characterized in that: The ResNet model consists of an input layer, convolutional layers, residual block groups, global average pooling layers, and fully connected layers. Input layer: Inputs the training set data into the convolutional layer; Convolutional layer: Receives data from the input layer, performs convolution calculations, and obtains the fingerprint image features after convolution processing; Each unit in the convolutional layer is activated using the ReLU activation function after convolution; Residual block group: Each residual block group contains multiple residual blocks. Each residual block group further extracts features from the fingerprint image features extracted from the previous group to obtain a fingerprint image feature map. Global average pooling layer: performs dimensionality reduction on each feature map; Fully connected layer: Receives the output of the global average pooling layer and generates the output through linear transformation and activation function.

3. The method for applying fingerprint recognition in collaborative office workflow approval according to claim 1, characterized in that: The specific steps of the encryption process include: performing SHA-256 hash processing on the target user's fingerprint information; taking the username as input, generating a 256-bit hash value using the SHA-256 algorithm; encrypting the hashed target user's fingerprint information using the AES symmetric encryption algorithm, generating a 128-bit key, and using CBC mode to encrypt the data in blocks to generate a unique encryption key; extracting fingerprint image feature data from the trained model and encrypting it using the RSA asymmetric encryption algorithm, and encrypting the fingerprint features using the user's public key to generate a digital signature.

4. The method for applying fingerprint recognition in collaborative office workflow approval according to claim 1, characterized in that: The fingerprint feature extraction includes: fingerprint image preprocessing, edge detection, thinning, Minutiae detection, and feature representation; The fingerprint image preprocessing involves grayscale processing, image denoising, and image enhancement of the fingerprint image; the edge detection involves detecting the fingerprint ridge contours in the fingerprint image; the thinning involves thinning the coarse fingerprint ridge contours into fine lines; the minutiae detection involves detecting intersections and endpoints in the fingerprint image; and the feature representation involves describing the detected intersections and endpoints using coordinates and directions to obtain fingerprint feature information in the fingerprint image.

5. A fingerprint recognition application system in collaborative office workflow approval, characterized in that, Includes the following steps: The operation initiation module triggers fingerprint recognition to confirm the fingerprint device connection when the target user initiates a sensitive business operation in the system. If the connection fails, a prompt is sent; otherwise, the target user's fingerprint information is judged. The fingerprint enrollment module, if the target user's fingerprint information is not found, proceeds with the fingerprint enrollment process, which includes: the system collecting the target user's fingerprint image, extracting fingerprint features to obtain fingerprint feature information from the fingerprint image; processing the fingerprint feature information to generate a corresponding fingerprint information string, and encrypting it; transmitting the encrypted fingerprint information string and fingerprint image to the user's fingerprint database; preprocessing the data in the user's fingerprint database, forming a training set from the preprocessed data, constructing a ResNet model based on a residual neural network, and training the ResNet model using the training set to obtain a trained fingerprint recognition model; if enrollment fails, an error message is displayed for re-enrollment; otherwise, the fingerprint recognition process continues. The fingerprint recognition module performs a fingerprint recognition process if the target user's fingerprint information is present. The fingerprint recognition process includes: the target user places the finger with the registered fingerprint information on the fingerprint scanner, the system acquires the target fingerprint image, extracts the fingerprint feature information from the fingerprint image, and generates a corresponding fingerprint information string; the fingerprint information string is input into the fingerprint recognition model to obtain the recognition result. The operation execution module receives the recognition result. If the recognition is successful, the system performs sensitive business operations; otherwise, the system sends out an error message and performs a re-recognition.

6. The fingerprint recognition application system in collaborative office workflow approval according to claim 5, characterized in that: The ResNet model consists of an input layer, convolutional layers, residual block groups, global average pooling layers, and fully connected layers. Input layer: Inputs the training set data into the convolutional layer; Convolutional layer: Receives data from the input layer, performs convolution calculations, and obtains the fingerprint image features after convolution processing; Each unit in the convolutional layer is activated using the ReLU activation function after convolution; Residual block group: Each residual block group contains multiple residual blocks. Each residual block group further extracts features from the fingerprint image features extracted from the previous group to obtain a fingerprint image feature map. Global average pooling layer: performs dimensionality reduction on each feature map; Fully connected layer: Receives the output of the global average pooling layer and generates the output through linear transformation and activation function.

7. The fingerprint recognition application system in collaborative office workflow approval according to claim 5, characterized in that: The specific steps of the encryption process include: performing SHA-256 hash processing on the target user's identity information; taking the username as input, generating a 256-bit hash value using the SHA-256 algorithm; encrypting the hashed target user's fingerprint information using the AES symmetric encryption algorithm, generating a 128-bit key, and using CBC mode to encrypt the data in blocks to generate a unique encryption key; extracting fingerprint image feature data from the trained model and encrypting it using the RSA asymmetric encryption algorithm, and encrypting the fingerprint features using the user's public key to generate a digital signature.

8. The fingerprint recognition application system in collaborative office workflow approval according to claim 5, characterized in that: The fingerprint feature extraction includes: fingerprint image preprocessing, edge detection, thinning, Minutiae detection, and feature representation; The fingerprint image preprocessing involves grayscale processing, image denoising, and image enhancement of the fingerprint image; the edge detection involves detecting the fingerprint ridge contours in the fingerprint image; the thinning involves thinning the coarse fingerprint ridge contours into fine lines; the minutiae detection involves detecting intersections and endpoints in the fingerprint image; and the feature representation involves describing the detected intersections and endpoints using coordinates and directions to obtain fingerprint feature information in the fingerprint image.

9. A physical device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements a fingerprint recognition application method in collaborative office workflow approval as described in any one of claims 1 to 4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements a fingerprint recognition application method in collaborative office workflow approval as described in any one of claims 1 to 4.

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