Forklift authority acquisition identity authentication management system

Abstract

The invention relates to forklift intelligent management, in particular to a forklift authority acquisition identity authentication management system, which is characterized in that an acquisition terminal acquires a face image, a fingerprint image and card information of a to-be-authenticated person through a face image acquisition module, a fingerprint image acquisition module and a card information acquisition module, and uploads the acquisition information to a cloud server; the cloud server carries out the matching recognition of the face image, the fingerprint image and the card information uploaded by the collection terminal through the face recognition module, the fingerprint recognition module and the card recognition module, and carries out the identity authentication of a to-be-authenticated person through the identity authentication module according to a face recognition result, a fingerprint recognition result and a card recognition result. The cloud server confirms authority information of the to-be-authenticated person through an authority confirmation module according to the identity authentication result; according to the technical scheme provided by the invention, the defects of low accuracy and safety of forklift operation authority management in the prior art can be effectively overcome.

Description

Technical Field

[0001] The present invention relates to intelligent management of forklifts, and in particular to a forklift authority collection and identity authentication management system. Background Art

[0002] With the rapid development and intelligent transformation of the logistics industry, forklifts, as essential transportation equipment in warehousing and logistics, are receiving increasing attention for their operational safety. However, traditional forklift management systems have numerous shortcomings, such as chaotic management of forklift operating permissions and lax driver identity authentication. These issues not only affect the normal operation of forklifts but are also more likely to lead to safety accidents.

[0003] Currently, several identity-authentication-based forklift management systems have emerged. These systems primarily rely on technologies such as radio frequency identification (RFID), fingerprint recognition, and facial recognition. By collecting the identity information of individuals to be authenticated and comparing it with a database, they control forklift operating permissions. However, these systems still have limitations in practical applications, such as low identity authentication accuracy and poor system stability. These issues have limited their widespread adoption and effectiveness.

[0004] Therefore, a more intelligent and reliable forklift permission collection and identity authentication management system is needed to improve the accuracy and security of forklift operation permission management while ensuring the stability and real-time performance of the system. Summary of the Invention

[0005] (1) Technical problems solved

[0006] In view of the above shortcomings of the prior art, the present invention provides a forklift authority collection and identity authentication management system, which can effectively overcome the defects of low accuracy and security of forklift operation authority management in the prior art.

[0007] (2) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] A forklift authority collection and identity authentication management system includes a collection terminal and a cloud server. The collection terminal collects the face image, fingerprint image, and card information of the person to be authenticated through a face image collection module, a fingerprint image collection module, and a card information collection module, and uploads the collected information to the cloud server.

[0010] The cloud server matches and identifies the face image, fingerprint image, and card information uploaded by the acquisition terminal through the face recognition module, the fingerprint recognition module, and the card recognition module, and uses the identity authentication module to authenticate the identity of the person to be authenticated based on the face recognition result, the fingerprint recognition result, and the card recognition result. The cloud server confirms the authority information of the person to be authenticated based on the identity authentication result through the authority confirmation module, and uses the instruction issuing module to issue an unlocking instruction to the acquisition terminal based on the authority confirmation result. The acquisition terminal forwards the unlocking instruction to the vehicle management system of the target forklift to unlock the target forklift.

[0011] Preferably, the face recognition module matches and recognizes the face image uploaded by the acquisition terminal, including:

[0012] Convert the face depth image uploaded by the acquisition terminal into a face mask image, and use the pre-trained face recognition model to match and recognize the face mask image to obtain the face recognition result;

[0013] Among them, the face depth image contains the distance information corresponding to each feature point on the face of the person to be authenticated, and the face mask image contains the pixel values corresponding to each feature point on the face of the person to be authenticated.

[0014] Preferably, the step of converting the face depth image uploaded by the acquisition terminal into a face mask image includes:

[0015] Converting the face depth image into a face mask image based on the distance information contained in the face depth image and the preset correspondence between the distance information and the pixel value;

[0016] Among them, there is a negative correlation between distance information and pixel value.

[0017] Preferably, after converting the face depth image into a face mask image, the method further comprises:

[0018] Determine whether the face angle in the face mask image meets the preset angle range and whether the number of invalid pixels in the face mask image is lower than a preset threshold;

[0019] If all the above conditions are met, the pre-trained face recognition model is used to match and identify the face mask image; otherwise, the face mask image is processed and the pre-trained face recognition model is used to match and identify the processed face mask image.

[0020] Preferably, the determining whether the face angle in the face mask image meets a preset angle range includes:

[0021] Determining first position information of a designated facial part in the facial mask image, and second position information of the designated facial part when the facial angle falls within a preset angle range;

[0022] It is determined whether the first position information is consistent with the second position information. If they are consistent, the face angle in the face mask image meets the preset angle range; otherwise, the face angle in the face mask image does not meet the preset angle range.

[0023] Preferably, performing image processing on the face mask image includes:

[0024] If the face angle in the face mask image does not conform to the preset angle range, determining the face angle in the face mask image according to the first position information;

[0025] The face angle in the face mask image is transformed to obtain a processed face mask image in which the face angle conforms to a preset angle range.

[0026] Preferably, the matching and identifying of the face mask image using a pre-trained face recognition model to obtain a face recognition result includes:

[0027] Extracting facial mask feature information from the facial mask image, matching the facial mask feature information, and obtaining a matching result of whether there is pre-recorded facial mask feature information that matches the facial mask feature information;

[0028] If there is pre-recorded face mask feature information that matches the face mask feature information, the face recognition passes, otherwise the face recognition fails;

[0029] The pre-recorded face mask feature information is extracted from the pre-recorded face mask image, and the pre-recorded face mask image is converted from the pre-recorded face depth image.

[0030] Preferably, the fingerprint recognition module matches and recognizes the fingerprint image uploaded by the acquisition terminal, including:

[0031] Extract key points from the fingerprint image uploaded by the acquisition terminal and obtain a key point image based on the key points;

[0032] The pre-trained fingerprint recognition model is used to match and identify the key point images to obtain the fingerprint recognition results.

[0033] Preferably, extracting key points from the fingerprint image uploaded by the acquisition terminal to obtain a key point image based on the key points includes:

[0034] Establish a pixel template, select a pixel in the fingerprint image as the center point of the pixel template, and collect pixels around the center point based on the pixel template;

[0035] Whether the center point is a key point is determined according to the number of black pixels around the center point, and the key point is extracted to obtain a key point image based on the key point.

[0036] Preferably, the use of a pre-trained fingerprint recognition model to match and identify key point images to obtain fingerprint recognition results includes:

[0037] Extracting feature vectors from key point images, matching them by calculating the Euclidean distance between the feature vectors and pre-recorded feature vectors, and obtaining a matching result of whether there is a pre-recorded feature vector that matches the feature vector;

[0038] If there is a pre-recorded feature vector that matches the feature vector, the fingerprint recognition passes, otherwise the fingerprint recognition fails;

[0039] The pre-recorded feature vector is extracted from the pre-recorded key point image, and the pre-recorded key point image is extracted from the pre-recorded fingerprint image.

[0040] (3) Beneficial effects

[0041] Compared with the existing technology, the forklift authority collection and identity authentication management system provided by the present invention has the following beneficial effects:

[0042] 1) Based on the distance information contained in the face depth image and the correspondence between the preset distance information and the pixel value, the face depth image is converted into a face mask image, and the face mask image that does not meet the preset conditions is processed. Then, the face mask feature information is extracted from the face mask image, and the face mask feature information is matched to obtain a matching result of whether there is pre-recorded face mask feature information that matches the face mask feature information, so as to accurately and efficiently perform face recognition on the person to be authenticated;

[0043] 2) Based on the pixel template, pixels around the center point of the fingerprint image are collected. The center point is determined to be a key point based on the number of black pixels around the center point. The key point is extracted to obtain a key point image based on the key point. A feature vector is extracted from the key point image. The feature vector is matched with a pre-recorded feature vector by calculating the Euclidean distance between the feature vector and the pre-recorded feature vector to obtain a matching result of whether there is a pre-recorded feature vector that matches the feature vector, thereby enabling accurate and efficient fingerprint recognition of the person to be authenticated;

[0044] 3) The identity authentication module authenticates the person to be authenticated based on the face recognition results, fingerprint recognition results and card recognition results. The authority confirmation module confirms the authority information of the person to be authenticated based on the identity authentication results. The instruction issuance module issues an unlocking instruction to the acquisition terminal based on the authority confirmation result. The acquisition terminal forwards the unlocking instruction to the on-board management system of the target forklift to unlock the target forklift, thereby effectively improving the accuracy and security of forklift operation authority management, while ensuring the stability and real-time performance of the system, which is conducive to better promotion and application of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0046] Figure 1 A schematic diagram of the system of the present invention;

[0047] Figure 2 This is a flow chart of the face recognition module in the present invention matching and identifying the face image uploaded by the acquisition terminal;

[0048] Figure 3 This is a flow chart of the fingerprint recognition module in the present invention matching and identifying the fingerprint image uploaded by the acquisition terminal. DETAILED DESCRIPTION

[0049] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0050] A forklift authority collection and identity authentication management system, such as Figure 1 As shown, it includes a collection terminal and a cloud server. The collection terminal collects the face image, fingerprint image and card information of the person to be authenticated through the face image collection module, fingerprint image collection module and card information collection module respectively, and uploads the collected information to the cloud server;

[0051] The cloud server uses the face recognition module, fingerprint recognition module, and card recognition module to match and identify the face image, fingerprint image, and card information uploaded by the collection terminal, and uses the identity authentication module to authenticate the person to be authenticated based on the face recognition results, fingerprint recognition results, and card recognition results. The cloud server uses the authority confirmation module to confirm the authority information of the person to be authenticated based on the identity authentication result, and uses the instruction issuing module to issue an unlocking instruction to the collection terminal based on the authority confirmation result. The collection terminal forwards the unlocking instruction to the on-board management system of the target forklift to unlock the target forklift.

[0052] In the above technical solution, the identity authentication module authenticates the person to be authenticated based on the face recognition results, fingerprint recognition results and card recognition results; the authority confirmation module confirms the authority information of the person to be authenticated based on the identity authentication results; the instruction issuing module issues an unlocking instruction to the acquisition terminal based on the authority confirmation result; the acquisition terminal forwards the unlocking instruction to the on-board management system of the target forklift to unlock the target forklift, thereby effectively improving the accuracy and security of forklift operation authority management, while ensuring the stability and real-time performance of the system, which is conducive to better promotion and application of the system.

[0053] like Figure 2 As shown, the face recognition module matches and recognizes the face image uploaded by the acquisition terminal, including:

[0054] Convert the face depth image uploaded by the acquisition terminal into a face mask image, and use the pre-trained face recognition model to match and recognize the face mask image to obtain the face recognition result;

[0055] Among them, the face depth image contains the distance information corresponding to each feature point on the face of the person to be authenticated, and the face mask image contains the pixel values corresponding to each feature point on the face of the person to be authenticated.

[0056] 1) Convert the face depth image uploaded by the acquisition terminal into a face mask image, including:

[0057] Converting the face depth image into a face mask image based on the distance information contained in the face depth image and the preset correspondence between the distance information and the pixel value;

[0058] Among them, there is a negative correlation between distance information and pixel value.

[0059] Specifically, after converting the face depth image into the face mask image, it includes:

[0060] Determine whether the face angle in the face mask image meets the preset angle range and whether the number of invalid pixels in the face mask image is lower than a preset threshold;

[0061] If all the above conditions are met, the pre-trained face recognition model is used to match and identify the face mask image; otherwise, the face mask image is processed and the pre-trained face recognition model is used to match and identify the processed face mask image.

[0062] a) Determine whether the face angle in the face mask image meets the preset angle range, including:

[0063] Determining first position information of a designated facial part in the facial mask image, and second position information of the designated facial part when the facial angle falls within a preset angle range;

[0064] It is determined whether the first position information is consistent with the second position information. If they are consistent, the face angle in the face mask image meets the preset angle range; otherwise, the face angle in the face mask image does not meet the preset angle range.

[0065] b) performing image processing on the face mask image, including:

[0066] If the face angle in the face mask image does not conform to the preset angle range, determining the face angle in the face mask image according to the first position information;

[0067] The face angle in the face mask image is transformed to obtain a processed face mask image in which the face angle conforms to a preset angle range.

[0068] 2) Use the pre-trained face recognition model to match and identify the face mask image to obtain the face recognition results, including:

[0069] Extracting facial mask feature information from the facial mask image, matching the facial mask feature information, and obtaining a matching result of whether there is pre-recorded facial mask feature information that matches the facial mask feature information;

[0070] If there is pre-recorded face mask feature information that matches the face mask feature information, the face recognition passes, otherwise the face recognition fails;

[0071] The pre-recorded face mask feature information is extracted from the pre-recorded face mask image, and the pre-recorded face mask image is converted from the pre-recorded face depth image.

[0072] The above technical solution converts the face depth image into a face mask image based on the distance information contained in the face depth image and the correspondence between the preset distance information and the pixel value, and performs image processing on the face mask image that does not meet the preset conditions. Then, the face mask feature information is extracted from the face mask image, and the face mask feature information is matched to obtain a matching result of whether there is pre-recorded face mask feature information that matches the face mask feature information, thereby enabling accurate and efficient face recognition of the person to be authenticated.

[0073] like Figure 3 As shown, the fingerprint recognition module matches and recognizes the fingerprint image uploaded by the acquisition terminal, including:

[0074] Extract key points from the fingerprint image uploaded by the acquisition terminal and obtain a key point image based on the key points;

[0075] The pre-trained fingerprint recognition model is used to match and identify the key point images to obtain the fingerprint recognition results.

[0076] 1) Extract key points from the fingerprint image uploaded by the acquisition terminal and obtain a key point image based on the key points, including:

[0077] Establish a pixel template, select a pixel in the fingerprint image as the center point of the pixel template, and collect pixels around the center point based on the pixel template;

[0078] Whether the center point is a key point is determined according to the number of black pixels around the center point, and the key point is extracted to obtain a key point image based on the key point.

[0079] 2) Use the pre-trained fingerprint recognition model to match and identify the key point image to obtain the fingerprint recognition results, including:

[0080] Extracting feature vectors from key point images, matching them by calculating the Euclidean distance between the feature vectors and pre-recorded feature vectors, and obtaining a matching result of whether there is a pre-recorded feature vector that matches the feature vector;

[0081] If there is a pre-recorded feature vector that matches the feature vector, the fingerprint recognition passes, otherwise the fingerprint recognition fails;

[0082] The pre-recorded feature vector is extracted from the pre-recorded key point image, and the pre-recorded key point image is extracted from the pre-recorded fingerprint image.

[0083] The above technical solution collects pixels around the center point in the fingerprint image based on the pixel template, determines whether the center point is a key point according to the number of black pixels around the center point, and extracts the key point to obtain a key point image based on the key point, extracts the feature vector from the key point image, and matches the feature vector with the pre-recorded feature vector by calculating the Euclidean distance between the feature vector and the pre-recorded feature vector to obtain a matching result of whether there is a pre-recorded feature vector that matches the feature vector, thereby enabling accurate and efficient fingerprint recognition of the person to be authenticated.

[0084] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A forklift authority collection and identity authentication management system, characterized by: It includes a collection terminal and a cloud server. The collection terminal collects the face image, fingerprint image and card information of the person to be authenticated through the face image collection module, fingerprint image collection module and card information collection module respectively, and uploads the collected information to the cloud server; The cloud server matches and identifies the face image, fingerprint image, and card information uploaded by the acquisition terminal through the face recognition module, the fingerprint recognition module, and the card recognition module, and uses the identity authentication module to authenticate the identity of the person to be authenticated based on the face recognition result, the fingerprint recognition result, and the card recognition result. The cloud server confirms the authority information of the person to be authenticated based on the identity authentication result through the authority confirmation module, and uses the instruction issuing module to issue an unlocking instruction to the acquisition terminal based on the authority confirmation result. The acquisition terminal forwards the unlocking instruction to the vehicle management system of the target forklift to unlock the target forklift.

2. The forklift authority collection and identity authentication management system according to claim 1 is characterized by: The face recognition module matches and recognizes the face image uploaded by the acquisition terminal, including: Convert the face depth image uploaded by the acquisition terminal into a face mask image, and use the pre-trained face recognition model to match and recognize the face mask image to obtain the face recognition result; Among them, the face depth image contains the distance information corresponding to each feature point on the face of the person to be authenticated, and the face mask image contains the pixel values corresponding to each feature point on the face of the person to be authenticated.

3. The forklift authority collection and identity authentication management system according to claim 2 is characterized by: The step of converting the face depth image uploaded by the acquisition terminal into a face mask image includes: Converting the face depth image into a face mask image based on the distance information contained in the face depth image and the preset correspondence between the distance information and the pixel value; Among them, there is a negative correlation between distance information and pixel value.

4. The forklift authority collection and identity authentication management system according to claim 3 is characterized by: After converting the face depth image into the face mask image, the method includes: Determine whether the face angle in the face mask image meets the preset angle range and whether the number of invalid pixels in the face mask image is lower than a preset threshold; If all the above conditions are met, the pre-trained face recognition model is used to match and identify the face mask image; otherwise, the face mask image is processed and the pre-trained face recognition model is used to match and identify the processed face mask image.

5. The forklift authority collection and identity authentication management system according to claim 4 is characterized in that: The determining whether the face angle in the face mask image meets the preset angle range includes: Determining first position information of a designated facial part in the facial mask image, and second position information of the designated facial part when the facial angle falls within a preset angle range; It is determined whether the first position information is consistent with the second position information. If they are consistent, the face angle in the face mask image meets the preset angle range; otherwise, the face angle in the face mask image does not meet the preset angle range.

6. The forklift authority collection and identity authentication management system according to claim 5, characterized in that: The performing image processing on the face mask image includes: If the face angle in the face mask image does not conform to the preset angle range, determining the face angle in the face mask image according to the first position information; The face angle in the face mask image is transformed to obtain a processed face mask image in which the face angle conforms to a preset angle range.

7. The forklift authority collection and identity authentication management system according to claim 2, characterized in that: The method of using the pre-trained face recognition model to match and recognize the face mask image to obtain the face recognition result includes: Extracting facial mask feature information from the facial mask image, matching the facial mask feature information, and obtaining a matching result of whether there is pre-recorded facial mask feature information that matches the facial mask feature information; If there is pre-recorded face mask feature information that matches the face mask feature information, the face recognition passes, otherwise the face recognition fails; The pre-recorded face mask feature information is extracted from the pre-recorded face mask image, and the pre-recorded face mask image is converted from the pre-recorded face depth image.

8. The forklift authority collection and identity authentication management system according to claim 1, characterized in that: The fingerprint recognition module matches and recognizes the fingerprint image uploaded by the acquisition terminal, including: Extract key points from the fingerprint image uploaded by the acquisition terminal and obtain a key point image based on the key points; The pre-trained fingerprint recognition model is used to match and identify the key point images to obtain the fingerprint recognition results.

9. The forklift authority collection and identity authentication management system according to claim 8, characterized in that: The key points are extracted from the fingerprint image uploaded by the acquisition terminal to obtain a key point image based on the key points, including: Establish a pixel template, select a pixel in the fingerprint image as the center point of the pixel template, and collect pixels around the center point based on the pixel template; Whether the center point is a key point is determined according to the number of black pixels around the center point, and the key point is extracted to obtain a key point image based on the key point.

10. The forklift authority collection and identity authentication management system according to claim 9, characterized in that: The method of using the pre-trained fingerprint recognition model to match and identify the key point image to obtain the fingerprint recognition result includes: Extracting feature vectors from key point images, matching them by calculating the Euclidean distance between the feature vectors and pre-recorded feature vectors, and obtaining a matching result of whether there is a pre-recorded feature vector that matches the feature vector; If there is a pre-recorded feature vector that matches the feature vector, the fingerprint recognition passes, otherwise the fingerprint recognition fails; The pre-recorded feature vector is extracted from the pre-recorded key point image, and the pre-recorded key point image is extracted from the pre-recorded fingerprint image.

Images