A permission verification device based on smart park management

Abstract

This invention discloses a permission verification device for smart park management, comprising: a verification request receiving module for receiving verification requests for users sent by smart devices in the smart park; a user information storage module for storing a user database; a verification processing module for comparing the verification request with the information in the user database and determining user permissions based on the comparison result; and a verification result feedback module for feeding back the user permissions to the smart devices. This improves the efficiency and accuracy of user identity and permission recognition, enabling secure and efficient verification of user permissions in smart parks and enhancing the level of intelligent park management.

Description

Technical Field

[0001] The present invention relates to the technical field of smart park authority verification, and in particular to a authority verification device based on smart park management. Background Art

[0002] At present, as intelligence penetrates into various parks, empowering the parks and making them intelligent, complex permission verification issues arise. People with different permissions can access different areas.

[0003] The efficiency and accuracy of identifying user identities and permissions in the existing technology are low, which leads to low management efficiency of smart parks and inability to achieve effective management. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems in the above-mentioned technologies to a certain extent. To this end, the first purpose of the present invention is to propose a permission verification device based on smart park management, which improves the efficiency and accuracy of identifying user identities and permissions, can realize safe and efficient verification of user permissions in smart parks, and improve the intelligent level of park management.

[0005] The second objective of the present invention is to propose a permission verification method based on smart park management.

[0006] To achieve the above-mentioned purpose, the first embodiment of the present invention proposes a permission verification device based on smart park management, comprising:

[0007] A verification request receiving module, used to receive a verification request about a user sent by a smart device in a smart park;

[0008] User information storage module, used to store user database;

[0009] A verification processing module is used to compare the verification request with the information in the user database and determine the user authority according to the comparison result;

[0010] The verification result feedback module is used to feedback user permissions to the smart device.

[0011] According to some embodiments of the present invention, the verification processing module performs image enhancement processing on the user image included in the verification request before comparing the verification request with information in the user database.

[0012] According to some embodiments of the present invention, the verification processing module includes:

[0013] A denoising module, used to perform image denoising on a user image to obtain a denoised image;

[0014] A partitioning module, used for partitioning the denoised image to obtain a number of image blocks;

[0015] The first determination module is used to determine the detail richness of each image block and compare it with a preset detail richness threshold; the image blocks whose detail richness is greater than the preset detail richness threshold are used as preliminary screening image blocks;

[0016] The second determination module is used to perform brightness screening on the pre-screened image blocks to determine the image blocks to be enhanced;

[0017] The enhancement module is used to perform image enhancement processing on the image block to be enhanced.

[0018] According to some embodiments of the present invention, the second determining module includes:

[0019] A screening module is used to convert the preliminary screening image block into the HSV color space, extract the image sequence component of the V channel, and use the preliminary screening image block corresponding to the largest image sequence component as the reference image block;

[0020] A first acquisition module is used to sort the image sequence components of the other preliminary screening image blocks except the reference image block from large to small, and calculate the absolute value of the difference with the reference image block;

[0021] The pre-screened image blocks whose absolute values ​​of the differences are greater than a preset threshold are used as the image blocks to be enhanced.

[0022] According to some embodiments of the present invention, the noise reduction module includes:

[0023] A grayscale processing module is used to perform grayscale processing on the user image to obtain a grayscale image;

[0024] A first calculation module is used to calculate a grayscale normalized histogram corresponding to the grayscale image;

[0025] The second calculation module is used to determine the cumulative distribution of the user image in grayscale based on the grayscale normalization histogram, and perform equalization calculation on all grayscales through a histogram equalization method to obtain a denoised image.

[0026] According to some embodiments of the present invention, the partitioning module includes:

[0027] The third calculation module is used to calculate the image information entropy of each pixel in the denoised image and compare them;

[0028] The judgment module is used to analyze the image information entropy deviation between any two pixel points within a preset range in the denoised image, and judge whether it is within the preset deviation range; when it is determined that the image information entropy deviation is within the preset deviation range, it means that the two pixel points are in an image block; all pixel points in the denoised image are judged to obtain a number of image blocks.

[0029] According to some embodiments of the present invention, the first determining module determines the detail richness of each image block, including:

[0030] Obtaining a binary image of each image block, and determining a connected domain in the image block according to the binary image;

[0031] The Euclidean distance between the center points of any two connected domains in the image block is calculated, and the average distance value is calculated based on all Euclidean distances as the detail richness of the image block.

[0032] According to some embodiments of the present invention, the enhancement module includes:

[0033] The fifth computing module is used for:

[0034] Determine the R color channel value, G color channel value, and B color channel value of each pixel on the image block to be enhanced, and determine the maximum color channel value as the brightness component of the pixel;

[0035] Determine the average brightness component, the maximum brightness component and the minimum brightness component of the image block to be enhanced;

[0036] Determine the enhancement parameter of each pixel according to the average brightness component, the maximum brightness component and the minimum brightness component of the image block to be enhanced;

[0037]

[0038] in, is the adjustment parameter of pixel (i, j); is the brightness component of pixel (i, j); is the average brightness component of the image block to be enhanced; is the minimum brightness component; is the maximum brightness component;

[0039] The adjustment module is used to adjust the brightness component of the corresponding pixel point according to the enhancement parameter of each pixel point. The adjustment module is used to adjust the brightness component of the corresponding pixel point according to the enhancement parameter of each pixel point.

[0040] To achieve the above-mentioned purpose, the second embodiment of the present invention proposes a permission verification method based on smart park management, comprising:

[0041] Receive user verification requests sent by smart devices in the smart park;

[0042] Compare the verification request with the information in the user database and determine the user's rights based on the comparison result;

[0043] Feedback user permissions to smart devices.

[0044] The present invention proposes a permission verification device based on smart park management, which improves the efficiency and accuracy of identifying user identities and permissions, can realize safe and efficient verification of user permissions in smart parks, and improve the intelligent level of park management.

[0045] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0046] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0048] Figure 1 is a block diagram of a permission verification device based on smart park management according to an embodiment of the present invention;

[0049] Figure 2 is a block diagram of a verification processing module according to an embodiment of the present invention;

[0050] Figure 3 It is a flowchart of a permission verification method based on smart park management according to an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0052] like Figure 1 As shown, the first embodiment of the present invention proposes a permission verification device based on smart park management, including:

[0053] A verification request receiving module, used to receive a verification request about a user sent by a smart device in a smart park;

[0054] User information storage module, used to store user database;

[0055] A verification processing module is used to compare the verification request with the information in the user database and determine the user authority according to the comparison result;

[0056] The verification result feedback module is used to feedback user permissions to the smart device.

[0057] Working principle of the above technical solution: In this embodiment, the verification processing module supports accurate acquisition and rapid recognition of high-resolution portraits, and uses GPU to accelerate face recognition and comparison.

[0058] In this embodiment, the user information storage module uses cloud database, cloud computing and other technologies to store the user database, ensuring the safe storage and fast access of user information. The user database includes the user's identity information, permission information and part of the biometric information. Part of the biometric information includes facial recognition.

[0059] In this embodiment, the smart device includes an access control system, a monitoring system, etc. The verification request includes the identity information and part of the biometric information of the user to be verified.

[0060] In this embodiment, the verification processing module processes the received verification request, processes and performs similarity comparison on the user's identity information and biometric information, and determines whether the user's identity and authority are legal based on a preset threshold; at the same time, it can determine whether the user is qualified to operate a certain resource based on the authority information corresponding to the user's identity information and biometric information.

[0061] The verification result feedback module feeds back the user's authority to the smart device, so that the smart device (gate) can perform the corresponding opening or closing operations.

[0062] The beneficial effects of the above technical solution are: improving the efficiency and accuracy of identifying user identities and permissions, enabling safe and efficient verification of user permissions in smart parks, and improving the level of intelligent park management.

[0063] According to some embodiments of the present invention, the verification processing module performs image enhancement processing on the user image included in the verification request before comparing the verification request with information in the user database.

[0064] The beneficial effect of the above technical solution is that it facilitates improving the accuracy of the acquired user image, thereby improving the accuracy of recognition.

[0065] like Figure 2 As shown, according to some embodiments of the present invention, the verification processing module includes:

[0066] A denoising module, used to perform image denoising on a user image to obtain a denoised image;

[0067] A partitioning module, used for partitioning the denoised image to obtain a number of image blocks;

[0068] The first determination module is used to determine the detail richness of each image block and compare it with a preset detail richness threshold; the image blocks whose detail richness is greater than the preset detail richness threshold are used as preliminary screening image blocks;

[0069] The second determination module is used to perform brightness screening on the pre-screened image blocks to determine the image blocks to be enhanced;

[0070] The enhancement module is used to perform image enhancement processing on the image block to be enhanced.

[0071] The working principle of the above technical solution is as follows: the user image is subjected to image denoising to facilitate the removal of noise information in the user image and to facilitate the improvement of the accuracy of the user image. The denoised image is partitioned to obtain a number of image blocks, and the image segmentation of the denoised image is realized to facilitate the subsequent determination of the image blocks to be enhanced, avoid processing all image blocks, reduce the amount of data processing, and improve data processing efficiency. The first determination module determines the detail richness of each image block, and the detail richness represents the richness of the content in the image block. The greater the detail richness, the greater the richness of the content in the image block, and the more critical the image block is. The image blocks with detail richness greater than the preset detail richness threshold are used as preliminary screening image blocks; the image blocks with detail richness less than or equal to the preset detail richness threshold are removed, that is, the image blocks that are not very important are removed, which is convenient for reducing the amount of data processing and improving data processing efficiency. The second determination module is used to perform brightness screening on the preliminary screening image blocks, and screen again in the key preliminary screening image blocks, further reducing the amount of data to be processed, and facilitating the accurate determination of the image blocks that need to be enhanced, that is, determining the image blocks to be enhanced. Image enhancement processing is performed on the image blocks to be enhanced based on the enhancement module.

[0072] The beneficial effects of the above technical solution are: image denoising is performed on user images, image zoning is performed on the denoised images, initial content screening is performed based on the detail richness of each image block, and important initial screening image blocks are determined; brightness screening is performed on the initial screening image blocks to determine the image blocks to be enhanced; image enhancement processing is performed on the image blocks to be enhanced, so as to accurately determine the image area that needs to be processed, avoid processing the entire image, reduce the amount of data processing, improve data processing efficiency, and thereby improve the efficiency and accuracy of identifying user identity and authority.

[0073] According to some embodiments of the present invention, the second determining module includes:

[0074] A screening module is used to convert the preliminary screening image block into the HSV color space, extract the image sequence component of the V channel, and use the preliminary screening image block corresponding to the largest image sequence component as the reference image block;

[0075] A first acquisition module is used to sort the image sequence components of the other preliminary screening image blocks except the reference image block from large to small, and calculate the absolute value of the difference with the reference image block;

[0076] The pre-screened image blocks whose absolute values ​​of the differences are greater than a preset threshold are used as the image blocks to be enhanced.

[0077] The working principle of the above technical solution is: Use image processing libraries such as OpenCV and PIL to convert the initial screening image blocks into the HSV color space. In the HSV color space, V stands for brightness (Value). The V channel represents the brightness level of the pixel, that is, the brightness or grayscale of the pixel. A V channel with a larger value corresponds to a pixel with higher brightness, and a V channel with a smaller value corresponds to a pixel with lower brightness. In the HSV color model, by adjusting the value of the V channel, the brightness of the image can be changed while keeping the color unchanged.

[0078] The reference image block has the largest brightness value. Sort the image sequence components of the other preliminary screening image blocks except the reference image block from large to small, and calculate the absolute value of the difference with the reference image block; that is, determine the brightness difference between the other preliminary screening image blocks and the reference image block. The preliminary screening image blocks whose absolute value of the difference is greater than the preset threshold are used as the image blocks to be enhanced.

[0079] The beneficial effect of the above technical solution is as follows: from the perspective of brightness component, the initially screened image blocks whose absolute values ​​of the differences are greater than a preset threshold are used as image blocks to be enhanced, so as to accurately determine the image areas that need to be enhanced.

[0080] According to some embodiments of the present invention, the noise reduction module includes:

[0081] A grayscale processing module is used to perform grayscale processing on the user image to obtain a grayscale image;

[0082] A first calculation module is used to calculate a grayscale normalized histogram corresponding to the grayscale image;

[0083] The second calculation module is used to determine the cumulative distribution of the user image in grayscale based on the grayscale normalization histogram, and perform equalization calculation on all grayscales through a histogram equalization method to obtain a denoised image.

[0084] Working principle of the above technical solution: In this embodiment, the grayscale normalized histogram corresponding to the grayscale image is calculated, including: counting the frequency of the grayscale level of each pixel in the grayscale image and calculating the proportion of each grayscale level; drawing a grayscale histogram. The horizontal axis represents the grayscale level, and the vertical axis represents the probability of the grayscale level appearing in the entire image or the proportion of the number of pixels; normalizing the histogram. The frequency of each grayscale level in the histogram is divided by the total number of pixels to obtain a normalized histogram, so that the sum of all the columns of the histogram is 1.

[0085] In this embodiment, the cumulative distribution of the user image in grayscale is determined based on the grayscale normalized histogram, and all grayscales are equalized by a histogram equalization method to obtain a denoised image, including: calculating the cumulative distribution function (CDF) of the grayscale level based on the grayscale normalized histogram of the grayscale image; performing histogram equalization on all grayscale levels according to the CDF: new intensity=CDF×(L-1), where new intensity is the grayscale level after equalization; L is the total number of grayscale levels. The equalized grayscale level is mapped back to the original image to obtain a denoised image.

[0086] The beneficial effect of the above technical solution is to achieve effective noise reduction of the user image and obtain an accurate noise-reduced image.

[0087] According to some embodiments of the present invention, the partitioning module includes:

[0088] The third calculation module is used to calculate the image information entropy of each pixel in the denoised image and compare them;

[0089] The judgment module is used to analyze the image information entropy deviation between any two pixel points within a preset range in the denoised image, and judge whether it is within the preset deviation range; when it is determined that the image information entropy deviation is within the preset deviation range, it means that the two pixel points are in an image block; all pixel points in the denoised image are judged to obtain a number of image blocks.

[0090] Working principle of the above technical solution: In this embodiment, the image information entropy of each pixel in the denoised image is calculated and compared, including: for each pixel, the information entropy is calculated according to its gray value. The information entropy can be calculated by the following formula: in, is the information entropy of pixel X; is the probability that the pixel X belongs to the i-th gray level; is the number of gray levels.

[0091] In this embodiment, the preset range is the distance between pixels.

[0092] The beneficial effects of the above technical solution are as follows: based on the image information entropy deviation between any two pixel points within a preset range in the denoised image and judging whether it is within the preset deviation range, when it is determined that the image information entropy deviation is within the preset deviation range, it indicates that the two pixel points are in one image block, thereby accurately partitioning the image to obtain a number of image blocks.

[0093] According to some embodiments of the present invention, the first determining module determines the detail richness of each image block, including:

[0094] Obtaining a binary image of each image block, and determining a connected domain in the image block according to the binary image;

[0095] The Euclidean distance between the center points of any two connected domains in the image block is calculated, and the average distance value is calculated based on all Euclidean distances as the detail richness of the image block.

[0096] Working principle of the above technical solution: In this embodiment, the threshold processing method is used to convert it into a binary image, a threshold is set according to the gray value of the pixel, pixels greater than the threshold are set to white, and pixels less than or equal to the threshold are set to black. The connected domain algorithm is used to find the connected domain in the image block. The connected domain is an area composed of pixels with the same pixel value and connected to each other in space.

[0097] In this embodiment, the Euclidean distance between the center points of any two connected domains in the image block is calculated, and an average distance value is calculated based on all the Euclidean distances, including: for each connected domain in the image block, the coordinates of its center point are calculated. The center point of the connected domain can be calculated by the bounding box information of the connected domain. For each pair of different connected domains, the Euclidean distance between their center points is calculated. The Euclidean distances between all different connected domains are accumulated, and the average distance value is calculated.

[0098] The beneficial effect of the above technical solution is: based on calculating the Euclidean distance between the center points of any two connected domains in the image block, and calculating the average distance value based on all Euclidean distances, it is convenient to accurately determine the detail richness of each image block.

[0099] According to some embodiments of the present invention, the enhancement module includes:

[0100] The fifth computing module is used for:

[0101] Determine the R color channel value, G color channel value, and B color channel value of each pixel on the image block to be enhanced, and determine the maximum color channel value as the brightness component of the pixel;

[0102] Determine the average brightness component, the maximum brightness component and the minimum brightness component of the image block to be enhanced;

[0103] Determine the enhancement parameter of each pixel according to the average brightness component, the maximum brightness component and the minimum brightness component of the image block to be enhanced;

[0104]

[0105] in, is the adjustment parameter of pixel (i, j); is the brightness component of pixel (i, j); is the average brightness component of the image block to be enhanced; is the minimum brightness component; is the maximum brightness component;

[0106] The adjustment module is used to adjust the brightness component of the corresponding pixel point according to the enhancement parameter of each pixel point.

[0107] The working principle of the above technical solution is as follows: determine the R color channel value, G color channel value, and B color channel value of each pixel on the image block to be enhanced, and determine the maximum color channel value as the brightness component of the pixel; determine the average brightness component, maximum brightness component, and minimum brightness component of the image block to be enhanced; determine the enhancement parameter of each pixel according to the average brightness component, maximum brightness component, and minimum brightness component of the image block to be enhanced; and adjust the brightness component of the corresponding pixel according to the enhancement parameter of each pixel.

[0108] The beneficial effects of the above technical solution are: it is easy to adjust the brightness component of each pixel on the image block to be enhanced, thereby achieving image enhancement and obtaining an accurate image.

[0109] like Figure 3 As shown, the second embodiment of the present invention proposes a permission verification method based on smart park management, including steps S1-S3:

[0110] S1. Receive a user verification request sent by a smart device in the smart park;

[0111] S2, comparing the verification request with the information in the user database, and determining the user authority according to the comparison result;

[0112] S3. Feedback the user authority to the smart device.

[0113] The beneficial effects of the above technical solution are: improving the efficiency and accuracy of identifying user identities and permissions, enabling safe and efficient verification of user permissions in smart parks, and improving the level of intelligent park management.

[0114] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

[0115] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.) and signals (including but not limited to signals transmitted between user terminals and other devices, etc.) involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions. For example, "face", "face recognition", "facial recognition", etc. involved in this disclosure are all obtained with full authorization."

Claims

1. A permission verification device based on smart park management, characterized in that: include: A verification request receiving module, used to receive a verification request about a user sent by a smart device in a smart park; User information storage module, used to store user database; A verification processing module is used to compare the verification request with the information in the user database and determine the user authority according to the comparison result; Verification result feedback module, used to feedback user permissions to the smart device; The verification processing module performs image enhancement processing on the user image included in the verification request before comparing the verification request with the information in the user database; The verification processing module includes: A denoising module, used to perform image denoising on a user image to obtain a denoised image; A partitioning module, used for partitioning the denoised image to obtain a number of image blocks; The first determination module is used to determine the detail richness of each image block and compare it with a preset detail richness threshold; the image blocks whose detail richness is greater than the preset detail richness threshold are used as preliminary screening image blocks; The second determination module is used to perform brightness screening on the pre-screened image blocks to determine the image blocks to be enhanced; An enhancement module, used for performing image enhancement processing on the image block to be enhanced; The enhancement module comprises: The fifth computing module is used for: Determine the R color channel value, G color channel value, and B color channel value of each pixel on the image block to be enhanced, and determine the maximum color channel value as the brightness component of the pixel; Determine the average brightness component, the maximum brightness component and the minimum brightness component of the image block to be enhanced; Determine the enhancement parameter of each pixel according to the average brightness component, the maximum brightness component and the minimum brightness component of the image block to be enhanced; in, is the adjustment parameter of pixel (i, j); is the brightness component of pixel (i, j); is the average brightness component of the image block to be enhanced; is the minimum brightness component; is the maximum brightness component; The adjustment module is used to adjust the brightness component of the corresponding pixel point according to the enhancement parameter of each pixel point.

2. The authority verification device based on smart park management according to claim 1, characterized in that: The second determining module includes: A screening module is used to convert the preliminary screening image block into the HSV color space, extract the image sequence component of the V channel, and use the preliminary screening image block corresponding to the largest image sequence component as the reference image block; The first acquisition module is used to: Sort the image sequence components of the other preliminary screening image blocks except the reference image block from large to small, and calculate the absolute value of the difference with the reference image block; The pre-screened image blocks whose absolute values ​​of the differences are greater than a preset threshold are used as the image blocks to be enhanced.

3. The authority verification device based on smart park management according to claim 1, characterized in that: The noise reduction module comprises: A grayscale processing module is used to perform grayscale processing on the user image to obtain a grayscale image; A first calculation module is used to calculate a grayscale normalized histogram corresponding to the grayscale image; The second calculation module is used to determine the cumulative distribution of the user image in grayscale based on the grayscale normalization histogram, and perform equalization calculation on all grayscales through a histogram equalization method to obtain a denoised image.

4. The authority verification device based on smart park management according to claim 1, characterized in that: The partition module comprises: The third calculation module is used to calculate the image information entropy of each pixel in the denoised image and compare them; The judgment module is used to analyze the image information entropy deviation between any two pixel points within a preset range in the denoised image, and judge whether it is within the preset deviation range; when it is determined that the image information entropy deviation is within the preset deviation range, it means that the two pixel points are in an image block; all pixel points in the denoised image are judged to obtain a number of image blocks.

5. The authority verification device based on smart park management according to claim 1, characterized in that: The first determining module determines the detail richness of each image block, including: Obtaining a binary image of each image block, and determining a connected domain in the image block according to the binary image; The Euclidean distance between the center points of any two connected domains in the image block is calculated, and the average distance value is calculated based on all Euclidean distances as the detail richness of the image block.

Images