Electronic license management system and method based on block chain

Abstract

The invention relates to the technical field of block chains, in particular to an electronic license management system based on a block chain, and the system comprises an image analysis module which is used for detecting the brightness distribution uniformity and the definition reference value of each to-be-processed license image, and determining the image quality category according to the brightness distribution uniformity and the definition reference value; the storage analysis module is used for determining a certificate storage mode according to the image quality category of each certificate image to be processed; the character processing module is used for carrying out character recognition and determining a character processing mode according to the proportion of the loss area in the character area; and the license storage module is used for storing the license image and character information of each electronic license, and the integrity of the obtained license information is improved.

Description

Technical Field

[0001] The present invention relates to the field of blockchain technology, and in particular to an electronic certificate management system and method based on blockchain. Background Art

[0002] The use of electronic certificates can effectively improve work efficiency in the actual work process. In addition, the management of electronic certificates through blockchain technology can further ensure the management efficiency and security of electronic certificates. However, the existing blockchain-based electronic certificate management methods often fail to determine the corresponding electronic certificate storage method according to the image quality of each certificate image uploaded by the user, which makes the integrity of the stored certificate information low. Therefore, how to ensure the integrity of the stored certificate information while ensuring the efficiency of certificate information upload is a problem that needs to be urgently solved by technical personnel in this field.

[0003] Chinese patent publication number CN116720824A discloses a blockchain-based electronic certificate library management system and method, wherein the system includes: an acquisition module, used to acquire a target electronic certificate, store the target electronic certificate in a corresponding preset blockchain, and obtain an electronic certificate library; a verification module, used to perform authority verification on the acquired user's retrieval request, and obtain a target retrieval request that passes the verification; a visual display module, used to determine the retrieval electronic certificate in the electronic certificate library and perform a visual display based on the target retrieval request and display processing rules; a permission restriction module, used to determine whether the user is a malicious user, and if so, restrict the malicious user's permissions within a preset time period. The above scheme has the following problems: it is impossible to determine a targeted certificate storage method according to the image quality of each certificate image to be processed in the actual work scenario, so that the integrity of the stored certificate information is low, which leads to low work efficiency in the actual work process. Summary of the invention

[0004] To this end, the present invention provides a blockchain-based electronic certificate management system and method to overcome the problem that the prior art is unable to determine a targeted certificate storage method according to the image quality of each certificate image to be processed in an actual working scenario, resulting in low integrity of the stored certificate information.

[0005] To achieve the above objectives, the present invention provides an electronic certificate management system based on blockchain, comprising:

[0006] An image analysis module is used to detect the brightness distribution uniformity and clarity reference value of each certificate image to be processed, and determine the image quality category according to the brightness distribution uniformity and clarity reference value;

[0007] A storage analysis module, which is connected to the image analysis module and is used to determine the storage mode of the certificate according to the image quality category of each certificate image to be processed;

[0008] A character processing module, which is connected to the storage analysis module and is used to perform character recognition and determine a character processing method according to the loss area ratio in the character area;

[0009] A correlation analysis module, which is connected to the storage analysis module and is used to determine the correlation coefficient of each stored preferred certificate image and determine the associated certificate image according to the correlation coefficient;

[0010] A region analysis module connected to the storage analysis module to determine whether to perform region filling for the loss region or to adjust the short side length and the long side length of the character region;

[0011] The certificate storage module is respectively connected to the image analysis module, the storage analysis module and the character processing module, and is used to store the preferred certificate image and character information of each electronic certificate.

[0012] Furthermore, the image quality categories include:

[0013] A quality image whose brightness distribution uniformity is greater than a preset brightness distribution uniformity and whose definition reference value is greater than a preset definition reference value;

[0014] A second-class quality image whose brightness distribution uniformity is less than or equal to a preset brightness distribution uniformity or whose clarity reference value is less than or equal to a preset clarity reference value.

[0015] Furthermore, for a single certificate to be uploaded, the certificate storage method is that the storage analysis module determines the preferred certificate image according to the quality coefficient of each type of quality image, or the storage analysis module determines the preferred certificate image according to the quality coefficient of each certificate image to be processed, and performs character recognition on the preferred certificate image through the character processing module.

[0016] Furthermore, the character processing module performs character recognition including:

[0017] Extract characters from the preferred certificate image and divide the characters into corresponding character areas;

[0018] A loss area detection is performed on the character area of ​​the unrecognized character, and a character processing method is determined according to the loss area ratio in the character area to determine the unrecognized character.

[0019] Furthermore, for a single character, if the loss area ratio of the corresponding character area is greater than the preset loss area ratio, the character recognition method includes:

[0020] If the character area is in the preset area, the character processing module determines the character information of the character area according to the character information of the same preset area in the associated certificate image;

[0021] If the character area is not within the preset area, the character processing module sends an image quality warning to the uploading user of the corresponding character.

[0022] Furthermore, for a preferred certificate image to be stored, the association analysis module determines the associated certificate image according to the association coefficient between each stored preferred certificate image and the preferred certificate image;

[0023] The correlation coefficient is determined according to the similarity between the certificate information of each stored preferred certificate image and the recognized certificate information of the preferred certificate image and the certificate interval time.

[0024] Further, for a single character, if the loss area ratio of the corresponding character area is less than or equal to the preset loss area ratio, the character processing module determines the character recognition method according to the reference spacing distance of the loss area in the character area and the spacing distance difference value;

[0025] The reference spacing distance and the spacing distance difference value are determined according to the spacing distance between adjacent loss areas in the character area.

[0026] Furthermore, if the reference spacing distance of the loss area in the corresponding character area is less than or equal to the preset reference distance and the spacing distance difference value is less than or equal to the preset distance difference value, the area analysis module determines whether to fill the loss area based on whether the character shape in the character area other than the loss area is symmetrical about the vertical center axis or the horizontal center axis of the corresponding character area.

[0027] Further, if the reference spacing distance of the corresponding character area is greater than the preset reference distance or the spacing distance difference value is greater than the preset distance difference value, the area analysis module determines whether to increase the short side length and the long side length of the corresponding character area according to the number of edge loss areas;

[0028] The increase in the short side length and the long side length is positively correlated with the number of edge loss areas.

[0029] The present invention also provides a method for applying the blockchain-based electronic certificate management system, comprising:

[0030] Determine the image quality category based on brightness distribution uniformity and clarity reference value;

[0031] Determine the storage method of the certificate according to the image quality category of each certificate image to be processed;

[0032] The certificate storage method is to determine a preferred certificate image according to the quality coefficient of each first-class quality image, or to determine a preferred certificate image according to the quality coefficient of each certificate image to be processed and perform character recognition on the preferred certificate image;

[0033] Determine the character processing method according to the loss area ratio and the distribution status of the loss area within the character area;

[0034] The character processing method is to determine the character recognition method according to the reference spacing distance and the spacing distance difference value of whether the corresponding character area is in the preset area or the loss area in the character area;

[0035] The preferred certificate image of the certificate to be uploaded is stored in a preset certificate directory. If it has character information, the preferred certificate image and its character information are stored together in the same preset certificate directory.

[0036] Compared with the prior art, the beneficial effect of the present invention lies in that the technical solution of the present invention determines the image quality category according to the brightness distribution uniformity and the clarity reference value, and determines the certificate storage method according to the image quality category of each certificate image to be processed, thereby ensuring that the determined certificate storage method conforms to the actual working scenario, while ensuring the efficiency of uploading the certificate information, and ensuring the integrity of the stored certificate information. The present invention improves the integrity of the acquired certificate information.

[0037] Furthermore, in the present invention, when there is a type of quality image among each of the certificate images to be processed for the uploaded certificate, the preferred certificate image is determined based on the quality coefficient of each type of quality image, thereby improving the work efficiency of the certificate information uploading process and ensuring the integrity of the certificate information stored in the system, thereby avoiding information missing during subsequent certificate use and resulting in low work efficiency.

[0038] Furthermore, in the present invention, when all the certificate images to be processed of the uploaded certificates are of Class II quality, a preferred certificate image is determined based on the quality coefficient of each certificate image to be processed, and character recognition is performed on the preferred certificate image. The preferred certificate image and its character information are stored together, and the certificate information is stored by coordinating the character information with the certificate image, which can effectively improve the integrity of the certificate information.

[0039] Furthermore, the present invention determines the character processing method according to the proportion of the loss area in the character area, and further selects a targeted character recognition method according to whether the character area is in a preset area and the distribution status of the loss area, so that the character recognition method conforms to the actual application scenario, thereby improving the efficiency of the character recognition process. The present invention improves the uploading efficiency of the certificate information while ensuring the integrity and validity of the certificate information stored in the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a module connection diagram of the blockchain-based electronic certificate management system of the present invention;

[0041] Figure 2This is a flow chart of the present invention for determining a certificate storage method according to the image quality category of each certificate image to be processed;

[0042] Figure 3 A flow chart of the present invention for determining a character processing method according to a loss area ratio in a character area;

[0043] Figure 4 It is a schematic diagram of the electronic certificate management method based on blockchain of the present invention. DETAILED DESCRIPTION

[0044] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0045] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0046] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0047] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0048] See also Figures 1 to 3 As shown, the present invention provides an electronic certificate management system based on blockchain, comprising:

[0049] An image analysis module is used to detect the brightness distribution uniformity and clarity reference value of each certificate image to be processed, and determine the image quality category according to the brightness distribution uniformity and clarity reference value;

[0050] A storage analysis module, which is connected to the image analysis module and is used to determine the storage mode of the certificate according to the image quality category of each certificate image to be processed;

[0051] A character processing module, which is connected to the storage analysis module and is used to perform character recognition and determine a character processing method according to the loss area ratio in the character area;

[0052] A correlation analysis module, which is connected to the storage analysis module and is used to determine the correlation coefficient of each stored preferred certificate image and determine the associated certificate image according to the correlation coefficient;

[0053] A region analysis module connected to the storage analysis module to determine whether to perform region filling for the loss region or to adjust the short side length and the long side length of the character region;

[0054] The certificate storage module is respectively connected to the image analysis module, the storage analysis module and the character processing module, and is used to store the preferred certificate image and character information of each electronic certificate.

[0055] Among them, the certificate image to be processed in the present invention is an image obtained by the user by taking a photo of the certificate to be uploaded and uploaded to the system. The certificate to be uploaded is the certificate that the user needs to store in the blockchain-based electronic certificate management system described in the present invention. The certificates in the present invention are all inspection certificates and quarantine certificates for meat products.

[0056] Specifically, the image quality categories include:

[0057] A quality image whose brightness distribution uniformity is greater than a preset brightness distribution uniformity and whose definition reference value is greater than a preset definition reference value;

[0058] A second-class quality image whose brightness distribution uniformity is less than or equal to a preset brightness distribution uniformity or whose clarity reference value is less than or equal to a preset clarity reference value.

[0059] The brightness distribution uniformity is determined according to the maximum brightness value and the minimum brightness value of each certificate image to be processed, and the brightness distribution uniformity is the ratio of the difference between the maximum brightness value and the minimum brightness value to the sum of the maximum brightness value and the minimum brightness value. The clarity reference value is determined according to the number of pixels and the noise intensity of each certificate image to be processed, and the clarity reference value is the natural logarithm of the product of the number of pixels and the noise intensity.

[0060] The values ​​of the preset brightness distribution uniformity and the preset clarity reference value can be set by the user according to actual needs and historical records. The higher the user's requirements for the integrity of the stored preferred certificate image, the greater the value of the preset brightness distribution uniformity and the smaller the value of the preset clarity reference value. A method for determining the preset brightness distribution uniformity is provided, and the average value of the brightness distribution uniformity of each preferred certificate image without stored character information in the historical records that meet the user's requirements for the integrity of the stored certificate information is recorded as the preset brightness distribution uniformity. A method for determining the preset clarity reference value is provided, and the average value of the clarity reference values ​​of each preferred certificate image without stored character information in the historical records that meet the user's requirements for the integrity of the stored certificate information is recorded as the preset clarity reference value.

[0061] Specifically, for a single certificate to be uploaded, the certificate storage method is that the storage analysis module determines the preferred certificate image according to the quality coefficient of each type of quality image, or the storage analysis module determines the preferred certificate image according to the quality coefficient of each certificate image to be processed, and performs character recognition on the preferred certificate image through the character processing module.

[0062] Among them, if there is a first-class quality image among the certificate images to be processed of the certificates to be uploaded, the storage and analysis module determines the preferred certificate image according to the quality coefficients of the first-class quality images, and stores the preferred certificate image in the certificate storage module; if the certificate images to be processed of the certificates to be uploaded are all second-class quality images, the storage and analysis module determines the preferred certificate image according to the quality coefficients of the certificate images to be processed, performs character recognition on the preferred certificate image, and stores the preferred certificate image and its character information together in the certificate storage module;

[0063] The image to be processed with a larger quality coefficient is set as the priority certificate image. The quality coefficient is determined according to the brightness distribution uniformity and the clarity reference value of each certificate image to be processed. The quality coefficient is the sum of the products of the brightness distribution uniformity and the clarity reference value and the corresponding influence coefficient. The user can set the values ​​of the influence coefficients corresponding to the brightness distribution uniformity and the clarity reference value according to actual needs. After the user writes the preferred certificate image, the smaller the influence of the wrinkle area or light on the image, the higher the requirement for the brightness uniformity of the preferred certificate image, and the larger the value of the influence coefficient corresponding to the brightness distribution uniformity. A method for determining the brightness distribution uniformity is provided, and a value of the influence coefficient corresponding to the brightness distribution uniformity is provided. The value of the influence coefficient corresponding to the brightness distribution uniformity is 0.4. The higher the user's clarity requirement for the preferred certificate image, the larger the value of the influence coefficient corresponding to the clarity reference value. A value of the influence coefficient corresponding to the clarity reference value is provided, and the value of the influence coefficient corresponding to the clarity reference value is 0.6.

[0064] Specifically, the character processing module performs character recognition in the following steps:

[0065] Extract characters from the preferred certificate image and divide the characters into corresponding character areas;

[0066] A loss area detection is performed on the character area of ​​the unrecognized character, and a character processing method is determined according to the loss area ratio in the character area to determine the unrecognized character.

[0067] Among them, the character area is a rectangular area with the maximum width of each character as the short side length and the maximum height of each character as the long side length, the loss area is the area in the character area where the character edges cannot be recognized, and the unrecognized character is a character whose character information cannot be obtained due to the existence of a loss area in the character area during the character extraction process.

[0068] Specifically, for a single character, if the loss area ratio of the corresponding character area is greater than the preset loss area ratio, the character recognition method includes:

[0069] If the character area is in the preset area, the character processing module determines the character information of the character area according to the character information of the same preset area in the associated certificate image;

[0070] If the character area is not within the preset area, the character processing module sends an image quality warning to the uploading user of the corresponding character.

[0071] The loss area ratio is the ratio of the area of ​​the loss area in the corresponding character area to the area of ​​the corresponding character area. The value of the preset loss area ratio can be set by the user according to actual needs and historical records. The higher the user's requirements for the accuracy of the character recognition result, the smaller the value of the preset loss area ratio. A method for setting the value of the preset loss area ratio is provided. The historical records that meet the user's requirements for the accuracy of the character recognition result are recorded as reference records. The average value of the loss area ratio of each character area in each character recognition in the reference record is recorded as the preset loss area ratio. A value of the preset loss area ratio is provided, and the value of the preset loss area ratio is 0.3.

[0072] The preferred certificate images in the same preset certificate directory are divided into a number of sub-image areas of the same area according to the same division method. The preset area is determined according to the reference information similarity in the sub-image areas at the same position of the preferred certificate images in the same preset certificate directory. The sub-image area with a reference information similarity greater than the preset reference information similarity is recorded as the preset area. The reference information similarity is the ratio of the number of identical characters in the sub-image area to the maximum number of characters in the sub-image area corresponding to the same position of each preferred certificate image. The value of the preset reference information similarity can be set by the user according to actual needs and historical records. The higher the user's requirements for the accuracy of the character recognition result, the greater the value of the preset reference information similarity. A value of the preset reference information similarity is provided, and the value of the preset reference information similarity is 50%.

[0073] Specifically, for a preferred certificate image to be stored, the association analysis module determines the associated certificate image according to the association coefficient between each stored preferred certificate image and the preferred certificate image;

[0074] The correlation coefficient is determined according to the similarity between the certificate information of each stored preferred certificate image and the recognized certificate information of the preferred certificate image and the certificate interval time.

[0075] Wherein, the associated certificate image is a stored preferred certificate image whose association coefficient is greater than a preset association coefficient. The value of the preset association coefficient can be set by the user according to actual needs and historical records. The higher the user's requirements for the accuracy of the character recognition result, the larger the preset association coefficient. A method for setting the value of the preset association coefficient is provided, and the historical records of determining the character information according to the associated certificate image are recorded as associated reference records, and the average value of the association coefficients of the associated certificate images in each associated reference record that meets the user's requirements for the accuracy of the character recognition result is recorded as the preset association coefficient.

[0076] The correlation coefficient is the natural logarithm of the product of the similarity between the certificate information of each stored preferred certificate image and the recognized certificate information of the preferred certificate image and the certificate interval time length. The certificate information similarity is determined according to the information overlap degree of the overlap information in the certificate information corresponding to the two certificate images and the weight coefficient of the corresponding category overlap information. S i is the degree of information overlap of the i-th type of overlap information, θi is the weight coefficient corresponding to the i-th type of overlap information, and the overlap information categories include but are not limited to the certificate inspection location, inspection goods and production units, etc. In the present invention, the weight coefficients corresponding to various types of overlap information are not specifically set. Users can set the weight coefficients corresponding to various types of overlap information according to actual needs. The certificate interval time is the interval time between the visa dates of the certificates corresponding to the two certificate images.

[0077] Specifically, for a single character, if the loss area ratio of the corresponding character area is less than or equal to the preset loss area ratio, the character processing module determines the character recognition method according to the reference spacing distance of the loss area in the character area and the spacing distance difference value;

[0078] The reference spacing distance and the spacing distance difference value are determined according to the spacing distance between adjacent loss areas in the character area.

[0079] Among them, the reference spacing distance is the average value of the spacing distances of adjacent loss areas in the character area, and the spacing distance difference value is the ratio of the sum of the absolute values ​​of the differences between the spacing distances of adjacent loss areas and the reference spacing distance to the product of the number of loss areas and the preset number of adjacent areas. For any loss area, the adjacent loss area is determined according to the spacing distance between the loss area and other loss areas, and the loss areas with a preset number of adjacent areas from small to large according to the spacing distance are recorded as adjacent loss areas of the loss area. The user can set the preset number of adjacent areas according to actual needs. The higher the user's requirements for the accuracy of the character recognition results, the more preset adjacent areas are set. A value of the preset number of adjacent areas is provided, and the value of the preset number of adjacent areas is 4.

[0080] Specifically, if the reference spacing distance of the loss area in the corresponding character area is less than or equal to the preset reference distance and the spacing distance difference value is less than or equal to the preset distance difference value, the area analysis module determines whether to fill the loss area based on whether the character shape in the character area other than the loss area is symmetrical about the vertical center axis or the horizontal center axis of the corresponding character area.

[0081] Among them, the vertical center axis is the line connecting the center points of the two short sides of the character area, and the horizontal center axis is the line connecting the center points of the two long sides of the character area. If the character shape in the character area other than the loss area is symmetrical about the vertical center axis or the horizontal center axis of the corresponding character area, the loss area is filled according to the character shape of the area symmetrical to the loss area, and secondary character recognition is performed on the character area after the loss area is filled. If the character information in the character area is still not recognized, the character information of the character area is determined according to the character information of the same preset area of ​​the associated certificate image.

[0082] Specifically, if the reference spacing distance of the corresponding character area is greater than the preset reference distance or the spacing distance difference value is greater than the preset distance difference value, the area analysis module determines whether to increase the short side length and the long side length of the corresponding character area according to the number of edge loss areas;

[0083] The increase in the short side length and the long side length is positively correlated with the number of edge loss areas.

[0084] Among them, the edge loss area is a loss area whose distance from the center point of the character area is greater than the preset edge distance, and the center point is the intersection of the vertical center axis and the horizontal center axis. A preset edge distance value is provided, and the preset edge distance value is 20% of the corresponding character area. When the number of edge loss areas accounts for a large proportion, the character area division has a greater impact on character recognition. The short side length and the long side length of the character area are increased and adjusted to avoid incomplete character information in the character area and resulting in unrecognizable. The proportion of the number of edge loss areas is the ratio of the number of edge loss areas to the number of loss areas in the character area.

[0085] The preset reference distance and the preset distance difference value can be set by the user according to the historical records. The historical record of determining whether to adjust the short side length and the long side length of the corresponding character area according to the number of edge loss areas is recorded as the distance reference record, the minimum value of the reference interval distance in the distance reference record is recorded as the preset reference distance, and the minimum value of the interval distance difference value is recorded as the preset distance difference value.

[0086] See also Figure 4 As shown, it is a schematic diagram of the electronic certificate management method based on blockchain of the present invention. The present invention provides an electronic certificate management method based on blockchain, including:

[0087] Determine the image quality category based on brightness distribution uniformity and clarity reference value;

[0088] Determine the storage method of the certificate according to the image quality category of each certificate image to be processed;

[0089] The certificate storage method is to determine a preferred certificate image according to the quality coefficient of each first-class quality image, or to determine a preferred certificate image according to the quality coefficient of each certificate image to be processed and perform character recognition on the preferred certificate image;

[0090] Determine the character processing method according to the loss area ratio and the distribution status of the loss area within the character area;

[0091] The character processing method is to determine the character recognition method according to the reference spacing distance and the spacing distance difference value of whether the corresponding character area is in the preset area or the loss area in the character area;

[0092] The preferred certificate image of the certificate to be uploaded is stored in a preset certificate directory. If it has character information, the preferred certificate image and its character information are stored together in the same preset certificate directory.

[0093] Among them, the user can divide the preset certificate directory according to the visa date and application area of ​​each certificate. It is worth noting that the user can choose the setting method of the preset certificate directory according to the actual work situation. This is easy for technical personnel in this field to understand and will not be elaborated here.

[0094] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0095] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An electronic certificate management system based on blockchain, characterized in that: include: An image analysis module is used to detect the brightness distribution uniformity and clarity reference value of each certificate image to be processed, and determine the image quality category according to the brightness distribution uniformity and clarity reference value; A storage analysis module, which is connected to the image analysis module and is used to determine the storage mode of the certificate according to the image quality category of each certificate image to be processed; A character processing module, which is connected to the storage analysis module and is used to perform character recognition and determine a character processing method according to the loss area ratio in the character area; A correlation analysis module, which is connected to the storage analysis module and is used to determine the correlation coefficient of each stored preferred certificate image and determine the associated certificate image according to the correlation coefficient; A region analysis module connected to the storage analysis module to determine whether to perform region filling for the loss region or to adjust the short side length and the long side length of the character region; The certificate storage module is respectively connected to the image analysis module, the storage analysis module and the character processing module, and is used to store the preferred certificate image and character information of each electronic certificate.

2. The blockchain-based electronic certificate management system according to claim 1 is characterized in that: The image quality categories include: A quality image whose brightness distribution uniformity is greater than a preset brightness distribution uniformity and whose definition reference value is greater than a preset definition reference value; A second-class quality image whose brightness distribution uniformity is less than or equal to a preset brightness distribution uniformity or whose clarity reference value is less than or equal to a preset clarity reference value.

3. The blockchain-based electronic certificate management system according to claim 2 is characterized in that: For a single certificate to be uploaded, the certificate storage method is that the storage analysis module determines the preferred certificate image according to the quality coefficient of each type of quality image, or the storage analysis module determines the preferred certificate image according to the quality coefficient of each certificate image to be processed, and performs character recognition on the preferred certificate image through the character processing module.

4. The blockchain-based electronic certificate management system according to claim 3 is characterized in that: The process of character recognition by the character processing module includes: Extract characters from the preferred certificate image and divide the characters into corresponding character areas; A loss area detection is performed on the character area of ​​the unrecognized character, and a character processing method is determined according to the loss area ratio in the character area to determine the unrecognized character.

5. The blockchain-based electronic certificate management system according to claim 4 is characterized in that: For a single character, if the loss area ratio of the corresponding character area is greater than the preset loss area ratio, the character recognition method includes: If the character area is in the preset area, the character processing module determines the character information of the character area according to the character information of the same preset area in the associated certificate image; If the character area is not within the preset area, the character processing module sends an image quality warning to the uploading user of the corresponding character.

6. The blockchain-based electronic certificate management system according to claim 5 is characterized in that: For a preferred certificate image to be stored, the association analysis module determines the associated certificate image according to the association coefficient between each stored preferred certificate image and the preferred certificate image; The correlation coefficient is determined according to the similarity between the certificate information of each stored preferred certificate image and the recognized certificate information of the preferred certificate image and the certificate interval time.

7. The blockchain-based electronic certificate management system according to claim 6 is characterized in that: For a single character, if the loss area ratio of the corresponding character area is less than or equal to the preset loss area ratio, the character processing module determines the character recognition method according to the reference spacing distance of the loss area in the character area and the spacing distance difference value; The reference spacing distance and the spacing distance difference value are determined according to the spacing distance between adjacent loss areas in the character area.

8. The blockchain-based electronic certificate management system according to claim 7 is characterized in that: If the reference spacing distance of the loss area in the corresponding character area is less than or equal to the preset reference distance and the spacing distance difference value is less than or equal to the preset distance difference value, the area analysis module determines whether to fill the loss area based on whether the character shape in the character area other than the loss area is symmetrical about the vertical center axis or the horizontal center axis of the corresponding character area.

9. The blockchain-based electronic certificate management system according to claim 8 is characterized in that: If the reference spacing distance of the corresponding character area is greater than the preset reference distance or the spacing distance difference value is greater than the preset distance difference value, the area analysis module determines whether to increase the short side length and the long side length of the corresponding character area according to the number of edge loss areas; The increase in the short side length and the long side length is positively correlated with the number of edge loss areas.

10. A blockchain-based electronic certificate management method according to any one of claims 1 to 9, characterized in that: include: Determine the image quality category based on brightness distribution uniformity and clarity reference value; Determine the storage method of the certificate according to the image quality category of each certificate image to be processed; The certificate storage method is to determine a preferred certificate image according to the quality coefficient of each first-class quality image, or to determine a preferred certificate image according to the quality coefficient of each certificate image to be processed and perform character recognition on the preferred certificate image; Determine the character processing method according to the loss area ratio and the distribution status of the loss area within the character area; The character processing method is to determine the character recognition method according to the reference spacing distance and the spacing distance difference value of whether the corresponding character area is in the preset area or the loss area in the character area; The preferred certificate image of the certificate to be uploaded is stored in a preset certificate directory. If it has character information, the preferred certificate image and its character information are stored together in the same preset certificate directory.

Images