Enterprise data uploading system

Abstract

The invention relates to the technical field of data transmission, in particular to an enterprise data uploading system, which obtains the byte size of a to-be-uploaded picture and the current network uploading speed through a data sending module, and calculates the uploading time according to the byte size of the to-be-uploaded picture and the current network uploading speed through a data calculation module. The method comprises the steps that a data acquisition module acquires to-be-uploaded pictures, judges whether the to-be-uploaded pictures are directly uploaded or not, a data adjustment module obtains pixels of the to-be-uploaded pictures and similarity values between the to-be-uploaded pictures and divides the to-be-uploaded pictures into categories, a data uploading strategy selection unit is used for selecting an uploading strategy according to the categories of the to-be-uploaded pictures, and a display unit displays the uploading process of the to-be-uploaded pictures. According to the method, the to-be-uploaded pictures are classified, and different uploading strategies are adopted for different types of to-be-uploaded pictures, so that the uploading efficiency is improved, and the resource waste is reduced.

Description

Technical Field

[0001] This invention relates to the field of data transmission technology, and in particular to an enterprise data uploading system. Background Technology

[0002] In digital office and enterprise data management systems, image data is a crucial component of core business data, possessing irreplaceable value in scenarios such as product development, production control, customer service, and compliance auditing. With the expansion of enterprise business scale and the upgrading of imaging technology, high-definition and high-resolution image data have become the mainstream trend for enterprise image data. The volume of image data uploaded in batches can reach GB levels, leading to a continuous increase in the overall volume of enterprise image data.

[0003] Chinese Patent Publication No. CN118297532A discloses an automatic review method and system based on corporate visual recognition specifications. The automatic review method includes: constructing and training a multi-task deep learning model for corporate logo target detection and scene recognition; obtaining the object to be reviewed and converting it into an image; performing information recognition and processing on the image to be reviewed; and constructing a corporate logo and corporate name review rule engine based on corporate visual recognition rules. This invention utilizes the image semantic understanding capabilities of the multi-task deep learning model for corporate logo detection and scene recognition, and the text detection and recognition model to detect and recognize corporate logos and corporate names in images. Through a rule engine based on image processing and the corporate visual system specification manual, it can accurately review batches of images in newly uploaded folder compressed packages according to corporate visual recognition specifications.

[0004] However, the following problems still exist in the existing technology: 1. Enterprise data upload systems often adopt a full direct upload mode without any filtering or optimization of image data. All images to be uploaded are directly transmitted to the target server via the network, resulting in excessive upload time and affecting business efficiency. 2. The image data uploaded by enterprises in batches generally contains a large amount of similar or redundant content. The storage of similar and redundant images occupies a lot of server storage space, which requires enterprises to invest additional costs to expand storage resources, resulting in a waste of hardware resources and network bandwidth. Summary of the Invention

[0005] To address this issue, the present invention provides an enterprise data upload system to overcome the problems of low upload efficiency and resource waste caused by the lack of classification, filtering, and optimization of image data during the upload process in the prior art.

[0006] To achieve the above objectives, the present invention provides an enterprise data upload system, comprising: The data sending module is used to obtain the size of the image to be uploaded in bytes and the current network upload speed; The data calculation module, connected to the data sending module, is used to calculate the upload time based on the size of the image to be uploaded in bytes and the current network upload speed, and to determine whether to upload directly based on the upload time. The data adjustment module, connected to the data calculation module, is used to obtain the pixels of each image to be uploaded and the similarity value between each image to be uploaded, and to classify each image to be uploaded into a category based on the pixels of each image to be uploaded and the similarity value between each image to be uploaded. The data upload module includes a data upload strategy selection unit and a display unit, and is connected to the data adjustment module. The data upload strategy selection unit is used to select an upload strategy based on the category of each image to be uploaded, including: Upload the image to be uploaded directly; Alternatively, feature images can be extracted based on the similarity between the images to be uploaded, the feature images can be sorted in ascending order by pixels, and feature images with a preset ratio can be selected in sequence to reject the upload, while the remaining images to be uploaded can be uploaded. The display unit is used to show the upload progress of the image to be uploaded.

[0007] Furthermore, the data calculation module calculates the upload time based on the size of the image to be uploaded in bytes and the current network upload speed. Retrieve all images to be uploaded; The upload time is the ratio of the total number of bytes of the images to be uploaded to the current network upload speed.

[0008] Furthermore, the data calculation module determines whether to upload directly based on the upload time, wherein, Compare the upload time with a preset upload time comparison threshold; If the upload time exceeds the preset upload time comparison threshold, it will be determined that the upload will not be performed directly. If the upload time is less than or equal to the preset upload time comparison threshold, it is determined that the upload will proceed directly.

[0009] Furthermore, the data adjustment module obtains the determination result from the data calculation module. If it is determined that the image will not be uploaded directly, the pixel count of each image to be uploaded and the similarity value between each image to be uploaded are obtained, and the images to be uploaded are classified into categories based on the pixel count of each image to be uploaded and the similarity value between each image to be uploaded.

[0010] Furthermore, the data adjustment module categorizes the images to be uploaded into different types based on their pixel count and the similarity values ​​between them. Iterate through all the images to be uploaded and calculate the similarity value of each image to be uploaded to all other images to be uploaded. If the image to be uploaded meets the preset conditions, then the image to be uploaded will be classified as a key data category; If the image to be uploaded does not meet the preset conditions, the image to be uploaded will be classified as a general data category. The preset conditions are that the pixel count of the image to be uploaded is greater than the average pixel count of the images to be uploaded, and there exists an image to be uploaded whose similarity value with all the remaining images to be uploaded is less than a preset similarity value comparison threshold.

[0011] Furthermore, the data upload strategy selection unit is used to select an upload strategy based on the category of each image to be uploaded, wherein, If the category of the image to be uploaded is a critical data category, then upload the image directly. If the category of the images to be uploaded is a general data category, feature images are extracted based on the similarity values ​​between the images to be uploaded. The feature images are then sorted in ascending order by pixels. Feature images with a preset ratio are selected in sequence and rejected from uploading. The remaining images to be uploaded are then uploaded.

[0012] Furthermore, the data upload strategy selection unit extracts feature images based on the similarity values ​​between the images to be uploaded. Iterate through all the images to be uploaded and calculate the similarity value of each image to be uploaded to all other images to be uploaded. If any image to be uploaded satisfies the similarity value condition with at least one other image to be uploaded, then the image to be uploaded and all images to be uploaded that satisfy the similarity value condition are extracted as feature images. The similarity condition is that the similarity value is greater than a preset feature similarity value comparison threshold.

[0013] Furthermore, it also includes a data encryption module to encrypt the images to be uploaded before uploading.

[0014] Furthermore, it also includes a resume upload module, which is used to obtain the upload progress of the image to be uploaded. If the upload of the image to be uploaded is interrupted, the interruption position is recorded and the upload is resumed from the interruption position.

[0015] Furthermore, the display unit displays the remaining time required to upload the image to be uploaded and the number of images remaining to be uploaded.

[0016] Compared with existing technologies, this invention obtains the byte size of the image to be uploaded and the current network upload speed through a data sending module; a data calculation module calculates the upload time based on the byte size of the image to be uploaded and the current network upload speed, and determines whether to upload directly; a data adjustment module obtains the pixel value of each image to be uploaded and the similarity value between each image to be uploaded to classify the images to be uploaded into different categories; a data upload strategy selection unit selects an upload strategy based on each image category; and a display unit displays the upload progress of the images to be uploaded. By classifying the images to be uploaded into different categories and adopting different upload strategies for different categories of images to be uploaded, this invention improves upload efficiency and reduces resource waste.

[0017] In particular, the data calculation module of this invention determines whether to upload directly based on the upload time. In enterprise image data upload scenarios, excessively long upload times can significantly increase the probability of network fluctuations and device malfunctions, such as power outages, crashes, and temporary server failures, which can lead to upload interruptions, data loss, or corruption. If the upload time is too long, the optimization process of the data adjustment module is triggered to reduce the amount of data uploaded and shorten the actual upload time from the source. This can significantly reduce the probability of various accidents during long uploads and improve the reliability of data uploads.

[0018] In particular, this invention uses a data adjustment module to categorize images to be uploaded based on their pixel count and similarity values. In practice, among the batch-uploaded images by enterprises, there are high-value key images (clear and indispensable) and low-value redundant images (unclear and numerous similar images). This invention's data adjustment module precisely defines key data through a dual-dimensional approach, ensuring that key images possess high clarity and unique information—meaning there are no substitute images and the information cannot be supplemented by other images if lost. This avoids the problem of core information loss due to upload optimization and ensures the integrity of enterprise business data.

[0019] In particular, this invention extracts feature images based on the similarity between images to be uploaded through the data upload module, arranges the feature images in ascending order by pixels, and selects a preset proportion of feature images to reject uploads in sequence. In practice, similar and redundant images are often accompanied by images of poor quality. For example, during production workshop inspections, among 10 similar images taken consecutively from the same equipment, there may be 3-4 low-resolution blurry images due to hand tremors or insufficient lighting. By deleting similar and redundant images, the upload efficiency is improved. Attached Figure Description

[0020] Figure 1 This is a structural diagram of an enterprise data upload system according to an embodiment of the invention; Figure 2 This is a structural diagram of the data upload module according to an embodiment of the invention; Figure 3 The data calculation module in this embodiment of the invention determines whether to upload directly based on the upload time; Figure 4 This is a logical decision diagram for the data adjustment module of the invention embodiment, which divides the images to be uploaded into different categories. Detailed Implementation

[0021] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0022] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0023] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the term "connection" should be interpreted broadly. 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 an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0024] Please see Figures 1-4 As shown, Figure 1 This is a structural diagram of an enterprise data upload system according to an embodiment of the invention. Figure 2 This is a structural diagram of the data upload module according to an embodiment of the invention. Figure 3 The data calculation module in this embodiment of the invention determines whether to upload directly based on the upload time. Figure 4 The data adjustment module of this invention divides the images to be uploaded into different categories using a logical decision diagram. The enterprise data upload system of this invention includes: The data sending module is used to obtain the size of the image to be uploaded in bytes and the current network upload speed; The data calculation module, connected to the data sending module, is used to calculate the upload time based on the size of the image to be uploaded in bytes and the current network upload speed, and to determine whether to upload directly based on the upload time. The data adjustment module, connected to the data calculation module, is used to obtain the pixels of each image to be uploaded and the similarity value between each image to be uploaded, and to classify each image to be uploaded into a category based on the pixels of each image to be uploaded and the similarity value between each image to be uploaded. The data upload module includes a data upload strategy selection unit and a display unit, and is connected to the data adjustment module. The data upload strategy selection unit is used to select an upload strategy based on the category of each image to be uploaded, including: Upload the image to be uploaded directly; Alternatively, feature images can be extracted based on the similarity between the images to be uploaded, the feature images can be sorted in ascending order by pixels, and feature images with a preset ratio can be selected in sequence to reject the upload, while the remaining images to be uploaded can be uploaded. The display unit is used to show the upload progress of the image to be uploaded.

[0025] In this embodiment, the similarity value between the images to be uploaded is calculated using image processing software, which can be MATLAB.

[0026] Specifically, the data calculation module calculates the upload time based on the size of the image to be uploaded in bytes and the current network upload speed. Retrieve all images to be uploaded; The upload time is the ratio of the total number of bytes of the images to be uploaded to the current network upload speed.

[0027] Specifically, the data calculation module determines whether to upload directly based on the upload time, wherein, Compare the upload time with a preset upload time comparison threshold; If the upload time exceeds the preset upload time comparison threshold, it will be determined that the upload will not be performed directly. If the upload time is less than or equal to the preset upload time comparison threshold, it is determined that the upload will proceed directly.

[0028] In this embodiment, the preset upload time comparison threshold is selected within the range of [20min, 30min].

[0029] Specifically, the data calculation module of this invention determines whether to upload directly based on the upload time. In enterprise image data upload scenarios, excessively long upload times significantly increase the probability of network fluctuations and device malfunctions, such as power outages, crashes, and temporary server failures, which can lead to upload interruptions, data loss, or corruption. If the upload time is too long, the optimization process of the data adjustment module is triggered to reduce the amount of data uploaded and shorten the actual upload time from the source. This can significantly reduce the probability of various accidents during long uploads and improve the reliability of data uploads.

[0030] Specifically, the data adjustment module obtains the determination result from the data calculation module. If it is determined that the image will not be uploaded directly, the pixel count of each image to be uploaded and the similarity value between each image to be uploaded are obtained, and the images to be uploaded are classified into categories based on the pixel count of each image to be uploaded and the similarity value between each image to be uploaded.

[0031] Specifically, the data adjustment module categorizes the images to be uploaded into different types based on their pixel count and the similarity values ​​between them. Iterate through all the images to be uploaded and calculate the similarity value of each image to be uploaded to all other images to be uploaded. If the image to be uploaded meets the preset conditions, then the image to be uploaded will be classified as a key data category; If the image to be uploaded does not meet the preset conditions, the image to be uploaded will be classified as a general data category. The preset conditions are that the pixel count of the image to be uploaded is greater than the average pixel count of the images to be uploaded, and there exists an image to be uploaded whose similarity value with all the remaining images to be uploaded is less than a preset similarity value comparison threshold.

[0032] Understandably, if there are 50 images to be uploaded, the similarity value of one image to be uploaded is obtained sequentially with the remaining 49 images to be uploaded. Each image to be uploaded has 49 similarity values. If all 49 similarity values ​​of an image to be uploaded are less than a preset similarity value comparison threshold, and the pixel value of the image to be uploaded is greater than the average pixel value of the images to be uploaded, then the image to be uploaded is classified as a key data category.

[0033] In this embodiment, the preset similarity comparison threshold is selected within the range of [40%, 50%].

[0034] Specifically, this invention uses a data adjustment module to categorize images to be uploaded based on their pixel count and similarity values. In practice, among the batch-uploaded images by enterprises, there are high-value key images (clear and indispensable) and low-value redundant images (unclear and numerous similar images). The data adjustment module of this invention accurately defines key data through dual dimensions, ensuring that key images have high clarity and unique information, meaning there are no substitute images and the information cannot be supplemented by other images if lost. This avoids the problem of core information loss due to upload optimization and ensures the integrity of enterprise business data.

[0035] Specifically, the data upload strategy selection unit is used to select an upload strategy based on the category of each image to be uploaded, wherein, If the category of the image to be uploaded is a critical data category, then upload the image directly. If the category of the images to be uploaded is a general data category, feature images are extracted based on the similarity values ​​between the images to be uploaded. The feature images are then sorted in ascending order by pixels. Feature images with a preset ratio are selected in sequence and rejected from uploading. The remaining images to be uploaded are then uploaded.

[0036] Specifically, the data upload strategy selection unit extracts feature images based on the similarity values ​​between the images to be uploaded. Iterate through all the images to be uploaded and calculate the similarity value of each image to be uploaded to all other images to be uploaded. If any image to be uploaded satisfies the similarity value condition with at least one other image to be uploaded, then the image to be uploaded and all images to be uploaded that satisfy the similarity value condition are extracted as feature images. The similarity condition is that the similarity value is greater than a preset feature similarity value comparison threshold.

[0037] Understandably, if there are 50 images to be uploaded, the similarity value of one image to be uploaded is obtained sequentially with the remaining 49 images to be uploaded. Each image to be uploaded has 49 similarity values. If one or more similarity values ​​of an image to be uploaded are greater than a preset feature similarity value comparison threshold, then the image to be uploaded, as well as the images to be uploaded with similarity values ​​greater than the preset feature similarity value comparison threshold, are extracted as feature images.

[0038] Understandably, the feature images are in several sets, and each set contains several images to be uploaded whose similarity value is greater than the preset feature similarity value comparison threshold.

[0039] In this embodiment, the preset feature similarity value comparison threshold is selected within the range of [80%, 90%].

[0040] In this embodiment, after obtaining the feature images, the feature images in each set are arranged in ascending order by pixels, and feature images with a preset percentage are selected in sequence to be rejected from uploading. The preset percentage is selected within the range of [30%, 40%], that is, the images with lower pixel counts in each set are deleted from the data to be uploaded.

[0041] It is understandable that some images with high similarity values ​​and low pixel values ​​will be deleted, and all remaining images will be uploaded.

[0042] Specifically, this invention extracts feature images based on the similarity between images to be uploaded through a data upload module. The feature images are then sorted in ascending order by pixels, and a preset proportion of feature images are selected in sequence to reject uploads. In practice, similar and redundant images are often accompanied by images of poor quality. For example, during production workshop inspections, among 10 similar images taken consecutively from the same equipment, there may be 3-4 low-resolution blurry images due to hand tremors or insufficient lighting. By deleting similar and redundant images, the upload efficiency is improved.

[0043] Specifically, it also includes a data encryption module, which is used to encrypt the images to be uploaded before uploading.

[0044] Specifically, it also includes a resume upload module, which is used to obtain the upload progress of the image to be uploaded. If the upload of the image to be uploaded is interrupted, the interruption position is recorded and the upload is resumed from the interruption position.

[0045] Specifically, the display unit displays the remaining time required to upload the image to be uploaded and the number of images remaining to be uploaded.

[0046] The modules described in the embodiments of this application can be implemented in software or hardware. The described modules can also be located in a processor, and the names of these modules do not necessarily limit the module itself.

[0047] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using dedicated hardware-based apparatus to perform the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0048] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles 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 all fall within the scope of protection of the present invention.

[0049] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An enterprise data upload system, characterized in that, include: The data sending module is used to obtain the size of the image to be uploaded in bytes and the current network upload speed; The data calculation module, connected to the data sending module, is used to calculate the upload time based on the size of the image to be uploaded in bytes and the current network upload speed, and to determine whether to upload directly based on the upload time. The data adjustment module, connected to the data calculation module, is used to obtain the pixels of each image to be uploaded and the similarity value between each image to be uploaded, and to classify each image to be uploaded into a category based on the pixels of each image to be uploaded and the similarity value between each image to be uploaded. The data upload module includes a data upload strategy selection unit and a display unit, and is connected to the data adjustment module. The data upload strategy selection unit is used to select an upload strategy based on the category of each image to be uploaded. include, Upload the image to be uploaded directly; Alternatively, feature images can be extracted based on the similarity between the images to be uploaded, the feature images can be sorted in ascending order by pixels, and feature images with a preset ratio can be selected in sequence to reject the upload, while the remaining images to be uploaded can be uploaded. The display unit is used to show the upload progress of the image to be uploaded.

2. The enterprise data upload system according to claim 1, characterized in that, The data calculation module calculates the upload time based on the size of the image to be uploaded in bytes and the current network upload speed. Get the total size in bytes of all images to be uploaded; The upload time is the ratio of the total number of bytes of the images to be uploaded to the current network upload speed.

3. The enterprise data upload system according to claim 1, characterized in that, The data calculation module determines whether to upload directly based on the upload time. Compare the upload time with a preset upload time comparison threshold; If the upload time exceeds the preset upload time comparison threshold, it will be determined that the upload will not be performed directly. If the upload time is less than or equal to the preset upload time comparison threshold, it is determined that the upload will proceed directly.

4. The enterprise data upload system according to claim 3, characterized in that, The data adjustment module obtains the judgment result from the data calculation module. If it is determined that the image will not be uploaded directly, the pixel count of each image to be uploaded and the similarity value between each image to be uploaded are obtained, and the images to be uploaded are classified into categories based on the pixel count of each image to be uploaded and the similarity value between each image to be uploaded.

5. The enterprise data upload system according to claim 1, characterized in that, The data adjustment module categorizes the images to be uploaded into different types based on their pixel count and the similarity values ​​between them. Iterate through all the images to be uploaded and calculate the similarity value of each image to be uploaded to all other images to be uploaded. If the image to be uploaded meets the preset conditions, then the image to be uploaded will be classified as a key data category; If the image to be uploaded does not meet the preset conditions, the image to be uploaded will be classified as a general data category. The preset conditions are that the pixel count of the image to be uploaded is greater than the average pixel count of the images to be uploaded, and there exists an image to be uploaded whose similarity value with all the remaining images to be uploaded is less than a preset similarity value comparison threshold.

6. The enterprise data upload system according to claim 5, characterized in that, The data upload strategy selection unit is used to select an upload strategy based on the category of each image to be uploaded, wherein, If the category of the image to be uploaded is a critical data category, then upload the image directly. If the category of the images to be uploaded is a general data category, feature images are extracted based on the similarity values ​​between the images to be uploaded. The feature images are then sorted in ascending order by pixels. Feature images with a preset ratio are selected in sequence and rejected from uploading. The remaining images to be uploaded are then uploaded.

7. The enterprise data upload system according to claim 1, characterized in that, The data upload strategy selection unit extracts feature images based on the similarity values ​​between the images to be uploaded. Iterate through all the images to be uploaded and calculate the similarity value of each image to be uploaded to all other images to be uploaded. If any image to be uploaded satisfies the similarity value condition with at least one other image to be uploaded, then the image to be uploaded and all images to be uploaded that satisfy the similarity value condition are extracted as feature images. The similarity condition is that the similarity value is greater than a preset feature similarity value comparison threshold.

8. The enterprise data upload system according to claim 1, characterized in that, It also includes a data encryption module, which is used to encrypt the images to be uploaded before uploading.

9. The enterprise data upload system according to claim 1, characterized in that, It also includes a resume upload module, which is used to obtain the upload progress of the image to be uploaded. If the upload of the image to be uploaded is interrupted, the interruption position is recorded and the upload is resumed from the interruption position.

10. The enterprise data upload system according to claim 1, characterized in that, The display unit shows the remaining time required to upload the image and the number of images remaining to be uploaded.

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