Photographic work management system based on geographic information aggregation

Abstract

The invention relates to a photographic work management system based on geographic information aggregation, and belongs to the technical field of mobile internet. According to the system, a national-provincial-municipal tree-shaped geocoding system is constructed, a dynamic storage allocation algorithm is combined to realize accurate geographic dimension aggregation of photographic works, and a cross-boundary map expansion interface is innovatively developed to support multi-coordinate system data fusion. The social matching engine establishes a vertical communication group based on a composite model of device features, geographic overlap ratio and theme similarity, and the multi-dimensional evaluation system integrates EXIF technical parameters and user feedback to generate a work quality score and matches a user level growth mechanism. In the aspect of copyright protection, a collaborative scheme of frequency domain watermark embedding and block chain evidence storage is adopted, and full-link tracking from a creation source to a propagation terminal is realized. The technical defects of regional information fragmentation, community interaction superficial layer and weak copyright protection of a traditional photography platform are solved, and the photography work management efficiency and the cultural exchange depth are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of mobile Internet technology, and in particular to a photographic work management system based on geographic information aggregation. Background Art

[0002] Currently, there are the following technical bottlenecks in the field of photographic work management and sharing:

[0003] Traditional image storage systems often use a single timeline or folder classification system, lacking a geographically based hierarchical organizational structure. This makes it difficult for users to quickly retrieve photographs from specific regions. While existing social media platforms support geotagging, they lack the ability to aggregate works with provincial-level precision. Furthermore, international photos are difficult to display uniformly due to differences in coordinate systems, limiting users' ability to systematically explore global image resources.

[0004] In terms of social interaction, mainstream platforms rely on general interest tags to match users, ignoring the multi-dimensional correlation between equipment characteristics, shooting subjects and geographical preferences, making it difficult for professional photography enthusiasts to form high-value communication circles.

[0005] In addition, copyright protection for digital works mostly remains at the basic watermark technology level, lacking a binding mechanism with the shooting location and creation time, making it difficult to collect evidence of infringement; and centralized evidence storage systems have the risk of data tampering, making it difficult to meet the security needs of managing photographic works throughout their life cycle.

[0006] The scalability limitations of existing systems also restrict the expansion of service boundaries. Traditional architectures cannot support the dynamic loading and seamless integration of multinational map data, hindering the global development of photographic cultural exchange. These technical shortcomings collectively lead to industry pain points such as inefficient photographic work management, shallow community interaction, weak copyright protection, and limited service scope.

[0007] Therefore, a photography management system based on geographic information aggregation is needed to solve the above problems. Summary of the Invention

[0008] In order to solve the problems of the prior art, the present invention provides a photographic work management system based on geographic information aggregation.

[0009] In order to solve the above technical problems, the present invention is implemented through the following technical solutions: 1. A photography work management system based on geographic information aggregation, comprising:

[0010] The geographic information layering module is used to construct a tree-like geographic coding system at the national, provincial, and municipal levels, including a national boundary vector data layer, a provincial administrative division raster layer, and a user-defined marker point cloud layer;

[0011] Dynamic storage allocation module, which automatically associates the GPS coordinates and timestamp metadata of user-uploaded photos to storage containers at the corresponding geographical level through spatial indexing algorithms;

[0012] The social relationship matching engine generates regional photography exchange groups based on users' historical uploaded regional distribution data and interactive behavior data;

[0013] A multi-dimensional evaluation calculation unit integrates photo EXIF ​​technical parameters, user rating data, and visit statistics to generate a composite quality score using the analytic hierarchy process.

[0014] The user growth modeling module calculates the user level promotion parameters based on the user's completed geographical level exploration progress and work quality score.

[0015] In a specific implementation of the first aspect, the geocoding system further includes a cross-border geo-calibration extension interface, supports the overlay and fusion of multi-country map data, and realizes seamless connection of cross-border geographic containers through a coordinate system conversion algorithm.

[0016] In this application, three core effects are achieved by constructing a collaborative mechanism between the geocoding system and the dynamic storage architecture: First, the geographic dimension aggregation technology increases the response speed of provincial-level photographic work queries by more than 40%, and the cross-border map expansion function supports seamless connection of multiple coordinate systems, so that users can view real images of more than 95% of the world's regions in real time; second, the social matching algorithm is based on a multi-dimensional similarity model of equipment, region, and subject matter, which increases the frequency of regional group interactions by 3 times, and the exposure rate of high-quality works through the screening of the composite evaluation model increases by 65%; third, the combination of blockchain evidence storage and improved image comparison technology increases the efficiency of copyright verification to milliseconds, and the infringement detection accuracy rate reaches more than 92%, forming a closed-loop protection system from creation evidence storage, authority control to infringement tracing, and finally constructing a full life cycle management system for photographic works with deep coupling of geographical attributes and social ecology.

[0017] In a specific implementation of the first aspect, a specific implementation method of the dynamic storage allocation module includes:

[0018] Use R-tree spatial index algorithm to quickly locate the GPS coordinates of photos;

[0019] A hash mapping table is used to record the correspondence between provincial containers and photo storage paths;

[0020] When a cross-border photo is detected, the coordinate conversion interface is called to map it to the extended geographic container.

[0021] In a specific implementation of the first aspect, the matching rules of the social relationship matching engine are:

[0022] Define the user similarity matrix:

[0023]

[0024] Among them, α+β+γ=1, default value α=0.5, β=0.2, γ=0.3;

[0025] When sim(u,v)≥0.6, it is automatically recommended to join the communication group in the same region.

[0026] In a specific embodiment of the first aspect, the multidimensional evaluation calculation unit includes:

[0027] Technical evaluation sub-model: Analyzes photo resolution, ISO value, exposure time parameters, and calculates the clarity score

[0028] Social evaluation sub-model: Statistical user rating mean and standard deviation σ, calculate the debiasing score

[0029] Regional association sub-model: Calculate S based on the feature matching degree between the photo GPS and the geographic container to which it belongs geo .

[0030] In a specific implementation of the first aspect, the level promotion parameters of the user growth modeling module include:

[0031] Geographic exploration coefficient:

[0032] Mass contribution coefficient: N is the total number of uploaded works;

[0033] User Level

[0034] In a specific embodiment of the first aspect, a visual interaction module is further included, which is used to:

[0035] Generate a heat map of China, using different color saturation to represent the number of photographs in each province;

[0036] When the user clicks on a provincial administrative region, the photo thumbnail and the associated communication group entrance of the region are asynchronously loaded;

[0037] Displays the user level identifier and the exploration progress required for the next level.

[0038] In a second aspect, a method for managing photographic works based on geographic information aggregation includes the following steps:

[0039] S1: Copyright logo generation

[0040] Automatically embed a digital watermark when a user uploads a photo. The digital watermark includes:

[0041] S11 photographer's identity hash value;

[0042] S12 upload timestamp;

[0043] S13 geographic container code;

[0044] S2: Access Control

[0045] Establish a three-level access rights model:

[0046] Public level: allows all users to view and comment

[0047] Group level: Access is limited to authenticated users within the same geographic container

[0048] Private level: requires the photographer to grant a temporary access token

[0049] Dynamically adjust permission policies: When a work is reported for infringement, it will be automatically downgraded to private and trigger the review process.

[0050] S3: Copyright Verification Mechanism

[0051] Develop a reverse watermark extraction algorithm and detect watermark integrity through Fourier transform

[0052] Build a blockchain evidence storage subsystem and write watermark key information into the smart contract (using Hyperledger Fabric architecture)

[0053] S4: Infringement Handling Process

[0054] Real-time crawler monitoring module: uses a deep learning-based image similarity comparison algorithm (ResNet-50 model) to scan third-party platforms

[0055] Infringement evidence solidification: When the similarity is >85%, an evidence package containing a spatiotemporal watermark verification report is automatically generated.

[0056] In a specific implementation of the second aspect, in step three:

[0057] The blockchain evidence storage subsystem performs the following operations:

[0058] Generate a Merkle tree structure containing a timestamp and upload watermark data to the chain in batches every 10 minutes

[0059] The smart contract automatically executes the copyright verification request and returns a verification certificate containing the following elements:

[0060] Copyright ownership status;

[0061] First release date;

[0062] Geographic container association proof.

[0063] In a specific implementation of the second aspect, in step 4:

[0064] The image similarity comparison algorithm adopts the improved SSIM evaluation method:

[0065]

[0066] The tampering coefficient is obtained by analyzing the abnormal values ​​of EXIF ​​metadata.

[0067] The beneficial effects of the present invention are:

[0068] 1. Through the collaborative architecture of geographic information aggregation and dynamic storage, a multi-dimensional management system for photographic works has been constructed. The hierarchical design of the geocoding system enables photographic works to be accurately grouped according to the shooting location, solving the problem of regional information fragmentation in traditional platforms; the social matching engine is based on a composite model of equipment, subject matter and geographical overlap, forming a vertical interest community, which significantly enhances the depth of professional communication between users; the multi-dimensional evaluation system organically combines technical parameter analysis with community feedback to establish a work screening mechanism that takes into account both artistic quality and communication value. The cross-border map expansion function breaks through geographical restrictions and realizes the unified calibration and visualization of global photographic works through the dynamic adaptation technology of the coordinate system, providing users with an immersive world image exploration experience;

[0069] 2. By coupling blockchain evidence storage with watermark embedding, a full-link copyright protection system has been established, from the source of creation to the end-user, effectively curbing the illegal tampering and theft of digital works. The infringement detection mechanism incorporates deep learning models and network forensics technology to achieve real-time early warning of infringements and automatically consolidate the chain of evidence, significantly improving the efficiency of rights protection. The system's layered architecture adopts a modular expansion strategy. The storage layer supports smooth upgrades from stand-alone deployment to distributed clusters, and the geographic layer achieves plug-and-play access to multi-national map data through standardized interfaces. This highly scalable technical framework not only meets the needs of individual users for lightweight use, but also supports the commercial operation of large-scale photography communities, providing the underlying technical support for building a sustainable photography ecosystem. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 It is a diagram of the overall system architecture of the present invention.

[0071] Figure 2 It is a geographical coding system diagram of the present invention.

[0072] Figure 3 It is a topological diagram of the three-level storage architecture of the present invention.

[0073] Figure 4It is a flow chart of the social matching algorithm of the present invention.

[0074] Figure 5 It is a rights management flow chart of the present invention.

[0075] Figure 6 It is the user interface state transition diagram of the present invention.

[0076] Figure 7 It is a timing diagram of infringement detection of the present invention. DETAILED DESCRIPTION

[0077] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0078] like Figures 1 to 7 A photographic works management system based on geographic information aggregation is shown.

[0079] 1. Geographic Information Processing

[0080] 1.1 Construction of geographic coding system

[0081] Data format: GeoJSON 2.0 is used to store China's provincial administrative divisions, and the coordinate system is WGS84 (EPSG:4326).

[0082] Coordinate conversion: Coordinate encryption is achieved through the WGS84 to GCJ-02 algorithm published by the National Administration of Surveying, Mapping and Geoinformation, and the conversion error is controlled within the range of ±3 meters.

[0083] Map rendering rules:

[0084] Zoom levels 1-5: Loads the country layer container (resolution ≤ 1280×720 pixels).

[0085] Zoom levels 6-10: Load provincial containers (resolution ≤ 2560 × 1440 pixels).

[0086] Zoom level ≥ 11: Load city-level markers (single-point rendering delay < 50ms).

[0087] 1.2 Cross-border expansion

[0088] Coordinate system compatibility: supports dynamic switching between WGS84 and Web Mercator (EPSG:3857).

[0089] Data Fusion: Automatically trigger coordinate system alignment compensation when the geographic bins of transnational photos overlap by ≥15%.

[0090] 2. Data Storage Architecture

[0091] 2.1 Three-level storage system

[0092]

[0093]

[0094] 2.2 Spatial Index Optimization

[0095] Index type: R-tree spatial index is used, the node capacity is set to 50 / node, and the minimum bounding rectangle accuracy is ±0.0001°.

[0096] Query optimization: Implement a maximum timeout of 500ms for provincial-level photo retrieval, and prioritize returning the top 100 works by popularity.

[0097] 3. Social interaction realization

[0098] 3.1 User Matching Rules

[0099] Weight distribution: regional overlap (55%), device similarity (25%), and subject matter matching (20%).

[0100] Trigger mechanism:

[0101] The similarity calculation of all users is performed at 2:00 am every day.

[0102] When the user similarity score is ≥0.62, it will be automatically recommended to join the group.

[0103] 3.2 Regional Chat Room Management

[0104] Creation conditions: Generate an exclusive chat room when the number of daily active users in a province is ≥50.

[0105] Message transmission:

[0106] Text message: All-node broadcast completed within 200ms.

[0107] Image message: First transmit a 100KB thumbnail, and then load the original image within 1 second after clicking.

[0108] Data retention: Text messages are retained for 7 days, and media files are transferred to cold storage after 30 days.

[0109] 4. Rights Management Methods

[0110] 4.1 Digital Watermark Embedding

[0111] Preprocessing: uniformly adjust the image size to 2048×1365 pixels.

[0112] Frequency domain processing: embed the watermark in the mid-frequency band (15th-30th frequency components) of DCT transform.

[0113] Watermark Strength: Set the invisibility standard of PSNR ≥ 48dB.

[0114] Data structure: Contains a 32-byte photographer hash value, an 8-byte timestamp, and a 16-byte geocode.

[0115] 4.2 Blockchain Evidence Storage

[0116] Network architecture: Built on Hyperledger Fabric 2.4, with a 3-node Kafka sorting service.

[0117] Evidence storage rules: Batch upload to the chain every 10 minutes, with a maximum tolerance of 15 seconds of delay.

[0118] Verification certificate: Contains three elements: original watermark hash, blockchain height and geographic fingerprint.

[0119] 5. Infringement Detection Process

[0120] 5.1 Image Comparison Technology

[0121] Feature extraction: ResNet-50 network is used to extract 2048-dimensional feature vectors.

[0122] Judgment criteria: When the cosine similarity is ≥ 0.85, an infringement alert is triggered.

[0123] Scanning strategy: Implement polling monitoring of key platforms every 15 minutes.

[0124] 5.2 Evidence solidification standards

[0125] Page forensics: Capture the complete page containing the infringing content and timestamp.

[0126] Network Forensics: Capture HTTPS communication metadata of infringing pages.

[0127] Hash evidence storage: Perform SHA-256 hash calculation on the infringement evidence package and then upload it to the chain.

[0128] Key Performance Indicators

[0129] Geographic service performance

[0130] Provincial map loading: average 83ms (95th percentile ≤ 120ms).

[0131] Coordinate conversion error: RMS ≤ 2.8 meters.

[0132] Infringement detection capabilities

[0133] Test scale Recall False positive rate 100,000 samples 93.7％ 0.23％

[0134] System throughput

[0135] Photo upload: 3800 photos / second (single photo ≤ 20MB).

[0136] Infringement scanning: 1200 pages / second.

[0137] This implementation is verified using an AWS EC2 (c5.9xlarge instance) cluster.

[0138] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A photography management system based on geographic information aggregation, characterized in that: include: The geographic information layering module is used to construct a tree-like geographic coding system at the national, provincial, and municipal levels, including a national boundary vector data layer, a provincial administrative division raster layer, and a user-defined marker point cloud layer; Dynamic storage allocation module, which automatically associates the GPS coordinates and timestamp metadata of user-uploaded photos to storage containers at the corresponding geographical level through spatial indexing algorithms; The social relationship matching engine generates regional photography exchange groups based on users' historical uploaded regional distribution data and interactive behavior data; A multi-dimensional evaluation calculation unit integrates photo EXIF ​​technical parameters, user rating data, and visit statistics to generate a composite quality score using the analytic hierarchy process. The user growth modeling module calculates the user level promotion parameters based on the user's completed geographical level exploration progress and work quality score.

2. The photographic work management system based on geographic information aggregation according to claim 1, characterized in that: The geocoding system further includes a cross-border geo-calibration extension interface, supports the overlay and fusion of multi-country map data, and realizes seamless connection of cross-border geo-containers through a coordinate system conversion algorithm.

3. The photographic work management system based on geographic information aggregation according to claim 1, characterized in that: The specific implementation method of the dynamic storage allocation module includes: Use R-tree spatial index algorithm to quickly locate the GPS coordinates of photos; A hash mapping table is used to record the correspondence between provincial containers and photo storage paths; When a cross-border photo is detected, the coordinate conversion interface is called to map it to the extended geographic container.

4. The photographic work management system based on geographic information aggregation according to claim 1, characterized in that: The matching rules of the social relationship matching engine are: Define the user similarity matrix: Among them, α+β+γ=1, default value α=0.5, β=0.2, γ=0.3; When sim(u,v)≥0.6, it is automatically recommended to join the communication group in the same region.

5. The photographic work management system based on geographic information aggregation according to claim 1, characterized in that: The multidimensional evaluation calculation unit comprises: Technical evaluation sub-model: Analyzes photo resolution, ISO value, exposure time parameters, and calculates the clarity score Social evaluation sub-model: Statistical user rating mean and standard deviation σ, calculate the debiasing score Regional association sub-model: Calculate S based on the feature matching degree between the photo GPS and the geographic container to which it belongs geo .

6. The photographic work management system based on geographic information aggregation according to claim 1, characterized in that: The level promotion parameters of the user growth modeling module include: Geographic exploration coefficient: Mass contribution coefficient: N is the total number of uploaded works; User Level 7. The photographic work management system based on geographic information aggregation according to claim 1, characterized in that: Also includes visual interaction modules for: Generate a heat map of China, using different color saturation to represent the number of photographs in each province; When the user clicks on a provincial administrative region, the photo thumbnail and the associated communication group entrance of the region are asynchronously loaded; Displays the user level identifier and the exploration progress required for the next level.

8. A method for managing photographic works based on geographic information aggregation, characterized by: The following steps are involved: S1: Copyright logo generation Automatically embed a digital watermark when a user uploads a photo. The digital watermark includes: S11 photographer's identity hash value; S12 upload timestamp; S13 geographic container code; S2: Access Control Establish a three-level access rights model: Public level: allows all users to view and comment; Group level: Access is limited to authenticated users within the same geographic container; Private level: The photographer needs to grant a temporary access token; Dynamically adjust permission policies: When a work is reported for infringement, it is automatically downgraded to private and the review process is triggered; S3: Copyright Verification Mechanism Develop a reverse watermark extraction algorithm and detect watermark integrity through Fourier transform; Build a blockchain evidence storage subsystem to write watermark key information into smart contracts (using the Hyperledger Fabric architecture); S4: Infringement Handling Process Real-time crawler monitoring module: uses a deep learning-based image similarity comparison algorithm (ResNet-50 model) to scan third-party platforms; Infringement evidence solidification: When the similarity is >85%, an evidence package containing a spatiotemporal watermark verification report is automatically generated.

9. The method for managing photographic works based on geographic information aggregation according to claim 8, characterized in that: In step three: The blockchain evidence storage subsystem performs the following operations: Generate a Merkle tree structure containing a timestamp and upload watermark data to the chain in batches every 10 minutes The smart contract automatically executes the copyright verification request and returns a verification certificate containing the following elements: Copyright ownership status; First release date; Geographic container association proof.

10. The method for managing photographic works based on geographic information aggregation according to claim 8, characterized in that: In step 4: The image similarity comparison algorithm adopts the improved SSIM evaluation method: The tampering coefficient is obtained by analyzing the abnormal values ​​of EXIF ​​metadata.

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