Marine-related enterprise mobile management platform based on 3S technology and implementation method thereof

Abstract

The invention relates to the technical field of spatial information, and discloses a sea-related enterprise mobile management platform based on the 3S technology and an implementation method thereof, and the sea-related enterprise mobile management platform based on the 3S technology comprises a business application layer which provides a map interface and spatial data collection function; the logic service layer completes identity verification and authority control and generates a space processing instruction; the GIS middleware layer fuses multi-source data and carries out coordinate unification; the database management layer analyzes the processing result and constructs a spatial index; and the high-precision positioning layer corrects positioning information through a difference technology and fuses the positioning information with acquired data to realize map updating and task feedback. According to the invention, by constructing a five-layer architecture composed of a business application layer, a logic service layer, a GIS middleware layer, a database management layer and a high-precision positioning layer, full-link cooperation of spatial data from acquisition, verification and processing to visual feedback is realized, and the real-time performance and reliability of mobile terminal data interaction in a sea-related scene are improved.

Description

Technical Field

[0001] The present invention relates to the field of spatial information technology, and in particular to a mobile management platform for maritime enterprises based on 3S technology and an implementation method thereof. Background Art

[0002] Amid the rapid growth of maritime economic activities, businesses are placing higher demands on real-time spatial information collection, precise positioning, and dynamic feedback during maritime use, operational supervision, and routine inspections. This is especially true in scenarios like port construction, offshore aquaculture, and sand mining and dredging, where vast operating areas and complex environments place extremely high demands on the timeliness and spatial consistency of field data. To enable rapid linkage between operational data and mapping systems, visual monitoring of task progress, and unified management of spatial data, a comprehensive mobile platform with both high-precision positioning and spatial fusion capabilities is crucial.

[0003] In the existing technology, some systems have realized the mobile collection function of spatial data and have certain layer overlay and map browsing capabilities. Some platforms support the collection of field point, line and surface elements through mobile terminals, saving them offline, and submitting them to the background server after the network is restored. This type of method can alleviate the data breakpoint problem caused by poor network conditions in the sea area to a certain extent. In addition, the existing systems are also relatively mature in map visualization, and can provide basic layer control, coordinate picking and map annotation functions. For static supervision tasks, such as map inspections, single-point verification and other applications, current technology has a relatively stable execution capability.

[0004] However, existing technologies generally have broken spatial data processing chains, with a serious disconnect between front-end collection and back-end analysis, a lack of unified reference system support, and the inability to directly overlay multi-source data. Spatial elements are significantly misaligned. Furthermore, traditional systems mostly rely on the built-in GPS module in mobile phones to provide location data, which has large fluctuations in accuracy and is prone to drift in areas such as the sea or open space. Regarding data query, existing platforms often use single-field indexing to support retrieval, resulting in low query efficiency when faced with large data volumes. Finally, in terms of information feedback mechanisms, existing collection platforms often use manual upload or delayed synchronization modes, resulting in significant lags in task status feedback. Therefore, those skilled in the art have proposed a mobile management platform for maritime enterprises based on 3S technology and its implementation method to address the above issues. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a mobile management platform for maritime enterprises based on 3S technology and its implementation method, which solves the problems in the existing technology of poor connection between data collection and map system, insufficient positioning accuracy and low spatial processing efficiency.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A mobile management platform for maritime enterprises based on 3S technology, comprising:

[0007] The business application layer is used to provide users with an operation interface, present maritime geographic information maps, collect spatial and attribute data input by users on site, and generate data access and processing requests;

[0008] The logical service layer is used to receive the data access and processing requests, verify the user identity based on the authentication algorithm, and filter the data range accessible to the user in combination with the permission control mechanism to build a space processing instruction set;

[0009] The GIS middleware layer is used to call the multi-source spatial data fusion method according to the spatial processing instruction set, use the Web Mercator projection model to perform projection transformation and coordinate correction on spatial elements, and generate processing results under a unified spatial reference system;

[0010] The database management layer is used to perform structured analysis and storage of the processing results under the unified spatial reference system and to construct query paths using a spatial index mechanism;

[0011] The high-precision positioning layer is used to connect to an external GPS receiving device through a wireless network, and to perform real-time correction of geographic location information based on differential positioning technology. The corrected positioning data is dynamically integrated with the spatial and task data collected by the business application layer to drive the update of map display and real-time feedback of task progress.

[0012] Preferably, the business application layer includes:

[0013] Map presentation module, used to display the sea area geographic base map and layer information based on the view range and zoom level selected by the user;

[0014] The data collection module is used to receive spatial point, line, and surface elements and their attribute information collected by users on-site in the sea area;

[0015] The request generation module is used to package the user's spatial editing operations and attribute inputs into standardized data access and processing requests and send them to the logical service layer.

[0016] Preferably, the logical service layer includes:

[0017] An identity authentication module, used to verify user accounts, passwords, or biometric information using a preset encryption algorithm;

[0018] The permission control module is used to match the corresponding data access boundaries based on user roles and permission levels and dynamically adjust the data processing process;

[0019] The instruction building module is used to parse user requests and convert them into a standard spatial processing instruction set that can be recognized by the GIS middleware layer.

[0020] Preferably, the identity verification module performs verification using the following formula:

[0021] C=AES_Encrypt(D raw ,K);

[0022] Where: C is the encrypted credential ciphertext; D raw is the plaintext credential data entered by the user; K is the preset symmetric key; AES_Encrypt(·) is the Advanced Encryption Standard symmetric encryption algorithm function;

[0023] The permission control module determines the accessible data space range through the following formula:

[0024] S perm =A(R(U id ));

[0025] Where: R(U id ) indicates the user ID U id The bound role type; A(·) is the permission mapping function; S perm It is the boundary of the data space that can be operated by the user;

[0026] The instruction set of the instruction building block includes the following:

[0027] I spatial ={T req ,D target ,B box ,M proc ,t};

[0028] Among them: I spatial For space processing instruction set; T req It is a request type parameter, indicating the operation type; D target B is the target dataset identifier, indicating the operation space data layer; box =(x min ,y min ,x max ,y max ) is the spatial range parameter, which limits the operation area; M proc is the processing method parameter, indicating the algorithm type of the spatial operation; t is the timestamp parameter, indicating the task generation time.

[0029] Preferably, the GIS middleware layer includes:

[0030] Data fusion module, used to unify coordinates and perform raster-vector fusion on the connected remote sensing images, aerial survey results and historical layer data;

[0031] A projection conversion module is used to convert the original spatial elements into a unified reference coordinate system based on a preset projection model;

[0032] The spatial correction module is used to perform geometric correction on boundary misalignment, angle deviation or scale distortion to generate spatial results that conform to map projection specifications.

[0033] Preferably, the projection conversion module uses the Web Mercator projection model to convert the latitude and longitude coordinates, and the conversion formula is:

[0034] Horizontal projection coordinate: x = R e ·λ;

[0035] Vertical projection coordinates:

[0036] Where: x, y are projection coordinates; λ is the input longitude; φ is the input latitude; R e is the Earth's radius constant.

[0037] Preferably, the database management layer includes:

[0038] The data structure parsing module is used to parse the spatial processing results returned by the GIS middleware layer according to the GeoJSON format;

[0039] The storage management module is used to store data according to the mapping rules between geographic feature types and attribute fields;

[0040] The index building module is used to build an R-tree structure-based index for spatial fields to improve data retrieval efficiency.

[0041] Preferably, the high-precision positioning layer includes:

[0042] GPS access module, used to connect to high-precision GNSS receiving equipment via Bluetooth or WiFi to obtain raw positioning data;

[0043] The differential processing module is used to call the real-time differential data stream RTCM to correct the original data;

[0044] The positioning fusion module is used to link and update the corrected location information with the spatial task data collected by the business application layer, triggering interface refresh and task status changes.

[0045] Preferably, the positioning result after difference correction of the difference processing module is calculated by the following model:

[0046] P corrected =P raw +Δ d ;

[0047] Where: P corrected is the corrected position coordinate; P raw is the original GPS coordinate; Δ d is the error vector calculated by the reference station.

[0048] The present invention also provides a method for implementing a mobile management platform for maritime enterprises based on 3S technology, comprising the following steps:

[0049] S1. Users access the platform interface through the business application layer, collect on-site spatial elements and input attribute data to generate data processing requests;

[0050] S2. The logic service layer receives the request and verifies the user's identity, and builds a space processing instruction set based on the user's permissions;

[0051] S3, the GIS middleware layer fuses multi-source spatial data according to the instruction set and uniformly processes spatial elements through the Web Mercator projection;

[0052] S4. Parse and store the spatial processing results after projection conversion in the database management layer, and build a spatial index to support subsequent queries;

[0053] S5. Connect to a high-precision GPS receiving device via a wireless network, use the differential positioning method to correct the original coordinates, and integrate them into the spatial data in real time;

[0054] S6. Dynamically update the map interface and task data status based on the fused location information to achieve mobile management of task progress.

[0055] The present invention provides a mobile management platform for maritime enterprises based on 3S technology and its implementation method. It has the following beneficial effects:

[0056] 1. This invention achieves full-link collaboration of spatial data from collection, verification, processing to visual feedback by constructing a five-layer architecture consisting of a business application layer, a logical service layer, a GIS middleware layer, a database management layer, and a high-precision positioning layer. This technical solution improves the real-time and reliability of mobile data interaction in marine scenarios. Compared with the existing approach of relying solely on desktop GIS systems or independent collection terminals, it effectively solves the problems of disconnection between field collection and system processing, and delayed data return.

[0057] 2. This invention adopts a unified projection and raster-vector fusion mechanism based on the Web Mercator model, enabling the overlay and geometric correction of multi-source heterogeneous spatial data in the same reference system. The processing results are presented more accurately on the map side, and the boundary consistency is significantly improved. Compared with traditional solutions that use the data source format as the processing unit and lack a unified coordinate framework, this method avoids common mismatch problems such as feature dislocation and projection conflicts, and significantly improves the quality of base map fusion.

[0058] 3. The present invention introduces a differential positioning correction mechanism, combining RTCM differential data with multi-system GNSS signal solutions to achieve centimeter-level accuracy in position reporting. The corrected coordinate information can be dynamically bound to the task and drive the interface to update. Compared with the existing positioning method that relies on the mobile phone's built-in GPS module and is limited by municipal signals, this solution solves the problems of on-site trajectory drift and insufficient spatial resolution, meeting the precise positioning requirements of maritime supervision.

[0059] 4. This invention constructs an R-tree spatial index and attribute joint query mechanism at the database management level, and combines it with a task version control strategy to enable the platform to maintain good responsiveness when processing large-scale spatial data. Unlike traditional solutions based on single-field indexes or that do not support time series data traceability, this design supports multi-level screening by layer, task, and time dimensions, solving the problems of low query efficiency and non-traceable task status under high concurrency. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 This is a schematic diagram of the platform architecture of the present invention;

[0061] Figure 2 This is a schematic diagram of the business application layer architecture of the present invention;

[0062] Figure 3 This is a schematic diagram of the logical service layer architecture of the present invention;

[0063] Figure 4 This is a schematic diagram of the GIS middleware layer architecture of the present invention;

[0064] Figure 5 This is a schematic diagram of the database management layer architecture of the present invention;

[0065] Figure 6 This is a schematic diagram of the high-precision positioning layer architecture of the present invention;

[0066] Figure 7 Schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0067] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the present specification. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0068] Please see the attached Figure 1 -Attached Figure 6 The embodiment of the present invention provides a mobile management platform for maritime enterprises based on 3S technology, including:

[0069] The business application layer is used to provide users with an operation interface, present maritime geographic information maps, collect spatial and attribute data input by users on site, and generate data access and processing requests;

[0070] Specifically, in the 3S technology-based mobile management platform for maritime enterprises, the business application layer is the user-facing front-end execution module, responsible for key functions such as spatial interaction presentation, user input collection, and data processing request generation. This layer directly interfaces with the logical service layer and completes data upload and transmission via standardized protocol formats, serving as a bridge between user operations and the system's back-end functions.

[0071] Generally speaking, the business application layer runs on smart terminals, including but not limited to Android handheld devices, vehicle-mounted mobile devices, maritime inspection tablets and other mobile hardware platforms. The front-end interface is presented in the form of a map main view, which can load vector and raster layer information, and supports layer overlay control and real-time data display.

[0072] In a feasible implementation, this layer establishes a two-way data channel with the logical service layer through the HTTPs protocol, and the request encapsulation adopts a structured data message format that conforms to the REST architectural style to ensure the portability and security of cross-platform communication.

[0073] In this embodiment, the service application layer mainly includes the following submodules:

[0074] In this embodiment, the map rendering module is used to load and render the geographic base map within the maritime area and overlay regulatory-related layer information on it, supporting layer management, transparency adjustment, and click-to-query functions. Map tiles are pre-sliced ​​using the Web Mercator projection coordinate system (EPSG:3857). The loading process dynamically calculates the required tile number based on the boundary coordinates of the current map window and the zoom level. The number calculation formula is as follows:

[0075]

[0076] Where: N x N is the number of tiles that need to be loaded horizontally; y is the number of tiles that need to be loaded vertically; X min , X max , Y min , Y max is the projection boundary coordinate value of the current map visual range, in meters; S is the pixel size of a single map tile, usually 256px; R is the ground resolution (Resolution) corresponding to the current zoom level, in meters / pixel.

[0077] The above ground resolution R can be calculated by the following formula:

[0078]

[0079] Where: R e is the radius of the earth in meters, usually 6,378,137 meters; Z is the pixel size of each tile in pixels (such as 256 or 512); L is the zoom level of the map, which is a non-negative integer.

[0080] As an option, this module uses the OpenLayers6.x rendering engine, combined with Canvas layer redrawing technology, to achieve a smooth interactive experience on mobile devices.

[0081] In this embodiment, the data collection module supports the collection and editing of three basic spatial elements: points, lines, and surfaces. Users can directly click on the map interface to mark points, draw tracks, or circle fences. The system will simultaneously record their spatial coordinates and guide users to fill in the corresponding attribute fields.

[0082] In some embodiments, attribute fields are organized as configuration templates, supporting common data types such as text, numeric, Boolean, enumeration, and date. Field content can be configured with mandatory fields, range validation rules, and linkage logic. For example, "Enterprise Operation Type" is an enumeration field whose value is selected from a preset dictionary; "Operation Area" is a numeric field that supports two decimal places and a configurable upper limit.

[0083] Specifically, the system standardizes the packaging of user-collected data, uses the GeoJSON format to describe the spatial structure, and adds attribute fields and metadata fields, the latter of which includes user account, device IMEI, collection timestamp, GPS accuracy, and other content.

[0084] In this embodiment, the request generation module is used to package all user operations into a standard request object and send data processing instructions to the logical service layer. Its core structure is based on JSON and contains the following main parameter fields:

[0085] reqType operation request type, string format, value is "create", "update", "delete", etc.

[0086] geomData spatial data field, point, line or area features represented in WKT or GeoJSON format;

[0087] attrData attribute data object, key-value pair structure, stores the collected field content;

[0088] layerId layer identifier, indicating the target layer of the operation;

[0089] bbox space bounding box, the format is a four-tuple (x min ,y min ,x max ,y max ), indicating the scope of influence of the current operation;

[0090] timestamp generates the timestamp of the request, in milliseconds;

[0091] sign integrity check field, used for anti-tampering verification.

[0092] The integrity signature field sign is calculated using the following formula:

[0093] sign=SHA256(reqType+layerId+timestamp+secretKey);

[0094] Where: SHA256(·) is a secure hash algorithm function that returns a 64-bit hexadecimal string; reqType is the operation type string field; layerId is the target layer identifier; timestamp is the current operation timestamp; secretKey is the key stored locally on the device side, which is first authorized and distributed by the platform.

[0095] As an extension, this module supports offline collection mode. In the absence of network, all data requests will be cached in the local SQLite database and automatically retried through the background thread after the network is restored to ensure data integrity.

[0096] In some embodiments, the platform also allows enterprise users to customize form templates. The templates define the field structure and constraint rules in XML or JSON format, and the platform parses them to dynamically generate input forms to adapt to different operational scenarios and regulatory requirements.

[0097] It's important to note that the business application layer doesn't include any complex spatial analysis capabilities. All processing logic beyond map rendering is handled by the logical service layer and GIS middleware layer. This design ensures a clear division of labor between the front-end and back-end, improving system responsiveness and maintainability.

[0098] The logical service layer is used to receive data access and processing requests, verify user identities based on authentication algorithms, and filter the data range accessible to users in combination with permission control mechanisms to build a spatial processing instruction set;

[0099] Specifically, after users complete data collection and construct operational instructions through the business application layer, the logical service layer receives the data access and processing request and, based on this, completes identity authentication, permission screening, and generates spatial operation instruction sets. In this invention, this layer plays a central role in instruction parsing and security control. Its output directly drives the GIS middleware layer to carry out subsequent spatial processing, so its architecture and data flow logic require a highly standardized and stable structure.

[0100] Typically, the logical service layer is deployed in the cloud or in an edge server cluster, within the same trusted network zone as the database management layer and the GIS middleware layer. It uses a service interface call mechanism to enable multi-module interoperability. This layer receives JSON structured requests from the business application layer and performs structural verification and security parsing on key fields in the request data.

[0101] In this embodiment, the logical service layer consists of the following main functional modules, including an identity authentication module, a permission control module, and an instruction construction module. The system operation logic is as follows:

[0102] In this embodiment, the identity authentication module is used to verify the login status and legitimacy of the received user request. The verification mechanism is implemented in a symmetric encryption manner, and the plain text credential information submitted by the user is encrypted and matched.

[0103] Specifically, the module uses the Advanced Encryption Standard (AES) algorithm to encrypt and compare the data fields submitted by the user. The encryption process follows the following formula:

[0104] C=AES_Encrypt(D raw ,K);

[0105] Where: C represents the encrypted certificate string; D raw represents the original plaintext credential data entered by the user, such as the combination of a username and a token; K represents the system's preset symmetric key, which uses a 128-bit or 256-bit length and is deployed in the server's secure storage area; AES_Encrypt(·) represents the AES encryption function, which uses CBC or GCM mode to complete the encryption conversion.

[0106] As a possible implementation method, after verification, the system will enter the permission screening stage based on the unique identity identifier (UID) associated with the user account.

[0107] In this embodiment, the permission control module is responsible for limiting the user's operable data space scope and system function access boundaries based on the user's role type.

[0108] Specifically, this module uses a two-layer mapping mechanism, first extracting the role identifier bound to the user UID, and then obtaining the corresponding data access area through the permission mapping function. The process formula is as follows:

[0109] S perm =A(R(U id ));

[0110] Among them: U id Indicates user identification code; R(U id ) represents the role type mapping function, which returns the role number or permission group code corresponding to the user; A(·) is the permission-space area mapping function, which takes the role type as input and outputs the data boundary area that the user can operate; S perm The spatial area that the end user is authorized to access is defined as a set of surface geometric objects.

[0111] In some embodiments, the data boundary is presented in GeoFence mode and encapsulated in WKT format. PostGIS is used in the background to perform spatial inclusion judgment and implement spatial clipping authorization logic.

[0112] In this embodiment, the instruction construction module is used to reorganize and standardize the authenticated user request data to generate a spatial processing instruction set for subsequent parsing by the GIS middleware layer.

[0113] Specifically, this module extracts and restructures the input parameter fields, ultimately forming the following spatial processing instruction set format:

[0114] I spatial ={T req ,D target ,B box ,M proc ,t};

[0115] Among them: I spatial For space processing instruction set; T req It is a request type parameter, indicating the operation type; D target B is the target dataset identifier, indicating the operation space data layer; box =(x min ,y min ,x max ,y max ) is the spatial range parameter, which limits the operation area; M proc is the processing method parameter, indicating the algorithm type of the spatial operation; t is the timestamp parameter, indicating the task generation time.

[0116] In one possible implementation, the spatial range parameter B box In the logical service layer, it will first determine whether it is consistent with the user authority area Sperm If there is no spatial intersection, the request is aborted and a "no permission" response status code is returned.

[0117] As a security control measure, the logical service layer generates a message digest field for data integrity verification while building the instruction set. The SHA-256 digest algorithm can be used for its calculation process. The structure is as follows:

[0118] digest=SHA256(T req +D target +t+K session );

[0119] Where: digest is the final generated summary value, which is used for subsequent link integrity comparison; K session Indicates the temporary key of the current session, issued by the platform login authorization.

[0120] In some embodiments, the instruction construction module is also responsible for intercepting illegal request types, unknown layer numbers, or non-standard timestamp fields, and recording abnormal logs and pushing them to the platform security audit subsystem.

[0121] The GIS middleware layer is used to call the multi-source spatial data fusion method based on the spatial processing instruction set, use the Web Mercator projection model to perform projection transformation and coordinate correction on spatial elements, and generate processing results in a unified spatial reference system;

[0122] Specifically, after the logical service layer constructs the instruction set and transmits it to the system's central processing unit, the GIS middleware layer invokes the multi-source spatial data fusion method based on this instruction set to perform projection transformation, geometric correction, and coordinate standardization of spatial elements. This layer is the key bridge connecting the logical instructions with the visual map results in this invention. This layer has a direct impact on the integrity, consistency, and accuracy of spatial data, with its core functions focusing on map projection transformation, heterogeneous data fusion, and geometric error correction.

[0123] Typically, the GIS middleware layer is deployed in a dedicated service environment with geospatial computing capabilities, relying on open-source projection and parsing components such as GDAL and PROJ to implement complex geoprocessing workflows. The system interfaces with the logical service layer through interfaces, receives structured spatial processing instructions, and locates, loads, and spatially processes target data sources.

[0124] In this embodiment, the GIS middleware layer includes the following core processing modules: a data fusion module, a projection conversion module, and a spatial correction module, which are described below respectively.

[0125] In this embodiment, the data fusion module is responsible for identifying the target data set D provided by the logical service layer. targetand processing parameter M proc , call the corresponding data source interface to load the original spatial data.

[0126] Specifically, supported input data formats include but are not limited to GeoJSON, Shapefile, GPKG, TIFF, etc. Different parsers are used to load data for different formats. For example, for the GPKG format, the system calls the ogr2ogr tool to load layer by layer and automatically identify the layer field structure.

[0127] As an option, the module includes layer fusion logic. When processing planar layer overlays, the system automatically constructs a topological structure and merges nodes along adjacent polygon boundaries to create a seamless boundary data set. The fusion results are then transmitted downstream in a unified structure.

[0128] In some embodiments, the module also supports multi-temporal remote sensing data fusion. For raster layers of the same area but different time dimensions, a composite image is constructed using a weighted algorithm. The weight calculation follows the following formula:

[0129]

[0130] Where: P fused (x, y) is the pixel value of the fused image at coordinate (x, y); P i (x, y) is the pixel value of the i-th layer at the coordinate point; w i is the fusion weight of the corresponding layer, satisfying n is the number of layers to be fused.

[0131] In this embodiment, the projection conversion module is responsible for projecting spatial elements from different data sources into a unified reference coordinate system to ensure the accuracy of spatial overlay operations. The system uses the Web Mercator projection model (EPSG:3857) as the primary projection reference system. This model is suitable for large-scale mapping in a web map environment and is expressed in meters.

[0132] Specifically, the latitude and longitude coordinates (λ, φ) are converted to projection coordinates (x, y) as follows:

[0133] x=R e ·λ;

[0134]

[0135] Where: x, y projected coordinate values, in meters; λ is the input longitude, in radians; φ is the input latitude, in radians; R e is the Earth's radius constant, which is 6,378,137 meters.

[0136] In one possible implementation, the module first detects the spatial reference of the original data (e.g., WKT or EPSG code). If it is not EPSG:3857, it automatically converts the projection using the PROJ library. All boundary points are refined using midpoint interpolation to avoid geometric distortion during the transformation.

[0137] In this embodiment, the spatial correction module primarily addresses geometric misalignment, shape distortion, and scale anomalies in the original data. The module incorporates an internal error detection mechanism that automatically determines the closure of boundary polylines, point sequence consistency, and topological legitimacy.

[0138] In some embodiments, to achieve boundary correction, the system repositions the coordinates of spatial elements through affine transformation, and its calculation model is as follows:

[0139]

[0140] Among them: (x, y) is the original coordinate point; (x ′ ,y ′ ) is the corrected coordinate point; a, b, c, d are the elements of the affine transformation matrix, representing the scaling and rotation coefficients; t x , t y Translation offset in meters.

[0141] The affine parameters are calculated from the sample control points and can be fitted using the least squares method, with the error controlled within the centimeter level.

[0142] As a complementary approach, the spatial correction module can also construct a correction reference surface based on the historical boundary layer, using nearest neighbor fitting or B-spline interpolation to perform flexible correction of distorted boundaries. The corrected spatial features are then written to the buffer cache for subsequent unified processing.

[0143] It's important to note that the GIS middleware layer doesn't store data long-term. Processing results are returned to the database management layer in a standard spatial object structure. All intermediate data and log information generated during processing are stored in memory or temporary directories and do not form persistent content.

[0144] The database management layer is used to perform structured analysis and storage of processing results under a unified spatial reference system, and to construct query paths using a spatial index mechanism;

[0145] Specifically, after the GIS middleware layer completes spatial processing operations within a unified reference system, the results are structured and stored in a standardized manner at the database management layer. This layer carries the core responsibilities of information integration, structural organization, and efficient retrieval within the system, providing the foundational support for subsequent business queries and dynamic map response performance.

[0146] Typically, the database management layer is built on a hybrid relational and spatial database engine, using an object-oriented geographic information model to define the data storage structure. It also integrates spatial indexing mechanisms and attribute mapping rules to achieve unified storage and rapid location of spatial entities. Before entering this layer, the processed results have been coordinate-projected, geometrically corrected, and format-standardized by the middleware, allowing for direct structural deconstruction and content analysis.

[0147] In this embodiment, the database management layer includes the following processing modules: a data structure analysis module, a storage management module and an index construction module, and each module collaborates to complete the data storage process.

[0148] In one possible implementation, the data structure parsing module receives spatial result data returned by the GIS middleware layer. Such result data is formatted as a GeoJSON structure, which contains spatial geometries (such as Point, LineString, Polygon, etc.) and their corresponding attribute key-value pairs.

[0149] Specifically, the system first parses the coordinate field in the GeoJSON object, extracts the vertex coordinates of each spatial feature point by point, and converts them into a spatial field format that is compatible with the database.

[0150] In some embodiments, if the feature type is a face polygon, the module performs a circular check on the vertex order to ensure that the end is closed and there are no self-intersection errors. For composite features (MultiPolygon or GeometryCollection), its sub-geometry is recursively extracted and a hierarchical nested structure is constructed.

[0151] As an option, the data structure parsing module can also extract timestamp fields, source layer information, and operation algorithm identifiers to build a metadata index table to provide auxiliary support for subsequent traceability and version control.

[0152] In this embodiment, the storage management module writes the elements and their attribute information into the spatial database based on the parsed results. Geographic elements are mapped to different storage logic tables according to their types and uniquely identified by combining spatial primary key generation rules.

[0153] Specifically, the system sets the spatial primary key generation rules as follows:

[0154] K spatial =H(D target ||t||S geom );

[0155] Among them: K spatial The unique primary key used to identify spatial features; D targetis the corresponding target dataset identifier; t is the data generation timestamp; S geom is the vertex set of the spatial geometry; H(·) is the hash function operator, using SHA-256 for digest generation; || represents the field concatenation operation.

[0156] In some embodiments, attribute information mapping follows a regular field mapping table, which maps the field names of the input data to predefined attribute models in the system, thereby ensuring consistency of attribute data types. For example, floating-point attributes will be uniformly converted to DOUBLE type fields in the database and their value ranges will be normalized according to the unit configuration table.

[0157] In this embodiment, the index building module triggers the index building process of the spatial field immediately after the data is stored. The system uses an R-tree structure to build the spatial index, and the index object is the minimum bounding rectangle (MBR) corresponding to the spatial field, that is, the minimum bounding box.

[0158] In general, the R-tree index construction algorithm follows the following construction principles:

[0159] During the initial node aggregation, multiple geometric objects are merged according to their spatial proximity;

[0160] Each index node stores the MBR range of its child nodes;

[0161] When a node splits, the area growth minimum strategy is implemented to ensure the balance of the tree structure.

[0162] In one implementation, to improve query efficiency, the system also creates a joint index based on attribute fields. For example, a composite query index is constructed for the "sea area usage" field and the spatial location field. This allows for dual-condition filtering based on both spatial and attribute conditions during queries, significantly reducing the scanning of non-target data.

[0163] To facilitate subsequent map service calls, the system also generates a unique data version identifier after each batch of data is written and records it in the version tracking table, which contains the following field information:

[0164] Dataset ID;

[0165] Write time;

[0166] Execution algorithm type;

[0167] Index update status;

[0168] Data integrity check code.

[0169] This identifier is used by the subsequent GIS service layer to obtain the latest available data snapshot on demand to ensure the consistency and response efficiency of map presentation.

[0170] To improve large-scale data processing performance, the database management layer supports geographic partitioning based on partitioning strategies. For example, data can be rasterized and sliced ​​according to longitude and latitude, and each slice can be stored in a separate physically partitioned table. In query requests, the system can directly locate the corresponding partition based on the target region boundary, reducing access pressure on the main table.

[0171] The high-precision positioning layer is used to connect to external GPS receiving devices through wireless networks and perform real-time corrections to geographic location information based on differential positioning technology. The corrected positioning data is dynamically integrated with the spatial and mission data collected by the business application layer to drive map display updates and real-time feedback on mission progress.

[0172] Specifically, after spatial processing results are structured and stored in the database management layer, the system must continuously receive dynamic information about the user's on-site location to enable real-time updates to the work interface and synchronized feedback on task status. At this point, the high-precision positioning layer takes on the responsibility of connecting external positioning devices to the platform's core systems, combining differential positioning technology to correct raw GNSS signals and integrate them with spatial feature data.

[0173] Typically, this layer connects to centimeter-level precision GNSS receivers via wireless methods (such as Bluetooth or WiFi) and receives raw positioning data in real time. Because GNSS signals are susceptible to factors such as sea surface reflections and ionospheric disturbances, the raw coordinates contain systematic errors that require correction using differential positioning methods.

[0174] In this embodiment, the high-precision positioning layer includes: a GPS access module, a differential processing module, and a positioning fusion module. There is a clear time sequence calling relationship between each module.

[0175] In this embodiment, the GPS access module establishes a communication connection with the GNSS receiver and supports parsing the standard NMEA data protocol. The module continuously monitors the received data stream and extracts fields such as the timestamp, satellite number, latitude, longitude, and altitude. Optionally, the module supports multi-GNSS system combination solutions, including GPS, BDS, GLONASS, and Galileo, and is capable of automatically selecting the optimal satellite combination in different regions.

[0176] In some embodiments, the GPS access module also integrates a signal quality evaluation mechanism to perform real-time evaluation of the received satellite data. If the number of visible satellites is less than a set threshold, the module enters a position hold mode and prompts the user to pause the acquisition.

[0177] In this embodiment, the differential processing module is used to perform error correction on the received GNSS coordinates. Specifically, the system calls the differential data stream interface to obtain real-time correction data (RTCM format) from the CORS reference station and calculates the correction value based on the original GNSS data.

[0178] In one possible implementation, the differential correction model can be expressed as follows:

[0179] P corrected =P raw +Δ d ;

[0180] Where: P corrected is the coordinate result after differential correction, in meters; P raw The original coordinates output by the GNSS receiving device, in meters; Δ d is the error vector calculated based on the baseline distance between the reference station and the rover, in meters.

[0181] The calculation of the error vector involves parameter components such as ionospheric delay, tropospheric delay, multipath effect, and receiver clock bias. In general, the system performs weighted aggregation of error sources based on the following model:

[0182] Δ d =w1·ε ion +w2·ε trop +w3·ε mp +w4·ε clk ;

[0183] Where: ε ion is the ionospheric delay error component; ε trop is the tropospheric delay error component; ε mp is the multipath reflection error component; ε clk is the receiver clock deviation; w1, w2, w3, w4 are error weight coefficients, satisfying w1+w2+w3+w4=1.

[0184] In actual deployment, the error weight can be dynamically adjusted according to the characteristics of the sea environment. For example, ε can be increased in high humidity areas. trop The weights are used to reflect the dominant effect of tropospheric disturbances.

[0185] In this embodiment, the positioning fusion module receives the corrected coordinates output by the differential processing module and integrates them with the spatial elements being collected by the business application layer in real time. Specifically, the system uses a spatial binding mechanism to associate the positioning coordinates with the collection task identifier and drive the update of the visual location on the map interface.

[0186] As an option, the positioning fusion module introduces dynamic position verification logic and sets the tolerance range threshold δtol , if the difference between two consecutive positioning results is greater than δ tol , then enter the steady-state judgment process, suspend the synchronization of position information, and record suspected abnormal points for manual review.

[0187] In some embodiments, the positioning fusion module also supports path reconstruction, which generates continuous operation trajectory line elements by performing spline interpolation on continuous position points. The interpolation function is expressed as:

[0188] S(x)=a0+a1x+a2x 2 +a3x 3 ;

[0189] Where: S(x) is the coordinate of the fitting point on the path curve; x is the index of the trajectory point sequence; a0, a1, a2, a3 fitting coefficients are determined by the least squares method.

[0190] The reconstructed path data is stored as linear features and attached with corresponding timestamps and task numbers for subsequent task progress retrospective analysis.

[0191] Generally, the positioning fusion module pushes positioning update events at a frequency of once per second or higher. The system dynamically refreshes the interface status through a subscription monitoring mechanism to achieve real-time response of task status icons, spatial layers, and form fields, ensuring the timeliness of user operations and consistency of spatial accuracy.

[0192] The implementation method of the maritime enterprise mobile management platform based on 3S technology described below can be referenced to the maritime enterprise mobile management platform based on 3S technology described above.

[0193] Please see the attached Figure 7 The implementation method of the mobile management platform for maritime enterprises based on 3S technology includes the following steps:

[0194] S1. Users access the platform interface through the business application layer, collect on-site spatial elements and input attribute data to generate data processing requests;

[0195] S2, the logical service layer receives the request and verifies the user's identity, and builds a space processing instruction set based on the user's permissions;

[0196] S3,GIS middleware layer fuses multi-source spatial data according to the instruction set and uniformly processes spatial elements through the Web Mercator projection;

[0197] S4. Parse and store the spatial processing results after projection conversion in the database management layer, and build a spatial index to support subsequent queries;

[0198] S5. Connect to a high-precision GPS receiving device via a wireless network, use the differential positioning method to correct the original coordinates, and integrate them into the spatial data in real time;

[0199] S6. Dynamically update the map interface and task data status based on the fused location information to achieve mobile management of task progress.

[0200] The implementation method of this embodiment can be used to execute the above platform embodiment. Its principles and technical effects are similar and will not be described in detail here.

[0201] 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. The mobile management platform for maritime enterprises based on 3S technology is characterized by: include: The business application layer is used to provide users with an operation interface, present maritime geographic information maps, collect spatial and attribute data input by users on site, and generate data access and processing requests; The logical service layer is used to receive the data access and processing requests, verify the user identity based on the authentication algorithm, and filter the data range accessible to the user in combination with the permission control mechanism to build a space processing instruction set; The GIS middleware layer is used to call the multi-source spatial data fusion method according to the spatial processing instruction set, use the Web Mercator projection model to perform projection transformation and coordinate correction on spatial elements, and generate processing results under a unified spatial reference system; The database management layer is used to perform structured analysis and storage of the processing results under the unified spatial reference system and to construct query paths using a spatial index mechanism; The high-precision positioning layer is used to connect to an external GPS receiving device through a wireless network, and to perform real-time correction of geographic location information based on differential positioning technology. The corrected positioning data is dynamically integrated with the spatial and task data collected by the business application layer to drive the update of map display and real-time feedback of task progress.

2. The mobile management platform for maritime enterprises based on 3S technology according to claim 1 is characterized in that: The business application layer includes: Map presentation module, used to display the sea area geographic base map and layer information based on the view range and zoom level selected by the user; The data collection module is used to receive spatial point, line, and surface elements and their attribute information collected by users on-site in the sea area; The request generation module is used to package the user's spatial editing operations and attribute inputs into standardized data access and processing requests and send them to the logical service layer.

3. The mobile management platform for maritime enterprises based on 3S technology according to claim 1 is characterized in that: The logical service layer includes: An identity authentication module, used to verify user accounts, passwords, or biometric information using a preset encryption algorithm; The permission control module is used to match the corresponding data access boundaries based on user roles and permission levels and dynamically adjust the data processing process; The instruction building module is used to parse user requests and convert them into a standard spatial processing instruction set that can be recognized by the GIS middleware layer.

4. The mobile management platform for maritime enterprises based on 3S technology according to claim 3 is characterized in that: The authentication module performs verification using the following formula: C=AES_Encrypt(D raw ,K); Where: C is the encrypted credential ciphertext; D raw is the plaintext credential data entered by the user; K is the preset symmetric key; AES_Encrypt(·) is the Advanced Encryption Standard symmetric encryption algorithm function; The permission control module determines the accessible data space range through the following formula: S perm =A(R(U id )); Where: R(U id ) indicates the user ID U id The bound role type; A(·) is the permission mapping function; S perm It is the boundary of the data space that can be operated by the user; The instruction set of the instruction building block includes the following: I spatial ={T req ,D target ,B box ,M proc ,t}; Among them: I spatial For space processing instruction set; T req It is a request type parameter, indicating the operation type; D target B is the target dataset identifier, indicating the operation space data layer; box =(x min ,y min ,x max ,y max ) is the spatial range parameter, which limits the operation area; M proc is the processing method parameter, indicating the algorithm type of the spatial operation; t is the timestamp parameter, indicating the task generation time.

5. The mobile management platform for maritime enterprises based on 3S technology according to claim 1 is characterized in that: The GIS middleware layer includes: Data fusion module, used to unify coordinates and perform raster-vector fusion on the connected remote sensing images, aerial survey results and historical layer data; A projection conversion module is used to convert the original spatial elements into a unified reference coordinate system based on a preset projection model; The spatial correction module is used to perform geometric correction on boundary misalignment, angle deviation or scale distortion to generate spatial results that conform to map projection specifications.

6. The mobile management platform for maritime enterprises based on 3S technology according to claim 5 is characterized in that: The projection conversion module uses the Web Mercator projection model to convert the latitude and longitude coordinates, and the conversion formula is: Horizontal projection coordinate: x = R e ·λ; Vertical projection coordinates: Where: x, y are projection coordinates; λ is the input longitude; φ is the input latitude; R e is the Earth's radius constant.

7. The mobile management platform for maritime enterprises based on 3S technology according to claim 1 is characterized in that: The database management layer includes: The data structure parsing module is used to parse the spatial processing results returned by the GIS middleware layer according to the GeoJSON format; The storage management module is used to store data according to the mapping rules between geographic feature types and attribute fields; The index building module is used to build an R-tree structure-based index for spatial fields to improve data retrieval efficiency.

8. The mobile management platform for maritime enterprises based on 3S technology according to claim 1 is characterized in that: The high-precision positioning layer includes: GPS access module, used to connect to high-precision GNSS receiving equipment via Bluetooth or WiFi to obtain raw positioning data; The differential processing module is used to call the real-time differential data stream RTCM to correct the original data; The positioning fusion module is used to link and update the corrected location information with the spatial task data collected by the business application layer, triggering interface refresh and task status changes.

9. The mobile management platform for maritime enterprises based on 3S technology according to claim 8 is characterized in that: The positioning result after differential correction of the differential processing module is calculated using the following model: P corrected =P raw +D d ; Where: P corrected is the corrected position coordinate; P raw is the original GPS coordinate; Δ d is the error vector calculated by the reference station.

10. A method for implementing a mobile management platform for maritime enterprises based on 3S technology, applied to the mobile management platform for maritime enterprises based on 3S technology according to any one of claims 1 to 9, characterized in that: The following steps are involved: S1. Users access the platform interface through the business application layer, collect on-site spatial elements and input attribute data to generate data processing requests; S2. The logic service layer receives the request and verifies the user's identity, and builds a space processing instruction set based on the user's permissions; S3, the GIS middleware layer fuses multi-source spatial data according to the instruction set and uniformly processes spatial elements through the Web Mercator projection; S4. Parse and store the spatial processing results after projection conversion in the database management layer, and build a spatial index to support subsequent queries; S5. Connect to a high-precision GPS receiving device via a wireless network, use the differential positioning method to correct the original coordinates, and integrate them into the spatial data in real time; S6. Dynamically update the map interface and task data status based on the fused location information to achieve mobile management of task progress.

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