Mobile management platform for sea-related enterprises based on 3S technology and implementation method thereof

Abstract

This application relates to the field of spatial information technology, and discloses a mobile management platform for marine enterprises based on 3S technology and its implementation method. The 3S-based mobile management platform for marine enterprises includes: a business application layer providing a map interface and spatial data acquisition functions; a logic service layer completing identity verification and access control, and generating spatial processing instructions; a GIS middleware layer integrating multi-source data and unifying coordinates; a database management layer parsing the processing results and constructing a spatial index; and a high-precision positioning layer correcting positioning information through differential technology and fusing it with the acquired data to achieve map updates and task feedback. This invention, by constructing a five-layer architecture consisting of a business application layer, a logic service layer, a GIS middleware layer, a database management layer, and a high-precision positioning layer, achieves end-to-end collaboration of spatial data from acquisition, verification, processing to visualization feedback, improving the real-time performance and reliability of mobile data interaction in marine scenarios.

Description

Technical Field

[0001] This invention relates to the field of spatial information technology, specifically to a mobile management platform for marine enterprises based on 3S technology and its implementation method. Background Technology

[0002] Against the backdrop of rapid development in marine-related economic activities, enterprises are placing higher demands on the real-time collection, precise positioning, and dynamic feedback of spatial information in their use of marine areas, operational supervision, and daily inspections. This is particularly true in scenarios such as port construction, marine aquaculture, and sand dredging, where the operational areas are vast and the environments complex, requiring extremely high timeliness and spatial consistency of on-site data. To achieve rapid linkage between operational data and map systems, visualized monitoring of task progress, and unified management of spatial data, a comprehensive mobile platform combining high-precision positioning and spatial fusion capabilities is crucial.

[0003] In existing technologies, some systems have implemented mobile spatial data acquisition capabilities, possessing certain layer overlay and map browsing abilities. Some platforms support the offline storage of field point, line, and polygon features collected via mobile terminals, and then submitted to the backend server after network recovery. These methods can alleviate data interruption issues caused by poor network conditions in marine areas to some extent. Furthermore, existing systems are relatively mature in map visualization, providing basic layer control, coordinate picking, and map annotation functions. For static monitoring tasks, such as patch inspections and single-point verification, current technologies already possess relatively stable execution capabilities.

[0004] However, existing spatial data processing technologies generally suffer from fragmentation, with a severe disconnect between front-end acquisition and back-end analysis, a lack of a unified reference system, and the inability to directly overlay multi-source data, resulting in significant misalignment between spatial elements. Furthermore, traditional systems largely rely on built-in GPS modules in mobile phones for location data, leading to large fluctuations in accuracy and a high risk of drifting over sea or open areas. Regarding data querying, existing platforms primarily use single-field indexes for retrieval, resulting in low query efficiency when dealing with large datasets. Finally, in terms of information feedback mechanisms, existing acquisition platforms often employ manual uploading or delayed synchronization modes, leading to significant lags in task status feedback. Therefore, those skilled in the art propose a mobile management platform for marine enterprises based on 3S technology and its implementation method to address these issues. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a mobile management platform for marine enterprises based on 3S technology and its implementation method, which solves the problems of poor data collection and map system integration, insufficient positioning accuracy, and low spatial processing efficiency in existing technologies.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a mobile management platform for marine enterprises based on 3S technology, comprising:

[0007] The business application layer is used to provide users with an operation interface, present a map of marine geographic information, 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's identity based on the authentication algorithm, filter the range of data that the user can access in combination with the access control mechanism, and construct a spatial 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, and to perform projection transformation and coordinate correction on spatial elements using the Web Mercator projection model to generate processing results under a unified spatial reference system.

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

[0011] The high-precision positioning layer is used to connect to an external GPS receiving device via a wireless network. Based on differential positioning technology, it corrects the geographical location information in real time. The corrected positioning data is dynamically fused 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] The map presentation module is used to display the geographic base map and layer information of the sea area based on the view range and zoom level selected by the user.

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

[0015] The request generation module is used to package the user's space editing operations and attribute inputs into a standardized data access and processing request and send it to the logic service layer.

[0016] Preferably, the logical service layer includes:

[0017] The identity verification module is used to verify user accounts, passwords, or biometric information using a preset encryption algorithm.

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

[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 authentication module performs authentication using the following formula:

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

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

[0023] The access control module determines the accessible data space range using the following formula:

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

[0025] Where: R(U id ) indicates the user identifier U id The bound role type; A(·) is the permission mapping function; S perm Defines the user-manageable data space boundary;

[0026] The instruction set of the instruction construction module 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 The request type parameter indicates the operation type; D target Identify the target dataset and specify the operational space data layer; B box =(x min ,y min ,x max ,y max ) represents the spatial range parameter, defining the operating area; M proc The processing method parameter indicates the algorithm type for the spatial operation; t is the timestamp parameter, indicating the task generation time.

[0029] Preferably, the GIS middleware layer includes:

[0030] The data fusion module is used to unify coordinates and fuse raster and vector data from the accessed remote sensing images, aerial survey results and historical layer data;

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

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

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

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

[0035] Vertical axis projection coordinates:

[0036] Where: x, y are the projected 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 in GeoJSON format;

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

[0040] The index building module is used to create indexes on spatial fields based on R tree structures to improve data retrieval efficiency.

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

[0042] The GPS access module is used to connect to a high-precision GNSS receiving device 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 location fusion module is used to link the corrected location information with the spatial task data collected by the business application layer, triggering interface refresh and task status change.

[0045] Preferably, the differentially corrected positioning result of the differential processing module is calculated using the following model:

[0046] P corrected =P raw +Δ d ;

[0047] Where: P corrected P represents the corrected position coordinates. raw Original GPS coordinates; Δ d This is the error vector calculated using the base station.

[0048] This invention also provides a method for implementing a mobile management platform for marine enterprises based on 3S technology, including 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 logical service layer receives the request and verifies the user's identity, and constructs a space processing instruction set according to the user's permissions;

[0051] S3. The GIS middleware layer integrates multi-source spatial data according to the instruction set and performs unified processing of spatial elements through Web Mercator projection.

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

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

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

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

[0056] 1. This invention constructs a five-layer architecture consisting of a business application layer, a logic service layer, a GIS middleware layer, a database management layer, and a high-precision positioning layer, realizing end-to-end collaboration of spatial data from collection, verification, processing to visualization feedback. This technical solution improves the real-time performance and reliability of mobile data interaction in marine scenarios. Compared with the existing approach that relies solely on desktop GIS systems or independent collection terminals, it effectively solves the problems of disconnect between field collection and system processing, and delayed data transmission.

[0057] 2. This invention adopts a unified projection and raster fusion mechanism based on the Web Mercator model, which enables multi-source heterogeneous spatial data to be superimposed and geometrically corrected under the same reference system. The processing results are presented more accurately on the map and the boundary consistency is significantly improved. Compared with the traditional scheme that uses the data source format as the processing unit and lacks a unified coordinate framework, this method avoids common mismatch problems such as feature misalignment and projection conflict, and significantly improves the quality of base map fusion.

[0058] 3. This invention introduces a differential positioning correction mechanism, which combines RTCM differential data with multi-system GNSS signal calculation 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 in a linked manner. Compared with the existing positioning methods that rely on the built-in GPS module of the mobile phone and whose accuracy is limited by municipal signals, this solution solves the problems of on-site trajectory drift and insufficient spatial resolution, and meets the precise positioning requirements of marine supervision.

[0059] 4. This invention constructs an R-tree spatial index and attribute joint query mechanism in the database management layer, and combines it with a task version control strategy to enable the platform to maintain good response performance 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, and solves the problems of low query efficiency and non-retrospective task status under high concurrency. Attached Figure Description

[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 logic 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 structure of the present invention;

[0066] Figure 7 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0067] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0068] Please see the appendix Figure 1 -Appendix Figure 6 This invention provides a mobile management platform for marine enterprises based on 3S technology, comprising:

[0069] The business application layer is used to provide users with an operation interface, present a map of marine geographic information, 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 marine enterprises of this invention, the business application layer is a user-facing front-end execution module, undertaking key functions such as spatial interaction presentation, user input collection, and data processing request generation. This layer directly connects to the logic service layer and completes data uploading and transmission through standardized protocol formats, serving as a bridge between user operations and system backend functions.

[0071] Generally, the business application layer runs on smart terminals, including but not limited to Android handheld devices, vehicle-mounted mobile devices, and maritime inspection tablets. 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 one feasible implementation, this layer establishes a bidirectional data channel with the logical service layer via the HTTPS protocol, and encapsulates requests using 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 business application layer mainly includes the following sub-modules:

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

[0075]

[0076] Where: N x N represents the number of tiles that need to be loaded laterally. y X represents the number of tiles that need to be loaded vertically; min X max Y min Y max The coordinates of the projected boundary of the current map's visible area are in meters; S is the pixel size of a single map tile, usually 256 pixels; R is the ground resolution corresponding to the current zoom level, in meters per pixel.

[0077] The aforementioned ground resolution R can be calculated using the following formula:

[0078]

[0079] Where: R e is the radius of the earth, in meters, usually taken as 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, a non-negative integer.

[0080] As an option, this module uses the OpenLayers 6.x rendering engine, combined with the Canvas layer redrawing technology, to achieve a smooth interaction experience on the mobile side.

[0081] In this embodiment, the data acquisition module supports the acquisition and editing of three types of basic spatial elements (points, lines, and surfaces). Users can directly click to mark points, draw trajectories, or select by enclosing areas on the map interface, and the system will synchronously record their spatial coordinates and guide users to fill in the corresponding attribute fields.

[0082] In some embodiments, the attribute fields are organized in a configuration template, supporting common data types including text, numeric, boolean, enumeration, date, etc. The field content can set mandatory items, range validation rules, and linkage logic. For example, "enterprise operation type" is an enumeration field, and its value is selected from a preset dictionary; "operation area" is a numeric field, supporting two decimal places and setting an upper limit.

[0083] Specifically, the system standardizes and encapsulates the data collected by users, describes the spatial structure in GeoJSON format, and attaches attribute fields and metadata fields, the latter including user account, device IMEI, collection timestamp, GPS accuracy, etc.

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

[0085] reqType The operation request type, in string format, with values such as "create", "update", "delete", etc.;

[0086] geomData Spatial data field, point, line, or surface elements represented in WKT or GeoJSON format;

[0087] attrData Attribute data object, in key-value pair structure, storing the content of the collected fields;

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

[0089] The bounding box of the bounding space, formatted as a quad (x) min ,y min ,x max ,y max ), indicating the scope of the current operation's impact;

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

[0091] The sign integrity verification field is used for tamper-proof verification.

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

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

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

[0095] As an extension, this module supports offline data collection. In the absence of a network connection, all data requests will be cached in a local SQLite database, and will be automatically retried and submitted via a background thread once the network is restored, ensuring data integrity.

[0096] Furthermore, in some implementations, the platform also allows enterprise users to customize form templates. The templates define field structures and constraint rules in XML or JSON format, which the platform parses and dynamically generates input forms to adapt to different types of operational scenarios and regulatory requirements.

[0097] It is important to note that the business application layer does not include any complex spatial analysis functions. All processing logic other than map rendering is handled by the logic service layer and the GIS middleware layer. This design ensures a clear division of labor between the front-end and back-end, improving system response efficiency and maintainability.

[0098] The logical service layer is used to receive data access and processing requests, verify user identity based on authentication algorithms, filter the range of data that users can access in conjunction with access control mechanisms, and build a spatial processing instruction set.

[0099] Specifically, after the user completes data collection and constructs operation instructions through the business application layer, the logic service layer receives the data access and processing requests, and on this basis, completes authentication, permission filtering, and the generation of spatial operation instruction sets. This layer plays a central role in instruction parsing and security control in this invention. Its output directly drives the GIS middleware layer to carry out subsequent spatial processing; therefore, its architecture and data flow logic require high standardization and stability.

[0100] Typically, the logical service layer is deployed in the cloud or edge server cluster, located in the same trusted network area as the database management layer and the GIS middleware layer, and uses a service interface call mechanism to achieve interoperability between multiple modules. This layer receives JSON structured requests from the business application layer and performs structure 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: an authentication module, an access control module, and an instruction construction module. The system operation logic is as follows:

[0102] In this embodiment, the authentication module is used to verify the login status and legitimacy of received user requests. The authentication mechanism is implemented using symmetric encryption, which performs encrypted matching processing on the plaintext credentials submitted by the user.

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

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

[0105] Where: C represents the encrypted credential string; D raw This represents the original plaintext credential data entered by the user, such as a combination of username and token; K represents the system's preset symmetric key, which is 128 or 256 bits long 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 one possible implementation, after successful verification, the system will enter the permission filtering stage based on the unique identifier (UID) associated with the user account.

[0107] In this embodiment, the access control module is responsible for restricting the range of data space that a user can operate and the boundaries of system function access based on the user's role type.

[0108] Specifically, this module uses a two-layer mapping mechanism. First, it extracts the role identifier bound to the user's UID, and then obtains the corresponding data access area through a permission mapping function. The formula for this process is as follows:

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

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

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

[0112] In this embodiment, the instruction construction module is used to reorganize and standardize the verified 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 space 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 The request type parameter indicates the operation type; D target Identify the target dataset and specify the operational space data layer; B box =(x min ,y min ,x max ,y max ) represents the spatial range parameter, defining the operating area; M proc The processing method parameter indicates the algorithm type for the spatial operation; t is the timestamp parameter, indicating the task generation time.

[0116] In one possible implementation, the spatial extent parameter B box The logic service layer will first determine whether it matches the user's permission area S.perm If there is a spatial intersection, the request will be aborted and a "Permission Denied" response status code will be returned.

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

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

[0119] Where: digest is the final generated digest value, used for subsequent link integrity comparison; K session This represents the temporary key for the current session, issued by the platform upon 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, recording exception logs, and pushing them to the platform security audit subsystem.

[0121] The GIS middleware layer is used to call multi-source spatial data fusion methods according to the spatial processing instruction set, and to perform projection transformation and coordinate correction on spatial elements using the Web Mercator projection model to generate processing results under a unified spatial reference system.

[0122] Specifically, after the logic service layer completes the construction of the instruction set and transmits it to the system's central processing unit, the GIS middleware layer, based on this instruction set, invokes multi-source spatial data fusion methods to complete the projection transformation, geometric correction, and coordinate standardization of spatial elements. This is the key link connecting logical instructions and visualization map results in this invention. This layer has a direct impact on the integrity, consistency, and accuracy of spatial data, and its core functions mainly focus 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 geographic processing workflows. The system interfaces with the logical service layer, receives structured spatial processing instruction sets, and performs location loading and spatial processing on the target data source.

[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 will be described separately below.

[0125] In this embodiment, the data fusion module is responsible for identifying the target dataset D provided by the logical service layer. targetWith processing method 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, and TIFF. Different parsers are used to perform data loading for different formats. For example, for GPKG format, the system calls the ogr2ogr tool to perform layer-by-layer loading and automatically identifies the layer field structure.

[0127] As an option, the module includes layer blending logic. When processing polygon layer overlay tasks, the system automatically constructs a topology structure and merges nodes at the boundaries of adjacent polygons to generate a seamless boundary data set. The blending result is transmitted downstream in a unified structure format.

[0128] In some embodiments, this module also supports multi-temporal remote sensing data fusion. For raster layers of the same region but different time dimensions, a composite image is constructed using a weighted algorithm, with the weights calculated according to the following formula:

[0129]

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

[0131] In this embodiment, the projection conversion module is responsible for projecting spatial elements from different data sources onto a unified reference coordinate system, ensuring 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 map creation in a web map environment, with units in meters.

[0132] Specifically, the conversion of latitude and longitude coordinates (λ, φ) into projected coordinates (x, y) is calculated as follows:

[0133] x = R e ·λ;

[0134]

[0135] Where: x and y are projected coordinates in meters; λ is the input longitude in radians; φ is the input latitude in radians; R e is the Earth's radius constant, with a value of 6,378,137 meters.

[0136] In one possible implementation, the module first detects the spatial reference information of the original data (such as WKT or EPSG code). If it is not EPSG:3857, it calls the PROJ library to automatically complete the projection transformation process. During the transformation, all boundary points are refined using midpoint interpolation to avoid geometric distortion during the transformation.

[0137] In this embodiment, the spatial correction module is mainly used to handle geometric misalignment, shape distortion, and scale anomalies in the original data. The module integrates an error detection mechanism that can automatically determine the closure of boundary polylines, the consistency of point order, and the legality of topological relationships.

[0138] In some embodiments, to achieve boundary correction, the system performs coordinate repositioning of spatial features through affine transformation, and its calculation model is as follows:

[0139]

[0140] Where: (x,y) are the original coordinates of the point; (x ′ ,y ′ ) represents the corrected coordinates; a, b, c, d are elements of the affine transformation matrix, representing 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 supplementary approach, the spatial correction module can also construct a correction reference surface based on the historical boundary layer, and use nearest neighbor fitting or B-spline interpolation to flexibly correct distorted boundaries. The corrected spatial features will be written to a buffer cache for subsequent unified processing.

[0143] It should be noted that the GIS middleware layer does not undertake the function of long-term data storage; its processing results are returned to the database management layer in a standard spatial object structure. All intermediate data and log information generated during the processing are stored in memory or a temporary directory and do not form persistent content.

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

[0145] Specifically, after spatial processing operations under a unified reference system are completed in the GIS middleware layer, the processing results need to be structured and stored in a standardized manner by the database management layer. This layer bears the core responsibility of information landing, structural organization, and efficient retrieval in this system, and is a fundamental support link to ensure the performance of subsequent business queries and dynamic map responses.

[0146] Typically, the database management layer is built on a hybrid relational and spatial database engine. It uses an object-oriented geographic information model to define the data storage structure, while integrating spatial indexing mechanisms and attribute mapping rules to achieve unified data entry and rapid location of spatial entities. Before entering this layer, the processing results have already undergone coordinate projection, geometric correction, and format standardization by middleware, so they can be directly deconstructed and parsed.

[0147] In this embodiment, the database management layer includes the following processing modules: a data structure parsing module, a storage management module, and an index building module. These modules work together to complete the data ingestion process.

[0148] In one possible implementation, the data structure parsing module receives spatial result data returned by the GIS middleware layer. This result is uniformly formatted as a GeoJSON structure, containing 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 element 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 polygon, the module performs a cyclic check on the vertex order to ensure that the beginning and end are closed and there are no self-intersection errors. For composite features (MultiPolygon or GeometryCollection), its sub-geometry is extracted recursively and a hierarchical nested structure is constructed.

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

[0152] In this embodiment, the storage management module writes the features and their attribute information into the spatial database based on the parsed results. Geographic features are mapped to different logical storage tables according to their type and are uniquely identified using 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] Where: K spatial A unique primary key used to identify spatial features; D targetThe corresponding target dataset identifier; t is the data generation timestamp; S geom The set of vertices of the spatial geometry; H(·) hash function operator, which uses SHA-256 to generate the digest; || represents the field concatenation operation.

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

[0157] In this embodiment, the index building module triggers the spatial field index building process immediately after the data is entered into the database. 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.

[0158] Generally, R-tree index building algorithms follow these construction principles:

[0159] During initial node aggregation, multiple geometric objects are merged based on spatial proximity.

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

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

[0162] In one implementation, to improve query efficiency, the system also creates a composite index by combining attribute fields. For example, a composite query index is built by combining the "sea area use" field with the spatial location field, which can achieve dual-condition filtering based on space and attribute during querying, thereby 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 includes the following fields:

[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 snapshots as needed, ensuring the consistency of map presentation and response efficiency.

[0170] In some embodiments, to improve the performance of large-scale data processing, the database management layer also supports geographic partitioning based on partitioning strategies. For example, data can be rasterized and sliced ​​according to latitude and longitude, and different slices can be stored in independent physical partition tables. In query requests, the system can directly locate the corresponding partition based on the boundary of the target area, thereby reducing the access pressure on the main table.

[0171] The high-precision positioning layer is used to connect to an external GPS receiving device via a wireless network. Based on differential positioning technology, it corrects the geographical location information in real time. The corrected positioning data is dynamically fused with the spatial and task data collected by the business application layer to drive map display updates and real-time feedback on task progress.

[0172] Specifically, after the spatial processing results are structured and stored by the database management layer, the system needs to continuously receive dynamic information about the user's on-site location to achieve real-time updates of the work interface and synchronous feedback of task status. At this time, the high-precision positioning layer is responsible for connecting external positioning equipment with the platform's core system, correcting the original GNSS signal using differential positioning technology, and linking and fusing it with spatial element data.

[0173] Typically, this layer connects to a GNSS receiver with centimeter-level accuracy via wireless means (such as Bluetooth or WiFi) and receives its raw positioning data in real time. Because GNSS signals are susceptible to sea surface reflection and ionospheric disturbances, the raw coordinates contain systematic errors and need to be corrected 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. These modules have a clearly defined timing relationship.

[0175] In this embodiment, the GPS access module is used to establish a communication connection with the GNSS receiving device and supports the parsing of the standard NMEA data protocol. The module continuously monitors the received data string and extracts fields such as timestamp, satellite number, latitude, longitude, and altitude. Alternatively, this module supports multi-system GNSS combination calculation, including GPS, BDS, GLONASS, and Galileo systems, and has the ability to automatically select the optimal satellite combination in different areas.

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

[0177] In this embodiment, the differential processing module is used to correct errors in 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 by combining it with the original GNSS data.

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

[0179] P corrected =P raw +Δ d ;

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

[0181] The calculation of the error vector involves parameters such as ionospheric delay, tropospheric delay, multipath effects, and receiver clock skew. Generally, the system uses the following model to weight and aggregate the error sources:

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

[0183] Where: ε ion ε is the ionospheric delay error component; trop ε represents the tropospheric delay error component; mp For multipath reflection error components; ε clk The receiver clock bias is represented by w1, w2, w3, and w4, which are error weighting coefficients that satisfy w1 + w2 + w3 + w4 = 1.

[0184] In practical deployments, the error weights can be dynamically adjusted according to the characteristics of the marine environment. For example, in high-humidity areas, ε can be increased. 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 performs real-time fusion with the spatial features being collected by the business application layer. Specifically, the system uses a spatial binding mechanism to associate the positioning coordinates with the collection task identifier and drives the update of the visualized location on the map interface.

[0186] As an alternative, the positioning fusion module introduces dynamic position verification logic and sets a tolerance range threshold δ.tol If the difference between two consecutive positioning results is greater than δ tol If the condition is not met, the steady-state discrimination process will begin, the location information synchronization will be temporarily suspended, and suspected anomalies will be recorded for manual review.

[0187] In some embodiments, the positioning fusion module also supports path reconstruction, generating continuous operation trajectory line elements by performing spline interpolation on continuous location 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 fitted point on the path curve; x is the index of the trajectory point sequence; the fitting coefficients a0, a1, a2, a3 are determined by the least squares method.

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

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

[0192] The implementation method of the 3S technology-based mobile management platform for marine enterprises described below can be referred to in correspondence with the 3S technology-based mobile management platform for marine enterprises described above.

[0193] Please see the appendix Figure 7 The implementation method of a mobile management platform for marine 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 requests and verifies the user's identity, and constructs a space processing instruction set based on the user's permissions;

[0196] The S3 and GIS middleware layers integrate multi-source spatial data according to the instruction set and process spatial elements uniformly through Web Mercator projection.

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

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

[0199] S6. Dynamically update the map interface and task data status based on the merged 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, and its principle and technical effect are similar, so it will not be described again here.

[0201] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A mobile management platform for marine enterprises based on 3S technology, characterized in that: include: The business application layer is used to provide users with an operation interface, present a map of marine geographic information, 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's identity based on an authentication algorithm, filter the range of data that the user can access using an access control mechanism, and construct a spatial processing instruction set; the spatial processing instruction set includes the following: ； in: For space processing instruction set; This is the request type parameter, indicating the operation type. Identify the target dataset and specify the data layer in the operation space; This is a spatial range parameter that defines the operating area. The processing method parameter indicates the type of algorithm for the space operation; This is a timestamp parameter, representing the time the task was generated. The GIS middleware layer is used to call the multi-source spatial data fusion method according to the spatial processing instruction set, and to perform projection transformation and coordinate correction on spatial elements using the Web Mercator projection model to generate processing results under a unified spatial reference system. The database management layer is used to perform structured parsing and storage of the processing results under the unified spatial reference system, and to construct query paths using a spatial indexing mechanism; The high-precision positioning layer is used to connect to an external GPS receiving device via a wireless network. Based on differential positioning technology, it corrects the geographical location information in real time. The corrected positioning data is dynamically fused 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 marine enterprises based on 3S technology according to claim 1, characterized in that, The business application layer includes: The map presentation module is used to display the geographic base map and layer information of the sea area based on the view range and zoom level selected by the user. The data acquisition module is used to receive spatial point, line, and area features and their attribute information collected by the user on-site in the sea area; The request generation module is used to package the user's space editing operations and attribute inputs into a standardized data access and processing request and send it to the logic service layer.

3. The mobile management platform for marine enterprises based on 3S technology according to claim 1, characterized in that, The logical service layer includes: The identity verification module is used to verify user accounts, passwords, or biometric information using a preset encryption algorithm. The access control module is used to match corresponding data access boundaries based on user roles and permission levels and dynamically adjust data processing flow. 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 marine enterprises based on 3S technology according to claim 3, characterized in that, The authentication module verifies using the following formula: ； in: The encrypted credential ciphertext; Plaintext credential data entered by the user; The preset symmetric key; This is a function for the Advanced Encryption Standard (AES) symmetric encryption algorithm. The access control module determines the accessible data space range using the following formula: ； in: Indicates user identifier The bound role type; This is a permission mapping function; This defines the boundaries of the user-manageable data space.

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

6. The mobile management platform for marine enterprises based on 3S technology according to claim 5, characterized in that, The projection conversion module uses the Web Mercator projection model to convert latitude and longitude coordinates, and the conversion formula is as follows: Horizontal projection coordinates: ; Vertical axis projection coordinates: ; in: , These are the projected coordinates; The longitude is the input value. The latitude is the input value. is the Earth's radius constant.

7. The mobile management platform for marine enterprises based on 3S technology according to claim 1, 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 in GeoJSON format; The storage management module is used to store data in the database according to the mapping rules between geographic feature types and attribute fields; The index building module is used to create indexes on spatial fields based on R tree structures to improve data retrieval efficiency.

8. The mobile management platform for marine enterprises based on 3S technology according to claim 1, characterized in that, The high-precision positioning layer includes: The GPS access module is used to connect to a high-precision GNSS receiving device 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 location fusion module is used to link the corrected location information with the spatial task data collected by the business application layer, triggering interface refresh and task status change.

9. The mobile management platform for marine enterprises based on 3S technology according to claim 8, characterized in that, The differentially corrected positioning result of the differential processing module is calculated using the following model: ； in: These are the corrected position coordinates; Original GPS coordinates; This is the error vector calculated using the base station.

10. A method for implementing a mobile management platform for marine enterprises based on 3S technology, applied to the mobile management platform for marine enterprises based on 3S technology as described in any one of claims 1-9, characterized in that, Includes the following steps: 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 logical service layer receives the request and verifies the user's identity, and constructs a space processing instruction set according to the user's permissions; S3. The GIS middleware layer integrates multi-source spatial data according to the instruction set and performs unified processing of spatial elements through Web Mercator projection. S4. Parse and store the spatial processing results after projection transformation to the database management layer, and build a spatial index to support subsequent queries; S5. Connect to a high-precision GPS receiver via a wireless network, use differential positioning to correct the original coordinates, and fuse them into the spatial data in real time; S6. Dynamically update the map interface and task data status based on the merged location information to achieve mobile management of task progress.

Images