Cloud-based collaborative methods and systems for geological and mineral exploration data analysis

Abstract

This application relates to the field of geological and mineral exploration, specifically disclosing a cloud-based collaborative method and system for geological and mineral exploration data analysis. It encapsulates user operations on a client-side WebGIS into WebSocket message frames containing authentication credentials, which are then securely submitted to a cloud-based central coordinator. The central coordinator first verifies user permissions, and then, based on the operation type field in the message frame and combined with intelligent judgment enhanced by cross-modal context, accurately routes the message frame to either the OT processing module or the lightweight state synchronization module. The OT processing module handles complex, high-conflict-risk editing operations, ensuring eventual data consistency under concurrent editing by multiple users through version comparison, operation conversion, and transactional applications. The lightweight state synchronization module handles non-editable or low-conflict-risk operations, quickly updating and broadcasting the state to improve system response speed and overall performance.

Description

Technical Field

[0001] This application relates to the field of geological and mineral exploration, and more specifically, to a cloud-based collaborative method and system for analyzing geological and mineral exploration data. Background Technology

[0002] Geological and mineral exploration is a crucial foundation for national economic development. Its core lies in the efficient acquisition, storage, management, analysis, and interpretation of massive amounts of heterogeneous data from multiple sources, including geological, geophysical, geochemical, remote sensing, and drilling data. Traditional exploration data analysis methods often rely on desktop software, resulting in fragmented data storage and inefficient data sharing and collaborative analysis among team members. This is particularly problematic when dealing with complex 3D geological model construction, multidisciplinary data fusion and interpretation, and real-time update requirements. Data consistency is difficult to guarantee, version conflicts are frequent, and this severely hinders the progress of exploration projects and the accuracy of decision-making.

[0003] While some cloud-based geographic information system (GIS) platforms have emerged, enabling online data storage, publishing, and basic query and browsing functions, and some have even introduced collaborative editing capabilities, these collaborative solutions often reveal numerous shortcomings when handling the complex spatial and attribute data unique to the geological and mineral exploration field. For example, a simple "last writer wins" strategy can easily lead to data loss or overwriting, while coarse-grained locking mechanisms can severely impact user experience and collaborative efficiency. Ensuring data consistency, integrity, and real-time performance while avoiding frequent conflicts and inefficient synchronization is a common challenge faced by existing cloud-based collaborative platforms for high-concurrency, high-complexity geological data editing operations. They typically lack sophisticated management and conflict resolution mechanisms for concurrent operations, making it difficult to meet the high standards required by geological and mineral exploration teams for real-time collaborative data editing.

[0004] Therefore, an optimized cloud-based collaborative solution for geological and mineral exploration data analysis is desired. Summary of the Invention

[0005] To address the aforementioned technical problems, this application is proposed. Embodiments of this application provide a cloud-based collaborative method and system for analyzing geological and mineral exploration data.

[0006] According to one aspect of this application, a cloud-based collaborative method for analyzing geological and mineral exploration data is provided, comprising:

[0007] Responding to user mouse / touch operations on the WebGIS client, generate client operation commands;

[0008] The client encapsulates the client operation instructions and the user's authentication credentials into instructions and securely submits them to obtain a WebSocket message frame;

[0009] The central coordinator in the cloud determines whether the user has permission to edit the target layer based on the user's authentication credentials in the WebSocket message frame;

[0010] In response to the user having permission to edit the target layer, the central coordinator in the cloud determines whether to send the WebSocket message frame to the OT processing module or the lightweight state synchronization module based on the operation type field in the WebSocket message frame.

[0011] In response to receiving the WebSocket message frame, the OT processing module performs version comparison, operation conversion, and transactional application based on the WebSocket message frame and the current server status to obtain an updated project status version;

[0012] In response to receiving the WebSocket message frame, the lightweight state synchronization module updates the state cache based on the WebSocket message frame and generates broadcast state update information.

[0013] According to another aspect of this application, a cloud-based collaborative geological and mineral exploration data analysis system is provided, comprising:

[0014] The client operation instruction generation module is used to generate client operation instructions in response to user mouse / touch operations on the WebGIS client.

[0015] The instruction encapsulation and submission module is used by the client to encapsulate and securely submit the client operation instructions and the user's authentication credentials to obtain a WebSocket message frame.

[0016] The permission detection module is used by the central coordinator in the cloud to determine whether the user has permission to edit the target layer based on the user's authentication credentials in the WebSocket message frame.

[0017] The message frame sending selection module is used to respond to the user having permission to edit the target layer. The central coordinator in the cloud determines whether to send the WebSocket message frame to the OT processing module or the lightweight state synchronization module based on the operation type field in the WebSocket message frame.

[0018] The OT processing module analysis module is used to respond to the received WebSocket message frame. The OT processing module performs version comparison, operation conversion and transaction application based on the WebSocket message frame and the current state of the server to obtain an updated project status version.

[0019] The lightweight state synchronization module is used to update the state cache and generate broadcast state update information based on the received WebSocket message frame in response to the WebSocket message frame.

[0020] Compared with existing technologies, this application provides a cloud-based collaborative geological and mineral exploration data analysis method and system. It encapsulates user operations on the client-side WebGIS into WebSocket message frames containing authentication credentials and securely submits them to a cloud-based central coordinator. The central coordinator first verifies user permissions, and then, based on the operation type field in the message frame and combined with intelligent judgment enhanced by cross-modal context, accurately routes the message frame to either the OT processing module or the lightweight state synchronization module. The OT processing module handles complex, high-conflict-risk editing operations, ensuring eventual data consistency under multi-user concurrent editing through version comparison, operation conversion, and transactional applications. The lightweight state synchronization module handles non-editable or low-conflict-risk operations, quickly updating and broadcasting the state to improve system response speed and overall performance. This intelligent routing and collaboration mechanism effectively overcomes the limitations of traditional methods, achieving efficient and reliable cloud-based collaborative data analysis. Attached Figure Description

[0021] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0022] Figure 1 This is a flowchart of a cloud-based collaborative geological and mineral exploration data analysis method according to an embodiment of this application;

[0023] Figure 2 This is a schematic diagram of data flow in a cloud-based collaborative geological and mineral exploration data analysis method according to an embodiment of this application;

[0024] Figure 3 The flowchart illustrates how, in response to the user having permission to edit the target layer, the cloud-based central coordinator determines whether to send the WebSocket message frame to the OT processing module or the lightweight state synchronization module based on the operation type field in the WebSocket message frame, according to the cloud-based collaborative geological and mineral exploration data analysis method of this application embodiment.

[0025] Figure 4This is a flowchart illustrating the process of enhancing the semantic embedding encoding vector of operation type information by performing cross-modal context enhancement on the operation type information semantic embedding encoding vector based on the operation type context supplementary embedding encoding graph according to the cloud-based collaborative geological and mineral exploration data analysis method of this application.

[0026] Figure 5 This is a block diagram of a cloud-based collaborative geological and mineral exploration data analysis system according to an embodiment of this application. Detailed Implementation

[0027] Hereinafter, exemplary embodiments according to this application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein.

[0028] As indicated in this application and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not specifically singular and may include plural forms. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0029] While this application makes various references to certain modules of the systems according to embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The modules described are merely illustrative, and different aspects of the systems and methods may use different modules.

[0030] Flowcharts are used in this application to illustrate the operations performed by the system according to embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0031] Hereinafter, exemplary embodiments according to this application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein.

[0032] In geological and mineral exploration data analysis, traditional methods suffer from inefficiencies and data loss in areas such as multi-user real-time collaboration, data consistency assurance, and complex conflict resolution. To address these issues, the technical solution in this application generates corresponding operation instructions when a user performs operations on the client's WebGIS platform. These instructions are then securely encapsulated with the user's authentication credentials to form a WebSocket message frame, which is subsequently securely submitted to a central coordinator in the cloud. This central coordinator first rigorously verifies the user's editing permissions. Crucially, once the user has editing permissions, the central coordinator no longer treats all operations indiscriminately. Instead, based on the operation type field in the WebSocket message frame and combined with cross-modal context-enhanced intelligent judgment of this operation type information, it dynamically and accurately decides whether to send the message frame to the OT processing module or the lightweight state synchronization module.

[0033] For operations involving complex data editing that may lead to concurrent conflicts, the system routes them to the OT (Operational Technology) processing module. This module ensures eventual consistency and integrity of data under multi-user concurrent editing through meticulous version comparison, operation transformation, and transactional application, effectively avoiding data overwriting and conflict issues common in traditional methods. For non-editable or low-conflict-risk operations, such as simple view operations or status queries, the system sends them to the lightweight state synchronization module. This module can quickly update the state cache and generate broadcast information, significantly improving system response speed and overall performance while avoiding unnecessary complex processing overhead. This intelligent routing mechanism combines the rigor of OT with the efficiency of lightweight synchronization, enabling this solution to effectively address the technical challenges of low real-time collaboration efficiency and difficulty in guaranteeing data consistency in existing technologies. It provides a highly efficient and reliable cloud-based collaborative data analysis platform for geological and mineral exploration.

[0034] The technical solution of this application proposes a cloud-based collaborative method for analyzing geological and mineral exploration data. Figure 1 This is a flowchart of a cloud-based collaborative geological and mineral exploration data analysis method according to an embodiment of this application. Figure 2 This is a schematic diagram illustrating the data flow of a cloud-based collaborative geological and mineral exploration data analysis method according to an embodiment of this application. Figure 1 and Figure 2As shown, the cloud-based collaborative geological and mineral exploration data analysis method according to an embodiment of this application includes the following steps: S100, in response to a user's mouse / touch operation on the client's WebGIS, a client operation command is generated; S200, the client encapsulates the client operation command and the user's authentication credentials and securely submits them to obtain a WebSocket message frame; S300, the central coordinator in the cloud determines whether the user has permission to edit the target layer based on the user's authentication credentials in the WebSocket message frame; S400, in response to the user having permission to edit the target layer, the central coordinator in the cloud determines, based on the operation type field in the WebSocket message frame, whether to send the WebSocket message frame to the OT processing module or the lightweight state synchronization module; S500, in response to receiving the WebSocket message frame, the OT processing module performs version comparison, operation conversion, and transactional application based on the WebSocket message frame and the current server state to obtain an updated project state version; S600, in response to receiving the WebSocket message frame, the lightweight state synchronization module updates the state cache based on the WebSocket message frame and generates broadcast state update information.

[0035] Specifically, in steps S100 and S200, in response to the user's mouse / touch operation on the WebGIS client, a client operation command is generated. The client encapsulates the client operation command and the user's authentication credentials and securely submits them to obtain a WebSocket message frame. It should be understood that geological and mineral exploration data analysis often involves real-time editing and analysis of shared spatial data by multiple users and professionals. Any user interaction needs to be accurately captured and transformed into standardized commands that can be understood and processed by the server. Simultaneously, to ensure data security and access control, the server must be able to identify the identity of the operation initiator and verify their operation permissions. Therefore, in the technical solution of this application, it is necessary to transform the user's operation into a client operation command, and encapsulate and securely submit the client operation command and the user's authentication credentials. This ensures the accurate transmission of the user's intent, providing a basic input for subsequent permission verification, operation type judgment, and data processing; at the same time, through the encapsulation of authentication credentials, it enables traceability and access management of user operations, preventing unauthorized access and data tampering, thereby maintaining the data integrity and security of the entire collaborative environment. In this way, the server can receive structured, authenticated, and secure real-time operation instructions, laying the foundation for subsequent intelligent traffic routing (OT processing or lightweight state synchronization) and improving collaborative efficiency and data security. Specifically, in this embodiment, the client encapsulates and securely submits the client operation instructions and the user's authentication credentials to obtain a WebSocket message frame, including: the client packages the client operation instructions and the user's authentication credentials into a WebSocket message frame and sends it to the central coordinator in the cloud via a WebSocket connection.

[0036] More specifically, in a concrete example of this application, firstly, the client-side WebGIS application continuously listens for user interaction events such as mouse clicks, drags, and touches. When the user draws points, lines, or polygons on the map, modifies attribute information, or performs operations such as zooming or panning, the WebGIS front-end framework (such as those based on OpenLayers, Leaflet, or Mapbox GL JS) captures these raw UI events. Secondly, the captured raw events are converted into standardized client operation commands by the front-end application logic. For example, a mouse drag event is parsed into a polygon drawing command, along with the polygon's geometric coordinate sequence; a click event is parsed into a feature attribute query command, along with the geographic coordinates or feature ID of the click location; a keyboard input event is parsed into a text attribute update command, along with the feature ID to be updated, the attribute field name, and the new value. These commands are represented in a structured data format (such as JSON), clearly describing the user's intent and operation content. Next, the client encapsulates these generated client operation commands with the user's authentication credentials. Authentication credentials are typically issued by the server and stored on the client side (e.g., as a JWT token or session ID) upon user login to prove the user's legitimate identity. The client packages the operation instructions and authentication credentials into a unified WebSocket message frame. This message frame is a self-contained data unit containing the specific content of the operation and the user's identity information. Finally, the client securely sends this encapsulated WebSocket message frame to a central coordinator in the cloud via a pre-established WebSocket connection. The WebSocket protocol provides a full-duplex, persistent communication channel that significantly reduces communication overhead, achieves lower latency and higher real-time performance compared to traditional HTTP requests. This is crucial for frequent interactions and real-time collaboration in geological and mineral exploration data analysis. Through a WebSocket Secure (WSS) connection, data transmission is further protected by TLS / SSL encryption, ensuring the confidentiality and integrity of instructions and credentials during transmission.

[0037] Specifically, in step S300, the central coordinator in the cloud determines whether the user has permission to edit the target layer based on the user's authentication credentials in the WebSocket message frame. It should be understood that geological and mineral data are important national strategic resources, and their accuracy and security are paramount. Unauthorized editing may lead to data errors, loss, or even malicious damage, seriously affecting exploration project decisions and national resource management. Therefore, before processing any operation that may modify data, the system must ensure that the operator has a legitimate identity and corresponding operating permissions. That is, in the cloud-based collaborative environment for geological and mineral exploration data analysis, any operation performed by a user on WebGIS, especially editing operations involving data modification, must undergo strict permission verification. Therefore, in the technical solution of this application, the central coordinator in the cloud determines whether the user has permission to edit the target layer based on the user's authentication credentials in the WebSocket message frame. This approach ensures the security and integrity of geological and mineral data, preventing unauthorized users from modifying core data. Simultaneously, it enables refined access control, ensuring that different user roles (such as data entry clerks, geological engineers, and project managers) can only perform their authorized operations. This maintains the order and efficiency of collaborative workflows, guarantees data quality and system security, and lays a secure foundation for subsequent intelligent data routing.

[0038] More specifically, in a concrete example of this application, firstly, when the central coordinator in the cloud receives a WebSocket message frame from the client, it immediately parses the user's authentication credentials from the message frame. This credential is typically a token (such as a JSON Web Token, JWT) or a session ID, representing the user's unique identity and authentication status. Secondly, the central coordinator uses the parsed authentication credentials to interact with the user management and permission management modules within the system. If the credential is a JWT, the coordinator decrypts and verifies it, checking its signature for validity and expiration, and extracts the user's unique identifier (such as a user ID) and its associated role information or permission list. If the credential is a session ID, the coordinator queries session storage (such as Redis or a database) to retrieve the user information and permission data associated with that session ID.

[0039] Next, the central coordinator identifies the specific target layer for the operation based on the operation instructions contained in the message frame. For example, if the operation instruction is to draw geological boundaries, then the target layer is the geological boundary layer; if the operation is to modify borehole attributes, then the target layer is the borehole layer. Finally, the central coordinator compares the obtained user permission information with the editing permission requirements of the target layer. The system typically maintains a permission policy database or configuration that clearly defines which user roles or specific users have different levels of granular operation permissions, such as editing, viewing, and deleting, for which layers. The central coordinator queries this permission policy to determine whether the current user is explicitly authorized to edit the specified target layer. If the comparison shows that the user does not have editing permissions, the central coordinator will reject the operation and return an insufficient permission error message to the client; otherwise, if the user has editing permissions, the operation is allowed to proceed to subsequent processing stages. The entire process is typically completed in milliseconds to ensure smooth real-time collaboration.

[0040] Specifically, in step S400, in response to the user having permission to edit the target layer, the central coordinator in the cloud determines whether to send the WebSocket message frame to the OT processing module or the lightweight state synchronization module based on the operation type field in the WebSocket message frame. It should be understood that user operations in geological and mineral exploration data analysis are diverse and complex. Not all operations have equally strict requirements for data consistency, nor do all operations pose a risk of high concurrency conflicts. For example, operations such as drawing new geological lines and modifying borehole data directly involve modifications to shared spatial data and may conflict with concurrent editing by other users, requiring a rigorous conflict resolution mechanism. Operations such as map panning, zooming, layer display / hiding, and simple attribute queries mainly affect the client's view state or modify the server state locally and are non-conflicting. If all operations undergo complex OT processing, it will introduce unnecessary computational overhead and latency, affecting system performance and user experience. Therefore, in a cloud-based collaborative environment for geological and mineral exploration data analysis, once the central coordinator in the cloud confirms that a user has permission to edit the target layer, its next key decision is based on the operation type field in the WebSocket message frame. It intelligently determines whether to send the message frame to the OT processing module or the lightweight state synchronization module. This enables optimized allocation of system resources and refined management of the processing flow. Through intelligent routing, editing operations that truly require complex conflict resolution and version synchronization (such as adding, deleting, or modifying geological elements) are routed to the OT processing module, ensuring the eventual consistency and integrity of data under concurrent editing by multiple users and avoiding data loss or logical errors. Simultaneously, operations with lower data consistency requirements, low conflict risk, or those only affecting the client's view are routed to the lightweight state synchronization module, thereby reducing processing latency, improving system response speed, and providing users with a smoother and more efficient real-time collaborative experience.

[0041] Figure 3 The flowchart illustrates a cloud-based collaborative geological and mineral exploration data analysis method according to an embodiment of this application, in response to the user having permission to edit the target layer. The central coordinator in the cloud determines whether to send the WebSocket message frame to the OT processing module or the lightweight state synchronization module based on the operation type field in the WebSocket message frame. (See also...) Figure 3As shown, according to the cloud-based collaborative geological and mineral exploration data analysis method of this application embodiment, step S400 includes: S410, extracting the operation type field; S420, performing semantic embedding encoding on the operation type field to obtain an operation type information semantic embedding encoding vector; S430, extracting other information besides the operation type field from the WebSocket message frame as operation type context supplement; S440, performing structured embedding encoding on the operation type context supplement to obtain an operation type context supplement embedding encoding graph; S450, based on the operation type context supplement embedding encoding graph, performing cross-modal context enhancement on the operation type information semantic embedding encoding vector to obtain an enhanced operation type information semantic embedding encoding vector; S460, based on the enhanced operation type information semantic embedding encoding vector, determining whether to send the WebSocket message frame to the OT processing module or the lightweight state synchronization module.

[0042] Specifically, in steps S410 and S420, the operation type field is extracted, and semantic embedding encoding is performed on the operation type field to obtain an operation type information semantic embedding encoding vector. It should be understood that the original operation type field may be a discrete text label (e.g., AddPoint, ModifyLine, DeletePolygon, PanMap, etc.), which itself cannot directly reflect the deep semantics, complexity, and potential conflict risks of the operation. For example, a Modify operation may refer to modifying a simple attribute value or modifying the geometry of a complex geological body, with drastically different requirements for system resources and conflict resolution complexity. To achieve more accurate and intelligent routing decisions, the system needs a representation method that can capture the inherent meaning and correlation of operation types. In other words, in cloud-based collaborative geological and mineral exploration data analysis methods, when the central coordinator needs to intelligently determine whether to distribute WebSocket message frames to the OT processing module or the lightweight state synchronization module, simply relying on the original operation type field string for simple rule matching is far from sufficient. Therefore, the operation type field is extracted and semantically embedded to obtain an operation type information semantic embedding encoding vector. Discrete, symbolic operation type fields are transformed into continuous, high-dimensional numerical vector representations. This vector representation captures the semantic similarity between operation types, ensuring that similar operations are close together in the vector space, thus providing richer feature information for subsequent intelligent decision-making. Through semantic embedding encoding, the system can go beyond simple string matching, understanding the intent and nature of operations, laying the foundation for subsequent cross-modal context enhancement and final routing decisions.

[0043] More specifically, in a concrete example of this application, firstly, the operation type field is extracted: When the central coordinator receives a WebSocket message frame, it parses the structure of the message frame to accurately identify and extract the predefined operation type field. This field is typically a specific key-value pair in the message frame, with the value being a string representing the operation type, such as operationType: AddDrillHole or operationType:UpdateGeologicalBoundary. Secondly, an operation type vocabulary is constructed: During system initialization or training, all possible operation type strings are collected, and a unique vocabulary is constructed. For example, {AddPoint, ModifyLine, DeletePolygon, PanMap, ZoomIn}.

[0044] Next, select a semantic embedding model: various commonly used semantic embedding techniques from Natural Language Processing (NLP) can be employed. Based on pre-trained word vector models: if the operation type field is a descriptive phrase (e.g., "add borehole data"), a pre-trained word vector model (e.g., Word2Vec, GloVe, or FastText) can be used to map each word to a vector, which is then combined into a vector of the operation type phrase using average pooling or weighted averaging. For complex, descriptive operation type strings, a Transformer encoder (e.g., BERT) can be used to encode the operation type string, resulting in a more context-aware semantic embedding vector. Finally, perform semantic embedding encoding: the extracted operation type field string is fed into the selected semantic embedding model as input. The model converts the string into a fixed-dimensional floating-point vector, i.e., the "operation type information semantic embedding encoded vector," based on its internal mapping relationships or learned weights. For example, AddPoint is encoded as [0.12, -0.05, 0.88, ...], while DeletePoint is encoded as [0.10, -0.06, 0.85, ...]. Their proximity in the vector space reflects their association with point operations. This vector will then serve as input for subsequent cross-modal context enhancement.

[0045] Specifically, in steps S430 and S440, information other than the operation type field is extracted from the WebSocket message frame as operation type context supplementation, and the operation type context supplementation is structured and embedded to obtain an operation type context supplementation embedding code diagram. It should be understood that the complexity of geological and mineral exploration operations lies not only in the operation type itself, but also in the specific object targeted by the operation, the attributes involved, and the system state at the time of the operation. For example, a modification operation targeting a simple point feature's non-critical attribute has a much lower risk of conflict and processing complexity than modifying the geometry or critical attributes of a complex geological body (such as a fault or ore body). This contextual information (such as target layer ID, feature ID, modified attribute fields, geometric range of the operation, user role, etc.) is structured, and together they constitute the complete context of the operation, which is crucial for accurately determining the nature of the operation. The true intent of an operation and its impact on the system state often depend on its specific contextual information. Therefore, information other than the operation type field is extracted from WebSocket message frames as operation type context supplementation, and this information is then structured and embedded to obtain an operation type context supplementation embedding encoding graph. These scattered and heterogeneous structured context supplementation information are integrated and transformed into a unified, high-dimensional numerical representation, i.e., the operation type context supplementation embedding encoding graph. This graph structure can capture the internal relationships within the context supplementation information; for example, an operation may simultaneously involve a specific layer, a specific feature, and a specific attribute field, and these pieces of information have logical relationships. Through structured embedding encoding, the system can gain a more comprehensive and in-depth understanding of the actual scope of impact and potential risks of operations, providing rich and organized supplementary information for subsequent cross-modal context enhancement.

[0046] More specifically, in a concrete example of this application, firstly, supplementary information about the operation type context is extracted: After the central coordinator receives the WebSocket message frame and parses the operation type field, it continues to parse the rest of the message frame, extracting all structured data related to the operation type. This data may include, but is not limited to: Target Layer ID / Name: Identifier of the geographic information layer targeted by the operation. Target Feature ID: Unique identifier of the specific geographic feature affected by the operation. Modified Attribute Field: If the operation modifies an attribute, it contains the name of the modified attribute. Geometric Information: If the operation involves geometric modifications (such as drawing or editing geometry), it may contain new geometric coordinates or geometric operation types (such as adding vertices or deleting edges). User Role / Permission Level: The current role or permission level of the operation initiator. Timestamp / Version Information: The time when the operation occurred or the client data version it is based on. This information typically exists in the JSON payload of the WebSocket message frame in the form of key-value pairs, arrays, or nested objects.

[0047] Secondly, the structured information needs to be characterized: For each type of extracted structured information, it needs to be converted into a numerical representation. Discrete feature encoding: For discrete identifiers such as layer IDs, feature IDs, and attribute field names, one-hot encoding or an embedding layer can be used to map them into vectors. The embedding layer can learn the potential relationships between different IDs during training. Continuous feature normalization: For continuous numerical values ​​such as geometric coordinates and timestamps, normalization can be performed to make them fall within a specific range. Complex structure encoding: For complex structures such as geometric information, a specialized geometric encoder (such as one based on graph neural networks or convolutional neural networks) can be used to convert them into vector representations.

[0048] Next, construct an operation type context-supplemented embedding encoding graph: integrate the various context information vectors after featureization to form a graph structure that can represent their internal relationships. This can be achieved in the following ways: Feature concatenation and multilayer perceptron: The simplest way is to concatenate all context information vectors and then input them into one or more fully connected layers to learn the non-linear relationships between them, ultimately outputting a unified context embedding vector. Graph neural network: If there are explicit graph structure relationships between context information (e.g., an operation affects multiple related elements, or a layer contains multiple elements), a graph can be constructed where nodes represent different context entities (e.g., layers, elements, attributes), and edges represent the relationships between them. Then, a GNN model such as a graph convolutional network or graph attention network is used to encode this graph to obtain an embedding encoding graph that can capture the complex relationships between context entities.

[0049] Finally, through the above steps, the original, scattered structured context information is transformed into a high-dimensional operation type context supplementary embedding encoding graph that captures its internal correlations. This encoding graph will serve as the input for subsequent cross-modal context enhancement, and will be fused with the operation type semantic embedding vector to achieve a more comprehensive and accurate understanding of the operation intent.

[0050] Specifically, in step S450, based on the operation type context supplement embedding coding diagram, the operation type information semantic embedding coding vector is enhanced across modal contexts to obtain an enhanced operation type information semantic embedding coding vector. It should be understood that operation type information provides an abstract semantic representation of the operation, while operation type context supplementation provides the specific context and details of the operation. These two types of information are complementary, but their respective encoding methods (semantic embedding vector and structured embedding coding diagram) are heterogeneous. To make the most accurate routing decision, it is necessary to deeply integrate these two different modalities of information, allowing the semantic understanding of the operation to be calibrated and enhanced by its specific context. For example, the semantics of a modification operation, in the context of modifying borehole data, has drastically different complexity and conflict risk compared to the context of modifying geological boundaries. Based on this, in the technical solution of this application, the operation type information semantic embedding coding vector is further enhanced across modal contexts based on the operation type context supplement embedding coding diagram to obtain an enhanced operation type information semantic embedding coding vector. Through a full-dimensional dynamic perception cross-modal interaction mechanism, the complex correlation between operation type semantics and operation context supplementation is deeply captured. Specifically, it aims to enable the semantic embedding encoding vector of operation type information to dynamically perceive and utilize the operation type context to supplement the rich information contained in the embedding encoding graph. Through this fusion, an enhanced semantic embedding encoding vector of operation type information is generated. This vector not only contains the semantics of the operation type itself, but also incorporates detailed considerations of its context, thereby more comprehensively and accurately representing the true intention, complexity, and potential impact of the operation.

[0051] Figure 4 This is a flowchart illustrating the process of enhancing the semantic embedding encoding vector of operation type information by performing cross-modal context enhancement on the operation type information semantic embedding encoding vector, based on the operation type context supplementary embedding encoding graph according to the cloud-based collaborative geological and mineral exploration data analysis method of this application. (See flowchart for example.) Figure 4As shown, according to the cloud-based collaborative geological and mineral exploration data analysis method of this application embodiment, step S450 includes: S451, performing global mean pooling on the operation type context supplementation embedding coding map to obtain the operation type context supplementation global pooling coding vector; S452, performing global dynamic interaction analysis on the operation type information semantic embedding coding vector and the operation type context supplementation global pooling coding vector to obtain the operation type context supplementation full-dimensional dynamic perception coding matrix; S453, performing nonlinear activation on the operation type context supplementation full-dimensional dynamic perception coding matrix and applying it to the operation type context supplementation embedding coding map to obtain the enhanced operation type information semantic embedding coding vector.

[0052] More specifically, in step S451, global mean pooling is performed on the operation type context supplementation embedding coding graph to obtain the operation type context supplementation global pooling coding vector, expressed by the formula:

[0053] ,

[0054] in, The operation type context supplement is embedded in the encoding graph. Location feature value, and The height and width of the embedded coding graph are supplemented for the context of the operation type, respectively. Supplement the global pooling encoding vector to the operation type context.

[0055] It is understandable that the operation type context supplement embedding coding graph is a high-dimensional, complex graph structure containing various fine-grained information such as layers, features, and attributes involved in the operation. Directly interacting the entire graph structure with the operation type semantic vector would introduce excessive computational complexity and the curse of dimensionality, and may also contain redundant information. To efficiently perform cross-modal fusion, it is necessary to extract the core, global context summary information from this complex graph structure while reducing the dimensionality of the data. Through global mean pooling, the high-dimensional operation type context supplement embedding coding graph, which may have a complex topological structure, is efficiently compressed into a compact, fixed-dimensional operation type context supplement global pooling coding vector. This aims to condense the overall semantic summary of the operation context, that is, to capture the macroscopic features and key information of the environment in which the operation occurs, while achieving dimensionality reduction and parameter regularization. This global pooling coding vector will serve as the global context anchor point for the operation type context supplement embedding coding, providing a stable and representative macroscopic view for subsequent cross-modal interactions with the operation type information semantic embedding coding vector.

[0056] More specifically, in step S452, a global dynamic interaction analysis is performed on the semantic embedding encoding vector of the operation type information and the global pooling encoding vector of the operation type context supplementation to obtain the full-dimensional dynamic perceptual encoding matrix of the operation type context supplementation, expressed by the formula:

[0057] ,

[0058] ,

[0059] in, For vector multiplication, For positional product, The semantic embedding encoding vector is used to define the operation type information. and Let be the projection matrix. for function, Supplement the operation type context with a dynamic weight-aware matrix. Supplement the operation type context with a fully perceptive trainable bias vector. Supplement the operation type context with a full-dimensional dynamic perceptual coding matrix.

[0060] It is understandable that there is a complex, non-linear interaction between operation type information and its contextual supplementary semantics. For example, the semantics of a modification operation, in the context of modifying a critical geological boundary, have a far greater importance and potential conflict than modifying a non-critical attribute. This interaction pattern is dynamic and cannot be simply covered by fixed rules. To capture this subtle, dimension-to-dimensional potential correlation, a mechanism is needed that can go beyond simple splicing or linear combination to achieve deep, adaptive cross-modal interaction. In other words, to achieve accurate routing of user operations, simply having the operation type semantic embedding vector and the operation context global pooling vector is insufficient. Although these two represent what the operation is and in what context, the deep correlation and mutual influence patterns between them are the key to determining the complexity of the operation and the risk of conflict. Therefore, a global dynamic interaction analysis is further performed on the operation type information semantic embedding encoding vector and the operation type context supplementary global pooling encoding vector to obtain the operation type context supplementary full-dimensional dynamic perceptual encoding matrix. In this way, it is possible to deeply explore all-dimensional potential correlations between the operation type information semantic embedding encoding vector and the operation type context supplementary global pooling encoding vector. This module aims to learn and dynamically generate a full-dimensional dynamic perceptual encoding matrix supplemented by operation type context. This matrix can be viewed as a dynamically generated, parameterized interaction kernel, whose internal structure finely encodes the specific patterns and strengths of mutual influence and interdependence between operation type semantics and global information of the operation context. It avoids the assumptions of locality or sparsity in interaction, ensuring a comprehensive capture of all potential correlations between the two, and this interaction pattern is adaptively generated based on the current specific input content, rather than being preset.

[0061] Accordingly, according to the embodiments of this application, step S453, after nonlinearly activating the operation type context supplementary full-dimensional dynamic perception coding matrix, applies it to the operation type context supplementary embedding coding graph to obtain the enhanced operation type information semantic embedding coding vector, includes: nonlinearly activating the operation type context supplementary full-dimensional dynamic perception coding matrix to obtain an activated operation type context supplementary full-dimensional dynamic perception coding matrix; performing positional dot product between the activated operation type context supplementary full-dimensional dynamic perception coding matrix and each operation type context supplementary feature matrix along the channel dimension in the operation type context supplementary embedding coding graph, so as to apply the activated operation type context supplementary full-dimensional dynamic perception coding matrix to the operation type context supplementary embedding coding graph to obtain the enhanced operation type information semantic embedding coding vector.

[0062] Specifically, in this embodiment, the activated operation type context supplementary full-dimensional dynamic perception encoding matrix and the operation type context supplementary embedding encoding map along the channel dimension are multiplied by position to apply the activated operation type context supplementary full-dimensional dynamic perception encoding matrix to the operation type context supplementary embedding encoding map to obtain the enhanced operation type information semantic embedding encoding vector. This includes: multiplying the activated operation type context supplementary full-dimensional dynamic perception encoding matrix and the operation type context supplementary embedding encoding map along the channel dimension by position to obtain the enhanced operation type information semantic embedding encoding map; and performing feature reshaping processing on the enhanced operation type information semantic embedding encoding map to obtain the enhanced operation type information semantic embedding encoding vector.

[0063] More specifically, the operation type context supplemented full-dimensional dynamic sensing coding matrix is ​​nonlinearly activated to obtain the activated operation type context supplemented full-dimensional dynamic sensing coding matrix, expressed by the formula:

[0064] ,

[0065] in, Supplement the operation type context with a full-dimensional dynamic perceptual coding matrix. for function, Supplement the activated operation type context with a full-dimensional dynamic sensing encoding matrix.

[0066] More specifically, the enhanced operation type information semantic embedding encoding map is obtained by performing positional dot multiplication on the full-dimensional dynamic perception encoding matrix of the activated operation type context supplementation and the operation type context supplementation embedding encoding map along the channel dimension. This is expressed by the formula:

[0067] ,

[0068] in, This is a positional dot product along the channel dimension. To enhance the semantic embedding of operation type information into the coding graph.

[0069] It is understandable that, firstly, the element values ​​of the original operation type context-supplemented full-dimensional dynamic perceptual encoding matrix may be distributed across any real number range. However, as attention weights or modulation factors, they typically need to be normalized to a specific range (e.g., between 0 and 1) to represent importance or gating strength. Non-linear activation functions (such as the sigmoid) can map these values ​​to the desired range and introduce non-linearity, enhancing the model's expressive power. Secondly, this activated operation type context-supplemented full-dimensional dynamic perceptual encoding matrix needs to be able to guide or reshape the original operation type context-supplemented embedding encoding map. This means it should be able to adaptively boost or suppress the importance of different regions or channels in the context feature map based on the operation type semantics. By using positional dot product, each element of the activated dynamic perceptual encoding matrix can be treated as a weight or scaling factor, directly acting on the feature value of the corresponding position or channel in the original context feature map, thereby achieving refined feature recalibration. This is achieved by transforming the abstract cross-modal interaction pattern (operation type context-supplemented full-dimensional dynamic perceptual encoding matrix) into concrete attention or modulation signals that can be applied to features. Non-linear activation ensures that these signals have a reasonable numerical range and interpretability. Subsequently, by performing positional dot products on these signals and the original operation type context supplementary embedding encoding map, adaptive recalibration of the original operation type context supplementary embedding encoding features is achieved. This allows context feature regions or channels that are highly relevant to the operation type semantics and are crucial for determining operation complexity and routing paths to receive higher weights, thus becoming significantly highlighted in the final enhanced operation type information semantic embedding encoding map, while irrelevant or unimportant features are suppressed.

[0070] More specifically, in step S423, the semantic embedding encoding map of the enhanced operation type information is subjected to feature reshaping processing to obtain the semantic embedding encoding vector of the enhanced operation type information, expressed by the formula:

[0071] ,

[0072] in, For feature reshaping processing, To enhance the semantic embedding of operation type information into the encoding vector.

[0073] It can be understood that the augmented operation type information semantic embedding encoding graph is a multidimensional tensor, or a set containing multiple feature vectors. While this form of representation is information-rich, subsequent classification or regression tasks typically require a single, fixed-length vector as input. Furthermore, feature reshaping processes can further refine and compress information, remove potential redundancy, and ensure that the final vector representation comprehensively and effectively captures all key information in the augmented graph while meeting the input format requirements of downstream modules. Therefore, the high-dimensional augmented operation type information semantic embedding encoding graph with its complex internal structure is transformed into a compact, fixed-dimensional augmented operation type information semantic embedding encoding vector through a series of feature reshaping operations. This vector serves as the final representation after deep fusion of operation type semantics and contextual information; it includes the semantics of the operation type itself, incorporates refined considerations of its context, and has been enhanced through cross-modal interaction.

[0074] Specifically, in step S460, based on the enhanced operation type information semantic embedding encoding vector, it is determined whether to send the WebSocket message frame to the OT processing module or the lightweight state synchronization module. It should be understood that in a cloud-based collaborative environment for geological and mineral exploration data analysis, after a series of complex semantic embedding, context supplementation encoding, and cross-modal enhancement processing, the central coordinator ultimately obtains a highly condensed and information-rich enhanced operation type information semantic embedding encoding vector. This vector is the final and most comprehensive numerical representation of the user's operation type, its context, and the complex interaction patterns between them. It already contains all the key information needed to determine the complexity of the operation, the risk of conflict, and the degree of data consistency requirements. At this point, the system needs a final decision-making mechanism that can map this abstract vector to specific routing selections, thereby achieving differentiated processing for operations of different natures. Therefore, based on the enhanced operation type information semantic embedding encoding vector, it is further determined whether to send the WebSocket message frame to the OT processing module or the lightweight state synchronization module. Based on the rich information contained in the enhanced operation type information semantic embedding encoding vector, the most optimized and intelligent module distribution decision is made. Specifically, accurately identifying which operations are high-conflict, high-complexity, and have extremely high requirements for data consistency requires routing to the OT processing module for rigorous version control and conflict resolution; while which operations are low-conflict, low-complexity, and non-editing operations that mainly affect the view or non-critical state can be routed to the lightweight state synchronization module for rapid response and broadcasting.

[0075] First, the central coordinator's decision module receives the semantic embedding encoding vector of the enhanced operation type information generated in the previous step. This vector is a fixed-dimensional sequence of floating-point numbers that comprehensively represents the semantic and contextual information of the current user operation. Second, a decision model is constructed: various commonly used machine learning or deep learning models can be used as the decision model, mapping the input enhancement vector to the classification results of the OT module or lightweight state synchronization module. In other words, the semantic embedding encoding vector of the enhanced operation type information is used as feature input to train a binary classification model. The model learns a decision boundary, dividing the vector space into two regions, corresponding to two processing modules. Alternatively, a simple neural network can be constructed, containing one or more fully connected layers and a non-linear activation function (such as ReLU), with the last layer using a Sigmoid or Softmax activation function to output the probabilities of the two classes. By learning a large number of operation samples (and their corresponding correct routing decisions), the network automatically learns the complex mapping relationship from the enhancement vector to the routing decision.

[0076] Finally, the decision is executed and message frames are distributed: the semantic embedding of enhanced operation type information is input into the trained decision model. The model outputs a classification result (e.g., the probability of the OT module or the lightweight module, or a direct class label). Based on the model's output, the central coordinator accurately distributes the raw WebSocket message frames to the corresponding processing modules. If the decision is for the OT processing module, the message frame is sent to the OT engine for version comparison, operation conversion, and transactional application; if the decision is for the lightweight state synchronization module, the message frame is quickly processed and broadcast to other relevant clients, enabling rapid state updates.

[0077] Specifically, in step S500, in response to receiving the WebSocket message frame, the OT processing module performs version comparison, operation transformation, and transactional application based on the WebSocket message frame and the current server state to obtain an updated project state version. It should be understood that geological and mineral exploration data (such as geological lines, boreholes, ore body boundaries, etc.) is shared and complex spatial data, and multi-user real-time concurrent editing is common. Traditional locking mechanisms can lead to severe performance bottlenecks and a degraded user experience, while a simple last-write-wins strategy can result in data loss and inconsistency. OT technology is designed to solve the data consistency problem under such high-concurrency, lock-free collaborative editing. It allows users to edit independently locally and submit their operations to the server. The server intelligently resolves conflicts through transformation algorithms, ensuring that all clients eventually converge to a consistent data state. In the cloud-based collaborative environment for geological and mineral exploration data analysis, when the central coordinator intelligently determines that a WebSocket message frame (representing a user operation) belongs to a high-conflict, high-complexity editing operation with extremely high data consistency requirements, and routes it to the OT (Operational Transformation) processing module, the OT processing module responds by receiving the message frame. At this point, the OT processing module performs version comparison, operation conversion, and transactional application based on the WebSocket message frame and the current server state to obtain an updated project state version. This ensures that when multiple users concurrently edit shared geological and mineral data, the server-side data maintains logical consistency and integrity, avoiding data loss, overwriting, or logical errors caused by concurrent operations. Version comparison identifies the differences between the client's operation and the server's current version; operation conversion intelligently resolves conflicts caused by version differences, "converting" the client's operation to the latest version on the server, enabling it to be correctly applied to the current state; finally, transactional application atomically commits the converted operation to the database, resulting in a logically correct and up-to-date project state version that reflects all operations that have occurred.

[0078] More specifically, in this embodiment, in response to receiving the WebSocket message frame, the OT processing module performs version comparison, operation conversion, and transactional application based on the WebSocket message frame and the current server state to obtain an updated project state version. This includes: parsing the basic version of the operation from the WebSocket message frame; comparing the basic version of the operation with the current server version number in the current server state to obtain a version comparison result; in response to a conflict in the version comparison result, extracting all operations from the basic version of the operation to the current server version number from the server operation history in the current server state as other operations; calling a conversion function to convert the client instructions in the WebSocket message frame and the other operations to obtain converted operation instructions; and executing the converted operation instructions in PostGIS to obtain the updated project state version.

[0079] More specifically, firstly, the OT processing module precisely parses the base version of the operation from the received WebSocket message frame. This base version is typically the server version number upon which the client's local data is based when executing the operation. This version number is the key basis for the OT algorithm's conflict detection and transformation. Subsequently, the OT processing module compares the parsed base version of the operation with the current server version number in the server's current state. This comparison aims to determine if the version based on the client's operation is consistent with the latest version of the server. If the version numbers are the same, it indicates that the client's operation is based on the latest state, and there is usually no direct conflict; if the client's base version is lower than the server's current version, it indicates that other users' operations have occurred on the server during the client's operation submission, and a potential conflict exists. In response to a conflict (i.e., the client's base version is lower than the server's current version), the OT processing module precisely extracts all operations from the server's operation history in the current server state, from the client's base version to the server's current version number. These extracted operations are considered as other users' operations; they are operations successfully applied to the server by other users during the client's operation submission.

[0080] Next, the OT processing module calls a predefined transformation function to transform the client instructions (i.e., the original operations submitted by the user) contained in the WebSocket message frame and the extracted operations from others. The transformation function is the core of the OT algorithm; it intelligently adjusts the client instructions based on the type and content of the operations, ensuring they are correctly applied to the latest state of the server while preserving the intent of the client operation and resolving conflicts with other operations. For example, if a client deletes a point, and someone else adds an attribute to that point, the transformation function will ensure the deletion operation remains valid, but may need to adjust its internal parameters to adapt to the new state. Finally, the transformed operation instructions are executed in PostGIS (the core database for storing geological and mineral spatial data). This execution process is transactional, ensuring the atomicity and durability of the operations. Once the transformed operation instructions are successfully executed in PostGIS, the server's geospatial data and related attribute data are updated, resulting in a logically consistent updated project state version reflecting all operations that have occurred. This new server version number is also updated accordingly, ready to respond to subsequent client operations or synchronization requests.

[0081] Specifically, in step S600, in response to receiving the WebSocket message frame, the lightweight state synchronization module updates the state cache based on the WebSocket message frame and generates broadcast state update information. It should be understood that in a cloud-based collaborative environment for geological and mineral exploration data analysis, when the central coordinator intelligently determines that a WebSocket message frame (representing a user operation) is a low-conflict, low-complexity, non-editable operation that primarily affects the view or non-critical state (e.g., map panning, zooming, layer visibility switching, simple queries, etc.) and routes it to the lightweight state synchronization module, the module responds by receiving the message frame. At this time, the lightweight state synchronization module updates the state cache based on the WebSocket message frame and generates broadcast state update information. For these types of operations, they typically do not involve modifications to core shared data, or their modifications are reversible and have extremely low conflict risk. Sending all operations into a complex OT processing flow would introduce unnecessary latency and computational overhead, severely impacting the user experience. Therefore, a more efficient and lightweight mechanism is needed to handle these operations to ensure high system responsiveness and smoothness. Based on this, in response to receiving the WebSocket message frame, the lightweight state synchronization module updates the state cache and generates broadcast state update information. This allows for the rapid and efficient handling of non-critical operations that do not involve complex conflict resolution, and quickly broadcasts these state changes to all relevant clients. By updating the state cache, it ensures that the lightweight state maintained on the server side (such as map viewport, layer visibility, currently selected features, etc.) remains synchronized with the client. Simultaneously, it generates concise state update information so that other clients can quickly receive and update their local views, thereby achieving a near real-time collaborative experience and improving the overall system's responsiveness and user-perceived performance.

[0082] In summary, the cloud-based collaborative geological and mineral exploration data analysis method according to the embodiments of this application is explained. It encapsulates user operations on the client-side WebGIS into WebSocket message frames containing authentication credentials and securely submits them to a cloud-based central coordinator. The central coordinator first verifies user permissions, and then, based on the operation type field in the message frame and combined with intelligent judgment enhanced by cross-modal context, accurately routes the message frame to either the OT processing module or the lightweight state synchronization module. The OT processing module handles complex, high-conflict-risk editing operations, ensuring eventual data consistency under concurrent editing by multiple users through version comparison, operation conversion, and transactional applications. The lightweight state synchronization module handles non-editable or low-conflict-risk operations, quickly updating and broadcasting the state to improve system response speed and overall performance. This intelligent routing and collaboration mechanism effectively overcomes the limitations of traditional methods, achieving efficient and reliable cloud-based collaborative data analysis.

[0083] Furthermore, a cloud-based collaborative geological and mineral exploration data analysis system is also provided.

[0084] Figure 5 This is a block diagram of a cloud-based collaborative geological and mineral exploration data analysis system according to an embodiment of this application. Figure 5 As shown, the cloud-based collaborative geological and mineral exploration data analysis system 100 according to an embodiment of this application includes: a client operation instruction generation module 110, used to generate client operation instructions in response to user mouse / touch operations on the client's WebGIS; an instruction encapsulation and submission module 120, used by the client to encapsulate and securely submit the client operation instructions and the user's authentication credentials to obtain a WebSocket message frame; an authorization detection module 130, used by the central coordinator in the cloud to determine whether the user has the authorization to edit the target layer based on the user's authentication credentials in the WebSocket message frame; and a message frame sending selection module 140, used in response to the user having the authorization to edit the target layer. The cloud-based central coordinator determines whether to send the WebSocket message frame to the OT processing module or the lightweight state synchronization module based on the operation type field in the WebSocket message frame. The OT processing module analysis module 150, in response to receiving the WebSocket message frame, performs version comparison, operation conversion, and transactional application based on the WebSocket message frame and the current server state to obtain an updated project state version. The lightweight state synchronization module processing module 160, in response to receiving the WebSocket message frame, updates the state cache based on the WebSocket message frame and generates broadcast state update information.

[0085] Here, those skilled in the art will understand that the specific operations of each module in the aforementioned cloud-based collaborative geological and mineral exploration data analysis system have been referenced above. Figures 1 to 4 The description of the cloud-based collaborative geological and mineral exploration data analysis method is detailed here, and therefore, its repeated description will be omitted.

[0086] As described above, the cloud-based collaborative geological and mineral exploration data analysis system 100 according to the embodiments of this application can be implemented in various wireless terminals, such as servers with cloud-based collaborative geological and mineral exploration data analysis algorithms. In one possible implementation, the cloud-based collaborative geological and mineral exploration data analysis system 100 according to the embodiments of this application can be integrated into the wireless terminal as a software module and / or hardware module. For example, the cloud-based collaborative geological and mineral exploration data analysis system 100 can be a software module in the operating system of the wireless terminal, or it can be an application developed for the wireless terminal; of course, the cloud-based collaborative geological and mineral exploration data analysis system 100 can also be one of many hardware modules of the wireless terminal.

[0087] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A cloud-based collaborative method for analyzing geological and mineral exploration data, characterized in that, include: Responding to user mouse / touch operations on the WebGIS client, generate client operation commands; The client encapsulates the client operation instructions and the user's authentication credentials into instructions and securely submits them to obtain a WebSocket message frame; The central coordinator in the cloud determines whether the user has permission to edit the target layer based on the user's authentication credentials in the WebSocket message frame; In response to the user having permission to edit the target layer, the central coordinator in the cloud determines whether to send the WebSocket message frame to the OT processing module or the lightweight state synchronization module based on the operation type field in the WebSocket message frame. In response to receiving the WebSocket message frame, the OT processing module performs version comparison, operation conversion, and transactional application based on the WebSocket message frame and the current server status to obtain an updated project status version; In response to receiving the WebSocket message frame, the lightweight state synchronization module updates the state cache based on the WebSocket message frame and generates broadcast state update information.

2. The cloud-based collaborative geological and mineral exploration data analysis method according to claim 1, characterized in that, The client encapsulates and securely submits the client operation instructions and user authentication credentials to obtain a WebSocket message frame, including: After the client packages the client operation instructions and the user's authentication credentials into a WebSocket message frame, it sends it to the central coordinator in the cloud via a WebSocket connection.

3. The cloud-based collaborative geological and mineral exploration data analysis method according to claim 1, characterized in that, In response to receiving the WebSocket message frame, the OT processing module performs version comparison, operation conversion, and transactional application based on the WebSocket message frame and the current server state to obtain an updated project state version, including: The basic version of the WebSocket message frame parsing operation; A version comparison is performed based on the basic version of the operation and the current version number of the server in the current server state to obtain the version comparison result. If the version comparison result indicates a conflict, all operations from the base version of the operation to the current version number of the server are extracted from the server operation history in the current state of the server as the operations of others. The conversion function is invoked to convert the client instructions and the other party's operations in the WebSocket message frame to obtain the converted operation instructions. Execute the post-conversion operation command in PostGIS to obtain the updated project status version.

4. The cloud-based collaborative geological and mineral exploration data analysis method according to claim 1, characterized in that, In response to the user having permission to edit the target layer, the central coordinator in the cloud determines, based on the operation type field in the WebSocket message frame, whether to send the WebSocket message frame to the OT processing module or the lightweight state synchronization module, including: Extract the operation type field; The operation type field is semantically embedded and encoded to obtain the operation type information semantic embedding encoding vector; Extract other information besides the operation type field from the WebSocket message frame as supplementary operation type context; The operation type context supplement is structured and embedded to obtain an operation type context supplement embedding encoding graph; Based on the operation type context supplementary embedding coding graph, cross-modal context enhancement is performed on the operation type information semantic embedding coding vector to obtain an enhanced operation type information semantic embedding coding vector. Based on the enhanced operation type information semantic embedding encoding vector, it is determined whether to send the WebSocket message frame to the OT processing module or the lightweight state synchronization module.

5. The cloud-based collaborative geological and mineral exploration data analysis method according to claim 4, characterized in that, Based on the operation type context supplementary embedding coding graph, cross-modal context enhancement is performed on the operation type information semantic embedding coding vector to obtain an enhanced operation type information semantic embedding coding vector, including: The operation type context supplement embedding coding graph is subjected to global mean pooling to obtain the operation type context supplement global pooling coding vector; A global dynamic interaction analysis is performed on the semantic embedding encoding vector of the operation type information and the global pooling encoding vector of the operation type context to obtain the full-dimensional dynamic perception encoding matrix of the operation type context. After performing nonlinear activation on the full-dimensional dynamic perceptual coding matrix of the operation type context supplement, it is applied to the operation type context supplement embedding coding graph to obtain the enhanced operation type information semantic embedding coding vector.

6. The cloud-based collaborative geological and mineral exploration data analysis method according to claim 5, characterized in that, After performing nonlinear activation on the full-dimensional dynamic perceptual coding matrix supplemented to the operation type context, it is applied to the operation type context supplemented embedding coding graph to obtain the enhanced operation type information semantic embedding coding vector, including: The operation type context supplemented full-dimensional dynamic sensing coding matrix is ​​non-linearly activated to obtain the activated operation type context supplemented full-dimensional dynamic sensing coding matrix. The activated operation type context supplement full-dimensional dynamic perception coding matrix and the operation type context supplement embedding coding graph along the channel dimension are multiplied by position to apply the activated operation type context supplement full-dimensional dynamic perception coding matrix to the operation type context supplement embedding coding graph to obtain the enhanced operation type information semantic embedding coding vector.

7. The cloud-based collaborative geological and mineral exploration data analysis method according to claim 6, characterized in that, The activated operation type context supplemented full-dimensional dynamic perceptual encoding matrix and the operation type context supplemented embedding encoding map are multiplied by position along each operation type context supplemented feature matrix along the channel dimension. The activated operation type context supplemented full-dimensional dynamic perceptual encoding matrix is ​​then applied to the operation type context supplemented embedding encoding map to obtain the enhanced operation type information semantic embedding encoding vector, including: The enhanced operation type information semantic embedding encoding map is obtained by performing positional dot multiplication on the full-dimensional dynamic perception encoding matrix of the activated operation type context supplementation and the operation type context supplementation embedding encoding map along the channel dimension. The semantic embedding encoding graph of the enhanced operation type information is subjected to feature reshaping processing to obtain the semantic embedding encoding vector of the enhanced operation type information.

8. A cloud-based collaborative geological and mineral exploration data analysis system, characterized in that, include: The client operation instruction generation module is used to generate client operation instructions in response to user mouse / touch operations on the WebGIS client. The instruction encapsulation and submission module is used by the client to encapsulate and securely submit the client operation instructions and the user's authentication credentials to obtain a WebSocket message frame. The permission detection module is used by the central coordinator in the cloud to determine whether the user has permission to edit the target layer based on the user's authentication credentials in the WebSocket message frame. The message frame sending selection module is used to respond to the user having permission to edit the target layer. The central coordinator in the cloud determines whether to send the WebSocket message frame to the OT processing module or the lightweight state synchronization module based on the operation type field in the WebSocket message frame. The OT processing module analysis module is used to respond to the received WebSocket message frame. The OT processing module performs version comparison, operation conversion and transaction application based on the WebSocket message frame and the current state of the server to obtain an updated project status version. The lightweight state synchronization module is used to update the state cache and generate broadcast state update information based on the received WebSocket message frame in response to the WebSocket message frame.

9. The cloud-based collaborative geological and mineral exploration data analysis system according to claim 8, characterized in that, The instruction encapsulation and submission module is used to: package the client operation instruction and the user's authentication credentials into a WebSocket message frame, and then send it to the central coordinator in the cloud via a WebSocket connection.

10. The cloud-based collaborative geological and mineral exploration data analysis system according to claim 9, characterized in that, The OT processing module analysis module is used for: The basic version of the WebSocket message frame parsing operation; A version comparison is performed based on the basic version of the operation and the current version number of the server in the current server state to obtain the version comparison result. If the version comparison result indicates a conflict, all operations from the base version of the operation to the current version number of the server are extracted from the server operation history in the current state of the server as the operations of others. The conversion function is invoked to convert the client instructions and the other party's operations in the WebSocket message frame to obtain the converted operation instructions. Execute the post-conversion operation command in PostGIS to obtain the updated project status version.

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