Geological mineral exploration data analysis method and system based on cloud collaboration

Abstract

The invention relates to the field of geological mineral exploration, and particularly discloses a geological mineral exploration data analysis method and system based on cloud collaboration, and the method comprises the steps: packaging the operation of a user on a client WebGIS into a WebSocket message frame containing an identity authentication certificate, and safely submitting the WebSocket message frame to a cloud central coordinator. A central coordinator firstly verifies user permission, and then the central coordinator accurately distributes message frames to an OT processing module or a lightweight state synchronization module based on operation type fields in the message frames in combination with intelligent judgment of cross-modal context enhancement. The OT processing module is responsible for processing complex and high-conflict-risk editing operation, and the final consistency of data under multi-user concurrent editing is ensured through version comparison, operation conversion and transactional application. And the lightweight state synchronization module is used for processing non-editable or low-conflict-risk operation, quickly updating the state and broadcasting the state so as to improve the response speed and the overall performance of the system.

Description

Technical Field

[0001] The present application relates to the field of geological and mineral exploration, and more specifically, to a geological and mineral exploration data analysis method and system based on cloud collaboration. Background Art

[0002] Geological and mineral exploration is a crucial foundation for national economic development. Its core lies in the efficient collection, storage, management, analysis, and interpretation of massive amounts of heterogeneous data from multiple sources, including geology, geophysics, geochemistry, remote sensing, and drilling. Traditional exploration data analysis methods often rely on desktop software, with decentralized data storage. This leads to inefficient data sharing and collaborative analysis among team members. This is especially true when faced with complex 3D geological model construction, multidisciplinary data fusion and interpretation, and real-time update requirements. Data consistency is difficult to ensure, and version conflicts are frequent, severely hindering the progress of exploration projects and the accuracy of decision-making.

[0003] In the existing technology, although there are some cloud-based geographic information system platforms that can realize online storage, publishing and basic query and browsing functions of data, some platforms have also attempted to introduce collaborative editing capabilities. However, these collaborative solutions often expose many shortcomings when dealing with complex spatial data and attribute data unique to the field of geological and mineral exploration. For example, a simple "last writer wins" strategy can easily lead to data loss or overwriting, while a coarse-grained locking mechanism will seriously affect user experience and collaborative efficiency. For high-concurrency and high-complexity geological data editing operations, how to ensure 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. They usually lack refined management of concurrent operations and conflict resolution mechanisms, making it difficult to meet the high standards of geological and mineral exploration teams for real-time collaborative editing of data.

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

[0005] In order to solve the above technical problems, the present application is proposed. The embodiments of the present application provide a geological and mineral exploration data analysis method and system based on cloud collaboration.

[0006] According to one aspect of the present application, a cloud-based collaborative geological and mineral exploration data analysis method is provided, which includes: Generate client operation instructions in response to the user's mouse / touch operation on the client's WebGIS; The client encapsulates the client operation instruction and the user's identity authentication credentials 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 identity 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, based on the operation type field in the WebSocket message frame, to send the WebSocket message frame to the OT processing module or the lightweight state synchronization module; 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 state of the server 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.

[0007] According to another aspect of the present application, a cloud-based collaborative geological and mineral exploration data analysis system is provided, which includes: The client operation instruction generation module is used to generate a client operation instruction in response to the user's mouse / touch operation on the client's WebGIS; The instruction encapsulation and submission module is used for the client to encapsulate and securely submit the client operation instruction and the user's identity authentication credentials to obtain a WebSocket message frame; An authority detection module, used for the central coordinator in the cloud to determine whether the user has the authority to edit the target layer based on the user's identity authentication credentials in the WebSocket message frame; a message frame sending selection module, configured to, in response to the user having permission to edit the target layer, determine by the central coordinator on the cloud side, 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; an OT processing module analysis module, configured to, in response to receiving the WebSocket message frame, perform version comparison, operation conversion, and transactional application based on the WebSocket message frame and the current state of the server to obtain an updated project state version; The lightweight state synchronization module processing module is configured to respond to receiving the WebSocket message frame, and the lightweight state synchronization module updates the state cache based on the WebSocket message frame and generates broadcast state update information.

[0008] Compared with the existing technology, the present application provides a cloud-based collaborative geological and mineral exploration data analysis method and system, which encapsulates the user's operations on the client WebGIS into a WebSocket message frame containing identity authentication credentials and securely submits it to the cloud central coordinator. The central coordinator first verifies the user's permissions, and then based on the operation type field in the message frame and combined with cross-modal context-enhanced intelligent judgment, the central coordinator accurately diverts the message frame to the OT processing module or the lightweight state synchronization module. The OT processing module is responsible for handling complex, high-conflict risk editing operations, and ensures the final consistency of data under multi-user concurrent editing through version comparison, operation conversion and transactional applications. The lightweight state synchronization module handles non-editing or low-conflict risk operations, quickly updates the status and broadcasts it to improve the system response speed and overall performance. This intelligent diversion and collaboration mechanism effectively overcomes the limitations of traditional methods and realizes efficient and reliable cloud-based collaborative data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The above and other purposes, features, and advantages of the present application will become more apparent through a more detailed description of the embodiments of the present application in conjunction with the accompanying drawings. The accompanying drawings are intended to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the drawings, the same reference numerals generally represent the same components or steps.

[0010] Figure 1 Flowchart of a geological and mineral exploration data analysis method based on cloud collaboration according to an embodiment of the present application; Figure 2 Schematic diagram of data flow of a geological and mineral exploration data analysis method based on cloud collaboration according to an embodiment of the present application; Figure 3 A flowchart of a method for analyzing geological and mineral exploration data based on cloud collaboration according to an embodiment of the present application, in which, in response to the user having permission to edit a target layer, a central coordinator on the cloud determines, based on an operation type field in the WebSocket message frame, to send the WebSocket message frame to an OT processing module or a lightweight state synchronization module; Figure 4 A flowchart of performing cross-modal context enhancement on the semantic embedding coding vector of the operation type information based on the operation type context supplemented embedding coding diagram of the cloud-based collaborative geological and mineral exploration data analysis method according to an embodiment of the present application to obtain an enhanced semantic embedding coding vector of the operation type information; Figure 5 This is a block diagram of a geological and mineral exploration data analysis system based on cloud collaboration according to an embodiment of the present application. DETAILED DESCRIPTION

[0011] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described herein.

[0012] As used in this application and the claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not intended to refer to the singular but may include the plural. Generally speaking, the terms "comprises" and "include" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include other steps or elements.

[0013] Although the present application makes various references to certain modules in the system according to embodiments of the present application, any number of different modules can be used and run on the user terminal and / or server. The modules are illustrative only, and different aspects of the system and method can use different modules.

[0014] Flowcharts are used in this application to illustrate the operations performed by the systems according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the various steps may be processed in reverse order or simultaneously, as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0015] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described herein.

[0016] In the analysis of geological and mineral exploration data, traditional methods have problems such as low efficiency and data loss in multi-user real-time collaboration, data consistency assurance and complex conflict resolution. In response to the above technical problems, in the technical solution of this application, when the user operates on the WebGIS of the client, the system will generate corresponding operation instructions, and securely encapsulate them with the user's identity authentication credentials to form a WebSocket message frame, which is then securely submitted to the central coordinator in the cloud. The central coordinator first strictly verifies the user's editing permissions. The key is that once the user has editing permissions, the central coordinator will no longer simply lump all operations together, but will dynamically and accurately decide whether to send the message frame to the OT processing module or the lightweight state synchronization module based on the operation type field in the WebSocket message frame and combined with cross-modal context enhancement of the operation type information.

[0017] For operations involving complex data editing and possible concurrent conflicts, the system will route them to the OT processing module. This module ensures the ultimate consistency and integrity of data under concurrent editing by multiple users through sophisticated version comparison, operation conversion, and transactional applications, effectively avoiding data overwriting and conflict problems common in traditional methods. For non-editing or low-conflict-risk operations, such as simple view operations or status queries, the system sends them to a lightweight state synchronization module. This module can quickly update the state cache and generate broadcast information, thereby significantly improving the system response speed and overall performance, and avoiding unnecessary complex processing overhead. This intelligent diversion processing mechanism combines the rigor of OT with the efficiency of lightweight synchronization, enabling this solution to effectively solve the technical problems of low real-time collaboration efficiency and difficulty in ensuring data consistency in existing technologies, providing an efficient and reliable cloud-based collaborative data analysis platform for geological and mineral exploration.

[0018] In the technical solution of this application, a cloud-based collaborative geological and mineral exploration data analysis method is proposed. Figure 1 This is a flowchart of a cloud-based collaborative geological and mineral exploration data analysis method according to an embodiment of the present application. Figure 2 Schematic diagram of data flow of geological and mineral exploration data analysis method based on cloud collaboration according to an embodiment of the present application. Figure 1 and Figure 2 As shown, according to an embodiment of the present application, a cloud-based collaborative geological and mineral exploration data analysis method includes the following steps: S100, in response to a user's mouse / touch operation on the client's WebGIS, generating a client operation instruction; S200, the client encapsulates and securely submits the client operation instruction and the user's identity authentication credentials to obtain a WebSocket message frame; S300, the cloud-based central coordinator determines whether the user has the authority to edit the target layer based on the user's identity authentication credentials in the WebSocket message frame; S400, in response to the user having the authority to edit the target layer, the cloud-based central coordinator determines to send the WebSocket message frame to the OT processing module or the lightweight status synchronization module based on the operation type field in the WebSocket message frame; 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 state of the server to obtain an updated project status version; S600, in response to receiving the WebSocket message frame, the lightweight status synchronization module updates the status cache based on the WebSocket message frame and generates broadcast status update information.

[0019] Specifically, in steps S100 and S200, in response to a user's mouse / touch operation on the client's WebGIS, a client operation instruction is generated. The client encapsulates and securely submits the client operation instruction and the user's identity authentication credentials to obtain a WebSocket message frame. It should be understood that geological and mineral exploration data analysis often involves multiple users and professionals editing and analyzing shared spatial data in real time. Any user interaction needs to be accurately captured and converted into standardized instructions that can be understood and processed by the server. At the same time, to ensure data security and permission 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 convert the user's operation into a client operation instruction, and then encapsulate and securely submit the client operation instruction and the user's identity authentication credentials. This ensures the accurate communication of user intent and provides basic input for subsequent permission verification, operation type determination, and data processing. Furthermore, through the encapsulation of the identity authentication credentials, user operation traceability and permission management are achieved, 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 offload (OT processing or lightweight state synchronization), improving collaborative efficiency and data security. Specifically, in an embodiment of the present application, the client encapsulates and securely submits the client operation instructions and the user's identity authentication credentials to obtain a WebSocket message frame, including: after the client packages the client operation instructions and the user's identity authentication credentials into a WebSocket message frame, it sends it to the central coordinator in the cloud via a WebSocket connection.

[0020] More specifically, in one specific example of this application, the client-side WebGIS application continuously listens for user interaction events such as mouse clicks, drags, and touches. When a user draws points, lines, or polygons on the map, modifies attribute information, or performs operations such as zooming and panning, the WebGIS front-end framework (such as those based on OpenLayers, Leaflet, or Mapbox GL JS) captures these raw UI events. Next, the captured raw events are converted into standardized client-side operation instructions by the front-end application logic. For example, a mouse drag event is parsed as a command to draw a polygon, accompanied by the polygon's geometric coordinate sequence; a click event is parsed as a command to query feature attributes, accompanied by the geographic coordinates of the click location or feature ID; and a keyboard input event is parsed as a command to update text attributes, accompanied by the feature ID to be updated, the attribute field name, and the new value. These instructions are represented in a structured data format (such as JSON) that clearly describes the user's intent and operation content. The client then encapsulates these generated client-side operation instructions with the user's authentication credentials. Authentication credentials are typically issued by the server when a user logs in and stored on the client (for example, as a JWT token or session ID) to verify the user's legitimate identity. The client packages the operation instructions and identity credentials into a unified WebSocket message frame. This message frame is a self-contained data unit that includes the specific content of the operation and the identity information of the user initiating the operation. Finally, the client securely sends this encapsulated WebSocket message frame to the central coordinator in the cloud through a pre-established WebSocket connection. The WebSocket protocol provides a full-duplex, persistent communication channel that significantly reduces communication overhead compared to traditional HTTP requests, achieving lower latency and higher real-time performance, which is crucial for the frequent interactions and real-time collaboration required in geological and mineral exploration data analysis. Through a WebSocket Secure (WSS) connection, data transmission is also protected by TLS / SSL encryption, further ensuring the confidentiality and integrity of instructions and credentials during transmission.

[0021] Specifically, in step S300, the central coordinator in the cloud determines whether the user has the permission to edit the target layer based on the user's identity authentication credentials in the WebSocket message frame. It should be understood that since geological and mineral data is an important strategic resource for the country, its accuracy and security are of paramount importance. Unauthorized editing may lead to data errors, loss or even malicious destruction, seriously affecting the decision-making of exploration projects and national resource management. Therefore, before processing any operation that may modify the data, the system must ensure that the operator has a legal identity and corresponding operating authority. That is, in the cloud-based collaborative environment for geological and mineral exploration data analysis, any operation performed by the user on WebGIS, especially editing operations involving data modification, must undergo strict permission verification. Therefore, in the technical solution of the present application, the central coordinator in the cloud determines whether the user has the permission to edit the target layer based on the user's identity authentication credentials in the WebSocket message frame. In this way, the security and integrity of geological and mineral data can be guaranteed, and unauthorized users can be prevented from modifying core data. At the same time, refined permission management can be achieved to ensure that different user roles (such as data entry clerks, geological engineers, project managers, etc.) can only perform authorized operations, thereby maintaining the order and efficiency of collaborative workflows, ensuring data quality and system security, and laying a secure foundation for subsequent intelligent diversion processing.

[0022] More specifically, in a specific example of this application, first, 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, which represents the user's unique identity and authenticated status. Second, the central coordinator uses the parsed authentication credentials to interact with the system's internal user management and permission management modules. If the credential is a JWT, the coordinator decrypts and verifies it, checks whether its signature is valid and expired, and extracts the user's unique identifier (such as a user ID) and the associated role information or permission list. If the credential is a session ID, the coordinator queries the session storage (such as Redis or a database) to obtain the user information and permission data associated with the session ID.

[0023] Next, the central coordinator identifies the specific target layer for the operation based on the operation instruction contained in the message frame. For example, if the operation instruction is to draw a geological boundary, the target layer is the geological boundary layer; if the operation is to modify borehole properties, the target layer is the borehole layer. Finally, the central coordinator compares the obtained user permission information with the editing permissions required for the target layer. The system typically maintains a permission policy database or configuration that clearly defines which user roles or specific users have access to which layers, with varying levels of granularity such as edit, view, and delete. The central coordinator queries this permission policy to determine whether the current user is explicitly authorized to perform editing operations on the specified target layer. If the comparison indicates that the user does not have edit permissions, the central coordinator rejects the operation and returns an insufficient permission error message to the client. Conversely, if the user has edit permissions, the operation is allowed to proceed to the next processing step. The entire process typically completes within milliseconds, ensuring smooth real-time collaboration.

[0024] Specifically, in step S400, in response to the user having the permission to edit the target layer, the central coordinator in the cloud determines 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 the types of user operations in geological and mineral exploration data analysis are diverse and complex. Not all operations have equally strict requirements on data consistency, and not all operations have the risk of high concurrency conflicts. For example, operations such as drawing new geological lines and modifying drilling data directly involve the modification of shared spatial data and may conflict with concurrent editing by other users, requiring a rigorous conflict resolution mechanism; while operations such as map panning, zooming, layer display and hiding, and simple attribute queries mainly affect the client's view state or the modification of the server state is local and non-conflicting. If all operations are processed through complex OT, unnecessary computing overhead and delays will be introduced, affecting system performance and user experience. Therefore, in a cloud-based collaborative environment for geological and mineral exploration data analysis, once the cloud-based central coordinator confirms that the user has permission to edit the target layer, its next key decision is to intelligently determine whether to send the message frame to the OT processing module or the lightweight state synchronization module based on the operation type field in the WebSocket message frame. This enables optimized allocation of system resources and refined management of processing flows. Through intelligent diversion, editing operations that truly require complex conflict resolution and version synchronization (such as the addition, deletion, and modification of geological elements) are routed to the OT processing module, ensuring the ultimate consistency and integrity of data under concurrent editing by multiple users and avoiding data loss or logical errors. At the same time, operations with lower data consistency requirements, low conflict risks, or that only affect the client view are routed to the lightweight state synchronization module, reducing processing latency, improving system responsiveness, and providing users with a smoother and more efficient real-time collaborative experience.

[0025] Figure 3 This is a flowchart of a method for analyzing geological and mineral exploration data based on cloud collaboration according to an embodiment of the present application, in which, in response to the user having permission to edit the target layer, the central coordinator on the cloud determines 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. Figure 3As shown, according to the cloud-collaborative geological and mineral exploration data analysis method of an embodiment of the present application, step S400 includes: S410, extracting the operation type field; S420, performing semantic embedding coding on the operation type field to obtain an operation type information semantic embedding coding vector; S430, extracting other information except the operation type field from the WebSocket message frame as an operation type context supplement; S440, performing structured embedding coding on the operation type context supplement to obtain an operation type context supplement embedding coding graph; S450, based on the operation type context supplement embedding coding graph, performing cross-modal context enhancement on the operation type information semantic embedding coding vector to obtain an enhanced operation type information semantic embedding coding vector; S460, based on the enhanced operation type information semantic embedding coding vector, determining to send the WebSocket message frame to the OT processing module or the lightweight state synchronization module.

[0026] Specifically, in steps S410 and S420, the operation type field is extracted and semantically embedded encoded 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 cannot directly reflect the operation's deeper semantics, complexity, or potential conflict risk. For example, a Modify operation can modify a simple attribute value or a complex geological geometry, requiring vastly different system resources and conflict resolution complexity. To achieve more accurate and intelligent routing decisions, the system requires a representation that captures the inherent meaning and relevance of the operation type. Specifically, in a cloud-based collaborative geological and mineral exploration data analysis method, when the central coordinator needs to intelligently determine whether to distribute a WebSocket message frame to an OT processing module or a lightweight state synchronization module, relying solely on simple rule matching based on the original operation type field string is insufficient. Therefore, the operation type field is extracted and semantically embedded encoded to obtain an operation type information semantic embedding encoding vector. The discrete, symbolic operation type field is converted into a continuous, high-dimensional numerical vector representation. This vector representation captures the semantic similarity between operation types, placing similar operations close together in the vector space, thereby providing richer feature information for subsequent intelligent decision-making. Through semantic embedding encoding, the system can go beyond simple string matching to understand the intent and nature of the operation, laying the foundation for subsequent cross-modal context enhancement and ultimate routing decisions.

[0027] More specifically, in a specific example of the present application, first, the operation type field is extracted: when the central coordinator receives the WebSocket message frame, it parses the structure of the message frame, accurately identifies and extracts the predefined operation type field. This field is usually a specific key-value pair in the message frame, and its value is a string representing the operation type, such as operationType: AddDrillHole or operationType: UpdateGeologicalBoundary. Secondly, an operation type vocabulary is constructed: during the system initialization or training phase, all possible operation type strings are collected and a unique vocabulary is constructed. For example, {AddPoint, ModifyLine, DeletePolygon, PanMap, ZoomIn}.

[0028] Next, select a semantic embedding model: You can use a variety of commonly used semantic embedding techniques from natural language processing (NLP). Based on a pre-trained word vector model: If the operation type field is a descriptive phrase (such as "Add drill data"), you can use a pre-trained word vector model (such as Word2Vec, GloVe, or FastText) to map each word into a vector. These vectors are then combined into a vector representing the operation type phrase through average pooling or weighted averaging. For complex, descriptive operation type strings, you can use a Transformer encoder (such as BERT) to encode the operation type string, thereby obtaining 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. The model converts the string into a fixed-dimensional floating-point vector based on its internal mapping relationships or learned weights, namely the "operation type information semantic embedding encoding vector." For example, AddPoint is encoded as [0.12, -0.05, 0.88, ...], while DeletePoint is encoded as [0.10, -0.06, 0.85, ...], which are close to each other in the vector space, reflecting that they are both related to point operations. This vector will then serve as the input for subsequent cross-modal context enhancement.

[0029] Specifically, in steps S430 and S440, information other than the operation type field is extracted from the WebSocket message frame as supplemental operation type context, and the supplemental operation type context is structured and embedded to generate an embedded encoding diagram of the supplemental operation type context. 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 objects targeted by the operation, the attributes involved, and the system state at the time of the operation. For example, a modification operation targeting a non-critical attribute of a simple point feature has a much lower conflict risk and processing complexity than modifying the geometry or critical attributes of a complex geological entity (such as a fault or ore body). This contextual information (such as the target layer ID, feature ID, modified attribute fields, the geometric scope of the operation, and the user role) is structured and, together, constitutes 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 the specific contextual information within which it occurs. Therefore, information other than the operation type field is extracted from the WebSocket message frame as operation type context supplement, and structured embedding coding is performed on it to obtain the operation type context supplement embedding coding graph. This scattered and heterogeneous structured context supplement information is integrated and converted into a unified, high-dimensional numerical representation, namely the operation type context supplement embedding coding graph. This graph structure can capture the internal correlation of context supplement information. For example, an operation may involve a specific layer, a specific feature, and a specific attribute field at the same time, and there is a logical relationship between these information. Through structured embedding coding, the system can more comprehensively and deeply understand the actual impact scope and potential risks of the operation, providing rich and organized supplementary information for subsequent cross-modal context enhancement.

[0030] More specifically, in a specific example of the present application, first, the operation type context supplementary information is extracted: after the central coordinator receives the WebSocket message frame and parses the operation type field, it will continue to parse the rest of the message frame and extract all structured data related to the operation type. This data may include but is not limited to: Target layer ID / name: The identifier of the geographic information layer targeted by the operation. Target feature ID: The unique identifier of the specific geographic feature affected by the operation. Modified attribute field: If the operation is to modify an attribute, it contains the name of the modified attribute. Geometry information: If the operation involves geometric modification (such as drawing or editing a geometric shape), it may contain new geometric coordinates or the type of geometric operation (such as adding a vertex or deleting an edge). User role / permission level: The current role or permission level of the initiator of the operation. Timestamp / version information: The time when the operation occurred or the client data version based on it. This information is usually present in the JSON payload of the WebSocket message frame in the form of key-value pairs, arrays, or nested objects.

[0031] Secondly, the structured information is characterized: For each type of structured information extracted, it needs to be converted into a numerical representation. Discrete feature encoding: For discrete identifiers such as layer IDs, feature IDs, attribute field names, one-hot encoding (One-Hot Encoding) or embedding layer (Embedding Layer) can be used to map them into vectors. The embedding layer can learn the potential relationship between different IDs during the training process. Continuous feature normalization: For continuous values such as geometric coordinates and timestamps, they can be normalized so that they fall within a specific range. Complex structure encoding: For complex structures such as geometric information, a special geometric encoder (such as one based on a graph neural network or a convolutional neural network) can be used to convert it into a vector representation.

[0032] Next, construct an operation type contextual supplementary embedding coding graph: integrate the various contextual information vectors after the above characterization to form a graph structure that can represent its internal correlation. This can be achieved in the following ways: Feature splicing and multi-layer perceptron: The simplest way is to splice all the contextual information vectors, and then input them into one or more fully connected layers to learn the nonlinear relationship between them, and finally output a unified contextual embedding vector. Graph neural network: If there is a clear graph structure relationship between the contextual information (for example, an operation affects multiple related elements, or a layer contains multiple elements), a graph can be constructed in which the nodes represent different contextual entities (such as layers, elements, attributes) and the edges represent the relationship between them. Then, use a GNN model such as a graph convolutional network or a graph attention network to encode this graph to obtain an embedding coding graph that can capture the complex relationship between contextual entities.

[0033] Ultimately, through the above steps, the original, dispersed structured context information is transformed into a high-dimensional, contextually supplemented embedding encoding graph of the action type that captures its internal relevance. This encoding graph serves as input for subsequent cross-modal context enhancement and is fused with the action type semantic embedding vector to achieve a more comprehensive and accurate understanding of action intent.

[0034] Specifically, in step S450, based on the operation type context supplemented embedding coding graph, the operation type information semantic embedding coding vector is cross-modally contextually enhanced to obtain an enhanced operation type information semantic embedding coding vector. It should be understood that the operation type information provides an abstract semantic representation of the operation, while the 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 graph) are heterogeneous. To make the most accurate routing decisions, it is necessary to be able to deeply integrate these two different modalities of information so that the semantic understanding of the operation can be calibrated and enhanced by the specific context in which it occurs. For example, the semantic complexity and conflict risk of a modification operation in the context of modifying borehole data are significantly different from those in the context of modifying geological boundaries. Based on this, in the technical solution of the present application, based on the operation type context supplemented embedding coding graph, the operation type information semantic embedding coding vector is further cross-modally contextually enhanced to obtain an enhanced operation type information semantic embedding coding vector. Through a cross-modal interaction mechanism with full-dimensional dynamic perception, the complex relationship between operation type semantics and operation context supplementation is deeply captured. Specifically, it aims to enable the semantic embedding vector of the operation type information to dynamically perceive and utilize the operation type context to supplement the rich information contained in the embedding graph. Through this fusion, an enhanced semantic embedding vector of the operation type information is generated. This vector not only contains the semantics of the operation type itself, but also incorporates the detailed consideration of its context, thereby more comprehensively and accurately representing the true intent, complexity, and potential impact of the operation.

[0035] Figure 4 This is a flow chart of performing cross-modal context enhancement on the semantic embedding coding vector of the operation type information to obtain an enhanced semantic embedding coding vector of the operation type information based on the operation type context supplemented embedding coding diagram according to the cloud-based collaborative geological and mineral exploration data analysis method of the embodiment of the present application. Figure 4As shown, according to the cloud-collaborative geological and mineral exploration data analysis method of an embodiment of the present application, step S450 includes: S451, performing global mean pooling on the operation type context supplement embedded coding map to obtain an operation type context supplement global pooled coding vector; S452, performing global dynamic interactive analysis on the operation type information semantic embedded coding vector and the operation type context supplement global pooled coding vector to obtain an operation type context supplement full-dimensional dynamic perception coding matrix; S453, performing nonlinear activation on the operation type context supplement full-dimensional dynamic perception coding matrix, and then applying it to the operation type context supplement embedded coding map to obtain the enhanced operation type information semantic embedded coding vector.

[0036] More specifically, in step S451, global mean pooling is performed on the operation type context supplement embedded coding map to obtain an operation type context supplement global pooled coding vector, which is expressed as: , in, The operation type context is supplemented by the embedded coding graph Position eigenvalue, and Supplement the height and width of the embedded coding graph for the operation type context respectively, Supplement the global pooling encoding vector with the operation type context.

[0037] It should be understood that the operation type contextual supplementary embedding graph is a high-dimensional, complex graph structure that contains a variety of fine-grained information, including 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, the curse of dimensionality, and potentially contain redundant information. For efficient cross-modal fusion, it is necessary to extract core, global contextual summaries from this complex graph structure while reducing the dimensionality of the data. Through a global mean pooling operation, the high-dimensional, potentially topologically complex operation type contextual supplementary embedding graph is efficiently compressed into a compact, fixed-dimensional operation type contextual supplementary global pooling encoding vector. This aims to condense a holistic semantic summary of the operation context, capturing the macroscopic features and key information of the operation's environment while achieving dimensionality reduction and parameter regularization. This global pooling encoding vector serves as the global contextual anchor for the operation type contextual supplementary embedding code, providing a stable and representative macroscopic view for subsequent cross-modal interaction with the operation type information semantic embedding vector.

[0038] More specifically, in step S452, a global dynamic interaction analysis is performed on the semantic embedding coding vector of the operation type information and the global pooling coding vector of the operation type context supplementation to obtain an operation type context supplementation full-dimensional dynamic perception coding matrix, which is expressed as follows: , , in, is vector multiplication, is the point product by position, is the semantic embedding encoding vector for the operation type information, and is the projection matrix, for function, Supplement the dynamic weight perception matrix for the operation type context, Supplement the operation type context with a fully dimensionally aware trainable bias vector, Supplement the full-dimensional dynamic perceptual encoding matrix for the operation type context.

[0039] It should be understood that there is a complex, nonlinear interaction between operation type information and its contextually supplemented semantics. For example, the semantics of a modification operation, in the context of modifying a critical geological boundary, are far more important and potentially more conflicting than those of a modification of a non-critical attribute. This interaction pattern is dynamic and cannot be simply captured by fixed rules. To capture this nuanced, dimension-by-dimension potential, a mechanism is required that goes beyond simple concatenation or linear combination to achieve deep, adaptive cross-modal interaction. In other words, to accurately route user operations, simply possessing the operation type semantic embedding vector and the operation context global pooling vector is insufficient. While each represents the nature of the operation and its context, the deep connections and mutual influence patterns between them are the key to determining operation complexity and conflict risk. Therefore, we further perform a global dynamic interaction analysis on the operation type information semantic embedding encoding vector and the operation type context supplemented global pooling encoding vector to obtain the operation type context supplemented full-dimensional dynamic perceptual encoding matrix. This allows for the deep exploration of all dimension-by-dimension potential correlations between the operation type information semantic embedding encoding vector and the operation type context supplemented global pooling encoding vector. This module aims to learn and dynamically generate a full-dimensional dynamic perceptual encoding matrix that complements the operation type context. This matrix can be viewed as a dynamically generated, parameterized interaction kernel, whose internal structure meticulously encodes the specific patterns and strengths of mutual influence and interdependence between the operation type semantics and the global information of the operation context. It avoids assumptions about locality or sparsity of interaction, ensuring that all potential connections between the two are fully captured. Furthermore, this interaction pattern is adaptively generated based on the current specific input content, rather than being pre-set.

[0040] Accordingly, according to an embodiment of the present application, step S453, after nonlinearly activating the operation type context supplement full-dimensional dynamic perceptual coding matrix, applies it to the operation type context supplement embedded coding map to obtain the enhanced operation type information semantic embedded coding vector, including: nonlinearly activating the operation type context supplement full-dimensional dynamic perceptual coding matrix to obtain an activated operation type context supplement full-dimensional dynamic perceptual coding matrix; performing positional dot multiplication on the activated operation type context supplement full-dimensional dynamic perceptual coding matrix and each operation type context supplement feature matrix along the channel dimension in the operation type context supplement embedded coding map, so as to apply the activated operation type context supplement full-dimensional dynamic perceptual coding matrix to the operation type context supplement embedded coding map to obtain the enhanced operation type information semantic embedded coding vector.

[0041] Specifically, in an embodiment of the present application, the post-activation operation type context supplement full-dimensional dynamic perception coding matrix and the various operation type context supplement feature matrices along the channel dimension in the operation type context supplement embedded coding map are point-multiplied by position, so that the post-activation operation type context supplement full-dimensional dynamic perception coding matrix is applied to the operation type context supplement embedded coding map to obtain the enhanced operation type information semantic embedding coding vector, including: performing positional point multiplication on the post-activation operation type context supplement full-dimensional dynamic perception coding matrix and the various operation type context supplement feature matrices along the channel dimension in the operation type context supplement embedded coding map to obtain an enhanced operation type information semantic embedding coding map; performing feature reshaping processing on the enhanced operation type information semantic embedding coding map to obtain the enhanced operation type information semantic embedding coding vector.

[0042] More specifically, the operation type context supplemented full-dimensional dynamic perception coding matrix is nonlinearly activated to obtain an activated operation type context supplemented full-dimensional dynamic perception coding matrix, which is expressed as: , in, Supplement the full-dimensional dynamic perception encoding matrix for the operation type context, for function, Supplement the full-dimensional dynamic perceptual encoding matrix for the post-activation action type context.

[0043] More specifically, the post-activation operation type context supplement full-dimensional dynamic perception coding matrix and each operation type context supplement feature matrix along the channel dimension in the operation type context supplement embedding coding map are multiplied by position to obtain an enhanced operation type information semantic embedding coding map, which is expressed as follows: , in, is the positional dot product along the channel dimension, To enhance the semantic embedding encoding graph of operation type information.

[0044] It should be understood that, first, the element values of the original action type context-supplemented full-dimensional dynamic perceptual encoding matrix may lie within 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. Nonlinear activation functions (such as sigmoid) can map these values to the desired range, introducing nonlinearity and enhancing the model's expressiveness. Second, this activated action type context-supplemented full-dimensional dynamic perceptual encoding matrix needs to be able to guide or reshape the original action type context-supplemented embedding map. This means it should be able to adaptively enhance or suppress the importance of different regions or channels in the context feature map based on the action type semantics. By using positional dot multiplication, each element of the activated dynamic perceptual encoding matrix can be treated as a weight or scaling factor, directly applied to the feature values at the corresponding position or channel in the original context feature map, thereby achieving refined feature rescaling. This approach transforms the abstract cross-modal interaction pattern (the action type context-supplemented full-dimensional dynamic perceptual encoding matrix) into concrete, feature-applicable attention or modulation signals. Nonlinear activation ensures that these signals have a reasonable numerical range and are interpretable. Subsequently, these signals are adaptively recalibrated by performing positional dot multiplication with the original operation type contextual supplementary embedding map. This allows those contextual feature regions or channels that are highly relevant to the operation type semantics and crucial for determining operation complexity and routing paths to receive higher weights, thus being significantly highlighted in the final enhanced operation type information semantic embedding map, while irrelevant or unimportant features are suppressed.

[0045] More specifically, in step S423, feature reshaping is performed on the semantic embedding coding map of the enhanced operation type information to obtain the semantic embedding coding vector of the enhanced operation type information, which is expressed as follows: , in, For feature reshaping, To enhance the semantic embedding of operation type information.

[0046] It should be understood that the enhanced operation type information semantic embedding encoding graph is a multidimensional tensor, or a collection of multiple feature vectors. While this form of representation is rich in information, subsequent classification or regression tasks typically require a single, fixed-length vector as input. Furthermore, feature reshaping 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 enhanced graph while meeting the input format requirements of downstream modules. Therefore, the high-dimensional enhanced operation type information semantic embedding encoding graph with complex internal structure is converted into a compact, fixed-dimensional enhanced operation type information semantic embedding encoding vector through a series of feature reshaping operations. This vector serves as the final representation of the deep fusion of operation type semantics and contextual information. It not only contains the semantics of the operation type itself, but also incorporates a detailed consideration of its context and is enhanced by cross-modal interaction.

[0047] Specifically, in step S460, based on the enhanced operation type information semantic embedding encoding vector, a determination is made as to whether the WebSocket message frame should be sent to the OT processing module or the lightweight state synchronization module. It should be understood that in the cloud-based collaborative environment of geological and mineral exploration data analysis, after a series of complex semantic embedding, contextual supplementary encoding, and cross-modal enhancement processes, the central coordinator ultimately obtains a highly condensed and information-rich enhanced operation type information semantic embedding encoding vector. This enhanced operation type information semantic embedding encoding vector is the final and most comprehensive numerical representation of the user operation type, its context, and the complex interaction pattern between the two. 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 required. At this point, the system requires a final decision-making mechanism that can map this abstract vector to specific routing options, thereby achieving differentiated processing for operations of different natures. Therefore, based on the enhanced operation type information semantic embedding encoding vector, a determination is made as to whether the WebSocket message frame should be sent 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, it is necessary to accurately identify which operations are high-conflict, high-complexity editing operations with extremely high data consistency requirements, and which operations need to be routed to the OT processing module for rigorous version control and conflict resolution; and which operations are low-conflict, low-complexity, non-editing operations that mainly affect views or non-critical states, and can be routed to the lightweight state synchronization module for rapid response and broadcasting.

[0048] First, the enhanced operation type information semantic embedding vector is received: The decision module of the central coordinator receives the enhanced operation type information semantic embedding vector generated in the previous step. This vector is a fixed-dimensional floating-point sequence that comprehensively represents the semantics and context of the current user operation. Second, a decision model is constructed: Various common machine learning or deep learning models can be used as decision models to map the input enhanced vector to the classification results of the OT module or lightweight state synchronization module. Specifically, a binary classification model is trained using the enhanced operation type information semantic embedding vector as a feature input. The model learns a decision boundary that divides the vector space into two regions, corresponding to the two processing modules. Alternatively, a simple neural network can be constructed, consisting of one or more fully connected layers and nonlinear activation functions (such as ReLU). The final layer uses 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 enhanced vectors to routing decisions.

[0049] Finally, the decision is executed and the message frame is distributed: the semantic embedding encoding vector of the enhanced operation type information is input into the trained decision model. The model outputs a classification result (for example, the probability of being an OT module or a lightweight module, or a direct category label). Based on the model's output, the central coordinator accurately distributes the original WebSocket message frame to the corresponding processing module. If the decision result is an OT processing module, the message frame is sent to the OT engine for version comparison, operation conversion, and transactional application. If the decision result is a lightweight state synchronization module, the message frame is quickly processed and broadcast to other relevant clients, achieving rapid status updates.

[0050] Specifically, in step S500, in response to receiving the WebSocket message frame, the Operational Transformation (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, drill holes, and ore body boundaries) is shared, complex spatial data, and real-time concurrent editing by multiple users is common. Traditional locking mechanisms can lead to severe performance bottlenecks and a degraded user experience, while simple last-writer-wins strategies can result in data loss and inconsistencies. OT technology is designed to address data consistency issues in this highly concurrent, lock-free collaborative editing environment. It allows users to independently edit locally and submit their operations to the server. The server intelligently resolves conflicts through a transformation algorithm, ensuring that all clients ultimately converge to a consistent data state. 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 high-conflict, high-complexity edit operation with extremely high data consistency requirements and routes it to the OT (Operational Transformation) processing module, the OT processing module responsively receives 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 state of the server to obtain an updated project status version. In this way, it can ensure that when multiple users concurrently edit and share geological and mineral data, the data on the server side always maintains logical consistency and integrity, avoiding data loss, overwriting, or logical errors caused by concurrent operations. Through version comparison, the difference between the version based on which the client operation is based and the current version of the server is identified; through operation conversion, conflicts caused by version differences are intelligently resolved, and the client operation is "converted" to the latest version of the server so that it can be correctly applied to the current state; finally, through transactional application, the converted operation is atomically submitted to the database, thereby obtaining a logically correct and latest project status version that reflects all operations that have occurred.

[0051] More specifically, in an embodiment of the present application, 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 state of the server to obtain an updated project status version, including: parsing the basic version of the operation from the WebSocket message frame; performing version comparison based on the basic version of the operation and the current version number of the server in the current state of the server to obtain a version comparison result; in response to the version comparison result being a conflict, extracting all operations between the basic version of the operation and the current version number of the server from the server operation history in the current state of the server as operations of others; calling a conversion function to convert the client instructions in the WebSocket message frame and the operations of others to obtain converted operation instructions; and executing the converted operation instructions in PostGIS to obtain the updated project status version.

[0052] More specifically, the OT processing module first accurately parses the base version of the operation from the received WebSocket message frame. This base version is typically the server version number that the client's local data is based on when executing the operation. This version number is the key basis for conflict detection and conversion in the OT algorithm. The OT processing module then performs a version comparison between the parsed base version of the operation and the server's current version number in the server's current state. This comparison determines whether the version based on the client's operation is consistent with the latest server version. If the two version numbers are the same, the client operation is based on the latest state, and there is usually no direct conflict. If the client base version is lower than the server's current version, it indicates that other users have already performed operations on the server during the submission of the client operation, which may lead to a potential conflict. In response to the version comparison result indicating a conflict (i.e., the client base version is lower than the server's current version), the OT processing module accurately extracts all operations between the client base version and the server's current version number from the server's operation history in the server's current state. These extracted operations are considered "other" operations and are those successfully applied to the server by other users during the submission of the client operation.

[0053] Next, the OT processing module calls a predefined transformation function to transform the client command (i.e., the original user-submitted operation) contained in the WebSocket message frame and the extracted other operations. The transformation function is the core of the OT algorithm. Based on the type and content of the operation, it intelligently adjusts the client command so that it correctly applies to the latest server state, preserves the client's intent, and resolves conflicts with other operations. For example, if the client deletes a point and someone else adds an attribute to it, the transformation function ensures that the deletion operation remains valid, but may need to adjust its internal parameters to accommodate the new state. Finally, the transformed operation command is executed in PostGIS, the core database for storing geological and mineral spatial data. This execution is transactional, ensuring the atomicity and durability of the operation. Once the transformed operation command is successfully executed in PostGIS, the server's geospatial data and related attribute data are updated, resulting in a logically consistent updated project state that reflects all operations. This new server version number is also updated, making it ready to respond to subsequent client operations or synchronization requests.

[0054] 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-editing operation that primarily affects the view or non-critical state (for example, map panning, zooming, layer visibility switching, simple queries, etc.), and routes it to the lightweight state synchronization module, the module responsively receives the message frame. At this point, the lightweight state synchronization module updates the state cache based on the WebSocket message frame and generates broadcast state update information. These operations typically do not modify core shared data, or the modifications are reversible and have minimal conflict risk. If all operations were to be processed through a complex over-the-air (OT) process, unnecessary delays and computational overhead would be introduced, 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, further 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. In this way, non-critical operations that do not involve complex conflict resolution can be processed quickly and efficiently, and these state changes can be quickly broadcast to all relevant clients. By updating the state cache, it is ensured that the lightweight state maintained by the server (such as map viewport, layer visibility, currently selected features, etc.) is synchronized with the client. At the same time, concise state update information is generated so that other clients can quickly receive and update their local views, thereby achieving a near real-time collaborative experience and improving the response speed and user-perceived performance of the overall system.

[0055] In summary, according to the embodiment of the present application, a cloud-based collaborative geological and mineral exploration data analysis method is illustrated, which encapsulates the user's operations on the client WebGIS into a WebSocket message frame containing identity authentication credentials and securely submits it to the cloud central coordinator. The central coordinator first verifies the user's permissions, and then based on the operation type field in the message frame and combined with cross-modal context-enhanced intelligent judgment, the central coordinator accurately diverts the message frame to the OT processing module or the lightweight state synchronization module. The OT processing module is responsible for handling complex, high-conflict-risk editing operations, and ensures the final consistency of data under multi-user concurrent editing through version comparison, operation conversion and transactional applications. The lightweight state synchronization module handles non-editing or low-conflict-risk operations, quickly updates the status and broadcasts it to improve the system response speed and overall performance. This intelligent diversion and collaboration mechanism effectively overcomes the limitations of traditional methods and realizes efficient and reliable cloud-based collaborative data analysis.

[0056] Furthermore, a geological and mineral exploration data analysis system based on cloud collaboration is also provided.

[0057] Figure 5 FIG is a block diagram of a geological and mineral exploration data analysis system based on cloud collaboration according to an embodiment of the present application. Figure 5 As shown, according to the embodiment of the present application, the geological and mineral exploration data analysis system 100 based on cloud collaboration includes: a client operation instruction generation module 110, which is used to generate a client operation instruction in response to the user's mouse / touch operation on the client's WebGIS; an instruction packaging submission module 120, which is used for the client to package and securely submit the client operation instruction and the user's identity authentication credentials to obtain a WebSocket message frame; a permission detection module 130, which is used for the central coordinator in the cloud to determine whether the user has the permission to edit the target layer based on the user's identity authentication credentials in the WebSocket message frame; a message frame sending selection module 140, which is used to send a message to the central coordinator in response to the user having the permission to edit the target layer. The central coordinator in the cloud determines to send the WebSocket message frame to the OT processing module or the lightweight status synchronization module based on the operation type field in the WebSocket message frame; the OT processing module analysis module 150 is used for responding to the receipt of the WebSocket message frame, and the OT processing module performs version comparison, operation conversion and transactional application based on the WebSocket message frame and the current state of the server to obtain an updated project status version; the lightweight status synchronization module processing module 160 is used for responding to the receipt of the WebSocket message frame, and the lightweight status synchronization module updates the status cache based on the WebSocket message frame and generates broadcast status update information.

[0058] Here, those skilled in the art will understand that the specific operations of each module in the above-mentioned cloud-based collaborative geological and mineral exploration data analysis system have been referred to above. Figures 1 to 4 The method has been introduced in detail in the description of the cloud-based collaborative geological and mineral exploration data analysis method, and therefore, its repeated description will be omitted.

[0059] As described above, the geological and mineral exploration data analysis system 100 based on cloud collaboration according to the embodiment of the present application can be implemented in various wireless terminals, such as a server with a geological and mineral exploration data analysis algorithm based on cloud collaboration. In one possible implementation, the geological and mineral exploration data analysis system 100 based on cloud collaboration according to the embodiment of the present application can be integrated into the wireless terminal as a software module and / or hardware module. For example, the geological and mineral exploration data analysis system 100 based on cloud collaboration 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 geological and mineral exploration data analysis system 100 based on cloud collaboration can also be one of the many hardware modules of the wireless terminal.

[0060] While various embodiments of the present disclosure have been described above, the above descriptions are illustrative, non-exhaustive, and not intended to be limiting of 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 selected to best explain the principles of the embodiments, their practical applications, or improvements to existing technologies, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A cloud-based collaborative geological and mineral exploration data analysis method, characterized in that: include: Generate client operation instructions in response to the user's mouse / touch operation on the client's WebGIS; The client encapsulates the client operation instruction and the user's identity authentication credentials 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 identity 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, based on the operation type field in the WebSocket message frame, to send the WebSocket message frame to the OT processing module or the lightweight state synchronization module; 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 state of the server 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 the client operation instruction and the user's identity authentication credentials and securely submits them to obtain a WebSocket message frame, including: The client packages the client operation instruction and the user's identity authentication credentials into a WebSocket message frame, and sends it to the central coordinator in the cloud through the 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 state of the server to obtain an updated project status version, including: A basic version of the operation that parses the WebSocket message frame. Performing version comparison based on the basic version of the operation and the current version number of the server in the current state of the server to obtain a version comparison result; In response to a conflict being found in the version comparison result, extracting all operations between the basic version of the operation and the current version number of the server from the server operation history in the current state of the server as operations of others; Calling a conversion function to convert the client instruction and the other person's operation in the WebSocket message frame to obtain a converted operation instruction; The post-conversion operation instruction is executed 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, to send the WebSocket message frame to the OT processing module or the lightweight state synchronization module, including: Extracting the operation type field; Performing semantic embedding coding on the operation type field to obtain a semantic embedding coding vector of the operation type information; Extracting other information except the operation type field from the WebSocket message frame as an operation type context supplement; Performing structured embedding coding on the operation type context supplement to obtain an operation type context supplement embedding coding 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 embedded coding vector, it is determined 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: Performing global mean pooling on the operation type context supplement embedding code map to obtain an operation type context supplement global pooling code vector; Performing a global dynamic interaction analysis on the operation type information semantic embedding coding vector and the operation type context supplement global pooling coding vector to obtain an operation type context supplement full-dimensional dynamic perception coding matrix; After nonlinear activation is performed on the operation type context supplement full-dimensional dynamic perception coding matrix, it is applied to the operation type context supplement embedding coding map 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 operation type context supplement full-dimensional dynamic perception coding matrix, applying it to the operation type context supplement embedding coding map to obtain the enhanced operation type information semantic embedding coding vector, including: Nonlinearly activating the operation type context supplemented full-dimensional dynamic perception coding matrix to obtain an activated operation type context supplemented full-dimensional dynamic perception coding matrix; The post-activation operation type context supplement full-dimensional dynamic perception coding matrix and each operation type context supplement feature matrix along the channel dimension in the operation type context supplement embedding coding map are positionally multiplied to apply the post-activation operation type context supplement full-dimensional dynamic perception coding matrix to the operation type context supplement embedding coding map 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: Performing positional point multiplication on the post-activation operation type context supplement full-dimensional dynamic perceptual coding matrix and each operation type context supplement feature matrix along the channel dimension in the operation type context supplement embedding coding map, so as to apply the post-activation operation type context supplement full-dimensional dynamic perceptual coding matrix to the operation type context supplement embedding coding map to obtain the enhanced operation type information semantic embedding coding vector, including: Performing positional point multiplication on the post-activation operation type context supplement full-dimensional dynamic perception coding matrix and each operation type context supplement feature matrix along the channel dimension in the operation type context supplement embedding coding map to obtain an enhanced operation type information semantic embedding coding map; A feature reshaping process is performed on the enhanced operation type information semantic embedding coding map to obtain the enhanced operation type information semantic embedding coding vector.

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 a client operation instruction in response to the user's mouse / touch operation on the client's WebGIS; The instruction encapsulation and submission module is used for the client to encapsulate and securely submit the client operation instruction and the user's identity authentication credentials to obtain a WebSocket message frame; An authority detection module, used for the central coordinator in the cloud to determine whether the user has the authority to edit the target layer based on the user's identity authentication credentials in the WebSocket message frame; a message frame sending selection module, configured to, in response to the user having permission to edit the target layer, determine by the central coordinator on the cloud side, 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; an OT processing module analysis module, configured to, in response to receiving the WebSocket message frame, perform version comparison, operation conversion, and transactional application based on the WebSocket message frame and the current state of the server to obtain an updated project state version; The lightweight state synchronization module processing module is configured to respond to receiving the WebSocket message frame, and the lightweight state synchronization module updates the state cache based on the WebSocket message frame and generates broadcast state update information.

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: after the client packages the client operation instruction and the user's identity authentication certificate into a WebSocket message frame, send it to the central coordinator in the cloud through the 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 to: A basic version of the operation that parses the WebSocket message frame. Performing version comparison based on the basic version of the operation and the current version number of the server in the current state of the server to obtain a version comparison result; In response to a conflict being found in the version comparison result, extracting all operations between the basic version of the operation and the current version number of the server from the server operation history in the current state of the server as operations of others; Calling a conversion function to convert the client instruction and the other person's operation in the WebSocket message frame to obtain a converted operation instruction; The post-conversion operation instruction is executed in PostGIS to obtain the updated project status version.

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