Rendering method and system for large-scale data in port scene

Abstract

The embodiment of the invention provides a rendering method and system for large-scale data in a port scene. The method comprises the following steps: performing preliminary screening on obtained large-scale data about a target port to obtain preliminary screening data, and classifying the preliminary screening data into structured data and unstructured data; performing standardization processing on the structured data and the unstructured data to obtain standard structured data and standard unstructured data; in response to the standard structured data and the standard unstructured data, completing merging, and performing optimization processing on the merged data to generate optimized data; in response to completion of verification of the optimization data, performing three-dimensional modeling and rendering based on the optimization data passing verification to generate a three-dimensional model; and pushing the visual information representing the three-dimensional model to a display terminal. By utilizing the rendering method disclosed by the embodiment of the invention, the overall efficiency of large-scale data processing and the system stability can be remarkably improved.

Description

Technical Field

[0001] This disclosure relates to the field of data modeling technology, specifically to a rendering method and system for large-scale data in port scenarios. Background Technology

[0002] In the digital and intelligent construction of ports, building digital twins of ports through 3D modeling and rendering technology has become a key means to improve operational efficiency and safety management. This process requires processing massive amounts of port data from multiple sources.

[0003] Currently, the preprocessing and rendering of multi-source port data primarily relies on a centralized server architecture. Under this architecture, all data format conversions and structuring are handled by the central server, placing immense performance pressure on it and making it prone to overload during peak port operations, thus becoming a system bottleneck. Furthermore, the data processing flow operates in a closed loop within the server, preventing management from effectively monitoring and tracing data cleaning and integration processes. This poses a significant deficiency in port safety management, which emphasizes process compliance and incident tracing. While some solutions have attempted to separate rendering tasks to dedicated servers, they have failed to optimize the core data preprocessing flow. Existing technologies for improving efficiency largely focus on refining rendering algorithms, lacking systematic optimization of the multi-source data flow architecture of ports, making it difficult to simultaneously improve data processing efficiency and process transparency. Summary of the Invention

[0004] To address the aforementioned technical problems, the present disclosure provides a solution. Embodiments of this disclosure offer a rendering method and system for large-scale data in port scenarios.

[0005] According to a first aspect of the present disclosure, a rendering method for large-scale data in a port scenario is provided, wherein the rendering method includes: In response to the initial screening of the large-scale data obtained about the target port, the initial screening data is classified into structured data and unstructured data. The structured data and the unstructured data are standardized to obtain standard structured data and standard unstructured data. In response to the completion of the merging of the standard structured data and the standard unstructured data, the merged data is optimized to generate optimized data; In response to the completion of the verification of the optimized data, 3D modeling and rendering are performed based on the verified optimized data to generate a 3D model; The visualization information representing the 3D model is pushed to the display terminal.

[0006] According to a second aspect of the present disclosure, a rendering system for large-scale data in port scenarios is provided, wherein the rendering system includes a port central platform, a data classification server, a data processing server, a standardization server, and a modeling server.

[0007] The port center platform is configured to: perform initial screening on the acquired large-scale data about the target port to obtain initial screening data, and send the initial screening data to the data classification server; in response to receiving optimized data, verify the optimized data, and send the optimized data that passes the verification to the modeling server; The data classification server is configured to: classify the initial screening data into structured data and unstructured data; standardize the structured data to obtain standard structured data and send it to the sorting server; send the unstructured data to the standardization server; and, in response to receiving merged data sent by the sorting server, optimize the merged data to obtain optimized data and send the optimized data to the port center platform. The standardization server is configured to: perform standardization processing on the unstructured data to obtain standard unstructured data and send it to the sorting server; The data processing server is configured to merge the standard structured data with the standard unstructured data to obtain the merged data and send it to the data classification server. The modeling server is configured to: perform 3D modeling and rendering based on the verified optimized data to generate a 3D model; and push the visualization information representing the 3D model to the display terminal.

[0008] As described above, the embodiments of this disclosure provide a rendering method for large-scale data in port scenarios. Based on a distributed processing architecture in which a port central platform, a data classification server, a sorting server, and a standardization server work together, a refined data processing flow is designed, including data initial screening, classification and diversion, parallel standardization, merging feedback, and verification. This effectively overcomes the performance bottlenecks and black-box problems of the traditional central server processing mode. It not only significantly reduces the load on a single server and improves the overall efficiency and system stability of large-scale data processing, but also realizes transparent management and accurate traceability of the entire data processing chain. Thus, it provides reliable technical support for the efficient generation, dynamic updating, and in-depth business applications of port 3D digital twins. Attached Figure Description

[0009] The above and other objects, features, and advantages of this disclosure will become more apparent from the more detailed description of the embodiments thereof in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this disclosure and form part of the specification. They are used together with the embodiments of this disclosure to explain the disclosure and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0010] Figure 1 This is a flowchart illustrating a rendering method for large-scale data in a port scenario provided by an exemplary embodiment of this disclosure; Figure 2 This is a public announcement Figure 1 One of the exemplary flowcharts of a rendering method for large-scale data in a port scenario provided in the embodiment; Figure 3 This is a public announcement Figure 1 The second exemplary flowchart of the rendering method for large-scale data in port scenarios provided in the embodiment; Figure 4 This is a public announcement Figure 1 The third exemplary flowchart of the rendering method for large-scale data in port scenarios provided in the embodiment; Figure 5 This is a public announcement Figure 1 The fourth exemplary flowchart of the rendering method for large-scale data in port scenarios provided in the embodiment; Figure 6 This is a public announcement Figure 1 The fifth exemplary flowchart of the rendering method for large-scale data in port scenarios provided in the embodiment; Figure 7 This is a schematic diagram of the structure of a rendering system for large-scale data in a port scenario provided by an exemplary embodiment of this disclosure. Detailed Implementation

[0011] The present disclosure will be further described below with reference to the embodiments shown in the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present disclosure, and not all embodiments of the present disclosure. It should be understood that the present disclosure is not limited to the exemplary embodiments described herein.

[0012] It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of this disclosure.

[0013] Those skilled in the art will understand that the terms "first," "second," etc., in the embodiments of this disclosure are only used to distinguish different steps, devices, or modules, and do not represent any specific technical meaning, nor do they indicate a necessary logical order between them.

[0014] It should also be understood that in the embodiments disclosed herein, "a plurality of" may refer to two or more, and "at least one" may refer to one, two or more.

[0015] It should also be understood that any component, data or structure mentioned in the embodiments of this disclosure can generally be understood as one or more unless expressly defined or given to the contrary in the context.

[0016] Furthermore, the term "and / or" in this disclosure is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this disclosure generally indicates that the preceding and following related objects have an "or" relationship.

[0017] It should also be understood that the description of the various embodiments in this disclosure emphasizes the differences between the various embodiments, and the similarities or similarities can be referred to each other. For the sake of brevity, they will not be described in detail.

[0018] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0019] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit this disclosure or its application or use.

[0020] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0021] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0022] Overview of the inventive concept The inventive concept of this disclosure lies in designing a data flow architecture in which a port central platform, a data classification server, a data processing server, and a standardization server work collaboratively. This involves initially screening and classifying the acquired raw port data according to its structured attributes, then distributing it to different servers for parallel standardization processing. The data processing server then merges the data and optimizes it based on feedback from the data classification server, ultimately forming a standardized data package that can be efficiently rendered by the modeling server. This innovative architecture reduces the load on individual servers while achieving precise tracking and traceability of the entire data processing flow, thereby significantly improving the rendering efficiency and system manageability of the port's 3D model.

[0023] Based on the above inventive concept, this disclosure can provide specific solutions as described in the following embodiments.

[0024] Example 1 Figure 1 This is a schematic diagram of a rendering method for large-scale data in a port scenario provided by an exemplary embodiment of the present disclosure. The rendering method can be executed by a rendering system for large-scale data in a port scenario, which may include a port central platform, a data classification server, a data processing server, a standardization server, and a modeling server.

[0025] In the rendering method embodiments described below, to facilitate understanding of the implementation logic of each step, the specific execution entities of each step (i.e., the aforementioned "port center platform, data classification server, sorting server, standardization server, and modeling server") will be adaptively added.

[0026] Specifically, refer to Figure 1 The rendering method for large-scale data in port scenarios includes: S110. In response to performing preliminary screening on the acquired large-scale data about the target port to obtain preliminary screening data, the preliminary screening data is classified into structured data and unstructured data.

[0027] The large-scale data includes static data on port infrastructure, dynamic data on port operations, and port environmental data.

[0028] As an optional implementation method, refer to Figure 2 The initial screening of the acquired large-scale data includes: S210. Extract the parameter list based on the preset standardized template.

[0029] As an optional example, this step is performed by the port center platform. Specifically, before initiating the data processing task, the port center platform receives and caches the preset standardized template (usually a configuration file in a structured format such as JSON or XML) corresponding to the target 3D model from the modeling server. The platform parses the template file, traverses the parameters nodes defined within it, and extracts the name and data_type fields of all parameters. Subsequently, the platform constructs a parameter list C in memory based on these fields, which can be quickly queried. This list is essentially a hash table or set data structure, where the key is the parameter name and the value is the corresponding data type, used to achieve efficient matching in subsequent steps. For example, the extracted parameters may include {"berth_length", "float"}, {"vessel_draft", "float"}, {"container_id", "string"}, etc.

[0030] S220. Data corresponding to parameter types that do not belong to the parameter list in the large-scale data are removed to obtain the initial screening data.

[0031] As an optional example, this step is also performed by the port center platform. Specifically, the platform iterates through each data unit in the massive dataset A (e.g., a record in a database, a JSON object, or a row in a data file). For each data unit, the platform examines all the data fields (or keys) it contains. Each data field is compared with the parameter list C generated in step S210: If the name of a data field exists in parameter list C, then that field and its data value are retained.

[0032] If the name of a data field does not exist in parameter list C, then the field is determined to be redundant data that is irrelevant to the current modeling task, and the field and its data value are removed from the current data unit.

[0033] After traversing and processing all data units, only the data fields that match parameter list C are retained, thus obtaining a simplified and targeted initial screening data B. This process significantly reduces the amount of data that the server needs to process subsequently, improving the overall workflow efficiency.

[0034] The core components of the preset standardized template include data format specifications, business parameter enumeration, and data encoding rules. The functions of each core component are as follows: The data format specification defines the storage format (e.g., binary little-endian), precision (e.g., coordinate values ​​are retained to 3 decimal places) and encoding standard for port geometric data (e.g., vertex coordinates, grid index) and attribute data (e.g., equipment number, status code), ensuring the consistency of data parsing across system components.

[0035] The enumerated business parameters, in the form of an enumeration list, explicitly define all parameter types that need to be identified and processed by the system in port business scenarios, such as "ship draft", "berth number", and "wind speed". This list is directly used to generate the above parameter list C, which serves as the fundamental basis for data filtering.

[0036] The data encoding rules specify the specific algorithms or mapping dictionaries followed when converting unstructured data (such as laser point clouds and raw sensor streams) into structured data. For example, they define how to encode specific types of radar signals into structured data packets with fields such as "range" and "azimuth".

[0037] As an optional implementation method, refer to Figure 3 The initial screening of the acquired large-scale data also includes: S310, Time-stamp the large-scale data received in different time periods.

[0038] As an optional example, this step is performed by the port center platform upon data access. Specifically, the platform has a data receiving buffer and is configured with a precise clock source. When the platform receives raw, large-scale data A from various data sources (such as AIS receivers, sensor networks, and business databases), it immediately attaches a timestamp to each data record or data file package. This timestamp typically uses the internationally standard UTC timestamp format, accurate to the millisecond level, and its content includes at least the date and time the data was successfully received by the port center platform (e.g., 2023-10-27 08:30:15.250). The platform stores the timestamped data in its persistent storage system (such as a distributed file system or time-series database) and establishes a time-range-based index for efficient subsequent retrieval. In this way, all data entering the database acquires time-series attributes.

[0039] S320. In response to the selection of a target time period, retrieve the data with the time tag within the target time period for subsequent processing.

[0040] As an optional example, this step is triggered when a data processing task needs to be started. Specifically, the management end (such as port dispatchers) inputs or selects a target time period Dm (e.g., selecting "Third Quarter of 2023" or "2023-10-01 00:00:00 to 2023-10-01 23:59:59") through the user interface (such as a web management backend) provided by the port center platform. After receiving this instruction, the platform's query engine quickly locates and retrieves all data whose timestamps fall within the time period Dm based on the start and end times of the time period Dm in the storage system. Subsequently, the platform loads this data as a unified dataset from storage into memory or a cache, and feeds it into the subsequent initial screening (S210-S220) and the entire processing pipeline as the "large-scale data A obtained" described in step S1.

[0041] As an optional implementation method, refer to Figure 4 The initial screening data is classified into structured data and unstructured data, including: S410. Based on the initial screening data, determine multiple data units.

[0042] As an optional example, this step is performed by the data classification server. Specifically, after receiving the initial screening data B from the port center platform, the data classification server first starts a data parsing and segmentation engine. This engine divides the initial screening data B stream into independent, individually processable basic logical units, i.e., data units, based on the data's source and inherent format characteristics.

[0043] For tabular data from a database, a data cell typically corresponds to a record in the table.

[0044] For self-describing data formats such as JSON or XML, a data unit typically corresponds to a complete JSON object or XML document with a root node.

[0045] For data in file format, one data unit may correspond to a complete file (such as a CAD drawing file or a LiDAR scan data file package).

[0046] The purpose of this step is to normalize the initial screening data B into a series of standardized data units, preparing for subsequent structured judgments.

[0047] S420. In response to any of the data units being parsed and mapped to a predefined field, the data unit is determined to be structured data.

[0048] As an optional example, this step is performed by the structured data recognition module of the data classification server. This module has a built-in or accessible data schema defined by the standardized template, which explicitly specifies predefined field names, data types (such as strings, floating-point numbers, and dates), and hierarchical relationships between fields.

[0049] The recognition module attempts to parse the current data unit. For example, it might attempt to parse it into a JSON object or parse the columns of a database record.

[0050] After successful parsing, the module attempts to precisely match and map the parsed field names and data types against the fields in the predefined pattern. For example, it checks whether a field named "berth_length" exists in the data cell and whether its value is a floating-point number.

[0051] If the main fields in a data unit can be successfully mapped to a predefined schema, the data unit is determined to be structured data (B2). Subsequently, the data unit is marked as type B2 and stored in the structured data queue to be processed.

[0052] S430. In response to any of the data units being unable to be parsed and mapped to a predefined field, the data unit is determined to be unstructured data.

[0053] As an optional example, this step is performed collaboratively with S420 by the same module.

[0054] If a data unit cannot be parsed (e.g., it is not in a valid JSON format, or its encoding cannot be recognized), or if the fields it contains after parsing cannot be effectively mapped to any field in the predefined pattern (e.g., the data unit is entirely a binary stream, or the field names are all unrecognizable gibberish), then the data unit is determined to be unstructured data (B1).

[0055] The data unit is then labeled as type B1 and stored in the queue of unstructured data to be processed, ready to be sent to the standardization server.

[0056] As an optional example, the predefined fields include at least one of the following: port facility static attribute fields, port operation dynamic attribute fields, and port environment data fields.

[0057] The static attribute fields of the port facilities include at least one of the following: berth number, berth length, design water depth, quay crane equipment number, and rated lifting capacity. Specifically, such as berth_id (berth number, string) and design_depth (design water depth, floating-point number), are used to map BIM / CAD model data.

[0058] The port operation dynamic attribute fields include at least one of the following: vessel draft, vessel real-time position, container number, container real-time position, and equipment operation status. Specifically, such as vessel_position (vessel real-time position, GPS coordinates) and crane_status (equipment operation status, enumeration type), are used to map AIS messages and operation system data.

[0059] The port environmental data fields include at least one of the following: real-time tidal level, wind speed, wind direction, and visibility. Specifically, fields such as tide_level (real-time tidal level, floating-point number) and wind_speed (wind speed, floating-point number) are used to map meteorological and hydrological sensor data.

[0060] Alternatively, the data classification server, as a multi-threaded server, can create multiple parallel classification threads, each of which independently processes one or more data units, thereby achieving high-speed concurrent classification of massive initial screening data B and greatly improving system throughput.

[0061] S120. The structured data and the unstructured data are standardized to obtain standard structured data and standard unstructured data.

[0062] As an optional implementation method, refer to Figure 5The structured data is standardized, including: S510. In response to determining that the structured data conforms to the preset standardized template, the structured data is used as the standard structured data.

[0063] As an optional example, this step is performed by the data classification server. Specifically, the server performs a consistency check on each data unit in the structured data B2 obtained from step S2 against the preset standardized template received from the port center platform.

[0064] The specific verification process includes: the server checking whether the field name, data type, data format (such as date format YYYY-MM-DD), and value range (such as whether the value is within a reasonable range) of the data unit fully match the specifications of the standardized template.

[0065] The specific judgment and processing logic includes: if all the check items of a data unit meet the template requirements, then the data unit is judged to meet the standard. The server directly marks it as standard structured data B21 and sends it to the processing server. For example, a berth record from a port business system, whose fields BerthID (string) and Length (floating-point number, unit: meters) are completely consistent with the template definition, will pass directly without any modification.

[0066] S520. In response to determining that the structured data does not conform to the preset standardized template, but can be rewritten to conform to the preset standardized template, the structured data is rewritten to obtain the standard structured data.

[0067] As an optional example, this step is also performed by the data classification server, which is a core function of its data cleaning and transformation engine.

[0068] Specifically, the rewritability identification means that when a data unit is found to be inconsistent with the template (for example, the field name is berth length instead of the template-defined Berth_Length, or the length unit is "feet" instead of "meter"), the server will determine whether a predefined conversion path exists based on the built-in conversion rule library.

[0069] Optionally, if a conversion path exists, rewriting is performed automatically. The rewriting operations may include field mapping, format conversion, unit conversion, and enumeration value mapping. Specifically, field mapping may involve renaming fields according to a mapping table (e.g., berth length -> Berth_Length). Format conversion may involve, for example, converting a date format from DD / MM / YYYY to YYYY-MM-DD. Unit conversion may involve, for example, converting a length value from feet multiplied by 0.3048 to meters. Enumeration value mapping may involve, for example, mapping the status text "Operating" to the enumeration code OPERATING.

[0070] After the rewriting is completed, the server performs the S510 verification on the rewritten data unit again. If it is correct, it is marked as B21 and sent to the sorting server.

[0071] S530. In response to determining that the structured data does not conform to the preset standardized template and cannot be rewritten to conform to the preset standardized template, the structured data is deleted.

[0072] As an optional example, the following situations are typically considered unwritable: Data cells lack key fields specified in the template (e.g., missing vessel MMSI number); data values ​​are seriously incorrect or outside a reasonable range (e.g., negative vessel length); or the data format is completely unparseable or does not match any known conversion rules.

[0073] For such data units, the data classification server treats them as invalid or unusable data, removes them directly from the processing flow, and optionally logs them for subsequent analysis, but does not pass them on to downstream servers.

[0074] This three-tiered judgment and processing mechanism ensures that the structured data ultimately flowing into the modeling and rendering stage is highly consistent, accurate, and reliable in terms of format, content, and quality, laying a solid data foundation for generating high-quality, unambiguous 3D models. This process achieves effective governance of multi-source heterogeneous port data.

[0075] As an optional implementation, the unstructured data is standardized, including: Perform a format conversion operation on the unstructured data to obtain the standard unstructured data; The format conversion operation includes at least one of the following: converting algebraic data into binary data, or converting binary data into algebraic data.

[0076] As an optional example, this step is performed by the standardization server. Its core task is to convert unstructured data B1 from diverse sources and with raw formats into standardized unstructured data B11 that the modeling server can recognize and process. The specific implementation is as follows: 1) Data Reception and Type Identification. Specifically, the standardization server receives unstructured data B1 from the data classification server. B1 may contain various types of data streams or files, such as LiDAR point cloud data packets, raw video stream frames, radar scan signals, image files, etc.

[0077] 2) Template Matching and Conversion Rule Selection. Specifically, the server calls a standardized template obtained from the port center platform. The "Data Encoding Rules" section of this template predefines a conversion rule library for different unstructured data types. The server matches the identified data type against the rule library, automatically selecting the corresponding conversion algorithm and output format requirements. For example, data identified as "PLY format point cloud" matches the rule "convert to binary format point cloud".

[0078] 3) Perform format conversion operations. The core of these conversion operations is data serialization / deserialization or encoding / decoding, specifically including but not limited to the following: 31) Convert algebraic data to binary data. Specifically, this operation mainly targets textual descriptive data or parametric geometric data. For example, mathematical equations (algebraic data) describing the contour of a device are serialized into a binary stream file containing vertex coordinates and face indices, occupying contiguous storage space, using a specific meshing algorithm. This conversion significantly reduces data volume and improves the efficiency of the modeling engine in reading data. 32) Convert binary data to algebraic data. Specifically, this operation mainly targets raw sensor binary streams. For example, the raw binary signal stream (binary data) transmitted from a radar device is decoded according to a predefined communication protocol to extract meaningful algebraic parameters, such as the distance, azimuth, and velocity of the target object, and these parameters are organized into structured records. This process essentially converts low-level signals that are difficult to understand directly into high-level data rich in semantic information that can be used for modeling.

[0079] 4) Output and Verification. Specifically, after the conversion is complete, the standardization server performs integrity verification on the generated data B11 (such as checking file size and header / footer flags) to ensure that no errors occurred during the conversion process. After passing the verification, the server sends the standard unstructured data B11 to the processing server.

[0080] Through the standardization process described above, the originally messy unstructured data is unified into a format that can be consumed by the modeling pipeline. Whether it is point cloud, video, or radar signal, it is ultimately transformed into standardized geometric or parametric data that is directly related to the construction of 3D models, laying the foundation for seamless integration with standard structured data and efficient modeling and rendering.

[0081] S130. In response to the completion of the merging of the standard structured data and the standard unstructured data, the merged data is optimized to generate optimized data.

[0082] As an optional implementation, the optimization processing of the merged data includes: packaging and compressing the merged data to generate the optimized data.

[0083] As an optional example, this step is performed by the data classification server. Upon receiving the merged data B3 from the sorting server, it initiates an optimization process aimed at reducing data transmission volume, improving the loading efficiency of subsequent modeling servers, and facilitating management. Its core operations are packaging and compression. The specific implementation is as follows: a) Data Classification and Grouping. Specifically, the data classification server first reclassifies the merged data B3. This is not a simple classification of data formats, but rather a logical grouping based on the semantics and usage of the data in the 3D scene. For example, all data related to "Berth No. 1" (including the berth's geometric model, real-time AIS data of docked vessels, timestamps of the monitoring video streams in the area, etc.) are grouped into one logical group; all environmental monitoring data (tides, wind speed) are grouped into another logical group. Subsequently, the server creates an independent data package for each logical group. Within each data package, the data is organized according to a predefined directory structure, for example, including a manifest.json file describing the metadata of the data within the package (such as the data types, data volume, and version number), and corresponding data files (such as berth_1_model.mesh, vessel_list.csv, video_ref.txt).

[0084] b) Data Compression Processing. Specifically, after packaging, the server performs compression on each data packet. Its compression module selects an appropriate lossless compression algorithm based on the characteristics of the data type. For text-formatted attribute data, configuration files, etc., algorithms like GZIP or DEFLATE are used to achieve a high compression ratio. For geometric data, point cloud data, etc., which are already in binary format, more specialized compression algorithms optimized for numerical arrays, such as LZ4 or Snappy, may be used to strike a balance between compression ratio and processing speed. The compression process significantly reduces the size of each data packet, generating a corresponding compressed package.

[0085] c) Generate optimized data B31. All these compressed packages together constitute the final optimized data B31. B31 is essentially a collection of categorized data compressed packages. The data classification server generates a master index list for the entire B31, recording the logical classification information corresponding to each compressed package, and then feeds it back to the port center platform.

[0086] Understandably, compression significantly reduces the amount of data that needs to be fed back to the port's central platform and ultimately transmitted to the modeling server, thus lowering network bandwidth and storage pressure. Furthermore, this semantically categorized packaging method greatly benefits the modeling server's ability to dynamically load data. When rendering a specific portion of the port, the modeling server can download and decompress only the corresponding data package, without needing to load the entire port's data, thereby greatly improving rendering efficiency and response speed.

[0087] S140. In response to completing the verification of the optimized data, perform 3D modeling and rendering based on the verified optimized data to generate a 3D model.

[0088] As an optional implementation, this step can be performed by the modeling server. After receiving the validated optimized data B31 (i.e., a series of categorized data compressed packages) from the port center platform, the modeling server initiates the 3D modeling and rendering pipeline. This pipeline is a continuous process of converting data into visual image frames. Specifically, refer to... Figure 6 Step S140 may include: S610. Based on the data type and hierarchical identifier embedded in the optimized data, perform parsing and node filling operations on the scene graph data structure to obtain the filled 3D scene graph.

[0089] Specifically, this step can be performed as follows: First, the modeling server decompresses the various compressed packages in the optimized data B31. Then, its scene manager module reads the metadata file (e.g., manifest.json) within each package. This file describes the data type (e.g., "berth geometry," "ship dynamics data") and hierarchical identifier (e.g., the berth belongs to the "container handling area" and is a child node of "Port Area 1"). Next, the scene manager creates a tree-structured scene graph in memory based on these hierarchical identifiers. The root node of the scene graph represents the entire port scene, child nodes represent logical partitions such as port areas and berths, and leaf nodes represent specific renderable objects (e.g., quay cranes, ships, warehouses). Finally, the modeling server populates the corresponding scene graph nodes with the decompressed actual data. For example, the geometric data of the berth is populated into the "berth" node, and the position and model index of the ship are populated into the "ship" node. Each node contains its corresponding transformation matrix (position, rotation, scaling) and references to the geometric and material data.

[0090] S620. Based on the predefined level of detail rules and the geometric parameters in the optimized data, perform an instantiation operation on the three-dimensional mesh model to obtain three-dimensional mesh models with different precision after instantiation.

[0091] Specifically, this step can be performed as follows: First, the resource manager module of the modeling server determines the level of detail (or precision) of the 3D mesh model to load for each object in the scene graph based on predefined level-of-detail rules. These rules are typically based on the distance between the object and the virtual camera; a lower precision (fewer faces) model is loaded when the object is far away, and a higher precision model is loaded when the object is close. Then, the resource manager loads the corresponding 3D mesh data from the model library based on the geometric parameters (such as model ID and dimensions) provided in the optimization data. Finally, an instantiation operation is performed: that is, the same mesh model (such as a standard quay crane model) is created at multiple locations in the scene (corresponding to multiple berths) according to different transformation matrices, instead of storing a separate mesh data for each object. This significantly saves memory.

[0092] S630. Based on the material texture index in the optimized data, perform the binding operation between the material map and the lighting parameters to obtain a three-dimensional model with attached material texture.

[0093] Specifically, this step can be performed as follows: First, the shader manager module of the modeling server reads the material texture index corresponding to each model in the optimization data. Based on this index, the corresponding diffuse map, normal map, specular map, etc., are loaded from the texture library. At the same time, the module combines the scene's lighting parameters (such as sunlight direction, intensity, and ambient light color) with the model's material properties (such as base color and smoothness) to generate the final surface shader. After that, after material mapping and lighting binding, the original "white model" with only geometric shapes becomes a 3D model with realistic surface details (such as the metallic paint of the quay crane and the markings on the container) and attached material textures, ready to receive light.

[0094] S640. Based on the viewpoint parameters and the rendering engine's drawing instructions, perform traversal and rasterization operations on the three-dimensional scene graph to obtain and output the image frame sequence of the three-dimensional model.

[0095] Specifically, this step can be performed as follows: First, the rendering engine receives viewpoint parameters (such as camera position, orientation, and field of view). Starting from the root node of the scene graph, it traverses all visible nodes in a certain order (such as depth-first search). Second, for each visible leaf node (i.e., the specific 3D model), the engine passes the transformation matrix and material information to its corresponding shader program and submits a drawing instruction to the graphics processor. Finally, the graphics processor executes the drawing instruction, converting the vertex data of the 3D model into 2D pixels on the screen; this process is called rasterization. It calculates the color and depth of each pixel, ultimately synthesizing a complete image frame. By continuously updating the viewpoint parameters (to achieve camera movement) and repeating this process, a continuous sequence of image frames can be output.

[0096] S150. The visualization information representing the three-dimensional model is pushed to the display terminal.

[0097] As an optional implementation, this step is the final output stage of the method and is executed primarily by the modeling server. Its core task is to efficiently and stably transmit the 3D model and its dynamic visuals generated in step S140 to one or more display terminals for user monitoring and analysis. The specific implementation is as follows: First, the encoder module within the modeling server compresses and encodes the image frame sequence generated in real-time by the rendering engine. Based on the type of display terminal and network conditions, it selects an appropriate encoding standard (such as H.264 or H.265) and bitrate to minimize data volume while maintaining image quality. Subsequently, the streaming media server module encapsulates the encoded video stream into a standard streaming media protocol format (such as RTMP, HLS, or WebRTC). These protocols are suitable for different network environments and latency requirements; for example, RTMP is suitable for low-latency monitoring screen pushes, while HLS is suitable for segmented loading on the web.

[0098] Second, in addition to the video stream, the pushed information package also includes metadata and interaction instructions. This data is pushed synchronously with the video stream or transmitted via a separate bidirectional communication channel such as WebSocket. The content includes: scene metadata, such as the IDs, names, and key attributes of important objects in the scene at the current viewpoint (e.g., displaying the ship's name and draft when the mouse hovers over a ship); and interaction instructions, such as instructions to allow the terminal's user interface to respond to user actions, such as click, pan, and zoom commands.

[0099] The modeling server actively pushes assembled streaming media and interactive data packets to one or more registered display terminals via a high-speed network. The server maintains a list of terminal sessions, managing the connection status and subscribed content of different terminals.

[0100] Third, dedicated client applications or web browsers run on display terminals (such as the monitoring screen in the port dispatch center, the desktop computers or mobile terminals of management personnel). After receiving the data stream, the client decodes it in real time and renders the image frames onto the screen. At the same time, the client parses metadata and interaction instructions, updates the overlay information on the user interface (such as data panels and legends), and ensures that user operations can be fed back to the modeling server in real time and smoothly, forming an interactive closed loop.

[0101] As described above, the embodiments of this disclosure provide a rendering method for large-scale data in port scenarios. Based on a distributed processing architecture in which a port central platform, a data classification server, a sorting server, and a standardization server work together, a refined data processing flow is designed, including data initial screening, classification and diversion, parallel standardization, merging feedback, and verification. This effectively overcomes the performance bottlenecks and black-box problems of the traditional central server processing mode. It not only significantly reduces the load on a single server and improves the overall efficiency and system stability of large-scale data processing, but also realizes transparent management and accurate traceability of the entire data processing chain. Thus, it provides reliable technical support for the efficient generation, dynamic updating, and in-depth business applications of port 3D digital twins.

[0102] Example 2 It should be understood that the rendering methods for large-scale data in port scenarios described in the foregoing embodiments in this document can also be similarly applied to the following rendering systems for large-scale data in port scenarios for similar extensions. For simplicity, they are not described in detail.

[0103] Figure 7 This is a schematic diagram of a rendering system architecture for large-scale data in a port scenario, provided by an exemplary embodiment of this disclosure. (Refer to...) Figure 7 The rendering system 700 includes a port center platform 710, a data classification server 720, a sorting server 730, a standardization server 740, and a modeling server 750.

[0104] The port center platform 710 is configured to: perform initial screening on the acquired large-scale data about the target port to obtain initial screening data, and send the initial screening data to the data classification server; in response to receiving optimized data, verify the optimized data, and send the verified optimized data to the modeling server.

[0105] The data classification server 720 is configured to: classify the initial screening data into structured data and unstructured data; standardize the structured data to obtain standard structured data and send it to the sorting server; send the unstructured data to the standardization server; and, in response to receiving merged data sent by the sorting server, optimize the merged data to obtain optimized data and send the optimized data to the port center platform.

[0106] The standardization server 730 is configured to: perform standardization processing on the unstructured data to obtain standard unstructured data and send it to the sorting server.

[0107] The sorting server 740 is configured to merge the standard structured data with the standard unstructured data to obtain the merged data and send it to the data classification server.

[0108] The modeling server 750 is configured to: perform 3D modeling and rendering based on the verified optimized data to generate a 3D model; and push the visualization information representing the 3D model to the display terminal.

[0109] As described above, the embodiments of this disclosure provide a rendering system for large-scale data in port scenarios, including a distributed processing architecture in which a port central platform, a data classification server, a sorting server, and a standardization server work together. By executing a refined data processing flow that includes data initial screening, classification and diversion, parallel standardization, merging feedback, and verification, it effectively overcomes the performance bottlenecks and process black-box problems of the traditional central server processing mode. It not only significantly reduces the load of a single server and improves the overall efficiency and system stability of large-scale data processing, but also realizes transparent management and accurate traceability of the entire data processing chain, thereby providing reliable technical support for the efficient generation, dynamic updating, and in-depth business applications of port 3D digital twins.

[0110] Example 3 Reference Figure 7 The rendering system 700 includes any one of the following: a port center platform 710, a data classification server 720, a sorting server 730, a standardization server 740, and a modeling server 750. Each of these components may include one or more processors and a memory. The processor may be a central processing unit (CPU) or other processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. The memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor may execute the program instructions to implement the steps of matching each server (as the execution subject) and / or other desired functions in the rendering methods for large-scale data in port scenarios described in the various embodiments of this disclosure above.

[0111] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.

[0112] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For system embodiments, since they largely correspond to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0113] The block diagrams of devices, apparatuses, devices, and systems disclosed herein are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0114] The methods and apparatus of this disclosure may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the methods is for illustrative purposes only, and the steps of the methods of this disclosure are not limited to the order specifically described above unless otherwise specifically stated. Furthermore, in some embodiments, this disclosure may also be implemented as a program recorded on a recording medium, the program including machine-readable instructions for implementing the methods according to this disclosure. Thus, this disclosure also covers recording media storing programs for performing the methods according to this disclosure.

[0115] It should also be noted that in the apparatus, devices, and methods of this disclosure, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions to this disclosure.

[0116] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0117] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A rendering method for large-scale data in a port scenario, characterized in that, The rendering method comprises: In response to the preliminary screening of the obtained large-scale data about the target port, initial screening data is obtained, and the initial screening data is classified into structured data and unstructured data; The structured data and the unstructured data are subjected to standardization processing to obtain standard structured data and standard unstructured data; In response to the completion of the merging of the standard structured data and the standard unstructured data, the merged data is subjected to optimization processing to generate optimized data; In response to the completion of the verification of the optimized data, three-dimensional modeling and rendering are performed based on the optimized data that passes the verification to generate a three-dimensional model; Visual information representing the three-dimensional model is pushed to a display terminal.

2. The rendering method of claim 1, wherein, The preliminary screening of the obtained large-scale data comprises: A parameter list is extracted based on a preset standardization template; Data corresponding to a parameter type not belonging to the parameter list in the large-scale data is removed to obtain the initial screening data; The preset standardization template comprises data format specifications, business parameter enumeration, and data coding rules, the data format specifications are used to define the storage format and precision of geometric data and attribute data, the business parameter enumeration is used to define a set of parameter types to be identified in a port business scenario, and the data coding rules are used to define the rules for converting unstructured data into structured data.

3. The rendering method of claim 2, wherein, The preliminary screening of the obtained large-scale data further comprises: The large-scale data received in different time periods is marked with a time label; In response to the selection of a target time period, data with the time label in the target time period is called for subsequent processing.

4. The rendering method of claim 2, wherein, The classification of the initial screening data into structured data and unstructured data comprises: Based on the initial screening data, a plurality of data units are determined; In response to the fact that any data unit can be parsed and mapped to a predefined field, it is determined that the data unit is structured data; In response to the fact that any data unit cannot be parsed and mapped to a predefined field, it is determined that the data unit is unstructured data.

5. The rendering method according to claim 4, wherein The large-scale data comprises port infrastructure static data, port operation dynamic data, and port environment data; The predefined field comprises at least one of a port facility static attribute field, a port operation dynamic attribute field, and a port environment data field; wherein The port facility static attribute field comprises at least one of the following: berth number, berth length, designed water depth, shore crane device number, and rated lifting capacity; The port operation dynamic attribute field comprises at least one of the following: ship draft, ship real-time position, container number, container real-time position, and device operation state; The port environment data field comprises at least one of the following: real-time tidal level, wind speed, wind direction, and visibility.

6. The rendering method of claim 4, wherein, The standardization processing of the structured data comprises: In response to the determination that the structured data conforms to the preset standardization template, the structured data is taken as the standard structured data; In response to judging that the structured data does not conform to the preset standardized template but can be rewritten to conform to the preset standardized template, rewriting is performed on the structured data to obtain the standard structured data; In response to judging that the structured data does not conform to the preset standardized template and cannot be rewritten to conform to the preset standardized template, the structured data is deleted.

7. The rendering method of claim 4, wherein, The non-structured data is standardized, including: The non-structured data is subjected to a format conversion operation to obtain the standard non-structured data; The format conversion operation includes at least one of the following: converting algebraic data into binary data, or converting binary data into algebraic data.

8. The rendering method of claim 1, wherein, The optimization processing of the merged data includes: The merged data is packed and compressed to generate the optimized data.

9. The rendering method of claim 1, wherein, Based on the optimized data that passes the verification, three-dimensional modeling and rendering are performed to generate a three-dimensional model, including: According to the data type and level identifier embedded in the optimized data, a scene graph data structure is parsed and a node filling operation is performed to obtain a filled three-dimensional scene graph; According to the predefined detail level rule and the geometric parameters in the optimized data, an instantiation operation is performed on the three-dimensional mesh model to obtain a three-dimensional mesh model with different precisions after instantiation; According to the material texture index in the optimized data, a material mapping and lighting parameter binding operation is performed to obtain a three-dimensional model with material texture; According to the viewpoint parameters and the rendering instructions of the rendering engine, a traversal and rasterization operation is performed on the three-dimensional scene graph to obtain and output an image frame sequence of the three-dimensional model.

10. A rendering system for large-scale data in a port scenario, characterized in that, The rendering system includes a port center platform, a data classification server, a sorting server, a standardization server, and a modeling server; The port center platform is configured to: perform preliminary screening on the obtained large-scale data about a target port to obtain preliminary screening data, and send the preliminary screening data to the data classification server; in response to receiving the optimized data, verify the optimized data, and send the optimized data that passes the verification to the modeling server; The data classification server is configured to: classify the preliminary screening data into structured data and non-structured data; standardize the structured data to obtain standard structured data and send the standard structured data to the sorting server; send the non-structured data to the standardization server; in response to receiving the merged data sent by the sorting server, optimize the merged data to obtain the optimized data, and send the optimized data to the port center platform; The standardization server is configured to standardize the non-structured data to obtain standard non-structured data and send the standard non-structured data to the sorting server; The sorting server is configured to merge the standard structured data and the standard non-structured data to obtain the merged data and send the merged data to the data classification server; The modeling server is configured to: based on the optimized data that passes the verification, perform three-dimensional modeling and rendering to generate a three-dimensional model; Visual information characterizing the three-dimensional model is pushed to a display terminal.

Images