Survey big data model construction method and device, equipment and medium

Abstract

The invention discloses an exploration big data model construction method and device, equipment and a medium, and relates to the technical field of data processing, and the method comprises the steps: carrying out the standardization processing of obtained exploration data to determine a data standard, and collecting multi-source heterogeneous exploration data based on the data standard, multi-source heterogeneous survey data is subjected to distributed classified storage according to a multi-level classification system, a survey big data model is constructed according to user project requirements, coordinate enabling is set based on the survey big data model so as to carry out GIS data visual interaction, and a unified survey data model is established through data standardization and survey data standard making. A surveying multi-specialty data unified storage center is constructed, and full-life-cycle data management is achieved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and specifically to a method, device, equipment and medium for constructing a survey big data model. Background Art

[0002] Existing big data centers or data management systems across various industries don't include survey data management. In related technologies, a standardized system for survey data has not been established. This results in a lack of unified classification specifications, coding structures, and data models for specialized data such as hydrology, surveying, and geotechnical engineering. Furthermore, data from different projects is not directly compatible. Existing systems lack specialized survey calculation modules, and specialized algorithms for hydrological calculations and geotechnical analysis are not integrated. Engineering calculations cannot be performed directly based on stored data, requiring manual offline processing of survey data. Summary of the Invention

[0003] In view of the above problems, the present application provides a method, device, equipment and medium for constructing a survey big data model to at least solve the problems existing in the related art.

[0004] In a first aspect, an embodiment of the present application provides a method for constructing a survey big data model, the method comprising:

[0005] Standardize the acquired survey data to determine the data standards;

[0006] Collecting multi-source heterogeneous survey data based on the data standard;

[0007] Distributed classification and storage of the multi-source heterogeneous survey data according to a multi-level classification system, and construction of a survey big data model based on user project requirements;

[0008] Coordinate empowerment is set based on the survey big data model to perform GIS data visualization interaction.

[0009] In some embodiments, setting coordinate enabling based on the survey big data model to perform GIS data visualization interaction includes:

[0010] Determining the measurement point numbers and coordinate positions in the survey big data model;

[0011] Associating the measuring point number and coordinate position with the engineering file and database for automatic coordinate recognition and attribute assignment;

[0012] The data request input by the user into the survey big data model is visualized and interacted with using GIS layers.

[0013] In some embodiments, the distributed classification storage of the multi-source heterogeneous survey data according to a multi-level classification system includes:

[0014] Establishing a distributed system, wherein the distributed system includes at least three nodes;

[0015] The multi-source heterogeneous survey data are distributed and classified and stored according to the classification system corresponding to the hydrological and meteorological data architecture and the rock mass data architecture, wherein the hydrological and meteorological data architecture and the rock mass data architecture use Yarn queues to allocate computing resources for resource isolation, and use data lineage tracking for index association and storage.

[0016] In some embodiments, the survey big data model construction method further includes:

[0017] Get user information;

[0018] Determine the minimum set of permissions required to complete the work based on user information;

[0019] Assign access rights and operation permissions to target users based on the minimum permission set.

[0020] In some embodiments, constructing a survey big data model according to user project requirements includes:

[0021] Determine a classification and coding system based on user project requirements, wherein the classification and coding system includes: project level, professional level and data type;

[0022] A survey big data model is constructed after integrating the data verification engine based on the classification and coding system.

[0023] In some embodiments, the hydrological and meteorological data architecture includes a measurement layer, a data entry and storage layer, and a computational analysis layer.

[0024] In some embodiments, the geotechnical data architecture includes an extension layer, a support layer, a storage layer, and an acquisition layer.

[0025] In a second aspect, an embodiment of the present application provides a survey big data model construction device, comprising:

[0026] A standardization module is used to standardize the acquired survey data to determine the data standard;

[0027] An acquisition module, configured to acquire multi-source heterogeneous survey data based on the data standard;

[0028] A construction module is used to perform distributed classification storage on the multi-source heterogeneous survey data according to a multi-level classification system, and to construct a survey big data model according to user project requirements;

[0029] The interactive module is used to set coordinate empowerment based on the survey big data model to perform GIS data visualization interaction.

[0030] In a third aspect, an embodiment of the present application provides an electronic device comprising a memory and a processor, wherein the memory stores program code that can be run on the processor, and when the program code is executed by the processor, the method for constructing a survey big data model as described in any implementation method of the first aspect is implemented.

[0031] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing one or more programs, which can be executed by the electronic device as described in the third aspect to implement the survey big data model construction method as described in any implementation method of the first aspect.

[0032] The embodiments of the present application provide a method, apparatus, device, and medium for constructing a survey big data model. The method standardizes the acquired survey data to determine the data standard, collects multi-source heterogeneous survey data based on the data standard, and distributes and classifies the multi-source heterogeneous survey data according to a multi-level classification system. A survey big data model is constructed according to user project requirements, and coordinate empowerment is set based on the survey big data model to perform GIS data visualization interaction. Through data standardization, survey data standards are formulated, a unified survey data model is established, and a unified storage center for multi-disciplinary survey data is constructed to achieve full life cycle data management.

[0033] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Hereinafter, the present application will be described in more detail based on embodiments with reference to the accompanying drawings.

[0035] Figure 1 A schematic diagram of a process for constructing a survey big data model proposed in one embodiment of the present application is shown;

[0036] Figure 2 A schematic diagram of an exemplary hydrological and meteorological data architecture proposed in an embodiment of the present application is shown;

[0037] Figure 3 A schematic diagram of an exemplary rock mass data architecture proposed in an embodiment of the present application is shown;

[0038] Figure 4 A schematic diagram of an exemplary coordinate enabling process proposed in one embodiment of the present application is shown;

[0039] Figure 5 A schematic diagram of an exemplary GIS visualization output structure proposed in one embodiment of the present application is shown;

[0040] Figure 6 The following is a structural block diagram of an exemplary survey big data model building device proposed in one embodiment of the present application;

[0041] Figure 7 The following is a structural block diagram of an electronic device for executing the survey big data model construction method according to an embodiment of the present application;

[0042] Figure 8 A computer-readable storage medium for storing or carrying a method for constructing a survey big data model according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0043] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with examples and drawings. The exemplary embodiments of the present invention and their descriptions are only used to explain the present invention and are not intended to limit the present invention.

[0044] In the field of surveying and engineering, the volume of data has exploded with the increase in the number of projects and the advancement of data collection technology. Data, including engineering management data, hydrological data, survey data, and geotechnical data, has become a vital enterprise asset. This data, with its diverse categories, diverse formats, and dispersed storage, is characterized by its diversity and complexity, requiring effective management and utilization to improve the efficiency and quality of surveying and engineering projects.

[0045] After analyzing the existing problems, the applicant found that there are some problems in the management and application of survey data, such as data dispersion, difficulty in query and analysis, inconsistent data formats, insufficient data security and confidentiality, etc., which limit the value of survey data.

[0046] Therefore, the inventors have proposed a method, apparatus, device, and medium for constructing a survey big data model. These methods standardize acquired survey data to determine data standards, collect multi-source heterogeneous survey data based on these data standards, and distribute and classify and store the multi-source heterogeneous survey data according to a multi-level classification system. Furthermore, a survey big data model is constructed based on user project requirements. Coordinates are enabled based on the survey big data model to enable interactive GIS data visualization. Through data standardization, survey data standards are formulated, a unified survey data model is established, and a unified storage center for multi-disciplinary survey data is constructed, enabling full lifecycle data management. The survey big data model construction method is described in detail in subsequent embodiments.

[0047] The following describes the application scenarios of the survey big data model construction method provided in the embodiments of the present application:

[0048] See also Figure 1 , Figure 1 This is a flow chart of a survey big data model construction method provided in an embodiment of the present application. In this embodiment, the survey big data model construction method can be applied to Figure 6 The survey big data model building device 300 and Figure 7 In the electronic device 200 shown, the electronic device may include one or more electronic devices, and information can be transmitted between multiple electronic devices in a wireless and / or wired manner. Multiple electronic devices can collaborate to complete the survey big data model construction method. For example, the electronic device may include a computer, a mobile terminal, a communication device, etc., which is not limited in this application. Figure 1 The process shown is described in detail, and the survey big data model construction method may include S110 to S140.

[0049] S110: Standardize the acquired survey data to determine data standards.

[0050] In the embodiment of the present application, data standardization is carried out by taking inventory and analyzing the existing survey data, formulating data standards based on the data characteristics and actual conditions, and constructing a unified survey data model standard.

[0051] S120: Collect multi-source heterogeneous survey data based on data standards.

[0052] In the embodiment of the present application, during the data collection process, combined with data standards, the collection and conversion technology of multi-source heterogeneous data is studied to achieve unified storage of professional data.

[0053] S130: Distributed classification and storage of multi-source heterogeneous survey data according to a multi-level classification system, and construction of a survey big data model based on user project requirements.

[0054] Among them, include:

[0055] S131: Establish a distributed system, where the distributed system includes at least three nodes.

[0056] In this embodiment, the survey big data platform construction plan adopts a distributed storage system. By distributing data across multiple nodes rather than centrally storing it on a single server, the system significantly improves data storage reliability and access efficiency. The system divides the data into multiple small blocks and then distributes these blocks across at least three nodes.

[0057] This redundant storage strategy allows the system to recover lost data from replicas stored on other nodes if a node fails or data becomes corrupted, significantly enhancing data security and system robustness. Furthermore, by sharing storage tasks across multiple nodes, load balancing is achieved, avoiding the performance bottlenecks that can occur in traditional centralized storage systems. The system is designed with node failures in mind. Through data replication and distributed algorithms, this ensures that the entire system remains operational even if some nodes experience issues, preventing data loss.

[0058] S132: Distributed classification and storage of multi-source heterogeneous survey data according to the classification system corresponding to the hydrological and meteorological data architecture and the rock mass data architecture. The hydrological and meteorological data architecture and the rock mass data architecture use Yarn queues to allocate computing resources for resource isolation, and use data lineage tracking for index association and storage.

[0059] In this embodiment, computing resources are allocated through the Yarn queue to avoid mutual interference between hydrological and measurement data synchronization tasks, while achieving collaboration with the data standardization module.

[0060] For example, Datax directly calls the JSON Schema validation engine during the synchronization process to filter out data that does not meet the specifications in real time; when the water_level of hydrological data exceeds 100 meters, Datax automatically marks the record and triggers an alarm.

[0061] Among them, a unique identifier is generated for the data entity, which includes the project identifier, professional type, timestamp and source hash; during the data collection, processing and storage process, the source information, processing algorithm, responsible party and output association are recorded; a graph database is used to store the blood relationship between data entities, and multi-dimensional traceability queries are supported.

[0062] In some embodiments, constructing a survey big data model according to user project requirements in S130 includes:

[0063] S133: Determine a classification and coding system based on user project requirements, wherein the classification and coding system includes: project level, professional level and data type.

[0064] In the embodiment of the present application, a three-level classification coding system (project-level coding → professional-level coding → data type coding) is established to integrate the data verification engine.

[0065] Examples include field integrity verification: all data records must contain core fields such as project_id, data_type, and timestamp; otherwise, the data will be considered invalid. Range verification: water_level in hydrological data must be greater than 0 and less than 100 meters, and bearing_capacity in geotechnical data must be between 0 and 5000 kPa. Uniqueness verification: data uniqueness is ensured through the combination of project_id, data_source, and unique_key (e.g., hydrological station numbers cannot be repeated within the same project).

[0066] S134: Build a survey big data model after integrating the data verification engine based on the classification coding system.

[0067] In this embodiment, the data coding system dynamically expands to automatically parse new data types (such as new sensor data) from hydrological and meteorological sites and generate coding rules that conform to the three-level classification system. Entity recognition is performed on unstructured text, extracting key parameters (such as "extreme wind speed") and mapping them to the existing coding system. A clustering model trained on historical data automatically identifies similar data types and generates coding expansion suggestions.

[0068] In the embodiment of the present application, the data classification method adopts a three-level classification to support cross-dimensional retrieval; the data model adopts a unified model based on JSON Schema with compatibility, which supports dynamic expansion; the data verification mechanism is an automated engine verification, which reduces the error rate by 90%, and the cross-project data analysis capability can automatically correlate and analyze, and the response time is shortened from hours to minutes.

[0069] S140: Set coordinate empowerment based on the survey big data model to perform GIS data visualization interaction.

[0070] In some embodiments, see Figure 4 The schematic diagram of an exemplary coordinate enabling process shown in FIG. 140 includes: S141 to S143, wherein:

[0071] S141: Determine the measurement point number and coordinate position in the survey big data model.

[0072] S142: Associate the measurement point number and coordinate position with the project file and database to automatically identify the coordinates and assign attributes.

[0073] S143: Using GIS layers to perform visual interaction on the data request input by the user into the survey big data model.

[0074] In this embodiment, refer to Figure 5A schematic diagram of an exemplary GIS visualization output structure, a geotechnical engineering survey database with GIS display capabilities in the geotechnical direction, completes the construction of the acquisition layer, storage layer, and support layer, and builds a system that can realize functions such as massive engineering data storage, rapid data retrieval and call, interoperability of in-hospital collaborative platforms, and engineering information visualization.

[0075] Geological database information systems, which utilize geographic information system technology to digitally acquire, manage, and apply spatial positioning information, and database technology to store and manage massive amounts of attribute data, have been applied across various engineering fields. The construction of a GIS-based geological database structure should begin with a basic understanding of GIS theory. Key areas of focus include spatial point, line, and surface graphic data, as well as spatial feature layer combinations and map classes. GIS layers are used as the foundation for managing each layer using a hierarchical tree structure. Structured tabular data, such as attribute data and text data, is managed by a relational database system. Communication technology is used to simultaneously store spatial and attribute data, enabling comprehensive query, data statistics, analysis and prediction, cartographic output, report generation, and data presentation.

[0076] In this embodiment, by configuring user and platform data call permissions and managing data service APIs, two-way real-time data sharing between terminals and data centers and GIS-based interactive functions are achieved, realizing data visualization and interactive query.

[0077] In some embodiments, the survey big data model construction method further includes:

[0078] S210: Obtain user information;

[0079] S220: Determine the minimum set of permissions required to complete the task based on the user information;

[0080] S230: Allocate access rights and operation rights to the target user based on the minimum permission set.

[0081] In the embodiment of the present application, by setting up fine-grained permission management, the system refines and allocates access rights and operation permissions for specific data for individual users or user groups. Strictly adhere to the principle of least privilege to ensure that each user or user group only obtains the minimum set of permissions required to complete their work, and does not have any permissions beyond the scope of their work. Through fine-grained permission management, potential security risks can be greatly reduced, and any unauthorized access or operation will be blocked due to permission restrictions. This helps to protect the security and integrity of data, while also complying with internal control and compliance requirements, creating a safe and reliable information environment for users.

[0082] In some embodiments, the hydrometeorological data architecture includes a measurement layer, a data entry and storage layer, and a computational analysis layer.

[0083] See Figure 2 A schematic diagram of an exemplary hydrometeorological data architecture is shown. This architecture includes a project database, data modeling / parsing / storage, hydrological data object management and attribute display, data calculation, and project reporting, enabling comprehensive management and in-depth application of hydrological data. The module can call calculation programs, retrieve required data from the database, and import results and parameters into the results report and database. Available analysis and calculation methods include: Pearson III and Extreme Value I frequency analysis, the inference formula method, Lin Ping I method, and the Second Institute of Railway method for small watershed rainstorm floods, 10% meteorological conditions, Manning's formula, high wind correction, ice cover calculation, water surface curve calculation, flood adjustment calculation, runoff regulation calculation, scour calculation, and air cooling calculation.

[0084] The hydrological and meteorological professional architecture is mainly divided into database (data entry and storage), analysis and calculation (analysis and calculation and results production), etc.

[0085] In some implementations, a professional measurement architecture is also constructed, including measurement engineering object management and data display, data modeling / parsing / storage, and measurement large-screen functions, to achieve effective management and visual display of measurement data.

[0086] It realizes the functions of uploading and downloading finished product data and searching engineering data, realizes a table of measurement data, displays workload statistics of line engineering, substation, power generation and new energy, project distribution range, project quantity and working hours, human resources and other information statistics, and uses the survey big data platform to process data and view data processing progress.

[0087] In some embodiments, the geotechnical data architecture includes an extension layer, a support layer, a storage layer, and an acquisition layer.

[0088] See Figure 3 The diagram of an exemplary rock mass data architecture is shown, which constructs a geotechnical professional architecture, including data modeling / parsing / storage, geotechnical professional data object management and attribute display, geotechnical data query, geotechnical data calculation and geotechnical large-screen functions, to achieve comprehensive management and professional application of geotechnical data.

[0089] The engineering project section (storage and coordinate empowerment) within the geotechnical professional architecture primarily uses engineering project information as the primary entry point, with geotechnical, geotechnical, and geophysical exploration as secondary entry points. The geotechnical section primarily includes relevant reports, drawings (DWG), raw databases (geoline, geoslope, plaxis, etc.), field data (photos, electronic records), and drill cores; the geotechnical section includes test results reports and test process photos; and the geophysical exploration section includes raw data, resistivity results, wave velocity results, and high-density results.

[0090] Supporting sections (storage, coordinate empowerment): primarily include document archiving for bidding, site selection, tower location processing, etc. In construction agent services, construction agent inspections should be documented, with online records (with real-time photo uploads) of construction sites, pile numbers, and draft signatures. This also allows for archiving of materials such as site quality inspections and on-site briefings by construction agents.

[0091] Learning section (storage, exploration): mainly includes built-in, QC, and scientific research materials from previous years, stored in the form of documents; and opens functions such as mandatory reference or standard keyword search, which can quickly extract current standards, glossaries, recommended measures and mandatory regulations in the manual.

[0092] Geological Information (storage, real-time updates, coordinate empowerment, partial confidentiality): Regional geological maps, mining rights information, and geological disaster information are classified as state secrets and must comply with relevant national confidentiality regulations and ensure data security. This section requires visualization, and some information can be directly exported to formats such as KML and Excel for easy direct use.

[0093] In some embodiments, to ensure that data is encrypted during transmission and storage, advanced encryption algorithms (such as AES-256) are used to protect data from unauthorized access. To enhance the security of data transmission, the system will deploy the TLS / SSL protocol to ensure that all data transmitted over the network is encrypted and encapsulated, effectively defending against man-in-the-middle attacks and data eavesdropping, whether communicating on internal or external networks. For critical data channels, IPSec or VPN tunneling technology will be used to establish an encrypted virtual private network for data transmission, further strengthening the security of the transport layer. In terms of data storage, the system adopts a multi-layer encryption strategy, encrypting not only the data files themselves but also the storage media (such as hard drives and cloud storage services). Through disk encryption technology (such as BitLocker or full disk encryption solutions), even if the physical device is stolen or lost, the data stored therein is difficult for unauthorized users to access. For sensitive data, such as geographic coordinates and personal identity information, dynamic desensitization technology is implemented to reduce the risk of data leakage without affecting analysis efficiency.

[0094] In some implementations, to ensure data and service security, the system implements network zoning and access control policies. Data and services with different security levels are isolated in separate network zones, preventing potential security threats such as data leaks and malicious attacks. This ensures that if a security threat is detected in one zone, other zones remain secure, preventing the risk from spreading. This helps more effectively monitor and control network access, ensuring that only authorized users can access data and services in specific zones. This significantly improves overall network security and protects the information security of survey data systems.

[0095] In some embodiments, data acquisition uses Datax as a data synchronization tool, and the parallel processing strategies adopted include: task sharding, dynamically adjusting the number of shards based on the data volume (e.g., 1GB of data is split into 10 shards, each shard is processed by an independent thread).

[0096] For example, in this application, data source adaptation enables multi-source data synchronization based on different data source types. The hydrological station API, based on HTTP Client encapsulation, supports JSON / XML format parsing and real-time water level and flow data collection. Report generation integrates Apache Tika for text extraction, combined with regular expressions, to generate key parameters (such as borehole depth) in unstructured reports. Geotechnical sensors use a developed serial port protocol reader that supports Modbus RTU / TCP for real-time stress and displacement data collection.

[0097] The initial migration uses a full table scan, combined with Datax's split mechanism for parallel reads (for example, splitting a 10GB hydrological data table into 100 shards for simultaneous reading). Incremental synchronization uses timestamp mode, capturing new and modified data using the "WHERE update_time > ?" condition (for example, a hydrological station updates data every 10 minutes). Log parsing mode also captures changes in real time (such as measurement equipment operation logs) using the database's Binlog or Kafka message queue.

[0098] In the embodiment of the present application, a unified storage center for multi-source heterogeneous data adopts hybrid storage technology. HDFS (Hadoop Distributed File System) is suitable for high-reliability storage of unstructured data (such as PDF reports and remote sensing images). Technical features include: a copy mechanism (3 copies by default) to ensure data redundancy; support for hot and cold data tiering (SSD / HDD hybrid storage); seamless integration with computing frameworks such as Spark / Flink. HBase (NoSQL database) is suitable for fast reading and writing of structured real-time data (such as real-time water levels and measurement coordinates of hydrological sensors). Technical features include: column-based storage, support for random queries of massive data; pre-partitioning and write-ahead logs (WAL) to improve write performance; integration of Phoenix to implement SQL interface. MinIO (object storage) is suitable for efficient storage and management of binary files (such as CAD drawings and 3D point cloud models). Technical features include: S3-compatible API, support for multi-terminal (Web, mobile) access; Erasure Code technology to reduce storage costs (storage space saved by 50%); version control and lifecycle management.

[0099] Leveraging the cache acceleration layer, Redis is used to cache frequently accessed geotechnical borehole data, achieving an 85% hit rate and reducing query latency from 100ms to 10ms. Cold data is automatically eliminated based on a least-repeated (LRU) strategy. Queries utilize HBase pre-aggregation, pre-calculating average values ​​for hydrological data by time window (e.g., daily or monthly), reducing query response time from 3s to 0.5s. MinIO multipart uploads support parallel upload of files over 1GB (default 10MB / block), improving transmission efficiency by 40%.

[0100] In the embodiment of the present application, the core hydrometeorological algorithms include: water balance calculation, a precipitation-evaporation-runoff model based on hydrological principles, for basin water resource assessment and reservoir scheduling; flood forecasting combined with an ARIMA-LSTM hybrid model of historical data for rainstorm and flood warning and emergency response; meteorological interpolation using the Kriging+IDW spatial interpolation algorithm to fill in data gaps in sparse areas of meteorological stations; engineering hydrological calculations using hydrological frequency analysis (P-III type distribution) and design flood calculations. At the same time, Optuna is used to automatically optimize hydrological model parameters (such as runoff coefficient and infiltration rate) for dynamic parameter tuning, enabling Redis to cache commonly used calculation results (such as the 10-year flood parameters for a certain basin), with a hit rate of 75%.

[0101] Algorithm task scheduling uses a priority queue, with real-time flood warning tasks taking precedence over historical data statistics tasks. Resource isolation uses Docker containers to deploy different algorithms to avoid mutual interference (for example, water quality models and flood models use independent resources).

[0102] In the embodiment of the present application, the survey data lineage tracking mechanism records the data source, processing path, responsible party and change history through full life cycle tracing from collection, processing to application. The data uses a unique identifier, which is a globally unique ID composed of project_id+data_type+timestamp+source_hash. Source information: records the collection device of the original data (such as the hydrological station number), geographic location (GIS coordinates), timestamp, etc. The processing process records the data conversion algorithm (such as the hydrological calculation model version), parameters (such as P-III distribution parameters), operator ID and timestamp.

[0103] The data lineage dynamic tracking strategy includes: real-time triggering, where data import, calculation, modification and other operations trigger the generation of lineage records; chain association, where the parent_id field is used to establish a parent-child relationship between data entities (such as original hydrological data → calculated water level prediction data); version control, where a new version of the lineage record is generated each time the data is changed, and historical versions are retained for auditing.

[0104] During the data collection phase, lineage injection is used to identify multi-source data. Sensor data is uniquely identified using the device ID and timestamp (e.g., WT001_20240510_0800). Report files use a hash algorithm (SHA-256) to generate a fingerprint for the file content and associate it with file metadata. Integration with existing systems adds a lineage information field to the JSON Schema to enforce integrity verification of data source identifiers. Upon completion of a computational task, the lineage API is automatically called to record the processing progress. The spatial relationship between data collection points and processing nodes is displayed on a map, supporting click-through tracing.

[0105] See also Figure 6 , Figure 6 This is a structural block diagram of a survey big data model construction device provided by this application. The survey big data model construction device 300 includes: a standardization module 310, a collection module 320, a construction module 330 and an interaction module 340, wherein:

[0106] The standardization module 310 is used to perform standardization processing on the acquired survey data to determine the data standard.

[0107] The acquisition module 320 is used to acquire multi-source heterogeneous survey data based on data standards.

[0108] The construction module 330 is used to classify and store multi-source heterogeneous survey data in a distributed manner according to a multi-level classification system, and to construct a survey big data model according to user project requirements.

[0109] The interaction module 340 is used to set coordinate empowerment based on the survey big data model to perform GIS data visualization interaction.

[0110] The device embodiment in this application may also include other modules, which specifically correspond to part of the content of the above method.

[0111] It should be noted that the device embodiments in this application correspond to the aforementioned method embodiments. The specific principles in the device embodiments can be found in the contents of the aforementioned method embodiments and will not be repeated here.

[0112] In several embodiments provided in this embodiment, the coupling between modules may be electrical, mechanical or other forms of coupling.

[0113] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing module, or each module may exist physically separately, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or software functional modules.

[0114] See also Figure 7 , Figure 7The present application provides a structural block diagram of an electronic device 200 that can execute the above-mentioned survey big data model construction method. The electronic device 200 can be a communication device, a mobile phone, a computer, a portable computer or other devices.

[0115] The electronic device 200 further includes a processor 202 and a memory 204 . The memory 204 stores a program that can execute the contents of the aforementioned embodiments, and the processor 202 can execute the program stored in the memory 204 .

[0116] Please refer to Figure 8 , Figure 8 The computer-readable storage medium 400 stores program code 410, which can be called by a processor to execute the method described in the above method embodiment.

[0117] The present application also provides a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the survey big data model construction method described in the various optional implementations above.

[0118] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for constructing a survey big data model, characterized in that: The method comprises: Standardize the acquired survey data to determine the data standards; Collecting multi-source heterogeneous survey data based on the data standard; Distributed classification and storage of the multi-source heterogeneous survey data according to a multi-level classification system, and construction of a survey big data model based on user project requirements; Coordinate empowerment is set based on the survey big data model to perform GIS data visualization interaction.

2. The method for constructing a survey big data model according to claim 1, characterized in that: The setting of coordinate enabling based on the survey big data model to perform GIS data visualization interaction includes: Determining the measurement point numbers and coordinate positions in the survey big data model; Associating the measuring point number and coordinate position with the engineering file and database for automatic coordinate recognition and attribute assignment; The data request input by the user into the survey big data model is visualized and interacted with using GIS layers.

3. The method for constructing a survey big data model according to claim 1, characterized in that: The distributed classification storage of the multi-source heterogeneous survey data according to a multi-level classification system includes: Establishing a distributed system, wherein the distributed system includes at least three nodes; The multi-source heterogeneous survey data are distributed and classified and stored according to the classification system corresponding to the hydrological and meteorological data architecture and the rock mass data architecture, wherein the hydrological and meteorological data architecture and the rock mass data architecture use Yarn queues to allocate computing resources for resource isolation, and use data lineage tracking for index association and storage.

4. The method for constructing a survey big data model according to claim 1, wherein: The survey big data model construction method further includes: Get user information; Determine the minimum set of permissions required to complete the work based on user information; Assign access rights and operation permissions to target users based on the minimum permission set.

5. The method for constructing a survey big data model according to claim 3, characterized in that: The construction of a survey big data model based on user project requirements includes: Determine a classification and coding system based on user project requirements, wherein the classification and coding system includes: project level, professional level and data type; A survey big data model is constructed after integrating the data verification engine based on the classification and coding system.

6. The method for constructing a survey big data model according to claim 3, characterized in that: The hydrological and meteorological data architecture includes a measurement layer, a data entry and storage layer, and a calculation and analysis layer.

7. The method for constructing a survey big data model according to claim 3, wherein: The geotechnical data architecture includes an extension layer, a support layer, a storage layer and a collection layer.

8. A survey big data model construction device, characterized in that: The device comprises: A standardization module is used to standardize the acquired survey data to determine the data standard; An acquisition module, configured to acquire multi-source heterogeneous survey data based on the data standard; A construction module is used to perform distributed classification storage on the multi-source heterogeneous survey data according to a multi-level classification system, and to construct a survey big data model according to user project requirements; The interactive module is used to set coordinate empowerment based on the survey big data model to perform GIS data visualization interaction.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, wherein the memory stores program code that can be run on the processor, and when the program code is executed by the processor, the survey big data model construction method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores program code, and the program code can be called by one or more processors to execute the survey big data model construction method according to any one of claims 1 to 7.

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