On-site entry software system for geological disaster emergency field survey data

Abstract

The invention discloses a geological disaster emergency field survey data on-site entry software system, and relates to the technical field of communication technology, the system comprises a data acquisition module, a data processing module, a data storage module and a data transmission module, through multi-source data automatic association, real-time verification and compression, local encryption storage and an intelligent increment synchronization mechanism, the real-time verification and compression of the data are realized, and the real-time verification and compression of the data are realized. Efficient integration and reliable management of geographic information, images and attribute data are achieved, and the data quality and operation continuity of field emergency investigation are improved. According to the invention, a special software system integrating data acquisition, processing, storage and transmission is constructed, so that the problem of data entry in geological disaster emergency field investigation is effectively solved; according to the system, a multi-source data automatic association technology is adopted, high integration of geographic information, image data and attribute texts is ensured, and data quality and subsequent analysis value are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of communication technology, in particular to a geological disaster emergency field investigation data field entry software system. BACKGROUND

[0002] In the field of geological disaster emergency response and field investigation, quickly and accurately collecting and transmitting field data is crucial for disaster assessment and rescue decision-making. Traditional geological disaster field investigation mainly relies on manual recording and paper media, resulting in low data collection efficiency, information loss and difficulty in real-time sharing. Among them, the geological disaster emergency field investigation data field entry software system as a key technology direction to improve the efficiency of field work aims to realize the digitization, structuring and instant processing of investigation data.

[0003] The existing field data entry system is usually developed based on general mobile devices, lacking depth optimization for geological disaster emergency scenarios. These systems often face problems such as unstable network signal, insufficient device battery life and data synchronization delay in complex field environments. The existing software architecture cannot effectively integrate multi-source heterogeneous field data such as geographic coordinates, disaster images and description text, resulting in weak data correlation and subsequent processing difficulties. The traditional entry interface design does not fully consider the particularity of field operations, and the user experience significantly decreases in strong light, rain or emergency situations, increasing the risk of data misrecording and omission. There is a prominent contradiction between the timeliness requirement of geological disaster emergency response and the functional limitations of existing software systems, and there is an urgent need to develop a field entry solution that can adapt to extreme field conditions, support offline efficient operation and ensure data integrity. SUMMARY

[0004] The purpose of the present application is to provide a geological disaster emergency field investigation data field entry software system to solve the problem of slow data field entry efficiency in the prior art.

[0005] To solve the above technical problems, the present application provides the following technical solutions: The geological disaster emergency field investigation data field entry software system comprises: a data collection module for collecting multi-source investigation data at the geological disaster site; a data processing module for structuring and correlation analysis of the collected multi-source investigation data; a data storage module for storing the structured data after processing on the local device; a data transmission module for uploading the locally stored structured data to a remote server when network conditions allow; The data collection module comprises a geographic information collection unit, an image collection unit and an attribute information collection unit; The data processing module comprises a data correlation unit, a data checking unit and a data compression unit. The data storage module adopts embedded database technology to construct a local data warehouse. The data transmission module comprises a network state monitoring unit and a data synchronization unit.

[0006] Compared with the prior art, the present application has the beneficial technical effects that: (1) The present application effectively solves the data entry problem in the geological disaster emergency field investigation by constructing a special software system integrating data acquisition, processing, storage and transmission; the system adopts multi-source data automatic correlation technology to ensure the high integration of geographic information, image data and attribute text, and significantly improves the data quality and subsequent analysis value.

[0007] (2) The combination design of the local data warehouse and the intelligent synchronization mechanism enables the system to maintain efficient and reliable data management capability in the unstable network signal field environment, ensuring the continuity of the investigation work and the integrity of the data.

[0008] (3) The user interface optimized according to the field operation characteristics greatly reduces the error rate and operation difficulty of data entry in emergency situations, improves the emergency response efficiency; the multi-thread architecture ensures the response speed and stability of the system when processing complex investigation tasks, and provides timely and accurate data support for geological disaster emergency decision-making. BRIEF DESCRIPTION OF DRAWINGS

[0009] Figure 1 is the system framework diagram of the geological disaster emergency field investigation data field entry software system provided by the embodiment of the present application; Figure 2 is the core principle framework schematic diagram of the geological disaster emergency field investigation data field entry software system provided by the embodiment of the present application. DETAILED DESCRIPTION

[0010] The features and exemplary embodiments of various aspects of the present application will be described in detail below, in order to make the purposes, technical solutions and advantages of the present application more clear and apparent, the present application will be further described in detail below in combination with the drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, but not to limit the present application. For those skilled in the art, the present application can be implemented without some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by showing examples of the present application.

[0011] It is to be understood that the terminology used herein such as first and second, and the like, is only used to distinguish one entity or action from another entity or action, without necessarily requiring or implying any such actual relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the presence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0012] In the embodiments of the present application, the same reference signs indicate the same components, and for the sake of brevity, detailed descriptions of the same components are omitted in different embodiments. It should be understood that the thickness, length, width, and other dimensions of various components in the embodiments of the present application shown in the drawings, and the overall thickness, length, width, and other dimensions of integrated devices are only exemplary and should not constitute any limitation on the present application; "a plurality of" appearing in the present application means two or more (including two).

[0013] Please refer to Figure 1 , Figure 1 is a system framework diagram of a geological disaster emergency field investigation data field entry software system provided by the embodiments of the present application, comprising: a data acquisition module, configured to acquire multi-source investigation data of a geological disaster site; a data processing module, configured to perform structured processing and correlation analysis on the acquired multi-source investigation data; a data storage module, configured to store the processed structured data on a local device; a data transmission module, configured to upload the locally stored structured data to a remote server when network conditions allow.

[0014] Embodiment 1 The present application relates to a geological disaster emergency field investigation data field entry software system. Please refer to the accompanying Figure 1 The system is composed of a data acquisition module, a data processing module, a data storage module, a data transmission module, and a user interface module, and each module cooperatively operates under a multi-thread architecture to ensure efficient and reliable data entry and management in a complex field environment.

[0015] The data acquisition module is responsible for collecting multi-source survey data of geological disaster sites, which includes a geographic information acquisition unit, an image acquisition unit, and an attribute information acquisition unit. The geographic information acquisition unit obtains accurate coordinates and elevation data of survey points by integrating a global positioning system receiver and an inertial measurement unit. The global positioning system receiver receives satellite signals in real time, analyzes latitude and longitude coordinates, and the accuracy can reach meters. The inertial measurement unit compensates for position drift when the global positioning system signal is lost through an accelerometer and a gyroscope, and combines a Kalman filtering algorithm to improve positioning stability. The image acquisition unit calls the mobile device camera to collect digital images and video materials of disaster sites, supports automatic focusing, exposure compensation, and anti-shake functions, and ensures that clear images can be obtained in low light or vibrating environments. The attribute information acquisition unit receives disaster description text and classification identifiers entered by survey personnel through a customized input interface. The interface uses a high-contrast color scheme and magnification control design, supports glove operation and wet hand touch, and reduces the probability of misoperation.

[0016] The data processing module performs structured processing and correlation analysis on the collected multi-source data, including a data correlation unit, a data verification unit, and a data compression unit. The data correlation unit automatically correlates data from different acquisition units into complete survey records based on a unified timestamp and spatial coordinates. The specific process is as follows: generate a globally unique identifier for each survey event, which is composed of device serial number, timestamp, and random array; attach the same timestamp and spatial coordinate index to all data items within the same survey event; establish a bidirectional reference relationship between geographic information data, image data, and attribute information data, such as associating image file paths to geographic coordinate points, and mapping text descriptions to image metadata; construct a data correlation model based on graph structure, including defining node types, defining edge types, creating nodes and edges using graph database API (such as Neo4j), applying Dijkstra algorithm to support shortest path retrieval, and implementing multi-dimensional topology query through SPARQL query language; the definition of node types includes geographic coordinate points, image files, and text description entities; the definition of edge types includes time correlation edges and spatial proximity edges.

[0017] The data verification unit verifies the completeness and logical consistency of the input data in real time through a preset rule base. The rule base includes completeness rules and logical consistency rules. The completeness rules require that each survey record must include at least one set of geographic coordinates, one field image, and one disaster description text. The logical consistency rules include the following aspects: elevation data matching requires calling a GIS library (such as GDAL) to calculate the DEM difference, and the error threshold is controlled within ±5 meters; disaster scale matching is based on the feature pixel scale extracted by OpenCV image processing, and is cross-verified with the field measurement value; the time sequence logic check uses a timestamp comparison algorithm to ensure that the disaster occurrence time is after the device startup and before the current system time. For example, landslide volume estimation requires cross-verification of image features and field measurement values; the disaster occurrence time must meet the time sequence logic, i.e., it cannot be earlier than the device startup time or later than the current system time. When the data verification unit detects a rule violation, it immediately sends a re-collection instruction to the collection module and records the verification error log to the local storage module. The log includes error type, timestamp, and associated data identifier.

[0018] The data compression unit uses a differentiated compression strategy to reduce storage occupancy. For image data, a lossy compression algorithm based on discrete cosine transform is applied, which divides the image into 8x8 pixel blocks, and each block is transformed, quantized, and entropy encoded. The compression ratio is controlled within 5 to 8 times, which is achieved by adjusting the quantization table of discrete cosine transform (such as the standard JPEG quantization matrix) and the character frequency threshold of Huffman coding. The compression parameters are set as follows: DCT block size 8x8 pixels, quantization step 10-15, Huffman tree depth limit 5 layers. For text data, a lossless compression algorithm based on Huffman coding is applied, which counts the character frequency to construct the optimal prefix code, and the compression ratio reaches more than 2 times. For geographic coordinate data, differential encoding technology is used to store the difference of consecutive coordinates as relative offset, reducing the data volume. The compressed data is attached with a metadata header, recording the compression algorithm type, original size, and checksum.

[0019] The data storage module adopts embedded database technology to build a local data warehouse. The warehouse adopts a hybrid storage architecture: structured data is stored in relational database tables, and the table structure includes survey record tables, geographic information tables, image index tables, and user operation log tables; unstructured data is stored in the form of binary large objects in the file system, organized by time and event classification directory. All data is encrypted by the Advanced Encryption Standard algorithm before storage, and the encryption key is generated by the device hardware identifier and user identity information through a hash function, with a key length of 256 bits. The database supports transaction processing to ensure data consistency through rollback mechanisms in the event of write interruptions.

[0020] The data transmission module is responsible for uploading local data to the remote server when network conditions permit, including a network status monitoring unit and a data synchronization unit. The network status monitoring unit continuously detects the signal strength and data transmission rate of 2G, 3G, 4G, and 5G networks. Signal strength is measured by the Received Signal Strength Indicator, with units of decibels-milliwatts; data transmission rate is evaluated by sending data packets to a test server and calculating round-trip time. Network status determination relies on the following preset thresholds and processing flow: signal strength must be better than -85 dBm (measured via RSSI sensor), and data transmission rate must be higher than 50 kbps (obtained by calculating round-trip time by pinging the test server). In implementation, the system performs periodic sampling of each parameter once per second and uses moving average filtering to smooth the data; the threshold itself will be dynamically adjusted based on environmental history. For any network type, only when both its signal strength and data transmission rate exceed the established threshold can it be determined to have transmissible conditions.

[0021] The data synchronization unit uses an incremental synchronization mechanism. This mechanism compares the data version numbers of the local data warehouse and the remote server, and only uploads new or modified survey records. The version number is generated based on the timestamp and operation sequence, and is incremented each time data is updated. The synchronization process includes: establishing a Transport Layer Security protocol connection to the remote server; sending a synchronization request containing the last synchronization timestamp and device identifier; receiving the server's return of incremental data list; uploading local new records and updating synchronization status markers. After each synchronization operation, the data synchronization unit stores the last synchronization timestamp and synchronization result log locally.

[0022] The user interface module includes a data entry interface and a state display interface. The data entry interface adopts a grid layout, and the size of the key input control is enlarged to 12 mm x 12 mm at least, and the color contrast is not less than 7:1. The state display interface displays a data collection progress bar, a storage remaining space percentage and a network connection state icon in real time, wherein the network state is distinguished by color: green indicates good connection, yellow indicates signal fluctuation, and red indicates disconnection. The user interface module integrates a voice prompt function, and audio feedback is played through a text-to-speech engine when a key operation is completed or the system is abnormal, for example, when data verification fails, a prompt for re-collection is given.

[0023] The system runs on a multi-thread architecture. The main thread is responsible for user interface response and event distribution; the data collection thread handles sensor data input in parallel, with a sampling frequency of 10 times per second; the data processing thread performs data association and compression operations in the background, with a high priority setting to ensure real-time performance; the data transmission thread independently manages network communication tasks, and processes data upload through asynchronous sockets. The threads exchange data through message queues, and the queue is implemented as a circular buffer with a capacity of 1000 messages, supporting mutual exclusion and condition variable synchronization mechanisms.

[0024] Please refer to Figure 2 , Figure 2 is the core principle framework diagram of the geological disaster emergency field investigation data field entry software system provided by the embodiment of the present application, comprising: The core principle framework of the multi-source data automatic association and verification processing further refines the data flow process. The coordinate data output by the geographic information collection unit and the media files generated by the image collection unit are first sent to the data association unit, matched into temporary records through spatial indexing and time alignment algorithms; the text data input by the attribute information collection unit is associated to the temporary records after syntax analysis, forming complete investigation records. The data verification unit performs rule verification on the records, and if it passes, the compression unit is triggered for processing, otherwise an error code is returned to the collection unit. The compressed records are written to the local data warehouse, and the transaction log is updated. The network status monitoring unit periodically triggers the data synchronization unit, and when the transmission conditions are met, the incremental upload process is started.

[0025] The implementation of the system fully considers the particularity of the field environment. The sensor unit of the data collection module has dustproof and waterproof level, which meets the international protection level standard IP68; the user interface module automatically switches to high brightness mode in strong light environment to ensure visibility; the data storage module supports storage card expansion, with a maximum support of 2 terabytes capacity; the data transmission module automatically caches data to be uploaded when the network is interrupted, and preferentially transmits high-priority records after connection recovery. Through the above design, the system realizes the efficiency, reliability and integrity of data entry in the geological disaster emergency scene.

[0026] Embodiment 2 This embodiment is based on embodiment 1, further optimizing the strategy of data compression unit and network status monitoring unit to adapt to extreme network environment and large-scale data scenarios.

[0027] The data compression unit introduces a hierarchical compression mechanism for image data. First, the original image is down-sampled to generate a preview map, which is compressed in Joint Photographic Experts Group format with a compression ratio of 10 times. The original map uses a compression algorithm based on discrete wavelet transform, which preserves detailed information through multi-resolution analysis, with a compression ratio adjustment of 3 to 5 times. Text data compression adds a dictionary encoding stage based on Huffman coding, mapping commonly used geological terms to short codes to further improve compression efficiency. Linear interpolation algorithm is used for geographic coordinate data compression, which sparsifies the trajectory points and reduces the storage by 50% while maintaining accuracy.

[0028] The network status monitoring unit is enhanced to a multi-path evaluation mode. It simultaneously monitors the availability of mobile networks, wireless local area networks, and satellite communication links. The mobile network evaluation adds signal-to-noise ratio and bit error rate indicators, with a signal-to-noise ratio threshold of 20 decibels and a bit error rate threshold of 0.001. The wireless local area network scans available hotspots and tests connection delays. The satellite communication link detects the signal lock state through a dedicated modem. When there is at least one path that meets the transmission conditions in the multi-path, the system automatically selects the optimal path, with the priority order being: wireless local area network, 5G network, 4G network, satellite link. The data synchronization unit adapts to multi-path transmission, supporting breakpoint resume and protocol adaptive switching, ensuring that data synchronization can still be completed when the link fluctuates.

[0029] The user interface module adds offline map integration function. Map data is preloaded into the local storage module, supporting zooming, panning, and marking operations. Investigators can view topographic maps, geological structure layers, and historical disaster points in offline environments, and mark collected data on the map in real time. The map rendering engine uses tile caching technology, keeping recently used tiles in memory to speed up display response.

[0030] The data storage module introduces data lifecycle management strategy. According to the data creation time and importance classification storage, high-frequency access data is retained in the embedded database, low-frequency data is archived to the compressed file package, and overdue data is automatically cleaned up. The storage status is visualized through the user interface module, including the used space ratio, data classification statistics, and cleaning suggestions.

[0031] The system adds a monitoring thread in a multi-thread architecture to collect real-time running indicators of each module, including central processing unit usage, memory occupancy, and thread blocking status. The indicator data is sampled every 5 seconds and stored as a performance log for system optimization and fault diagnosis. When an abnormal state is detected, the monitoring thread triggers a voice prompt of the user interface module and automatically degrades non-core functions to ensure system stability.

[0032] Through the above improvements, the embodiment further improves data compression efficiency, network adaptability, and system maintainability in extreme environments, and is suitable for long-term field investigation tasks and large-scale disaster emergency response scenarios.

[0033] Embodiment 3 The embodiment is aimed at ultra-large-scale geological disaster investigation projects, and extends the distributed processing and collaborative work capability of the system.

[0034] The data acquisition module supports multi-device collaborative data acquisition. Through wireless ad hoc network technology, multiple mobile devices are formed into an acquisition cluster. The devices in the cluster automatically elect a master node, which is responsible for assigning acquisition tasks, aggregating data, and synchronizing time. The data collected by the slave nodes is transmitted to the master node in real time, and the data processing module of the master node performs association and verification. The data synchronization between clusters uses a conflict detection and resolution mechanism. When multiple devices collect data for the same area, the record with the earliest time or the highest accuracy is used as the reference.

[0035] The data processing module introduces a distributed computing framework. The data association unit divides large-scale investigation records into multiple subsets, which are processed in parallel through the map-reduce model. In the mapping stage, an association graph is independently constructed for each subset, and in the reduction stage, the subgraphs are combined and the global association relationship is resolved. The data verification unit deploys a rule base to the edge computing nodes, and each node is responsible for local data verification, and the results are aggregated to the master node to generate a global verification report.

[0036] The data storage module is upgraded to a distributed database architecture. The local data warehouse uses a multi-copy mechanism, with the same data backed up on at least three devices, and version numbers are used to coordinate consistency. The data transmission module supports point-to-point synchronization, with devices directly exchanging incremental data, reducing dependence on the central server. The synchronization protocol is based on blockchain technology, with each record attached with a digital signature and a hash value to ensure data integrity and traceability of the source.

[0037] The user interface module adds a collaborative work view. Investigators can view the locations, acquisition progress, and data quality scores of other members in real time. The view supports filtering and sorting functions, such as arranging the task list by disaster type, urgency, or data integrity. The voice prompt function is expanded to a group broadcast mode, and the master node can send unified instructions or warning information to all members.

[0038] The system introduces a dynamic load balancing mechanism in a multi-thread architecture. A monitoring thread evaluates the length of each thread queue and processing delay in real time, and automatically adjusts the thread priority or allocates additional computing resources when a bottleneck is detected. For computationally intensive tasks such as image compression and spatial analysis, the system can call a graphics processor for accelerated processing.

[0039] Through the above design, the embodiment realizes efficient data acquisition, processing and synchronization in a multi-device and large-scale scene, and is suitable for regional-level geological disaster monitoring networks and cross-department emergency cooperation tasks.

[0040] The above is only a preferred specific embodiment of the present application, not to limit the protection scope of the present application, therefore: all equivalent changes made according to the structure, shape, principle of the present application should be covered within the protection scope of the present application.

Claims

1. A software system for on-site data entry in geological disaster emergency field investigations, characterized in that: include: The data acquisition module is used to collect multi-source survey data from geological disaster sites. The data processing module is used to perform structured processing and correlation analysis on the collected multi-source survey data; The data storage module is used to store processed structured data on a local device; The data transmission module is used to upload locally stored structured data to a remote server when network conditions permit; The data acquisition module includes a geographic information acquisition unit, an image acquisition unit, and an attribute information acquisition unit; The data processing module includes a data association unit, a data verification unit, and a data compression unit; The data storage module uses embedded database technology to build a local data warehouse; The data transmission module includes a network status monitoring unit and a data synchronization unit.

2. The geological disaster emergency field investigation data on-site entry software system as described in claim 1, characterized in that: The geographic information acquisition unit obtains the precise coordinates and elevation data of the survey points by integrating a global positioning system receiver and an inertial measurement unit; The image acquisition unit captures digital images and video data of the disaster site by calling the camera of a mobile device; The attribute information collection unit receives disaster description text and classification labels entered by investigators through a customized input interface.

3. The geological disaster emergency field investigation data on-site entry software system as described in claim 1, characterized in that: The data association unit automatically associates data from different collection units into a complete survey record based on a unified timestamp and spatial coordinates. The specific workflow of the data association unit includes: Generate a globally unique identifier for each survey event; attach the same timestamp and spatial coordinate index to all data items within the same survey event; Establish bidirectional referencing relationships between geographic information data, image data, and attribute information data; A graph-based data association model is constructed, with each survey event, geographic information data, image data, and attribute information data as a node, and each node is assigned a globally unique identifier, timestamp, and spatial coordinate index attribute. By establishing edges between nodes through bidirectional references, an interconnected data graph is formed. By leveraging the traversal and query mechanisms of graph databases, multi-dimensional data retrieval based on time, space, and content can be achieved through node attributes and edge relationships; The globally unique identifier is composed of the device serial number, timestamp, and random number; The bidirectional referencing relationship includes associating image file paths with geographic coordinate points, while mapping text descriptions to image metadata; In the graph-based data association model, nodes represent data entities and edges represent association relationships, supporting multi-dimensional retrieval and topological queries.

4. The geological disaster emergency field investigation data on-site entry software system as described in claim 3, characterized in that: The data verification unit verifies the integrity and logical consistency of the entered data in real time through a preset rule base. The preset rule base of the data verification unit includes integrity rules and logical consistency rules. The integrity rules require that each survey record must contain at least one set of geographic coordinates, one on-site image, and one disaster description text. The logical consistency rules require that the elevation data match the topographic map data, the disaster scale data match the image evidence, and the occurrence time data conform to the time series logic. When the data verification unit detects a rule violation, it immediately sends a re-acquisition instruction to the acquisition module and records the verification error log.

5. The geological disaster emergency field investigation data on-site entry software system as described in claim 4, characterized in that: The data compression unit uses a lossless compression algorithm to compress image and text data to save storage space, and the data compression unit adopts a differentiated compression strategy for different types of data. For image data, a lossy compression algorithm based on discrete cosine transform is applied, with the compression ratio controlled within the range of 5 to 8 times; for text data, a lossless compression algorithm based on Huffman coding is applied, with the compression ratio reaching more than 2 times; and for geographic coordinate data, differential coding technology is used to reduce the amount of data stored.

6. The geological disaster emergency field investigation data on-site entry software system as described in claim 1, characterized in that: The data warehouse supports transaction processing and data encryption functions. The local data warehouse adopts a hybrid storage architecture. Structured data is stored in relational database tables, and unstructured data is stored in the file system in the form of binary large objects. All data is encrypted by advanced encryption standard algorithms before storage, and the encryption key is generated by the device hardware identifier and user identity information.

7. The geological disaster emergency field investigation data on-site entry software system as described in claim 1, characterized in that: The network status monitoring unit continuously detects the current network connection quality and assesses available bandwidth. The network status monitoring unit assesses network connection quality by simultaneously monitoring the signal strength and data transmission rate of 2G, 3G, 4G, and 5G networks. The preset thresholds include a signal strength threshold set to -85 dBmW and a data transmission rate threshold set to 50 kilobits per second. When the signal strength and data transmission rate of any network type simultaneously exceed the corresponding threshold, the network status is determined to meet the transmission conditions.

8. The geological disaster emergency field investigation data on-site entry software system as described in claim 7, characterized in that: The data synchronization unit automatically initiates the incremental data upload process when the network status reaches a preset threshold. The data synchronization unit adopts an incremental synchronization mechanism. This mechanism compares the data version numbers of the local data warehouse and the remote server, and only uploads newly added or modified survey records. After each synchronization operation, the data synchronization unit updates the synchronization status flag and the last synchronization timestamp stored locally.

9. The geological disaster emergency field investigation data on-site entry software system as described in claim 1, characterized in that: The system also includes a user interface module; the user interface module includes a data entry interface and a status display interface; the data entry interface adopts a high-contrast color scheme and a magnifying control design, and supports operation with gloves and wet hands; the status display interface displays the data acquisition progress, remaining storage space and network connection status in real time; the user interface module also integrates a voice prompt function, providing audio feedback when key operations are completed or when the system malfunctions.

10. The geological disaster emergency field investigation data on-site entry software system as described in claim 1, characterized in that: The system runs on a multi-threaded architecture; the main thread is responsible for user interface response, the data acquisition thread processes multiple sensor data inputs in parallel, the data processing thread performs data association and compression operations in the background, and the data transmission thread independently manages network communication tasks; the threads exchange data and synchronize states with each other through message queues.

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