Emergency disposal data transmission analysis method based on cloud platform

Abstract

The invention relates to the technical field of cloud platform data processing, in particular to an emergency disposal data transmission analysis method based on a cloud platform, which comprises the following steps of: constructing a seven-dimensional data global coverage acquisition system, implementing differentiated storage management of structured and unstructured data, and storing the structured and unstructured data; extracting core features including the data volume, the generation time and the data source, and establishing a dynamic tag library; a three-level communication architecture of a data source layer, a transfer node layer and a cloud platform layer is designed, a space-air-ground integrated heterogeneous transmission network is fused, a transfer node is selected based on an analytic hierarchy process, and routing is dynamically optimized by using a Dijkstra algorithm; according to a data type and a security level, matching strategies such as fragment transmission, user datagram protocol priority or block chain encryption and the like; establishing an integrity, timeliness and consistency three-layer verification system and an abnormal data closed-loop processing flow, and deploying a remote dual-active data center, three-copy redundant storage and a zero-trust security protection mechanism.

Description

Technical Field

[0001] This invention relates to the field of cloud platform data processing technology, and more specifically to a method for emergency response data transmission and analysis based on a cloud platform. Background Technology

[0002] With the deepening of the construction of digital emergency management systems, improving emergency response capabilities based on information technology has become a key path to ensure public safety. In complex emergency scenarios such as earthquakes, floods, chemical leaks, and public health emergencies, the rapid acquisition, efficient transmission, and accurate analysis of multi-source heterogeneous data are prerequisites for scientific decision-making and optimal resource allocation. However, traditional emergency data processing systems have long relied on distributed collection, localized storage, and static analysis models, which are insufficient to meet the demands of modern emergency response, which require high timeliness, high collaboration, and high precision. Especially under extreme conditions of explosive data growth and highly uncertain communication environments, their systemic bottlenecks are becoming increasingly prominent.

[0003] The core challenge in emergency response data transmission and analysis lies in constructing an intelligent closed-loop mechanism covering the entire "collection-transmission-analysis" chain. This mechanism needs to support seven types of heterogeneous data sources, including geographic information, sensor monitoring, on-site imagery, and command instructions, achieving unified access and characteristic representation of structured and unstructured data, and relying on a resilient network architecture to complete cross-domain, multi-protocol, and highly reliable dynamic transmission. However, existing technologies still have significant shortcomings in this area.

[0004] Existing technologies generally lack the ability to standardize and perceive the characteristics of multi-source emergency data, resulting in high redundancy and semantic ambiguity in raw data, making it difficult to support subsequent intelligent analysis. Simultaneously, transmission links often employ fixed routing and single protocols, failing to dynamically adjust transmission strategies based on data type, priority, and network status. This makes critical information easily lost or delayed when communication is interrupted or bandwidth is limited in disaster areas. Furthermore, data analysis often relies on offline batch processing or simple rule engines, which are unable to deeply mine massive amounts of real-time data and lack multi-dimensional cross-validation mechanisms for analysis results, leading to insufficient reliability of decision-making. These problems are particularly prominent in large-scale emergency events involving cross-regional and multi-departmental collaboration, severely restricting response efficiency and accuracy. There is an urgent need for an integrated data transmission and analysis method based on a cloud platform, possessing feature-driven, elastic transmission, and intelligent verification capabilities. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an emergency response data transmission and analysis method based on a cloud platform. By constructing a seven-dimensional data full-domain coverage acquisition system and a dynamic feature tag library, it achieves standardized feature perception and unified management of multi-source emergency data, significantly reducing data redundancy and improving data semantic clarity.

[0006] To achieve the above objectives, this invention provides a cloud platform-based emergency response data transmission and analysis method, comprising: Step 1: Construct a multi-source data classification and collection system to form a seven-dimensional data full-domain coverage collection system covering basic support data, real-time monitoring data, on-site handling data, disaster and demand data, command and dispatch data, cross-departmental collaboration data, and multimedia data. Collect data through multiple channels such as IoT terminals, application programming interfaces, and manual input to form a complete set of structured and unstructured data. Step 2: Implement a differentiated data management strategy. For structured data, use a relational database for storage and structured query language for efficient retrieval. For unstructured data, use a distributed file system or non-relational database for storage and combine metadata tags for fast indexing. Step 3: Perform key data feature extraction, define quantitative feature dimensions, core features include data volume, data generation time and data source, auxiliary attributes cover data format, update frequency and security level, use extraction, transformation and loading tools to clean the raw data and remove duplicate and erroneous records, and build a dynamic feature label library. Step 4: Design the core architecture of the cloud platform, adopting a three-level communication system consisting of a data source layer, a relay node layer, and a cloud platform layer. The data source layer deploys edge computing devices such as industrial gateways and 5G mobile communication technology terminals to achieve data noise reduction, protocol conversion, and lightweight processing. The relay node layer adopts a distributed server cluster to support multi-protocol conversion and traffic load balancing, and is configured with dual power supplies and dual links to achieve automatic fault switching. The cloud platform layer integrates elastic storage, elastic computing, and intelligent analysis modules based on a microservice architecture and has cross-regional disaster recovery capabilities. Step 5: Construct a heterogeneous transmission network, integrating multiple network technologies to form an integrated air-space-ground transmission network. Wide-area transmission adopts 5G mobile communication technology private network and satellite communication, while local interconnection uses mesh self-organizing network technology to solve the last-mile interconnection problem in disaster areas. Step 6: Deploy and optimize relay node links. Based on disaster risk maps and historical data, use the hierarchical analysis method to select node locations. Comprehensively evaluate node capacity, transmission delay and resilience. Configure an intelligent monitoring system to achieve 24 / 7 status monitoring. Develop a routing optimization model based on Dijkstra's algorithm to collect link load data in real time and dynamically select the optimal transmission path based on data priority. Step 7: Perform data packaging and feature tag association, design a standardized packaging protocol that conforms to emergency industry standards, encapsulate structured data using JavaScript object representation format, generate 256-bit hash values ​​using a secure hash algorithm for unstructured data and attach metadata tags to form a composite data packet containing the data body, feature tags, and verification information. Step 8: Establish a tag and policy mapping mechanism. Multimedia data with a data volume greater than 1 gigabyte and high real-time requirements should be transmitted in segments. Sensor data updated at the second level should be transmitted with priority using the User Datagram Protocol. Top-secret data should be encrypted using Transport Layer Security 1.3 and stored on the blockchain. Step 9: Implement transmission verification and checking, and build a three-layer verification system of integrity, timeliness and consistency. Use message digest algorithm 5 or secure hash algorithm 256-bit hash algorithm to verify data integrity, compare the receiving time with the generation time to verify timeliness, and ensure data consistency through cross-data source cross-comparison. Step 10: Design a closed-loop processing flow for abnormal data. When the link fails, the backup node will be automatically switched to retransmit the data. If the format is incorrect, a special tool will be called to convert and repair it. If the tag is missing, the data source will be re-collected. The entire process is logged to provide data support for transmission optimization. Step 11: Deploy redundant backup and disaster recovery measures, configure a geographically distributed active-active data center, adopt a three-replica redundant storage, scheduled snapshot and incremental backup mechanism to ensure that the recovery time target is less than or equal to 15 minutes and the recovery point target is less than or equal to 1 minute, and configure hot-swappable power supplies and network cards in the relay nodes to support online replacement of faulty components. Step 12: Build a security protection system that follows a zero-trust architecture, adopts two-factor authentication and biometric technology, implements access control based on the principle of least privilege, uses Transport Layer Security Protocol version 1.3 for encryption at the transport layer, uses Advanced Encryption Standard 256-bit encryption at the storage layer, and deploys a machine learning-based abnormal traffic detection system.

[0007] Preferably, in the seven-dimensional data full-domain coverage acquisition system, basic support data includes geographic information data and material reserve data; real-time monitoring data includes sensor data and environmental parameter data; on-site handling data includes rescue progress data and equipment status data; disaster and demand data includes casualty data and material shortage data; command and dispatch data includes instruction log data and resource allocation data; cross-departmental collaboration data includes linkage agreement data and list of rights and responsibilities data; and multimedia data includes on-site video data and audio data.

[0008] Preferably, during the construction of the dynamic feature tag library, real-time monitoring data generates a combined tag of sensor identifier, timestamp, and geographic location, while multimedia data generates metadata tags of shooting time, event type, and resolution, supporting custom tag expansion according to specific emergency scenario requirements.

[0009] Preferably, when the edge computing device at the data source headend layer performs protocol conversion, it supports converting the Modbus protocol to the message queue telemetry transmission protocol, thereby achieving seamless integration between industrial equipment data and cloud platform communication protocols.

[0010] Preferably, the distributed server cluster of the relay node layer supports bidirectional protocol conversion between Message Queuing Telemetry Transport Protocol and Hypertext Transfer Protocol, and the traffic load balancing adopts a weighted round-robin algorithm to dynamically allocate data transmission tasks according to the real-time load of the nodes.

[0011] Preferably, in the integrated air-space-ground transmission network, the fifth-generation mobile communication technology private network provides a peak rate of greater than or equal to 1 gigabit per second and an end-to-end latency of less than 10 milliseconds, and the satellite communication coverage includes remote areas and disaster-damaged areas that cannot be covered by the terrestrial network.

[0012] Preferably, the mesh self-organizing network technology adopts a dynamic routing protocol, supports automatic node discovery and multi-hop relay, and can still maintain local area communication even in the event of infrastructure damage, with a single-hop transmission distance of up to 1000 meters.

[0013] Preferably, the analytic hierarchy process (AHP) location model includes three core evaluation indicators: node capacity weight 0.4, transmission delay weight 0.35, and resilience weight 0.25. The reliability of the evaluation results is ensured by a judgment matrix with a consistency ratio of less than 0.1.

[0014] Preferably, the routing optimization model collects link load data every 5 seconds, prioritizes transmission paths with latency of less than 50 milliseconds for real-time monitoring data, and allocates transmission channels with bandwidth greater than 100 megabits per second for multimedia data.

[0015] Preferably, the standardized encapsulation protocol specifies that data volume characteristics are identified in kilobytes, megabytes, or gigabytes, the data generation time uses a Unix timestamp format accurate to milliseconds, and the data source characteristics are encoded using a globally unique identifier.

[0016] Preferably, the segmented transmission mechanism divides multimedia data larger than 1 gigabyte into uniformly sized data blocks, each ranging from 1 megabyte to 10 megabytes in size, and reassembles the data at the cloud platform layer after transmission is completed.

[0017] Preferably, the User Datagram Protocol (UDP) prioritizes the transmission of sensor data with a data update frequency higher than once per second, and the transmission process allows for a packet loss rate of less than or equal to 5% to ensure real-time data transmission.

[0018] Preferably, the Transport Layer Security Protocol version 1.3 encrypted channel uses a forward secrecy algorithm suite, and top-secret data is simultaneously transmitted and stored using blockchain technology. The stored information includes the data hash value, timestamp, and sender's digital signature.

[0019] Preferably, in the three-layer verification system, integrity verification adopts a dual mechanism of cyclic redundancy check and hash check, timeliness verification requires that the time difference between data generation and reception is less than a preset threshold, and consistency verification is achieved by comparing the logical relationship of multi-source data of the same event.

[0020] Preferably, in the abnormal data closed-loop processing flow, the link failure switching response time is less than 200 milliseconds, and the format error conversion supports mutual conversion of multiple data formats, including Extensible Markup Language, JavaScript Object Notation, and comma-separated value files.

[0021] Preferably, the full-process log recording adopts a structured log format, and the recording fields include operation time, operation type, data identifier, processing result and exception information, and the log data is retained for no less than 3 years.

[0022] Preferably, the off-site active-active data center uses synchronous replication technology to ensure data consistency, and the distance between the two data centers is greater than 100 kilometers, which enables it to withstand regional natural disasters.

[0023] Preferably, the triple-replica redundant storage uses erasure coding technology to store data in blocks on different servers in different racks, so that a single point of failure does not affect data availability and integrity.

[0024] Preferably, the hot-swappable power supply adopts a redundant power module design. When a single power module fails, the system automatically switches to the backup module, and the switching process does not affect the normal operation of the node.

[0025] Preferably, the two-factor authentication combines dynamic password and fingerprint recognition technology, and the access control policy is based on a role-based access control model, whereby each role can only access data and functions within its scope of responsibility.

[0026] Preferably, the machine learning abnormal traffic detection system adopts the isolated forest algorithm to analyze network traffic patterns in real time. When abnormal behavior is detected, it triggers a security alarm within 100 milliseconds and automatically blocks suspicious connections.

[0027] Compared with the closest existing technology, the present invention has the following advantages: By constructing a seven-dimensional data full-domain coverage acquisition system and a dynamic feature tag library, standardized feature perception and unified management of multi-source emergency data were achieved, significantly reducing data redundancy and improving data semantic clarity. A three-tier communication system and an integrated air-ground-space transmission network, combined with intelligent algorithm-based routing optimization and dynamic strategy matching, ensured high reliability and timeliness of data transmission in complex communication environments. A complete three-layer verification system and a closed-loop abnormal data processing flow were established, providing a multi-dimensional data quality assurance mechanism. Comprehensive redundant backup, disaster recovery, and security protection measures were deployed to ensure the system's continuous stable operation and data security under extreme conditions. Ultimately, standardized data acquisition, intelligent transmission, and precise analysis were achieved throughout the entire emergency response process, significantly improving the efficiency of emergency response and the reliability of decision-making. Attached Figure Description

[0028] Figure 1 This is a flowchart of an emergency response data transmission and analysis method based on a cloud platform provided by the present invention. Detailed Implementation

[0029] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] Example 1: This invention provides a cloud-based emergency response data transmission and analysis method, such as... Figure 1 As shown, it includes: Step (1) Construct a multi-source data classification and collection system to form a seven-dimensional data full-domain coverage collection system covering basic support data, real-time monitoring data, on-site disposal data, disaster and demand data, command and dispatch data, cross-departmental collaboration data, and multimedia data. Data is collected through multiple channels, including IoT terminals, application programming interfaces (APIs), and manual input, forming a complete set of structured and unstructured data. Specifically, in the seven-dimensional data full-domain coverage collection system, basic support data includes geographic information data and material reserve data; real-time monitoring data includes sensor data and environmental parameter data; on-site disposal data includes rescue progress data and equipment status data; disaster and demand data includes casualty data and material shortage data; command and dispatch data includes instruction log data and resource allocation data; cross-departmental collaboration data includes linkage agreement data and responsibility list data; and multimedia data includes on-site video data and audio data. This data acquisition system utilizes various IoT terminals deployed at disaster sites, emergency command centers, material warehouses, mobile rescue units, and public infrastructure. These terminals include temperature and humidity sensors, gas concentration detectors, GPS positioning modules, high-definition cameras, drone payloads, handheld terminals, and vehicle-mounted communication units, enabling comprehensive perception of the physical world's condition. Simultaneously, by using open and standardized application programming interfaces (APIs), it connects to real-time data streams from external professional systems such as meteorology, transportation, power, water conservancy, and healthcare. This is supplemented by manual data entry by emergency management personnel through dedicated mobile applications or web interfaces, ensuring that critical information that cannot be automatically obtained is supplemented. All acquisition channels are equipped with data format pre-validation mechanisms to reject data that does not conform to predefined patterns from flowing into subsequent processing stages, thereby controlling data quality at the source.

[0032] In the above method, step (2) implements a data differentiation management strategy. Structured data is stored in a relational database and retrieved efficiently using a structured query language. Unstructured data is stored in a distributed file system or a non-relational database and indexed quickly using metadata tags. Specifically, structured data, such as personnel casualty statistics tables, material allocation lists, and instruction execution logs, are imported into a relational database instance built on PostgreSQL or MySQL. The table structure strictly follows the emergency industry data model standard, and the primary keys, foreign keys, and indexes are optimized to support complex relational queries and aggregation operations. Unstructured data, such as on-site video streams, high-resolution satellite images, 3D point cloud scan results, and voice call recordings, are stored in a Hadoop distributed file system or Ceph object storage cluster. Each file is associated with an independent metadata record, which contains key attributes such as file type, creation time, source device identifier, geographic location coordinates, and event association ID. This record is synchronously written to non-relational databases such as Elasticsearch or MongoDB to support millisecond-level retrieval based on keywords, time range, spatial region, and other dimensions. The data storage strategy is dynamically adjusted based on the data's popularity. Hot data resides on high-performance solid-state drive arrays, warm data is migrated to large-capacity hard disk drives, and cold data is archived to tape libraries or cloud object storage, achieving the optimal balance between cost and performance.

[0033] In the above method, step (3) involves extracting key data features, defining quantitative feature dimensions, and including core features such as data volume, data generation time, and data source. Auxiliary attributes cover data format, update frequency, and security level. The original data is cleaned using an extraction, transformation, and loading tool, and duplicate or erroneous records are removed. A dynamic feature tag library is then constructed. Specifically, the data volume feature is precisely identified in kilobytes, megabytes, or gigabytes. The data generation time uses a Unix timestamp format accurate to milliseconds. The data source feature uses a globally unique identifier to ensure that each data unit has a unique identity globally. Among the auxiliary attributes, the data format field explicitly records the file extension or MIME type, the update frequency field is quantified as the number of updates per second, per minute, or per hour, and the security level field is divided into five levels: public, internal, secret, confidential, and top secret, according to the national information security level protection standard. The extraction, transformation, and loading tool first performs syntax and semantic verification on the original data, filtering out records with format errors, logical contradictions, or exceeding reasonable limits. Secondly, it identifies and merges duplicates by comparing historical data fingerprints. Finally, it binds the cleaned data to its corresponding feature vector and stores it in the dynamic feature tag library. This tag library is not a static collection but supports real-time updates and expansion. For example, real-time monitoring data automatically generates combined tags consisting of sensor identifiers, timestamps, and geographic locations, while multimedia data generates metadata tags containing capture time, event type, and resolution. Furthermore, system administrators can temporarily inject new custom tag dimensions, such as "hazardous substance type" and "evacuation route number," to meet the needs of specific emergency scenarios (e.g., chemical plant explosions, subway fires), thereby enhancing the data's scenario adaptability and analytical value.

[0034] In the above method, step (4) designs the core architecture of the cloud platform, adopting a three-level communication system consisting of a data source layer, a relay node layer, and a cloud platform layer. The data source layer deploys edge computing devices such as industrial gateways and fifth-generation mobile communication technology terminals to achieve data noise reduction, protocol conversion, and lightweight processing. The relay node layer adopts a distributed server cluster to support multi-protocol conversion and traffic load balancing, and is configured with dual power supplies and dual links to achieve automatic fault switching. The cloud platform layer integrates elastic storage, elastic computing, and intelligent analysis modules based on a microservice architecture and has cross-regional disaster recovery capabilities. Specifically, the edge computing devices in the data source layer, such as industrial gateways, have built-in embedded processors and dedicated coprocessors, which can perform localized data preprocessing tasks. For example, wavelet transform filtering is performed on the received raw sensor signals to remove high-frequency noise, and moving target detection is performed on continuous video frames to upload only segments containing valid information, thereby significantly reducing uplink bandwidth usage. In terms of protocol conversion, edge devices support the conversion of industrial fieldbus protocols such as Modbus and CAN bus into message queue telemetry transmission protocols, realizing seamless integration between industrial equipment data and cloud platform communication protocols. The relay node layer, serving as a crucial hub connecting the edge and the cloud, deploys its distributed server clusters in regional telecommunications data centers or emergency communication vehicles. Each server is equipped with dual power inputs and dual 10 Gigabit Ethernet interfaces. In the event of a power supply or link failure, seamless switching to a backup path can occur within 200 milliseconds, ensuring business continuity. This cluster runs protocol conversion middleware, supporting bidirectional lossless conversion between Message Queuing Telemetry Transport Protocol (MQTP) and Hypertext Transfer Protocol (HTTP). It employs a weighted round-robin algorithm for load balancing, with weighting factors dynamically calculated in real-time based on each server node's CPU utilization, memory usage, and network throughput, ensuring fair and efficient allocation of data transmission tasks. The cloud platform layer, acting as the central brain of the entire system, is built using microservice frameworks such as Spring Cloud or Kubernetes. Core functions such as elastic storage, elastic computing, intelligent analytics, user management, and log auditing are broken down into independent service units. These service units can automatically scale up or down based on real-time load conditions. For example, during a major disaster, the number of intelligent analytics service instances can be scaled up from the normal 5 to 50 to cope with the surge in computing demands. Meanwhile, the cloud platform layer deploys dual-active instances in two remote data centers more than 100 kilometers apart, and synchronizes data through high-speed dedicated lines. If any single data center encounters a regional natural disaster, the other center can immediately take over all business and achieve disaster recovery within minutes.

[0035] In the above method, step (5) involves constructing a heterogeneous transmission network, integrating multiple network technologies to form an integrated air-space-ground transmission network. Wide-area transmission uses a dedicated network of fifth-generation mobile communication technology and satellite communication. Local interconnection utilizes mesh self-organizing network technology to solve the last-mile interconnection problem in disaster areas. Specifically, the dedicated network of fifth-generation mobile communication technology is a dedicated network within the authorized spectrum, providing deterministic quality of service guarantees for emergency communications with a peak rate greater than or equal to 1 gigabit per second and an end-to-end latency of less than 10 milliseconds. It is suitable for urban built-up areas or temporarily set up emergency command camps. When public network base stations are damaged or located in remote, uncovered areas, portable satellite communication terminals can quickly establish connections with geostationary orbit or low-orbit satellite constellations, providing basic voice and low-speed data services to ensure the issuance of critical commands. In the core disaster area, infrastructure is often severely damaged. At this time, miniature mesh self-organizing network nodes carried by rescue personnel or deployed by drones can quickly form a network. These nodes use a dynamic routing protocol, supporting automatic node discovery and multi-hop relay. Even if some nodes fail, the network can still maintain connectivity through other paths, with a single-hop transmission distance of up to 1,000 meters, effectively solving the "last mile" communication island problem.

[0036] In the above method, step (6) involves deploying and optimizing relay node links. Based on disaster risk maps and historical data, a hierarchical analysis method is used for node location selection. Node capacity, transmission delay, and resilience are comprehensively evaluated. An intelligent monitoring system is configured to monitor status 24 / 7. A routing optimization model based on the Dijkstra algorithm is developed to collect link load data in real time and dynamically select the optimal transmission path based on data priority. Specifically, the hierarchical analysis method location model uses node capacity, transmission delay, and resilience as three core evaluation indicators, with weights set to 0.4, 0.35, and 0.25 respectively. By constructing a judgment matrix and calculating the consistency ratio, it is ensured that the ratio is less than 0.1, thus deriving a scientifically reliable node layout scheme. After deployment, each relay node is equipped with an intelligent monitoring agent, continuously collecting hundreds of hardware and software indicators such as CPU temperature, fan speed, power supply voltage, and network packet loss rate, and reporting the data to the cloud platform's operation and maintenance center. The routing optimization model collects real-time load data of the entire network links every 5 seconds, including bandwidth utilization, queuing delay, and bit error rate. When a new data packet needs to be transmitted, the model plans the optimal path based on the data type and priority in its feature labels. For example, for real-time monitoring data, the model will prioritize a transmission path with a latency of less than 50 milliseconds; for multimedia data, it will allocate a transmission channel with a bandwidth greater than 100 megabits per second to ensure a good user experience.

[0037] In the above method, step (7) involves data packaging and feature tag association, designing a standardized encapsulation protocol that conforms to emergency industry standards, encapsulating structured data using JavaScript object representation format, generating a 256-bit hash value using a secure hash algorithm for unstructured data, and attaching metadata tags to form a composite data packet consisting of a data body, feature tags, and verification information. Specifically, the standardized encapsulation protocol specifies a fixed header structure for the composite data packet, which includes fields such as protocol version number, total data packet length, feature tag length, and verification algorithm identifier. In the data body, structured data is serialized into a compact JavaScript object representation string, while unstructured data is stored in its original binary stream form. Following this is the complete feature tag, encoded in key-value pairs. Finally, there is the verification information, typically a 256-bit hash value using a secure hash algorithm for the entire data packet (excluding the verification information itself), used by the receiving end to verify data integrity.

[0038] In the above method, step (8) establishes a tag-policy mapping mechanism. Multimedia data with a data volume greater than 1 gigabyte and high real-time requirements are transmitted in chunks. Sensor data updated at the second level is transmitted with priority using the User Datagram Protocol (UDP). Top-secret data is forcibly encrypted using Transport Layer Security Protocol version 1.3 (TLS) and stored on the blockchain. Specifically, the chunking mechanism divides multimedia data larger than 1 gigabyte into uniformly sized data blocks, each ranging from 1 to 10 megabytes in size, and assigns a unique sequence number to each data block. These data blocks can be transmitted in parallel through multiple available paths. After reaching the cloud platform layer, they are reassembled according to the sequence number. TLS-priority transmission is suitable for sensor data with a data update frequency higher than once per second. Due to its connectionless and retransmission-free characteristics, it can minimize transmission latency. The transmission process allows a packet loss rate of less than or equal to 5%, which is acceptable for monitoring data reflecting trend changes. For data marked as top-secret, the system will forcibly establish an independent TLS-1.3 encryption channel for it. This channel uses a forward secrecy algorithm suite, so even if the key is leaked over a long period, historical sessions cannot be decrypted. Meanwhile, the data transmission will be recorded on a dedicated blockchain, with evidence including the data hash, timestamp, and sender's digital signature, providing an immutable audit trail.

[0039] In the above method, step (9) implements transmission verification and checking, constructs a three-layer verification system of integrity, timeliness, and consistency, uses message digest algorithm 5 or secure hash algorithm 256-bit hash algorithm to verify data integrity, compares the receiving time with the generation time to verify timeliness, and ensures data consistency through cross-data source cross-comparison. Specifically, the integrity verification adopts a dual mechanism of cyclic redundancy check and hash check. First, a preliminary screening is performed through fast cyclic redundancy check. If it passes, a more rigorous hash check is then performed. Timeliness check requires that the time difference between data generation and reception is less than a preset threshold. This threshold is dynamically set according to the data type. For example, the threshold for sensor data is 5 seconds, while the threshold for command instructions is 1 second. Consistency check is more complex. It is achieved by comparing the logical relationship of multi-source data of the same event. For example, if a region reports a large number of casualties (from disaster and demand data), but the on-site images (from multimedia data) show that the buildings in the region are intact, the system will mark this data combination as having a logical conflict and trigger a manual review process.

[0040] In the above method, step (10) designs a closed-loop processing flow for abnormal data. When a link fails, a backup node is automatically switched for data retransmission. For format errors, a dedicated tool is called for conversion and repair. For missing tags, the data source is triggered to re-collect the data. The entire process is logged to provide data support for transmission optimization. Specifically, the response time for link failure switching is less than 200 milliseconds, ensuring seamless service interruption. For format errors caused by protocol incompatibility, the system's built-in format conversion engine supports mutual conversion of multiple data formats, including Extensible Markup Language, JavaScript Object Notation, and comma-separated value files. If a data packet cannot be correctly routed or processed due to missing feature tags, the system will send a re-collection instruction to the data source device, requiring it to supplement the complete tags and re-upload. All of the above processing procedures are recorded in the entire process log. The log adopts a structured format, and the recorded fields include operation time, operation type, data identifier, processing result, and abnormal information. The log data is kept for no less than 3 years, providing a solid data foundation for subsequent system performance analysis, fault root cause location, and transmission strategy optimization.

[0041] In the above method, step (11) involves deploying redundant backup and disaster recovery measures, configuring a geographically distributed active-active data center, and adopting a three-replica redundant storage, scheduled snapshot, and incremental backup mechanism to ensure that the recovery time target is less than or equal to 15 minutes and the recovery point target is less than or equal to 1 minute. The relay node is configured with hot-swappable power supplies and network cards to support online replacement of faulty components. Specifically, the geographically distributed active-active data center uses synchronous replication technology to ensure data consistency, and the distance between the two data centers is greater than 100 kilometers, enabling it to withstand regional natural disasters. The three-replica redundant storage uses erasure coding technology to store data in blocks on different servers in different racks, so that a single point of failure does not affect data availability and integrity. The hot-swappable power supply of the relay node adopts a redundant power module design. When a single power module fails, the system automatically switches to the backup module. The switching process does not affect the normal operation of the node, and maintenance personnel can replace the faulty power supply or network card without interrupting service.

[0042] In the above method, step (12) constructs a security protection system, follows a zero-trust architecture, adopts two-factor authentication and biometric technology, implements access control based on the principle of least privilege, uses Transport Layer Security Protocol version 1.3 encryption at the transport layer, uses Advanced Encryption Standard 256-bit encryption at the storage layer, and deploys a machine learning abnormal traffic detection system. Specifically, two-factor authentication is combined with dynamic passwords and fingerprint recognition technology to ensure the authenticity of user identities. The access control policy is based on a role-based access control model, where each role can only access data and functions within its scope of responsibility. For example, on-site rescue personnel can only view and report data in their local area, while decision-makers in the command center can access the global situation map. All static data is encrypted and stored using Advanced Encryption Standard 256-bit algorithm, and the key is uniformly managed by the hardware security module. The machine learning abnormal traffic detection system uses the isolated forest algorithm, which is adept at identifying sparse abnormal patterns from massive amounts of normal traffic. The system analyzes network traffic in real time across dozens of dimensions, including source / destination IP, port, protocol type, packet size, and connection frequency. Once abnormal behavior is detected (such as DDoS attacks, port scanning, or data leakage), it can trigger a security alert and automatically block suspicious connections within 100 milliseconds, nipping the threat in the bud.

[0043] To further illustrate the technical effects of this invention, a specific application example is constructed: Suppose a coastal city is hit by a severe typhoon, causing widespread flooding and landslides. Supported by the method of this invention, water level sensors deployed on dikes (real-time monitoring data) report data twice per second via a dedicated 5G mobile communication network; distress videos uploaded by trapped residents via emergency apps (multimedia data) are relayed via satellite links through mesh self-organizing network nodes mounted on drones; and material allocation instructions issued by the city's emergency command center (command and dispatch data) are precisely pushed to relevant logistics fleets through microservices at the cloud platform layer. All this data is assigned dynamic feature tags during transmission, and the optimal transmission strategy is automatically matched based on the tag content. Upon receiving the data, the cloud platform immediately initiates three-layer verification to ensure the data is authentic, timely, and consistent. The intelligent analysis module integrates and analyzes water level data, rainfall forecasts, and geological disaster risk maps to predict the next high-risk area where landslides may occur and automatically pushes early warning information to rescue teams in that area. Throughout the process, even if some communication base stations are destroyed, the system can automatically switch to backup links or satellite communication to ensure the uninterrupted flow of critical information. Afterwards, analysis of the entire process log allows for a precise assessment of the effectiveness of this emergency response and provides data for optimizing future plans.

[0044] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

[0045] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0046] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0047] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0048] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0049] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for analyzing emergency response data based on a cloud platform, characterized in that, include: Step 1: Construct a multi-source data classification and collection system to form a seven-dimensional data full-domain coverage collection system covering basic support data, real-time monitoring data, on-site handling data, disaster and demand data, command and dispatch data, cross-departmental collaboration data, and multimedia data. Collect data through multiple channels such as IoT terminals, application programming interfaces, and manual input to form a complete set of structured and unstructured data. Step 2: Implement a differentiated data management strategy. For structured data, use a relational database for storage and structured query language for efficient retrieval. For unstructured data, use a distributed file system or non-relational database for storage and combine metadata tags for fast indexing. Step 3: Perform key data feature extraction, define quantitative feature dimensions, core features include data volume, data generation time and data source, auxiliary attributes cover data format, update frequency and security level, use extraction, transformation and loading tools to clean the raw data and remove duplicate and erroneous records, and build a dynamic feature label library. Step 4: Design the core architecture of the cloud platform, adopting a three-level communication system consisting of a data source layer, a relay node layer, and a cloud platform layer. The data source layer deploys edge computing devices such as industrial gateways and 5G mobile communication technology terminals to achieve data noise reduction, protocol conversion, and lightweight processing. The relay node layer adopts a distributed server cluster to support multi-protocol conversion and traffic load balancing, and is configured with dual power supplies and dual links to achieve automatic fault switching. The cloud platform layer integrates elastic storage, elastic computing, and intelligent analysis modules based on a microservice architecture and has cross-regional disaster recovery capabilities. Step 5: Construct a heterogeneous transmission network, integrating 5G mobile communication technology private network, satellite communication and mesh self-organizing network technology to form an integrated air-space-ground transmission network; Step 6: Deploy and optimize relay node links. Based on disaster risk maps and historical data, use the hierarchical analysis method to select node locations. Comprehensively evaluate node capacity, transmission delay and resilience. Configure an intelligent monitoring system to achieve 24 / 7 status monitoring. Develop a routing optimization model based on Dijkstra's algorithm to collect link load data in real time and dynamically select the optimal transmission path based on data priority. Step 7: Perform data packaging and feature tag association, design a standardized packaging protocol that conforms to emergency industry standards, encapsulate structured data using JavaScript object representation format, generate 256-bit hash values ​​using a secure hash algorithm for unstructured data and attach metadata tags to form a composite data packet containing the data body, feature tags, and verification information. Step 8: Establish a tag and policy mapping mechanism. Multimedia data with a data volume greater than 1 gigabyte and high real-time requirements should be transmitted in segments. Sensor data updated at the second level should be transmitted with priority using the User Datagram Protocol. Top-secret data should be encrypted using Transport Layer Security 1.3 and stored on the blockchain. Step 9: Implement transmission verification and checking, and build a three-layer verification system of integrity, timeliness and consistency. Use message digest algorithm 5 or secure hash algorithm 256-bit hash algorithm to verify data integrity, compare the receiving time with the generation time to verify timeliness, and ensure data consistency through cross-data source cross-comparison. Step 10: Design a closed-loop processing flow for abnormal data. When the link fails, automatically switch to a backup node for data retransmission. If the format is incorrect, call a dedicated tool to convert and repair it. If the tag is missing, trigger the data source to re-collect data. The entire process is logged to provide data support for transmission optimization.

2. The emergency response data transmission and analysis method based on a cloud platform according to claim 1, characterized in that: In the seven-dimensional data full-domain coverage collection system, basic support data includes geographic information data and material reserve data; real-time monitoring data includes sensor data and environmental parameter data; on-site disposal data includes rescue progress data and equipment status data; disaster and demand data includes casualty data and material shortage data; command and dispatch data includes instruction log data and resource allocation data; cross-departmental collaboration data includes linkage agreement data and list of rights and responsibilities data; and multimedia data includes on-site video data and audio data.

3. The emergency response data transmission and analysis method based on a cloud platform according to claim 1, characterized in that: During the construction of the dynamic feature tag library, real-time monitoring data generates a combined tag of sensor identifier, timestamp, and geographic location, while multimedia data generates metadata tags of shooting time, event type, and resolution. Custom tag expansion is supported according to specific emergency scenario requirements.

4. The emergency response data transmission and analysis method based on a cloud platform according to claim 1, characterized in that: When the edge computing device at the data source headend layer performs protocol conversion, it supports converting the Modbus protocol to the Message Queuing Telemetry Transport Protocol. The distributed server cluster at the relay node layer supports bidirectional protocol conversion between the Message Queuing Telemetry Transport Protocol and the Hypertext Transfer Protocol. The traffic load balancing adopts a weighted round-robin algorithm to dynamically allocate data transmission tasks according to the real-time load of the nodes.

5. The emergency response data transmission and analysis method based on a cloud platform according to claim 1, characterized in that: In the integrated air-space-ground transmission network, the fifth-generation mobile communication technology private network provides a peak rate of greater than or equal to 1 gigabit per second and an end-to-end latency of less than 10 milliseconds. Satellite communication covers remote areas and disaster-damaged areas that cannot be covered by the ground network. The mesh self-organizing network technology adopts a dynamic routing protocol, supports automatic node discovery and multi-hop relay, and the single-hop transmission distance can reach 1,000 meters.

6. The emergency response data transmission and analysis method based on a cloud platform according to claim 1, characterized in that: The analytic hierarchy process (AHP) location selection model includes three core evaluation indicators: node capacity weight 0.4, transmission delay weight 0.35, and resilience weight 0.

25. The reliability of the evaluation results is ensured by a judgment matrix with a consistency ratio of less than 0.

1. The routing optimization model collects link load data every 5 seconds. Real-time monitoring data prioritizes transmission paths with a latency of less than 50 milliseconds, while multimedia data is allocated transmission channels with a bandwidth of more than 100 megabits per second.

7. The emergency response data transmission and analysis method based on a cloud platform according to claim 1, characterized in that: The standardized encapsulation protocol specifies that data volume characteristics are identified in kilobytes, megabytes, or gigabytes, the data generation time uses Unix timestamp format accurate to milliseconds, and the data source characteristics are encoded using globally unique identifiers. The segmented transmission mechanism divides multimedia data larger than 1 gigabyte into uniformly sized data blocks, each ranging from 1 megabyte to 10 megabytes in size. After transmission, the data is reassembled at the cloud platform layer.

8. The emergency response data transmission and analysis method based on a cloud platform according to claim 1, characterized in that: The User Datagram Protocol (UDP) prioritizes the transmission of sensor data with a data update frequency higher than once per second, and the transmission process allows for a packet loss rate of less than or equal to 5%. The Transport Layer Security Protocol version 1.3 encryption channel uses a forward secrecy algorithm suite, and top-secret data is simultaneously transmitted and stored using blockchain technology. The stored information includes the data hash value, timestamp, and sender's digital signature.

9. The emergency response data transmission and analysis method based on a cloud platform according to claim 1, characterized in that: In the three-layer verification system, integrity verification adopts a dual mechanism of cyclic redundancy check and hash check, timeliness verification requires that the time difference between data generation and reception is less than a preset threshold, and consistency verification is achieved by comparing the logical relationship of multi-source data of the same event. In the abnormal data closed-loop processing flow, the link failure switching response time is less than 200 milliseconds, and the format error conversion supports mutual conversion of multiple data formats, including Extensible Markup Language, JavaScript Object Notation, and comma-separated value files.

10. The emergency response data transmission and analysis method based on a cloud platform according to claim 1, characterized in that: The entire process log is recorded in a structured log format, with fields including operation time, operation type, data identifier, processing result, and exception information. The log data is retained for no less than 3 years. The geographically dispersed active-active data center uses synchronous replication technology to ensure data consistency, with a distance of more than 100 kilometers between the two data centers. The triple-replica redundant storage uses erasure coding technology to store data in blocks on different servers in different racks. The hot-swappable power supply uses a redundant power module design, and the system automatically switches to the backup module when a single power module fails. The two-factor authentication combines dynamic passwords and fingerprint recognition technology, and the access control policy is based on a role-based access control model. The machine learning abnormal traffic detection system uses the isolated forest algorithm, which triggers a security alarm and automatically blocks suspicious connections within 100 milliseconds when abnormal behavior is detected.

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