Intelligent Disaster Prevention and Early Warning Management System and Method Based on Cloud Computing and Multi-Architecture Collaboration

Abstract

This invention discloses an intelligent disaster prevention and early warning management system and method based on cloud computing and multi-architecture collaboration, belonging to the field of disaster prevention and early warning management technology. The system includes: constructing a correlation database of disaster characteristics and architectural capabilities; generating an initial responsibility allocation table based on disaster precursor signals; dynamically adjusting responsibilities through collaboration between edge nodes, regional nodes, and the cloud to form a final responsibility allocation table; dividing the system into minimum early warning units according to disaster type and binding them with spatiotemporal IDs; normalizing and conflict-checking multi-source data to generate a global fusion dataset; calculating the similarity score of the current disaster based on a historical disaster case database to generate local prediction results; arbitrating cross-regional conflicts through cloud arbitration to form a global risk prediction map; and issuing early warning instructions hierarchically to edge nodes and regional nodes based on the global risk prediction map, and scheduling edge, regional, and global resources based on priority, significantly improving the accuracy of disaster early warning and the efficiency of emergency response.

Description

Technical Field

[0001] This invention relates to the field of disaster prevention and early warning management technology, specifically to an intelligent disaster prevention and early warning management system and method based on cloud computing and multi-architecture collaboration. Background Technology

[0002] In recent years, with the frequent occurrence of natural disasters, traditional disaster prevention and early warning systems mainly rely on single-architecture data collection and processing methods, such as centralized data centers or decentralized local monitoring stations. These systems typically employ fixed data collection frequencies and static responsibility allocation mechanisms, triggering early warning signals through preset thresholds. In existing technologies, some systems have attempted to introduce multi-source data fusion technology, such as combining meteorological data, hydrological data, and geographic information data, to improve the accuracy of early warnings. In addition, some systems also utilize historical disaster data for simple similarity comparisons to assist in predicting the development trend of disasters.

[0003] However, existing technologies have significant shortcomings in multi-architecture collaboration and dynamic resource scheduling. First, traditional systems lack dynamic collaborative management of edge nodes, regional nodes, and cloud resources, resulting in low efficiency in data collection and processing and difficulty in adapting to changing needs at different stages of a disaster. For example, the frequency and scope of data collection requirements differ significantly during the early and developing stages of a disaster, but existing systems cannot adjust the allocation of responsibilities in real time, leading to resource waste or insufficient data. Second, when fusing multi-source data, existing technologies often neglect the verification of data conflicts and dynamic weight allocation, resulting in low reliability of the fusion results. For example, data from different sources may differ in dimensions and precision, but existing systems lack effective normalization and conflict resolution mechanisms, affecting the accuracy of early warnings.

[0004] In addition, existing technologies also have limitations in cross-regional collaboration and resource allocation. Due to the lack of a conflict arbitration mechanism with a global perspective, the prediction results of adjacent areas may be contradictory, leading to confusion in early warning information. At the same time, resource allocation often relies on static priorities and cannot be dynamically adjusted according to real-time risks, making it difficult to achieve efficient emergency response. For example, in areas with high population density or near critical infrastructure, the priority of resource allocation needs to change dynamically, but existing systems cannot respond flexibly, reducing the efficiency and effectiveness of disaster emergency response. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] To address the shortcomings of existing technologies, this invention provides an intelligent disaster prevention and early warning management system and method based on cloud computing and multi-architecture collaboration. By dynamically coordinating edge nodes, regional nodes, and cloud resources, and optimizing the allocation of responsibilities and data fusion mechanisms, it solves the deficiencies of traditional systems in multi-architecture collaboration, data conflict verification, and dynamic resource scheduling, significantly improving the accuracy of disaster early warning and the efficiency of emergency response.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] Firstly, this application proposes an intelligent disaster prevention and early warning management method based on cloud computing and multi-architecture collaboration, including:

[0010] Construct a database linking disaster characteristics and architectural capabilities, generate an initial responsibility allocation table based on disaster precursor signals, and dynamically adjust responsibilities through collaboration among edge nodes, regional nodes, and the cloud to form a final responsibility allocation table;

[0011] The smallest early warning unit is divided according to the disaster type and bound to a spatiotemporal ID. Multi-source data is normalized and conflict checked to generate a global fusion dataset.

[0012] Based on a historical disaster case database, a similarity score for the current disaster is calculated to generate local prediction results. Cross-regional conflicts are then arbitrated through cloud-based arbitration to form a global risk prediction map.

[0013] Early warning instructions are issued to edge nodes and regional nodes in a hierarchical manner based on the global risk prediction map, and edge, regional and global resources are scheduled based on priority.

[0014] Furthermore, the database for linking disaster characteristics with architectural capabilities includes:

[0015] A cloud-based pre-built disaster feature architecture capability association library stores the mapping relationship between core disaster monitoring requirements and architecture capability parameters. Among them, core disaster monitoring requirements include high-frequency rainfall / water level data for rainstorms, temperature / wind speed data for forest fires, and P-wave acquisition data for earthquakes. Architecture capability parameters include the single-device computing power and data transmission latency of edge nodes, the storage capacity and aggregation latency of regional nodes, the bandwidth of the cloud, and the global data processing capabilities.

[0016] When an edge node or regional node captures an initial disaster precursor signal, it uploads the signal to the cloud. The cloud then calls a related database to match the core requirements of the current disaster with the capabilities of each architecture, generating an initial responsibility allocation table, which includes the data collection frequency, jurisdiction, and upload requirements for edge nodes, regional nodes, and the cloud.

[0017] Furthermore, the dynamic adjustment of rights and responsibilities includes:

[0018] Edge nodes perform targeted data collection according to their initial responsibilities, collecting only core disaster data. They then perform lightweight preprocessing on this core disaster data, using a sliding window anomaly detection method to remove outliers, generating valid data and quality labels, which are then uploaded to regional nodes. Regional nodes statistically analyze the distribution of quality labels for edge node data within their jurisdiction, and determine the disaster stage based on the local real-time disaster status. If the proportion of low-quality labels exceeds a preset threshold or the disaster stage escalates, the regional node temporarily takes over the responsibility for secondary data verification or applies to the cloud for a change in responsibilities. The cloud receives the responsibilities adjustment applications from each regional node, performs global collaborative arbitration based on cross-regional bandwidth status, generates a final responsibilities allocation table, and synchronizes it to all nodes.

[0019] Furthermore, the smallest early warning unit is divided according to disaster type and bound to a spatiotemporal ID, including:

[0020] The cloud-based system divides the smallest early warning units according to the disaster type and granularity: 1km×1km for rainstorms, 500m×500m for forest fires, and 2km×2km for earthquakes. Each unit is assigned a unique spatial ID. Regional nodes determine the time granularity based on the collection frequency in the final responsibility allocation table, generate timestamps, and bind data from each architecture layer to spatiotemporal IDs by spatial IDs and timestamps. Under the same spatiotemporal ID, edge data, regional data, and cloud data are included. Regional nodes unify the dimensions of multi-source data under the same spatiotemporal ID, generate spatiotemporally aligned normalized data, and calculate the unit state change rate to determine the disaster evolution stage.

[0021] Furthermore, conflict checking includes:

[0022] The cloud assigns stage-adaptive weights to data from different sources based on the disaster stage, generating a data source weight table. Regional nodes perform conflict detection on multi-source data under the same spatiotemporal ID. If a conflict exists, a three-level verification mechanism is initiated: the first level of verification involves re-collecting data, the second level involves retrieving the average data from adjacent early warning units, and the third level involves the cloud arbitrating the average historical data. Regional nodes perform weighted fusion of non-conflicting data according to the data source weight table, generating a unit fusion dataset, which is then uploaded to the cloud for integration to form a global fusion dataset.

[0023] Furthermore, generating local prediction results includes:

[0024] Regional nodes pre-store a local historical disaster case database, which contains fragments of the global fusion dataset of historical disasters, disaster spread paths, impact ranges, and risk levels. Regional nodes retrieve the global fusion dataset within their jurisdiction from the cloud, extract the current unit's fusion data features, calculate case similarity scores, and select the top 3 historical cases with the highest similarity. Local prediction results are generated, including spread direction, spread speed, risk level, and confidence level. The spread direction is calculated using multiple values ​​from historical cases, the spread speed is calculated using similarity weighting, the risk level is adjusted based on historical cases and current feature deviations, and the confidence level is the average of the similarity scores of historical cases.

[0025] Furthermore, cross-regional conflicts resolved through cloud-based arbitration include:

[0026] The cloud receives local prediction results from all regional nodes and detects cross-regional conflicts. A conflict is defined as an overlap in the predicted diffusion range of adjacent regions and an opposite direction, or the same unit being predicted to have different risk levels by different regions. When there are multiple regional prediction conflicts for the same unit, the prediction result of the main region that governs the unit is used first, determined according to administrative divisions or a preset unit region mapping table. The cloud integrates and corrects all regional local prediction results, generates a global risk prediction map, marks the risk level, diffusion time, confidence level, and impact range of each early warning unit, and distributes it to each regional node and edge node.

[0027] Furthermore, the tiered issuance of early warning instructions includes:

[0028] The cloud generates hierarchical early warning information based on the global risk prediction map and distributes it according to the architecture responsibilities: edge nodes receive unit-level early warning instructions and activate audible and visual alarms and local personnel evacuation prompts; regional nodes receive regional-level early warning instructions and activate rescue team assembly and pre-positioned material reserves; the early warning information includes risk basis and response suggestions, the risk basis is based on the characteristics of unit fusion data, and the response suggestions include evacuation routes and resource allocation plans.

[0029] Furthermore, resource scheduling includes:

[0030] The cloud-based system uses a global resource pool to prioritize and schedule resources according to risk. High-priority units, such as those under red alert and with a population density > 500 people / km² or containing critical infrastructure, are scheduled using a three-tiered approach: edge-regional-global. Medium-priority units, such as those under orange alert and with a population density > 300 people / km² or containing general infrastructure, are scheduled using a regional coordinated approach. Low-priority units, such as those under yellow / blue alert or with a population density ≤ 300 people / km², are allocated idle resources by regional nodes.

[0031] Upon receiving a resource scheduling request, the edge node immediately performs allocation. If local resources are insufficient, it sends a shortage request to the regional node. The regional node schedules idle resources from the regional resources. If it is still insufficient, it requests cross-regional support from the cloud. The cloud selects the cross-regional resources with the shortest distance and the least time, coordinates the opening of an emergency channel, and synchronizes the adjusted scheduling plan to the relevant nodes.

[0032] Secondly, this application proposes an intelligent disaster prevention and early warning management system based on cloud computing and multi-architecture collaboration, including:

[0033] The responsibility allocation and collaborative management module constructs a database linking disaster characteristics and architectural capabilities. It generates an initial responsibility allocation table based on disaster precursor signals and dynamically adjusts responsibilities through collaboration between edge nodes, regional nodes, and the cloud to form the final responsibility allocation table.

[0034] The data fusion and conflict resolution module divides the smallest early warning units according to disaster type and binds them with spatiotemporal IDs. It performs normalization processing and conflict verification on multi-source data and generates a global fusion dataset.

[0035] The disaster prediction and conflict arbitration module calculates the similarity score of the current disaster based on the historical disaster case database, generates local prediction results, and arbitrates cross-regional conflicts through the cloud to form a global risk prediction map;

[0036] The early warning distribution and resource scheduling module distributes early warning instructions to edge nodes and regional nodes in a hierarchical manner based on the global risk prediction map, and schedules edge, regional and global resources based on priority.

[0037] (III) Beneficial Effects

[0038] This invention provides an intelligent disaster prevention and early warning management system and method based on cloud computing and multi-architecture collaboration, which has the following beneficial effects:

[0039] (1) By constructing a database linking disaster characteristics and architectural capabilities, and dynamically adjusting the allocation of responsibilities based on disaster precursor signals, efficient collaboration between edge nodes, regional nodes and the cloud is achieved, resource allocation is optimized, the accuracy of data collection and processing is ensured, the system flexibility is improved through dynamic adjustment of responsibilities, adapting to the needs of different stages of disasters, reducing redundant data transmission, and reducing bandwidth pressure. At the same time, quality labeling and anomaly detection mechanisms ensure data reliability, laying a solid foundation for subsequent disaster early warning and emergency response.

[0040] (2) By dividing the smallest early warning unit according to the disaster type and binding it with spatiotemporal ID, the precise alignment and efficient fusion of multi-source data are realized. The spatiotemporal ID is used to uniformly manage edge, regional and cloud data to ensure data consistency. Normalization processing eliminates the difference in dimensions and improves data comparability. The three-level conflict verification mechanism significantly improves data reliability. Dynamic weight allocation adapts to the needs of different stages of disaster and optimizes the accuracy of data fusion. The final global fusion dataset provides a high-quality and highly consistent data foundation for disaster prediction and emergency response.

[0041] (3) The similarity score is calculated through the historical disaster case database to generate local prediction results, and cross-regional conflicts are arbitrated in the cloud to form a global risk prediction map. Based on historical data, the prediction accuracy is improved. The reliability of diffusion direction and speed is optimized by similarity weighted calculation. The cloud arbitration mechanism effectively resolves cross-regional prediction conflicts, ensures the consistency of global results, and dynamically adjusts the risk level and confidence level to enhance the credibility of the early warning. The final global risk prediction map provides a precise basis for hierarchical early warning and resource scheduling, and significantly improves the efficiency of disaster emergency response.

[0042] (4) By issuing early warning instructions in a hierarchical manner and prioritizing resource scheduling, efficient coordination of disaster emergency response is achieved. The hierarchical early warning mechanism ensures that resources at the edge, regional and global levels are accurately matched with disaster risk levels, thereby improving response speed. Priority scheduling optimizes resource allocation, giving priority to areas with high population density and key facilities. Dynamic resource allocation maximizes emergency efficiency and reduces redundancy. Risk basis and response suggestions enhance the credibility and operability of early warning, ultimately achieving a rapid and orderly disaster emergency response and minimizing disaster losses. Attached Figure Description

[0043] Figure 1 This is a schematic diagram illustrating the steps of the intelligent disaster prevention and early warning management method based on cloud computing and multi-architecture collaboration of the present invention.

[0044] Figure 2 This is a schematic diagram of the intelligent disaster prevention and early warning management method based on cloud computing and multi-architecture collaboration of the present invention.

[0045] Figure 3 This is a schematic diagram of the intelligent disaster prevention and early warning management system based on cloud computing and multi-architecture collaboration of the present invention. Detailed Implementation

[0046] 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, and 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.

[0047] Example 1

[0048] Please see Figure 1 and Figure 2 Embodiment 1 of the present invention provides an intelligent disaster prevention and early warning management method based on cloud computing and multi-architecture collaboration, comprising the following steps:

[0049] Step 1: Construct a database linking disaster characteristics and architectural capabilities; generate an initial responsibility allocation table based on disaster precursor signals; and dynamically adjust responsibilities through collaboration between edge nodes, regional nodes, and the cloud to form the final responsibility allocation table.

[0050] Step one includes the following:

[0051] Step 101: Pre-build a disaster feature architecture capability association library in the cloud: The library stores the mapping relationship between core disaster monitoring requirements and architecture capability parameters. Core disaster monitoring requirements include high-frequency rainfall / water level data for rainstorms, temperature / wind speed data for forest fires, and P-wave acquisition data for earthquakes. Architecture capability parameters include edge nodes: single device computing power ≤ 20 GFLOPS, data transmission latency ≤ 50 ms; regional nodes: storage capacity ≥ 10 TB, aggregation latency ≤ 1 min; cloud: bandwidth ≥ 10 Gbps, global data processing capability ≥ 1000 GFLOPS.

[0052] Step 102: When edge nodes / regional nodes capture initial disaster precursor signals, such as when meteorological departments push out rainstorm warnings or seismic stations capture microseismic signals, the signals are uploaded to the cloud. The cloud then calls the relevant database to match the core needs of the current disaster with the capabilities of each architecture, generating an initial responsibility allocation table. For example: Rainstorm precursor period—Edge nodes: 1 min / time of rainfall data collection + outlier removal; Regional nodes: 5 min / time of regional rainfall data summary; Cloud: 10 min / time of global trend analysis;

[0053] Step 103: Edge nodes perform targeted data collection according to their initial responsibilities, collecting only core disaster data. For example, redundant soil moisture data is not collected for heavy rain. The collected data undergoes lightweight preprocessing and a three-step sliding window anomaly detection is used: if the fluctuation of three consecutive collected values ​​exceeds 50%, it is marked as an anomaly and removed. Valid data + quality labels are generated, with quality labels divided into three levels: A, B, and C: A = calibrated within 1 hour, B = calibrated within 1-24 hours, and C = not calibrated for more than 24 hours. The data is then uploaded to the regional nodes.

[0054] Step 104: Statistically analyze the distribution of quality labels for edge data within the jurisdiction of regional nodes. For example, during the rainstorm development phase, if 30% of the edge node data is at level C, and combined with the real-time status of local disasters, such as if 50% of the edge nodes in the region collect rainfall exceeding 50 mm / h, it is determined that the disaster has entered the development phase from the precursor stage. Then, determine whether the initial rights and responsibilities are appropriate.

[0055] If the proportion of low-quality labels exceeds the preset threshold, such as the proportion of C-level labels exceeding 60%, the regional node temporarily takes over the responsibility of secondary data verification, compares the data of adjacent edge nodes, and corrects outliers; if the disaster stage escalates from the precursor stage to the development stage, the regional node applies to the cloud for an upgrade of its responsibilities, such as increasing the edge collection frequency from 1 minute / time to 30 seconds / time and the regional aggregation frequency from 5 minutes / time to 2 minutes / time, forming an adjusted responsibility application;

[0056] Step 105: The cloud receives the adjusted rights and responsibilities requests from nodes in each region, and, combined with the cross-regional bandwidth status (e.g., if region A's request to increase the collection frequency may occupy the bandwidth of downstream region B), performs global collaborative arbitration.

[0057] If there is a bandwidth conflict in the application, the cloud will prioritize the disaster-related areas. For example, if rainstorms in area A may cause floods in area B, bandwidth will be allocated to area A first. If the application involves overlapping responsibilities across regions, such as both areas A and B applying for the right to aggregate data in the same watershed, the cloud will allocate the primary responsible node according to the area's jurisdiction and data processing capabilities.

[0058] Step 106: Generate the final responsibility allocation table in the cloud, clarifying the data collection frequency, jurisdiction, and upload requirements of each architecture layer, and synchronize it to all edge nodes and regional nodes; subsequent steps such as data fusion, prediction, and scheduling must be executed according to the final responsibility allocation table to ensure consistent operation between architectures.

[0059] When using this method, combine the content of steps 101 to 106:

[0060] By constructing a correlation database between disaster characteristics and architectural capabilities, and dynamically adjusting the allocation of responsibilities based on disaster precursor signals, efficient collaboration between edge nodes, regional nodes, and the cloud is achieved. This optimizes resource allocation, ensures the accuracy of data collection and processing, enhances system flexibility through dynamic adjustment of responsibilities, adapts to the needs of different stages of a disaster, reduces redundant data transmission, and lowers bandwidth pressure. Meanwhile, quality labeling and anomaly detection mechanisms ensure data reliability, laying a solid foundation for subsequent disaster early warning and emergency response.

[0061] Step 2: Divide the data into the smallest early warning units according to the disaster type and bind them with spatiotemporal IDs. Perform normalization processing and conflict verification on the multi-source data to generate a global fusion dataset.

[0062] Step two includes the following:

[0063] Step 201: For the raw data collected by each architecture layer according to its responsibilities, the cloud divides the smallest early warning unit according to the disaster type and the granularity: rainstorm → 1km×1km, forest fire → 500m×500m, earthquake → 2km×2km. Each unit is assigned a unique spatial ID, such as A01-118.2°E-30.5°N. The regional nodes determine the time granularity according to the collection frequency of the final responsibility allocation table, such as rainstorm development period → 1min / slice, outbreak period → 30s / slice, and generate a timestamp, such as 202407201400.

[0064] Step 202: Bind the data of each architecture layer to a spatiotemporal ID by spatial ID + timestamp, such as A01-118.2°E-30.5°N_202507201400, to ensure that the same spatiotemporal ID contains edge data, regional data, and cloud data. Edge data is such as rainfall of 25mm / h at 14:00 in unit A01, regional data is such as water depth of 0.5m in video at 14:00 in unit A01, and cloud data is such as forecast rainfall of 30mm / h at 14:00 in unit A01.

[0065] Step 203: The regional node performs dimensional unification on multi-source data under the same spatiotemporal ID, such as converting video water accumulation levels 1-5 into actual depths of 0-1m and wind speed levels 1-12 into actual wind speeds of 0-32.7m / s, generating spatiotemporally aligned normalized data.

[0066] Step 204: The regional nodes calculate the unit state change rate based on the spatiotemporally aligned normalized data. For example, if the rainfall in unit A01 increases from 20 mm / h to 80 mm / h within 10 minutes, the current disaster evolution stage is determined by combining the disaster feature database. The disaster feature database contains the evolution stages of various disasters, such as the precursor stage, development stage, and outbreak stage, and is uploaded to the cloud.

[0067] Step 205: The cloud assigns stage-adaptive weights to data from different sources based on the disaster stage, with a total weight of 100%, generating a data source weight table:

[0068] Early warning stage: 60% global data in the cloud, 30% local data at the edge, and 10% regional aggregated data. Local data fluctuates greatly during the early warning stage, so global trends are prioritized. Development stage: 50% real-time data at the edge, 30% regional aggregated data, and 20% global data in the cloud. Local disasters have already manifested, and real-time data is more critical. Outbreak stage: 70% edge data, 25% regional data, and 5% cloud data. This data is used only for global collaboration to avoid delays.

[0069] Step 206: Regional nodes perform conflict detection on multi-source data under the same spatiotemporal ID. For example, if edge node A01 displays a water level of 1.2m, while regional video analysis shows 1.8m, a difference exceeding 30% is considered a conflict. If a conflict exists, a three-level verification mechanism is initiated.

[0070] Level 1 Verification: The regional node sends a re-collection command to the edge node corresponding to the conflicting data. The edge node returns new data within 30 seconds. If the relative deviation after re-collection is ≤30%, the new data is adopted. Level 2 Verification: If the conflict still exists, the regional node retrieves the average of the same type of data from the three adjacent early warning units. For example, if there is a conflict in unit A01, the average of the data from units A02, A03, and A04 is retrieved. If the relative deviation of the current data from the average is ≤15%, the current data is retained; otherwise, the average is used. Level 3 Verification: If the conflict still cannot be resolved, the regional node uploads the conflicting data to the cloud. The cloud retrieves the average of historical data for the same type of disaster over the past 5 years. For example, in the case of heavy rain, the average of the historical rainfall data for the same unit is ±10%. The data with the smallest deviation from the historical average is taken as the reliable value.

[0071] Step 207: Regional nodes perform weighted fusion of non-conflicting data under the same spatiotemporal ID according to the data source weight table. For example, edge water level 1.2m×50% + regional video 1.0m×30% + cloud prediction 1.1m×20% = 1.12m to generate a unit fusion dataset. All regional nodes upload the unit fusion dataset to the cloud. The cloud integrates the data to form a global fusion dataset, which includes the spatiotemporal ID, fusion data value, and data quality of all early warning units.

[0072] When using it, combine steps 201 to 207:

[0073] By dividing the smallest early warning unit according to disaster type and binding it with spatiotemporal ID, accurate alignment and efficient fusion of multi-source data are achieved. The spatiotemporal ID is used to uniformly manage edge, regional and cloud data to ensure data consistency. Normalization processing eliminates dimensional differences and improves data comparability. The three-level conflict verification mechanism significantly improves data reliability. Dynamic weight allocation adapts to the needs of different stages of disaster and optimizes data fusion accuracy. The final global fusion dataset provides a high-quality and highly consistent data foundation for disaster prediction and emergency response.

[0074] Step 3: Calculate the similarity score of the current disaster based on the historical disaster case database, generate local prediction results, and form a global risk prediction map by arbitrating cross-regional conflicts through cloud-based arbitration;

[0075] Step three includes the following:

[0076] Step 301: Each regional node pre-stores a local historical disaster case library, which contains fragments of the global fusion dataset of historical disasters (in spatiotemporal ID format), disaster spread paths, impact ranges, and risk levels; the regional node obtains the global fusion dataset within its jurisdiction from the cloud and extracts the fusion data features of the current unit, such as unit A01: rainfall 80mm / h, water level 1.16m, wind speed 5m / s;

[0077] Step 302: Calculate the case similarity score using a multi-dimensional weighted matching method. For example, for rainstorm disasters: rainfall 30%, water level 40%, wind speed 30%; for forest fires: temperature 40%, wind speed 30%, vegetation type 30%. Select the top 3 historical cases with the highest similarity from the case database, such as 92%, 88%, and 85% similarity.

[0078] Step 303: Generate local prediction results: The diffusion direction is taken from the majority value of the diffusion direction of the top 3 historical cases. For example, if 2 cases are southeast and 1 is northeast, then the predicted direction is southeast. The diffusion speed is calculated by weighting similarity. For example, if case 1 has a similarity of 95% and a speed of 0.5 km / h; case 2 has a similarity of 90% and a speed of 0.6 km / h; and case 3 has a similarity of 85% and a speed of 0.55 km / h, then the predicted speed = (95% × 0.5 + 90% × 0.6 + 85% × 0.55) / (95% + 90% + 85%). %)≈0.55km / h; The risk level is based on the risk level of the top 3 historical cases in the corresponding time and space, combined with the deviation between the current characteristics and the cases. If the deviation is higher than the deviation threshold, such as the current rainfall being 10% higher than the case, the risk level is increased by 1 level, such as yellow → orange; The confidence level is the average of the similarity of the top 3 historical cases, such as (95%+90%+85%) / 3=90%; Finally, a local prediction result table is formed, including the warning unit ID, risk level, diffusion direction, diffusion speed, prediction time, and confidence level, and uploaded to the cloud;

[0079] Step 304: The cloud receives the local prediction results of all regional nodes and performs cross-regional conflict detection. Conflict definition: The predicted diffusion range of adjacent regions overlaps and the directions are opposite. For example, region A predicts to spread southeast to the boundary of region B, while region B predicts to spread northwest to the boundary of region A. Or the same unit is predicted to have different risk levels by different regions. For example, region A predicts that unit C is orange, while region B predicts that unit C is yellow.

[0080] Step 305: When there are multiple regional prediction conflicts in the same unit, the prediction result of the main region that governs the unit shall be used first, and determined according to administrative division or preset unit-region mapping table; the cloud integrates and corrects all regional local prediction results, generates a global risk prediction map, marks the risk level, diffusion time, confidence level and impact range of each early warning unit, and distributes it to each regional node and edge node;

[0081] When using this method, refer to steps 301 to 305:

[0082] By calculating similarity scores from a historical disaster case database to generate local prediction results, and using cloud-based arbitration to resolve cross-regional conflicts, a global risk prediction map is formed. Based on historical data, prediction accuracy is improved, and the reliability of diffusion direction and speed is optimized through similarity weighted calculation. The cloud-based arbitration mechanism effectively resolves cross-regional prediction conflicts, ensuring the consistency of global results. The risk level and confidence level are dynamically adjusted to enhance the credibility of early warnings. The final global risk prediction map provides a precise basis for hierarchical early warning and resource allocation, significantly improving the efficiency of disaster emergency response.

[0083] Step 4: Issue early warning instructions to edge nodes and regional nodes in layers according to the global risk prediction map, and schedule edge, regional and global resources based on priority.

[0084] Step four includes the following:

[0085] Step 401: The cloud generates tiered early warning information based on the global risk prediction map and distributes it according to architectural responsibilities:

[0086] Edge nodes receive unit-level early warning instructions, such as if unit A01 will reach red risk at 14:30, instructing edge nodes to activate audible and visual alarms and local personnel evacuation prompts, and push the information to terminal devices within the unit, such as residents' mobile apps and on-site monitoring terminals; regional nodes receive regional-level early warning instructions, such as if five units in region A will reach orange risk within the next hour, instructing regional nodes to activate regional rescue team mobilization and pre-positioning of supplies, and simultaneously notify local disaster prevention departments; the early warning information includes risk basis and response suggestions, such as risk basis based on a predicted water level of 1.2m and rainfall of 80mm / h in unit A01, and response suggestions such as immediate evacuation of residents in low-lying areas, improving the credibility of the early warning;

[0087] Step 402: The cloud, based on a global resource pool, accesses local resources from edge nodes (e.g., temporary water pumps), regional resources from regional nodes (e.g., fire trucks), cross-regional global resources (e.g., drones), and predicted risk priorities (sorted by risk level + population density + distribution of critical facilities) in real time, and executes scheduling:

[0088] High priority units are red alert units with a population density >500 people / km², or alert units containing critical infrastructure such as reservoirs and hospitals. They adopt a three-level dispatch system: edge-region-global. First, the nearest emergency resources of the edge nodes are dispatched, such as temporary water pumps within 300 meters. Then, the main resources of the regional nodes are dispatched, such as fire trucks that can be reached within 5 minutes. Finally, global support resources in the cloud are coordinated, such as drone fire reconnaissance.

[0089] Medium-priority units are orange alert units with a population density >300 people / km², or alert units containing general infrastructure such as schools and substations. Low-priority units are yellow / blue alert units, or alert units with a population density ≤300 people / km². Regional coordination and scheduling are adopted, with regional nodes allocating idle resources, such as spare materials from neighboring towns, to avoid occupying high-priority resources.

[0090] Step 403: Upon receiving a resource scheduling request, the edge node immediately performs resource allocation: If local resources are insufficient, for example, if 10 water pumps are needed but the edge node only has 5, it immediately sends a resource shortage request to the regional node, indicating the shortage quantity and the type of resource required; upon receiving the resource shortage request, the regional node schedules idle resources from the regional resources, such as allocating 5 water pumps from adjacent edge nodes. If the regional resources are still insufficient, it sends a cross-regional resource support request to the cloud; upon receiving the cross-regional support request, the cloud selects the cross-regional resources with the shortest distance and the least scheduling time from the global resource pool, such as scheduling fire trucks from adjacent regions, and coordinates with the transportation department to open an emergency channel, while simultaneously synchronizing the adjusted scheduling plan to the relevant regional nodes and edge nodes;

[0091] When using this method, please refer to the content of steps 401 to 403:

[0092] By issuing early warning instructions in a tiered manner and prioritizing resource allocation, highly efficient coordination in disaster emergency response is achieved. The tiered early warning mechanism ensures that resources at the periphery, regional, and global levels are accurately matched to disaster risk levels, improving response speed. Prioritization optimizes resource allocation, prioritizing areas with high population density and critical facilities. Dynamic resource allocation maximizes emergency response efficiency and reduces redundancy. Risk assessment and response recommendations enhance the credibility and operability of early warnings. Ultimately, this achieves a rapid and orderly disaster emergency response, minimizing disaster losses.

[0093] Example 2

[0094] Please see Figure 3 Embodiment 2 of the present invention also provides an intelligent disaster prevention and early warning management system based on cloud computing and multi-architecture collaboration, including: a responsibility allocation and collaborative management module, a data fusion and conflict handling module, a disaster prediction and conflict arbitration module, and an early warning distribution and resource scheduling module, wherein:

[0095] The responsibility allocation and collaborative management module constructs a database linking disaster characteristics and architectural capabilities. It generates an initial responsibility allocation table based on disaster precursor signals and dynamically adjusts responsibilities through collaboration between edge nodes, regional nodes, and the cloud to form the final responsibility allocation table.

[0096] The data fusion and conflict resolution module divides the smallest early warning units according to disaster type and binds them with spatiotemporal IDs. It performs normalization processing and conflict verification on multi-source data and generates a global fusion dataset.

[0097] The disaster prediction and conflict arbitration module calculates the similarity score of the current disaster based on the historical disaster case database, generates local prediction results, and arbitrates cross-regional conflicts through the cloud to form a global risk prediction map;

[0098] The early warning distribution and resource scheduling module distributes early warning instructions to edge nodes and regional nodes in a hierarchical manner based on the global risk prediction map, and schedules edge, regional and global resources based on priority.

[0099] In the application, the various formulas mentioned are all calculated by removing dimensions and taking their numerical values. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The coefficients in the formulas are set by those skilled in the art according to the actual situation.

[0100] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, and combinations thereof. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0101] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0102] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A smart disaster prevention and early warning management method based on cloud computing and multi-architecture collaboration, characterized by: include: A correlation database of disaster characteristics and architectural capabilities is constructed. When edge nodes or regional nodes capture initial disaster precursor signals, they upload the signals to the cloud. The cloud calls the correlation database to match the core requirements of the current disaster with the capabilities of each architecture, generating an initial responsibility allocation table, including the data collection frequency, jurisdiction, and upload requirements of edge nodes, regional nodes, and the cloud. Edge nodes perform targeted data collection according to the initial responsibilities, collecting only core disaster data and performing lightweight preprocessing on the core disaster data. If the disaster stage escalates, they apply to the cloud for responsibility adjustment. The cloud receives responsibility adjustment applications from each regional node, performs global collaborative arbitration based on cross-regional bandwidth status, generates a final responsibility allocation table, and synchronizes it to all nodes. The smallest early warning unit is divided according to the disaster type and bound to a spatiotemporal ID. Multi-source data is normalized and conflict checked to generate a global fusion dataset. Regional nodes obtain the global fusion dataset within their jurisdiction from the cloud, extract the current unit fusion data features, calculate the similarity score of the current disaster based on the historical disaster case database, generate local prediction results, and form a global risk prediction map by arbitrating cross-regional conflicts through the cloud. Early warning instructions are issued to edge nodes and regional nodes in a hierarchical manner based on the global risk prediction map, and edge, regional and global resources are scheduled based on priority.

2. The intelligent disaster prevention and early warning management method based on cloud computing and multi-architecture collaboration according to claim 1, characterized in that, The library that links disaster characteristics with architectural capabilities includes: A cloud-based pre-built disaster feature architecture capability association library stores the mapping relationship between core disaster monitoring requirements and architecture capability parameters. Among them, core disaster monitoring requirements include high-frequency rainfall / water level data for rainstorms, temperature / wind speed data for forest fires, and P-wave acquisition data for earthquakes. Architecture capability parameters include the single-device computing power and data transmission latency of edge nodes, the storage capacity and aggregation latency of regional nodes, the bandwidth of the cloud, and the global data processing capabilities.

3. The intelligent disaster prevention and early warning management method based on cloud computing and multi-architecture collaboration according to claim 2, characterized in that, Dynamically adjusting responsibilities includes: Outliers are removed using a sliding window anomaly detection system, generating valid data and quality labels, which are then uploaded to the regional node. The regional node statistically analyzes the distribution of quality labels for edge node data within its jurisdiction and determines the disaster stage based on the real-time status of the local disaster. If the proportion of low-quality labels exceeds a preset threshold, the regional node temporarily takes over the responsibility for secondary data verification.

4. The intelligent disaster prevention and early warning management method based on cloud computing and multi-architecture collaboration according to claim 1, characterized in that, The smallest early warning unit is divided according to disaster type and bound to a spatiotemporal ID, including: The cloud-based system divides the smallest early warning units according to the disaster type and granularity: 1km×1km for rainstorms, 500m×500m for forest fires, and 2km×2km for earthquakes. Each unit is assigned a unique spatial ID. Regional nodes determine the time granularity based on the collection frequency in the final responsibility allocation table, generate timestamps, and bind data from each architecture layer to spatiotemporal IDs by spatial IDs and timestamps. Under the same spatiotemporal ID, edge data, regional data, and cloud data are included. Regional nodes unify the dimensions of multi-source data under the same spatiotemporal ID, generate spatiotemporally aligned normalized data, and calculate the unit state change rate to determine the disaster evolution stage.

5. The intelligent disaster prevention and early warning management method based on cloud computing and multi-architecture collaboration according to claim 4, characterized in that, Conflict checks include: The cloud assigns stage-adaptive weights to data from different sources based on the disaster stage, generating a data source weight table. Regional nodes perform conflict detection on multi-source data under the same spatiotemporal ID. If a conflict exists, a three-level verification mechanism is initiated: the first level of verification involves re-collecting data, the second level involves retrieving the average data from adjacent early warning units, and the third level involves the cloud arbitrating the average historical data. Regional nodes perform weighted fusion of non-conflicting data according to the data source weight table, generating a unit fusion dataset, which is then uploaded to the cloud for integration to form a global fusion dataset.

6. The intelligent disaster prevention and early warning management method based on cloud computing and multi-architecture collaboration according to claim 1, characterized in that, The generated local prediction results include: Regional nodes pre-store a local historical disaster case library, which contains fragments of the global fusion dataset of historical disasters, disaster spread paths, impact ranges, and risk levels. Case similarity scores are calculated, and the top 3 historical cases with the highest similarity are selected. Local prediction results are generated, including spread direction, spread speed, risk level, and confidence level. The spread direction is taken from multiple values ​​of historical cases, the spread speed is calculated by weighting similarity, the risk level is adjusted by referring to historical cases and combining current feature biases, and the confidence level is taken from the average similarity of historical cases.

7. The intelligent disaster prevention and early warning management method based on cloud computing and multi-architecture collaboration according to claim 6, characterized in that, Cloud-based arbitration of cross-regional conflicts includes: The cloud receives local prediction results from all regional nodes and detects cross-regional conflicts. A conflict is defined as an overlap in the predicted diffusion range of adjacent regions and an opposite direction, or the same unit being predicted to have different risk levels by different regions. When there are multiple regional prediction conflicts for the same unit, the prediction result of the main region that governs the unit is used first, determined according to administrative divisions or a preset unit region mapping table. The cloud integrates and corrects all regional local prediction results, generates a global risk prediction map, marks the risk level, diffusion time, confidence level, and impact range of each early warning unit, and distributes it to each regional node and edge node.

8. The intelligent disaster prevention and early warning management method based on cloud computing and multi-architecture collaboration according to claim 1, characterized in that, The tiered issuance of early warning instructions includes: The cloud generates hierarchical early warning information based on the global risk prediction map and distributes it according to the architecture responsibilities: edge nodes receive unit-level early warning instructions and activate audible and visual alarms and local personnel evacuation prompts; regional nodes receive regional-level early warning instructions and activate rescue team assembly and pre-positioned material reserves; the early warning information includes risk basis and response suggestions, the risk basis is based on the characteristics of unit fusion data, and the response suggestions include evacuation routes and resource allocation plans.

9. The intelligent disaster prevention and early warning management method based on cloud computing and multi-architecture collaboration according to claim 8, characterized in that, Resource scheduling includes: The cloud-based system uses a global resource pool to prioritize and schedule resources according to risk. High-priority units, such as those under red alert and with a population density > 500 people / km² or containing critical infrastructure, are scheduled using a three-tiered approach: edge-regional-global. Medium-priority units, such as those under orange alert and with a population density > 300 people / km² or containing general infrastructure, are scheduled using a regional coordinated approach. Low-priority units, such as those under yellow / blue alert or with a population density ≤ 300 people / km², are allocated idle resources by regional nodes. Upon receiving a resource scheduling request, the edge node immediately performs allocation. If local resources are insufficient, it sends a shortage request to the regional node. The regional node schedules idle resources from the regional resources. If it is still insufficient, it requests cross-regional support from the cloud. The cloud selects the cross-regional resources with the shortest distance and the least time, coordinates the opening of an emergency channel, and synchronizes the adjusted scheduling plan to the relevant nodes.

10. An intelligent disaster prevention and early warning management system based on cloud computing and multi-architecture collaboration, characterized in that: include: The responsibility allocation and collaborative management module constructs a correlation library of disaster characteristics and architectural capabilities. When an edge node or regional node captures an initial disaster precursor signal, it uploads the signal to the cloud. The cloud then calls the correlation library to match the core requirements of the current disaster with the capabilities of each architecture, generating an initial responsibility allocation table. This table includes the data collection frequency, jurisdiction, and upload requirements for edge nodes, regional nodes, and the cloud. Edge nodes perform targeted data collection according to their initial responsibilities, collecting only core disaster data and performing lightweight preprocessing on it. If the disaster stage escalates, the edge node requests a responsibility adjustment from the cloud. The cloud receives responsibility adjustment requests from each regional node, performs global collaborative arbitration based on cross-regional bandwidth status, generates a final responsibility allocation table, and synchronizes it to all nodes. The data fusion and conflict resolution module divides the smallest early warning units according to disaster type and binds them with spatiotemporal IDs. It performs normalization processing and conflict verification on multi-source data and generates a global fusion dataset. The disaster prediction and conflict arbitration module allows regional nodes to obtain the global fusion dataset within their jurisdiction from the cloud, extract the current unit's fusion data features, calculate the similarity score of the current disaster based on the historical disaster case database, generate local prediction results, and arbitrate cross-regional conflicts through the cloud to form a global risk prediction map. The early warning distribution and resource scheduling module distributes early warning instructions to edge nodes and regional nodes in a hierarchical manner based on the global risk prediction map, and schedules edge, regional and global resources based on priority.

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