Early warning service-oriented rainstorm event identification and evaluation integrated method and system

Abstract

The invention discloses an early warning service-oriented rainstorm event identification and evaluation integrated method and system, and the method comprises the steps: obtaining hourly rainfall observation data, rainstorm early warning signal data and geographic information data from multi-source meteorological data, and integrating the data into a structured data set; on the basis of sliding accumulation judgment, administrative division space aggregation and time merge, automatically identifying a standard regional hourly rainfall event; executing space-time matching and multi-dimensional evaluation analysis, and outputting a quantitative result; and generating a visual report and a differential optimization strategy, and forming an evaluation-optimization-re-evaluation service closed loop through a feedback mechanism. According to the method, full-process automatic evaluation and objective diagnosis are realized, an accuracy + timeliness multi-dimensional index system is constructed, a'station-region 'scale conversion effect is quantified, deep problems such as excessive early warning, missing report and level imbalance are accurately diagnosed, a differentiated optimization strategy is intelligently recommended, and an intelligent closed loop of evaluation-optimization-re-evaluation is formed.

Description

Technical Field

[0001] This invention relates to the field of meteorological early warning and assessment technology, and in particular to an integrated method and system for identifying and assessing rainstorm events for early warning operations. Background Technology

[0002] Against the backdrop of global warming, extreme precipitation events are showing a significant increase in frequency and intensity, with their time scales shifting from traditional daily and next-day scales to hourly and next-hourly scales of short-duration heavy rainfall. Due to their suddenness, significant locality, and high risk of causing disasters, this type of precipitation has become a primary factor triggering secondary geological disasters such as flash floods and mudslides, posing a serious threat to people's lives and property. In this context, rainstorm warning signals, as a key information product for short-duration heavy rainfall within the disaster prevention and mitigation system, are regarded by the industry as the "message tree" and "starting gun" of emergency response; their scientific validity and accuracy directly determine the effectiveness of disaster prevention and mitigation. In recent years, meteorological departments and academia have focused their attention on the scientific revision of rainstorm warning standards themselves. For example, they have optimized warning thresholds by introducing indicators such as hourly rainfall intensity, or redefined disaster probability-driven grading standards based on encrypted observation data. These studies have significantly improved the objectivity and local applicability of warning "benchmarks." However, warning standards, as static "benchmarks," are only the starting point of the warning operational chain. The more prominent contradiction lies in the fact that even with scientifically optimized early warning standards, the assessment, diagnosis, and optimization mechanisms in actual meteorological operations suffer from fundamental technical flaws, making it difficult for the entire early warning system to form a closed loop of continuous improvement. This core bottleneck is mainly reflected in the following aspects:

[0003] First, it is highly subjective and has a low level of automation: the core identification and assessment process relies heavily on manual operation, which is not only inefficient but also significantly affected by the forecaster's experience, making it difficult to guarantee the objectivity and consistency of the assessment results.

[0004] Second, the assessment dimensions are singular and lack a systematic approach: the existing assessment system relies excessively on limited indicators such as hit rate and false alarm rate, making it difficult to systematically and comprehensively measure the effectiveness of early warnings. In particular, it fails to adequately identify the overall underreporting of eligible regional hourly precipitation events, resulting in an inability to effectively assess the coverage capacity of early warning signals for regional extreme precipitation. Simultaneously, existing methods severely neglect multi-dimensional quantitative analysis of early warning timeliness, failing to comprehensively evaluate the effectiveness of early warnings from different issuance stages such as advance warnings, process warnings, and actual situation warnings, thus failing to systematically reflect the overall performance of the early warning mechanism at the timeliness level. The existing assessment method (the "Verification Method") only statistically analyzes issued early warning signals, failing to automatically and systematically identify all objectively eligible actual heavy rain events. Therefore, the assessment system cannot accurately determine which events should be warned (i.e., "reportable events"), making it difficult to truly and scientifically calculate the false alarm rate and achieve a comprehensive quantitative assessment of hit rate, false alarm rate, false alarm rate, and timeliness.

[0005] Third, the lack of objective rules for cross-scale assessment leads to insufficient basis for delineating warning areas: In current operations, provincial and municipal meteorological warnings are issued at the county / district level as the smallest unit, while real-time observations are based on discretely distributed meteorological stations, resulting in a significant scale mismatch. Currently, no scientific and unified automated rules have been established to achieve objective conversion and evaluation from station observations to county / district warning units, leading to vague assessment criteria and insufficient reliability of results. This problem is further exacerbated as warning issuance authority is delegated to counties / districts: when determining whether issuance standards are met and delineating warning areas, counties / districts mainly rely on forecasters' subjective experience, lacking quantitative and objective decision support tools. The consequence is poor consistency in warning issuance strategies; some counties / districts tend to over-expand warning areas, leading to decreased accuracy, while others may leave risks due to incomplete coverage, ultimately affecting the effectiveness of emergency response. The root cause lies in the lack of systematic mining of the spatiotemporal distribution patterns of historical rainstorm events by existing technology, making it impossible to provide a scientific basis for delineating warning areas based on climatic probability.

[0006] Fourth, it is disconnected from business operations, making it difficult to form an optimization loop: The current assessment function is isolated and only used for post-event statistics. It cannot provide forward-looking and differentiated decision support for early warning issuance (such as accurate delineation of the affected area and dynamic adjustment of the level), thus limiting its business guidance value.

[0007] Fifth, limited technical approaches: Existing technologies (such as CN202510976888) only statistically analyze the early warning effect, lacking mechanisms for precipitation event identification and operational optimization. Although significant progress has been made in improving forecasting capabilities, their technical approaches are fundamentally different from the "early warning signal quality assessment" problem that this invention aims to solve, and cannot be directly applied to conduct systematic and quantitative quality verification and effectiveness evaluation of operational early warning signals.

[0008] Therefore, there is an urgent need to develop an innovative method and system to automatically identify eligible regional hourly precipitation events and to conduct systematic, objective, and quantitative evaluation of early warning signals. This initiative aims to directly support operational optimization, overcome existing technological bottlenecks, and thus drive a fundamental shift in rainstorm early warning from experience-driven to data-driven intelligent decision-making. Summary of the Invention

[0009] This invention provides an integrated method and system for rainstorm event identification and assessment for early warning services, addressing the technical problem in the prior art of how to provide an automated, objective, and quantitative scheme for regional precipitation event identification and early warning signal assessment that meets rainstorm early warning standards, thereby achieving accurate evaluation of early warning signals and effective support for service optimization.

[0010] An integrated method for identifying and assessing rainstorm events for early warning operations includes:

[0011] S1. Obtain historical hourly precipitation observation data from automatic ground stations, rainstorm warning signals issued by meteorological stations, and geographic information data from multiple meteorological data sources, and integrate them into a structured dataset;

[0012] S2. Based on structured datasets and by identifying hourly precipitation events at qualifying stations through sliding cumulative precipitation, regional hourly precipitation events that meet the warning criteria are automatically identified by utilizing spatial aggregation of administrative divisions and merging of time windows.

[0013] S3. Receive information on regional hourly precipitation events that meet the warning criteria, perform spatiotemporal matching between the qualified regional hourly precipitation events and the warning signals, and then perform spatiotemporal distribution characteristic analysis, accuracy assessment, and timeliness assessment respectively; and output spatiotemporal distribution characteristics and multi-dimensional quantitative assessment results.

[0014] S4. Based on the multi-dimensional evaluation results, automatically generate differentiated early warning strategy optimization schemes, and form an operational evaluation-optimization-re-evaluation closed loop through early warning strategy execution and re-evaluation mechanisms.

[0015] An integrated system for rainstorm event identification and assessment for early warning operations, comprising:

[0016] The data acquisition module is used to acquire historical hourly precipitation observation data from ground automatic stations, rainstorm warning signals issued by meteorological stations, and geographic information data from multiple meteorological data sources, and integrate them into a structured dataset;

[0017] The regional hourly precipitation event identification module, connected to the data acquisition module, is based on a structured dataset. It calculates multi-scale cumulative precipitation using a variable sliding window to identify events at eligible stations and their highest levels. By utilizing administrative division spatial aggregation and time window merging, it automatically identifies regional hourly precipitation events that meet the warning standards.

[0018] The assessment and analysis module is connected to the regional hourly precipitation event identification module. It is used to receive information on regional hourly precipitation events that meet the warning standards, perform spatiotemporal matching between regional hourly precipitation events and warning signals, and then perform spatiotemporal distribution characteristic analysis, accuracy assessment, and timeliness assessment respectively; and output spatiotemporal distribution characteristics and multi-dimensional quantitative assessment results.

[0019] The evaluation results output and optimization module, connected to the evaluation analysis module, is used to generate historical statistical pattern analysis charts, visual evaluation reports, and business optimization suggestions based on spatiotemporal distribution characteristics and multi-dimensional quantitative evaluation results; automatically generate differentiated early warning optimization strategies and provide strategy parameter configuration and constraint interfaces; and transform evaluation results into specific parameters and strategy configuration instructions that can guide business through a feedback mechanism, automatically triggering re-evaluation to achieve a continuous iterative "evaluation-optimization-re-evaluation" business closed loop.

[0020] This invention brings three significant benefits by constructing a fully automated early warning, assessment, diagnosis, and optimization system:

[0021] First, it automates and objectifies the assessment process, effectively solving the problems of traditional methods that rely on manual labor, are inefficient, and are highly subjective. By automatically acquiring massive amounts of site observation data and early warning information through data interfaces, and matching and verifying them based on preset rules, it can automatically complete the statistical work that previously required a lot of manpower. This not only greatly improves efficiency but also eliminates the uncertainty caused by human intervention, ensuring the objectivity and reproducibility of the assessment process.

[0022] Secondly, it enables precise quantitative diagnosis of core business challenges, solving the problem that traditional assessment conclusions are one-sided and unable to reveal deep structural defects. By introducing multi-level timeliness indicators (T1, T2, T3) and a multi-dimensional assessment system, it can quantify the "site-region" scale conversion effect and systematically diagnose business pain points that are difficult to detect with traditional single indicators, such as "over-prevention of regional warnings," "under-reporting of regional warnings," and "imbalance in the structure of warning levels." This technical solution can directly output quantitative indicators such as false alarm rate, under-reporting rate, and level proportion, providing accurate data support for problem localization.

[0023] Third, it achieves an intelligent closed loop from diagnosis to strategy generation, solving the core problem of the disconnect between evaluation and optimization in traditional business processes. The core advantage of this invention lies in using preset strategy generation rules to call data-driven discriminative models to calculate and adjust warning parameters. These discriminative models include, but are not limited to, motion analysis models based on optical flow characteristics, random forest models, and XGBoost models, to generate warning strategy parameters that match the diagnostic results. For example, for diagnosed high-reporting-rate areas, the system automatically suggests strategies to increase the release threshold and optimize the landing area determination algorithm; for high-missing-rate areas, it automatically generates optimized "site-county" aggregation rules, adjusts trigger thresholds, and introduces models better at capturing nascent signals to improve capture sensitivity. The strategy configuration scheme can be confirmed through a human-computer interaction interface regarding the parameter activation status, applicable scope, or risk constraints. The system automatically integrates the confirmed strategy configuration into the warning business process, thereby driving adaptive adjustments to warning parameters and release logic. This integrated "precise diagnosis-intelligent recommendation" output mechanism changes the traditional business model that relies on expert experience and has a slow response, achieving accurate and rapid decision optimization based on data.

[0024] In summary, the technical solution of this invention effectively overcomes a series of core problems in existing early warning operations, such as one-sided assessment, reliance on manual labor, lack of scale conversion, and disconnect between assessment and optimization, and has outstanding practical value and significant technological progress. Attached Figure Description

[0025] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a flowchart of an integrated method for rainstorm event identification and assessment for early warning services according to an embodiment of the present invention;

[0027] Figure 2 This is a schematic diagram of the structure of an integrated system for rainstorm event identification and assessment for early warning services according to an embodiment of the present invention;

[0028] Figure 3 This is a comparison chart of the spatiotemporal distribution of rainstorm warning signals and precipitation events in Chongqing from 2014 to 2022.

[0029] Figure 4 This is a distribution map showing the hit rate and underreporting rate of rainstorm warnings in various districts and counties of Chongqing from 2014 to 2022.

[0030] Figure 5This is a correlation chart showing the relationship between the false alarm rate of rainstorm warnings and the number of warning signals in various districts and counties of Chongqing from 2014 to 2022. Detailed Implementation

[0031] 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, not all, of the embodiments of the present invention. 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.

[0032] The present invention provides an integrated method for rainstorm event identification and assessment for early warning services, which is applied in an integrated system for rainstorm event identification and assessment for early warning services.

[0033] In one embodiment, such as Figure 1 As shown, an integrated method for identifying and assessing rainstorm events for early warning operations is provided, including the following steps:

[0034] S1. Historical hourly precipitation observation data from ground automatic stations, rainstorm warning signals issued by meteorological stations, and geographic information data are obtained from multiple meteorological data sources and integrated into a structured dataset.

[0035] S2. Based on structured datasets and by identifying hourly precipitation events at qualifying stations through sliding cumulative precipitation, the system automatically identifies regional hourly precipitation events that meet the warning criteria by utilizing spatial aggregation of administrative divisions and merging of time windows.

[0036] S3. Receive information on regional hourly precipitation events that meet the warning criteria, perform spatiotemporal matching between the regional hourly precipitation events and the warning signals, and then perform spatiotemporal distribution characteristic analysis, accuracy assessment, and timeliness assessment respectively; and output spatiotemporal distribution characteristics and multi-dimensional quantitative assessment results.

[0037] S4. Based on spatiotemporal distribution characteristics and multi-dimensional quantitative evaluation results, generate historical statistical pattern analysis charts, visual evaluation reports, and business optimization suggestions; and through a feedback mechanism, transform the evaluation results into specific parameters and strategies that can guide business, forming a business closed loop of evaluation-optimization-re-evaluation.

[0038] In one embodiment, the method for identifying regional hourly precipitation events that meet the warning criteria in step S2 includes:

[0039] S201. Preprocess the structured dataset and calculate the sliding cumulative precipitation at multiple time scales using a variable sliding window width (including but not limited to 3h, 6h, 12h or adaptive window) for hourly precipitation observation data.

[0040] Understandably, a station hourly precipitation event is defined as a continuous precipitation process observed at a station, with hourly rainfall ≥ 0.1 mm as the precipitation criterion, and a maximum of 1 hour of precipitation interruption is allowed; if there is no precipitation for at least 2 hours after continuous precipitation, the event is considered to have ended.

[0041] For each hourly precipitation event at a station, the sliding cumulative precipitation is calculated for 3-hour, 6-hour, and 12-hour periods or an adaptive window, forming a basis for multi-timescale determination. The adaptive window can be dynamically adjusted according to the persistence of the precipitation process, seasonal characteristics, or topographic factors, for example, a shorter 3-hour window is used in the summer when convection is active, and a longer 12-hour window is used in the season when stable precipitation is dominant.

[0042] S202. Based on the early warning standard, threshold determination is performed on station-level events to identify hourly precipitation events at stations that meet the rainstorm warning standard and record their highest warning level.

[0043] Understandably, the threshold determination involves comparing the maximum sliding cumulative precipitation (3 / 6 / 12 hours) of each station event with the China Meteorological Administration's rainstorm warning standards (blue / yellow / orange / red levels).

[0044] Event tagging: Filter out site events where the maximum cumulative sliding value reaches the warning threshold, and record the highest warning level reached.

[0045] S203. Through a two-level fusion mechanism of spatial aggregation and temporal merging, discrete compliant site events are integrated into regional hourly precipitation events with clear spatiotemporal boundaries that meet the warning standards.

[0046] Intelligible spatial aggregation: Load district and county-level administrative boundary data, spatially group all hourly precipitation events at all stations according to their respective administrative divisions, and ensure that each district and county forms an independent set of events;

[0047] Time merging: Time merging is performed on events within the same district / county. The merging rule is: if the start time of events at the same site differs by ≤2 hours, or if the time window overlaps / discontinues with the time window of existing events within the group by ≤2 hours, then the events are merged.

[0048] Event Judgment and Output: The earliest start time and latest end time of all events in the merged group are used to define the unified time boundary of the regional precipitation process. If the regional hourly precipitation process includes at least one hourly precipitation event at a station that meets the rainstorm warning standard, it is automatically judged as a qualified regional hourly precipitation event, and its key attributes (start time, duration, highest warning level, spatial range) are output as input data for subsequent evaluation and analysis modules.

[0049] In one embodiment, step S3 further includes the following steps:

[0050] S301. Perform spatiotemporal matching between the list of eligible regional hourly precipitation events and the actual warning signals issued in meteorological operations to generate a spatiotemporal matching result set and a non-matching result set.

[0051] Understandably, the matching principle is as follows: Spatially, the area where the warning signal is issued must be consistent with the area where the eligible regional hourly precipitation event occurs (accurate to the same district / county-level administrative region). Temporally, it should fully cover various warning scenarios in actual operations, including advance warnings issued before precipitation begins, process warnings issued after precipitation has begun but before reaching the warning standard, and real-time warnings issued after the actual situation has reached the warning standard. Based on this matching principle, a spatiotemporal matching result set (hit warning signals and events) and a non-matching result set (false alarms, missed events) are generated. Furthermore, the system needs to statistically analyze the issuance patterns of warning signals, the spatiotemporal distribution characteristics of eligible stations and regional hourly precipitation events. This step S301 aims to establish a precise correlation or relationship between warning signals and actual precipitation events, generate matching and non-matching result sets, and output a report on the spatiotemporal characteristics of warning signals and eligible regional hourly precipitation events.

[0052] S302. Based on the generated spatiotemporal matching result set and the non-matching result set, perform binary classification judgment and calculate the core accuracy index to support deep structure diagnosis.

[0053] In one embodiment, step S302 further includes the following steps:

[0054] S3021. To focus on the core matching relationship between "whether an early warning was issued" and "whether the event actually occurred", a non-tiered test is conducted. When determining whether an early warning signal is hit or whether a precipitation event is missed, the specific level (blue / yellow / orange / red) of the early warning signal or precipitation event is not distinguished. Only the basic attributes as "early warning event" and "meeting precipitation event" are considered. This framework aims to provide a standardized and comparable benchmark dataset to simplify the initial evaluation process.

[0055] S3022. In the non-grading test, based on the spatiotemporal matching and non-matching result sets, five types of event determinations are performed in parallel:

[0056] Site event hit: If the hourly precipitation event at a station reaches the warning standard during the issuance of any level of rainstorm warning signal, it is judged as a site event hit;

[0057] Site event underreporting: If a site's hourly precipitation event reaches the warning standard, but no rainstorm warning signal of any level for the corresponding area and time is issued, then the site event is underreported.

[0058] Regional event hit: For a qualified regional hourly precipitation process, if at least one station in the region has a qualified hourly precipitation event, the regional hourly precipitation process is considered to have hit.

[0059] Regional event underreporting: If all hourly precipitation events that meet the rainstorm warning criteria based on stations within a region are underreported, then the hourly precipitation process in that region is considered to have been underreported.

[0060] Warning signal false alarm: If at least one station in the corresponding area meets the hourly precipitation event standard during the issuance period, the warning signal is considered to be true; otherwise, it is considered false alarm.

[0061] S3023. Calculate the core accuracy indicators based on the judgment results of the five types of events;

[0062] Hit rate (POD) = (Number of hits NA) / (Number of hits NA + Number of missed hits NC) × 100%;

[0063] False Alarm Rate (FAR) = Number of false alarms NB / (Number of hits NA + Number of false alarms NB) × 100%;

[0064] TS score (CSI) = (Number of hits NA) / (Number of hits NA + Number of false alarms NB + Number of missed alarms NC) × 100%.

[0065] The above steps ultimately output an accuracy assessment report, completing a quantitative evaluation of the rainstorm warning signal from an accuracy perspective.

[0066] S3024. Based on the standardized benchmark dataset provided by the non-grading test, the system further mines and utilizes the "early warning level" attribute contained in the dataset to perform grading statistics and structural analysis.

[0067] Understandably, building upon this foundation, the rich attributes of the dataset (such as the level of actual events meeting the criteria and the level of missed events) are further utilized to conduct in-depth hierarchical statistics and structural analysis. This allows for the diagnosis of systemic operational problems that are difficult to detect in traditional assessments, namely, "imbalance in the warning level structure." For example, when historical data indicates that there have been multiple rainstorm events meeting the red warning standard, but the actual issuance of red warning signals is extremely rare, the system identifies a serious mismatch between this level issuance strategy and the actual risk distribution. "Non-level testing" and "in-depth hierarchical analysis" together constitute a progressive assessment system from a unified benchmark to in-depth diagnosis.

[0068] S303. Based on the results of the hit, miss and false alarm judgments, the timeliness of the warning signal is quantitatively evaluated in multiple dimensions, and a timeliness evaluation report is output.

[0069] In one embodiment, step S303 further includes the following steps:

[0070] S3031. First, establish the basic variable for timeliness assessment: warning timeliness C. Warning timeliness C is defined as the time difference between the warning signal issuance time A and the actual precipitation reaching the warning standard time B (C=BA).

[0071] S3032. Construct three core multi-level timeliness assessment indicators, namely, accurate early warning time lead T1, early warning time lead T2, and effective early warning time lead T3; among which, the three core indicators are:

[0072] Accurate warning lead time (T1): Only for the hit warning samples, the average lead time of all hit samples is calculated, reflecting the timeliness of successful warnings;

[0073] Warning lead time (T2): To measure the overall response capability of the warning system to reported events, the average is calculated by including missed reports (whose warning lead time C is counted as 0) on the basis of the hit samples in T1, and measuring the overall response timeliness.

[0074] Effective warning lead time (T3): In order to comprehensively evaluate the overall effectiveness of the system, T3 further considers false alarm samples (whose warning lead time C is calculated as 0), and evaluates the overall warning effectiveness of the warning system under the conditions of hit, false alarm and false alarm.

[0075] Statistical output: Calculate the statistical distribution (mean / median / quantile) of the three types of indicators and generate a timeliness assessment report.

[0076] The above three indicators systematically quantify the timeliness performance of the early warning system from different dimensions. T1 emphasizes the hit quality, T2 takes into account the response capability, and T3 reflects the overall performance of the system. The system ultimately outputs a timeliness assessment report based on these indicators. Specifically, for missed or false alarm samples where no early warning signal was issued, the early warning timeliness C is uniformly recorded as 0. The definitions of these indicators are based on the unified business agreement of "no advance notice if not issued" in early warning operations, which aligns with the objective response logic of early warning signals in the business system.

[0077] S304. Based on the quantitative relationship between identified hourly precipitation events at stations and regional rainstorm events, a scale conversion quantitative index is generated to reveal the scale conversion effect of "station-region". The scale conversion quantitative index is the "average number of stations for regional events" or the station-region aggregation index. The specific calculation method of this index is: Station-region aggregation index = total number of qualifying station events included in all qualifying regional events / total number of qualifying regional hourly precipitation events. The physical and operational significance of this index is that: the higher the value, the more station events are required to constitute a regional event on average, that is, the regional event has a larger spatial coverage, the regional scale characteristics are more significant, and the regional scale is less sensitive to the precipitation fluctuations of individual stations. Under this case, the spatial range of the regional warning area is relatively stable, and the warning issuance has a high degree of consistency. Conversely, when the index value is low, it indicates that a small number of station events can constitute a regional event, the regional scale is more sensitive to the response of station information, the local characteristics of station precipitation are more prominent at the regional scale, and higher requirements are placed on the spatial refinement of the warning area. Therefore, by calculating and analyzing the "site-region aggregation index", this invention can objectively quantify the spatial characteristics of precipitation events in different administrative divisions from the site scale to the regional scale from historical data, providing a quantitative decision-making basis for differentiated and refined early warning release strategies, thereby alleviating the early warning service problems caused by the mismatch between the "site-region" scale.

[0078] The above steps S301-S304, through a three-step progressive architecture of "spatiotemporal matching → accuracy assessment → timeliness assessment," quantify the accuracy and timeliness of the early warning signals. First, the spatiotemporal correspondence between the early warning and the actual situation is established; then, the matching results are subjected to multi-dimensional quantitative analysis, and finally, a comprehensive evaluation report is generated.

[0079] In one embodiment, the early warning service optimization method for service closure in step S4 further includes the following steps:

[0080] S401. Conduct a comprehensive analysis of the multi-dimensional evaluation results, and integrate the analysis results to generate a structured comprehensive report on optimization and early warning strategies.

[0081] In one embodiment, step S401 further includes:

[0082] S4011. Conduct spatiotemporal distribution statistics, statistically analyze the spatiotemporal distribution characteristics (including frequency, intensity, spatial distribution and seasonal variation) of hourly precipitation events and warning signals at qualified stations or in the region, generate event climate probability distribution maps and warning release pattern maps, and identify typical areas and time periods with missed reports, false reports and warning level deviations.

[0083] S4012. Conduct correlation diagnosis of early warning effect. Based on accuracy indicators (hit rate, false alarm rate, TS score) and timeliness indicators (T1, T2, T3), construct a correlation model between early warning effect and release strategy, diagnose performance differences under different regions, different time periods and different early warning levels, generate matching degree analysis results between early warning weak areas / transition areas and early warning levels, and identify the local or regional characteristics of rainstorm events.

[0084] S4013. Identify differentiated problem areas, use cluster analysis to accurately locate differentiated problem areas such as low hit rate, high false alarm rate, high false alarm rate, or time lag, and mismatch between actual situation and warning level, and integrate the analysis results to generate a structured comprehensive report on optimized warning strategies.

[0085] S402. Based on the comprehensive report of optimized early warning strategy, and in accordance with the preset strategy generation rule base, automatically generate a configurable differentiated early warning optimization strategy parameter set. The strategy parameter set includes at least one or more of the following: early warning threshold parameters, spatial aggregation rule parameters, early warning level mapping parameters, and timeliness triggering parameters.

[0086] In one embodiment, step S402 further includes:

[0087] S4021, Strategy for underreporting high-frequency areas: It is recommended to lower the warning issuance threshold, optimize site aggregation rules to identify scattered initial signals of heavy rainfall, and integrate radar extrapolation and numerical forecast products to improve the ability to identify and track initial convection. At the same time, it is recommended to continue to carry out research on the physical mechanism of heavy precipitation and the evolution law of precipitation in complex terrain in this region to support the localization optimization of short-term forecast models.

[0088] S4022, High-Frequency Warning Strategy: It is recommended to increase the warning issuance threshold and combine the echo evolution trend judgment of optical flow method with the heavy precipitation potential identification of random forest model to accurately eliminate invalid warning targets.

[0089] S4023, Time Lag Zone Strategy: It is recommended to introduce precursor indicators such as boundary layer convergence lines and vertical integral liquid water content, and optimize the early warning triggering logic to achieve early release.

[0090] S4024. Strategy for areas where warning levels do not match actual intensity: In response to the problem of insufficient issuance of high-level warnings such as red, in one embodiment, the strategy generation rule can call an intensity discrimination model based on machine learning. The model includes, but is not limited to, random forest model, XGBoost model, etc., to generate warning level mapping parameters based on multi-source meteorological feature data to support dynamic adjustment of warning levels.

[0091] S4025. Adaptation strategy for complex terrain areas: It is recommended to build a zonal early warning model based on elevation, slope aspect and historical rainstorm distribution to achieve spatially differentiated configuration of thresholds and level standards.

[0092] All strategies are integrated into the comprehensive report of optimized early warning strategies through parameterized instructions, providing a quantitative basis for the precise adjustment of the early warning business system.

[0093] S403. Through the interactive interface, the system automatically generates differentiated early warning optimization strategies, which are then subject to parameter constraint confirmation and activation status management. This allows for risk control and business integration of the strategies without altering their intelligent generation logic.

[0094] S404. Automatically integrate the enabled and confirmed strategies into the early warning business system in the form of parameterized instructions, apply them to the actual early warning release process, and realize the dynamic adjustment of early warning thresholds, coverage areas, hierarchical structure and triggering logic.

[0095] S405. Automatically collect hourly precipitation observation data and newly released early warning signal data generated after the application of the new strategy, triggering the evaluation and analysis module to enter a new round of evaluation iteration.

[0096] S406. Form a continuous business closed loop of assessment-optimization-reassessment, continuously explore historical patterns, diagnose business shortcomings, and optimize release strategies through multiple iterations, and promote the transformation of rainstorm warning business from traditional experience-based model to data-driven and intelligent decision-making.

[0097] The above steps S401-S406, through a six-level progressive architecture of "core diagnosis → intelligent policy generation → policy governance and activation control → business application → data feedback → closed-loop iteration", transform multi-dimensional evaluation results into executable business optimization decisions, forming a continuous improvement mechanism.

[0098] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0099] In one embodiment, an integrated system for identifying and assessing rainstorm events for early warning services is provided. This integrated system corresponds one-to-one with the integrated method for identifying and assessing rainstorm events for early warning services described in the above embodiments. Figure 2 As shown, this integrated system for rainstorm event identification and assessment for early warning operations includes:

[0100] The data acquisition module is used to acquire historical hourly precipitation observation data from automatic ground stations, rainstorm warning signals issued by meteorological stations, and geographic information data from multiple meteorological data sources, and integrate them into a structured dataset.

[0101] The regional hourly precipitation event identification module, connected to the data acquisition module, is used to identify eligible site events based on structured datasets and cumulative precipitation using variable sliding windows (3h / 6h / 12h / adaptive). It automatically identifies regional hourly precipitation events that meet the warning standards by utilizing spatial aggregation of administrative divisions and merging of time windows.

[0102] The assessment and analysis module is connected to the regional hourly precipitation event identification module. It is used to receive regional hourly precipitation event information that meets the warning standard, perform spatiotemporal matching between regional hourly precipitation events and warning signals, and then perform spatiotemporal distribution characteristic analysis, accuracy assessment and timeliness assessment respectively; and output spatiotemporal distribution characteristics and multi-dimensional quantitative assessment results.

[0103] The evaluation results output and optimization module, connected to the evaluation analysis module, is used to generate historical statistical pattern analysis charts, visual evaluation reports, and business optimization suggestions based on spatiotemporal distribution characteristics and multi-dimensional quantitative evaluation results; automatically generate differentiated early warning optimization strategies and provide strategy parameter configuration and constraint interfaces; and transform the evaluation results into specific parameters and strategy configuration instructions that can guide business through a feedback mechanism, automatically triggering re-evaluation to form a business closed loop of evaluation-optimization-re-evaluation.

[0104] In one embodiment, the data acquisition module includes:

[0105] The data acquisition unit is used to acquire historical hourly precipitation observation data from automatic ground stations, rainstorm warning signals issued by meteorological observatories, and geographic information data from multiple meteorological data sources.

[0106] The data cleaning unit is used to perform targeted cleaning of early warning signal data, removing test early warnings, de-escalation early warnings, duplicate records, and invalid information, retaining only the initial early warnings and updated early warnings with disaster prediction value, and performing format standardization and invalid value cleaning on station observation data, unifying the time format and precipitation unit, and removing records with empty fields and incorrect formats.

[0107] The quality control unit is used to perform quality control on the cleaned data through climatic limit values, internal consistency and spatiotemporal consistency tests, and to remove station data with a missing rate of more than 15%.

[0108] In one embodiment, the regional hourly precipitation event identification module includes:

[0109] The site event identification unit is used to automatically identify and record hourly precipitation events at stations that meet the warning standards and their highest levels, based on the China Meteorological Administration's rainstorm warning standards and by calculating and determining sliding cumulative precipitation.

[0110] Understandably, based on the China Meteorological Administration's rainstorm warning standards, the hourly precipitation data from various meteorological stations transmitted by the data acquisition module are processed. By calculating the sliding cumulative precipitation and comparing it with the precipitation thresholds corresponding to different levels in the warning standards, hourly precipitation events at stations that meet the warning standards are automatically identified and recorded. At the same time, the highest warning level corresponding to the event is determined, laying the foundation for the identification of subsequent regional events.

[0111] The regional hourly precipitation event identification unit is used to spatially aggregate station events based on administrative divisions and to merge them temporally according to the principle of similar start times or continuous processes. The time boundary of the regional precipitation process is defined by the earliest start and latest end times of events within the group. Finally, it automatically identifies the regional hourly precipitation events that meet the criteria and records their key attributes.

[0112] Understandably, the regional hourly precipitation event identification module is the core of the system. Based on the identification results of the station event identification unit, it spatially aggregates station events that meet the warning criteria within the same district or county, according to administrative divisions. Furthermore, based on the principles of similar start times or continuous precipitation processes, the spatially aggregated events undergo temporal merging. The earliest start time of an event within a group is used as the start time of the regional precipitation process, and the latest end time is used as the end time, thus defining the time boundary of the regional precipitation process. Finally, it automatically identifies regional hourly precipitation events that meet the warning criteria and records their key attributes in detail, such as the involved region, the start and end times of the precipitation process, and the highest warning level, to comprehensively and accurately reflect the characteristics of regional hourly precipitation events.

[0113] In one embodiment, the evaluation and analysis module includes:

[0114] The spatiotemporal distribution feature analysis unit is used to statistically analyze the spatiotemporal distribution features of regional hourly precipitation events that meet the warning criteria and the actual warning signals issued.

[0115] Understandably, for regional hourly precipitation events that meet the criteria, the distribution patterns of their occurrence in different geographical regions and time periods are analyzed; for early warning signals, the frequency and severity distribution characteristics of their issuance in different regions and time periods are statistically analyzed. The spatiotemporal distribution characteristics of both are then compared and analyzed, and the system identifies their spatial matching and temporal consistency, providing basic data support for subsequent accuracy and timeliness assessments.

[0116] The accuracy assessment unit is used to perform spatiotemporal matching between regional hourly precipitation events that meet the warning criteria and the actual warning signals issued, and to calculate quantitative assessment indicators related to accuracy from multiple perspectives based on the matching results.

[0117] Understandably, metrics such as hit rate (the proportion of correctly warned events out of the total number of events that should be warned), false alarm rate (the proportion of falsely warned events out of the total number of events that are actually warned), and TS score (the proportion of correctly warned events out of all events that should be monitored (including hits, false alarms, and false alarms)) are used to comprehensively and objectively measure the ability of warning signals to accurately capture actual precipitation events.

[0118] The timeliness assessment unit is used to construct a multi-dimensional timeliness assessment index system for early warning signals based on the occurrence and development process of regional hourly precipitation events and the release time of early warning signals.

[0119] Understandably, for example, multiple quantitative evaluation indicators of timeliness are calculated for different early warning scenarios, such as early warning (the warning time point is defined as before the precipitation event has started), process warning (the warning time point is defined as before the precipitation event has started but has not reached the warning standard), and real-time warning (the warning time point is defined as after the real-time precipitation event has reached the warning standard). These indicators include early warning time intervals and process warning timeliness rates. The timeliness performance of the warning signal at different stages is comprehensively evaluated, thereby systematically reflecting the overall performance of the early warning mechanism in terms of timeliness.

[0120] In one embodiment, the evaluation result output and optimization module includes:

[0121] The report production unit is used to deeply mine and organize various types of data output by the evaluation and analysis module, and generate detailed, comprehensive and targeted historical statistical pattern analysis reports and visual evaluation reports according to preset analysis rules, indicator systems and report templates.

[0122] Understandably, the report covers the specific values, trends, and comparative analysis results of the assessment indicators, as well as the assessment of different regions and different types of precipitation events, providing meteorological operators and decision-makers with clear and intuitive decision-making references.

[0123] The visualization unit is used to present key information from assessment data and reports in an intuitive and vivid way, using graphics, charts, and other formats.

[0124] Understandably, for example, by plotting line graphs showing the time-series changes in the accuracy and timeliness of warning signals, their evolution trend can be presented intuitively; by using map overlays to display the spatiotemporal distribution of warning signals and precipitation events, it is easy to quickly locate problem areas and discover potential patterns; and by creating bar charts to compare and analyze the differences in assessment indicators for different types of precipitation events, etc. Through these visualization methods, users can intuitively understand complex assessment data and quickly grasp the core points.

[0125] The decision support unit is used to automatically generate business optimization suggestions adapted to different regions and event types based on the output of the report production unit and the visualization unit, combined with preset meteorological business rules, threshold configuration strategies and historical statistical models.

[0126] Understandably, these recommendations include, but are not limited to, automatically recommending differentiated early warning issuance standards (such as setting precipitation thresholds based on the geographical climate and disaster resilience of different regions), generating optimized early warning issuance processes (such as clarifying the approval process and timeliness requirements for different levels of early warnings), and proposing suggestions to strengthen monitoring and early warning in specific areas (such as increasing monitoring stations or increasing monitoring frequency for areas with high frequency of missed or false alarms). The decision support unit aims to transform the evaluation results into structured and parameterized strategy recommendations, providing a quantitative basis for the automatic configuration or strategy governance of subsequent early warning operational systems.

[0127] In one specific embodiment, using hourly precipitation observation data and concurrent rainstorm warning signal data from Chongqing Municipality from 2014 to 2022 as the analysis object, the full-process technical efficiency of the integrated rainstorm event identification and assessment system for early warning operations was verified. This application example not only fully realizes the automated processing of the entire process from data acquisition, event identification, matching evaluation to result output, but more importantly, it accurately diagnoses deep structural defects that are difficult to detect in traditional operations, and automatically generates highly differentiated optimization strategies accordingly, demonstrating the complete closed-loop value from data to decision-making.

[0128] This embodiment demonstrates the superior capabilities of the present invention in automated assessment and in-depth diagnosis. The system successfully and automatically identified 52,586 station-level and 2,844 regional hourly precipitation events that met the standard across the city, and completed the matching assessment with all early warning signals (such as...). Figure 3 As shown). The significant difference in magnitude between the two clearly reveals the core business challenge of "site-district / county" scale conversion, providing crucial data support for subsequent in-depth diagnosis. Based on this, the multi-dimensional evaluation system constructed in this invention systematically quantifies three major deep-seated business problems: First, the system accurately diagnoses the phenomenon of "over-warning in the main urban area." Through spatial distribution analysis of hits and false alarms, it was found that there are serious false alarms in the main urban area, with the false alarm rate in Yuzhong District reaching as high as 90.73% (e.g., Figure 5 As shown in the figure), the quantitative data confirms a severe mismatch between the frequency of early warning signal issuance and the actual risk of heavy rain in the region; secondly, the system accurately identifies the persistent problem of "underreporting in the southeastern mountainous area." The assessment shows that underreporting is particularly prominent in the southeastern mountainous area, with a hit rate of only about 21% in the Youyang area (e.g., Figure 4As shown in the figure, the quantitative analysis reveals significant shortcomings in the existing operational capabilities for early warning of localized and sudden rainstorms; thirdly, the system reveals the problem of "imbalance in the structure of early warning levels." After completing the basic assessment without grading, the system conducted a special statistical analysis of the distribution of all early warning signals. The results showed that yellow warnings accounted for as high as 67.2% of the city's total, while red warnings, representing extreme risks, were issued only 17 times. This data confirms a serious disconnect between the early warning issuance strategy and the distribution of extreme precipitation risks.

[0129] Based on the aforementioned accurate diagnosis, the system further demonstrates its powerful ability to generate differentiated strategies. Addressing the issue of excessive warnings in the main urban area, the system automatically generates corresponding warning release threshold adjustment schemes based on the evaluation results. It also optimizes the landing area determination rules by fusing optical flow and random forest algorithms, accurately eliminating invalid warning targets from both trend judgment and entity recognition perspectives. Regarding the issue of missed warnings in the southeastern mountainous area, the system suggests appropriately lowering the warning release threshold and optimizing the "site-county" aggregation rules. By identifying the initial signals of localized and sudden heavy rainfall, it enhances the sensitivity of capturing such signals. Addressing the imbalance in the warning level structure, the system can use a data-driven discriminant model to construct intensity classification calculations. This discriminant model includes, but is not limited to, the XGBoost algorithm. By fusing multi-dimensional features, it achieves accurate prediction of the potential intensity of heavy rainfall, thereby dynamically adjusting the warning level to ensure that high-level alerts such as red warnings accurately cover actual extreme events. It should be understood that the data-driven model is only one of the optional implementation methods for generating strategy parameters. Its output is used to assist in warning evaluation and strategy optimization and does not replace the existing release process and decision-making mechanism in warning operations.

[0130] Crucially, the system leverages the quantification scale conversion effect to provide a direct optimization solution for addressing the core challenge of "site-county" precipitation delineation. Based on historical data, the system intelligently identifies the spatial distribution types of hourly precipitation events in eligible areas and transforms the quantified results into actionable precipitation delineation strategies. For areas like Kaixian and Wuxi, with a high total number of site events (>3000) but a relatively low proportion of regional events, data analysis indicates that their rainstorm events are characterized by "wide spatial coverage and concentrated time." The "one-vote veto" rule is highly applicable in these areas, so the system automatically generates a "large-scale precipitation warning" strategy to align with the nature of their regional and systemic events. Conversely, for areas like Youyang, with a relatively low total number of site events but the highest number of regional events, data reveals that their heavy rainfall is characterized by "discrete spatial distribution and strong localized suddenness." Mechanically applying the "one-vote veto" rule could easily lead to an over-expansion of the warning area. Therefore, the system accurately addresses this diagnosis by generating an optimization strategy of "adopting smaller, more precise warning areas" to improve the accuracy of capturing scattered localized rainstorms.

[0131] It should be noted that in the technical system of this invention, the entire process, from the automatic identification, assessment, and analysis of rainstorm events to the generation of optimization strategies and closed-loop iteration, is automatically completed by the system based on preset rules and a data-driven mechanism. The human role serves only as an external, optional source of policy parameter constraints and governance input, used to configure and manage the parameter value range or activation conditions, and does not participate in the core automated decision-making processes such as event identification, assessment calculation, and strategy generation. This ensures that this invention, as a complete intelligent system, can operate independently and achieve continuous adaptive optimization through a feedback mechanism without continuous human intervention.

[0132] The technical effects demonstrated in this embodiment are significantly superior to existing technologies. Compared with the inefficient traditional model that relies on manual labor, takes weeks, and can only perform sampling assessments, this invention achieves, for the first time, full automated analysis of nine years of historical data, improving assessment efficiency by several orders of magnitude. More importantly, this solution overcomes the limitations of manual assessment, accurately quantifying the "site-district / county" scale conversion effect at the city-wide level for the first time, and systematically diagnosing complex operational problems such as "false reporting," "missed reporting," and "imbalanced hierarchical structure," which were previously difficult to detect. This provides unprecedented data support and decision-making basis for the precise optimization of early warning operations.

[0133] This embodiment, through a complete technical path of "quantifying scale conversion effects → identifying spatial distribution patterns → generating differentiated application strategies," fully verifies the invention's ability to systematically apply, quantitatively diagnose, and optimize existing business rules. The system's output optimization strategy accurately addresses the core challenge of "site-county" scale conversion: for areas dominated by regional events, it recommends "wide-area application" to align with climate characteristics; for areas dominated by local events, it recommends "precise application" to correct rule application biases. This fully demonstrates that the invention not only achieves automated assessment but also accurately reveals the consequences of business rule application and provides quantitative decision support. It effectively solves core problems in existing early warning operations, such as one-sided assessment, reliance on manual labor, lack of scale conversion, and disconnect between assessment and optimization, demonstrating significant practical value and superiority.

[0134] This invention offers significant advantages in terms of systematicity, objectivity, and operational applicability. First, the solution automates the entire process from data acquisition and event identification to evaluation and analysis, effectively overcoming the inherent subjectivity and inefficiency of traditional methods. Second, it constructs a multi-dimensional evaluation index system encompassing accuracy and timeliness, comprehensively and quantitatively reflecting the overall effectiveness of the early warning system. Crucially, this solution provides an objective set of rules and decision support methods for the "site-county" scale conversion. By deeply mining the spatiotemporal distribution characteristics of historical hourly precipitation events that meet early warning standards, it transforms early warning decision-making from relying on purely subjective experience to objective analysis based on historical statistical patterns. This significantly improves the spatial accuracy and cross-regional consistency of early warning issuance, effectively alleviating operational pain points caused by scale mismatch. Finally, by directly transforming evaluation results into actionable operational optimization suggestions and forming a continuous closed loop of "evaluation-optimization-re-evaluation," this solution establishes a virtuous cycle mechanism for continuous self-improvement of early warning operations, comprehensively propelling rainstorm early warning operations from experience-driven to a new stage of data-driven and intelligent decision-making.

[0135] When faced with the technical problem of "early warning operation evaluation and optimization," those skilled in the art may naturally think of several alternative technical paths. However, upon in-depth analysis, these solutions all fail to fully achieve the purpose of this invention due to inherent defects. Alternative solution one (the "direct forecast" evaluation method based on high-resolution numerical models) deviates from the core of actual early warning operations: its evaluation object is the performance of numerical models rather than the actual early warning signals issued by meteorological departments, and it cannot reflect the real operational decision-making process that integrates multiple factors such as model forecasts, forecaster experience, and disaster prevention needs. At the same time, since the numerical model output is a forecast sequence with a fixed start time, it is difficult to correspond to the flexible early warning issuance nodes in operations, resulting in the inability to calculate multi-level timeliness indicators such as T1, T2, and T3. Its conclusions can only be used to improve model parameters and cannot provide directly executable operational optimization strategies. Alternative Solution Two (the machine learning-based "classification prediction" method) suffers from operational interpretability flaws: its "black box" decision-making mechanism makes it difficult for forecasters to understand and trust; this solution typically only outputs general "success / failure" probabilities, failing to systematically decompose and output hit rates, false alarm rates, TS scores, and multi-level timeliness indicators, thus failing to comprehensively reveal specific problems in operations; and as a forecaster, it is difficult to generate specific optimization instructions such as "adjusting the landing area range" or "modifying the release threshold." Alternative Solution Three (the "purely manual expert review" method) relies entirely on expert subjective experience and cannot be standardized or quantified, resulting in varying evaluation results from person to person; its efficiency is extremely low, unable to comprehensively process massive amounts of historical data, and can only perform sampling evaluations; furthermore, the review conclusions are mostly qualitative summaries, making it difficult to transform into data-driven, precise optimization strategies. In summary, the aforementioned alternative solutions either deviate from the actual early warning business in terms of the assessment object, suffer from poor business interpretability due to the existence of a "black box" problem, or are inefficient and unable to be quantified. None of these solutions can comprehensively and effectively address the core technical problems that this invention aims to solve, such as "one-sided assessment, reliance on manual labor, lack of scale conversion, and disconnect between assessment and optimization." This invention, by establishing an objective, transparent, and fully automated multi-dimensional assessment and strategy generation rule system, successfully overcomes the inherent defects of these alternative solutions, achieving a complete technical closed loop from automated assessment to accurate diagnosis and strategy generation.

[0136] The above-mentioned technical solution of the present invention systematically solves the four major core problems that have long existed in rainstorm warning operations:

[0137] Firstly, regarding the automation and objectification of the assessment process, this invention addresses the inefficiencies and inconsistent results caused by the traditional methods' heavy reliance on human experience and lack of unified objective standards in identifying eligible regional hourly precipitation events and assessing early warning quality. By establishing standardized data processing rules, automated event recognition algorithms, and objective quantitative assessment indicators, this invention achieves full automation of the entire process from data preprocessing, event recognition, spatiotemporal matching to multi-dimensional assessment, completely eliminating the uncertainty caused by human intervention and ensuring that the assessment process is efficient, objective, and reproducible.

[0138] Secondly, regarding the completeness of the early warning effectiveness evaluation system, existing methods only focus on limited accuracy indicators such as hit rate and false alarm rate, severely lacking a multi-dimensional and refined quantitative analysis of early warning timeliness. This invention innovatively constructs a multi-dimensional comprehensive evaluation system covering both accuracy and timeliness. By introducing three levels of timeliness indicators—T1, T2, and T3—it achieves differentiated and precise quantification of time lead, invalid early warning duration, and delayed response duration under different early warning scenarios such as hit, false alarm, and missed alarm. This comprehensively and systematically measures the overall effectiveness of the early warning system, providing a three-dimensional evaluation perspective for in-depth diagnosis.

[0139] Then, regarding the scientific nature of scale conversion and area delineation, this invention addresses the problem that the lack of objective and unified automated rules in operations makes it impossible to scientifically solve the inherent scale mismatch between discrete "site observation" and "district / county early warning" units, leading to strong subjectivity in early warning area delineation, vague evaluation benchmarks, and low reliability of results. It provides a set of scientific reference criteria for scale conversion and area delineation. By systematically analyzing the spatiotemporal distribution patterns and statistical regularities of historical rainstorm events, especially by quantitatively analyzing the climatic probability differences between "single-point achievement" and "multi-point cluster achievement," it provides objective quantitative scientific basis for differentiated and refined early warning area delineation in different regions, significantly improving the spatial accuracy and cross-regional consistency of early warnings.

[0140] Finally, regarding the closed-loop mechanism for evaluation and business optimization, this invention addresses the shortcomings of existing evaluation methods, which can only provide post-event statistics, cannot directly empower early warning business optimization, and are difficult to form a continuous improvement closed loop. It achieves the intelligent transformation of evaluation results into business strategies. By deeply mining evaluation data and thoroughly diagnosing business shortcomings, it automatically generates structured and parameterized strategy configuration instructions, including dynamic threshold adjustment, precise area delineation, early warning level structure optimization, and algorithm model selection. These strategy configuration instructions can be directly applied to the early warning business system, enabling automatic updates of parameters and rules. The system automatically collects the results and triggers a new round of evaluation and analysis, thus forming a closed-loop iterative mechanism of "evaluation-diagnosis-optimization-verification-re-evaluation" that does not rely on continuous manual intervention. This fundamentally transforms the early warning business from an experience-driven to a data-driven intelligent decision-making model.

[0141] Specific limitations regarding the integrated system for rainstorm event identification and assessment for early warning services can be found in the limitations of the integrated method for rainstorm event identification and assessment for early warning services mentioned above, and will not be repeated here. Each module in the aforementioned integrated system for rainstorm event identification and assessment for early warning services can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0142] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When executed by a processor, the computer program implements the integrated method for rainstorm event identification and assessment for early warning services described in the above embodiment. To avoid repetition, it will not be described again here. Alternatively, when executed by a processor, the computer program implements the functions of each module / unit in the integrated system for rainstorm event identification and assessment for early warning services described in this embodiment. To avoid repetition, it will not be described again here.

[0143] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0144] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0145] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. An integrated method for identifying and assessing rainstorm events for early warning operations, characterized in that, include: S1. Obtain hourly precipitation observation data, rainstorm warning signal data, and geographic information data from multi-source meteorological data, and construct a structured dataset; S2. Based on sliding cumulative calculation, station threshold determination, administrative division spatial aggregation, and process time merging, it automatically identifies regional hourly precipitation events that meet the warning criteria. S3. Perform spatiotemporal matching between the qualified regional hourly precipitation events and the rainstorm warning signals, and perform accuracy and timeliness assessments based on the matching results to obtain multi-dimensional assessment results; S4. Based on the multi-dimensional evaluation results, generate a rule base or parameter mapping model according to the preset strategy, automatically generate a differentiated early warning strategy optimization scheme, and form a business closed loop of evaluation-optimization-re-evaluation through early warning strategy execution and re-evaluation mechanism.

2. The integrated method for rainstorm event identification and assessment for early warning services as described in claim 1, characterized in that, The method for identifying regional hourly precipitation events that meet the warning criteria in step S2 includes: S201. Preprocess the structured dataset and calculate the sliding cumulative precipitation at multiple time scales using a sliding window width for hourly precipitation observation data. S202. Based on the rainstorm warning standard, threshold judgment is made for station-level events, automatically identifying hourly precipitation events at stations that meet the rainstorm warning standard, and recording their highest warning level. S203. Through the fusion mechanism of spatial aggregation and temporal merging, discrete qualifying site events are integrated into qualifying regional hourly precipitation events with clear spatiotemporal boundaries.

3. The integrated method for rainstorm event identification and assessment for early warning services as described in claim 1, characterized in that, Step S3 further includes the following steps: S301. The list of qualified regional hourly precipitation events is spatiotemporally matched with the actual warning signals issued in meteorological operations to generate a set of spatiotemporally matched results and a set of non-matched results. The warning issuance effect is comprehensively evaluated and analyzed based on the set. Furthermore, the station-region aggregation index can be calculated based on the relationship between the qualified regional hourly precipitation events and the number of qualified station events contained therein, which is used to describe the aggregation characteristics of station-scale events to regional-scale events.

4. The integrated method for rainstorm event identification and assessment for early warning services according to claim 3, characterized in that, Step S301 further includes determining the hit, missed, and false alarm situations under a unified evaluation benchmark that does not distinguish between warning levels.

5. The integrated method for rainstorm event identification and assessment for early warning services according to claim 1, characterized in that, The early warning strategy optimization scheme generated in step S4 includes adjusting at least one of the following: early warning threshold setting, early warning release area range, hierarchical structure, or triggering logic.

6. An integrated system for rainstorm event identification and assessment for early warning operations, characterized in that, The method for integrated identification and assessment of rainstorm events for early warning services, as described in any one of claims 1-5, includes: The data acquisition module is used to acquire hourly precipitation observation data, rainstorm warning signal data and geographic information data from multi-source meteorological data, and to construct a structured dataset; The regional hourly precipitation event identification module is used to automatically identify regional hourly precipitation events that meet the warning standards based on sliding cumulative calculation, station threshold determination, administrative division spatial aggregation, and process time merging. The evaluation and analysis module is used to perform spatiotemporal matching between the regional hourly precipitation events and rainstorm warning signals, and to perform accuracy and timeliness evaluation based on the matching results to obtain multi-dimensional evaluation results. The evaluation result output and optimization module is used to automatically generate differentiated early warning strategy optimization schemes based on the multi-dimensional evaluation results, and form a business closed loop of evaluation-optimization-re-evaluation through early warning strategy execution and re-evaluation mechanism.

7. The integrated system for rainstorm event identification and assessment for early warning operations as described in claim 6, characterized in that, The data acquisition module includes: The data acquisition unit, data cleaning unit, and quality control unit are used to complete data acquisition, validity screening, and quality control.

8. The integrated system for rainstorm event identification and assessment for early warning services as described in claim 6, characterized in that, The qualified regional hourly precipitation event identification module includes: The site event identification unit and the regional event identification unit are used to identify hourly precipitation events at sites and in regions that meet the early warning criteria.

9. The integrated system for rainstorm event identification and assessment for early warning services according to claim 6, characterized in that, The evaluation and analysis module includes: Functional unit used for performing spatiotemporal distribution feature analysis, accuracy assessment, and timeliness assessment.

10. The integrated system for rainstorm event identification and assessment for early warning services according to claim 6, characterized in that, The evaluation result output and optimization module includes: The report production unit is used to output statistical analysis reports and visualization results; The strategy generation unit is used to generate differentiated early warning strategy optimization schemes; The strategy execution and re-evaluation unit is used to support strategy parameter configuration and trigger a new round of evaluation process.

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