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CN-121997766-A - Urban waterlogging water accumulation point analysis and prediction algorithm and system based on big model and big data technology

CN121997766ACN 121997766 ACN121997766 ACN 121997766ACN-121997766-A

Abstract

The invention relates to the technical field of urban waterlogging monitoring and prediction, and discloses an urban waterlogging water accumulation point analysis and prediction algorithm and system based on a large model and a large data technology, wherein S1, an all-weather three-dimensional monitoring network is constructed, multi-source heterogeneous data including hydrological data, real-time weather forecast data, topographic data and historical waterlogging water accumulation data of key nodes of a rainwater pipe network are collected, S2, the multi-source heterogeneous data are cleaned, standardized and fused to form a structured input data matrix, and the prediction error of waterlogging occurrence time can be controlled within 10 minutes by means of an advanced urban waterlogging early warning monitoring system and combining a large data analysis and machine learning technology, meanwhile, the prediction accuracy of a flooding range is improved by more than 40%, accurate time and space references are provided for urban managers and emergency response teams, and the efficiency of coping with waterlogging disasters is greatly improved.

Inventors

  • HE PENG
  • XU FENG

Assignees

  • 国研数字科技(北京)有限公司

Dates

Publication Date
20260508
Application Date
20260210

Claims (10)

  1. 1. An urban waterlogging water accumulation point analysis and prediction algorithm based on a big model and big data technology is characterized by comprising the following steps: s1, constructing an all-weather three-dimensional monitoring network, and collecting multi-source heterogeneous data including hydrological data, real-time weather forecast data, topographic data and historical waterlogging and ponding data of key nodes of a rainwater pipe network; S2, cleaning, standardizing and fusing the multi-source heterogeneous data to form a structured input data matrix; s3, constructing a large waterlogging forecast model, and when the monitored rainfall intensity reaches a preset threshold value, dynamically simulating a waterlogging evolution process based on an input data matrix by using the large waterlogging forecast model to predict the occurrence time, the submerging range and the water depth change trend of a waterlogging event; s4, based on the submerged range, automatically identifying the affected preset important positions through space superposition analysis, and evaluating the risk level of each important position according to a multi-dimensional index system comprising the submerged depth, duration time, position importance and historical disaster degree; S5, outputting a report containing the prediction result and the risk assessment result, and synchronizing to the emergency management platform.
  2. 2. The urban waterlogging water accumulation point analysis and prediction algorithm based on the big model and big data technology according to claim 1, wherein in S1, the all-weather three-dimensional monitoring network adopts an edge computing architecture, and edge computing equipment deployed at the local position of a sensor node is used for completing local preprocessing of data.
  3. 3. The urban waterlogging water accumulation point analysis and prediction algorithm based on the big model and the big data technology as claimed in claim 1, wherein in the S3, the waterlogging forecast big model is constructed by adopting a deep neural network based on a transform architecture and combining with a hydrodynamics principle; the model is trained by using historical multi-source heterogeneous data, and weight distribution on core influence factors is enhanced through a self-attention mechanism of the model.
  4. 4. The urban waterlogging water accumulation analysis and prediction algorithm based on the big model and big data technology according to claim 1, wherein in the step S4, the preset important areas include but are not limited to underpass tunnels, low-lying zones and residential communities, and the risk level is divided into three levels of high, medium and low.
  5. 5. A system for analyzing and predicting urban waterlogging water accumulation points based on a large model and large data technology according to any one of claims 1-4, which is characterized by comprising a multi-source data acquisition module, a data preprocessing module, a waterlogging forecast large model module, a risk assessment module and a result output and early warning module; the multi-source data acquisition module is connected with the data preprocessing module, the data preprocessing module is connected with the waterlogging forecast large model module, the waterlogging forecast large model module is connected with the risk assessment module, and the risk assessment module is connected with the result output and early warning module; the multi-source data acquisition module is used for acquiring and converging multi-source heterogeneous data in real time; the data preprocessing module is used for cleaning, normalizing and fusing the multi-source heterogeneous data to generate high-quality input data; The waterlogging forecast large model module is used for dynamically deducting and predicting a waterlogging evolution process based on input data; the risk assessment module is used for identifying important positions and assessing the risk level of the important positions based on the prediction result; And the result output and early warning module is used for visually displaying the evaluation result, generating early warning information and pushing the early warning information to the emergency management platform.
  6. 6. The system of the urban waterlogging water accumulation point analysis and prediction algorithm based on the big model and big data technology according to claim 5, wherein the multi-source data acquisition module comprises a hydrological sensing unit, a meteorological data access unit, a topographic data integration unit and a historical data management unit; The hydrologic sensing unit is used for sensing and collecting data of hydrologic conditions of the key area; the meteorological data access unit is used for being configured to be in butt joint with an API interface of a meteorological department to acquire real-time meteorological forecast data; The terrain data integration unit is used for loading and processing a high-precision terrain elevation data set; The history data management unit is used for storing and managing history waterlogging event data.
  7. 7. The system for analyzing and predicting urban waterlogging water accumulation points based on the large model and large data technology according to claim 5, wherein the data preprocessing module comprises a data cleaning unit, a data standardization unit, a data fusion unit and a data quality verification unit; The data cleaning unit is connected with the data standardization unit, the data standardization unit is connected with the data fusion unit, and the data fusion unit is connected with the data quality check unit; the data cleaning unit is used for removing abnormal values and invalid values in the original data; The data standardization unit is used for unifying the formats and dimensions of the data from different sources; The data fusion unit is used for constructing a space-time association relation among the multi-source data to form a structured input data matrix; the data quality checking unit is used for checking the preprocessed data.
  8. 8. The system for analyzing and predicting urban waterlogging water accumulation points based on the big model and big data technology according to claim 5, wherein the waterlogging forecast big model module comprises a model training unit, a real-time deduction unit and a model self-adaption unit; The model training unit is connected with the real-time deduction unit, and the real-time deduction unit is connected with the model self-adaptive unit; The model training unit is used for optimizing and training model parameters by utilizing historical data; the real-time deduction unit is used for starting waterlogging evolution simulation when the rainfall intensity is monitored to reach a preset threshold value; The model self-adapting unit is used for dynamically adjusting model parameters according to real-time data and predictive feedback.
  9. 9. The system of an urban waterlogging water accumulation point analysis and prediction algorithm based on a big model and big data technology according to claim 5, wherein the risk assessment module comprises a key location identification unit, an assessment index calculation unit and a risk level judgment unit; The important area position identification unit is connected with the evaluation index calculation unit, and the evaluation index calculation unit is connected with the risk grade judgment unit; the important position identification unit is used for automatically identifying the affected preset important position in the predicted submerging range through space superposition analysis; the evaluation index calculation unit is used for calculating the numerical value and weight of each index in the multi-dimensional index system; and the risk level judging unit is used for outputting high, medium and low three-level risk levels according to the comprehensive evaluation result.
  10. 10. The system of the urban waterlogging water accumulation point analysis and prediction algorithm based on the big model and big data technology according to claim 5, wherein the result output and early warning module comprises a visual display unit, a platform docking unit and an early warning pushing unit; The visual display unit is used for generating a waterlogging prediction chart and a risk level distribution map; the platform docking unit is used for being configured to perform data synchronization with the urban emergency management system; the early warning pushing unit is used for pushing early warning information to the relevant management departments.

Description

Urban waterlogging water accumulation point analysis and prediction algorithm and system based on big model and big data technology Technical Field The invention relates to the technical field of urban waterlogging monitoring and prediction, in particular to an urban waterlogging water accumulation point analysis and prediction algorithm and system based on a big model and big data technology. Background With the continuous aggravation of global climate change and the continuous promotion of urban progress, extreme rainfall events are more frequent, and urban waterlogging has become one of key disasters restricting urban safe operation. Urban inland inundation has the characteristics of strong burstiness, wide influence range, large destructive power and the like, and is easy to cause traffic paralysis, infrastructure damage, property loss and even casualties. Therefore, accurate analysis and early prediction of waterlogging ponding points are realized, and the method has important significance for improving the disaster prevention and reduction capability of cities and guaranteeing the urban safety. At present, urban waterlogging monitoring and prediction technologies mainly depend on traditional hydrologic models, such as SWMM models, MIKE models or simple statistical analysis methods, and have the obvious defects that firstly, data acquisition dimension is single, data of local rainfall stations or a small amount of water level monitoring points are mostly depended, hydrologic information, high-precision topographic data and real-time weather forecast information of key nodes of a rainwater pipe network cannot be integrated, monitoring coverage is limited, data timeliness is poor, secondly, the adaptability of the prediction models is poor, the traditional models are mainly static parameter models, real-time dynamic factors such as rainfall intensity changes and pipe network operation states are difficult to dynamically adapt, waterlogging evolution processes cannot be accurately simulated, thirdly, the practicability of prediction results is insufficient, only waterlogging areas can be roughly judged, the occurrence time, the flooding range and the water depth change trend cannot be accurately pre-judged, risk grade assessment is difficult to be carried out on heavy point areas such as down tunnels, low-lying zones and residential communities in combination with historical water accumulation conditions, and subsequent disaster prevention and disaster reduction responses cannot be targeted, and emergency disposal time cannot be effectively obtained. In recent years, the rapid development of big data technology and big model technology provides a new technical idea for urban waterlogging prediction. However, at present, no technical scheme can fully fuse multi-source big data, realize dynamic simulation and accurate prediction of a waterlogging evolution process based on a big model, and finish important location risk identification and grade assessment at the same time, so that an urban waterlogging ponding point analysis and prediction algorithm and system based on the big model and big data technology are provided. Disclosure of Invention The invention aims to provide an urban waterlogging water accumulation point analysis and prediction algorithm and system based on a large model and a large data technology, so as to solve the problems in the background technology. In order to achieve the purpose, the invention provides the following technical scheme that the urban waterlogging water accumulation point analysis and prediction algorithm based on the big model and big data technology comprises the following steps: s1, constructing an all-weather three-dimensional monitoring network, and collecting multi-source heterogeneous data including hydrological data, real-time weather forecast data, topographic data and historical waterlogging and ponding data of key nodes of a rainwater pipe network; S2, cleaning, standardizing and fusing the multi-source heterogeneous data to form a structured input data matrix; s3, constructing a large waterlogging forecast model, and when the monitored rainfall intensity reaches a preset threshold value, dynamically simulating a waterlogging evolution process based on an input data matrix by using the large waterlogging forecast model to predict the occurrence time, the submerging range and the water depth change trend of a waterlogging event; s4, based on the submerged range, automatically identifying the affected preset important positions through space superposition analysis, and evaluating the risk level of each important position according to a multi-dimensional index system comprising the submerged depth, duration time, position importance and historical disaster degree; S5, outputting a report containing the prediction result and the risk assessment result, and synchronizing to the emergency management platform. Preferably, in S1, the all-weather stereo monitoring netw