A flood risk communication assessment method, device, equipment and storage medium

Abstract

The present invention discloses a flood risk propagation assessment method, device, equipment and storage medium. The method includes: calculating the flood risk value of each sub-basin in the study area based on the acquired flood risk assessment indicator variables of the study area; classifying the flood risk values ​​to obtain the flood risk level of each sub-basin in the study area; constructing a Markov chain spatiotemporal model; determining the spatiotemporal propagation law of flood risk based on the Markov chain spatiotemporal model; fitting a flood risk propagation function based on the spatiotemporal propagation law of flood risk; determining the flood risk transfer hindering effect of flood control and water conservancy project based on the flood risk propagation function, flood control and water conservancy project operation information data and historical flood data; constructing a flood risk spatiotemporal propagation network model with spatiotemporal dependency; and evaluating the study area based on the flood risk spatiotemporal propagation network model to obtain a flood risk propagation assessment result.

Description

Technical Field

[0001] The present invention relates to the field of flood risk propagation analysis, and in particular to a flood risk propagation assessment method, device, equipment and storage medium. Background Art

[0002] The dual impacts of ongoing climate change and rapid urbanization have exacerbated the risk of floods and waterlogging, posing severe challenges to urban infrastructure, residents' livelihoods, and economic development. According to the Emergency Events Database (EM-DAT), 8,591 natural disasters occurred globally between 2000 and 2020, of which floods accounted for 40.2%. Therefore, strengthening research on flood risk and its transmission mechanisms not only provides theoretical basis and technical support for scientific disaster prevention and mitigation decision-making but also has important practical significance for promoting the sustainable development of economic, social, and ecological systems.

[0003] Traditional research has primarily focused on the mechanisms of flood risk transmission within a river basin or region, lacking a deep understanding of the spatiotemporal transmission of flood risk across river basins and regions. Current research is limited in the following ways: first, there is a lack of a comprehensive understanding of the spatiotemporal transmission patterns of flood risk between years, upstream and downstream, and between main and tributary rivers; second, there is still a lack of in-depth understanding of the risk transfer and barrier effects of water conservancy and flood control projects; and third, continuous simulation of flood risk trends across river basins and regions has yet to be achieved. Summary of the Invention

[0004] In order to solve the above-mentioned problems existing in the prior art, the present invention provides a flood risk communication assessment method, device, equipment and storage medium.

[0005] In order to achieve the above-mentioned purpose, the technical solution of the embodiment of the present invention is:

[0006] In a first aspect, the present invention provides a flood risk communication assessment method, the method comprising:

[0007] Calculate the flood risk value of each sub-basin in the study area based on the obtained flood risk assessment indicator variables of the study area;

[0008] Classifying the flood risk values ​​to obtain the flood risk level of each sub-basin in the study area;

[0009] According to the flood risk level, a Markov chain spatiotemporal model is constructed;

[0010] Determine the spatiotemporal propagation pattern of flood risk based on the Markov chain spatiotemporal model;

[0011] Fitting the flood risk propagation function according to the spatiotemporal propagation law of flood risk;

[0012] Determining the flood risk transfer hindering effect of the flood control and water conservancy project based on the flood risk propagation function, the flood control and water conservancy project operation information data, and the historical flood data;

[0013] Based on the preset river network topology and the flood risk transfer barrier effect, a spatiotemporal flood risk propagation network model with spatiotemporal dependencies is constructed;

[0014] Based on the spatiotemporal propagation network model of flood risk, the study area is evaluated to obtain a flood risk propagation assessment result.

[0015] In a second aspect, the present invention provides a flood risk communication assessment device, the device comprising:

[0016] A calculation module, configured to calculate the flood risk value of each sub-basin in the study area based on the acquired flood risk assessment indicator variables of the study area;

[0017] A classification module, configured to classify the flood risk values ​​to obtain a flood risk level for each sub-basin;

[0018] A construction module, configured to construct a Markov chain spatiotemporal model according to the flood risk level;

[0019] A determination module, configured to determine the spatiotemporal propagation law of flood risk based on the Markov chain spatiotemporal model;

[0020] A fitting module, configured to fit a flood risk propagation function according to the spatiotemporal propagation law of flood risk;

[0021] The determination module is further configured to determine the flood risk transfer obstruction effect of the flood control and water conservancy project based on the flood risk propagation function, the flood control and water conservancy project operation information data, and the historical flood data;

[0022] The construction module is further used to construct a spatiotemporal propagation network model of flood risk with spatiotemporal dependencies based on a preset river network topology and the flood risk transfer obstruction effect;

[0023] The evaluation module is used to evaluate the study area according to the spatiotemporal propagation network model of flood risk and obtain a flood risk propagation evaluation result.

[0024] In some embodiments, the calculation module is further used to obtain multiple flood risk assessment indicator variables and flood risk assessment indicator observation values ​​in the study area; perform standardization on the flood risk assessment indicator variables to obtain normalized flood risk assessment indicator variables; calculate the information entropy of each flood risk assessment indicator based on a preset entropy weight method and the flood risk assessment indicator observation values; determine the weight coefficient of each flood risk assessment indicator based on each information entropy; the calculation formula of the weight coefficient is: ; ; In the formula, m represents the number of flood risk assessment indicator variables; n represents the number of sub-basins; represents the observed value of the i-th flood risk assessment indicator variable in the j-th sub-basin; Represents the information entropy of each flood risk assessment indicator variable; Represents the weight coefficient of each flood risk assessment indicator variable; the flood risk value is calculated based on the weight coefficient and the normalized flood risk assessment indicator variable; the calculation formula of the flood risk value is: ; ; In the formula, R represents the flood risk value; H represents the risk of disaster-causing factors; V represents the vulnerability of the disaster-prone environment; E represents the exposure of the disaster-bearing body; C represents the disaster prevention and mitigation capacity; H i 、E i 、V i 、C i Represents the normalized value of the flood risk assessment indicator variable.

[0025] In some embodiments, the construction module is further configured to determine a first transition number for each flood risk level in each sub-basin within two consecutive years; and determine a first transition probability between flood risk levels based on the first transition number; the calculation formula for the first transition probability is: Where, In the same sub-basin, the flood risk increases from the previous year to Transferring grades to the next year The number of levels; when i=j, it means that the flood risk has shifted to the same level; Indicates that the flood risk in the previous year was The number of levels; based on the first transition probability, determining the Markov risk state time transition probability matrix of each sub-basin; based on the preset adjacency principle, determining the second transition number of each flood risk level in adjacent sub-basins in the same year; based on the second transition number, determining the second transition probability; the calculation formula of the second transition probability is: Where, In the same year, the flood risk of sub-basin A is The level is propagated to the adjacent sub-basin B The number of levels; when i=j, it means that the flood risk has shifted to the same level; The flood risk of sub-basin A is The number of levels; determining the Markov risk state space transition probability matrix of the adjacent sub-basin according to the second transition probability; constructing the Markov chain spatiotemporal model according to the Markov risk state time transition probability matrix and the Markov risk state space transition probability matrix.

[0026] In some embodiments, the determination module is also used to predict the flood risk level of each sub-basin in the next year based on the Markov risk state time transition probability matrix of each sub-basin and the preset maximum transition probability principle; and to identify the cross-basin and cross-regional transmission path of flood risk based on the Markov risk state space transition probability matrix of the adjacent sub-basin and the preset maximum transition probability principle.

[0027] In some embodiments, the fitting module is further used to analyze the spatiotemporal propagation law of the flood risk to obtain a key section for flood risk spillover propagation; and to fit the flood risk propagation function based on the key section for flood risk spillover propagation; the flood risk propagation function refers to a calculation function of the flood risk level of the downstream sub-basin B at time k, which is characterized based on the key section for flood risk spillover propagation and the spatiotemporal propagation law of flood risk; the calculation function is jointly determined by the flood risk level of the sub-basin B at the previous moment and the flood risk level of the upstream sub-basin A at time k; the calculation formula of the flood risk propagation function is: Where, represents the flood risk level of the downstream sub-basin B at time k; represents the flood risk level of the downstream sub-basin B at time k-1; represents the flood risk level of the upstream sub-basin A at time k.

[0028] In some embodiments, the assessment module is further used to calculate the flood risk propagation speed and propagation range based on the flood risk propagation network model to obtain the flood risk propagation assessment result.

[0029] In a third aspect, an embodiment of the present invention provides an electronic device, comprising: a memory for storing executable instructions; and a processor for implementing the above-mentioned flood risk propagation assessment method when executing the executable instructions stored in the memory.

[0030] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium storing executable instructions for causing a processor to execute the executable instructions to implement the above-mentioned flood risk propagation assessment method.

[0031] The flood risk propagation assessment method provided by the present invention first calculates the flood risk value of each sub-basin in the study area based on the flood risk assessment indicator variables obtained in the study area; classifies the flood risk values ​​to obtain the flood risk level of each sub-basin in the study area; constructs a Markov chain spatiotemporal model based on the flood risk level; determines the spatiotemporal propagation law of flood risk based on the Markov chain spatiotemporal model; fits a flood risk propagation function based on the spatiotemporal propagation law of flood risk; determines the flood risk transfer obstruction effect of flood control and water conservancy projects based on the flood risk propagation function, flood control and water conservancy project operation information data and historical flood data; constructs a flood risk spatiotemporal propagation network model with spatiotemporal dependence based on the preset river network water system topology structure and the flood risk transfer obstruction effect; and evaluates the study area based on the flood risk spatiotemporal propagation network model to obtain a flood risk propagation assessment result. Thus, this invention comprehensively assesses flood risk in the sub-basins of the study area by considering the hazard of hazard-causing factors, the exposure, vulnerability, and disaster prevention and mitigation capabilities of the basin, elucidating the spatiotemporal propagation patterns of flood risk across regions and basins. Furthermore, by establishing a risk propagation function for flood risk at key sections / control points, it quantifies the role of reservoirs, flood storage areas, levees, and other flood control and water conservancy projects in hindering flood risk transfer. Furthermore, by considering the risk propagation characteristics and the operational characteristics and interactions of hydraulic engineering facilities such as reservoirs, flood storage areas, levees, and sluices, and utilizing complex network theory, a spatiotemporal propagation network model for flood risk in the basin is constructed, enabling continuous deduction of the cross-regional and cross-basin flood risk propagation process. This invention is applicable to the study of flood risk and its propagation mechanisms in different basins and has strong versatility. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 Schematic diagram of the structure of the flood risk communication assessment system provided by an embodiment of the present invention;

[0033] Figure 2 1 is a flow chart of a flood risk communication assessment method provided by an embodiment of the present invention;

[0034] Figure 3 Schematic diagram of the structure of the flood risk assessment model provided by an embodiment of the present invention;

[0035] Figure 4 This is a technical roadmap for a method for studying flood risk and its spatiotemporal propagation mechanism provided by an embodiment of the present invention;

[0036] Figure 5 Schematic diagram of the structure of a flood risk communication assessment device provided by an embodiment of the present invention;

[0037] Figure 6It is a schematic diagram of the composition structure of the flood risk communication assessment device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0039] In the following description, references to "some embodiments" describe a subset of all possible embodiments. However, it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict. Unless otherwise defined, all technical and scientific terms used in the embodiments of the present invention have the same meaning as commonly understood by those skilled in the art to which the embodiments of the present invention pertain. The terms used in the embodiments of the present invention are for the purpose of describing the embodiments of the present invention only and are not intended to limit the present invention.

[0040] The following describes exemplary applications of the flood risk communication assessment device according to an embodiment of the present invention. The flood risk communication assessment device provided by the embodiment of the present invention can be implemented as either a terminal or a server. In one implementation, the flood risk communication assessment device provided by the embodiment of the present invention can be implemented as various types of terminals, such as laptops, tablets, desktop computers, and mobile devices. In another implementation, the flood risk communication assessment device provided by the embodiment of the present invention can also be implemented as a server. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. The terminal and server can be connected directly or indirectly via wired or wireless communication, which is not limited in the embodiments of the present invention. The following describes exemplary applications of the flood risk communication assessment device as a server.

[0041] See also Figure 1 , Figure 1is a schematic diagram of the structure of a flood risk communication assessment system 10 provided in an embodiment of the present invention. To implement flood risk communication assessment, an embodiment of the present invention can provide a flood risk communication assessment platform, which can be implemented as a flood risk communication assessment application. The flood risk communication assessment system 10 provided in an embodiment of the present invention includes a terminal 110, a network 120, and a server 130, wherein server 130 is a server of the flood risk communication assessment application. Server 130 can constitute the flood risk communication assessment device of an embodiment of the present invention. Terminal 110 is connected to server 130 via network 120, which can be a wide area network or a local area network, or a combination of the two.

[0042] In some embodiments, please refer to Figure 1 When conducting flood risk propagation assessment in the study area, the terminal 110 sends the acquired flood risk assessment indicator variables of the study area to the server 130 through the network 120. The server 130 receives the flood risk assessment indicator variables in response to the terminal 110, calculates the flood risk value of each sub-basin in the study area; classifies the flood risk values ​​to obtain the flood risk level of each sub-basin in the study area; constructs a Markov chain spatiotemporal model based on the flood risk level; determines the spatiotemporal propagation law of flood risk based on the Markov chain spatiotemporal model; fits the flood risk propagation function based on the spatiotemporal propagation law of flood risk; determines the flood risk transfer obstruction effect of flood control and water conservancy projects based on the flood risk propagation function, flood control and water conservancy project operation information data and historical flood data; constructs a flood risk spatiotemporal propagation network model with spatiotemporal dependence based on the preset river network water system topology and the flood risk transfer obstruction effect; evaluates the study area based on the flood risk spatiotemporal propagation network model to obtain the flood risk propagation assessment result. After obtaining the flood risk communication assessment result, the server 130 sends the flood risk communication assessment result to the terminal 110 through the network 120 .

[0043] The embodiment of the present invention provides a flood risk communication assessment method, see Figure 2 , Figure 2 This is a flow chart of a flood risk communication assessment method provided by an embodiment of the present invention, which combines Figure 2 The steps shown are explained.

[0044] Step S210 , calculating the flood risk value of each sub-basin in the study area according to the acquired flood risk assessment indicator variables of the study area.

[0045] In some embodiments, flood risk assessment indicator variables refer to a series of parameters used to measure flood risk in a study area. These assessment indicator variables may include, but are not limited to, maximum three-day precipitation, flood frequency, flood peak, flood volume, sub-basin boundaries, digital elevation, slope, river network density, land use rate, density of water conservancy and flood control facilities, percentage of flood storage and detention area, administrative divisions, population distribution, and socioeconomic indicators. These assessment indicator variables can reflect the physical and geographical characteristics of the study area and factors that may affect flood occurrence from different perspectives, and serve as the basic data for calculating flood risk values.

[0046] In some embodiments, a sub-basin refers to a relatively independent and complete small-scale watershed unit divided according to the natural geographical characteristics of the study area, such as the topography, landform, and water system distribution. Each sub-basin has its own unique catchment area, and its boundaries are defined by watersheds, so that precipitation and surface runoff converge within the area and eventually flow into the main river or other water bodies. Sub-basins have relatively uniform topography, soil, vegetation cover and other characteristics, and their hydrological processes have certain similarities and independence. For example, in mountainous areas, a sub-basin may consist of a valley and surrounding hillsides, and water flows converge along the valley; in plain areas, sub-basins may be divided according to the micro-undulations of the terrain and the distribution of the river network. Conducting research at this smaller spatial scale can more accurately analyze and assess flood risks, because different sub-basins may have completely different flood responses when facing the same rainfall conditions due to their own differences in characteristics.

[0047] In this paper, the flood risk value is used to quantify the risk level of flood disasters faced by each sub-basin. This value quantitatively represents the likelihood of flood disasters and the potential extent of losses faced by each sub-basin. Here, a larger value indicates a higher likelihood of flood disasters occurring in that sub-basin and a greater potential degree of damage.

[0048] Step S220 , classifying the flood risk values ​​to obtain the flood risk level of each sub-basin in the study area.

[0049] In some embodiments, flood risk level refers to the classification of flood risk values ​​according to certain standards based on pre-established calculation rules to obtain different levels. These level classifications include but are not limited to qualitative descriptions, such as low risk, medium risk, and high risk; or quantitative numerical levels, such as 1-5, where level 1 represents the lowest risk and level 5 represents the highest risk. Through level classification, the risk status of each sub-basin can be understood more quickly so that appropriate measures can be taken. For example, for sub-basins with low risk levels, relatively conventional flood control measures and monitoring frequencies can be adopted; while for sub-basins with high risk levels, it is necessary to strengthen flood control project construction, improve the accuracy and frequency of monitoring and early warning, and formulate detailed emergency plans.

[0050] Step S230: constructing a Markov chain spatiotemporal model according to the flood risk level.

[0051] In some embodiments, the model is constructed based on Markov chain theory and is a mathematical model used to describe the state transition laws of a system in time and space dimensions. In the field of flood risk research, the flood risk levels of different sub-basins at different time points are regarded as the state of the system. By analyzing a large amount of historical data, the probability of a sub-basin's current flood risk level transferring to a different risk level in the adjacent sub-basin at the next moment is determined. For example, at a certain moment, if a sub-basin is at a medium risk level, according to statistical analysis of historical data, the probability of it transferring to a high risk level in the future is 0.3, the probability of transferring to a low risk level is 0.4, and the probability of maintaining a medium risk level is 0.3. By establishing such a transition probability matrix and combining time and space factors, a Markov chain spatiotemporal model is constructed that can dynamically simulate the spread of flood risk between different sub-basins over time, providing a powerful tool for in-depth research on the spatiotemporal evolution of flood risk.

[0052] Step S240: Determine the spatiotemporal propagation pattern of flood risk based on the Markov chain spatiotemporal model.

[0053] In some embodiments, the spatiotemporal propagation law of flood risk refers to a law describing how flood risk propagates and changes across both time and space. In the temporal dimension, this manifests as the temporal trend of flood risk. For example, before the rainy season, the flood risk value of each sub-basin begins to rise due to increasing precipitation. After the rainy season, as precipitation decreases and floods recede, the flood risk value gradually decreases. In the spatial dimension, flood risk propagates from the sub-basin experiencing a flood disaster to neighboring sub-basins. The direction and speed of propagation are influenced by various factors, such as topography. Floods typically flow along lower-lying areas, propagating from upstream sub-basins to downstream sub-basins. The connectivity of river networks also affects flood risk. Regions with dense and well-connected river networks experience faster flood risk propagation. Furthermore, the layout and operational status of flood control and water conservancy projects also significantly influence risk propagation. For example, dams and sluice gates can block or slow the spread of flood water. In-depth research on the temporal and spatial propagation patterns of flood risks will help predict the development trend of flood disasters in advance and provide a basis for formulating scientific and reasonable flood prevention and disaster reduction measures.

[0054] In the present invention, by conducting in-depth analysis of the Markov chain spatiotemporal model constructed in step S230, the propagation patterns of flood risk in both temporal and spatial dimensions, as embodied in the model, can be explored. For example, by observing the probability of increased risk levels in adjacent sub-basins surrounding a high-risk sub-basin over time, as well as the shifting trends of risk levels in different seasons and time periods, universal and regular spatiotemporal propagation patterns of flood risk can be summarized. These patterns provide important references for the formulation of subsequent prevention and control measures.

[0055] Step S250: fitting a flood risk propagation function according to the spatiotemporal propagation law of flood risk.

[0056] In some embodiments, a mathematical function is constructed based on the determined spatiotemporal propagation patterns of flood risk using mathematical fitting methods. This function quantitatively describes the temporal and spatial propagation of flood risk in the form of a mathematical formula. The fitting process typically requires the use of a large amount of historical data and data simulated using a Markov chain spatiotemporal model. The function parameters are continuously adjusted to ensure that the function accurately reflects the actual flood risk propagation.

[0057] In the present invention, based on the spatiotemporal propagation patterns of flood risk determined in step S240, mathematical fitting methods, such as linear regression and nonlinear regression, can be used to find a suitable mathematical function to describe these propagation patterns. Parameter estimation and optimization are performed using a large amount of historical data and model simulation results. The resulting flood risk propagation function accurately reflects the spatiotemporal propagation of flood risk, and can be used to predict changes in flood risk at different temporal and spatial locations.

[0058] Step S260: determining the flood risk transfer hindering effect of the flood control and water conservancy project based on the flood risk propagation function, the flood control and water conservancy project operation information data, and the historical flood data.

[0059] In some embodiments, flood control and water conservancy project operation information data covers detailed information on the operation of various flood control and water conservancy projects. These projects include reservoirs, flood storage areas, sluice gates, etc. Reservoir operation information data includes water level, water storage capacity, inflow, outflow, water release time, and water release flow. These data reflect the reservoir's ability to regulate floods. By reasonably controlling the reservoir's storage and discharge capacity, the peak flow can be effectively reduced, alleviating the flood control pressure in downstream areas. Sluice gate operation information includes the time of opening and closing, the size of the opening, etc. By controlling the opening and closing of the sluice gate, the water level and flow in the river channel can be adjusted to achieve reasonable flood control. Accurately obtaining and analyzing the operation information data of these flood control and water conservancy projects is crucial for evaluating their role in flood disaster prevention and control.

[0060] In the present invention, the flood risk propagation function obtained in step S250 can be combined with flood control and hydropower project operational data and historical flood data. This allows analysis of the differences between the actual flood risk propagation during historical floods and the predicted results based on the propagation function without project intervention, under different operational conditions of flood control and hydropower projects. Through comparison and calculation, a quantitative assessment can be made of the impact of flood control and hydropower projects on flood risk transfer.

[0061] Step S270 , constructing a spatiotemporal propagation network model of flood risk with spatiotemporal dependency based on the preset river network topology and the flood risk transfer barrier effect.

[0062] In some embodiments, a preset river network topology refers to a structural model pre-defined based on the actual river network morphology and geographic characteristics of the study area, used to describe the connectivity and spatial layout between rivers and nodes (such as river confluences and diversion points) in the river network. In this model, the network's primary connecting edges are determined based on the dynamic propagation characteristics of flood risk in different regions and the topological structure of the river network. Sub-basins, reservoirs, flood storage areas, sluice gates, and other hydraulic engineering facilities are used as primary nodes. By establishing topological relationships between important nodes and connecting edges, the connectivity and flow direction of the river network can be accurately reflected. For example, in a complex river network system, the topological structure can clearly indicate which rivers are connected, from which nodes water flows in and out, and the upstream and downstream relationships between different rivers.

[0063] In some embodiments, the flood risk transfer obstruction effect refers to the inhibitory or obstructive effect of flood control and water conservancy projects on the spatial transfer and spread of flood risk through their own structure and functions. This effect is primarily manifested in their ability to alter flood flow paths, reduce flood velocity, reduce peak discharge, and minimize flood inundation. For example, reservoirs can store water during floods, thereby reducing downstream river flows and mitigating flood risk in downstream sub-basins. Levees can prevent flood overflows and limit their spread, confining them to specific river channels and reducing the likelihood of flooding in surrounding areas. Sluice gates can regulate water levels and flow within river channels by controlling their opening. When flood flows are excessive, closing the gates can prevent backflow and protect upstream areas. By quantitatively evaluating the flood risk transfer obstruction effect of flood control and water conservancy projects, we can accurately measure the actual effectiveness of these projects in flood prevention and disaster reduction, providing a basis for further optimizing project design and operation management.

[0064] Step S280: Evaluate the study area based on the spatiotemporal flood risk propagation network model to obtain a flood risk propagation evaluation result.

[0065] In some embodiments, a comprehensive assessment of flood risk propagation within a study area is obtained through comprehensive analysis and calculation of the spatiotemporal flood risk propagation network model. This assessment includes multiple aspects of information, including but not limited to the speed and scope of risk propagation.

[0066] In this invention, the spatiotemporal flood risk propagation network model constructed in step S270 and the flood risk propagation function obtained in step S250 can be used to comprehensively assess the study area. By inputting different initial conditions (such as flood occurrence at different locations and the operational status of flood control and flood control projects), the propagation of flood risk throughout the study area is simulated, resulting in a series of simulation results.

[0067] The flood risk propagation assessment method provided by the present invention first calculates the flood risk value of each sub-basin in the study area based on the flood risk assessment indicator variables obtained in the study area; classifies the flood risk values ​​to obtain the flood risk level of each sub-basin in the study area; constructs a Markov chain spatiotemporal model based on the flood risk level; determines the spatiotemporal propagation law of flood risk based on the Markov chain spatiotemporal model; fits a flood risk propagation function based on the spatiotemporal propagation law of flood risk; determines the flood risk transfer obstruction effect of flood control and water conservancy projects based on the flood risk propagation function, flood control and water conservancy project operation information data and historical flood data; constructs a flood risk spatiotemporal propagation network model with spatiotemporal dependence based on the preset river network water system topology structure and the flood risk transfer obstruction effect; and evaluates the study area based on the flood risk spatiotemporal propagation network model to obtain a flood risk propagation assessment result. In this way, the present invention comprehensively evaluates the flood risk of the sub-basin in the study area by considering the danger of the basin's disaster factors, the exposure, vulnerability and disaster prevention and mitigation capabilities of the disaster-bearing bodies, and clarifies the spatiotemporal propagation law of cross-regional and cross-basin flood risks; on the other hand, by establishing a risk propagation function of flood risks at key sections / control points, the transfer barrier effect of flood control water conservancy projects such as reservoirs, flood storage areas, and levees on flood risks is quantified; thirdly, by considering the risk propagation characteristics and the operating characteristics and interactions of water conservancy facilities such as reservoirs, flood storage areas, levees, and gates, a spatiotemporal propagation network model of basin flood risk is constructed using complex network theory to achieve continuous deduction of the cross-regional and cross-basin flood risk propagation process; fourthly, the present invention is suitable for the study of flood risks and their propagation mechanisms in different basins, and has strong versatility.

[0068] In some embodiments, the above step S210 can be implemented by the following steps S211 to S215:

[0069] Step S211: Acquire multiple flood risk assessment indicator variables and flood risk assessment indicator observation values ​​in the study area.

[0070] Step S212: normalize the flood risk assessment indicator variables to obtain normalized flood risk assessment indicator variables.

[0071] Step S213 , calculating the information entropy of each flood risk assessment indicator according to a preset entropy weight method and the observed value of the flood risk assessment indicator.

[0072] Step S214: Determine a weight coefficient for each flood risk assessment indicator based on each information entropy; the weight coefficient is calculated as follows: ; ; In the formula, m represents the number of flood risk assessment indicator variables; n represents the number of sub-basins; represents the observed value of the i-th flood risk assessment indicator variable in the j-th sub-basin; Represents the information entropy of each flood risk assessment indicator variable; Represents the weight coefficient of each flood risk assessment indicator variable.

[0073] Step S215: Calculate the flood risk value based on the weight coefficient and the normalized flood risk assessment indicator variable. The calculation formula for the flood risk value is:

[0074] ; ; In the formula, R represents the flood risk value; H represents the risk of disaster-causing factors; V represents the vulnerability of the disaster-prone environment; E represents the exposure of the disaster-bearing body; C represents the disaster prevention and mitigation capacity; H i 、E i 、V i 、C i Represents the normalized value of the flood risk assessment indicator variable.

[0075] In some embodiments, the above step S230 can be implemented by the following steps S231 to S237:

[0076] Step S231: determining the first transfer times of each flood risk level in each sub-basin within two consecutive years.

[0077] In the present invention, the first transfer number is the number of times that a transfer occurs at each flood risk level in a sub-basin within two consecutive years, which can be directly obtained.

[0078] Step S232: Determine a first transfer probability between flood risk levels based on the first transfer times.

[0079] Step S233: determining a Markov risk state time transition probability matrix of each of the sub-basins according to the first transition probability.

[0080] Step S234: determining the second transfer times of each flood risk level in adjacent sub-basins in the same year according to a preset adjacency principle.

[0081] In the present invention, the second transfer number is the number of times that transfer occurs in each flood risk level in adjacent sub-basins in the same year that can be directly obtained.

[0082] Step S235: Determine a second transition probability according to the second transition number.

[0083] Step S236: Determine the Markov risk state space transition probability matrix of the adjacent sub-basin according to the second transition probability.

[0084] Step S237 : constructing the Markov chain spatiotemporal model according to the Markov risk state time transition probability matrix and the Markov risk state space transition probability matrix.

[0085] In some embodiments, the spatiotemporal propagation law of flood risk includes the risk transfer trend and risk propagation path; the above step S240 can be implemented by the following steps S241 to S242:

[0086] Step S241 : predicting the flood risk level of each sub-basin in the next year based on the Markov risk state time transition probability matrix of each sub-basin and the preset maximum transition probability principle.

[0087] In this invention, the principle of maximum preset transition probability refers to the decision criterion for determining the deterministic transition path based on the path with the highest probability of transitioning from the current state to the next state in the Markov state transition probability matrix. In other words, the risk level corresponding to the highest probability among all possible flood risk levels for the next year in the Markov risk state time transition probability matrix is ​​selected as the predicted flood risk level for the sub-basin for the next year.

[0088] In addition, the principle of maximum preset transfer probability can be explained through the following case.

[0089] Assuming that sub-basin A is currently at a medium flood risk level, its Markov risk state time transition probability matrix for the next year is shown in the following table:

[0090]

[0091] According to the preset maximum transfer probability principle, when the current risk level is medium, the probability of transferring to medium risk in the next year is 0.5, which is the largest. Therefore, the flood risk level of sub-basin A in the next year is predicted to be medium.

[0092] Step S242 , identifying the cross-basin and cross-regional propagation path of flood risk based on the Markov risk state space transition probability matrix of the adjacent sub-basins and the preset maximum transition probability principle.

[0093] In some embodiments, the above step S250 can be implemented by the following steps S251 to S252:

[0094] Step S251: Analyze the spatiotemporal propagation pattern of the flood risk to obtain the key sections of the flood risk spillover propagation.

[0095] Step S252: fitting the flood risk propagation function according to the key sections of flood risk spillover propagation.

[0096] The flood risk propagation function refers to a calculation function of the flood risk level of the downstream sub-basin B at time k, which is characterized based on the key sections of the flood risk spillover propagation and the spatiotemporal propagation law of the flood risk; the calculation function is jointly determined by the flood risk level of the sub-basin B at the previous moment and the flood risk level of the upstream sub-basin A at time k.

[0097] The calculation formula of the flood risk propagation function is: Where, represents the flood risk level of the downstream sub-basin B at time k; represents the flood risk level of the downstream sub-basin B at time k-1; represents the flood risk level of the upstream sub-basin A at time k.

[0098] In the present invention, the key section for risk spillover transmission refers to the process in which, during the flood risk propagation process, if the flood risk in the adjacent sub-basin shows a significant aggravating trend, for example, from the medium risk in the upstream sub-basin to the medium-high risk or high risk in the downstream sub-basin, then the outlet section of the upstream sub-basin is called the key section for risk spillover transmission.

[0099] In some embodiments, the above step S280 can be implemented by the following: calculating the flood risk propagation speed and propagation range according to the flood risk propagation network model to obtain the flood risk propagation assessment result.

[0100] Below, an exemplary application of the embodiment of the present application in a practical application scenario will be described.

[0101] This invention targets the key scientific issues of the cross-regional and cross-basin flood risk transmission mechanism, conducts research on flood risk and its transmission mechanism, completes basin flood risk assessment based on historical flood disaster investigations and basin basic data, establishes risk transmission function and flood risk spatiotemporal transmission network model, reveals the dynamic mechanism of risk transmission, and realizes continuous deduction of the basin flood risk transmission process. The invention results will provide key and accurate risk prediction information for basin flood control command decision-making.

[0102] This embodiment also provides a method for studying flood risk and its spatiotemporal propagation mechanism, which includes the following steps:

[0103] Step 1: Flood risk assessment based on the four-factor theory: Build a flood risk assessment indicator system to calculate the danger of flood-causing factors, exposure of disaster-bearing bodies, vulnerability, and disaster prevention and mitigation capabilities year by year; integrate the four disaster factors to dynamically assess the flood risk rate of each sub-basin and classify the risk levels, and identify medium- and high-risk areas for floods in the basin.

[0104] Step 2: Study the transfer-impeding effect of water conservancy and flood control projects on flood risk: propose a flood risk propagation function, and study the temporal and spatial propagation patterns of typical cross-regional and cross-basin historical flood risks; classify and identify the transfer-impeding effects of reservoirs, flood storage areas, sluices, etc. on risks, and study the dynamic mechanism of flood risk propagation.

[0105] Step 3: Study on the spatiotemporal propagation network model of flood risk: Based on the topological structure of the river network and the risk transfer barrier effect of different types of flood control and water conservancy projects, a spatiotemporal propagation network model of flood risk with spatiotemporal dependence is constructed; for key control points and sections of flood risk, the risk propagation speed and range are calculated to realize the continuous deduction of the flood risk propagation process in the basin.

[0106] Specifically, the above step 1 can be implemented by the following:

[0107] First, collect meteorological and historical flood data, basic geographic information data, and socioeconomic and demographic data for the study area. The meteorological and historical flood data should include at least the maximum three-day precipitation, flood frequency, flood peak, and flood volume; the basic geographic information data should include at least sub-basin boundaries, digital elevation, slope, river network density, land use rate, water conservancy and flood control facility density, and the proportion of flood storage and detention areas; and the socioeconomic and demographic data should include at least administrative divisions, population distribution, and socioeconomic data.

[0108] Then, based on the natural disaster risk theory and indicator system assessment method, in accordance with the principles of comprehensiveness, scientificity, operability and systematicity, starting from the formation mechanism of flood disasters and the four disaster factors (disaster-causing factors, disaster-prone environment, disaster-bearing bodies, and disaster prevention and mitigation capabilities), a flood risk assessment index system consisting of a target layer, a criterion layer and an indicator layer was constructed to calculate the multi-year flood risk value of each sub-basin.

[0109] Finally, the collected meteorological, historical flood data, basic geographic information, and socioeconomic and demographic data were standardized to eliminate the influence of different units and value ranges, ensuring that all indicators could be compared and weighted on a unified scale. The entropy weight method was used to determine the annual weight of each indicator in the indicator layer, and the final weight of the evaluation indicator was obtained through weighted average. A flood risk assessment model constructed based on disaster risk theory and indicator system assessment methods was used to calculate flood risk values ​​for each sub-basin in each year. The flood risk value data calculated for all sub-basins in each year were classified using the natural breakpoint method to identify medium- and high-risk areas in the basin.

[0110] The data is standardized by expression:

[0111] ;

[0112] ;

[0113] Where, It represents the normalized value of each flood risk assessment indicator variable, dimensionless; Indicates the Observed values ​​of flood risk assessment indicators; and Represent flood risk assessment indicators The maximum and minimum observed values.

[0114] The entropy weight method is used to determine the indicator weight calculation expression:

[0115] ;

[0116] ;

[0117] Where m represents the number of flood risk assessment indicator variables; n represents the number of sub-basins; represents the observed value of the i-th flood risk assessment indicator variable in the j-th sub-basin; I i Represents the information entropy of each flood risk assessment indicator variable; Represents the weight coefficient of each flood risk assessment indicator variable.

[0118] Flood risk value calculation expression:

[0119] ;

[0120] ;

[0121] Where R represents the flood risk value; H represents the hazard of the disaster-causing factor; V represents the vulnerability of the disaster-prone environment; E represents the exposure of the disaster-bearing body; C represents the disaster prevention and mitigation capability; W i Indicates the weight coefficient of each evaluation index; H i 、E i 、V i 、C i Represents the normalized value of the flood risk assessment indicator variable.

[0122] Specifically, the above step 2 can be implemented by the following:

[0123] First, based on the multi-year risk levels of each sub-basin, and taking both temporal and spatial factors into account, the transitions in flood risk status over two consecutive years were analyzed and calculated to produce a Markov risk state transition probability matrix. Temporally, the Markov risk state temporal transition probability matrix for each sub-basin was constructed by analyzing the number of flood risk levels and transitions within each sub-basin over two consecutive years and calculating the transition probabilities. Spatially, the Markov risk state spatial transition probability matrix for adjacent sub-basins was constructed by analyzing the number of flood risk levels and transitions within the same year, combining the adjacency principle. This spatiotemporal Markov chain model was used to determine risk transition trends, identify risk propagation paths, and explore the temporal and spatial propagation patterns of risk within the basin.

[0124] Then, by deeply analyzing the flood risk transmission patterns between each sub-basin and adjacent basins, the key sections of flood risk spillover transmission were identified. Based on the above, flood risk transmission functions were fitted at different times and spaces.

[0125] Finally, by collecting relevant water conservancy project operation information data and historical flood data, and using the fitted spatiotemporal propagation function of flood risk, we predict and simulate flood risk propagation scenarios at different times and spaces, classify and identify the risk transfer hindering effects of reservoirs, flood storage areas, sluices, etc., and reveal the dynamic mechanism of flood risk propagation.

[0126] Markov transition matrix:

[0127] Assume that the flood risk level is divided into s levels, denoted as ξ1, ξ2, …, ξ s , the transfer matrix P is:

[0128] ;

[0129] Where, ; a represents the number of times the risk state ξi is transferred to ξj within the flood risk assessment year. When i=j, it means that the flood risk is transferred to the same level; t represents the number of times the flood risk level is transferred.

[0130] Specifically, the above step 3 can be implemented by the following:

[0131] First, using complex network communication theory, the main connecting edges of the network are determined based on the dynamic propagation characteristics of flood risks in different regions and the topological structure of the river network. With sub-basins, reservoirs, flood storage areas, sluices and other water conservancy engineering facilities as the main nodes, a hierarchical propagation network model of spatiotemporal dependency is constructed.

[0132] Then, using historical flood data from the basin, risk propagation pathways and control nodes were identified. The network model was then verified and calibrated to ensure the accuracy and reliability of important nodes and connecting edges within the constructed network model. By calculating the importance of nodes and edges within the network model, key nodes and main pathways for flood risk propagation within the basin were identified. For critical control points and sections of flood risk, the speed of risk propagation was determined by calculating the response time of the risk at different nodes in the network model. The extent of the risk was determined based on the impact area.

[0133] Finally, the propagation of flood risk at different nodes and over time is simulated to reveal the basin's flood risk and its transmission mechanisms. By analyzing historical data and combining it with predictions and simulations of extreme weather events, the risk level is input for a node at a specific point in time in the flood risk propagation network. Based on the established risk propagation function and spatiotemporal propagation network model, the risk level of that node at other points in time, or at different nodes, is inferred, thereby enabling a continuous simulation of the basin's flood risk propagation process.

[0134] Edge betweenness calculation expression:

[0135] ;

[0136] Where, is the edge betweenness number, For nodes and The shortest path between times.

[0137] Among them, edge betweenness is used to reflect the role and influence of the corresponding edge in the entire network, and can be used to evaluate which edges in the flood risk propagation network play an important role in the connection stability of the network.

[0138] Vulnerability calculation expression:

[0139] ;

[0140] Where, For vulnerability, is the average path length, the average number of edges in the shortest path between any two nodes in the flood risk propagation network; is the connectivity.

[0141] Vulnerability refers to the impact of deleting a network node or edge on the entire network structure, and can be used to reflect the importance of nodes in the flood risk transmission network. Connectivity refers to the ratio of the number of connected nodes to the total number of nodes in the flood risk transmission network, starting from a starting node.

[0142] Based on the four-factor theory of disasters, this invention comprehensively assesses the flood risk level in a river basin, quantifies the transfer and obstruction effects of flood control and water conservancy projects such as reservoirs, flood storage areas, and embankments on flood risk, proposes a flood risk propagation function, and constructs a spatiotemporal propagation network model of flood risk with spatiotemporal dependencies, thus realizing dynamic simulation of flood risk in a river basin.

[0143] Figure 5 FIG. 1 is a schematic diagram of the structure of a flood risk propagation assessment device provided by an embodiment of the present invention. Figure 5 As shown, the flood risk propagation assessment device 500 includes: a calculation module 501, which is used to calculate the flood risk value of each sub-basin in the study area according to the obtained flood risk assessment indicator variables of the study area; a classification module 502, which is used to classify the flood risk values ​​and obtain the flood risk level of each sub-basin; a construction module 503, which is used to construct a Markov chain spatiotemporal model according to the flood risk level; a determination module 504, which is used to determine the spatiotemporal propagation law of flood risk according to the Markov chain spatiotemporal model; a fitting module 505, which is used to determine the spatiotemporal propagation law of flood risk according to the flood risk level; The flood risk propagation function is fitted according to the spatiotemporal propagation law of flood risks; the determination module 504 is further used to determine the flood risk transfer obstruction effect of flood control and water conservancy projects based on the flood risk propagation function, the operation information data of flood control and water conservancy projects and the historical flood data; the construction module 503 is further used to construct a spatiotemporal propagation network model of flood risk with spatiotemporal dependence according to the preset river network topology and the flood risk transfer obstruction effect; the evaluation module 506 is used to evaluate the study area according to the spatiotemporal propagation network model of flood risk to obtain a flood risk propagation evaluation result.

[0144] In some embodiments, the calculation module is further used to obtain multiple flood risk assessment indicator variables and flood risk assessment indicator observation values ​​in the study area; perform standardization on the flood risk assessment indicator variables to obtain normalized flood risk assessment indicator variables; calculate the information entropy of each flood risk assessment indicator based on a preset entropy weight method and the flood risk assessment indicator observation values; determine the weight coefficient of each flood risk assessment indicator based on each information entropy; the calculation formula of the weight coefficient is: ; ; In the formula, m represents the number of flood risk assessment indicator variables; n represents the number of sub-basins; represents the observed value of the i-th flood risk assessment indicator variable in the j-th sub-basin; Represents the information entropy of each flood risk assessment indicator variable; Represents the weight coefficient of each flood risk assessment indicator variable; the flood risk value is calculated based on the weight coefficient and the normalized flood risk assessment indicator variable; the calculation formula of the flood risk value is: ; ; In the formula, R represents the flood risk value; H represents the risk of disaster-causing factors; V represents the vulnerability of the disaster-prone environment; E represents the exposure of the disaster-bearing body; C represents the disaster prevention and mitigation capacity; H i 、E i 、V i 、C i Represents the normalized value of the flood risk assessment indicator variable.

[0145] In some embodiments, the construction module is further configured to determine a first transition number for each flood risk level in each sub-basin within two consecutive years; and determine a first transition probability between flood risk levels based on the first transition number; the calculation formula for the first transition probability is: Where, In the same sub-basin, the flood risk increases from the previous year to Transferring grades to the next year The number of levels; when i=j, it means that the flood risk has shifted to the same level; Indicates that the flood risk in the previous year was The number of levels; based on the first transition probability, determining the Markov risk state time transition probability matrix of each sub-basin; based on the preset adjacency principle, determining the second transition number of each flood risk level in adjacent sub-basins in the same year; based on the second transition number, determining the second transition probability; the calculation formula of the second transition probability is: Where, In the same year, the flood risk of sub-basin A is The level is propagated to the adjacent sub-basin B The number of levels; when i=j, it means that the flood risk has shifted to the same level; The flood risk of sub-basin A is The number of levels; determining the Markov risk state space transition probability matrix of the adjacent sub-basin according to the second transition probability; constructing the Markov chain spatiotemporal model according to the Markov risk state time transition probability matrix and the Markov risk state space transition probability matrix.

[0146] In some embodiments, the determination module is also used to predict the flood risk level of each sub-basin in the next year based on the Markov risk state time transition probability matrix of each sub-basin and the preset maximum transition probability principle; and to identify the cross-basin and cross-regional transmission path of flood risk based on the Markov risk state space transition probability matrix of the adjacent sub-basin and the preset maximum transition probability principle.

[0147] In some embodiments, the fitting module is further used to analyze the spatiotemporal propagation law of the flood risk to obtain a key section for flood risk spillover propagation; and to fit the flood risk propagation function based on the key section for flood risk spillover propagation; the flood risk propagation function refers to a calculation function of the flood risk level of the downstream sub-basin B at time k, which is characterized based on the key section for flood risk spillover propagation and the spatiotemporal propagation law of flood risk; the calculation function is jointly determined by the flood risk level of the sub-basin B at the previous moment and the flood risk level of the upstream sub-basin A at time k; the calculation formula of the flood risk propagation function is: Where, represents the flood risk level of the downstream sub-basin B at time k; represents the flood risk level of the downstream sub-basin B at time k-1; represents the flood risk level of the upstream sub-basin A at time k.

[0148] In some embodiments, the assessment module is further used to calculate the flood risk propagation speed and propagation range based on the flood risk propagation network model to obtain the flood risk propagation assessment result.

[0149] It should be noted that the description of the device embodiment of the present invention is similar to the description of the above-mentioned method embodiment, and has similar beneficial effects as the same method embodiment, so it will not be repeated. For technical details not disclosed in the device embodiment, please refer to the description of the method embodiment of the present invention for understanding.

[0150] It should be noted that in the embodiments of the present invention, if the flood risk propagation assessment method described above is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of the present invention, or the portion that contributes to the relevant technology, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a terminal to execute all or part of the methods described in various embodiments of the present invention. The aforementioned storage media include various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), magnetic disks, or optical disks. As such, the embodiments of the present invention are not limited to any specific combination of hardware and software.

[0151] Correspondingly, an embodiment of the present invention provides a flood risk propagation assessment device, Figure 6 FIG. 1 is a schematic diagram of the structure of a flood risk communication assessment device provided by an embodiment of the present invention. Figure 6 As shown, flood risk propagation assessment device 600 includes at least: a processor 601 and a computer-readable storage medium 602 configured to store executable instructions. Processor 601 generally controls the overall operation of flood risk propagation assessment device 600. Computer-readable storage medium 602 is configured to store instructions and applications executable by processor 601 and to cache data to be processed or processed by processor 601 and various modules within flood risk propagation assessment device 600. This can be achieved using flash memory (FLASH) or random access memory (RAM).

[0152] An embodiment of the present invention provides a storage medium storing executable instructions, wherein the executable instructions are stored. When the executable instructions are executed by a processor, the processor will be caused to execute the method provided by the embodiment of the present invention, for example, Figure 2 The method shown.

[0153] In some embodiments, the storage medium can be a computer-readable storage medium, such as a ferroelectric random access memory (FRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); it can also be various devices including one or any combination of the above memories.

[0154] In some embodiments, executable instructions may be in the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0155] By way of example, executable instructions may, but need not necessarily, correspond to a file in a file system, may be stored as part of a file storing other programs or data, such as one or more scripts in a Hypertext Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinating files (e.g., files storing one or more modules, subroutines, or code portions). By way of example, executable instructions may be deployed to be executed on one electronic device, or on multiple electronic devices located in one location, or on multiple electronic devices distributed across multiple locations and interconnected via a communication network.

[0156] The above description is merely an embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and scope of the present invention are included in the scope of protection of the present invention.

[0157] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present invention. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean 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 embodiment of the present invention. The serial numbers of the above-mentioned embodiments of the present invention are for description only and do not represent the advantages and disadvantages of the embodiments.

[0158] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method or device. In the absence of further restrictions, an element defined by the statement "comprises a..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed.

[0159] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A flood risk communication assessment method, characterized in that: The method comprises: Calculate the flood risk value of each sub-basin in the study area based on the obtained flood risk assessment indicator variables of the study area; Classifying the flood risk values ​​to obtain the flood risk level of each sub-basin in the study area; According to the flood risk level, a Markov chain spatiotemporal model is constructed; Determine the spatiotemporal propagation pattern of flood risk based on the Markov chain spatiotemporal model; Fitting the flood risk propagation function according to the spatiotemporal propagation law of flood risk; Determining the flood risk transfer hindering effect of the flood control and water conservancy project based on the flood risk propagation function, the flood control and water conservancy project operation information data, and the historical flood data; Based on the preset river network topology and the flood risk transfer barrier effect, a spatiotemporal flood risk propagation network model with spatiotemporal dependencies is constructed; According to the spatiotemporal propagation network model of flood risk, the study area is evaluated to obtain a flood risk propagation assessment result; Among them, constructing a spatiotemporal Markov chain model based on the flood risk level includes: determining the first transfer number of each flood risk level in each sub-basin within two consecutive years; determining the first transfer probability between flood risk levels based on the first transfer number; determining the Markov risk state time transfer probability matrix of each sub-basin based on the first transfer probability; determining the second transfer number of each flood risk level in adjacent sub-basins in the same year based on a preset adjacency principle; determining the second transfer probability based on the second transfer number; determining the Markov risk state space transfer probability matrix of the adjacent sub-basins based on the second transfer probability; constructing the Markov chain spatiotemporal model based on the Markov risk state time transfer probability matrix and the Markov risk state space transfer probability matrix.

2. The method according to claim 1, characterized in that Calculating the flood risk value of each sub-basin in the study area based on the acquired flood risk assessment indicator variables of the study area includes: Obtain multiple flood risk assessment indicator variables and flood risk assessment indicator observations in the study area; Standardizing the flood risk assessment indicator variables to obtain normalized flood risk assessment indicator variables; Calculating the information entropy of each flood risk assessment indicator according to a preset entropy weight method and the observed value of the flood risk assessment indicator; Determining a weight coefficient for each flood risk assessment indicator based on each of the information entropies; The flood risk value is calculated according to the weight coefficient and the normalized flood risk assessment indicator variable.

3. The method according to claim 1, characterized in that Determining the spatiotemporal propagation law of flood risk based on the Markov chain spatiotemporal model includes: Predicting the flood risk level of each sub-basin in the next year based on the Markov risk state time transition probability matrix of each sub-basin and the preset maximum transition probability principle; According to the Markov risk state space transition probability matrix of the adjacent sub-basins and the preset maximum transition probability principle, the cross-basin and cross-regional propagation path of flood risk is identified.

4. The method according to claim 1, wherein The step of fitting a flood risk propagation function according to the spatiotemporal propagation law of flood risk comprises: Analyze the spatiotemporal propagation patterns of flood risk and obtain the key sections for spillover propagation of flood risk; The flood risk propagation function is fitted based on the key sections of flood risk spillover propagation.

5. The method according to claim 1, wherein The study area is evaluated based on the spatiotemporal flood risk propagation network model to obtain flood risk propagation assessment results, including: According to the spatiotemporal propagation network model of flood risk, the propagation speed and range of flood risk are calculated to obtain the flood risk propagation assessment result.

6. A flood risk communication assessment device, characterized in that: The device comprises: A calculation module, configured to calculate the flood risk value of each sub-basin in the study area based on the acquired flood risk assessment indicator variables of the study area; A classification module, configured to classify the flood risk values ​​to obtain a flood risk level for each sub-basin; A construction module, configured to construct a Markov chain spatiotemporal model according to the flood risk level; A determination module, configured to determine the spatiotemporal propagation law of flood risk based on the Markov chain spatiotemporal model; A fitting module, configured to fit a flood risk propagation function according to the spatiotemporal propagation law of flood risk; The determination module is further configured to determine the flood risk transfer obstruction effect of the flood control and water conservancy project based on the flood risk propagation function, the flood control and water conservancy project operation information data, and the historical flood data; The construction module is further used to construct a spatiotemporal propagation network model of flood risk with spatiotemporal dependencies based on a preset river network topology and the flood risk transfer obstruction effect; An evaluation module, configured to evaluate the study area based on the spatiotemporal flood risk propagation network model to obtain a flood risk propagation evaluation result; The construction module is also used to determine the first transfer number of each flood risk level in each sub-basin within two consecutive years; determine the first transfer probability between flood risk levels based on the first transfer number; determine the Markov risk state time transfer probability matrix of each sub-basin based on the first transfer probability; determine the second transfer number of each flood risk level in adjacent sub-basins in the same year based on a preset adjacency principle; determine the second transfer probability based on the second transfer number; determine the Markov risk state space transfer probability matrix of the adjacent sub-basins based on the second transfer probability; and construct the Markov chain spatiotemporal model based on the Markov risk state time transfer probability matrix and the Markov risk state space transfer probability matrix.

7. An electronic device, characterized in that: include: a memory for storing executable instructions; The processor is configured to implement the flood risk propagation assessment method according to any one of claims 1 to 5 when executing the executable instructions stored in the memory.

8. A computer-readable storage medium, characterized in that Executable instructions are stored, which are used to cause a processor to execute the executable instructions to implement the flood risk propagation assessment method according to any one of claims 1 to 5.

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