Flood risk propagation assessment method, apparatus and device, and storage medium

Abstract

The invention discloses a flood risk propagation assessment method, device and equipment and a storage medium, and the method comprises the steps: calculating a flood risk value of each sub-basin in a research region according to an obtained flood risk assessment index variable of the research region; grading the flood risk value to obtain a flood risk grade of each sub-basin in the research area; constructing a Markov chain space-time model; determining a flood risk space-time propagation rule according to the Markov chain space-time model; fitting a flood risk propagation function according to a flood risk space-time propagation rule; according to the flood risk propagation function, the flood control water conservancy project operation information data and the historical flood data, determining a flood risk transfer blocking effect of the flood control water conservancy project; constructing a flood risk space-time propagation network model with a space-time dependency relationship; and according to the flood risk space-time propagation network model, evaluating the research area to obtain a flood risk propagation evaluation 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 impact of ongoing climate change and rapid urbanization has increased the risk of flood disasters, posing severe challenges to urban infrastructure, residents' lives and economic development. According to the Emergency Events Database (EM-DAT), there were 8,591 natural disasters worldwide between 2000 and 2020, of which flood disasters accounted for 40.2%. Therefore, strengthening the research on flood risks and their transmission mechanisms can not only provide theoretical basis and technical support for scientific disaster prevention and mitigation decision-making, but also have important practical significance for promoting the sustainable development of economic, social and ecological environmental systems.

[0003] Traditional research mainly focuses on the flood risk transmission mechanism within a basin or region, but lacks in-depth understanding of the spatiotemporal transmission mechanism of flood risk across basins and regions. The limitations of current research are mainly reflected in the following aspects: first, there is a lack of overall understanding of the spatiotemporal transmission law of flood risk between years, upstream and downstream, and between main and tributary rivers; second, the understanding of the risk transfer and obstruction effect of water conservancy and flood control projects is still not in-depth; third, the continuous deduction of cross-basin and cross-regional flood risk situation has not yet been achieved. Summary of the invention

[0004] In order to solve the above 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 object, 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 according to 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] According to the Markov chain spatiotemporal model, the spatiotemporal propagation law of flood risk is determined;

[0011] According to the spatiotemporal propagation law of flood risk, a flood risk propagation function is fitted;

[0012] Determine the flood risk transfer barrier 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] According to the preset river network topology and the flood risk transfer barrier effect, a spatiotemporal propagation network model of flood risk with spatiotemporal dependency is constructed;

[0014] According to 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, used for 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;

[0017] A classification module, used for classifying the flood risk values ​​to obtain the flood risk level of each sub-basin;

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

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

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

[0021] The determination module is further used 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 dependency according to the preset river network water system topology structure and the flood risk transfer barrier effect;

[0023] The evaluation module is used to evaluate the study area according to the flood risk spatiotemporal propagation network model to 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 observations in the study area; standardize the flood risk assessment indicator variables to obtain normalized flood risk assessment indicator variables; calculate the information entropy of each flood risk assessment indicator according to a preset entropy weight method and the flood risk assessment indicator observations; determine the weight coefficient of each flood risk assessment indicator according to 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; calculates the flood risk value according to 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 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 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 used to determine the first transfer times of each flood risk level in each sub-basin within two consecutive years; determine the first transfer probability between flood risk levels according to the first transfer times; the calculation formula of the first transfer probability is: ; In the formula, In the same sub-basin, the flood risk increases from the previous year to Grade transfer to next year The number of levels; when i=j, it means that the flood risk is transferred to the same level; Indicates that the flood risk in the previous year was The number of times the flood risk level is changed; according to the first transfer probability, the Markov risk state time transfer probability matrix of each sub-basin is determined; according to the preset adjacency principle, the second transfer number of each flood risk level of adjacent sub-basins in the same year is determined; according to the second transfer number, the second transfer probability is determined; the calculation formula of the second transfer probability is: ; In the formula, 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 is transferred 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 transfer probability principle; and to identify 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 transfer probability principle.

[0027] In some embodiments, the fitting module is further used to analyze the spatiotemporal propagation law of the flood risk to obtain the key section of flood risk spillover propagation; according to the key section of flood risk spillover propagation, fit the flood risk propagation function; 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 of 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: ; In the formula, 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 according to 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 comprises the following steps: first, calculating the flood risk value of each sub-basin in the study area according to the flood risk assessment index variables obtained in 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 according to the flood risk level; determining the spatiotemporal propagation law of flood risk according to the Markov chain spatiotemporal model; fitting a flood risk propagation function according to the spatiotemporal propagation law of flood risk; determining the flood risk transfer hindering effect of flood control and water conservancy projects according to 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 according to the preset river network water system topology structure and the flood risk transfer hindering effect; and evaluating the study area according to the flood risk spatiotemporal propagation network model to obtain a flood risk propagation assessment result. Thus, the present invention comprehensively evaluates the flood risk of the sub-basin of the study area by considering the danger of the basin disaster factors, the exposure degree of the disaster-bearing body, the vulnerability and the disaster prevention and mitigation capacity, and clarifies the spatiotemporal propagation law of cross-regional and cross-basin flood risk; at the same time, by establishing the risk propagation function of flood risk 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 risk is quantified; in addition, by considering the risk propagation characteristics and the operation characteristics and interactions of water conservancy engineering facilities such as reservoirs, flood storage areas, levees, and gates, the complex network theory is used to construct a spatiotemporal propagation network model of basin flood risk, and the continuous deduction of the cross-regional and cross-basin flood risk propagation process is realized. The present invention is suitable for the study of flood risk and its propagation mechanism in different basins, and has strong versatility. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 is a structural schematic diagram of a flood risk communication assessment system provided by an embodiment of the present invention;

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

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

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

[0036] Figure 5 It is a schematic diagram of the composition structure of a flood risk propagation 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 purpose, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present invention. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present invention.

[0039] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but 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 those 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 only for the purpose of describing the embodiments of the present invention and are not intended to limit the present invention.

[0040] The following describes an exemplary application of the flood risk communication assessment device of the embodiment of the present invention. The flood risk communication assessment device provided by the embodiment of the present invention can be implemented as 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, tablet computers, desktop computers, mobile devices, etc.; in another implementation, the flood risk communication assessment device provided by the embodiment of the present invention can also be implemented as a server, wherein the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides 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 distribution networks (CDN, Content Delivery Network), and big data and artificial intelligence platforms. The terminal and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in the embodiment of the present invention. The following describes an exemplary application of the flood risk communication assessment device as a server.

[0041] See also Figure 1 , Figure 11 is a schematic diagram of the structure of the flood risk communication assessment system 10 provided in an embodiment of the present invention. In order to realize the assessment of flood risk communication, the 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 the embodiment of the present invention includes a terminal 110, a network 120 and a server 130, wherein the server 130 is a server of the flood risk communication assessment application. The server 130 can constitute the flood risk communication assessment device of the embodiment of the present invention. The terminal 110 is connected to the server 130 via the network 120, and the network 120 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 the flood risk propagation assessment is conducted in the study area, the terminal 110 sends the acquired flood risk assessment index variable of the study area to the server 130 through the network 120. The server 130 calculates the flood risk value of each sub-basin in the study area in response to the terminal 110 receiving the flood risk assessment index variable; the flood risk value is graded to obtain the flood risk level of each sub-basin in the study area; a Markov chain spatiotemporal model is constructed according to the flood risk level; the spatiotemporal propagation law of flood risk is determined according to the Markov chain spatiotemporal model; the flood risk propagation function is fitted according to the spatiotemporal propagation law of flood risk; the flood risk transfer hindering effect of flood control and water conservancy projects is determined according to the flood risk propagation function, the operation information data of flood control and water conservancy projects and the historical flood data; a spatiotemporal propagation network model of flood risk with spatiotemporal dependency is constructed according to the preset river network water system topology structure and the flood risk transfer hindering effect; the study area is evaluated according to the spatiotemporal propagation network model of flood risk, and the flood risk propagation assessment result is obtained. 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 present invention provides a flood risk assessment method. Figure 2 , Figure 2 is a flow chart of a flood risk communication assessment method provided by an embodiment of the present invention, which is combined with 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 the 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 boundary, digital elevation, slope, river network density, land utilization rate, water conservancy and flood control facility density, flood storage area area ratio, administrative divisions, population distribution, and socioeconomic data. These assessment indicator variables can reflect the natural geographical characteristics of the study area and the factors that may affect the occurrence of floods from different aspects, and are 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 features such as the topography and landforms of the study area, the distribution of water systems, etc. Each sub-basin has its own unique catchment area, and its boundaries are defined by watersheds, so that precipitation and surface runoff converge in 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, sub-basins may consist of valleys and surrounding hillsides, and water flows converge along the valleys; in plain areas, sub-basins may be divided according to the micro-undulations of the terrain and the distribution of river networks. Research at this smaller spatial scale can more accurately analyze and assess flood risks, because different sub-basins may have completely different flood responses due to differences in their own characteristics when facing the same rainfall conditions.

[0047] In the present invention, the flood risk value is used to quantify the risk level of flood disasters faced by each sub-basin. This value is a quantitative representation of the possibility and potential loss level of flood disasters faced by each sub-basin. Here, the larger the value, the higher the possibility of flood disasters occurring in the sub-basin, and the greater the potential damage level.

[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 digital levels, such as 1-5, where 1 represents the lowest risk and 5 represents the highest risk. Through level classification, the risk status of each sub-basin can be understood more quickly so that corresponding 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 the Markov chain theory, which is a mathematical model used to describe the state transition law of the 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 transferring from the current flood risk level of a sub-basin to a different risk level of 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 the statistical analysis of historical data, the probability of 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 transfer probability matrix and combining time and space factors, a Markov chain spatiotemporal model that can dynamically simulate the spread of flood risk between different sub-basins over time is constructed, providing a powerful tool for in-depth research on the spatiotemporal evolution of flood risk.

[0052] Step S240: determining the spatiotemporal propagation law of flood risk according to the Markov chain spatiotemporal model.

[0053] In some embodiments, the spatiotemporal propagation law of flood risk refers to the law used to describe how flood risk propagates and changes in both time and space. In the time dimension, it is manifested as the changing trend of flood risk over time. For example, before the rainy season, due to the gradual increase in precipitation, the flood risk value of each sub-basin begins to gradually increase; and after the rainy season, as precipitation decreases and floods recede, the flood risk value gradually decreases. In the spatial dimension, flood risk will spread from the sub-basin where the flood disaster occurs to the surrounding adjacent sub-basins. The direction and speed of propagation are affected by many factors, such as topography. Floods usually flow along the lower-lying areas and propagate from the upstream sub-basin to the downstream sub-basin; the connectivity of the river network and water system. In areas with dense river networks and good connectivity, the speed of flood risk propagation is faster; in addition, the layout and operation status of flood control and water conservancy projects will also have an important impact on risk propagation. For example, dams, sluices and other projects can block or slow down the spread of floods. 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, the Markov chain spatiotemporal model constructed in step S230 can be deeply analyzed to explore the propagation law of flood risk in the temporal and spatial dimensions contained in the model. For example, the probability of the risk level of adjacent sub-basins around high-risk sub-basins is observed to increase over time, as well as the transfer trend of risk levels in different seasons and time periods, so as to summarize the spatiotemporal propagation pattern of flood risk with universality and regularity, which provides important reference 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 law of flood risk by using a mathematical fitting method. The function quantitatively describes the propagation process of flood risk in time and space in the form of a mathematical formula. In the fitting process, it is usually necessary to use a large amount of historical data and data simulated based on the Markov chain spatiotemporal model, and by continuously adjusting the parameters of the function, the function can reflect the actual flood risk propagation as accurately as possible.

[0057] In the present invention, based on the spatiotemporal propagation law of flood risk determined in step S240, a mathematical fitting method such as linear regression, nonlinear regression, etc. can be used to find a suitable mathematical function to describe the propagation law. Parameter estimation and optimization are performed through a large amount of historical data and model simulation results, so that the fitted flood risk propagation function can accurately reflect the spatiotemporal propagation process of flood risk, and the function can predict the changes in flood risk at different time and spatial locations.

[0058] Step S260, determining the flood risk transfer barrier 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, the 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. The reservoir operation information data includes water level, water storage capacity, inflow flow, outflow flow, water release time and water release flow, etc. These data reflect the reservoir's ability to regulate floods. By reasonably controlling the reservoir's storage and discharge volume, the peak flow can be effectively reduced and the flood control pressure in downstream areas can be reduced. The sluice operation information includes the time of opening and closing, the size of the opening, etc. By controlling the switch of the sluice gate, the water level and flow in the river channel can be adjusted to achieve reasonable scheduling of floods. Accurately obtaining and analyzing the operation information data of these flood control and water conservancy projects is crucial to evaluating their role in flood control and prevention.

[0060] In the present invention, the flood risk propagation function obtained in step S250 can be combined with the operation information data of flood control and water conservancy projects and historical flood data. The difference between the actual situation of flood risk propagation when historical floods occur and the predicted situation based on the propagation function when there is no engineering intervention under different operation states of flood control and water conservancy projects is analyzed. Through comparison and calculation, the hindering effect of flood control and water conservancy projects on flood risk transfer is quantitatively evaluated.

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

[0062] In some embodiments, the preset river network water system topology structure refers to a structural model that is pre-set based on the actual river network water system morphology and geographical characteristics of the study area, and is used to describe the connection relationship and spatial layout between the rivers and nodes (such as river confluences, diversion points, etc.) in the river network. In this model, 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 water system. The sub-basins, reservoirs, flood storage areas, gates and other water conservancy engineering facilities are used as the main nodes. By establishing the topological relationship between important nodes and connecting edges, the connectivity and water flow direction of the river network water system can be accurately reflected. For example, in a complex river network system, the topological structure can clearly show which rivers are connected to each other, which nodes the water flows in or out from, and the upstream and downstream relationships between different rivers.

[0063] In some embodiments, the flood risk transfer hindering effect refers to the inhibitory or hindering effect of flood control and water conservancy projects on the spatial transfer and propagation of flood risks through their own structure and function. This effect is mainly reflected in the ability of flood control and water conservancy projects to change the flow path of floods, reduce the flow rate of floods, reduce the peak flow, and reduce the flood inundation range. For example, reservoirs can store water when floods come, store a large amount of floods, thereby reducing the flow of downstream rivers and reducing the flood risk of downstream sub-basins; dams can prevent floods from overflowing and limit the spread of floods, so that floods can only flow in specific rivers, reducing the possibility of floods in surrounding areas; sluices can adjust the water level and flow in the river by controlling the opening. When the flood flow is too large, closing the sluice can prevent floods from backflowing and protect the safety of upstream areas. By quantitatively evaluating the flood risk transfer hindering effect of flood control and water conservancy projects, the actual effect of these projects in flood control and disaster reduction can be accurately measured, providing a basis for further optimizing project design and operation management.

[0064] Step S280: evaluating the study area according to the spatiotemporal propagation network model of flood risk to obtain a flood risk propagation evaluation result.

[0065] In some embodiments, a comprehensive evaluation conclusion on the flood risk propagation situation in the study area is finally obtained by comprehensively analyzing and calculating the spatiotemporal propagation network model of flood risk. The result covers multiple aspects of information, including but not limited to the speed and scope of risk propagation.

[0066] In the present invention, the spatiotemporal propagation network model of flood risk constructed in step S270 and the flood risk propagation function obtained in step S250 can be used to comprehensively evaluate the study area. By inputting different initial conditions (such as flood occurrence in different locations, operation status of flood control and water conservancy projects, etc.), the propagation process of flood risk in the entire study area is simulated to obtain a series of simulation results.

[0067] The flood risk propagation assessment method provided by the present invention comprises the following steps: first, calculating the flood risk value of each sub-basin in the study area according to the flood risk assessment index variables obtained in 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 according to the flood risk level; determining the spatiotemporal propagation law of flood risk according to the Markov chain spatiotemporal model; fitting a flood risk propagation function according to the spatiotemporal propagation law of flood risk; determining the flood risk transfer hindering effect of flood control and water conservancy projects according to 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 according to the preset river network water system topology structure and the flood risk transfer hindering effect; and evaluating the study area according to 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 disaster factors in the basin, the exposure, vulnerability and disaster prevention and mitigation capabilities of the disaster-bearing body, and clarifies the spatiotemporal propagation law of cross-regional and cross-basin flood risk; on the other hand, by establishing the risk propagation function of flood risk 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 risk is quantified; thirdly, by considering the risk propagation characteristics and the operating characteristics and interactions of water conservancy engineering facilities such as reservoirs, flood storage areas, levees, and gates, the complex network theory is used to construct a spatiotemporal propagation network model of basin flood risk, so as to realize the 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, obtaining multiple flood risk assessment indicator variables and flood risk assessment indicator observation values ​​in the study area.

[0070] Step S212, normalizing the flood risk assessment indicator variable to obtain a normalized flood risk assessment indicator variable.

[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 of each flood risk assessment indicator according to 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.

[0073] Step S215, calculating the flood risk value according to the weight coefficient and the normalized flood risk assessment indicator variable; the calculation formula of the flood risk value is:

[0074] ; ; In the formula, 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 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 transfer occurs at each flood risk level in the sub-basin within two consecutive years, which can be directly obtained.

[0078] Step S232: determining a first transfer probability between flood risk levels according to 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 transfer occurs in each flood risk level of adjacent sub-basins in the same year, which can be directly obtained.

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

[0083] Step S236: determining a 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 according to the Markov risk state time transition probability matrix of each sub-basin and the preset maximum transition probability principle.

[0087] In the present invention, the preset maximum transfer probability principle refers to taking the path with the maximum transfer probability from the current state to the next state as the decision criterion for the deterministic transfer path in the Markov state transfer probability matrix. In other words, the risk level corresponding to the maximum transfer probability corresponding to each possible flood risk level in the next year in the Markov risk state time transfer probability matrix is ​​selected as the predicted flood risk level of the sub-basin in 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] 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 maximum. Therefore, it is predicted that the flood risk level of sub-basin A in the next year will be medium.

[0091] 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.

[0092] In some embodiments, the above step S250 can be implemented by the following steps S251 to S252:

[0093] Step S251, analyzing the spatiotemporal propagation law of the flood risk to obtain the key sections of the flood risk spillover propagation.

[0094] Step S252: fitting the flood risk propagation function according to the key section of flood risk spillover propagation.

[0095] 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.

[0096] The calculation formula of the flood risk propagation function is: ; In the formula, 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.

[0097] 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 increasing 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 exit section of the upstream sub-basin is called the key section for risk spillover transmission.

[0098] In some embodiments, the above step S280 can be implemented by the following contents: according to the flood risk propagation network model, the flood risk propagation speed and propagation range are calculated to obtain the flood risk propagation assessment result.

[0099] The following is an explanation of an exemplary application of an embodiment of the present application in a practical application scenario.

[0100] The present invention aims at 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 the basin flood risk assessment based on the historical flood disaster investigation and basin basic data, establishes the risk propagation function and the flood risk spatiotemporal propagation network model, reveals the dynamic mechanism of risk propagation, and realizes the continuous deduction of the basin flood risk propagation process. The invention results will provide key and accurate risk prediction information for basin flood control command decision-making.

[0101] This embodiment also provides a method for studying flood risk and its spatiotemporal propagation mechanism, the method comprising the following steps:

[0102] Step 1: Flood risk assessment based on the four-factor theory: Construct a flood risk assessment index system to calculate the danger of flood-causing factors, exposure, vulnerability and disaster prevention and mitigation capabilities of disaster-bearing bodies year by year; integrate the four disaster factors to dynamically assess the flood risk rate of each sub-basin and divide the risk level to identify medium and high-risk areas for floods in the basin.

[0103] Step 2: Study the transfer barrier effect of water conservancy and flood control projects on flood risk: propose a flood risk propagation function, study the temporal and spatial propagation laws of typical cross-regional and cross-basin historical flood risks; classify and identify the transfer barrier effects of reservoirs, flood storage areas, sluices, etc. on risks, and study the dynamic mechanism of flood risk propagation.

[0104] Step three, study on the spatiotemporal propagation network model of flood risk: Based on the topological structure of river network and the risk transfer barrier effect of different types of flood control and water conservancy projects, construct a spatiotemporal propagation network model of flood risk with spatiotemporal dependency; for key control points and sections of flood risk, calculate the risk propagation speed and range, and realize the continuous deduction of the flood risk propagation process in the basin.

[0105] Specifically, the above step 1 can be implemented by the following contents:

[0106] First, collect meteorological and historical flood data, basic geographic information data, and socio-economic and demographic data of the study area. The meteorological and historical flood data include at least the maximum three-day precipitation, flood frequency, flood peak, and flood volume; the basic geographic information data include at least the sub-basin boundaries, digital elevation, slope, river network density, land utilization rate, water conservancy and flood control facility density, and the proportion of flood storage and detention area area; the socio-economic and demographic data include at least administrative divisions, population distribution, and socio-economic data.

[0107] 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 body, 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.

[0108] Finally, the collected meteorological, historical flood data, basic geographic information data, and socio-economic population data were standardized to eliminate the impact of different units and value ranges, ensuring that each indicator can be compared and weighted on a unified scale. The entropy weight method was used to determine the weight of each indicator in the indicator layer for each year, and the final weight of the evaluation indicator was obtained by weighted average. The flood risk assessment model constructed based on the disaster risk theory and the indicator system assessment method was used to calculate the flood risk value of each sub-basin in each year, and the flood risk value data calculated for all sub-basins in each year were graded using the natural breakpoint method to identify medium and high risk areas in the basin.

[0109] The data is standardized by expression:

[0110] ;

[0111] ;

[0112] In the formula, It represents the normalized value of each flood risk assessment indicator variable, dimensionless; Indicates Observed values ​​of flood risk assessment indicators; and Represent flood risk assessment indicators The maximum and minimum observed values ​​of .

[0113] The entropy weight method determines the indicator weight calculation expression:

[0114] ;

[0115] ;

[0116] 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; I i Represents the information entropy of each flood risk assessment indicator variable; Represents the weight coefficient of each flood risk assessment indicator variable.

[0117] Flood risk value calculation expression:

[0118] ;

[0119] ;

[0120] In the formula, 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 capacity; W i Represents 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.

[0121] Specifically, the above step 2 can be implemented by the following contents:

[0122] First, based on the multi-year risk levels of each sub-basin, considering both time and space factors, the transfer of flood risk status in two consecutive years is analyzed and calculated to obtain the Markov risk state transfer probability matrix. In terms of time, by analyzing the number of occurrences and transfers of flood risk levels in the same sub-basin every two consecutive years, the transfer probability is calculated to form the Markov risk state time transfer probability matrix of each sub-basin; in terms of space, combined with the adjacency principle, by analyzing the number of occurrences and transfers of flood risk levels in adjacent sub-basins in the same year, the transfer probability is calculated to form the Markov risk state space transfer probability matrix of adjacent sub-basins. Through the above-constructed spatiotemporal Markov chain model, the risk transfer trend is judged, the risk propagation path is identified, and the spatiotemporal propagation law of risk in the basin is explored.

[0123] Then, by deeply analyzing the flood risk propagation law between each sub-basin and the adjacent basin, the key sections of flood risk spillover propagation are identified. On the above basis, the flood risk propagation function in different time and space is fitted.

[0124] 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 in different time and space, classify and identify the risk transfer barrier effects of reservoirs, flood storage areas, sluices, etc., and reveal the dynamic mechanism of flood risk transmission.

[0125] Markov transition matrix:

[0126] Assume that the flood risk level is divided into s levels, denoted by ξ 1 ,ξ 2 ,…,ξ s , the transfer matrix P is:

[0127] ;

[0128] In the formula, ; 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.

[0129] Specifically, the above step three can be implemented by the following contents:

[0130] First, the complex network communication theory is used to determine the main connecting edges of the network 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 time and space dependency is constructed.

[0131] Then, the historical flood data of the basin is used to identify the risk propagation paths and control nodes, and the network model is verified and calibrated to ensure the accuracy and reliability of the important nodes and connecting edges in the constructed network model. By calculating the importance of nodes and edges in the network model, the key nodes and main paths of flood risk propagation in the basin are identified. For the key control points and sections of flood risk, the speed of risk propagation is determined by calculating the response time of the risk at different nodes of the network model, and the scope of flood risk propagation is determined by the affected area.

[0132] Finally, the propagation of flood risk at different nodes and at different times is simulated to reveal the flood risk and its propagation mechanism in the basin. By analyzing historical data and combining the prediction and simulation of extreme weather events, the risk level is input for a node at a certain time point in the flood risk propagation network, and the risk level of the node at other time points or the risk level of different nodes is inferred based on the established risk propagation function and spatiotemporal propagation network model, thereby realizing the continuous deduction of the flood risk propagation process in the basin.

[0133] Edge betweenness calculation expression:

[0134] ;

[0135] In the formula, is the edge betweenness number, For Node and The shortest path between The number of times.

[0136] 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.

[0137] Vulnerability calculation expression:

[0138] ;

[0139] In the formula, For vulnerability, is the average path length, the average number of edges of the shortest path between any two nodes in the flood risk propagation network; For connectivity.

[0140] Among them, vulnerability refers to the impact on the entire network structure after a network node or edge is deleted, which 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 certain starting node.

[0141] Based on the four-factor theory of disasters, the present invention comprehensively evaluates the flood risk level in the basin, quantifies the transfer and barrier effects of flood control and water conservancy projects such as reservoirs, flood storage areas, and levees on flood risks, proposes a flood risk propagation function, and constructs a spatiotemporal propagation network model of flood risk with spatiotemporal dependence, thus realizing dynamic simulation of flood risks in the basin.

[0142] Figure 5 is a schematic diagram of the structure of a flood risk propagation assessment device provided by an embodiment of the present invention, such as 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 acquired flood risk assessment indicator variables of the study area; a classification module 502, which is used to classify the flood risk values ​​to 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 also used to determine the flood risk transfer barrier effect of flood control and water conservancy projects according to 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 also used to construct a spatiotemporal propagation network model of flood risk with spatiotemporal dependency according to the preset river network topology structure and the flood risk transfer barrier 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.

[0143] In some embodiments, the calculation module is further used to obtain multiple flood risk assessment indicator variables and flood risk assessment indicator observations in the study area; standardize the flood risk assessment indicator variables to obtain normalized flood risk assessment indicator variables; calculate the information entropy of each flood risk assessment indicator according to a preset entropy weight method and the flood risk assessment indicator observations; determine the weight coefficient of each flood risk assessment indicator according to 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; calculates the flood risk value according to 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 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 capacity; H i 、E i 、V i , C i Represents the normalized value of the flood risk assessment indicator variable.

[0144] In some embodiments, the construction module is further used to determine the first transfer times of each flood risk level in each sub-basin within two consecutive years; determine the first transfer probability between flood risk levels according to the first transfer times; the calculation formula of the first transfer probability is: ; In the formula, In the same sub-basin, the flood risk increases from the previous year to Grade transfer to next year The number of levels; when i=j, it means that the flood risk is transferred to the same level; Indicates that the flood risk in the previous year was The number of times the flood risk level is changed; according to the first transfer probability, the Markov risk state time transfer probability matrix of each sub-basin is determined; according to the preset adjacency principle, the second transfer number of each flood risk level of adjacent sub-basins in the same year is determined; according to the second transfer number, the second transfer probability is determined; the calculation formula of the second transfer probability is: ; In the formula, 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 is transferred 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.

[0145] 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 transfer probability principle; and to identify 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 transfer probability principle.

[0146] In some embodiments, the fitting module is further used to analyze the spatiotemporal propagation law of the flood risk to obtain the key section of flood risk spillover propagation; according to the key section of flood risk spillover propagation, fit the flood risk propagation function; 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 of 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: ; In the formula, 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.

[0147] In some embodiments, the assessment module is further used to calculate the flood risk propagation speed and propagation range according to the flood risk propagation network model to obtain the flood risk propagation assessment result.

[0148] It should be noted that the description of the device of the embodiment of the present invention is similar to the description of the above 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 embodiment of the device, please refer to the description of the method embodiment of the present invention for understanding.

[0149] It should be noted that in the embodiments of the present invention, if the above-mentioned flood risk propagation assessment method is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. According to this understanding, the technical solution of the embodiment of the present invention, or the part that contributes to the relevant technology, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions for a terminal to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read Only Memory), disk or optical disk, etc. Various media that can store program codes. In this way, the embodiments of the present invention are not limited to any specific combination of hardware and software.

[0150] 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, the flood risk propagation assessment device 600 at least includes: a processor 601 and a computer-readable storage medium 602 configured to store executable instructions, wherein the processor 601 generally controls the overall operation of the flood risk propagation assessment device 600. The computer-readable storage medium 602 is configured to store instructions and applications executable by the processor 601, and can also cache data to be processed or processed by the processor 601 and each module in the flood risk propagation assessment device 600, which can be implemented by flash memory (FLASH) or random access memory (RAM, Random Access Memory).

[0151] 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.

[0152] In some embodiments, the storage medium can be a computer-readable storage medium, for example, 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 disk, or a compact disk read-only memory (CD-ROM); it can also be various devices including one or any combination of the above memories.

[0153] 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.

[0154] As an example, executable instructions may, but need not 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 Hyper Text Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files storing one or more modules, subroutines, or code portions). As an example, executable instructions may be deployed to be executed on one electronic device, or on multiple electronic devices located at one location, or on multiple electronic devices distributed at multiple locations and interconnected by a communication network.

[0155] The above description is only an embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement and improvement made within the spirit and scope of the present invention are included in the protection scope of the present invention.

[0156] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that 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 number 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 only for description and do not represent the advantages and disadvantages of the embodiments.

[0157] 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 including a series of elements includes not only those elements, but also includes 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 one..." does not exclude the presence of other identical elements in the process, method, article or device including the element. In 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. There may be other division methods in actual implementation, 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.

[0158] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope 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 according to 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; According to the Markov chain spatiotemporal model, the spatiotemporal propagation law of flood risk is determined; According to the spatiotemporal propagation law of flood risk, a flood risk propagation function is fitted; Determine the flood risk transfer barrier 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; According to the preset river network topology and the flood risk transfer barrier effect, a spatiotemporal propagation network model of flood risk with spatiotemporal dependency 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.

2. The method according to claim 1, characterized in that The method of calculating the flood risk value of each sub-basin in the study area according to the acquired flood risk assessment indicator variable of the study area includes: Obtain multiple flood risk assessment indicator variables and flood risk assessment indicator observation values ​​in the study area; Standardizing the flood risk assessment indicator variable to obtain a normalized flood risk assessment indicator variable; Calculate 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; According to each of the information entropies, a weight coefficient of each of the flood risk assessment indicators is determined; 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 according to the weight coefficient and the normalized flood risk assessment index variable; the calculation formula of the flood risk value is: ; ; In the formula, 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 capacity; H i 、E i 、V i , C i Represents the normalized value of the flood risk assessment indicator variable.

3. The method according to claim 1, characterized in that The step of constructing a spatiotemporal Markov chain model according to the flood risk level includes: Determine the number of first shifts in each of said flood risk levels for each of said sub-basins within two consecutive years; According to the first transfer times, a first transfer probability between flood risk levels is determined; the calculation formula of the first transfer probability is: ; In the formula, In the same sub-basin, the flood risk increases from the previous year to Grade transfer to next year The number of levels; when i=j, it means that the flood risk is transferred to the same level; Indicates that the flood risk in the previous year was The number of levels; Determine a Markov risk state time transition probability matrix of each of the sub-basins according to the first transition probability; Determine the number of second transfers for each of the flood risk levels in adjacent sub-basins within the same year based on the preset adjacency principle; According to the second transfer number, a second transfer probability is determined; the calculation formula of the second transfer probability is: ; In the formula, 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 is transferred to the same level; The flood risk of sub-basin A is The number of levels; Determining a Markov risk state space transition probability matrix of the adjacent sub-basin according to the second transition probability; The Markov chain spatiotemporal model is constructed according to the Markov risk state time transition probability matrix and the Markov risk state space transition probability matrix.

4. The method according to claim 3, characterized in that Determining the spatiotemporal propagation law of flood risk according to the Markov chain spatiotemporal model includes: Predicting the flood risk level of each sub-basin in the next year according to 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.

5. The method according to claim 4, characterized in that The step of fitting a flood risk propagation function according to the spatiotemporal propagation law of flood risk includes: Analyze the spatiotemporal propagation law of flood risk and obtain the key sections of flood risk spillover propagation; According to the key sections of flood risk spillover propagation, fitting the flood risk propagation function; 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 combined with 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; The calculation formula of the flood risk propagation function is: ; In the formula, 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.

6. The method according to claim 1, characterized in that The study area is evaluated according to the spatiotemporal propagation network model of flood risk to obtain flood risk propagation evaluation results, including: According to the flood risk propagation network model, the flood risk propagation speed and propagation range are calculated to obtain the flood risk propagation assessment result.

7. A flood risk communication assessment device, characterized in that: The device comprises: A calculation module, used for 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; A classification module, used for classifying the flood risk values ​​to obtain the flood risk level of each sub-basin; A construction module, for constructing a Markov chain spatiotemporal model according to the flood risk level; A determination module, used to determine the spatiotemporal propagation law of flood risk according to the Markov chain spatiotemporal model; A fitting module, used for fitting a flood risk propagation function according to the spatiotemporal propagation law of the flood risk; The determination module is further used 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 dependency according to the preset river network water system topology structure and the flood risk transfer barrier effect; The evaluation module is used to evaluate the study area according to the flood risk spatiotemporal propagation network model to obtain a flood risk propagation evaluation result.

8. An electronic device, characterized in that: include: A memory for storing executable instructions; The processor is used to implement the flood risk propagation assessment method described in any one of claims 1 to 6 when executing the executable instructions stored in the memory.

9. A computer-readable storage medium, characterized in that: Executable instructions are stored, which are used to cause the processor to execute the executable instructions to implement the flood risk propagation assessment method described in any one of claims 1 to 6.

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