A data intelligent simulation processing method and system for emergency response to breaches

Abstract

This invention discloses a data intelligent simulation processing method and system for dike breach emergency repair, belonging to the field of simulation processing technology. At the dike breach, drones and multimodal sensors are used to collect real-time data on the breach depth, width, and length, constructing a three-dimensional distribution matrix of the breach. Water flow velocity and sediment content data are also collected. The risk value of the deployment disturbance corresponding to a single row, column, and layer in the three-dimensional distribution matrix is ​​calculated. The risk weight of the deployment disturbance is calculated. The single row and column of the breach are used as deployment paths, and based on the risk weight, the safety factor of the deployment disturbance for each path is calculated. A threshold for the safety factor of the deployment disturbance is preset, and the deployment paths are analyzed and dynamically updated. This invention, through a three-in-one technical system of spatial modeling, data fusion, and simulation evaluation, effectively improves the scientific nature, safety, and real-time adaptability of deployment path selection during dike breach emergency repair.

Description

Technical Field

[0001] This invention relates to the field of simulation processing technology, specifically to a data intelligent simulation processing method and system for breach rescue. Background Technology

[0002] Levee breaches, as one of the most sudden and impactful disasters in water conservancy projects, have long been an important topic in flood control, disaster relief, and hydrological engineering research. Traditional breach control mainly relies on on-site experience, manual inspections, and static deployment methods, determining the location and method of material deployment through manual mapping, experience-based judgment, and post-event analysis. However, this approach suffers from problems such as slow response, insufficient decision-making basis, and uncertain intervention effects. In recent years, with the development of remote sensing technology, sensor networks, and UAV platforms, the ability to acquire real-time data on breach areas has significantly improved, gradually driving the transition of disaster relief methods from experience-driven to data-driven. Simultaneously, breach evolution modeling methods based on numerical simulation and modeling technologies have gradually emerged, capable of simulating breach expansion, water flow, and sediment distribution processes, providing theoretical support for disaster relief deployment. However, these simulation methods currently generally suffer from problems such as lagging model updates, difficulties in parameter estimation, and insufficient integration with real-time on-site data, making it difficult to achieve accurate assessment of dynamic risks and intelligent planning of disaster relief routes.

[0003] Most existing studies focus on breach hydrodynamic simulation or sediment transport pattern analysis, lacking precise characterization and quantitative analysis of microscopic disturbance risks in three-dimensional space. Current methods struggle to achieve spatial integration modeling of breach depth, width, and length data, and are unable to integrate disturbance factors such as flow velocity and sediment content to construct fine-grained deployment disturbance models. Consequently, path planning still relies heavily on manual judgment, making it difficult to address sudden disturbances and deployment failures that occur during breach evolution. Summary of the Invention

[0004] The purpose of this invention is to provide a data intelligent simulation processing method and system for breach emergency repair, so as to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] A data intelligent simulation processing method for dike breach emergency response includes the following steps: Step S1: At the dike breach, real-time data on breach depth, width, and length are collected using drones and multimodal sensors, and a three-dimensional distribution matrix of the breach is constructed; Step S2: Buoys are deployed on the breach surface using a buoy-type sensor chain to collect data on water flow velocity and sediment content; Step S3: The deployment disturbance risk value corresponding to a single row, single column, and single layer in the three-dimensional distribution matrix is ​​calculated; based on the single row and single column, all deployment disturbance risk values ​​in the corresponding layer are extracted, and the deployment disturbance risk weight of the single row and single column is calculated; Step S4: The single row and single column of the breach are used as deployment paths, and the deployment disturbance safety factor of the deployment path is calculated based on the deployment disturbance risk weight; a preset deployment disturbance safety factor threshold is established, and the deployment path is analyzed and dynamically updated.

[0007] As a preferred embodiment of the intelligent data simulation processing method for breach emergency response described in this invention, at the breach point, a drone and a multimodal sensor are used to collect basic data of the breach in real time. The basic data includes breach depth data, breach width data, and breach length data. The multimodal sensor includes a laser detection sensor, which is used to collect the breach depth data. The drone is equipped with an optical camera, which is used to collect the breach width data and breach length data.

[0008] Based on the breach depth data, breach width data, and breach length data, the breach depth data, breach width data, and breach length data are uniformly divided into N depth segments, I width segments, and J length segments, respectively, and a three-dimensional distribution matrix of the breach is constructed. The row dimension of the three-dimensional distribution matrix is ​​the J length segments, the column dimension of the three-dimensional distribution matrix is ​​the I width segments, and the layer dimension of the three-dimensional distribution matrix is ​​the N depth segments.

[0009] As a preferred embodiment of the intelligent data simulation processing method for breach emergency rescue described in this invention, a buoy-type sensor chain is used to deploy I×J buoys along I width segments and J length segments on the breach surface, and N water flow sensors and sediment content sensors are suspended below each buoy along N depth segments. The water flow sensors are used to collect water flow velocity data, and the sediment content sensors are used to collect sediment content data.

[0010] Obtain the flow velocity data and sediment content data corresponding to the j-th row, i-th column, and n-th layer in the three-dimensional distribution matrix, and denote the flow velocity data and sediment content data corresponding to the j-th row, i-th column, and n-th layer as WFR, respectively. j,i,n and SC j,i,n .

[0011] As a preferred embodiment of the intelligent data simulation processing method for breach emergency response described in this invention, each cell in the three-dimensional distribution matrix represents a disturbance risk value, based on the water flow velocity data WFR corresponding to the j-th row, i-th column, and n-th layer. j,i,n and sediment content data SC j,i,n Calculate the delivery disturbance risk value corresponding to the j-th row, i-th column, and n-th layer in the three-dimensional distribution matrix. The calculation formula is as follows:

[0012]

[0013] Among them, TDRF j,i,n This represents the risk value of the release disturbance corresponding to the j-th row, i-th column, and n-th layer in the three-dimensional distribution matrix, where α represents the preset water flow velocity data WFR. j,i,n Impact Factor, WFR max β represents the preset maximum reference value for water flow velocity, and β represents the preset sediment content data SC. j,i,n Impact factor, SC max This indicates the maximum reference value for the preset sediment content;

[0014] In this invention, the formula comprehensively considers the impact of water flow velocity and sediment content on the risk of disturbance during deployment. The characteristic of the exponential function exp(x) is that the larger x is, the larger the value of exp(x), and the larger the value of 1-exp(-x). In the formula, and These represent the ratios of the current flow velocity and sediment content at position (j, i, n) relative to the maximum reference value. α and β are preset influence factors used to adjust the relative importance of flow velocity and sediment content in risk calculation. When the flow velocity WFR... j,i,n Or sediment content SC j,i,n When it is larger, The larger the value, the better. The closer the value is to 1, the higher the risk of material release disturbance. This formula converts the water flow velocity and sediment content at different locations along the breach into corresponding risk values ​​for material release disturbance, providing fundamental data for subsequent assessments of the safety of the release path. The risk value corresponding to each cell reflects the degree of risk from water flow and sediment during material release at that location, helping to determine which areas have a higher risk and which have a relatively lower risk.

[0015] Based on the j-th length segment and the i-th width segment, the corresponding row and column dimensions are locked in the three-dimensional distribution matrix. According to the locked row and column dimensions, all delivery perturbation risk values ​​in the corresponding layer dimensions are extracted, and the delivery perturbation risk weights of the j-th length segment and the i-th width segment are calculated. The calculation formula is as follows:

[0016]

[0017] Among them, ARWD j,i The projection disturbance risk weights are represented by the j-th length segment and the ith width segment in the breach, and N represents the total number of depth segments.

[0018] As a preferred embodiment of the data intelligent simulation processing method for breach emergency repair described in this invention, the j-th length segment and the i-th width segment of the breach are used as the delivery path, and the delivery disturbance risk weight ARWD is based on the j-th length segment and the i-th width segment. j,i The delivery path is simulated as follows:

[0019] The safety factor for the delivery disturbance along the delivery path is calculated using the following formula:

[0020]

[0021] Among them, ISFD j,i This represents the safety factor for delivery disturbance along the delivery path;

[0022] In this invention, the formula is based on the application of perturbation risk weights (ARWD). j,i Based on this, the safety factor of the delivery path is calculated by further considering the combined effects of water flow velocity and sediment content. This indicates the ratio of the average water flow velocity along the delivery path to the maximum reference value. This represents the ratio of the average sediment content along the delivery path to the maximum reference value. The safety factor is obtained by multiplying the risk weight by these two ratio factors, and also considers a correction term for the sediment content ratio factor to more comprehensively reflect the impact of various factors on delivery safety. Delivery Disturbance Safety Factor (ISFD) j,i This method is used to quantitatively assess the safety of each delivery path. The safety coefficient calculated using this formula allows for a direct comparison of the safety levels of different delivery paths. By setting a preset safety coefficient threshold for delivery disturbance, the system can determine the safety of a delivery path based on this coefficient, thereby selecting safe delivery paths for emergency rescue operations. These paths are then dynamically updated and managed to ensure that relatively safe delivery paths are always selected as the breach situation changes, thus improving the safety and effectiveness of emergency rescue work.

[0023] A preset safety factor threshold for delivery disturbance is set; if the safety factor for delivery disturbance of the delivery path is ISFD... j,i If the result is greater than or equal to the safety factor threshold for the delivery disturbance, it indicates that the simulation result of the delivery path is safe, and the delivery path is a safe delivery path.

[0024] Calculate the safety factor of the delivery disturbance for all I×J delivery paths, obtain all delivery paths whose safety factor of the delivery disturbance is greater than or equal to the threshold of the delivery disturbance safety factor, and manage them in a unified manner. Obtain the delivery disturbance risk value in real time and perform dynamic update management of safe delivery paths.

[0025] A data intelligent simulation processing system for breach emergency response, the system includes: a data acquisition and matrix construction module, a water flow and sediment data acquisition module, a risk value and weight calculation module, and a safety factor calculation and analysis processing module;

[0026] The data acquisition and matrix construction module: at the breach point of the dike, it uses drones and multimodal sensors to collect real-time data on the breach depth, width, and length, and constructs a three-dimensional distribution matrix of the breach.

[0027] The water flow and sediment data acquisition module: uses a buoy-type sensor chain to deploy buoys on the surface of the breach to collect water flow velocity data and sediment content data of the breach.

[0028] The risk value and weight calculation module calculates the delivery disturbance risk value corresponding to a single row, single column, and single layer in the three-dimensional distribution matrix; based on the single row and single column, it extracts all delivery disturbance risk values ​​in the corresponding layer and calculates the delivery disturbance risk weight for the single row and single column.

[0029] The safety factor calculation and analysis module: takes the single row and single column of the breach as the delivery path, and calculates the delivery disturbance safety factor of the delivery path based on the delivery disturbance risk weight; presets the delivery disturbance safety factor threshold, analyzes and performs dynamic update management of the delivery path.

[0030] Furthermore, the data acquisition and matrix construction module includes a data acquisition unit and a matrix construction unit;

[0031] The data acquisition unit: at the breach in the dike, uses a drone and multimodal sensors to collect basic data of the breach in real time. The basic data includes breach depth data, breach width data, and breach length data. The multimodal sensors include a laser detection sensor, which is used to collect the breach depth data. The drone is equipped with an optical camera, which is used to collect the breach width data and breach length data.

[0032] The matrix construction unit: Based on the breach depth data, breach width data, and breach length data, it uniformly divides the breach depth data, breach width data, and breach length data into N depth segments, I width segments, and J length segments, respectively, and constructs a three-dimensional distribution matrix of the breach. The row dimension of the three-dimensional distribution matrix is ​​the J length segments, the column dimension of the three-dimensional distribution matrix is ​​the I width segments, and the layer dimension of the three-dimensional distribution matrix is ​​the N depth segments.

[0033] Furthermore, the water flow and sediment data acquisition module includes a water flow and sediment data acquisition unit;

[0034] The water flow and sediment data acquisition unit utilizes a buoy-type sensor chain to deploy I×J buoys along I width segments and J length segments on the breach surface. Below each buoy, N water flow sensors and sediment content sensors are suspended along N depth segments. The water flow sensors are used to collect water flow velocity data, and the sediment content sensors are used to collect sediment content data.

[0035] Furthermore, the risk value and weight calculation module includes a risk value calculation unit and a weight calculation unit;

[0036] The risk value calculation unit: In the three-dimensional distribution matrix, each cell represents a release disturbance risk value. Based on the water flow velocity data and sediment content data corresponding to the j-th row, i-th column, and n-th layer, the release disturbance risk value corresponding to the j-th row, i-th column, and n-th layer in the three-dimensional distribution matrix is ​​calculated.

[0037] The weight calculation unit: based on the j-th length segment and the i-th width segment, locks the corresponding row dimension and column dimension in the three-dimensional distribution matrix, extracts all the corresponding delivery disturbance risk values ​​in the corresponding layer dimension according to the locked row dimension and column dimension, and calculates the delivery disturbance risk weight of the j-th length segment and the i-th width segment.

[0038] Furthermore, the safety factor calculation and analysis module includes a safety factor calculation unit and an analysis and processing unit;

[0039] The safety factor calculation unit: takes the j-th length segment and the i-th width segment of the breach as the delivery path, and simulates the delivery path based on the delivery disturbance risk weight of the j-th length segment and the i-th width segment, specifically: calculates the delivery disturbance safety factor of the delivery path;

[0040] The analysis and processing unit: presets a safety factor threshold for the delivery disturbance; if the safety factor for the delivery disturbance of the delivery path is greater than or equal to the safety factor threshold, it indicates that the simulation result of the delivery path is safe, and the delivery path is a safe delivery path; calculates the safety factor for the delivery disturbance of all I×J delivery paths, obtains all delivery paths whose safety factor for the delivery disturbance is greater than or equal to the safety factor threshold, and manages them uniformly; obtains the delivery disturbance risk value in real time and performs dynamic update management of safe delivery paths.

[0041] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: The intelligent data simulation processing method and system for breach emergency response provided by this invention utilizes a drone equipped with a laser detection sensor and an optical camera to collect real-time data on the depth, width, and length of the breach, dividing it into multiple spatial segments to construct a three-dimensional distribution matrix with spatial resolution. This enables visualized modeling of the breach's spatial structure, providing high-precision basic data support for subsequent simulations. Furthermore, by deploying a buoy-type sensor chain in the breach area, key dynamic information such as water flow velocity and sediment content is collected and accurately mapped to a three-dimensional matrix. Each spatial unit within the array enables three-dimensional perception of the dynamic environment of the breach, enhancing the system's responsiveness to local disturbances. By integrating multi-source data, the disturbance risk value is calculated based on the flow velocity and sediment content of each cell, and further, risk weights for each delivery path are extracted on a two-dimensional surface. This imbues the risk assessment with hierarchy and regional focus, effectively supporting the refined evaluation of delivery paths. By fusing the two-dimensional paths with their corresponding disturbance risk weights, a delivery disturbance safety coefficient model is constructed, and a safety threshold mechanism is introduced to screen and dynamically update all potential paths, achieving real-time management and intelligent optimization of safe delivery paths. In summary, this invention, through a three-in-one technical system of spatial modeling, data fusion, and simulation evaluation, effectively improves the scientific rigor, safety, and real-time adaptability of delivery path selection during breach rescue operations, demonstrating significant emergency rescue application value and potential for widespread adoption. Attached Figure Description

[0042] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0043] Figure 1 This is a schematic diagram illustrating the steps of a data intelligent simulation processing method for breach emergency rescue according to the present invention;

[0044] Figure 2 This is a schematic diagram of the structure of a data intelligent simulation processing system for breach rescue according to the present invention. Detailed Implementation

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] Please see Figure 1 In this first embodiment: a data intelligent simulation processing method for breach emergency response is provided, which includes the following steps:

[0047] Step S1: At the breach in the dike, use drones and multimodal sensors to collect real-time data on the breach depth, width, and length, and construct a three-dimensional distribution matrix of the breach.

[0048] Specifically, at the breach in the dike, drones and multimodal sensors are used to collect basic data about the breach in real time. The basic data includes breach depth, breach width, and breach length. The multimodal sensors include laser detection sensors, which are used to collect the breach depth data. The drones are equipped with optical cameras, which are used to collect the breach width and breach length data.

[0049] Furthermore, based on the breach depth data, breach width data, and breach length data, the breach depth data, breach width data, and breach length data are uniformly divided into N depth segments, I width segments, and J length segments, respectively, and a three-dimensional distribution matrix of the breach is constructed. The row dimension of the three-dimensional distribution matrix is ​​the J length segments, the column dimension of the three-dimensional distribution matrix is ​​the I width segments, and the layer dimension of the three-dimensional distribution matrix is ​​the N depth segments.

[0050] Step S2: Deploy buoys on the breach surface using a buoy-type sensor chain to collect data on water flow velocity and sediment content in the breach.

[0051] Specifically, using a buoy-type sensor chain, I×J buoys are deployed along I width segments and J length segments on the breach surface, and N flow sensors and sediment content sensors are suspended below each buoy along N depth segments. The flow sensors are used to collect water flow velocity data, and the sediment content sensors are used to collect sediment content data.

[0052] Furthermore, the flow velocity data and sediment content data corresponding to the j-th row, i-th column, and n-th layer in the three-dimensional distribution matrix are obtained, and the flow velocity data and sediment content data corresponding to the j-th row, i-th column, and n-th layer are respectively denoted as WFR. j,i,n and SC j,i,n .

[0053] Step S3: Calculate the delivery disturbance risk value corresponding to a single row, single column, and single layer in the three-dimensional distribution matrix; based on the single row and single column, extract all delivery disturbance risk values ​​in the corresponding layer, and calculate the delivery disturbance risk weight of the single row and single column.

[0054] Specifically, in the three-dimensional distribution matrix, each cell represents a disturbance risk value, based on the water flow velocity data WFR corresponding to the j-th row, i-th column, and n-th layer. j,i,n and sediment content data SC j,i,n Calculate the delivery disturbance risk value corresponding to the j-th row, i-th column, and n-th layer in the three-dimensional distribution matrix. The calculation formula is as follows:

[0055]

[0056] Among them, TDRF j,i,n This represents the risk value of the release disturbance corresponding to the j-th row, i-th column, and n-th layer in the three-dimensional distribution matrix, where α represents the preset water flow velocity data WFR. j,i,n Impact Factor, WFR max β represents the preset maximum reference value for water flow velocity, and β represents the preset sediment content data SC. j,i,n Impact factor, SC max This indicates the maximum reference value for the preset sediment content;

[0057] It should be noted that this formula uses the water flow velocity WFR j,i,n and sediment content SC j,i,n By comparing each value with its maximum reference value and combining this with preset influencing factors α and β, the risk at a specific location can be accurately reflected. For example, in areas with rapid water flow and high sediment content, the risk value calculated by the formula will be significantly higher, intuitively demonstrating the high risk of operations in that area. The formula also utilizes the characteristics of an exponential function to non-linearly characterize risk changes. When the water flow velocity or sediment content increases slightly, the risk value will rise slowly; however, when these factors approach or exceed a certain limit, the risk value will increase rapidly. This characteristic is consistent with reality, because during breach repair operations, once water flow and sediment conditions deteriorate to a certain extent, the risk of operations will increase dramatically.

[0058] Furthermore, based on the j-th length segment and the i-th width segment, the corresponding row and column dimensions are locked in the three-dimensional distribution matrix. According to the locked row and column dimensions, all delivery perturbation risk values ​​in the corresponding layer dimensions are extracted, and the delivery perturbation risk weights of the j-th length segment and the i-th width segment are calculated. The calculation formula is as follows:

[0059]

[0060] Among them, ARWDj,i The projection disturbance risk weights are represented by the j-th length segment and the ith width segment in the breach, and N represents the total number of depth segments.

[0061] It should be noted that by averaging the risk values ​​at different depths within a specific two-dimensional area, the overall risk level of that area is comprehensively reflected. In actual breach control operations, water flow and sediment conditions vary at different depths at the same horizontal position. This formula integrates these differences, avoiding misjudgments of risk due to focusing only on a single depth. For example, in some areas, surface water flows faster than deeper water flows; averaging yields a more reasonable risk weight. Furthermore, it simplifies the complex risk distribution in three-dimensional space into a single risk weight index for a two-dimensional area, facilitating subsequent calculations and analysis. When planning deployment paths, different paths can be directly compared based on this weight, improving decision-making efficiency.

[0062] Step S4: Use the single row and single column of the breach as the delivery path, and calculate the delivery disturbance safety factor of the delivery path based on the delivery disturbance risk weight; preset the delivery disturbance safety factor threshold, analyze and perform dynamic update management of the delivery path.

[0063] Specifically, the j-th length segment and the i-th width segment of the breach are used as the delivery path, and the delivery disturbance risk weight ARWD is based on the j-th length segment and the i-th width segment. j,i The delivery path is simulated as follows:

[0064] The safety factor for the delivery disturbance along the delivery path is calculated using the following formula:

[0065]

[0066] Among them, ISFD j,i This represents the safety factor for delivery disturbance along the delivery path;

[0067] It should be noted that the safety factor is calculated by combining risk weights, average flow velocity ratios, and average sediment content ratios, comprehensively considering key factors affecting deployment safety. For example, a path with a high risk weight but relatively low flow velocity and sediment content may not necessarily have a low safety factor; conversely, even with a low risk weight, excessively high flow velocity or sediment content can affect the safety factor. During breach repair operations, flow velocity and sediment content constantly change, and this formula can recalculate the safety factor based on real-time data. By comparing with preset thresholds, deployment paths can be adjusted promptly to ensure that repair work is always carried out on relatively safe routes.

[0068] Furthermore, a preset delivery disturbance safety factor threshold is set; if the delivery disturbance safety factor (ISFD) of the delivery path is... j,iIf the result is greater than or equal to the safety factor threshold for the delivery disturbance, it indicates that the simulation result of the delivery path is safe, and the delivery path is a safe delivery path.

[0069] Furthermore, the safety factor of the delivery disturbance for all I×J delivery paths is calculated, and all delivery paths with a safety factor of delivery disturbance greater than or equal to the threshold of the safety factor of delivery disturbance are obtained and managed in a unified manner. The risk value of delivery disturbance is obtained in real time, and dynamic update management of safe delivery paths is performed.

[0070] Please see Figure 2 In this second embodiment: a data intelligent simulation processing system for breach emergency response is provided. The system includes: a data acquisition and matrix construction module, a water flow and sediment data acquisition module, a risk value and weight calculation module, and a safety factor calculation and analysis processing module.

[0071] The data acquisition and matrix construction module: at the breach point of the dike, it uses drones and multimodal sensors to collect real-time data on the breach depth, width, and length, and constructs a three-dimensional distribution matrix of the breach.

[0072] The water flow and sediment data acquisition module: uses a buoy-type sensor chain to deploy buoys on the surface of the breach to collect water flow velocity data and sediment content data of the breach.

[0073] The risk value and weight calculation module calculates the delivery disturbance risk value corresponding to a single row, single column, and single layer in the three-dimensional distribution matrix; based on the single row and single column, it extracts all delivery disturbance risk values ​​in the corresponding layer and calculates the delivery disturbance risk weight for the single row and single column.

[0074] The safety factor calculation and analysis module: takes the single row and single column of the breach as the delivery path, and calculates the delivery disturbance safety factor of the delivery path based on the delivery disturbance risk weight; presets the delivery disturbance safety factor threshold, analyzes and performs dynamic update management of the delivery path.

[0075] Furthermore, the data acquisition and matrix construction module includes a data acquisition unit and a matrix construction unit;

[0076] The data acquisition unit: at the breach in the dike, uses a drone and multimodal sensors to collect basic data of the breach in real time. The basic data includes breach depth data, breach width data, and breach length data. The multimodal sensors include a laser detection sensor, which is used to collect the breach depth data. The drone is equipped with an optical camera, which is used to collect the breach width data and breach length data.

[0077] The matrix construction unit: Based on the breach depth data, breach width data, and breach length data, it uniformly divides the breach depth data, breach width data, and breach length data into N depth segments, I width segments, and J length segments, respectively, and constructs a three-dimensional distribution matrix of the breach. The row dimension of the three-dimensional distribution matrix is ​​the J length segments, the column dimension of the three-dimensional distribution matrix is ​​the I width segments, and the layer dimension of the three-dimensional distribution matrix is ​​the N depth segments.

[0078] Furthermore, the water flow and sediment data acquisition module includes a water flow and sediment data acquisition unit;

[0079] The water flow and sediment data acquisition unit utilizes a buoy-type sensor chain to deploy I×J buoys along I width segments and J length segments on the breach surface. Below each buoy, N water flow sensors and sediment content sensors are suspended along N depth segments. The water flow sensors are used to collect water flow velocity data, and the sediment content sensors are used to collect sediment content data.

[0080] Furthermore, the risk value and weight calculation module includes a risk value calculation unit and a weight calculation unit;

[0081] The risk value calculation unit: In the three-dimensional distribution matrix, each cell represents a release disturbance risk value. Based on the water flow velocity data and sediment content data corresponding to the j-th row, i-th column, and n-th layer, the release disturbance risk value corresponding to the j-th row, i-th column, and n-th layer in the three-dimensional distribution matrix is ​​calculated.

[0082] The weight calculation unit: based on the j-th length segment and the i-th width segment, locks the corresponding row dimension and column dimension in the three-dimensional distribution matrix, extracts all the corresponding delivery disturbance risk values ​​in the corresponding layer dimension according to the locked row dimension and column dimension, and calculates the delivery disturbance risk weight of the j-th length segment and the i-th width segment.

[0083] Furthermore, the safety factor calculation and analysis module includes a safety factor calculation unit and an analysis and processing unit;

[0084] The safety factor calculation unit: takes the j-th length segment and the i-th width segment of the breach as the delivery path, and simulates the delivery path based on the delivery disturbance risk weight of the j-th length segment and the i-th width segment, specifically: calculates the delivery disturbance safety factor of the delivery path;

[0085] The analysis and processing unit: presets a safety factor threshold for the delivery disturbance; if the safety factor for the delivery disturbance of the delivery path is greater than or equal to the safety factor threshold, it indicates that the simulation result of the delivery path is safe, and the delivery path is a safe delivery path; calculates the safety factor for the delivery disturbance of all I×J delivery paths, obtains all delivery paths whose safety factor for the delivery disturbance is greater than or equal to the safety factor threshold, and manages them uniformly; obtains the delivery disturbance risk value in real time and performs dynamic update management of safe delivery paths.

[0086] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0087] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A data intelligent simulation processing method for breach rescue, characterized in that, The method comprises the following steps: Step S1: at the breach of the dike, the breach depth data, the breach width data and the breach length data of the breach are collected in real time by using the unmanned aerial vehicle and the multi-modal sensor, and a three-dimensional distribution matrix of the breach is constructed; Step S2: a buoy type sensor chain is used to arrange buoys on the surface of the breach to collect the water flow velocity data and the sediment content data of the breach; Step S3: the corresponding disturbance risk value of a single row, a single column and a single layer in the three-dimensional distribution matrix is calculated; based on the single row and the single column, all the disturbance risk values in the corresponding layer are extracted, and the disturbance risk weight of the single row and the single column is calculated; Step S4: the single row and the single column of the breach are taken as a drop path, and the drop disturbance safety coefficient of the drop path is calculated based on the drop disturbance risk weight; a drop disturbance safety coefficient threshold is preset, and the dynamic update management of the drop path is analyzed and performed; The specific implementation process of step S3 comprises: A cell in the three-dimensional distribution matrix represents a risk value of a release disturbance, which is based on the flow velocity data corresponding to the jth row, the ith column, and the nth layer and the sediment content data The risk value of the release disturbance corresponding to the jth row, the ith column, and the nth layer in the three-dimensional distribution matrix is calculated according to the following formula: ； wherein, represents a risk value of the delivery disturbance corresponding to the jth row, the ith column and the nth layer in the three-dimensional distribution matrix, represents an influence factor of preset water flow velocity data , represents a maximum reference value of the preset water flow velocity, represents an influence factor of preset sediment content data , represents a maximum reference value of the preset sediment content. Based on the jth length section and the ith width section, the corresponding row dimension and column dimension in the three-dimensional distribution matrix are locked, the corresponding all disturbance risk values in the corresponding layer dimension are extracted according to the locked row dimension and column dimension, and the drop disturbance risk weight of the jth length section and the ith width section is calculated, and the calculation formula is as follows: ； wherein, represents the risk weight of the launch disturbance for the jth length section and the ith width section in the breach, and N represents the total number of depth sections. The specific implementation process of step S4 comprises: The jth length section and the ith width section of the breach are taken as a delivery path, and a delivery disturbance risk weight of the jth length section and the ith width section is calculated The delivery path is simulated, specifically as follows: The drop disturbance safety coefficient of the drop path is calculated, and the calculation formula is as follows: ； wherein, a drop disturbance safety factor representing the drop path; A preset safety factor threshold for delivery disturbance is set; if the safety factor for delivery disturbance along the delivery path is... If the result is greater than or equal to the safety factor threshold for the delivery disturbance, it indicates that the simulation result of the delivery path is safe, and the delivery path is a safe delivery path. The drop disturbance safety coefficients of all I×J drop paths are calculated, all drop paths with a drop disturbance safety coefficient greater than or equal to the drop disturbance safety coefficient threshold are obtained, and unified management is performed; the drop disturbance risk value is obtained in real time, and the dynamic update management of the safe drop path is performed.

2. The data intelligent simulation processing method for breach rescue according to claim 1, characterized in that, The specific implementation process of step S1 comprises: At the breach of the dike, the basic data of the breach is collected in real time by using the unmanned aerial vehicle and the multi-modal sensor, the basic data includes the breach depth data, the breach width data and the breach length data, the multi-modal sensor includes a laser detection sensor, and the laser detection sensor is used to collect the breach depth data; an optical camera is carried on the unmanned aerial vehicle, and the optical camera is used to collect the breach width data and the breach length data; Based on the breach depth data, the breach width data and the breach length data, the breach depth data, the breach width data and the breach length data are evenly divided into N depth sections, I width sections and J length sections, and a three-dimensional distribution matrix of the breach is constructed, the row dimension of the three-dimensional distribution matrix is the J length sections, the column dimension of the three-dimensional distribution matrix is the I width sections, and the layer dimension of the three-dimensional distribution matrix is the N depth sections.

3. The data intelligent simulation processing method for breach rescue according to claim 2, characterized in that, The specific implementation process of step S2 comprises: An I×J buoy type sensor chain is used to arrange I×J buoys on the surface of the breach along the I width sections and the J length sections, and N water flow sensors and sediment content sensors are hung under each buoy along the N depth sections, the water flow sensors are used to collect the water flow velocity data, and the sediment content sensors are used to collect the sediment content data; obtain water flow velocity data and sediment content data corresponding to the jth row, the ith column and the nth layer in the three-dimensional distribution matrix, and denote the water flow velocity data and the sediment content data corresponding to the jth row, the ith column and the nth layer as Vj, in, n and Cj, in, n respectively and .

4. A data intelligent simulation processing system for breach rescue, which executes a data intelligent simulation processing method for breach rescue according to any one of claims 1 to 3, characterized by, The system comprises a data acquisition and matrix construction module, a water flow and sediment data acquisition module, a risk value and weight calculation module, and a safety factor calculation and analysis processing module. The data acquisition and matrix construction module: at the dike breach, uses a drone and a multi-modal sensor to acquire real-time breach depth data, breach width data and breach length data of the breach, and constructs a three-dimensional distribution matrix of the breach. The water flow and sediment data acquisition module: uses a floating sensor chain to arrange floating buoys on the surface of the breach along the I width sections and J length sections, and acquires water flow velocity data and sediment content data of the breach. The risk value and weight calculation module: calculates the corresponding risk value of a single row, a single column and a single layer in the three-dimensional distribution matrix; based on a single row and a single column, extracts all the risk values of the corresponding layer, and calculates the risk weight of the single row and the single column. The safety factor calculation and analysis processing module: takes the single row and the single column of the breach as a release path, and calculates the safety factor of the release path based on the risk weight; a preset safety factor threshold is used to analyze and dynamically update the release path.

5. The data intelligent simulation processing system for breach rescue according to claim 4, characterized in that: The data acquisition and matrix construction module comprises a data acquisition unit and a matrix construction unit. The data acquisition unit: at the dike breach, uses a drone and a multi-modal sensor to acquire real-time breach depth data, breach width data and breach length data of the breach, and the multi-modal sensor comprises a laser detection sensor for acquiring the breach depth data; the drone is equipped with an optical camera for acquiring the breach width data and the breach length data. The matrix construction unit: based on the breach depth data, the breach width data and the breach length data, divides the breach depth data, the breach width data and the breach length data into N depth sections, I width sections and J length sections respectively, and constructs a three-dimensional distribution matrix of the breach, wherein the row dimension of the three-dimensional distribution matrix is the J length sections, the column dimension of the three-dimensional distribution matrix is the I width sections, and the layer dimension of the three-dimensional distribution matrix is the N depth sections.

6. The data intelligent simulation processing system for breach rescue according to claim 5, characterized in that: The water flow and sediment data acquisition module comprises a water flow and sediment data acquisition unit. The water flow and sediment data acquisition unit: uses a floating sensor chain to arrange I×J floating buoys on the surface of the breach along the I width sections and the J length sections, and suspends N water flow sensors and sediment content sensors along the N depth sections under each floating buoy, wherein the water flow sensors are used to acquire water flow velocity data, and the sediment content sensors are used to acquire sediment content data.

7. The data intelligent simulation processing system for breach rescue according to claim 6, characterized in that: The risk value and weight calculation module comprises a risk value calculation unit and a weight calculation unit. The risk value calculation unit: one cell in the three-dimensional distribution matrix represents one risk value of a release disturbance, and based on the water flow velocity data and the sediment content data corresponding to the jth row, the ith column and the nth layer, the risk value of the jth row, the ith column and the nth layer in the three-dimensional distribution matrix is calculated. The weight calculation unit: locks corresponding row dimension and column dimension in the three-dimensional distribution matrix based on the jth length section and the ith width section, extracts all corresponding launch disturbance risk values in the corresponding layer dimension according to the locked row dimension and column dimension, and calculates the launch disturbance risk weight of the jth length section and the ith width section.

8. The data intelligent simulation processing system for breach rescue according to claim 7, characterized in that: The safety factor calculation and analysis processing module comprises a safety factor calculation unit and an analysis processing unit. The safety factor calculation unit: takes the jth length section and the ith width section of the breach as a launch path, and simulates the launch path based on the launch disturbance risk weight of the jth length section and the ith width section, specifically: calculates the launch disturbance safety factor of the launch path; The analysis processing unit: presets a launch disturbance safety factor threshold value, and if the launch disturbance safety factor of the launch path is greater than or equal to the launch disturbance safety factor threshold value, it indicates that the simulation result of the launch path is safe, and the launch path is a safe launch path; The launch disturbance safety factors of all IxJ launch paths are calculated, all launch paths with launch disturbance safety factors greater than or equal to the launch disturbance safety factor threshold value are obtained and uniformly managed, and the launch disturbance risk values are obtained in real time, and the dynamic update management of the safe launch path is performed.

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