Water transportation emergency evacuation deployment method based on balanced flow distribution model

Abstract

The invention discloses a water transportation emergency evacuation deployment method based on a balance flow distribution model, and relates to the technical field of emergency management, and the method comprises the steps: collecting ship distribution data and ship behavior data, and carrying out the preprocessing of the ship distribution data and the ship behavior data; according to the preprocessed ship distribution data and behavior data, the evacuation demand intensity of each evacuation area is predicted; hydrological data and meteorological data are collected, and risk assessment is carried out on each evacuation area; constructing a balanced flow distribution model, and predicting an evacuation priority score of each evacuation area based on the evacuation demand intensity and the risk assessment result of each evacuation area; according to the evacuation priority score of each evacuation area, generating a ship evacuation scheduling scheme; and executing the ship evacuation scheduling scheme, and performing real-time adjustment. According to the method, the ship feedback data is received in real time, and the balance flow distribution model and the scheduling scheme are dynamically updated, so that the whole process of efficient resource distribution, real-time adjustment and accurate evacuation deployment is realized.

Description

Technical Field

[0001] The invention relates to the technical field of emergency management, and in particular to a method for emergency evacuation deployment of water transport based on a balanced flow distribution model. Background Art

[0002] With the rapid development of the global economy and the growing demand for water transportation, water transportation plays an increasingly important role in logistics transportation, urban evacuation and emergency response to emergencies. However, the complexity of the water transportation network and its high sensitivity to the dynamic environment make it an important issue to be solved in the face of emergencies, such as how to efficiently dispatch ships, reasonably deploy evacuation resources, and quickly respond to and evacuate threatened ships and personnel. In the prior art, water evacuation scheduling methods based on static models are more common, and these methods usually rely on a single environmental variable or a simple linear programming model. However, the water environment is highly dynamic and changeable, and factors such as wind speed, tide height, and flow rate will have complex effects on ship scheduling and resource allocation. In addition, the distribution of evacuation needs is often non-uniform. How to dynamically adjust the evacuation priority based on risk assessment to ensure efficient use of resources is a difficulty in the prior art.

[0003] The existing water transport emergency evacuation technology has the following two main deficiencies: First, the existing methods usually rely on simple statistical models in evacuation demand prediction, which cannot fully capture the complex dynamic characteristics of ship distribution and behavior status. The information such as the real-time speed, load status and navigation direction of the ship has not been effectively integrated in the existing methods, resulting in low accuracy in evacuation demand prediction and easy imbalance in resource allocation. Secondly, in terms of risk assessment and evacuation priority setting, the existing technologies mostly use a single risk factor or a simple weighted model, ignoring the nonlinear interaction between multiple dynamic variables, such as the combined impact of hydrological and meteorological conditions, and the potential risk amplification effect of the uncertainty of these conditions on evacuation efficiency. This deficiency may make it difficult for the evacuation scheduling plan to adapt to complex and changeable actual scenarios and unable to fully respond to the resource scheduling needs in emergency situations. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method for emergency evacuation deployment of water transport based on a balanced flow distribution model to solve the problems of inaccurate evacuation demand prediction and incomplete risk assessment in the prior art.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for emergency evacuation deployment of water transport based on a balanced flow distribution model, which includes collecting ship distribution data and ship behavior data, and preprocessing the ship distribution data and ship behavior data; predicting the evacuation demand intensity of each evacuation area based on the preprocessed ship distribution data and behavior data; collecting hydrological data and meteorological data, and performing risk assessment on each evacuation area; constructing a balanced flow distribution model, and predicting the evacuation priority score of each evacuation area based on the evacuation demand intensity and risk assessment results of each evacuation area; generating a ship evacuation scheduling plan based on the evacuation priority score of each evacuation area; executing the ship evacuation scheduling plan, and making real-time adjustments.

[0008] As a preferred solution of the water transport emergency evacuation deployment method based on the balanced flow distribution model of the present invention, wherein: the ship distribution data includes the location, quantity, and load status of the ship;

[0009] The ship behavior data includes the ship's heading, speed and navigation status.

[0010] As a preferred solution of the water transport emergency evacuation deployment method based on the balanced flow distribution model of the present invention, the specific steps of preprocessing the ship distribution data and the ship behavior data are as follows:

[0011] Data cleaning is used to remove invalid and abnormal data from ship distribution data and ship behavior data;

[0012] Through data format conversion, the formats of ship distribution data and ship behavior data are unified;

[0013] The vessel distribution data and vessel behavior data are standardized through data filtering and transformation rules.

[0014] As a preferred solution of the water transport emergency evacuation deployment method based on the balanced flow distribution model of the present invention, wherein: the evacuation demand intensity of each evacuation area is predicted according to the pre-processed ship distribution data and behavior data, and the specific steps are as follows:

[0015] By combining the ship density analysis with the weight adjustment of the ship behavior status and the nonlinear correction of the evacuation resource availability, the evacuation demand intensity of each evacuation area is predicted through the dynamic interaction between the preprocessed ship distribution data and the behavior data. The expression is:

[0016]

[0017] Among them, S i represents the evacuation demand intensity of the i-th evacuation area, N i represents the total number of ships in the i-th evacuation area, represents the average load status of the ships in the i-th evacuation area, represents the area of ​​the i-th evacuation area, V j represents the speed of the jth ship, H j represents the course change rate of the jth ship, Z j Represents the navigation status coefficient of the jth ship.

[0018] As a preferred solution of the water transport emergency evacuation deployment method based on the balanced flow distribution model of the present invention, wherein: the hydrological data and meteorological data are collected to conduct risk assessment on each evacuation area, and the specific steps are as follows:

[0019] Using SAR satellite images and real-time hydrological monitoring stations, the water velocity, direction, tidal height and water depth of each evacuation area are collected in real time;

[0020] Use weather radar and wind profiler radar to collect wind speed, wind direction, rainfall and visibility in each evacuation area in real time;

[0021] Taking hydrological data and meteorological data as dynamic variables, the comprehensive interactive relationship between different risks is analyzed. The interaction between dynamic variables is integrated based on geometric characteristics to calculate the risk level. At the same time, combined with the uncertainty of the distribution of dynamic variables, the overall risk level is adjusted by information entropy, and the risk is amplified nonlinearly using the variance of rainfall volatility. Finally, the comprehensive risk assessment level of the evacuation area is obtained, which is expressed as:

[0022]

[0023] Among them, R i is the comprehensive risk level of the i-th evacuation area, T is the evaluation time interval, G(t) represents the geometric eigenvalue changing with time t, H is the information entropy, δ is the information entropy adjustment factor, r is the volatility variance of rainfall, and γ is the rainfall volatility amplification coefficient.

[0024] As a preferred solution of the water transport emergency evacuation deployment method based on the balanced flow distribution model of the present invention, wherein: the balanced flow distribution model is constructed to predict the evacuation priority score of each evacuation area based on the evacuation demand intensity and risk assessment results of each evacuation area. The specific steps are as follows:

[0025] According to the speed and load capacity of ships in each evacuation area, the nonlinear effect of the evacuation area is introduced to analyze the resource flow capacity value of each evacuation area. The expression is:

[0026]

[0027] Among them, C i is the resource flow capacity value of the i-th evacuation area, L jis the carrying capacity of the jth ship, λ is the nonlinear adjustment coefficient of the area;

[0028] Based on the evacuation demand intensity S i and the overall risk level R i The dynamic interaction of the ship speed, load capacity and area, as well as the nonlinear influence of the ship speed, load capacity and area, weight the priority through the nonlinear relationship and convert the evacuation demand intensity S i , comprehensive risk level R i , the resource circulation capacity value C and the dynamic control mechanism of resource response efficiency are combined to construct a balanced flow distribution model to predict the evacuation priority score of each evacuation area. The expression is:

[0029]

[0030] Among them, P i is the evacuation priority score of the i-th evacuation area, φ is the circulation adjustment smoothing factor, M i represents the total number of resource distribution sources in the i-th evacuation area, E l is the available resources of the lth resource source, T l The response time of the lth resource source, ψ is the response time smoothing factor, and η is the nonlinear adjustment coefficient of resource distribution.

[0031] As a preferred solution of the water transport emergency evacuation deployment method based on the balanced distribution model of the present invention, wherein: the ship evacuation scheduling plan is generated according to the evacuation priority score of each evacuation area, and the specific steps are as follows:

[0032] According to the evacuation priority score P i Arrange the evacuation areas in descending order, and select the area with the highest priority score as the target evacuation area;

[0033] Sort all ships in ascending order based on their arrival time at the target evacuation area;

[0034] Ships are dispatched in sequence according to the ship sorting results until the resource flow capacity value of the target evacuation area is exhausted and automatically allocated to the next priority evacuation area. Finally, a complete evacuation dispatch plan including all ship target evacuation areas and dispatch times is generated.

[0035] As a preferred solution of the water transport emergency evacuation deployment method based on the balanced flow distribution model of the present invention, the specific steps of executing the ship evacuation scheduling plan and making real-time adjustments are as follows:

[0036] When the ship is executing the evacuation dispatch plan, the ship feedback data is received in real time through GPS and AIS;

[0037] The balanced flow distribution model is optimized based on the ship feedback data. According to the optimized balanced flow distribution model, the evacuation priority score of the evacuation area is updated in real time, and the ship evacuation scheduling plan is adjusted in real time.

[0038] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the method for emergency evacuation deployment of water transport based on the balanced distribution model as described in the first aspect of the present invention is implemented.

[0039] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the method for emergency evacuation deployment of water transport based on a balanced distribution model as described in the first aspect of the present invention.

[0040] The beneficial effects of the present invention are as follows: by collecting ship distribution data and behavior data, pre-processing and dynamic analysis combined with hydrological data and meteorological data, the evacuation demand intensity of each evacuation area is first predicted, and then based on the balanced flow distribution model, the demand intensity is dynamically combined with multiple factors such as the comprehensive risk level and resource circulation capacity value to generate a priority score for each evacuation area; on this basis, the priority score is used to generate an optimized ship evacuation scheduling plan, and by receiving ship feedback data in real time, the balanced flow distribution model and scheduling plan are dynamically updated, thereby realizing the whole process of efficient resource allocation, real-time adjustment and precise evacuation deployment. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0042] Figure 1 This is a flow chart of the water transport emergency evacuation deployment method based on the balanced flow distribution model in Example 1.

[0043] Figure 2 This is a flow chart of generating a ship evacuation scheduling plan according to the evacuation priority score of each evacuation area in Example 1. DETAILED DESCRIPTION

[0044] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0045] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0046] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0047] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a method for emergency evacuation deployment of water transportation based on a balanced flow distribution model, comprising the following steps:

[0048] S1: Collecting ship distribution data and ship behavior data, and preprocessing the ship distribution data and ship behavior data;

[0049] S1.1: Vessel distribution data includes the location, number, and load status of vessels;

[0050] Furthermore, the ship distribution data is collected through real-time monitoring systems (such as AIS systems and radar monitoring equipment), which can accurately reflect the dynamic layout characteristics of ships in each area;

[0051] S1.2: Vessel behavior data includes the vessel’s heading, speed, and navigation status.

[0052] Furthermore, ship behavior data is obtained through multi-source sensors (such as GPS equipment and ship status monitoring systems), which can accurately reflect the behavior patterns of ships in each area.

[0053] S1.3: Use data cleaning to remove invalid and abnormal data from ship distribution data and ship behavior data;

[0054] For example, when there are obvious errors in the collected ship position data (such as the ship position is beyond the water range or invalid position points appear repeatedly), or the speed in the ship behavior data is displayed as a negative value or exceeds the upper limit of the ship's design speed, these invalid or abnormal data are eliminated through cleaning rules to ensure that subsequent analysis is based on real and reliable data.

[0055] S1.4: Unify the formats of ship distribution data and ship behavior data through data format conversion;

[0056] For example, if some ship position data are expressed in longitude and latitude, while data from other sources are expressed in grid coordinates, all position data are converted into the same format (such as the standard WGS84 longitude and latitude format) through format conversion rules; at the same time, the ship speed data is converted from "meters / second" to "knots" (1 knot = 1.852 kilometers / hour) so that data from different sources can be directly compared and processed.

[0057] S1.5: Standardize the vessel distribution data and vessel behavior data through data filtering and transformation rules.

[0058] For example, the ship's load status is converted from a variety of inconsistent descriptions (such as "full load", "partial load", and "empty load") into a standardized numerical ratio (such as 1 for full load and 0 for empty load); at the same time, the normalization method is used to map data such as speed, load status, and heading change rate to the [0,1] interval to eliminate the influence of different variable dimensions and provide a unified input data format for subsequent model calculation and analysis.

[0059] S2: Predict the evacuation demand intensity of each evacuation area based on the preprocessed ship distribution data and behavior data;

[0060] S2.1: By combining the ship density analysis with the weight adjustment of the ship behavior status and the nonlinear correction of the evacuation resource availability, the evacuation demand intensity of each evacuation area is predicted through the dynamic interaction between the preprocessed ship distribution data and the behavior data. The expression is:

[0061]

[0062] Among them, S i represents the evacuation demand intensity of the i-th evacuation area, N i represents the total number of ships in the i-th evacuation area, represents the average load status of the ships in the i-th evacuation area, represents the area of ​​the i-th evacuation area, V j represents the speed of the jth ship, H j represents the course change rate of the jth ship, Z j Represents the navigation status coefficient of the jth ship.

[0063] The specific process is to perform ship density analysis on the preprocessed ship distribution data and behavior data, and adjust the weights of the ship behavior status (such as heading, speed, and navigation status). Specifically, we calculate the number of ships N in each evacuation area i and the average load status of these ships Also consider the area of ​​the region Next, by introducing the speed V of each shipj , heading change rate H j And the navigation state coefficient Z i , assessing the dynamic characteristics of the vessel’s behavior, and combining these factors to reflect the vessel’s response capabilities and needs in emergency situations.

[0064] On this basis, combined with the nonlinear correction of evacuation resource availability, the actual allocation and limitation of rescue resources are taken into account to ensure that the prediction results are closer to the actual situation. The whole process involves a complex analysis of the dynamic interaction between ships, that is, not only focusing on the status of a single ship, but also paying attention to the mutual influence between ships and their impact on the overall evacuation demand. Finally, by integrating all the above factors, the evacuation demand intensity S of each evacuation area can be accurately predicted. i , providing an important basis for the subsequent formulation of effective evacuation scheduling plans. This method ensures that the prediction model can not only reflect the current ship distribution and behavior patterns, but also adapt to the changing emergency evacuation needs.

[0065] It should be noted that the availability of evacuation resources refers to the actual availability of resources that can be dispatched and allocated within a specific time and area during the emergency evacuation process, including the number of ships, load capacity, current position, speed, navigation status and other dynamic characteristics, while also considering the environmental adaptability of the evacuation area (such as water depth, flow rate, tide height and meteorological conditions) as well as the response time and accessibility of resources. This indicator reflects the effectiveness and reliability of resources in a complex dynamic environment.

[0066] The ship behavior status includes heading, speed, navigation status, load status, speed change rate, heading change rate and navigation status coefficient.

[0067] S3: Collect hydrological and meteorological data and conduct risk assessment for each evacuation area;

[0068] S3.1: Use SAR satellite images and real-time hydrological monitoring stations to collect real-time data on water flow velocity, direction, tidal height, and water depth in each evacuation area;

[0069] S3.2: Use weather radar and wind profiler radar to collect wind speed, wind direction, rainfall and visibility in each evacuation area in real time;

[0070] S3.3: Take hydrological data and meteorological data as dynamic variables, analyze the comprehensive interactive relationship between different risks, integrate the interaction between dynamic variables based on geometric characteristics, calculate the risk level, and combine the uncertainty of dynamic variable distribution. Adjust the overall risk level through information entropy, use rainfall volatility variance to perform nonlinear amplification on the risk, and finally obtain the comprehensive risk assessment level of the evacuation area. The expression is:

[0071]

[0072] Among them, R i is the comprehensive risk level of the i-th evacuation area, T is the evaluation time interval, G(t) represents the geometric eigenvalue changing with time t, H is the information entropy, δ is the information entropy adjustment factor, r is the volatility variance of rainfall, and γ is the rainfall volatility amplification coefficient.

[0073] The specific process is to collect hydrological and meteorological data, including water flow velocity, tidal height, wind speed, rainfall and other factors, through real-time monitoring systems such as SAR satellite images and hydrological and meteorological radars, and use these data as dynamic variables to analyze the interaction between different risks. Then, consider the geometric characteristics of the evacuation area, such as shape and size, and integrate the interaction between these dynamic variables to more accurately reflect the actual risk situation. At the same time, in order to deal with the uncertainty in the data, the information entropy method is used to adjust the risk assessment results to ensure its accuracy. In addition, special attention is paid to the impact of rainfall volatility on risk, and nonlinear amplification processing is used to emphasize the additional risks that extreme weather may bring. Finally, all these analyses and adjustments help obtain the comprehensive risk assessment level of each evacuation area, providing a scientific basis for formulating effective emergency evacuation plans.

[0074] Furthermore, the expression of the geometric eigenvalue G(t) changing with time t is:

[0075]

[0076] Among them, f(t) is the water velocity at time t, h(t) is the tide height at time t, w(t) is the wind speed at time t, α is the weight coefficient of the tide height, and β is the adjustment coefficient of the wind speed;

[0077] The expression of information entropy value H is:

[0078]

[0079] Where n is the total number of dynamic variables, p k represents the normalized weight coefficient of the kth variable;

[0080] The expression of the volatility variance r of rainfall is:

[0081]

[0082] Where m is the total number of time steps, r(t u ) is the rainfall value at the u-th time step, is the average rainfall amount, t u represents the time step;

[0083] S4: Construct a balanced flow distribution model to predict the evacuation priority score of each evacuation area based on the evacuation demand intensity and risk assessment results of each evacuation area;

[0084] S4.1: Based on the speed and load capacity of the ships in each evacuation area, the nonlinear effect of the evacuation area is introduced to analyze the resource flow capacity value of each evacuation area. The expression is:

[0085]

[0086] Among them, C i is the resource flow capacity value of the i-th evacuation area, L j is the carrying capacity of the jth ship, λ is the nonlinear adjustment coefficient of the area;

[0087] It should be noted that by calculating the resource circulation capacity value based on the speed and load capacity of the ships in each evacuation area, while introducing the nonlinear influence of the evacuation area area, the resource transportation and allocation capacity of each evacuation area under specific conditions can be accurately evaluated. This calculation method comprehensively considers the dynamic performance of the ship (such as speed and load) and the regional characteristics (such as the influence of area on resource circulation efficiency), and corrects the actual constraint of regional area on circulation capacity through nonlinear adjustment factors. Through this evaluation, the circulation efficiency of available resources in each evacuation area can be objectively reflected, providing an accurate quantitative basis for the formulation of a scientific and reasonable evacuation scheduling plan, thereby improving the resource allocation efficiency and the overall optimization level of the evacuation process.

[0088] S4.2: Based on the evacuation demand intensity S i and the overall risk level R i The dynamic interaction of the ship speed, load capacity and area, as well as the nonlinear influence of the ship speed, load capacity and area, weight the priority through the nonlinear relationship and convert the evacuation demand intensity S i , comprehensive risk level R i , the resource circulation capacity value C and the dynamic control mechanism of resource response efficiency are combined to construct a balanced flow distribution model to predict the evacuation priority score of each evacuation area. The expression is:

[0089]

[0090] Among them, P i is the evacuation priority score of the i-th evacuation area, φ is the circulation adjustment smoothing factor, M i represents the total number of resource distribution sources in the i-th evacuation area, E l is the available resources of the lth resource source, T l The response time of the lth resource source, ψ is the response time smoothing factor, and η is the nonlinear adjustment coefficient of resource distribution.

[0091] It should be noted that the "total number of resource distribution sources" refers to the number of all sources in a certain evacuation area that can provide resource support to the area, such as nearby ports, anchored rescue ships, resources deployed from other evacuation areas, etc.

[0092] For "M i The total number of resource distribution sources for the i-th evacuation area is obtained by counting all resource sources associated with the evacuation area, including the number of ports that can be supported based on the geographical location, the number of ship resources in the adjacent area, and the number of externally deployed resources that meet the response time requirements. This indicator reflects the diversity and richness of resource supply in the evacuation area and is an important parameter for evaluating regional evacuation capacity.

[0093] S5: Generate a ship evacuation dispatch plan based on the evacuation priority score of each evacuation area;

[0094] S5.1: Score P according to evacuation priority i Arrange the evacuation areas in descending order, and select the area with the highest priority score as the target evacuation area;

[0095] Specifically, the priority score P i The higher the area, the higher the evacuation demand intensity, the greater the risk level and the lower the resource circulation capacity in the area, so resources need to be dispatched as a priority for evacuation.

[0096] For example, assuming there are three evacuation areas A, B, and C, with priority scores of PA=85, PB=70, and PC=60, area A with the highest priority score is selected for evacuation first. After the needs of area A are met or the resources are exhausted, areas B and C with higher scores are selected for evacuation in turn. Through this process, it can ensure that limited resources are allocated to high-risk and high-demand areas first, thereby maximizing evacuation efficiency and reducing overall risk.

[0097] S5.2: Sort all ships in ascending order according to their arrival time at the target evacuation area;

[0098] Specifically, the arrival time can be calculated by the current position, speed and position of the target evacuation area of ​​the ship, for example, arrival time = distance / speed. For example, assuming that the current evacuation area is area A, there are three ships 1, 2 and 3, and their arrival times in area A are T1 = 10 minutes, T2 = 15 minutes, and T3 = 5 minutes, then after sorting in ascending order of arrival time, the dispatch order is ship 3 → ship 1 → ship 2. In this way, available resources can be used as soon as possible, evacuation efficiency can be accelerated and waiting time can be reduced.

[0099] S5.3: Ships are dispatched in sequence according to the ship sorting results until the resource flow capacity value of the target evacuation area is exhausted and automatically allocated to the next priority evacuation area. Finally, a complete evacuation dispatch plan including all ship target evacuation areas and dispatch time is generated.

[0100] Specifically: according to the results of the ascending sorting of the arrival time of the ships, the ships are dispatched to the current evacuation area to perform the evacuation task in sequence until the resource flow capacity value of the evacuation area is exhausted. When the current evacuation area can no longer accommodate or dispatch more ships, it automatically switches to the evacuation area of ​​the next priority, and continues to sort and dispatch ships in the same way until all ships are assigned to the target evacuation area. Finally, a complete evacuation dispatch plan containing the target evacuation area and dispatch time of each ship is generated.

[0101] For example, suppose the evacuation area with the highest current priority is area A, and its resource circulation capacity value is CA = 100. After ships 1, 2, and 3 are sorted in sequence, their carrying capacities are 30, 50, and 40, respectively. First, ship 1 is dispatched to area A, and CA is reduced to 70; then ship 2 is dispatched, and CA is reduced to 20; since the carrying capacity of ship 3 is 40, which exceeds the remaining circulation capacity, it can no longer be dispatched to area A. At this time, switch to the next priority evacuation area B, and continue to sort and dispatch the remaining ships. Through this process, it is ensured that evacuation resources are reasonably allocated and efficiently utilized, and finally a complete dispatch plan is formed, such as "ship 1 is dispatched to area A, time T1; ship 2 is dispatched to area A, time T2; ship 3 is dispatched to area B, time T3".

[0102] S6: Execute the ship evacuation dispatch plan and make real-time adjustments.

[0103] S6.1: During the evacuation process, the ship receives the ship feedback data in real time through GPS and AIS;

[0104] It should be noted that the vessel feedback data includes current position, speed, heading, estimated time to reach the target evacuation area and current load status.

[0105] S6.2: Optimize the balanced flow distribution model based on the ship feedback data, update the evacuation priority score of the evacuation area in real time according to the optimized balanced flow distribution model, and adjust the ship evacuation scheduling plan in real time.

[0106] Specifically, in the process of executing the evacuation scheduling plan, the ship receives real-time feedback data (such as the current position, speed, heading, estimated arrival time and load status of the ship) through GPS and AIS, and dynamically adjusts the input parameters of the balanced flow allocation model based on these feedback data, including key factors such as evacuation demand intensity, resource circulation capacity and comprehensive risk level. The optimized balanced flow allocation model will recalculate the priority score of the evacuation area in real time, thereby dynamically updating the evacuation scheduling plan. For example, assuming that the priority score of an evacuation area A decreases due to real-time data updates, while the priority score of evacuation area B increases, the ships that have not completed the evacuation task will be reallocated and dispatched to area B first. In addition, if a ship cannot reach the target area as planned due to an emergency (such as speed reduction or navigation delay), the target evacuation area of ​​the ship will be adjusted or the task will be rescheduled according to the feedback data. Through this process, it is ensured that the evacuation task can flexibly adapt to dynamic changes and improve the overall evacuation efficiency and emergency response capabilities.

[0107] This embodiment also provides a computer device, which is suitable for the case of a water transport emergency evacuation deployment method based on a balanced flow distribution model, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the water transport emergency evacuation deployment method based on a balanced flow distribution model as proposed in the above embodiment.

[0108] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0109] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements the water transport emergency evacuation deployment method based on the balanced distribution model proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, disk or optical disk.

[0110] In summary, the present invention collects ship distribution data and behavior data, combines hydrological data with meteorological data for preprocessing and dynamic analysis, first predicts the evacuation demand intensity of each evacuation area, and then dynamically combines the demand intensity with multiple factors such as comprehensive risk level and resource circulation capacity value based on the balanced flow distribution model to generate a priority score for each evacuation area; on this basis, the priority score is used to generate an optimized ship evacuation scheduling plan, and by receiving ship feedback data in real time, the balanced flow distribution model and scheduling plan are dynamically updated, thereby realizing the whole process of efficient resource allocation, real-time adjustment and precise evacuation deployment.

[0111] Example 2 is the second example of the present invention. In order to further verify the technical solution of the present invention, experimental simulation data of a water transport emergency evacuation deployment method based on a balanced flow distribution model is provided.

[0112] In order to verify the effectiveness of the water transport emergency evacuation deployment method based on the balanced flow distribution model, a simulation experiment was designed. Three evacuation areas (A, B, C) in a coastal waters and 8 ships were selected to simulate the emergency evacuation process in a complex environment. The advantages and disadvantages of the proposed method and the existing static priority allocation method were compared in terms of evacuation efficiency, risk control and resource utilization.

[0113] First, the experiment collected distribution data (such as location, speed, and load status) and behavior data (such as heading and navigation status) of eight ships through AIS and GPS equipment. At the same time, SAR satellite images and hydrological monitoring stations were used to obtain hydrological data (such as water flow velocity and tide height) and meteorological data (such as wind speed and rainfall) in the evacuation area. These data were preprocessed to ensure their accuracy and consistency, providing reliable input for subsequent calculations.

[0114] Secondly, based on the pre-processed ship distribution and behavior data and the availability of regional evacuation resources, the evacuation demand intensity (S i ), and combined hydrological and meteorological data through a dynamic interactive model to assess the comprehensive risk level of each region (R i ). These calculation results laid the foundation for building a balanced flow distribution model and clarified the initial evacuation priority of each area.

[0115] Then, the priority score (P i ), and sort the evacuation areas in descending order according to the scores, and prioritize the dispatch of ships to the areas with the highest scores. Sort by ship arrival time, and dispatch ships in sequence until the resource flow capacity of the priority area is exhausted, and then switch to the second-best area. During the dispatch process, the system receives real-time ship feedback data through GPS and AIS, dynamically adjusts the model and re-optimizes the evacuation plan.

[0116] Finally, the experiment generated a complete ship dispatching plan and recorded the target evacuation area and arrival time of each ship. Through the analysis of the test data, it was found that the method of the present invention showed significant advantages in dynamically adjusting priorities, improving evacuation efficiency, optimizing resource utilization and reducing risks. Compared with the traditional static priority model, the dispatching time of the present invention was shortened by about 22%, and the resource utilization rate was higher. At the same time, it effectively avoided excessive concentration of resources in high-risk areas, verifying the innovation and practicality of the method.

[0117] Through data analysis, it can be clearly seen that the water transport emergency evacuation deployment method based on the balanced flow distribution model of the present invention has significant advantages in terms of ship scheduling priority, resource allocation efficiency and evacuation time control. The data clearly shows that by dynamically adjusting the evacuation area priority score, more efficient resource allocation and shorter evacuation time can be achieved in the actual evacuation process.

[0118] For example, ships 1 and 2 were assigned to evacuation area B with priority, with speeds of 10.2 knots and 11.8 knots, respectively, and shorter arrival times of 14.8 minutes and 18.2 minutes. Area B, as the evacuation area with the highest priority score, can respond quickly to emergency evacuation needs and ensure efficient use of its resource flow capacity. In contrast, although ships 3 and 4 are located farther away (2.67 km and 4.05 km), they can still be reasonably dispatched due to the suboptimal priority score of evacuation area A, completing the evacuation task within 12.5 minutes and 14.2 minutes, respectively.

[0119] In summary, the present invention can adjust the scheduling plan in real time according to the feedback data of the ship and the risk status of the evacuation area, thereby significantly improving the evacuation efficiency. Compared with the static priority scheduling method, it reduces the evacuation time, optimizes the resource utilization, and effectively reduces the resource scheduling pressure in high-risk areas, which is innovative and practical.

[0120] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for emergency evacuation deployment of water transport based on a balanced flow distribution model, characterized in that: include, Collecting ship distribution data and ship behavior data, and preprocessing the ship distribution data and ship behavior data; By combining the ship density analysis with the weight adjustment of the ship behavior status and the nonlinear correction of the evacuation resource availability, the evacuation demand intensity of each evacuation area is predicted through the dynamic interaction between the pre-processed ship distribution data and the behavior data; Collect hydrological and meteorological data to conduct risk assessments for each evacuation area; A balanced flow distribution model is constructed to predict the evacuation priority score of each evacuation area based on the evacuation demand intensity and risk assessment results of each evacuation area; Generate a ship evacuation dispatch plan based on the evacuation priority score of each evacuation area; Execute the ship evacuation dispatch plan and make real-time adjustments; The hydrological data and meteorological data are collected to conduct risk assessment on each evacuation area. The specific steps are as follows: Using SAR satellite images and real-time hydrological monitoring stations, the water velocity, direction, tidal height and water depth of each evacuation area are collected in real time; Use weather radar and wind profiler radar to collect wind speed, wind direction, rainfall and visibility in each evacuation area in real time; Taking hydrological data and meteorological data as dynamic variables, the comprehensive interactive relationship between different risks is analyzed. The interaction between dynamic variables is integrated based on geometric characteristics, and the risk level is calculated. At the same time, combined with the uncertainty of the distribution of dynamic variables, the overall risk level is adjusted through information entropy, and the risk is nonlinearly amplified using rainfall volatility variance. Finally, the comprehensive risk assessment level of the evacuation area is obtained.

2. The method for emergency evacuation deployment of water transport based on the balanced flow distribution model according to claim 1, characterized in that: The vessel distribution data includes the location, quantity and load status of the vessels; The ship behavior data includes the ship's heading, speed and navigation status.

3. The method for emergency evacuation deployment of water transport based on the balanced flow distribution model according to claim 2, characterized in that: The specific steps of preprocessing the ship distribution data and the ship behavior data are as follows: Data cleaning is used to remove invalid and abnormal data from ship distribution data and ship behavior data; Through data format conversion, the formats of ship distribution data and ship behavior data are unified; The vessel distribution data and vessel behavior data are standardized through data filtering and transformation rules.

4. The method for emergency evacuation deployment of water transport based on a balanced flow distribution model as claimed in claim 3, characterized in that: The evacuation demand intensity is calculated by the following formula: ； in, Indicates The evacuation demand intensity of each evacuation area is Indicates Total number of vessels evacuating the area, Indicates The average load status of the vessels in the evacuation area, Indicates The area of ​​the evacuation zone, Indicates The speed of the ship, Indicates The rate of change of the ship's heading, Indicates The navigation status coefficient of a ship.

5. The method for emergency evacuation deployment of water transport based on the balanced flow distribution model according to claim 4, characterized in that: The comprehensive risk assessment level of the evacuation area is calculated by the following formula: ； in, It is The overall risk level of the evacuation area is is the evaluation time interval, Indicates that over time The changing geometric eigenvalues, is the information entropy value, is the information entropy adjustment factor, is the volatility variance of rainfall, is the rainfall volatility amplification factor.

6. The method for emergency evacuation deployment of water transport based on the balanced flow distribution model according to claim 5, characterized in that: The said balanced flow distribution model is constructed to predict the evacuation priority score of each evacuation area based on the evacuation demand intensity and risk assessment results of each evacuation area. The specific steps are as follows: According to the speed and load capacity of ships in each evacuation area, the nonlinear effect of the evacuation area is introduced to analyze the resource flow capacity value of each evacuation area. The expression is: ； in, It is The resource flow capacity value of the evacuation area, It is The carrying capacity of ships, λ is the nonlinear adjustment coefficient of the area; Based on the intensity of evacuation demand and overall risk level The dynamic interaction of the ship speed, load capacity and area, as well as the nonlinear effects of the ship speed, load capacity and area, weight the priority through the nonlinear relationship and convert the evacuation demand intensity into , comprehensive risk level , Resource circulation capacity value Combined with the dynamic control mechanism of resource response efficiency, a balanced flow distribution model is constructed to predict the evacuation priority score of each evacuation area. The expression is: ； in, For the The evacuation priority score of each evacuation area, is the circulation adjustment smoothing factor, Indicates Total number of resource distribution sources in the evacuation area, For the The available resources of each resource source, No. The response time of each resource source, is the response time smoothing factor, is the nonlinear adjustment coefficient of resource distribution.

7. The method for emergency evacuation deployment of water transport based on the balanced flow distribution model according to claim 6, characterized in that: The specific steps of generating a ship evacuation dispatch plan according to the evacuation priority score of each evacuation area are as follows: Based on evacuation priority score Arrange the evacuation areas in descending order, and select the area with the highest priority score as the target evacuation area; Sort all ships in ascending order based on their arrival time at the target evacuation area; Ships are dispatched in sequence according to the ship sorting results until the resource flow capacity value of the target evacuation area is exhausted and automatically allocated to the next priority evacuation area. Finally, a complete evacuation dispatch plan including all ship target evacuation areas and dispatch times is generated.

8. The method for emergency evacuation deployment of water transport based on the balanced flow distribution model according to claim 7, characterized in that: The specific steps of executing the ship evacuation dispatch plan and making real-time adjustments are as follows: When the ship is executing the evacuation dispatch plan, the ship feedback data is received in real time through GPS and AIS; The balanced flow distribution model is optimized based on the ship feedback data. According to the optimized balanced flow distribution model, the evacuation priority score of the evacuation area is updated in real time, and the ship evacuation scheduling plan is adjusted in real time.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the water transport emergency evacuation deployment method based on the balanced distribution model described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the water transport emergency evacuation deployment method based on the balanced flow distribution model described in any one of claims 1 to 8 are implemented.

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