A method and system for intelligent configuration of meteorological disaster emergency plans based on disaster survey results

Abstract

The present invention discloses a method and system for intelligently configuring meteorological disaster emergency plans based on disaster survey results, which relates to the field of intelligent meteorological disaster emergency plans and includes the following steps: receiving and analyzing a disaster data set to obtain a disaster basic parameter set; receiving a disaster situation data set, analyzing and obtaining a disaster site status demand data set and disaster assessment results; and receiving a reinforcement data set. The present invention mainly has the following beneficial effects: by receiving disaster data from sensor networks, meteorological monitoring stations, and manual reports, and analyzing the disaster situation data, the present invention can obtain detailed information on the specific location of the disaster site, the type of damage, the extent of damage, and the required rescue materials, personnel, and medical equipment, and quickly extract the disaster basic parameters and the disaster site status demand, providing accurate data support for subsequent material allocation, rescue route planning, and rescue task allocation, thereby improving the timeliness and pertinence of emergency response.

Description

Technical Field

[0001] The present invention relates to the field of intelligent meteorological disaster emergency plans, and in particular to an intelligent configuration method and system for meteorological disaster emergency plans based on disaster census results. Background Art

[0002] As the impact of global climate change becomes increasingly significant, meteorological disasters are becoming more frequent. Extreme weather events such as typhoons, heavy rains, and droughts pose a serious threat to people's lives and property. As a key component of emergency response, the intelligent meteorological disaster plan configuration system integrates advanced information technology and intelligent algorithms to improve the efficiency and accuracy of disaster response, providing strong support for disaster reduction and relief efforts.

[0003] Compared to traditional manual planning methods, the intelligent meteorological disaster plan configuration system demonstrates significant advantages in data processing, plan generation, and real-time adjustments. Manual plans often rely on the experience and expertise of experts, developed through meetings and manual development. This process is time-consuming and susceptible to human error. The intelligent configuration system, on the other hand, automatically receives and processes disaster data from sensor networks, meteorological monitoring stations, and human reports. Using intelligent algorithms, it rapidly generates preliminary plans, significantly reducing planning time.

[0004] The current manual emergency plan development process typically involves gathering disaster information, holding emergency meetings with expert teams, discussing and assessing the nature of the disaster, developing a plan based on historical experience and expertise, and then reviewing and adjusting the plan. Due to the limitations of manual operations and information processing delays, this process is often time-consuming and makes it difficult to provide a comprehensive and accurate emergency response plan immediately. This is especially true after a disaster, when time is of the essence and every minute of delay can result in immeasurable losses.

[0005] Compared with the intelligent configuration system for meteorological disaster plans, existing manual plans have obvious shortcomings in terms of timeliness and pertinence of emergency response plans. Due to the time-consuming manual operation and the limitations of data processing capabilities, existing manual plans often find it difficult to quickly generate effective emergency response plans after a disaster occurs, resulting in delayed rescue operations and waste of resources. In addition, existing manual plans are also unable to cope with the real-time adjustment of plan planning. Since it is difficult for humans to fully track the development of disasters and the progress of rescue in real time, it is difficult to make timely adjustments and optimizations to details during the implementation of the plan, resulting in inefficient and inaccurate rescue operations. These problems limit the application effect of existing manual plans in meteorological disaster emergency response, highlighting the importance and necessity of the intelligent configuration system for meteorological disaster plans. Summary of the Invention

[0006] This application provides an intelligent configuration method and system for meteorological disaster plans based on disaster census results, which is used to solve the technical problems that existing emergency response plans lack timeliness and pertinence, and it is difficult to take into account details in real-time adjustment of plan planning.

[0007] In view of the above problems, the present application provides an intelligent method for meteorological disaster emergency response, including the following steps: receiving and analyzing a disaster data set to obtain a disaster basic parameter set; receiving a disaster situation data set, analyzing to obtain a disaster-affected point status demand data set and a disaster assessment result; receiving a reinforcement data set, wherein the reinforcement data set comes from data from multiple access departments, which is the currently available reinforcement force and material reserves; constructing a material allocation environment based on the disaster basic parameter set, reinforcement data set, disaster assessment results and disaster-affected point status demand data set, and analyzing to obtain a rescue scheduling plan.

[0008] In summary, the present invention mainly has the following beneficial effects:

[0009] 1. The present invention receives disaster data from sensor networks, meteorological monitoring stations and manual reports, and analyzes the disaster situation data to obtain detailed information on the specific location of the disaster site, the type of damage, the extent of damage, and the required rescue materials, personnel and medical equipment. It quickly extracts basic disaster parameters and the status requirements of the disaster site, providing accurate data support for subsequent material deployment, rescue route planning and rescue task allocation, thereby improving the timeliness and pertinence of emergency response.

[0010] 2. Based on the full reception and analysis of reinforcement data, the present invention constructs a material allocation environment, comprehensively considers the current number of available rescue personnel, the number of rescue vehicles, the reserves of rescue materials, and the status requirements of each disaster-stricken site, and allocates rescue tasks and dispatches resources to ensure that rescue resources can reach the demand points according to priority and urgency, and can be dynamically adjusted according to the actual situation of the disaster and rescue needs to provide support for rescue operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 This is a logic diagram of an intelligent configuration method for meteorological disaster emergency plans based on disaster survey results of the present invention;

[0012] Figure 2 This is a system framework diagram of an intelligent configuration system for meteorological disaster plans based on disaster census results of the present invention. DETAILED DESCRIPTION

[0013] This application provides an intelligent configuration method and system for meteorological disaster plans based on disaster census results, which is used to solve the technical problems that existing emergency response plans lack timeliness and pertinence, and it is difficult to take into account details in real-time adjustments to plan planning.

[0014] After introducing the basic principles of the present application, the technical solutions in the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. It should also be noted that, for the sake of ease of description, only the parts related to the present application, not all, are shown in the accompanying drawings.

[0015] Example 1

[0016] A method and system for intelligently configuring meteorological disaster emergency plans based on disaster survey results, comprising:

[0017] S100: Receive and analyze a disaster data set to obtain a disaster basic parameter set;

[0018] Disaster-related data is received from multiple channels and analyzed and processed to extract basic disaster parameters that provide guidance for subsequent rescue efforts. The system receives disaster data from multiple sources in real time. The sensor network includes meteorological and geological sensors deployed at key locations to monitor and report real-time meteorological and geological conditions. Meteorological monitoring stations provide more comprehensive and professional meteorological data, including wind direction, wind speed, and rainfall. Manual reports are also a crucial data source, especially during disasters, when on-site personnel can report the actual situation through specific channels.

[0019] The S100 includes:

[0020] S110: Receive disaster data in real time through sensor networks, weather monitoring stations, and manual reports;

[0021] S120: performing data cleaning, format conversion, and outlier processing on the received disaster data;

[0022] S130: Analyze the pre-processed disaster data to extract basic disaster parameters such as the intensity, scope, and duration of the disaster;

[0023] S140: Arrange the extracted basic disaster parameters into a basic disaster parameter set as input for subsequent steps.

[0024] For example, when a flood disaster occurs, the water level sensors in the sensor network will monitor the water level changes in real time and upload the data to the system; the meteorological monitoring station will provide meteorological data such as rainfall and wind speed in the area; at the same time, on-site personnel will report the actual situation of the flood disaster through communication equipment, including the scope of inundation and the disaster situation.

[0025] Because disaster data comes from multiple sources, its format and quality may vary. Data cleaning aims to remove duplicate, invalid, or erroneous data; format conversion converts data into a unified format that the system can recognize; and outlier processing identifies and addresses data points that differ significantly from normal data to ensure data accuracy and reliability.

[0026] For example, flood disaster data may contain data anomalies from certain sensors, including missing data and abnormal values. The system will remove or correct these abnormal data through data cleaning and outlier processing to ensure the accuracy of subsequent analysis.

[0027] The system conducts in-depth analysis of pre-processed disaster data to extract essential disaster parameters crucial for subsequent rescue efforts. These parameters include, but are not limited to, the intensity, scope, and duration of the disaster. For example, in the case of a flood, the system can analyze data from water level sensors to extract the peak water level and inundated area. Simultaneously, by analyzing data from meteorological monitoring stations, it can derive the duration and rainfall of the flood. These parameters will provide crucial reference for subsequent rescue efforts.

[0028] S200: Receive a disaster situation data set, analyze and obtain a disaster site status demand data set and disaster assessment results;

[0029] By receiving and analyzing disaster data, determining the specific status and needs of the disaster-stricken areas, and conducting an overall assessment of the disaster, a calculation basis is provided for subsequent rescue work.

[0030] The S200 includes:

[0031] S210: receiving disaster situation data;

[0032] S220: Analyze the received disaster data to determine the status information of the disaster site, wherein the status information of the disaster site includes the specific location, damage type, and damage extent;

[0033] S230: Analyze and obtain a state demand data set of the disaster-affected site based on the state information of the disaster-affected site, wherein the state demand data set of the disaster-affected site includes the demand for rescue supplies, rescue personnel, and medical equipment;

[0034] S240: Comprehensively evaluate the disaster based on the disaster basic parameter set and the disaster-affected point status information to obtain a disaster assessment result; the disaster assessment result includes the severity of the disaster, the scope of impact, and the potential risks.

[0035] Disaster data is received from multiple sources. This data comes from on-site personnel, rescue teams, government departments, and more, including the specific location of the affected area, damage, and casualties. This data forms the basis for subsequent analysis and is crucial for determining the status and needs of the affected area. For example, during an earthquake, the system received reports from on-site personnel describing collapsed buildings in residential areas, casualties, and the types of relief supplies urgently needed. Rescue teams also provided information about the difficulties and needs they encountered during the rescue process.

[0036] The received disaster data is analyzed to determine the specific location of the affected area, the type of damage, and the extent of the damage. For example, in an earthquake disaster, the system analyzes the received data to determine the specific location of residential areas, as well as the type and severity of damage to collapsed buildings in the area.

[0037] Based on the status information of the disaster site, further analysis is performed to obtain a data set of the status needs of the disaster site. This data set includes the required quantity and type of relief supplies (including food, water, medicine, tents, etc.), rescue personnel (including search and rescue teams, medical teams, etc.), and medical equipment (such as ambulances, stretchers, surgical instruments, etc.). For example, in an earthquake disaster, the system analyzes the damage and casualties in a residential area to determine the quantity and type of relief supplies such as food, water, and medicine that are urgently needed in the area, as well as the required number and type of search and rescue teams and medical teams.

[0038] A comprehensive disaster assessment is conducted by integrating a set of basic disaster parameters (including the intensity, scope, and duration of the disaster) with information on the status of the affected sites. The assessment results include the severity of the disaster (including minor, moderate, and severe), the scope of impact (including the size of the affected area and the number of people affected), and the potential risks (including the possibility of secondary disasters).

[0039] S300: receiving a reinforcement data set, wherein the reinforcement data set is data from multiple access departments and is the current available reinforcement force and material reserves;

[0040] Receive reinforcement data from multiple access departments, and integrate, analyze and evaluate this data to determine the currently available reinforcement forces and material reserves, providing strong support for subsequent rescue work.

[0041] The S300 includes:

[0042] S310: receiving reinforcement data from multiple access departments, including the number of available rescue personnel, the number of rescue vehicles, and the status of rescue material reserves;

[0043] S320: Integrate and process the received reinforcement data to ensure the accuracy and completeness of the data;

[0044] S330: Analyze the integrated reinforcement data, evaluate the currently available reinforcement forces and material reserves, and package the reinforcement types, quantities, mobilization time and mobilization feasibility as a reinforcement data set.

[0045] Reinforcement data is received from multiple connected departments, including but not limited to fire departments, medical departments, and material storage centers. This data includes the number of available rescue personnel, the number of rescue vehicles, and the status of rescue material reserves. This data forms the basis for subsequent analysis and evaluation and is crucial for determining the availability of reinforcement forces and material reserves. For example, during a flood disaster, the system received data on the number of available rescue personnel and rescue vehicles from the fire department; data on the number of medical rescue teams and the status of medical equipment reserves from the medical department; and data on the status of rescue material reserves such as food, water, and medicine from the material storage center.

[0046] Integrate and process the received reinforcement data to ensure its accuracy and completeness. This includes removing duplicate data, correcting erroneous data, and supplementing missing data to ensure the accuracy of subsequent analysis and evaluation.

[0047] The integrated reinforcement data is analyzed and evaluated to determine the currently available reinforcement forces and material reserves. This includes evaluating the types, quantities, mobilization time and feasibility of reinforcements. The system packages information such as the types, quantities, mobilization time and feasibility of reinforcements as a reinforcement data set. For example, in a flood disaster, the system analyzes and evaluates the integrated reinforcement data. The evaluation results show that the number of available rescue personnel and the number of rescue vehicles are sufficient to meet the needs of the disaster; at the same time, the reserves of medical rescue teams and medical equipment are also good; in addition, the reserves of rescue materials such as food, water, and medicines are also sufficient to meet the needs during the disaster. Based on these evaluation results, the system packages information such as the types of reinforcements (including rescue personnel, rescue vehicles, medical teams, rescue materials, etc.), quantities, mobilization time and mobilization feasibility as a reinforcement data set.

[0048] S400: Building a material allocation environment based on the disaster basic parameter set, reinforcement data set, disaster assessment results and disaster-affected site status demand data set, and analyzing and obtaining a rescue dispatch plan.

[0049] Based on basic disaster parameters, reinforcement data, disaster assessment results, and disaster site status and demand data, a comprehensive material allocation environment is constructed, and on this basis, the optimal rescue dispatch plan is analyzed. This step is crucial to improving rescue efficiency and ensuring the rational allocation of rescue resources.

[0050] The S400 includes:

[0051] S410: Build a material allocation environment;

[0052] S420: Planning a rescue path based on available vehicles to obtain a rescue path plan;

[0053] S430: Rescue task allocation, obtaining a rescue task queue;

[0054] S440: formulate rescue dispatch plan and obtain resource allocation queue;

[0055] S450: Using the rescue path plan, rescue task queue, and resource allocation queue as a rescue scheduling plan.

[0056] Integrate the disaster type, intensity, impact range, number of rescue personnel, number of rescue vehicles, rescue material reserves, disaster severity, impact range, potential risks, rescue material needs, rescue personnel needs, and medical equipment needs to build a comprehensive material allocation environment. This environment will serve as the basis for subsequent analysis of rescue dispatch plans. For example, in an earthquake disaster, the system integrates basic disaster parameters (including earthquake magnitude, focal depth, and affected area), reinforcement data (including the number of rescue teams, number of rescue vehicles, and rescue material reserves), disaster assessment results (including the number of houses collapsed by the earthquake, casualties, and potential secondary disaster risks), and disaster site status demand data (including a list of rescue material needs, the number of rescue personnel needed, and the type of medical equipment needed) to build a comprehensive material allocation computing environment.

[0057] The system will plan rescue routes based on available rescue vehicles, road conditions, traffic control, and other factors. The system will comprehensively consider factors such as the time, safety, and feasibility of the rescue route, generate multiple rescue route plans, and select the optimal one. For example, in an earthquake disaster, the system planned rescue routes based on the number and type of available rescue vehicles, the road damage caused by the earthquake, and traffic control information. The system generated multiple rescue route plans and comprehensively considered factors such as the time, safety, and feasibility of the route, ultimately selecting the rescue route plan with the shortest time and the highest safety.

[0058] The S410 includes:

[0059] S411: Determine rescue priorities using the SP formula based on the disaster assessment results and the disaster site status and demand data set;

[0060] S412: Determine the urgency of material demand using the UN formula based on the disaster site status demand data set and the reinforcement data set;

[0061] The SP formula is as follows:

[0062] SP(Di)=α*Sp(Di)+β*Sr(Di)+γ*Sin(Di)+δ*In(Di)

[0063] Among them, Sp(Di) represents the quantitative value of the proportion of the affected population at the i-th disaster site, which can be obtained by counting the population in the disaster area and comparing it with the total population; Sr(Di) represents the rescue accessibility of the i-th disaster site. Sin(Di) represents the quantitative value of the infrastructure condition of the i-th disaster site before the disaster, which is evaluated based on the type, quantity, and maintenance status of the infrastructure at the disaster site; In(Di) represents the interaction term between the various factors at the i-th disaster site; α, β, γ, δ are the weight coefficients of each item, which are used to represent the relative importance of each factor in determining the rescue priority;

[0064] The UN formula is as follows:

[0065] UN(Di)=Qmax(Ti)Qi+(1-Δtmax(Ti)Δti)+Scrit(Ti,Di)+In(Di)

[0066] Among them, Qi represents the quantitative value of demand at the i-th disaster-affected point; Qmax(Ti) represents the maximum possible demand under demand type Ti, which is used to normalize the demand; Δti represents the quantitative value of time urgency at the i-th disaster-affected point, which is based on the difference between the rescue arrival time and the disaster occurrence time; Δtmax(Ti) represents the maximum acceptable time urgency under demand type Ti, which is used to normalize time urgency; Scrit(Ti,Di) represents the criticality score of demand type Ti at the i-th disaster-affected point, which is based on the disaster type, the number of affected people, and the degree of infrastructure damage; In(Di) represents the interaction term between the various factors at the i-th disaster-affected point.

[0067] Based on the results of the disaster assessment and the data on the status and needs of the affected sites, a specific formula is used to determine rescue priorities and the urgency of material needs. The SP formula is used to assess the rescue priority for each affected site. This formula takes into account the proportion of the population affected, rescue accessibility, the condition of pre-disaster infrastructure, and the interactions between these factors. For example, in a flood disaster, we have site A with an affected population proportion of 30%. Rescue accessibility is high because the roads are intact, and the area's pre-disaster infrastructure is in good condition due to regular maintenance. Based on these factors, we can calculate the rescue priority for site A.

[0068] The UN formula is used to assess the urgency of the supply needs at each disaster site. This formula takes into account the quantitative value of the need, the time urgency, the criticality score, and the interaction between these factors. For example, in a flood disaster, the quantitative value of the need at disaster site A is high because the rescue team is expected to arrive within a few hours. The time urgency is moderate because the area has a large number of residents and critical infrastructure, and the criticality score is also high. Based on these factors, we can calculate the urgency of the supply needs at site A: UN(A)

[0069] The S430 includes:

[0070] S431: Evaluate the team's equipment type, quantity, personnel skill level, and available disaster sites based on the reinforcement data set;

[0071] S432: Using the TA formula to quantitatively arrange the priorities of the rescue tasks, and generating a rescue task queue for each rescue team based on the result;

[0072] The TA formula is as follows:

[0073] TA(Tk,Mj)=SPj+UNj+Pr(Tk,Mj)

[0074] Among them, TA(Tk,Mj) represents the quantitative value of the priority of rescue team Tk in performing rescue task Mj; SPj and UNj represent the rescue priority and urgency of material demand of disaster site j, respectively; Pr(Tk,Mj) represents the degree of matching between rescue team Tk and rescue task Mj.

[0075] Based on the reinforcement data set, the types and quantity of tools, personnel skill levels and accessible disaster sites of the rescue team are evaluated, and the TA formula is used to quantitatively arrange the priorities of rescue tasks.

[0076] Based on the information in the reinforcement data set, we assess each rescue team's equipment type, number of rescue vehicles, personnel skill levels, and the disaster site requirements they can reach. For example, in a flood disaster, we have a rescue team B with sufficient rescue vehicles and personnel, and its personnel are highly skilled. Based on this information, we can determine that Team B is capable of reaching and meeting the needs of disaster site A.

[0077] The TA formula is used to calculate the quantitative priority of each rescue team for each rescue mission. This formula takes into account the rescue priority of the disaster site, the urgency of the supply needs, and the matching degree between the rescue team and the rescue mission. For example, in a flood disaster, we can calculate the quantitative priority value TA(B, A) for rescue team B to carry out the rescue mission, that is, to reach and meet the needs of disaster site A. Based on this value, we can calculate the optimal team to be assigned to site A for rescue.

[0078] The S440 includes:

[0079] S441: Calculate the remaining resources using the RR formula, analyze and prioritize the disaster-stricken areas, and obtain the rescue queue;

[0080] S442: Analyze the rescue queue and reinforcement data set to obtain a rescue evaluation value, perform a comprehensive evaluation using the OE formula, and allocate resources using the AR formula to obtain a resource allocation queue;

[0081] The RR formula is as follows:

[0082] RR=TR-Di∈SortedList∑A(Di)

[0083] Among them, RR represents the remaining resources, SortedList is the list of disaster-affected points sorted by priority, A(Di) is the amount of resources allocated to Di, and TR represents the total amount of resources.

[0084] The OE formula is as follows:

[0085]

[0086] Among them, w1, w2, and w3 are weight coefficients, and w1+w2+w3=1. TR represents the total resources, RR represents the remaining resources, and RR_Impact(Di) represents the impact of the remaining resources on the disaster point Di, which is determined by manual evaluation. When the impact cannot be determined, the value is 1 and does not affect the calculation results of other values.

[0087] The AR formula is as follows:

[0088]

[0089] Among them, A(Di) represents the amount of resources that should be allocated to the disaster-stricken point Di, OE(Di) represents the comprehensive evaluation value of the disaster-stricken point Di, which is calculated by the OE formula, ∑ Dj∈DPs OE(Dj) represents all disaster-affected points, that is, the sum of the comprehensive evaluation values ​​of (each disaster-affected point Dj in the DPs set), TR represents the total amount of resources, RRAll represents the remaining amount of resources that have been allocated so far, and RRAdd(Di) represents the amount of resources additionally allocated to the disaster-affected point Di. This value is an indicator adjusted manually.

[0090] Calculate remaining resources, analyze and prioritize disaster-affected locations, obtain rescue assessments, allocate resources, and analyze and prioritize disaster-affected locations. For example, in a flood disaster, we can calculate the remaining resources (RR) and prioritize the affected locations based on their rescue priority and urgency of material needs.

[0091] The OE formula is used to comprehensively assess each disaster site, and the AR formula is used to allocate resources. The OE formula takes into account the rescue priority, the urgency of material needs, and the impact of remaining resources on the disaster site. The AR formula allocates resources based on the comprehensive assessment value and the total amount of resources. For example, in a flood disaster, we can calculate the comprehensive assessment value OE(A) of disaster site A and allocate resources to site A based on this value, the total amount of resources, and the amount of resources already allocated. When site A has a greater need and a higher comprehensive assessment value, it will receive more resources.

[0092] The method further includes step S500, which is composed of the following steps:

[0093] S510: Change the execution and monitoring of the plan to be used for drills, collect manually input disaster basic parameter sets, reinforcement data sets, disaster assessment results, and disaster site status demand data sets;

[0094] S520: Start the drill and generate a preliminary rescue dispatch plan;

[0095] S530: Collecting drill evaluation parameters, wherein the drill evaluation parameters are quantitative values ​​input by manual evaluation;

[0096] S540: Use the ES formula to digitize the execution results and score the drill;

[0097] S550: accepting manual input adjustments to optimize the rescue dispatch plan formulation step;

[0098] S560: The drill results are adjusted and applied as the results of the subsequent reinforcement data collection and disaster site status demand data collection steps.

[0099] Adjust the plan execution and monitoring environment to a mode suitable for drills and collect necessary input data. Set the system to drill mode to test and optimize the dispatch plan without actually triggering a rescue operation. Next, manually input a set of basic disaster parameters, reinforcement data, disaster assessment results, and disaster site status and demand data. This data forms the basis for developing a preliminary rescue dispatch plan. For example, in a simulated flood disaster drill, we input basic disaster parameters (including flood depth and flow rate), reinforcement data (including the location, number of people, and equipment of the rescue team), disaster assessment results (including the area of ​​the affected area and population distribution), and disaster site status and demand data (including emergency evacuation needs and medical assistance needs). Evaluation parameters are collected during the drill to evaluate the results. During the drill, various evaluation parameters are recorded and collected, including the rescue team's response time, speed of arrival at the disaster site, and efficiency of relief supply distribution. These parameters are manually evaluated and quantified. For example, during a flood disaster drill, the rescue team's response time, speed of arrival at the disaster site, and efficiency of relief supply distribution were recorded. The ES formula was then used to digitally score the drill's execution results. This score reflects the overall effectiveness of the drill and is used to evaluate its success or failure.

[0100] After the drill, we collect feedback from human input regarding adjustments, including adjustments to the allocation of rescue teams, optimization of rescue routes, and distribution strategies for relief supplies. These feedback is then taken into account to refine the initial rescue dispatch plan. For example, after a flood disaster drill, human input revealed that the allocation of some rescue teams was not rational and that some rescue routes presented congestion risks. Based on these feedback, the initial rescue dispatch plan was optimized, adjusting the allocation of rescue teams and the planning of rescue routes.

[0101] After the drill, the results will be used as the basis for collecting data on subsequent reinforcement and disaster site status needs. This means that in future actual rescue operations, we can adjust and optimize rescue strategies based on these results.

[0102] This application also includes an intelligent meteorological disaster emergency response system, which is used to implement the intelligent meteorological disaster emergency response method. It includes:

[0103] A first receiving unit is configured to receive and analyze a disaster data set to obtain a disaster basic parameter set;

[0104] The first computing unit is configured to receive a disaster situation data set, analyze and obtain a disaster site status demand data set and a disaster assessment result;

[0105] The second receiving unit is used to receive a reinforcement data set, wherein the reinforcement data set is data from multiple access departments and is the current available reinforcement force and material reserves;

[0106] The second computing unit is used to build a material allocation environment based on the disaster basic parameter set, reinforcement data set, disaster assessment results and disaster-affected site status demand data set, and analyze and obtain a rescue scheduling plan.

[0107] The system further includes a drill mode unit for carrying out various sub-steps in the step of S500.

[0108] Those skilled in the art will understand that the various numerical numbers such as the first and second involved in this application are only for the convenience of description, and are not used to limit the scope of this application, nor do they indicate the order of precedence. "And / or" describes the association relationship of the associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one" refers to one or more. At least two refers to two or more. "At least one", "any one" or similar expressions refer to any combination of these items, including any combination of single items (individuals) or plural items (individuals). For example, at least one item (individual, kind) of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0109] In the above embodiments, all or part of the embodiments may be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments may be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described herein are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0110] The steps of the method or algorithm described in this application can be directly embedded in hardware, software units executed by a processor, or a combination of the two. The software units can be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM or any other form of storage medium in the art. Exemplarily, the storage medium can be connected to the processor so that the processor can read information from the storage medium and write information to the storage medium. Optionally, the storage medium can also be integrated into the processor. The processor and the storage medium can be arranged in an ASIC, and the ASIC can be arranged in a terminal. Optionally, the processor and the storage medium can also be arranged in different components in the terminal. These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are performed on the computer or other programmable device to produce computer-implemented processing, so that the instructions executed on the computer or other programmable device provide for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0111] Although the present application has been described with reference to specific features and embodiments thereof, it will be apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and drawings are merely illustrative of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, the present application is intended to include such modifications and variations as fall within the scope of the present application and its equivalents.

Claims

1. An intelligent method for meteorological disaster emergency response, characterized in that: The following steps are involved: Receive and analyze disaster data sets to obtain disaster basic parameter sets; Receive disaster situation data sets, analyze and obtain disaster site status demand data sets and disaster assessment results; Receive reinforcement data sets, where the reinforcement data sets come from data from multiple access departments and are information on currently available reinforcement forces and material reserves; Building a material allocation environment based on the disaster basic parameter set, reinforcement data set, disaster assessment results, and disaster site status demand data set, and analyzing and obtaining a rescue dispatch plan; The disaster basic parameter set, reinforcement data set, disaster assessment results and disaster site status demand data set are used to construct a material allocation environment, and the rescue dispatch plan is analyzed and obtained, further comprising: Build a material allocation environment; Plan rescue routes based on available vehicles and obtain rescue route plans; Rescue mission allocation, obtaining rescue mission queue; Formulate rescue dispatch plans and obtain resource allocation queues; Using the rescue path plan, rescue task queue, and resource allocation queue as a rescue scheduling plan; The material allocation environment construction further includes: Based on the disaster assessment results and the data set of the disaster site status and needs, the SP formula is used to determine the rescue priority; Based on the disaster site status demand data set and the reinforcement data set, the UN formula is used to determine the urgency of material demand; The SP formula is as follows: SP(Di)=α*Sp(Di)+β*Sr(Di)+γ*Sin(Di)+δ*In(Di) Where Sp(Di) represents the quantitative value of the proportion of the affected population at the i-th disaster site, which is obtained by counting the population in the disaster area and comparing it with the total population; Sr(Di) represents the rescue accessibility of the i-th disaster site; Sin(Di) represents the quantitative value of the infrastructure condition of the i-th disaster site before the disaster, which is evaluated based on the type, quantity, and maintenance status of the infrastructure at the disaster site; In(Di) represents the interaction term between the various factors at the i-th disaster site; α, β, γ, and δ are the weight coefficients of each term, which are used to indicate the relative importance of each factor in determining the rescue priority. The UN formula is as follows: UN(Di)=Qmax(Ti)Qi+(1−Δtmax(Ti)Δti)+Scrit(Ti,Di)+In(Di) Where Qi represents the quantitative value of the demand at the i-th disaster site; Qmax(Ti) represents the maximum possible demand under the demand type Ti, which is used to normalize the demand; Δti represents the quantitative value of the time urgency of the i-th disaster site, which is based on the difference between the rescue arrival time and the disaster occurrence time; Δtmax(Ti) represents the maximum acceptable time urgency under the demand type Ti, which is used to normalize the time urgency; Scrit(Ti,Di) represents the criticality score of the demand type Ti at the i-th disaster site, which is based on the disaster type, the number of affected people, and the degree of infrastructure damage; In(Di) represents the interaction term between the various factors at the i-th disaster site; The rescue task allocation and obtaining of the rescue task queue further include: Assess the team's equipment type, quantity, personnel skill level, and available disaster sites based on reinforcement data sets; The TA formula is used to quantitatively prioritize rescue missions, and the rescue mission queues of each rescue team are generated based on the results. The TA formula is as follows: TA(Tk,Mj)=SPj+UNj+Pr(Tk,Mj) Among them, TA(Tk,Mj) represents the priority quantification value of rescue team Tk in executing rescue mission Mj; SPj and UNj represent the rescue priority and material demand urgency of disaster site j respectively; Pr(Tk,Mj) represents the matching degree between rescue team Tk and rescue mission Mj; Further including: Change the execution and monitoring of the plan to be used for drills, collect manually input disaster basic parameter sets, reinforcement data sets, disaster assessment results, and disaster site status demand data sets; Initiate drills and generate preliminary rescue dispatch plans; Collecting drill evaluation parameters, wherein the drill evaluation parameters are quantitative values ​​input by manual evaluation; Use the ES formula to digitize execution results and score the drills; Accept adjustments based on manual input to optimize the rescue dispatch plan formulation step; The drill results are adjusted and applied as the results of the post-reinforcement data collection and disaster site status demand data collection steps.

2. The intelligent method for meteorological disaster emergency response according to claim 1, characterized in that: The receiving and analyzing the disaster data set to obtain the disaster basic parameter set further includes: Receive disaster data in real time through sensor networks, weather monitoring stations, and manual reporting; Perform data cleaning, format conversion and outlier processing on the received disaster data; Analyze the pre-processed disaster data and extract basic disaster parameters such as disaster intensity, scope, and duration; The extracted basic disaster parameters are organized into a basic disaster parameter set as input for subsequent steps.

3. The intelligent method for meteorological disaster emergency response according to claim 1, characterized in that: The receiving of the disaster situation data set and analyzing to obtain the disaster site status demand data set and the disaster assessment result further includes: Receive disaster damage data; Analyze the received disaster data to determine the status information of the disaster site, wherein the status information of the disaster site includes the specific location, damage type, and damage extent; According to the status information of the disaster-stricken site, the state demand data set of the disaster-stricken site is analyzed and obtained, wherein the state demand data set of the disaster-stricken site includes the demand for rescue materials, rescue personnel, and medical equipment; Comprehensively evaluate the disaster by combining the basic disaster parameter set and the state information of the disaster-affected site to obtain a disaster assessment result; the disaster assessment result includes the severity of the disaster, the scope of impact, and the potential risks.

4. The intelligent method for meteorological disaster emergency response according to claim 1, characterized in that: The receiving of the reinforcement data set further comprises: Receive reinforcement data from multiple access departments, including the number of available rescue personnel, number of rescue vehicles, and the status of rescue material reserves; Integrate and process the received reinforcement data to ensure the accuracy and completeness of the data; Analyze the integrated reinforcement data, evaluate the currently available reinforcement forces and material reserves, and package the reinforcement types, quantities, mobilization time and mobilization feasibility as a reinforcement data set.

5. The intelligent method for meteorological disaster emergency response according to claim 1, characterized in that: The rescue dispatch plan formulation and resource allocation queue acquisition further include: The RR formula is used to calculate the remaining resources, analyze and prioritize the disaster-stricken areas, and obtain the rescue queue; Analyze the rescue queue and reinforcement data set to obtain a rescue evaluation value, use the OE formula to perform a comprehensive evaluation, and allocate resources through the AR formula to obtain a resource allocation queue; The RR formula is as follows: RR=TR−Di∈SortedList∑A(Di) Among them, RR represents the remaining resources, SortedList is the list of disaster-affected points sorted by priority, A(Di) is the amount of resources allocated to Di, and TR represents the total amount of resources; The OE formula is as follows: OE(Di)=w1*SP(Di)+w2*UN(Di)+w3* *RRImpact(Di) Among them, w1, w2, w3 are weight coefficients, and w1+w2+w3=1, TR represents the total resources, RR represents the remaining resources, and RR_Impact(Di) represents the impact of the remaining resources on the disaster point Di, which is determined by manual evaluation. When the impact cannot be determined, the value is 1 and does not affect the calculation results of other values; The AR formula is as follows: ； Among them, A(Di) represents the amount of resources that should be allocated to the disaster-stricken point Di, and OE(Di) represents the comprehensive assessment value of the disaster-stricken point Di, which is calculated by the OE formula. It represents all disaster-affected points, that is, the sum of the comprehensive evaluation values ​​of each disaster-affected point Dj in the DPs set. TR represents the total amount of resources. RRAll represents the remaining amount of resources that have been allocated so far. RRAdd(Di) represents the amount of resources additionally allocated to the disaster-affected point Di. This value is an indicator adjusted manually.

6. An intelligent system for emergency response to meteorological disasters, used to carry the intelligent method for emergency response to meteorological disasters according to any one of claims 1 to 5, characterized in that Includes the following units: A first receiving unit is configured to receive and analyze a disaster data set to obtain a disaster basic parameter set; The first computing unit is configured to receive a disaster situation data set, analyze and obtain a disaster site status demand data set and a disaster assessment result; The second receiving unit is used to receive a reinforcement data set, wherein the reinforcement data set is data from multiple access departments and is the current available reinforcement force and material reserves; The second computing unit is used to build a material allocation environment based on the disaster basic parameter set, reinforcement data set, disaster assessment results and disaster-affected site status demand data set, and analyze and obtain a rescue scheduling plan.

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