Meteorological disaster plan intelligent configuration method and system based on disaster census result

Abstract

The invention discloses a meteorological disaster plan intelligent configuration method and system based on a disaster census result, and relates to the field of meteorological disaster plan intelligentization, and the method comprises the following steps: receiving and analyzing a disaster data set, and obtaining a disaster basic parameter set; receiving the disaster condition data set, and analyzing to obtain a disaster point state demand data set and a disaster assessment result; a set of reinforcement data is received. The system mainly has the following beneficial effects: by receiving disaster data from a sensor network, a meteorological monitoring station and an artificial report and analyzing disaster situation data, the specific position, the damage type and the damage degree of a disaster site and information such as required relief supplies, personnel and medical equipment are mastered in detail; according to the method, disaster basic parameters and disaster-affected point state requirements are rapidly extracted, accurate data support is provided for subsequent material allocation, rescue path planning and rescue task distribution, and the timeliness and pertinence of emergency response are improved.

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 occur frequently, such as typhoons, rainstorms, droughts and other extreme weather events, which pose a serious threat to people's lives and property. As an important part of emergency response, the intelligent configuration system of meteorological disaster plans aims to improve the efficiency and accuracy of disaster response by integrating advanced information technology and intelligent algorithms, and provide strong support for disaster reduction and relief work.

[0003] Compared with the traditional existing manual plan-making method, the meteorological disaster plan intelligent configuration system has shown significant advantages in data processing, plan generation and real-time adjustment. Manual plans often rely on the experience and expertise of experts, and are completed through meeting discussions and manual formulation. This process is time-consuming and easily affected by human factors. The intelligent configuration system can automatically receive and process disaster data from sensor networks, meteorological monitoring stations and manual reports, and quickly generate preliminary plans through intelligent algorithms, greatly shortening the time for plan formulation.

[0004] The existing manual emergency plan formulation process usually includes the collection of disaster information, emergency meetings of expert teams, discussion and evaluation of the nature of the disaster, formulation of emergency plans based on historical experience and professional knowledge, and review and adjustment of emergency plans. Due to the limitations of manual operations and delays in information processing, this process often takes a long time and it is difficult to provide a comprehensive and accurate emergency response plan in the first place. Especially after a disaster occurs, time is tight and every minute of delay may bring immeasurable losses.

[0005] Compared with the intelligent configuration system for meteorological disaster plans, the existing manual plans have obvious deficiencies in the timeliness and pertinence of emergency response plans. Due to the time-consuming manual operation and the limitation of data processing capacity, the existing manual plans often fail to quickly generate effective emergency response plans after a disaster occurs, resulting in delays in rescue operations and waste of resources. In addition, the existing manual plans are also unable to cope with the real-time adjustment of plan planning. Since it is difficult for humans to comprehensively 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] The present application provides a method and system for intelligent configuration of 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, the reinforcement data set, the disaster assessment result and the disaster-affected point status demand data set, and analyzing to obtain a rescue dispatch 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 degree of damage, and the required rescue materials, personnel and medical equipment, etc., and quickly extracts the basic disaster parameters and the status requirements of the disaster site, providing accurate data support for subsequent material allocation, rescue route planning and rescue task allocation, thereby improving the timeliness and pertinence of emergency response.

[0010] 2. Based on fully receiving and analyzing the 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 reserve of rescue materials, and the status requirements of each disaster-stricken point, 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 A logic diagram of an intelligent configuration method for meteorological disaster emergency plans based on disaster census results of the present invention;

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

[0013] The present application provides a method and system for intelligent configuration of 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 the details in real-time adjustment of 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, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described here. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0015] Embodiment 1

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

[0017] S100: receiving and analyzing a disaster data set to obtain a disaster basic parameter set;

[0018] Receive disaster-related data from multiple channels and analyze and process these data to extract basic disaster parameters that are instructive for subsequent rescue work. The system will receive disaster data in real time from multiple sources. The sensor network includes meteorological sensors and geological sensors deployed in key geographical 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, rainfall, etc. At the same time, manual reports are also an important source of data, especially when disasters occur, on-site personnel can report the actual situation of the disaster through specific channels.

[0019] The S100 includes:

[0020] S110: Real-time reception of disaster data 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] Since disaster data comes from multiple different sources, their formats 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; outlier processing identifies and processes data points that differ significantly from normal data to ensure data accuracy and reliability.

[0026] For example, in flood disaster data, there may be data anomalies of some sensors, including missing data and numerical anomalies. 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 will conduct an in-depth analysis of the pre-processed disaster data to extract basic disaster parameters that are critical to subsequent rescue work. These parameters include but are not limited to the intensity, scope, and duration of the disaster. For example, in a flood disaster, the system can extract the highest water level and flooded area parameters of the flood by analyzing the data of the water level sensor; at the same time, by analyzing the data of the meteorological monitoring station, the duration and rainfall parameters of the flood can be obtained. These parameters will provide an important reference for subsequent rescue work.

[0028] S200: receiving a disaster situation data set, analyzing and obtaining a disaster point status demand data set and a disaster assessment result;

[0029] By receiving and analyzing disaster data, the specific status and needs of the disaster-stricken areas are determined, and an overall assessment of the disaster is conducted to provide a calculation basis 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-affected point, wherein the status information of the disaster-affected point includes specific location, damage type, and damage degree;

[0033] S230: Analyze and obtain a data set of state requirements of the disaster-affected points according to the state information of the disaster-affected points, wherein the data set of state requirements of the disaster-affected points includes requirements for rescue materials, rescue personnel, and medical equipment;

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

[0035] Receive disaster data from multiple channels. This data comes from on-site personnel, rescue teams, government departments, etc., including the specific location of the disaster site, damage, and casualties. This data is the basis for subsequent analysis and is crucial to determining the status and needs of the disaster site. For example, in an earthquake disaster, the system received reports from on-site personnel describing the collapse of houses in residential areas, casualties, and the types of relief supplies that are urgently needed. At the same time, the rescue team also provided the difficulties and needs they encountered during the rescue process.

[0036] The received disaster data is analyzed in depth to determine the specific location of the disaster site, the type of damage, and the extent of damage. For example, in an earthquake disaster, the system determines the specific location of a residential area, as well as the type and severity of damage to collapsed houses in the area by analyzing the received data.

[0037] According to the status information of the disaster site, further analysis is performed to obtain the status demand data set of the disaster site. These data sets include the required quantity and type of relief materials (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 determines the quantity and type of relief materials such as food, water, and medicine that are urgently needed in the area, as well as the required quantity and type of search and rescue teams and medical teams by analyzing the damage and casualties in a residential area.

[0038] The disaster basic parameter set (including the intensity, scope, duration, etc. of the disaster) and the status information of the disaster site are integrated to conduct an overall assessment of the disaster. The assessment results include the severity of the disaster (including minor, moderate, severe, etc.), the scope of impact (including the size of the disaster area, the number of people affected, etc.) and the potential risks (including the possibility of secondary disasters, etc.).

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

[0040] Receive reinforcement data from multiple access departments, and integrate, analyze and evaluate the 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 reserve of rescue materials;

[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] Receive reinforcement data from multiple access departments, including but not limited to fire departments, medical departments, material reserve centers, etc. These data include the number of available rescue personnel, the number of rescue vehicles, and the reserve of rescue materials. These data are the basis for subsequent analysis and evaluation, and are crucial to determining the availability of reinforcement forces and material reserves. For example, in a flood disaster, the system received data on the number of available rescue personnel and rescue vehicles from the fire department; received data on the number of medical rescue teams and the reserve of medical equipment from the medical department; and received data on the reserve of rescue materials such as food, water, and medicine from the material reserve center.

[0046] The received reinforcement data is integrated and processed to ensure the accuracy and completeness of the data. 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 reinforcement. The system packages information such as the types, quantities, mobilization time and feasibility of reinforcement 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 is sufficient and the number of rescue vehicles is 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 reinforcement (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 point status demand data set, and analyzing and obtaining a rescue dispatch plan.

[0049] Based on the basic disaster parameters, reinforcement data, disaster assessment results and disaster-affected site status demand data, a comprehensive material allocation environment is constructed, and on this basis, the optimal rescue dispatch plan is analyzed and obtained. This step is reasonable for improving rescue efficiency and ensuring that rescue resources are reasonably allocated.

[0050] The S400 includes:

[0051] S410: Build a material allocation environment;

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

[0053] S430: Allocate rescue tasks and obtain a rescue task queue;

[0054] S440: formulate a rescue dispatch plan and obtain a 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, severity of the disaster, 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, the number of rescue vehicles, and the amount of rescue material reserves), disaster assessment results (including the number of collapsed houses caused 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 needs, and the type of medical equipment needs), to build a comprehensive material allocation computing environment.

[0057] The system will plan the rescue route based on the available rescue vehicles, road conditions, traffic control and other factors. The system will comprehensively consider factors such as the time consumption, safety, and feasibility of the rescue route, generate multiple rescue route plans, and select the best one from them. For example, in an earthquake disaster, the system plans the rescue route based on the number and type of available rescue vehicles, the road damage caused by the earthquake, and traffic control information. The system generates multiple rescue route plans, and comprehensively considers factors such as the time consumption, safety, and feasibility of the route, and finally selects the rescue route plan with the shortest time consumption and the highest safety.

[0058] The S410 includes:

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

[0060] S412: 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;

[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] Among them, 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-stricken 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-stricken point, based on the difference between the arrival time of rescue and the time of disaster occurrence; 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-stricken point, based on the disaster type, the number of affected people, and the degree of damage to infrastructure; In(Di) represents the interaction term between the various factors at the i-th disaster-stricken point.

[0067] Based on the disaster assessment results and the data set of the state and demand of the disaster-stricken points, a specific formula is used to determine the rescue priority and the urgency of material needs. The SP formula is used to evaluate the rescue priority of each disaster-stricken point. This formula takes into account the proportion of the affected population, rescue accessibility, pre-disaster infrastructure conditions, and the interaction between various factors. For example, in a flood disaster, we have a disaster point A with a population proportion of 30%, because the roads are not damaged and the rescue accessibility is high, because the area is frequently maintained and the pre-disaster infrastructure is in good condition. Based on these factors, we can calculate the rescue priority of point A.

[0068] The UN formula is used to assess the urgency of the material 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 the 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, and the criticality score is also high because there are a large number of residents and critical infrastructure in the area. Based on these factors, we can calculate the urgency of the material needs at site A: UN(A)

[0069] The S430 includes:

[0070] S431: Evaluate the team's equipment types, quantity, personnel skill levels, 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 according to the result;

[0072] Among them, 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 the rescue team Tk in performing the rescue task Mj; SPj and UNj represent the rescue priority and the urgency of material demand of the disaster site j respectively; Pr(Tk,Mj) represents the matching degree between the rescue team Tk and the rescue task Mj.

[0075] According to the reinforcement data set, the types of tools, quantity, skill level of personnel and the need for accessible disaster sites of the rescue team are evaluated, and the TA formula is used to quantitatively arrange the priorities of rescue task allocation.

[0076] Based on the information in the reinforcement data set, evaluate the types of tools, quantity, personnel skill level, and reachable disaster site requirements of each rescue team. For example, in a flood disaster, we have a rescue team B, which has sufficient rescue vehicles and personnel, and the personnel skill level is high. Based on this information, we can determine that team B can reach and meet the needs of disaster site A.

[0077] The TA formula is used to calculate the priority quantification value of each rescue team in performing each rescue mission. This formula takes into account the rescue priority of the disaster site, the urgency of material needs, and the matching degree between the rescue team and the rescue mission. For example, in a flood disaster, we can calculate the priority quantification value TA(B,A) of rescue team B in performing the rescue mission, that is, reaching and meeting the needs of disaster site A. Based on this value, we can calculate the best 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-affected locations, and obtain the rescue queue;

[0080] S442: Analyze the rescue queue and the reinforcement data set to obtain a rescue evaluation value, use the OE formula to perform a comprehensive evaluation, and use the AR formula to allocate resources 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] Among them, the OE formula is as follows:

[0085]

[0086] 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.

[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-affected point Di, OE(Di) represents the comprehensive evaluation value of the disaster-affected 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 resource volume, RRAll represents the remaining resources that have been allocated so far, and RRAdd(Di) represents the additional resources 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 disaster-affected locations based on their rescue priorities and the urgency of material needs.

[0091] The OE formula is used to comprehensively evaluate 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 evaluation value and the total amount of resources. For example, in a flood disaster, we can calculate the comprehensive evaluation value OE(A) of the disaster site A, and allocate resources to point A based on this value as well as the total amount of resources and the amount of resources that have been allocated. When point A has a greater demand and a higher comprehensive evaluation value, it will receive more resource allocation.

[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 exercise evaluation parameters, wherein the exercise evaluation parameters are quantitative values ​​input by manual evaluation;

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

[0097] S550: accepting manual input adjustment for optimizing 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 execution and monitoring environment of the program to a mode suitable for the drill and collect necessary input data. Set the system to the drill mode so that the scheduling plan can be tested and optimized without actually triggering the rescue operation. Next, manually input the set of basic disaster parameters, the set of reinforcement data, the disaster assessment results, and the set of state demand data of the disaster-affected points. These data are the basis for formulating a preliminary rescue scheduling plan. For example: In a simulated flood disaster drill, we input the basic parameters of the disaster (including flood depth, flow rate), reinforcement data (including the location, number of people, equipment, etc. of the rescue team), disaster assessment results (including the area of ​​the disaster-affected area, population distribution, etc.), and the state demand data of the disaster-affected points (including emergency evacuation needs, medical assistance needs, etc.). Collect the evaluation parameters during the drill to evaluate the results of the drill. During the drill, record and collect various evaluation parameters, including the response time of the rescue team, the speed of reaching the disaster site, and the efficiency of the distribution of rescue materials. These parameters are manually judged and input quantitative values. For example, in a flood disaster drill, the response time of the rescue team, the speed of reaching the disaster site, and the efficiency of the distribution of relief supplies were recorded. The ES formula was used to digitally score the execution results of the drill. This score reflects the overall effect of the drill and is used to evaluate the success or failure of the drill.

[0100] After the drill, we will collect manually inputted adjustment opinions, including the allocation adjustment of rescue teams, optimization of rescue routes, and distribution strategies of rescue materials. Then, these opinions are taken into consideration to optimize the preliminary rescue dispatch plan. Example: After the flood disaster drill, manual input of adjustment opinions pointed out that the allocation of some rescue teams was not reasonable enough and that some rescue routes were at risk of congestion. Based on these opinions, the preliminary rescue dispatch plan was optimized to adjust the allocation of rescue teams and the planning of rescue routes.

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

[0102] The present application also includes an intelligent system for emergency response to meteorological disasters, which is used to carry the intelligent method for emergency response to meteorological disasters. The system includes:

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

[0104] The first computing unit is used to receive a disaster situation data set, analyze and obtain a disaster point 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 comes from data of multiple access departments and is the current available reinforcement force and material reserve situation;

[0106] The second computing unit is used to construct 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 analyze and obtain a rescue dispatch plan.

[0107] The system further comprises 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 digital 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 a sequence of precedence. "And / or" describes the association relationship of 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 with each other 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, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part 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 process or function described in this application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[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 unit can be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, register, 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 a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0111] Although the present application has been described in conjunction with specific features and embodiments thereof, it is obvious that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the accompanying drawings are merely exemplary illustrations of the present application and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. An intelligent method for emergency response to meteorological disasters, characterized in that: The following steps are involved: Receive and analyze disaster data sets and 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 set, where the reinforcement data set comes from data from multiple access departments, which is the current available reinforcement force and material reserve situation; Based on the disaster basic parameter set, reinforcement data set, disaster assessment results and disaster-affected point status demand data set, a material allocation environment is constructed to analyze and obtain a rescue dispatch plan.

2. The intelligent method for emergency response to meteorological disasters according to claim 1 is 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 reports; Perform data cleaning, format conversion and outlier processing on the received disaster data; Analyze the pre-processed disaster data to 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 emergency response to meteorological disasters according to claim 1 is 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 degree; According to the status information of the disaster-affected point, the state demand data set of the disaster-affected point is analyzed and obtained, wherein the state demand data set of the disaster-affected point includes the demand for rescue materials, rescue personnel, and medical equipment; The disaster basic parameter set and the state information of the disaster-affected points are comprehensively evaluated 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 emergency response to meteorological disasters according to claim 1 is characterized in that: The receiving of 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 emergency response to meteorological disasters according to claim 1 is characterized in that: 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 solutions; Rescue mission allocation, obtain rescue mission queue; Formulate rescue dispatch plans and obtain resource allocation queues; The rescue path plan, rescue task queue, and resource allocation queue are used as a rescue scheduling plan.

6. The intelligent method for emergency response to meteorological disasters according to claim 5 is characterized in that: The material allocation environment construction further includes: Based on the disaster assessment results and the data set of the disaster site status requirements, 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) 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; Among them, the UN formula is as follows: UN(Di)=Qmax(Ti)Qi+(1-Δtmax(Ti)Δti)+Scrit(Ti,Di)+In(Di) Among them, Qi represents the quantitative value of demand at the i-th disaster-stricken 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-stricken point, based on the difference between the arrival time of rescue and the time of disaster occurrence; 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-stricken point, based on the disaster type, the number of affected people, and the degree of damage to infrastructure; In(Di) represents the interaction term between the various factors at the i-th disaster-stricken point.

7. The intelligent method for emergency response to meteorological disasters according to claim 5 is characterized in that: 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 the reinforcement data set; The TA formula is used to quantitatively arrange the priority of rescue task allocation, and the rescue task queue of each rescue team is generated based on the results; Among them, the TA formula is as follows: TA(Tk,Mj)=SPj+UNj+Pr(Tk,Mj) Among them, TA(Tk,Mj) represents the quantitative value of the priority of the rescue team Tk in performing the rescue task Mj; SPj and UNj represent the rescue priority and the urgency of material demand of the disaster site j respectively; Pr(Tk,Mj) represents the matching degree between the rescue team Tk and the rescue task Mj.

8. The intelligent method for emergency response to meteorological disasters according to claim 5 is characterized in that: The rescue dispatch plan formulation and obtaining of resource allocation queue further include: The RR formula is used to calculate the remaining resources, analyze and prioritize the disaster-affected locations, 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 use the AR formula to allocate resources 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. Among them, the OE formula is as follows: 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-affected point Di, OE(Di) represents the comprehensive evaluation value of the disaster-affected 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 resource volume, RRAll represents the remaining resources that have been allocated so far, and RRAdd(Di) represents the additional resources allocated to the disaster-affected point Di. This value is an indicator adjusted manually.

9. The intelligent method for emergency response to meteorological disasters according to claim 1, characterized in that: 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 exercise evaluation parameters, wherein the exercise evaluation parameters are quantitative values ​​input by manual evaluation; Use the ES formula to digitize the 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.

10. According to the intelligent method for emergency response to meteorological disasters as described in claim 1, a meteorological disaster emergency response intelligent system is also provided, which is used to carry the intelligent method for emergency response to meteorological disasters, and is characterized in that The following units are included: A first receiving unit is used to receive and analyze a disaster data set to obtain a disaster basic parameter set; The first computing unit is used to receive a disaster situation data set, analyze and obtain a disaster point 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 comes from data of multiple access departments and is the current available reinforcement force and material reserve situation; The second computing unit is used to construct 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 analyze and obtain a rescue dispatch plan.

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