Object list data processing method for urban extreme weather pressure test

Abstract

The invention discloses an object list data processing method for an urban extreme weather pressure test. Comprising the following steps: clearly defining the type and identification standard of extreme weather, collecting and classifying various types of acceptor objects which are possibly influenced by the extreme weather in a city, and allocating unique types and codes to the acceptor objects; the attributes of the objects are subdivided, and corresponding coding systems are established; according to the coding result of each object, a risk function is set to predict the risk probability of each object under the extreme weather condition; acquiring related extreme weather data, and matching the related extreme weather data with the coding result of the acceptor object; applying the data to a risk function to obtain a pressure test result of each object; in addition, the economic value of each object is coded, and the overall economic loss estimation is calculated in combination with a pressure test result; and finally, performing precision optimization on the risk function according to a test result. The technical defect of insufficient accuracy of risk assessment in urban extreme weather risk management in the prior art is solved.

Description

[0001] This application is a divisional application of patent number "202410457254X", application date "2024-04-16", and name "An object-oriented urban extreme weather stress testing method". Technical Field

[0002] The invention relates to the field of meteorology, and in particular to an object list data processing method for urban extreme weather stress testing. Background Art

[0003] Against the backdrop of global climate change, the increase in extreme weather events has become a real problem that cannot be ignored. As a region with a dense population and concentrated economic activities, cities are particularly important for their resilience and ability to cope with extreme weather. However, the existing urban emergency management system often shows certain fragility and shortcomings when facing extreme weather.

[0004] Although existing numerical weather forecasting technology has made progress in providing short-term forecasts, its accuracy and reliability still have significant limitations when it comes to medium- and long-term forecasts. This technical shortcoming makes it difficult for city managers to predict and assess the risks of extreme weather events, causing cities to often adopt passive defense strategies in the face of extreme weather, mainly relying on near-occurrence or post-event processing. This passivity not only causes inefficient and wasteful resource allocation, but also fails to provide sufficient protection measures to ensure the safety of citizens' lives and property when extreme weather actually occurs. Summary of the invention

[0005] The purpose of the present invention is to solve the above technical problems and provide an object list data processing method for urban extreme weather stress testing.

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

[0007] The present invention provides an object list data processing method for urban extreme weather stress testing, characterized in that the method comprises:

[0008] Define the criteria for determining extreme weather in cities;

[0009] Acquire first receptor object information of urban extreme weather, wherein the first receptor object information includes a first receptor object category and a first receptor object code, and the first receptor object category and the first receptor object code correspond one to one;

[0010] Subdividing the first receptor object category to obtain a plurality of first receptor object attributes and first receptor object attribute codes, wherein the first receptor object attributes and the first receptor object attribute codes correspond to each other one by one;

[0011] Encode each of the first receptor object codes according to a coding rule formula, and obtain a plurality of first receptor object coding results accordingly;

[0012] According to each first receptor object coding result, a corresponding risk function is set when each first receptor object coding result is impacted by extreme weather, and the risk function is used to predict the probability value of the corresponding first receptor object coding result having a risk under the corresponding extreme weather;

[0013] Acquire extreme weather data, and select and obtain a plurality of specific extreme weather data according to the urban extreme weather discrimination standard, wherein the specific extreme weather data corresponds to the first receptor object coding result one by one;

[0014] Substituting the specific extreme weather data into the risk function corresponding to the first receptor object coding result to obtain the first receptor object stress test result;

[0015] Encode each first receptor object encoding result according to the economic attribute encoding formula to obtain the corresponding first receptor object economic value result;

[0016] The total economic loss of the stress test is calculated based on the stress test results of the first receptor object and the economic value results of the first receptor object;

[0017] According to the stress test result of the first receptor object, the accuracy of the risk function corresponding to the encoding result of the first receptor object is optimized.

[0018] Preferably, the extreme weather in the city includes extremely high temperature weather, extremely low temperature weather, strong wind weather and heavy rainfall weather; the criterion for judging the extreme high temperature is that when the temperature data is higher than 0.95 times the highest temperature recorded in the city's history, it is defined as extremely high temperature; the criterion for judging the extreme low temperature is that when the temperature data is lower than 0.95 times the lowest temperature recorded in the city, it is defined as extremely low temperature; the criterion for judging the strong wind is that when the wind speed data is higher than 20m / s, it is defined as strong wind weather; the criterion for judging the heavy rainfall is that when the rainfall data is higher than 300mm / d, it is judged as heavy rainfall.

[0019] Preferably, the first receptor object category of urban extreme weather includes people, buildings, trees, underground spaces, low-lying and flood-prone ground, rivers, reservoirs, water supply and drainage facilities and the like.

[0020] Preferably, the encoding rule formula includes:

[0021]

[0022] Wherein, O represents the first receptor object code, x represents the longitude of the first receptor object code, y represents the latitude of the first receptor object code, z represents the ground height of the first receptor object code, t represents the time information of the first receptor object code performing the pressure test, i represents the serial number coding information of the first receptor object code, and k represents the first receptor object attribute code;

[0023] The encoding results of the plurality of first receptor objects are stored in a database.

[0024] Preferably, the risk function comprises:

[0025]

[0026] in, represents the probability value of the extreme weather risk occurring within time t for the first receptor object coded as i, k represents the attribute code of the first receptor object, w t A physical quantity representing extreme weather at time t.

[0027] Preferably, setting a corresponding risk function for each of the first receptor object coding results when being impacted by extreme weather comprises the following steps: determining the sensitivity of the first receptor object coding result to different types of extreme weather according to the attributes of the first receptor object coding result; acquiring historical data, and determining the physical quantity of extreme weather that affects the first receptor object coding result according to historical data analysis; and setting a corresponding risk function for each of the first receptor object coding results when being impacted by extreme weather according to the sensitivity and the physical quantity.

[0028] Preferably, the economic attribute coding formula includes:

[0029]

[0030] The economic value results of the first recipient object are stored in a database.

[0031] Preferably, the stress test result of the first receptor object and the economic value result of the first receptor object are substituted into the total economic loss formula of the stress test to calculate the total economic loss of the stress test;

[0032] The formula for the total amount of economic losses from the stress test includes:

[0033]

[0034] Wherein, LY represents the total economic loss of the stress test, m represents the number of coding results of the first receptor object, and t represents the time of the stress test.

[0035] Preferably, the accuracy of the risk function is optimized using a Bayesian formula, the Bayesian formula comprising:

[0036]

[0037] Among them, A represents an event in which the risk of the first receptor object coding result actually occurs, and B represents an event in which the probability value of the extreme weather risk of the first receptor object coding result exceeds the risk warning line.

[0038] Preferably, the step of optimizing the accuracy of the risk function corresponding to the encoding result of the first receptor object includes: acquiring historical data;

[0039] By statistically analyzing historical data, the event of the actual risk of the first receptor object coding result is defined as A;

[0040] Set a risk warning line, and define the event B when the probability value of the extreme weather risk caused by the coding result of the first receptor object exceeds the risk warning line;

[0041] Determine the number of coded results for the first recipient subjects who received stress testing;

[0042] Construct a Bayesian formula and calculate the value of the risk warning line through the Bayesian formula;

[0043] Verify that the risk functions corresponding to the encoding results of all first receptor objects are convergent.

[0044] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention provides an object list data processing method for urban extreme weather stress testing, including defining urban extreme weather discrimination criteria; obtaining first receptor object information of urban extreme weather, the first receptor object information including a first receptor object category and a first receptor object code, the first receptor object category and the first receptor object code corresponding one to one; subdividing the first receptor object category to obtain multiple first receptor object attributes and first receptor object attribute codes, the first receptor object attributes and the first receptor object attribute codes corresponding one to one; encoding each of the first receptor object codes according to the encoding rules, and correspondingly obtaining multiple first receptor object encoding results; according to each first receptor object encoding result, correspondingly setting each of the first receptor object encoding results to be affected by extreme weather A risk function is provided when the risk function is used to predict the probability value of the risk of the corresponding first receptor object coding result under the corresponding extreme weather; extreme weather data is obtained, and a plurality of specific extreme weather data are obtained by screening according to the urban extreme weather discrimination standard, and the specific extreme weather data corresponds to the first receptor object coding result one by one; the specific extreme weather data is substituted into the risk function corresponding to the first receptor object coding result to obtain the first receptor object stress test result; each first receptor object coding result is encoded according to the economic attribute coding formula to obtain the first receptor object economic value result; the total amount of economic loss of the stress test is calculated according to the first receptor object stress test result and the first receptor object economic value result; according to the first receptor object stress test result, the risk function corresponding to the first receptor object coding result is optimized.

[0045] The present invention can more accurately evaluate the impact of extreme weather on the city by defining clear extreme weather discrimination criteria and classifying and encoding the first receptor object information in detail, thereby improving the accuracy of risk assessment. By subdividing and encoding the categories of the first receptor objects, multiple first receptor object attributes and first receptor object attribute codes are obtained, and then each first receptor object code is encoded using the first receptor object attribute code and the encoding rule formula to obtain multiple first receptor object encoding results, so that city managers can perform refined management on each specific object in the city and set a specific risk function for each object, thereby achieving more targeted risk prevention and response. By calculating the economic value and potential total economic loss of the first receptor object encoding results, it can help city managers understand the economic impact that extreme weather events may cause, so as to more reasonably allocate resources and formulate emergency budgets. Finally, the present invention obtains stress test results by substituting extreme weather data into the risk function, which helps city managers adjust and optimize response strategies according to the test results and improve the effectiveness of response measures. The shortcomings of the existing technology in urban extreme weather risk assessment and management are solved, the response efficiency and resilience of the city are improved, and the negative impact of extreme weather is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The specific embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings, wherein:

[0047] Figure 1 is a flow chart of an object list data processing method for urban extreme weather stress testing according to an embodiment of the present invention;

[0048] Figure 2 It is a flowchart of the steps of setting a risk function in a method for processing object list data for urban extreme weather stress testing according to an embodiment of the present invention;

[0049] Figure 3 It is a flow chart of the steps of optimizing the accuracy of risk function in an object list data processing method for urban extreme weather stress testing according to an embodiment of the present invention;

[0050] Figure 4 It is a schematic diagram of the first recipient object information in the object list data;

[0051] Figure 5 It is a schematic diagram of a partial breakdown of the first recipient object category in the object inventory data. DETAILED DESCRIPTION

[0052] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0053] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0054] With the rapid development of urbanization, the impact of extreme weather events on cities has become increasingly prominent. In the long-term urban planning and management practice, the applicant of this patent has deeply realized the shortcomings of the existing urban emergency management system in the face of extreme weather. Especially after a series of extreme weather events, the applicant found that although the city has taken a variety of response measures, due to the lack of accurate prior risk assessment, these measures often fail to achieve the expected results, and even cause a waste of resources in some cases.

[0055] The applicant has discovered a core problem through in-depth analysis of the existing urban emergency management system and numerical weather forecasting technology: the passive defense state of cities in the face of extreme weather. This state stems from the inaccurate prediction and assessment of the impact of extreme weather, as well as the inadequate classification and risk assessment of urban receptor objects. These problems have led to cities often being able to only take immediate or post-event measures when extreme weather strikes, lacking effective prevention and preparation measures.

[0056] In order to solve this problem, the applicant conducted extensive research and exploration. During the research process, the applicant found that by classifying and coding various receptor objects in the city in detail and combining them with extreme weather data, a refined stress testing model can be constructed. This model can predict the risk probability of various receptor objects under different extreme weather scenarios, thereby providing city managers with more accurate risk assessment and response strategies. The applicant further studied and created the urban extreme weather stress testing method of the present invention.

[0057] like Figure 1 As shown, an object list data processing method for urban extreme weather stress testing according to an embodiment of the present invention is characterized in that the method comprises:

[0058] Step 101: define the urban extreme weather identification criteria.

[0059] The extreme weather in the city includes extremely high temperature weather, extremely low temperature weather, strong wind weather and heavy rainfall weather; the criteria for judging extremely high temperature is that when the temperature data is higher than 0.95 times of the highest temperature recorded in the city's history, it is defined as extremely high temperature; the criteria for judging extremely low temperature is that when the temperature data is lower than 0.95 times of the lowest temperature recorded in the city, it is defined as extremely low temperature; the criteria for judging strong wind is that when the wind speed data is higher than 20m / s, it is defined as strong wind weather; the criteria for judging heavy rainfall is that when the rainfall data is higher than 300mm / d, it is judged as heavy rainfall.

[0060] It is important to note that in this link, based on the analysis of historical meteorological data, a series of quantitative thresholds are set to clearly define what constitutes an urban extreme weather event. These criteria provide a clear basis for the identification and classification of urban extreme weather, making the interpretation of meteorological data more objective and consistent, thus providing a solid foundation for subsequent risk assessment and management decisions.

[0061] In this embodiment, by defining the criteria for distinguishing extreme weather, potential extreme weather events can be quickly identified, providing timely warning information for city managers. When conducting stress testing, meteorological data related to extreme weather can be screened out according to these criteria to ensure the accuracy and relevance of the test. When setting risk functions for various receptor objects in the city (such as buildings, infrastructure, residential areas, etc.), accurate extreme weather data is required as input, and these criteria provide the necessary data support. Understanding the specific types and extent of extreme weather can help city managers develop more accurate and effective emergency preparedness measures.

[0062] Step 102: Acquire first receptor object information of urban extreme weather, wherein the first receptor object information includes a first receptor object category and a first receptor object code, and the first receptor object category and the first receptor object code correspond one to one.

[0063] like Figure 4 As shown, in the object list data of the present invention, the first receptor object category of the urban extreme weather includes people, buildings, trees, underground space, low-lying and flood-prone ground, rivers, reservoirs, water supply and drainage facilities, and each receptor object category has a corresponding code, namely the first receptor object code. It should be noted that in some embodiments, the first receptor object type includes but is not limited to the facilities mentioned in this embodiment.

[0064] In this phase, various objects in the city that may be affected by extreme weather are classified and coded to facilitate the systematic management and analysis of the vulnerability and risk level of these objects in extreme weather events. The purpose of this step is to ensure that each receptor object can be accurately identified and tracked, so that risk assessment and response measures are more targeted and effective.

[0065] In this embodiment, by classifying and coding various receptor objects in the city, a detailed risk profile can be established for each object to assess the damage they may suffer under different extreme weather scenarios. Understanding the characteristics and location information of various receptor objects will help city planners develop more accurate emergency plans to ensure that measures can be taken quickly and effectively to protect the most vulnerable groups and critical infrastructure when extreme weather occurs. Clear receptor object information will help city managers to reasonably allocate emergency resources, such as deploying flood control measures in advance in low-lying areas susceptible to floods, or strengthening structural safety in old buildings.

[0066] The benefit of this is that, through systematic classification and coding, the risks of various receptor objects can be quickly identified and responded to, improving the risk management efficiency of the entire city. Clear receptor object information makes emergency response more targeted, and specific response measures can be formulated according to the characteristics and needs of different objects. Accurate risk assessment and object classification help avoid wasting emergency resources on unnecessary areas or objects, ensuring that resources are used most effectively.

[0067] Step 103: subdivide the first receptor object category to obtain a plurality of first receptor object attributes and first receptor object attribute codes, wherein the first receptor object attributes and the first receptor object attribute codes correspond one to one.

[0068] In this phase, based on the first receptor object categories of urban extreme weather that have been defined and classified, each category is further subdivided to more finely characterize the specific attributes of each receptor object. The purpose of this step is to ensure that each receptor object can be accurately identified and analyzed according to its specific attributes, so that risk assessment and response measures are more targeted and accurate.

[0069] like Figure 5As shown, in this embodiment, by subdividing the attributes of the first receptor object category, the potential impact of extreme weather on each specific object can be more accurately assessed, for example, differentiating the risk differences of different age groups (infants, teenagers, young people, etc.) under extreme high temperatures. The subdivided receptor object attribute information helps to formulate more customized emergency response measures. For example, special reinforcement measures may be required for old buildings, while different protection strategies may be required for new buildings. Understanding the specific attributes of the receptor objects helps to allocate emergency resources more effectively. For example, wind protection facilities can be deployed in advance in high-rise building areas that are susceptible to strong winds, and drainage systems can be strengthened in flood-prone areas.

[0070] The benefit of this is that the subdivided receptor object attributes make risk assessment more refined and can more accurately predict the damage that extreme weather events may cause. Targeted resource allocation can avoid wasting resources on unnecessary areas or objects and ensure that resources are used most effectively. Detailed receptor object information provides city managers with more data support, which helps them make more scientific and reasonable decisions.

[0071] Step 104: Encode each of the first receptor object codes according to a coding rule formula, and obtain a plurality of first receptor object coding results accordingly.

[0072] The encoding rule formula includes:

[0073]

[0074] Wherein, O represents the first receptor object code, x represents the longitude of the first receptor object code, y represents the latitude of the first receptor object code, z represents the ground height of the first receptor object code, t represents the time information of the first receptor object code performing the pressure test, i represents the serial number coding information of the first receptor object code, and k represents the first receptor object attribute code;

[0075] The encoding results of the plurality of first receptor objects are stored in a database.

[0076] It should be noted that the first receptor object coding result is a unique identifier for each specific receptor object in the city. It integrates the spatial location, time information and attribute characteristics of the object to form a detailed, object-specific coding system. This coding not only provides a clear "identity" for each receptor object, but also ensures that each object can be accurately identified and referenced in subsequent risk assessments and management decisions.

[0077] The subsequent risk function setting is based on these specific first receptor object encoding results.

[0078] During the entire extreme weather stress test operation, all receptor object information and analysis involved are centered around the first receptor object coding result. Whether it is data collection, risk assessment, resource allocation or emergency response plan formulation, this coding result will be referenced to ensure that each decision and measure is designed for the actual situation of the specific object. This approach improves the accuracy and effectiveness of the entire stress test process, allowing cities to respond to the challenges brought by extreme weather in a more scientific and systematic manner.

[0079] In this step, the receptor objects in the city are coded with unique identification, and finally the first receptor object coding result is obtained to facilitate effective data management and analysis in extreme weather stress testing. This coding rule integrates the spatial location information (longitude, latitude, ground height) and time information of the receptor object, as well as the serial number and attribute information of the receptor object, to ensure that each object can be accurately identified and tracked in the database.

[0080] Through coding, data from different sources and types can be integrated into a unified database for easy storage, retrieval and analysis. When conducting extreme weather stress testing, coding allows the selection of specific first receptor object coding results for testing, that is, coding allows the selection of specific receptor objects or samples for testing, improving the pertinence and efficiency of the test. The coded first receptor object coding results can be used in risk analysis models to assess the risk levels of different receptor objects under extreme weather conditions.

[0081] The benefit of this is that unified coding rules simplify the data management process and improve the speed and efficiency of data processing. Accurate coding ensures the accuracy of data analysis and makes risk assessment results more reliable. Through accurate coding of receptor objects, monitoring and protection resources can be allocated more reasonably to ensure that key areas and objects receive sufficient attention. The unified coding system promotes information sharing and collaboration between different departments and improves the synergy of the entire urban emergency management system.

[0082] Step 105: According to each first receptor object coding result, a corresponding risk function is set for each first receptor object coding result when it is impacted by extreme weather, and the risk function is used to predict the probability value of the risk of the corresponding first receptor object coding result under the corresponding extreme weather.

[0083] The risk function includes:

[0084]

[0085] in, represents the probability value of the extreme weather risk occurring within time t for the first receptor object coded as i, k represents the attribute code of the first receptor object, w t A physical quantity representing extreme weather at time t.

[0086] It should be noted that the risk function is set based on the first receptor coding result. The risk function is a mathematical model used to evaluate and predict the risk level that a specific receptor may face under extreme weather conditions. Since each receptor has unique characteristics and environment, the risk function set for each object will also be customized based on the detailed information in its coding results to reflect its unique risk exposure.

[0087] In this phase, a risk function is set for each first receptor object based on the coding results. The risk function is used to predict the probability of the receptor object facing risk under specific extreme weather conditions. The setting of the risk function takes into account the properties of the receptor object and its sensitivity to extreme weather, as well as the physical quantities of extreme weather, such as temperature, rainfall, wind speed, etc. A risk function is established for each receptor object, which can calculate the risk probability based on the physical quantities of extreme weather. Based on the risk prediction results, emergency preparedness measures are formulated and adjusted to mitigate the possible impacts of extreme weather.

[0088] The benefit of this is that by setting a specific risk function for each receptor object, the impact of extreme weather on it can be predicted more accurately. Accurate risk prediction helps to allocate emergency resources more effectively and ensure that key areas and objects are given priority protection. It enables cities to more accurately assess and manage the risks brought by extreme weather, thereby improving the city's response capacity and overall resilience.

[0089] Correspondingly setting the risk function when each encoding result of the first receptor object is impacted by extreme weather, comprises the following steps:

[0090] Step 1051, determining the sensitivity of the first receptor object encoding result to different types of extreme weather according to the attribute of the first receptor object encoding result;

[0091] Analyze the properties of the first receptor object encoding results to determine its sensitivity to different types of extreme weather. This step involves in-depth understanding of the receptor object's physical structure, location, function and other attributes to assess their potential impact under extreme weather conditions.

[0092] Step 1052: Obtain historical extreme weather and disaster data, and determine the extreme weather physical quantity that affects the coding result of the first receptor object based on historical data analysis;

[0093] Obtain and analyze historical data to determine which physical quantities of extreme weather (such as wind speed, rainfall, temperature, etc.) have a significant impact on the encoding results of the first receptor object. This helps to understand the impact of similar weather events in the past on the receptor object, and thus predict the potential risks of future events. For example, for the risk of falling objects from high altitudes, what needs to be considered is the instantaneous wind pressure at the receiving point of the falling object receptor object, and the input of extreme weather data is the wind speed at the receiving point of the receptor object. For the risk of reservoir water level, what needs to be considered is the catchment area, runoff coefficient and slope ratio of the reservoir receptor object, and the input of extreme weather data is the grid data of rainfall in the catchment area.

[0094] Step 1053: According to the sensitivity and the physical quantity, a risk function is correspondingly set when the encoding result of each of the first receptor objects is impacted by extreme weather.

[0095] The risk function is set by combining the sensitivity of the coding results of the first receptor object and the extreme weather physical quantities that affect them. This function will be used as an evaluation tool to predict the risk probability of the receptor object under specific extreme weather conditions.

[0096] By setting a dedicated risk function for each receptor object, risks can be identified and quantified more accurately. By preventing and mitigating the impact of extreme weather events, economic losses and recovery costs can be significantly reduced.

[0097] Step 106: Acquire extreme weather data, and select a plurality of specific extreme weather data according to the urban extreme weather discrimination standard, wherein the specific extreme weather data corresponds to the first receptor object coding result one by one;

[0098] The purpose of this link is to ensure that the coding results of each first receptor object can be used for accurate risk assessment under extreme weather conditions, and it is necessary to obtain the corresponding specific extreme weather data. These data include forecast data, measured data and preset data, which are obtained in different ways: forecast data comes from numerical forecast models, measured data comes from real-time or historical records of monitoring stations, and preset data is artificially set according to specific scenarios.

[0099] Before conducting stress testing, relevant extreme weather data must be collected to match the receptor object coding results and provide input values ​​for the calculation of the risk function. By combining extreme weather data with the first receptor object coding results, the specific impact of extreme weather events on the city can be analyzed in a simulated environment. Understanding the risk level of different receptor objects under specific extreme weather conditions will help to rationally allocate emergency resources such as personnel, materials and equipment.

[0100] The benefit of this is that by understanding the risks of each receptor object under specific extreme weather conditions, more targeted emergency measures can be formulated. Reasonable data matching and analysis can help avoid waste of resources and ensure that resources are used most effectively during critical periods. Timely acquisition and processing of extreme weather data can speed up the decision-making process and improve the response speed of urban managers.

[0101] In addition, this embodiment also needs to define the spatial scope of the urban extreme weather stress test to effectively implement the test. This means that you can choose to conduct a comprehensive stress test on the receptor objects of the entire city, or you can conduct more targeted tests on specific areas or specific types of receptor objects in the city according to actual needs. By selecting relevant receptor object information from a database or table, that is, selecting the first receptor object encoding result, you can construct a sample set for this stress test.

[0102] Step 107: Substitute the specific extreme weather data into the risk function corresponding to the first receptor object coding result to obtain the first receptor object stress test result;

[0103] The purpose of this step is to apply the collected specific extreme weather data to the risk function that has been set for each first receptor object coding result to calculate and predict the risk value of these objects under extreme weather conditions. This process is the core of stress testing, which combines actual weather data with the characteristics of the receptor object to quantify the disaster risk of the hazard-prone body.

[0104] The benefit of this is that by setting a risk function for a single type of receptor, the potential risk of each receptor object in the city under a specific extreme weather event can be analyzed and evaluated efficiently and quickly based on extreme weather data. The risk assessment results can directly provide decision support for city managers and help them formulate strategies and measures to deal with extreme weather.

[0105] Step 108, encoding each first receptor object encoding result according to the economic attribute encoding formula, and obtaining the corresponding first receptor object economic value result;

[0106] The above economic attribute coding formula includes:

[0107]

[0108] The economic value results of the first recipient object are stored in a database.

[0109] The purpose of this link is to evaluate the economic losses that extreme weather events may cause to urban disaster-prone objects. It is necessary to encode the economic attributes of each first receptor object coding result. This step involves quantifying the economic value of specific receptor objects and combining it with the object's coding information so that the factor of economic loss can be considered in risk assessment. Through economic attribute coding, the potential economic losses of various extreme weather events to various types of urban disaster-prone objects can be evaluated. Understanding the economic value of different receptor objects will help decision makers to rationally allocate resources for disaster prevention and mitigation measures. Economic attribute coding allows risk assessment to consider not only physical damage, but also economic losses, providing a more comprehensive risk perspective.

[0110] Step 109: Calculate the total economic loss of the stress test according to the stress test result of the first receptor object and the economic value result of the first receptor object;

[0111] Substituting the stress test result of the first receptor object and the economic value result of the first receptor object into the total economic loss formula of the stress test to calculate the total economic loss of the stress test;

[0112] The formula for the total amount of economic losses from the stress test includes:

[0113]

[0114] Wherein, LY represents the total economic loss of the stress test, m represents the number of the first receptor object coding results, t represents the time of the stress test, i represents the serial number of the receptor object, which varies from 1 to m and is used to traverse all receptor objects. k represents the attribute code of the receptor object with serial number coded as i.

[0115] The purpose of this link is to assess the economic losses that extreme weather events may cause to the city, and to calculate the total economic losses of a stress test by combining the stress test results and economic value results of the first receptor objects. This calculation process involves multiplying the potential risk value of each receptor object by its economic value and summarizing the results of the coded results of all tested first receptor objects to derive the overall economic losses that may be caused under specific extreme weather conditions.

[0116] Step 110: Optimize the accuracy of the risk function corresponding to the encoding result of the first receptor object according to the stress test result of the first receptor object;

[0117] The Bayesian probability theory method is used to optimize the accuracy of the risk function. The formula is:

[0118]

[0119] Among them, A represents an event in which the risk of the first receptor object coding result actually occurs, and B represents an event in which the probability value of the extreme weather risk of the first receptor object coding result exceeds the risk warning line.

[0120] The Bayesian formula is a statistical method that calculates the posterior probability based on the prior probability and new evidence. In this step, the Bayesian formula is used to update the probability prediction of the risk function by combining event A, which actually occurs, and event B, which is above the risk warning line. The Bayesian method allows prior knowledge (such as historical data) to be combined with new observational data to form a more comprehensive posterior probability. This helps to make reasonable risk assessments even when data is scarce. Extreme weather events are often accompanied by uncertainty. The Bayesian formula helps quantify and manage this uncertainty by considering the possibility of different probability distributions, thereby providing decision makers with more reliable information.

[0121] The step of optimizing the accuracy of the risk function corresponding to the encoding result of the first receptor object includes:

[0122] Step 1101: Obtain historical data;

[0123] Historical extreme weather event data related to the first receptor object coding results are collected and used as the basis for assessing risks.

[0124] Step 1102: By statistically analyzing historical data, define the event of the actual risk of the first receptor object coding result as A;

[0125] Based on historical data, it is determined that the first receptor object encoding result actually suffered damage or risk in past extreme weather events, which are defined as event A.

[0126] Step 1103, setting a risk warning line, defining the event B in which the probability value of the extreme weather risk of the first receptor object coding result exceeds the risk warning line;

[0127] Determine a threshold, i.e., the risk warning line, to predict future events that may be subject to risk. When the probability calculated by the risk function exceeds this threshold, it is defined as event B.

[0128] Step 1104, determining the number of coding results of the first recipient object subjected to the stress test;

[0129] The total number of recipient subjects who will participate in the stress test is counted, which helps in the subsequent Bayesian probability analysis.

[0130] Step 1105: construct a Bayesian formula, and calculate and analyze the value of the risk warning line through Bayesian probability theory;

[0131] The optimal value of the risk warning line is calculated by combining the occurrence frequencies of event A and event B. This calculation takes into account the prior probability and new evidence to obtain the posterior probability.

[0132] Step 1106: Verify that the risk functions corresponding to all first receptor object encoding results are convergent.

[0133] Ensure that the risk functions of all first receptor object encoding results are convergent after optimization, that is, as the risk warning line increases, the probability of actual risk should also increase accordingly. In this step, if the risk function corresponding to the first receptor object encoding result of an individual case is divergent, it is necessary to perform iterative corrections and finally optimize it to convergence, ensuring that all risk functions of all first receptor object encoding results are convergent after optimization.

[0134] The working principle of the object list data processing method for urban extreme weather stress testing described in the present invention is: define urban extreme weather discrimination criteria; obtain first receptor object information of urban extreme weather, the first receptor object information includes a first receptor object category and a first receptor object code, and the first receptor object category and the first receptor object code correspond one to one; subdivide the first receptor object category to obtain multiple first receptor object attributes and first receptor object attribute codes, and the first receptor object attributes and the first receptor object attribute codes correspond one to one; encode each of the first receptor object codes according to the encoding rules, and obtain multiple first receptor object encoding results; according to each first receptor object encoding result, set a risk function for each first receptor object encoding result when it is impacted by extreme weather. The risk function is used to predict the probability value of the risk of the corresponding first receptor object coding result under the corresponding extreme weather; extreme weather data is obtained, and multiple specific extreme weather data are obtained by screening according to the urban extreme weather discrimination standard, and the specific extreme weather data corresponds to the first receptor object coding result one by one; the specific extreme weather data is substituted into the risk function corresponding to the first receptor object coding result to obtain the first receptor object stress test result; each first receptor object coding result is encoded according to the economic attribute coding formula to obtain the first receptor object economic value result; based on the first receptor object stress test result and the first receptor object economic value result, the total amount of economic loss from the stress test is calculated; based on the first receptor object stress test result, the risk function corresponding to the first receptor object coding result is optimized.

[0135] By setting clear extreme weather types and discrimination criteria, the present invention can provide city managers with a simple and clear framework to identify and classify extreme weather events, so as to respond quickly. By encoding various receptor objects in the city (such as people, buildings, infrastructure, etc.) in detail, the present invention enables each object to be accurately identified and tracked, providing data support for subsequent risk assessment and management decisions. Risk functions are set for each receptor object. These functions are based on the attributes and historical data of the object and are used to predict the risk probability that the object may face under extreme weather conditions. By substituting extreme weather data into the risk function, the present invention can simulate the disaster risk status of a single type of disaster-stricken body in the city under different extreme weather scenarios, and provide decision support for city managers. Combined with the risk test results and economic value coding of the receptor object, the present invention can calculate the total amount of economic losses that may be caused by extreme weather events, providing a basis for resource allocation and emergency preparedness. Using the Bayesian formula and historical data, the present invention continuously optimizes the risk function to ensure that risk assessment continues to improve as new information is acquired. The present invention provides a more proactive and accurate method for cities to improve their ability to predict extreme weather events in advance and formulate response strategies, reduce the negative impact of extreme weather, protect the lives and property of urban residents, and prevent inefficiency and waste in the allocation of urban rescue resources under extreme weather conditions.

[0136] For other structures of the object list data processing method for urban extreme weather stress testing described in this embodiment, refer to the prior art.

[0137] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0138] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Therefore, any modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention without departing from the technical solution of the present invention shall still fall within the scope of the technical solution of the present invention.

Claims

1. A method for processing object list data for urban extreme weather stress testing, characterized in that: The method comprises: Define the criteria for determining extreme weather in cities; Acquire first receptor object information of urban extreme weather, wherein the first receptor object information includes a first receptor object category and a first receptor object code, and the first receptor object category and the first receptor object code correspond one to one; The first receptor object categories of the urban extreme weather include people, buildings, trees, underground spaces, low-lying and flood-prone ground, rivers, reservoirs, and water supply and drainage facilities; the first receptor object attribute codes include h, b, y, u, f, r, s, and e, and the first receptor object attribute codes h, b, y, u, f, r, s, and e correspond to people, buildings, trees, underground spaces, low-lying and flood-prone ground, rivers, reservoirs, and water supply and drainage facilities, respectively; Subdividing the first receptor object category to obtain a plurality of first receptor object attributes and first receptor object attribute codes, wherein the first receptor object attributes and the first receptor object attribute codes correspond to each other one by one; Encode each of the first receptor object codes according to a coding rule formula, and obtain a plurality of first receptor object coding results accordingly; According to each first receptor object coding result, a corresponding risk function is set when each first receptor object coding result is impacted by extreme weather, and the risk function is used to predict the probability value of the corresponding first receptor object coding result having a risk under the corresponding extreme weather; Acquire extreme weather data, and select and obtain a plurality of specific extreme weather data according to the urban extreme weather discrimination standard, wherein the specific extreme weather data corresponds to the first receptor object coding result one by one; Substituting the specific extreme weather data into the risk function corresponding to the first receptor object coding result to obtain the first receptor object stress test result; Encode each first receptor object encoding result according to the economic attribute encoding formula to obtain the corresponding first receptor object economic value result; According to the stress test results of the first recipient object and the economic value results of the first recipient object, the total economic loss of the stress test is calculated, including: Substituting the stress test result of the first receptor object and the economic value result of the first receptor object into the total economic loss formula of the stress test to calculate the total economic loss of the stress test; The formula for the total amount of economic losses from the stress test includes: Wherein, LY represents the total economic loss of the stress test, m represents the number of coding results of the first receptor object, and t represents the time of the stress test; According to the stress test result of the first receptor object, the accuracy of the risk function corresponding to the encoding result of the first receptor object is optimized, specifically including: The Bayesian probability analysis method is used to optimize the accuracy of the risk function. The formula is: Wherein, A represents an event in which the risk of the first receptor object coding result actually occurs, and B represents an event in which the probability value of the extreme weather risk of the first receptor object coding result exceeds the risk warning line; The step of optimizing the accuracy of the risk function corresponding to the encoding result of the first receptor object comprises: Acquiring historical data, wherein the historical data is historical extreme weather event data collected and related to the encoding result of the first receptor object; By statistically analyzing historical data, the event of the actual risk of the first receptor object coding result is defined as A. The event of the actual risk of the first receptor object coding result is specifically that the first receptor object coding result actually suffered damage or risk in the past extreme weather events; Set a risk warning line, and define the event B when the probability value of the extreme weather risk caused by the coding result of the first receptor object exceeds the risk warning line; Determine the number of coded results for the first recipient subjects who received stress testing; Construct a Bayesian formula and calculate the value of the risk warning line through the Bayesian formula; Verify that the risk functions corresponding to all first receptor object encoding results are convergent. If the risk function corresponding to the first receptor object encoding result is divergent, it is necessary to perform iterative correction to optimize the risk function to converge, thereby ensuring that the risk functions of all first receptor object encoding results are convergent.

2. The object list data processing method for urban extreme weather stress testing according to claim 1 is characterized in that: Define urban extreme weather standards, including: The extreme weather in the city includes extremely high temperature weather, extremely low temperature weather, strong wind weather, and heavy rainfall weather; The criterion for determining extreme high temperatures is that when the temperature data is higher than 0.95 times the highest temperature recorded in the city's history, it is defined as extreme high temperatures; The criterion for determining extreme low temperatures is that when the temperature data is lower than 0.95 times the lowest temperature ever recorded in the city, it is defined as extreme low temperatures; The criterion for judging strong wind is that when the wind speed data is higher than 20m / s, it is defined as strong wind weather; The criterion for judging heavy rainfall is that when the rainfall data is higher than 300 mm / d, it is judged as heavy rainfall.

3. The object list data processing method for urban extreme weather stress testing according to claim 1 is characterized in that: Acquiring first receptor object information of urban extreme weather, the first receptor object information including a first receptor object category and a first receptor object code, the first receptor object category and the first receptor object code corresponding one to one, including: The first receptor object category of the urban extreme weather includes people, buildings, trees, underground spaces, low-lying and flood-prone ground, rivers, reservoirs, and water supply and drainage facilities.

4. The object list data processing method for urban extreme weather stress testing according to claim 1 is characterized in that: Each of the first receptor object codes is encoded according to a coding rule formula, and a plurality of first receptor object encoding results are obtained accordingly. The coding rule formula includes: Wherein, O represents the first receptor object code, x represents the longitude of the first receptor object code, y represents the latitude of the first receptor object code, z represents the ground height of the first receptor object code, t represents the time information of the first receptor object code performing the pressure test, i represents the serial number coding information of the first receptor object code, and k represents the first receptor object attribute code; The encoding results of the plurality of first receptor objects are stored in a database.

5. The object list data processing method for urban extreme weather stress testing according to claim 1 is characterized in that: According to each first receptor object coding result, a corresponding risk function is set when each first receptor object coding result is impacted by extreme weather. The risk function is used to predict the probability value of the risk of the corresponding first receptor object coding result under the corresponding extreme weather. The risk function includes: in, represents the probability value of the extreme weather risk occurring within time t for the first receptor object coded as i, k represents the attribute code of the first receptor object, w t A physical quantity representing extreme weather at time t.

6. The object list data processing method for urban extreme weather stress testing according to claim 1 is characterized in that: Correspondingly setting the risk function when each encoding result of the first receptor object is impacted by extreme weather, comprises the following steps: Determining the sensitivity of the encoding result of the first receptor object to different types of extreme weather according to the attribute of the encoding result of the first receptor object; Acquire historical data, and determine the physical quantity of extreme weather that affects the coding result of the first receptor object based on the historical data analysis; According to the sensitivity and the physical quantity, a risk function is set correspondingly when the encoding result of each first receptor object is impacted by extreme weather.

7. The object list data processing method for urban extreme weather stress testing according to claim 1 is characterized in that: The coding result of each first receptor object is coded according to the economic attribute coding formula to obtain the economic value result of the first receptor object. The economic attribute coding formula includes: The economic value results of the first recipient object are stored in a database.

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