Urban heat island effect emergency monitoring system and method based on Internet of Things large model

Abstract

The invention provides an urban heat island effect emergency monitoring system and method based on an Internet of Things large model, and relates to the field of heat island effect monitoring. The system comprises an emergency supervision and management platform, and the emergency supervision and management platform is configured to determine heat island levels of a plurality of candidate nodes based on remote sensing data, meteorological station data, environmental data and urban activity data; determining a target node and a cooling parameter of the target node based on the heat island levels of the plurality of candidate nodes and a preset threshold; determining a watering cart corresponding to the target node based on the heat island grade of the target node; sending the target node to a navigation terminal of a corresponding watering cart through a wireless network, and controlling the navigation terminal to display a navigation route to the target node; and based on the cooling parameters, controlling a watering cart and a spraying system corresponding to the target node to carry out watering and water spraying. According to the invention, accurate monitoring and efficient emergency disposal can be carried out on the urban heat island effect, and the coping capability of the heat island effect and the intelligent level of public service are improved.

Description

Technical Field

[0001] This specification relates to the field of urban heat island effect monitoring, and in particular to an emergency monitoring system and method for urban heat island effect based on a large-scale Internet of Things (IoT) model. Background Technology

[0002] With the accelerating pace of climate change and urbanization, the urban heat island effect has become increasingly significant, posing a major threat to urban environmental quality and residents' health. The urban heat island effect refers to the phenomenon where urban temperatures are significantly higher than those in surrounding suburbs due to frequent human activities and changes in land cover. Traditional urban meteorological monitoring relies primarily on a limited number of ground-based meteorological stations, resulting in limited coverage and an inability to comprehensively and meticulously reflect the complex changes in the urban thermal environment. This hinders the accurate identification of high-temperature risks and the effective implementation of countermeasures.

[0003] Therefore, there is a need to provide an emergency monitoring system and method for the urban heat island effect based on a large-scale Internet of Things model, so as to improve the ability to cope with the heat island effect and the level of intelligence of public services. Summary of the Invention

[0004] To address the problems of insufficient monitoring accuracy, delayed emergency cooling response, and lack of targeted cooling measures in existing urban heat island effect monitoring systems, this specification proposes an emergency monitoring system and method for urban heat island effect based on a large-scale Internet of Things (IoT) model.

[0005] The invention includes an emergency monitoring system for urban heat island effect based on a large-scale Internet of Things (IoT) model, comprising an emergency monitoring and management platform configured to execute an emergency monitoring method for urban heat island effect based on a large-scale IoT model.

[0006] The invention includes an emergency monitoring method for urban heat island effect based on a large-scale Internet of Things (IoT) model, comprising: determining the heat island level of multiple candidate nodes based on remote sensing data, meteorological station data, environmental data, and urban activity data; determining a target node and its cooling parameters based on the heat island level of the candidate nodes and a preset threshold, the cooling parameters including water spraying parameters of a sprinkler truck and spraying parameters of a fixed spray system; determining the sprinkler truck corresponding to the target node based on its heat island level; transmitting the target node data via a wireless network to the navigation terminal of the sprinkler truck corresponding to the target node, and controlling the navigation terminal to display a navigation route to the target node; and controlling the sprinkler truck and spray system corresponding to the target node to perform water spraying based on the cooling parameters of the target node.

[0007] The beneficial effects of the above invention include, but are not limited to: (1) by integrating multi-source data to determine the heat island level, the comprehensiveness and accuracy of urban heat island effect monitoring are improved, providing reliable data support for the formulation of subsequent emergency cooling measures; (2) based on the heat island level, the target node, cooling parameters and sprinkler truck resources are accurately matched, realizing personalized and targeted management of emergency cooling, effectively improving cooling efficiency and avoiding resource waste; (3) by using wireless networks to realize accurate push of navigation routes and remote control of cooling equipment, the emergency response time is shortened, and the timeliness and intelligence level of emergency response to urban heat island effect are enhanced. Attached Figure Description

[0008] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0009] Figure 1 This is a schematic diagram of the platform structure of an emergency monitoring system for the urban heat island effect based on a large-scale Internet of Things model, as shown in some embodiments of this specification. Figure 2 This is an exemplary flowchart of an emergency monitoring method for the urban heat island effect according to some embodiments of this specification; Figure 3 This is an exemplary schematic diagram of the structure of a prediction model according to some embodiments of this specification; Figure 4 This is an exemplary flowchart of a method for determining a target cooling strategy according to some embodiments of this specification. Detailed Implementation

[0010] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0011] It should be understood that the terms “system,” “device,” “unit,” and / or “module” used herein are one way to distinguish different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0012] Unless the context clearly indicates an exception, words such as "a," "an," "a kind," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0013] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0014] Figure 1 This is a schematic diagram of the platform structure of an emergency monitoring system for the urban heat island effect based on a large-scale Internet of Things model, as shown in some embodiments of this specification.

[0015] In some embodiments, such as Figure 1 As shown, the urban heat island effect emergency monitoring system 100 based on an IoT big data model may include an emergency monitoring service platform 110, an emergency monitoring management platform 120, an emergency monitoring sensor network platform 130, and an emergency monitoring object platform 140. In some embodiments, the emergency monitoring service platform 110, the emergency monitoring management platform 120, the emergency monitoring sensor network platform 130, and the emergency monitoring object platform 140 may be interconnected sequentially. The IoT big data model refers to an IoT model architecture used to enable the efficient operation of large amounts of data in the urban heat island effect emergency monitoring system 100 based on the IoT big data model. In some embodiments, artificial intelligence models (e.g., ChatGPT, Gemini, Deepseek) may be applied to the IoT model architecture for data perception and processing.

[0016] Emergency monitoring service platform 110 refers to a platform that provides emergency monitoring services, such as emergency monitoring services for the urban heat island effect. In some embodiments, emergency monitoring service platform 110 is configured as a server and / or processor, etc. Emergency monitoring service platform 110 can interact bidirectionally with data center 121 in emergency monitoring management platform 120. In some embodiments, emergency monitoring management platform 120 can obtain urban activity data from emergency monitoring service platform 110 and store the obtained urban activity data in database 121-1. For more information on urban activity data, please refer to [link to relevant documentation]. Figure 2 And its related descriptions.

[0017] The emergency monitoring and management platform 120 refers to a comprehensive management platform that coordinates and integrates the connections and collaboration between multiple platforms. In some embodiments, the emergency monitoring and management platform 120 may be a platform for monitoring and managing information related to the urban heat island effect. In some embodiments, the emergency monitoring and management platform 120 may include servers, processors, data storage systems, large-screen display systems, IoT platform software, communication components (e.g., communication interfaces, gateways), etc. In some embodiments, the emergency monitoring and management platform 120 may be a software platform running on a server or in the cloud, used to process data and / or information obtained from other platforms (e.g., emergency monitoring service platform 110, emergency monitoring sensor network platform 130). Based on the acquired data, information, and / or corresponding processing results, the emergency monitoring and management platform 120 may execute program instructions to perform the functions and / or steps described in this specification.

[0018] In some embodiments, the emergency monitoring and management platform 120 may include a data center 121. The data center 121 may include a database 121-1, a data processing model library 121-2, and a computing unit 121-3.

[0019] Database 121-1 is used to collect, store, and manage data related to the urban heat island effect, such as remote sensing data, weather station data, environmental data, urban activity data, cooling parameters, urban heat effect maps, initial cooling strategies, candidate cooling strategies, and target cooling strategies. Database 121-1 can include MySQL, PostgreSQL, InfluxDB, and Prometheus. For more information on remote sensing data, weather station data, environmental data, urban activity data, cooling parameters, urban heat effect maps, initial cooling strategies, candidate cooling strategies, and target cooling strategies, please refer to [link to relevant documentation]. Figures 2-4 And its related descriptions.

[0020] Data processing model library 121-2 is used to store pre-trained large data processing models. In some embodiments, data processing model library 121-2 may include chatbots, prediction models, etc. For more information on prediction models, please refer to [link to relevant documentation]. Figure 3 And its related descriptions.

[0021] Computing unit 121-3 refers to a functional module that performs arithmetic, logical, and other instruction operations. Computing unit 121-3 may include a processor. The processor may include a central processing unit (CPU), a graphics processing unit (GPU), a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), an application-specific instruction set processor (ASIP), etc.

[0022] The emergency monitoring sensor network platform 130 refers to a platform used for the comprehensive management of sensor information. In some embodiments, the emergency monitoring sensor network platform 130 can be configured as a communication network and / or gateway, etc. The emergency monitoring sensor network platform 130 can interact bidirectionally with the data center 121 and the emergency monitoring object platform 140. In some embodiments, the emergency monitoring sensor network platform 130 can collect remote sensing data, weather station data, environmental data, and urban activity data, and send them to the emergency monitoring management platform 120. The emergency monitoring management platform 120 can store the received remote sensing data, weather station data, environmental data, and urban activity data in database 121-1. For more information on remote sensing data, weather station data, environmental data, and urban activity data, please refer to [link to relevant documentation]. Figure 2 And its related descriptions.

[0023] The emergency monitoring object platform 140 refers to a platform for monitoring entities or systems, used to display, manage, and analyze the operational status and data of the monitored entities or systems. In some embodiments, the emergency monitoring object platform 140 may include a processor, server, gateway, etc. In some embodiments, the emergency monitoring object platform 140 can be used to manage and control sprinkler trucks and spray systems. For example, the emergency monitoring object platform 140 can communicate with sprinkler trucks and spray systems to achieve two-way data interaction. The emergency monitoring object platform 140 can receive instructions from the emergency monitoring management platform 120 through the emergency monitoring sensor network platform 130 to control the sprinkler trucks and spray systems to perform cooling operations.

[0024] In the embodiments described in this specification, the urban heat island effect emergency monitoring system 100 based on an IoT big data model can automatically and intelligently realize emergency monitoring of the urban heat island effect. Through early identification of the urban heat island effect and precise deployment and guidance of sprinkler trucks and spray systems, it significantly improves cooling efficiency and optimizes cooling effects. At the same time, the use of the IoT big data model makes data fusion, analysis, and decision-making more efficient and comprehensive.

[0025] It should be noted that the above description of the urban heat island effect emergency monitoring system 100 and its platform based on an IoT big data model is for convenience only and should not be construed as limiting this specification to the scope of the embodiments described. It is understood that those skilled in the art, after understanding the principles of this system, may arbitrarily combine the various modules or construct subsystems connected to other platforms without departing from these principles.

[0026] Figure 2 This is an exemplary flowchart illustrating an emergency monitoring method for the urban heat island effect according to some embodiments of this specification. Figure 2 As shown, process 200 includes the following steps. In some embodiments, process 200 may be executed by an emergency monitoring and management platform.

[0027] Step 210: Based on remote sensing data, weather station data, environmental data, and urban activity data, determine the heat island level of multiple candidate nodes. In some embodiments, the emergency monitoring and management platform can obtain remote sensing data, weather station data, environmental data, and urban activity data from a database.

[0028] Remote sensing data refers to information about the Earth's surface and its environment acquired through remote sensing technology. In some embodiments, remote sensing data may include surface temperature, vegetation cover, etc. For example, an emergency monitoring and management platform can analyze the distribution of urban buildings, roads, and green spaces using WorldView satellite imagery and Shapefile vector data. As another example, an emergency monitoring and management platform can assess surface temperature differences in different areas and locate heat island regions using nighttime thermal infrared images.

[0029] Meteorological station data refers to real-time or historical observational information collected through meteorological observation stations (e.g., ground stations, radiosonde stations, automatic weather stations, etc.) that reflects weather phenomena, climate change, and atmospheric environmental conditions. In some embodiments, meteorological station data may include air temperature (e.g., real-time ambient air temperature in different regions), wind speed, and wind direction.

[0030] Environmental data refers to real-time or historical information about the environmental state collected through Internet of Things (IoT) environmental sensors, smart light pole integrated sensors, and other similar devices. IoT environmental sensors can be deployed in locations or nodes such as streets, parks, and building complexes. In some embodiments, environmental data may include local surface temperature and local air temperature, for example, the surface temperature and air temperature at locations or nodes such as streets, parks, and building complexes.

[0031] Urban activity data refers to real-time or historical information on urban activities collected through urban traffic management systems (e.g., intelligent traffic lights, roadside radar) and power company energy consumption monitoring systems. In some embodiments, urban activity data may include traffic flow, vehicle congestion index, building energy consumption data, etc. The vehicle congestion index is a quantitative indicator that comprehensively reflects road congestion, typically expressed as a value between 0 and 10, with higher values ​​indicating more severe congestion. Building energy consumption data refers to the electricity consumption data of buildings.

[0032] In some embodiments, the emergency monitoring and management platform can align remote sensing data, weather station data, environmental data, and urban activity data in time and space using spatiotemporal interpolation models (e.g., spatiotemporal kriging (STK), Bayesian spatiotemporal models) and data assimilation (e.g., four-dimensional variational, ensemble Kalman filtering), and standardize or normalize the aligned remote sensing data, weather station data, environmental data, and urban activity data using Z-score standardization, Min-Max standardization, etc.

[0033] Candidate nodes refer to basic spatial units and / or functional areas within a city, such as streets, communities, residential areas, commercial areas, industrial areas, transportation hubs, and ecological and recreational areas. In some embodiments, candidate nodes may be multiple nodes or locations pre-set by the emergency monitoring and management platform.

[0034] The heat island rating is a quantitative indicator reflecting the intensity of the heat island effect. In some embodiments, the heat island rating can be the intensity level of the heat island effect at a future time or over a future time period. The future time or time period can be set based on experience or by the system. In some embodiments, the heat island rating can be represented by a numerical value ranging from 0 to 10.

[0035] In some embodiments, the emergency monitoring and management platform can determine the heat island level of multiple candidate nodes using a predictive model. For more information on determining the heat island level of multiple candidate nodes using a predictive model, please refer to [link to relevant documentation]. Figure 3 And its related descriptions.

[0036] In some embodiments, the emergency monitoring and management platform can determine the heat island level based on the heat island level and heat island coefficient at the current moment or time period. For example, the heat island level can be the product of the heat island level at the current moment or time period and the heat island coefficient.

[0037] In some embodiments, the heat island level at the current moment or time period may include the heat island intensity of air temperature, the heat island intensity of surface temperature, and the temperature level of multiple candidate nodes. The emergency monitoring and management platform can determine the heat island level of multiple candidate nodes based on their air temperature heat island intensity, surface temperature heat island intensity, temperature level, and heat island coefficient. For example, for one candidate node among multiple candidate nodes, its heat island level can be characterized as: (Heat island intensity of the candidate node at air temperature + Heat island intensity of the candidate node at surface temperature + Temperature level of the candidate node). Heat island coefficient.

[0038] The intensity of the heat island effect is related to the temperature difference between urban and suburban air temperatures. For example, if the temperature difference is less than or equal to 0°C, the heat island intensity is level 0; if the temperature difference is greater than 0°C and less than or equal to 2°C, the intensity is level 1; if the temperature difference is greater than 2°C and less than or equal to 4°C, the intensity is level 2; and if the temperature difference is greater than 4°C, the intensity is level 3. In some embodiments, the emergency monitoring and management platform can obtain the heat island intensity from meteorological station data.

[0039] The intensity of the land surface temperature heat island is related to the temperature difference between urban and suburban land surface temperatures. The correspondence between land surface temperature heat island intensity and the temperature difference is similar to the correspondence between air temperature heat island intensity and the temperature difference; for details, please refer to the correspondence between air temperature heat island intensity and the temperature difference. In some embodiments, the emergency monitoring and management platform can obtain the land surface temperature heat island intensity from remote sensing data.

[0040] Temperature level refers to the intensity level corresponding to the temperature value of a node at the current moment or during the current time period. In some embodiments, the temperature level can be set based on experience or by the system. For example, if the temperature value is less than 30°C, the temperature level is 0; if the temperature value is greater than or equal to 30°C and less than 35°C, the temperature level is 1; if the temperature value is greater than or equal to 35°C and less than 40°C, the temperature level is 2; and if the temperature value is greater than or equal to 40°C, the temperature level is 3.

[0041] The urban heat island coefficient is used to characterize the degree to which weather conditions at a future time or over a future period will affect the urban heat island rating. In some embodiments, the urban heat island coefficient may be represented by a value greater than 0.

[0042] In some embodiments, the emergency monitoring and management platform can determine the heat island coefficient based on the air temperature at a future time or time period. For example, the platform can determine the heat island coefficient by looking up a preset table based on the temperature difference between the air temperature at a future time or time period and the air temperature at the current time or time period. The preset table includes the correspondence between air temperature differences and heat island coefficients, for example, {index (air temperature difference is 3°C) -> query result (heat island coefficient is 1.3)}. Another example is {index (air temperature difference is -3°C) -> query result (heat island coefficient is 0.8)}. In some embodiments, the preset table can be built based on historical data or preset by the system.

[0043] The embodiments described in this specification determine the heat island level at each node or location by comprehensively considering the heat island intensity of air temperature, the heat island intensity of land surface temperature, temperature level, and heat island coefficient, effectively improving the monitoring of the urban heat island effect. These comprehensive indicators can more fully and multidimensionally assess the intensity and impact of the urban heat island effect, thereby supporting scientific decision-making, optimizing resource allocation, and improving the living environment.

[0044] Step 220: Based on the heat island level and preset threshold of multiple candidate nodes, determine the target node and the cooling parameters of the target node.

[0045] Cooling parameters include the water spraying parameters of the sprinkler truck and the spraying parameters of the misting system.

[0046] A target node refers to the node among multiple candidate nodes that requires cooling. In some embodiments, the target node can be the node among multiple candidate nodes whose heat island level is higher than a preset threshold. The preset threshold can be set based on experience or by the system. For example, the preset threshold can be set to level 6.

[0047] Sprinkling parameters refer to the parameters used to control the water spraying of a sprinkler truck. In some embodiments, sprinkling parameters may include sprinkling mode (e.g., side spray mode, high-pressure water cannon mode, spray mode), sprinkling intensity (e.g., water pressure, flow rate, spray distance, coverage volume), and sprinkling duration. The sprinkler truck can be an automatic sprinkler truck or a sprinkler truck that requires a human operator.

[0048] A spray system is an automated system that provides continuous or intermittent spraying through a fixed network of pipes and nozzles. In some embodiments, spray systems can be applied to agriculture, landscaping, industry, fire protection, and other fields. For example, spray systems can be deployed in urban public areas such as parks and squares.

[0049] Spraying parameters refer to the parameters used to control the spraying of a spraying system. In some embodiments, spraying parameters may include spraying mode (e.g., continuous spraying mode, intermittent spraying mode, timed spraying mode), spraying intensity (e.g., water pressure, flow rate, coverage volume, spray pattern), and spraying duration.

[0050] In some embodiments, it is known that there are two scenarios: a heat island level less than or equal to a preset threshold, and a heat island level greater than the preset threshold. In response to at least one candidate node having a heat island level greater than the preset threshold among multiple candidate nodes, the emergency monitoring and management platform can identify at least one candidate node as the target node. In some embodiments, the cooling parameters of the target node can be positively correlated with its heat island level. For example, the higher the heat island level of the target node, the greater the water spraying intensity and / or spraying intensity, and the longer the water spraying duration and / or spraying duration. The water spraying mode can be set to a high-pressure water cannon mode, where the pressure can be between 10-20 MPa. The spraying mode can be set to a continuous spraying mode.

[0051] In some embodiments, the emergency monitoring and management platform can send the cooling parameters of the target node to the emergency monitoring object platform through the emergency monitoring sensor network platform to cool the target node. For example, if the target node is an open-air plaza, the emergency monitoring object platform can receive the following cooling parameters: {intermittent spraying mode; water pressure 4 MPa; flow rate 10 L / min; coverage area 200m}. 2 From 10:00 to 18:00 daily, the data is sent to the spray system. The emergency monitoring and management platform can then control the spray system to cool the open-air plaza based on the cooling parameters.

[0052] Step 230: Based on the heat island level of the target node, determine the water truck corresponding to the target node.

[0053] In some embodiments, if the number of target nodes is one, the emergency monitoring and management platform can determine the sprinkling parameters of the target node based on its heat island level, and dispatch a water truck matching the sprinkling parameters to the target node based on these parameters. For example, the emergency monitoring and management platform can send the target node and its sprinkling parameters to the emergency monitoring object platform through the emergency monitoring sensor network platform. The emergency monitoring object platform can then dispatch a water truck that meets the sprinkling parameters to the target node based on the received sprinkling parameters. For more information on sprinkling parameters, please refer to step 220 and its related description.

[0054] In some embodiments, for at least two of the multiple target nodes, the emergency monitoring and management platform can also determine the priority of at least two target nodes based on the heat island level of the at least two target nodes, and obtain the current location of the idle sprinkler truck, and dispatch the idle sprinkler truck with the closest location to the at least two target nodes based on the priority of the at least two target nodes.

[0055] Priority refers to the order in which nodes are cooled. For example, the higher the priority, the earlier the node is cooled.

[0056] In some embodiments, the priority of a target node can be positively correlated with its heat island level. For example, the higher the heat island level of a target node, the higher its priority.

[0057] An idle sprinkler truck refers to a sprinkler truck that is not performing a sprinkler task and is in a standby or non-operational state. The current location of an idle sprinkler truck refers to its current geographic coordinates or location. In some embodiments, the emergency monitoring and management platform can obtain the current location of idle sprinkler trucks through a Global Positioning System (GPS), IoT devices, a Geographic Information System (GIS), and an emergency monitoring object platform. In some embodiments, the current location of an idle sprinkler truck may include latitude and longitude, address information, parking lot number, etc.

[0058] In some embodiments, the emergency monitoring and management platform can, based on the priority of the target nodes, sequentially send the target nodes to the emergency monitoring object platform through the emergency monitoring sensor network platform. The emergency monitoring object platform can then, based on the received target node and the current location of the available sprinkler truck, dispatch the nearest available sprinkler truck to the target node.

[0059] The embodiments in this specification assess the severity of the heat island effect at each node through heat island classification, and prioritize dispatching water trucks to high-temperature areas. This ensures that limited water and vehicle resources are used preferentially for areas that need cooling the most, avoiding resource waste and improving cooling efficiency.

[0060] Step 240: Send the target node to the navigation terminal of the sprinkler truck corresponding to the target node via wireless network, and control the navigation terminal to display the navigation route to the target node.

[0061] A navigation terminal refers to a dedicated device or system installed on a sprinkler truck to achieve functions such as real-time positioning, route navigation, task reception and execution, and status monitoring. In some embodiments, the navigation terminal may include an in-vehicle navigation terminal, a mobile terminal equipped with navigation software (e.g., a mobile phone, a personal digital assistant), etc.

[0062] In some embodiments, the emergency monitoring and management platform can send the target node and the navigation route to the target node to the emergency monitoring object platform via an emergency monitoring sensor network platform and / or wireless network. The emergency monitoring object platform can then send the received target node and navigation route to the water truck's navigation terminal.

[0063] Step 250: Based on the cooling parameters of the target node, control the water truck and spray system corresponding to the target node to perform water sprinkling and spraying.

[0064] In some embodiments, the emergency monitoring and management platform can send the target node and its cooling parameters to the emergency monitoring object platform via the emergency monitoring sensor network platform. The emergency monitoring object platform can then generate a first control signal corresponding to the water spraying parameters and a second control signal corresponding to the spraying parameters based on the received target node and its cooling parameters (i.e., water sprinkling parameters and spraying parameters). The platform can then control the water truck and the spraying system using the first and second control signals to perform water sprinkling and spraying, respectively. For more information on the water sprinkling parameters and spraying parameters, please refer to step 220 and its related description.

[0065] In some embodiments, the sprinkler truck includes onboard sensors. The emergency monitoring and management platform can also use the onboard sensors to obtain the surface temperature, air humidity, and wind speed at the sprinkler truck's current location, and adjust the sprinkler truck's spraying mode, spraying intensity, and duration based on the surface temperature, air humidity, and wind speed at the current location. For more information about the current location, please refer to step 230 and its related description.

[0066] The on-board sensors of a water sprinkler truck refer to various sensing devices installed in key parts of the water sprinkler truck (e.g., water tank, water sprinkler system, chassis, cab, etc.). In some embodiments, the on-board sensors of a water sprinkler truck may include high-precision surface temperature sensors, air temperature sensors, air humidity sensors, and wind speed sensors.

[0067] In some embodiments, the emergency monitoring and management platform can obtain the updated water spraying mode, spraying intensity, and duration of the sprinkler truck based on the current node's surface temperature, air humidity, and wind speed through a vector database. For example, the emergency monitoring and management platform can construct feature vectors based on the current node's surface temperature, air humidity, and wind speed, and then retrieve the updated water spraying mode, spraying intensity, and duration of the sprinkler truck based on these feature vectors in the vector database. A vector database is a database used to store, index, and query vectors. Through a vector database, similarity queries and other vector management can be performed quickly on a large number of vectors. The vector database can be stored within a database.

[0068] In some embodiments, the emergency monitoring and management platform can acquire reference surface temperature (e.g., surface temperature sequences at multiple historical moments or time periods), reference air humidity (e.g., air humidity sequences at multiple historical moments or time periods), and reference wind speed (e.g., wind speeds at multiple historical moments or time periods) based on historical data from reference nodes. Based on the reference surface temperature, reference air humidity, and reference wind speed, multiple reference vectors are constructed. Each reference vector has a corresponding vector label, which may include the reference sprinkler pattern with the fastest cooling rate, the reference sprinkler intensity, and the reference duration. The emergency monitoring and management platform can store multiple reference vectors and their corresponding vector labels in a vector database.

[0069] In some embodiments, the computing unit of the emergency monitoring and management platform can calculate the similarity (e.g., cosine similarity, Euclidean distance) between the feature vector and multiple reference vectors, and determine the vector label of the reference vector with the highest similarity as the updated water spraying mode, water spraying intensity, and duration of the sprinkler truck. For more information on water spraying mode and water spraying intensity, please refer to step 220 and its related description.

[0070] The embodiments described in this manual transmit real-time data on ground temperature, air humidity, and wind speed to the emergency monitoring and management platform via onboard sensors during water spraying operations. Based on the current ground temperature, air humidity, and wind speed, the emergency monitoring and management platform intelligently adjusts or updates the water spraying mode, spraying intensity, and duration of the water truck, thereby achieving real-time adaptive adjustment of the water spraying strategy and improving cooling efficiency.

[0071] The embodiments in this specification determine the heat island level of candidate nodes or locations based on remote sensing data, weather station data, environmental data, and urban activity data. They automatically plan and dispatch sprinkler trucks to perform precise cooling operations. Simultaneously, they remotely activate and adjust the spray systems in public areas such as parks and squares, rapidly reducing local temperatures through water evaporation. Furthermore, the embodiments in this specification, through the spray system, can automatically advance or increase the irrigation volume of green spaces, maximizing the transpiration cooling effect of plants, increasing water circulation, and enhancing evaporative cooling capacity, thereby achieving automated water mist or sprinkler cooling.

[0072] It should be noted that the above description of process 200 is merely for illustration and explanation, and does not limit the scope of the invention. Those skilled in the art can make various modifications and changes to process 200 under the guidance of this invention. However, these modifications and changes are still within the scope of this invention.

[0073] Figure 3 This is an exemplary schematic diagram of the structure of a prediction model according to some embodiments of this specification.

[0074] In some embodiments, the emergency monitoring and management platform can determine the heat island level of multiple candidate nodes using prediction model 310. Prediction model 310 is a machine learning model. For example, the machine learning model may include a neural network (NN), a convolutional neural network (CNN), a recurrent neural network (RNN), a graph neural network (GNN), etc.

[0075] In some embodiments, the prediction model 310 may be a machine learning model with a custom structure including multiple layers. For example... Figure 3 As shown, the prediction model 310 may include a thermal effect processing layer 311 and a heat island prediction layer 312. In some embodiments, the thermal effect processing layer 311 may be a neural network model, and the heat island prediction layer 312 may be a graph neural network model.

[0076] In some embodiments, the input to the thermal effect processing layer 311 may include remote sensing data 311-1, weather station data 311-2, environmental data 311-3, and urban activity data 311-4 of one of the multiple candidate nodes, and the output may include the thermal effect latent vector 311-5 of the candidate node.

[0077] The latent thermal effect vector is a feature vector used to determine the level of a heat island. For example, elements of the latent thermal effect vector may include surface temperature, vegetation cover, air temperature, wind direction, wind speed, and traffic flow. In some embodiments, the latent thermal effect vector can be normalized or standardized remote sensing data, weather station data, environmental data, and urban activity data. For more information on remote sensing data 311-1, weather station data 311-2, environmental data 311-3, and urban activity data 311-4, please refer to [link to relevant documentation]. Figure 2 And its related descriptions.

[0078] In some embodiments, the input to the heat island prediction layer 312 may include an urban heat effect map 320 and a latent heat effect vector 311-5, and the output may include the heat island level 330 of the candidate nodes. For more information on the heat island level 330 of the candidate nodes, please refer to [link to relevant documentation]. Figure 2 And its related descriptions.

[0079] The Urban Thermal Effect Map 320 is a network model of urban thermal effect distribution built based on nodes and edges in graph theory. It presents the differences in thermal effects, heat flow paths and spatial relationships between different areas of the city in a visual way.

[0080] In some embodiments, the nodes 320-1 of the urban thermal effect map 320 can be multiple candidate nodes, and the nodes 320-1 that meet the preset location conditions have edges 320-2 between them. The node attributes can include the thermal effect latent vector 311-5 of the candidate node output by the thermal effect processing layer 311, the reference sprinkling parameters of the candidate node, and the reference spraying parameters of the candidate node. The edge attributes can include the features of the segmented zone corresponding to the edge and the cooling factor.

[0081] Preset location conditions refer to the preset conditions under which a connecting edge exists between two nodes. In some embodiments, preset location conditions refer to the existence of a dividing zone between two nodes, and the distance between the two nodes being less than a preset distance threshold.

[0082] In some embodiments, the reference sprinkling parameters and reference spraying parameters can be determined based on historical data of the candidate nodes. Similar to sprinkling parameters and spraying parameters, more details can be found in [link to relevant documentation]. Figure 2 And its related descriptions.

[0083] A dividing zone refers to a transitional or boundary area used to separate multiple candidate nodes in geographical space or functional layout. For example, a dividing zone may include roads, greenbelts, etc. The characteristics of a dividing zone refer to its physical form, ecological function, and other features. For example, the characteristics of a dividing zone may include the width of roads, the shape of greenbelts (e.g., strip-shaped or area-shaped), vegetation type, and vegetation coverage. In some embodiments, the characteristics of the dividing zone may be set based on experience or preset by the system.

[0084] The cooling factor is used to characterize the impact of sprinkler trucks and spray systems on the cooling effect. In some embodiments, the cooling factor can be determined based on reference sprinkler parameters, reference spray parameters, distance between nodes, wind direction, and wind speed. For example, the emergency monitoring and management platform can determine the water mist density of node 320-1 based on the reference sprinkler parameters and reference spray parameters. Water mist density It can characterize the mass of water mist particles per unit volume within the coverage area. Water mist density. The larger the value, the higher the mass of the water mist particles per unit volume. The more water mist particles there are, the faster the evaporation and diffusion rate, the higher the heat absorption efficiency, and the better the cooling effect.

[0085] For example, taking the use of a water truck for cooling as an example, the water mist density It can be characterized as: = (Reference Flow) Reference watering duration (Water density) / Reference coverage volume. Emergency monitoring and management platforms can use Z-score normalization, Min-Max normalization, etc., to determine the water mist density. The distance between nodes, wind direction, and wind speed are standardized or normalized, and the water mist density is based on the normalized or normalized values. The cooling factor is determined by considering the distance between nodes, wind direction, and wind speed, using a Gaussian plume model or computational fluid dynamics (CFD). The cooling factor can be a value between 0 and 1; a higher value indicates a better cooling effect.

[0086] In some embodiments, the wind direction and / or the direction of water mist diffusion is the direction of edge 320-2. A node may have outgoing edges and / or incoming edges. An incoming edge is an edge that points to the node, and an outgoing edge is an edge that starts from the node and points to another node.

[0087] In some embodiments, in response to a sprinkler truck moving along edge 320-2 and / or a spray system deploying along edge 320-2 and performing sprinkling and / or spraying operations, the emergency monitoring and management platform can, based on the sprinkler truck's reference sprinkling parameters (e.g., reference sprinkling intensity and reference sprinkling duration), the spray system's reference spraying parameters (e.g., reference spraying intensity and reference spraying duration), and the distance between nodes 320-1, [the platform can be configured to manage the system based on these parameters]. d Determine the cooling factor. For example, the cooling factor can be positively correlated with reference sprinkler intensity, reference spray intensity, reference sprinkler duration, and reference spray duration, and negatively correlated with distance. d For example, the higher the reference sprinkling intensity and reference spraying intensity, and the longer the reference sprinkling duration and reference spraying duration, the higher the cooling factor; distance d The larger the value, the smaller the cooling factor.

[0088] The embodiments in this specification employ a graph-structured urban thermal effect map with embedded prediction models. This not only enhances the visualization and quantitative analysis capabilities of thermal effect distribution but also clarifies heat propagation paths and key areas of influence of thermal effects, thereby improving the accuracy of determining the thermal effect levels at different locations.

[0089] In some embodiments, the emergency monitoring and management platform can determine a prediction model 310 through joint training based on the thermal effect processing layer 311 and the heat island prediction layer 312. Training samples can be constructed based on historical data, which can be obtained from a database. Training samples may include at least one set of sample remote sensing data, sample meteorological station data, sample environmental data, sample urban activity data, and sample urban thermal effect maps constructed based on the sample nodes at a first historical time point or a first historical time period. The label can be the actual heat island level of the sample node at a second historical time point or a second historical time period. The second historical time point or second historical time period is after the first historical time point or the first historical time period. In some embodiments, the label can be determined and annotated based on the historical data of the sample nodes.

[0090] During training, sample remote sensing data, sample meteorological station data, sample environmental data, and sample urban activity data are input into the thermal effect processing layer 311 to obtain the latent thermal effect vector output by the thermal effect processing layer 311. This latent thermal effect vector, along with the sample urban thermal effect map, is input into the heat island prediction layer 312 to obtain the heat island level output by the heat island prediction layer 312. A loss function is constructed based on the labels and the heat island level output by the heat island prediction layer 312. The parameters of the thermal effect processing layer 311 and the heat island prediction layer 312 are synchronously and iteratively updated (e.g., using gradient descent) based on the loss function until preset training conditions are met. Training then ends, and the trained prediction model 310 is obtained. These preset training conditions may include, but are not limited to, loss function convergence and reaching a threshold training period.

[0091] For more information on remote sensing data, weather station data, environmental data, urban activity data, and urban heat island classification, please refer to [link to relevant resources]. Figure 2 And its related descriptions.

[0092] The embodiments in this specification obtain a prediction model through joint training of the thermal effect processing layer and the heat island prediction layer. This not only improves the prediction accuracy and spatial resolution of the prediction model, but also enhances its dynamic adaptability and resource optimization efficiency.

[0093] The embodiments in this specification are based on urban heat effect maps and utilize trained prediction models to determine the heat island level of multiple candidate nodes or locations. They can combine actual conditions to more accurately predict key areas affected by the heat island effect and reduce the manpower costs and resource waste required for human assessment.

[0094] Figure 4 This is an exemplary flowchart illustrating a method for determining a target cooling strategy according to some embodiments of this specification. Figure 4 As shown, process 400 includes the following steps. In some embodiments, process 400 may be executed by an emergency monitoring and management platform.

[0095] Step 410: Based on the target node, generate at least one candidate cooling strategy.

[0096] Candidate cooling strategies refer to proposed cooling schemes or measures to address the urban heat island effect. In some embodiments, a candidate cooling strategy may include candidate cooling parameters and a water truck corresponding to the target node. Candidate cooling parameters are similar to cooling parameters. For more information on cooling parameters and target nodes, please refer to [link to relevant documentation]. Figure 2 And its related descriptions.

[0097] In some embodiments, the emergency monitoring and management platform can generate multiple initial cooling strategies based on the current locations and target nodes of multiple sprinkler trucks; for one of the multiple initial cooling strategies, the load balancing score of the initial cooling strategy is determined based on the workload of the multiple sprinkler trucks; and based on the load balancing scores of the multiple initial cooling strategies, at least one candidate cooling strategy is determined.

[0098] For more information on the current location of the water truck, please refer to [link / reference]. Figure 2 And its related descriptions.

[0099] An initial cooling strategy is used to determine candidate cooling strategies. For example, an emergency monitoring and management platform can select candidate cooling strategies from the initial cooling strategies based on their load balancing scores. The content of the initial cooling strategy is similar to that of the candidate cooling strategies; for more information, please refer to the candidate cooling strategies and their related descriptions.

[0100] In some embodiments, the emergency monitoring and management platform can generate multiple initial cooling strategies based on the current locations and target nodes of multiple sprinkler trucks, through at least one of the following methods: nearest, random allocation, and random exchange.

[0101] In some embodiments, in response to the nearest-neighbor approach, the emergency monitoring and management platform can assign the nearest water truck to each of the multiple target nodes based on their priorities and current locations. It can also determine multiple initial cooling strategies using the system's default initial cooling parameters and the assigned water trucks for each target node. For more information on target node priorities and the current locations of water trucks, please refer to [link to relevant documentation]. Figure 2 And its related descriptions.

[0102] In some embodiments, in response to the random allocation method, the emergency monitoring and management platform can randomly sort multiple target nodes and randomly generate initial cooling parameters, randomly allocate water trucks based on the sorting of multiple target nodes, and determine multiple initial cooling strategies by combining the randomly generated initial cooling parameters and the allocated water trucks corresponding to multiple target nodes.

[0103] In some embodiments, in response to a random exchange, the emergency monitoring and management platform can generate multiple initial cooling strategies to be exchanged using either the nearest neighbor or random allocation method. From these multiple initial cooling strategies, at least two are randomly selected, and the initial cooling parameters of these at least two strategies are randomly exchanged to generate at least two initial cooling strategies. The initial cooling parameters are similar to the cooling parameters; more details can be found in [link to relevant documentation]. Figure 2 And its related descriptions.

[0104] The embodiments in this specification generate multiple initial cooling strategies based on the current locations of multiple sprinkler trucks and target nodes, using at least one of the following methods: nearest, random allocation, and random exchange. This ensures the diversity of cooling strategies for the urban heat island effect and improves the cooling effect.

[0105] The workload of a water truck refers to the working time of the water truck. In some embodiments, the workload of a water truck may include the driving time without watering operations and the watering time. For example, the workload of a water truck may be the weighted sum of the driving time without watering operations and the watering time.

[0106] The load balancing score is used to evaluate the balance of workload of sprinkler trucks performing sprinkler operations at corresponding target nodes based on the initial cooling parameters (e.g., initial sprinkler parameters) in the initial cooling strategy. In some embodiments, the load balancing score can be determined based on the workload of multiple sprinkler trucks. The load balancing score can be represented by a numerical value between 0 and 100. The initial cooling parameters (e.g., initial sprinkler parameters) are similar to the cooling parameters (e.g., sprinkler parameters); for more information, please refer to [link to relevant documentation]. Figure 2 And its related descriptions.

[0107] In some embodiments, for one of a plurality of initial cooling strategies, the load balancing score of the initial cooling strategy may be negatively correlated with the standard deviation or variance of the workload of the multiple sprinkler trucks performing water spraying operations based on the initial cooling strategy. For example, the smaller the standard deviation or variance of the workload of the multiple sprinkler trucks, the higher the load balancing score of the initial cooling strategy.

[0108] In some embodiments, the emergency monitoring and management platform can sort multiple initial cooling strategies from highest to lowest based on their load balancing scores, and determine the top N initial cooling strategies as at least one candidate cooling strategy. N is a positive integer greater than or equal to 1. The value of N can be set based on experience or by the system. In some embodiments, the emergency monitoring and management platform can determine initial cooling strategies with scores higher than a preset score threshold as at least one candidate cooling strategy based on their load balancing scores. The preset score threshold can be set based on experience or by the system. For example, the preset score threshold can be set to 60.

[0109] The embodiments in this specification, when allocating watering tasks to multiple sprinkler trucks, not only consider the distance between the sprinkler trucks and the target node and the priority of the target node, but also the workload of the sprinkler trucks. This enables the priority allocation of sprinkler trucks that are closer to the target node and have a lighter load, thus avoiding the situation of overloading the sprinkler trucks.

[0110] Step 420: Based on at least one candidate cooling strategy, determine the candidate cooling factor of at least one candidate cooling strategy.

[0111] Candidate cooling factors are used to characterize the degree of influence of candidate cooling strategies on the cooling effect. In some embodiments, candidate cooling factors are similar to cooling factors; for more information, please refer to [link to relevant documentation]. Figure 3 And its related descriptions.

[0112] In some embodiments, the emergency monitoring and management platform can determine the candidate cooling factor of a candidate cooling strategy based on the candidate cooling parameters in the candidate cooling strategy. For example, the emergency monitoring and management platform can determine the candidate cooling factor based on the candidate sprinkling parameters (e.g., sprinkling intensity, sprinkling duration) and / or the candidate spraying parameters (e.g., spraying intensity, spraying duration) in the candidate cooling parameters. Candidate cooling parameters are similar to cooling parameters; for more information, please refer to [link to relevant documentation]. Figure 2 And related descriptions. The process of determining the candidate cooling factor based on candidate cooling parameters is similar to the process of determining the cooling factor based on cooling parameters. For more details, please refer to [link to relevant documentation]. Figure 3 And its related descriptions.

[0113] Step 430: Based on the candidate cooling factors of at least one candidate cooling strategy, determine the target heat island level through a prediction model.

[0114] The target heat island rating refers to the heat island rating after implementing candidate cooling strategies on the target node. In some embodiments, the target heat island rating is similar to the heat island rating; for more information, please refer to [link to relevant documentation]. Figure 2 And its related descriptions.

[0115] In some embodiments, the emergency monitoring and management platform can determine the edges pointing to the target node based on the urban heat effect map, update the cooling factor in the edge attributes of the edge with the candidate cooling factor of the candidate cooling strategy, and, based on the updated edge attributes based on the urban heat effect map, output the target heat island level corresponding to the candidate cooling strategy through a prediction model. For more information on urban heat effect maps and prediction models, please refer to [link to relevant documentation]. Figure 3 And its related descriptions.

[0116] Step 440: Determine the target cooling strategy based on the target heat island level of at least one candidate cooling strategy.

[0117] The target cooling strategy refers to the cooling strategy selected from at least one candidate cooling strategy and applied to the target node. In some embodiments, the target cooling strategy is similar to the candidate cooling strategies; for more details, please refer to step 410 and its related description.

[0118] In some embodiments, the emergency monitoring and management platform can identify candidate cooling strategies with a target heat island level less than or equal to a preset threshold as the target cooling strategy. For more information on the preset threshold, please refer to [link to relevant documentation]. Figure 2 And related descriptions. In some embodiments, the emergency monitoring and management platform can sort the target heat island levels of candidate cooling strategies based on the target heat island level of at least one candidate cooling strategy, and determine the candidate cooling strategy with the lowest target heat island level as the target cooling strategy.

[0119] The embodiments in this specification predict the target heat island level of multiple candidate cooling strategies, predict the cooling effect of the candidate cooling strategies, and then select the target cooling strategy with the best cooling effect, thereby effectively improving the cooling effect and cooling efficiency of the urban heat island effect.

[0120] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0121] Furthermore, this invention uses specific terms to describe embodiments of the invention. For example, "some embodiments" refers to a particular feature, structure, or characteristic associated with at least one embodiment of the invention. Additionally, certain features, structures, or characteristics in one or more embodiments of the invention can be appropriately combined.

[0122] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this specification are not intended to limit the order of the processes and methods described herein. Although various examples have been discussed in the foregoing disclosure of some embodiments of the invention that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the spirit and scope of the embodiments described herein. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely using software solutions, such as installing the described system on existing servers or mobile devices.

[0123] Similarly, it should be noted that, in order to simplify the description disclosed herein and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of embodiments in this specification may sometimes combine multiple features into a single embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the subject matter of this specification requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of a single embodiment disclosed above.

[0124] For each patent, patent application, patent application publication, and other material such as articles, books, specifications, publications, and documents referenced in this specification, the entire contents of which are incorporated herein by reference. This excludes historical application documents that are inconsistent with or conflict with the content of this specification, as well as documents that limit the broadest scope of the claims in this specification (currently or subsequently appended to this specification). It should be noted that in the event of any inconsistency or conflict between the descriptions, definitions, and / or terminology used in the supplementary materials to this specification and the content of this specification, the descriptions, definitions, and / or terminology used in this specification shall prevail.

[0125] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. An emergency monitoring system for the urban heat island effect based on a large-scale Internet of Things (IoT) model, characterized in that, This includes an emergency monitoring and management platform, which is configured as follows: Based on remote sensing data, meteorological station data, environmental data, and urban activity data, the heat island level of multiple candidate nodes was determined; Based on the heat island level and preset threshold of the multiple candidate nodes, the target node and the cooling parameters of the target node are determined. The cooling parameters include the water spraying parameters of the sprinkler truck and the spraying parameters of the spray system. Based on the heat island level of the target node, determine the corresponding sprinkler truck for the target node; The target node is sent to the navigation terminal of the sprinkler truck corresponding to the target node via a wireless network, and the navigation terminal is controlled to display the navigation route to the target node; Based on the cooling parameters of the target node, the water truck and spray system corresponding to the target node are controlled to perform water sprinkling and spraying.

2. The urban heat island effect emergency monitoring system as described in claim 1, characterized in that, The emergency monitoring and management platform is also configured as follows: The heat island level of the multiple candidate nodes is determined by a prediction model, which is a machine learning model and includes a heat effect processing layer and a heat island prediction layer.

3. The urban heat island effect emergency monitoring system as described in claim 2, characterized in that, The input to the heat island prediction layer includes an urban heat effect map, the nodes of which are the multiple candidate nodes. Nodes that meet the preset location conditions have edges between them. The node attributes include the latent heat effect vector output by the heat effect processing layer, and the edge attributes include the cooling factor.

4. The urban heat island effect emergency monitoring system as described in claim 2, characterized in that, The emergency monitoring and management platform is also configured as follows: Based on the target node, at least one candidate cooling strategy is generated, and the candidate cooling strategy includes candidate cooling parameters and the sprinkler truck corresponding to the target node. Based on the at least one candidate cooling strategy, determine the candidate cooling factor of the at least one candidate cooling strategy; Based on the candidate cooling factors of the at least one candidate cooling strategy, the target heat island level is determined through the prediction model; A target cooling strategy is determined based on the target heat island level of the at least one candidate cooling strategy.

5. The urban heat island effect emergency monitoring system as described in claim 1, characterized in that, The water truck includes onboard sensors, and the emergency monitoring and management platform is further configured as follows: The vehicle-mounted sensors acquire the surface temperature, air humidity, and wind speed at the current location of the sprinkler truck. Based on the surface temperature, air humidity, and wind speed at the current location, the water spraying mode, spraying intensity, and duration of the water truck are adjusted.

6. An emergency monitoring method for the urban heat island effect based on a large-scale Internet of Things model, characterized in that, The method is executed by the emergency monitoring and management platform of the urban heat island effect emergency monitoring system based on an Internet of Things (IoT) big data model. The method includes: Based on remote sensing data, meteorological station data, environmental data, and urban activity data, the heat island level of multiple candidate nodes was determined; Based on the heat island level and preset threshold of the multiple candidate nodes, the target node and the cooling parameters of the target node are determined. The cooling parameters include the water spraying parameters of the sprinkler truck and the spraying parameters of the spray system. Based on the heat island level of the target node, determine the corresponding sprinkler truck for the target node; The target node is sent to the navigation terminal of the sprinkler truck corresponding to the target node via a wireless network, and the navigation terminal is controlled to display the navigation route to the target node; Based on the cooling parameters of the target node, the water truck and spray system corresponding to the target node are controlled to perform water sprinkling and spraying.

7. The emergency monitoring method for urban heat island effect as described in claim 6, characterized in that, The determination of the heat island level of multiple candidate nodes includes: The heat island level of the multiple candidate nodes is determined by a prediction model, which is a machine learning model and includes a heat effect processing layer and a heat island prediction layer.

8. The emergency monitoring method for urban heat island effect as described in claim 7, characterized in that, The input to the heat island prediction layer includes an urban heat effect map, the nodes of which are the multiple candidate nodes. Nodes that meet the preset location conditions have edges between them. The node attributes include the latent heat effect vector output by the heat effect processing layer, and the edge attributes include the cooling factor.

9. The emergency monitoring method for urban heat island effect as described in claim 7, characterized in that, Also includes: Based on the target node, at least one candidate cooling strategy is generated, and the candidate cooling strategy includes candidate cooling parameters and the sprinkler truck corresponding to the target node. Based on the at least one candidate cooling strategy, determine the candidate cooling factor of the at least one candidate cooling strategy; Based on the candidate cooling factors of the at least one candidate cooling strategy, the target heat island level is determined through the prediction model; A target cooling strategy is determined based on the target heat island level of the at least one candidate cooling strategy.

10. The emergency monitoring method for urban heat island effect as described in claim 6, characterized in that, The sprinkler truck includes onboard sensors, and the method further includes: The vehicle-mounted sensors acquire the surface temperature, air humidity, and wind speed at the current location of the sprinkler truck. Based on the surface temperature, air humidity, and wind speed at the current location, the water spraying mode, spraying intensity, and duration of the water truck are adjusted.

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