City public place gas emergency system and method based on internet of things large model

Abstract

The application provides an urban public place gas emergency system and method based on an Internet of Things large model, and relates to the field of public place gas emergency. The system comprises an emergency supervision management platform configured to execute the method, which comprises: acquiring gas monitoring data of a target area; in response to meeting evacuation conditions, generating evacuation parameters based on the gas monitoring data and regional data of the target area; and sending an evacuation control signal to an evacuation device deployed in the target area based on the evacuation parameters to control the evacuation device to guide personnel to evacuate, including: controlling a display device to display an evacuation path; controlling the display color and indication direction of an electronic sign based on sign control parameters; controlling the brightness and mode of a lighting device based on lighting control parameters; and controlling a gate to open based on gate control parameters. The application can formulate an evacuation scheme for different environments and gas leakage conditions, and control the evacuation device to guide personnel to evacuate quickly and effectively.

Description

Technical Field

[0001] This manual pertains to the field of gas emergency response in public places, and in particular to urban public place gas emergency response systems and methods based on a large-scale Internet of Things (IoT) model. Background Technology

[0002] In the process of urban development, the safety management of public places faces multiple bottlenecks. In the event of a gas leak in densely populated public places such as scenic spots, squares, and transportation hubs, the complex space and dense crowds may lead to difficulties in evacuation, resulting in regional stampede risks and secondary disaster hazards.

[0003] Therefore, it is necessary to provide a gas emergency system and method for urban public places based on the Internet of Things (IoT) big data model, which can formulate targeted evacuation plans for different environments and different gas leak situations, and send evacuation control signals to evacuation devices to control the evacuation devices to guide people to evacuate quickly and effectively. Summary of the Invention

[0004] To address the challenge of developing personalized evacuation plans for different environments and gas leak situations, this invention provides a gas emergency system and method for urban public places based on a large-scale Internet of Things (IoT) model.

[0005] The invention includes an urban public place gas emergency system based on an Internet of Things (IoT) big data model. The system includes an emergency monitoring and management platform and an emergency monitoring object platform. The emergency monitoring and management platform is configured to execute an urban public place gas emergency IoT big data model method.

[0006] The invention includes a gas emergency response method for urban public places based on an Internet of Things (IoT) big data model. The method is executed by an emergency monitoring and management platform within an urban public place gas emergency response system based on an IoT big data model. The method includes: acquiring gas monitoring data of a target area through monitoring devices deployed within the monitoring area, based on the emergency monitoring platform; generating evacuation parameters based on the gas monitoring data and the area data of the target area when evacuation conditions are met, the evacuation parameters including evacuation routes, sign control parameters, lighting control parameters, and turnstile control parameters; and sending evacuation control signals to evacuation devices deployed within the target area based on the evacuation parameters to control the evacuation devices to guide the evacuation of personnel within the target area, including: controlling a display device to display the evacuation route; controlling the display color and direction of electronic signs based on the sign control parameters; controlling the brightness and lighting mode of lighting equipment based on the lighting control parameters; and controlling the open / closed state of turnstiles based on the turnstile control parameters.

[0007] The beneficial effects of the above invention include, but are not limited to: (1) When evacuation conditions are met, evacuation parameters are generated by coordinating gas monitoring data and regional data to control evacuation devices and guide people in the target evacuation area, which can effectively improve the efficiency of crowd evacuation, reduce the risk of stampedes, enhance the ability to respond to emergencies, and ensure public safety. Among them, the precise adjustment of the light sign control parameters can enhance the dynamic adaptability of directional signs and meet the guidance needs of different scenarios. Adjusting the lighting control parameters can ensure visibility in low visibility environments and further enhance the guidance of the path. The intelligent control of the gate status realizes the precise coordination of physical isolation and path control, effectively preventing people from accidentally entering dangerous areas. The entire control signal linkage mechanism ensures the reliability and response speed of multi-device collaborative work through parameterized instruction transmission, significantly improving the emergency response efficiency of public safety incidents in complex environments; (2) By integrating gas monitoring data, regional data generation, alternative evacuation routes, and screening evacuation routes through actual regional personnel data and obstacle data, the feasibility and rationality of evacuation routes are effectively balanced while ensuring evacuation efficiency, avoiding congestion caused by insufficient evacuation routes or chaos caused by choosing unreasonable evacuation channels; (3) By dynamically correcting the alarm threshold and precisely controlling the monitoring frequency of the image acquisition device, the system can adapt to changes in environmental conditions, so as to achieve reasonable energy consumption while ensuring evacuation efficiency. Attached Figure Description

[0008] The present invention 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, and in these embodiments, the same reference numerals denote the same structures, wherein:

[0009] Figure 1 This is a platform structure diagram of an urban public place gas emergency system based on an Internet of Things (IoT) big data model, as shown in some embodiments of this specification.

[0010] Figure 2 This is an exemplary flowchart of an emergency gas supply method for urban public places based on an Internet of Things (IoT) big data model, as shown in some embodiments of this specification.

[0011] Figure 3 This is an exemplary flowchart illustrating the determination of evacuation routes according to some embodiments of this specification;

[0012] Figure 4 This is an exemplary flowchart illustrating the updating of safe zones and alternative evacuation routes according to some embodiments of this specification;

[0013] Figure 5 This is a schematic diagram illustrating the determination of modal parameters according to some embodiments of this specification. Detailed Implementation

[0014] The accompanying drawings used in the description of the embodiments will be briefly introduced below. The drawings do not represent all embodiments.

[0015] In the embodiments of the present invention, when describing the operations performed step by step, unless otherwise specified, the order of the steps is interchangeable, the steps can be omitted, and other steps may be included in the operation process.

[0016] Figure 1 This is a platform structure diagram of an urban public place gas emergency system based on an Internet of Things (IoT) big data model, as shown in some embodiments of this specification.

[0017] In some embodiments, such as Figure 1 As shown, the urban public place gas emergency system 100 based on the Internet of Things big model may include an emergency supervision user platform 110, an emergency supervision service platform 120, an emergency supervision management platform 130, an emergency supervision sensor network platform 140, and an emergency supervision object platform 150.

[0018] The Emergency Monitoring User Platform 110 refers to an interactive platform that provides emergency service operations for users, including user terminals. Examples include computers and other devices with input and / or output functions.

[0019] Users include both higher-level regulatory authorities and ordinary citizens. Higher-level regulatory authorities refer to the superior management departments of the emergency supervision and management platform 130, who can deploy emergency supervision tasks to the platform through the emergency supervision user platform 110 and receive dynamic emergency supervision information from the platform. Ordinary citizens refer to individuals in public places who require evacuation, such as tourists in shopping malls, hotel guests, and restaurant staff.

[0020] The Emergency Monitoring Service Platform 120 refers to an interactive service platform that receives and transmits data, including servers, gateways, and routers.

[0021] In some embodiments, the emergency monitoring service platform 120 interacts upward with the emergency monitoring user platform 110 and downward with the emergency monitoring management platform 130.

[0022] The Emergency Supervision and Management Platform 130 refers to a comprehensive platform for processing and managing emergency supervision data.

[0023] In some embodiments, the emergency monitoring and management platform includes processors and / or servers, data centers, etc. The data center is equipped with storage devices.

[0024] In some embodiments, the emergency monitoring and management platform 130 can be configured to execute a gas emergency response method for urban public places based on an IoT big data model. For more information on this method, see [link to relevant documentation]. Figure 2 Related descriptions.

[0025] The emergency monitoring sensor network platform 140 refers to a management platform that transmits emergency monitoring-related sensor data or information, including communication transmission networks and routing devices.

[0026] In some embodiments, the emergency monitoring sensor network platform 140 interacts upward with the emergency monitoring management platform 130 and downward with the emergency monitoring object platform 150.

[0027] The Emergency Supervision Target Platform 150 refers to the platform for emergency supervision data collection and execution of instructions.

[0028] In some embodiments, the emergency monitoring platform is equipped with various monitoring devices, storage devices, and evacuation devices. Monitoring devices may include image acquisition devices, environmental monitoring devices, gas monitoring devices, etc.

[0029] Image acquisition devices refer to devices used to acquire image data, such as electronic cameras. Image data can include image sensor data and external image data.

[0030] Environmental monitoring devices refer to devices used to monitor environmental data, such as temperature sensors, humidity sensors, and wind direction detectors.

[0031] Gas monitoring devices are devices used to monitor gas data, such as gas concentration monitoring devices.

[0032] Evacuation facilities are devices that provide evacuation support in emergency situations. In some embodiments, evacuation facilities include turnstiles, electronic signs, lighting equipment, display devices, ventilation systems, projection devices, broadcasting systems, etc.

[0033] Electronic signs are used to guide or provide directions. Display devices are used to present visual information (such as evacuation routes). Ventilation systems are used to disperse harmful gases to reduce the concentration of flammable gases in the air. Examples include fans, exhaust fans, and air purifiers. Projection devices are used to project images to guide directions. For example, projecting evacuation routes. Broadcasting systems are used to transmit audio information. For example, broadcasting evacuation routes.

[0034] In some embodiments, the emergency monitoring platform is also equipped with sensing devices, such as light sensors and volume detectors. These sensing devices are used to acquire environmental perception data. For example, a light sensor can detect ambient light intensity, and a volume detector can monitor ambient sound intensity.

[0035] In some embodiments of this specification, the urban public place gas emergency system based on the Internet of Things big data model can form an information operation closed loop between various functional platforms, operate in a coordinated and regular manner, and improve emergency evacuation efficiency and avoid stampede accidents by efficiently and accurately determining the actual evacuation control parameters.

[0036] Figure 2 This is an exemplary flowchart illustrating an emergency gas supply method for urban public places based on an IoT big data model, according to some embodiments of this specification. In some embodiments, the emergency gas supply method for urban public places based on an IoT big data model can be executed by an emergency monitoring and management platform. Figure 2 As shown, process 200 includes the following steps.

[0037] Step 210: Based on the emergency monitoring platform, acquire gas monitoring data of the target area through monitoring devices deployed within the monitoring area.

[0038] A monitoring area refers to an area that requires safety monitoring and control. In some embodiments, the monitoring area includes the target area and the external environment area.

[0039] The target area refers to the area where there is a need for gas safety evacuation. For example, the interior areas of public places that use gas, such as shopping malls, hotels, and restaurants.

[0040] The external environment area refers to the surrounding environment of the target area. For example, the roads, buildings, etc., around the target area.

[0041] Gas monitoring data refers to relevant parameters of gases within a target area. Examples include the concentration of combustible gas and oxygen within the target area. Combustible gas concentration can be expressed as the concentration of harmful gases (such as methane).

[0042] In some embodiments, gas monitoring data can be acquired through a gas monitoring device.

[0043] In some embodiments, in response to the evacuation conditions being met, the emergency monitoring and management platform may execute step 220, which includes steps 221 and 222.

[0044] Evacuation conditions refer to the critical conditions that trigger the evacuation of personnel when a gas leak hazard occurs.

[0045] In some embodiments, evacuation conditions may include a gas concentration in gas monitoring data that is higher than a first preset concentration threshold. The first preset concentration threshold may be preset manually based on experience or set by default by the processor.

[0046] Step 221: Generate evacuation parameters based on gas monitoring data and regional data of the target area.

[0047] Regional data refers to data describing the conditions of a target area. Examples include terrain data, the location of each passageway, and passageway dimensions within the target area. Regional data can be obtained based on a planar and / or three-dimensional structural map of the target area.

[0048] Evacuation parameters refer to the relevant parameters used to guide people to evacuate, including evacuation routes, sign control parameters, lighting control parameters, and turnstile control parameters.

[0049] Evacuation routes refer to the routes taken by people to evacuate.

[0050] Light sign control parameters refer to the operational parameters of electronic signs used to guide people during evacuation. Examples include the direction indicated and the display color of the electronic sign.

[0051] Lighting control parameters refer to the operating parameters of lighting equipment used to guide personnel evacuation. Examples include the brightness and lighting mode of the lighting equipment. The lighting mode can include constant light intensity mode, flashing mode, etc.

[0052] Turnstile control parameters refer to the operating parameters of a turnstile used to guide the evacuation of people. For example, the open / closed state of the turnstile.

[0053] In some embodiments, the emergency monitoring and management platform can generate multiple connecting paths based on regional data of the target area using a path planning algorithm; and select one or more of these connecting paths as evacuation routes according to selection criteria. For example, the selection criteria could be the shortest path length or the highest path score.

[0054] In this context, a connected path refers to a path connecting the target region to the external environment region. The path score can be represented by a first weighted sum of the normalized path length and the exit capacity. The exit capacity refers to the capacity at the exit of the connected path; the exit capacity of each region can be preset manually. Based on the region where the exit of each connected path is located, the path score of each connected path can be determined. The path planning algorithm can be Dijkstra's algorithm. The normalization method can be Min-Max normalization.

[0055] In some embodiments, the emergency monitoring and management platform can determine the control parameters of the signboard, lighting control parameters, and turnstile control parameters based on the evacuation route.

[0056] For example, the emergency monitoring and management platform can use the direction of the evacuation route as the direction of the electronic signs on the evacuation route, and display the signs in a preset emergency color (such as red); use a first preset brightness as the illumination brightness of the lighting equipment on the evacuation route, and set the lighting mode to flashing mode; use a second preset brightness as the illumination brightness of the lighting equipment on non-evacuation routes, and set the lighting mode to constant light intensity mode; open the turnstiles on the evacuation route, and close the turnstiles outside the evacuation route. The first preset brightness is higher than the second preset brightness, and the first preset brightness, second preset brightness, and preset emergency color are manually preset.

[0057] In some embodiments, the emergency monitoring and management platform can acquire image sensor data of the target area, thereby acquiring personnel data and obstacle data of the target area; based on gas monitoring data and area data, it can determine safe zones and generate alternative evacuation routes; and based on the personnel data and obstacle data, it can determine an evacuation route from the alternative evacuation routes. For more information on this section, please refer to [link to relevant documentation]. Figure 3 And its related descriptions.

[0058] Step 222: Based on the evacuation parameters, send evacuation control signals to the evacuation devices deployed in the target area to control the evacuation devices to guide the evacuation of personnel in the target area.

[0059] Evacuation control signals are signals used during the evacuation process to control evacuation devices to operate according to evacuation parameters.

[0060] In some embodiments, the evacuation control signal can be used to control the evacuation device to guide personnel within the evacuation target area, including controlling the display device to display the evacuation route; controlling the display color and direction of the electronic sign based on the sign control parameters; controlling the lighting brightness and lighting mode of the lighting equipment based on the lighting control parameters; and controlling the opening and closing state of the gate based on the gate control parameters.

[0061] In some embodiments of this specification, when evacuation conditions are met, evacuation parameters are generated collaboratively using gas monitoring data and regional data. These parameters are then used to control evacuation devices and guide personnel within the target evacuation area, effectively improving crowd evacuation efficiency, reducing the risk of stampedes, enhancing emergency response capabilities, and ensuring public safety. Specifically, precise adjustment of sign control parameters enhances the dynamic adaptability of directional signage, meeting the guidance needs of different scenarios. Adjustment of lighting control parameters ensures visibility in low-visibility environments and further strengthens path guidance. Intelligent control of gate status achieves precise coordination between physical isolation and path management, effectively preventing personnel from accidentally entering dangerous areas. The entire control signal linkage mechanism, through parameterized command transmission, ensures the reliability and response speed of multi-device collaborative operation, significantly improving the emergency response efficiency for public safety incidents in complex environments.

[0062] In some embodiments, evacuation parameters also include ventilation control parameters.

[0063] In some embodiments, the emergency monitoring and management platform can control the air supply intensity and direction of the ventilation device based on ventilation control parameters.

[0064] Ventilation control parameters refer to the parameters used to control the operation of ventilation devices, including air supply direction and air supply intensity.

[0065] In some embodiments, the emergency monitoring and management platform can determine ventilation control parameters based on gas monitoring data and regional data. For example, the platform can generate evacuation routes based on gas monitoring data and regional data; determine the direction of gas concentration decrease as the gas diffusion direction and the gas diffusion rate as the gas leakage rate based on the gas monitoring data; activate ventilation devices located along the evacuation routes and determine the air supply direction of the ventilation devices as the opposite direction of gas diffusion; perform a second weighted summation on the normalized gas leakage rate and the evacuation route length, with the air supply intensity of the ventilation devices being positively correlated with the result of the second weighted summation. For details on how to generate evacuation routes based on gas monitoring data and regional data, please refer to the relevant description in step 221.

[0066] In some embodiments of this specification, ventilation devices in the corresponding areas are automatically activated to supply air to the evacuation routes, effectively preventing the intrusion of toxic or harmful gases or smoke and ensuring the safety and unobstructed flow of the evacuation routes.

[0067] Figure 3 This is an exemplary flowchart illustrating the determination of evacuation routes according to some embodiments of this specification. In some embodiments, the method for determining evacuation routes may be performed by an emergency monitoring and management platform. Figure 3 As shown, process 300 includes the following steps.

[0068] Step 310: Based on the image acquisition device, acquire image sensing data of the target area.

[0069] Image sensing data refers to image data acquired by an image acquisition device.

[0070] Step 320: Based on image sensing data, acquire regional personnel data and obstacle data of the target area.

[0071] Regional population data refers to data describing the population situation in a target area. For example, the number of people, population density, and the direction of population movement.

[0072] In some embodiments, the emergency monitoring and management platform can identify the number of people, the direction of people movement, and the size of the area based on image sensor data and image recognition algorithms, and determine the population density as the ratio of the number of people to the area size. The image recognition algorithm can be a Haar feature cascade algorithm.

[0073] Obstacle data refers to data describing the situation of obstacles within a target area. Examples include obstacle size, obstacle location, obstacle area percentage, and number of obstacles.

[0074] In some embodiments, the emergency monitoring and management platform can identify and obtain obstacle data based on image sensing data and image recognition algorithms.

[0075] Step 330: Based on gas monitoring data and regional data, determine the safe area and generate alternative evacuation routes.

[0076] A safe zone is an area where gas hazards can be effectively avoided. In some embodiments, a safe zone may refer to an area where the gas concentration is below a second preset concentration threshold.

[0077] In some embodiments, the emergency monitoring and management platform can divide the target area into multiple functional sub-regions based on terrain data in the regional data. A functional sub-region refers to a sub-region divided according to the area's function, such as a corridor, stairwell, elevator, lobby, or room. The emergency monitoring and management platform can then input gas monitoring data and regional data into a Gaussian plume model to output the estimated gas concentration for each functional sub-region. Functional sub-regions with estimated gas concentrations below a second preset concentration threshold are designated as safe areas, while those with estimated gas concentrations above a third preset concentration threshold are designated as high-risk areas.

[0078] The preset grid size, second preset concentration threshold, and third preset concentration threshold can be set manually based on experience. The second preset concentration threshold is less than the first preset concentration threshold, and the first preset concentration threshold is less than the third preset concentration threshold.

[0079] Alternative evacuation routes are candidate routes used to determine evacuation routes.

[0080] In some embodiments, the emergency monitoring and management platform can eliminate connecting paths that pass through high-risk areas and use the remaining connecting paths as alternative evacuation routes. For more information on connecting paths and evacuation routes, please refer to the relevant descriptions above.

[0081] Step 340: Based on the area's personnel data and obstacle data, determine the evacuation route from the alternative evacuation routes.

[0082] In some embodiments, the emergency monitoring and management platform can determine the degree of obstacle impact and congestion of alternative evacuation routes based on regional personnel data and obstacle data; determine the path length and path complexity of alternative evacuation routes based on regional data; determine the evacuation difficulty by querying an evacuation difficulty table based on the path complexity, path length, degree of obstacle impact, and degree of congestion of alternative evacuation routes; and select the N alternative evacuation routes with the lowest evacuation difficulty as the evacuation routes. The evacuation difficulty table can be constructed based on historical data or experimental data. The greater the path complexity, path length, degree of obstacle impact, and degree of congestion of the alternative evacuation routes, the greater the evacuation difficulty.

[0083] For example, the emergency monitoring and management platform can determine the area ratio and number of obstacles on alternative evacuation routes; perform a third weighted summation on the normalized obstacle area ratio and number of obstacles to obtain the degree of obstacle impact of the alternative evacuation routes; the congestion level of alternative evacuation routes is positively correlated with the population density; determine the ratio of the straight-line distance between the start and end points of the alternative evacuation routes to the route length, and multiply this ratio by the curvature weight to determine the path complexity of the alternative evacuation routes.

[0084] The curvature weight is positively correlated with the number of right-angle bends in the candidate evacuation routes, the value of N is positively correlated with the number of people in the target area, and the weight of the third weighted sum can be preset manually based on experience. The number of right-angle bends can be obtained based on the candidate evacuation routes using a corner detection algorithm (such as Harris corner detection).

[0085] In some embodiments, the emergency monitoring and management platform may acquire external image data based on an image acquisition device; determine the evacuation efficiency of alternative evacuation routes based on regional personnel data, obstacle data, and regional data; and determine an evacuation route from the alternative evacuation routes based on external image data and evacuation efficiency.

[0086] External image data refers to image data of the external environment area acquired by an image acquisition device.

[0087] The evacuation efficiency of alternative evacuation routes refers to the number of people evacuated through alternative evacuation routes per unit time, and can be used to measure the evacuation capacity of alternative evacuation routes.

[0088] In some embodiments, the emergency monitoring and management platform can construct a first target feature vector based on the regional personnel data, obstacle data, and regional data of each functional sub-area traversed by the alternative evacuation route. The platform then searches a first vector database to determine a first reference feature vector with the highest vector similarity to the first target feature vector, and uses the corresponding first reference evacuation efficiency as the evacuation efficiency of the alternative evacuation route. The first vector database includes multiple sets of first reference feature vectors and their corresponding first reference evacuation efficiencies, which can be constructed based on historical data. For example, the first reference feature vector can be constructed based on historical regional personnel data, historical obstacle data, and historical regional data of each functional sub-area traversed by historical evacuation routes, and the first reference evacuation efficiency can be represented by the number of people actually evacuated via historical evacuation routes per unit time. Vector similarity can be represented by cosine similarity, Euclidean distance, etc.

[0089] In some embodiments, the emergency monitoring and management platform can determine the evacuation efficiency of alternative evacuation routes based on regional personnel data, obstacle data, regional data, and access control data.

[0090] Gateway control data refers to the data related to gateway control devices. These devices may include turnstiles, and the gateway control data may include the turnstile's position, opening speed, maximum throughput, and gate size.

[0091] In some embodiments, port control data may be obtained through manual input.

[0092] In some embodiments, the emergency monitoring and management platform can construct a second target feature vector based on regional personnel data, obstacle data, area data, and access control data of each functional sub-area traversed by the alternative evacuation route. The platform then searches a second vector database to determine the second reference feature vector with the highest vector similarity to the second target feature vector, and uses the corresponding second reference evacuation efficiency as the evacuation efficiency of the alternative evacuation route. The second vector database includes multiple sets of second reference feature vectors and their corresponding second reference evacuation efficiencies. The construction method of the second vector database is the same as that of the first vector database, and will not be elaborated here.

[0093] In some embodiments of this specification, by taking into account port control data, the evacuation efficiency of each path can be calculated more accurately, so that the system can quickly match the optimal evacuation plan, shorten the decision-making time, and improve the accuracy of the decision.

[0094] In some embodiments, the emergency monitoring and management platform can identify and obtain the personnel density, vehicle density, and environmental type (such as lanes, squares, etc.) of the external environment area based on external image data and through image recognition algorithms; determine the correction coefficient by querying a correction coefficient lookup table based on the environmental type, personnel density, and vehicle density of the external environment area; determine the corrected evacuation efficiency by multiplying the evacuation efficiency by the correction coefficient; sort multiple candidate evacuation routes in descending order according to the magnitude of the corrected evacuation efficiency, and select the top M candidate evacuation routes as evacuation routes. M and N can be the same or different.

[0095] The correction coefficient reference table is determined manually based on historical or experimental data. In some embodiments, the emergency monitoring and management platform determines the required evacuation efficiency as the ratio of the number of people in the target area to the preset evacuation time; multiple alternative evacuation routes are sorted in descending order according to their corrected evacuation efficiencies; the corrected evacuation efficiencies corresponding to the sorted alternative evacuation routes are summed sequentially to obtain the total evacuation efficiency; the minimum number of alternative evacuation routes required when the total evacuation efficiency is greater than the required evacuation efficiency is defined as M. The required evacuation efficiency refers to the minimum evacuation efficiency required for the safe evacuation of people in the target area, and the preset evacuation time is set manually based on experience.

[0096] In some embodiments of this specification, by comprehensively assessing the internal evacuation routes of a building and the external environmental conditions of the exits, the risk of congestion caused by insufficient evacuation route capacity can be effectively reduced, thereby improving the safety and efficiency of the evacuation process.

[0097] In some embodiments of this specification, by integrating gas monitoring data, generating regional data, and selecting alternative evacuation routes, and by using actual regional personnel data and obstacle data, evacuation routes are screened. This effectively balances the feasibility and rationality of evacuation routes while ensuring evacuation efficiency, and avoids congestion caused by insufficient evacuation routes or chaos caused by choosing unreasonable evacuation routes.

[0098] Figure 4 This is an exemplary flowchart illustrating the updating of safe zones and alternative evacuation routes according to some embodiments of this specification. In some embodiments, the method for updating safe zones and alternative evacuation routes may be performed by an emergency monitoring and management platform. Figure 4 As shown, process 400 includes the following steps.

[0099] Step 410: Based on the environmental monitoring device, acquire environmental monitoring data of the target area.

[0100] Environmental monitoring data refers to environmental condition data acquired by environmental monitoring devices. Examples include ambient temperature, humidity, and wind direction data.

[0101] In some embodiments, in response to the fulfillment of pre-evacuation conditions, the emergency monitoring and management platform may execute step 420, which includes steps 421 and 422.

[0102] Preparatory evacuation conditions refer to the conditions that trigger a preliminary warning when a gas leak hazard occurs. In some embodiments, preparatory evacuation conditions may refer to conditions that have not yet met the evacuation requirements but require a warning.

[0103] In some embodiments, pre-evacuation conditions may include a gas concentration in gas monitoring data that is higher than a fourth preset concentration threshold. The fourth preset concentration threshold may be preset manually based on experience, and is lower than a first preset concentration threshold. For more information on evacuation conditions and the first preset concentration threshold, please refer to [link to relevant documentation]. Figure 2 And its related descriptions.

[0104] Step 421: Based on regional data, obstacle data, gas monitoring data, and environmental monitoring data, determine the estimated leakage data.

[0105] Predicted leakage data refers to forecast data on gas leakage in a target area. In some embodiments, predicted leakage data may be a sequence consisting of gas concentrations at multiple future time points within a future time period for multiple functional sub-regions of the target area. The future time period and future time points can be preset manually based on actual conditions.

[0106] In some embodiments, the emergency monitoring and management platform can determine the estimated leakage data based on regional data, obstacle data, gas monitoring data, and environmental monitoring data through a safe zone determination model.

[0107] In some embodiments, the safe region determination model can be a machine learning model, such as a graph neural network (GNN) model.

[0108] In some embodiments, the input to the security region determination model may include a leakage feature map, and the output may be the estimated leakage data for each node of the leakage feature map.

[0109] A leak feature map is a map that describes the gas leak situation in a target area. A leak feature map can consist of at least one node and at least one edge.

[0110] In some embodiments, a node corresponds to a functional sub-region of the target area. The node attributes may include area data, obstacle data, gas monitoring data, environmental monitoring data, gas leak rate, and wind direction data within that functional sub-region. More information on functional sub-regions, area data, gas monitoring data, gas leak rate, and wind direction data can be found in [link to relevant documentation]. Figure 2 Related descriptions.

[0111] In some embodiments, the edges of the leaked feature map can be physical connection edges. When two functional sub-regions corresponding to two nodes are spatially physically connected, then an edge exists between the two nodes. The attributes of the edge include the connection data of the two functional sub-regions connected by the edge.

[0112] Connection data includes the connection method, connection type, and connection size of the spatial physical connection. The connection method includes direct and indirect connections. A direct connection refers to a connection where two functional sub-areas exchange gases through a physical channel (such as a door or window). An indirect connection refers to a connection where two functional sub-areas exchange gases through methods other than physical channels (such as a ventilation system). The connection type refers to the type of equipment used for gas exchange between the two functional sub-areas, such as a two-way door or a sliding window. The connection size refers to the dimensions of the equipment used for gas exchange between the two functional sub-areas, such as the dimensions of a one-way door.

[0113] In some embodiments, the safe region determination model can be obtained by training a large number of first training samples with a first label. The first training samples may include a sample leakage feature map constructed based on historical data collected at a first historical time point. The first label may be the historical leakage data of each node in the sample leakage feature map at multiple second historical time points within a second historical period. The first historical time point is before the second historical period. The first training samples may be constructed manually based on historical data, and the first label may be manually annotated based on historical data.

[0114] In some embodiments, the emergency monitoring and management platform can train a safe area determination model based on a first training sample and a first label. Training methods may include, but are not limited to, gradient descent. As an example only, the emergency monitoring and management platform can input multiple first training samples into the initial safe area determination model, construct a loss function using the first label and the output of the initial safe area determination model, and iteratively update the parameters of the initial safe area determination model based on the loss function. Model training is complete when preset conditions are met, resulting in a trained safe area determination model. These preset conditions may include loss function convergence, the number of iterations reaching a preset threshold, etc.

[0115] Step 422: Update the safe zone and alternative evacuation routes based on the estimated leakage data.

[0116] In some embodiments, the emergency monitoring and management platform can designate functional sub-regions in the estimated leak data where the gas concentration at multiple future time points is below a second preset concentration threshold and there is no continuous leak source as updated safe zones. For an explanation of the second preset concentration threshold, please refer to the above.

[0117] In some embodiments, the emergency monitoring and management platform may use alternative evacuation routes that pass through updated safe zones as updated alternative evacuation routes.

[0118] In some embodiments, the emergency monitoring and management platform can update safe zones and alternative evacuation routes based on estimated leakage data and ventilation device data of the target area.

[0119] Ventilation device data refers to operating parameters of the ventilation device, such as maximum airflow. In some embodiments, the ventilation device data can be pre-entered manually.

[0120] In some embodiments, the emergency monitoring and management platform can determine the actual gas concentration in a functional sub-zone according to the following formula: .

[0121] in, This represents the actual gas concentration. To estimate the leakage data, The volume of the target region. This is the maximum airflow of the ventilation device. For correction factor, Ventilation time; Ventilation time Correction coefficients are pre-input manually. It is set manually based on experience.

[0122] The emergency monitoring and management platform can designate functional sub-areas with actual gas concentrations below the second preset concentration threshold and no continuous leakage source as updated safe zones, and select alternative evacuation routes that pass through updated safe zones as updated alternative evacuation routes.

[0123] In some embodiments of this specification, the dilution of the gas concentration in the space by the ventilation device is taken into account, which makes the subsequent determination of evacuation routes more consistent with the actual development of evacuation conditions.

[0124] In some embodiments of this specification, the evacuation routes selected by taking into account the gas concentration in various parts of the space can, to a certain extent, avoid secondary damage that may be caused during the evacuation process and improve the safety and effectiveness of the evacuation routes.

[0125] In some embodiments, the emergency monitoring and management platform may update alarm thresholds and monitoring frequencies based on estimated leakage data and updated alternative evacuation routes; send alarm control signals to alarm devices deployed in the target area based on the updated alarm thresholds to control the alarm devices to update their settings based on the updated alarm thresholds; and send monitoring control signals to image acquisition devices based on the updated monitoring frequencies to control the image acquisition devices to update their settings based on the updated monitoring frequencies.

[0126] An alarm threshold refers to the gas concentration threshold at which an alarm device triggers an alarm. In some embodiments, the alarm threshold may be the same as or close to a first preset concentration threshold (e.g., differing by no more than 5%).

[0127] In some embodiments, the alarm threshold can be preset manually.

[0128] Monitoring frequency refers to the frequency at which the image acquisition device performs monitoring.

[0129] In some embodiments, when the number of updated alternative evacuation routes is less than a preset number, the emergency monitoring and management platform can calculate the gas leak rate based on the estimated leak data using a rate formula, determine the reduction range of the alarm threshold and the monitoring frequency based on the gas leak rate, and update the alarm threshold and monitoring frequency based on the reduction range, wherein the reduction range is positively correlated with the gas leak rate. The preset number can be manually preset.

[0130] In some embodiments, the emergency monitoring and management platform can send alarm control signals to alarm devices deployed in the target area based on the updated alarm thresholds, driving the alarm devices to perform alarm threshold update operations; and send monitoring control signals to image acquisition devices based on the updated monitoring frequency, driving the image acquisition devices to perform monitoring frequency update operations.

[0131] In some embodiments of this specification, the emergency monitoring and management platform ensures that the system can adapt to changes in environmental conditions by dynamically correcting alarm thresholds and precisely controlling the monitoring frequency of image acquisition devices, so as to achieve reasonable energy consumption while ensuring evacuation efficiency.

[0132] Figure 5 This is a schematic diagram illustrating the determination of modal parameters according to some embodiments of this specification.

[0133] In some embodiments, the evacuation parameters also include modal parameters of the evacuation device. For example... Figure 5As shown, the emergency monitoring and management platform can obtain candidate modal parameters 514; based on evacuation routes 512, regional data 513, gas monitoring data 511 and candidate modal parameters 514, it determines the emergency evacuation data 530 corresponding to the candidate modal parameters through parameter determination model 520, and the parameter determination model is a machine learning model; and, based on emergency evacuation data 530, it determines modal parameters 540.

[0134] Modal parameters refer to the setting parameters of the specific performance of evacuation devices during evacuation guidance. Examples include the display brightness of electronic signs, the flicker frequency of lighting equipment, the display brightness of display devices, the projection brightness of projection devices, and the volume of broadcast devices.

[0135] Candidate modal parameters refer to the modal parameters that are available for selection during the modal parameter confirmation process. Candidate modal parameters can be preset manually or randomly generated by the system.

[0136] Emergency evacuation data refers to data describing the estimated evacuation situation along evacuation routes. In some embodiments, emergency evacuation data may include the evacuation rate and evacuation completion time for each evacuation route.

[0137] In some embodiments, the emergency monitoring and management platform can determine the emergency evacuation data corresponding to the candidate modal parameters by using parameter determination models based on evacuation routes, regional data, gas monitoring data, and candidate modal parameters.

[0138] A parameter determination model is a model used to determine modal parameters.

[0139] In some embodiments, the parameter determination model is a machine learning model, such as a multimodal fusion model (MFM).

[0140] In some embodiments, such as Figure 5 As shown, the inputs to the parameter determination model can include evacuation routes (512), area data (513), gas monitoring data (511), candidate modal parameters (514), area personnel data (516), environmental perception data (517), and obstacle data (518). The output can be emergency evacuation data (530) corresponding to the candidate modal parameters. For more information on evacuation routes, area data, area personnel data, gas monitoring data, and obstacle data, please refer to [link to relevant documentation]. Figure 2 and Figure 3 Related descriptions.

[0141] Environmental sensing data refers to environmental data related to sensing. Examples include ambient light levels and ambient noise levels. In some embodiments, environmental sensing data can be acquired by sensing devices. For more information on sensing devices, please see [link to relevant documentation]. Figure 1 And its related descriptions.

[0142] In some embodiments, the parameter determination model can be obtained by training a large number of second training samples with second labels. The second training samples may include sample evacuation routes, sample area data, sample gas monitoring data, sample modal parameters, sample area personnel data, sample environmental perception data, and sample obstacle data. The second training samples can be obtained based on historical data. The second label can be the actual emergency evacuation data corresponding to the second training sample, and the second label corresponding to the second training sample can be obtained manually.

[0143] The training process for the parameter determination model is similar to that for the safe region determination model, as described above, and will not be repeated here.

[0144] In some embodiments, such as Figure 5 As shown, the input to the parameter determination model 520 can also include environmental monitoring data 515 of the target area. For more information on environmental monitoring data, please refer to [link to relevant documentation]. Figure 4 Related descriptions.

[0145] In some embodiments, the second training sample may further include environmental monitoring data, and the second label may be the actual emergency evacuation data corresponding to the second training sample. The methods for obtaining the second training sample and the second label, as well as the training process of the parameter determination model, can be found in the description above.

[0146] In some embodiments of this specification, environmental parameters such as temperature and humidity constitute key regulatory factors of human physiological state. By using environmental monitoring data of the target area as input variables of the model, the dynamic impact of the environment on path traffic efficiency can be quantitatively analyzed, and the prediction accuracy of personnel behavior response can be improved, thereby optimizing the effectiveness of evacuation strategies.

[0147] In some embodiments, the emergency monitoring and management platform may select the candidate modal parameter with the best evacuation effect as the modal parameter. The best evacuation effect may refer to the highest weighted score of evacuation rate and evacuation completion time among the emergency evacuation data corresponding to the candidate modal parameter. The faster the evacuation rate and the shorter the evacuation completion time, the higher the score. The weighting weights can be preset manually.

[0148] In some embodiments of this specification, by considering multi-dimensional dynamic parameter optimization, the accuracy and response efficiency of evacuation control in complex emergency scenarios are significantly improved, the system's ability to autonomously adapt to sudden variable factors is enhanced, and the dynamic allocation efficiency of emergency resources is optimized while ensuring evacuation safety.

[0149] The embodiments described in this invention are merely illustrative and do not limit the scope of the invention. Various modifications and alterations that can be made by those skilled in the art under the guidance of this invention are still within its scope.

[0150] Furthermore, certain features, structures, or characteristics in one or more embodiments of the present invention can be appropriately combined.

[0151] If there is any inconsistency or conflict between the descriptions, definitions, and / or terminology used in the appended materials of this invention and the content described in this invention, the descriptions, definitions, and / or terminology used in this invention shall prevail.

Claims

1. A gas emergency system for urban public places based on an Internet of Things (IoT) big data model, characterized in that: This includes an emergency monitoring and management platform and an emergency monitoring target platform; The emergency monitoring and management platform is configured as follows: Based on the aforementioned emergency monitoring platform, gas monitoring data of the target area is acquired through monitoring devices deployed within the monitoring area; In response to the fulfillment of evacuation conditions, Based on the gas monitoring data and the regional data of the target area, evacuation parameters are generated, including evacuation route and sign control parameters, lighting control parameters, and gate control parameters; and... Based on the evacuation parameters, an evacuation control signal is sent to evacuation devices deployed within the target area to control the evacuation devices to guide the evacuation of personnel within the target area, including: The control display device displays the evacuation route; Based on the light sign control parameters, control the display color and direction of the electronic sign; Based on the aforementioned lighting control parameters, the lighting brightness and lighting mode of the lighting equipment are controlled; and, Based on the gate control parameters, control the gate's opening and closing status; The evacuation parameters also include the modal parameters of the evacuation device, which are configured to control the modal settings of the evacuation device; the emergency monitoring and management platform is further configured to: Obtain candidate modal parameters; Based on the evacuation routes, the regional data, the gas monitoring data, and the candidate modal parameters, an emergency evacuation data corresponding to the candidate modal parameters is determined using a parameter determination model, wherein the parameter determination model is a machine learning model; and, Based on the emergency evacuation data, the modal parameters are determined.

2. The system as described in claim 1, characterized in that, The monitoring device also includes an image acquisition device, and the emergency monitoring and management platform is further configured as follows: Based on the image acquisition device, image sensing data of the target area is acquired; Based on the image sensing data, obtain the area personnel data and obstacle data of the target area; Based on the gas monitoring data and the regional data, safe areas are determined and alternative evacuation routes are generated; as well as, Based on the personnel data and obstacle data of the area, the evacuation route is determined from the alternative evacuation routes.

3. The system as described in claim 2, characterized in that, The emergency monitoring and management platform is further configured as follows: Based on the image acquisition device, external image data is acquired; Based on the personnel data, obstacle data, and area data of the region, the evacuation efficiency of the alternative evacuation routes is determined. as well as, Based on the external image data and the evacuation efficiency, the evacuation path is determined from the candidate evacuation paths.

4. The system as described in claim 2, characterized in that, The monitoring device also includes an environmental monitoring device, and the emergency monitoring and management platform is further configured as follows: Based on the environmental monitoring device, environmental monitoring data of the target area is acquired; In response to the fulfillment of pre-evacuation conditions, Based on the area data, the obstacle data, the gas monitoring data, and the environmental monitoring data, the estimated leakage data is determined; and, Based on the estimated leakage data, the safe zone and the alternative evacuation routes are updated.

5. A gas emergency response method for urban public places based on an Internet of Things (IoT) big data model, characterized in that: The method is executed by an emergency monitoring and management platform in an urban public place gas emergency system based on an Internet of Things (IoT) big data model. The method includes: Based on the emergency monitoring platform, gas monitoring data of the target area is obtained through monitoring devices deployed in the monitoring area; In response to the fulfillment of evacuation conditions, Based on the gas monitoring data and the regional data of the target area, evacuation parameters are generated, including evacuation route and sign control parameters, lighting control parameters, and gate control parameters; and... Based on the evacuation parameters, an evacuation control signal is sent to evacuation devices deployed within the target area to control the evacuation devices to guide the evacuation of personnel within the target area, including: The control display device displays the evacuation route; Based on the light sign control parameters, control the display color and direction of the electronic sign; Based on the aforementioned lighting control parameters, the lighting brightness and lighting mode of the lighting equipment are controlled; and, Based on the gate control parameters, control the gate's opening and closing status; The evacuation parameters further include modal parameters of the evacuation device, the modal parameters being configured to control the modal settings of the evacuation device; the method further includes: Obtain candidate modal parameters; Based on the evacuation routes, the regional data, the gas monitoring data, and the candidate modal parameters, an emergency evacuation data corresponding to the candidate modal parameters is determined using a parameter determination model, wherein the parameter determination model is a machine learning model; and, Based on the emergency evacuation data, the modal parameters are determined.

6. The method as described in claim 5, characterized in that, The monitoring device further includes an image acquisition device, which generates evacuation parameters based on the gas monitoring data and the area data of the target area, including: Based on the image acquisition device, image sensing data of the target area is acquired; Based on the image sensing data, obtain the area personnel data and obstacle data of the target area; Based on the gas monitoring data and the regional data, safe zones are determined and alternative evacuation routes are generated; and, Based on the personnel data and obstacle data of the area, the evacuation route is determined from the alternative evacuation routes.

7. The method as described in claim 6, characterized in that, The step of determining the evacuation route from the candidate evacuation routes based on the area's personnel data and the obstacle data includes: Based on the image acquisition device, external image data is acquired; Based on the personnel data, obstacle data, and area data of the region, the evacuation efficiency of the alternative evacuation routes is determined; and, Based on the external image data and the evacuation efficiency, the evacuation path is determined from the candidate evacuation paths.

8. The method as described in claim 6, characterized in that, The monitoring device further includes an environmental monitoring device, and the method further includes: Based on the environmental monitoring device, environmental monitoring data of the target area is acquired; In response to the fulfillment of pre-evacuation conditions, Based on the area data, the obstacle data, the gas monitoring data, and the environmental monitoring data, the estimated leakage data is determined; and, Based on the estimated leakage data, the safe zone and the alternative evacuation routes are updated.

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