Smart city integrated emergency management and control method, Internet of Things large model system and medium

Abstract

The invention provides a smart city integrated emergency management and control method, an Internet of Things large model system and a medium. The method comprises the following steps: acquiring a plurality of pieces of emergency management and control data, calling uploading time, data types and belonging geographic areas of the plurality of pieces of emergency management and control data by the method, and generating a to-be-processed data group; marking an emergency management and control range corresponding to a temporary management and control region in an interactive terminal of a smart city GIS system according to a geographic range of a geographic region related to the to-be-processed data set; according to emergency control streets in the emergency control range and the crowd scale corresponding to the emergency control streets, generating a first driving route and sending the first driving route to the emergency inspection vehicle; and the signal lamp phase is updated in combination with the driving demand of the emergency inspection vehicle and the traffic flow condition of the emergency management and control street. According to the method, the operation efficiency and the response speed for the emergency event can be effectively improved, and the effective degree of emergency management and control is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of smart city emergency management and control, and particularly relates to a smart city integrated emergency management and control method, an Internet of Things large model system and a medium. Background Art

[0002] With the development of smart cities, urban emergency management also needs to be gradually improved. However, in the face of a large amount of emergency management and control data from different sources, it is difficult to discover the correlation between different types of emergency management and control data by means of separate and independent processing. How to determine the associated emergency management and control data and emergency management and control geographical regions is a problem that needs to be solved.

[0003] Therefore, providing a smart city integrated emergency management and control method, an Internet of Things large model system and a medium can effectively improve the operation efficiency and the response speed of emergency events, and ensure the effectiveness of emergency management and control. Summary of the Invention

[0004] One or more embodiments of the present invention provide a smart city integrated emergency management and control method, which includes: being implemented based on a smart city integrated emergency management and control Internet of Things large model system; the method is executed based on an emergency supervision and management platform, and includes: through the communication transmission network of the emergency supervision sensor network platform configured in multiple geographical regions, obtaining multiple emergency management and control data and storing them in the memory of the general data center; retrieving the upload time, data type, and geographical region to which the multiple emergency management and control data belong from the memory of the general data center to generate a data group to be processed; according to the geographical range of the geographical region involved in the data group to be processed, marking the emergency management and control range corresponding to the temporary control area in the interactive terminal of the smart city GIS system, and displaying the emergency management and control range corresponding to the temporary control area on the interface of the emergency management and control terminal; generating a first driving route according to the emergency management and control streets within the emergency management and control range and the population scale corresponding to the emergency management and control streets, and sending it to the emergency inspection vehicle; controlling the emergency inspection vehicle to execute the first driving route and conduct inspections; combining the driving requirements of the emergency inspection vehicle and the traffic flow conditions of the emergency management and control streets to update the signal light phase; controlling the operation of the traffic lights within the emergency management and control range based on the updated signal light phase, and controlling the update of the indication signs of the variable lanes within the emergency management and control range to indicate the driving direction.

[0005] One or more embodiments of the present invention provide an Internet of Things large model system for integrated emergency management and control in a smart city. The system includes an emergency supervision user platform, an emergency supervision service platform, an emergency supervision management platform, an emergency supervision sensor network platform, and an emergency supervision object platform. The emergency supervision user platform includes a third-party terminal. The emergency supervision service platform includes a communication terminal. The emergency supervision management platform includes a processor, a general data center, and emergency sub-platforms. The general data center is configured with a memory and a data processing model library. The emergency supervision management platform is configured to execute the above-mentioned integrated emergency management and control method for a smart city. The emergency supervision object platform is configured with sensors and a memory. The emergency supervision object platform is configured to operate based on multiple emergency inspection vehicles and communication devices.

[0006] One or more embodiments of the present invention provide a computer-readable storage medium. The storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the above-mentioned integrated emergency management and control method for a smart city.

[0007] Beneficial effects: The integrated emergency management and control method, Internet of Things large model system, and medium of the present invention can improve the processing efficiency of emergencies by reasonably determining the actual emergency management and control scope. By reasonably determining the driving routes of emergency inspection vehicles, traffic control parameters conducive to the smooth driving and inspection of emergency inspection vehicles can be further set to improve the inspection efficiency. Description of the Drawings

[0008] The present invention will be further described by way of exemplary embodiments, which will be described in detail through the accompanying drawings. These embodiments are not restrictive. In these embodiments, the same numbers represent the same structures, where: Figure 1 is a schematic diagram of the platform structure of the Internet of Things large model system for integrated emergency management and control in a smart city according to some embodiments of the present invention; Figure 2 is an exemplary flowchart of the integrated emergency management and control method for a smart city according to some embodiments of the present invention; Figure 3 is an exemplary flowchart of generating a data group to be processed according to some embodiments of the present invention; Figure 4 is an exemplary diagram of collecting updated emergency management and control data according to some embodiments of the present invention. Detailed Embodiments

[0009] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some examples or embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, the present invention can also be applied to other similar scenarios based on these drawings. Unless obvious from the language context or otherwise stated, the same reference numerals in the figures represent the same structure or operation.

[0010] It should be understood that the "system", "device", "unit" and / or "module" used herein is a way to distinguish different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the said words can be replaced by other expressions.

[0011] Figure 1 is the platform structure diagram of the Internet of Things large model system for integrated emergency management and control in a smart city shown according to some embodiments of the present invention.

[0012] In some embodiments, as Figure 1 shown, the Internet of Things large model system 100 for integrated emergency management and control in a smart city includes 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.

[0013] The emergency supervision user platform refers to the management platform for the superior department to comprehensively coordinate emergency supervision.

[0014] In some embodiments, the emergency supervision user platform includes a third-party terminal.

[0015] The third-party terminal refers to an external terminal device or system software. For example, the third-party terminal can be a mobile device, a computer, or other devices with input and / or output functions provided by other institutions, or any combination thereof.

[0016] The emergency supervision service platform refers to an interactive service platform for receiving and transmitting data.

[0017] In some embodiments, the emergency supervision service platform interacts with the emergency supervision user platform upward and with the emergency supervision management platform downward.

[0018] In some embodiments, the emergency supervision service platform includes a communication terminal.

[0019] The communication terminal refers to a device or software for realizing real-time information interaction. For example, the communication terminal can be a wireless mobile phone, a video monitor, a multimedia computer, etc.

[0020] The emergency supervision management platform refers to a comprehensive platform for processing and managing emergency supervision data.

[0021] In some embodiments, the emergency supervision management platform includes a processor, a general data center 131, and multiple emergency sub-platforms. Each of the multiple emergency sub-platforms includes a corresponding sub-data center. As Figure 1 shown, the emergency sub-platform 132-1 includes the sub-data center 133-1, the emergency sub-platform 132-2 includes the sub-data center 133-2,..., and the emergency sub-platform 132-n includes the sub-data center 133-n.

[0022] The processor can be used to process information such as the obtained emergency supervision data. The processor can execute program instructions based on these data, information, and / or processing results to perform one or more functions described in this application. For example, the processor can include a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), etc., or any combination thereof.

[0023] The general data center can be used to overall manage the collected emergency supervision data.

[0024] In some embodiments, the general data center is configured with a memory and a data processing model library.

[0025] The memory can be used to store emergency supervision data and / or instructions. The memory can include one or more storage components, and each storage component can be an independent device or a part of other devices. For example, the memory can include a random access memory (RAM), a read-only memory (ROM), etc., or any combination thereof.

[0026] The data processing model library can be used to store relevant calculation models for overall processing of the emergency supervision management platform data. For example, the calculation models can include models for overall classification of emergency supervision data, comprehensive analysis models for emergency supervision data, etc. Among them, the classification models can include binary classification, decision tree, random forest, etc.

[0027] The emergency sub-platform refers to a sub-platform for supervising emergency supervision data.

[0028] The sub-data center can be used to store and process the emergency supervision data allocated by the general data center.

[0029] In some embodiments, the sub-data center can include a sub-database and a sub-data processing model library. Among them, the sub-database is used to store emergency supervision data, and the sub-data processing model library is used to store data processing models.

[0030] In some embodiments, the general data center 131 can interact with multiple sub-data centers.

[0031] The emergency supervision sensing network platform refers to a management platform that transmits sensing data or information related to emergency supervision.

[0032] In some embodiments, the emergency supervision sensing network platform interacts with multiple sub-data centers in the emergency supervision management platform upward and with the emergency supervision object platform downward.

[0033] In some embodiments, the emergency supervision sensing network platform includes a communication transmission network and a routing device. Among them, the communication transmission network can realize the functions of sensing communication of perception information and sensing communication of control information.

[0034] The routing device is a hardware device that realizes information sensing communication.

[0035] The emergency supervision object platform refers to a platform for collecting emergency supervision data and implementing execution instructions.

[0036] In some embodiments, the emergency supervision object platform is configured with sensors and a memory. Among them, the memory can be used to store the monitored and collected information and data.

[0037] The sensor is a device for receiving and converting various monitoring information. For example, the sensor can include a temperature sensor, a pressure sensor, an ultrasonic sensor, etc.

[0038] In some embodiments, the emergency supervision object platform includes emergency inspection vehicles and communication devices, and the emergency supervision object platform is configured to operate based on multiple emergency inspection vehicles and communication devices.

[0039] The emergency inspection vehicle refers to a patrol vehicle used to prevent or respond to emergency events. For example, the emergency inspection vehicle can be an unmanned vehicle.

[0040] The communication device refers to a device used to realize data transmission between the emergency inspection vehicle and the emergency supervision sensing network platform. For example, the communication device can be an in-vehicle terminal.

[0041] In some embodiments, the emergency inspection vehicle can carry devices such as sensors, monitoring equipment, and a memory.

[0042] For more detailed descriptions of the smart city integrated emergency management and control Internet of Things large model system and its implementation of the smart city integrated emergency management and control method, reference can be made to Figures 2 to 4 the relevant descriptions.

[0043] In some embodiments of the present invention, the smart city integrated emergency management and control Internet of Things large model system can form an information operation closed loop among various functional platforms, coordinate and operate regularly, and improve the processing efficiency of sudden emergency events by accurately determining the actual emergency management and control scope.

[0044] Figure 2 is an exemplary flowchart of the integrated emergency management and control method for a smart city according to some embodiments of the present invention. As Figure 2 shown, process 200 includes the following steps. In some embodiments, process 200 can be executed by an emergency supervision and management platform.

[0045] Step 210, obtain a plurality of emergency management and control data through the communication transmission network of the emergency supervision sensor network platforms configured in multiple geographical regions, and store them in the memory of the total data center.

[0046] The geographical region refers to the region obtained by dividing the national territory. In some embodiments, the geographical region can be obtained by various methods. For example, a plurality of geographical regions can be obtained based on administrative divisions.

[0047] In some embodiments, each geographical region is configured with at least one communication transmission network of an emergency supervision sensor network platform.

[0048] The emergency management and control data refers to the data related to emergency management and control. For example, the emergency management and control data includes combustible gas concentration, environmental temperature, environmental humidity, etc.

[0049] Emergency management and control refers to the response mechanism proposed for the dangerous problems of major accident disasters (such as gas leakage, fire explosion, traffic accidents, etc.).

[0050] In some embodiments, the emergency supervision and management platform can interact with the emergency supervision sensor network platform through the communication transmission network to obtain the emergency management and control data collected by the sensors configured in different geographical regions in the emergency supervision object platform, and store the emergency management and control data in the memory of the total data center.

[0051] Step 220, retrieve the upload time, data type, and geographical region to which the plurality of emergency management and control data belongs from the memory of the total data center, and generate a data group to be processed.

[0052] The upload time of the emergency management and control data refers to the time when the emergency management and control data is uploaded to the emergency supervision sensor network platform.

[0053] The data type of the emergency management and control data includes, but is not limited to, one or more of image data, sound data, text data, etc.

[0054] The geographical region to which the emergency management and control data belongs refers to the geographical region where the emergency management and control data is collected.

[0055] The data group to be processed refers to the combination of a plurality of emergency management and control data. The emergency supervision and management platform can generate the data group to be processed in various ways.

[0056] In some embodiments, the emergency supervision and management platform may classify the emergency control data based on preset rules to obtain multiple data groups to be processed. Among them, the preset rules may be system default rules or set in advance by users. For example, the preset rules may include classifying the emergency control data with an upload time between 8-11h, a geographical area belonging to Area A, and a data type of text data into a data group to be processed.

[0057] In some other embodiments, the emergency supervision and management platform may generate data groups to be processed through a data classification model. For more information about the data classification model, see Figure 3 and related descriptions.

[0058] Step 230: According to the geographical scope of the geographical area involved in the data group to be processed, mark the emergency control scope corresponding to the temporary control area in the interaction terminal of the smart city GIS system, and display the emergency control scope corresponding to the temporary control area on the interface of the emergency control terminal.

[0059] The smart city Geographic Information System (GIS) is a technical system that collects, stores, manages, calculates, analyzes, displays, and describes geographical distribution data in the entire or part of the earth's surface space under the support of computer hardware and software systems. The emergency supervision and management platform can perform data interaction with the smart city GIS system through the interaction terminal. In some embodiments, the interaction terminal includes a visual interface.

[0060] The emergency control scope refers to the scope of the area that needs to be subject to emergency control.

[0061] In some embodiments, the emergency supervision and management platform may determine whether there are major accident disasters based on the data group to be processed. In response to the possible existence of major accident disasters, the emergency supervision and management platform determines the geographical area corresponding to the data group to be processed as the temporary control area, and determines the area range it contains as the emergency control scope. In some embodiments, the emergency supervision and management platform may mark multiple emergency control scopes with different colors and display the emergency control scope corresponding to the temporary control area on the interface of the emergency control terminal.

[0062] Step 240: Generate the first driving route based on the emergency control streets within the emergency control scope and the population scale corresponding to the emergency control streets, and send it to the emergency inspection vehicle to control the emergency inspection vehicle to execute the first driving route and conduct inspections.

[0063] The emergency control street refers to the street within the emergency control scope. The emergency supervision and management platform can determine the emergency control street through the smart city GIS system.

[0064] The population scale includes the pedestrian flow and vehicle flow within the streets under emergency control. In some embodiments, the emergency supervision and management platform may determine the population scale based on image data and the like in the data group to be processed corresponding to the emergency control scope.

[0065] The first driving route refers to the driving route determined based on the population scale. In some embodiments, the emergency supervision and management platform may screen the streets under emergency control where the population scale is greater than the preset scale, and unidirectionally connect these streets under emergency control in the order from near to far based on the distance between the streets under emergency control and the emergency inspection vehicles, to obtain the first driving route.

[0066] The emergency inspection vehicle refers to the vehicle that conducts inspections during emergency control. The emergency inspection vehicle may be an unmanned vehicle. The inspection includes one or more of, but is not limited to, sounding an alarm through an alarm device to evacuate the crowd, continuously collecting emergency control data through sensors, etc.

[0067] Step 250, combining the driving requirements of the emergency inspection vehicle and the traffic flow conditions of the streets under emergency control, update the signal light phase.

[0068] The driving requirement refers to the requirements of the emergency inspection vehicle during inspection. For example, the driving requirements include the streets under emergency control that the emergency inspection vehicle needs to pass through and the driving direction, etc.

[0069] At intersections, the signal lights display different colors in sequence according to a continuous time sequence, and the colors of the signal lights are different at different times. Based on this, multiple signal light phases can be divided. The signal light phase can be for one driving route. For example, if there are signal lights at 4 consecutive intersections on a driving route, the signal light phase may include the green light interval time between intersection 1 and intersection 2, the green light interval time between intersection 2 and intersection 3, … when the emergency inspection vehicle conducts inspections on the driving route.

[0070] The green light interval time refers to the time interval between the end moment of the green light of the signal light at the previous intersection on the driving route and the start moment of the green light of the signal light at the next intersection.

[0071] The emergency supervision and management platform can update the signal light phase in various ways. For example, the emergency supervision and management platform may estimate the time for the emergency inspection vehicle to reach each intersection based on the position information of the emergency inspection vehicle, the driving requirements of the emergency inspection vehicle, and the traffic flow conditions of the streets under emergency control, and update the signal light phase based on the time for the emergency inspection vehicle to reach each intersection, so that the signal light is green when the emergency inspection vehicle reaches each intersection.

[0072] In some embodiments, the emergency supervision and management platform may generate the updated signal light phase through a signal light phase model based on the position information of the emergency inspection vehicle, the street information of the streets under emergency control, and the current signal light phase.

[0073] The signal light phase model is a model used to generate updated signal light phases. In some embodiments, the signal light phase model is a machine learning model. For example, the signal light phase model is a Neural Networks (NN) model or other user-defined models, etc.

[0074] The input of the signal light phase model includes the position information and driving route of the emergency inspection vehicle, the street information of the emergency control street, and the candidate signal light phases. The output of the signal light phase model includes the number of times the emergency inspection vehicle encounters a red light on the driving route corresponding to the candidate signal light phases.

[0075] The position information of the emergency inspection vehicle refers to the current position of the emergency inspection vehicle.

[0076] The street information refers to the information related to the emergency control street. For example, the street information includes the street distribution, signal light distribution, traffic flow, current signal light phase, etc. of the emergency control street.

[0077] In some embodiments, the emergency supervision and management platform can obtain the position information of the emergency inspection vehicle, the street information of the emergency control street, and the current signal light phase through a positioning system. The positioning system includes the Global Positioning System (GPS), the Beidou satellite navigation system, etc.

[0078] In some embodiments, the emergency supervision and management platform can randomly generate multiple candidate signal light phases.

[0079] The emergency supervision and management platform can train a signal light phase model based on multiple first training samples with first labels. The emergency supervision and management platform can input the first training samples into the initial signal light phase model, construct a loss function based on the output of the initial signal light phase model and the first labels, and iteratively update the parameters of the initial signal light phase model based on the loss function. When the iteration end condition is met, the iteration ends, and a trained signal light phase model is obtained. Among them, the method of iterative update includes but is not limited to the gradient descent method, and the iteration end condition can be that the loss function converges or the number of iterations reaches a threshold.

[0080] In some embodiments, the emergency supervision and management platform can determine the historical position information and historical driving route of the emergency inspection vehicle, the historical street information of the emergency control street, and the historical signal light phase during the historical inspection process as the first training samples, and use the actual number of times the emergency inspection vehicle encounters a red light during the historical inspection process as the first label corresponding to the first training samples.

[0081] In some embodiments, the emergency supervision and management platform may determine the candidate signal light phase corresponding to the minimum number of times the emergency inspection vehicle encounters a red light as the updated signal light phase.

[0082] In some embodiments of the present invention, by determining the number of times the emergency inspection vehicle encounters a red light under multiple candidate signal light phases through the signal light phase model, and determining the candidate signal light phase with the minimum number of times of encountering a red light as the updated signal light phase, the waiting time for red lights can be reduced, and the inspection efficiency can be effectively improved.

[0083] Step 260, based on the updated signal light phase, control the operation of the traffic signal lights within the emergency control range, and control the update of the indication signs of the variable lanes within the emergency control range to indicate the driving direction.

[0084] In some embodiments, the emergency supervision and management platform may set the current signal light phase as the updated signal light phase to control the operation of the traffic signal lights within the emergency control range.

[0085] In some embodiments, the emergency supervision and management platform may set the driving direction of the variable lane entering the emergency control range to the opposite direction by controlling the indication sign, so as to reduce the traffic flow entering the emergency control range.

[0086] In some embodiments of the present invention, by reasonably determining the actual emergency control range, the handling efficiency of emergencies can be improved; by reasonably determining the driving route of the emergency inspection vehicle, traffic control parameters beneficial to the smooth driving and inspection of the emergency inspection vehicle can be further set, and the inspection efficiency can be improved.

[0087] Figure 3 It is an exemplary flowchart for generating a data group to be processed shown in some embodiments of the present invention. As Figure 3 shown, process 300 includes the following steps. In some embodiments, process 300 may be executed by the emergency supervision and management platform.

[0088] Step 310, based on the total data center, retrieve the data classification model from the data processing model library.

[0089] The data classification model is a model for classifying emergency control data. For example, the data classification model is a binary classification model, etc.

[0090] The input of the data classification model includes emergency control data, and the output of the data classification model includes the data group to be processed.

[0091] In some embodiments, the multiple emergency control data input into the data classification model are data after preprocessing and screening, and the preprocessing and screening are determined based on the historical processing information corresponding to the multiple emergency control data.

[0092] Historical processing information refers to the information related to the classification of emergency control data by the emergency supervision and management platform. The historical processing information can be obtained based on historical data.

[0093] In some embodiments, the emergency supervision and management platform determines the frequency of dividing the emergency control data into different data groups to be processed based on the historical processing information, and marks multiple emergency control data based on this frequency.

[0094] In some embodiments, the preprocessing screening can be to classify and process the emergency control data based on the marks, and then determine the data groups to be processed to which each marked emergency control data belongs. For example, the emergency control data is divided into the data group to be processed with the highest frequency value that is greater than the preset frequency threshold among its marks. Among them, the preset frequency threshold can be set based on historical experience.

[0095] The data after the preprocessing screening is the emergency control data for which it is impossible to determine the data group to be processed to which it belongs based on the foregoing marking process. For example, the data without historical grouping records in the historical processing information or the data with the frequency value of the mark not greater than the preset frequency threshold.

[0096] In some embodiments of the present invention, by preprocessing and screening the emergency control data, the data processing efficiency can be improved, and the data processing burden on the total data center can be reduced.

[0097] The emergency supervision and management platform can train a data classification model based on multiple second training samples with second tags. The training process of the data classification model is the same as that of the signal light phase model. For more details, see the relevant description in step 250.

[0098] The second training sample includes sample emergency control data, and the second tag is the sample data group to be processed corresponding to the sample emergency control data. The second training sample and the second tag can be determined based on the historical processing information.

[0099] Step 320, classify and process multiple emergency control data based on the data classification model to generate data groups to be processed.

[0100] In some embodiments, the emergency supervision and management platform can classify and process multiple emergency control data in the total data center based on the data classification model to generate data groups to be processed.

[0101] In some other embodiments, a sub-data center is configured in the emergency sub-platform, and the emergency supervision and management platform can classify and process multiple emergency control data in the sub-data center based on the sub-data classification model to generate data groups to be processed.

[0102] For more information about the emergency control data and the data groups to be processed, see Figure 2 and the relevant description.

[0103] Step 330: Generate routing information based on the data group to be processed.

[0104] Routing information refers to information related to data transmission. For example, routing information includes the data group to be processed that needs to be sent, as well as the corresponding sending end and receiving end.

[0105] In some embodiments, the emergency supervision and management platform can determine the routing information based on actual needs. For example, the emergency supervision and management platform sets the total data platform as the sending end and determines the sub-data center of the emergency sub-platform in the geographical area corresponding to the data group to be processed as the receiving end. Among them, the geographical area corresponding to the data group to be processed refers to the geographical area to which the emergency control data in the data group to be processed belongs.

[0106] For more content about the geographical area to which it belongs, see Figure 2 and related descriptions.

[0107] Step 340: Based on the routing information, control the routing device of the emergency supervision sensor network platform to send the data group to be processed to the sub-data center of the emergency sub-platform in the corresponding geographical area.

[0108] In some embodiments, the emergency supervision and management platform can control the routing device corresponding to the sending end based on the routing information to send the data group to be processed to the receiving end.

[0109] In some embodiments of the present invention, by classifying the emergency control data through a data classification model to obtain a data group to be processed and sending the data group to be processed based on actual needs, the transmission and processing efficiency of the emergency control data can be improved.

[0110] In some embodiments, the emergency supervision and management platform can also generate the association values between multiple emergency control data and emergency events according to the value range, geographical area to which it belongs, type of emergency event, and location where the emergency event occurs within a preset time period; determine the data groups to be processed corresponding to different time periods based on the association values; divide the emergency control scope corresponding to the temporary control area according to the data group to be processed and the geographical scope of the geographical area involved in the data group to be processed; and control the interactive terminal of the smart city GIS system to display the electronic map corresponding to the temporary control area according to the temporary control area.

[0111] The value range of the emergency control data refers to the fluctuation range of the emergency control data within a preset time period. Different emergency control data correspond to different value ranges. For example, if the lowest environmental temperature within the preset time period is 10°C and the highest environmental temperature is 20°C, then the value range corresponding to the environmental temperature is 10~20°C. The preset time period can be the system default value or set manually by the user.

[0112] An emergency event refers to an event that requires emergency management and control. For example, emergency events can be various major accident disasters (such as gas leakage, fire and explosion, traffic accidents, etc.). Emergency events include major accident disasters that are occurring or may occur. The location where an emergency event occurs can be determined based on emergency management and control data.

[0113] The correlation value refers to the degree of correlation between the emergency management and control data and the emergency event. The larger the correlation value, the higher the correlation between the emergency management and control data and the emergency event. For example, the correlation between the concentration of combustible gas and gas leakage is greater than the correlation between environmental humidity and gas leakage. In some embodiments, the correlation value can be represented by a number between 0 and 1, and the larger the numerical value, the larger the correlation value.

[0114] The emergency supervision and management platform can determine the correlation value in various ways.

[0115] In some embodiments, the emergency supervision and management platform can obtain historical emergency events of the same type as the current emergency event and located in the same geographical area based on historical data, and at the same time obtain the historical numerical range of the emergency management and control data corresponding to the historical emergency events.

[0116] The emergency supervision and management platform determines the correlation value based on the degree of overlap between the numerical range of the emergency management and control data corresponding to the current emergency event and the historical numerical range. The greater the degree of overlap, the larger the correlation value. When the historical numerical range contains the numerical range of the emergency management and control data corresponding to the current emergency event, the correlation value is the maximum value of 1.

[0117] In some embodiments, the emergency supervision and management platform can determine the product of the above correlation value and the type coefficient as the final correlation value. The type coefficient refers to the coefficient related to the type of the current emergency event, and the type coefficient can be the system default value.

[0118] In some embodiments, the emergency supervision and management platform can generate the correlation values between multiple emergency management and control data and the emergency event through an emergency correlation model according to the numerical ranges, the geographical areas to which they belong, the types of emergency events, and the occurrence locations of the emergency events within a preset time period.

[0119] The emergency correlation model is a model for determining the correlation value. In some embodiments, the emergency correlation model is a machine learning model. For example, the emergency correlation model is a neural network (NN) model or other user-defined models, etc.

[0120] The inputs of the emergency correlation model include the numerical ranges, the geographical areas to which they belong, the types of emergency events, and the occurrence locations of the emergency events within a preset time period, and the outputs of the emergency correlation model include the correlation values between multiple emergency management and control data and the emergency event.

[0121] In some embodiments, multiple emergency events may occur at the same time. At this time, the input of the emergency association model may include the types and occurrence locations of multiple emergency events.

[0122] The emergency supervision and management platform can train an emergency association model based on multiple third training samples with third tags. The training process of the emergency association model is the same as that of the signal light phase model. For more details, see the relevant description in step 250.

[0123] The third training sample includes the sample value range of the emergency control data, the geographical area to which the sample belongs, the type of the sample emergency event, and the occurrence location of the sample emergency event in the historical data. The third tag is the actual association value corresponding to the third training sample. The emergency supervision and management platform can determine the third tag corresponding to the third training sample based on the third training sample by the above method for determining the association value.

[0124] In some embodiments, the training data of the emergency association model includes multiple labeled training samples. The label is determined according to the data group to be processed corresponding to multiple sample emergency control data included in the training sample. The emergency supervision and management platform can obtain the event processing result corresponding to the training sample (i.e., the third training sample); estimate the association pre-estimation value (i.e., the third tag) corresponding to the training sample based on the event processing result; in response to the association pre-estimation value being greater than the preset sample value, set the value of the label corresponding to the training sample to the preset label value, where the preset label value is greater than the preset threshold; divide the multiple training samples into multiple training sets based on the values of the multiple labels corresponding to the multiple training samples; and train the emergency association model based on the multiple training sets.

[0125] The event processing result refers to the result of processing the sample emergency event corresponding to the third training sample.

[0126] The association pre-estimation value refers to the estimated value of the association value. In some embodiments, the emergency supervision and management platform can obtain the historical value range of the historical emergency control data for a period of time after the historical moment corresponding to the third training sample, determine the association value between the historical value range and the sample emergency event corresponding to the third training sample by the above method for determining the association value based on the degree of coincidence, and determine this association value as the association pre-estimation value.

[0127] In some embodiments, based on the processing results of historical events in a large amount of historical data, it is possible to determine, through prior experience, which emergency control data is effective for the handling of emergency events, and store the results in the emergency supervision and management platform. For example, when it is found that the emergency event is a fire, and there are significant changes in environmental temperature, traffic flow, and smoke concentration, then these emergency control data are determined to be effective data for the handling of the fire. If the emergency control data is effective data for the handling of a sample emergency event, in response to the associated estimated value being greater than the preset sample value, the emergency supervision and management platform sets the value of the third label to the preset label value. The preset label value is greater than the preset threshold (for example, 0.75). If the emergency control data is not effective data for the handling of a sample emergency event, the value of the third label remains unchanged.

[0128] The historical emergency control data being effective data for the handling of a sample emergency event indicates that the actual degree of association between the historical emergency control data and the sample emergency event is relatively high. When the associated estimated value is greater than the preset sample value, increasing the value of the third label enables the trained emergency association model to accurately predict the associated values of subsequent emergency control data with relatively high associated values.

[0129] In some embodiments, the emergency supervision and management platform can divide the third training samples into two training sets. Among them, the first training set includes the third training samples with the value of the third label being the preset label value, and the remaining third training samples belong to the second training set. The learning rate corresponding to the second training set is less than the learning rate corresponding to the first training set.

[0130] The emergency supervision and management platform can train an emergency association model based on the first training set and the second training set. The training process of the emergency association model is the same as that of the signal light phase model. For more details, refer to the relevant description in step 250.

[0131] In some embodiments of the present invention, considering that the associated value may change over time, the third label is adjusted based on the associated value within a subsequent period of time, and the third training samples corresponding to the adjusted and unadjusted third labels are divided into different training sets, and model training is performed based on different learning rates, so that the model parameters of the emergency association model are more accurate.

[0132] In some embodiments of the present invention, through the trained emergency association model, it is possible to quickly determine the associated values between a large amount of emergency control data and at least one emergency event, so as to improve the data processing efficiency.

[0133] In some embodiments, the emergency supervision and management platform can divide the emergency control data into multiple groups of data to be processed based on the type of emergency event and the associated value interval (for example, 0.9 - 1, 0.8 - 0.9... each corresponds to an associated value interval).

[0134] In some embodiments, the emergency supervision and management platform may determine the emergency control scope based on the data group to be processed by the method described in step 230.

[0135] In some embodiments, the emergency supervision and management platform may also determine the control time corresponding to each emergency control scope. For example, the control time is positively correlated with the data volume of the emergency control data in the data group to be processed. In addition, the control time is also related to the associated value interval corresponding to the data group to be processed. The larger the value of the associated value interval, the longer the control time. The control time is also related to the severity of the emergency event. The more serious the emergency event, the longer the control time.

[0136] For more content on how to display the electronic map, refer to step 230 and related descriptions.

[0137] In some embodiments of the present invention, by determining the associated values between different emergency control data and emergency events, important emergency control data can be preferentially processed to ensure the timeliness of data processing.

[0138] Figure 4 It is an exemplary diagram of collecting and updating emergency control data shown in some embodiments of the present invention.

[0139] In some embodiments, as Figure 4 shown, the emergency supervision and management platform adjusts the first driving route 421 according to the associated value 411 between multiple emergency control data and the emergency event and the emergency control street 412, and generates a second driving route 431; controls the emergency inspection vehicle to execute the second driving route 431 and conduct inspections to collect updated emergency control data.

[0140] In some embodiments, the associated value 411 between multiple emergency control data and the emergency event may include the associated value 1 between the emergency control data and the emergency event, the associated value 2 between the emergency control data and the emergency event,..., the associated value n between the emergency control data and the emergency event.

[0141] In some embodiments, for more descriptions of the emergency control street, the first driving route, the emergency inspection vehicle, and the associated value between multiple emergency control data and the emergency event, reference can be made to Figure 2 、 Figure 3 and its related content.

[0142] The second driving route refers to the driving route obtained by adjusting the first driving route based on the associated value between the emergency control data and the emergency event and the emergency control street.

[0143] In some embodiments, the second driving route can be determined through the following steps: Step 1: Determine the emergency control scope corresponding to the geographical area to which the emergency control street belongs. For more instructions on the emergency control scope and its determination, please refer to Figure 2 and its related content; Step 2: Obtain the correlation values between multiple emergency control data and emergency events. For more instructions on the correlation values, please refer to Figure 3 and its related content; Step 3: The emergency supervision management platform can use the emergency control street corresponding to the emergency control data corresponding to the correlation value as the emergency control street corresponding to the correlation value, and sort the emergency control streets corresponding to the correlation value from largest to smallest according to the correlation value; the emergency supervision management platform can determine the correlation values corresponding to the emergency control streets during different preset time periods; and then determine the increase amplitude of the correlation values in the adjacent time periods of multiple preset time periods. For example, if the correlation value in the previous time period is 0.2 and the correlation value in the current time period is 0.4, then the increase amplitude of the correlation value is 100%. The inspection frequency of the emergency control street is positively correlated with the increase amplitude of the correlation value in the adjacent time periods of multiple preset time periods. For example, every time the increase amplitude of the correlation value in the adjacent time periods increases by 30%, the inspection frequency increases by one time; Step 4: The emergency inspection vehicle generates an adjusted driving route, that is, the second driving route, according to the sorting result of the emergency control streets; It can be understood that when the emergency inspection vehicle goes to the emergency control street with a larger correlation value, it can complete the inspection of other emergency control streets with relatively smaller correlation values along the way.

[0144] In some embodiments, the updated emergency control data can be collected in various ways. For example, the emergency inspection vehicle and other processors can collect the updated emergency control data during the inspection process based on the second driving route.

[0145] In some embodiments, the emergency supervision management platform adjusts the second driving route according to the updated emergency control data, in combination with the correlation value and the emergency control street, to generate the third driving route.

[0146] The third driving route is the driving route obtained by adjusting the second driving route based on the updated emergency control data.

[0147] In some embodiments, the third driving route can be determined through the following steps: Step 1: Generate new correlation values according to the updated emergency control data. For more instructions on generating correlation values based on the emergency control data, please refer to Figure 3 and its related content; Step 2: Sort the emergency control streets corresponding to the new correlation values from largest to smallest according to the new correlation values; Step 3: The emergency inspection vehicle generates an adjusted driving route, i.e., the third driving route, according to the sorting result of the new emergency control streets.

[0148] In some embodiments of the present invention, according to the updated emergency control data, the correlation values of different emergency control data are re-determined to update in real time the priority of the emergency control streets to be inspected, which can further improve the real-time performance of the emergency inspection vehicle.

[0149] In some embodiments of the present invention, the priority of the emergency control streets to be inspected and the inspection frequency are updated in real time according to the size of the correlation value, which can improve the inspection effect of the emergency inspection vehicle and avoid the situation of untimely inspection.

[0150] Some embodiments of the present invention also provide a computer-readable storage medium, which stores computer instructions. When the computer reads the computer instructions in the storage medium, the computer executes the method described in any one of the above embodiments.

[0151] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only an example and does not constitute a limitation to the present invention. Although not explicitly stated here, those skilled in the art may make various modifications, improvements, and corrections to the present invention. Such modifications, improvements, and corrections are proposed in the present invention, so such modifications, improvements, and corrections still belong to the spirit and scope of the exemplary embodiments of the present invention.

[0152] Finally, it should be understood that the embodiments described in the present invention are only used to illustrate the principles of the embodiments of the present invention. Other variations may also fall within the scope of the present invention. Therefore, by way of example and not limitation, alternative configurations of the embodiments of the present invention may be considered to be in accordance with the teachings of the present invention. Accordingly, the embodiments of the present invention are not limited to the embodiments explicitly introduced and described in the present invention.

Claims

1. A smart city integrated emergency management and control method, characterized in that: The method is performed based on the emergency supervision management platform and includes: Obtain multiple emergency control data; Retrieving the upload time, data type, and geographical area of ​​the multiple emergency control data to generate a data group to be processed; According to the geographical scope of the geographical area involved in the data group to be processed, marking the emergency control scope corresponding to the temporary control area in the interactive terminal of the smart city GIS system; According to the emergency control streets within the emergency control range and the crowd size corresponding to the emergency control streets, a first driving route is generated and sent to the emergency inspection vehicle; In combination with the driving requirements of the emergency inspection vehicle and the traffic flow conditions of the emergency control street, the signal light phase is updated; The operation of the traffic lights within the emergency control range is controlled based on the updated signal light phase, and the signboards of the variable lanes within the emergency control range are controlled to update and indicate the driving direction.

2. The method according to claim 1, characterized in that The retrieving the upload time, data type, and geographical area of ​​the plurality of emergency management and control data to generate a data group to be processed includes: Based on the total data center, retrieve the data classification model from the data processing model library; Classify the multiple emergency management and control data based on the data classification model to generate the data group to be processed; generating routing information based on the data group to be processed, Based on the routing information, the routing device of the emergency supervision sensor network platform is controlled to send the data group to be processed to the sub-data center of the emergency sub-platform in the corresponding geographical area.

3. The method according to claim 2, characterized in that The method further comprises: Generate association values ​​between the multiple emergency management and control data and the emergency event according to the numerical range of the multiple emergency management and control data within a preset time period, the geographical area to which they belong, the type of emergency event, and the location where the emergency event occurs; Based on the association value, determining the to-be-processed data groups corresponding to different time periods; According to the data group to be processed and the geographical range of the geographical area involved in the data group to be processed, the emergency control range corresponding to the temporary control area is divided; and, According to the temporary control area, the interactive terminal of the smart city GIS system is controlled to display an electronic map corresponding to the temporary control area.

4. The method according to claim 1, characterized in that: The method further comprises: According to the association values ​​of the plurality of emergency control data and the emergency events, combined with the emergency control street, the first driving route is adjusted to generate a second driving route; The emergency inspection vehicle is controlled to execute the second driving route and conduct inspections to collect updated emergency management and control data.

5. The method according to claim 4, characterized in that The method further comprises: According to the updated emergency control data, combined with the associated value and the emergency control street, the second driving route is adjusted to generate a third driving route.

6. A large-scale model system of the Internet of Things for integrated emergency control in smart cities, characterized in that: The system includes an emergency supervision user platform, an emergency supervision service platform, an emergency supervision management platform, an emergency supervision sensor network platform and an emergency supervision object platform; Among them, the emergency supervision user platform includes a third-party terminal, the emergency supervision service platform includes a communication terminal, the emergency supervision management platform includes a processor, a general data center and an emergency sub-platform, and the general data center is configured with a memory and a data processing model library; the emergency supervision sensor network platform includes a communication transmission network and a routing device; the emergency supervision object platform is configured with sensors and memory, and the emergency supervision object platform is configured to operate based on emergency inspection vehicles and communication devices; The emergency supervision management platform is configured to execute the smart city integrated emergency management and control method as described in claim 1.

7. The system according to claim 6, characterized in that The emergency supervision management platform is further configured as follows: Retrieving a data classification model from the data processing model library based on the total data center; Classify the multiple emergency management and control data based on the data classification model to generate the data group to be processed; generating routing information based on the data group to be processed; Based on the routing information, the routing device of the emergency supervision sensor network platform is controlled to send the data group to be processed to the sub-data center of the emergency sub-platform in the corresponding geographical area.

8. The system according to claim 7, characterized in that The emergency supervision management platform is further configured as follows: Generate association values ​​between the multiple emergency management and control data and the emergency event according to the numerical range of the multiple emergency management and control data within a preset time period, the geographical area to which they belong, the type of emergency event, and the location where the emergency event occurs; Based on the association value, determining the to-be-processed data groups corresponding to different time periods; According to the data group to be processed and the geographical range of the geographical area involved in the data group to be processed, the emergency control range corresponding to the temporary control area is divided; and, According to the temporary control area, the interactive terminal of the smart city GIS system is controlled to display an electronic map corresponding to the temporary control area.

9. The system according to claim 6, characterized in that The emergency supervision management platform is further configured as follows: According to the association values ​​of the plurality of emergency control data and the emergency events, combined with the emergency control street, the first driving route is adjusted to generate a second driving route; The emergency inspection vehicle is controlled to execute the second driving route and conduct inspections to collect updated emergency management and control data.

10. A computer-readable storage medium, characterized in that: The storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the method according to any one of claims 1 to 5.

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