Smart city center division type emergency management method and system based on Internet of Things large model

Abstract

The invention provides a smart city split emergency management method and system based on an Internet of Things large model, and the method is executed by an emergency supervision management platform of the system, and comprises the steps: determining at least one accident based on target data; determining a data criticality based on the target data, the data basic features and the at least one accident; determining a data emergency feature based on the target data, the data abnormal feature and the data criticality; determining at least one target sub-platform based on the target data, the data exception features and the division conditions; determining emergency parameters based on the emergency type, the emergency degree and the data emergency characteristics; determining working parameters of the emergency vehicle based on the emergency degree, the emergency parameters and the data emergency characteristics; and based on the emergency parameters and the working parameters, generating and sending a scheduling instruction, and controlling the emergency vehicle to drive to the geographic area to work. According to the method and the system, accurate determination and dynamic allocation of emergency resources can be realized, and the overall efficiency of urban emergency management is improved.

Description

Technical Field

[0001] This specification relates to the field of emergency monitoring, and particularly to a mid - type emergency management method and system for smart cities based on the Internet of Things large model. Background Art

[0002] In the construction of smart cities, emergency management is a key link to ensure the safe operation of the city. However, the current emergency management system still has deficiencies in data processing and resource allocation. On the one hand, the data sources are extensive and complex, and it is difficult to effectively integrate and share data between different departments, resulting in a serious information island phenomenon. On the other hand, there is a lack of effective means for analyzing the emergency characteristics of data, making it difficult to quickly determine the criticality and priority of data.

[0003] Therefore, it is desired to provide a mid - type emergency management method and system for smart cities based on the Internet of Things large model, which can accurately determine and dynamically allocate the emergency characteristics of data, ensure the efficient use of emergency resources, and improve the overall efficiency of urban emergency management. Summary of the Invention

[0004] One or more embodiments of this specification provide a mid - type emergency management method for smart cities based on the Internet of Things large model, which is executed based on an emergency supervision management platform and includes: through an emergency supervision sensing network platform, obtaining multiple target data from an emergency supervision object platform based on a preset period; for each of the multiple target data: based on the target data, determining at least one accident corresponding to the target data; based on the target data, the data basic characteristics of the target data, and the at least one accident, determining the data criticality of the target data; based on the target data, the data abnormal characteristics of the target data, and the data criticality, determining the data emergency characteristics of the target data; based on the target data, the data abnormal characteristics, and the division conditions corresponding to the target data, determining at least one target sub - platform corresponding to the target data; obtaining the emergency type and emergency degree within the geographical area corresponding to the target data from the at least one target sub - platform; based on the emergency type, the emergency degree within the geographical area, and the data emergency characteristics of the at least one target data, determining emergency parameters; based on the emergency degree, the emergency parameters within the geographical area, and the data emergency characteristics of the at least one target data, determining the working parameters of the rescue vehicle; generating a dispatch instruction based on the emergency parameters and the working parameters, and sending the dispatch instruction to the emergency supervision object platform; based on the emergency supervision object platform, controlling the rescue vehicle to drive towards the geographical area and controlling the rescue vehicle to work based on the emergency parameters and the working parameters.

[0005] One or more embodiments of this specification provide a fractional emergency management system for a smart city based on an Internet of Things large model, including an emergency supervision and management platform; the emergency supervision and management platform is configured to: through the emergency supervision sensing network platform, obtain multiple target data from the emergency supervision object platform based on a preset period; for each target data in the multiple target data: based on the target data, determine at least one accident corresponding to the target data; based on the target data, the data basic characteristics of the target data, and the at least one accident, determine the data criticality of the target data; based on the target data, the data anomaly characteristics of the target data, and the data criticality, determine the data emergency characteristics of the target data; based on the target data, the data anomaly characteristics, and the division conditions corresponding to the target data, determine at least one target sub-platform corresponding to the target data; from the at least one target sub-platform, obtain the emergency type and emergency level within the geographical area corresponding to the target data; based on the emergency type, the emergency level within the geographical area, and the data emergency characteristics of the at least one target data, determine emergency parameters; based on the emergency level, the emergency parameters, and the data emergency characteristics of the at least one target data within the geographical area, determine the working parameters of the emergency rescue vehicle; based on the emergency parameters and the working parameters, generate a scheduling instruction, and send the scheduling instruction to the emergency supervision object platform; based on the emergency supervision object platform, control the emergency rescue vehicle to drive towards the geographical area, and control the emergency rescue vehicle to work based on the emergency parameters and the working parameters.

[0006] The beneficial effects brought by the above invention content include but are not limited to: (1) By analyzing the target data, the data basic characteristics of the target data, the data anomaly characteristics, etc., different emergency parameters and scheduling instructions are determined, which is beneficial to accurately respond to accidents, allocate emergency rescue resources in a hierarchical and classified manner, improve the efficiency of emergency rescue, reduce the possibility of accidents occurring, and reduce the losses caused by accidents; (2) For the target data in different accident stages, by determining the division conditions, the target data corresponding to different accidents can be targeted and allocated to the appropriate target sub-platforms, improving the efficiency and accuracy of data processing; (3) Based on the associated anomaly characteristics, the data anomaly characteristics of the target data, and the data criticality, the data emergency characteristics are determined, which is beneficial to improving the accuracy of data pairs, thereby improving the risk assessment effect and enhancing the accident warning ability. Brief Description of the Drawings

[0007] This specification will be further described in the form of exemplary embodiments, and these exemplary embodiments will be described in detail through the drawings. These embodiments are not restrictive. In these embodiments, the same numbers represent the same structures, where: Figure 1It is an exemplary platform structure diagram of a fractional emergency management system for a smart city based on an Internet of Things large model shown in some embodiments of this specification; Figure 2 It is an exemplary flowchart of a fractional emergency management method for a smart city based on an Internet of Things large model shown in some embodiments of this specification; Figure 3 It is an exemplary flowchart for controlling the operation of a display device shown in some embodiments of this specification; Figure 4 It is a schematic diagram of an emergency prediction model shown in some embodiments of this specification; Figure 5 It is an exemplary schematic diagram of sending network data packets shown in some embodiments of this specification. Detailed implementation manners

[0008] To more clearly illustrate the technical solutions of the present invention, the accompanying drawings required for description in the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some examples or embodiments of this specification. For those of ordinary skill in the art, without creative efforts, this specification 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.

[0009] It should be understood that the "system", "unit" and / or "module" used herein is a method for distinguishing 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.

[0010] As shown in this specification, unless the context clearly indicates an exception, words such as "a", "one", "a kind of" and / or "the" are not specifically singular and may also include plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the clearly identified steps and elements, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0011] Flowcharts are used in this specification to illustrate the operations performed by the systems according to the embodiments of this specification. It should be understood that the operations before or after do not necessarily need to be executed precisely in sequence. On the contrary, the steps can be processed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several steps can be removed from these processes.

[0012] Figure 1It is an exemplary platform structure diagram of a distributed emergency management system for a smart city based on an Internet of Things large model shown in some embodiments of this specification. It should be noted that the following embodiments are only used to explain this specification and do not constitute a limitation to this specification.

[0013] In some embodiments, as Figure 1 shown, the distributed emergency management system 100 for a smart city based on an Internet of Things large 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. The emergency supervision user platform 110, the emergency supervision service platform 120, the emergency supervision management platform 130, the emergency supervision sensor network platform 140, and the emergency supervision object platform 150 are communicatively connected in sequence.

[0014] The emergency supervision user platform 110 is a platform for interacting with supervision users. The supervision users may be the superior supervision departments of the current-level supervision department, local government departments, and local public.

[0015] The emergency supervision service platform 120 refers to a platform for receiving emergency feedback information from the emergency supervision management platform 130 and conveying user requirements and control information. In some embodiments, the emergency supervision service platform 120 may perform data interaction with the emergency supervision user platform 110 and the emergency supervision general platform 131 of the emergency supervision management platform 130.

[0016] The emergency supervision management platform 130 refers to a platform for supervising and managing data related to the emergency management process. The emergency supervision management platform 130 may interact with the current-level supervision department. The current-level supervision department corresponds to the superior supervision department corresponding to the emergency supervision user platform 110. For example, when the superior supervision department is a provincial emergency supervision department, the current-level supervision department is a municipal emergency supervision department.

[0017] In some embodiments, the emergency supervision management platform 130 may be configured to: through the emergency supervision sensing network platform 140, obtain multiple target data from the emergency supervision object platform 150 based on a preset period; for each of the multiple target data: based on the target data, determine at least one type of accident corresponding to the target data; based on the target data, the data basic characteristics of the target data, and at least one type of accident, determine the data criticality of the target data; based on the target data, the data abnormal characteristics of the target data, and the data criticality, determine the data emergency characteristics of the target data; based on the target data, the data abnormal characteristics, and the division conditions corresponding to the target data, determine at least one target sub-platform corresponding to the target data; from at least one target sub-platform, obtain the emergency type and emergency level within the geographical area corresponding to the target data; based on the emergency type, emergency level within the geographical area, and the data emergency characteristics of at least one target data, determine the emergency parameters; based on the emergency level within the geographical area, the emergency parameters, and the data emergency characteristics of at least one target data, determine the working parameters of the emergency rescue vehicle; based on the emergency parameters and the working parameters, generate a scheduling instruction, and send the scheduling instruction to the emergency supervision object platform 150; based on the emergency supervision object platform 150, control the emergency rescue vehicle to drive towards the geographical area, and control the emergency rescue vehicle to work based on the emergency parameters and the working parameters. For more content of the emergency supervision management platform 130, please refer to Figures 2 - 5 the relevant description.

[0018] In some embodiments, the emergency supervision management platform 130 may include an emergency supervision general platform 131 and multiple emergency supervision sub-platforms.

[0019] The emergency supervision general platform 131 refers to the general platform for managing multiple emergency supervision sub-platforms. In some embodiments, the emergency supervision general platform 131 includes an emergency supervision general data center 1311.

[0020] The emergency supervision general data center 1311 refers to the platform for overall management of all emergency supervision data. The emergency supervision data includes target data, scheduling instructions, or other data generated during the emergency management process, etc.

[0021] In some embodiments, the emergency supervision general data center 1311 may include a data overall processing model library, an overall management database, and a computing unit, etc. The data overall processing model library refers to the relevant computing model library for storing and overall processing of emergency supervision data. The data overall processing model can perform overall classification, comprehensive analysis, etc. on the received emergency supervision data. The overall management database can be used to store relevant data for comprehensive analysis and management. The computing unit can be a processing device (such as a central processing unit, an embedded processor, etc.).

[0022] The emergency supervision sub-platform refers to a platform for processing different types of data. In some embodiments, the emergency supervision sub-platform may include a prevention sub-platform, a monitoring sub-platform, a response sub-platform, a prevention sub-platform, etc. Among them, the prevention sub-platform is configured to process abnormal data to predict the occurrence of emergency accidents. The monitoring sub-platform is configured to process normal data to monitor abnormal situations in real time. The response sub-platform is configured to process abnormal data to determine the real-time status of emergency accidents. The prevention sub-platform is configured to process accident prevention measure data to prevent accidents from occurring again.

[0023] In some embodiments, the emergency supervision sub-platform includes a sub-data center. The sub-data center refers to a platform for managing and storing data of the emergency supervision sub-platform. The sub-data center may include a sub-database and a sub-data processing model library.

[0024] In some embodiments, the sub-data center may interact with the emergency supervision general data center 1311.

[0025] In some embodiments, as Figure 1 shown, the emergency supervision sub-platform may include emergency supervision sub-platform 133-1, emergency supervision sub-platform 133-2,..., emergency supervision sub-platform 133-n. The sub-data center may include sub-data center 132-1, sub-data center 132-2,..., sub-data center 132-n. The emergency supervision sub-platform 133-n corresponds to the sub-data center 132-n and conducts data interaction.

[0026] The emergency supervision sensing network platform 140 is a platform for conducting sensing communication. In some embodiments, the emergency supervision sensing network platform 140 may be configured as a communication network or a gateway, etc.

[0027] In some embodiments, the emergency supervision sensing network platform 140 may interact with the emergency supervision general platform 131 and the emergency supervision object platform 150.

[0028] The emergency supervision object platform 150 is a platform for collecting data or executing instructions. In some embodiments, the emergency supervision object platform 150 may include a monitoring device system directly managed by the emergency supervision management platform 130, a monitoring device system managed by lower-level supervision departments, etc.

[0029] The monitoring device system includes a variety of data acquisition devices, for example, pressure sensors, temperature sensors, flow meters, gas sensors, video surveillance, infrared sensors, etc.

[0030] For more information about the above platforms, reference can be made to Figures 2 - 5 and its related descriptions.

[0031] With the fractional emergency management system 100 for smart cities based on the large model of the Internet of Things, communication connections can be established between various functional platforms, an information operation closed-loop can be formed between the functional platforms, and coordinated and regular operations can be carried out under the unified management of the emergency supervision and management platform, realizing the informatization and intelligence of emergency supervision perception and control.

[0032] It should be noted that the above description of the fractional emergency management system 100 for smart cities based on the large model of the Internet of Things and its component platforms is only for convenience of description and does not limit this specification within the scope of the examples given. It can be understood that for those skilled in the art, after understanding the principle of the system, various platforms may be arbitrarily combined, or subsystems may be formed and connected to other platforms without departing from this principle.

[0033] Figure 2 It is an exemplary flowchart of the fractional emergency management method for smart cities based on the large model of the Internet of Things shown in some embodiments of this specification. Process 200 is an exemplary process of the fractional emergency management method for smart cities based on the large model of the Internet of Things. In some embodiments, process 200 is executed by the emergency supervision and management platform 130. Process 200 includes steps 201 - step 210.

[0034] Step 201, through the emergency supervision sensing network platform, obtain multiple target data from the emergency supervision object platform based on a preset period.

[0035] The preset period refers to the period for the emergency supervision object platform 150 to obtain target data.

[0036] In some embodiments, the preset period can be preset by technicians based on experience.

[0037] The target data is the emergency management data of the object to be processed that needs to be processed. For example, the target data may include temperature, pressure, etc. The object to be processed can be a gas system, a power system, a water resource system, a traffic safety system. Taking the gas system as an example, the target data may include at least one of gas temperature, gas pressure, gas flow rate, concentration of combustible gas or toxic gas, population scale around the pipeline, quantity of inflammable and explosive items, and duration of gas outage.

[0038] In some embodiments, the emergency supervision and management platform 130 can obtain the target data from the acquisition device of the emergency supervision object platform 150 through the emergency supervision sensing network platform 140.

[0039] For each target data among the multiple target data, execute steps 202 - 210 until all target data are processed.

[0040] Step 202: Based on the target data, determine at least one type of accident corresponding to the target data.

[0041] Accidents can include leakage accidents, fire accidents, explosion accidents, poisoning accidents, equipment failure accidents, etc.

[0042] In some embodiments, the emergency supervision and management platform 130 can, based on the target data, search a first preset table to determine at least one type of accident corresponding to the target data. The first preset table can include the correspondence between the target data and at least one type of accident. The first preset table can be constructed by technicians based on experience or historical data.

[0043] Step 203: Based on the target data, the data basic features of the target data, and at least one type of accident, determine the data criticality of the target data.

[0044] Data basic features refer to the basic features related to the target data. For example, the data basic features can include the geographical area to which the target data belongs and the data type (such as text, image, etc.).

[0045] In some embodiments, the emergency supervision and management platform 130 can determine the data basic features of the target data through a preset algorithm. For example, the preset algorithm can be an identification algorithm, etc.

[0046] Data criticality refers to the importance level of the target data. The higher the data criticality, the higher the importance level of the target data.

[0047] In some embodiments, the emergency supervision and management platform 130 can, based on the target data and at least one type of accident, search a second preset table to obtain the development speed, harm level, importance level of the data type of the target data, and importance level of the geographical area to which the target data belongs for each type of accident; take the weighted sum of the development speed, harm level, importance level of the data type, and importance level of the geographical area as the data criticality, and the weights can be set according to experience. The second preset table can include the development speed and harm level of each type of accident corresponding to the target data, the importance levels of different data types, and the importance levels of different geographical areas. The second preset table can be obtained through historical data statistics or preset by technicians according to requirements.

[0048] Step 204: Based on the target data, the data abnormal features of the target data, and the data criticality, determine the data emergency features of the target data.

[0049] Data abnormal features are features that characterize whether the target data is in an abnormal state. For example, the data abnormal features can include the current value of the target data, the numerical change trend of the target data (such as rising or falling), etc.

[0050] In some embodiments, the emergency supervision and management platform 130 may obtain target data at multiple time points within a preset period to form a target data sequence; obtain historical target data composed of multiple historical time points and the next time point within the preset period in the historical data to form multiple historical change sequences, and perform clustering on the multiple historical change sequences; among the clustering clusters to which the historical change sequences similar to the target data sequence belong, the category with the largest proportion of the change trend of the subsequent data is used as the change trend of the target data. The historical change sequence reflects the change trend of the historical target data. For example, if the target data sequence is composed of target data at time points t1 to t4, then the historical change sequence is composed of historical target data at historical time points t1 to t5, and t5 is the next time point after t4. Similarity may mean that the similarity between the target data sequence and the historical change sequence is greater than the similarity threshold, and the similarity is negatively correlated with the vector distance.

[0051] In some embodiments, the emergency supervision and management platform 130 may determine data anomaly features based on the changes in the target data within a preset period. For specific content, please refer to Figure 3 and its related descriptions.

[0052] The data emergency feature is a feature that reflects the importance of the target data. For example, the data emergency feature may include the importance of the target data and the change trend of the importance.

[0053] In some embodiments, the emergency supervision and management platform 130 may use the weighted sum of the data criticality of the target data and the difference between the value of the target data and the normal range as the importance of the target data. The normal range is the range where the target data is located when no accident occurs.

[0054] In some embodiments, the emergency supervision and management platform 130 may determine the change trend of the importance based on the change trend of the target data. For example, when the value of the target data is less than the normal range, the smaller the value of the target data, the greater the importance of the target data; when the value of the target data is greater than the normal range, the larger the value of the target data, the greater the importance of the target data.

[0055] In some embodiments, the emergency supervision and management platform 130 may determine the data emergency feature of the target data based on at least one associated data, data anomaly feature, and data criticality of the target data. For more content, please refer to Figure 3 and its related descriptions.

[0056] Step 205: Determine at least one target sub-platform corresponding to the target data based on the target data, the data anomaly feature, and the division condition corresponding to the target data.

[0057] The partitioning condition refers to the condition for partitioning the target data to different target sub - platforms. The partitioning condition may include the numerical range and change trend of the target data to be partitioned to each target sub - platform.

[0058] In some embodiments, the partitioning condition can be preset by technicians based on experience.

[0059] In some embodiments, the emergency supervision management platform 130 can determine the partitioning condition corresponding to the target data based on the target data and the historical sensing data of the sensor corresponding to the target data. For more details, see Figure 5 and its related description.

[0060] The target sub - platform refers to the platform for performing different processing on the target data.

[0061] In some embodiments, the target sub - platforms include platforms in the emergency supervision sub - platforms (such as prevention sub - platform, monitoring sub - platform, response sub - platform, prevention sub - platform, etc.). For more explanations, see Figure 1 and related descriptions.

[0062] In some embodiments, the emergency supervision management platform 130 can match the partitioning condition based on the target data and data anomaly characteristics, determine the change trend of the target data and the numerical range to which it belongs in the partitioning condition, so as to determine the target sub - platform to which the target data should be sent.

[0063] Step 206, obtain the emergency type and emergency level within the geographical area corresponding to the target data from at least one target sub - platform.

[0064] The geographical area refers to the geographical location information where the target data is located. Each geographical area includes at least one target data.

[0065] The emergency type refers to the type of work that needs to be carried out in the geographical area where the target data is located. For example, it is necessary to monitor and patrol, prevent and / or conduct emergency rescue.

[0066] The emergency level refers to the urgency of the accident occurring in the geographical area where the target data is located.

[0067] In some embodiments, the target sub - platform can analyze and process the received target data to determine the emergency type and emergency level within the geographical area corresponding to the target data.

[0068] In some embodiments, the emergency supervision management platform 130 obtains the emergency type and emergency level from the corresponding target sub - platform in the emergency supervision sub - platform.

[0069] Step 207, determine the emergency parameter based on the emergency type, emergency level within the geographical area and the data emergency characteristics of at least one target data.

[0070] Emergency parameters refer to the relevant parameters for carrying out emergency rescue work within a geographical area. For example, the emergency parameters may include the types of emergency rescue vehicles required for the geographical area corresponding to the target data, the number of emergency rescue vehicles, and the arrival time of the emergency rescue vehicles. The types of emergency rescue vehicles include emergency rescue vehicles, communication vehicles, patrol vehicles, power supply vehicles, etc.

[0071] In some embodiments, the emergency supervision and management platform 130 may determine the types of emergency rescue vehicles based on the type of emergency. For example, if the type of emergency is rescue required, the types of emergency rescue vehicles include emergency rescue vehicles, power supply vehicles, and communication vehicles. Another example is that if the type of emergency is prevention required, the types of emergency rescue vehicles include emergency rescue vehicles, power supply vehicles, communication vehicles, and patrol vehicles. Still another example is that if the type of emergency is monitoring and patrol required, the type of emergency rescue vehicle includes patrol vehicles.

[0072] In some embodiments, the emergency supervision and management platform 130 may determine the number of emergency rescue vehicles based on the weighted sum of the emergency level within the geographical area and the average importance level of at least one target data; in response to the weighted sum being greater than the first preset threshold, dispatch the maximum number of emergency rescue vehicles to the geographical area; in response to the weighted sum being less than or equal to the first preset threshold, dispatch the standard number of emergency rescue vehicles to the geographical area.

[0073] Among them, the first preset threshold, the maximum number, and the standard number can be preset by technicians based on experience.

[0074] Step 208: Determine the working parameters of the emergency rescue vehicle based on the emergency level within the geographical area, the emergency parameters, and the data emergency characteristics of at least one target data.

[0075] In some embodiments, the working parameters may include the emergency rescue parameters of the emergency rescue vehicle, the patrol parameters of the patrol vehicle, and / or the power supply parameters of the power supply vehicle. Among them, the emergency rescue parameters include the monitoring frequency of sensors on the emergency rescue vehicle (such as temperature sensors, pressure sensors, gas concentration sensors, etc.) and the operating parameters of the ventilation equipment (for example, the operating parameters may include the ventilation period and ventilation power of the ventilation equipment). The patrol parameters include the patrol frequency and / or patrol time of the patrol vehicle, and the power supply parameters include the power generation power of the power supply equipment.

[0076] In some embodiments, the emergency supervision and management platform 130 may determine the working parameters of the emergency rescue vehicle by looking up a third preset table based on the emergency level, the emergency parameters, and the data emergency characteristics of at least one target data. The third preset table includes the emergency level, the emergency parameters, the data emergency characteristics of the target data, and the corresponding working parameters of the emergency rescue vehicle. The third preset table can be preset by technicians according to historical data or requirements.

[0077] Step 209: Generate a dispatch instruction based on the emergency parameters and the working parameters, and send the dispatch instruction to the emergency supervision object platform.

[0078] A dispatching instruction refers to an instruction for dispatching a rescue vehicle. The dispatching instruction may include a control instruction for dispatching the rescue vehicle to a geographical area and a control instruction for controlling each dispatched rescue vehicle to work according to working parameters.

[0079] Step 210: Based on the emergency supervision object platform, control the rescue vehicle to drive towards the geographical area, and control the rescue vehicle to work based on emergency parameters and working parameters.

[0080] In some embodiments, the emergency supervision object platform 150 can control the sensors installed on the rescue vehicle to monitor the environment around the rescue vehicle at a monitoring frequency; control the ventilation equipment installed on the rescue vehicle to exhaust air at a ventilation power during the ventilation period; control the power supply equipment installed on the power supply vehicle to supply power to the geographical area at a power generation power; control the patrol vehicle to patrol within the geographical area at a patrol frequency and patrol time.

[0081] In some embodiments of this specification, determining an accident based on target data, and then determining the data criticality, data emergency characteristics, and their corresponding target sub-platforms, geographical area, emergency type, emergency level, emergency parameters of the target data, and then determining the working parameters of the rescue vehicle and controlling the rescue vehicle to work is beneficial for accurately responding to accidents, allocating rescue resources in a classified and hierarchical manner, improving the efficiency of emergency rescue, reducing the possibility of accidents occurring, and reducing the losses caused by accidents.

[0082] It should be noted that the above description of process 200 is only for illustration and explanation, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to process 200 under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0083] Figure 3 is an exemplary flowchart for controlling the operation of the control display device according to some embodiments of this specification. Process 300 is an exemplary process for controlling the operation of the control display device. In some embodiments, process 300 can be executed by the emergency supervision management platform 130. As Figure 3 shown, process 300 includes the following steps 310-step 330.

[0084] Step 310: Determine the data anomaly characteristics of the target data based on the change situation of the target data within a preset period.

[0085] The change situation of the target data within a preset period can be a change curve. Among them, the abscissa of the change curve represents multiple time points within the preset period, and the ordinate represents the numerical values of the target data corresponding to each time point.

[0086] In some embodiments, the emergency supervision management platform 130 may use multiple time points of a preset period as the abscissa and the numerical values of the target data corresponding to the multiple time points as the ordinate to construct multiple coordinate points; in response to the number of coordinate points being greater than or equal to a preset number threshold, connect the multiple coordinate points in sequence to form a change curve. In response to the number of coordinate points being less than the preset number threshold, the emergency supervision management platform 130 may divide all the coordinate points into different point sets according to a preset time range; for each point set, supplement and obtain the target data of at least one time point within the time range corresponding to the point set from the emergency supervision object platform 150 through the emergency supervision sensing network platform 140, and construct supplementary coordinate points based on the numerical values and time points of the supplemented target data, and fit the supplementary coordinate points and the original coordinate points to obtain a change curve.

[0087] In some embodiments, the preset time range and the preset number threshold may be preset by technicians based on requirements or experience. In some embodiments, the preset number threshold is positively correlated with the geographical area distribution breadth of multiple target data. The geographical area distribution breadth may be represented by the total number of geographical areas to which the multiple target data respectively belong. The larger the geographical area distribution breadth, the more complex the data law, and the larger the preset data threshold.

[0088] In some embodiments, the emergency supervision management platform 130 may add the supplementary coordinate points to the point set and perform fitting on each point set through a fitting model or a fitting algorithm to obtain a change curve.

[0089] In some embodiments, the emergency supervision object platform 150 may supplement and obtain the target data of at least one time point within the time range corresponding to the point set through a data acquisition device. The time point corresponding to the supplemented target data is different from the original time point.

[0090] In some embodiments, for each target data, the emergency supervision management platform 130 may use the numerical value of the target data corresponding to the current time point in the change curve of the target data and the first derivative of the change curve at the current time point as the data anomaly feature of the target data.

[0091] In some embodiments, there is an association relationship between multiple target data. For each target data, the emergency supervision management platform 130 may also obtain at least one associated data of the target data and generate at least one associated data pair; based on the change situation of the at least one associated data pair within a preset period, determine at least one associated anomaly feature of the at least one associated data pair; based on the at least one associated anomaly feature, the data anomaly feature and the data criticality of the target data, determine the data emergency feature of the target data.

[0092] There is an association relationship among multiple target data, which reflects the associated impact of multiple target data on the same accident. For example, taking the emergency supervision of a gas system as an example, for gas pressure data, when a gas leakage or gas blockage accident occurs, there is an association relationship between gas temperature, gas flow rate, etc. and gas pressure.

[0093] Associated data refers to data that has an association relationship with the current target data.

[0094] In some embodiments, the emergency supervision management platform 130 can obtain the associated data of the target data from the target sub-platform corresponding to the target data.

[0095] An associated data pair can include a pair of target data with an association relationship.

[0096] In some embodiments, the emergency supervision management platform 130 can generate multiple associated data pairs respectively based on the target data and multiple associated data of the target data.

[0097] In some embodiments, the associated data pair can further include an associated accident, and the emergency supervision management platform 130 can also generate at least one associated data pair based on the target data, at least one associated data of the target data, and at least one type of accident.

[0098] An associated accident refers to an accident jointly affected by the data in the associated data pair. For example, taking the emergency supervision of a gas system as an example, both gas pressure data and gas temperature have an impact on gas leakage and gas blockage. The associated data pair composed of gas pressure data and gas temperature also includes gas leakage and gas blockage.

[0099] In some embodiments of this specification, the associated data pair also takes into account the specific associated accident, so that the associated data pair clearly reflects the association relationship between the target data, making the parameters determined based on the associated data pair more accurate and reliable subsequently.

[0100] An associated anomaly feature refers to a feature that characterizes whether the associated data has an abnormal state. For example, the associated anomaly feature can include the value of the associated data, the change trend of the associated data.

[0101] The determination method of the associated anomaly feature is the same as Figure 2 the determination method of the data anomaly feature in Figure 2 the relevant description, which will not be elaborated here.

[0102] In some embodiments, the emergency supervision management platform 130 can determine the data emergency feature of the target data through various methods based on at least one associated anomaly feature, the data anomaly feature of the target data, and the data criticality.

[0103] For example, the emergency supervision management platform 130 may use the weighted sum of the data criticality, the differences between the values of multiple associated data and the normal range, and the difference between the value of the target data and the normal range as the importance level of the target data; based on the change trends of multiple associated data, update the change trend of the target data, and based on the updated change trend of the target data, determine the change trend of the importance level. For more information on how to determine the change trend of the importance level, refer to Figure 2 and its related descriptions.

[0104] The emergency supervision management platform 130 may determine the change trend of the target data through a fourth preset table based on the change trends of multiple associated data. The fourth preset table includes the correspondence between the change trends of the associated data and the change trend of the target data. The fourth preset table can be obtained by statistically analyzing historical data.

[0105] In some embodiments, the emergency supervision management platform 130 may determine the data emergency characteristics of the target data through an emergency prediction model. For specific content, refer to Figure 4 and related descriptions.

[0106] In some embodiments of this specification, determining the data emergency characteristics based on the associated anomaly characteristics, the data anomaly characteristics of the target data, and the data criticality is beneficial to improving the accuracy of the data pair, thereby improving the risk assessment effect and enhancing the accident warning ability.

[0107] Step 320, in response to the data anomaly characteristics satisfying the first preset condition, based on the target data and the data anomaly characteristics of the target data, determine the display parameters of at least one display device within the preset area range of the target data.

[0108] The first preset condition includes the numerical range of the target data sent to the response sub-platform.

[0109] In some embodiments, the emergency supervision management platform 130 may use the partitioning condition of the target data sent to the response sub-platform in the partitioning condition as the first preset condition.

[0110] The preset area range refers to the geographical range within the geographical area to which the target data belongs.

[0111] In some embodiments, the preset area range may be preset by technicians based on experience.

[0112] The display device refers to a device used to display and alert abnormal situations. For example, the display device may include warning lights, warning signs, etc. Multiple display devices may be set within each geographical area.

[0113] The display parameters refer to the operating parameters of the display device. For example, the display parameters may include the display frequency of the display device (such as the blinking frequency of the warning light) and the display color, etc.

[0114] In some embodiments, the emergency supervision and management platform 130 may determine the display parameters of the display device by querying the fifth preset table based on the target data and the data anomaly characteristics. The fifth preset table may include the target data, the data anomaly characteristics of the target data, and the corresponding display parameters (including the display frequency and the display color). The fifth preset table may be set by technicians according to historical data or experience.

[0115] Step 330, generate an emergency evacuation instruction based on the display parameters, and send the emergency evacuation instruction to the emergency supervision object platform to control at least one display device to operate according to the display parameters.

[0116] The emergency evacuation instruction refers to an instruction for controlling the operation of the display device. In some embodiments, the emergency supervision and management platform 130 may send the emergency evacuation instruction to the emergency supervision object platform 150 through the emergency supervision sensing network platform 140 to control the display device in the emergency supervision object platform 150 to operate according to the display parameters.

[0117] In some embodiments of this specification, based on the change situation of the target data within a preset period, determine the data anomaly characteristics of the target data, determine the display parameters of the target data, and then generate an emergency evacuation instruction, send and control the display device to operate, which is beneficial to dynamically adjust the display parameters, quickly generate and execute the emergency evacuation instruction, improve the emergency response efficiency, and improve the public's emergency evacuation ability.

[0118] It should be noted that the above description of the process 300 is only for illustration and explanation, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to the process under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0119] In some embodiments, the emergency supervision and management platform 130 may determine the data emergency characteristics of the target data through an emergency prediction model based on at least one associated anomaly characteristic, the target data, the data anomaly characteristics, and the data criticality.

[0120] The emergency prediction model refers to a model for determining the data emergency characteristics of the target data. In some embodiments, the emergency prediction model may be a machine learning model, for example, a deep neural network (DNN), etc.

[0121] Figure 4 It is an exemplary schematic diagram of the emergency prediction model shown in some embodiments of this specification.

[0122] In some embodiments, as Figure 4 shown, the input of the emergency prediction model 420 may include at least one associated anomaly feature 411, target data 412, data anomaly feature 413, and data criticality 414, and the output may be the data emergency feature 430 of the target data.

[0123] In some embodiments, the emergency prediction model can be obtained by training a large number of training samples with training labels. A set of training samples may include multiple sample associated anomaly features, sample target data, sample data anomaly features, and sample data criticality. The training label corresponding to a set of training samples is the data emergency feature of the sample target data.

[0124] The training samples can be determined based on historical data. The historical data includes historical associated anomaly features, historical target data, historical data anomaly features, and historical data criticality. For each training sample, the emergency supervision and management platform 130 can search for historical data, obtain multiple historical accidents at the historical time point of the sample target data corresponding to the training sample, and use the weighted sum of the influence range and loss of the historical accidents (the weight can be preset according to experience), as well as the change trend of the loss caused by the historical accidents in the next time period at the historical time point, as the training label.

[0125] In some embodiments, the emergency supervision and management platform 130 can perform multiple rounds of iterative training on the initial emergency prediction model based on multiple sets of training samples with training labels until the training is terminated when the iteration condition is met, and a trained emergency prediction model is obtained. One round of iterative training includes: inputting a set of training samples with training labels into the initial emergency prediction model, determining the loss function value based on the training label and the output result of the initial emergency prediction model, and iteratively updating the parameters of the initial emergency prediction model based on the loss function value. The iteration method may include the gradient descent method, etc. The iteration condition may be that the loss function converges, the number of iterations reaches a preset number threshold, the loss function value is less than a preset function value threshold, etc.

[0126] In some embodiments of this specification, by determining the data emergency feature of the target data through the emergency prediction model and considering the influence of multiple factors (such as associated anomaly features, target data, data anomaly features, data criticality, etc.) on the data emergency feature, it is beneficial to utilize the learning ability of the machine learning model to accurately predict the data emergency feature (including the importance degree of the target data and the change trend of the importance degree), thereby improving the emergency response efficiency.

[0127] Figure 5 is an exemplary schematic diagram of sending network data packets shown according to some embodiments of this specification.

[0128] In some embodiments, as Figure 5As shown, the emergency supervision management platform 130 can obtain the historical sensing data of the sensor corresponding to the target data, where the historical sensing data includes normal historical sensing data and abnormal historical sensing data; determine the division conditions corresponding to the target data based on the historical sensing data and the target data; generate at least one network data packet and at least one network route based on the target data, the data anomaly characteristics of the target data, and the division conditions; and send at least one network data packet to at least one target sub-platform based on at least one network route.

[0129] The sensor corresponding to the target data refers to the sensor used to obtain the target data in the emergency supervision object platform 150.

[0130] The historical sensing data refers to the target data obtained by the sensor corresponding to the target data at historical time points. The normal historical sensing data refers to the relevant target data obtained when no accident occurs. The abnormal historical sensing data refers to the relevant target data obtained when an accident occurs.

[0131] In some embodiments, the historical sensing data can obtain a preset number of data obtained by the sensor during the historical accident occurrence period as the historical sensing data. The preset number can be set according to requirements.

[0132] In some embodiments, the emergency supervision object platform 150 can respectively count the upper limit values and lower limit values of multiple normal historical sensing data and multiple abnormal historical sensing data, and use the numerical range of the upper limit values and lower limit values of the multiple normal historical sensing data as the numerical range of the target data to be sent to the monitoring sub-platform; use the intersection of the upper limit values and lower limit values of the multiple normal historical sensing data and the upper limit values and lower limit values of the multiple abnormal historical sensing data as the numerical range of the target data to be sent to the prevention sub-platform or the prevention and control sub-platform; and use the numerical range of the upper limit values and lower limit values of the multiple abnormal historical sensing data as the numerical range of the target data to be sent to the response sub-platform.

[0133] The network data packet is the basic unit in network communication, including the source address, target address, the data actually to be transmitted (such as text, image, video stream), etc. The network route refers to the transmission process and path for transmitting the network data packet from the emergency supervision object platform 150 to the target sub-platform.

[0134] In some embodiments, the emergency supervision object platform 150 can determine the target sub-platform to which the target data needs to be sent based on the target data, the data anomaly characteristics of the target data, and the division conditions; generate the corresponding number of network data packets for the target sub-platform and at least one network route for communicating with the target sub-platform based on the target sub-platform and the target data.

[0135] In some embodiments of this specification, determining the partitioning conditions of the target data based on historical sensing data can make the determined partitioning conditions more accurate, so that the target data can be correctly allocated to the corresponding target sub-platforms.

[0136] In some embodiments, the partitioning conditions further include sub-partitioning conditions for at least one type of accident corresponding to the target data. The emergency supervision object platform 150 can, for each type of accident among the at least one type of accident: obtain at least one associated data corresponding to the target data and the accident; determine the sub-partitioning conditions corresponding to the accident based on the target data, the historical sensing data corresponding to the target data, and the associated historical sensing data corresponding to the at least one associated data; determine at least one target sub-platform corresponding to the target data based on the data anomaly characteristics of the target data and the sub-partitioning conditions; generate at least one network data packet and at least one network route based on the target data, the data anomaly characteristics of the target data, and the corresponding at least one sub-partitioning condition; and send the at least one network data packet to the at least one target sub-platform based on the at least one network route.

[0137] For the method of obtaining the target data and the associated data, refer to Figure 3 the relevant description.

[0138] The associated historical sensing data refers to the target data at the historical time points collected by the sensors corresponding to the associated data, including the first sensing data and the second sensing data. The first sensing data and the second sensing data respectively represent the target data collected by the sensors corresponding to the associated data when an accident occurred and did not occur at the historical time.

[0139] In some embodiments, the emergency supervision object platform 150 can cluster the abnormal historical sensing data corresponding to a certain target data based on at least one accident corresponding to the target data. For each cluster obtained after clustering: obtain the first sensing data of multiple associated data corresponding to the abnormal historical sensing data in the cluster. If the acquisition period corresponding to the first sensing data is earlier than or partially earlier than the acquisition period corresponding to the abnormal historical sensing data, then add the first sensing data corresponding to the acquisition period earlier than the acquisition period of the abnormal historical sensing data to the abnormal historical sensing data and update it. For the abnormal historical sensing data, refer to the Figure 5 relevant description above.

[0140] The sub-partitioning conditions refer to the partitioning conditions of the target data corresponding to different accidents. The sub-partitioning conditions can include the numerical range and change trend of the target data corresponding to different accidents partitioned to each target sub-platform.

[0141] In some embodiments, the emergency supervision object platform 150 may determine sub - conditions for different accidents based on historical normal sensing data and the upper and lower limit values of historical abnormal sensing data within each clustering cluster. For the specific determination method, refer to Figure 5 and the relevant descriptions.

[0142] In some embodiments, the emergency supervision management platform 130 may match sub - conditions based on target data and data anomaly characteristics, determine the change trend of the target data and the numerical range to which it belongs in the sub - conditions, so as to determine the target sub - platform to which the target data should be sent.

[0143] The determination methods of network data packets and network routes are similar to Figure 5 that, refer to Figure 5 and the relevant descriptions.

[0144] In some embodiments of this specification, for different accidents, the target data may be in different accident stages. By dividing sub - conditions, the target data corresponding to different accidents can be allocated to appropriate target sub - platforms. For some accidents, the target data has hysteresis. Considering associated data and associated historical sensing data, the time period when the accident actually occurs can be determined, so as to determine the true numerical range of the target data during the accident, and then determine accurate sub - conditions.

[0145] In some embodiments, the emergency supervision management platform 130 may determine the abnormal duration distribution of sensors based on historical sensing data; determine the accident stage in which the target data is located according to the historical sensing data and the target data; determine the data upload parameters in the next preset period according to the target data, accident stage and abnormal duration distribution; generate a sensing regulation instruction based on the data upload parameters, and send it to the emergency supervision object platform to control the sensors to work according to the data upload parameters.

[0146] Regarding the historical sensing data, refer to Figure 5 and the relevant descriptions.

[0147] The abnormal duration distribution includes the time period when the sensor continuously collects abnormal data (i.e., the data during an accident) and the corresponding abnormal data.

[0148] In some embodiments, the abnormal duration distribution can be obtained by statistically analyzing historical sensing data.

[0149] The accident stage includes the accident initial stage, accident peak stage, accident calming stage and accident prevention stage.

[0150] In some embodiments, the emergency supervision and management platform 130 may draw multiple candidate phase curves based on the abnormal historical sensing data corresponding to multiple accidents; perform weighted summation on the multiple candidate phase curves (the weights can be set according to experience) to determine the standard accident phase curve. The method for drawing the candidate phase curve is similar to that of the change curve. For details, see Figure 3 and related descriptions.

[0151] In some embodiments, the emergency supervision and management platform 130 may determine the accident phase in which the target data is located through the standard accident phase curve based on the value and change trend of the target data. If the value of the target data is within the value range corresponding to the first time period in the standard accident phase curve, the target data is in the accident peak phase. The first time period is a preset time period before and after the peak value (set according to requirements). If the value of the target data is within the value range corresponding to the second time period in the standard accident phase curve, the target data is in the accident initial phase or the accident subsiding phase. The second time period is a time period far from the peak value, and the second time period may be the time period other than the first time period in the standard accident phase curve. If the value of the target data is not within the value range in the standard accident phase curve, the target data is in the accident prevention phase.

[0152] The data upload parameters include the upload frequency and upload volume of the target data.

[0153] In some embodiments, the emergency supervision and management platform 130 may search the sixth preset table to determine the data upload parameters in the next preset cycle based on the target data, accident phase, and abnormal duration distribution. The sixth preset table includes the corresponding relationship between the target data, accident phase, abnormal duration distribution, and data upload parameters. The sixth preset table can be preset by technicians.

[0154] The sensing regulation instruction is an instruction for adjusting the upload parameters of the sensor.

[0155] Considering the abnormal duration distribution and accident phase and adjusting the data upload parameters of the sensor in the next cycle can make the parameters collected and uploaded by the sensor more in line with the actual requirements, thereby improving the parameter collection efficiency of the sensor.

[0156] In some embodiments, a display element is provided on the emergency rescue vehicle and / or the power supply vehicle. The emergency supervision and management platform 130 may generate a display instruction based on the accident phase in which the target data is located and send it to the emergency supervision object platform to control the display element to work based on the display instruction.

[0157] The display element is an element for displaying the accident occurrence situation. The display element may be a warning light or the like.

[0158] The display instruction is an instruction for controlling the display element to work. The display instruction includes the display frequency and display color of the display element.

[0159] In some embodiments, during the accident stage, the emergency supervision and management platform 130 may determine different display modes of the display element through the seventh preset table and convert the different display modes into display instructions. Different display modes correspond to different display frequencies and / or display colors. The seventh preset table includes display modes corresponding to different accident stages. The seventh preset table can be set according to requirements.

[0160] By setting the display element to display different accident stages of the accident, the occurrence of the accident can be timely and accurately warned, thus facilitating the timely investigation and handling of the accident.

[0161] 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 this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification, so such modifications, improvements, and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0162] At the same time, this specification uses specific terms to describe the embodiments of this specification. For example, "some embodiments" means a certain feature, structure, or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "some embodiments" mentioned twice or more at different positions in this specification do not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0163] In addition, the order of the processing elements and sequences described in this specification, the use of numbers, letters, or other names, is not used to limit the order of the processes and laminar flow hoods in this specification. Although the system components described above can be implemented by hardware devices, they can also be implemented only through software solutions, such as installing the described system on existing servers or mobile devices.

[0164] Similarly, it should be noted that in order to simplify the expression of this specification disclosure and thus help the understanding of one or more embodiments of the invention, in the previous description of the present invention, sometimes multiple features are merged into one embodiment, drawing, or description thereof. In fact, the features of the embodiment are less than all the features of the single embodiment disclosed above.

[0165] In some embodiments, the numerical parameters used in the specification are all approximate values, and these approximate values may change according to the characteristics required by individual embodiments. In some embodiments, the numerical parameters should consider the specified significant digits and adopt the method of retaining the general number of digits. Although the numerical ranges and parameters used in some embodiments of this specification to confirm the breadth of their scope are approximate values, in specific embodiments, the setting of such numerical values is as precise as possible within the feasible range.

[0166] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the present invention. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the present invention may be regarded as consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A smart city split emergency management method based on the Internet of Things large model, characterized in that: Executed based on the emergency supervision management platform, including: Through the emergency supervision sensor network platform, multiple target data are obtained from the emergency supervision object platform based on a preset period; For each target data in the plurality of target data: Based on the target data, determining at least one accident corresponding to the target data; Determining data criticality of the target data based on the target data, data basic characteristics of the target data, and the at least one accident; Determining data emergency characteristics of the target data based on the target data, data anomaly characteristics of the target data, and the data criticality; Determine at least one target sub-platform corresponding to the target data based on the target data, the data abnormality characteristics and the division condition corresponding to the target data; Acquire, from the at least one target sub-platform, the emergency type and emergency degree in the geographical area corresponding to the target data; determining an emergency parameter based on the emergency type, the emergency degree and the data emergency characteristic of the at least one target data within the geographic area; determining working parameters of a rescue vehicle based on the emergency level in the geographic area, the emergency parameters and the data emergency characteristics of the at least one target data; Based on the emergency parameters and the working parameters, a dispatch instruction is generated, and the dispatch instruction is sent to the emergency supervision object platform, Based on the emergency supervision object platform, the rescue vehicle is controlled to drive to the geographical area, and the rescue vehicle is controlled to work based on the emergency parameters and the working parameters.

2. The method according to claim 1, characterized in that The method further comprises: Determining the data abnormality feature of the target data based on the change of the target data within a preset period; In response to the data abnormality feature satisfying a first preset condition, determining a display parameter of at least one display device within a preset area of ​​the target data based on the target data and the data abnormality feature of the target data; An emergency risk avoidance instruction is generated based on the display parameters, and the emergency risk avoidance instruction is sent to the emergency supervision object platform, and the at least one display device is controlled to work according to the display parameters.

3. The method according to claim 2, characterized in that There is an association relationship between the multiple target data, and the method further includes: For each of the plurality of target data: Acquire at least one associated data of the target data, and generate at least one associated data pair; Determining at least one associated abnormal feature of the at least one associated data pair based on changes in the at least one associated data pair within the preset period; The data contingency feature of the target data is determined based on the at least one associated abnormality feature, the data abnormality feature of the target data, and the data criticality.

4. The method according to claim 1, characterized in that The method further comprises: Acquire historical sensor data of the sensor corresponding to the target data, wherein the historical sensor data includes normal historical sensor data and abnormal historical sensor data; Determining the division condition corresponding to the target data based on the historical sensor data and the target data; Based on the target data, the data anomaly characteristics of the target data, and the partitioning condition, generating at least one network data packet and at least one network route; Based on the at least one network route, the at least one network data packet is sent to the at least one target sub-platform.

5. The method according to claim 4, characterized in that The division condition further includes a division sub-condition of the at least one accident corresponding to the target data, and the method further includes: For each of the at least one incident: Acquire the target data and at least one associated data corresponding to the accident; Determining the division sub-condition corresponding to the accident based on the target data, the historical sensor data corresponding to the target data, and the associated historical sensor data corresponding to the at least one associated data; Determine the at least one target sub-platform corresponding to the target data based on the data anomaly feature of the target data and the division sub-condition; Based on the target data, the data anomaly feature of the target data, and the corresponding at least one division sub-condition, generating the at least one network data packet and the at least one network route; Based on the at least one network route, the at least one network data packet is sent to the at least one target sub-platform.

6. A smart city split emergency management system based on the Internet of Things large model, characterized in that: Including emergency supervision and management platform; The emergency supervision management platform is configured as follows: Acquiring multiple target data from the emergency supervision object platform based on a preset period through the emergency supervision sensor network platform; For each target data in the plurality of target data: Based on the target data, determining at least one accident corresponding to the target data; Determining data criticality of the target data based on the target data, data basic characteristics of the target data, and the at least one accident; Determining data emergency characteristics of the target data based on the target data, data anomaly characteristics of the target data, and the data criticality; Determine at least one target sub-platform corresponding to the target data based on the target data, the data abnormality characteristics and the division condition corresponding to the target data; Acquire, from the at least one target sub-platform, the emergency type and emergency degree in the geographical area corresponding to the target data; determining an emergency parameter based on the emergency type, the emergency degree and the data emergency characteristic of the at least one target data within the geographic area; determining working parameters of a rescue vehicle based on the emergency level in the geographic area, the emergency parameters and the data emergency characteristics of the at least one target data; Generate a dispatch instruction based on the emergency parameter and the working parameter, and send the dispatch instruction to the emergency supervision object platform; Based on the emergency supervision object platform, the rescue vehicle is controlled to drive to the geographical area, and the rescue vehicle is controlled to work based on the emergency parameters and the working parameters.

7. The system according to claim 6, characterized in that: The system also includes an emergency supervision user platform, an emergency supervision service platform, an emergency supervision sensor network platform and an emergency supervision object platform. The emergency supervision user platform, the emergency supervision management platform, the emergency supervision service platform, the emergency supervision sensor network platform and the emergency supervision object platform are communicatively connected in sequence.

8. The system according to claim 6, characterized in that The emergency supervision management platform is further configured as follows: Determining the data abnormality feature of the target data based on the change of the target data within a preset period; In response to the data abnormality feature satisfying a first preset condition, determining a display parameter of at least one display device within a preset area of ​​the target data based on the target data and the data abnormality feature of the target data; An emergency risk avoidance instruction is generated based on the display parameters, and the emergency risk avoidance instruction is sent to the emergency supervision object platform, and the at least one display device is controlled to work according to the display parameters.

9. The system according to claim 8, characterized in that There is an association relationship between the multiple target data, and the emergency supervision management platform is further configured as follows: For each of the plurality of target data: Acquire at least one associated data of the target data, and generate at least one associated data pair; Determining at least one associated abnormal feature of the at least one associated data pair based on changes in the at least one associated data pair within the preset period; The data contingency feature of the target data is determined based on the at least one associated abnormality feature, the data abnormality feature of the target data, and the data criticality.

10. The system according to claim 6, characterized in that The emergency supervision management platform is further configured as follows: Acquire historical sensor data of the sensor corresponding to the target data, wherein the historical sensor data includes normal historical sensor data and abnormal historical sensor data; Determining the division condition corresponding to the target data based on the historical sensor data and the target data; Based on the target data, the data anomaly characteristics of the target data, and the partitioning condition, generating at least one network data packet and at least one network route; Based on the at least one network route, the at least one network data packet is sent to the at least one target sub-platform.

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