# US 2026/0089477 A1 · Systems and methods for multimodal emergency management of smart cities based on large models of internet of things

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- Number: US 2026/0089477 A1
- Document ID: US20260089477A1
- Status: Published application
- Kind: A1
- Application number: 19/408,304
- Filed: 2025-12-03
- Published: 2026-03-26
- Earliest claimed: 2025-10-22
- Assignees: Chengdu Qinchuan Iot Technology Co., Ltd.
- Applicants: Chengdu Qinchuan Iot Technology Co., Ltd.
- Inventors: Hanshu SHAO
- Web page: https://patentgrep.com/patent/US20260089477A1

## CPC

- H04W 4/90: Services for handling of emergency or hazardous situations, e.g. earthquake and tsunami warning systems [ETWS]
- G06N 20/00: Machine learning
- G06Q 10/06315: {Needs-based resource requirements planning or analysis}
- H04L 67/1029: using data related to the state of servers by a load balancer
- H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)

## Abstract

A system and a method for multimodal emergency management of a smart city based on a large model of internet of things are provided. The method is executed by an emergency supervision management platform. The method includes: based on a preset cycle, for each of a plurality of sub-data centers, determining a second target dataset and a target processing order based on a remaining computing resource, a reference computing resource, and a first target dataset; predicting a pending data volume based on first historical data; based on the reference computing resource and the pending data volume, predicting a resource occupancy condition, and generating an overload condition; determining a data transmission order based on the target processing orders of the plurality of sub-data centers, and transmitting the second target dataset based on the data transmission order.

## Claims

### Claim 1

A system for multimodal emergency management of a smart city based on a large model of Internet of Things (IoT), comprising: an emergency supervision management platform, an emergency supervision sensor network platform, and an emergency supervision object platform; wherein
the emergency supervision management platform includes an emergency supervision central platform, a plurality of emergency supervision sub-platforms, and a plurality of sub-data centers;
the emergency supervision management platform is configured to:
based on a preset cycle, for each of the plurality of sub-data centers,
determine a second target dataset and a target processing order based on a remaining computing resource, a reference computing resource, and a first target dataset;
predict a pending data volume based on first historical data;
based on the reference computing resource and the pending data volume, predict a resource occupancy condition, and generate an overload condition; and
determine a data transmission order for each of the plurality of sub-data centers based on target processing orders of the plurality of sub-data centers, and transmit the second target dataset of each of the plurality of sub-data centers based on the data transmission order of the sub-data center.

### Claim 2 (depends on 1)

The system of claim 1, wherein the emergency supervision management platform is further configured to:
based on the overload conditions and the pending data volumes of the plurality of sub-data centers, generate a resource control instruction, and send the resource control instruction to the plurality of sub-data centers to control each of the plurality of sub-data centers to clear a cache space and/or adjust a transmission bandwidth.

### Claim 3 (depends on 1)

The system of claim 1, wherein the emergency supervision management platform is further configured to:
based on the second target datasets, the target processing orders, and the overload conditions of the plurality of sub-data centers, generate at least one of a valve control instruction, a power vehicle control instruction, and a rescue vehicle control instruction, and send the at least one instruction to the emergency supervision object platform to control a smart gas valve to automatically open or close based on an open-close state, control a mobile emergency power vehicle to travel based on a driving route and supply power based on a power output, and/or control a display terminal disposed on a rescue vehicle to display a rescue route based on a rescue arrival deadline.

### Claim 4 (depends on 3)

The system of claim 3, wherein, during a process in which the rescue vehicle travels based on the rescue route, the rescue vehicle is configured to determine a risk region based on road condition data and transmit the risk region to the emergency supervision management platform.

### Claim 5 (depends on 3)

The system of claim 3, wherein the rescue vehicle control instruction includes a rescue vehicle type and a rescue vehicle count;
the emergency supervision management platform is further configured to:
determine, based on the second target datasets, the target processing orders and the overload conditions of the plurality of sub-data centers, a disaster development trend corresponding to each of the target processing orders through a trend prediction model, the trend prediction model being a machine learning model; and
generate the rescue vehicle control instruction based on the disaster development trends corresponding to the target processing orders of the plurality of sub-data centers.

### Claim 6 (depends on 1)

The system of claim 1, wherein the emergency supervision management platform is further configured to:
for each of the plurality of sub-data centers, based on the first historical data, second historical data, and a regional feature, predict the pending data volume of the sub-data center through a data volume prediction model, the data volume prediction model being a machine learning model.

### Claim 7 (depends on 6)

The system of claim 6, wherein an input of the data volume prediction model further includes the disaster development trend corresponding to the target processing order.

### Claim 8

A method for multimodal emergency management of a smart city based on a large model of Internet of Things (IoT), executed by an emergency supervision management platform, comprising:
based on a preset cycle, for each of a plurality of sub-data centers,
determining a second target dataset and a target processing order based on a remaining computing resource, a reference computing resource, and a first target dataset;
predicting a pending data volume based on first historical data;
based on the reference computing resource and the pending data volume, predicting a resource occupancy condition, and generating an overload condition; and
determining a data transmission order for each of the plurality of sub-data centers based on target processing orders of the plurality of sub-data centers, and transmitting the second target dataset of each of the plurality of sub-data centers based on the data transmission order of the sub-data center.

### Claim 9 (depends on 8)

The method of claim 8, further comprising:
based on the overload conditions and the pending data volumes of the plurality of sub-data centers, generating a resource control instruction, and sending the resource control instruction to the plurality of sub-data centers to control each of the plurality of sub-data centers to clear a cache space and/or adjust a transmission bandwidth.

### Claim 10 (depends on 8)

The method of claim 8, further comprising:
based on the second target datasets, the target processing orders, and the overload conditions of the plurality of sub-data centers, generating at least one of a valve control instruction, a power vehicle control instruction, and a rescue vehicle control instruction, and sending the at least one instruction to the emergency supervision object platform to control a smart gas valve to automatically open or close based on an open-close state, control a mobile emergency power vehicle to travel based on a driving route and supply power based on a power output, and/or control a display terminal disposed on a rescue vehicle to display a rescue route based on a rescue arrival deadline.

### Claim 11 (depends on 10)

The method of claim 10, wherein, during a process in which the rescue vehicle travels based on the rescue route, the rescue vehicle is configured to determine a risk region based on road condition data and transmit the risk region to the emergency supervision management platform.

### Claim 12 (depends on 10)

The method of claim 10, wherein the rescue vehicle control instruction includes a rescue vehicle type and a rescue vehicle count; and the generating at least one of a valve control instruction, a power vehicle control instruction, and a rescue vehicle control instruction based on the second target datasets, the target processing orders, and the overload conditions of the plurality of sub-data centers includes:
determining, based on the second target datasets, the target processing orders and the overload conditions of the plurality of sub-data centers, a disaster development trend corresponding to each of the target processing orders through a trend prediction model, the trend prediction model being a machine learning model; and
generating the rescue vehicle control instruction based on the disaster development trends corresponding to the target processing orders of the plurality of sub-data centers.

### Claim 13 (depends on 8)

The method of claim 8, wherein the predicting a pending data volume based on first historical data includes:
for each of the plurality of sub-data centers, based on the first historical data, second historical data, and a regional feature, predicting the pending data volume of the sub-data center through a data volume prediction model, the data volume prediction model being a machine learning model.

### Claim 14 (depends on 13)

The method of claim 13, wherein an input of the data volume prediction model further includes the disaster development trend corresponding to the target processing order.

### Claim 15

A non-transitory computer-readable storage medium, storing computer instructions, wherein when a computer reads the computer instructions from the storage medium, the computer executes a method for multimodal emergency management of a smart city based on a large model of Internet of Things (IoT), executed by an emergency supervision management platform, the method comprising:
based on a preset cycle, for each of a plurality of sub-data centers,
determining a second target dataset and a target processing order based on a remaining computing resource, a reference computing resource, and a first target dataset;
predicting a pending data volume based on first historical data;
based on the reference computing resource and the pending data volume, predicting a resource occupancy condition, and generating an overload condition; and
determining a data transmission order for each of the plurality of sub-data centers based on target processing orders of the plurality of sub-data centers, and transmitting the second target dataset of each of the plurality of sub-data centers based on the data transmission order of the sub-data center.