Emergency shelter intelligent early warning system and method based on Internet of Things technology

Abstract

The invention discloses an emergency shelter intelligent early warning system and method based on the Internet of Things technology, and relates to the technical field of disaster early warning. Comprising the following steps: acquiring environment data and image data based on a sensor module and an image acquisition module, generating a data set and preprocessing the data set, constructing evaluation indexes based on the preprocessed data set, determining weight information of different evaluation indexes, and integrating the indexes and the corresponding weight information to obtain evaluation grades. And judging whether the disaster evaluation grade exceeds a threshold value or not, if so, giving an alarm, and prompting the crowd to enter a corresponding shelter. The method can effectively improve the evacuation speed of the organization, prevents the waste of guard resources, reduces the number of casualties, and improves the practicability and reliability of the system.

Description

Technical Field

[0001] The present invention relates to the field of disaster warning technology, and in particular to an intelligent warning system and method for emergency shelters based on Internet of Things technology. Background Art

[0002] With the increasing pace of information construction, the urban emergency response system has become an important part of people's daily life and even a symbol of the level of urban management. Modern urban public places are usually highly densely populated, with concentrated property, special environment, three-dimensional functions, and many potential accidents that are difficult to prevent. These characteristics make cities more vulnerable to disasters and increase their vulnerability to disasters. Once an accident occurs, the consequences are serious and rescue is not timely, which will lead to huge casualties and property losses.

[0003] In emergency situations, such as natural disasters, accidents, disasters, and terrorist attacks, timely and effective warning information release is crucial to ensure personnel safety. However, existing warning facilities often have single functions and poor adaptability in indoor and outdoor environments, and cannot meet diverse emergency needs.

[0004] Therefore, it is an urgent problem for those skilled in the art to provide an intelligent early warning system and method for emergency shelters based on Internet of Things technology to solve the defects of the prior art. Summary of the invention

[0005] In view of this, the present invention provides an intelligent early warning system and method for emergency shelters based on Internet of Things technology, which can effectively improve the speed of organizing evacuation and prevent the waste of security resources, reduce the number of casualties, and improve the practicality and reliability of the system.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] An intelligent early warning system for emergency shelters based on Internet of Things technology, comprising a sensor module, an image acquisition module, a data processing module, an early warning module, a communication module, an alarm module and an emergency module;

[0008] A sensor module, connected to the first input terminal of the data processing module, for detecting environmental data;

[0009] An image acquisition module, connected to the second input terminal of the data processing module, for acquiring image data;

[0010] A data processing module, connected to the first input terminal of the early warning module, for processing environmental data and image data, and obtaining a risk index corresponding to the data based on the risk indicator;

[0011] A communication module is connected to the second input terminal of the early warning module and is used to receive data information from the meteorological bureau;

[0012] The first output terminal of the early warning module is connected to the alarm module, and is used to determine the weights of different risk indexes, obtain a comprehensive risk assessment, and issue an early warning signal;

[0013] An alarm module, used to receive warning signals and issue an alarm;

[0014] Emergency module: connected to the second output terminal of the early warning module, used to open the emergency passage and emergency lights to the shelter.

[0015] In the above system, optionally, the sensor module includes a plurality of parallel sensors for collecting environmental data.

[0016] An intelligent early warning method for emergency shelters based on the Internet of Things technology, executing any of the above-mentioned intelligent early warning systems for emergency shelters based on the Internet of Things technology, comprising the following steps:

[0017] Data acquisition step: acquiring environmental data and image data based on the sensor module and the image acquisition module to generate a data set;

[0018] Data preprocessing step: preprocess the data set to obtain the preprocessed data set;

[0019] Indicator construction steps: construct evaluation indicators based on the preprocessed data set;

[0020] Grade generation step: determine the weight information of different evaluation indicators, and obtain the evaluation grade by combining the indicators and their corresponding weight information;

[0021] Risk warning steps: Determine whether the disaster assessment level exceeds the threshold. If so, issue an alarm and prompt people to enter the corresponding shelter.

[0022] In the above method, optionally, in the indicator construction step, the evaluation indicators include disaster source indicators, evacuation path indicators and resource scheduling indicators;

[0023] Disaster source indicators include disaster source location, disaster source impact range, and disaster source diffusion speed;

[0024] Evacuation route indicators include the number of people that can be accommodated in the shelter, the distance to the shelter, and the number of people to be evacuated;

[0025] Resource scheduling indicators include the difficulty of emergency rescue and the arrival time of rescue personnel.

[0026] In the above method, optionally, in the level generation step, the entropy weight method is used to determine the weights of different indicators, and the indicator evaluation levels are divided into disaster indicators and refuge site indicators.

[0027] The above method, optionally, also includes path planning in the risk warning step. When the need for evacuation is detected, a shelter is determined according to shelter indicators, and emergency lights are used to instruct people to evacuate.

[0028] It can be seen from the above technical solution that compared with the prior art, the present invention provides an intelligent early warning system and method for emergency shelters based on Internet of Things technology, which has the following beneficial effects: 1) The present invention communicates with the Meteorological Bureau in real time, and can combine multiple sensors to obtain disaster information in a timely manner, and classify it into levels to determine whether it is in the scope of the disaster, effectively improving the speed of organizing evacuation and preventing the waste of security resources; 2) The present invention can determine the number of people inside the shelter in real time, adjust the capacity risk and accessibility of the shelter in time, and reduce the number of casualties; 3) The present invention is also equipped with emergency lights to enhance communication and lighting capabilities in emergency situations. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0030] Figure 1 A block diagram of an intelligent early warning system for emergency shelters based on Internet of Things technology disclosed in the present invention;

[0031] Figure 2 The present invention discloses a flow chart of an intelligent early warning method for emergency shelters based on Internet of Things technology. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] In this application, relational terms such as first and second, etc. are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of more restrictions, the elements defined by the sentence "comprise one..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0034] Reference Figure 1 As shown, the present invention discloses an intelligent early warning system for emergency shelters based on Internet of Things technology, including a sensor module, an image acquisition module, a data processing module, an early warning module, a communication module, an alarm module and an emergency module;

[0035] A sensor module, connected to the first input terminal of the data processing module, for detecting environmental data;

[0036] An image acquisition module, connected to the second input terminal of the data processing module, for acquiring image data;

[0037] A data processing module, connected to the first input terminal of the early warning module, for processing environmental data and image data, and obtaining a risk index corresponding to the data based on the risk indicator;

[0038] A communication module is connected to the second input terminal of the early warning module and is used to receive data information from the meteorological bureau;

[0039] The first output terminal of the early warning module is connected to the alarm module, and is used to determine the weights of different risk indexes, obtain a comprehensive risk assessment, and issue an early warning signal;

[0040] An alarm module, used to receive warning signals and issue an alarm;

[0041] Emergency module: connected to the second output terminal of the early warning module, used to open the emergency passage and emergency lights to the shelter.

[0042] Furthermore, the sensor module includes multiple parallel sensors to collect environmental data and monitor environmental changes in real time, such as temperature, humidity, smoke, etc.

[0043] Furthermore, the communication module adopts wireless communication technology, such as 4G / 5G or satellite communication.

[0044] An intelligent early warning method for emergency shelters based on the Internet of Things technology, executing any of the above-mentioned intelligent early warning systems for emergency shelters based on the Internet of Things technology, referring to Figure 2 As shown, the following steps are included:

[0045] Data acquisition step: acquiring environmental data and image data based on the sensor module and the image acquisition module to generate a data set;

[0046] Data preprocessing step: preprocess the data set to obtain the preprocessed data set;

[0047] Indicator construction steps: construct evaluation indicators based on the preprocessed data set;

[0048] Grade generation step: determine the weight information of different evaluation indicators, and obtain the evaluation grade by combining the indicators and their corresponding weight information;

[0049] Risk warning steps: Determine whether the disaster assessment level exceeds the threshold. If so, issue an alarm and prompt people to enter the corresponding shelter.

[0050] Furthermore, in the indicator construction step, the evaluation indicators include disaster source indicators, evacuation path indicators, and resource scheduling indicators;

[0051] Disaster source indicators include disaster source location, disaster source impact range, and disaster source diffusion speed;

[0052] Evacuation route indicators include the number of people that can be accommodated in the shelter, the distance to the shelter, and the number of people to be evacuated;

[0053] Resource scheduling indicators include the difficulty of emergency rescue and the arrival time of rescue personnel.

[0054] Furthermore, the location of the disaster source is determined by sensors and the meteorological bureau. After the corresponding disaster source is obtained through sensors and the meteorological bureau, the impact range and diffusion speed of the disaster source are determined based on the corresponding model. For example, when the disaster source is detected to be chemical gas, the Gaussian plume diffusion model is used to determine the impact range and diffusion speed of the disaster source.

[0055] Furthermore, in the level generation step, the entropy weight method is used to determine the weights of different indicators, and the indicator evaluation levels are divided into disaster indicators and shelter indicators.

[0056] Furthermore, the objective assignment method is used to assign the initial indicator weights, and the weights of different indicators are determined after optimization. The calculated disaster source indicator is used as the disaster indicator, and the calculated evacuation path indicator and resource scheduling indicator are used as the shelter indicator.

[0057] Specifically, firstly, the data corresponding to the disaster source is obtained as the original sample, and the original data is standardized by minimization and maximization processing to eliminate the influence of the dimension. The corresponding expression is:

[0058]

[0059] Among them, x ij is the value of the i-th sample at the j-th index, x min(j) is the minimum value of the jth index, x max(j) is the maximum value of the jth indicator. After standardization, all data are normalized to the interval [0,1]. The proportional coefficient of the sample is calculated as follows:

[0060] Then calculate the information entropy of each indicator, the expression is: Among them, e j is the information entropy of the jth indicator, k is a constant used to standardize the entropy value, and the expression is When p ij = 0, define p ij ln(p ij )=0, and the weight of each indicator is calculated according to the information entropy, and the expression is:

[0061] Among them, ω j is the weight of the jth indicator, and finally the comprehensive score of each sample is calculated, expressed as:

[0062]

[0063] Furthermore, the risk warning step also includes path planning. When the need for evacuation is detected, the shelter is determined based on the shelter indicators, and the crowd is instructed to evacuate through emergency lights.

[0064] The number of people that can be accommodated in the shelters includes obtaining the current number of people accommodated in each shelter in real time, determining the corresponding upper limit of the number of people according to each shelter, calculating the remaining number of people that can be accommodated based on the current number of people accommodated and the upper limit of the number of people, determining the shortest route for the crowd to go to the shelter as the planned path, collecting the current population density of each section on the planned path, judging whether the current number of people in each section exceeds the remaining number of people that can be accommodated in the shelter, and if so, obtaining the location information of the section, redetermining the shelter based on the current location information, and planning the forward path.

[0065] Furthermore, the emergency module in the planned path can control the opening and closing of the emergency passage to control the forward distance of the emergency path. When there is a path in the planned path that passes through the emergency passage and has a shorter time, the emergency passage is controlled to open. When there is a fork in the planned path caused by the emergency passage, the emergency passage is closed to ensure that the crowd evacuation path remains unchanged.

[0066] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can refer to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiment. The system and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without creative work.

[0067] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An intelligent early warning system for emergency shelters based on Internet of Things technology, characterized in that: It includes sensor module, image acquisition module, data processing module, early warning module, communication module, alarm module and emergency module; A sensor module, connected to the first input terminal of the data processing module, for detecting environmental data; An image acquisition module, connected to the second input terminal of the data processing module, for acquiring image data; A data processing module, connected to the first input terminal of the early warning module, for processing environmental data and image data, and obtaining a risk index corresponding to the data based on the risk indicator; A communication module is connected to the second input terminal of the early warning module and is used to receive data information from the meteorological bureau; The first output terminal of the early warning module is connected to the alarm module, and is used to determine the weights of different risk indexes, obtain a comprehensive risk assessment, and issue an early warning signal; An alarm module, used to receive warning signals and issue an alarm; Emergency module: connected to the second output terminal of the early warning module, used to open the emergency passage and emergency lights to the shelter.

2. According to claim 1, an intelligent early warning system for emergency shelters based on Internet of Things technology is characterized in that: The sensor module includes multiple parallel sensors to collect environmental data.

3. An intelligent early warning method for emergency shelters based on the Internet of Things technology, executed in an intelligent early warning system for emergency shelters based on the Internet of Things technology as described in any one of claims 1-2, characterized in that: The following steps are involved: Data acquisition step: acquiring environmental data and image data based on the sensor module and the image acquisition module to generate a data set; Data preprocessing step: preprocess the data set to obtain the preprocessed data set; Indicator construction steps: construct evaluation indicators based on the preprocessed data set; Grade generation step: determine the weight information of different evaluation indicators, and obtain the evaluation grade by combining the indicators and their corresponding weight information; Risk warning steps: Determine whether the disaster assessment level exceeds the threshold. If so, issue an alarm and prompt people to enter the corresponding shelter.

4. According to claim 3, an intelligent early warning method for emergency shelters based on Internet of Things technology is characterized in that: In the indicator construction step, the evaluation indicators include disaster source indicators, evacuation path indicators and resource scheduling indicators; Disaster source indicators include disaster source location, disaster source impact range, and disaster source diffusion speed; Evacuation route indicators include the number of people that can be accommodated in the shelter, the distance to the shelter, and the number of people to be evacuated; Resource scheduling indicators include the difficulty of emergency rescue and the arrival time of rescue personnel.

5. According to claim 3, the intelligent early warning method for emergency shelters based on Internet of Things technology is characterized in that: In the level generation step, the entropy weight method is used to determine the weights of different indicators, and the indicator evaluation levels are divided into disaster indicators and shelter indicators.

6. According to claim 5, an intelligent early warning method for emergency shelters based on Internet of Things technology is characterized in that: The risk warning step also includes route planning. When the need for evacuation is detected, the shelter is determined based on the shelter indicators, and emergency lights are used to instruct people to evacuate.

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