Multi-source information fusion and intelligent decision-making method for emergency calling terminal

Abstract

The invention discloses a multi-source information fusion and intelligent decision-making method of an emergency call terminal, and relates to the technical field of emergency call terminals, the emergency call terminal comprises a system body, the system body is internally provided with a plurality of hierarchies, and the hierarchies comprise a sensing layer, a transmission layer, a data processing layer, a decision-making layer and an application layer, the sensing layer is internally provided with a data acquisition module, the transmission layer is internally provided with a communication module, the data processing layer is internally provided with a data preprocessing module and a multi-source information fusion module, the decision layer is internally provided with an intelligent decision module, and the application layer is internally provided with an instruction output module. The data acquisition module is internally provided with an image acquisition unit used for acquiring image data of a disaster site, a voice acquisition unit used for collecting voice information of the site, and a sensor unit used for acquiring environment temperature and humidity.

Description

Technical Field

[0001] The present invention relates to the technical field of emergency response terminals, and in particular to a multi-source information fusion and intelligent decision-making method for emergency response terminals. Background Art

[0002] With the increasing frequency of various disasters and emergencies, the requirements for emergency response terminals are getting higher and higher. Although the existing emergency response terminals have certain functions, there is still room for improvement in multi-source information fusion and intelligent decision-making. For example, the information obtained is less, which easily leads to the limitation of a single information source. Secondly, the operation efficiency is low, and the response data is not accurate enough.

[0003] The present invention aims to provide a more efficient and accurate emergency call terminal multi-source information fusion and intelligent decision-making method. Summary of the invention

[0004] The purpose of the present invention is to provide a multi-source information fusion and intelligent decision-making method for an emergency call response terminal to solve the problems raised in the above background technology.

[0005] By adopting the above technical solution, a more efficient and accurate operation function is achieved.

[0006] In view of the above problems, the technical solution proposed by the present invention is: An emergency call response terminal system includes a system body. Several layers are arranged inside the system body, including a perception layer, a transmission layer, a data processing layer, a decision layer and an application layer. The perception layer is provided with a data acquisition module, the transmission layer is provided with a communication module, the data processing layer is provided with a data preprocessing module and a multi-source information fusion module, the decision layer is provided with an intelligent decision module, and the application layer is provided with a command output module.

[0007] As a preferred technical solution of the present invention, the data acquisition module includes an image acquisition unit for acquiring image data of the disaster site, such as a camera and other equipment, whose performance must meet the requirements of not less than 1080P at low definition as mentioned in the document, and be able to work in harsh environments; a voice acquisition unit for collecting voice information at the scene, such as a microphone and other equipment, which can ensure normal operation in an environment of -40℃ to +65℃ through the terminal's protection design; a sensor unit for collecting relevant data such as ambient temperature and humidity to assist in judging the environmental conditions of the disaster site.

[0008] As a preferred technical solution of the present invention, the communication module includes a 4G / 5G communication unit, which is responsible for transmitting data through the ground mobile network to ensure timely transmission of data and realize the rapid sending and receiving of information such as short messages; a satellite communication unit, including Ku-band and L-band satellite communication sub-units, to realize long-distance data transmission in areas without ground network coverage, such as obtaining macro data such as meteorology and geography from satellites; a BeiDou-3 short message communication unit, to realize positioning and sending and receiving short message information in a specific area, and to ensure information interaction in remote areas.

[0009] As a preferred technical solution of the present invention, the data preprocessing module performs format conversion and standardization operations on various types of collected data, such as converting image data into a format suitable for analysis, digitizing voice data, and using the hardware encryption and decryption functions mentioned in the document (such as using national standard algorithms SM4, SM3, SM2) to securely process the data and prevent the data from being tampered with during the processing process.

[0010] As a preferred technical solution of the present invention, the multi-source information fusion module performs feature extraction and semantic analysis on the preprocessed data based on artificial intelligence algorithms, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), to achieve the fusion of different types of data (images, voice, text, sensor data, etc.).

[0011] As a preferred technical solution of the present invention, the intelligent decision-making module is based on the fused data and establishes an intelligent decision-making model, wherein the result of the intelligent decision-making model generates an emergency response plan.

[0012] As a preferred technical solution of the present invention, a loudspeaker broadcast unit and a text message sending unit are respectively provided inside the instruction output module.

[0013] The present invention provides a multi-source information fusion method for an emergency call response terminal, comprising the following steps: Step 1: Data collection: Through the terminal's 4G / 5G network, satellite communications, including Ku-band and L-band, and BeiDou-3 short message communication means, collect meteorological data including disaster site images, voice, text reports, satellite monitoring data, and geographic change data, such multi-source information; Step 2: Data preprocessing: Unify and standardize the format of the collected information. The image data is adjusted and compressed, the voice data is converted into an analyzable digital format, the text information is encoded and converted, and the data is securely processed using information encryption technology to prevent data leakage or tampering during the fusion process; Step 3: Fusion algorithm: An artificial intelligence-based fusion algorithm is used, in which the convolutional neural network (CNN) in deep learning extracts features from image data, and the recurrent neural network (RNN) performs semantic analysis on voice and text data. It also associates and integrates different types of data features, mainly correlating the degree of damage in the disaster site images with the meteorological data monitored by satellites to determine the development trend of the disaster.

[0014] The present invention provides an intelligent decision-making method for an emergency call response terminal, comprising the following steps: Step 1: Establish a decision model: Establish an intelligent decision model based on the integrated multi-source information. The model can be based on machine learning algorithms, including support vector machines (SVM) and decision trees, to classify and evaluate disaster types, severity, impact range, etc.; Step 2: Parameter setting and optimization: Set the parameters of the decision model based on historical data and experience, and improve the accuracy of the decision model through continuous training and optimization; Step 3: Decision output: Based on the results of the decision model, an emergency response plan is generated, including rescue route planning, personnel evacuation plan, and material allocation plan. The decision results are communicated to relevant personnel through the terminal's loudspeaker broadcast and text messages.

[0015] Compared with the prior art, the present invention has the following beneficial effects: It achieves the effect of comprehensive information: specifically, by integrating multi-source information, including satellite data, on-site images, voice and text, etc., it can have a more comprehensive understanding of the disaster situation and avoid the limitations of a single information source; it improves the accuracy of decision-making: specifically, by using advanced artificial intelligence algorithms for data fusion and intelligent decision-making, it can more accurately judge the nature and development trend of disasters and improve the efficiency of emergency response; it improves security: mainly uses built-in hardware encryption and decryption technology to ensure the security of data during transmission and processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 An architecture diagram of an emergency call response terminal system provided by the present invention; Figure 2 A flowchart of a multi-source information fusion method for an emergency call response terminal provided by the present invention; Figure 3 A flowchart of an intelligent decision-making method for an emergency call response terminal provided by the present invention. DETAILED DESCRIPTION

[0017] 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.

[0018] See also Figure 1 The present invention provides a technical solution: an emergency call response terminal system, including a system body, wherein several layers are arranged inside the system body, including a perception layer, a transmission layer, a data processing layer, a decision layer and an application layer, wherein a data acquisition module is arranged inside the perception layer, a communication module is arranged inside the transmission layer, a data preprocessing module and a multi-source information fusion module are arranged inside the data processing layer, an intelligent decision-making module is arranged inside the decision layer, and a command output module is arranged inside the application layer.

[0019] The data acquisition module is internally provided with an image acquisition unit for acquiring image data of the disaster site, a voice acquisition unit for collecting voice information at the site, and a sensor unit for collecting ambient temperature and humidity.

[0020] The communication module is equipped with a 4G / 5G communication unit responsible for transmitting data through the ground mobile network, and a satellite communication unit for realizing long-distance data transmission in areas without ground network coverage. The satellite communication unit includes Ku-band and L-band satellite communication sub-units, and a BeiDou-3 short message communication unit for realizing positioning in specific areas and sending and receiving short message information.

[0021] Among them, the data preprocessing module is equipped with a format conversion unit and a digital processing unit, and the multi-source information fusion module is based on an artificial intelligence algorithm, specifically any one or combination of a convolutional neural network (CNN) or a recurrent neural network (RNN).

[0022] Among them, the intelligent decision-making module is based on the integrated data and establishes an intelligent decision-making model, wherein the result of the intelligent decision-making model generates an emergency response plan.

[0023] The command output module is internally provided with a loudspeaker broadcast unit and a text message sending unit.

[0024] 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.

[0025] See also Figure 1 and Figure 2 The present invention provides a technical solution: a multi-source information fusion method for an emergency call terminal, comprising the following steps: Step 1: Data collection: Through the terminal's 4G / 5G network, satellite communications, including Ku-band and L-band, and BeiDou-3 short message communication means, collect multi-source information including disaster site images, voice, text reports, satellite monitoring data, meteorological data, and geographic change data; Step 2: Data preprocessing: Unify and standardize the format of the collected information. The image data is adjusted and compressed, the voice data is converted into an analyzable digital format, the text information is encoded and converted, and the data is securely processed using information encryption technology to prevent data leakage or tampering during the fusion process; Step 3: Fusion algorithm: An artificial intelligence-based fusion algorithm is used, in which the convolutional neural network (CNN) in deep learning extracts features from image data, and the recurrent neural network (RNN) performs semantic analysis on voice and text data. It also associates and integrates different types of data features, mainly correlating the degree of damage in the disaster site images with the meteorological data monitored by satellites to determine the development trend of the disaster.

[0026] See also Figure 1 and Figure 3 The present invention provides a technical solution: a multi-source information fusion and intelligent decision-making method for an emergency call terminal, comprising the following steps: Step 1: Establish a decision model: Establish an intelligent decision model based on the integrated multi-source information. The model can be based on machine learning algorithms, including support vector machines (SVM) and decision trees, to classify and evaluate disaster types, severity, impact range, etc.; Step 2: Parameter setting and optimization: Set the parameters of the decision model based on historical data and experience, and improve the accuracy of the decision model through continuous training and optimization; Step 3: Decision output: Based on the results of the decision model, an emergency response plan is generated, including rescue route planning, personnel evacuation plan, and material allocation plan. The decision results are communicated to relevant personnel through the terminal's loudspeaker broadcast and text messages.

Claims

1. An emergency call response terminal system, characterized in that: The system comprises a system body, wherein several layers are arranged inside the system body, including a perception layer, a transmission layer, a data processing layer, a decision layer and an application layer. The perception layer is provided with a data acquisition module, the transmission layer is provided with a communication module, the data processing layer is provided with a data preprocessing module and a multi-source information fusion module, the decision layer is provided with an intelligent decision module, and the application layer is provided with a command output module.

2. The emergency call response terminal system according to claim 1, characterized in that: The data acquisition module is internally provided with an image acquisition unit for acquiring image data of the disaster site, a voice acquisition unit for collecting voice information at the site, and a sensor unit for collecting ambient temperature and humidity.

3. The emergency call response terminal system according to claim 1, characterized in that: The communication module is internally provided with a 4G / 5G communication unit responsible for transmitting data through the ground mobile network, and a satellite communication unit for realizing long-distance data transmission in areas without ground network coverage. The satellite communication unit includes Ku-band and L-band satellite communication sub-units, and a BeiDou-3 short message communication unit for realizing positioning of a specific area and sending and receiving short message information.

4. The emergency call response terminal system according to claim 1, characterized in that: The data preprocessing module has a format conversion unit and a digital processing unit inside.

5. The emergency call response terminal system according to claim 1, characterized in that: The multi-source information fusion module is based on an artificial intelligence algorithm, specifically any one or a combination of a convolutional neural network (CNN) or a recurrent neural network (RNN).

6. The emergency call response terminal system according to claim 1, characterized in that: The intelligent decision-making module establishes an intelligent decision-making model based on the fused data, wherein the result of the intelligent decision-making model generates an emergency response plan.

7. The emergency call response terminal system according to claim 1, characterized in that: The command output module is internally provided with a loudspeaker broadcast unit and a short message sending unit.

8. A multi-source information fusion method for an emergency call response terminal according to any one of claims 1 to 7, characterized in that: The following steps are involved: Step 1: Data collection: Through the terminal's 4G / 5G network, satellite communications, including Ku-band and L-band, and BeiDou-3 short message communication means, collect meteorological data including disaster site images, voice, text reports, satellite monitoring data, and geographic change data, such multi-source information; Step 2: Data preprocessing: Unify and standardize the format of the collected information. The image data is adjusted and compressed, the voice data is converted into an analyzable digital format, the text information is encoded and converted, and the data is securely processed using information encryption technology to prevent data leakage or tampering during the fusion process; Step 3: Fusion algorithm: An artificial intelligence-based fusion algorithm is used, in which the convolutional neural network (CNN) in deep learning extracts features from image data, and the recurrent neural network (RNN) performs semantic analysis on voice and text data. It also associates and integrates different types of data features, mainly correlating the degree of damage in the disaster site images with the meteorological data monitored by satellites to determine the development trend of the disaster.

9. An intelligent decision-making method for an emergency call response terminal according to any one of claims 1 to 7, characterized in that: The following steps are involved: Step 1: Establish a decision model: Establish an intelligent decision model based on the integrated multi-source information. The model can be based on machine learning algorithms, including support vector machines (SVM) and decision trees, to classify and evaluate disaster types, severity, impact range, etc.; Step 2: Parameter setting and optimization: Set the parameters of the decision model based on historical data and experience, and improve the accuracy of the decision model through continuous training and optimization; Step 3: Decision output: Based on the results of the decision model, an emergency response plan is generated, including rescue route planning, personnel evacuation plan, and material allocation plan. The decision results are communicated to relevant personnel through the terminal's loudspeaker broadcast and text messages.

Images