Intelligent fire-fighting emergency method and system based on base station, and storage medium

Abstract

The invention discloses an intelligent fire-fighting emergency method and system based on a base station and a storage medium, and relates to the field of safety fire fighting. A fire sensing device is arranged in a large building, radio frequency signals are transmitted through the fire sensing device to establish communication with a surrounding base station, and the base station positions the fire sensing device through the transmitting position of the radio frequency signals; the method comprises the following steps: acquiring multi-modal total data of a large building, including spatial characteristics, ventilation characteristics and material characteristics, and constructing a building digital twinborn body according to the multi-modal total data and a built-in fire sensing device; constructing a fire-fighting emergency knowledge graph; determining a mapping relation between the fire-fighting emergency knowledge graph and the building digital twinborn body to obtain a fire-fighting emergency model; and after the radio frequency signal of the fire sensing device is received, the radio frequency signal is input into the fire emergency model, and the fire emergency model generates an emergency strategy according to the radio frequency signal, the fire position and the multi-mode total data of the large building. According to the invention, the fire-fighting work is more efficient, accurate and scientific.

Description

Technical Field

[0001] The present invention relates to the field of safety firefighting, and more particularly to a base station-based intelligent firefighting emergency method, system and storage medium. Background Art

[0002] With the rapid development of society and the economy, various industrial plants and high-rise buildings have sprung up like mushrooms after rain, and infrastructure such as subway stations has been increasingly improved. This has enabled industrial automation and the digitalization of people's lives, but at the same time, firefighting work faces unprecedented challenges.

[0003] In some special areas, such as underground garages without base station network coverage, communication equipment using public networks falls into communication "blind spots" and fails to function properly. This results in firefighters being unable to communicate with each other and the outside world while carrying out firefighting operations in these areas, severely hampering the smooth progress of firefighting operations. Furthermore, leaders at the command center lack an intuitive, real-time understanding of the actual situation of on-site firefighting operations, hindering their ability to accurately assess the extent of the disaster and, in turn, affecting the formulation and adjustment of overall rescue strategies.

[0004] Traditional fire emergency response methods and technologies rely heavily on manual inspections and on-site assessments, making it difficult to achieve real-time, comprehensive fire monitoring and effectively control fires before firefighters arrive. In remote areas or large-scale industrial plants, due to their vast geographical reach, traditional fire monitoring methods have gaps, making it difficult to detect early-stage fires. Fires often go unnoticed until they have grown to a larger scale, often resulting in severe property damage and even casualties.

[0005] Therefore, there is an urgent need for an innovative fire emergency method that can overcome the shortcomings of traditional firefighting methods with the help of widely covered base stations, realize intelligent judgment and control of fire conditions, and transmit key information to the fire control center in a timely and stable manner, thereby buying valuable time for fire rescue work and minimizing losses. Summary of the Invention

[0006] In view of this, the present invention provides a base station-based intelligent fire emergency method, system and storage medium, which realizes the intelligent process from fire monitoring and positioning to emergency decision-making, comprehensively improves the intelligent level of fire emergency in large buildings, and makes firefighting work more efficient, accurate and scientific.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] A base station-based intelligent fire emergency method includes the following steps:

[0009] Fire sensors are installed in large buildings. The fire sensors transmit radio frequency signals to establish communication with surrounding base stations. The base stations locate the fire sensors based on the location of the radio frequency signal transmission.

[0010] Acquire multimodal, full-volume data on large buildings, including spatial characteristics, ventilation characteristics, and material properties, and construct a digital twin of the building based on this multimodal, full-volume data and built-in fire sensors.

[0011] The location information of the fire sensor device is used as the node of the knowledge graph, and the fire-related information corresponding to the parsed radio frequency signal is used as the edge of the knowledge graph to construct a fire emergency knowledge graph;

[0012] Determine the mapping relationship between the fire emergency knowledge graph and the building digital twin, complete data mapping and association matching processing, and obtain the fire emergency model;

[0013] After receiving the radio frequency signal from the fire sensor device, it is input into the fire emergency model. The fire emergency model generates an emergency strategy based on the radio frequency signal, the fire location and the multimodal full data of the large building.

[0014] Optionally, the fire sensing device has a built-in RFID chip, and each chip corresponds to a unique code. The code corresponds to the location information of the fire sensing device and the fire risk level of the current location. The fire risk level is related to the multimodal full data of the current location.

[0015] Optionally, the fire sensing device collects thermal field information and smoke concentration information at the current location, analyzes and compares the thermal field information with the thermal field data of the fire information in the database, and analyzes and compares the smoke concentration information with the smoke concentration information of the fire information in the database, assigns weights to the analysis and comparison results, and comprehensively evaluates them to obtain the current fire level.

[0016] Optionally, in the fire emergency knowledge graph, a resource description framework is selected to describe the edges and nodes in the fire emergency knowledge graph; based on the characteristics of the fire protection field, a "fire event" class is defined, with attributes corresponding to "smoke concentration", "heat map" and "time", and a "fire sensor device" class is defined, with attributes corresponding to "RFID code"; the fire emergency model is used to perform fire situation analysis, emergency resource scheduling and decision support, and as the fire sensor device continuously collects new data and the radio frequency signal changes, the knowledge graph is updated in real time, and new data is extracted and integrated into the knowledge graph regularly or in real time.

[0017] Optionally, it also includes simulating and deducing the current radio frequency signal and the transmission position corresponding to the radio frequency signal through a fire emergency model to obtain the best rescue plan that is compatible with the current fire information.

[0018] Optionally, for the transmission of radio frequency signals, a double-layer wireless coverage network is constructed as follows: based on the same-frequency and simulcast self-organizing network technology, wireless links are used between base stations to realize automatic networking and build a wide-area coverage network, fixed self-organizing base stations are used, and portable self-organizing base stations are used to deeply extend the auxiliary to build a double-layer communication network to ensure the safe transmission of radio frequency signals.

[0019] A base station-based intelligent fire emergency system, comprising:

[0020] Fire-related information acquisition module: Fire sensors are installed in large buildings. The fire sensors transmit radio frequency signals to establish communication with surrounding base stations. The base stations locate the fire sensors based on the location of the radio frequency signal transmission.

[0021] Digital twin construction module: This module is used to obtain multimodal, full-volume data on large buildings, including spatial characteristics, ventilation characteristics, and material properties, and to construct a building digital twin based on this multimodal, full-volume data and built-in fire sensors.

[0022] Fire emergency knowledge graph construction module: This module is used to construct a fire emergency knowledge graph by using the location information of fire sensors as nodes in the knowledge graph and parsing the fire-related information corresponding to the radio frequency signals as edges in the knowledge graph.

[0023] Fire response model construction module: used to determine the mapping relationship between the fire emergency knowledge graph and the building digital twin, complete data mapping and association matching processing, and obtain the fire emergency model;

[0024] Fire emergency strategy range module: It is used to receive the radio frequency signal of the fire sensor device and input it into the fire emergency model. The fire emergency model generates an emergency strategy based on the radio frequency signal, the fire location and the multimodal full data of large buildings.

[0025] A computer storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the computer program implements the steps of any one of the base station-based smart fire emergency methods.

[0026] It can be seen from the above technical solutions that, compared with the prior art, the present invention provides a base station-based smart fire emergency method, system, and storage medium, which have the following beneficial effects:

[0027] 1. Acquiring comprehensive multimodal data from large buildings and constructing digital twins of them enables firefighters to fully understand the building's spatial, ventilation, and material properties. This helps them consider factors such as the impact of building structure on fire spread, the effect of ventilation conditions on smoke diffusion, and the combustion characteristics of building materials when formulating emergency response strategies, thereby improving the scientific nature and effectiveness of emergency response strategies.

[0028] 2. A knowledge graph is constructed using the location of fire sensors as nodes and the fire-related information corresponding to the radio frequency signals as edges. This integrates various fire-related information into a structured knowledge system. This facilitates rapid retrieval and analysis of fire-related information, providing comprehensive and accurate knowledge support for emergency decision-making.

[0029] 3. Determine the mapping relationship between the fire emergency knowledge graph and the building digital twin, complete data mapping and association matching, and obtain the fire emergency model. This allows the various data involved in the fire emergency process to be interconnected and integrated, avoiding the emergence of information silos and improving data utilization efficiency and decision-making accuracy.

[0030] 4. The fire emergency response model comprehensively considers received radio frequency signals, fire locations, and multimodal data from large buildings to generate scientific and reasonable emergency response strategies. This helps optimize the deployment of firefighting resources, improve the speed and effectiveness of emergency response, and maximize the safety of personnel and minimize property losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0032] Figure 1 Schematic diagram of the method flow of the present invention;

[0033] Figure 2 Schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0035] The embodiment of the present invention discloses a base station-based intelligent fire emergency method, comprising the following steps:

[0036] S1: Fire sensors are installed in large buildings. The fire sensors transmit radio frequency signals to establish communication with surrounding base stations. The base stations locate the fire sensors based on the location of the radio frequency signal transmission.

[0037] S2: Obtain multimodal, full-volume data on large buildings, including spatial characteristics, ventilation characteristics, and material properties. Build a digital twin of the building based on this multimodal, full-volume data and built-in fire sensors.

[0038] S3: Use the location information of the fire sensor device as the node of the knowledge graph, and the fire-related information corresponding to the parsed radio frequency signal as the edge of the knowledge graph to build a fire emergency knowledge graph;

[0039] S4: Determine the mapping relationship between the fire emergency knowledge graph and the building digital twin, complete data mapping and association matching processing, and obtain the fire emergency model;

[0040] S5: After receiving the radio frequency signal from the fire sensor device, it is input into the fire emergency model. The fire emergency model generates an emergency strategy based on the radio frequency signal, the fire location, and the multimodal full data of the large building.

[0041] Optionally, further, in S1, the fire sensing device has a built-in RFID chip, each chip corresponds to a unique code, the code corresponds to the location information of the fire sensing device and the fire risk level of the current location, and the fire risk level is related to the multimodal full data of the current location.

[0042] Furthermore, the fire sensing device collects thermal field information and smoke concentration information at the current location, analyzes and compares the thermal field information with the thermal field data of the fire information in the database, and analyzes and compares the smoke concentration information with the smoke concentration information of the fire information in the database, assigns weights to the analysis and comparison results, and comprehensively evaluates them to obtain the current fire level.

[0043] Furthermore, in S3, key entities are extracted from the fire sensor location information as nodes in the knowledge graph. For example, building name, floor number, room number, and area name are each treated as a different node, and a hierarchical relationship is established between them. Radio frequency signals are parsed to extract fire-related information as edges in the knowledge graph. For example, the relationship between smoke concentration and sensor location, the relationship between temperature change and location, and the relationship between flame intensity and location can be determined. The edge type and weight can be determined based on signal characteristics and pre-defined rules.

[0044] In the fire emergency knowledge graph, a resource description framework is selected to describe the edges and nodes in the fire emergency knowledge graph; according to the characteristics of the fire protection field, the "fire event" class is defined, and the attributes correspond to "smoke concentration", "heat map" and "time", and the "fire sensor device" class is defined, and the attributes correspond to "RFID code"; the fire emergency model is used to perform fire situation analysis, emergency resource scheduling and decision support, and as the fire sensor device continuously collects new data and the radio frequency signal changes, the knowledge graph is updated in real time, and new data is extracted and integrated into the knowledge graph regularly or in real time.

[0045] Specifically, knowledge graphs are used in fire emergency response to analyze fire situations, dispatch emergency resources, and provide decision support. For example, by querying the knowledge graph, the fire's location, surrounding firefighting facilities, and evacuation routes can be quickly determined, providing firefighters with accurate information. As fire sensors continuously collect new data and radio frequency signals change, the knowledge graph needs to be updated in real time. New data is extracted and integrated into the knowledge graph periodically or in real time to ensure the timeliness and accuracy of the knowledge.

[0046] When building a fire emergency knowledge graph, data security and privacy protection must also be considered to ensure that sensitive information related to fire emergency response is not leaked. At the same time, integration with the fire department's business processes and systems will enable the knowledge graph to better serve fire emergency response efforts. To ensure data security and privacy protection, this invention uses blockchain to store sensitive information related to fire emergency response, ensuring that data can only be accessed with the object's private key and secret key.

[0047] Furthermore, if there are multiple data sources or different types of fire sensors, knowledge fusion is required to integrate the knowledge from different data sources into the same knowledge graph. Through techniques such as entity alignment, the same entities are uniquely identified in the knowledge graph, and their related information is merged and integrated.

[0048] Furthermore, it also includes simulating and processing the current radio frequency signal and the transmission position corresponding to the radio frequency signal through a fire emergency model to obtain the best rescue plan that is compatible with the current fire information.

[0049] In order to ensure data transmission, this embodiment constructs a wireless coverage double-layer network for the transmission of radio frequency signals. Specifically, based on the same-frequency and simulcast self-organizing network technology, wireless links are used between base stations to realize automatic networking and build a wide-area coverage network. Fixed self-organizing network base stations are used, and portable self-organizing network base stations are used to deeply extend the auxiliary to build a double-layer communication network to ensure the safe transmission of radio frequency signals.

[0050] and Figure 1Corresponding to the method shown, the present invention also discloses a base station-based intelligent fire emergency system for Figure 1 The implementation of the method, the specific structure is as follows Figure 2 Shown, including:

[0051] Fire-related information acquisition module: Fire sensors are installed in large buildings. The fire sensors transmit radio frequency signals to establish communication with surrounding base stations. The base stations locate the fire sensors based on the location of the radio frequency signal transmission.

[0052] Digital twin construction module: This module is used to obtain multimodal, full-volume data on large buildings, including spatial characteristics, ventilation characteristics, and material properties, and to construct a building digital twin based on this multimodal, full-volume data and built-in fire sensors.

[0053] Fire emergency knowledge graph construction module: This module is used to construct a fire emergency knowledge graph by using the location information of fire sensors as nodes in the knowledge graph and parsing the fire-related information corresponding to the radio frequency signals as edges in the knowledge graph.

[0054] Fire response model construction module: used to determine the mapping relationship between the fire emergency knowledge graph and the building digital twin, complete data mapping and association matching processing, and obtain the fire emergency model;

[0055] Fire emergency strategy range module: It is used to receive the radio frequency signal of the fire sensor device and input it into the fire emergency model. The fire emergency model generates an emergency strategy based on the radio frequency signal, the fire location and the multimodal full data of large buildings.

[0056] Finally, this embodiment further discloses a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the base station-based smart fire emergency methods are implemented.

[0057] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0058] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily 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 is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A smart fire emergency method based on a base station, characterized in that: The following steps are involved: Fire sensors are installed in large buildings. The fire sensors transmit radio frequency signals to establish communication with surrounding base stations. The base stations locate the fire sensors based on the location of the radio frequency signal transmission. Acquire multimodal, full-volume data on large buildings, including spatial characteristics, ventilation characteristics, and material properties, and construct a digital twin of the building based on this multimodal, full-volume data and built-in fire sensors. The location information of the fire sensor device is used as the node of the knowledge graph, and the fire-related information corresponding to the parsed radio frequency signal is used as the edge of the knowledge graph to construct a fire emergency knowledge graph; Determine the mapping relationship between the fire emergency knowledge graph and the building digital twin, complete data mapping and association matching processing, and obtain the fire emergency model; After receiving the radio frequency signal from the fire sensor device, it is input into the fire emergency model. The fire emergency model generates an emergency strategy based on the radio frequency signal, the fire location and the multimodal full data of the large building.

2. The base station-based intelligent fire emergency method according to claim 1, characterized in that: The fire sensor device has a built-in RFID chip, and each chip corresponds to a unique code. The code corresponds to the location information of the fire sensor device and the fire risk level of the current location. The fire risk level is related to the multimodal full data of the current location.

3. The base station-based intelligent fire emergency method according to claim 1, characterized in that: The fire sensing device collects the thermal field information and smoke concentration information of the current location, analyzes and compares the thermal field information with the thermal field data of the fire information in the database, and analyzes and compares the smoke concentration information with the smoke concentration information of the fire information in the database. The analysis and comparison results are weighted respectively, and a comprehensive evaluation is performed to obtain the current fire level.

4. The base station-based intelligent fire emergency method according to claim 1, characterized in that: In the fire emergency knowledge graph, a resource description framework is selected to describe the edges and nodes in the fire emergency knowledge graph; based on the characteristics of the fire protection field, a "fire event" class is defined, with attributes corresponding to "smoke concentration", "heat map", and "time"; a "fire sensor device" class is defined, with attributes corresponding to "RFID code"; the fire emergency model is used to conduct fire situation analysis, emergency resource scheduling, and decision support. As the fire sensor device continuously collects new data and the radio frequency signal changes, the knowledge graph is updated in real time, and new data is extracted and integrated into the knowledge graph regularly or in real time.

5. The base station-based intelligent fire emergency method according to claim 1, characterized in that: It also includes simulating and deducing the current radio frequency signal and the transmission position corresponding to the radio frequency signal through a fire emergency model to obtain the best rescue plan that is compatible with the current fire information.

6. The base station-based intelligent fire emergency method according to claim 1, characterized in that: For the transmission of radio frequency signals, a double-layer wireless coverage network is constructed as follows: based on the same-frequency and same-broadcast self-organizing network technology, wireless links are used between base stations to realize automatic networking and build a wide-area coverage network. Fixed self-organizing base stations are used, and portable self-organizing base stations are used to extend the depth to assist and build a double-layer communication network to ensure the safe transmission of radio frequency signals.

7. A smart fire emergency system based on a base station, characterized in that: include: Fire-related information acquisition module: Fire sensors are installed in large buildings. The fire sensors transmit radio frequency signals to establish communication with surrounding base stations. The base stations locate the fire sensors based on the location of the radio frequency signal transmission. Digital twin construction module: This module is used to obtain multimodal, full-volume data on large buildings, including spatial characteristics, ventilation characteristics, and material properties, and to construct a building digital twin based on this multimodal, full-volume data and built-in fire sensors. Fire emergency knowledge graph construction module: This module is used to construct a fire emergency knowledge graph by using the location information of fire sensors as nodes in the knowledge graph and parsing the fire-related information corresponding to the radio frequency signals as edges in the knowledge graph. Fire response model construction module: used to determine the mapping relationship between the fire emergency knowledge graph and the building digital twin, complete data mapping and association matching processing, and obtain the fire emergency model; Fire emergency strategy range module: It is used to receive the radio frequency signal of the fire sensor device and input it into the fire emergency model. The fire emergency model generates an emergency strategy based on the radio frequency signal, the fire location and the multimodal full data of large buildings.

8. A computer storage medium, characterized in that The computer storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the base station-based smart fire emergency method as described in any one of claims 1 to 6 are implemented.

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