Cloud platform building fire safety supervision system and method based on artificial intelligence

Abstract

The invention belongs to the technical field of fire safety supervision, and provides a cloud platform building fire safety supervision system and method based on artificial intelligence, and the system comprises a data collection module which is used for carrying out the cloud platform building fire safety supervision through a plurality of sensors and image collection devices which are distributed in all regions of a building, environment data and real-time monitoring pictures in the building are collected; the data transmission module is used for transmitting the collected data to the cloud platform; the cloud platform is used for analyzing the received transmission data based on a fire prediction model and a potential safety hazard identification model, and judging whether a fire hazard and a fire-fighting treatment potential hazard exist in the building or not; and the early warning and processing module is used for generating an early warning signal according to the position of the hidden danger and sending the early warning signal to related management personnel when the fire hazard or the fire-fighting treatment hidden danger exists in the building, and starting the fire-fighting equipment at the corresponding position for emergency treatment when the fire hazard occurs. According to the invention, comprehensive and intelligent supervision of building fire safety is realized.

Description

Technical Field

[0001] This invention relates to the field of fire safety supervision technology, and in particular to a cloud platform-based building fire safety supervision system and method based on artificial intelligence. Background Technology

[0002] With the acceleration of urbanization and the continuous increase in the scale and number of buildings, building fire safety issues have become increasingly prominent. Traditional building fire safety supervision mainly relies on manual inspections and simple fire-fighting equipment, which suffers from problems such as low supervision efficiency, untimely response, and inability to achieve comprehensive coverage. For example, manual inspections may miss safety hazards due to human negligence and cannot continuously monitor a large number of buildings in real time. Although existing fire monitoring systems have certain automation functions, they still have significant shortcomings in intelligent analysis and cloud platform integration, making it difficult to meet the complex and ever-changing needs of building fire safety management. Summary of the Invention

[0003] This invention provides a cloud platform-based building fire safety supervision system and method based on artificial intelligence, aiming to solve the problems of low efficiency, slow response and insufficient intelligence in existing building fire safety supervision, and to achieve comprehensive, real-time and intelligent supervision of building fire safety.

[0004] This invention provides an artificial intelligence-based cloud platform building fire safety monitoring system, comprising: The data acquisition module is used to collect environmental data and real-time monitoring images through various sensors and image acquisition devices distributed throughout the building. The data transmission module is used to transmit environmental data and real-time monitoring images to the cloud platform using wireless communication technology. The cloud platform is used to receive and store the data transmitted by the data transmission module, and analyze the received data based on the safety hazard identification model to determine whether there are fire hazards and fire-fighting hazards in the building. The early warning and handling module is used to generate early warning signals based on the location of fire hazards or fire-fighting hazards in the building and send them to relevant management personnel. When a fire hazard occurs, the corresponding fire-fighting equipment at the location will be activated for emergency handling.

[0005] Preferably, in an AI-based cloud platform building fire safety monitoring system, the data acquisition module includes: The fire data acquisition unit is used to collect environmental data and real-time monitoring images from various sensors and image acquisition devices distributed throughout the building. The monitoring equipment status monitoring unit is used to collect network status data of various sensors and image acquisition devices. A system access status monitoring unit is used to collect access status information of various building fire protection facilities systems. Preferably, in an artificial intelligence-based cloud platform building fire safety supervision system, the cloud platform includes: The data storage unit is used to automatically store the data transmitted by the data transmission module in chronological order. The safety hazard analysis unit is used to analyze the data transmitted by the data transmission module based on the safety hazard identification model, to determine whether there are fire hazards or fire-fighting hazards in various areas of the building, and to determine the type of hazard. The fire prediction unit is used to acquire ignition point information when a fire is confirmed to exist in a building, and to determine various relevant sensors and image acquisition devices based on the ignition point information. The system acquires transmission data from various relevant sensors and image acquisition devices, extracts current fire features, and predicts fire trends by combining the correlations between various fire features and environmental data corresponding to the ignition point.

[0006] Preferably, in an artificial intelligence-based cloud platform building fire safety monitoring system, the cloud platform includes: The equipment data analysis unit is used to analyze the collected network information to obtain the monitoring results of the monitoring equipment within the building, including: The online data analysis subunit is used to calculate the overall online rate and offline rate of equipment in the building, as well as the regional offline rate and online rate of each area, based on the collected network status data, and generate equipment online information; The equipment coverage analysis subunit is used to acquire the installation and distribution information of sensors and image acquisition equipment within the building, as well as the effective monitoring area information of the equipment. Based on the online equipment information and installation and distribution information, the location of the online equipment is determined, and combined with the effective monitoring area information of the online equipment, the current monitoring coverage range and monitoring blind spots of each area are determined, and monitoring coverage information is generated. The equipment monitoring information sending subunit is used to generate equipment monitoring data based on equipment online information and monitoring coverage information and send it to relevant management personnel.

[0007] Preferably, in an artificial intelligence-based cloud platform building fire safety monitoring system, the cloud platform includes: The system status monitoring unit is used to determine whether there is any abnormal access to the building fire protection system based on the access status information of various building fire protection facilities systems; If such an abnormal access occurs, a corresponding access anomaly warning signal will be generated and sent to the relevant management personnel. The control status of the target fire protection equipment corresponding to the abnormal access to the building fire protection system will be marked as unknown. After the abnormal access to the building fire protection system is restored, the control status mark of the target fire protection equipment will be updated synchronously.

[0008] Preferably, in an AI-based cloud platform building fire safety monitoring system, the safety hazard analysis unit includes: The environment determination subunit is used to acquire target transmission data corresponding to relevant image acquisition devices, perform environmental analysis on the target transmission data, acquire environmental characteristics corresponding to the ignition point, and determine the location of flammable and explosive materials in the area where the ignition point is located, as well as the actual distance between the flammable and explosive materials and the ignition point based on the environmental characteristics. The feature extraction subunit is used to extract features from the transmitted data of various related sensors and image acquisition devices to obtain various current fire features. The trend prediction subunit is used to determine the trend of fire characteristics and predict the outcome of the fire based on the correlation of fire characteristics and the actual distance and combustion characteristics between flammable and explosive materials and the ignition point.

[0009] Preferably, in an AI-based cloud platform building fire safety monitoring system, the safety hazard analysis unit further includes: The fire prediction subunit is used to acquire various fire characteristics of non-fire areas, generate a feature matrix and compare it with a standard feature matrix to obtain a difference matrix. Then, the maximum eigenvalue of the matrix is ​​calculated and compared with a preset threshold. If the maximum eigenvalue exceeds the threshold, the area is determined to have a fire hazard.

[0010] Preferably, in an artificial intelligence-based cloud platform building fire safety monitoring system, the early warning and processing module includes: The early warning signal generation unit is used to generate a corresponding early warning signal and send it to relevant management personnel based on the type of hazard and the location of the hazard when it is confirmed that there is a fire hazard or fire-fighting hazard in any area of ​​the building. The emergency response unit is used to acquire information on the ignition point and the location of flammable and explosive materials at the fire scene, determine the appropriate fire-fighting equipment based on the combustion characteristics of flammable and explosive materials and the ignition point information, and acquire monitoring data and control status of fire-fighting equipment in the fire area to determine whether fire emergency response can be completed. If possible, a device activation signal is generated based on the type of fire-fighting activation equipment and the area location information, and the equipment is controlled to aim at the fire point and extinguish it.

[0011] The ignition point combustion information includes ignition point information and ignition point type information.

[0012] This invention provides a cloud platform-based building fire safety supervision method based on artificial intelligence, comprising: Step 1: Collect environmental data and real-time monitoring images using various sensors and image acquisition devices distributed throughout the building; Step 2: Use wireless communication technology to transmit environmental data and real-time monitoring images to the cloud platform; Step 3: Receive and store the data transmitted by the data transmission module through the cloud platform, and analyze the received data based on the fire prediction model and the safety hazard identification model to determine whether there are fire hazards and fire-fighting hazards in the building. The fire safety hazards mentioned include obstructed fire exits and malfunctioning or abnormal fire equipment. Step 4: When there are fire hazards or fire-fighting risks in the building, generate an early warning signal based on the location of the hazard and send it to the relevant management personnel. When a fire hazard occurs, activate the fire-fighting equipment at the corresponding location for emergency response.

[0013] Preferably, in a cloud platform-based building fire safety supervision method based on artificial intelligence, step 3 includes: The data transmitted by the data transmission module is automatically stored based on the time sequence. Based on the safety hazard identification model, the data transmitted by the data transmission module is analyzed to determine whether there are fire hazards or fire-fighting hazards in various areas of the building, and to determine the type of hazard. When a fire is confirmed to exist inside a building, the ignition point information is obtained, and based on the ignition point information, various relevant sensors and image acquisition devices are identified. The system acquires transmission data from various relevant sensors and image acquisition devices, extracts current fire features, and predicts fire trends by combining the correlations between various fire features and environmental data corresponding to the ignition point.

[0014] Compared with the prior art, the present invention has at least the following beneficial effects: This invention, by deploying multiple sensors and image acquisition devices throughout various areas of a building, enables comprehensive and seamless collection of environmental data and real-time monitoring images. This results in more complete information acquisition, accurately capturing subtle changes in smoke concentration and the actual condition of fire escape routes, providing a solid data foundation for subsequent analysis and decision-making. This significantly improves the accuracy of assessing building fire safety and reduces regulatory loopholes caused by missing information. Furthermore, wireless communication technology enables rapid and stable transmission of environmental data and real-time monitoring images to the cloud platform, ensuring the platform can promptly acquire the latest data and guaranteeing real-time monitoring of the building's fire safety. Upon receiving the transmitted data, the cloud platform, based on fire prediction and safety hazard identification models, analyzes the data... The input data is analyzed in depth to accurately determine whether there are fire hazards in various areas of the building, as well as fire-fighting hazards such as obstructed fire exits, malfunctioning or abnormal fire-fighting equipment. Compared with traditional manual judgment, this intelligent analysis method greatly improves the accuracy and efficiency of hazard identification, avoids the omission of hazards due to human negligence, and can quickly generate early warning signals based on the location of the hazard and send them to relevant management personnel when a fire hazard occurs. At the same time, when a fire hazard occurs, the corresponding fire-fighting equipment is immediately activated for emergency treatment, ensuring that countermeasures can be taken at the first moment of danger, controlling the fire in its infancy, effectively reducing the risk of casualties and property losses caused by fire, and providing a strong guarantee for the life safety and property safety of people in the building. This invention achieves centralized storage and management of data through the application of a cloud platform, enabling managers to view and analyze building fire safety conditions anytime and anywhere. It also facilitates data sharing and collaborative work among different departments. By analyzing a large amount of fire monitoring data in buildings through artificial intelligence algorithms (safety hazard identification models) on the cloud platform, fire hazards and safety issues can be identified quickly and accurately, greatly improving the efficiency and accuracy of supervision. Furthermore, the invention provides immediate and automatic emergency response functions in the event of fire hazards and safety issues, enabling timely measures to be taken in the early stages of a fire to reduce fire losses and protect the lives and property of people. This achieves comprehensive, real-time, and intelligent supervision of building fire safety.

[0015] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in this application.

[0016] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a structural diagram of a cloud platform-based building fire safety monitoring system based on artificial intelligence. Figure 2 This is a structural diagram of a data acquisition module for a cloud-based building fire safety monitoring system based on artificial intelligence. Figure 3 This is a structural diagram of a cloud platform for a building fire safety monitoring system based on artificial intelligence. Figure 4 This is a structural diagram of an early warning and processing module for a cloud-based building fire safety monitoring system based on artificial intelligence. Figure 5 This is a flowchart of a cloud platform-based building fire safety supervision method based on artificial intelligence. Detailed Implementation

[0018] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0019] Example 1: This invention provides a cloud platform-based building fire safety monitoring system based on artificial intelligence, such as... Figure 1 As shown, it includes: The data acquisition module is used to collect environmental data and real-time monitoring images through various sensors and image acquisition devices distributed throughout the building. The data transmission module is used to transmit environmental data and real-time monitoring images to the cloud platform using wireless communication technology. The cloud platform is used to receive and store the data transmitted by the data transmission module, and to analyze the received data based on the fire prediction model and the safety hazard identification model to determine whether there are fire hazards and fire protection hazards in the building. The fire safety hazards mentioned include obstructed fire exits, malfunctioning or abnormal fire equipment, improper placement of fire extinguishers, and haphazard wiring. The early warning and handling module is used to generate early warning signals based on the location of fire hazards or fire-fighting hazards in the building and send them to relevant management personnel. When a fire hazard occurs, the corresponding fire-fighting equipment at the location will be activated for emergency handling.

[0020] The beneficial effects of the above technical solution are as follows: By deploying multiple sensors and image acquisition devices in various areas of the building, environmental data and real-time monitoring images can be collected comprehensively and without blind spots. This makes the information acquired by the system more comprehensive, accurately capturing even subtle changes in smoke concentration or the actual condition of fire escape routes. This provides a solid data foundation for subsequent analysis and decision-making, greatly improving the accuracy of assessing the building's fire safety status and reducing regulatory loopholes caused by missing information. Subsequently, wireless communication technology is used to achieve rapid and stable transmission of environmental data and real-time monitoring images to the cloud platform, ensuring that the cloud platform can obtain the latest data in a timely manner. This, in turn, guarantees the real-time monitoring of the building's fire safety status by the entire system. After receiving the transmitted data, the cloud platform, based on fire prediction models and safety hazard identification... The model performs in-depth analysis of the transmitted data, accurately determining whether there are fire hazards in various areas of the building, as well as fire-fighting hazards such as obstructed fire exits, malfunctioning or abnormal fire-fighting equipment. Compared with traditional manual judgment, this intelligent analysis method greatly improves the accuracy and efficiency of hazard identification, avoids the omission of hazards due to human negligence, and can quickly generate early warning signals based on the location of the hazard and send them to relevant management personnel when a fire hazard occurs. At the same time, it can immediately activate the fire-fighting equipment in the corresponding location for emergency treatment when a fire hazard occurs, ensuring that countermeasures can be taken at the first moment of danger, controlling the fire in its infancy, effectively reducing the risk of casualties and property losses caused by fire, and providing strong protection for the life and property safety of people in the building. This invention achieves centralized storage and management of data through the application of a cloud platform, enabling managers to view and analyze building fire safety conditions anytime and anywhere. It also facilitates data sharing and collaborative work among different departments. By analyzing a large amount of fire monitoring data in buildings through artificial intelligence algorithms (safety hazard identification models) on the cloud platform, fire hazards and safety issues can be identified quickly and accurately, greatly improving the efficiency and accuracy of supervision. Furthermore, the invention provides immediate and automatic emergency response functions in the event of fire hazards and safety issues, enabling timely measures to be taken in the early stages of a fire to reduce fire losses and protect the lives and property of people. This achieves comprehensive, real-time, and intelligent supervision of building fire safety.

[0021] Example 2: Based on Example 1, the data acquisition module, such as Figure 2 As shown, it includes: The fire data acquisition unit is used to collect environmental data and real-time monitoring images from various sensors and image acquisition devices distributed throughout the building. The monitoring equipment status monitoring unit is used to collect network status data of various sensors and image acquisition devices. The system access status monitoring unit is used to collect access status information of various building fire protection facilities systems; Among them, building fire protection facilities systems include, but are not limited to, automatic fire alarm systems, automatic fire extinguishing systems (including automatic sprinkler systems, automatic tracking and positioning jet fire extinguishing systems, fixed fire monitor fire extinguishing systems, gas fire extinguishing systems, foam fire extinguishing systems, dry powder fire extinguishing systems, fine water mist fire extinguishing systems, water spray fire extinguishing systems, etc.), electrical fire monitoring systems, fire water supply and fire hydrant systems, and smoke control and exhaust systems.

[0022] The beneficial effects of the above technical solution are as follows: The present invention acquires environmental data and real-time monitoring images from various sensors and image acquisition devices in the building area through the fire data acquisition unit, thereby achieving a comprehensive perception of the building's fire safety status; and by acquiring network status data from various sensors and image acquisition devices through the monitoring equipment status monitoring unit, it is possible to understand in real time whether these devices are working properly and whether the data transmission is stable. Once a network anomaly is detected, such as a sensor going offline or an image acquisition device interrupting its operation, the faulty device can be located promptly, facilitating quick handling by maintenance personnel. This prevents critical safety data from being lost or not being transmitted due to equipment failure, ensuring the reliability and integrity of the entire fire monitoring system and keeping the system in an effective operating state at all times. The system access status monitoring unit collects access status information of various building fire protection facilities systems, covering multiple important systems such as automatic fire alarm systems, automatic fire extinguishing systems (automatic sprinkler systems, automatic tracking and positioning jet extinguishing systems, fixed fire monitor extinguishing systems, gas extinguishing systems, foam extinguishing systems, dry powder extinguishing systems, fine water mist extinguishing systems, and water spray extinguishing systems). By monitoring the access status information of these systems in real time, it can be ensured that all building fire protection facilities systems can work together normally and coordinate their functions in the event of a fire or other emergency. For example, when an automatic fire alarm system is triggered, automatic fire extinguishing systems (automatic sprinkler systems, automatic tracking and positioning jet extinguishing systems, fixed fire monitor extinguishing systems, gas extinguishing systems, foam extinguishing systems, dry powder extinguishing systems, fine water mist extinguishing systems, water spray extinguishing systems, etc.) can respond and start in a timely manner, avoiding delays or failures in emergency response due to system access failures, significantly improving the overall emergency response capability and reliability of the building's fire protection system, and minimizing fire hazards.

[0023] Example 3: Based on Examples 1-2, a cloud platform, such as... Figure 3 As shown, it includes: The data storage unit is used to automatically store the data transmitted by the data transmission module in chronological order. The safety hazard analysis unit is used to analyze the data transmitted by the data transmission module based on the safety hazard identification model, to determine whether there are fire hazards or fire-fighting hazards in various areas of the building, and to determine the type of hazard. The fire prediction unit is used to acquire ignition point information when a fire is confirmed to exist in a building, and to determine various relevant sensors and image acquisition devices based on the ignition point information. The system acquires transmission data from various relevant sensors and image acquisition devices, extracts current fire features, and predicts fire trends by combining the correlation between various fire features and environmental data corresponding to the ignition point. The equipment data analysis unit is used to analyze the collected network information to obtain the monitoring results of the monitoring equipment within the building, including: The online data analysis subunit is used to calculate the overall online rate and offline rate of equipment in the building, as well as the regional offline rate and online rate of each area, based on the collected network status data, and generate equipment online information; The equipment coverage analysis subunit is used to acquire the installation and distribution information of sensors and image acquisition equipment within the building, as well as the effective monitoring area information of the equipment. Based on the online equipment information and installation and distribution information, the location of the online equipment is determined, and combined with the effective monitoring area information of the online equipment, the current monitoring coverage range and monitoring blind spots of each area are determined, and monitoring coverage information is generated. The equipment monitoring information sending subunit is used to generate equipment monitoring data based on equipment online information and monitoring coverage information and send it to relevant management personnel. The cloud platform also includes: The system status monitoring unit is used to determine whether there is any abnormal access to the building fire protection system based on the access status information of various building fire protection facilities systems; If such an abnormal access occurs, a corresponding access anomaly warning signal will be generated and sent to the relevant management personnel. The control status of the target fire protection equipment corresponding to the abnormal access to the building fire protection system will be marked as unknown. After the abnormal access to the building fire protection system is restored, the control status mark of the target fire protection equipment will be updated synchronously.

[0024] In this embodiment, the safety hazard identification model is trained using massive amounts of building hazard data (including fire safety hazards and general safety hazards).

[0025] In this embodiment, the various related sensors refer to multi-type and multi-dimensional sensing devices that are directly related to building fire safety monitoring, fire hazard identification, fire analysis and emergency response, and are distributed in key areas of the building, including but not limited to temperature sensors, smoke sensors and flame sensors.

[0026] The beneficial effects of the above technical solution are as follows: This invention automatically stores transmitted data in chronological order, providing a systematic and coherent data record for building fire safety management. This facilitates managers in reviewing historical data, analyzing environmental changes and equipment operating status over different time periods, summarizing patterns, and identifying potential safety risks. It also provides accurate and reliable data support for subsequent safety hazard analysis and fire prediction. Through in-depth analysis of transmitted data using a safety hazard identification model, it can quickly and accurately determine whether there are fire hazards or fire-fighting hazards in a building area, and clarify the type of hazard. This not only significantly improves the efficiency of hazard investigation, covering a large amount of data in a short time, but also reduces oversights caused by human factors, allowing for timely detection of hazards such as blocked fire exits and damaged fire-fighting equipment, achieving early warning and precise prevention and control of building fire safety hazards. Furthermore, after confirming a fire, by acquiring ignition point information, accurately locating relevant sensors and image acquisition equipment, extracting current fire characteristics, and combining ignition point environmental data to predict fire trends, it can provide firefighters and managers with accurate information on the fire development situation, providing a basis for relevant personnel to formulate emergency rescue plans, minimizing losses caused by fires, and ensuring the safety of people's lives and property.

[0027] The system calculates the overall online and offline rates of the equipment, as well as the regional online and offline rates for each area, through a data analysis unit. This quantifies the operational status of the building's monitoring equipment, allowing managers to intuitively understand the overall operational status and online status of equipment in different areas. This provides a basis for the operation and maintenance of fire monitoring equipment. For example, the regional online rate can quickly locate areas with low online rates, allowing for timely maintenance and preventing monitoring gaps caused by equipment offline. This ensures that the monitoring equipment can function continuously and stably, providing a reliable equipment foundation for fire safety supervision. By combining information such as equipment installation distribution, online information, and effective monitoring areas, the system accurately determines the current monitoring coverage area and blind spots for each area. This allows managers to clearly know which areas within the building are under effective monitoring and which areas have monitoring loopholes, facilitating focused attention and patrols of blind spots. Furthermore, the system integrates online and monitoring coverage information to generate equipment monitoring data, which is promptly sent to relevant managers. This enables managers to make quick decisions, rationally arrange equipment maintenance, and adjust the monitoring layout, significantly improving the efficiency and response speed of fire safety management and reducing safety risks caused by untimely equipment management.

[0028] The system status monitoring unit can quickly and accurately determine whether there are any abnormal access situations based on the access status information of the building fire protection system in real time. Once an abnormality is detected, an early warning signal is immediately generated and sent to relevant management personnel, enabling them to be aware of the problems with the building fire protection system at the first time, ensuring the stable operation of the building fire protection system, and avoiding the failure of fire accident handling due to equipment malfunction. The control status of the target fire protection equipment corresponding to the abnormal access to the building fire protection system is marked as unknown. After the abnormal access to the building fire protection system is restored, the control status mark of the target fire protection equipment is updated synchronously, providing accurate data for judging the feasibility of subsequent fire emergency measures.

[0029] Example 4: Based on Example 3, the safety hazard analysis unit includes: The environment determination subunit is used to acquire target transmission data corresponding to relevant image acquisition devices, perform environmental analysis on the target transmission data, acquire environmental characteristics corresponding to the ignition point, and determine the location of flammable and explosive materials in the area where the ignition point is located, as well as the actual distance between the flammable and explosive materials and the ignition point based on the environmental characteristics. The feature extraction subunit is used to extract features from the transmitted data of various related sensors and image acquisition devices to obtain various current fire features. The trend prediction subunit is used to determine the trend of fire characteristics and predict the outcome of the fire based on the correlation of fire characteristics and the actual distance and combustion characteristics between flammable and explosive materials and the ignition point.

[0030] The beneficial effects of the above technical solution are as follows: By analyzing the data transmitted by image acquisition equipment, this invention accurately obtains the environmental characteristics of the fire point, thereby determining the flammable and explosive materials around the fire point and their actual distance from the fire point. This helps firefighters and managers gain a deeper understanding of potential risk sources at the fire scene. For example, in a chemical plant fire, quickly identifying the location and distance of hazardous chemicals provides crucial information for subsequent evacuation, rescue, and fire control strategy development, preventing passive rescue operations or secondary disasters due to a lack of understanding of hazardous materials. Furthermore, by extracting various current fire characteristics from data transmitted by multiple sensors and image acquisition equipment, covering information such as temperature, smoke concentration, and flame morphology, this invention comprehensively and meticulously reflects the actual situation at the fire scene, providing rich data dimensions for fire analysis and avoiding... The potential for bias in judgments from a single data source necessitates a more comprehensive and accurate basis for fire response. Then, based on the correlation of fire characteristics, the changing trends of each characteristic are determined in conjunction with the current fire situation. Furthermore, the actual distance and combustion characteristics of flammable and explosive materials to the ignition point are comprehensively considered to make predictions, resulting in fire development forecasts. This approach fully considers multiple key factors in the fire development process and their interactions, enabling scientific and accurate prediction of the fire's trajectory. For example, it can predict whether the fire will spread more rapidly due to nearby flammable and explosive materials or whether environmental factors will change. This provides a foundation for fire departments and management personnel to develop more targeted and effective emergency rescue plans and rationally allocate rescue resources, thereby significantly improving the ability to respond to fires, minimizing fire-related losses, and achieving comprehensive and intelligent supervision of building fire safety.

[0031] Example 5: Based on Example 4, the safety hazard analysis unit further includes: The fire prediction subunit is used to acquire various fire characteristics of non-fire areas, generate a feature matrix and compare it with a standard feature matrix to obtain a difference matrix. Then, the maximum eigenvalue of the matrix is ​​calculated and compared with a preset threshold. If the maximum eigenvalue exceeds the threshold, the area is determined to have a fire hazard.

[0032] In this embodiment, the standard feature matrix is ​​a feature matrix generated based on feature threshold data corresponding to various fire characteristics in non-fire-occurring areas.

[0033] The beneficial effects of the above technical solution are as follows: This invention generates a feature matrix by analyzing various fire characteristics in areas where no fire has occurred, and compares it with a standard feature matrix. This allows for the early detection of potential abnormal changes in the environment before a fire actually occurs. For example, when parameters such as temperature and smoke concentration in the area show slight but continuous abnormal fluctuations, the system can detect them promptly through feature matrix comparison. Compared to traditional methods relying on obvious fire signs, this significantly advances the identification time of fire hazards. It provides a basis for managers to take proactive measures (e.g., troubleshooting faulty equipment, clearing flammable materials), maximizing the prevention of fires in their early stages and effectively reducing the probability of fire occurrence. By calculating the difference matrix and obtaining its maximum eigenvalue, the process of judging fire hazards is transformed into a quantitative numerical comparison, enabling a more objective and accurate measurement of the deviation between the current fire characteristics and the normal state. For example, even if there are differences in the environmental baselines of different areas, the system can accurately locate areas with actual hazards through feature matrix comparison and maximum eigenvalue analysis, avoiding misjudgments or omissions caused by environmental interference or subjective judgment, significantly improving the accuracy and reliability of fire hazard assessment.

[0034] Example 6: Based on Example 1, the early warning and processing module, such as Figure 4 As shown, it includes: The early warning signal generation unit is used to generate a corresponding early warning signal and send it to relevant management personnel based on the type of hazard and the location of the hazard when it is confirmed that there is a fire hazard or fire-fighting hazard in any area of ​​the building. The emergency response unit is used to acquire information on the ignition point and the location of flammable and explosive materials at the fire scene, determine the appropriate fire-fighting equipment based on the combustion characteristics of flammable and explosive materials and the ignition point information, and acquire monitoring data and control status of fire-fighting equipment in the fire area to determine whether fire emergency response can be completed. If possible, a device activation signal is generated based on the type of fire-fighting activation equipment and the area location information, and the equipment is controlled to aim at the fire point and extinguish it.

[0035] The ignition point combustion information includes ignition point information and ignition point type information.

[0036] The beneficial effects of the above technical solution are as follows: This invention generates corresponding hazard warning signals based on the type and location of the hazard, achieving accurate transmission of hazard information and facilitating relevant management personnel to quickly determine targeted emergency response plans. For example, when the system detects a blocked fire exit, management personnel can immediately know the specific location and quickly arrange for personnel to clear the blockage; if a fire hazard is found in electrical wiring, electricians can be organized to inspect and repair it in a timely manner. Furthermore, through the emergency handling unit, the system comprehensively analyzes the combustion information of the ignition point (ignition point location, type of burning material) with the location and combustion characteristics of flammable and explosive materials to scientifically determine the appropriate fire-fighting activation equipment. For example, if the ignition point is an oil-based substance and there is a gasoline storage tank nearby, the system will prioritize foam fire extinguishers or dry powder fire extinguishers and pre-deploy cooling spray equipment at the location of the storage tank to prevent the fire from spreading and causing an explosion. This data-driven intelligent decision-making approach changes the traditional "one-size-fits-all" firefighting method, significantly improving the targeting and effectiveness of firefighting plans. It combines monitoring data and control status of fire equipment in the target area to determine emergency response capabilities, ensuring that activated fire equipment is available. For example, if an automatic sprinkler system in a certain area malfunctions and cannot start normally, the system will promptly adjust the plan, activating backup fire equipment or taking manual intervention measures to avoid delays in firefighting due to equipment failure, ensuring the reliability of fire emergency response. Based on the type of fire equipment and its location, the system generates equipment activation signals, enabling precise deployment of fire resources. The system can also dynamically adjust equipment activation strategies according to the development of the fire. For example, when the fire expands, it automatically links more fire equipment to assist in firefighting, ensuring that fire resources are concentrated in key areas, avoiding resource dispersion, and maximizing control of fire spread. It can take timely measures in the early stages of a fire, reducing fire losses and protecting the lives and property of people, achieving comprehensive, real-time, and intelligent monitoring of building fire safety.

[0037] Example 7: Based on Example 1, a cloud platform-based building fire safety supervision method based on artificial intelligence, such as... Figure 5 As shown, it includes: Step 1: Collect environmental data and real-time monitoring images from various sensors and image acquisition devices distributed throughout the building. Step 2: Use wireless communication technology to transmit environmental data and real-time monitoring images to the cloud platform; Step 3: Receive and store the data transmitted by the data transmission module through the cloud platform, and analyze the received data based on the fire prediction model and the safety hazard identification model to determine whether there are fire hazards and fire-fighting hazards in the building. Step 4: When there are fire hazards or fire-fighting risks in the building, generate an early warning signal based on the location of the hazard and send it to the relevant management personnel. When a fire hazard occurs, activate the fire-fighting equipment at the corresponding location for emergency response.

[0038] The beneficial effects of the above technical solution are as follows: By deploying multiple sensors and image acquisition devices in various areas of the building, environmental data and real-time monitoring images can be collected comprehensively and without blind spots. This makes the information acquired by the system more comprehensive, accurately capturing even subtle changes in smoke concentration or the actual condition of fire escape routes. This provides a solid data foundation for subsequent analysis and decision-making, greatly improving the accuracy of assessing the building's fire safety status and reducing regulatory loopholes caused by missing information. Subsequently, wireless communication technology is used to achieve rapid and stable transmission of environmental data and real-time monitoring images to the cloud platform, ensuring that the cloud platform can obtain the latest data in a timely manner. This, in turn, guarantees the real-time monitoring of the building's fire safety status by the entire system. After receiving the transmitted data, the cloud platform, based on fire prediction models and safety hazard identification... The model performs in-depth analysis of the transmitted data, accurately determining whether there are fire hazards in various areas of the building, as well as fire-fighting hazards such as obstructed fire exits, malfunctioning or abnormal fire-fighting equipment. Compared with traditional manual judgment, this intelligent analysis method greatly improves the accuracy and efficiency of hazard identification, avoids the omission of hazards due to human negligence, and can quickly generate early warning signals based on the location of the hazard and send them to relevant management personnel when a fire hazard occurs. At the same time, it can immediately activate the fire-fighting equipment in the corresponding location for emergency treatment when a fire hazard occurs, ensuring that countermeasures can be taken at the first moment of danger, controlling the fire in its infancy, effectively reducing the risk of casualties and property losses caused by fire, and providing strong protection for the life and property safety of people in the building. This invention achieves centralized storage and management of data through the application of a cloud platform, making it convenient for managers to view and analyze building fire safety conditions anytime and anywhere. It also facilitates data sharing and collaborative work between different departments. By analyzing a large amount of fire monitoring data in buildings through artificial intelligence algorithms (fire prediction model and safety hazard identification model) on the cloud platform, fire hazards and safety problems can be identified quickly and accurately, greatly improving the efficiency and accuracy of supervision. Furthermore, the invention provides immediate and automatic emergency response functions when fire hazards and safety problems occur, enabling timely measures to be taken in the early stages of a fire to reduce fire losses and protect the lives and property of people.

[0039] Example 8: Based on Example 7, step 3 includes: The data transmitted by the data transmission module is automatically stored based on the time sequence. Based on the safety hazard identification model, the data transmitted by the data transmission module is analyzed to determine whether there are fire hazards or fire-fighting hazards in various areas of the building, and to determine the type of hazard. When a fire is confirmed to exist inside a building, the ignition point information is obtained, and based on the ignition point information, various relevant sensors and image acquisition devices are identified. The system acquires transmission data from various relevant sensors and image acquisition devices, extracts current fire features, and predicts fire trends by combining the correlations between various fire features and environmental data corresponding to the ignition point.

[0040] The beneficial effects of the above technical solution are as follows: This invention automatically stores transmitted data in chronological order, providing a systematic and coherent data record for building fire safety management. This facilitates managers in reviewing historical data, analyzing environmental changes and equipment operating status over different time periods, summarizing patterns, and identifying potential safety risks. It also provides accurate and reliable data support for subsequent safety hazard analysis and fire prediction. Through in-depth analysis of transmitted data using a safety hazard identification model, it can quickly and accurately determine whether there are fire hazards or fire-fighting hazards in a building area, and clarify the type of hazard. This not only significantly improves the efficiency of hazard investigation, covering a large amount of data in a short time, but also reduces oversights caused by human factors, allowing for timely detection of hazards such as blocked fire exits and damaged fire-fighting equipment, achieving early warning and precise prevention and control of building fire safety hazards. Furthermore, after confirming a fire, by acquiring ignition point information, accurately locating relevant sensors and image acquisition equipment, extracting current fire characteristics, and combining ignition point environmental data to predict fire trends, it can provide firefighters and managers with accurate information on the fire development situation, providing a basis for relevant personnel to formulate emergency rescue plans, minimizing losses caused by fires, and ensuring the safety of people's lives and property.

[0041] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A cloud platform-based building fire safety monitoring system based on artificial intelligence, characterized in that, include: The data acquisition module is used to collect environmental data and real-time monitoring images through various sensors and image acquisition devices distributed throughout the building. The data transmission module is used to transmit environmental data and real-time monitoring images to the cloud platform using wireless communication technology. The cloud platform is used to receive and store the data transmitted by the data transmission module, and analyze the received data based on the safety hazard identification model to determine whether there are fire hazards and fire-fighting hazards in the building. The early warning and handling module is used to generate early warning signals based on the location of fire hazards or fire-fighting hazards in the building and send them to relevant management personnel. When a fire hazard occurs, the corresponding fire-fighting equipment at the location will be activated for emergency handling.

2. The cloud platform building fire safety monitoring system based on artificial intelligence according to claim 1, characterized in that, The data acquisition module includes: The fire data acquisition unit is used to collect environmental data and real-time monitoring images from various sensors and image acquisition devices distributed throughout the building. The monitoring equipment status monitoring unit is used to collect network status data of various sensors and image acquisition devices. The system access status monitoring unit is used to collect access status information of various building fire protection facilities systems.

3. The cloud platform building fire safety monitoring system based on artificial intelligence according to claim 1, characterized in that, Cloud platforms, including: The data storage unit is used to automatically store the data transmitted by the data transmission module in chronological order. The safety hazard analysis unit is used to analyze the transmitted data of the data transmission module based on the safety hazard identification model, to determine whether there are fire hazards or fire-fighting hazards in various areas of the building, and to determine the type of hazard. The fire prediction unit is used to acquire ignition point information when a fire is confirmed to exist in a building, and to determine various relevant sensors and image acquisition devices based on the ignition point information. The system acquires transmission data from various relevant sensors and image acquisition devices, extracts current fire features, and predicts fire trends by combining the correlations between various fire features and environmental data corresponding to the ignition point.

4. The cloud platform building fire safety monitoring system based on artificial intelligence according to claim 2, characterized in that, Cloud platforms, including: The equipment data analysis unit is used to analyze the collected network information to obtain the monitoring results of the monitoring equipment within the building, including: The online data analysis subunit is used to calculate the overall online rate and offline rate of equipment in the building, as well as the regional offline rate and online rate of each area, based on the collected network status data, and generate equipment online information; The equipment coverage analysis subunit is used to acquire the installation and distribution information of sensors and image acquisition equipment within the building, as well as the effective monitoring area information of the equipment. Based on the online equipment information and installation and distribution information, the location of the online equipment is determined, and combined with the effective monitoring area information of the online equipment, the current monitoring coverage range and monitoring blind spots of each area are determined, and monitoring coverage information is generated. The equipment monitoring information sending subunit is used to generate equipment monitoring data based on equipment online information and monitoring coverage information and send it to relevant management personnel.

5. A cloud platform building fire safety monitoring system based on artificial intelligence according to claim 2, characterized in that, Cloud platforms, including: The system status monitoring unit is used to determine whether there is any abnormal access to the building fire protection system based on the access status information of various building fire protection facilities systems; If such an abnormal access occurs, a corresponding access anomaly warning signal will be generated and sent to the relevant management personnel. The control status of the target fire protection equipment corresponding to the abnormal access to the building fire protection system will be marked as unknown. After the abnormal access to the building fire protection system is restored, the control status mark of the target fire protection equipment will be updated synchronously.

6. The cloud platform building fire safety monitoring system based on artificial intelligence according to claim 3, characterized in that, The safety hazard analysis unit includes: The environment determination subunit is used to acquire target transmission data corresponding to relevant image acquisition devices, perform environmental analysis on the target transmission data, acquire environmental characteristics corresponding to the ignition point, and determine the location of flammable and explosive materials in the area where the ignition point is located, as well as the actual distance between the flammable and explosive materials and the ignition point based on the environmental characteristics. The feature extraction subunit is used to extract features from the transmitted data of various related sensors and image acquisition devices to obtain various current fire features. The trend prediction subunit is used to determine the trend of fire characteristics and predict the outcome of the fire based on the correlation of fire characteristics and the actual distance and combustion characteristics between flammable and explosive materials and the ignition point.

7. The cloud platform building fire safety monitoring system based on artificial intelligence according to claim 6, characterized in that, The safety hazard analysis unit also includes: The fire prediction subunit is used to acquire various fire characteristics of non-fire areas, generate a feature matrix and compare it with a standard feature matrix to obtain a difference matrix. Then, the maximum eigenvalue of the matrix is ​​calculated and compared with a preset threshold. If the maximum eigenvalue exceeds the threshold, the area is determined to have a fire hazard.

8. The cloud platform building fire safety monitoring system based on artificial intelligence according to claim 1, characterized in that, The early warning and processing module includes: The early warning signal generation unit is used to generate a corresponding early warning signal and send it to relevant management personnel based on the type of hazard and the location of the hazard when it is confirmed that there is a fire hazard or fire-fighting hazard in any area of ​​the building. The emergency response unit is used to acquire information on the ignition point and the location of flammable and explosive materials at the fire scene, determine the appropriate fire-fighting equipment based on the combustion characteristics of flammable and explosive materials and the ignition point information, and acquire monitoring data and control status of fire-fighting equipment in the fire area to determine whether fire emergency response can be completed. If possible, generate a device activation signal based on the type of fire-fighting activation device and the area location information, and control the activation device to aim at the fire point and extinguish the fire. The ignition point combustion information includes ignition point information and ignition point type information.

9. A cloud platform-based method for supervising building fire safety based on artificial intelligence, characterized in that, include: Step 1: Collect environmental data and real-time monitoring images using various sensors and image acquisition devices distributed throughout the building; Step 2: Use wireless communication technology to transmit environmental data and real-time monitoring images to the cloud platform; Step 3: Receive and store the data transmitted by the data transmission module through the cloud platform, and analyze the received data based on the fire prediction model and the safety hazard identification model to determine whether there are fire hazards and fire-fighting hazards in the building. The fire safety hazards mentioned include obstructed fire exits and malfunctioning or abnormal fire equipment. Step 4: When there are fire hazards or fire-fighting risks in the building, generate an early warning signal based on the location of the hazard and send it to the relevant management personnel. When a fire hazard occurs, activate the fire-fighting equipment at the corresponding location for emergency response.

10. A cloud platform-based building fire safety supervision method based on artificial intelligence according to claim 9, characterized in that, Step 3 includes: The data transmitted by the data transmission module is automatically stored based on the time sequence. Based on the safety hazard identification model, the data transmitted by the data transmission module is analyzed to determine whether there are fire hazards or fire-fighting hazards in various areas of the building, and to determine the type of hazard. When a fire is confirmed to exist inside a building, the ignition point information is obtained, and based on the ignition point information, various relevant sensors and image acquisition devices are identified. The system acquires transmission data from various relevant sensors and image acquisition devices, extracts current fire features, and predicts fire trends by combining the correlations between various fire features and environmental data corresponding to the ignition point.

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