Intelligent fire-fighting closed-loop management method and system based on multistage early warning and six-stage linkage

Abstract

The invention relates to the technical field of fire-fighting management, in particular to an intelligent fire-fighting closed-loop management method and system based on multistage early warning and six-stage linkage, and the method comprises the steps: firstly, obtaining multi-source sensing data, generating a comprehensive fire risk index through a fire risk analysis model, and achieving the precise risk assessment; a threshold value is dynamically adjusted by combining a real-time environment and a historical model so as to determine a multi-level early warning level, and intelligent and accurate early warning is ensured; starting a corresponding six-level linkage response scheme according to the early warning level, predicting a fire behavior and dynamically optimizing a response strategy according to a risk index change trend, and synchronizing an emergency decision and a real-time risk situation; and finally, a closed-loop management system integrating intelligent sensing, dynamic early warning, self-adaptive linkage and continuous optimization is formed by executing a feedback correction analysis model, and the fire prevention and control efficiency is comprehensively improved.

Description

Technical Field

[0001] This invention relates to the field of fire management technology, specifically to a smart fire closed-loop management method and system based on multi-level early warning and six-level linkage. Background Technology

[0002] With the acceleration of urbanization and the increasing number of high-rise and complex buildings, the complexity and challenges of fire safety management have significantly increased. Traditional fire safety systems mainly rely on single sensors such as smoke detectors and heat detectors for threshold alarms, and their response modes are relatively fixed and passive. These systems typically lack the ability to integrate and analyze multi-source risk information, making it difficult to conduct early and accurate comprehensive assessments of fire risks. Their warning thresholds are often statically set and cannot be adaptively adjusted according to dynamic environmental changes (such as diurnal temperature variations, seasonal changes, and changes in population density in specific areas), easily leading to false alarms or missed alarms. In the emergency response phase after an alarm is triggered, the linkage between departments and systems largely relies on pre-set procedures or post-event coordination, lacking the ability to dynamically optimize and coordinate command based on real-time risk situations.

[0003] The entire management process is typically in an open-loop state, meaning that alarm, response, and handling stages are isolated from each other. The system cannot obtain effective feedback from each event for self-learning and performance optimization, resulting in limited intelligence and overall effectiveness in dealing with complex and ever-changing fire scenarios. Therefore, there is an urgent need to develop a comprehensive new fire management method that can achieve accurate risk perception, intelligent early warning, dynamic linkage, and continuous evolution capabilities. Summary of the Invention

[0004] The purpose of this invention is to address the problems raised in the background section by providing a smart fire protection closed-loop management method and system based on multi-level early warning and six-level linkage. Specifically, the technical problems include how to construct an adaptive closed-loop management system integrating multi-source information fusion analysis, dynamic intelligent early warning, multi-level collaborative linkage, and continuous self-optimization, to solve the problems of insufficient overall fire prevention and control effectiveness caused by single perception, rigid early warning, disconnect between response and real-time risk situation, and lack of learning and evolution mechanisms in existing technologies.

[0005] To achieve the above objectives, one of the objectives of this invention is a smart fire protection closed-loop management method based on multi-level early warning and six-level linkage, comprising the following method steps:

[0006] S1. Acquire fire-related multi-source sensing data through a variety of deployed sensors, including temperature sensors, smoke sensors, gas sensors, and flame sensors; the multi-source sensing data includes temperature, smoke concentration, gas concentration, and infrared radiation.

[0007] Step S1 systematically acquires multi-source sensing data related to fire, such as temperature, smoke, gas, and flame, by deploying various sensors including temperature, smoke concentration, gas concentration, and infrared radiation. The core function of this step is to construct a comprehensive and heterogeneous on-site information acquisition layer, providing a rich data foundation for subsequent in-depth analysis. This solves the technical problem of insufficient risk assessment basis caused by single sensing in traditional methods and is a prerequisite for realizing multi-source information fusion analysis.

[0008] S2. Using a fire risk analysis model, multi-source sensing data is analyzed and processed to generate a comprehensive fire risk index. The analysis and processing include normalization preprocessing and deep learning processing. The normalization preprocessing uses the minimum and maximum normalization methods to linearly transform different data in the multi-source sensing data to a common numerical range, generating a comprehensive feature vector. The deep learning processing involves inputting the comprehensive feature vector into a pre-trained deep learning network. The deep learning network extracts high-level abstract features of the data layer by layer through multi-layer nonlinear transformations, and maps the high-level abstract features to the comprehensive fire risk index through a regressor in the output layer.

[0009] Step S2 utilizes a fire risk analysis model to perform normalization preprocessing and deep learning processing on multi-source sensing data, ultimately generating a comprehensive fire risk index. Its technical effectiveness lies in achieving high-level abstract feature extraction and deep fusion of heterogeneous multi-source data through multi-layer nonlinear transformations of deep learning networks. This transforms isolated physical quantity signals into a quantitative indicator that comprehensively reflects the overall risk level. This marks a leap from simple threshold judgment to intelligent feature learning, and is a core component in building multi-source information fusion analysis capabilities, laying a solid foundation for accurate risk assessment.

[0010] S3. Based on real-time environmental parameters and historical fire models, dynamically adjust the threshold for determining the warning level, and determine multiple warning levels based on the comparison between the fire risk index and the threshold; the process of determining multiple warning levels specifically includes:

[0011] Historical fire models are queried based on real-time environmental parameters to obtain statistical characteristics of historical fire data that match the current environment. Based on these statistical characteristics, dynamic judgment thresholds are generated for each multi-level warning level using a threshold calculation function. The comprehensive fire risk index is compared with the dynamic judgment thresholds level by level, and the multi-level warning level is determined based on the comparison results.

[0012] Step S3 dynamically adjusts the judgment thresholds for each level of early warning by combining real-time environmental parameters with historical fire models, and determines the final multi-level early warning level based on the comparison results between the comprehensive fire risk index and these dynamic thresholds. Its key role is to introduce an environmentally adaptive decision-making mechanism, which enables the early warning judgment criteria to be flexibly adjusted with changes in the environment. This effectively overcomes the false alarm and missed alarm problems caused by the rigidity of traditional early warning, realizes dynamic intelligent early warning, and significantly improves the accuracy and context relevance of early warning signals.

[0013] S4. Based on the multi-level warning levels, generate and activate the corresponding six-level linkage response plan. A built-in contingency plan library is used, which pre-stores the six-level linkage response plan bound to each multi-level warning level. When the current multi-level warning level is determined, the six-level linkage response plan corresponding to that level is called from the contingency plan library.

[0014] Based on the changing trends of the fire risk index, the development of the fire is predicted, and the six-level linkage response plan is dynamically optimized according to the development trend, specifically including:

[0015] The comprehensive fire risk index is continuously acquired at fixed time intervals to form a time series. The instantaneous rate of change and the average trend of change of the fire risk index are calculated. The instantaneous rate of change and the average trend of change are input into the fire prediction model to predict the development level of the fire. Based on the development level, the six-level linkage response plan is dynamically optimized through the plan adjustment rule set.

[0016] Step S4 first calls upon and activates the corresponding six-level linkage response plan from the preset plan library based on the determined multi-level early warning levels, achieving preliminary structuring and automation of the response. Then, it predicts the fire development trend based on the changing trend of the fire risk index and dynamically optimizes the activated response plan accordingly. The direct technical effect of this step is to elevate emergency response from static, fixed plan execution to an intelligent process that can dynamically adjust according to the real-time risk situation, solving the problem of the disconnect between response and real-time risk situation. This is key to achieving multi-level collaborative linkage and ensuring its high efficiency.

[0017] S5. Execute the six-level linkage response plan and obtain feedback information, including the latest multi-source sensing data and key nodes and final results of the plan execution; compare and analyze the feedback information with the output of the fire risk analysis model, and if false alarms or omissions are confirmed, determine that the model has deviations.

[0018] The fire risk analysis model is revised based on feedback information to form a closed-loop management system, which specifically includes:

[0019] Based on the bias, feedback information is used to correct the connection weights and bias parameters of the deep learning network in the fire risk analysis model. This includes combining the latest multi-source sensing data with real labels as training samples and using the error backpropagation algorithm to fine-tune and update the parameters.

[0020] Step S5, while executing the six-level linkage response plan, collects feedback information, including the latest multi-source sensing data and plan execution results, and uses this information to revise the fire risk analysis model. Its fundamental role is to introduce a continuous self-optimization mechanism into the entire management system. By comparing the model output with the actual feedback and using the error backpropagation algorithm to fine-tune the model parameters, the system can learn from each warning-response event, continuously reduce model deviation, and thus gradually improve the perception accuracy and decision-making intelligence of the entire system. Ultimately, it forms a continuously evolving adaptive closed-loop management system, solving the deficiency of lacking a learning and evolution mechanism.

[0021] The second objective of this invention is to provide a system based on a multi-level early warning and six-level linkage intelligent fire protection closed-loop management method, comprising a multi-source sensing data acquisition module, a fire risk analysis module, an adaptive early warning decision-making module, an intelligent response strategy module, and an intelligent response strategy module, wherein:

[0022] The multi-source sensing data acquisition module acquires fire-related multi-source sensing data through a variety of deployed sensors;

[0023] The fire risk analysis module uses a fire risk analysis model to analyze and process multi-source sensing data to generate a comprehensive fire risk index.

[0024] The adaptive early warning decision module dynamically adjusts the judgment threshold of the early warning level based on real-time environmental parameters and historical fire models, and determines multiple early warning levels based on the comparison results between the fire risk index and the judgment threshold.

[0025] The intelligent response strategy module generates and activates a corresponding six-level linkage response plan based on the multi-level early warning levels; it predicts the development trend of the fire based on the changing trend of the fire risk index, and dynamically optimizes the six-level linkage response plan according to the development trend.

[0026] The closed-loop optimization management module executes a six-level linkage response plan, obtains feedback information, and corrects the fire risk analysis model based on the feedback information, thus forming a closed-loop management system.

[0027] Compared with the prior art, the beneficial effects of the present invention are:

[0028] This invention generates a comprehensive fire risk index through deep fusion and intelligent analysis of multi-source sensing data, enabling early and accurate assessment of fire risks. By dynamically adjusting warning thresholds and activating corresponding linkage schemes, it ensures the accuracy and timeliness of warnings and responses. Furthermore, it predicts fire development based on risk trends and dynamically optimizes response strategies, ensuring that emergency decisions are always synchronized with the real-time risk situation. Finally, it utilizes execution feedback to continuously self-correct the analysis model, thereby constructing a closed-loop management system integrating intelligent sensing, dynamic early warning, adaptive linkage, and continuous evolution. This effectively overcomes the inherent defects of traditional methods, such as single sensing, rigid early warning, disconnected response, and lack of learning mechanisms, significantly improving the overall effectiveness and intelligence level of fire prevention and control. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the overall method steps of the present invention;

[0030] Figure 2 This is a schematic diagram of the closed-loop core process of the method of this invention;

[0031] Figure 3 This is a schematic diagram of the overall module flow of the present invention.

[0032] In the diagram: 100, Multi-source sensing data acquisition module; 200, Fire risk analysis module; 300, Adaptive early warning decision module; 400, Intelligent response strategy module; 500, Intelligent response strategy module. Detailed Implementation

[0033] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] Next, please refer to Figure 1-2 One of the objectives of this embodiment is to provide a smart fire protection closed-loop management method based on multi-level early warning and six-level linkage.

[0035] The specific steps are as follows:

[0036] S1. By deploying multiple sensors in the actual monitoring area, including temperature sensors, smoke sensors, gas sensors, and flame sensors, these sensors are fixedly installed in key locations such as inside buildings, corridors, and hazardous materials storage areas to collect multi-source sensing data related to fire in real time. The built-in detection elements of the sensors convert environmental physical quantities such as temperature, smoke concentration, gas concentration, and infrared radiation into electrical signals, which are then transmitted to the data aggregation node through wired or wireless communication modules, thereby completing the acquisition of multi-source sensing data.

[0037] S2. Using a fire risk analysis model, analyze and process multi-source sensing data to generate a comprehensive fire risk index, specifically including:

[0038] First, the multi-source sensing data undergoes normalization preprocessing to eliminate the dimensional differences between data from different sensors, and is then concatenated into a comprehensive feature vector. The process of normalizing the multi-source sensing data is as follows:

[0039] For multi-source sensing data with different dimensions and numerical ranges, such as temperature, smoke concentration, gas concentration, and infrared radiation, a minimum and maximum normalization method is adopted to linearly transform the raw data collected by each sensor into a common numerical range. Specifically, the minimum and maximum values ​​of the data for each type of sensor are calculated, and each data is converted into a dimensionless value within the range according to the formula (current value - minimum value) / (maximum value - minimum value), thereby eliminating the dimensional differences between different sensor data.

[0040] Then, the comprehensive feature vector is processed by deep learning. This comprehensive feature vector is input into a pre-trained deep learning network. The deep learning network extracts high-level abstract features of the data layer by layer through multiple nonlinear transformations, and maps the high-level abstract features into a comprehensive fire risk index through a regressor in the output layer. Specifically, the comprehensive feature vector is input into the input layer of the deep learning network, and then passed to multiple hidden layers in sequence. Each hidden layer performs a weighted summation of the features output by the previous layer and applies a nonlinear activation function transformation, thereby generating more abstract feature representations layer by layer. Finally, the network output layer linearly combines these high-level abstract features, and maps the linear combination result into a continuous value representing the overall risk level, i.e., the comprehensive fire risk index, through a regressor.

[0041] The connection weights and bias parameters of the deep learning network in the fire risk analysis model are adjustable. These parameters are corrected in step S5 based on feedback information to form a closed-loop management.

[0042] S3. Based on real-time environmental parameters and historical fire models, dynamically adjust the threshold for determining the warning level, and determine the current multi-level warning level based on the comparison between the fire risk index and the threshold, specifically including:

[0043] First, based on real-time environmental parameters, historical fire models are queried to obtain statistical features of historical fire data that match the current environment. Specifically, a historical fire model is a database that stores fire case data under different combinations of environmental parameters in history. Using the current real-time environmental parameters as query conditions, all historical fire cases with environmental parameters similar to the current conditions are retrieved from the historical fire model. The statistical values ​​of key data (such as the average value and distribution range of various sensor data) at the time of the fire in these matching cases are calculated, and these statistical values ​​are used as the statistical features of historical fire data that match the current environment.

[0044] Then, based on the statistical characteristics of historical fire data, a dynamic judgment threshold is generated for each multi-level warning level through a threshold calculation function. Specifically, a basic threshold is preset for each multi-level warning level, and the statistical characteristics of historical fire data obtained from the historical fire model are used as input variables. The threshold calculation function uses these basic thresholds and input variables as parameters and calculates them through a linear weighting formula. The output result is the dynamic judgment threshold that is adjusted according to the historical fire risk status and is applicable to the current specific environmental conditions.

[0045] Next, the comprehensive fire risk index generated in step S2 is compared step by step with this series of dynamic judgment thresholds;

[0046] Ultimately, based on the comparison results, the level corresponding to the threshold range into which the comprehensive fire risk index falls is determined as the current multi-level warning level.

[0047] S4. Based on the determined multi-level early warning levels, generate and activate the corresponding six-level linkage response plan, predict the development trend of the fire based on the changing trend of the fire risk index, and dynamically optimize the six-level linkage response plan according to the development trend. The specific process includes:

[0048] First, a built-in contingency plan library is established, which pre-stores standard six-level linkage response schemes strictly bound to each multi-level warning level. Once the current multi-level warning level is determined, the corresponding six-level linkage response scheme is immediately retrieved from the contingency plan library. The contingency plan library is a relational data table stored in the system database, containing two core fields: warning level and standard scheme content. The warning level field stores all multi-level warning level identifiers, each identifier being unique. The standard scheme content field stores the complete standard six-level linkage response scheme text and instruction set, strictly bound to the corresponding warning level, in serialized string format. When the system determines the current multi-level warning level, it queries the data table for records that perfectly match the level identifier and reads the value of their standard scheme content field, thereby completing the retrieval of the corresponding standard six-level linkage response scheme.

[0049] Subsequently, the comprehensive fire risk index is continuously acquired at fixed time intervals to form a time series, and the instantaneous rate of change and average trend of the fire risk index are calculated using this time series. Then, the instantaneous rate of change and average trend of change are input into a pre-set fire prediction model, which classifies the development of the fire into different predefined levels based on the speed and persistence of risk growth.

[0050] Then, based on the determined development trend level, a predefined set of adjustment rules is invoked. The set of adjustment rules clearly specifies the adjustment operations that need to be performed on the ongoing Level 6 linkage response plan under different development trend levels, including but not limited to increasing or decreasing the response intensity, changing the focus of rescue force deployment, or adjusting the evacuation range; and the adjustment operations are executed to complete the dynamic optimization of the Level 6 linkage response plan.

[0051] S5. Implement the six-level linkage response plan, obtain feedback information, and revise the fire risk analysis model based on the feedback information to form a closed-loop management system, specifically including:

[0052] While executing the dynamically optimized six-level linkage response plan, the latest multi-source sensing data during the fire handling process is continuously acquired through various sensors in step S1, and the key nodes and final results of the plan execution are recorded as feedback information.

[0053] Next, the feedback information is compared with the output of the fire risk analysis model in step S2. If the feedback information confirms a false alarm or a missed alarm, the model is judged to have a deviation. Specifically, the final result in the feedback information regarding whether the fire actually occurred is taken as the true label. If the feedback information confirms that the fire did not occur, but the comprehensive fire risk index output by the fire risk analysis model in step S2 was higher than the dynamic judgment threshold of the multi-level warning level determined in step S3 and triggered a warning, then the event is judged to be a false alarm. If the feedback information confirms that the fire occurred, but the comprehensive fire risk index output by the fire risk analysis model in step S2 before the fire occurred was lower than the dynamic judgment threshold of its proper level and did not trigger the corresponding warning, then the event is judged to be a missed alarm. When either a false alarm or a missed alarm occurs, the fire risk analysis model is judged to have a deviation.

[0054] Then, based on this deviation, the connection weights and bias parameters of the deep learning network in the fire risk analysis model are corrected using feedback information. Specifically, the system combines the latest multi-source sensing data corresponding to the event that caused the false alarm or missed alarm with the real label in the feedback information into a training sample. Subsequently, the backpropagation algorithm is used to calculate the gradient of the parameters of each layer of the deep learning network with the goal of narrowing the gap between the comprehensive fire risk index output by the model and the real label, and the connection weights and bias parameters in the network are fine-tuned and updated in the opposite direction of the gradient, thereby completing the correction.

[0055] The revised fire risk analysis model will be used for data analysis and processing in the next step S2, thus forming a closed-loop management system.

[0056] Please see Figure 3 The second objective of this embodiment is to provide a system for intelligent fire protection closed-loop management based on multi-level early warning and six-level linkage, including a multi-source sensing data acquisition module 100, a fire risk analysis module 200, an adaptive early warning decision module 300, an intelligent response strategy module 400, and a closed-loop optimization management module 500, wherein:

[0057] The multi-source sensing data acquisition module 100 acquires fire-related multi-source sensing data through a variety of deployed sensors;

[0058] The fire risk analysis module 200 uses a fire risk analysis model to analyze and process multi-source sensing data to generate a comprehensive fire risk index.

[0059] The adaptive early warning decision module 300 dynamically adjusts the judgment threshold of the early warning level based on real-time environmental parameters and historical fire models, and determines multiple early warning levels based on the comparison results between the fire risk index and the judgment threshold.

[0060] The intelligent response strategy module 400 generates and activates a corresponding six-level linkage response plan based on the multi-level early warning levels; it predicts the development trend of the fire based on the changing trend of the fire risk index, and dynamically optimizes the six-level linkage response plan according to the development trend.

[0061] The closed-loop optimization management module 500 executes a six-level linkage response plan, obtains feedback information, and corrects the fire risk analysis model based on the feedback information, thus forming a closed-loop management system.

[0062] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A smart fire protection closed-loop management method based on multi-level early warning and six-level linkage, characterized in that, The methods and steps include the following: S1. Acquire multi-source sensing data related to fire by deploying multiple sensors; S2. Using a fire risk analysis model, the multi-source sensing data is analyzed and processed to generate a comprehensive fire risk index. S3. Based on real-time environmental parameters and historical fire models, dynamically adjust the judgment threshold of the early warning level, and determine the multi-level early warning level based on the comparison result between the fire risk index and the judgment threshold. S4. Based on the multi-level early warning levels, generate and activate the corresponding six-level linkage response scheme; Based on the changing trend of the fire risk index, the development trend of the fire is predicted, and the six-level linkage response plan is dynamically optimized according to the development trend. S5. Execute the six-level linkage response plan, obtain feedback information, and revise the fire risk analysis model based on the feedback information to form a closed-loop management.

2. The intelligent fire protection closed-loop management method based on multi-level early warning and six-level linkage as described in claim 1, characterized in that, The various sensors include temperature sensors, smoke sensors, gas sensors, and flame sensors; the multi-source sensing data includes temperature, smoke concentration, gas concentration, and infrared radiation.

3. The intelligent fire protection closed-loop management method based on multi-level early warning and six-level linkage as described in claim 1, characterized in that, The analysis and processing in step S2 includes normalization preprocessing and deep learning processing. The normalization preprocessing uses the minimum and maximum normalization methods to linearly transform different data in the multi-source sensing data into a common numerical range, generating a comprehensive feature vector.

4. The intelligent fire protection closed-loop management method based on multi-level early warning and six-level linkage according to claim 3, characterized in that, The deep learning process involves inputting a comprehensive feature vector into a pre-trained deep learning network. The deep learning network extracts high-level abstract features from the data layer by layer through multi-layer nonlinear transformations, and maps these high-level abstract features into a comprehensive fire risk index through a regressor at the output layer.

5. The intelligent fire protection closed-loop management method based on multi-level early warning and six-level linkage according to claim 1, characterized in that, The process for determining the multi-level early warning system specifically includes: Historical fire models are queried based on real-time environmental parameters to obtain statistical characteristics of historical fire data that match the current environment; based on the statistical characteristics of the historical fire data, dynamic judgment thresholds are generated for each multi-level warning level through a threshold calculation function; the comprehensive fire risk index is compared with the dynamic judgment thresholds level by level, and the multi-level warning level is determined based on the comparison results.

6. The intelligent fire protection closed-loop management method based on multi-level early warning and six-level linkage according to claim 1, characterized in that, The activation process of the six-level linkage response scheme specifically includes: It has a built-in contingency plan library, which pre-stores six-level linkage response schemes bound to each multi-level warning level; when the current multi-level warning level is determined, the six-level linkage response scheme corresponding to that level is called from the contingency plan library.

7. The intelligent fire protection closed-loop management method based on multi-level early warning and six-level linkage according to claim 1, characterized in that, The optimization process of the six-level linkage response scheme specifically includes: The comprehensive fire risk index is continuously acquired at fixed time intervals to form a time series, and the instantaneous change rate and average change trend of the fire risk index are calculated. The instantaneous change rate and average change trend are input into the fire prediction model to predict the development status level of the fire. Based on the development status level, the six-level linkage response scheme is dynamically optimized through the scheme adjustment rule set.

8. The intelligent fire protection closed-loop management method based on multi-level early warning and six-level linkage according to claim 1, characterized in that, The feedback information includes the latest multi-source sensing data and key nodes and final results of the scheme execution; the feedback information is compared and analyzed with the output of the fire risk analysis model, and if false alarms or omissions are confirmed, the model is determined to have deviations.

9. The intelligent fire protection closed-loop management method based on multi-level early warning and six-level linkage as described in claim 8, characterized in that, The correction process in step S5 specifically includes: Based on the aforementioned deviation, feedback information is used to correct the connection weights and bias parameters of the deep learning network in the fire risk analysis model. This includes combining the latest multi-source sensing data with real labels as training samples and using an error backpropagation algorithm to fine-tune and update the parameters.

10. A system using the intelligent fire protection closed-loop management method based on multi-level early warning and six-level linkage as described in any one of claims 1-9, characterized in that, It includes a multi-source sensing data acquisition module (100), a fire risk analysis module (200), an adaptive early warning decision module (300), an intelligent response strategy module (400), and an intelligent response strategy module (500), wherein: The multi-source sensing data acquisition module (100) acquires fire-related multi-source sensing data through a variety of deployed sensors; The fire risk analysis module (200) uses a fire risk analysis model to analyze and process the multi-source sensing data to generate a comprehensive fire risk index. The adaptive early warning decision module (300) dynamically adjusts the judgment threshold of the early warning level according to real-time environmental parameters and historical fire models, and determines the multi-level early warning level based on the comparison result between the fire risk index and the judgment threshold. The intelligent response strategy module (400) generates and activates a corresponding six-level linkage response scheme according to the multi-level early warning level; predicts the development trend of the fire based on the changing trend of the fire risk index, and dynamically optimizes the six-level linkage response scheme according to the development trend. The closed-loop optimization management module (500) executes the six-level linkage response scheme, obtains feedback information, and corrects the fire risk analysis model based on the feedback information to form closed-loop management.

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