Automatic patrol intelligent fire extinguishing method based on data analysis and control system

Abstract

The invention discloses an automatic patrol intelligent fire extinguishing method and control system based on data analysis, and relates to the technical field of intelligent fire extinguishing control, and the method comprises the steps: obtaining multi-dimensional data through a multi-dimensional data integration platform, and carrying out the preprocessing; calculating a multi-dimensional risk assessment index based on the preprocessed data, performing nonlinear standardization, constructing a multi-dimensional risk assessment feature vector, and performing clustering analysis on all the to-be-patrolled areas based on the multi-dimensional risk assessment feature vector according to a distance metric function; according to the clustering analysis result, comprehensive risk score calculation is carried out on all the to-be-patrolled areas, a priority to-be-patrolled list is generated, automatic patrolling is carried out on the to-be-patrolled areas, and when a fire behavior is detected, fire extinguishing equipment in the areas is started to extinguish fire. Key elements of fire occurrence and spreading are comprehensively described through multi-dimensional risk assessment, and risk scoring and grading are performed on all to-be-patrolled areas after clustering analysis, so that dynamic priority patrolling of high-risk areas is realized, and the patrolling efficiency is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent fire extinguishing control, and specifically to an intelligent fire extinguishing method and control system for automatic patrol based on data analysis. Background Art

[0002] With the continuous advancement of urbanization and the increasing complexity of building structures, the challenges faced by traditional manual inspection and fire protection models have become increasingly prominent, mainly manifested in the following aspects: insufficient inspection coverage and timeliness. In large buildings or industrial parks, traditional manual inspections are often restricted by human and time costs and it is difficult to cover all key areas in a timely manner. Especially in multi-story buildings or industrial scenarios with many partitions, relying solely on manual patrols is not only inefficient but also unable to achieve real-time attention to high-risk areas; data silos and lack of comprehensive analysis. In modern buildings, various sensing devices are often deployed, such as temperature sensors, smoke sensors, air flow detection devices, etc., but these data are often scattered or isolated. Lack of systematic and in-depth data fusion and analysis means, making it difficult to predict fire risks in a timely manner or identify the fire interaction effects between regions; imperfect fire risk assessment methods. In existing fire protection management, simple alarm indicators are often relied on, including single thresholds such as temperature and smoke sensors, and it is impossible to dynamically and comprehensively evaluate the fire risks of each area. Insufficient consideration is given to factors such as the adaptability of the building environment, the concentration of combustibles, and the impact of air flow on the spread of flames. Once a fire occurs, it is difficult to accurately judge high-risk areas and make real-time responses; lack of automated inspection and fire extinguishing strategies. In the initial stage of a fire, if the danger can be automatically identified and extinguishing operations can be carried out immediately, losses can be effectively reduced and personnel safety can be improved. However, traditional fire protection mostly relies on manual scheduling and command, and the response speed is often not ideal, and it is difficult to take into account the real-time adjustment of multiple regions and multiple indicators in complex fire situations. Summary of the Invention

[0003] Based on the above-mentioned disadvantages of the prior art, the purpose of the present invention is to provide an intelligent fire extinguishing method and control system for automatic patrol based on data analysis to solve the above technical problems.

[0004] To achieve the above purpose, the present invention provides the following technical solution: An intelligent fire extinguishing method for automatic patrol based on data analysis, including:

[0005] S1: Obtain multi-dimensional data through a multi-dimensional data integration platform, where the multi-dimensional data includes real-time sensor data and environmental monitoring data, and preprocess the collected data;

[0006] S2: Calculate a multi-dimensional risk assessment index based on the preprocessed data, where the multi-dimensional risk assessment index includes a comprehensive thermo-dynamic interaction index, a spatial fire interaction complexity index, a fire thermo-dynamic synergistic effect index, and an environmental fire risk adaptability index;

[0007] S3: Non-linearly standardize the multi-dimensional risk assessment index, construct a multi-dimensional risk assessment feature vector based on the standardized multi-dimensional risk assessment index, and perform cluster analysis on all regions to be patrolled based on the distance metric function and the multi-dimensional risk assessment feature vector;

[0008] S4: Calculate the comprehensive risk scores for all regions to be patrolled according to the cluster analysis results, generate a list of regions to be patrolled preferentially based on the comprehensive risk scores, automatically patrol the regions to be patrolled according to the list of regions to be patrolled preferentially, and when a fire is detected, activate the fire extinguishing equipment in the area for fire extinguishing.

[0009] The present invention is further configured such that the multi-dimensional data includes temperature, temperature gradient, radiant heat flux, air flow velocity, air flow direction, combustible gas concentration, relative wind direction angle, convective heat transfer rate, radiative heat transfer rate, total air flow volume of air circulation, and total heat load; clean, format, and correct the collected data to ensure the accuracy and consistency of the data, and handle missing values, outliers, and data noise.

[0010] The present invention is further configured such that based on the pre-processed data, calculate a multi-dimensional risk assessment index, including:

[0011] Calculate a comprehensive thermo-dynamic interaction index according to temperature, radiant heat flux, temperature gradient, and air flow velocity;

[0012] Calculate a spatial fire interaction complexity index according to combustible gas concentration, relative wind direction angle, and temperature;

[0013] Calculate a fire thermo-dynamic synergy effect index according to air flow velocity, air flow direction, convective heat transfer rate, and radiative heat transfer rate;

[0014] Calculate an environmental fire risk adaptability index according to the total air flow volume of air circulation and the total heat load.

[0015] The present invention is further configured such that the calculation logic of the comprehensive thermo-dynamic interaction index is: Where CTII is the comprehensive thermo-dynamic interaction index, T max is the highest temperature in the region to be patrolled, Q rad is the radiant heat flux, is the temperature gradient, V air is the air flow velocity, and K1 is the temperature-air flow interaction sensitivity constant.

[0016] The present invention is further configured such that the calculation logic of the spatial fire interaction complexity index is: Where SFICI is the spatial fire interaction complexity index, N is the number of regions to be patrolled, and is the concentration of combustible substances in the i-th and j-th areas to be patrolled, θ ij is the relative wind direction angle of the i-th and j-th areas to be patrolled, d ij is the relative distance between the i-th and j-th areas to be patrolled, T i and T j is the highest temperature of the i-th and j-th areas to be patrolled, T threshold is the temperature threshold for measuring temperature gain.

[0017] The present invention is further configured such that the calculation logic of the fire thermal synergy effect index is: where FTSSEI is the fire thermal synergy effect index, Q conv is the convective heat transfer rate, Q rad is the radiative heat transfer rate, φ wind is the air flow direction, C fuel is the concentration of combustible substances, V air is the air flow velocity, V critical is the critical air flow velocity threshold.

[0018] The present invention is further configured such that the calculation logic of the environmental fire risk adaptability index is: where EFRAI is the environmental fire risk adaptability index, W air is the total air volume of air circulation, S heat is the total heat load, L struct is the building structure fire resistance rating score, B fireproof is the fireproof material coverage rate, D access is the number of safety exits, J is the total number of fire spread paths, T j is the highest temperature of the area to be patrolled corresponding to the j-th fire spread path, K is the number of fire prevention obstacle factors on the path, including firewalls and fire doors, is the k-th fire prevention obstacle factor on the j-th fire spread path, The obstacle effectiveness score of the k-th fire prevention obstacle factor on the j-th fire spread path.

[0019] The present invention is further configured such that step S3 includes:

[0020] Performing non-linear standardization processing on the comprehensive thermo-dynamic interaction index, the spatial fire interaction complexity index, and the fire thermal synergy effect index, and performing reverse standardization processing on the environmental fire risk adaptability index;

[0021] Based on the standardized comprehensive thermo-dynamic interaction index, the spatial fire interaction complexity index, the fire thermal synergy effect index, and the environmental fire risk adaptability index, constructing a multi-dimensional risk assessment feature vector for each monitoring area;

[0022] Cluster analysis is performed on the multi-dimensional risk assessment feature vectors through a density-based spatial clustering algorithm according to the distance metric function to identify and divide regions with different risk levels.

[0023] The present invention is further configured such that step S4 includes:

[0024] Calculate the comprehensive risk scores of all regions to be patrolled according to the standardized comprehensive thermo-dynamic interaction index, spatial fire interaction complexity index, fire thermal synergy effect index, and environmental fire risk adaptability index;

[0025] Perform threshold judgment on the comprehensive risk scores. When the comprehensive risk score is greater than or equal to the first risk threshold, determine that the region to be patrolled is a high-risk region; when the comprehensive risk score is less than the first risk threshold and greater than or equal to the second risk threshold, determine that the region to be patrolled is a medium-risk region; when the comprehensive risk score is less than the second risk threshold, determine that the region to be patrolled is a low-risk region;

[0026] Sort the high-risk regions in descending order according to the comprehensive risk scores to generate a priority patrol list, automatically patrol the regions to be patrolled according to the priority patrol list, and when a fire is detected, activate the fire extinguishing equipment in the region to extinguish the fire.

[0027] The present invention also provides an intelligent fire extinguishing control system for automatic patrol based on data analysis for implementing the above-mentioned intelligent fire extinguishing method for automatic patrol based on data analysis. The system includes:

[0028] Data acquisition module: Acquire multi-dimensional data through a multi-dimensional data integration platform. The multi-dimensional data includes real-time sensor data and environmental monitoring data, and preprocess the collected data;

[0029] Index calculation module: Calculate multi-dimensional risk assessment indices based on the preprocessed data. The multi-dimensional risk assessment indices include comprehensive thermo-dynamic interaction index, spatial fire interaction complexity index, fire thermal synergy effect index, and environmental fire risk adaptability index;

[0030] Cluster analysis module: Non-linearly standardize the multi-dimensional risk assessment indices, construct multi-dimensional risk assessment feature vectors based on the standardized multi-dimensional risk assessment indices, and perform cluster analysis on all regions to be patrolled according to the distance metric function based on the multi-dimensional risk assessment feature vectors;

[0031] Automatic patrol module: Calculate the comprehensive risk scores for all regions to be patrolled according to the cluster analysis results, generate a priority patrol list according to the comprehensive risk scores, automatically patrol the regions to be patrolled according to the priority patrol list, and when a fire is detected, activate the fire extinguishing equipment in the region to extinguish the fire.

[0032] The present invention provides an intelligent fire extinguishing method and control system for automatic patrol based on data analysis. The method obtains multi-dimensional data through a multi-dimensional data integration platform. The multi-dimensional data includes real-time sensor data and environmental monitoring data, and preprocesses the collected data. Based on the preprocessed data, a multi-dimensional risk assessment index is calculated. The multi-dimensional risk assessment index includes a comprehensive thermo-dynamic interaction index, a spatial fire interaction complexity index, a fire thermo-dynamic synergy index, and an environmental fire risk adaptability index. Non-linear normalization is performed on the multi-dimensional risk assessment index. Based on the normalized multi-dimensional risk assessment index, a multi-dimensional risk assessment feature vector is constructed. According to the distance metric function, cluster analysis is performed on all regions to be patrolled based on the multi-dimensional risk assessment feature vector. According to the results of the cluster analysis, a comprehensive risk score is calculated for all regions to be patrolled. A priority patrol list is generated according to the comprehensive risk score, and the regions to be patrolled are automatically patrolled according to the priority patrol list. When a fire is detected, the fire extinguishing equipment in the area is activated to extinguish the fire. The beneficial effects generated include:

[0033] 1. Comprehensive index evaluation to achieve refined quantification of fire risks: The comprehensive thermo-dynamic interaction index, spatial fire interaction complexity index, fire thermo-dynamic synergy index, and environmental fire risk adaptability index are used to quantify fire risks from four dimensions: thermo-dynamics, spatial interaction, thermo-dynamic synergy, and environmental adaptability respectively. Different from the traditional method that only relies on a single threshold or simple alarm signal, multi-dimensional risk assessment can more precisely depict the key elements and potential risk points in the occurrence and spread of fires, providing a scientific basis for subsequent cluster analysis and patrol strategies.

[0034] 2. Combining cluster analysis and comprehensive risk scoring to achieve priority patrol of high-risk areas: After completing the cluster analysis, a comprehensive risk score is further calculated for all regions to be patrolled, and high, medium, and low risk levels are classified and sorted in descending order based on the score results. Compared with traditional priori or fixed-threshold patrol schemes, the method of the present invention has adaptability and dynamics: it can automatically determine newly emerging high-risk areas based on real-time data changes and promptly place them in the priority patrol list, thereby improving the pertinence and efficiency of patrols.

[0035] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of this application more obvious and understandable, the specific embodiments of this application are specifically given below. Description of the Drawings

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings. In the accompanying drawings:

[0037] Figure 1 It is a flowchart of an intelligent fire extinguishing method based on data analysis for automatic patrol in an exemplary embodiment of the present invention;

[0038] Figure 2 It is a schematic structural diagram of an intelligent fire extinguishing control system based on data analysis for automatic patrol in an exemplary embodiment of the present invention. Detailed implementation manners

[0039] The following will illustrate the implementation manners of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for explaining the present invention and not for limiting the protection scope of the present invention.

[0040] It should be noted that the illustrations provided in the following embodiments only schematically illustrate the basic concept of the present invention. Therefore, only the components related to the present invention are shown in the drawings, rather than being drawn according to the number, shape, and size of the components in actual implementation. The type, quantity, and ratio of each component in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0041] In the following description, a large number of details are explored to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0042] Embodiment 1

[0043] An intelligent fire extinguishing method based on data analysis for automatic patrol, as Figure 1 shown, includes:

[0044] S1: Obtain multi-dimensional data through a multi-dimensional data integration platform. The multi-dimensional data includes real-time sensor data and environmental monitoring data, and preprocess the collected data;

[0045] S2: Calculate a multi-dimensional risk assessment index based on the pre-processed data. The multi-dimensional risk assessment index includes a comprehensive thermal dynamic interaction index, a spatial fire interaction complexity index, a fire thermal synergy effect index, and an environmental fire risk adaptability index;

[0046] S3: Perform non-linear standardization on the multi-dimensional risk assessment index, construct a multi-dimensional risk assessment feature vector based on the standardized multi-dimensional risk assessment index, and perform cluster analysis on all regions to be patrolled according to the distance metric function based on the multi-dimensional risk assessment feature vector;

[0047] S4: Calculate the comprehensive risk scores for all regions to be patrolled according to the cluster analysis results, generate a priority patrol list according to the comprehensive risk scores, automatically patrol the regions to be patrolled according to the priority patrol list, and activate the fire extinguishing equipment in the region when a fire is detected to extinguish the fire.

[0048] The present invention is further configured such that the multi-dimensional data includes temperature, temperature gradient, radiant heat flux, air flow velocity, air flow direction, combustible gas concentration, relative wind direction angle, convective heat transfer rate, radiant heat transfer rate, total air volume of air circulation, and total heat load; the collected data is cleaned, formatted, and corrected to ensure the accuracy and consistency of the data, and missing values, outliers, and data noise are processed. Specifically, temperature is a basic physical quantity for measuring the thermal energy state of the environment, representing the change in the thermal state within the monitored area, and is one of the important parameters for evaluating fire risks. It is obtained through temperature sensors distributed at key positions in the area to be patrolled. The sensors measure the temperature in real time and transmit the data to the multi-dimensional data integration platform through wired or wireless communication modules; the temperature gradient represents the rate of change of the ambient temperature in space, reflecting the diffusion speed and direction of heat in space, and is an important indicator for analyzing the dynamic spread of fires. Multiple temperature sensors are arranged in the area to be patrolled to form a temperature measurement network. Using the temperature difference and physical distance between adjacent sensors, the temperature gradient vector is calculated using the finite difference method or interpolation algorithm, and the temperature gradient is updated regularly based on the latest temperature data to reflect the real-time dynamics of fire spread; the radiant heat flux represents the radiant heat energy per unit area and is one of the main ways of heat transfer in fires, having an important impact on fire spread. Radiant heat flux meters or infrared radiation sensors are arranged in the area to be patrolled to measure the radiant heat intensity in the area to be patrolled. The sensors measure the radiant heat flux in real time and transmit the data to the data integration platform through the communication module; the air flow velocity represents the flow velocity of air in space, affecting the spread speed and direction of the fire, and is an important parameter for evaluating fire risks. An anemometer is used to measure the air flow velocity, including a hot-wire anemometer and an ultrasonic anemometer; the air flow direction represents the direction of air flow, affecting the spread path of the fire and the propagation direction of smoke, and is an important parameter for fire risk assessment. A wind vane or a multi-dimensional wind speed sensor is used to measure the air flow direction; the combustible gas concentration represents the concentration of combustible gases in the environment and is an important parameter for evaluating fire and explosion risks, including methane, propane, and carbon monoxide. Combustible gas sensors are used to detect the concentration of combustible gases in the environment, including MQ series sensors; the relative wind direction angle represents the angle between the wind direction and the fire occurrence point or a predetermined direction, affecting the spread path of the fire and the diffusion direction of smoke, and the relative wind direction angle is calculated through multi-point wind direction sensors or by combining air flow velocity and direction data; the convective heat transfer rate represents the rate of thermal energy transferred by convection and is an important way of heat transfer in fires, having a significant impact on fire spread. Q conv = h·A·ΔT, where Q conv is the convective heat transfer rate, h is the convective heat transfer coefficient, A is the heat transfer area, and ΔT is the temperature difference. Based on real-time air flow velocity, temperature difference, and heat transfer area data, the convective heat transfer rate is dynamically calculated as a key parameter for fire risk assessment; the radiant heat transfer rate represents the rate of thermal energy transferred by radiation and acts together with the convective heat transfer rate to affect fire heat transfer. Q rad= σ·∈·B·T 4 , Q rad The radiation heat transfer rate, σ is the Stefan-Boltzmann constant, ∈ is the emissivity, B is the area affected by radiation, T is the temperature. Based on the radiation heat transfer rate formula, combined with real-time temperature and emissivity data, the radiation heat transfer rate is calculated; the total air flow rate represents the total air flow in the space per unit time, which affects the oxygen supply and heat removal ability in a fire; the total heat load represents the total heat released by all equipment, personnel and other heat sources in the space, which directly affects the initiation and development of a fire and is an important parameter for comprehensive fire risk assessment. It is approximately calculated through the energy consumption in the area. By monitoring the power consumption of various electrical and mechanical equipment in the building and accumulating it, the total heat load is obtained; the accurate collection and high-quality preprocessing of multi-dimensional data are the basis for achieving accurate fire risk assessment and efficient automatic inspection. By collecting multi-dimensional data including temperature, temperature gradient, radiation heat flux, air flow velocity, air flow direction, combustible gas concentration, relative wind direction angle, convective heat transfer rate, radiation heat transfer rate, total air flow rate and total heat load, and cleaning, formatting and correcting them, it can ensure the accurate and reliable calculation of subsequent multi-dimensional risk assessment indexes.

[0049] The present invention is further configured to calculate a multi-dimensional risk assessment index based on the preprocessed data, including:

[0050] Calculate a comprehensive thermo-dynamic interaction index according to temperature, radiation heat flux, temperature gradient and air flow velocity; the present invention is further configured that the calculation logic of the comprehensive thermo-dynamic interaction index is: Wherein, CTII is the comprehensive thermo-dynamic interaction index, T max is the highest temperature in the area to be inspected, Q rad is the radiation heat flux, is the temperature gradient, V air is the air flow velocity, and K1 is the temperature-air flow interaction sensitivity constant. Specifically, the comprehensive thermo-dynamic interaction index (CTII) aims to comprehensively evaluate the impact of thermo-dynamic interaction in the area to be inspected on fire risk. This index combines four key parameters: the highest temperature, radiation heat flux, temperature gradient and air flow velocity, and models their mutual relationship through a non-linear function to quantify the potential risk of heat energy transfer and fire spread in the area. The numerator T max ·Q rad combines the highest temperature and radiation heat flux in the area, reflecting the concentration and radiation intensity of heat energy in this area, and serving as a basic quantitative index for fire risk. Represents the dot product of the temperature gradient and the air flow velocity, quantifying the heat transfer efficiency through the air flow. The larger the dot product, the more effective the heat transfer through the air flow, and the faster the fire spreads. K1 is a dimensionless constant used to adjust the influence degree of the temperature gradient and the air flow velocity on the comprehensive thermo-dynamic interaction index. The selection is based on historical data fitting or expert experience, usually between 1 and 10. The denominator Non-linearly scales the product of the temperature gradient and the air flow velocity through an exponential function, enhancing the sensitivity of the comprehensive thermo-dynamic interaction index to high-risk factors, and ensuring that the comprehensive thermo-dynamic interaction index tends to T when the temperature gradient and the air flow velocity are extremely low max ·Q rad while at high temperature gradients and high air flow velocities, the exponential term approaches zero, making the growth of the comprehensive thermo-dynamic interaction index tend to T max ·Q rad The comprehensive thermo-dynamic interaction index comprehensively reflects the concentration and diffusion dynamics of thermal energy in the region by taking the product of the highest temperature and the radiant heat flux as a reference and combining the non-linear adjustment of the temperature gradient and the air flow velocity. This design enables the comprehensive thermo-dynamic interaction index to significantly increase the index value under the combined action of high temperature, high radiant heat flux, rapid heat diffusion and high air flow velocity, and accurately capture the key characteristics of fire risk.

[0051] Calculate the spatial fire interaction complexity index according to the combustible gas concentration, relative wind direction angle and temperature; The present invention is further set that the calculation logic of the spatial fire interaction complexity index is: where SFICI is the spatial fire interaction complexity index, N is the number of areas to be patrolled, and are the combustible substance concentrations of the i-th and j-th areas to be patrolled, θ ij is the relative wind direction angle of the i-th and j-th areas to be patrolled, d ij is the relative distance between the i-th and j-th areas to be patrolled, T i and T j are the highest temperatures of the i-th and j-th areas to be patrolled, T threshold is the temperature threshold for measuring temperature gain. Specifically, the spatial fire interaction complexity index (SFICI) aims to quantify the interaction and complexity of fire risks between areas to be patrolled. This index evaluates the interactive influence and diffusion potential of fire risks between areas by comprehensively considering factors such as combustible substance concentration, relative wind direction angle, temperature and relative distance in multiple areas. Specifically, SFICI uses a double summation method to analyze the interaction complexity between each pair of areas and combines the temperature gain factor to quantify the overall fire risk complexity. The outer summation ensures that each pair of areas to be patrolled is only calculated once, avoiding double counting. The temperature gain factor Reflecting the enhancing effect of temperature on the interaction of fire risks between regions, higher temperature combinations will significantly increase the SFICI value, indicating that the interaction of fire risks is more complex and severe. By introducing the relative wind direction angle and the temperature gain factor, SFICI can more accurately predict the propagation path and speed of the fire between different regions. This helps to identify high-risk area interactions in advance, take targeted prevention and control measures, and prevent the spread of fire.

[0052] Calculate the fire thermal synergy effect index according to the air flow velocity, air flow direction, convective heat transfer rate, and radiative heat transfer rate; the present invention is further configured such that the calculation logic of the fire thermal synergy effect index is: Among them, FTSSEI is the fire thermal synergy effect index, Q conv is the convective heat transfer rate, Q rad is the radiative heat transfer rate, φ wind is the air flow direction, C fuel is the concentration of combustible substances, V air is the air flow velocity, V critical is the critical air flow velocity threshold. Specifically, the fire thermal synergy effect index (FTSSEI) aims to quantify the impact of the synergistic effects of air flow velocity, air flow direction, convective heat transfer rate, and radiative heat transfer rate on fire risk during a fire. By comprehensively analyzing these thermal parameters, FTSSEI provides an indicator to measure the synergistic effect of fire heat energy transfer and diffusion, helping the intelligent fire extinguishing system to more accurately assess fire risk, optimize fire extinguishing strategies. The calculation logic, through the composition of the numerator and denominator and the introduction of the exponential function, comprehensively reflects the synergistic effect of heat energy transfer within the region and the driving effect of air flow on fire spread, thereby providing a refined quantitative basis for fire risk assessment. By comprehensively considering the convective heat transfer rate, radiative heat transfer rate, the sine value of the air flow direction, and the air flow velocity, the fire thermal synergy effect between regions can be quantified in detail. This multi-dimensional assessment method is more comprehensive than traditional single-index assessments and provides a more accurate basis for fire risk analysis; by introducing the sine value of the air flow direction and the exponential adjustment of the air flow velocity, FTSSEI can more accurately predict the propagation path and speed of the fire between different regions. This helps to identify high-risk area interactions in advance, take targeted prevention and control measures, and prevent the spread of fire. The calculation of FTSSEI depends on real-time monitoring data and can dynamically reflect changes in fire risk. When significant changes occur in the convective heat transfer rate, radiative heat transfer rate, air flow velocity, or air flow direction within the monitored area, FTSSEI quickly reflects an increase in risk, supporting the system to timely adjust inspection and fire extinguishing strategies and improve the efficiency and effectiveness of fire emergency response.

[0053] Calculate the environmental fire risk adaptability index based on the total air circulation volume and the total heat load; the present invention is further configured such that the calculation logic of the environmental fire risk adaptability index is: Wherein, EFRAI is the environmental fire risk adaptability index, W air is the total air circulation volume, S heat is the total heat load, L struct is the building structure fire resistance rating score, B fireproof is the fireproof material coverage rate, D access is the number of safety exits, J is the total number of fire spread paths, T j is the highest temperature of the area to be patrolled corresponding to the j-th fire spread path, K is the number of fire prevention obstruction factors on the path, including firewalls and fire doors, is the k-th fire prevention obstruction factor on the j-th fire spread path, The obstruction effectiveness score of the k-th fire prevention obstruction factor on the j-th fire spread path. Specifically, the environmental fire risk adaptability index (EFRAI) aims to comprehensively evaluate the adaptation and control ability of air circulation and heat load in the building environment to fire risk. By comprehensively considering multiple key parameters such as the total air circulation volume, total heat load, building structure fire resistance rating, fireproof material coverage rate, number of safety exits, and temperature and fire prevention obstruction factors on the fire spread path, EFRAI provides an index to quantify the environment's adaptability to fire risk. The above calculation logic, through the composition of the numerator and denominator, comprehensively reflects the balance between air circulation and heat load in the building environment and the fire prevention measure effect on the fire spread path, thus providing a refined quantitative basis for fire risk adaptability. The building structure fire resistance rating score L struct According to national or industry standards, score the fire resistance performance of the building structure, comprehensively considering building materials, structural design and fire prevention measures, with a value range of [1, 10]. The fire prevention obstruction factor Scores are determined based on factors such as the type of fire prevention obstruction factor, installation quality, and maintenance status, usually determined by fire experts or through historical fire data analysis, with a value range of (0, 10]. By integrating multi-dimensional data such as the total air flow rate, total heat load, fire resistance rating of the building structure, coverage rate of fireproof materials, number of safety exits, and temperature and fire prevention obstruction factors on the fire diffusion path, the system can comprehensively and meticulously capture the key elements affecting fire risk adaptability in the building environment. This multi-dimensional data fusion not only makes up for the deficiencies of a single data source but also enhances the system's adaptability to complex fire environments, improving the comprehensiveness and accuracy of the overall fire risk assessment. By real-time collecting and processing multi-dimensional data, the system can promptly capture dynamic changes in the fire environment, such as changes in air flow rate, increase in heat load, and sharp rise in temperature. This enables the system to have the ability of real-time monitoring and rapid response, being able to identify potential risks immediately in the early stage of a fire, adjust fire prevention and control strategies in a timely manner, significantly improving the efficiency and effectiveness of fire emergency response and reducing the possibility and losses of fire spread.

[0054] The present invention is further configured such that step S3 includes:

[0055] Perform non-linear standardization on the comprehensive thermal dynamic interaction index, spatial fire interaction complexity index, and fire thermal synergy effect index, and perform reverse standardization on the environmental fire risk adaptability index; specifically, non-linear standardization is for high-risk indicators, including the comprehensive thermal dynamic interaction index, spatial fire interaction complexity index, and fire thermal synergy effect index. The larger the value, the higher the risk; reverse standardization is for the environmental fire risk adaptability index. The larger the value, the lower the risk. Adopt the reverse standardization method to map the value after reverse standardization processing to a unified comparison range where the larger the value, the higher the risk;

[0056] Based on the standardized comprehensive thermal dynamic interaction index, spatial fire interaction complexity index, fire thermal synergy effect index, and environmental fire risk adaptability index, construct a multi-dimensional risk assessment feature vector for each monitoring area; use the four standardized indicators as different dimensions of the feature vector respectively, and a multi-dimensional risk feature vector with four components can be obtained for each monitoring area;

[0057] Based on the distance metric function, cluster analysis is performed on the multi-dimensional risk assessment feature vectors through a density-based spatial clustering algorithm to identify and divide regions with different risk levels. Specifically, a distance metric function, including the Euclidean distance, is used to cluster the multi-dimensional risk feature vectors of all monitored regions. Through cluster analysis, regions with similar risk features are identified and divided into different risk levels. At this time, only different risk levels can be obtained, and the magnitudes of the risk levels are not clear. All feature vectors after standardization or inverse standardization can be updated in real time; combined with the density-based spatial clustering algorithm, when the monitored data changes (such as a sudden increase in the fire risk in some regions), the clustering results can be recalculated in a timely manner, quickly identifying high-risk clusters and paying attention to and preventing them. Through a unified and reasonable standardization mapping method, each index has been smoothed or mapped to an appropriate interval before cluster analysis, reducing the analysis deviation caused by outliers and dimensional differences, making the clustering more stable.

[0058] The present invention is further configured such that step S4 includes:

[0059] Calculate the comprehensive risk score of all regions to be patrolled according to the standardized comprehensive thermo-dynamic interaction index, spatial fire interaction complexity index, fire thermo-dynamic synergistic effect index, and environmental fire risk adaptability index; specifically, in step S3, non-linear standardization processing has been performed on the comprehensive thermo-dynamic interaction index, spatial fire interaction complexity index, and fire thermo-dynamic synergistic effect index, and inverse standardization processing has been performed on the environmental fire risk adaptability index. Each monitored region has four standardized index values, which can be combined into a multi-dimensional risk assessment feature vector. The four standardized indexes are combined in a weighted manner to obtain the comprehensive risk score of each monitored region, which is used to uniformly measure the fire risk level;

[0060] Perform threshold judgment on the comprehensive risk score. When the comprehensive risk score is greater than or equal to the first risk threshold, it is determined that the region to be patrolled is a high-risk region; when the comprehensive risk score is less than the first risk threshold and greater than or equal to the second risk threshold, it is determined that the region to be patrolled is a medium-risk region; when the comprehensive risk score is less than the second risk threshold, it is determined that the region to be patrolled is a low-risk region; specifically, by presetting two thresholds: the first risk threshold and the second risk threshold, the comprehensive risk score interval is divided into three levels: high risk, medium risk, and low risk. The first risk threshold and the second risk threshold here are obtained through analysis of historical data; each region can be determined to be the corresponding risk level according to the comprehensive risk score falling into the corresponding interval;

[0061] Sort the high-risk areas in descending order according to the comprehensive risk score to generate a priority list of areas to be patrolled. Automatically patrol the areas to be patrolled according to the priority list. When a fire is detected, activate the fire extinguishing equipment in the area to extinguish the fire. Specifically, by integrating four standardized indicators in different dimensions through the comprehensive risk score, it is convenient to intuitively compare the fire risks between areas on the same numerical scale. At the same time, the complementarity between indicators is reflected, thus improving the ability and accuracy of capturing fire risks. A two-level threshold is used for fire risk zoning, which can achieve a three-level management mode of high, medium, and low. The priority attention to high-risk areas can ensure the centralized allocation of inspection equipment and fire protection resources to the most urgent areas at critical moments, improving the prevention and control efficiency; as the environmental data, fire factors, or building usage conditions change, the system can calculate the comprehensive risk score in real time and re-judge the risk level to ensure a rapid response to sudden fires; after the high-risk list is refreshed, ensure that the most dangerous areas are patrolled first through descending order. When the inspection equipment confirms a fire, the system immediately activates the fire extinguishing device and issues an alarm to contain the spread of the fire with the shortest path and the least time, effectively reducing property and personnel losses.

[0062] Embodiment 2

[0063] Please refer to Figure 2 , an exemplary intelligent fire extinguishing control system for automatic patrol based on data analysis includes:

[0064] Data acquisition module: Obtain multi-dimensional data through a multi-dimensional data integration platform. The multi-dimensional data includes real-time sensor data and environmental monitoring data, and preprocess the collected data;

[0065] Index calculation module: Based on the preprocessed data, calculate a multi-dimensional risk assessment index. The multi-dimensional risk assessment index includes a comprehensive thermo-dynamic interaction index, a spatial fire interaction complexity index, a fire thermo-dynamic synergy index, and an environmental fire risk adaptability index;

[0066] Clustering analysis module: Perform non-linear standardization on the multi-dimensional risk assessment index, construct a multi-dimensional risk assessment feature vector based on the standardized multi-dimensional risk assessment index, and perform clustering analysis on all areas to be patrolled according to the distance metric function based on the multi-dimensional risk assessment feature vector;

[0067] Automatic inspection module: According to the clustering analysis results, calculate the comprehensive risk score for all areas to be patrolled, generate a priority list of areas to be patrolled according to the comprehensive risk score, automatically patrol the areas to be patrolled according to the priority list, and when a fire is detected, activate the fire extinguishing equipment in the area to extinguish the fire.

[0068] It should be noted that an intelligent fire extinguishing control system based on data analysis for automatic patrol provided in the above embodiments and an intelligent fire extinguishing method based on data analysis for automatic patrol provided in the above embodiments belong to the same concept. The specific manners in which each module and unit perform operations have been described in detail in the method embodiments, and will not be elaborated herein. In practical applications, the intelligent fire extinguishing control system based on data analysis for automatic patrol provided in the above embodiments can, according to needs, allocate the above functions to different functional modules, that is, divide the internal structure of the system into different functional modules to complete all or part of the functions described above. No limitation is imposed herein either.

[0069] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that contains one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0070] It should be understood that the term “and / or” in this article is merely a description of the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character “ / ” in this article generally represents an “or” relationship between the associated objects before and after, but it may also represent an “and / or” relationship, which can be specifically understood with reference to the context before and after.

[0071] In this application, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following" or a similar expression means any combination of these items, including any combination of single item or plural items. For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0072] It should be understood that in various embodiments of this application, the magnitudes of the sequence numbers of the above - mentioned processes do not mean the sequence of execution. The execution sequence of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of this application.

[0073] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0074] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0075] In several embodiments provided in this application, it should be understood that the disclosed system can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the devices or units can be in an electrical, mechanical, or other form.

[0076] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0077] In addition, in each embodiment of the present application, each functional unit may be integrated into one processing unit, may exist physically alone for each unit, or two or more units may be integrated into one unit.

[0078] If the above-mentioned function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc.

[0079] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An intelligent fire extinguishing method for automatic patrol based on data analysis, characterized in that, Including: S1: Obtain multi-dimensional data through a multi-dimensional data integration platform. The multi-dimensional data includes real-time sensor data and environmental monitoring data, and preprocess the collected data. S2: Calculate a multi-dimensional risk assessment index based on the preprocessed data. The multi-dimensional risk assessment index includes a comprehensive thermo-dynamic interaction index, a spatial fire interaction complexity index, a fire thermo-dynamic synergistic effect index, and an environmental fire risk adaptability index. S3: Perform non-linear standardization on the multi-dimensional risk assessment index, construct a multi-dimensional risk assessment feature vector based on the standardized multi-dimensional risk assessment index, and perform cluster analysis on all regions to be patrolled according to the distance metric function based on the multi-dimensional risk assessment feature vector. S4: Calculate the comprehensive risk score for all regions to be patrolled according to the cluster analysis result, generate a priority patrol list based on the comprehensive risk score, automatically patrol the regions to be patrolled according to the priority patrol list, and when a fire is detected, activate the fire extinguishing equipment in the area to extinguish the fire.

2. The automatic patrol intelligent fire extinguishing method based on data analysis according to claim 1, characterized in that, The multi-dimensional data includes temperature, temperature gradient, radiant heat flux, air flow velocity, air flow direction, combustible gas concentration, relative wind direction angle, convective heat transfer rate, radiative heat transfer rate, total air volume of air circulation, and total heat load; clean, format, and correct the collected data to ensure the accuracy and consistency of the data, and handle missing values, outliers, and data noise.

3. An intelligent fire extinguishing method based on data analysis for automatic patrol according to claim 2, characterized in that, Based on the preprocessed data, calculate the multi-dimensional risk assessment index, including: Calculate the comprehensive thermo-dynamic interaction index according to temperature, radiant heat flux, temperature gradient, and air flow velocity. Calculate the spatial fire interaction complexity index according to combustible gas concentration, relative wind direction angle, and temperature. Calculate the fire thermo-dynamic synergistic effect index according to air flow velocity, air flow direction, convective heat transfer rate, and radiative heat transfer rate. Calculate the environmental fire risk adaptability index according to the total air volume of air circulation and the total heat load.

4. An intelligent fire extinguishing method for automatic patrol based on data analysis according to claim 3, characterized in that The calculation logic of the comprehensive thermo-dynamic interaction index is as follows: Among them, CTII is the comprehensive thermo-dynamic interaction index, T max is the highest temperature in the area to be patrolled, Q rad is the radiant heat flux, is the temperature gradient, V air is the air flow velocity, and K1 is the temperature-air flow interaction sensitivity constant.

5. The automatic patrol intelligent fire extinguishing method based on data analysis according to claim 3, characterized in that, The calculation logic of the spatial fire interaction complexity index is as follows: where SFICI is the spatial fire interaction complexity index, N is the number of areas to be patrolled, and are the combustible substance concentrations of the i-th and j-th areas to be patrolled, θ ij is the relative wind direction angle of the i-th and j-th areas to be patrolled, d ij is the relative distance of the i-th and j-th areas to be patrolled, T i and T j are the maximum temperatures of the i-th and j-th areas to be patrolled, T threshold is the temperature threshold used to measure the temperature gain.

6. The automatic patrol intelligent fire extinguishing method based on data analysis according to claim 3, characterized in that The calculation logic of the fire thermal synergy effect index is as follows: Among them, FTSSEI is the fire thermal synergy effect index, Q conv is the convective heat transfer rate, Q rad is the radiative heat transfer rate, φ wind is the air flow direction, C fuel is the concentration of combustible substances, V air is the air flow velocity, V critical is the critical air flow velocity threshold.

7. An intelligent fire extinguishing method for automatic patrol based on data analysis according to claim 3, characterized in that The calculation logic of the environmental fire risk adaptability index is as follows: Among them, EFRAI is the environmental fire risk adaptability index, W air is the total air volume of air circulation, S heat is the total heat load, L struct is the fire resistance rating score of the building structure, B fireproof is the coverage rate of fireproof materials, D access is the number of safety exits, J is the total number of fire spread paths, T j is the highest temperature of the area to be patrolled corresponding to the j-th fire spread path, K is the number of fire prevention obstacle factors on the path, including firewalls and fire doors, is the k-th fire prevention obstacle factor on the j-th fire spread path, The obstacle effectiveness score of the k-th fire prevention obstacle factor on the j-th fire spread path.

8. The automatic fire extinguishing method for intelligent patrol based on data analysis according to claim 3, characterized in that, Step S3 includes: Perform non-linear standardization processing on the comprehensive thermo-dynamic interaction index, the spatial fire interaction complexity index, and the fire thermo-dynamic synergistic effect index, and perform reverse standardization processing on the environmental fire risk adaptability index. Based on the standardized comprehensive thermo-dynamic interaction index, the spatial fire interaction complexity index, the fire thermo-dynamic synergistic effect index, and the environmental fire risk adaptability index, construct a multi-dimensional risk assessment feature vector for each monitoring area. According to the distance metric function, perform cluster analysis on the multi-dimensional risk assessment feature vector through a density-based spatial clustering algorithm to identify and divide regions with different risk levels.

9. An intelligent fire extinguishing method for automatic patrol based on data analysis according to claim 8, characterized in that, Step S4 includes: Calculate the comprehensive risk score for all regions to be patrolled according to the standardized comprehensive thermo-dynamic interaction index, the spatial fire interaction complexity index, the fire thermo-dynamic synergistic effect index, and the environmental fire risk adaptability index. Perform a threshold judgment on the comprehensive risk score. When the comprehensive risk score is greater than or equal to the first risk threshold, determine that the region to be patrolled is a high-risk region; when the comprehensive risk score is less than the first risk threshold and greater than or equal to the second risk threshold, determine that the region to be patrolled is a medium-risk region; when the comprehensive risk score is less than the second risk threshold, determine that the region to be patrolled is a low-risk region. Sort the high-risk areas in descending order according to the comprehensive risk score to generate a priority list of areas to be patrolled, and automatically patrol the areas to be patrolled according to the priority list of areas to be patrolled. When a fire is detected, activate the fire extinguishing equipment in the area to extinguish the fire.

10. An automatic patrol and intelligent fire extinguishing control system based on data analysis, which is used to implement an automatic patrol and intelligent fire extinguishing method based on data analysis according to any one of claims 1-9, and is characterized in that, Including: Data acquisition module: Obtain multi-dimensional data through a multi-dimensional data integration platform. The multi-dimensional data includes real-time sensor data and environmental monitoring data, and preprocess the collected data; Index calculation module: Calculate a multi-dimensional risk assessment index based on the preprocessed data. The multi-dimensional risk assessment index includes a comprehensive thermal dynamic interaction index, a spatial fire interaction complexity index, a fire thermal synergy effect index, and an environmental fire risk adaptability index; Cluster analysis module: Non-linearly standardize the multi-dimensional risk assessment index, construct a multi-dimensional risk assessment feature vector based on the standardized multi-dimensional risk assessment index, and perform cluster analysis on all areas to be patrolled according to the distance metric function based on the multi-dimensional risk assessment feature vector; Automatic patrol module: Calculate the comprehensive risk score for all areas to be patrolled according to the cluster analysis result, generate a priority list of areas to be patrolled according to the comprehensive risk score, automatically patrol the areas to be patrolled according to the priority list of areas to be patrolled, and when a fire is detected, activate the fire extinguishing equipment in the area to extinguish the fire.

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