Intelligent fire extinguishing management system and method based on multi-sensor fusion

Abstract

The invention provides an intelligent fire extinguishing management system and method based on multi-sensor fusion, and belongs to the technical field of fire extinguishing management.The method comprises the steps that temperature data are collected to analyze reburning risk area positioning and reburning risk area temperature changes; collecting smoke concentration data to analyze smoke change of the recombustion risk area; fire scene residue data are collected to analyze the influence of fire scene residues on reburning; building data of the fire scene are collected to analyze the influence of the building on reburning; the recombustion probability is analyzed by combining the temperature change, the smoke change, the influence of fire scene residues on recombustion and the influence of buildings on recombustion, and whether fire extinguishing succeeds or not is judged according to the recombustion probability; according to the method, the re-combustion probability is comprehensively analyzed by combining the fire scene temperature change, the smoke change, the residue influence and the building influence, the influence of different building scenes and different residue conditions on re-combustion is considered, the re-combustion probability of the re-combustion risk area is accurately monitored, and the fire extinguishing management efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of fire extinguishing management, and in particular to an intelligent fire extinguishing management system and method based on multi-sensor fusion. Background Art

[0002] The re-ignition phenomenon refers to the phenomenon that in a closed space with poor ventilation, when the fire has been maintained for a period of time, the oxygen concentration in the air decreases, and the incompletely burned carbon particles and carbon monoxide (combustible gas) produced by the combustion accumulate. When enough air is introduced into the fire scene, it mixes with the concentrated combustible gas and reaches the combustion limit. Under the instant ignition of the original fire source, the combustion occurs violently, and an explosive flame spreads and rushes out of the air toward the air inlet. This phenomenon usually occurs in the later stage of the fire. Therefore, in fire extinguishing management, it is of great significance to judge whether there will be a re-ignition. In the existing related technologies, the re-ignition judgment in fire extinguishing management is usually based on the re-ignition status of the monitored area and the analysis of the re-ignition risk of combustible data. There is a lack of solutions that comprehensively analyze the re-ignition probability by combining the changes in fire temperature, smoke, residue effects and building effects. As a result, it is impossible to analyze the re-ignition risk according to the different residue conditions in different building scenarios, resulting in inaccurate re-ignition risk judgment; In order to solve the above problems, the present invention provides an intelligent fire extinguishing management system and method based on multi-sensor fusion. Summary of the invention

[0003] In view of this, the present invention provides an intelligent fire extinguishing management system and method based on multi-sensor fusion, the present invention collects temperature data to analyze the positioning of the re-ignition risk area and the temperature change of the re-ignition risk area; collects smoke concentration data to analyze the smoke change in the re-ignition risk area; collects fire residue data to analyze the influence of the fire residue on the re-ignition; collects building data of the fire scene to analyze the influence of the building on the re-ignition; combines the temperature change, smoke change, the influence of the fire residue on the re-ignition and the influence of the building on the re-ignition to analyze the re-ignition probability, and judges whether the fire is successfully extinguished according to the re-ignition probability; the present invention combines the fire scene temperature change, smoke change, residue influence and building influence to comprehensively analyze the re-ignition probability, takes into account the influence of different building scenes and different residue conditions on the re-ignition, realizes accurate monitoring of the re-ignition probability in the re-ignition risk area, and improves the efficiency of fire extinguishing management.

[0004] To achieve the above object, the present invention provides the following technical solutions: The intelligent fire extinguishing management method based on multi-sensor fusion includes the following specific steps: S1. Collect temperature data to analyze the location of the re-ignition risk area and the temperature changes in the re-ignition risk area; S2. Collect smoke concentration data and analyze smoke changes in re-ignition risk areas; S3, collect fire residue data and analyze the impact of fire residue on re-ignition; S4. Collect building data at the fire scene to analyze the impact of buildings on re-ignition; S5. Analyze the probability of re-ignition based on the changes in temperature, smoke, the impact of fire residues on re-ignition, and the impact of buildings on re-ignition, and determine whether the fire is successfully extinguished based on the probability of re-ignition.

[0005] Optionally, S1 includes the following specific steps: S101, collecting temperature data of the fire scene through a temperature sensor; S102, comparing the temperature data with a preset fire extinguishing temperature threshold, and screening out areas exceeding the fire extinguishing temperature threshold as re-ignition risk areas; S103, importing the temperature data into a temperature change abnormal value calculation formula to calculate the temperature change abnormal value of the re-ignition risk area, the temperature change abnormal value calculation formula is: ; in, is the temperature collected at time t, is the temperature collected at time t-1, is the temperature collected at the initial moment, is the collection time of temperature data, is the time integral.

[0006] Optionally, S2 includes the following specific steps: S201, collecting smoke concentration data of the re-ignition risk area through a smoke sensor; S202, importing the smoke density data into a calculation formula for abnormal value of smoke density change to calculate the abnormal value of smoke density change in the re-ignition risk area, wherein the calculation formula for abnormal value of smoke density change is: ; in, is the smoke concentration collected at time t, is the smoke concentration collected at time t-1, is the smoke concentration collected at the initial moment, is the collection time of smoke density data, is the time integral.

[0007] Optionally, S3 includes the following specific steps: S301, collecting fire scene residue data, wherein the fire scene residue data includes residue type, residue volume, residue ignition point and residue area location; S302, importing the residue area positioning and the re-ignition risk area positioning into the overlap rate calculation formula to calculate the overlap rate of the residue area and the re-ignition risk area, the overlap rate calculation formula is: ; in, is the lower left corner abscissa of the residue area, is the upper right corner abscissa of the residue area, is the lower left corner ordinate of the residue area, is the upper right corner ordinate of the residue area, is the length of the residue region, is the width of the residue region, is the area of ​​the residue region, is the lower left corner abscissa of the resurgence risk area, is the upper right corner horizontal coordinate of the resurgence risk area, is the lower left ordinate of the re-ignition risk area, is the upper right corner ordinate of the resurgence risk area, is the length of the re-ignition risk area, is the width of the re-ignition risk area, is the area of ​​the re-ignition risk area, It is the overlapping area of ​​the residue area and the re-ignition risk area; The overlap area is obtained according to the overlap area calculation formula, which is: ; in, is the minimum value of the two coordinates, is the maximum value of the two coordinates, when or hour, The value is 0, which means that there is no overlap between the residue area and the re-ignition risk area; S303, the residue volume, residue ignition point and overlap rate are introduced into the residue impact value calculation formula to calculate the impact value of the residue on the re-ignition, and the residue impact value calculation formula is: ; in, is the residue influence value of the ith residue region, is the overlap rate between the ith residue area and the re-ignition risk area, is the residual volume of the ith residual region, is the total volume of the fire residues, is the residue ignition point of the ith residue region, is the temperature of the re-ignition risk area closest to the i-th residue area, It is an exponential function with the natural constant e as its base.

[0008] Optionally, S4 includes the following specific steps: S401, collecting building data of the fire scene, wherein the building data includes building material temperature and wind speed in the building; S402, introducing the building material temperature, the building material ignition point and the wind speed in the building into the building impact value calculation formula to calculate the impact value of the building on the re-ignition, the building impact value calculation formula is: ; in, is the building material decomposition coefficient, is the current temperature collected, Ignition of building materials, is the reaction order of building materials, is the standard decomposition rate, is the building ventilation volume at time t, is the collection time of building ventilation volume, is the standard ventilation volume per unit time, is the time integral, is an exponential function with the natural constant e as base; The building ventilation volume is obtained according to the building ventilation volume calculation formula, which is: ; in, is the building ventilation volume, is the interception coefficient, representing and The ventilation volume, is the temperature coefficient, is the temperature inside the building collected for the jth time, is the building external temperature collected for the jth time, is the temperature difference between the inside and outside of the building, is the wind speed coefficient, is the wind speed collected for the jth time.

[0009] Optionally, S5 includes the following specific steps: S501, the temperature change abnormal value, the smoke concentration change abnormal value, the residue impact value and the building impact value are introduced into the re-ignition probability calculation formula to calculate the re-ignition probability, and the re-ignition probability calculation formula is: ; in, is the temperature change weight, is the smoke variation weight, is the residual impact weight, is the building influence weight, , is the number of residue regions; S502, comparing the re-ignition probability with a preset re-ignition probability threshold; If the re-ignition probability is greater than the preset re-ignition probability threshold, the fire extinguishing failure is judged and feedback is sent to the fire extinguishing management platform; If the re-ignition probability is less than or equal to the preset re-ignition probability threshold, the fire is judged to be extinguished successfully.

[0010] An intelligent fire extinguishing management system based on multi-sensor fusion is used to realize an intelligent fire extinguishing management method based on multi-sensor fusion, including a control unit, a data acquisition unit, a re-ignition risk area positioning unit, a temperature change analysis unit, a smoke change analysis unit, a residue impact analysis unit, a building impact unit, a re-ignition probability analysis unit and a fire extinguishing judgment unit; Optionally, the data acquisition unit is used to collect temperature data, smoke density data, fire scene residue data and fire scene building data; The re-ignition risk area positioning unit is used to compare the temperature data with a preset fire extinguishing temperature threshold, and screen out an area exceeding the fire extinguishing temperature threshold as a re-ignition risk area; The temperature change analysis unit is used to import the temperature data into the temperature change abnormal value calculation formula to calculate the temperature change abnormal value of the re-ignition risk area; The smoke change analysis unit is used to import the smoke concentration data into the smoke concentration change abnormal value calculation formula to calculate the smoke concentration change abnormal value of the re-ignition risk area; The residue impact analysis unit is used to import the residue volume, residue ignition point and overlap rate into the residue impact value calculation formula to calculate the impact value of the residue on the re-ignition; The building impact unit is used to import the building material temperature, the building material ignition point and the wind speed in the building into the building impact value calculation formula to calculate the building impact value on the re-ignition; The re-ignition probability analysis unit is used to import the temperature change abnormal value, the smoke concentration change abnormal value, the residue impact value and the building impact value into the re-ignition probability calculation formula to calculate the re-ignition probability; The fire extinguishing judgment unit is used to compare the re-ignition probability with a preset re-ignition probability threshold; if the re-ignition probability is greater than the preset re-ignition probability threshold, it is judged that the fire extinguishing has failed and feedback is sent to the fire extinguishing management platform; if the re-ignition probability is less than or equal to the preset re-ignition probability threshold, it is judged that the fire extinguishing is successful; Optionally, the control unit is used to control the operation of a data acquisition unit, a re-ignition risk area positioning unit, a temperature change analysis unit, a smoke change analysis unit, a residue impact analysis unit, a building impact unit, a re-ignition probability analysis unit and a fire extinguishing judgment unit.

[0011] A storage medium includes stored instructions, wherein when the instructions are executed, the device where the storage medium is located is controlled to execute the above-mentioned intelligent fire extinguishing management method based on multi-sensor fusion.

[0012] An electronic device includes a memory and one or more instructions, wherein the one or more instructions are stored in the memory and are configured to be executed by one or more processors to perform the above-mentioned intelligent fire extinguishing management method based on multi-sensor fusion.

[0013] The beneficial effects of the present invention are specifically as follows: The present invention collects temperature data to analyze the location of the re-ignition risk area and the temperature change in the re-ignition risk area; collects smoke concentration data to analyze the smoke change in the re-ignition risk area; collects fire residue data to analyze the influence of the fire residue on the re-ignition; collects building data of the fire scene to analyze the influence of the building on the re-ignition; combines the temperature change, smoke change, the influence of the fire residue on the re-ignition and the influence of the building on the re-ignition to analyze the re-ignition probability, and judges whether the fire is successfully extinguished according to the re-ignition probability; the present invention combines the fire scene temperature change, smoke change, the residue influence and the building influence to comprehensively analyze the re-ignition probability, takes into account the influence of different building scenes and different residue conditions on the re-ignition, realizes accurate monitoring of the re-ignition probability in the re-ignition risk area, and improves the efficiency of fire extinguishing management. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 It is a flow chart of the intelligent fire extinguishing management method based on multi-sensor fusion of the present invention; Figure 2 A schematic diagram is constructed for the coordinate system of the residue area and the re-ignition risk area of ​​the present invention; Figure 3 It is a schematic diagram of the overall framework of the intelligent fire extinguishing management system based on multi-sensor fusion of the present invention; Figure 4 The figure is a schematic diagram of an electronic device framework of the present invention. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0017] In the present invention, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article, or apparatus.

[0018] See also Figure 1 The present invention provides an intelligent fire extinguishing management method based on multi-sensor fusion, which includes the following specific steps: S1. Collect temperature data to analyze the location of the re-ignition risk area and the temperature changes in the re-ignition risk area; In this embodiment, S1 includes the following specific steps: S101, collecting temperature data of the fire scene through a temperature sensor; Furthermore, collecting the fire scene temperature can monitor the fire scene environment and evaluate the fire extinguishing effect. The steps for collecting fire scene temperature data through temperature sensors are as follows: select appropriate sensors according to the fire scene environment, such as fire scene-specific thermocouples, infrared temperature sensors, etc., install the sensor at a location that can accurately reflect the fire scene temperature after extinguishing, such as near the fire source or in a key area, connect the output end of the temperature sensor to a data acquisition device, such as a data recorder, computer or other display device, start the data acquisition device, monitor temperature changes in real time, analyze the fire scene temperature trend, and after the acquisition is completed, save the temperature data and record important information such as the sensor location, timestamp, and environmental conditions.

[0019] S102, comparing the temperature data with a preset fire extinguishing temperature threshold, and screening out areas exceeding the fire extinguishing temperature threshold as re-ignition risk areas; Furthermore, in the fire scene after extinguishing, if the temperature of certain areas continues to be much higher than the ambient temperature (for example, exceeding 100°C to 150°C), this may indicate that there are unextinguished fire sources or residual heat in these areas, and there is a high risk of re-ignition. In this embodiment, the preset fire extinguishing temperature threshold value range is 100°C-150°C, and the specific value is set according to the actual situation of the fire scene.

[0020] S103, importing the temperature data into a temperature change abnormal value calculation formula to calculate the temperature change abnormal value of the re-ignition risk area, the temperature change abnormal value calculation formula is: ; in, is the temperature collected at time t, is the temperature collected at time t-1, is the temperature collected at the initial moment, is the collection time of temperature data, is the time integral.

[0021] S2. Collect smoke concentration data and analyze smoke changes in re-ignition risk areas; In this embodiment, S2 includes the following specific steps: S201, collecting smoke concentration data of the re-ignition risk area through a smoke sensor; Furthermore, by collecting smoke density data and analyzing the changing trend of smoke density data over a period of time, we can understand whether the smoke generation in the area is stable or has an increasing trend. If the smoke density is found to be on the rise, it may be a precursor to re-ignition. A warning can be issued when the fire source is just beginning to form or in the early stage of re-ignition, so as to buy precious time for timely fire-fighting measures. After the fire is extinguished, by analyzing the time point of re-ignition, diffusion speed and other records, it can help to review the specific process of the fire re-ignition. The steps for collecting smoke concentration data in the re-ignition risk area through smoke sensors are as follows: select a suitable smoke sensor according to the characteristics of the re-ignition risk area, including the type of sensor (such as ion type, photoelectric type, etc.) and the range, install the smoke sensor at the predetermined location, connect the sensor to the data acquisition system, turn on the data acquisition system, the sensor will monitor the smoke concentration in the air in real time, and transmit the data to the data acquisition system, the data acquisition system records the readings of each sensor, including timestamp, smoke concentration and other information, and analyzes the smoke concentration data to detect any abnormal changes in smoke concentration.

[0022] S202, importing the smoke density data into the smoke density change abnormal value calculation formula to calculate the smoke density change abnormal value of the re-ignition risk area, the smoke density change abnormal value calculation formula is: ; in, is the smoke concentration collected at time t, is the smoke concentration collected at time t-1, is the smoke concentration collected at the initial moment, is the collection time of smoke density data, is the time integral.

[0023] Furthermore, in order to more accurately judge temperature changes and smoke changes, this embodiment can set the same collection start time and collection time interval for the temperature sensor and the smoke sensor, that is, synchronously collect temperature data and smoke concentration data, and the collection time of the temperature data is equal to the collection time of the smoke concentration data.

[0024] S3, collect fire residue data and analyze the impact of fire residue on re-ignition; In this embodiment, S3 includes the following specific steps: S301, collecting fire scene residue data, the fire scene residue data including residue type, residue volume, residue ignition point and residue area location; Furthermore, the deployment of sensors and data collection equipment at the fire scene is as follows: Gas and particle sensors: used to detect chemicals and particles in the air; Infrared thermal imager: used to detect surface temperature and heat distribution; Spectrum analyzer: used for rapid on-site analysis of the chemical composition of solid or liquid residues; Drones / remote control robots: used to enter dangerous areas to collect data. Drones equipped with laser scanners or high-definition cameras are used to perform aerial scans of the fire scene. The data collected by the drones are processed to generate a three-dimensional model and calculate the volume of the residue. Use a spectrometer (such as a portable Raman spectrometer) to quickly scan suspicious residues, analyze the scan results, identify the chemical structure of the residues, retrieve the ignition point data of the residues, use a drone equipped with a laser scanner or high-definition camera to perform an aerial scan of the fire scene, and generate a three-dimensional model and calculate the volume of the residues by processing the data collected by the drone. Use an infrared thermal imager to identify abnormal heat sources or heat distribution areas in the fire scene, use a drone or remote-controlled robot to take photos and videos on site, locate the residue area, upload the collected data to a rapid processing and analysis system, and use algorithms to automatically identify and analyze the type, volume, ignition point, and distribution of the residues.

[0025] S302, importing the residue area positioning and the re-ignition risk area positioning into the overlap rate calculation formula to calculate the overlap rate of the residue area and the re-ignition risk area, the overlap rate calculation formula is: ; in, is the lower left corner abscissa of the residue area, is the upper right corner abscissa of the residue area, is the lower left corner ordinate of the residue area, is the upper right corner ordinate of the residue area, is the length of the residue region, is the width of the residue region, is the area of ​​the residue region, is the lower left corner abscissa of the resurgence risk area, is the upper right corner horizontal coordinate of the resurgence risk area, is the lower left ordinate of the re-ignition risk area, is the upper right corner ordinate of the resurgence risk area, is the length of the re-ignition risk area, is the width of the re-ignition risk area, is the area of ​​the re-ignition risk area, It is the overlapping area of ​​the residue area and the re-ignition risk area; The overlap area is obtained according to the overlap area calculation formula, which is: ; in, is the minimum value of the two coordinates, is the maximum value of the two coordinates, when or hour, The value is 0, which means that there is no overlap between the residue area and the re-ignition risk area; For further information, see Figure 2 In order to facilitate calculation, this embodiment makes circumscribed rectangles for the residue area and the re-ignition risk area respectively, and obtains the overlapping area of ​​the two circumscribed rectangles, that is, Figure 2 In the shaded part, a coordinate system is constructed according to the residue area and the re-ignition risk area so that the residue area and the re-ignition risk area are in the first quadrant of the coordinate system.

[0026] S303, the residue volume, residue ignition point and overlap rate are introduced into the residue impact value calculation formula to calculate the impact value of the residue on the re-ignition, and the residue impact value calculation formula is: ; in, is the residue influence value of the ith residue region, is the overlap rate between the ith residue area and the re-ignition risk area, is the residual volume of the ith residual region, is the total volume of the fire residues, is the residue ignition point of the ith residue region, is the temperature of the re-ignition risk area closest to the i-th residue area, It is an exponential function with the natural constant e as its base.

[0027] S4. Collect building data at the fire scene to analyze the impact of buildings on re-ignition; In this embodiment, S4 includes the following specific steps: S401, collecting building data of the fire scene, the building data including the temperature of building materials and the wind speed in the building; Furthermore, the steps for obtaining the building material temperature are: select representative measurement points according to the building design and the type of building materials, install thermocouples or infrared temperature sensors at the selected measurement points, ensure a stable environment during measurement, and continuously record temperature data changes; The steps for obtaining wind speed inside a building are as follows: select measurement points at the entrance, exit and key locations inside the building, install anemometers at the selected measurement locations, record wind speed data, and analyze the patterns and characteristics of air flow inside the building.

[0028] S402, the building material temperature, the building material ignition point and the wind speed in the building are introduced into the building impact value calculation formula to calculate the building impact value on the re-ignition. The building impact value calculation formula is: ; in, is the building material decomposition coefficient, It is a proportional constant that is related to the chemical properties and physical structure of building materials and reflects the ease with which the material decomposes at a specific temperature. is the current temperature collected, Ignition of building materials, The ignition temperature of the material, that is, the lowest temperature at which the material begins to decompose and burn meaningfully. is the reaction order of building materials, is a constant related to the material properties, is the standard decomposition rate, is the building ventilation volume at time t, is the collection time of building ventilation volume, is the standard ventilation volume per unit time, is the time integral, is an exponential function with the natural constant e as base; Further, commonly used methods for obtaining standard decomposition rates and ignition points of building materials include: Thermogravimetric analysis: evaluates thermal decomposition by measuring the change in mass of a material during heating; Differential Scanning Calorimetry: Determines thermal decomposition characteristics by measuring the heat change of a material during heating; Cone calorimetry: thermal decomposition and combustion characteristics of materials under simulated fire conditions; Before the experiment, prepare a sample of a size and shape representing the actual material used, set the experimental parameters: heating rate, final temperature, nitrogen, air, etc., heat the sample under controlled conditions, and record the changes in temperature and sample mass over time, record the experimental data, including temperature, time, mass change, etc., and determine the temperature at which the material begins to decompose (i.e., the ignition point of the building material) and the decomposition rate by analyzing the TGA curve, obtain the decomposition rate by the ratio of the sample mass to the time, repeat the experiment multiple times, and take the average value as the standard decomposition rate of this embodiment; The current temperature of the area closest to the building materials is collected by a temperature sensor; Furthermore, the decomposition rate of building materials increases with increasing temperature, and the rate of increase depends on the material-specific constant and , when the current temperature approaches or exceeds the ignition point of the building material, the decomposition rate will increase significantly because the material begins to produce a large amount of flammable gases; in, and The steps to obtain the value are: select a representative material sample, heat the material sample at different constant temperatures, and record the thermal decomposition rate at each temperature, collect experimental data including temperature, thermal decomposition rate, etc., and use the formula Fit the experimental data and adjust the and until the formula best fits the experimental data; For example, a plastic sample is placed in a heating furnace and the temperature is gradually increased from room temperature. Every time the temperature is increased by 10°C, the mass loss of the plastic sample within a certain period of time is measured. The collected data is as follows: Temperature: 200°C, 210°C, 220°C, 230°C, 240°C; Decomposition rate: 0.01 g / min, 0.02 g / min, 0.05 g / min, 0.1 g / min, 0.2 g / min; Substitute the data into the formula , and use statistical software to perform nonlinear regression analysis to determine the best and .

[0029] Among them, the building ventilation volume is obtained according to the building ventilation volume calculation formula, and the building ventilation volume calculation formula is: ; in, is the building ventilation volume, is the interception coefficient, representing and The ventilation volume, is the temperature coefficient, is the temperature inside the building collected for the jth time, is the building external temperature collected for the jth time, is the temperature difference between the inside and outside of the building, is the wind speed coefficient, is the wind speed collected for the jth time.

[0030] Furthermore, the temperature coefficient reflects the effect of temperature difference on air density and ventilation volume. The acquisition steps are: Under different temperature conditions, the density change of air is measured experimentally. The density calculation formula is: ,in, is the actual air density, is the air density at the reference temperature, is the actual temperature, is the reference temperature, is the temperature coefficient; The wind speed coefficient reflects the effect of wind speed on ventilation effect, which is usually related to the shape of the building, wind direction and surrounding environment. The steps to obtain it are: in the wind tunnel experiment, the building model is tested to measure the ventilation effect under different wind speeds. Through the experimental data, the relationship between wind speed and ventilation volume is established to obtain the wind speed coefficient. The wind speed and ventilation volume can also be measured on the actual building, and the wind speed coefficient can be determined by comparing the ventilation effects under different wind speeds. The temperature inside and outside the building is obtained by collecting temperature sensors at set positions; the steps for obtaining the interception coefficient are: setting experimental parameters: temperature inside the building, temperature outside the building, wind speed, where the temperature inside the building is equal to the temperature outside the building, and the wind speed is zero, and the ventilation volume under the experimental conditions is collected as the interception coefficient.

[0031] S5. Analyze the probability of re-ignition based on the changes in temperature, smoke, the impact of fire residues on re-ignition, and the impact of buildings on re-ignition, and determine whether the fire is successfully extinguished based on the probability of re-ignition.

[0032] In this embodiment, S5 includes the following specific steps: S501, the temperature change abnormal value, the smoke concentration change abnormal value, the residue impact value and the building impact value are introduced into the re-ignition probability calculation formula to calculate the re-ignition probability. The re-ignition probability calculation formula is: ; in, is the temperature change weight, is the smoke variation weight, is the residual impact weight, is the building influence weight, , is the number of residue regions; Furthermore, the temperature change weight , Smoke change weight , Residue influence weight and building impact weight The acquisition steps are as follows: select fifty representative fire scenes, collect temperature data to analyze the abnormal values ​​of temperature changes in the re-ignition risk area; collect smoke density data to analyze the abnormal values ​​of smoke density changes in the re-ignition risk area; collect fire residue data to analyze the impact of fire residues on re-ignition; collect building data of the fire scene to analyze the impact of buildings on re-ignition; combine the abnormal values ​​of temperature changes, abnormal values ​​of smoke density changes, residue impact values ​​and building impact values ​​to analyze the re-ignition probability; import the actual re-ignition situation of the fire scene and the analyzed re-ignition probability into the fitting software to obtain a set of weight values ​​with the highest judgment accuracy as the temperature change weights of this embodiment , Smoke change weight , Residue impact weight and building impact weights The value of .

[0033] S502, comparing the re-ignition probability with a preset re-ignition probability threshold; If the re-ignition probability is greater than the preset re-ignition probability threshold, the fire extinguishing failure is judged and feedback is sent to the fire extinguishing management platform; If the re-ignition probability is less than or equal to the preset re-ignition probability threshold, the fire is judged to be extinguished successfully.

[0034] See also Figure 3 The embodiment of the present invention also provides an intelligent fire extinguishing management system based on multi-sensor fusion, which is used to implement an intelligent fire extinguishing management method based on multi-sensor fusion, including a control unit, a data acquisition unit, a re-ignition risk area positioning unit, a temperature change analysis unit, a smoke change analysis unit, a residue impact analysis unit, a building impact unit, a re-ignition probability analysis unit and a fire extinguishing judgment unit.

[0035] In this embodiment, the data collection unit is used to collect temperature data, smoke density data, fire residue data and building data of the fire scene; The re-ignition risk area positioning unit is used to compare the temperature data with the preset fire extinguishing temperature threshold, and screen out the area exceeding the fire extinguishing temperature threshold as the re-ignition risk area; The temperature change analysis unit is used to import the temperature data into the temperature change abnormal value calculation formula to calculate the temperature change abnormal value of the re-ignition risk area; The smoke change analysis unit is used to import the smoke density data into the smoke density change abnormal value calculation formula to calculate the smoke density change abnormal value of the re-ignition risk area; The residue impact analysis unit is used to import the residue volume, residue ignition point and overlap rate into the residue impact value calculation formula to calculate the residue impact value on re-ignition; The building impact unit is used to import the building material temperature, building material ignition point and wind speed in the building into the building impact value calculation formula to calculate the building impact value on the re-ignition; The re-ignition probability analysis unit is used to import the temperature change abnormal value, the smoke concentration change abnormal value, the residue impact value and the building impact value into the re-ignition probability calculation formula to calculate the re-ignition probability; The fire extinguishing judgment unit is used to compare the re-ignition probability with the preset re-ignition probability threshold; if the re-ignition probability is greater than the preset re-ignition probability threshold, the fire extinguishing failure is judged and fed back to the fire extinguishing management platform; if the re-ignition probability is less than or equal to the preset re-ignition probability threshold, the fire extinguishing is judged to be successful; In this embodiment, the control unit is used to control the operation of the data acquisition unit, the re-ignition risk area positioning unit, the temperature change analysis unit, the smoke change analysis unit, the residue impact analysis unit, the building impact unit, the re-ignition probability analysis unit and the fire extinguishing judgment unit.

[0036] An embodiment of the present invention further provides a storage medium, which includes stored instructions, wherein when the instructions are executed, the device where the storage medium is located is controlled to execute the intelligent fire extinguishing management method based on multi-sensor fusion as described above.

[0037] See also Figure 4 The embodiment of the present invention further provides an electronic device, specifically comprising a memory and one or more instructions, wherein the one or more instructions are stored in the memory and are configured to be executed by one or more processors to perform the following operations: Collect temperature data to analyze the location of re-ignition risk areas and temperature changes in re-ignition risk areas; Collect smoke concentration data to analyze smoke changes in areas with re-ignition risk; Collect fire residue data to analyze the impact of fire residue on re-ignition; Collect building data at the fire scene to analyze the impact of the building on the re-ignition; analyze the probability of re-ignition by combining the impact of temperature changes, smoke changes, and the impact of fire residues and buildings on the re-ignition, and determine whether the fire is successfully extinguished based on the re-ignition probability.

[0038] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can refer to each other. Each embodiment focuses on the differences from other embodiments. Especially for the system or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiment. The system and system embodiment described above are only schematic, in which the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment, and ordinary technicians in this field can understand and implement it without creative work.

[0039] Professionals may further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0040] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art. The general principles defined in the present invention may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An intelligent fire extinguishing management method based on multi-sensor fusion, characterized in that: The specific steps include: S1. Collect temperature data to analyze the location of the re-ignition risk area and the temperature changes in the re-ignition risk area; S2. Collect smoke concentration data and analyze smoke changes in re-ignition risk areas; S3, collect fire residue data and analyze the impact of fire residue on re-ignition; S4. Collect building data at the fire scene to analyze the impact of buildings on re-ignition; S5. Analyze the probability of re-ignition based on the changes in temperature, smoke, the impact of fire residues on re-ignition, and the impact of buildings on re-ignition, and determine whether the fire is successfully extinguished based on the probability of re-ignition.

2. The intelligent fire extinguishing management method based on multi-sensor fusion according to claim 1 is characterized in that: The S1 comprises the following specific steps: S101, collecting temperature data of the fire scene through a temperature sensor; S102, comparing the temperature data with a preset fire extinguishing temperature threshold, and screening out areas exceeding the fire extinguishing temperature threshold as re-ignition risk areas; S103, importing the temperature data into a temperature change abnormal value calculation formula to calculate the temperature change abnormal value of the re-ignition risk area, the temperature change abnormal value calculation formula is: ； in, is the temperature collected at time t, is the temperature collected at time t-1, is the collection time of temperature data, is the time integral.

3. The intelligent fire extinguishing management method based on multi-sensor fusion as claimed in claim 2, characterized in that: The S2 comprises the following specific steps: S201, collecting smoke concentration data of the re-ignition risk area through a smoke sensor; S202, importing the smoke density data into a calculation formula for abnormal value of smoke density change to calculate the abnormal value of smoke density change in the re-ignition risk area, wherein the calculation formula for abnormal value of smoke density change is: ； in, is the smoke concentration collected at time t, is the smoke concentration collected at time t-1, is the collection time of smoke concentration data, is the time integral.

4. The intelligent fire extinguishing management method based on multi-sensor fusion as claimed in claim 3, characterized in that: The S3 includes the following specific steps: S301, collecting fire scene residue data, wherein the fire scene residue data includes residue type, residue volume, residue ignition point and residue area location; S302, importing the residue area positioning and the re-ignition risk area positioning into the overlap rate calculation formula to calculate the overlap rate of the residue area and the re-ignition risk area, the overlap rate calculation formula is: ； in, is the lower left corner abscissa of the residue area, is the upper right corner abscissa of the residue area, is the lower left corner ordinate of the residue area, is the upper right corner ordinate of the residue area, is the lower left corner abscissa of the resurgence risk area, is the upper right corner horizontal coordinate of the resurgence risk area, is the lower left ordinate of the re-ignition risk area, is the upper right corner ordinate of the resurgence risk area, It is the overlapping area of ​​the residue area and the re-ignition risk area; The overlap area is obtained according to the overlap area calculation formula, which is: ； in, is the minimum value of the two coordinates, is the maximum value of the two coordinates, when or hour, The value is 0; S303, the residue volume, residue ignition point and overlap rate are introduced into the residue impact value calculation formula to calculate the impact value of the residue on the re-ignition, and the residue impact value calculation formula is: ； in, is the residue influence value of the ith residue region, is the overlap rate between the ith residue area and the re-ignition risk area, is the residual volume of the ith residual region, is the total volume of the fire residues, is the residue ignition point of the ith residue region, is the temperature of the re-ignition risk area closest to the i-th residue area, It is an exponential function with the natural constant e as its base.

5. The intelligent fire extinguishing management method based on multi-sensor fusion as claimed in claim 4, characterized in that: The S4 comprises the following specific steps: S401, collecting building data of the fire scene, wherein the building data includes building material temperature and wind speed in the building; S402, introducing the building material temperature, the building material ignition point and the wind speed in the building into the building impact value calculation formula to calculate the impact value of the building on the re-ignition, the building impact value calculation formula is: ； in, is the building material decomposition coefficient, is the current temperature collected, Ignition of building materials, is the reaction order of building materials, is the standard decomposition rate, is the building ventilation volume at time t, is the collection time of building ventilation volume, is the standard ventilation volume per unit time, is the time integral, is an exponential function with the natural constant e as base; The building ventilation volume is obtained according to the building ventilation volume calculation formula, which is: ； in, is the building ventilation volume, is the interception coefficient, is the temperature coefficient, is the temperature inside the building collected for the jth time, is the building external temperature collected for the jth time, is the wind speed coefficient, is the wind speed collected for the jth time.

6. The intelligent fire extinguishing management method based on multi-sensor fusion according to claim 5, characterized in that: The S5 comprises the following specific steps: S501, the temperature change abnormal value, the smoke concentration change abnormal value, the residue impact value and the building impact value are introduced into the re-ignition probability calculation formula to calculate the re-ignition probability, and the re-ignition probability calculation formula is: ； in, is the temperature change weight, is the smoke variation weight, is the residual impact weight, is the building influence weight, , is the number of residue regions; S502, comparing the re-ignition probability with a preset re-ignition probability threshold; If the re-ignition probability is greater than the preset re-ignition probability threshold, the fire extinguishing failure is judged and feedback is sent to the fire extinguishing management platform; If the re-ignition probability is less than or equal to the preset re-ignition probability threshold, the fire is judged to be extinguished successfully.

7. An intelligent fire extinguishing management system based on multi-sensor fusion, used to implement the intelligent fire extinguishing management method based on multi-sensor fusion as claimed in any one of claims 1 to 6, characterized in that: It includes a control unit, a data acquisition unit, a re-ignition risk area positioning unit, a temperature change analysis unit, a smoke change analysis unit, a residue impact analysis unit, a building impact unit, a re-ignition probability analysis unit and a fire extinguishing judgment unit; The data acquisition unit is used to collect temperature data, smoke density data, fire residue data and building data of the fire scene; The re-ignition risk area positioning unit is used to compare the temperature data with a preset fire extinguishing temperature threshold, and screen out an area exceeding the fire extinguishing temperature threshold as a re-ignition risk area; The temperature change analysis unit is used to import the temperature data into the temperature change abnormal value calculation formula to calculate the temperature change abnormal value of the re-ignition risk area; The smoke change analysis unit is used to import the smoke concentration data into the smoke concentration change abnormal value calculation formula to calculate the smoke concentration change abnormal value of the re-ignition risk area; The residue impact analysis unit is used to import the residue volume, residue ignition point and overlap rate into the residue impact value calculation formula to calculate the impact value of the residue on the re-ignition; The building impact unit is used to import the building material temperature, the building material ignition point and the wind speed in the building into the building impact value calculation formula to calculate the building impact value on the re-ignition; The re-ignition probability analysis unit is used to import the temperature change abnormal value, the smoke concentration change abnormal value, the residue impact value and the building impact value into the re-ignition probability calculation formula to calculate the re-ignition probability; The fire extinguishing judgment unit is used to compare the re-ignition probability with a preset re-ignition probability threshold; If the re-ignition probability is greater than the preset re-ignition probability threshold, the fire extinguishing is judged to have failed and feedback is sent to the fire extinguishing management platform; if the re-ignition probability is less than or equal to the preset re-ignition probability threshold, the fire extinguishing is judged to have been successful.

8. The intelligent fire extinguishing management system based on multi-sensor fusion as claimed in claim 7, characterized in that: The control unit is used to control the operation of a data acquisition unit, a re-ignition risk area positioning unit, a temperature change analysis unit, a smoke change analysis unit, a residue impact analysis unit, a building impact unit, a re-ignition probability analysis unit and a fire extinguishing judgment unit.

9. A storage medium, characterized in that: The storage medium includes stored instructions, wherein when the instructions are executed, the device where the storage medium is located is controlled to execute the intelligent fire extinguishing management method based on multi-sensor fusion according to any one of claims 1 to 6.

10. An electronic device, characterized in that: It comprises a memory and one or more instructions, wherein the one or more instructions are stored in the memory and are configured to be executed by one or more processors to execute the intelligent fire extinguishing management method based on multi-sensor fusion as described in any one of claims 1-6.

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