An unattended fire alarm system based on real-time comprehensive analysis of environmental characteristics

Abstract

The invention discloses an unmanned fire alarm system based on real-time comprehensive analysis of environmental characteristics, which relates to the field of unmanned fire alarm, and comprises: a dust distribution simulation module, which establishes a dynamic dust concentration distribution model in an industrial production area, and analyzes the distribution of dust in space in real time; a secondary risk area marking module, which performs three-dimensional coverage of the electrostatic field distribution in a monitoring area and the distribution of dust in space, and marks the overlapping area as a secondary risk area; a primary risk area marking module, which determines the dust-gas synergistic explosion risk in the current risk area based on the energy of electrostatic discharge, and performs primary risk marking; an explosion propagation path simulation module, which establishes an explosion propagation path map based on explosion energy estimation and the propagation attenuation of thermal radiation; and a risk alarm module, which performs color marking on the graded risk areas and the explosion propagation path map, and sends out an alarm signal.

Description

Technical Field

[0001] The invention relates to the field of unmanned fire alarm, and in particular to an unmanned fire alarm system based on real-time comprehensive analysis of environmental characteristics. Background Art

[0002] In industrial production environments, especially in the chemical, pharmaceutical, and food processing industries, the presence of dust and combustible gases greatly increases the risk of explosion. Dust particles are unevenly distributed in the air, and their concentration may reach the critical explosive value under certain conditions. Especially in an environment with static electricity accumulation, electrostatic discharge can become a trigger for dust explosions. At the same time, if there are combustible gases in the air, they will be distributed together with dust in areas with strong electrostatic fields, which will lead to more complex dust-gas synergistic explosion risks. Therefore, in order to ensure the safety of industrial production areas, real-time monitoring and analysis of dust concentration, electrostatic field strength, and combustible gas distribution are necessary.

[0003] Therefore, how to establish risk area markings and possible explosion propagation path simulations for industrial production areas, visually mark risk areas and explosion paths, and issue early warnings to help safety managers take protective measures in a timely manner to ensure production safety is an urgent problem that needs to be solved. Summary of the invention

[0004] In order to solve the above technical problems, an unmanned fire alarm system based on real-time comprehensive analysis of environmental characteristics is provided. This technical solution solves the problems raised in the above background technology.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] An unattended fire alarm system based on real-time comprehensive analysis of environmental characteristics, comprising:

[0007] A dust distribution simulation module, which establishes a dynamic dust concentration distribution model in the industrial production area and analyzes the distribution of dust in space in real time;

[0008] A secondary risk area labeling module, which performs three-dimensional coverage of the electrostatic field distribution in the monitoring area and the distribution of dust in space, and labels the overlapping area as a secondary risk area;

[0009] A first-level risk area labeling module, which determines the dust-gas synergistic explosion risk in the current risk area based on the energy of electrostatic discharge through the combustible gas information in the second-level detection risk area, and performs first-level risk labeling;

[0010] An explosion propagation path simulation module, wherein the explosion propagation path simulation module establishes an explosion propagation path map based on explosion energy estimation and thermal radiation propagation attenuation;

[0011] A risk alarm module is provided, wherein the risk alarm module colors and marks the graded risk areas and the explosion propagation path map, and issues an alarm signal.

[0012] Preferably, the dust distribution simulation module establishes a dynamic dust concentration distribution model in the industrial production area, and real-time analysis of the distribution of dust in space specifically includes:

[0013] Identify the main dust sources in industrial environments, extract monitoring data of dust source equipment in industrial production sites, and quantitatively describe the dust generation rate;

[0014] Measure air velocity, temperature, humidity and physical properties of dust in the space;

[0015] Use 3D CAD models of industrial production sites to determine ventilation systems, equipment layouts, and the impact of obstacles on air flow;

[0016] Based on the CFD dynamic model and the physical characteristic parameters of dust, a motion model of dust particles in the flow field is established, and a dust concentration sensor network is set up in the operating area of ​​the dust source equipment to collect dust concentration data in real time to provide data input for the dynamic model;

[0017] The real-time dust concentration data provided by the sensor is integrated with the prediction results of the CFD dynamic model, and the dynamic model is updated in real time using the data assimilation algorithm;

[0018] Based on the prediction of the motion model of dust particles in the flow field, the Lagrangian method is used to track the motion of individual dust particles, and the expected spatial distribution of dust particles is calculated in real time according to the air flow velocity field and the dynamic characteristics of dust particles.

[0019] Mark the areas in the space where the density of dust particles exceeds the set safety range.

[0020] Preferably, the secondary risk area labeling module performs three-dimensional coverage of the electrostatic field distribution in the monitoring area and the distribution of dust in space, and labels the overlapping area as the secondary risk area, specifically including:

[0021] The monitoring area is divided into three-dimensional grid monitoring units, and a three-dimensional array of electric field sensors is arranged in each unit. The collected data is transmitted to the data center for processing via a wireless network;

[0022] The collected data is subjected to the inverse distance weighted interpolation algorithm to integrate the electric field strength in the entire monitoring unit, and the comprehensive electric field strength is calculated for each monitoring unit in the three-dimensional space of the electrostatic field monitoring area to obtain a three-dimensional model of the electrostatic field distribution;

[0023] Project the three-dimensional model of electrostatic field distribution and the expected spatial distribution of dust particles into the same three-dimensional space to generate a comprehensive spatial analysis model;

[0024] In the spatial analysis model, the overlapping areas of electric field distribution and dust particle distribution are marked as risk areas and recorded as secondary risk areas.

[0025] Preferably, the first-level risk area labeling module determines the dust-gas synergistic explosion risk in the current risk area based on the energy of electrostatic discharge through the combustible gas information in the second-level detection risk area, and performs the first-level risk labeling specifically including:

[0026] Use a combustible gas detector to identify the type of combustible gas present in each secondary risk area, and simultaneously measure the concentration of oxygen and combustible gas to determine whether the combustible gas has reached the lower explosion limit concentration;

[0027] Retrieve the minimum ignition energy data of combustible gas and dust, calculate the electrostatic discharge energy of the risk area based on the three-dimensional model data of the electrostatic field distribution, and if the energy of the electrostatic discharge reaches the set risk column of the minimum ignition energy of gas and dust, it is determined that the current risk area has the risk of dust-gas synergistic explosion, and the current secondary risk area is marked as a primary risk area;

[0028] The reaction kinetics model is used to analyze dust and combustible gas, and the synergistic explosion reaction equation is constructed. The instantaneous explosion pressure is calculated by combining the chemical reaction rate and heat release with the ideal gas state equation to evaluate the synergistic explosion energy level.

[0029] Preferably, the explosion propagation path simulation module establishes an explosion propagation path diagram based on explosion energy estimation and thermal radiation propagation attenuation, specifically comprising:

[0030] In three-dimensional space, an explosion propagation path is established for each explosion risk unit, which is marked as a first-level risk area explosion propagation chain. The node of each explosion risk unit represents a first-level risk area, and the edges between the nodes represent the propagation path and energy transfer of the explosion wave.

[0031] Based on the distance between adjacent explosion risk units, the time it takes for the explosion shock wave to reach the next explosion risk unit is calculated. According to the expected explosion heat release of the explosion risk unit, the heat attenuation in the air is calculated in combination with the thermal radiation formula. A propagation attenuation model of the thermal radiation of the explosion risk unit is established, and the thermal radiation intensity of the explosion risk unit within the effective range of the thermal radiation is estimated. Specifically, ;

[0032] In the formula, is the thermal radiation intensity, is the total amount of heat released, is the estimated energy of the synergistic explosion, is the density of air, It is the time from the current explosion risk unit to the next explosion risk unit;

[0033] A recursive algorithm is used to traverse the propagation path diagram, and the total received heat of the next explosion risk unit is calculated based on the thermal radiation intensity and the time of receiving radiation. When the total received heat is lower than the minimum ignition energy of the next explosion risk unit, the traversal is terminated and the final propagation path diagram is uploaded to the fire monitoring center.

[0034] Preferably, the risk alarm module performs color marking on the graded risk areas and the explosion propagation path map, and issues an alarm signal, specifically including:

[0035] Determine whether there is a risk area. If so, mark the risk area with different colors in the fire monitoring center;

[0036] If there is a first-level risk area, an alarm signal will be generated for the first-level area. At the same time, based on the explosion propagation path map received by the fire monitoring center, an alarm will be broadcast to the involved areas.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] This solution can significantly improve the safety monitoring capabilities of industrial production areas. Through the dust distribution simulation module and real-time monitoring of the electrostatic field, the system can accurately analyze the dust concentration and its overlap with static electricity, thereby effectively identifying and marking potential explosion risk areas. The secondary and primary risk area labeling modules implement graded risk assessment by integrating dust concentration, electrostatic field strength and the distribution of combustible gases, further clarifying the risk levels of different areas and making safety management more scientific and efficient. At the same time, the explosion propagation path simulation module simulates the explosion propagation path in advance based on energy estimation and thermal radiation attenuation, reducing the possibility of accident spread. Finally, the risk alarm module visualizes the graded risks and explosion propagation paths and issues early warnings to ensure that operators can take emergency measures in a timely manner and effectively reduce the probability of explosion accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a structural schematic diagram of an unattended fire alarm system based on real-time comprehensive analysis of environmental characteristics of the present invention;

[0040] Figure 2 A schematic diagram of the structure of the dust distribution simulation module of the present invention establishing a dynamic dust concentration distribution model in an industrial production area and analyzing the distribution of dust in space in real time;

[0041] Figure 3 The secondary risk area marking module of the present invention performs three-dimensional coverage of the electrostatic field distribution in the monitoring area and the distribution of dust in space, and marks the overlapping area as a schematic diagram of the secondary risk area structure;

[0042] Figure 4 The first-level risk area marking module of the present invention detects the combustible gas information in the risk area through the second-level detection, determines the dust-gas coordinated explosion risk in the current risk area based on the energy of electrostatic discharge, and performs a first-level risk marking structure schematic diagram;

[0043] Figure 5 The explosion propagation path simulation module of the present invention establishes a schematic diagram of the explosion propagation path diagram based on the explosion energy estimation and the propagation attenuation of thermal radiation;

[0044] Figure 6 The risk alarm module of the present invention colors and marks the graded risk areas and the explosion propagation path diagram, and issues an alarm signal structure schematic diagram. DETAILED DESCRIPTION

[0045] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0046] Reference Figure 1 As shown, an unattended fire alarm system based on real-time comprehensive analysis of environmental characteristics includes:

[0047] A dust distribution simulation module, which establishes a dynamic dust concentration distribution model in the industrial production area and analyzes the distribution of dust in space in real time;

[0048] A secondary risk area labeling module, which performs three-dimensional coverage of the electrostatic field distribution in the monitoring area and the distribution of dust in space, and labels the overlapping area as a secondary risk area;

[0049] A first-level risk area labeling module, which determines the dust-gas synergistic explosion risk in the current risk area based on the energy of electrostatic discharge through the combustible gas information in the second-level detection risk area, and performs first-level risk labeling;

[0050] An explosion propagation path simulation module, wherein the explosion propagation path simulation module establishes an explosion propagation path map based on explosion energy estimation and thermal radiation propagation attenuation;

[0051] A risk alarm module is provided, wherein the risk alarm module colors and marks the graded risk areas and the explosion propagation path map, and issues an alarm signal.

[0052] Reference Figure 2 As shown in the figure, the dust distribution simulation module establishes a dynamic dust concentration distribution model in the industrial production area, and analyzes the distribution of dust in space in real time, including:

[0053] According to the production process of the industrial site, determine whether various types of equipment (such as grinders, conveyor belts, crushers, etc.) generate dust. Number and classify different equipment and record their location coordinates. Identify the main dust sources in the industrial environment, extract the monitoring data of dust source equipment in the industrial production site, and quantitatively describe the dust generation rate. Assuming that the dust concentration generated by a certain equipment increases by 50 mg / m³ in 10 seconds, the dust generation rate is 5 mg / m³ / s.

[0054] The air velocity, temperature, humidity and physical properties of dust in the space are measured, and the physical properties of dust such as particle size, density, diffusion coefficient, etc. are obtained through experiments. These parameters will be used for subsequent dust movement simulation.

[0055] In narrow areas between equipment, the air flow rate may be low due to obstacles, which affects dust diffusion. Therefore, the 3D CAD model of the industrial production site is used to make initial settings for the ventilation system, equipment layout, and the impact of obstacles on air flow based on historical or experimental data.

[0056] Based on the CFD dynamic model and the physical characteristic parameters of dust, a motion model of dust particles in the flow field is established, and a dust concentration sensor network is set up in the operating area of ​​the dust source equipment to collect dust concentration data in real time to provide data input for the dynamic model;

[0057] The real-time dust concentration data provided by the sensor is integrated with the prediction results of the CFD dynamic model, and the dynamic model is updated in real time using a data assimilation algorithm. For example, the Kalman filter method is used to achieve data assimilation, and the error analysis of the sensor measurement data and the model prediction results is performed. The model is adjusted by calculating the gain to make it closer to the actual data.

[0058] Based on the prediction of the motion model of dust particles in the flow field, the Lagrangian method is used to track the motion of individual dust particles. According to the air flow velocity field and the dynamic characteristics of dust particles, the expected spatial distribution of dust particles is calculated in real time. Specifically:

[0059] Establish the kinetic equation of dust particles: ;in is the position vector of the dust particle, is the velocity of the particle, It is the velocity field obtained based on CFD model or field data. is the settling velocity of the particle, is the diffusion rate of the particles;

[0060] The sedimentation velocity of particles is determined by the density and size of the particles and the viscosity and density of the fluid. The basic model is calculated by Stokes' law. The calculation formula is ; where d is the diameter of the dust particle, and are the density of the particle and the density of air, is the acceleration due to gravity, is the dynamic viscosity of air.

[0061] The diffusion of particles reflects the random movement of particles in space due to air turbulence, collision and other aerodynamic effects. The diffusion rate formula is: ,in is the Boltzmann constant, It's the temperature.

[0062] The numerical integration method is used to calculate the position update of the particle in each time step, and the above process is repeated to track each particle and calculate its motion trajectory in space. During the simulation process, a large number of virtual particles can be generated to approximate the dust concentration distribution in the real environment.

[0063] Mark areas in the space where the density of dust particles exceeds the set safety range (set according to industry standard safety regulations).

[0064] Reference Figure 3 As shown in the figure, the secondary risk area labeling module covers the electrostatic field distribution in the monitoring area and the distribution of dust in space in three dimensions, and labels the overlapping areas as secondary risk areas, including:

[0065] The monitoring area is divided into three-dimensional grid monitoring units, and a three-dimensional array of electric field sensors is arranged in each unit. The collected data is transmitted to the data center for processing via a wireless network;

[0066] The inverse distance weighted interpolation algorithm is used for the collected data to integrate the electric field strength in the entire monitoring unit. The comprehensive electric field strength is calculated for each monitoring unit in the three-dimensional space of the electrostatic field monitoring area to obtain a three-dimensional model of the electrostatic field distribution. The formula of the inverse distance weighted interpolation algorithm is: ,in, is the detected electric field intensity of the mth three-dimensional array point, To the destination The actual distance It is the target point The estimated electric field strength is is the comprehensive electric field strength of the monitoring unit, is the volume of the monitoring unit.

[0067] Project the three-dimensional model of electrostatic field distribution and the expected spatial distribution of dust particles into the same three-dimensional space to generate a comprehensive spatial analysis model;

[0068] In the spatial analysis model, the overlapping areas of electric field distribution and dust particle distribution are marked as risk areas and recorded as secondary risk areas.

[0069] Reference Figure 4 As shown, the first-level risk area labeling module detects the combustible gas information in the risk area through the second-level detection, determines the dust-gas synergistic explosion risk in the current risk area based on the energy of electrostatic discharge, and performs the first-level risk labeling, which specifically includes:

[0070] Use a combustible gas detector to identify the type of combustible gas present in each secondary risk area, measure the concentration of oxygen and combustible gas at the same time, compare the detected combustible gas concentration with the lower explosion limit (LEL) of the gas, and determine whether the combustible gas has reached the lower explosion limit concentration.

[0071] Retrieve the minimum ignition energy data of combustible gas and dust, calculate the electrostatic discharge energy of the risk area based on the three-dimensional model data of the electrostatic field distribution, and if the electrostatic discharge energy reaches the set risk column of the minimum ignition energy of gas and dust, for example, the electrostatic discharge energy (15 mJ) is greater than the minimum ignition energy of methane (0.28 mJ) and carbon powder (10 mJ), it is determined that the current risk area has a dust-gas synergistic explosion risk, and the current secondary risk area is marked as a primary risk area;

[0072] Use the reaction kinetics model to analyze dust and combustible gas, construct a coordinated explosion reaction equation, and calculate the instantaneous explosion pressure through the chemical reaction rate and heat release combined with the ideal gas state equation to evaluate the coordinated explosion energy level. The following is a specific example:

[0073] Assuming that methane (CH4) and aluminum powder (Al) coexist in the air, the synergistic explosion reaction equation is:

[0074] Methane combustion: ,

[0075] Aluminum powder burning: ,

[0076] Use the Arrhenius equation to calculate the rate constants for the two reactions, assuming that the activation energy of combustion of aluminum is , frequency factor 1.2× , the combustion activation energy of methane , frequency factor 1.5× , put the parameters into the Arrhenius equation (in The gas constant is 8.314, T is the temperature, 1000K) then:

[0077] Aluminum combustion reaction rate constant ,

[0078] Methane combustion reaction rate constant ,

[0079] Based on the combustion reaction rate constant, the reaction rates of aluminum and methane combustion are further obtained according to the reaction rate equation. Assuming that the initial concentrations of aluminum and methane are 0.01 mol / L and 0.05 mol / L respectively, and the initial concentration of oxygen is 0.2 mol / L, the reaction rates are: ,

[0080] in To set the unit time, calculate the instantaneous heat release rate based on the reaction rate and the combustion heat value: ;

[0081] Finally, according to the ideal gas state equation Estimate the instantaneous explosion pressure, where n is the amount of reactant, which is used to describe the sum of the instantaneous reaction rates in this process + , calculate the instantaneous explosion pressure P, and classify the current explosion energy release level based on the TNT equivalent method and the instantaneous explosion pressure P:

[0082] < 0.1 kg TNT: low energy explosion, usually does not cause serious damage to structures.

[0083] 0.1 - 1 kg TNT: A medium energy explosion that may damage closed containers or small structures.

[0084] 1 - 10 kg TNT: High energy explosion, which may pose a serious threat to surrounding buildings and personnel.

[0085] 10 kg TNT: Extremely high energy explosion, which will cause large-scale structural damage and is a high-risk level.

[0086] For different instantaneous explosion pressure P calculation results, a reference value for coordinated explosion energy estimation is set. The deviation of the coordinated explosion energy estimation is estimated in proportion to the deviation ratio of the actual calculated instantaneous explosion pressure P relative to the reference value to obtain an energy estimation value.

[0087] Reference Figure 5 As shown in the figure, the explosion propagation path simulation module establishes the explosion propagation path diagram based on the explosion energy estimation and the propagation attenuation of thermal radiation, which specifically includes:

[0088] In three-dimensional space, an explosion propagation path is established for each explosion risk unit, which is marked as a first-level risk area explosion propagation chain. The node of each explosion risk unit represents a first-level risk area, and the edges between the nodes represent the propagation path and energy transfer of the explosion wave.

[0089] Based on the distance between adjacent explosion risk units, the time it takes for the explosion shock wave to reach the next explosion risk unit is calculated. According to the expected explosion heat release of the explosion risk unit, the heat attenuation in the air is calculated in combination with the thermal radiation formula. A propagation attenuation model of the thermal radiation of the explosion risk unit is established, and the thermal radiation intensity of the explosion risk unit within the effective range of the thermal radiation is estimated. Specifically, ;

[0090] In the formula, is the thermal radiation intensity, is the total amount of heat released, is the estimated value of the coordinated explosion energy (calculated in the first-level risk area annotation module), is the density of air, It is the time from the current explosion risk unit to the next explosion risk unit. This formula combines the propagation attenuation model of thermal radiation with the Taylor-Sedov solution to estimate the thermal radiation intensity through the shock wave propagation time.

[0091] A recursive algorithm is used to traverse the propagation path diagram, and the total received heat of the next explosion risk unit is calculated based on the thermal radiation intensity and the time of receiving radiation. When the total received heat is lower than the minimum ignition energy of the next explosion risk unit, the traversal is terminated and the final propagation path diagram is uploaded to the fire monitoring center.

[0092] Reference Figure 6As shown in the figure, the risk alarm module colors and marks the graded risk areas and explosion propagation path map, and issues alarm signals, including:

[0093] Determine whether there is a risk area. If so, mark the risk area with different colors in the fire monitoring center;

[0094] If there is a first-level risk area, an alarm signal will be generated for the first-level area. At the same time, based on the explosion propagation path map received by the fire monitoring center, an alarm will be broadcast to the involved areas.

[0095] Furthermore, the present solution also proposes a storage medium for an unmanned fire alarm system based on real-time comprehensive analysis of environmental characteristics, on which a computer-readable program is stored. When the computer-readable program is called, it executes the above-mentioned unmanned fire alarm system based on real-time comprehensive analysis of environmental characteristics.

[0096] It is understandable that the storage medium may be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; an optical medium, such as a DVD; or a semiconductor medium, such as a solid state drive (SSD).

[0097] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. An unattended fire alarm system based on real-time comprehensive analysis of environmental characteristics, characterized in that: include: A dust distribution simulation module, which establishes a dynamic dust concentration distribution model in the industrial production area and analyzes the distribution of dust in space in real time; A secondary risk area labeling module, which performs three-dimensional coverage of the electrostatic field distribution in the monitoring area and the distribution of dust in space, and labels the overlapping area as a secondary risk area; A first-level risk area labeling module, which determines the dust-gas synergistic explosion risk in the current risk area based on the energy of electrostatic discharge through the combustible gas information in the second-level detection risk area, and performs first-level risk labeling; An explosion propagation path simulation module, wherein the explosion propagation path simulation module establishes an explosion propagation path map based on explosion energy estimation and thermal radiation propagation attenuation; A risk alarm module, which colors and marks the graded risk areas and explosion propagation path map, and issues an alarm signal; The first-level risk area labeling module determines the dust-gas synergistic explosion risk in the current risk area based on the energy of electrostatic discharge through the combustible gas information in the second-level detection risk area, and performs the first-level risk labeling, specifically including: Use a combustible gas detector to identify the type of combustible gas present in each secondary risk area, and simultaneously measure the concentration of oxygen and combustible gas to determine whether the combustible gas has reached the lower explosion limit concentration; Retrieve the minimum ignition energy data of combustible gas and dust, calculate the electrostatic discharge energy of the risk area based on the three-dimensional model data of the electrostatic field distribution, and if the energy of the electrostatic discharge reaches the set risk column of the minimum ignition energy of gas and dust, it is determined that the current risk area has the risk of dust-gas synergistic explosion, and the current secondary risk area is marked as a primary risk area; The reaction kinetics model is used to analyze dust and combustible gas, and the synergistic explosion reaction equation is constructed. The instantaneous explosion pressure is calculated by combining the chemical reaction rate and heat release with the ideal gas state equation to evaluate the synergistic explosion energy level.

2. The unmanned fire alarm system based on real-time comprehensive analysis of environmental characteristics according to claim 1 is characterized in that: The dust distribution simulation module establishes a dynamic dust concentration distribution model in the industrial production area and analyzes the distribution of dust in space in real time, including: Identify the main dust sources in industrial environments, extract monitoring data of dust source equipment in industrial production sites, and quantitatively describe the dust generation rate; Measure air velocity, temperature, humidity and physical properties of dust in the space; Use 3D CAD models of industrial production sites to determine ventilation systems, equipment layouts, and the impact of obstacles on air flow; Based on the CFD dynamic model and the physical characteristic parameters of dust, a motion model of dust particles in the flow field is established, and a dust concentration sensor network is set up in the operating area of ​​the dust source equipment to collect dust concentration data in real time to provide data input for the dynamic model; The real-time dust concentration data provided by the sensor is integrated with the prediction results of the CFD dynamic model, and the dynamic model is updated in real time using the data assimilation algorithm; Based on the prediction of the motion model of dust particles in the flow field, the Lagrangian method is used to track the motion of individual dust particles, and the expected spatial distribution of dust particles is calculated in real time according to the air flow velocity field and the dynamic characteristics of dust particles. Mark the areas in the space where the density of dust particles exceeds the set safety range.

3. The unattended fire alarm system based on real-time comprehensive analysis of environmental characteristics according to claim 2 is characterized in that: The secondary risk area labeling module performs three-dimensional coverage of the electrostatic field distribution in the monitoring area and the distribution of dust in space, and labels the overlapping area as the secondary risk area, specifically including: The monitoring area is divided into three-dimensional grid monitoring units, and a three-dimensional array of electric field sensors is arranged in each unit. The collected data is transmitted to the data center for processing via a wireless network; The collected data is subjected to the inverse distance weighted interpolation algorithm to integrate the electric field strength in the entire monitoring unit, and the comprehensive electric field strength is calculated for each monitoring unit in the three-dimensional space of the electrostatic field monitoring area to obtain a three-dimensional model of the electrostatic field distribution; Project the three-dimensional model of electrostatic field distribution and the expected spatial distribution of dust particles into the same three-dimensional space to generate a comprehensive spatial analysis model; In the spatial analysis model, the overlapping areas of electric field distribution and dust particle distribution are marked as risk areas and recorded as secondary risk areas.

4. The unattended fire alarm system based on real-time comprehensive analysis of environmental characteristics according to claim 3 is characterized in that: The explosion propagation path simulation module establishes an explosion propagation path diagram based on the explosion energy estimation and the propagation attenuation of thermal radiation, and specifically includes: In three-dimensional space, an explosion propagation path is established for each explosion risk unit, which is marked as a first-level risk area explosion propagation chain. The node of each explosion risk unit represents a first-level risk area, and the edges between the nodes represent the propagation path and energy transfer of the explosion wave. Based on the distance between adjacent explosion risk units, the time it takes for the explosion shock wave to reach the next explosion risk unit is calculated. Based on the expected explosion heat release of the explosion risk unit, the heat attenuation in the air is calculated in combination with the thermal radiation formula. A propagation attenuation model of the thermal radiation of the explosion risk unit is established to estimate the thermal radiation intensity received by the explosion risk unit within the effective range of the thermal radiation. A recursive algorithm is used to traverse the propagation path diagram, and the total received heat of the next explosion risk unit is calculated based on the thermal radiation intensity and the time of receiving radiation. When the total received heat is lower than the minimum ignition energy of the next explosion risk unit, the traversal is terminated and the final propagation path diagram is uploaded to the fire monitoring center.

5. The unmanned fire alarm system based on real-time comprehensive analysis of environmental characteristics according to claim 4 is characterized in that: The risk alarm module colors and marks the graded risk areas and the explosion propagation path map, and issues an alarm signal, specifically including: Determine whether there is a risk area. If so, mark the risk area with different colors in the fire monitoring center; If there is a first-level risk area, an alarm signal will be generated for the first-level area. At the same time, based on the explosion propagation path map received by the fire monitoring center, an alarm will be broadcast to the involved areas.

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