A real-time fire monitoring data transmission system for fire engineering construction

Abstract

The present invention relates to the field of fire protection engineering technology and discloses a real-time fire protection monitoring and data transmission system for fire protection engineering construction. The system includes a fire parameter analysis module, an environmental data analysis module, a fire source location module, a model construction module, and a security database. The fire parameter analysis module generates a temperature change record table using gridded temperature measurement nodes, estimates the smoke diffusion rate, and assesses the thermal stress and deformation risk of the building structure. The environmental data analysis module correlates air flow rate with smoke diffusion rate and corrects the parameter weights of the assessment model. The fire source location module uses multi-spectral imaging equipment to generate a three-dimensional fire source location map. The model construction module constructs a dynamic fire assessment model using multi-dimensional data and outputs a comprehensive threat level. The security database stores building material parameters, historical case characteristics, and dynamic threshold rules. Through multi-module collaboration and multi-data fusion, the system improves the accuracy and timeliness of fire monitoring and is suitable for fire protection engineering safety management.

Description

Technical Field

[0001] The present invention relates to the technical field of fire protection engineering, in particular to a real-time fire protection monitoring and data transmission system for fire protection engineering construction. Background Art

[0002] In the field of modern building fire protection engineering, with the continuous expansion of building scale and increasing structural complexity, traditional fire monitoring methods are no longer able to meet the needs of real-time, accurate fire monitoring and dynamic assessment. Most existing fire monitoring systems suffer from problems such as limited data collection, crude analysis models, and delayed risk assessment. This makes it difficult to accurately grasp the development of fires in a timely and accurate manner, making it difficult to implement effective prevention and control measures in the early stages of a fire.

[0003] From a data collection perspective, traditional systems often rely solely on single-point temperature monitoring, lacking a comprehensive understanding of the temperature distribution in the monitored area. This makes it impossible to construct a complete temperature change record table, making it difficult to accurately estimate the smoke diffusion rate. Furthermore, the correlation analysis between environmental factors such as air velocity and fire parameters is insufficient, making it impossible to dynamically measure the distribution relationship between air velocity and smoke concentration. This results in incomplete input parameters for fire assessment models, impacting the accuracy of assessment results.

[0004] In terms of fire source positioning, existing technologies mostly use a single sensor or simple signal detection method, which makes it difficult to quickly and accurately obtain the coordinate data of each fire source and the thermal radiation distribution data under the corresponding fire source intensity. It is impossible to generate a three-dimensional fire source positioning map and update it to the evaluation model in real time, resulting in a lag in fire source positioning, affecting the targetedness and effectiveness of fire prevention and control.

[0005] Traditional fire risk assessment systems typically use simple calculation models with fixed weights, lacking the dynamic integration and analysis of multi-dimensional data. For example, they are unable to standardize temperature data, building structure data, air velocity data, fire source data, and other data to generate feature vector sets. They also struggle to dynamically adjust the weighting coefficients of various indicators based on real-time data. As a result, the output of comprehensive fire spread threat levels is not scientifically sound and reliable, and cannot provide a reliable basis for firefighting decision-making.

[0006] Furthermore, the construction of security databases also suffers from significant flaws. Existing databases often store only basic data, such as the thermal conductivity of building materials. They lack a database of historical fire case characteristics and a set of dynamic threshold adjustment rules. This makes them unable to provide a rich reference for fire parameter analysis and risk assessment, and makes them difficult to adapt to the fire monitoring needs of diverse building structures and environmental conditions.

[0007] With the development of smart cities and Internet of Things technologies, the demand for real-time, accurate, and intelligent fire monitoring systems in the field of fire protection engineering is becoming increasingly urgent. How to integrate multi-source data, build a dynamic and intelligent fire assessment model, and achieve real-time monitoring, precise positioning, and risk warning of fires has become a key issue that needs to be urgently addressed in the current field of fire protection engineering technology. Based on the above-mentioned deficiencies in the existing technology, the present invention proposes a real-time fire monitoring data transmission system for fire protection engineering construction, which aims to improve the accuracy and timeliness of fire monitoring through multi-module collaborative work and multi-dimensional data fusion, and provide more reliable technical support for the safety management of fire protection engineering. Summary of the Invention

[0008] The purpose of the present invention is to provide a real-time fire monitoring data transmission system for fire engineering construction to solve the problems raised in the above background technology.

[0009] To achieve the above-mentioned object, the present invention provides the following technical solution: a real-time fire monitoring data transmission system for fire engineering construction, the system comprising:

[0010] The fire parameter analysis module is used to generate a temperature change record table based on the temperature data of the monitoring area, from which the smoke diffusion rate is estimated, and then a safety assessment of the thermal stress and material deformation of the building structure is conducted;

[0011] Environmental data analysis module, used to obtain air velocity data, correlate it with smoke diffusion rate, and measure the dynamic distribution relationship between air velocity and smoke concentration;

[0012] Fire source positioning module, used to obtain the coordinate data of each fire source and the thermal radiation distribution data under the corresponding fire source intensity;

[0013] The model building module is used to construct a fire dynamic assessment model using temperature data, building structure data, air velocity data, and fire source data as input parameters, and output a comprehensive threat level of fire spread;

[0014] The security database is used to store the corresponding thermal conductivity coefficient of each building material, the set smoke concentration threshold and the structural deformation threshold.

[0015] Preferably, the fire parameter analysis module specifically includes the following steps:

[0016] S1. Deploy several temperature measurement nodes in the monitoring area according to the grid principle. During the fire monitoring process, according to the set collection cycle, the temperature data of each temperature measurement node is acquired through infrared sensors. The coordinates, temperature value and collection time of each node are recorded, and a temperature change record table is generated;

[0017] S2. Calculate the heat accumulation gradient of the monitored area based on the temperature change of each node during the current acquisition cycle. Simultaneously, obtain the spatial parameters of the building structure and calculate the smoke diffusion range based on the heat gradient distribution to estimate the fire spread rate.

[0018] S3. Analyze the thermal stress of the building structure based on the uniformity of temperature distribution, determine the tolerance coefficient of the building materials, and determine the risk level of the structure;

[0019] S4. Analyze the material deformation based on the temperature difference between adjacent areas, and compare it with the preset deformation threshold to determine whether there is a risk of structural failure. If so, calculate the deformation risk value.

[0020] Preferably, the specific operation method of step S2 is:

[0021] Obtain the length and floor height of the building, and calculate the smoke diffusion volume based on the heat distribution range. Obtain the type of building material and match it with the corresponding thermal conductivity coefficient of each material stored in the security database to obtain the material thermal conductivity coefficient. Then, calculate the smoke diffusion intensity of each temperature measurement node based on the set thermal radiation attenuation rate.

[0022] Based on the smoke diffusion volume, material thermal conductivity and smoke diffusion intensity, the smoke concentration value of each monitoring point is derived through a spatial distribution algorithm, and a three-dimensional diffusion model is constructed based on the spatial coordinates of the monitoring points and the smoke concentration values.

[0023] Preferably, the specific operation method of step S3 is:

[0024] S31. Read the temperature data of each monitoring point and calculate the temperature distribution dispersion by standard deviation;

[0025] S32. Calculate the temperature difference and spatial spacing of each group of adjacent monitoring points. Calculate the local thermal stress by multiplying the temperature gradient by the material's thermal expansion coefficient. Calculate the weighted average to obtain the overall thermal stress index.

[0026] S33. Extract the material yield strength set in the safety database and compare it with the overall thermal stress index. If it is lower than the set threshold, it is determined to be a high-risk area; otherwise, it is marked as a safe area.

[0027] Preferably, the specific operation method of step S4 is:

[0028] S41. Obtain the original geometric parameters of the building structure, calculate the temperature difference between the high-temperature area and the adjacent areas, and calculate the theoretical deformation based on the material linear expansion coefficient;

[0029] S42. Obtain actual deformation data through a laser rangefinder. If the theoretical deformation exceeds a preset threshold, an alarm is triggered and a deformation risk coefficient is output.

[0030] Preferably, the specific operation method of the environmental data analysis module is:

[0031] Multiple groups of wind speed sensors are deployed in the monitoring area to collect air velocity data in time series, and the smoke concentration values ​​at the corresponding time points are extracted synchronously. The dynamic correlation between air velocity and smoke diffusion is calculated through covariance analysis, and the velocity-concentration distribution matrix is ​​generated to correct the parameter weights of the fire dynamic assessment model.

[0032] Preferably, the specific operation method of the fire source locating module is:

[0033] Multispectral imaging equipment is used to obtain heat source distribution data in the monitored area. Abnormal heat source points are separated through background difference algorithm. A three-dimensional positioning map of the fire source is generated by combining temperature intensity and spatial coordinates, and is updated to the fire dynamic assessment model in real time.

[0034] Preferably, the specific analysis method of the input parameters and output parameters of the fire dynamic assessment model is:

[0035] The input parameters include temperature data, building structure parameters, air velocity data, and fire source coordinate data. The temperature data includes the temperature value, acquisition time, and spatial coordinates of each node. The building structure parameters include material type, geometric dimensions, and thermal conductivity.

[0036] The output parameters are defined to include smoke diffusion range, structural thermal stress index, deformation risk value and fire source threat level, and comprehensive threat assessment results are generated through multi-dimensional data fusion.

[0037] Preferably, the specific operation method of the model building module is:

[0038] The input parameters are standardized and a feature vector set is generated. Dynamic weight coefficients are assigned to each indicator in the output parameters. The comprehensive fire threat index is calculated using a weighted summation algorithm, and the threat level is generated according to the preset level classification standard.

[0039] The time series prediction algorithm is used to train the model parameters, and the real-time data is input into the trained fire dynamic assessment model to output the current threat level and risk parameter list.

[0040] Preferably, the safety database includes a library of thermodynamic parameters of building materials, a library of historical fire case characteristics, and a set of dynamic threshold adjustment rules.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] In terms of fire parameter analysis, by deploying temperature measurement nodes in a grid and combining them with infrared sensors, it is possible to obtain temperature data from each node in real time according to the set acquisition cycle, generating a detailed temperature change record table. Based on this table, the system can not only calculate the heat accumulation gradient in the monitored area, and accurately estimate the smoke diffusion range and fire spread rate by combining the spatial parameters of the building structure, but also analyze the thermal stress of the building structure through the uniformity of temperature distribution, obtain the tolerance coefficient of the building material and determine the structural risk level. At the same time, the material deformation is analyzed through the temperature difference between adjacent areas, and the risk of structural failure is determined by comparing it with the preset threshold. This comprehensive fire parameter analysis realizes a dynamic and accurate assessment of the fire development trend and the safety of the building structure, providing key data support for early fire warning and structural protection.

[0043] The environmental data analysis module deploys multiple sets of wind speed sensors to collect air velocity data in a time series format. It also simultaneously extracts smoke concentration values ​​at corresponding time points. Covariance analysis is used to calculate the dynamic correlation between the two, generating a velocity-concentration distribution matrix. This matrix effectively adjusts the parameter weights of the dynamic fire assessment model, allowing the model to fully account for the impact of environmental factors on fire development, thereby improving the scientific nature and reliability of the assessment results. This analytical approach, combining environmental factors with fire parameters, is more consistent with actual fire scenarios and provides a more realistic reference for firefighting decision-making.

[0044] The fire source location module utilizes multispectral imaging equipment and a background difference algorithm to quickly and accurately acquire heat source distribution data within the monitored area. It then isolates abnormal heat source points and combines temperature intensity with spatial coordinates to generate a three-dimensional fire source location map, which is then updated in real time to the fire dynamic assessment model. This technology enables precise positioning and dynamic tracking of fire sources, enabling firefighters to instantly identify their location and intensity. This provides critical information for developing targeted firefighting and rescue plans, significantly improving the efficiency and effectiveness of fire prevention and control.

[0045] The model building module generates a feature vector set by standardizing the input parameters, assigning dynamic weight coefficients to each output parameter indicator, calculating the comprehensive fire threat index using a weighted summation algorithm, and training the model parameters in conjunction with a time series prediction algorithm. This intelligent model building approach enables the dynamic integration and comprehensive evaluation of multi-dimensional data. It can output the current threat level and risk parameter list based on real-time data, providing fire management personnel with timely and accurate fire risk assessment results, helping them make scientific and reasonable decisions. Furthermore, the model's dynamic training and optimization mechanisms enable it to adapt to fire monitoring needs in different scenarios and time periods, further enhancing the system's applicability and reliability.

[0046] The safety database not only stores the thermodynamic parameters of building materials but also includes a library of historical fire case characteristics and a set of dynamic threshold adjustment rules. This rich database provides a comprehensive reference for fire parameter analysis and risk assessment, enabling the system to automatically adjust assessment criteria and thresholds based on different building structures and environmental conditions, enhancing the system's flexibility and adaptability. For example, by analyzing the characteristics of historical fire cases, the system can draw on past experience to more accurately determine current fire trends and potential risks. The dynamic threshold adjustment rules ensure that the system maintains excellent monitoring and assessment performance across different time periods and environmental conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a working principle diagram of the real-time fire protection monitoring and data transmission system for fire protection engineering construction according to the present invention;

[0048] Figure 2 Flowchart for smoke dispersion calculation;

[0049] Figure 3 Flowchart for structural deformation analysis;

[0050] Figure 4 This is the flowchart of the environmental data analysis module;

[0051] Figure 5 This is the flow chart of the fire source location module. DETAILED DESCRIPTION

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0053] See also Figure 1-Figure 5 The present invention relates to a real-time fire monitoring and data transmission system for fire engineering construction. The system realizes real-time monitoring and data transmission of fire through the collaborative work of multiple modules, and specifically includes the following steps:

[0054] The system includes fire parameter analysis module, environmental data analysis module, fire source location module, model building module and safety database.

[0055] The fire parameter analysis module is used to generate a temperature change record table based on the temperature data of the monitoring area, from which the smoke diffusion rate is estimated, and then a safety assessment of the thermal stress and material deformation of the building structure is conducted. The specific process is as follows: First, a number of temperature measurement nodes are arranged in the monitoring area according to the grid principle. During the fire monitoring process, according to the set collection cycle, the temperature data of each temperature measurement node is acquired by infrared sensors. The coordinates, temperature value, and collection time of each node are recorded to generate a temperature change record table (step S1). Next, the heat accumulation gradient of the monitoring area is calculated based on the temperature change of each node during the current collection cycle. At the same time, the spatial parameters of the building structure are obtained and the smoke diffusion range is calculated based on the heat gradient distribution, thereby estimating the fire spread rate (step S2). Then, the thermal stress of the building structure is analyzed based on the uniformity of the temperature distribution, the tolerance coefficient of the building material is obtained, and the risk level of the structure is determined (step S3). Finally, the material deformation is analyzed based on the temperature difference between adjacent areas. The deformation is compared with the preset deformation threshold to determine whether there is a risk of structural failure. If so, the deformation risk value is calculated (step S4).

[0056] The environmental data analysis module is used to acquire air velocity data, correlate it with smoke diffusion rate, and measure the dynamic distribution relationship between air velocity and smoke concentration. Multiple wind speed sensors are deployed in the monitoring area to collect air velocity data in a time series. Smoke concentration values ​​at corresponding time points are simultaneously extracted. Covariance analysis is used to calculate the dynamic correlation between air velocity and smoke diffusion, generating a velocity-concentration distribution matrix that is used to modify the parameter weights of the fire dynamic assessment model.

[0057] The fire source location module is used to obtain the coordinate data of each fire source and the thermal radiation distribution data at the corresponding fire source intensity. Multispectral imaging equipment is used to obtain heat source distribution data in the monitored area. Anomalous heat source points are separated using a background difference algorithm. A three-dimensional fire source location map is generated by combining temperature intensity and spatial coordinates, and is updated in real time to the fire dynamic assessment model.

[0058] The model construction module is used to construct a dynamic fire assessment model using temperature data, building structure data, air velocity data, and fire source data as input parameters, outputting a comprehensive threat level for fire spread. Input parameters are defined as temperature data, building structure parameters, air velocity data, and fire source coordinate data. Temperature data includes the temperature value, acquisition time, and spatial coordinates of each node, and building structure parameters include material type, geometric dimensions, and thermal conductivity. Output parameters are defined as smoke diffusion range, structural thermal stress index, deformation risk value, and fire source threat level. Comprehensive threat assessment results are generated through multi-dimensional data fusion. Input parameters are standardized and a feature vector set is generated. Dynamic weight coefficients are assigned to each indicator in the output parameters. A weighted summation algorithm is used to calculate a comprehensive fire threat index, and a threat level is generated based on preset classification standards. A time series prediction algorithm is used to train model parameters. Real-time data is input into the trained dynamic fire assessment model, outputting the current threat level and a list of risk parameters.

[0059] The safety database is used to store the corresponding thermal conductivity coefficient of each building material, the set smoke concentration threshold and the structural deformation threshold. The safety database also includes a building material thermodynamic parameter library, a historical fire case feature library and a dynamic threshold adjustment rule set.

[0060] Example 1

[0061] In the fire parameter analysis module, step S2 is specifically implemented as follows: Basic spatial parameters of the building structure, including its length and floor height, are obtained. These parameters can be obtained from architectural design drawings or on-site surveys to clarify the building's three-dimensional spatial dimensions. Furthermore, the heat accumulation gradient, calculated based on the temperature changes at each temperature measurement node during the current acquisition cycle, is used to determine the heat distribution within the monitored area. The heat accumulation gradient reflects the rate of heat accumulation and distribution within a unit space and is a key foundational data for assessing smoke spread.

[0062] Obtain the type of building material, such as concrete, steel, or glass. Each material has different thermal conductivity characteristics. By matching the acquired material type with the corresponding thermal conductivity coefficients stored in the secure database, the specific material thermal conductivity coefficient can be obtained. The secure database contains a pre-stored table of thermal conductivity coefficients for common building materials, based on the material's physical properties and relevant industry standards, ensuring data accuracy and authority.

[0063] After obtaining the material's thermal conductivity, the smoke diffusion intensity at each temperature measurement node must be calculated based on the set thermal radiation attenuation rate. The thermal radiation attenuation rate is a preset physical parameter that simulates the attenuation of thermal radiation during propagation due to factors such as air absorption and scattering. The calculation combines the heat distribution range, the material's thermal conductivity, and the thermal radiation attenuation rate with a specific mathematical formula (such as one based on the laws of heat conduction and radiation propagation models) to determine the smoke diffusion intensity at each temperature measurement node. This value reflects the smoke's diffusion capacity and potential threat at that node.

[0064] Based on the smoke diffusion volume, material thermal conductivity, and smoke diffusion intensity, a spatial distribution algorithm is used to derive smoke concentration values ​​at each monitoring point. The smoke diffusion volume is calculated by multiplying the building's length and floor height by the heat distribution range, reflecting the range occupied by smoke in three-dimensional space. The spatial distribution algorithm can employ diffusion models from fluid mechanics, such as the Gaussian diffusion model or the finite volume method. These algorithms simulate the concentration distribution of smoke at different locations based on smoke diffusion characteristics and spatial parameters. Taking the Gaussian diffusion model as an example, its basic principle is to assume that smoke follows a normal distribution during diffusion. By inputting parameters such as smoke source intensity, average wind speed, and atmospheric stability, smoke concentration values ​​at different spatial coordinates are calculated. In this embodiment, smoke diffusion intensity is used as the source intensity parameter. The influence of material thermal conductivity on the diffusion process is taken into account (for example, materials with high thermal conductivity accelerate heat transfer, thereby affecting smoke diffusion speed). The smoke concentration value at each monitoring point is calculated through an iterative algorithm.

[0065] A three-dimensional diffusion model is constructed based on the spatial coordinates of the monitoring points and their corresponding smoke concentration values. The spatial coordinates of the monitoring points are determined by a positioning system (such as GPS or on-site coordinate calibration) during grid layout. Each monitoring point corresponds to a coordinate point (x, y, z) in three-dimensional space. The smoke concentration values ​​at each point are used as attribute values, and three-dimensional modeling software or algorithms are used to convert the discrete monitoring point data into a continuous three-dimensional spatial field distribution model. For example, kriging interpolation or triangulation can be used to interpolate discrete data to generate continuous smoke concentration surfaces or volumes, visually displaying the three-dimensional diffusion pattern of smoke within the monitoring area, including key information such as the diffusion range, peak concentration area, and diffusion front.

[0066] In practice, the measurement accuracy of building length and floor height must meet engineering requirements. Laser rangefinders or total stations are typically used for measurement, with errors controlled to the centimeter level. The density of temperature measurement nodes is determined by the importance of the monitored area and the fire risk level. For example, in densely populated or flammable areas, the node spacing can be set to 1-2 meters, while in ordinary areas, the spacing can be increased to 3-5 meters. Infrared sensors must achieve an accuracy of ±0.5°C to ensure the accuracy of temperature data. The thermal conductivity coefficient table in the security database is regularly updated to incorporate data on new building materials to ensure the timeliness and reliability of the matching process.

[0067] The choice of spatial distribution algorithm should be tailored to the complexity of the monitored area. For regularly shaped architectural spaces, such as rectangular rooms, a Gaussian diffusion model can be used for rapid calculations. For complex structures, such as multi-story buildings with corridors and stairwells, a finite volume method or computational fluid dynamics (CFD) model should be employed to more accurately simulate smoke diffusion between obstacles. The visualization interface of the three-dimensional diffusion model must be able to update in real time, dynamically displaying the smoke diffusion process, allowing monitoring personnel to promptly monitor the fire's development.

[0068] Furthermore, when calculating the smoke diffusion volume, it is necessary to consider the barrier effect of internal building structures, such as walls and partitions, on smoke diffusion. For areas with partitions, the monitoring area can be divided into multiple sub-areas, and the smoke diffusion volume of each sub-area calculated separately. Diffusion coupling calculations are then performed through the vents or gaps between the sub-areas to improve the accuracy of the overall calculation. The thermal conductivity of materials not only affects the transfer rate of thermal radiation but also indirectly affects air flow by changing the temperature distribution of the building structure, thereby affecting the diffusion path and speed of smoke. Therefore, the coupling of multiple physical fields must be comprehensively considered during model construction.

[0069] Through the above steps, step S2 completes the complete process from temperature data to smoke diffusion rate estimation. Through multi-parameter fusion and algorithmic modeling, it provides critical smoke diffusion data for fire parameter analysis, providing an important basis for subsequent fire spread assessments and building structure safety assessments. This implementation method is closely integrated with engineering practice. Through standardized data collection processes, precise parameter matching, and scientific algorithmic models, it ensures the accuracy and reliability of smoke diffusion rate estimation, meeting the needs of real-time fire monitoring in fire protection engineering construction.

[0070] Example 2

[0071] In the fire parameter analysis module, step S3 is implemented as follows: First, temperature data from each monitoring point is read. This data, including the temperature value, acquisition time, and spatial coordinates of each node, is transmitted to the analysis module in real time via the data acquisition system. The temperature data is read at a frequency consistent with the acquisition cycle to ensure the latest monitoring information is obtained. Subsequently, the temperature distribution dispersion is calculated using the standard deviation to measure the temperature uniformity within the monitoring area. The standard deviation is calculated as follows:

[0072] ;

[0073] in, is the temperature value of each monitoring point, is the average temperature, and n is the number of monitoring points. A larger dispersion indicates a more uneven temperature distribution, which may indicate the risk of local thermal stress concentration.

[0074] Next, we construct a temperature gradient analysis unit using adjacent monitoring points as a group. For each group, we calculate two key parameters: temperature difference and spatial distance. The temperature difference is calculated by subtracting the temperature values ​​of two adjacent points and reflects the driving force of heat transfer. The spatial distance is calculated by the coordinate difference between the two points using the Euclidean distance formula:

[0075] ;

[0076] in, and is the spatial coordinate of adjacent monitoring points. The temperature gradient is defined as the ratio of the temperature difference to the spatial distance, that is, Its physical meaning is the temperature change rate per unit distance, which is the core parameter for thermal stress calculation.

[0077] The local thermal stress is then calculated by multiplying the temperature gradient by the material's thermal expansion coefficient. In the thermodynamic parameter library of building materials stored in the secure database, different materials (such as concrete, steel, and aluminum alloy) have different values. For example, the thermal expansion coefficient of steel is approximately , concrete is about , local thermal stress The calculation formula is:

[0078] ;

[0079] Where E is the elastic modulus of the material, which is also stored in the security database. The elastic modulus reflects the ability of the material to resist elastic deformation. For example, the elastic modulus of steel is approximately , concrete is about Through this formula, the distribution of the temperature field is converted into the stress distribution inside the structure, and the magnitude of the local thermal stress is quantified.

[0080] After calculating the local thermal stress of all adjacent monitoring point groups, a weighted average is performed to obtain the overall thermal stress index. The weighting factor is determined based on the spatial importance of the monitoring point. For example, monitoring points close to load-bearing structures or flammable areas are given a higher weight. The weighted average formula is:

[0081] ;

[0082] Where m is the total number of adjacent monitoring point groups, is the weight coefficient of group i ( ), is the local thermal stress value of group i. The overall thermal stress index comprehensively considers the thermal stress contribution of each local area and reflects the overall thermal stress level of the entire monitoring area.

[0083] Finally, extract the material yield strength set in the safety database , and compare it with the overall heat stress index For comparison. The yield strength of a material is the critical stress value at which the material undergoes significant plastic deformation. For example, the yield strength of Q235 steel is 235MPa, and the axial compressive yield strength of C30 concrete is approximately 14.3MPa. , then the area is judged to be a safe area, indicating that the structural material has not yet reached the plastic deformation stage; if , it is determined to be a high-risk area, indicating that the structure may face the risk of failure due to excessive thermal stress.

[0084] In practice, monitoring points should be arranged in a grid-like pattern, ensuring uniform spacing between adjacent points to facilitate standardized temperature gradient calculations. For irregular building structures, adaptive meshing techniques can be used to increase the number of monitoring points in complex or heat-sensitive areas, improving the accuracy of local thermal stress calculations. The data acquisition system must be resistant to interference, preventing electromagnetic radiation and environmental noise from affecting the accuracy of temperature data. For example, shielded cables should be used to transmit temperature signals, or sensors should be designed for electromagnetic compatibility.

[0085] Material mechanical parameters in the security database (such as thermal expansion coefficient, elastic modulus, and yield strength) must be calibrated according to national standards (such as GB50010 "Code for Design of Concrete Structures" and GB50017 "Standard for Design of Steel Structures") or international standards (such as ISO6892 "Tensile Tests of Metallic Materials") to ensure their authority and reliability. Weighting factors should be set in conjunction with mechanical analysis of the building structure, such as through finite element simulation to determine the stress sensitivity coefficients of different regions, so that the weighted average results are closer to the actual stress conditions.

[0086] The calculation of temperature distribution dispersion can be achieved through programming, such as using Python's NumPy library or MATLAB's statistical functions, to enable rapid data processing and analysis. For large-scale monitoring areas, a distributed computing architecture can be adopted to process temperature data in blocks to improve computational efficiency. When comparing the thermal stress index with the yield strength, the system must trigger a real-time early warning mechanism. If a high-risk area is identified, monitoring personnel are immediately alerted through audio and visual alarms, text message notifications, and other means. The spatial location of the high-risk area is also marked on the monitoring interface to facilitate rapid response and disposal.

[0087] Furthermore, the time series nature of temperature data must be considered, and thermal stress trends can be analyzed by comparing historical data. For example, if the overall thermal stress index in a region continues to rise over multiple consecutive acquisition cycles, even if it has not yet exceeded the yield strength, it should be considered a potential risk area, requiring increased monitoring frequency or the activation of emergency response plans. The thermal expansion coefficient and elastic modulus of a material may exhibit nonlinear changes with temperature. Temperature-dependent material property parameters can be incorporated into high-precision assessments, and corrections can be made through piecewise functions or polynomial fitting to improve the accuracy of thermal stress calculations.

[0088] Through the above steps, step S3 completes the entire process from temperature data to building structure thermal stress assessment. Through statistical methods, material mechanics theory, and data comparative analysis, the thermal stress risk level of the structure is quantified. This implementation approach, in line with actual project needs, employs standardized data processing procedures and scientific mechanical models to ensure the accuracy and reliability of thermal stress assessments, providing a key basis for fire monitoring systems to promptly identify structural safety hazards and formulate protective measures.

[0089] Example 3

[0090] In the fire parameter analysis module, step S4 is specifically implemented as follows: The original geometric parameters of the building structure are obtained. These parameters include basic dimensional data such as the length, width, and height of building components, as well as spatial topological information such as node coordinates and component connection relationships. These parameters are typically obtained from architectural design drawings or 3D modeling files (such as BIM models) to ensure that the data is consistent with the actual structure. The original geometric parameters serve as the basis for calculating material deformation, and their accuracy directly affects the reliability of subsequent analysis.

[0091] Determine the division between high-temperature areas and adjacent areas. A high-temperature area is defined as a monitoring zone where the temperature exceeds a preset high-temperature threshold. This threshold is set based on the building's function and the heat resistance of the materials (e.g., 200°C for ordinary residential buildings, or adjusted based on the type of combustible materials in industrial plants). Adjacent areas are monitoring areas directly adjacent to a high-temperature area, determined by ensuring that the spatial coordinate distance between the monitoring points is less than or equal to a preset spacing (e.g., 1 meter).

[0092] Calculate the temperature difference between the high temperature area and the adjacent area The temperature difference is the average temperature value of the monitoring point in the high temperature area The average temperature value of the monitoring points in the adjacent areas Subtracting them, we get:

[0093] ;

[0094] in, is the arithmetic mean of the temperature values ​​of all monitoring points in the high temperature area, The temperature difference is the arithmetic mean of the temperature values ​​of all monitoring points in adjacent areas. The temperature difference reflects the heat transfer gradient between adjacent areas and is the key factor driving the thermal deformation of the material.

[0095] The theoretical deformation is calculated based on the linear expansion coefficient of the material. The building material thermodynamic parameter library stored in the secure database characterizes the linear elongation of the material under unit temperature change, in units of The linear expansion coefficients of different materials vary significantly. For example, steel is approximately , concrete is about , glass is about Theoretical deformation The calculation formula is:

[0096] ;

[0097] in, is the original length of the building component (in meters), taking the geometric length of the component at the junction of the high-temperature area and the adjacent area. This formula is based on the physical principle of thermal expansion and contraction, assuming that the material expands or contracts uniformly under temperature changes and that deformation is in the elastic phase.

[0098] The actual deformation data is obtained by using a laser rangefinder. The laser rangefinder should be placed at key monitoring locations of the building structure (such as beams, column nodes, wall corners, etc.). By emitting a laser beam and measuring the time difference of the reflected signal, the actual length change of the component can be accurately obtained. The laser rangefinder must achieve submillimeter accuracy (e.g., ±0.1 mm) to meet the high-precision requirements of deformation monitoring. The actual deformation data must be collected at a frequency synchronized with the temperature data to ensure a one-to-one correspondence between the two in the time dimension.

[0099] The theoretical deformation With the preset deformation threshold The preset deformation threshold is set according to the design safety standard of the building structure, usually 0.1%-0.5% of the original length of the component (for example, for a steel beam with an original length of 5 meters, the threshold can be set to 5 mm-25 mm). , then it is determined that there is a risk of structural failure, and the system automatically triggers the alarm mechanism and sends an alarm to the fire monitoring center through sound and light signals, text messages or network notifications. At the same time, the deformation risk value is calculated based on the ratio of the theoretical deformation variable to the threshold value. , the calculation formula is:

[0100] ;

[0101] This risk value is dimensionless and is used to quantify the degree of danger of structural deformation. A larger value indicates a higher risk. , it is marked as safe and continues to be monitored in real time.

[0102] In practice, the acquisition of original geometric parameters requires two-person verification to ensure that the drawing data is consistent with the on-site structure. For complex structures (such as curved curtain walls and special-shaped components), 3D laser scanning technology can be used to generate point cloud data, and reverse modeling can be used to obtain accurate geometric parameters. The demarcation of high-temperature areas can be dynamically adjusted based on the temperature field cloud map. For example, by setting a temperature gradient threshold (e.g., a temperature change exceeding 50°C per meter), the boundaries of high-temperature areas can be determined to avoid misjudgments caused by a single threshold.

[0103] Laser rangefinder placement should adhere to the following principles: 1) Cover all boundaries between high-temperature areas and adjacent areas; 2) Avoid obstacles that could obstruct the laser path; and 3) Ensure the instrument is securely mounted to prevent vibration from affecting measurement accuracy. For multi-story buildings or high-altitude structures, drones equipped with laser rangefinder modules can be used for periodic scanning to address deformation monitoring in areas inaccessible to humans.

[0104] Preset deformation thresholds in the security database must be categorized and stored by building structure type. For example, different threshold systems correspond to steel structures, reinforced concrete structures, and masonry structures. The threshold setting process should refer to national standards such as the Code for Loads on Building Structures (GB50009) and the Standard for Design of Steel Structures (GB50017), and be determined through a combination of structural mechanics calculations and engineering experience. For buildings undergoing renovation or reinforcement, the thresholds can be dynamically adjusted based on structural inspection reports.

[0105] When calculating theoretical deformation, the effects of multi-directional thermal deformation must be considered. For example, for rectangular cross-section components, in addition to linear deformation along the length, deformation along the width and height may also occur. However, for preliminary assessments, only deformation along the principal load direction can be calculated. For higher-precision analysis, a three-dimensional thermal expansion model can be introduced to account for the anisotropic properties of the material. Furthermore, temperature difference calculations must exclude interference from environmental factors (such as sunlight and air conditioning airflow). Time series analysis of temperature data (e.g., removing periodic fluctuations) can be used to extract the actual temperature variation caused by the fire.

[0106] When the system triggers an alarm, a 3D model of the risk area automatically pops up on the monitoring interface, noting the high-temperature zone, adjacent areas, and deformation monitoring points. The distribution of deformation risk values ​​is displayed using a color gradient. The system also automatically generates a risk report containing key information such as temperature differences, theoretical deformation values, actual deformation data, and risk values. This information is then used by fire and structural engineers for joint analysis and development of targeted emergency response plans (such as temporary shoring, cooling, and evacuation).

[0107] In order to improve the reliability of deformation assessment, a redundant monitoring mechanism can be introduced. For example, a laser rangefinder and a strain gauge are arranged at the same monitoring point, and the strain value measured by the strain gauge is Inverse deformation , and compare it with the laser ranging results. If the difference between the two exceeds a preset error range (such as 5%), the system automatically marks the data as abnormal and prompts you to calibrate the device or troubleshoot the problem.

[0108] Through the above steps, step S4 completes the entire process from temperature data to material deformation risk assessment. Through geometric parameter analysis, thermal deformation calculation, measured data comparison, and risk quantification, a real-time early warning mechanism for structural failure risk is established. This implementation combines engineering measurement technology with material mechanics theory, employing standardized data processing procedures and precise monitoring methods to ensure the accuracy and timeliness of deformation assessments, providing key technical support for structural safety protection in fire protection projects.

[0109] Example 4

[0110] In the environmental data analysis module, its specific implementation method revolves around the dynamic correlation analysis of air flow rate and smoke concentration. First, multiple groups of wind speed sensors need to be reasonably deployed in the monitoring area, and the layout principle needs to comprehensively consider the building space structure and fire risk distribution. For example, in key air flow nodes such as atriums, corridor intersections, and stairwell entrances of high-rise buildings, as well as areas prone to smoke accumulation such as gaps between warehouse shelves and around factory equipment, sensors are deployed in a grid or array manner. The distance between adjacent sensors is set to 5-10 meters according to the spatial scale to ensure coverage of the main air flow channels and the surrounding areas of potential fire sources. Wind speed sensors can be selected from ultrasonic or thermal anemometers. The former is suitable for high dust or harsh environments, and the latter has high sensitivity and can accurately capture micro-wind speed changes as low as 0.1 meters per second.

[0111] The sensor collects air velocity data in a time series, synchronized with the temperature data collection cycle of the fire parameter analysis module (e.g., once per second) to ensure data alignment in the temporal dimension. During each acquisition, the sensor simultaneously records the current timestamp and three-dimensional spatial coordinates (obtained via built-in GPS or on-site coordinate calibration) to form a structured data frame. For example, if a sensor at coordinates (X=10m, Y=8m, Z=3m) records an air velocity of 2.3m / s at 14:00:01, the data frame format is [Time: 14:00:01, Coordinates: (10,8,3), Velocity: 2.3m / s].

[0112] The smoke concentration values ​​at the corresponding time points are extracted simultaneously. This data is derived from the concentration data at each monitoring point output by the three-dimensional diffusion model in the fire parameter analysis module. For example, at 2:00:01 PM, the smoke concentration at the temperature measurement node adjacent to the wind speed sensor coordinates (10, 8, 3) is calculated to be 150 ppm (parts per million). Using a spatial matching algorithm, these two values ​​are linked to form a "flow rate-concentration" data pair (2.3 m / s, 150 ppm).

[0113] Covariance analysis is used to calculate the dynamic correlation between air velocity and smoke dispersion. Covariance analysis is a statistical method used to measure the coordinated variation trends of two variables in a time series. In practice, the system collects all "flow rate-concentration" data pairs over a period of time (e.g., 30 minutes) to form a two-dimensional dataset. For example, in a certain monitoring area, 1,800 data pairs were obtained, covering different wind speed ranges (0.5-5.0 m / s) and corresponding smoke concentration values ​​(50-500 ppm). The direction and strength of the correlation between the two are determined by calculating the covariance value: a positive covariance indicates a positive correlation between wind speed and smoke concentration (e.g., concentration increases with increasing wind speed); a negative covariance indicates a negative correlation (e.g., concentration decreases with increasing wind speed). The larger the absolute value, the stronger the correlation.

[0114] Generating a velocity-concentration distribution matrix is ​​the core output of this module. The matrix uses air velocity as the row dimension and smoke concentration as the column dimension. Data pairs are divided into intervals and the distribution frequency is calculated. For example, wind speed is divided into five intervals: 0-1 m / s, 1-2 m / s, 2-3 m / s, 3-4 m / s, and 4-5 m / s, and smoke concentration is divided into five intervals: 0-100 ppm, 100-200 ppm, 200-300 ppm, 300-400 ppm, and 400-500 ppm, constructing a 5×5 matrix. Each matrix cell records the number of data pairs within the corresponding wind speed and concentration interval, reflecting the probability of occurrence of that combination. For example, the value of 230 in the matrix cell for "Wind speed 1-2 m / s, concentration 100-200 ppm" indicates that this wind speed and concentration combination occurred 230 times during the statistical period, accounting for 12.8% of the total data volume.

[0115] This distribution matrix is ​​used to modify the parameter weights of the fire dynamic assessment model. In the model construction module, parameters such as temperature, building structure, air velocity, and fire source are weighted and summed to generate a comprehensive threat index. For example, the original weight distribution is 40% for temperature, 30% for building structure, 20% for air velocity, and 10% for fire source. By analyzing the matrix, it was found that when the wind speed is greater than 3m / s, the probability of smoke concentration exceeding 300ppm increases significantly, indicating that the impact of high wind speed on smoke diffusion is far greater than expected. Therefore, the system automatically increases the weight of the air velocity parameter to 25% and adjusts the weights of other parameters accordingly (such as temperature to 35%), so that the model focuses more on risk assessment in high wind speed scenarios.

[0116] In practice, wind speed sensors should be placed away from sources of human interference, such as air conditioning vents and areas with frequently opened doors and windows, to ensure that the collected data reflects actual air movement caused by natural airflow or fire. For large buildings (such as stadiums and airport terminals), a layered deployment strategy can be adopted: focusing on monitoring low-speed airflow in areas with human activity at ground level, and placing sensors below the roof to monitor high-speed airflow caused by the stack effect.

[0117] During data synchronization, timestamp errors (such as clock skew between devices) may occur. Network Time Protocol (NTP) clock calibration is required to ensure precise temporal alignment of wind speed and concentration data. The spatial matching algorithm uses nearest neighbor interpolation. When wind speed sensors and temperature measurement nodes do not completely overlap, the concentration value from the nearest temperature measurement node is selected as the associated data, with the error controlled to within half the distance between the sensors.

[0118] The covariance analysis time window can be dynamically adjusted: in the early stages of a fire (when smoke concentrations are low), a longer time window (e.g., 60 minutes) is used to accumulate sufficient data; during the fire's development phase (when concentrations rise rapidly), a shorter window (e.g., 5 minutes) is used to track correlation changes in real time. The distribution matrix is ​​updated every 10 minutes to ensure that model parameters reflect the current environmental characteristics.

[0119] For example, a commercial complex deployed 10 wind speed sensors in the basement supermarket area. Three of these sensors were located near the aisles (wind speed 0.5-1.5 m / s), four at the atrium escalator entrances (wind speed 1.0-2.5 m / s), and three near the smoke exhaust vents (wind speed 2.0-4.0 m / s). During a simulated fire experiment, after a fire broke out in the atrium, the wind speed sensors detected a gradual increase in wind speed to 1.2 m / s in the aisles, corresponding to a rise in smoke concentration from 80 ppm to 220 ppm. The wind speed at the atrium escalator entrances remained stable at 1.8 m / s, with the concentration rising to 350 ppm. The wind speed near the smoke exhaust vents reached 3.5 m / s, with the concentration remaining around 450 ppm. Covariance analysis revealed a strong positive correlation between wind speed and concentration in the atrium (covariance value 0.85), indicating that airflow accelerated the spread of smoke into the atrium. In contrast, due to the mechanical smoke extraction in the smoke vent area, wind speed and concentration showed a negative correlation (covariance value -0.68), indicating that high-speed airflow effectively reduced local concentration. Based on this, the system increased the weight of the air velocity parameter in the atrium from 20% to 30%, and strengthened the diffusion prediction for this area in the model.

[0120] Through the above process, the environmental data analysis module achieves a closed loop from data collection, correlation analysis, and model modification. By capturing the dynamic relationship between air velocity and smoke concentration in real time, it provides environmental dynamics support for fire assessment. This implementation approach, combined with specific building scenarios, ensures the dynamic adaptability of model parameters through scientific deployment strategies, precise data synchronization, and statistical analysis, enhancing the fire monitoring system's responsiveness to complex environments.

[0121] Example 5

[0122] The fire source location module uses multispectral imaging technology and data processing algorithms to accurately locate and dynamically track fire sources. For example, an industrial plant, containing complex structures such as production line equipment, storage racks, and ventilation ducts, requires real-time monitoring of potential fire sources (such as overheated electrical equipment and chemical leaks).

[0123] Deploy multispectral imaging equipment. Based on the factory building's height (approximately 10 meters) and monitoring range (80 meters long and 50 meters wide), four multispectral cameras were evenly distributed on the roof, providing comprehensive coverage. The cameras support multi-band imaging in visible light (400-700nm), near-infrared (700-1400nm), mid-infrared (3-5μm), and far-infrared (8-14μm), capturing the radiation signatures of heat sources at different temperatures. For example, initial overheating of electrical equipment (approximately 100-200°C) primarily emits radiation in the near-infrared band, while open flames (temperatures >500°C) produce strong signals in the mid-infrared and far-infrared bands.

[0124] The device continuously captures multispectral images of the monitored area at a set frame rate (e.g., 25 frames per second). Each image contains data from both visible and infrared bands at the same instant. For example, in one frame, the visible light image shows the conveyor belt in area A of production line operating normally, while the near-infrared image reveals an abnormally high temperature on the motor casing in that area (appearing as a bright red spot). The mid-infrared image shows a concentrated temperature distribution in the spot, and the far-infrared image shows thermal radiation spreading to surrounding shelves.

[0125] Abnormal heat sources are isolated using a background difference algorithm. This algorithm first establishes a background model for the monitored area. Based on multispectral image data from a fire-free environment (e.g., a continuous one-hour image of a factory in normal production), it calculates the mean and variance of each pixel in different bands to form a background feature library. When real-time images are received, the system performs a band-by-band difference calculation on each pixel, comparing the current pixel value with the corresponding value in the background model. If the brightness value of a pixel in the near-infrared band exceeds three standard deviations of the background mean, it is identified as an abnormal heat source. For example, the brightness value of the pixel in the motor housing area mentioned above in the near-infrared band is 4.2 times the background mean, triggering an abnormal flag.

[0126] A three-dimensional map of the fire source is generated by combining temperature intensity and spatial coordinates. Temperature intensity is calculated by converting infrared radiance values. According to Planck's radiation law, the relationship between radiance and temperature in different bands is nonlinear. The system includes built-in temperature conversion tables for each band. For example, the correspondence between radiance L and temperature T in the mid-infrared band (3-5μm) is obtained through table interpolation. When L = 1500W / m²·sr, it converts to a temperature T = 300°C. Spatial coordinates are calculated using camera calibration parameters. Using binocular vision, the three-dimensional coordinates (X, Y, Z) of the heat source are calculated based on the parallax of the four cameras' images of the same heat source point. For example, the coordinates of an abnormal heat source on a motor casing were determined to be (X = 20 meters, Y = 15 meters, Z = 2 meters) through triangulation using four cameras. This corresponds to an equipment floor height of 2 meters above the ground.

[0127] The real-time update process to the dynamic fire assessment model is as follows: The fire source location module generates a three-dimensional fire source location map every second, including parameters such as the heat source coordinates, temperature intensity, and radiation range. For example, at t=10:05:00, the map shows a heat source in the motor area with a temperature of 300°C and a radiation radius of 1.5 meters, marked as a Level 1 fire source (low intensity). At t=10:05:05, the temperature rises to 450°C and the radiation radius expands to 2.8 meters, upgrading it to a Level 2 fire source (medium intensity). After receiving this data, the model automatically updates the fire threat level and adjusts the source intensity parameters of the smoke dispersion model, for example, increasing the smoke dispersion intensity from the initial value of 1000 m³ / s to 1800 m³ / s.

[0128] In practice, multispectral imaging equipment requires regular radiometric and geometric calibration. Radiometric calibration is performed using a blackbody furnace (a standard heat source of known temperature) to ensure accurate temperature conversion. Geometric calibration utilizes a checkerboard calibration plate to correct image distortion caused by lens distortion. For example, cameras are calibrated monthly using a blackbody furnace. If a camera's temperature error exceeds ±5°C, the calibration process is automatically triggered.

[0129] The background difference algorithm must possess adaptive learning capabilities to address environmental changes (such as equipment movement within the factory or changes in lighting conditions). The system automatically collects 30 minutes of background images every morning (during non-production hours) and updates the background model. If there are unexpected environmental changes during daytime production (such as the addition of large equipment blocking part of the monitoring area), the operator can manually trigger a background model update to avoid false alarms.

[0130] For example, a multispectral camera detected an abnormal temperature (150°C) at a location on the bottom shelf of a warehouse using near-infrared wavelengths, marking it as a suspected heat source. As the fire progressed, the mid-infrared wavelength detected a temperature rise to 400°C, and smoke appeared in the visible light image, confirming the fire source. Three-dimensional positioning calculations determined the fire source's coordinates (X = 50 meters, Y = 30 meters, Z = 1.5 meters), located on the second shelf. A location map was pushed to the monitoring interface in real time, marking the fire source with a red cube, its edges flashing to indicate dynamic changes. A pseudo-color temperature gradient map was superimposed to display the thermal radiation distribution. Based on the fire source intensity data, the fire dynamic assessment model predicted that smoke would spread to the warehouse entrance within three minutes, triggering an evacuation alarm.

[0131] The fire source location module is deeply integrated with the video surveillance system. Monitoring personnel can click on a heat source in the location map to directly access the corresponding camera's real-time video feed, viewing the fire scene. For example, clicking on a heat source in the motor area brings up a split-screen display of visible light and infrared images from the cameras in that area. The visible light image on the left shows no obvious abnormalities on the device's exterior, while the infrared image on the right clearly reveals uneven temperature distribution on the motor casing, with a local hotspot reaching 350°C, assisting in the diagnosis of overheating caused by an internal coil short circuit.

[0132] Through the above process, the fire source location module implements a complete chain of processing, from multispectral image acquisition and anomaly detection to three-dimensional positioning. Integrating this with the specific scenario of an industrial plant, multi-band data fusion and real-time algorithm response ensures the accuracy and timeliness of fire source location. This implementation leverages the technical advantages of multispectral imaging to effectively distinguish different types of heat sources, providing the fire monitoring system with accurate basic fire source data, supporting the dynamic updating of fire spread models and risk level assessment.

[0133] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, 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 list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0134] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A real-time fire monitoring data transmission system for fire engineering construction, characterized in that: The system specifically includes the following modules: The fire parameter analysis module is used to generate a temperature change record table based on the temperature data of the monitoring area, from which the smoke diffusion rate is estimated, and then a safety assessment of the thermal stress and material deformation of the building structure is conducted; Environmental data analysis module, used to obtain air velocity data, correlate it with smoke diffusion rate, and measure the dynamic distribution relationship between air velocity and smoke concentration; Fire source positioning module, used to obtain the coordinate data of each fire source and the thermal radiation distribution data under the corresponding fire source intensity; The model building module is used to construct a fire dynamic assessment model using temperature data, building structure data, air velocity data, and fire source data as input parameters, and output a comprehensive threat level of fire spread; A security database is used to store the corresponding thermal conductivity coefficient of each building material, the set smoke concentration threshold and the structural deformation threshold; The fire parameter analysis module performs the following steps: S1. Deploy several temperature measurement nodes in the monitoring area according to the grid principle. During the fire monitoring process, according to the set collection cycle, the temperature data of each temperature measurement node is acquired through infrared sensors. The coordinates, temperature value and collection time of each node are recorded, and a temperature change record table is generated; S2. Calculate the heat accumulation gradient of the monitored area based on the temperature change of each node during the current acquisition cycle. Simultaneously, obtain the spatial parameters of the building structure and calculate the smoke diffusion range based on the heat gradient distribution to estimate the fire spread rate. S3. Analyze the thermal stress of the building structure based on the uniformity of temperature distribution, determine the tolerance coefficient of the building materials, and determine the risk level of the structure; S4. Analyze the material deformation based on the temperature difference between adjacent areas, compare it with a preset deformation threshold to determine whether there is a risk of structural failure, and if so, calculate the deformation risk value; The specific operation method of step S2 is: Obtain the length and floor height of the building, and calculate the smoke diffusion volume based on the heat distribution range. Obtain the type of building material and match it with the corresponding thermal conductivity coefficient of each material stored in the security database to obtain the material thermal conductivity coefficient. Then, calculate the smoke diffusion intensity of each temperature measurement node based on the set thermal radiation attenuation rate. Based on the smoke diffusion volume, material thermal conductivity and smoke diffusion intensity, the smoke concentration value of each monitoring point is derived through a spatial distribution algorithm, and a three-dimensional diffusion model is constructed based on the spatial coordinates of the monitoring points and the smoke concentration values; The specific analysis method of the input parameters and output parameters of the fire dynamic assessment model is as follows: The input parameters include temperature data, building structure parameters, air velocity data, and fire source coordinate data. The temperature data includes the temperature value, acquisition time, and spatial coordinates of each node. The building structure parameters include material type, geometric dimensions, and thermal conductivity. Output parameters are defined, including smoke diffusion range, structural thermal stress index, deformation risk value, and fire threat level, and comprehensive threat assessment results are generated through multi-dimensional data fusion; The specific operation method of the model construction module is: The input parameters are standardized and a feature vector set is generated. Dynamic weight coefficients are assigned to each indicator in the output parameters. The comprehensive fire threat index is calculated using a weighted summation algorithm, and the threat level is generated according to the preset level classification standard. The time series prediction algorithm is used to train the model parameters, and the real-time data is input into the trained fire dynamic assessment model to output the current threat level and risk parameter list.

2. A real-time fire protection monitoring and data transmission system for fire protection engineering construction according to claim 1, characterized in that: The specific operation method of step S3 is: S31. Read the temperature data of each monitoring point and calculate the temperature distribution dispersion by standard deviation; S32. Calculate the temperature difference and spatial spacing of each group of adjacent monitoring points. Calculate the local thermal stress by multiplying the temperature gradient by the material's thermal expansion coefficient. Calculate the weighted average to obtain the overall thermal stress index. S33. Extract the material yield strength set in the safety database and compare it with the overall thermal stress index. If it is lower than the set threshold, it is determined to be a high-risk area; otherwise, it is marked as a safe area.

3. A real-time fire protection monitoring and data transmission system for fire protection engineering construction according to claim 1, characterized in that: The specific operation method of step S4 is: S41. Obtain the original geometric parameters of the building structure, calculate the temperature difference between the high-temperature area and the adjacent areas, and calculate the theoretical deformation based on the material linear expansion coefficient; S42. Obtain actual deformation data through a laser rangefinder. If the theoretical deformation exceeds a preset threshold, an alarm is triggered and a deformation risk coefficient is output.

4. A real-time fire protection monitoring and data transmission system for fire protection engineering construction according to claim 1, characterized in that: The specific operation method of the environmental data analysis module is as follows: Multiple groups of wind speed sensors are deployed in the monitoring area to collect air velocity data in time series, and the smoke concentration values ​​at the corresponding time points are extracted synchronously. The dynamic correlation between air velocity and smoke diffusion is calculated through covariance analysis, and the velocity-concentration distribution matrix is ​​generated to correct the parameter weights of the fire dynamic assessment model.

5. A real-time fire protection monitoring and data transmission system for fire protection engineering construction according to claim 1, characterized in that: The specific operation method of the fire source positioning module is as follows: Multispectral imaging equipment is used to obtain heat source distribution data in the monitored area. Abnormal heat source points are separated through background difference algorithm. A three-dimensional positioning map of the fire source is generated by combining temperature intensity and spatial coordinates, and is updated to the fire dynamic assessment model in real time.

6. A real-time fire protection monitoring and data transmission system for fire protection engineering construction according to claim 1, characterized in that: The safety database includes a building material thermodynamic parameter library, a historical fire case feature library, and a dynamic threshold adjustment rule set.

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