A control method for a spraying device used to extinguish oil fires on the outer surface of an armored vehicle

Abstract

The present invention relates to the technical field of armored vehicle safety protection, and specifically to a control method for a spray device used to extinguish oil fires on the outer surface of an armored vehicle, including the following steps. Step 1: Based on the actual fire extinguishing requirements on the outer surface of the armored vehicle, corresponding sensors are matched for each important area on the outer surface of the armored vehicle, and the temperature data and environmental data on the outer surface of the armored vehicle are collected in real time to generate a basic data collection form. Step 2: Based on the basic data collection form, various types of data are preprocessed, and the sliding window method is used to calculate the amplitude of the temperature change on the outer surface of the armored vehicle in real time. A warning is issued when the amplitude of the temperature change reaches a set value, and an abnormal temperature detection report is generated. In the present invention, by analyzing the oxygen concentration provided by the airflow and its influence on the spread of the flame, a vehicle speed range that is conducive to reducing the flame intensity is matched, so as to prevent the rapid spread of the flame under the combined action of oxygen and airflow at the initial stage of the flame, and thus ensure that the foam can extinguish the flame efficiently in a short time.

Description

Technical Field

[0001] The present invention relates to the technical field of armored vehicle safety protection control, and particularly relates to a control method for a spraying device used to extinguish oil fires on the outer surface of an armored vehicle. Background Art

[0002] After petroleum is processed, its products can be divided into two categories. One category is oils or raw materials used in industrial production, such as solvent oil used in the production of grease, rubber, and paint; lubricating oil used as a lubricant on mechanical equipment; petrolatum used for rust prevention and pharmaceutical purposes; paraffin wax used in the production of wax paper and insulating materials; asphalt used for paving, construction, and as a preservative; and petroleum coke used for making electrodes and producing silicon carbide. The other category is oil-based fuels, and common oil-based fuels are mainly divided into 4 types: petroleum, kerosene, diesel, and heavy oil, which are extremely easy to obtain in daily life. Therefore, the outer surface of an armored vehicle is also vulnerable to attacks by oil substances. Moreover, due to the strong adhesion and spreadability of oils, when they catch fire, they may cause serious damage to some important parts of the armored vehicle body along the gaps between the armors.

[0003] Currently, the extinguishment of oil fires on the outer surface of armored vehicles mainly relies on manual fire-fighting equipment or automatic fire extinguishing devices. Among them, manual fire-fighting equipment requires personnel to respond quickly when a fire occurs. However, in a high-pressure combat environment, personnel often cannot handle it in time. And automatic fire extinguishing devices mostly rely on the triggering of fire detectors and the spraying of foam fire extinguishing agents. However, affected by the complex external environment, the spraying of foam fire extinguishing agents may face problems such as mis-triggering, unformed spraying, and poor extinguishing effect, which may easily lead to the escalation of the incident and cause more personnel injuries.

[0004] Therefore, how to automatically control the spraying device on the armored vehicle to spray and extinguish oil combustibles is a technical problem that needs to be solved by technical personnel at present. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art, and a control method for a spraying device used to extinguish oil fires on the outer surface of an armored vehicle is proposed.

[0006] To achieve the above purpose, the present invention adopts the following technical scheme: A control method for a spraying device used to extinguish oil fires on the outer surface of an armored vehicle, including the following steps:

[0007] Step 1: Based on the actual fire extinguishing requirements on the outer surface of the armored vehicle, collect the vehicle surface temperature data and environmental data in real time through corresponding sensors, and generate a basic data collection table;

[0008] Step 2: Based on the basic data collection form, preprocess various types of data, calculate the amplitude of the temperature change on the outer surface of the armored vehicle, issue a warning according to the set threshold, estimate the coordinate range of the temperature anomaly area, analyze the relationship between various environmental factors in combination with the environmental parameters of the time stamp, and generate the current data analysis report;

[0009] Step 3: Based on the current data analysis report, establish a flame propagation model, simulate the interaction between vehicle movement and environmental wind speed, predict the flame state through a regression model, and optimize the vehicle speed through a simulated annealing algorithm to generate a recommended vehicle speed report;

[0010] Step 4: Based on the recommended vehicle speed report, determine the basic flow rate of the sprayed foam, calculate the basic pressure required to be applied according to the foam basic flow rate, and dynamically adjust the foam injection pressure according to the foam coverage area to generate a spraying treatment report;

[0011] Step 5: Based on the spraying treatment report, conduct follow-up monitoring, display the data changes in the form of charts through a regression algorithm and Tableau drawing tool, obtain a visualization chart set, and back up all data information and operation logs to generate an event handling record form.

[0012] Preferably, the environmental data includes wind speed, wind direction, vehicle speed, terrain, humidity, and environmental temperature, where wind speed and wind direction are used to evaluate the risk of flame spread and provide a judgment basis for adjusting the actual driving route of the armored vehicle, terrain is used to analyze the impact on the acceleration and deceleration capabilities of the armored vehicle, and together with the vehicle speed, it reflects the air fluidity and oxygen supply, and humidity and environmental temperature are used to judge the influence range of the external environment on the flame spread speed.

[0013] Preferably, the specific steps for preprocessing various types of data are as follows:

[0014] Based on the data collected by various sensors, screen out outliers one by one through the box plot method, and correct them through interpolation or mean substitution to generate a preliminary data screening package;

[0015] Based on the preliminary data screening package, perform exponentially weighted moving average to reduce short-term fluctuations, and synchronize the data obtained by each sensor through resampling to generate a data sorting package;

[0016] Based on the data sorting package, perform normalization and standardization processing to convert various data features into the same scale to generate a standard data package.

[0017] Preferably, the specific steps for estimating the spatial coordinate range of the temperature anomaly area are as follows:

[0018] Based on Kriging interpolation, interpolate the temperature data on the outer surface of the armored vehicle, estimate the temperature values at all positions in space in real time, obtain a three-dimensional temperature distribution map, and evaluate the accuracy of the interpolation results through cross-validation to generate a statistical report on the temperature distribution on the outer surface of the armored vehicle;

[0019] Based on the statistical report on the temperature distribution on the outer surface of the armored vehicle, use the DBSCAN algorithm to identify temperature anomaly regions in space by density clustering, divide the temperature value anomaly regions into different clusters, and represent the spatial range of the temperature anomaly regions with bounding boxes, convex hulls, and polygon regions in space to generate a table of abnormal temperature ranges;

[0020] Based on the table of abnormal temperature ranges, match and regularly refresh the corresponding three-dimensional space coordinates to generate a table of coordinate markers for abnormal regions.

[0021] Preferably, the specific steps for analyzing the relationships between various environmental factors are as follows:

[0022] Build a model for the temperature data based on a time series model to capture the time dependence of the data and generate a temperature-time correlation table;

[0023] Based on the temperature-time correlation table, analyze the linear correlation between different variables through the Pearson correlation coefficient, establish a regression model, and perform a regression analysis on temperature and environmental factors based on historical data to generate an analysis report on the temperature-environment relationship;

[0024] Based on the analysis report on the temperature-environment relationship, construct a multivariate time series model to quantify the relationships between the data of each variable, analyze the influence of multiple time series data on each other, and generate an analysis report on external factors.

[0025] Preferably, the flame propagation model is established based on heat conduction, convection, and radiation. Among them, heat conduction is used to reflect the propagation of heat from the fire source to nearby combustibles, described by Fourier's law; convection is used to reflect the process of heat transfer through air and smoke, described by the Navier-Stokes equation and the energy equation; radiation is used to reflect the process of heat transfer through electromagnetic waves, described by the Stefan-Boltzmann law, and numerical simulation is carried out through the FDS tool. According to the simulated fire development and heat transfer process, analyze the oxygen supply and heat flow promoted by the airflow flowing around the vehicle to improve the flame propagation.

[0026] Preferably, based on the historical flame state data under different experimental environments and vehicle speeds, establish and train the regression model through machine learning to predict the flame state under different given conditions, and optimize the vehicle speed through the simulated annealing algorithm based on the given environmental data to limit the flame spread.

[0027] Preferably, the specific steps for generating the spray treatment report are as follows:

[0028] Based on the adjusted vehicle speed, collect the current wind speed, combine the basic formula of fluid mechanics, and adjust the rheological model, Kelvin-Helmholtz instability model, and hydrodynamic model parameters according to the actual physical properties of the foam. Determine the foam property constant from the density, viscosity, stability of the foam to be sprayed, and nozzle characteristics.

[0029] Based on the foam property constant and the current wind speed, determine the basic flow rate of the currently sprayed foam and calculate the required basic pressure in real time.

[0030] Based on the basic pressure, combine the temperature change trend collected by the sensor to determine the foam coverage area and coverage effect, dynamically adjust the pressure applied to the foam, and generate a spraying treatment report.

[0031] Preferably, the specific generation steps of the visualization chart set are as follows:

[0032] Based on the data information collected in real time by various sensors, sort by timestamp through the heap sort algorithm within the set time window.

[0033] Based on the regression algorithm, establish a mathematical model, fit various sorted data, output the obtained fitting curve, and add it as a new column to the data set.

[0034] Based on the Tableau drawing tool, match the appropriate type of chart to the imported fitting result, display the data change and regression curve, and generate a visualization chart set.

[0035] Preferably, the data information and operation logs are stored through the local hard disk and the cloud, and the operation logs include the IP address, operation type, operation time, operation object, operation result, and system response.

[0036] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0037] 1. In the present invention, through the flame propagation model and the FDS tool, when the armored vehicle is moving, the oxygen concentration provided by the air flow and the influence on the flame spread can be obtained. Furthermore, in combination with historical training data, the vehicle speed range beneficial to reducing the flame intensity can be matched according to the current environmental data. Further, according to the current wind speed, foam properties, basic formulas of fluid mechanics, and related models, the foam spray flow rate required to extinguish the flame and the pressure to be applied can be determined. In summary, when the flame initially appears, the oxygen supply to the flame can be reduced in a short time by reducing the vehicle speed, preventing the flame from spreading rapidly under the combined action of oxygen and air flow. At the same time, through the spray flow rate and pressure calculated in real time, it can be ensured that the foam can automatically cover all the burning areas in a formed state to resist the wind force, and then the flame can be extinguished efficiently in a short time, preventing it from continuously burning to block the driver's vision, affecting the driving of the armored vehicle, or causing damage to some important areas of the armored vehicle, resulting in the armored vehicle being unable to move normally.

[0038] 2. In the present invention, based on the temperature data collected by sensors, a temperature distribution map of the outer surface of the armored vehicle can be constructed through the spatial interpolation algorithm and the clustering algorithm. Furthermore, when an abnormal temperature is detected, the burning area can be accurately inferred, and the spatial coordinates of the corresponding range can be marked to facilitate subsequent precise processing of the burning area and reduce the probability of false alarms. At the same time, various environmental data can be quantitatively correlated according to the time series model to facilitate subsequent analysis of the influence of the external environment on fire extinguishing.

[0039] 3. In the present invention, various types of data related to the flame can be displayed by appropriate charts through the Tableau drawing tool, so that the personnel inside the armored vehicle can understand the development degree and processing results of the flame, and further facilitate them to master the overall status of the armored vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a schematic diagram of the main steps of the present invention.

[0041] Figure 2 It is a schematic diagram of the steps for estimating the spatial coordinate range of the temperature abnormal area of the present invention.

[0042] Figure 3 It is a schematic diagram of the visualization chart set of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0043] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0044] Please refer to Figure 1 - Appendix Figure 3, the present invention provides a technical solution: a control method for a spray device for extinguishing oil fires on the outer surface of an armored vehicle, comprising the following steps:

[0045] Step 1: Based on the actual fire extinguishing requirements on the outer surface of the armored vehicle, the vehicle surface temperature data and environmental data are collected in real time through corresponding sensors. Various sensors distributed in different areas on the outer surface of the armored vehicle can obtain different types of data parameters such as the surface temperature of the armored vehicle, environmental temperature, humidity, and wind speed in real time, generate a basic data collection table, and then transmit it through the sensor network, and be centrally processed and analyzed by the subsequent host to deeply understand key factors such as the operating state of the armored vehicle under different environmental conditions, the heat absorption and dissipation of the vehicle surface, and the influence of external meteorological factors on the performance of the armored vehicle. Further, it can help optimize the safety of the vehicle and timely warn of potential faults or abnormal conditions to ensure that the armored vehicle can always maintain the best state in complex and changeable environments;

[0046] Step 2: Based on the basic data collection form, preprocess various types of data, calculate the amplitude of the temperature change on the outer surface of the armored vehicle, and issue a warning according to the set threshold. By preprocessing the obtained various types of data, the rationality of the data can be improved. Moreover, through classic sorting algorithms such as quicksort, mergesort, or heapsort algorithms, a large amount of data collected by temperature sensors can be sorted to effectively reduce data redundancy. And during the sorting process, by selecting an appropriate time window, effective data within a specific time range can be effectively screened out, thereby avoiding the impact of a large amount of irrelevant data in historical data on the system performance. At the same time, through a preset sliding window, the time range of the data can be dynamically adjusted to ensure that only the latest data within the current period is concerned and avoid calculating too much useless information. Subsequently, in cooperation with another preset sliding window with a shorter period, the temperature data can be compared and calculated one by one. During this process, abnormal temperature fluctuations can be identified by comparing the temperature data within consecutive time periods. For example, when the amplitude of the temperature change continuously exceeds a certain threshold, it may mean that there is a fire somewhere on the outer surface of the armored vehicle. At this time, the system will automatically trigger an alarm to prompt the armored vehicle driver or the command center to pay attention to the potential fire risk and estimate the coordinate range of the temperature anomaly area. Further, through spatial interpolation algorithms and clustering algorithms, the temperature data of each area on the outer surface of the armored vehicle can be converted into a three-dimensional temperature distribution map. Among them, the spatial interpolation algorithm interpolates and calculates the surrounding area in space through known temperature data points to obtain a continuous temperature field, achieving the purpose of filling the gaps between temperature sensors and providing a more accurate temperature distribution. And the clustering algorithm can cluster and analyze the temperature data according to different regions to identify temperature anomalies within specific regions. In summary, through the temperature distribution map, not only can the temperature changes in each area be visually presented, but also it can assist in judging the location and scope of the fire. Analyze the relationships between various environmental factors by combining the environmental parameters with timestamps to generate the current data analysis report. On this basis, using timestamps, all temperature data collected at a specific time point can be associated with other environmental data. By combining relevant models, data such as temperature and humidity in the current environment can be comprehensively analyzed to evaluate the mutual influence between various environmental factors. For example, the change in wind speed may exacerbate the spread of the fire, and the increase in humidity may affect the combustion state of the flame. Furthermore, through the integrated analysis of environmental data, it can more reliably assist in subsequent fire prediction;

[0047] Step 3: Based on the current data analysis report, establish a flame propagation model. According to the specific area where the flame appears and the currently integrated environmental factors, establish a flame propagation model from three aspects: heat conduction, convection, and radiation. Heat conduction refers to the transfer of heat from the flame source to the surroundings by direct contact. Since metals usually have high thermal conductivity, heat will be quickly transferred to other parts of the vehicle body. Convection refers to the flow generated by hot air rising around the fire source and being replaced by cooler air around it. Therefore, the air flow inside and outside the carriage, the ventilation system, and the temperature gradient inside the vehicle will all affect the spread speed and range of the flame. Radiation refers to the large amount of thermal radiation released by the flame itself, which raises the temperature of other areas without direct contact. In summary, by quantitatively modeling heat conduction, convection, and radiation, the flame propagation path and speed can be predicted relatively completely. By quantitatively modeling these physical processes, the interaction between vehicle movement and environmental wind speed can be simulated. In addition, combined with computational fluid dynamics technology, the air flow and heat distribution can be simulated to accurately calculate the oxygen concentration and the trend of heat flow, and further reveal the flame spread path and possible affected areas. Furthermore, the possible consequences caused by the flame if not dealt with in time can be preliminarily understood, such as the flame continuously burning and blocking the driver's vision, or continuously burning in the key areas of the armored vehicle, resulting in the normal use of these areas being affected by high temperature, etc. Predict the flame state through a regression model. Further, a regression model can be trained by a large amount of historical experimental data, and the regression model can predict the trend and direction of the flame's continued development under the current environmental conditions. At the same time, optimize the vehicle speed through the simulated annealing algorithm to generate a recommended vehicle speed report. The simulated annealing algorithm can also be used to speculate on the flame state at different vehicle speeds to obtain the appropriate vehicle speed for reducing the oxygen concentration and the flame spread speed, so as to achieve the purpose of reducing the flame intensity and assist subsequent fire extinguishing operations. Moreover, the vehicle speed range also needs to consider the actual adhesion and forming effect of the foam sprayed by the foam fire extinguishing agent to prevent the foam from being affected by external wind speed and other factors and unable to maintain its own shape normally;

[0048] Step 4: Based on the recommended vehicle speed report, determine the basic flow rate of the sprayed foam. After adjusting the vehicle speed to reduce the flame intensity and spread speed, calculate the foam property constant after combining the physical properties of the foam and the characteristics of the spraying device according to the basic formula of fluid mechanics and related models. Then, according to the fact that the foam spraying speed needs to be greater than the actual wind speed, the foam spraying speed can be preliminarily determined from the actually collected wind speed data, and the basic pressure to be applied can be calculated according to the basic flow rate of the foam. Further, after determining the spraying speed, the foam pressure to be applied can be calculated using the foam property constant and the spraying speed. At the same time, dynamically adjust the foam spraying pressure according to the foam coverage area to generate a spraying treatment report. Since the role of the foam is not only to isolate oxygen, but also to effectively absorb heat and quickly reduce the flame temperature, in actual applications, real-time adjustment needs to be carried out according to the design redundancy principle and the changes in the on-site fire to maintain sufficient coverage area, so as to avoid the continuous spread of the fire to the greatest extent;

[0049] Step Five: Based on the spray treatment report, conduct follow-up monitoring. Through the monitoring of temperature sensors, various key data related to the flame state can be captured in real time. Then, through regression algorithms, the data is sorted and analyzed to reveal the laws and trends of flame development. The data changes are presented in the form of charts through regression algorithms and the Tableau plotting tool (an existing Tableu visual data analysis software) to obtain a set of visualization charts. Further, through the Tableau visualization tool, the sorted data can be intuitively presented in the form of charts, helping the personnel inside the armored vehicle to more clearly understand the dynamic changes of the flame. At the same time, when there is a trend of the flame reigniting, the operator can also detect it in time and take corresponding fire extinguishing measures before the fire further develops. All data information and operation logs are backed up to generate an event handling record form. In addition, the data collected by the sensors can not only provide support for the current fire extinguishing process but also serve as historical data for subsequent model training. Through the continuously accumulated historical data, relevant machine learning models and algorithms can be continuously optimized, thereby improving the overall fire extinguishing efficiency and system response speed. At the same time, for the situation where the flame cannot be effectively extinguished, problem troubleshooting and analysis can be carried out later through historical records and data backups, and then a more accurate flame recognition and fire extinguishing plan can be optimized to further reduce the risk of reignition and improve the fire emergency response ability.

[0050] Please refer to Figure 1 , the environmental data includes wind speed, wind direction, vehicle speed, terrain, humidity, and ambient temperature. Among them, wind speed and wind direction are used to evaluate the risk of flame spread and provide a basis for judging the adjustment of the actual driving route of the armored vehicle. The changes in wind speed and wind direction can directly affect the path and speed of flame propagation. Therefore, during a fire, by real-time monitoring of wind speed and wind direction, the trend of flame spread can be effectively predicted. In some special cases, the driving route of the armored vehicle can be adjusted to avoid the spread of the fire to key areas, thereby ensuring the safety of important facilities and personnel. The terrain is used to analyze the impact on the acceleration and deceleration capabilities of the armored vehicle and, in combination with the vehicle speed, jointly reflects the air fluidity and oxygen supply. At the same time, the undulation of the terrain will also affect the driving speed and variable speed ability of the armored vehicle, and thus may affect the heat flow and oxygen supply. Since the spread speed of the flame is also closely related to heat propagation and oxygen supply, the change in terrain may indirectly affect the fire spread to a certain extent by changing the speed of the armored vehicle. Humidity and ambient temperature are used to judge the influence range of the external environment on the flame spread speed. In addition, in a high-temperature and dry environment, the flame is more likely to obtain sufficient oxygen and heat to accelerate its spread. Therefore, the actual humidity value in the air can inhibit the spread of the flame to a certain extent. In summary, during a fire, it is necessary to comprehensively consider main factors such as wind speed, wind direction, terrain, humidity, and ambient temperature for full analysis and judgment.

[0051] Please refer to Figure 1 , the specific steps for preprocessing various types of data are as follows:

[0052] Based on the data collected by various sensors, outlier screening is carried out one by one through the box plot method. Since the data obtained by sensors may mutate due to factors such as hardware failures, signal noise, or environmental changes in actual applications, abnormal data can be automatically identified and removed through statistical threshold judgment or machine learning algorithms, and corrected through interpolation or mean substitution to generate a preliminary data screening package. Furthermore, interpolation or mean substitution methods can be used to complete data filling to fill in missing data, maintain data integrity, ensure that subsequent analysis and decision-making are not affected by data missing, and achieve the purpose of improving data reliability and credibility;

[0053] Based on the preliminary data screening set, exponential weighted moving average is performed to reduce short-term fluctuations. Through exponential weighted moving average, short-term fluctuations and noise in the data can be reduced, a more stable signal can be provided, the change trend can be highlighted, and time synchronization of the data obtained by each sensor is performed through resampling to generate a data sorting package. In a multi-sensor system, the sampling frequencies of different sensors may vary, which may lead to inconsistent timestamps of the data and affect data synchronization and comparability. Therefore, through resampling, signals with inconsistent sampling frequencies can be aligned to ensure that the data of each sensor is at the same timestamp;

[0054] Based on the data sorting package, normalization and standardization processing are performed to convert various data features into the same scale to generate a standard data package. Through normalization and standardization processing, the data can be used for corresponding algorithms or models.

[0055] Please refer to Figure 2 , the specific steps for estimating the spatial coordinate range of the temperature anomaly area are as follows:

[0056] Based on Kriging interpolation, interpolation is performed on the temperature data of the outer surface of the armored vehicle to estimate the temperature values at all positions in space in real time. Through Kriging interpolation, the distance and temperature difference between measurement points can be calculated based on the known temperature data, and the semi-variogram is used for spatial interpolation to obtain the temperature estimation value of each target point, obtaining a three-dimensional temperature distribution map. Furthermore, a three-dimensional temperature distribution map can be constructed based on the estimated temperature to more intuitively understand the temperature distribution on the outer surface of the armored vehicle, and the accuracy of the interpolation result is evaluated through cross-validation to generate a statistical report on the temperature distribution on the outer surface of the armored vehicle;

[0057] Based on the statistical report of the temperature distribution on the outer surface of the armored vehicle, the DBSCAN algorithm is used to identify the temperature anomaly regions in space through density clustering. The DBSCAN clustering algorithm can effectively identify multiple clusters in the data, and divide the temperature value anomaly regions into different clusters. Since each cluster is composed of a set of spatially adjacent data points with similar characteristics, the DBSCAN algorithm can be used to discover spatially abnormal temperatures from a large amount of data during temperature monitoring, and represent the spatial range of the temperature anomaly regions with bounding boxes, convex hulls, and polygon regions in space, generating a table of abnormal temperature ranges. Furthermore, the DBSCAN clustering algorithm can generate cluster information to achieve the digital positioning of the temperature anomaly regions, and thus provide information support for subsequent temperature monitoring and safety warning; among them, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm. Different from partitioning and hierarchical clustering methods, it can define clusters as the largest set of density-connected points, and can divide regions with sufficient high density into clusters, and can discover clusters of any shape in a spatial database with noise.

[0058] Based on the table of abnormal temperature ranges, match and refresh the corresponding three-dimensional space coordinates at regular intervals, generate a table of coordinate markers for the abnormal regions, and update the range coordinates of the temperature anomaly regions, that is, each cluster, through real-time refreshing, so as to accurately determine the coordinates and area of the fire area.

[0059] Please refer to Figure 1 , and the specific steps for analyzing the relationships between various environmental factors are as follows:

[0060] Based on the time series model, model the temperature data, capture the time dependence of the data, generate a temperature-time association table, and associate time with the corresponding temperature according to the time series model;

[0061] Based on the temperature-time association table, analyze the linear correlation between different variables through the Pearson correlation coefficient, calculate the linear relationship between environmental factors such as humidity, wind direction, and air pressure and temperature through the Pearson correlation coefficient, and quantify the correlation between environmental factors and temperature changes, establish a regression model, and conduct a regression analysis of temperature and environmental factors based on historical data to generate a temperature-environment relationship analysis report. Furthermore, regression analysis can be carried out according to the selected environmental factors with relatively high correlation, and the regression coefficients can be obtained through the fitting of the regression model. Further, the influence degree of each environmental factor on temperature changes can be reflected by the regression coefficients; among them, the Pearson Correlation Coefficient is a statistical index used to measure the degree of linear correlation between two variables, and its value ranges from -1 to 1, mainly used to analyze the linear relationship between two continuous variables.

[0062] Based on the temperature-environment relationship analysis report, a multivariate time series model is constructed to quantify the relationships between various variable data. Through the vector autoregressive model and dynamic causality analysis, multiple time-varying environmental factors can be analyzed, and the mutual influences and interaction relationships between different environmental factors can be quantified. Among them, by constructing a vector autoregressive model, the dynamic changes of each environmental factor and its dependence on the time series can be described, the influences among multiple time series data can be analyzed, and an external factor analysis report can be generated. Furthermore, through dynamic causality analysis, causal relationships can be revealed from the time series data, and the direct influence of one environmental factor on another can be determined. In summary, the purpose of comprehensively revealing the complex interactions among multiple environmental factors can be achieved, accurate quantitative results can be provided, and the integration of diversified environmental factors can be completed.

[0063] Please refer to Figure 1 , the flame propagation model is established based on heat conduction, convection, and radiation. Among them, heat conduction is used to reflect the propagation of heat from the fire source to nearby combustibles, described by Fourier's law; convection is used to reflect the process of heat transfer through air and smoke, described by the Navier-Stokes equation and the energy equation; radiation is used to reflect the process of heat transfer through electromagnetic waves, described by the Stefan-Boltzmann law. The flame propagation model established by heat conduction, convection, and radiation is numerically simulated through the FDS tool. According to the simulated fire development and heat transfer process, the oxygen supply and heat flow promoted by the airflow around the vehicle are analyzed to improve the flame propagation. By using the FDS tool to simulate the changes in the current flame propagation model due to the changes in oxygen concentration and heat flow under the action of the airflow, the accuracy of the flame propagation model is further improved.

[0064] It should be noted that the Navier-Stokes equation (abbreviated as the N-S equation) is a partial differential equation describing the motion of viscous fluids and is used to simulate the weather fluid mechanics system; the FDS tool is a fire dynamics simulation tool, which is a model of computational fluid dynamics (CFD) and simulates the energy-driven fluid flow of fire.

[0065] Please refer to Figure 1, based on the historical flame state data under different experimental environments and vehicle speeds, a regression model is established and trained through machine learning. The machine learning builds and trains a suitable regression model based on the historical experimental data to predict the flame state under different given conditions. Then, combined with the currently integrated environmental data, the current flame development trend is predicted, and the vehicle speed is optimized through the simulated annealing algorithm to limit the flame spread. Moreover, by simulating the air flow speed after changing the vehicle speed in cooperation with the simulated annealing algorithm, the integrated environmental data can be changed accordingly. Further, the flame intensity and flame spread speed at different vehicle speeds can be predicted by the regression model. In summary, through multiple simulations, a suitable vehicle speed for reducing the flame intensity and spread range can be obtained to reduce the difficulty of using foam extinguishing subsequently.

[0066] Please refer to Figure 1 , the specific generation steps of the spray treatment report are as follows:

[0067] Based on the adjusted vehicle speed, the current wind speed is collected, combined with the basic formula of fluid mechanics, and the rheology model, Kelvin-Helmholtz instability model, and fluid dynamics model parameters are adjusted according to the actual physical properties of the foam. The foam property constant is determined by the density, viscosity, stability of the foam to be sprayed and the nozzle characteristics. By determining the foam property constant, the interference of the foam type and nozzle type used on the pressure required for spraying the foam can be reduced. Among them, the Kelvin-Helmholtz instability model refers to the dynamic instability caused by the shear of the horizontal wind speed at the interface of two fluids with different densities, simply referred to as K-H instability.

[0068] Based on the foam property constant and the current wind speed, the current basic flow rate of the sprayed foam is determined. Since the basic flow rate of the foam needs to be greater than the actual wind speed, the basic flow rate of the foam can be initially inferred through the actual wind speed, and the required basic pressure is calculated in real time. Then, the required applied basic pressure can be calculated jointly by the foam property constant and the inferred basic flow rate of the foam.

[0069] Based on the basic pressure, combined with the temperature change trend collected by the sensor, the foam coverage area and coverage effect are determined. The foam coverage area and coverage effect are determined by the temperature change actually detected by the sensor, and the pressure applied to the foam is dynamically adjusted to generate a spray treatment report. Furthermore, the pressure applied can be adjusted according to the actual requirements and design redundancy to ensure that the foam can quickly extinguish the flame in a short time.

[0070] Please refer to Figure 3 , the specific generation steps of the visualization chart set are as follows:

[0071] Based on the data information collected in real time by various sensors, within the set time window, it is sorted by timestamp through the heap sort algorithm, and the data is organized in chronological order for subsequent retrieval and use;

[0072] Based on the regression algorithm, a mathematical model is established to fit various sorted data, and the obtained fitting curve is output, which is added as a new column to the data set. By inputting the data into the regression model, the data change curve can be obtained through its output result;

[0073] Based on the Tableau plotting tool, the appropriate type of chart is matched to the imported fitting result to display the data change and the regression curve, and a visualization chart set is generated. According to the data change curve, which is the result output by the regression model, the data change can be intuitively reflected by the appropriate visualization chart through the Tableau plotting tool.

[0074] Please refer to Figure 1 , the data information and operation logs are stored through the local hard disk and the cloud. Through the local hard disk and the cloud, the appropriate channels can be selected for backup recording according to the needs during experiments or actual emergencies. Furthermore, the data parameters related to the flame and all operation information can be used as historical data to enrich the subsequent model and algorithm training content, and can be retrieved in the future to find the root cause of problems. The operation logs include the IP address, operation type, operation time, operation object, operation result, and system response. Among them, the IP address can assist in tracing the operation source, the operation type records the specific operation behavior performed by the user or administrator, the operation time is used to track the specific time when the operation occurs to help analyze the timeliness of the operation, the operation object is the specific data and steps, the operation result is used to determine whether the operation meets the expectations, and the system response is the returned information, error code, response time, etc.

[0075] The above is only the preferred embodiment of the present invention, and it is not intended to limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for controlling a spray device for extinguishing oil fire on the outer surface of an armored vehicle, characterized in that: The following steps are involved: Step 1: Collect vehicle surface temperature data and environmental data in real time through corresponding sensors to generate a basic data collection table; the environmental data includes wind speed, wind direction, vehicle speed, terrain, humidity and ambient temperature; Step 2: Based on the basic data collection table, pre-process various types of data, calculate the temperature change amplitude of the outer surface of the armored vehicle, issue a warning according to the set threshold, estimate the coordinate range of the temperature abnormal area, analyze the relationship between various environmental factors in combination with the environmental parameters of the timestamp, and generate a current data analysis report; Step 3: Based on the current data analysis report, a flame propagation model is established to simulate the interaction between vehicle movement and environmental wind speed, the flame state is predicted by a regression model, and the vehicle speed is optimized by a simulated annealing algorithm to generate a recommended vehicle speed report; in the step 3, according to the flame state data under different historical experimental environments and vehicle speeds, a regression model is established and trained by machine learning to predict the flame state under different given conditions, and then the current flame development trend is predicted in combination with the currently integrated environmental data, and the vehicle speed is optimized by a simulated annealing algorithm based on the given environmental data to limit the flame spread, and the airflow speed after the vehicle speed is changed is simulated by the simulated annealing algorithm, so that the integrated environmental data can be changed accordingly, and the flame intensity and flame spread speed under different vehicle speeds are further predicted by the regression model, and the appropriate vehicle speed for reducing the flame intensity and spread range can be obtained through multiple simulations; Step 4: Based on the recommended vehicle speed report, determine the basic flow rate of the sprayed foam, calculate the basic pressure to be applied according to the basic flow rate of the foam, and dynamically adjust the foam spray pressure according to the foam coverage area to generate a spray treatment report; in step 4, the specific steps of generating the spray treatment report are: Based on the adjusted vehicle speed, the current wind speed is collected, combined with the basic formula of fluid mechanics, and the rheological model, Kelvin-Helmholtz instability model and fluid dynamics model parameters are adjusted according to the actual physical properties of the foam, and the foam property constant is determined by the density, viscosity, stability and nozzle characteristics of the foam to be sprayed; Based on the foam property constant and the current wind speed, the current spray foam basic flow rate is determined, and the required basic pressure is calculated in real time; Based on the basic pressure and in combination with the temperature change trend collected by the sensor, the foam coverage area and coverage effect are determined, the pressure applied to the foam is dynamically adjusted, and a spray treatment report is generated; Step 5: Based on the spray treatment report, the data changes are displayed in the form of charts through regression algorithms and Tableau drawing tools to obtain a visual chart set, and all data information and operation logs are backed up to generate an event processing record table.

2. A control method for a spray device for extinguishing oil fire on the outer surface of an armored vehicle according to claim 1, characterized in that: In step one, wind speed and direction are used to assess the risk of flame spread and provide a basis for adjusting the actual driving route of the armored vehicle. Terrain is used to analyze the impact on the acceleration and deceleration capabilities of the armored vehicle, and together with the vehicle speed, it reflects air mobility and oxygen supply. Humidity and ambient temperature are used to determine the extent of the impact of the external environment on the speed of flame spread.

3. The control method of a spray device for extinguishing oil fire on the outer surface of an armored vehicle according to claim 1 is characterized in that: In step 2, the specific steps of preprocessing various types of data are: Based on the data collected by various sensors, outliers are screened one by one through the box plot method, and corrections are made through interpolation or mean substitution to generate a preliminary data screening package; Based on the data, a preliminary screening package is performed, an exponentially weighted moving average is performed, and the data obtained by each sensor is synchronized in time by resampling to generate a data sorting package; Based on the data sorting package, normalization and standardization processing is performed to convert various data features into the same scale and generate a standard data package.

4. The control method of a spray device for extinguishing oil fire on the outer surface of an armored vehicle according to claim 1 is characterized in that: In step 2, the specific steps of estimating the coordinate range of the temperature anomaly area are: Based on Kriging interpolation, the temperature data of the outer surface of the armored vehicle is interpolated, and the temperature values ​​of all locations in space are estimated in real time to obtain a three-dimensional temperature distribution map. The accuracy of the interpolation results is evaluated through cross-validation, and a statistical report on the temperature distribution of the outer surface of the armored vehicle is generated; Based on the statistical report of the temperature distribution on the outer surface of the armored vehicle, the temperature abnormality area in the space is identified by density clustering through the DBSCAN algorithm, the temperature abnormality area is divided into different clusters, and the spatial range of the temperature abnormality area is represented by the bounding box, convex hull and polygonal area in space to generate an abnormal temperature range table; Based on the abnormal temperature range table, the corresponding three-dimensional space coordinates are matched and periodically updated to generate an abnormal area coordinate marking table.

5. The control method of a spray device for extinguishing oil fire on the outer surface of an armored vehicle according to claim 1 is characterized in that: In the step 2, the specific steps of analyzing the relationship between various environmental factors in combination with the environmental parameters of the timestamp are: Model the temperature data based on the time series model, capture the time dependency of the data, and generate a temperature-time correlation table; Based on the temperature-time correlation table, the linear correlation between different variables is analyzed by Pearson correlation coefficient, a regression model is established, and regression analysis is performed on temperature and environmental factors according to historical data to generate a temperature-environment relationship analysis report; Based on the temperature-environment relationship analysis report, a multivariate time series model is constructed to quantify the relationship between the variable data, analyze the mutual influence of multiple time series data, and generate an external factor analysis report.

6. The control method of a spray device for extinguishing oil fire on the outer surface of an armored vehicle according to claim 1 is characterized in that: In the step 3, the flame propagation model is established based on heat conduction, convection and radiation; Among them, the heat conduction is used to reflect the heat transfer from the fire source to the nearby combustibles, which is described by Fourier's law; the convection is used to reflect the process of heat transfer through air and smoke, which is described by the Navier-Stokes equation and the energy equation; the radiation is used to reflect the process of heat transfer through electromagnetic waves, which is described by the Stefan-Boltzmann law and numerically simulated by the FDS tool; Based on the simulated fire development and heat transfer process, the oxygen supply and heat flow caused by the airflow around the vehicle are analyzed to improve the flame propagation.

7. The control method of a spray device for extinguishing oil fire on the outer surface of an armored vehicle according to claim 1 is characterized in that: In step 5, the specific steps of generating the visualization chart set are: Based on the data information collected in real time by various sensors, the data is sorted by timestamp within the set time window using the heap sort algorithm; Establish a mathematical model based on the regression algorithm, fit the sorted data, output the obtained fitting curve, and add it to the data set as a new column; Based on the Tableau drawing tool, the imported fitting results are matched with appropriate types of charts to display data changes and regression curves, and generate a visual chart set.

8. The control method of a spray device for extinguishing oil fire on the outer surface of an armored vehicle according to claim 1 is characterized in that: In the step five, backing up all data information and operation logs includes: the data information and operation logs are stored on the local hard disk and in the cloud, and the operation logs include the IP address, operation type, operation time, operation object, operation result and system response.

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