Rapid cable bridge fire extinguishing method based on superfine dry powder fire extinguishing agent

Abstract

The invention discloses a cable bridge rapid fire extinguishing method based on a superfine dry powder fire extinguishing agent. The method comprises the steps that a fire disaster is judged by monitoring temperature and smoke data, particle components of the fire extinguishing agent are adjusted through a particle swarm optimization algorithm, and injection parameters and a structure integration scheme are optimized. And in combination with special environmental factors, dry powder particle behaviors are simulated, a distributed system architecture is constructed, and fire positioning and resource coordination are realized. A dry powder deposition law is simulated, a cleaning process is improved, linkage control logic is integrated to an intelligent system, a storage environment is monitored, performance degradation is predicted, a fire extinguishing process is circularly optimized, and rapid fire extinguishing and long-term standby application are achieved. According to the invention, the fire prevention and control efficiency and recovery speed of the cable bridge are obviously improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of cable bridge protection, and particularly relates to a rapid fire extinguishing method for cable bridges based on superfine dry powder extinguishing agent. BACKGROUND

[0002] As an important part of power and communication systems, cable bridges carry a large number of cables and are widely used in industrial plants, underground tunnels, high-rise buildings and other scenarios. The safe operation of cable bridges is directly related to the stability of the power system and public safety. Cable bridge fires, due to their strong concealment, fast spread and difficulty in extinguishing, have become an important challenge in the field of power safety. Once a fire occurs, it may not only cause power outages, but also cause significant economic losses and even casualties. Therefore, developing efficient rapid fire extinguishing technology for cable bridges has become a key issue to ensure the safety of power infrastructure.

[0003] Currently, the extinguishing of cable bridge fires mainly relies on traditional extinguishing methods such as water-based extinguishing, gas extinguishing or ordinary dry powder extinguishing. These methods have certain effects in open spaces or conventional fire scenes, but they have significant shortcomings in the special environment of cable bridges. For example, water-based extinguishing may cause short circuits and damage cable equipment; gas extinguishing is difficult to distribute evenly in semi-closed bridges, resulting in low extinguishing efficiency; and ordinary dry powder extinguishing agent has large particles that are difficult to penetrate the complex structure of the bridge interior, resulting in poor coverage. These limitations make it difficult for existing methods to quickly control bridge fires, especially in complex environments such as high temperature, high humidity or strong electromagnetic interference, where the extinguishing effect is further limited.

[0004] In the study of rapid fire extinguishing for cable bridges, superfine dry powder extinguishing agent is considered a potential efficient solution due to its small particle size and strong coverage. However, its application faces core technical difficulties. First, the suspension time and coverage density of superfine dry powder in the bridge interior are difficult to accurately control. Cable bridges are usually long and semi-closed structures with complex internal airflow, and dry powder particles can quickly settle due to airflow resistance or particle-to-particle collisions, resulting in insufficient coverage of the extinguishing agent in the burning area. For example, in a bridge that is tens of meters long, the dry powder sprayed may only cover the first few meters, and the rear-end fire source continues to burn due to a lack of extinguishing agent.

[0005] The uneven distribution of the extinguishing agent in the bridge is further exacerbated by the control difficulties of the suspension time and the coverage density. Especially in high-temperature or high-humidity environments, dry powder particles are easily affected by environmental factors, causing aggregation or adhesion, resulting in ineffective dispersion after spraying and reducing the chemical inhibition effect with fire radicals. For example, in a high-temperature and high-humidity underground tunnel bridge, dry powder may be affected by moisture and form lumps, making it difficult to form a uniform fire-extinguishing cloud after spraying and making it difficult to extinguish hidden fires. These two technical factors are interrelated: insufficient suspension time leads to reduced coverage density, and insufficient coverage density makes it difficult for dry powder to play a sustained fire-extinguishing role in complex environments.

[0006] Therefore, how to accurately control the suspension time and coverage density of ultra-fine dry powder in the complex structure and special environment of the cable bridge so that it can be evenly distributed and efficiently extinguish fires has become a key problem in rapid fire extinguishing technology. This problem not only involves the spraying and distribution technology of the extinguishing agent, but also is closely related to the structural characteristics of the bridge, the environmental adaptability, and the intelligent control of the fire extinguishing system, and requires systematic technical breakthroughs. SUMMARY

[0007] To solve the above technical problems, the present application provides a cable bridge rapid fire extinguishing method based on ultra-fine dry powder extinguishing agent, comprising:

[0008] Obtain the internal temperature and smoke data of the cable bridge, judge the fire occurrence signal through the preset threshold value, extract the bridge specification parameters from the signal, use the particle swarm optimization algorithm to determine the component adjustment ratio of the ultra-fine dry powder extinguishing agent particles, and obtain the optimized particle component distribution ratio of chemical inhibition and physical suffocation cooperation;

[0009] According to the particle component distribution ratio, calculate the airflow resistance value in the bridge space, simulate the spraying process through a multi-stage pressure release model, determine the spraying lift and angle parameters, and obtain a coverage density distribution map for guiding the deployment of the lift control device;

[0010] Extract the force arm distribution data of the key parts of the bridge from the coverage density distribution map, use the finite element analysis method to evaluate the influence of the integrated equipment load on the structure, adjust the equipment installation position if the load exceeds the preset threshold value, and obtain a fused structure force arm integration scheme;

[0011] For the fused structure force arm integration scheme, obtain real-time data of special environmental factors, simulate the behavior of dry powder particles in complex airflow through a particle collision dynamics model, determine the spraying speed angle time sequence optimization value, and obtain an enhanced effective utilization rate parameter;

[0012] The bridge network paragraph division information is extracted from the enhanced effective utilization rate parameter, a distributed system architecture is constructed to deploy sensor nodes, a deep learning algorithm is used to process monitored temperature smoke current abnormal data, fire positioning coordinates are judged, and an automatic starting sequence for coordinating adjacent area fire extinguishing resources is obtained;

[0013] According to the automatic starting sequence, the deposition law after dry powder injection is simulated, the model is improved by adding an easy-to-clean formula to predict the flow characteristics of the residue, the process parameters of the cleaning equipment are determined, and a deposition control scheme is obtained;

[0014] The linkage control logic is obtained from the deposition control scheme and integrated into the intelligent system framework, the fire detection and fire extinguishing starting power cut-off ventilation operation is coordinated through a preset algorithm, the environmental adaptation adjustment is executed if the response time is lower than the threshold, and the storage parameters are updated and obtained;

[0015] Based on the storage parameter monitoring of the storage environment temperature and humidity data, the support vector machine algorithm is used to predict the performance degradation trend of the fire extinguishing agent, the adjustment system setting of the moisture-proof and anti-caking is determined, and the long-term standby state adaptation technology output is obtained for cyclic optimization of the entire fire extinguishing process.

[0016] Compared with the prior art, the present application has the following advantages and technical effects:

[0017] The present application realizes accurate positioning and efficient fire extinguishing by integrating temperature smoke monitoring, particle swarm optimization algorithm, finite element analysis and deep learning technology. The system first collects the temperature and smoke data inside the bridge through the sensor in real time, judges the fire signal in combination with the preset threshold, extracts the bridge specification parameters, adjusts the particle ratio of the ultra-fine dry powder extinguishing agent by using the particle swarm optimization algorithm, and improves the synergistic effect of chemical inhibition and physical suffocation. Based on the optimized ratio, the system simulates the injection process through a multi-stage pressure release model, calculates the air resistance and coverage density, determines the injection lift angle, and guides the equipment deployment. At the same time, the system uses finite element analysis to evaluate the influence of the attached load on the bridge structure, optimizes the installation position, and ensures the structural stability. In complex environment, the system optimizes the injection speed and angle through the particle collision dynamics model, and improves the utilization rate of dry powder. The distributed sensor network combines with the deep learning algorithm to accurately locate the fire coordinates and automatically coordinate the fire extinguishing resources. The system also improves the formula and deposition control scheme, optimizes the cleaning process of the residue, integrates the linkage control logic, and realizes fast response and environmental adaptation. Finally, the system predicts the performance degradation of the fire extinguishing agent by using the support vector machine, optimizes the storage parameters, and ensures long-term reliability. The present application significantly improves the efficiency and recovery speed of cable bridge fire prevention and control. BRIEF DESCRIPTION OF DRAWINGS

[0018] The accompanying drawings, which form a part of this application, are intended to provide further understanding of the application and are incorporated herein in their entirety, and together with the general description of the application and its detailed description help to explain the present application. In the drawings:

[0019] Figure 1 The flow chart of the method of the embodiment of the present application. DETAILED DESCRIPTION

[0020] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0021] It should be noted that the steps shown in the flow chart of the drawings can be executed in a computer system such as a group of computer executable instructions, and although the logical order is shown in the flow chart, in some cases, the steps shown or described herein can be executed in a different order.

[0022] As Figure 1 shown, the embodiment provides a cable bridge rapid fire extinguishing method based on superfine dry powder extinguishing agent, comprising:

[0023] Obtain the internal temperature and smoke data of the cable bridge, judge the fire occurrence signal through the preset threshold value, extract the bridge specification parameters from the signal, use the particle swarm optimization algorithm to determine the component adjustment ratio of the superfine dry powder extinguishing agent particles, and obtain the optimized particle component distribution ratio of chemical inhibition and physical suffocation cooperation;

[0024] According to the particle component distribution ratio, calculate the airflow resistance value in the bridge space, simulate the spraying process through the multi-stage pressure release model, determine the spraying lift and angle parameters, and obtain the coverage density distribution map for guiding the deployment of the lift control device;

[0025] Extract the force arm distribution data of the key parts of the bridge from the coverage density distribution map, use the finite element analysis method to evaluate the influence of the integrated device additional load on the structure, adjust the equipment installation position if the load exceeds the preset threshold value, and obtain the integrated structure force arm integration scheme;

[0026] For the integrated structure force arm integration scheme, obtain real-time data of special environmental factors, simulate the behavior of dry powder particles in complex airflow through the particle collision dynamics model, determine the spraying speed angle time sequence optimization value, and obtain the enhanced effective utilization rate parameter;

[0027] Extract the bridge network paragraph division information from the enhanced effective utilization rate parameter, construct a distributed system architecture to deploy sensor nodes, use a deep learning algorithm to process the monitored temperature, smoke and current abnormal data, judge the fire positioning coordinates, and obtain the automatic start sequence for coordinating the adjacent area fire extinguishing resources;

[0028] According to the automatic starting sequence, the deposition law after the simulation of dry powder injection is simulated, the model is improved to predict the flow characteristics of the residual by adding the formula of easy-to-clean agent, the process parameters of the cleaning equipment are determined, and the deposition control scheme is obtained;

[0029] The linkage control logic is obtained from the deposition control scheme and integrated into the intelligent system framework. Through the preset algorithm, the fire detection and fire extinguishing starting power cut and ventilation operation are coordinated. If the response time is lower than the threshold value, the environmental adaptation adjustment is executed, and the storage parameters are updated and obtained;

[0030] Based on the storage parameters, the storage environment temperature and humidity data are monitored, the support vector machine algorithm is used to predict the performance degradation trend of the fire extinguishing agent, the moisture-proof and anti-caking adjustment system settings are determined, and the long-term standby state adaptation technology output is obtained for cyclic optimization of the entire fire extinguishing process.

[0031] Further, the process of obtaining the optimized particle component proportion of the synergistic combination of chemical inhibition and physical suffocation includes:

[0032] Obtain the internal temperature and smoke data of the cable bridge, collect real-time temperature values and smoke concentration values through sensors, and obtain original environment data;

[0033] If the temperature value or smoke concentration value in the original environment data exceeds the preset threshold value, a fire signal is generated through a logic judgment module to determine the fire occurrence state;

[0034] Extract the specification parameters of the cable bridge from the fire signal, and generate structured specification data using a data analysis algorithm;

[0035] According to the structured specification data, calculate the internal space volume and fire spread characteristics of the bridge, and obtain the fire scene parameters;

[0036] Using a particle swarm optimization algorithm, the proportion of chemical inhibitors and physical suffocation agents is iteratively adjusted for the fire scene parameters and the particle components of ultra-fine dry powder, and a preliminary proportioning scheme is obtained;

[0037] The preliminary proportioning scheme is evaluated for free radical capture ability, and the optimized synergistic proportioning is determined;

[0038] According to the optimized synergistic proportioning, generate the particle component adjustment instructions of the ultra-fine dry powder extinguishing agent, and output the final particle component proportioning data.

[0039] For example, temperature and smoke data collection inside the cable bridge is achieved by deploying high-precision sensors. The temperature sensor uses a thermocouple to monitor the internal temperature of the bridge in real time, with an accuracy of ±0.5°C; the smoke sensor uses a photoelectric sensor to detect smoke concentration, with a sensitivity of 0.01% obs / m. The sensor collects data every second and transmits it to the data processing unit, generating raw environmental data containing time stamp, temperature value and smoke concentration value. Assuming that the collected data is temperature 45°C and smoke concentration 0.15% obs / m, it is stored in the local database.

[0040] In one possible implementation, the logic judgment module analyzes the fire risk based on preset thresholds. The temperature threshold is set to 60°C and the smoke concentration threshold is set to 0.2% obs / m. If the collected data shows that the temperature is 65°C or the smoke concentration is 0.25% obs / m, the module determines it as a fire signal, triggers an alarm and records the fire status.

[0041] For example, if the temperature of a certain bridge is 70°C and the smoke concentration is 0.3% obs / m, the module generates a fire signal and marks it as "fire occurrence".

[0042] Specifically, after the fire signal is triggered, the system extracts the bridge specification parameters. Assuming that the bridge height is 0.2m, the width is 0.5m and the length is 10m, the data analysis algorithm converts them into structured specification data, including geometric dimensions and material information. Based on this, the internal volume of the bridge is calculated as 0.2x0.5x10=1m 3 , combined with the fire spread characteristics such as heat release rate, to generate fire scene parameters.

[0043] For example, the heat release rate is estimated to be 500kW / m 2 , indicating that the fire may spread rapidly.

[0044] For example, for the fire scene parameters, the particle swarm optimization algorithm adjusts the ratio of ultra-fine dry powder extinguishing agent. In the initial ratio, the proportion of chemical inhibitor is 60% and the proportion of physical smothering agent is 40%. The algorithm iterates 10 times, each time evaluating the inhibition effect, and optimizes to 55% chemical inhibitor and 45% physical smothering agent, improving the extinguishing efficiency.

[0045] Preferably, the simulation analysis module evaluates the free radical capture ability, and the test shows that the free radical capture rate is improved by 20% under the optimized ratio, and the extinguishing time is shortened by 30%.

[0046] In one embodiment, the final ratio data generates particle composition adjustment instructions.

[0047] For example, the instructions require that the proportion of sodium bicarbonate in the ultra-fine dry powder be reduced by 5% and the proportion of inert gas particles be increased, ensuring that the particle diameter is controlled within 5-10μm, improving the coverage rate of injection. This ratio significantly reduces the risk of residual flame in the bridge, ensuring the safety of the cable and prolonging the service life of the equipment.

[0048] It can be understood that the above scheme realizes early fire detection and efficient fire extinguishing through the synergistic effect of sensors, algorithms and proportioning optimization. Real-time data acquisition ensures timely warning, structured data supports accurate analysis, optimized proportioning improves fire extinguishing effect, and the overall scheme has high efficiency and reliability in cable bridge fire prevention and control.

[0049] Further, the process of obtaining the coverage density distribution map for guiding the deployment of the head control device includes:

[0050] By particle composition proportioning data, the airflow resistance in the bridge space is calculated by using a fluid mechanics model to obtain the airflow resistance value;

[0051] According to the airflow resistance value, a multi-stage pressure release model is constructed to simulate the pressure change in the spraying process to obtain pressure distribution data;

[0052] By pressure distribution data, a particle motion simulation algorithm is used to calculate the spraying head and angle parameters to determine the spraying trajectory parameters;

[0053] If the spraying trajectory parameters meet the preset coverage density threshold, a coverage density distribution map is generated to obtain distribution map data;

[0054] According to the distribution map data, a geometric optimization algorithm is used to calculate the deployment position of the head control device to determine the deployment coordinates;

[0055] By deployment coordinates, configuration parameters of the head control device are generated to obtain device control instructions;

[0056] According to the device control instructions, an automatic scheduling algorithm is used to perform the deployment operation of the head control device to complete the deployment task.

[0057] For example, in the cable bridge fire prevention and control scene, the airflow resistance is calculated by particle composition proportioning data, which needs to analyze the airflow characteristics inside the bridge based on a fluid mechanics model. The airflow resistance is mainly determined by the roughness of the bridge inner wall, air flow rate and particle concentration. Assuming that the bridge cross section is rectangular, the width is 0.5 meters, the height is 0.3 meters, the air flow rate is 2 meters / second, and the particle concentration is 10 grams / cubic meter, the resistance value is estimated by the fluid mechanics model to reflect the resistance influence when the fire extinguishing agent is sprayed. High resistance value may cause uneven spraying, affecting the fire extinguishing efficiency, so accurate calculation is needed to optimize the subsequent spraying design.

[0058] In one possible implementation, when building the multi-stage pressure release model, the pressure change during the spraying process is simulated according to the airflow resistance value. It is assumed that the initial spraying pressure is 5 bar, there are multiple narrow areas inside the bridge, and the pressure release points are set in stages, such as setting release valves at the entrance, middle, and end of the bridge, and the pressure is reduced to 4.5 bar, 4 bar, and 3.5 bar in turn. This staged release ensures uniform pressure distribution and avoids local overpressure causing damage to the spraying device, while ensuring the coverage range of the extinguishing agent.

[0059] Specifically, the particle motion simulation algorithm is used to calculate the spraying lift and angle parameters. It is assumed that the spraying device is installed at the top of the bridge, and the spraying angle can be adjusted in the range of 30° to 60°. Through simulation, it is determined that the optimal angle is 45°, and the lift reaches 3 meters of the bridge length. This parameter selection ensures that the extinguishing agent uniformly covers the inside of the bridge, especially in the corners and narrow areas, avoiding missing the fire source.

[0060] For example, when generating the coverage density distribution map, the spatial distribution of the extinguishing agent particles is drawn according to the spraying trajectory parameters combined with the bridge geometry. It is assumed that the bridge length is 5 meters, and the coverage density threshold is 0.8 g / m³. The distribution map shows that the density in the middle area reaches 0.9 g / m³, and the edge is slightly lower at 0.7 g / m³. The distribution map data intuitively reflects the coverage effect of the extinguishing agent, guiding subsequent optimization.

[0061] In one possible implementation, the geometric optimization algorithm is used to determine the deployment position of the lift control device. It is assumed that there are multiple high-temperature points inside the bridge, and the algorithm selects the deployment coordinates close to the high-temperature points according to the distribution map data, such as the left position (2 meters, 0.25 meters) in the middle of the bridge. This coordinate ensures that the device covers the high-temperature area and improves the extinguishing efficiency.

[0062] Specifically, when generating the configuration parameters of the lift control device, the spraying frequency and flow rate are set according to the deployment coordinates.

[0063] For example, the device is configured to spray 0.5 liters of extinguishing agent per second at a frequency of 2 times per second, ensuring stable coverage density. The configuration parameters directly affect the response speed and coverage range of the device.

[0064] For example, when the automatic scheduling algorithm performs the deployment operation, the central control system sends instructions to the lift control device. It is assumed that there are 3 control devices inside the bridge, and the scheduling algorithm activates them in priority order, such as activating the device closest to the fire source first, and then activating the other devices in turn. This scheduling method ensures efficient allocation of extinguishing resources and rapid response to fire scenarios.

[0065] In one possible implementation, after the deployment task is completed, the coverage effect is verified by the sensor. Assuming that the sensor detects that the average coverage density in the bridge is 0.85 g / m3, which meets the threshold requirement, indicating that the deployment task is successful. This verification link ensures that the fire extinguishing agent distribution meets the expectations, improving the reliability of fire prevention and control.

[0066] Further, the process of obtaining the integrated structure force arm scheme after fusion includes:

[0067] Obtain the force arm distribution data of the main beam cross arm from the bridge structure density distribution, use the stereomicroscope technology to perform three-dimensional scanning on the bridge structure, and obtain an initial data set of the force arm distribution data;

[0068] According to the initial data set of the force arm distribution data, construct a finite element analysis model, input the integrated device attachment load parameters, and calculate the structural stress distribution of the main beam cross arm;

[0069] If the structural stress distribution exceeds the preset deformation threshold, adjust the equipment installation position through an iterative optimization algorithm to generate a new position coordinate set;

[0070] According to the new position coordinate set, re-run the finite element analysis model to obtain the adjusted structural stress distribution, and judge whether the preset deformation threshold is met;

[0071] Through the adjusted structural stress distribution, calculate the load distribution uniformity, use the K-means clustering algorithm to classify the load distribution, and obtain the classified load distribution characteristics;

[0072] According to the classified load distribution characteristics, generate a force arm adjustment strategy, use a linear regression algorithm to predict the optimization trend of the force arm distribution, and obtain an optimized force arm scheme;

[0073] Through the optimized force arm scheme, update the main beam cross arm position of the bridge structure, and generate a final force arm integration scheme.

[0074] For example, the embodiment obtains the force arm distribution data of the main beam cross arm through three-dimensional laser scanning technology combined with stereomicroscope technology for high-precision data acquisition. The stereomicroscope can capture the micro-geometric characteristics of the main beam cross arm by magnifying the surface of the bridge structure, and generate three-dimensional point cloud data.

[0075] For example, in the scanning of a certain bridge main beam cross arm, assuming that the cross arm length is 2 meters and the width is 0.5 meters, the stereomicroscope scans with an accuracy of 0.01 millimeters to generate a point cloud data set containing the geometric changes of the cross arm surface. This data set can accurately reflect the force arm distribution of the cross arm, providing a reliable basis for subsequent analysis.

[0076] It should be noted that the imaging depth and light source adjustment of the stereomicroscope need to be adjusted according to the bridge material to ensure data integrity.

[0077] In one possible implementation, when constructing a finite element analysis model based on an initial data set of force arm distribution data, it is assumed that the material of the main beam cross arm is steel, and the initial data set shows that the force arm distribution of the cross arm in the length direction is uneven, with a maximum force arm of 1.2 meters and a minimum force arm of 0.8 meters. The integrated device load is input in the model, for example, the total weight of the device is 500 kilograms, distributed at both ends of the cross arm. Through finite element analysis, the stress concentration area in the middle of the cross arm is calculated, with a maximum stress value of 200 megapascals. If the preset deformation threshold is 150 megapascals, the device installation position needs to be optimized.

[0078] It should be noted that the model needs to consider the boundary conditions of the bridge, such as fixed end constraints, to ensure the accuracy of the stress distribution. For example, when adjusting the device installation position, the genetic algorithm is used in the iterative optimization algorithm. The initial position coordinates are the two end points A(0, 0) and B(2, 0) of the cross arm, and through iterative calculation, the device installation position is offset from point A to (0.3, 0). After re-running the finite element analysis, the stress value is reduced to 120 megapascals, meeting the threshold requirement. This adjustment is achieved through multiple iterations to ensure uniform stress distribution.

[0079] It should be noted that the optimization process needs to balance the calculation efficiency and accuracy to avoid excessive iteration and waste of resources.

[0080] In one possible implementation, when calculating the uniformity of load distribution, the K-means clustering algorithm is used to classify the load distribution of the cross arm into three categories: high load area, medium load area, and low load area.

[0081] For example, the high load area is concentrated in the middle of the cross arm, with a load value of 400 kilograms, the medium load area is 200 kilograms, and the low load area is 50 kilograms. Based on this classification, a force arm adjustment strategy is generated to prioritize adjusting the device position in the high load area. When the linear regression algorithm predicts the optimization trend of the force arm, it is assumed that historical data shows that adjusting the force arm from 1.2 meters to 1.0 meters can reduce the stress concentration by 20%. The final optimization scheme adjusts the force arm in the middle of the cross arm to 1.0 meter, and generates a force arm integration scheme to ensure the stability of the bridge structure.

[0082] It should be noted that the classification and prediction need to be combined with the actual working conditions to avoid overfitting of the algorithm.

[0083] For example, when updating the position of the main beam cross arm of the bridge structure, the adjustment operation is performed by an automatic mechanical arm.

[0084] For example, the position of the cross arm is adjusted from the initial coordinates (0, 0) to (0.2, 0), and the mechanical arm accurately positions according to the optimization scheme. This method can improve the adjustment efficiency and ensure the implementation accuracy of the force arm integration scheme.

[0085] Further, the process of obtaining the enhanced effective utilization rate parameter includes:

[0086] Real-time data of high temperature and high humidity environment is obtained, temperature, humidity and air flow speed data are collected by sensors, and an environment parameter data set is generated;

[0087] Through a particle collision dynamics model, the environment parameter data set is input, the motion trajectory of dry powder particles in complex airflow is simulated, and the particle distribution characteristics are obtained;

[0088] According to the particle distribution characteristics, the Monte Carlo method is used to optimize the injection speed and angle parameters, and an initial optimization parameter set is generated;

[0089] If the utilization value of the initial optimization parameter set is lower than the preset threshold, the speed and angle are iteratively adjusted by the genetic algorithm to obtain an improved parameter set;

[0090] According to the improved parameter set, a time sequence analysis method is used to adjust the injection time sequence, and an optimized time sequence scheme is generated;

[0091] The optimized time sequence scheme is verified by real-time data, and if the utilization value reaches the preset threshold, the final injection parameters are determined;

[0092] If not, repeat the Monte Carlo method and genetic algorithm optimization steps;

[0093] According to the final injection parameters, an enhanced effective utilization value is generated.

[0094] For example, in a dry powder injection system in a high temperature and high humidity environment, the high temperature and high humidity environment usually refers to a scenario where the temperature exceeds 38℃ and the relative humidity is higher than 70%. The sensor can deploy a multi-point temperature and humidity meter and an anemometer to collect data in real time.

[0095] For example, the temperature sensor records the temperature in the workshop as 39℃, the humidity as 73%, and the air flow speed as 2.5m / s, generating an environment parameter data set containing time stamp and spatial coordinates. These data provide a basis for subsequent simulation, ensuring that the calculation of particle motion trajectory is close to the actual environment.

[0096] In one possible implementation, the particle collision dynamics model is used to simulate the motion trajectory of dry powder particles in complex airflow. Dry powder particles such as talc have a diameter usually between 10-50 microns, and are greatly affected by air flow speed and humidity. The model simulates the force on the particles in the airflow, such as drag force and Brownian motion, by inputting environmental parameters, and calculates the spatial distribution characteristics of the particles.

[0097] For example, the simulation shows that in an air flow of 2.5m / s, the particles are concentrated in the downstream 0.5-1.2 meters of the injection path, showing a conical diffusion. This distribution characteristic provides a basis for optimizing the injection parameters.

[0098] Specifically, the Monte Carlo method is used to optimize the injection speed and angle. The Monte Carlo method evaluates the effects of multiple parameter combinations through random sampling.

[0099] For example, the initial injection speed is set to 5 m / s and the angle is 30°. After 1000 simulations, it is found that the particle utilization rate is only 60%, which is lower than the preset threshold of 80%. By analyzing the distribution characteristics, the speed is adjusted to 6 m / s and the angle is adjusted to 45° to generate an initial optimized parameter set. This method finds a better parameter combination through a large number of random experiments.

[0100] In one embodiment, if the utilization rate of the initial optimized parameter set does not meet the standard, a genetic algorithm is used for iterative adjustment. The genetic algorithm simulates natural selection and optimizes parameters through "mutation" and "cross" operations.

[0101] For example, the injection speed and angle are taken as "genes", and the initial population contains 10 parameter combinations. After 5 iterations, an improved parameter set with a speed of 6.2 m / s and an angle of 42° is found, and the utilization rate is increased to 75%. This method efficiently approaches the optimal solution through step-by-step screening.

[0102] For example, the time sequence analysis method is used to adjust the injection time sequence. Based on the improved parameter set, the periodic fluctuations of the airflow speed are analyzed, such as the airflow speed decreasing from 2.5 m / s to 2.0 m / s every 10 seconds. Time sequence analysis adjusts the injection time window by predicting the fluctuation pattern, such as increasing the injection frequency when the airflow speed is high, to ensure more uniform particle distribution. The optimized time sequence scheme may adjust the injection interval from a fixed 5 seconds to a dynamic 3-7 seconds.

[0103] In one possible implementation, real-time data verifies the optimized time sequence scheme. The adjusted injection effect is monitored by sensors, and if the utilization rate reaches 80%, the final injection parameters are confirmed as a speed of 6.2 m / s, an angle of 42°, and a time sequence of dynamic 3-7 seconds. If the standard is not met, the Monte Carlo and genetic algorithm optimization is repeated until the requirements are met. After the final injection parameters are generated, the particle utilization rate can be increased to 85%, significantly improving the efficiency of dry powder injection.

[0104] Specifically, the application of the final injection parameters can be realized through an automatic control system.

[0105] For example, a PLC controller adjusts the angle of the servo motor and the power of the air pump of the injection device in real time according to the optimized parameters to ensure the stability of the injection process. This scheme optimizes multiple links to ensure efficient distribution and utilization of dry powder in high-temperature and high-humidity environments.

[0106] Further, the process of obtaining an automatic start sequence for coordinating fire extinguishing resources in adjacent areas includes:

[0107] Paragraph division information is obtained from the bridge network, an effective utilization rate parameter is extracted, a deployment scheme of a distributed system architecture is generated, and the distribution position of the sensor nodes is determined;

[0108] According to the distribution position of the sensor nodes, temperature, smoke, and current sensors are deployed, real-time monitoring data is obtained, and a multidimensional data stream is generated;

[0109] The convolutional neural network is used to process the multidimensional data stream, extract the abnormal features of temperature, smoke, and current, and generate an abnormal event set;

[0110] If the temperature data in the abnormal event set exceeds the preset threshold, the smoke data and the current data are combined, the convolutional neural network is analyzed, the probability of fire occurrence is judged, and a fire risk assessment result is generated;

[0111] According to the fire risk assessment result, the distribution position of the sensor nodes is combined, the fire positioning coordinates are calculated, and the fire location information is generated;

[0112] Through the fire location information, the extinguishing resources of the adjacent area are matched, the resource allocation sequence is generated, and the coordination start order is determined;

[0113] According to the resource allocation sequence, the extinguishing equipment of the adjacent area is activated, the automatic start sequence is generated, and the extinguishing resources are coordinated.

[0114] Specifically, when obtaining paragraph division information in the bridge network, the physical and logical structures of the bridge are extracted by a network topology analysis tool.

[0115] For example, in the bridge network of a large industrial plant, a three-dimensional model of the bridge is generated by laser scanning, and 100 independent bridge paragraphs are divided by combining node connectivity analysis, each paragraph covering a physical area of about 50 meters. The effective utilization rate parameter of each paragraph can be calculated by the load data collected by the sensor. Assuming that the current load of a paragraph is 80% of the rated value, the utilization rate parameter is 0.8. This division method helps to accurately locate the high-load area and facilitates subsequent sensor deployment. For the distribution position of the sensor nodes, according to the utilization rate parameter of the bridge paragraph, the sensor is preferentially deployed in the high-load area.

[0116] For example, in paragraphs with a load higher than 0.7, temperature, smoke, and current sensors are deployed, with a node set every 10 meters, totaling 30 nodes. The sensors collect real-time data, such as temperature reaching 50 degrees Celsius, smoke concentration exceeding 100 ppm, and current fluctuation amplitude greater than 10 amperes, forming a multidimensional data stream. This distribution strategy ensures comprehensive monitoring of critical areas.

[0117] In one possible implementation, when processing multi-dimensional data streams with a convolutional neural network, the temperature, smoke, and current data are first normalized to form a standardized input matrix. For example, an input matrix containing 1000 groups of data is provided, with a matrix dimension of 1000x3, corresponding to the values of temperature, smoke, and current, respectively. The convolutional neural network extracts abnormal features such as temperature mutations or abnormal increases in smoke concentration through three layers of convolution and pooling operations, and generates a set of abnormal events. This method can quickly identify potential risk points. For fire risk assessment, data in the set of abnormal events can be analyzed.

[0118] For example, a node detects that the temperature is 60 degrees Celsius, the smoke concentration is 150 ppm, and the current fluctuation is 15 amperes, and the convolutional neural network calculates the fire probability as 0.85. This high probability result triggers fire risk assessment, and combined with the sensor node location, the fire location coordinates are calculated to within 3 meters of the bridge segment. This precise positioning helps to respond quickly.

[0119] Preferably, when generating the resource allocation sequence, the extinguishing resources closest to the fire point can be matched according to the fire location information.

[0120] For example, the fire location coordinates are bridge segment number 50, and the system identifies that there are 3 dry powder extinguishers and 2 sprinkler devices within a 10-meter range, and generates a resource allocation sequence to activate the closest extinguisher first. This allocation ensures maximum resource utilization efficiency.

[0121] In one possible implementation, the coordinated start sequence is generated by the system to activate the extinguishers within 5 meters of the fire point first, and the sprinkler devices are activated 2 seconds later. This timing coordination avoids resource conflicts and ensures extinguishing efficiency. This step-by-step activation strategy can effectively control the spread of fire.

[0122] It can be understood that the above scheme forms a complete fire monitoring and response system through the division of bridge network segments, sensor deployment, data processing, and resource coordination. Each step is closely related to the actual needs of the bridge network, with a logical and progressive structure.

[0123] Further, the process of obtaining the deposition control scheme includes:

[0124] Obtain deposition rule data by simulating the dry powder injection process to determine a deposition distribution model;

[0125] According to the deposition distribution model, analyze the flow characteristics of the residual on different surfaces to generate a flow characteristic data set;

[0126] Classify the flow characteristic data set using a support vector machine algorithm to determine the optimization direction of the easy-to-clean agent formula and obtain the formula adjustment parameters;

[0127] By adjusting the formula parameters, the cleaning effect of the optimized easy-to-clean agent on the deposition surface is simulated to determine the cleaning efficiency index;

[0128] If the cleaning efficiency index is lower than the preset threshold value, the process flow parameters are adjusted, the cleaning effect is simulated again, and the optimized process parameters are obtained;

[0129] According to the optimized process parameters, the running sequence of the cleaning equipment is generated, and the deposition control scheme is determined;

[0130] Through the deposition control scheme, the equipment running state is simulated, the effect of rapid recovery operation is verified, and the final running parameters are generated.

[0131] In one possible implementation, simulating the dry powder spraying process requires building a physical simulation environment to capture the deposition rules of dry powder under different environmental conditions.

[0132] For example, the embodiment simulates the deposition behavior of dry powder under high temperature, high humidity, or different wind speed environments through a laboratory spraying device. Assuming that under the conditions of high temperature 40°C and wind speed 2m / s, the dry powder forms a uniform deposition layer with a thickness of 0.5mm on the metal surface, and the thickness on the rough wood surface is only 0.3mm. This difference data can be used to build a deposition distribution model to reflect the adhesion characteristics of dry powder on different material surfaces, thereby providing a basis for subsequent analysis.

[0133] Specifically, the construction of the deposition distribution model is based on experimental data combined with statistical analysis methods.

[0134] For example, by measuring the deposition thickness, coverage rate, and particle distribution density on different surfaces, a three-dimensional distribution model is generated. Assuming that the model shows that dry powder tends to be uniformly distributed on smooth surfaces, while it presents cluster-like deposition on porous surfaces. This model can help analyze the adhesion stability of dry powder on different surfaces and provide a basis for subsequent flow characteristic analysis.

[0135] In one possible implementation, when analyzing the flow characteristics of the residue, the migration behavior of dry powder on the surface is simulated through fluid mechanics experiments.

[0136] For example, on a 45° inclined glass surface, after adding a small amount of water flow, the dry powder residue presents a fast sliding flow characteristic, while on the wood surface it stays longer due to capillary action. The flow characteristic data set can record parameters such as sliding speed, residual amount, and surface roughness, such as the sliding speed on the glass surface is 0.2m / s, and on the wood surface it is only 0.05m / s. These data provide key inputs for optimizing the cleaning agent formula.

[0137] For example, a support vector machine algorithm is used to classify the flow characteristics dataset to determine which surface residues are more difficult to clean. Suppose that through algorithm analysis, the residues on the glass surface are classified as "easy to clean", and the residues on the wooden surface are classified as "difficult to clean". Based on this, the optimization direction may be to increase the proportion of surfactants in the cleaning agent to improve the penetration ability on porous surfaces. The formula adjustment parameter can be specifically to increase the surfactant concentration from 5% to 8%, thereby improving the cleaning effect.

[0138] Specifically, the cleaning effect of the optimized easy-to-clean agent is simulated, and the virtual experiment is verified.

[0139] For example, under the adjusted formula, the cleaning of the wooden surface is simulated, and the results show that the residue removal rate is increased from 60% to 85%, and the cleaning efficiency index reaches the preset threshold 0.8. If it does not meet the standard, continue to adjust the process parameters, such as increasing the injection pressure from 0.3 MPa to 0.5 MPa, and re-simulating to confirm the efficiency improvement.

[0140] In one possible implementation, when generating the cleaning device operation sequence, the optimized process parameters are designed according to the optimized process parameters. For example, the device can be set to first inject the cleaning agent at a high pressure of 0.5 MPa for 5 seconds, and then flush at a low pressure of 0.2 MPa for 10 seconds, forming an efficient operation sequence. This sequence can ensure rapid removal of deposits while reducing waste of cleaning agents.

[0141] For example, the verification of the deposition control scheme is through the simulation of the running state of the device in the actual scene.

[0142] For example, in a simulated industrial environment with a high temperature of 40°C, the device works according to the operation sequence, and the surface cleanliness is restored to 95% within 10 minutes, verifying the efficiency of the scheme. The final running parameters can be determined as injection pressure 0.5 MPa and cleaning time 15 seconds, ensuring rapid recovery of operation.

[0143] Further, the process of updating the obtained storage parameters includes:

[0144] The linkage control logic is obtained from the deposition control scheme, the trigger conditions of fire detection, fire extinguishing start, power cut-off and ventilation operation are extracted by analyzing the preset rule set, and the control logic sequence is obtained;

[0145] According to the control logic sequence, the logic sequence is embedded into the intelligent system framework through system integration, and each operation module is matched through interface mapping to obtain the integrated system configuration;

[0146] Through preset algorithm coordination, based on the integrated system configuration, the fire detection operation is executed, the sensor data stream is analyzed, and if a fire signal is detected, the fire extinguishing start mechanism is triggered to obtain a fire extinguishing instruction set;

[0147] According to the fire extinguishing instruction set, activate the power cut-off function, cut off the power supply of the designated area, and at the same time start the ventilation operation management, adjust the running state of the ventilation equipment, and obtain the environmental control parameters;

[0148] According to the environmental control parameters, calculate the response time, if the response time is lower than the preset threshold, execute the environmental adaptation adjustment, optimize the cooperative operation of ventilation and fire extinguishing operation, and obtain the adjusted running parameters;

[0149] By analyzing the adjusted running parameters through the random forest algorithm, predict the stability of the storage environment, and update the storage parameters;

[0150] According to the storage parameters, dynamically adjust the control strategy of the intelligent system framework, optimize the execution path of the linkage control logic, and obtain the optimized system running state.

[0151] For example, in the linkage logic of fire detection and deposition control, the trigger condition of fire detection is usually based on multi-sensor data fusion. The sensors may include smoke sensors, temperature sensors and infrared flame sensors.

[0152] For example, when the smoke sensor detects that the concentration of particulate matter in the air exceeds 100 micrograms per cubic meter, or the temperature sensor records that the environmental temperature exceeds 60 degrees Celsius, the system will trigger a preliminary alarm.

[0153] It should be noted that these thresholds need to be calibrated according to the material characteristics of the actual storage environment, such as the flammable chemical storage area, which may require a lower trigger threshold to ensure timely response. This multi-condition trigger mechanism can improve the accuracy of fire detection and reduce false alarms.

[0154] In one embodiment, when the control logic sequence is embedded in the intelligent system framework, the fire detection module, fire extinguishing start module, power cut-off module and ventilation control module are connected through standardized interface protocols such as Modbus or OPCUA.

[0155] For example, the fire detection module generates a fire signal data set containing time stamp and signal strength by analyzing the sensor data stream. The system compares this data set with the preset rule set, and if it matches the condition of "smoke concentration exceeding standard and temperature anomaly", it generates a fire extinguishing instruction set. This interface mapping method ensures the cooperative work of each module and improves the response efficiency of the system.

[0156] For example, after the fire extinguishing instruction set is generated, the system will activate the power cut-off function.

[0157] Preferably, the power cut-off is preferentially directed to high-risk areas such as electrical equipment in the storage area, rather than the entire facility, to avoid unnecessary operation interruption.

[0158] Specifically, if the fire signal indicates a fire in zone A, the system will cut off the power supply to zone A while maintaining normal power supply to other zones. This zoning control strategy can effectively isolate fire risks while ensuring the continued operation of equipment in other zones.

[0159] In one embodiment, the ventilation operation management optimizes the environmental control parameters by adjusting the operating state of the ventilation equipment.

[0160] For example, the ventilation equipment can increase the air volume from 500 cubic meters / hour in normal mode to 1000 cubic meters / hour in emergency mode according to the intensity of the fire signal to quickly exhaust smoke. This dynamic adjustment can improve the air quality of the storage environment and reduce the risk of secondary damage.

[0161] For example, the calculation of response time is based on the time consumed from fire detection to the start of fire extinguishing. Assuming that the system completes instruction generation and starts fire extinguishing within 2 seconds after detecting the fire signal, and the response time is less than the preset threshold of 5 seconds, no further adjustment is needed. If the threshold is exceeded, the system will compress the response time to the target range by optimizing the sensor data processing algorithm or shortening the instruction transmission path. This fast response mechanism can significantly improve the efficiency of fire extinguishing.

[0162] In one embodiment, the random forest algorithm is used to predict the stability of the storage environment, which can analyze historical environmental data such as temperature, humidity, and smoke concentration trends.

[0163] For example, the system predicts whether the adjusted ventilation and fire extinguishing strategy can maintain the environmental stability above 95% based on the operating parameters of the past 30 days. This prediction result can be used to dynamically adjust the control strategy to ensure the long-term safety of the storage environment.

[0164] For example, the optimized system operating state is achieved by dynamically adjusting the execution path of the control logic.

[0165] Specifically, the system prioritizes fire extinguishing and ventilation operations in high-risk areas based on real-time fire risk levels, while low-risk areas enter a monitoring mode. This hierarchical response strategy can effectively allocate system resources and improve overall operational efficiency.

[0166] Further, the adaptation technology output for long-term standby state is used to optimize the entire fire extinguishing process, which includes:

[0167] Real-time collection of temperature and humidity data in the storage environment using a sensor array to obtain a structured environmental data set;

[0168] Analysis of the structured environmental data set using a support vector machine algorithm to build a fire extinguishing agent performance decay prediction model and obtain the performance decay trend;

[0169] According to the performance attenuation trend, calculate the moisture-proof adjustment system parameters, if the predicted humidity exceeds the preset threshold, adjust the dehumidification equipment operation frequency, determine the moisture-proof adjustment parameters;

[0170] According to the performance attenuation trend, calculate the anti-caking adjustment parameters, if the predicted caking risk exceeds the preset threshold, adjust the ventilation equipment operation cycle, determine the anti-caking adjustment parameters;

[0171] According to the moisture-proof adjustment parameters and the anti-caking adjustment parameters, update the storage environment control system configuration, obtain the long-term standby state optimization setting;

[0172] According to the long-term standby state optimization setting, adjust the fire extinguishing agent storage equipment operation strategy, generate the fire extinguishing process optimization instruction;

[0173] According to the fire extinguishing process optimization instruction, update the storage environment control system cyclically, obtain the continuously optimized fire extinguishing process configuration for fire extinguishing.

[0174] In one possible implementation, the process of collecting temperature and humidity data in real time in the storage environment is realized by deploying a high-precision sensor array. The sensor array is composed of multiple temperature and humidity sensors, for example, in a storage warehouse, a sensor is placed every 5 meters, covering an area of 1000 square meters, ensuring the comprehensiveness of data collection. The sensor collects data every minute, records the temperature range of 20-30 degrees Celsius, and the humidity range of 40%-70%, and stores it to the cloud database through wireless transmission, generating a structured environment data set. This way ensures the real-time nature of the data, which helps the accuracy of subsequent analysis.

[0175] For example, when analyzing the structured environment data set based on the support vector machine algorithm, the data set is divided into training set and test set, and the relationship between temperature, humidity and fire extinguishing agent performance attenuation is extracted. Suppose historical data shows that when the humidity exceeds 60%, the injection efficiency of the fire extinguishing agent decreases by 5%. Through algorithm modeling, it is predicted that the humidity may reach 65% in the next 24 hours, thus obtaining the performance attenuation trend. This prediction provides data support for subsequent adjustment and avoids the failure of fire extinguishing agents due to environmental factors.

[0176] Specifically, when calculating the moisture-proof adjustment system parameters, if the predicted humidity exceeds the preset threshold of 60%, the dehumidification equipment operation frequency is adjusted.

[0177] For example, increase the dehumidifier from running 4 hours a day to 6 hours, and keep the warehouse humidity stable at about 50%. This adjustment prolongs the storage life of the fire extinguishing agent by precisely controlling the humidity.

[0178] In one possible implementation, the calculation of the anti-caking adjustment parameters is based on the performance attenuation trend. If the predicted caking risk exceeds the threshold, for example, the combination of humidity and temperature leads to a caking probability of 30%, the ventilation equipment operation cycle is adjusted.

[0179] For example, changing the ventilation equipment from running 30 minutes every 4 hours to running 45 minutes every 2 hours increases air circulation and reduces the risk of caking. This method maintains the physical stability of the extinguishing agent through dynamic adjustment.

[0180] For example, when updating the storage environment control system configuration with moisture prevention adjustment parameters and anti-caking adjustment parameters, the parameters are sent to the equipment through the central control system.

[0181] For example, the dehumidifier and ventilation equipment automatically adjust the operation mode according to the new parameters to form a long-term standby state optimization setting. This setting ensures that the equipment works cooperatively and improves the stability of the storage environment.

[0182] Specifically, when adjusting the extinguishing agent storage equipment operation strategy, extinguishing process optimization instructions are generated according to the optimization setting.

[0183] For example, the instructions specify that when the humidity is below 50%, the low-pressure injection mode is preferred to reduce the loss of extinguishing agent. This strategy optimizes the efficiency of the extinguishing process.

[0184] In one possible implementation, the circulating update storage environment control system dynamically adjusts the instructions by analyzing sensor data daily.

[0185] For example, the parameters are recalculated every 24 hours based on the latest environmental data to update the extinguishing process configuration, ensuring that the system always adapts to environmental changes. This continuous optimization approach improves the reliability of the storage environment.

[0186] The above is only the preferred specific implementation of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for rapid extinguishing of cable tray based on ultra-fine dry powder extinguishing agent, characterized in that, The method comprises the following steps: acquiring cable bridge internal temperature and smoke data, judging fire occurrence signal through preset threshold, extracting bridge specification parameters from the signal, using particle swarm optimization algorithm to determine the composition adjustment ratio of ultra-fine dry powder extinguishing agent particles, and obtaining the optimized particle composition ratio of chemical inhibition and physical suffocation cooperation; According to the particle composition ratio, the air flow resistance value in the bridge space is calculated, the injection process is simulated through a multi-stage pressure release model, the injection lift and angle parameters are determined, and the coverage density distribution map for guiding the deployment of the lift control device is obtained; From the coverage density distribution map, the force arm distribution data of the key parts of the bridge are extracted, the influence of the integrated equipment load on the structure is evaluated by using the finite element analysis method, and if the load exceeds the preset threshold, the equipment installation position is adjusted, and the integrated structure force arm scheme is obtained; For the integrated structure force arm integration scheme, real-time data of special environmental factors are obtained, the behavior of dry powder particles in complex airflow is simulated through a particle collision dynamics model, the injection speed angle time sequence optimization value is determined, and the enhanced effective utilization rate parameter is obtained; From the enhanced effective utilization rate parameter, the bridge network paragraph division information is extracted, the distributed system architecture is deployed to deploy sensor nodes, the deep learning algorithm is used to process the monitored temperature, smoke and current abnormal data, the fire positioning coordinates are judged, and the automatic start sequence for coordinating adjacent area fire extinguishing resources is obtained; According to the automatic start sequence, the deposition law after dry powder injection is simulated, the residual flow characteristics are predicted through an improved model with the addition of an easy-to-clean agent, the cleaning equipment process parameters are determined, and the deposition control scheme is obtained; From the linkage control logic obtained from the deposition control scheme, integrate it into the intelligent system framework, coordinate the fire detection, fire extinguishing start, power cut-off and ventilation operation through the preset algorithm, and if the response time is lower than the threshold, execute the environmental adaptation adjustment, and update the obtained storage parameters; Based on the storage parameter monitoring storage environment temperature and humidity data, using support vector machine algorithm to predict the performance degradation trend of fire extinguishing agent, determining the adjustment system setting of moisture-proof and anti-caking, obtaining the adaptation technology output of long-term standby state for circulating optimization of the whole fire extinguishing process.

2. The method of claim 1, wherein the process of obtaining the optimized particle composition ratio of chemical inhibition and physical suffocation cooperation comprises: acquiring cable bridge internal temperature and smoke data, collecting real-time temperature and smoke concentration values through sensors to obtain original environmental data; if the temperature value or smoke concentration value in the original environmental data exceeds the preset threshold, a fire signal is generated through a logic judgment module to determine the fire occurrence state; extracting the specification parameters of the cable bridge from the fire signal, and generating structured specification data using a data analysis algorithm; According to the structured specification data, the internal space volume and fire spread characteristics of the bridge are calculated to obtain fire scene parameters; using a particle swarm optimization algorithm, iteratively adjusting the proportion of chemical inhibitors and physical suffocation agents for the fire scene parameters and the particle composition of ultra-fine dry powder, and obtaining a preliminary matching scheme. ​ The free radical capture capacity of the preliminary matching scheme is evaluated to determine an optimized synergistic matching; According to the optimized synergistic matching, a particle component adjustment instruction of the superfine dry powder extinguishing agent is generated, and final particle component matching data is output.

3. The method of claim 1, wherein, The process of obtaining a coverage density distribution map for guiding the deployment of the head control device includes: By using the particle component matching data, the airflow resistance in the bridge space is calculated by using a fluid mechanics model to obtain an airflow resistance value; According to the airflow resistance value, a multi-stage pressure release model is constructed to simulate the pressure change in the spraying process to obtain pressure distribution data; By using the pressure distribution data, a particle motion simulation algorithm is used to calculate the spraying head and angle parameters to determine the spraying trajectory parameters; If the spraying trajectory parameters meet the preset coverage density threshold, a coverage density distribution map is generated to obtain distribution map data; According to the distribution map data, a geometric optimization algorithm is used to calculate the deployment position of the head control device to determine the deployment coordinates; By using the deployment coordinates, configuration parameters of the head control device are generated to obtain device control instructions; According to the device control instructions, an automatic scheduling algorithm is used to perform the deployment operation of the head control device to complete the deployment task.

4. The method of claim 1, wherein, The process of obtaining the integrated structure force arm scheme includes: Obtain the force arm distribution data of the main beam cross arm from the bridge structure density distribution, and use a stereomicroscope technique to perform three-dimensional scanning on the bridge structure to obtain an initial data set of the force arm distribution data; According to the initial data set of the force arm distribution data, a finite element analysis model is constructed, and the integrated equipment attachment load parameters are input to calculate the structural stress distribution of the main beam cross arm; If the structural stress distribution exceeds the preset deformation threshold, the equipment installation position is adjusted by an iterative optimization algorithm to generate a new position coordinate set; According to the new position coordinate set, the finite element analysis model is re-run to obtain the adjusted structural stress distribution, and it is judged whether the preset deformation threshold is met; By using the adjusted structural stress distribution, the load distribution uniformity is calculated, and a K-means clustering algorithm is used to classify the load distribution to obtain the classified load distribution features; According to the classified load distribution features, a force arm adjustment strategy is generated, and a linear regression algorithm is used to predict the optimization trend of the force arm distribution to obtain an optimized force arm scheme; By using the optimized force arm scheme, the main beam cross arm position of the bridge structure is updated to generate a final force arm integration scheme.

5. The method of claim 1, wherein, The process of obtaining the enhanced effective utilization rate parameter includes: Obtain real-time data of a high-temperature and high-humidity environment, collect temperature, humidity and airflow speed data by using sensors to generate an environment parameter data set; By using a particle collision dynamics model, the environment parameter data set is input to simulate the motion trajectory of dry powder particles in complex airflow to obtain particle distribution features; According to the particle distribution features, a Monte Carlo method is used to optimize the spraying speed and angle parameters to generate an initial optimization parameter set; If the utilization value of the initial optimization parameter set is lower than the preset threshold, the speed and angle are iteratively adjusted by a genetic algorithm to obtain an improved parameter set; According to the improved parameter set, a time sequence analysis method is used to adjust the injection time sequence to generate an optimized time sequence scheme; The optimized time sequence scheme is verified by real-time data, and if the utilization value reaches the preset threshold, the final injection parameters are determined; If not, repeat the Monte Carlo method and genetic algorithm optimization steps; According to the final injection parameters, an enhanced effective utilization value is generated.

6. The method of claim 1, wherein The process of obtaining an automatic start-up sequence for coordinating fire extinguishing resources in adjacent areas includes: Obtain segment division information from the bridge network, extract the effective utilization rate parameter, generate a deployment scheme for the distributed system architecture, and determine the distribution position of the sensor nodes; According to the distribution position of the sensor nodes, deploy temperature, smoke and current sensors, obtain real-time monitoring data, and generate multi-dimensional data streams; The multi-dimensional data streams are processed using a convolutional neural network to extract abnormal features of temperature, smoke and current, and generate an abnormal event set; If the temperature data in the abnormal event set exceeds the preset threshold, combine the smoke data and current data, analyze the probability of fire occurrence by convolutional neural network, and generate a fire risk assessment result; According to the fire risk assessment result, combined with the distribution position of the sensor nodes, calculate the fire positioning coordinates, and generate the fire location information; Through the fire location information, match the fire extinguishing resources in adjacent areas, generate a resource allocation sequence, and determine the coordinated start-up sequence; According to the resource allocation sequence, activate the fire extinguishing equipment in adjacent areas, generate an automatic start-up sequence, and coordinate the fire extinguishing resources.

7. The method of claim 1, wherein The process of obtaining a deposition control scheme includes: Obtain deposition rule data by simulating the dry powder injection process, and determine the deposition distribution model; According to the deposition distribution model, analyze the flow characteristics of residues on different surfaces to generate a flow characteristic data set; Classify the flow characteristic data set using a support vector machine algorithm to determine the optimization direction of the easy-to-clean agent formula, and obtain the formula adjustment parameters; Simulate the cleaning effect of the optimized easy-to-clean agent on the deposition surface through the formula adjustment parameters to determine the cleaning efficiency index; If the cleaning efficiency index is lower than the preset threshold, adjust the process flow parameters, re-simulate the cleaning effect, and obtain the optimized process parameters; According to the optimized process parameters, generate the operation sequence of the cleaning equipment, and determine the deposition control scheme; Simulate the equipment operation state through the deposition control scheme to verify the effect of rapid recovery operation, and generate the final operation parameters.

8. The method of claim 1, wherein The process of updating the obtained storage parameters includes: Obtain the linkage control logic from the deposition control scheme, extract the trigger conditions of fire detection, fire extinguishing start, power cut-off and ventilation operation by analyzing the preset rule set, and obtain the control logic sequence; According to the control logic sequence, the logic sequence is embedded into the intelligent system framework through system integration, each operation module is matched through interface mapping, and an integrated system configuration is obtained; Through preset algorithm coordination, based on the integrated system configuration, a fire detection operation is performed, sensor data streams are analyzed, if a fire signal is detected, a fire extinguishing start mechanism is triggered, and a fire extinguishing instruction set is obtained; According to the fire extinguishing instruction set, the power cut-off function is activated, the power supply of the specified area is cut off, the ventilation operation management is started, the running state of the ventilation equipment is adjusted, and the environment control parameters are obtained; According to the environment control parameters, the response time is calculated, if the response time is lower than the preset threshold, the environment adaptation adjustment is performed, the cooperative operation of the ventilation and fire extinguishing operation is optimized, and the adjusted running parameters are obtained; The adjusted running parameters are analyzed by a random forest algorithm to predict the storage environment stability, and the storage parameters are updated; According to the storage parameters, the control strategy of the intelligent system framework is dynamically adjusted, the execution path of the linkage control logic is optimized, and the optimized system running state is obtained.

9. The method of claim 1, wherein The adaptive technology output of the long-term standby state is obtained for circulating optimization of the entire fire extinguishing process to extinguish the fire, comprising: Using a sensor array to collect temperature and humidity data in the storage environment in real time to obtain a structured environment data set; An SVM algorithm is used to analyze the structured environment data set to build a fire extinguishing agent performance decay prediction model to obtain a performance decay trend; According to the performance decay trend, the moisture-proof adjustment system parameters are calculated, if the predicted humidity exceeds the preset threshold, the running frequency of the dehumidification equipment is adjusted, and the moisture-proof adjustment parameters are determined; According to the performance decay trend, the anti-caking adjustment parameters are calculated, if the predicted caking risk exceeds the preset threshold, the running cycle of the ventilation equipment is adjusted, and the anti-caking adjustment parameters are determined; According to the moisture-proof adjustment parameters and the anti-caking adjustment parameters, the storage environment control system configuration is updated to obtain long-term standby state optimization settings; According to the long-term standby state optimization settings, the running strategy of the fire extinguishing agent storage equipment is adjusted to generate fire extinguishing process optimization instructions; According to the fire extinguishing process optimization instructions, the storage environment control system is updated in a loop to obtain a continuously optimized fire extinguishing process configuration for fire extinguishing.

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