Fire equipment management method and system based on railway tunnel fire-fighting monitoring system

Abstract

The invention discloses a fire equipment management method and system based on a railway tunnel fire-fighting monitoring system, and relates to the technical field of tunnel fire fighting, and the system comprises a fire-fighting monitoring module which is used for collecting multi-dimensional state data of fire equipment in real time; the redundant data processing module adopts a hot standby redundancy mechanism and is used for analyzing and processing the multi-dimensional state data of the fire equipment in real time and generating a fire risk assessment report; an emergency scheduling plan is called based on the fire risk assessment report, and an execution instruction is generated and issued to the execution module; the execution module is used for controlling fire equipment to work according to the execution instruction; and the data display module is used for displaying the multi-dimensional state data, the fire risk assessment report and the emergency scheduling plan of the fire equipment in real time. According to the invention, potential safety hazards can be found earlier, early warning of fire risks is realized, the accuracy and timeliness of fire warning can be obviously improved, and thus precious time is won for taking effective prevention measures.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel fire protection, and more particularly to a fire equipment management method and system based on a railway tunnel fire protection monitoring system. Background Art

[0002] At present, railway tunnels, as an important part of modern transportation infrastructure, play a vital role in ensuring traffic safety and improving transportation efficiency. However, with the continuous expansion of the railway network and the increase in technical complexity, the problem of ensuring the safe operation of railway tunnels, especially fire safety, has become increasingly prominent. Railway tunnels have a special environment. Once a fire occurs, it is easy to cause serious casualties and property losses due to factors such as closed space and difficult evacuation. Although existing fire protection measures can provide certain safety guarantees to a certain extent, these measures often rely on traditional monitoring methods and equipment management methods, and there are problems such as untimely information processing, low data accuracy, and low emergency response efficiency.

[0003] Especially when faced with complex multi-dimensional status data, traditional systems have difficulty in achieving effective assessment and rapid response to fire risks. For example, when the initial signs of a fire appear, if they cannot be identified and effective countermeasures are not taken in a timely and accurate manner, the fire may spread rapidly and the best time to extinguish the fire may be missed. Therefore, how to build an efficient and reliable railway tunnel fire monitoring system and fire equipment management system is an urgent problem that technicians in this field need to solve. Summary of the invention

[0004] In view of this, the present invention provides a fire equipment management method and system based on a railway tunnel fire monitoring system, which overcomes the above-mentioned defects.

[0005] In order to achieve the above object, the present invention adopts the following technical solution:

[0006] A fire equipment management system based on a railway tunnel fire monitoring system, comprising a fire monitoring module, a redundant data processing module, an execution module and a data display module;

[0007] The fire monitoring module is used to collect multi-dimensional status data of fire equipment in real time and transmit the multi-dimensional status data of the fire equipment to the redundant data processing module;

[0008] The redundant data processing module adopts a hot standby redundant mechanism to analyze and process the multi-dimensional status data of the fire equipment in real time and generate a fire risk assessment report; and based on the fire risk assessment report, the emergency dispatch plan is called to generate an execution instruction and sent to the execution module;

[0009] The execution module is used to control the operation of the fire equipment according to the execution instruction;

[0010] The data display module is used to display the multi-dimensional status data of the fire equipment, the fire risk assessment report and the emergency dispatch plan in real time.

[0011] Optionally, the fire monitoring module includes a data acquisition unit and an abnormality identification unit;

[0012] The data acquisition unit is used to acquire multi-dimensional status data of the fire equipment in real time;

[0013] The abnormality identification unit is used to input the multi-dimensional state data into an abnormality identification model to identify and filter abnormal data.

[0014] Optionally, the redundant data processing module includes a data preprocessing unit, a fire risk assessment unit, an emergency dispatch unit and an instruction generation unit;

[0015] The data preprocessing unit is used to clean and format the multi-dimensional state data of the fire equipment to generate preprocessed data;

[0016] The fire risk assessment unit is used to analyze the pre-processed data using a deep reinforcement learning model and an adaptive clustering algorithm to generate a fire risk assessment report;

[0017] The emergency dispatch unit is used to generate an emergency dispatch plan using a dynamic emergency dispatch plan library according to the fire risk assessment report;

[0018] An instruction generating unit is used to split the emergency dispatch plan into a plurality of execution instructions.

[0019] Optionally, the emergency dispatch module includes a database construction unit, a model construction unit and a strategy generation unit;

[0020] The database construction unit is used to collect historical fire case data and real-time data of fire monitoring equipment in railway tunnels, and integrate and process them to build a dynamic emergency dispatch plan library;

[0021] The model building unit is used to train the initial strategy generation model using the dynamic emergency dispatch plan library to obtain the strategy generation model;

[0022] The strategy generation unit is used to input the fire risk assessment report into the strategy generation model to generate an emergency dispatch plan.

[0023] Optionally, a redundant power supply module is further included, including a power supply unit and a battery pack unit;

[0024] The power supply unit includes two power supply circuits for supplying power to various modules of the system;

[0025] The battery pack unit is used to seamlessly switch to supply power to various units of the system when the power supply unit is powered off.

[0026] A fire equipment management method based on a railway tunnel fire monitoring system, the specific steps are:

[0027] Obtain multi-dimensional status data of fire equipment in real time;

[0028] Analyzing the multidimensional state data using a deep reinforcement learning model and an adaptive clustering algorithm to generate a fire risk assessment report;

[0029] The emergency dispatch plan is called based on the fire risk assessment report, and an execution instruction is generated according to the emergency dispatch plan and issued to the corresponding fire equipment.

[0030] Optionally, the status data includes operating status and fault information of the fire equipment.

[0031] Optionally, the multi-dimensional status data of the fire equipment needs to be input into an abnormality recognition model before analysis to identify and filter abnormal data.

[0032] Optionally, the emergency dispatch plan includes the location, type, level and response measures of the fire hazard.

[0033] Optionally, an adaptive control strategy is introduced during the execution of the emergency dispatch plan to automatically adjust the working parameters of the fire equipment according to the real-time fire risk assessment report.

[0034] It can be seen from the above technical solutions that the present invention provides a fire equipment management method and system based on a railway tunnel fire monitoring system, which has the following beneficial effects compared with the prior art:

[0035] Improve fire warning capability: The present invention can detect potential safety hazards earlier, achieve early warning of fire risks, and can significantly improve the accuracy and timeliness of fire warnings, thereby buying valuable time for taking effective preventive measures.

[0036] Enhance emergency response efficiency: The hot standby redundancy mechanism ensures high availability and reliability of the system. Even if the main system fails, the backup system can take over the work seamlessly, ensuring a rapid response to emergencies.

[0037] Optimize resource allocation and management: Through centralized management and intelligent analysis of fire equipment status data, managers can better understand the working status of each device, arrange maintenance plans reasonably, and avoid resource waste. At the same time, in an emergency, resource allocation can be flexibly adjusted according to actual conditions to maximize the use of existing resources for fire fighting and rescue. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0039] Figure 1 A schematic diagram of the system structure provided by the present invention;

[0040] Figure 2 The present invention provides a flow chart of the method. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] On the one hand, an embodiment of the present invention discloses a fire equipment management system based on a railway tunnel fire monitoring system. Figure 1 As shown, it includes a fire monitoring module, a redundant data processing module, an execution module and a data display module;

[0043] A fire monitoring module, used to collect multi-dimensional status data of fire equipment in real time and transmit the multi-dimensional status data of fire equipment to a redundant data processing module;

[0044] The redundant data processing module adopts a hot standby redundant mechanism to analyze and process the multi-dimensional status data of fire equipment in real time and generate a fire risk assessment report; based on the fire risk assessment report, the emergency dispatch plan is called to generate execution instructions and sent to the execution module;

[0045] An execution module, used to control the operation of fire equipment according to execution instructions;

[0046] The data display module is used to display the multi-dimensional status data of fire equipment, fire risk assessment reports and emergency dispatch plans in real time.

[0047] In one embodiment, the fire monitoring module includes a data acquisition unit and an abnormality identification unit;

[0048] Data acquisition unit, used to collect multi-dimensional status data of fire equipment in real time;

[0049] The anomaly recognition unit is used to input the multi-dimensional state data into the anomaly recognition model to recognize and filter the anomaly data.

[0050] Furthermore, the multi-dimensional status data of the fire equipment includes data such as the operating status, fault information and battery power of the detection device, the fire extinguishing device, the lighting device and the smoke exhaust device.

[0051] Among them, the detection devices are pyrolysis particle detectors, optical fiber temperature measurement equipment, smoke sensors, CO sensors, CO 2 A multi-dimensional fire monitoring network is formed by multiple devices such as sensors, oxygen sensors, wind speed sensors, humidity sensors, infrared thermopile array sensors and high-temperature resistant cameras.

[0052] After obtaining the multi-dimensional status data of the fire equipment, the image data is input into the pre-trained abnormality recognition model. By extracting and identifying the flame and smoke features in the image, abnormal images are screened out and deleted. Flame recognition preliminarily screens out possible flame areas through color space conversion and color threshold segmentation methods, and makes judgments based on the extracted features and the dynamic characteristics of the flame; smoke recognition adopts a method based on texture and dynamic features, and recognizes by calculating the image's local binary pattern (LBP), HOG and other texture features, as well as the smoke's diffusion speed, transparency and other dynamic features.

[0053] The steps to obtain the anomaly recognition model are:

[0054] First, a large number of fire pictures are collected and accurately labeled to build a training dataset containing normal and abnormal (flame, smoke) images to provide reliable labels for model training.

[0055] An initial anomaly recognition model is constructed based on the Faster R-CNN network, and the initial anomaly recognition model is alternately trained using the training data set to obtain the final anomaly recognition model.

[0056] In one embodiment, the redundant data processing module includes a data preprocessing unit, a fire risk assessment unit, an emergency dispatch unit, and an instruction generation unit;

[0057] A data preprocessing unit, used for cleaning and formatting the multi-dimensional status data of the fire equipment to generate preprocessed data;

[0058] A fire risk assessment unit, which is used to analyze the preprocessed data using a deep reinforcement learning model and an adaptive clustering algorithm to generate a fire risk assessment report;

[0059] An emergency dispatch unit, used to generate an emergency dispatch plan using a dynamic emergency dispatch plan library according to a fire risk assessment report;

[0060] The instruction generation unit is used to split the emergency dispatch plan into multiple execution instructions.

[0061] Furthermore, the redundant data processing module is composed of dual CPUs and adopts a hot standby redundancy mechanism, in which the first CPU is used to process and analyze the multi-dimensional status data from the fire monitoring module in real time; the second CPU synchronously backs up the data and monitors the operating status of the first CPU, and automatically switches control when either CPU fails.

[0062] A data preprocessing module, a fire risk assessment module, an emergency dispatch module and an instruction generation module are configured in both the first CPU and the second CPU;

[0063] Among them, the specific data processing steps of the data preprocessing module are: first, integrate the data of different devices into a unified data storage system to ensure the consistency and integrity of the data; then clean and normalize the stored data; data cleaning includes missing value processing, outlier processing and noise filtering.

[0064] Further, the fire risk assessment module includes a first fire generation unit, a second fire prediction unit and a report generation unit;

[0065] In the first fire identification unit, the preprocessed data is firstly subjected to feature extraction to extract fire-related features, such as temperature, smoke density, flame shape, etc. Then, the extracted features are clustered using an adaptive clustering algorithm, and potential fire hazard areas or objects are identified based on the clustering results.

[0066] In the second fire identification unit, the preprocessed data is input into the trained deep reinforcement learning model, and the prediction results of fire hazards are output;

[0067] The report generating unit outputs a fire risk assessment report according to the prediction results of the first fire identification unit and the second fire identification unit in accordance with the set extraction rules.

[0068] In one embodiment, the emergency dispatch module includes a database construction unit, a model construction unit, and a strategy generation unit;

[0069] Database construction unit, used to collect historical fire case data and real-time data of fire monitoring equipment in railway tunnels, integrate and process them, and build a dynamic emergency dispatch plan library;

[0070] A model building unit, used to train the initial strategy generation model using the dynamic emergency dispatch plan library to obtain the strategy generation model;

[0071] The strategy generation unit is used to input the fire risk assessment report into the strategy generation model to generate an emergency dispatch plan.

[0072] Furthermore, in the database construction module, historical fire cases are first collected and analyzed to extract key information from the cases, such as the cause of the fire, the process of fire fighting, and the loss situation. At the same time, real-time data from various fire monitoring equipment in the railway tunnel, such as temperature, smoke concentration, flame detection, etc., are extracted to build a dynamic emergency dispatch plan library; the real-time data of various fire monitoring equipment in the railway tunnel is used to set the triggering conditions of the emergency dispatch plan;

[0073] In the model building module, the initial strategy generation model is built based on the long short-term memory network (LSTM), and the dynamic emergency dispatch plan library is used to train the initial strategy generation model to obtain the final strategy generation model;

[0074] In the strategy generation module, an emergency dispatch plan for the current situation is automatically generated based on the real-time fire risk assessment report and the similarity of historical fire cases, and a dynamic update mechanism for the emergency dispatch plan is established to timely adjust the content of the emergency dispatch plan according to changes in fire risks or new situations.

[0075] Among them, virtual simulation technology is used to construct tunnel fire scenarios during the initial strategy generation model training process, and the generated emergency dispatch plan is tested and verified to evaluate its feasibility and effectiveness, and the emergency dispatch plan is optimized based on the evaluation results.

[0076] Furthermore, the instruction generation module also includes a collaborative emergency and information sharing unit. When the risk value in the fire risk assessment report exceeds the limit, the relevant emergency dispatch plan is automatically triggered, and an information sharing system is built to integrate emergency resources and information to achieve unified command and dispatch. The emergency dispatch plan can be dynamically adjusted according to real-time data to improve the accuracy of fire risk assessment and the efficiency of emergency response; the emergency dispatch plan is split into system instructions, and the system instructions are distributed to the corresponding execution modules.

[0077] The emergency dispatch plan contains detailed information such as the location, type, level of fire hazards and recommended response measures.

[0078] In one embodiment, based on the location signal of the fire hazard, the optimal evacuation route is generated and dynamically indicated through the emergency lighting equipment; the smoke exhaust device and fire extinguishing device of the corresponding partition are started, and the ventilation system is closed to prevent the fire from spreading.

[0079] The introduction of adaptive control strategies in the execution module can automatically adjust the working parameters of fire-fighting equipment according to the fire risk assessment results, such as water spray intensity, smoke exhaust system activation, etc., to achieve precise fire extinguishing.

[0080] In one embodiment, a storage module is also included, which uses an industrial-grade SSD storage chip to store data such as device status data, fault logs, and linkage operation records.

[0081] In one embodiment, a redundant power supply module is further included, and the redundant power supply module includes a power supply unit and a battery unit;

[0082] The power supply unit includes two power supply circuits for supplying power to various modules of the system;

[0083] The battery pack unit is used to seamlessly switch to supply power to various units of the system when the power supply unit is powered off.

[0084] Furthermore, the power supply unit adopts dual-channel independent AC / DC power conversion, with AC220V input and DC24V output to power each module of the system; and each circuit is equipped with an input voltage monitoring module. When an abnormal input voltage is detected, it automatically switches to the backup power supply path and triggers an alarm in the user interface.

[0085] The battery pack module is a linear UPS with a high-density lithium battery pack, which gives priority to powering key modules (CPU, communication, storage) when external power is cut off to avoid data loss.

[0086] In one embodiment, the system includes a multi-protocol communication module, and an Ethernet ring network + two-bus hybrid networking mode is used for data transmission, wherein the Ethernet communication adopts a dual network port redundant design, and automatically switches to a backup link when any link fails to ensure data transmission continuity.

[0087] Among them, the Ethernet ring network switch connects the distributed fire-fighting equipment in the tunnel through the optical fiber interface to form a redundant ring network topology, and supports VLAN division and priority scheduling to ensure that the transmission priority of fire alarm data is higher than that of normal status data.

[0088] The second bus interface of the multi-protocol communication module adopts a non-polarity wiring design, supporting a maximum transmission distance of ≥ 2000 meters, which is suitable for long-distance and multi-device fire monitoring needs in tunnels.

[0089] In one embodiment, a self-check module is also included, which automatically detects the operating status of each module through a built-in regular maintenance and self-check mechanism, promptly discovers and repairs potential faults, and ensures long-term stable operation of the system.

[0090] On the other hand, this embodiment discloses a fire equipment management method based on a railway tunnel fire monitoring system. Figure 2As shown, the specific steps are:

[0091] Step 1: Acquire multi-dimensional status data of fire equipment in real time;

[0092] Step 2: Use deep reinforcement learning model and adaptive clustering algorithm to analyze multi-dimensional state data and generate fire risk assessment report;

[0093] Step 3: Call the emergency dispatch plan based on the fire risk assessment report, generate execution instructions based on the emergency dispatch plan, and send them to the corresponding fire equipment.

[0094] In one embodiment, the status data includes the operating status and fault information of the fire equipment.

[0095] In one embodiment, the multi-dimensional status data of the fire equipment needs to be input into an abnormality recognition model before analysis to identify and filter abnormal data.

[0096] In one embodiment, the emergency dispatch plan includes the location, type, level and response measures of the fire hazard.

[0097] In one embodiment, an adaptive control strategy is introduced during the execution of the emergency dispatch plan to automatically adjust the working parameters of the fire equipment according to the real-time fire risk assessment report.

[0098] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0099] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A fire equipment management system based on a railway tunnel fire monitoring system, characterized in that: It includes fire monitoring module, redundant data processing module, execution module and data display module; The fire monitoring module is used to collect multi-dimensional status data of fire equipment in real time and transmit the multi-dimensional status data of the fire equipment to the redundant data processing module; The redundant data processing module adopts a hot standby redundant mechanism to analyze and process the multi-dimensional status data of the fire equipment in real time and generate a fire risk assessment report; and based on the fire risk assessment report, the emergency dispatch plan is called to generate an execution instruction and sent to the execution module; The execution module is used to control the operation of the fire equipment according to the execution instruction; The data display module is used to display the multi-dimensional status data of the fire equipment, the fire risk assessment report and the emergency dispatch plan in real time.

2. A fire equipment management system based on a railway tunnel fire monitoring system according to claim 1, characterized in that: The fire monitoring module includes a data acquisition unit and an abnormality identification unit; The data acquisition unit is used to acquire multi-dimensional status data of the fire equipment in real time; The abnormality identification unit is used to input the multi-dimensional state data into an abnormality identification model to identify and filter abnormal data.

3. A fire equipment management system based on a railway tunnel fire monitoring system according to claim 1, characterized in that: The redundant data processing module includes a data preprocessing unit, a fire risk assessment unit, an emergency dispatch unit and an instruction generation unit; The data preprocessing unit is used to clean and format the multi-dimensional state data of the fire equipment to generate preprocessed data; The fire risk assessment unit is used to analyze the pre-processed data using a deep reinforcement learning model and an adaptive clustering algorithm to generate a fire risk assessment report; The emergency dispatch unit is used to generate an emergency dispatch plan using a dynamic emergency dispatch plan library according to the fire risk assessment report; An instruction generating unit is used to split the emergency dispatch plan into a plurality of execution instructions.

4. A fire equipment management system based on a railway tunnel fire monitoring system according to claim 3, characterized in that: The emergency dispatch module includes a database construction unit, a model construction unit and a strategy generation unit; The database construction unit is used to collect historical fire case data and real-time data of fire monitoring equipment in railway tunnels, and integrate and process them to build a dynamic emergency dispatch plan library; The model building unit is used to train the initial strategy generation model using the dynamic emergency dispatch plan library to obtain the strategy generation model; The strategy generation unit is used to input the fire risk assessment report into the strategy generation model to generate an emergency dispatch plan.

5. A fire equipment management system based on a railway tunnel fire monitoring system according to claim 1, characterized in that: Also included is a redundant power supply module, including a power supply unit and a battery pack unit; The power supply unit includes two power supply circuits for supplying power to various modules of the system; The battery pack unit is used to seamlessly switch to supply power to various units of the system when the power supply unit is powered off.

6. A fire equipment management method based on a railway tunnel fire monitoring system, characterized in that: The specific steps are: Obtain multi-dimensional status data of fire equipment in real time; Analyzing the multidimensional state data using a deep reinforcement learning model and an adaptive clustering algorithm to generate a fire risk assessment report; The emergency dispatch plan is called based on the fire risk assessment report, and an execution instruction is generated according to the emergency dispatch plan and issued to the corresponding fire equipment.

7. A fire equipment management method based on a railway tunnel fire monitoring system according to claim 6, characterized in that: The status data includes the operating status and fault information of the fire equipment.

8. A fire equipment management method based on a railway tunnel fire monitoring system according to claim 6, characterized in that: The multi-dimensional status data of the fire equipment needs to be input into an abnormality recognition model before analysis to identify and filter abnormal data.

9. A fire equipment management method based on a railway tunnel fire monitoring system according to claim 6, characterized in that: The emergency dispatch plan includes the location, type, level and response measures of fire hazards.

10. A fire equipment management method based on a railway tunnel fire monitoring system according to claim 6, characterized in that: An adaptive control strategy is introduced when the emergency dispatch plan is executed to automatically adjust the working parameters of the fire equipment according to the real-time fire risk assessment report.

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